From 57bbed21de888b4b58f20fe753630c231547a40d Mon Sep 17 00:00:00 2001
From: Vinoth Chandar <vinoth@uber.com>
Date: Thu, 14 Mar 2019 17:23:36 -0700
Subject: [PATCH] Removing docs folder from master branch

 - Only asf-site branch contains the docs
 - Helps streamline doc contributions
---
 docs/.gitignore                               |    4 -
 docs/404.md                                   |    6 -
 docs/CNAME                                    |    1 -
 docs/Dockerfile                               |   26 -
 docs/Gemfile                                  |   10 -
 docs/Gemfile.lock                             |  156 -
 docs/_config.yml                              |   87 -
 docs/_data/alerts.yml                         |   15 -
 docs/_data/sidebars/mydoc_sidebar.yml         |   66 -
 docs/_data/strings.yml                        |    5 -
 docs/_data/tags.yml                           |   10 -
 docs/_data/topnav.yml                         |   30 -
 docs/_includes/archive.html                   |   15 -
 docs/_includes/callout.html                   |    1 -
 .../custom/getting_started_series.html        |   19 -
 .../custom/getting_started_series_next.html   |   10 -
 docs/_includes/custom/series_acme.html        |   19 -
 docs/_includes/custom/series_acme_next.html   |   30 -
 docs/_includes/custom/sidebarconfigs.html     |   19 -
 docs/_includes/custom/usermap.html            |   14 -
 docs/_includes/custom/usermapcomplex.html     |   91 -
 docs/_includes/disqus.html                    |   16 -
 docs/_includes/feedback.html                  |   13 -
 docs/_includes/footer.html                    |    9 -
 docs/_includes/google_analytics.html          |    6 -
 docs/_includes/head.html                      |   47 -
 docs/_includes/head_print.html                |   33 -
 docs/_includes/image.html                     |    1 -
 docs/_includes/important.html                 |    1 -
 docs/_includes/initialize_shuffle.html        |  130 -
 docs/_includes/inline_image.html              |    1 -
 docs/_includes/links.html                     |   44 -
 docs/_includes/note.html                      |    1 -
 docs/_includes/sidebar.html                   |   56 -
 docs/_includes/taglogic.html                  |   32 -
 docs/_includes/tip.html                       |    1 -
 docs/_includes/toc.html                       |   21 -
 docs/_includes/topnav.html                    |   75 -
 docs/_includes/warning.html                   |    1 -
 docs/_layouts/default.html                    |   80 -
 docs/_layouts/default_print.html              |   25 -
 docs/_layouts/none.html                       |    3 -
 docs/_layouts/page.html                       |   70 -
 docs/_layouts/page_print.html                 |   15 -
 docs/_layouts/post.html                       |   41 -
 docs/_posts/2016-12-30-strata-talk-2017.md    |   11 -
 docs/admin_guide.md                           |  444 --
 docs/api_docs.md                              |   10 -
 docs/code_and_design.md                       |   38 -
 docs/community.md                             |   22 -
 docs/comparison.md                            |   61 -
 docs/concepts.md                              |  159 -
 docs/configurations.md                        |  203 -
 docs/css/bootstrap.min.css                    |    5 -
 docs/css/customstyles.css                     | 1181 ----
 docs/css/font-awesome.min.css                 |    4 -
 docs/css/fonts/FontAwesome.otf                |  Bin 93888 -> 0 bytes
 docs/css/fonts/fontawesome-webfont.eot        |  Bin 60767 -> 0 bytes
 docs/css/fonts/fontawesome-webfont.svg        |  565 --
 docs/css/fonts/fontawesome-webfont.ttf        |  Bin 122092 -> 0 bytes
 docs/css/fonts/fontawesome-webfont.woff       |  Bin 71508 -> 0 bytes
 docs/css/fonts/fontawesome-webfont.woff2      |  Bin 56780 -> 0 bytes
 docs/css/lavish-bootstrap.css                 | 5898 -----------------
 docs/css/modern-business.css                  |   93 -
 docs/css/printstyles.css                      |  160 -
 docs/css/syntax.css                           |   60 -
 docs/css/theme-blue.css                       |  103 -
 docs/css/theme-green.css                      |   99 -
 docs/dev_setup.md                             |   13 -
 docs/feed.xml                                 |   32 -
 docs/fonts/FontAwesome.otf                    |  Bin 85908 -> 0 bytes
 docs/fonts/fontawesome-webfont.eot            |  Bin 56006 -> 0 bytes
 docs/fonts/fontawesome-webfont.svg            |  520 --
 docs/fonts/fontawesome-webfont.ttf            |  Bin 112160 -> 0 bytes
 docs/fonts/fontawesome-webfont.woff           |  Bin 65452 -> 0 bytes
 docs/fonts/glyphicons-halflings-regular.eot   |  Bin 20127 -> 0 bytes
 docs/fonts/glyphicons-halflings-regular.svg   |  288 -
 docs/fonts/glyphicons-halflings-regular.ttf   |  Bin 45404 -> 0 bytes
 docs/fonts/glyphicons-halflings-regular.woff  |  Bin 23424 -> 0 bytes
 docs/fonts/glyphicons-halflings-regular.woff2 |  Bin 18028 -> 0 bytes
 docs/gcs_filesystem.md                        |   62 -
 docs/images/androidsdkmanagericon.png         |  Bin 795 -> 0 bytes
 docs/images/async_compac_1.png                |  Bin 60344 -> 0 bytes
 docs/images/async_compac_2.png                |  Bin 54164 -> 0 bytes
 docs/images/async_compac_3.png                |  Bin 70516 -> 0 bytes
 docs/images/async_compac_4.png                |  Bin 66932 -> 0 bytes
 docs/images/authorizegithubscreen2.png        |  Bin 76388 -> 0 bytes
 docs/images/authorizeongithub.png             |  Bin 22571 -> 0 bytes
 docs/images/company_logo.png                  |  Bin 3105 -> 0 bytes
 docs/images/company_logo_big.png              |  Bin 9588 -> 0 bytes
 docs/images/favicon.ico                       |  Bin 1150 -> 0 bytes
 docs/images/helpapi-01.png                    |  Bin 91640 -> 0 bytes
 docs/images/helpapi.svg                       | 1661 -----
 docs/images/hoodie_commit_duration.png        |  Bin 869144 -> 0 bytes
 docs/images/hoodie_cow.png                    |  Bin 31136 -> 0 bytes
 docs/images/hoodie_intro_1.png                |  Bin 23478 -> 0 bytes
 docs/images/hoodie_log_format_v2.png          |  Bin 223676 -> 0 bytes
 docs/images/hoodie_mor.png                    |  Bin 56002 -> 0 bytes
 docs/images/hoodie_query_perf_hive.png        |  Bin 158481 -> 0 bytes
 docs/images/hoodie_query_perf_presto.png      |  Bin 33727 -> 0 bytes
 docs/images/hoodie_query_perf_spark.png       |  Bin 29384 -> 0 bytes
 docs/images/hoodie_timeline.png               |  Bin 23093 -> 0 bytes
 docs/images/hoodie_upsert_dag.png             |  Bin 503771 -> 0 bytes
 docs/images/hoodie_upsert_perf1.png           |  Bin 15984 -> 0 bytes
 docs/images/hoodie_upsert_perf2.png           |  Bin 18954 -> 0 bytes
 docs/images/illustratoroptions.png            |  Bin 118175 -> 0 bytes
 docs/images/itermexample.png                  |  Bin 68886 -> 0 bytes
 docs/images/jekyll.png                        |  Bin 5375 -> 0 bytes
 docs/images/killalljekyll.png                 |  Bin 66686 -> 0 bytes
 docs/images/liningup.png                      |  Bin 74728 -> 0 bytes
 docs/images/workflowarrow.png                 |  Bin 3595 -> 0 bytes
 docs/implementation.md                        |  275 -
 docs/incremental_processing.md                |  237 -
 docs/index.md                                 |   26 -
 docs/js/customscripts.js                      |   55 -
 docs/js/jekyll-search.js                      |    1 -
 docs/js/jquery.ba-throttle-debounce.min.js    |    9 -
 docs/js/jquery.localScroll.min.js             |    7 -
 docs/js/jquery.navgoco.min.js                 |    8 -
 docs/js/jquery.scrollTo.min.js                |    7 -
 docs/js/jquery.shuffle.min.js                 | 1588 -----
 docs/js/mydoc_scroll.html                     |  240 -
 docs/js/toc.js                                |   82 -
 docs/licenses/LICENSE                         |   21 -
 docs/licenses/LICENSE-BSD-NAVGOCO.txt         |   27 -
 docs/migration_guide.md                       |   71 -
 docs/pages/news/news.html                     |   32 -
 docs/pages/news/news_archive.html             |   41 -
 docs/powered_by.md                            |   40 -
 docs/quickstart.md                            | 1248 ----
 docs/roadmap.md                               |   14 -
 docs/s3_filesystem.md                         |   79 -
 docs/search.json                              |   33 -
 docs/sitemap.xml                              |   24 -
 docs/sql_queries.md                           |   68 -
 docs/use_cases.md                             |   77 -
 136 files changed, 17358 deletions(-)
 delete mode 100644 docs/.gitignore
 delete mode 100644 docs/404.md
 delete mode 100644 docs/CNAME
 delete mode 100644 docs/Dockerfile
 delete mode 100644 docs/Gemfile
 delete mode 100644 docs/Gemfile.lock
 delete mode 100644 docs/_config.yml
 delete mode 100644 docs/_data/alerts.yml
 delete mode 100644 docs/_data/sidebars/mydoc_sidebar.yml
 delete mode 100644 docs/_data/strings.yml
 delete mode 100644 docs/_data/tags.yml
 delete mode 100644 docs/_data/topnav.yml
 delete mode 100644 docs/_includes/archive.html
 delete mode 100644 docs/_includes/callout.html
 delete mode 100644 docs/_includes/custom/getting_started_series.html
 delete mode 100644 docs/_includes/custom/getting_started_series_next.html
 delete mode 100644 docs/_includes/custom/series_acme.html
 delete mode 100644 docs/_includes/custom/series_acme_next.html
 delete mode 100644 docs/_includes/custom/sidebarconfigs.html
 delete mode 100644 docs/_includes/custom/usermap.html
 delete mode 100644 docs/_includes/custom/usermapcomplex.html
 delete mode 100644 docs/_includes/disqus.html
 delete mode 100644 docs/_includes/feedback.html
 delete mode 100755 docs/_includes/footer.html
 delete mode 100644 docs/_includes/google_analytics.html
 delete mode 100644 docs/_includes/head.html
 delete mode 100644 docs/_includes/head_print.html
 delete mode 100644 docs/_includes/image.html
 delete mode 100644 docs/_includes/important.html
 delete mode 100644 docs/_includes/initialize_shuffle.html
 delete mode 100644 docs/_includes/inline_image.html
 delete mode 100644 docs/_includes/links.html
 delete mode 100644 docs/_includes/note.html
 delete mode 100644 docs/_includes/sidebar.html
 delete mode 100644 docs/_includes/taglogic.html
 delete mode 100644 docs/_includes/tip.html
 delete mode 100644 docs/_includes/toc.html
 delete mode 100644 docs/_includes/topnav.html
 delete mode 100644 docs/_includes/warning.html
 delete mode 100644 docs/_layouts/default.html
 delete mode 100644 docs/_layouts/default_print.html
 delete mode 100644 docs/_layouts/none.html
 delete mode 100644 docs/_layouts/page.html
 delete mode 100644 docs/_layouts/page_print.html
 delete mode 100644 docs/_layouts/post.html
 delete mode 100644 docs/_posts/2016-12-30-strata-talk-2017.md
 delete mode 100644 docs/admin_guide.md
 delete mode 100644 docs/api_docs.md
 delete mode 100644 docs/code_and_design.md
 delete mode 100644 docs/community.md
 delete mode 100644 docs/comparison.md
 delete mode 100644 docs/concepts.md
 delete mode 100644 docs/configurations.md
 delete mode 100755 docs/css/bootstrap.min.css
 delete mode 100644 docs/css/customstyles.css
 delete mode 100644 docs/css/font-awesome.min.css
 delete mode 100644 docs/css/fonts/FontAwesome.otf
 delete mode 100644 docs/css/fonts/fontawesome-webfont.eot
 delete mode 100644 docs/css/fonts/fontawesome-webfont.svg
 delete mode 100644 docs/css/fonts/fontawesome-webfont.ttf
 delete mode 100644 docs/css/fonts/fontawesome-webfont.woff
 delete mode 100644 docs/css/fonts/fontawesome-webfont.woff2
 delete mode 100644 docs/css/lavish-bootstrap.css
 delete mode 100755 docs/css/modern-business.css
 delete mode 100644 docs/css/printstyles.css
 delete mode 100644 docs/css/syntax.css
 delete mode 100644 docs/css/theme-blue.css
 delete mode 100644 docs/css/theme-green.css
 delete mode 100644 docs/dev_setup.md
 delete mode 100644 docs/feed.xml
 delete mode 100644 docs/fonts/FontAwesome.otf
 delete mode 100644 docs/fonts/fontawesome-webfont.eot
 delete mode 100644 docs/fonts/fontawesome-webfont.svg
 delete mode 100644 docs/fonts/fontawesome-webfont.ttf
 delete mode 100644 docs/fonts/fontawesome-webfont.woff
 delete mode 100644 docs/fonts/glyphicons-halflings-regular.eot
 delete mode 100644 docs/fonts/glyphicons-halflings-regular.svg
 delete mode 100644 docs/fonts/glyphicons-halflings-regular.ttf
 delete mode 100644 docs/fonts/glyphicons-halflings-regular.woff
 delete mode 100644 docs/fonts/glyphicons-halflings-regular.woff2
 delete mode 100644 docs/gcs_filesystem.md
 delete mode 100644 docs/images/androidsdkmanagericon.png
 delete mode 100644 docs/images/async_compac_1.png
 delete mode 100644 docs/images/async_compac_2.png
 delete mode 100644 docs/images/async_compac_3.png
 delete mode 100644 docs/images/async_compac_4.png
 delete mode 100644 docs/images/authorizegithubscreen2.png
 delete mode 100644 docs/images/authorizeongithub.png
 delete mode 100644 docs/images/company_logo.png
 delete mode 100644 docs/images/company_logo_big.png
 delete mode 100644 docs/images/favicon.ico
 delete mode 100644 docs/images/helpapi-01.png
 delete mode 100644 docs/images/helpapi.svg
 delete mode 100644 docs/images/hoodie_commit_duration.png
 delete mode 100644 docs/images/hoodie_cow.png
 delete mode 100644 docs/images/hoodie_intro_1.png
 delete mode 100644 docs/images/hoodie_log_format_v2.png
 delete mode 100644 docs/images/hoodie_mor.png
 delete mode 100644 docs/images/hoodie_query_perf_hive.png
 delete mode 100644 docs/images/hoodie_query_perf_presto.png
 delete mode 100644 docs/images/hoodie_query_perf_spark.png
 delete mode 100644 docs/images/hoodie_timeline.png
 delete mode 100644 docs/images/hoodie_upsert_dag.png
 delete mode 100644 docs/images/hoodie_upsert_perf1.png
 delete mode 100644 docs/images/hoodie_upsert_perf2.png
 delete mode 100644 docs/images/illustratoroptions.png
 delete mode 100644 docs/images/itermexample.png
 delete mode 100644 docs/images/jekyll.png
 delete mode 100644 docs/images/killalljekyll.png
 delete mode 100644 docs/images/liningup.png
 delete mode 100644 docs/images/workflowarrow.png
 delete mode 100644 docs/implementation.md
 delete mode 100644 docs/incremental_processing.md
 delete mode 100644 docs/index.md
 delete mode 100644 docs/js/customscripts.js
 delete mode 100644 docs/js/jekyll-search.js
 delete mode 100644 docs/js/jquery.ba-throttle-debounce.min.js
 delete mode 100644 docs/js/jquery.localScroll.min.js
 delete mode 100755 docs/js/jquery.navgoco.min.js
 delete mode 100644 docs/js/jquery.scrollTo.min.js
 delete mode 100644 docs/js/jquery.shuffle.min.js
 delete mode 100644 docs/js/mydoc_scroll.html
 delete mode 100644 docs/js/toc.js
 delete mode 100644 docs/licenses/LICENSE
 delete mode 100644 docs/licenses/LICENSE-BSD-NAVGOCO.txt
 delete mode 100644 docs/migration_guide.md
 delete mode 100644 docs/pages/news/news.html
 delete mode 100644 docs/pages/news/news_archive.html
 delete mode 100644 docs/powered_by.md
 delete mode 100644 docs/quickstart.md
 delete mode 100644 docs/roadmap.md
 delete mode 100644 docs/s3_filesystem.md
 delete mode 100644 docs/search.json
 delete mode 100644 docs/sitemap.xml
 delete mode 100644 docs/sql_queries.md
 delete mode 100644 docs/use_cases.md

diff --git a/docs/.gitignore b/docs/.gitignore
deleted file mode 100644
index f380cae1b..000000000
--- a/docs/.gitignore
+++ /dev/null
@@ -1,4 +0,0 @@
-_site
-.sass-cache
-.jekyll-metadata
-.ruby-version
diff --git a/docs/404.md b/docs/404.md
deleted file mode 100644
index a7b58c002..000000000
--- a/docs/404.md
+++ /dev/null
@@ -1,6 +0,0 @@
----
-title: "Page Not Found"
-search: exclude
----  
-
-Sorry, but the page you were trying to view does not exist. Try searching for it or looking at the URL to see if it looks correct.
diff --git a/docs/CNAME b/docs/CNAME
deleted file mode 100644
index c8dd5d09f..000000000
--- a/docs/CNAME
+++ /dev/null
@@ -1 +0,0 @@
-hudi.apache.org
\ No newline at end of file
diff --git a/docs/Dockerfile b/docs/Dockerfile
deleted file mode 100644
index b1fa52c47..000000000
--- a/docs/Dockerfile
+++ /dev/null
@@ -1,26 +0,0 @@
-FROM ruby:2.1
-MAINTAINER mrafayaleem@gmail.com
-
-RUN apt-get clean \
-  && mv /var/lib/apt/lists /var/lib/apt/lists.broke \
-  && mkdir -p /var/lib/apt/lists/partial
-
-RUN apt-get update
-
-RUN apt-get install -y \
-    node \
-    python-pygments \
-  && apt-get clean \
-  && rm -rf /var/lib/apt/lists/
-
-WORKDIR /tmp
-ADD Gemfile /tmp/
-ADD Gemfile.lock /tmp/
-RUN bundle install
-
-VOLUME /src
-EXPOSE 4000
-
-WORKDIR /src
-ENTRYPOINT ["jekyll"]
-
diff --git a/docs/Gemfile b/docs/Gemfile
deleted file mode 100644
index b301edabe..000000000
--- a/docs/Gemfile
+++ /dev/null
@@ -1,10 +0,0 @@
-source "https://rubygems.org"
-
-
-gem "jekyll", "3.3.1"
-
-
-group :jekyll_plugins do
-   gem "jekyll-feed", "~> 0.6"
-   gem 'github-pages', '~> 106'
-end
diff --git a/docs/Gemfile.lock b/docs/Gemfile.lock
deleted file mode 100644
index 8359d5757..000000000
--- a/docs/Gemfile.lock
+++ /dev/null
@@ -1,156 +0,0 @@
-GEM
-  remote: https://rubygems.org/
-  specs:
-    activesupport (4.2.7)
-      i18n (~> 0.7)
-      json (~> 1.7, >= 1.7.7)
-      minitest (~> 5.1)
-      thread_safe (~> 0.3, >= 0.3.4)
-      tzinfo (~> 1.1)
-    addressable (2.4.0)
-    coffee-script (2.4.1)
-      coffee-script-source
-      execjs
-    coffee-script-source (1.12.2)
-    colorator (1.1.0)
-    concurrent-ruby (1.1.4)
-    ethon (0.12.0)
-      ffi (>= 1.3.0)
-    execjs (2.7.0)
-    faraday (0.15.4)
-      multipart-post (>= 1.2, < 3)
-    ffi (1.10.0)
-    forwardable-extended (2.6.0)
-    gemoji (2.1.0)
-    github-pages (106)
-      activesupport (= 4.2.7)
-      github-pages-health-check (= 1.2.0)
-      jekyll (= 3.3.1)
-      jekyll-avatar (= 0.4.2)
-      jekyll-coffeescript (= 1.0.1)
-      jekyll-feed (= 0.8.0)
-      jekyll-gist (= 1.4.0)
-      jekyll-github-metadata (= 2.2.0)
-      jekyll-mentions (= 1.2.0)
-      jekyll-paginate (= 1.1.0)
-      jekyll-redirect-from (= 0.11.0)
-      jekyll-relative-links (= 0.2.1)
-      jekyll-sass-converter (= 1.3.0)
-      jekyll-seo-tag (= 2.1.0)
-      jekyll-sitemap (= 0.12.0)
-      jekyll-swiss (= 0.4.0)
-      jemoji (= 0.7.0)
-      kramdown (= 1.11.1)
-      liquid (= 3.0.6)
-      listen (= 3.0.6)
-      mercenary (~> 0.3)
-      minima (= 2.0.0)
-      rouge (= 1.11.1)
-      terminal-table (~> 1.4)
-    github-pages-health-check (1.2.0)
-      addressable (~> 2.3)
-      net-dns (~> 0.8)
-      octokit (~> 4.0)
-      public_suffix (~> 1.4)
-      typhoeus (~> 0.7)
-    html-pipeline (2.10.0)
-      activesupport (>= 2)
-      nokogiri (>= 1.4)
-    i18n (0.9.5)
-      concurrent-ruby (~> 1.0)
-    jekyll (3.3.1)
-      addressable (~> 2.4)
-      colorator (~> 1.0)
-      jekyll-sass-converter (~> 1.0)
-      jekyll-watch (~> 1.1)
-      kramdown (~> 1.3)
-      liquid (~> 3.0)
-      mercenary (~> 0.3.3)
-      pathutil (~> 0.9)
-      rouge (~> 1.7)
-      safe_yaml (~> 1.0)
-    jekyll-avatar (0.4.2)
-      jekyll (~> 3.0)
-    jekyll-coffeescript (1.0.1)
-      coffee-script (~> 2.2)
-    jekyll-feed (0.8.0)
-      jekyll (~> 3.3)
-    jekyll-gist (1.4.0)
-      octokit (~> 4.2)
-    jekyll-github-metadata (2.2.0)
-      jekyll (~> 3.1)
-      octokit (~> 4.0, != 4.4.0)
-    jekyll-mentions (1.2.0)
-      activesupport (~> 4.0)
-      html-pipeline (~> 2.3)
-      jekyll (~> 3.0)
-    jekyll-paginate (1.1.0)
-    jekyll-redirect-from (0.11.0)
-      jekyll (>= 2.0)
-    jekyll-relative-links (0.2.1)
-      jekyll (~> 3.3)
-    jekyll-sass-converter (1.3.0)
-      sass (~> 3.2)
-    jekyll-seo-tag (2.1.0)
-      jekyll (~> 3.3)
-    jekyll-sitemap (0.12.0)
-      jekyll (~> 3.3)
-    jekyll-swiss (0.4.0)
-    jekyll-watch (1.5.1)
-      listen (~> 3.0)
-    jemoji (0.7.0)
-      activesupport (~> 4.0)
-      gemoji (~> 2.0)
-      html-pipeline (~> 2.2)
-      jekyll (>= 3.0)
-    json (1.8.6)
-    kramdown (1.11.1)
-    liquid (3.0.6)
-    listen (3.0.6)
-      rb-fsevent (>= 0.9.3)
-      rb-inotify (>= 0.9.7)
-    mercenary (0.3.6)
-    mini_portile2 (2.4.0)
-    minima (2.0.0)
-    minitest (5.11.3)
-    multipart-post (2.0.0)
-    net-dns (0.9.0)
-    nokogiri (1.10.1)
-      mini_portile2 (~> 2.4.0)
-    octokit (4.13.0)
-      sawyer (~> 0.8.0, >= 0.5.3)
-    pathutil (0.16.2)
-      forwardable-extended (~> 2.6)
-    public_suffix (1.5.3)
-    rb-fsevent (0.10.3)
-    rb-inotify (0.10.0)
-      ffi (~> 1.0)
-    rouge (1.11.1)
-    safe_yaml (1.0.4)
-    sass (3.7.3)
-      sass-listen (~> 4.0.0)
-    sass-listen (4.0.0)
-      rb-fsevent (~> 0.9, >= 0.9.4)
-      rb-inotify (~> 0.9, >= 0.9.7)
-    sawyer (0.8.1)
-      addressable (>= 2.3.5, < 2.6)
-      faraday (~> 0.8, < 1.0)
-    terminal-table (1.8.0)
-      unicode-display_width (~> 1.1, >= 1.1.1)
-    thread_safe (0.3.6)
-    typhoeus (0.8.0)
-      ethon (>= 0.8.0)
-    tzinfo (1.2.5)
-      thread_safe (~> 0.1)
-    unicode-display_width (1.4.1)
-
-PLATFORMS
-  ruby
-
-DEPENDENCIES
-  github-pages (~> 106)
-  jekyll (= 3.3.1)
-  jekyll-feed (~> 0.6)
-
-BUNDLED WITH
-   1.14.3
diff --git a/docs/_config.yml b/docs/_config.yml
deleted file mode 100644
index baf6748dc..000000000
--- a/docs/_config.yml
+++ /dev/null
@@ -1,87 +0,0 @@
-
-output: web
-# this property is useful for conditional filtering of content that is separate from the PDF.
-
-topnav_title: Hoodie
-# this appears on the top navigation bar next to the home button
-
-site_title: Hoodie
-# this appears in the html browser tab for the site title (seen mostly by search engines, not users)
-
-company_name: Uber Technologies Inc.
-# this appears in the footer
-
-github_editme_path:
-# if you're using Github, provide the basepath to the branch you've created for reviews, following the sample here. if not, leave this value blank.
-
-disqus_shortname:
-# if you're using disqus for comments, add the shortname here. if not, leave this value blank.
-
-host: 127.0.0.1
-# the preview server used. Leave as is.
-
-port: 4000
-# the port where the preview is rendered. You can leave this as is unless you have other Jekyll builds using this same port that might cause conflicts. in that case, use another port such as 4006.
-
-exclude:
-  - .idea/
-  - .gitignore
-# these are the files and directories that jekyll will exclude from the build
-
-feedback_subject_line: Hoodie Documentation
-
-feedback_email: hoodie-dev-group@uber.com
-# used as a contact email for the Feedback link in the top navigation bar
-
-# feedback_disable: true
-# if you uncomment the previous line, the Feedback link gets removed
-
-# feedback_text: "Need help?"
-# if you uncomment the previous line, it changes the Feedback text
-
-# feedback_link: "http://helpy.io/"
-# if you uncomment the previous line, it changes where the feedback link points to
-
-highlighter: rouge
-# library used for syntax highlighting
-
-markdown: kramdown
-kramdown:
- input: GFM
- auto_ids: true
- hard_wrap: false
- syntax_highlighter: rouge
-
-# filter used to process markdown. note that kramdown differs from github-flavored markdown in some subtle ways
-
-defaults:
-  -
-    scope:
-      path: ""
-      type: "pages"
-    values:
-      layout: "page"
-      comments: true
-      search: true
-      sidebar: mydoc_sidebar
-
-  -
-    scope:
-      path: ""
-      type: "posts"
-    values:
-      layout: "post"
-      comments: true
-      search: true
-      sidebar: mydoc_sidebar
-
-# these are defaults used for the frontmatter for these file types
-
-sidebars:
-- mydoc_sidebar
-
-description: "Hoodie is a Spark Library, that provides upserts and incremental processing capaibilities on Hadoop datasets"
-# the description is used in the feed.xml file
-
-# needed for sitemap.xml file only
-url: http://github.com/uber/hoodie
diff --git a/docs/_data/alerts.yml b/docs/_data/alerts.yml
deleted file mode 100644
index 157e1622b..000000000
--- a/docs/_data/alerts.yml
+++ /dev/null
@@ -1,15 +0,0 @@
-tip: '<div class="alert alert-success" role="alert"><i class="fa fa-check-square-o"></i> <b>Tip: </b>'
-note: '<div class="alert alert-info" role="alert"><i class="fa fa-info-circle"></i> <b>Note: </b>'
-important: '<div class="alert alert-warning" role="alert"><i class="fa fa-warning"></i> <b>Important: </b>'
-warning: '<div class="alert alert-danger" role="alert"><i class="fa fa-exclamation-circle"></i> <b>Warning: </b>'
-end: '</div>'
-
-callout_danger: '<div class="bs-callout bs-callout-danger">'
-callout_default: '<div class="bs-callout bs-callout-default">'
-callout_primary: '<div class="bs-callout bs-callout-primary">'
-callout_success: '<div class="bs-callout bs-callout-success">'
-callout_info: '<div class="bs-callout bs-callout-info">'
-callout_warning: '<div class="bs-callout bs-callout-warning">'
-
-hr_faded: '<hr class="faded"/>'
-hr_shaded: '<hr class="shaded"/>'
\ No newline at end of file
diff --git a/docs/_data/sidebars/mydoc_sidebar.yml b/docs/_data/sidebars/mydoc_sidebar.yml
deleted file mode 100644
index c4f6c46f2..000000000
--- a/docs/_data/sidebars/mydoc_sidebar.yml
+++ /dev/null
@@ -1,66 +0,0 @@
-# This is your sidebar TOC. The sidebar code loops through sections here and provides the appropriate formatting.
-
-entries:
-- title: sidebar
-  product: Documentation
-  version:
-  folders:
-
-  - title: Getting Started
-    output: web
-    folderitems:
-
-    - title: Overview
-      url: /index.html
-      output: web
-      type: homepage
-
-    - title: Quickstart
-      url: /quickstart.html
-      output: web
-
-    - title: Use Cases
-      url: /use_cases.html
-      output: web
-
-    - title: Talks & Powered By
-      url: /powered_by.html
-      output: web
-
-  - title: Documentation
-    output: web
-    folderitems:
-
-    - title: Concepts
-      url: /concepts.html
-      output: web
-
-    - title: Implementation
-      url: /implementation.html
-      output: web
-
-    - title: Configurations
-      url: /configurations.html
-      output: web
-
-    - title: SQL Queries
-      url: /sql_queries.html
-      output: web
-
-    - title: Migration Guide
-      url: /migration_guide.html
-      output: web
-
-    - title: Incremental Processing
-      url: /incremental_processing.html
-      output: web
-
-    - title: Admin Guide
-      url: /admin_guide.html
-      output: web
-
-    - title: Comparison
-      url: /comparison.html
-      output: web
-
-
diff --git a/docs/_data/strings.yml b/docs/_data/strings.yml
deleted file mode 100644
index d7c13924f..000000000
--- a/docs/_data/strings.yml
+++ /dev/null
@@ -1,5 +0,0 @@
-
-
-# placed here for translation purposes
-search_placeholder_text: search...
-search_no_results_text: No results found.
diff --git a/docs/_data/tags.yml b/docs/_data/tags.yml
deleted file mode 100644
index 0ad9ce56b..000000000
--- a/docs/_data/tags.yml
+++ /dev/null
@@ -1,10 +0,0 @@
-allowed-tags:
-  - getting_started
-  - overview
-  - quickstart
-  - concepts
-  - sql_queries
-  - incremental_processing
-  - configurations
-  - news
-  - troubleshooting
diff --git a/docs/_data/topnav.yml b/docs/_data/topnav.yml
deleted file mode 100644
index 190573a3e..000000000
--- a/docs/_data/topnav.yml
+++ /dev/null
@@ -1,30 +0,0 @@
-## Topnav single links
-## if you want to list an external url, use external_url instead of url. the theme will apply a different link base.
-topnav:
-- title: Topnav
-  items:
-    - title: News
-      url: /news
-    - title: Community
-      url: /community.html
-    - title: Github
-      external_url: https://github.com/uber/hoodie
-
-#Topnav dropdowns
-topnav_dropdowns:
-- title: Topnav dropdowns
-  folders:
-    - title: Developer Resources
-      folderitems:
-          - title: Setup
-            url: /dev_setup.html
-            output: web
-          - title: API Docs
-            url: /api_docs.html
-            output: web
-          - title: Code Structure
-            url: /code_and_design.html
-            output: web
-          - title: Roadmap
-            url: /roadmap.html
-            output: web
diff --git a/docs/_includes/archive.html b/docs/_includes/archive.html
deleted file mode 100644
index 275850c9c..000000000
--- a/docs/_includes/archive.html
+++ /dev/null
@@ -1,15 +0,0 @@
----
-layout: default
-type: archive
----
-
-<div class="post-header">
-  <h1 class="post-title-main">{{ page.title }}</h1>
-</div>
-<div class="post-content">
-
-{{ content }}
-</div>
-
- 
-
diff --git a/docs/_includes/callout.html b/docs/_includes/callout.html
deleted file mode 100644
index d492b1830..000000000
--- a/docs/_includes/callout.html
+++ /dev/null
@@ -1 +0,0 @@
-<div markdown="span" class="bs-callout bs-callout-{{include.type}}">{{include.content}}</div>
diff --git a/docs/_includes/custom/getting_started_series.html b/docs/_includes/custom/getting_started_series.html
deleted file mode 100644
index 5b573a9d0..000000000
--- a/docs/_includes/custom/getting_started_series.html
+++ /dev/null
@@ -1,19 +0,0 @@
-<div class="seriesContext">
-    <div class="btn-group">
-        <button type="button" data-toggle="dropdown" class="btn btn-primary dropdown-toggle">Getting Started <span class="caret"></span></button>
-        <ol class="dropdown-menu">
-            {% assign pages = site.pages | sort:"weight"  %}
-            {% for p in pages %}
-            {% if p.series == "Getting Started" %}
-            {% if p.url == page.url %}
-            <li class="active"> → {{p.title}}</li>
-            {% else %}
-            <li>
-                <a href="{{p.url | remove: "/"}}">{{p.title}}</a>
-            </li>
-            {% endif %}
-            {% endif %}
-            {% endfor %}
-        </ol>
-    </div>
-</div>
\ No newline at end of file
diff --git a/docs/_includes/custom/getting_started_series_next.html b/docs/_includes/custom/getting_started_series_next.html
deleted file mode 100644
index c464214b0..000000000
--- a/docs/_includes/custom/getting_started_series_next.html
+++ /dev/null
@@ -1,10 +0,0 @@
-<p>{% assign series_pages = site.tags.series_acme %}
-    {% for p in pages %}
-    {% if p.series == "Getting Started" %}
-    {% assign nextTopic = page.weight | plus: "1"  %}
-    {% if p.weight == nextTopic  %}
-    <a href="{{p.url | remove: "/"}}"><button type="button" class="btn btn-primary">Next: {{p.title}}</button></a>
-    {% endif %}
-    {% endif %}
-    {% endfor %}
-</p>
\ No newline at end of file
diff --git a/docs/_includes/custom/series_acme.html b/docs/_includes/custom/series_acme.html
deleted file mode 100644
index b9ebf4437..000000000
--- a/docs/_includes/custom/series_acme.html
+++ /dev/null
@@ -1,19 +0,0 @@
-<div class="seriesContext">
-    <div class="btn-group">
-        <button type="button" data-toggle="dropdown" class="btn btn-primary dropdown-toggle">Series Demo <span class="caret"></span></button>
-        <ol class="dropdown-menu">
-            {% assign pages = site.pages | sort:"weight"  %}
-            {% for p in pages %}
-            {% if p.series == "ACME series" %}
-            {% if p.url == page.url %}
-            <li class="active"> → {{p.weight}}. {{p.title}}</li>
-            {% else %}
-            <li>
-                <a href="{{p.url | remove: "/"}}">{{p.weight}}. {{p.title}}</a>
-            </li>
-            {% endif %}
-            {% endif %}
-            {% endfor %}
-        </ol>
-    </div>
-</div>
\ No newline at end of file
diff --git a/docs/_includes/custom/series_acme_next.html b/docs/_includes/custom/series_acme_next.html
deleted file mode 100644
index 9c9a11a5e..000000000
--- a/docs/_includes/custom/series_acme_next.html
+++ /dev/null
@@ -1,30 +0,0 @@
-<div class="seriesContext">
-    <a>
-        {% assign pages = site.pages | sort:"weight"  %}
-        {% for pg in pages %}
-        {% if pg.series == "ACME series" %}
-        {% if pg.weight > page.weight %}
-        <a href="{{pg.url | remove: '/'}}"><button type="button" class="btn btn-primary">Next: {{pg.title}}</button></a>
-        {% break %}
-        {% endif %}
-        {% endif %}
-        {% endfor %}
-    </a>
-    &nbsp;
-    <div class="btn-group">
-        <button type="button" data-toggle="dropdown" class="btn btn-primary dropdown-toggle">Jump to: <span class="caret"></span></button>
-        <ol class="dropdown-menu">
-            {% for pg in pages %}
-            {% if pg.series == "ACME series" %}
-            {% if pg.url == page.url %}
-            <li class="active"> → {{pg.weight}}. {{pg.title}}</li>
-            {% else %}
-            <li>
-                <a href="{{pg.url | remove: '/'}}">{{pg.title}}</a>
-            </li>
-            {% endif %}
-            {% endif %}
-            {% endfor %}
-        </ol>
-    </div>
-</div>
\ No newline at end of file
diff --git a/docs/_includes/custom/sidebarconfigs.html b/docs/_includes/custom/sidebarconfigs.html
deleted file mode 100644
index cfdacc4c4..000000000
--- a/docs/_includes/custom/sidebarconfigs.html
+++ /dev/null
@@ -1,19 +0,0 @@
-{% if page.sidebar == "home_sidebar" %}
-{% assign sidebar = site.data.sidebars.home_sidebar.entries %}
-
-{% elsif page.sidebar == "mydoc_sidebar" %}
-{% assign sidebar = site.data.sidebars.mydoc_sidebar.entries %}
-
-{% else %}
-{% assign sidebar = site.data.sidebars.home_sidebar.entries %}
-{% endif %}
-
-
-
-{% comment %}
-sidebar configuration for print files
-{% endcomment %}
-
-{% if site.product == "mydoc" %}
-{% assign sidebar_pdf = site.data.sidebars.mydoc_sidebar.entries %}
-{% endif %}
diff --git a/docs/_includes/custom/usermap.html b/docs/_includes/custom/usermap.html
deleted file mode 100644
index f90b5c2cb..000000000
--- a/docs/_includes/custom/usermap.html
+++ /dev/null
@@ -1,14 +0,0 @@
-
-<div id="userMap">
-<div class="content"><a href="p2_sample1.html"><div class="box box1">Connect to ADB</div></a></div>
-<div class="arrow">→</div>
-<div class="content"><a href="p2_sample2.html"><div class="box box2">Download and Build the Starter Kit</div></a></div>
-<div class="arrow">→</div>
-<div class="content"><a href="p2_sample3.html"><div class="box box3">Take a Tour</div></a></div>
-<div class="arrow">→</div>
-<div class="content"><a href="p2_sample4.html"><div class="box box4">Load Your Widgets</div></a></div>
-<div class="arrow">→</div>
-<div class="content"><a href="p2_sample5.html"><div class="box box5">Query for Something</div></a></div>
-<div class="clearfix"></div>
-
-</div>
diff --git a/docs/_includes/custom/usermapcomplex.html b/docs/_includes/custom/usermapcomplex.html
deleted file mode 100644
index c75a65254..000000000
--- a/docs/_includes/custom/usermapcomplex.html
+++ /dev/null
@@ -1,91 +0,0 @@
-<div id="userMap">
-
-    <!-- Button trigger modal -->
-<button type="button" class="btn btn-default btn-lg modalButton1" data-toggle="modal" data-target="#myModal1">Get Started</button>
-
-    <!-- Modal -->
-    <div class="modal fade" id="myModal1" tabindex="-1" role="dialog" aria-labelledby="myModalLabel">
-      <div class="modal-dialog" role="document">
-        <div class="modal-content">
-          <div class="modal-header">
-            <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">&times;</span></button>
-            <h4 class="modal-title" id="myModalLabel">Get Started</h4>
-          </div>
-          <div class="modal-body">
-<p>This is just dummy text ... Your first steps should be to get started. You will need to do the following:</p>
-
-    <ul>
-        <li><a href="p2_sample6.html">Sample 6</a></li>
-        <li><a href="p2_sample7.html">Sample 7</a></li>
-        <li><a href="p2_sample8.html">Sample 8</a></li>
-    </ul>
-    <p>If you run into any of these setup issues, you must solve them before you can continue on.</p>
-
-          </div>
-          <div class="modal-footer">
-            <button type="button" class="btn btn-default" data-dismiss="modal">Close</button>
-          </div>
-        </div>
-      </div>
-    </div>
-            <span class="complexArrow">→</span>
-
-            <!-- Button trigger modal -->
-            <button type="button" class="btn btn-default btn-lg modalButton2" data-toggle="modal" data-target="#myModal2">Build your widgets</button>
-            <!-- Modal -->
-            <div class="modal fade" id="myModal2" tabindex="-1" role="dialog" aria-labelledby="myModalLabel">
-              <div class="modal-dialog" role="document">
-                <div class="modal-content">
-                  <div class="modal-header">
-                    <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">&times;</span></button>
-                    <h4 class="modal-title" id="myModalLabel">Build your widgets</h4>
-                  </div>
-                  <div class="modal-body">
-                    <p>In this step, you will build the widgets for your system. The widgets form the various components that blah blah blah this is dummy text power the nuclear capabilities of your energy transformer into deep space using wormhole technology and warp drive speeds.</p>
-
-                    <p>In order to configure your widgets, you will need to follow these topics:</p>
-                    <ul>
-                        <li><a href="p2_sample9.html">Sample 9</a></li>
-                        <li><a href="p2_sample10.html">Sample 10</a></li>
-                        <li><a href="p2_sample11.html">Sample 11</a></li>
-                    </ul>
-                  </div>
-                  <div class="modal-footer">
-                    <button type="button" class="btn btn-default" data-dismiss="modal">Close</button>
-                  </div>
-                </div>
-              </div>
-            </div>
-
-            <span class="complexArrow">→</span>
-
-            <!-- Button trigger modal -->
-<button type="button" class="btn btn-default btn-lg modalButton3" data-toggle="modal" data-target="#myModal3">Publish your app</button>
-            <!-- Modal -->
-            <div class="modal fade" id="myModal3" tabindex="-1" role="dialog" aria-labelledby="myModalLabel">
-              <div class="modal-dialog" role="document">
-                <div class="modal-content">
-                  <div class="modal-header">
-                    <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">&times;</span></button>
-                    <h4 class="modal-title" id="myModalLabel">Publish your app</h4>
-                  </div>
-                  <div class="modal-body">
-                    <p>After you've configured all the necessary components to build your space transformer, you need to publish your app. Of course this content is also just dummy text. Pay no particular attention to the content but rather the format and placement of the map.</p>
-                    <p>To publish your app, see the following:
-                        <ul>
-                            <li><a href="p2_sample12.html">Sample 12</a></li>
-                            <li><a href="p2_sample13.html">Sample 13</a></li>
-                            <li><a href="p2_sample14.html">Sample 14</a></li>
-                        </ul>
-                  </div>
-                  <div class="modal-footer">
-                    <button type="button" class="btn btn-default" data-dismiss="modal">Close</button>
-                  </div>
-                </div>
-              </div>
-            </div>
-
-
-<div class="clearfix"></div>
-
-</div>
diff --git a/docs/_includes/disqus.html b/docs/_includes/disqus.html
deleted file mode 100644
index 4fad7a25f..000000000
--- a/docs/_includes/disqus.html
+++ /dev/null
@@ -1,16 +0,0 @@
-{% if site.disqus_shortname %}
-
-    <div id="disqus_thread"></div>
-    <script type="text/javascript">
-        /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */
-        var disqus_shortname = '{{site.disqus_shortname}}'; // required: replace example with your forum shortname
-
-        /* * * DON'T EDIT BELOW THIS LINE * * */
-        (function() {
-            var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true;
-            dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js';
-            (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq);
-        })();
-    </script>
-    <noscript>Please enable JavaScript to view the <a href="https://disqus.com/?ref_noscript">comments powered by Disqus.</a></noscript>
-{% endif %}
\ No newline at end of file
diff --git a/docs/_includes/feedback.html b/docs/_includes/feedback.html
deleted file mode 100644
index 4b55e44f8..000000000
--- a/docs/_includes/feedback.html
+++ /dev/null
@@ -1,13 +0,0 @@
-<li>
-{% if site.feedback_text %}
-  {% assign feedback_text = site.feedback_text %}
-{% else %}
-  {% assign feedback_text = "Feedback" %}
-{% endif %}
-
-{% if site.feedback_link %}
-  <a class="email" title="Submit feedback" href="{{site.feedback_link}}">{{feedback_text}}</a>
-{% else %}
-  <a class="email" title="Submit feedback" href="#" onclick="javascript:window.location='mailto:{{site.feedback_email}}?subject={{site.feedback_subject_line}} feedback&body=I have some feedback about the {{page.title}} page: ' + window.location.href;"><i class="fa fa-envelope-o"></i> {{feedback_text}}</a>
-{% endif %}
-<li>
diff --git a/docs/_includes/footer.html b/docs/_includes/footer.html
deleted file mode 100755
index 9847670a3..000000000
--- a/docs/_includes/footer.html
+++ /dev/null
@@ -1,9 +0,0 @@
-<footer>
-            <div class="row">
-                <div class="col-lg-12 footer">
-               &copy;{{ site.time | date: "%Y"  }} {{site.company_name}}. All rights reserved. <br />
-{% if page.last_updated %}<span>Page last updated:</span> {{page.last_updated}}<br/>{% endif %} Site last generated: {{ site.time | date: "%b %-d, %Y"  }} <br />
-                    <!--<p><img src="{{ "images/company_logo.png" }}" alt="Company logo"/></p>-->
-                </div>
-            </div>
-</footer>
diff --git a/docs/_includes/google_analytics.html b/docs/_includes/google_analytics.html
deleted file mode 100644
index 56b2ee88c..000000000
--- a/docs/_includes/google_analytics.html
+++ /dev/null
@@ -1,6 +0,0 @@
-<!-- the google_analytics_id gets auto inserted from the config file -->
-
-{% if site.google_analytics %}
-
-<script>(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)})(window,document,'script','//www.google-analytics.com/analytics.js','ga');ga('create','{{site.google_analytics}}','auto');ga('require','displayfeatures');ga('send','pageview');</script>
-{% endif %}
\ No newline at end of file
diff --git a/docs/_includes/head.html b/docs/_includes/head.html
deleted file mode 100644
index 9a6f74e7d..000000000
--- a/docs/_includes/head.html
+++ /dev/null
@@ -1,47 +0,0 @@
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1">
-<meta name="description" content="{% if page.summary %}{{ page.summary | strip_html | strip_newlines | truncate: 160 }}{% endif %}">
-<meta name="keywords" content="{{page.tags}}{% if page.tags %}, {% endif %} {{page.keywords}}">
-<title>{{ page.title }} | {{ site.site_title }}</title>
-<link rel="stylesheet" href="{{ "css/syntax.css" }}">
-
-
-<link rel="stylesheet" type="text/css" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css">
-<!--<link rel="stylesheet" type="text/css" href="css/bootstrap.min.css">-->
-<link rel="stylesheet" href="{{ "css/modern-business.css" }}">
-<link rel="stylesheet" href="{{ "css/lavish-bootstrap.css" }}">
-<link rel="stylesheet" href="{{ "css/customstyles.css" }}">
-<link rel="stylesheet" href="{{ "css/theme-blue.css" }}">
-
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.1.4/jquery.min.js"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery-cookie/1.4.1/jquery.cookie.min.js"></script>
-<script src="{{ "js/jquery.navgoco.min.js" }}"></script>
-
-
-<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.4/js/bootstrap.min.js"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/2.0.0/anchor.min.js"></script>
-<script src="{{ "js/toc.js" }}"></script>
-<script src="{{ "js/customscripts.js" }}"></script>
-
-<script>
-  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
-  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
-  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
-  })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
-
-  ga('create', 'UA-93561550-1', 'auto');
-  ga('send', 'pageview');
-
-</script>
-
-<link rel="shortcut icon" href="{{ "images/favicon.ico"  }}">
-
-<!-- HTML5 Shim and Respond.js IE8 support of HTML5 elements and media queries -->
-<!-- WARNING: Respond.js doesn't work if you view the page via file:// -->
-<!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/libs/html5shiv/3.7.0/html5shiv.js"></script>
-<script src="https://oss.maxcdn.com/libs/respond.js/1.4.2/respond.min.js"></script>
-<![endif]-->
-
-<link rel="alternate" type="application/rss+xml" title="{{ site.title }}" href="{{ "feed.xml" | prepend: site.url }}">
diff --git a/docs/_includes/head_print.html b/docs/_includes/head_print.html
deleted file mode 100644
index 8f10c28f8..000000000
--- a/docs/_includes/head_print.html
+++ /dev/null
@@ -1,33 +0,0 @@
-<meta charset="utf-8">
-<meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1">
-<meta name="description" content="{% if page.summary %}{{ page.summary | strip_html | strip_newlines | truncate: 160 }}{% endif %}">
-<meta name="keywords" content="{{page.tags}}{% if page.tags %}, {% endif %} {{page.keywords}}">
-<title>{% if page.homepage == true %} {{site.homepage_title}} {% elsif page.title %}{{ page.title }}{% endif %}  | {{ site.site_title }}</title>
-
-
-<link rel="stylesheet" href="{{ "/css/syntax.css" | prepend: site.baseurl | prepend: site.url }}">
-<link rel="stylesheet" href="{{ "/css/font-awesome.min.css" | prepend: site.baseurl | prepend: site.url }}">
-<link rel="stylesheet" href="{{ "/css/bootstrap.min.css" | prepend: site.baseurl | prepend: site.url }}">
-<link rel="stylesheet" href="{{ "/css/modern-business.css" | prepend: site.baseurl | prepend: site.url }}">
-<link rel="stylesheet" href="{{ "/css/lavish-bootstrap.css" | prepend: site.baseurl | prepend: site.url }}">
-<link rel="stylesheet" href="{{ "/css/customstyles.css" | prepend: site.baseurl | prepend: site.url }}">
-<link rel="stylesheet" href="{{ "/css/theme-green.css" | prepend: site.baseurl | prepend: site.url }}">
-<link rel="stylesheet" href="{{ "/css/syntax.css" | prepend: site.baseurl | prepend: site.url }}">
-<link rel="stylesheet" href="{{ "/css/printstyles.css" | prepend: site.baseurl }}">
-
-<script>
-    Prince.addScriptFunc("datestamp", function() {
-        return "PDF last generated: {{ site.time | date: '%B %d, %Y' }}";
-    });
-</script>
-
-<script>
-    Prince.addScriptFunc("guideName", function() {
-        return "{{site.print_title}} User Guide";
-    });
-</script>
-
-
-
-
diff --git a/docs/_includes/image.html b/docs/_includes/image.html
deleted file mode 100644
index 7b3b0fa2c..000000000
--- a/docs/_includes/image.html
+++ /dev/null
@@ -1 +0,0 @@
-<figure>{% if {{include.url}} %}<a class="no_icon" target="_blank" href="{{include.url}}">{% endif %}<img class="docimage" src="images/{{include.file}}" alt="{{include.alt}}" {% if {{include.max-width}} %}style="max-width: {{include.max-width}}px"{% endif %} />{% if {{include.url}} %}</a>{% endif %}{% if {{include.caption}} %}<figcaption>{{include.caption}}</figcaption>{% endif %}</figure>
diff --git a/docs/_includes/important.html b/docs/_includes/important.html
deleted file mode 100644
index af8824b9f..000000000
--- a/docs/_includes/important.html
+++ /dev/null
@@ -1 +0,0 @@
-<div markdown="span" class="alert alert-warning" role="alert"><i class="fa fa-warning"></i> <b>Important:</b> {{include.content}}</div>
\ No newline at end of file
diff --git a/docs/_includes/initialize_shuffle.html b/docs/_includes/initialize_shuffle.html
deleted file mode 100644
index 9a0f048dd..000000000
--- a/docs/_includes/initialize_shuffle.html
+++ /dev/null
@@ -1,130 +0,0 @@
-<script type="text/javascript">
-$(document).ready(function() {
-    $('#toc').toc({ minimumHeaders: 0, listType: 'ul', showSpeed: 0, headers: 'h2,h3,h4' });
-});
-
-</script>
-<!-- shuffle -->
-<script>
-var shuffleme = (function( $ ) {
-  'use strict';
-
-  var $grid = $('#grid'),
-      $filterOptions = $('.filter-options'),
-      $sizer = $grid.find('.shuffle_sizer'),
-
-  init = function() {
-
-    // None of these need to be executed synchronously
-    setTimeout(function() {
-      listen();
-      setupFilters();
-    }, 100);
-
-    // instantiate the plugin
-    $grid.shuffle({
-      itemSelector: '[class*="col-"]',
-      sizer: $sizer    
-    });
-  },
-
-  // Set up button clicks
-  setupFilters = function() {
-    var $btns = $filterOptions.children();
-    $btns.on('click', function() {
-      var $this = $(this),
-          isActive = $this.hasClass( 'active' ),
-          group = isActive ? 'all' : $this.data('group');
-
-      // Hide current label, show current label in title
-      if ( !isActive ) {
-        $('.filter-options .active').removeClass('active');
-      }
-
-      $this.toggleClass('active');
-
-      // Filter elements
-      $grid.shuffle( 'shuffle', group );
-    });
-
-    $btns = null;
-  },
-
-  // Re layout shuffle when images load. This is only needed
-  // below 768 pixels because the .picture-item height is auto and therefore
-  // the height of the picture-item is dependent on the image
-  // I recommend using imagesloaded to determine when an image is loaded
-  // but that doesn't support IE7
-  listen = function() {
-    var debouncedLayout = $.throttle( 300, function() {
-      $grid.shuffle('update');
-    });
-
-    // Get all images inside shuffle
-    $grid.find('img').each(function() {
-      var proxyImage;
-
-      // Image already loaded
-      if ( this.complete && this.naturalWidth !== undefined ) {
-        return;
-      }
-
-      // If none of the checks above matched, simulate loading on detached element.
-      proxyImage = new Image();
-      $( proxyImage ).on('load', function() {
-        $(this).off('load');
-        debouncedLayout();
-      });
-
-      proxyImage.src = this.src;
-    });
-
-    // Because this method doesn't seem to be perfect.
-    setTimeout(function() {
-      debouncedLayout();
-    }, 500);
-  };      
-
-  return {
-    init: init
-  };
-}( jQuery ));
-
-
-
-$(document).ready(function() {
-  shuffleme.init();
-});
-
-    </script>
-
-    <!-- new attempt-->
-
-    <script>
-    $(document).ready(function() {
-     
-    /* initialize shuffle plugin */
-    var $grid = $('#grid');
-         
-    $grid.shuffle({
-        itemSelector: '.item' // the selector for the items in the grid
-    });
- 
-});</script>
-
-<script>
-$('#filter a').click(function (e) {
-    e.preventDefault();
-         
-    // set active class
-    $('#filter a').removeClass('active');
-    $(this).addClass('active');
-         
-    // get group name from clicked item
-    var groupName = $(this).attr('data-group');
-         
-    // reshuffle grid
-    $grid.shuffle('shuffle', groupName );
-});</script>
-
-
diff --git a/docs/_includes/inline_image.html b/docs/_includes/inline_image.html
deleted file mode 100644
index 1e7fd187c..000000000
--- a/docs/_includes/inline_image.html
+++ /dev/null
@@ -1 +0,0 @@
-<img class="inline" src="images/{{include.file}}" alt="{{include.alt}}" />
diff --git a/docs/_includes/links.html b/docs/_includes/links.html
deleted file mode 100644
index 4f99e9422..000000000
--- a/docs/_includes/links.html
+++ /dev/null
@@ -1,44 +0,0 @@
-{% comment %}Get links from each sidebar, as listed in the _config.yml file under sidebars{% endcomment %}
-
-{% for sidebar in site.sidebars %}
-{% for entry in site.data.sidebars[sidebar].entries %}
-{% for folder in entry.folders %}
-{% for folderitem in folder.folderitems %}
-{% if folderitem.url contains "html#" %}
-[{{folderitem.url | remove: "/" }}]: {{folderitem.url | remove: "/"}}
-{% else %}
-[{{folderitem.url | remove: "/"  | remove: ".html"}}]: {{folderitem.url | remove: "/"}}
-{% endif %}
-{% for subfolders in folderitem.subfolders %}
-{% for subfolderitem in subfolders.subfolderitems %}
-[{{subfolderitem.url | remove: "/"  | remove: ".html"}}]: {{subfolderitem.url | remove: "/"}}
-{% endfor %}
-{% endfor %}
-{% endfor %}
-{% endfor %}
-{% endfor %}
-{% endfor %}
-
-
-{% comment %} Get links from topnav {% endcomment %}
-
-{% for entry in site.data.topnav.topnav %}
-{% for item in entry.items %}
-{% if item.external_url == null %}
-[{{item.url | remove: "/" | remove: ".html"}}]: {{item.url | remove: "/"}}
-{% endif %}
-{% endfor %}
-{% endfor %}
-
-{% comment %}Get links from topnav dropdowns {% endcomment %}
-
-{% for entry in site.data.topnav.topnav_dropdowns %}
-{% for folder in entry.folders %}
-{% for folderitem in folder.folderitems %}
-{% if folderitem.external_url == null %}
-[{{folderitem.url | remove: "/"  | remove: ".html"}}]: {{folderitem.url | remove: "/"}}
-{% endif %}
-{% endfor %}
-{% endfor %}
-{% endfor %}
-
diff --git a/docs/_includes/note.html b/docs/_includes/note.html
deleted file mode 100644
index 2c1cfe967..000000000
--- a/docs/_includes/note.html
+++ /dev/null
@@ -1 +0,0 @@
-<div markdown="span" class="alert alert-info" role="alert"><i class="fa fa-info-circle"></i> <b>Note:</b> {{include.content}}</div>
diff --git a/docs/_includes/sidebar.html b/docs/_includes/sidebar.html
deleted file mode 100644
index 1a9c23617..000000000
--- a/docs/_includes/sidebar.html
+++ /dev/null
@@ -1,56 +0,0 @@
-{% include custom/sidebarconfigs.html %}
-
-<ul id="mysidebar" class="nav">
-    <li class="sidebarTitle">{{sidebar[0].product}} {{sidebar[0].version}}</li>
-    {% for entry in sidebar %}
-    {% for folder in entry.folders %}
-    {% if folder.output contains "web" %}
-    <li>
-        <a href="#">{{ folder.title }}</a>
-        <ul>
-            {% for folderitem in folder.folderitems %}
-            {% if folderitem.output contains "web" %}
-            {% if folderitem.external_url %}
-            <li><a href="{{folderitem.external_url}}" target="_blank">{{folderitem.title}}</a></li>
-            {% elsif page.url == folderitem.url %}
-            <li class="active"><a href="{{folderitem.url | remove: "/"}}">{{folderitem.title}}</a></li>
-            {% else %}
-            <li><a href="{{folderitem.url | remove: "/"}}">{{folderitem.title}}</a></li>
-            {% endif %}
-            {% for subfolders in folderitem.subfolders %}
-            {% if subfolders.output contains "web" %}
-            <li class="subfolders">
-                <a href="#">{{ subfolders.title }}</a>
-                <ul>
-                    {% for subfolderitem in subfolders.subfolderitems %}
-                    {% if subfolderitem.output contains "web" %}
-                    {% if subfolderitem.external_url %}
-                    <li><a href="{{subfolderitem.external_url}}" target="_blank">{{subfolderitem.title}}</a></li>
-                    {% elsif page.url == subfolderitem.url %}
-                    <li class="active"><a href="{{subfolderitem.url | remove: "/"}}">{{subfolderitem.title}}</a></li>
-                    {% else %}
-                    <li><a href="{{subfolderitem.url | remove: "/"}}">{{subfolderitem.title}}</a></li>
-                    {% endif %}
-                    {% endif %}
-                    {% endfor %}
-                </ul>
-            </li>
-            {% endif %}
-            {% endfor %}
-            {% endif %}
-            {% endfor %}
-        </ul>
-        {% endif %}
-        {% endfor %}
-        {% endfor %}
-        <!-- if you aren't using the accordion, uncomment this block:
-           <p class="external">
-               <a href="#" id="collapseAll">Collapse All</a> | <a href="#" id="expandAll">Expand All</a>
-           </p>
-           -->
-    </li>
-</ul>
-</div>
-
-<!-- this highlights the active parent class in the navgoco sidebar. this is critical so that the parent expands when you're viewing a page. This must appear below the sidebar code above. Otherwise, if placed inside customscripts.js, the script runs before the sidebar code runs and the class never gets inserted.-->
-<script>$("li.active").parents('li').toggleClass("active");</script>
diff --git a/docs/_includes/taglogic.html b/docs/_includes/taglogic.html
deleted file mode 100644
index c2f76b014..000000000
--- a/docs/_includes/taglogic.html
+++ /dev/null
@@ -1,32 +0,0 @@
-<p>The following pages and posts are tagged with <button type="button" style="cursor: default" class="btn btn-default navbar-btn">{{page.tagName}}</button></p>
-<table><thead><tr><th>Title</th><th>Type</th><th>Excerpt</th></tr></thead>
-    <tbody>
-    {% assign thisTag = page.tagName %}
-  {% for page in site.pages %}
-    {% for tag in page.tags %}
-        {% if tag == thisTag %}
-
-        <tr><td><a href="{{ page.url | remove: "/" }}">{{page.title}}</a></td>
-            <td><span class="label label-default">Page</span></td>
-          <td>{% if page.summary %} {{ page.summary | strip_html | strip_newlines | truncate: 160 }} {% else %} {{ page.content | truncatewords: 50 | strip_html }} {% endif %}</td>
-        </tr>
-        {% endif %}
-     {% endfor %}
-   {% endfor %}
-
-        {% assign thisTag = page.tagName %}
-        {% for post in site.posts %}
-        {% for tag in post.tags %}
-        {% if tag == thisTag %}
-
-        <tr><td><a href="{{ post.url | remove: "/" }}">{{post.title}}</a></td>
-            <td><span class="label label-primary">Post</span></td>
-            <td>{% if post.summary %} {{ post.summary | strip_html | strip_newlines | truncate: 160 }} {% else %} {{ post.content | truncatewords: 50 | strip_html }} {% endif %}</td>
-        </tr>
-        {% endif %}
-        {% endfor %}
-        {% endfor %}
-
-   </tbody>
-</table>
-
diff --git a/docs/_includes/tip.html b/docs/_includes/tip.html
deleted file mode 100644
index faf48afd8..000000000
--- a/docs/_includes/tip.html
+++ /dev/null
@@ -1 +0,0 @@
-<div markdown="span" class="alert alert-success" role="alert"><i class="fa fa-check-square-o"></i> <b>Tip:</b> {{include.content}}</div>
\ No newline at end of file
diff --git a/docs/_includes/toc.html b/docs/_includes/toc.html
deleted file mode 100644
index 067141a6a..000000000
--- a/docs/_includes/toc.html
+++ /dev/null
@@ -1,21 +0,0 @@
-
-<!-- this handles the automatic toc. use ## for subheads to auto-generate the on-page minitoc. if you use html tags, you must supply an ID for the heading element in order for it to appear in the minitoc. -->
-<script>
-$( document ).ready(function() {
-  // Handler for .ready() called.
-
-$('#toc').toc({ minimumHeaders: 0, listType: 'ul', showSpeed: 0, headers: 'h2,h3,h4' });
-
-/* this offset helps account for the space taken up by the floating toolbar. */
-$('#toc').on('click', 'a', function() {
-  var target = $(this.getAttribute('href'))
-    , scroll_target = target.offset().top
-
-  $(window).scrollTop(scroll_target - 10);
-  return false
-})
-  
-});
-</script>
-
-<div id="toc"></div>
diff --git a/docs/_includes/topnav.html b/docs/_includes/topnav.html
deleted file mode 100644
index 87486057c..000000000
--- a/docs/_includes/topnav.html
+++ /dev/null
@@ -1,75 +0,0 @@
-<!-- Navigation -->
-<nav class="navbar navbar-inverse navbar-fixed-top">
-    <div class="container topnavlinks">
-        <div class="navbar-header">
-            <button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#bs-example-navbar-collapse-1">
-                <span class="sr-only">Toggle navigation</span>
-                <span class="icon-bar"></span>
-                <span class="icon-bar"></span>
-                <span class="icon-bar"></span>
-            </button>
-            <a class="fa fa-home fa-lg navbar-brand" href="index.html">&nbsp;<span class="projectTitle"> {{site.topnav_title}}</span></a>
-        </div>
-        <div class="collapse navbar-collapse" id="bs-example-navbar-collapse-1">
-            <ul class="nav navbar-nav navbar-right">
-                <!-- entries without drop-downs appear here -->
-                {% for entry in site.data.topnav.topnav %}
-                {% for item in entry.items %}
-                {% if item.external_url %}
-                <li><a href="{{item.external_url}}" target="_blank">{{item.title}}</a></li>
-                {% elsif page.url contains item.url %}
-                <li class="active"><a href="{{item.url | remove: "/"}}">{{item.title}}</a></li>
-                {% else %}
-                <li><a href="{{item.url | remove: "/"}}">{{item.title}}</a></li>
-                {% endif %}
-                {% endfor %}
-                {% endfor %}
-                <!-- entries with drop-downs appear here -->
-                <!-- conditional logic to control which topnav appears for the audience defined in the configuration file.-->
-                {% for entry in site.data.topnav.topnav_dropdowns %}
-                {% for folder in entry.folders %}
-                <li class="dropdown">
-                    <a href="#" class="dropdown-toggle" data-toggle="dropdown">{{ folder.title }}<b class="caret"></b></a>
-                    <ul class="dropdown-menu">
-                        {% for folderitem in folder.folderitems %}
-                        {% if folderitem.external_url %}
-                        <li><a href="{{folderitem.external_url}}" target="_blank">{{folderitem.title}}</a></li>
-                        {% elsif page.url contains folderitem.url %}
-                        <li class="dropdownActive"><a href="{{folderitem.url |  remove: "/"}}">{{folderitem.title}}</a></li>
-                        {% else %}
-                        <li><a href="{{folderitem.url | remove: "/"}}">{{folderitem.title}}</a></li>
-                        {% endif %}
-                        {% endfor %}
-                    </ul>
-                </li>
-                {% endfor %}
-                {% endfor %}
-                {% if site.feedback_disable == null or site.feedback_disable == false %}
-			{% include feedback.html %}
-		{% endif %}
-                <!--comment out this block if you want to hide search-->
-                <li>
-                    <!--start search-->
-                    <div id="search-demo-container">
-                        <input type="text" id="search-input" placeholder="{{site.data.strings.search_placeholder_text}}">
-                        <ul id="results-container"></ul>
-                    </div>
-                    <script src="{{ "js/jekyll-search.js"}}" type="text/javascript"></script>
-                    <script type="text/javascript">
-                            SimpleJekyllSearch.init({
-                                searchInput: document.getElementById('search-input'),
-                                resultsContainer: document.getElementById('results-container'),
-                                dataSource: '{{ "search.json" }}',
-                                searchResultTemplate: '<li><a href="{url}" title="{{page.title | replace: "'", "\"}}">{title}</a></li>',
-                    noResultsText: '{{site.data.strings.search_no_results_text}}',
-                            limit: 10,
-                            fuzzy: true,
-                    })
-                    </script>
-                    <!--end search-->
-                </li>
-            </ul>
-        </div>
-        </div>
-        <!-- /.container -->
-</nav>
diff --git a/docs/_includes/warning.html b/docs/_includes/warning.html
deleted file mode 100644
index e08268c93..000000000
--- a/docs/_includes/warning.html
+++ /dev/null
@@ -1 +0,0 @@
-<div markdown="span" class="alert alert-danger" role="alert"><i class="fa fa-exclamation-circle"></i> <b>Warning:</b> {{include.content}}</div>
\ No newline at end of file
diff --git a/docs/_layouts/default.html b/docs/_layouts/default.html
deleted file mode 100644
index 90c1c4e5a..000000000
--- a/docs/_layouts/default.html
+++ /dev/null
@@ -1,80 +0,0 @@
-<!DOCTYPE html>
-<head>
-    {% include head.html %}
-    <script>
-        $(document).ready(function() {
-            // Initialize navgoco with default options
-            $("#mysidebar").navgoco({
-                caretHtml: '',
-                accordion: true,
-                openClass: 'active', // open
-                save: false, // leave false or nav highlighting doesn't work right
-                cookie: {
-                    name: 'navgoco',
-                    expires: false,
-                    path: '/'
-                },
-                slide: {
-                    duration: 400,
-                    easing: 'swing'
-                }
-            });
-
-            $("#collapseAll").click(function(e) {
-                e.preventDefault();
-                $("#mysidebar").navgoco('toggle', false);
-            });
-
-            $("#expandAll").click(function(e) {
-                e.preventDefault();
-                $("#mysidebar").navgoco('toggle', true);
-            });
-
-        });
-
-    </script>
-    <script>
-        $(function () {
-            $('[data-toggle="tooltip"]').tooltip()
-        })
-    </script>
-    {% if page.datatable == true %}
-    <link rel="stylesheet" type="text/css" href="//cdn.datatables.net/1.10.5/css/jquery.dataTables.css">
-    <script type="text/javascript" charset="utf8" src="//cdn.datatables.net/1.10.5/js/jquery.dataTables.js"></script>
-    <script>
-        $(document).ready(function(){
-
-            $('table.datatable').DataTable( {
-                        paging: false,
-                        stateSave: true
-                    }
-            );
-    </script>
-    {% endif %}
-
-</head>
-<body>
-{% include topnav.html %}
-<!-- Page Content -->
-<div class="container">
-    <div class="col-lg-12">&nbsp;</div>
-    <!-- Content Row -->
-    <div class="row">
-        <!-- Sidebar Column -->
-        <div class="col-md-3">
-
-          {% include sidebar.html %}
-    <!-- Content Column -->
-    <div class="col-md-9">
-        {{content}}
-    </div>
-    <!-- /.row -->
-</div>
-<!-- /.container -->
-    </div>
-
-</body>
-{% if site.google_analytics %}
-{% include google_analytics.html %}
-{% endif %}
-</html>
\ No newline at end of file
diff --git a/docs/_layouts/default_print.html b/docs/_layouts/default_print.html
deleted file mode 100644
index 4bf619b47..000000000
--- a/docs/_layouts/default_print.html
+++ /dev/null
@@ -1,25 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<html>
-<head>
-    {% include head_print.html %}
-
-
-</head>
-
-<body class="{% if page.type == "title"%}title{% elsif page.type == "frontmatter" %}frontmatter{% elsif page.type == "first_page" %}first_page{% endif %} print">
-
-<!-- Page Content -->
-<div class="container">
-    <!-- Content Column -->
-    <div class="col-md-9">
-
-        {{content}}
-    </div>
-
-</div>    <!-- /.container -->
-
-</body>
-
-</html>
-
diff --git a/docs/_layouts/none.html b/docs/_layouts/none.html
deleted file mode 100644
index 60887a920..000000000
--- a/docs/_layouts/none.html
+++ /dev/null
@@ -1,3 +0,0 @@
----
----
-{{content}}
\ No newline at end of file
diff --git a/docs/_layouts/page.html b/docs/_layouts/page.html
deleted file mode 100644
index d7aedc4c1..000000000
--- a/docs/_layouts/page.html
+++ /dev/null
@@ -1,70 +0,0 @@
----
-layout: default
----
-
-<div class="post-header">
-   <h1 class="post-title-main">{{ page.title }}</h1>
-</div>
-
-{% if page.simple_map == true %}
-
-<script>
-    $(document).ready ( function(){
-        $('.box{{page.box_number}}').addClass('active');
-    });
-</script>
-
-{% include custom/{{page.map_name}}.html %}
-
-{% elsif page.complex_map == true %}
-
-<script>
-    $(document).ready ( function(){
-        $('.modalButton{{page.box_number}}').addClass('active');
-    });
-</script>
-
-{% include custom/{{page.map_name}}.html %}
-
-{% endif %}
-
-<div class="post-content">
-
-   {% if page.summary %}
-    <div class="summary">{{page.summary}}</div>
-   {% endif %}
-
-    {% unless page.toc == false %}
-    {% include toc.html %}
-    {% endunless %}
-
-
-    {% if site.github_editme_path %}
-
-    <a target="_blank" href="https://github.com/{{site.github_editme_path}}{% unless page.url contains "index.html" %}pages/{% endunless %}{{page.folder}}{{page.url | remove: ".html" | append: ".md"}}" class="btn btn-default githubEditButton" role="button"><i class="fa fa-github fa-lg"></i> Edit me</a>
-
-    {% endif %}
-
-  {{content}}
-
-    <div class="tags">
-        {% if page.tags != null %}
-        <b>Tags: </b>
-        {% assign projectTags = site.data.tags.allowed-tags %}
-        {% for tag in page.tags %}
-        {% if projectTags contains tag %}
-        <a href="{{ "tag_" | append: tag | append: ".html" }}" class="btn btn-default navbar-btn cursorNorm" role="button">{{page.tagName}}{{tag}}</a>
-        {% endif %}
-        {% endfor %}
-        {% endif %}
-    </div>
-
-    {% if site.disqus %}
-{% include disqus.html %}
-    {% endif %}
-
-</div>
-
-{{site.data.alerts.hr_shaded}}
-
-{% include footer.html %}
diff --git a/docs/_layouts/page_print.html b/docs/_layouts/page_print.html
deleted file mode 100644
index 9e04604a9..000000000
--- a/docs/_layouts/page_print.html
+++ /dev/null
@@ -1,15 +0,0 @@
----
-layout: default_print
-comments: true
----
-<div class="post-header">
-    <h1 class="post-title-main" id="{{page.permalink | replace: '/', '' }}">{{ page.title }}</h1>
-</div>
-
-<div class="post-content">
-
-    {% if page.summary %}
-    <div class="summary">{{page.summary}}</div>
-    {% endif %}
-    {{ content }}
-</div>
diff --git a/docs/_layouts/post.html b/docs/_layouts/post.html
deleted file mode 100644
index 9067ebd5d..000000000
--- a/docs/_layouts/post.html
+++ /dev/null
@@ -1,41 +0,0 @@
----
-layout: default
----
-<article class="post" itemscope itemtype="http://schema.org/BlogPosting">
-
-    <header class="post-header">
-        <h1 class="post-title" itemprop="name headline">{{ page.title }}</h1>
-        <p class="post-meta"><time datetime="{{ page.date | date_to_xmlschema }}" itemprop="datePublished">{{ page.date | date: "%b %-d, %Y" }}</time> {% if page.author %}<span itemprop="author" itemscope itemtype="http://schema.org/Person"><span itemprop="name">/ {{ page.author }}</span></span>{% endif %}{% if page.tags != null %}/
-            {% assign projectTags = site.data.tags.allowed-tags %}
-            {% for tag in page.tags %}
-            {% if projectTags contains tag %}
-            <a href="{{ "tag_" | append: tag | append: ".html"}}">{{tag}}</a>{% unless forloop.last %}, {% endunless%}
-            {% endif %}
-            {% endfor %}
-            {% endif %}
-
-        </p>
-
-
-    </header>
-
-    <div class="post-content" itemprop="articleBody">
-
-        {% if page.summary %}
-        <div class="summary">{{page.summary}}</div>
-        {% endif %}
-
-        {{ content }}
-    </div>
-
-
-
-</article>
-
-{% if site.disqus %}
-{% include disqus.html %}
-{% endif %}
-
-{{site.data.alerts.hr_shaded}}
-
-{% include footer.html %}
diff --git a/docs/_posts/2016-12-30-strata-talk-2017.md b/docs/_posts/2016-12-30-strata-talk-2017.md
deleted file mode 100644
index 88b923e46..000000000
--- a/docs/_posts/2016-12-30-strata-talk-2017.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title:  "Connect with us at Strata San Jose March 2017"
-categories:  update
-permalink: strata-talk.html
-tags: [news]
----
-
-We will be presenting Hoodie & general concepts around how incremental processing works at Uber.
-Catch our talk **"Incremental Processing on Hadoop At Uber"**
-
-{% include links.html %}
diff --git a/docs/admin_guide.md b/docs/admin_guide.md
deleted file mode 100644
index f7fe91f8d..000000000
--- a/docs/admin_guide.md
+++ /dev/null
@@ -1,444 +0,0 @@
----
-title: Admin Guide
-keywords: admin
-sidebar: mydoc_sidebar
-permalink: admin_guide.html
-toc: false
-summary: This section offers an overview of tools available to operate an ecosystem of Hoodie datasets
----
-
-Admins/ops can gain visibility into Hoodie datasets/pipelines in the following ways
-
- - Administering via the Admin CLI
- - Graphite metrics
- - Spark UI of the Hoodie Application
-
-This section provides a glimpse into each of these, with some general guidance on troubleshooting
-
-## Admin CLI
-
-Once hoodie has been built via `mvn clean install -DskipTests`, the shell can be fired by via  `cd hoodie-cli && ./hoodie-cli.sh`.
-A hoodie dataset resides on HDFS, in a location referred to as the **basePath** and we would need this location in order to connect to a Hoodie dataset.
-Hoodie library effectively manages this HDFS dataset internally, using .hoodie subfolder to track all metadata
-
-To initialize a hoodie table, use the following command.
-
-```
-18/09/06 15:56:52 INFO annotation.AutowiredAnnotationBeanPostProcessor: JSR-330 'javax.inject.Inject' annotation found and supported for autowiring
-============================================
-*                                          *
-*     _    _                 _ _           *
-*    | |  | |               | (_)          *
-*    | |__| | ___   ___   __| |_  ___      *
-*    |  __  |/ _ \ / _ \ / _` | |/ _ \     *
-*    | |  | | (_) | (_) | (_| | |  __/     *
-*    |_|  |_|\___/ \___/ \__,_|_|\___|     *
-*                                          *
-============================================
-
-Welcome to Hoodie CLI. Please type help if you are looking for help.
-hoodie->create --path /user/hive/warehouse/table1 --tableName hoodie_table_1 --tableType COPY_ON_WRITE
-.....
-18/09/06 15:57:15 INFO table.HoodieTableMetaClient: Finished Loading Table of type COPY_ON_WRITE from ...
-```
-
-To see the description of hoodie table, use the command:
-```
-hoodie:hoodie_table_1->desc
-18/09/06 15:57:19 INFO timeline.HoodieActiveTimeline: Loaded instants []
-    _________________________________________________________
-    | Property                | Value                        |
-    |========================================================|
-    | basePath                | ...                          |
-    | metaPath                | ...                          |
-    | fileSystem              | hdfs                         |
-    | hoodie.table.name       | hoodie_table_1               |
-    | hoodie.table.type       | COPY_ON_WRITE                |
-    | hoodie.archivelog.folder|                              |
-```
-
-Following is a sample command to connect to a Hoodie dataset contains uber trips.
-
-```
-hoodie:trips->connect --path /app/uber/trips
-
-16/10/05 23:20:37 INFO model.HoodieTableMetadata: Attempting to load the commits under /app/uber/trips/.hoodie with suffix .commit
-16/10/05 23:20:37 INFO model.HoodieTableMetadata: Attempting to load the commits under /app/uber/trips/.hoodie with suffix .inflight
-16/10/05 23:20:37 INFO model.HoodieTableMetadata: All commits :HoodieCommits{commitList=[20161002045850, 20161002052915, 20161002055918, 20161002065317, 20161002075932, 20161002082904, 20161002085949, 20161002092936, 20161002105903, 20161002112938, 20161002123005, 20161002133002, 20161002155940, 20161002165924, 20161002172907, 20161002175905, 20161002190016, 20161002192954, 20161002195925, 20161002205935, 20161002215928, 20161002222938, 20161002225915, 20161002232906, 20161003003028, 20161003005958, 20161003012936, 20161003022924, 20161003025859, 20161003032854, 20161003042930, 20161003052911, 20161003055907, 20161003062946, 20161003065927, 20161003075924, 20161003082926, 20161003085925, 20161003092909, 20161003100010, 20161003102913, 20161003105850, 20161003112910, 20161003115851, 20161003122929, 20161003132931, 20161003142952, 20161003145856, 20161003152953, 20161003155912, 20161003162922, 20161003165852, 20161003172923, 20161003175923, 20161003195931, 20161003210118, 20161003212919, 20161003215928, 20161003223000, 20161003225858, 20161004003042, 20161004011345, 20161004015235, 20161004022234, 20161004063001, 20161004072402, 20161004074436, 20161004080224, 20161004082928, 20161004085857, 20161004105922, 20161004122927, 20161004142929, 20161004163026, 20161004175925, 20161004194411, 20161004203202, 20161004211210, 20161004214115, 20161004220437, 20161004223020, 20161004225321, 20161004231431, 20161004233643, 20161005010227, 20161005015927, 20161005022911, 20161005032958, 20161005035939, 20161005052904, 20161005070028, 20161005074429, 20161005081318, 20161005083455, 20161005085921, 20161005092901, 20161005095936, 20161005120158, 20161005123418, 20161005125911, 20161005133107, 20161005155908, 20161005163517, 20161005165855, 20161005180127, 20161005184226, 20161005191051, 20161005193234, 20161005203112, 20161005205920, 20161005212949, 20161005223034, 20161005225920]}
-Metadata for table trips loaded
-hoodie:trips->
-```
-
-Once connected to the dataset, a lot of other commands become available. The shell has contextual autocomplete help (press TAB) and below is a list of all commands, few of which are reviewed in this section
-are reviewed
-
-
-```
-hoodie:trips->help
-* ! - Allows execution of operating system (OS) commands
-* // - Inline comment markers (start of line only)
-* ; - Inline comment markers (start of line only)
-* addpartitionmeta - Add partition metadata to a dataset, if not present
-* clear - Clears the console
-* cls - Clears the console
-* commit rollback - Rollback a commit
-* commits compare - Compare commits with another Hoodie dataset
-* commit showfiles - Show file level details of a commit
-* commit showpartitions - Show partition level details of a commit
-* commits refresh - Refresh the commits
-* commits show - Show the commits
-* commits sync - Compare commits with another Hoodie dataset
-* connect - Connect to a hoodie dataset
-* date - Displays the local date and time
-* exit - Exits the shell
-* help - List all commands usage
-* quit - Exits the shell
-* records deduplicate - De-duplicate a partition path contains duplicates & produce repaired files to replace with
-* script - Parses the specified resource file and executes its commands
-* stats filesizes - File Sizes. Display summary stats on sizes of files
-* stats wa - Write Amplification. Ratio of how many records were upserted to how many records were actually written
-* sync validate - Validate the sync by counting the number of records
-* system properties - Shows the shell's properties
-* utils loadClass - Load a class
-* version - Displays shell version
-
-hoodie:trips->
-```
-
-
-#### Inspecting Commits
-
-The task of upserting or inserting a batch of incoming records is known as a **commit** in Hoodie. A commit provides basic atomicity guarantees such that only commited data is available for querying.
-Each commit has a monotonically increasing string/number called the **commit number**. Typically, this is the time at which we started the commit.
-
-To view some basic information about the last 10 commits,
-
-
-```
-hoodie:trips->commits show --sortBy "Total Bytes Written" --desc true --limit 10
-    ________________________________________________________________________________________________________________________________________________________________________
-    | CommitTime    | Total Bytes Written| Total Files Added| Total Files Updated| Total Partitions Written| Total Records Written| Total Update Records Written| Total Errors|
-    |=======================================================================================================================================================================|
-    ....
-    ....
-    ....
-hoodie:trips->
-
-```
-
-At the start of each write, Hoodie also writes a .inflight commit to the .hoodie folder. You can use the timestamp there to estimate how long the commit has been inflight
-
-
-```
-$ hdfs dfs -ls /app/uber/trips/.hoodie/*.inflight
--rw-r--r--   3 vinoth supergroup     321984 2016-10-05 23:18 /app/uber/trips/.hoodie/20161005225920.inflight
-```
-
-
-#### Drilling Down to a specific Commit
-
-To understand how the writes spread across specific partiions,
-
-
-```
-hoodie:trips->commit showpartitions --commit 20161005165855 --sortBy "Total Bytes Written" --desc true --limit 10
-    __________________________________________________________________________________________________________________________________________
-    | Partition Path| Total Files Added| Total Files Updated| Total Records Inserted| Total Records Updated| Total Bytes Written| Total Errors|
-    |=========================================================================================================================================|
-     ....
-     ....
-```
-
-If you need file level granularity , we can do the following
-
-
-```
-hoodie:trips->commit showfiles --commit 20161005165855 --sortBy "Partition Path"
-    ________________________________________________________________________________________________________________________________________________________
-    | Partition Path| File ID                             | Previous Commit| Total Records Updated| Total Records Written| Total Bytes Written| Total Errors|
-    |=======================================================================================================================================================|
-    ....
-    ....
-```
-
-
-#### FileSystem View
-
-Hudi views each partition as a collection of file-groups with each file-group containing a list of file-slices in commit
-order (See Concepts). The below commands allow users to view the file-slices for a data-set.
-
-```
- hoodie:stock_ticks_mor->show fsview all
- ....
-  _______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
- | Partition | FileId | Base-Instant | Data-File | Data-File Size| Num Delta Files| Total Delta File Size| Delta Files |
- |==============================================================================================================================================================================================================================================================================================================================================================================================================|
- | 2018/08/31| 111415c3-f26d-4639-86c8-f9956f245ac3| 20181002180759| hdfs://namenode:8020/user/hive/warehouse/stock_ticks_mor/2018/08/31/111415c3-f26d-4639-86c8-f9956f245ac3_0_20181002180759.parquet| 432.5 KB | 1 | 20.8 KB | [HoodieLogFile {hdfs://namenode:8020/user/hive/warehouse/stock_ticks_mor/2018/08/31/.111415c3-f26d-4639-86c8-f9956f245ac3_20181002180759.log.1}]|
-
-
-
- hoodie:stock_ticks_mor->show fsview latest --partitionPath "2018/08/31"
- ......
- __________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
- | Partition | FileId | Base-Instant | Data-File | Data-File Size| Num Delta Files| Total Delta Size| Delta Size - compaction scheduled| Delta Size - compaction unscheduled| Delta To Base Ratio - compaction scheduled| Delta To Base Ratio - compaction unscheduled| Delta Files - compaction scheduled | Delta Files - compaction unscheduled|
- |=================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================|
- | 2018/08/31| 111415c3-f26d-4639-86c8-f9956f245ac3| 20181002180759| hdfs://namenode:8020/user/hive/warehouse/stock_ticks_mor/2018/08/31/111415c3-f26d-4639-86c8-f9956f245ac3_0_20181002180759.parquet| 432.5 KB | 1 | 20.8 KB | 20.8 KB | 0.0 B | 0.0 B | 0.0 B | [HoodieLogFile {hdfs://namenode:8020/user/hive/warehouse/stock_ticks_mor/2018/08/31/.111415c3-f26d-4639-86c8-f9956f245ac3_20181002180759.log.1}]| [] |
- 
- hoodie:stock_ticks_mor->
-```
-
-
-#### Statistics
-
-Since Hoodie directly manages file sizes for HDFS dataset, it might be good to get an overall picture
-
-
-```
-hoodie:trips->stats filesizes --partitionPath 2016/09/01 --sortBy "95th" --desc true --limit 10
-    ________________________________________________________________________________________________
-    | CommitTime    | Min     | 10th    | 50th    | avg     | 95th    | Max     | NumFiles| StdDev  |
-    |===============================================================================================|
-    | <COMMIT_ID>   | 93.9 MB | 93.9 MB | 93.9 MB | 93.9 MB | 93.9 MB | 93.9 MB | 2       | 2.3 KB  |
-    ....
-    ....
-```
-
-In case of Hoodie write taking much longer, it might be good to see the write amplification for any sudden increases
-
-
-```
-hoodie:trips->stats wa
-    __________________________________________________________________________
-    | CommitTime    | Total Upserted| Total Written| Write Amplifiation Factor|
-    |=========================================================================|
-    ....
-    ....
-```
-
-
-#### Archived Commits
-
-In order to limit the amount of growth of .commit files on HDFS, Hoodie archives older .commit files (with due respect to the cleaner policy) into a commits.archived file.
-This is a sequence file that contains a mapping from commitNumber => json with raw information about the commit (same that is nicely rolled up above).
-
-
-#### Compactions
-
-To get an idea of the lag between compaction and writer applications, use the below command to list down all 
-pending compactions.
-
-```
-hoodie:trips->compactions show all
-     ___________________________________________________________________
-    | Compaction Instant Time| State    | Total FileIds to be Compacted|
-    |==================================================================|
-    | <INSTANT_1>            | REQUESTED| 35                           |
-    | <INSTANT_2>            | INFLIGHT | 27                           |
-
-```
-
-To inspect a specific compaction plan, use
-
-```
-hoodie:trips->compaction show --instant <INSTANT_1>
-    _________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
-    | Partition Path| File Id | Base Instant  | Data File Path                                    | Total Delta Files| getMetrics                                                                                                                    |
-    |================================================================================================================================================================================================================================================
-    | 2018/07/17    | <UUID>  | <INSTANT_1>   | viewfs://ns-default/.../../UUID_<INSTANT>.parquet | 1                | {TOTAL_LOG_FILES=1.0, TOTAL_IO_READ_MB=1230.0, TOTAL_LOG_FILES_SIZE=2.51255751E8, TOTAL_IO_WRITE_MB=991.0, TOTAL_IO_MB=2221.0}|
-
-```
-
-To manually schedule or run a compaction, use the below command. This command uses spark launcher to perform compaction
-operations. NOTE : Make sure no other application is scheduling compaction for this dataset concurrently
-
-```
-hoodie:trips->help compaction schedule
-Keyword:                   compaction schedule
-Description:               Schedule Compaction
- Keyword:                  sparkMemory
-   Help:                   Spark executor memory
-   Mandatory:              false
-   Default if specified:   '__NULL__'
-   Default if unspecified: '1G'
-
-* compaction schedule - Schedule Compaction
-```
-
-```
-hoodie:trips->help compaction run
-Keyword:                   compaction run
-Description:               Run Compaction for given instant time
- Keyword:                  tableName
-   Help:                   Table name
-   Mandatory:              true
-   Default if specified:   '__NULL__'
-   Default if unspecified: '__NULL__'
-
- Keyword:                  parallelism
-   Help:                   Parallelism for hoodie compaction
-   Mandatory:              true
-   Default if specified:   '__NULL__'
-   Default if unspecified: '__NULL__'
-
- Keyword:                  schemaFilePath
-   Help:                   Path for Avro schema file
-   Mandatory:              true
-   Default if specified:   '__NULL__'
-   Default if unspecified: '__NULL__'
-
- Keyword:                  sparkMemory
-   Help:                   Spark executor memory
-   Mandatory:              true
-   Default if specified:   '__NULL__'
-   Default if unspecified: '__NULL__'
-
- Keyword:                  retry
-   Help:                   Number of retries
-   Mandatory:              true
-   Default if specified:   '__NULL__'
-   Default if unspecified: '__NULL__'
-
- Keyword:                  compactionInstant
-   Help:                   Base path for the target hoodie dataset
-   Mandatory:              true
-   Default if specified:   '__NULL__'
-   Default if unspecified: '__NULL__'
-
-* compaction run - Run Compaction for given instant time
-```
-
-##### Validate Compaction
-
-Validating a compaction plan : Check if all the files necessary for compactions are present and are valid
-
-```
-hoodie:stock_ticks_mor->compaction validate --instant 20181005222611
-...
-
-   COMPACTION PLAN VALID
-   
-    ___________________________________________________________________________________________________________________________________________________________________________________________________________________________
-    | File Id                             | Base Instant Time| Base Data File                                                                                                                   | Num Delta Files| Valid| Error|
-    |==========================================================================================================================================================================================================================|
-    | 05320e98-9a57-4c38-b809-a6beaaeb36bd| 20181005222445   | hdfs://namenode:8020/user/hive/warehouse/stock_ticks_mor/2018/08/31/05320e98-9a57-4c38-b809-a6beaaeb36bd_0_20181005222445.parquet| 1              | true |      |
-
-
-
-hoodie:stock_ticks_mor->compaction validate --instant 20181005222601
-
-   COMPACTION PLAN INVALID
-
-    _______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
-    | File Id                             | Base Instant Time| Base Data File                                                                                                                   | Num Delta Files| Valid| Error                                                                           |
-    |=====================================================================================================================================================================================================================================================================================================|
-    | 05320e98-9a57-4c38-b809-a6beaaeb36bd| 20181005222445   | hdfs://namenode:8020/user/hive/warehouse/stock_ticks_mor/2018/08/31/05320e98-9a57-4c38-b809-a6beaaeb36bd_0_20181005222445.parquet| 1              | false| All log files specified in compaction operation is not present. Missing ....    |
-
-
-```
-
-##### NOTE
-
-The following commands must be executed without any other writer/ingestion application running.
-
-Sometimes, it becomes necessary to remove a fileId from a compaction-plan inorder to speed-up or unblock compaction 
-operation. Any new log-files that happened on this file after the compaction got scheduled will be safely renamed 
-so that are preserved. Hudi provides the following CLI to support it
-
-
-##### UnScheduling Compaction
-
-```
-hoodie:trips->compaction unscheduleFileId --fileId <FileUUID>
-....
-No File renames needed to unschedule file from pending compaction. Operation successful.
-
-```
-
-In other cases, an entire compaction plan needs to be reverted. This is supported by the following CLI
-```
-hoodie:trips->compaction unschedule --compactionInstant <compactionInstant>
-.....
-No File renames needed to unschedule pending compaction. Operation successful.
-```
-  
-##### Repair Compaction
-
-The above compaction unscheduling operations could sometimes fail partially (e:g -> HDFS temporarily unavailable). With
-partial failures, the compaction operation could become inconsistent with the state of file-slices. When you run 
-`compaction validate`, you can notice invalid compaction operations if there is one.  In these cases, the repair
-command comes to the rescue, it will rearrange the file-slices so that there is no loss and the file-slices are
-consistent with the compaction plan
-
-```
-hoodie:stock_ticks_mor->compaction repair --instant 20181005222611
-......
-Compaction successfully repaired
-.....
-```
-
-
-## Metrics
-
-Once the Hoodie Client is configured with the right datasetname and environment for metrics, it produces the following graphite metrics, that aid in debugging hoodie datasets
-
- - **Commit Duration** - This is amount of time it took to successfully commit a batch of records
- - **Rollback Duration** - Similarly, amount of time taken to undo partial data left over by a failed commit (happens everytime automatically after a failing write)
- - **File Level metrics** - Shows the amount of new files added, versions, deleted (cleaned) in each commit
- - **Record Level Metrics** - Total records inserted/updated etc per commit
- - **Partition Level metrics** - number of partitions upserted (super useful to understand sudden spikes in commit duration)
-
-These metrics can then be plotted on a standard tool like grafana. Below is a sample commit duration chart.
-
-{% include image.html file="hoodie_commit_duration.png" alt="hoodie_commit_duration.png" max-width="1000" %}
-
-
-## Troubleshooting Failures
-
-Section below generally aids in debugging Hoodie failures. Off the bat, the following metadata is added to every record to help triage  issues easily using standard Hadoop SQL engines (Hive/Presto/Spark)
-
- - **_hoodie_record_key** - Treated as a primary key within each HDFS partition, basis of all updates/inserts
- - **_hoodie_commit_time** - Last commit that touched this record
- - **_hoodie_file_name** - Actual file name containing the record (super useful to triage duplicates)
- - **_hoodie_partition_path** - Path from basePath that identifies the partition containing this record
-
-{% include callout.html content="Note that as of now, Hoodie assumes the application passes in the same deterministic partitionpath for a given recordKey. i.e the uniqueness of record key is only enforced within each partition" type="warning" %}
-
-
-#### Missing records
-
-Please check if there were any write errors using the admin commands above, during the window at which the record could have been written.
-If you do find errors, then the record was not actually written by Hoodie, but handed back to the application to decide what to do with it.
-
-#### Duplicates
-
-First of all, please confirm if you do indeed have duplicates **AFTER** ensuring the query is accessing the Hoodie datasets [properly](sql_queries.html) .
-
- - If confirmed, please use the metadata fields above, to identify the physical files & partition files containing the records .
- - If duplicates span files across partitionpath, then this means your application is generating different partitionPaths for same recordKey, Please fix your app
- - if duplicates span multiple files within the same partitionpath, please engage with mailing list. This should not happen. You can use the `records deduplicate` command to fix your data.
-
-#### Spark failures
-
-Typical upsert() DAG looks like below. Note that Hoodie client also caches intermediate RDDs to intelligently profile workload and size files and spark parallelism.
-Also Spark UI shows sortByKey twice due to the probe job also being shown, nonetheless its just a single sort.
-
-
-{% include image.html file="hoodie_upsert_dag.png" alt="hoodie_upsert_dag.png" max-width="1000" %}
-
-
-At a high level, there are two steps
-
-**Index Lookup to identify files to be changed**
-
- - Job 1 : Triggers the input data read, converts to HoodieRecord object and then stops at obtaining a spread of input records to target partition paths
- - Job 2 : Load the set of file names which we need check against
- - Job 3  & 4 : Actual lookup after smart sizing of spark join parallelism, by joining RDDs in 1 & 2 above
- - Job 5 : Have a tagged RDD of recordKeys with locations
-
-**Performing the actual writing of data**
-
- - Job 6 : Lazy join of incoming records against recordKey, location to provide a final set of HoodieRecord which now contain the information about which file/partitionpath they are found at (or null if insert). Then also profile the workload again to determine sizing of files
- - Job 7 : Actual writing of data (update + insert + insert turned to updates to maintain file size)
-
-Depending on the exception source (Hoodie/Spark), the above knowledge of the DAG can be used to pinpoint the actual issue. The most often encountered failures result from YARN/HDFS temporary failures.
-In the future, a more sophisticated debug/management UI would be added to the project, that can help automate some of this debugging.
diff --git a/docs/api_docs.md b/docs/api_docs.md
deleted file mode 100644
index 24bfd6b20..000000000
--- a/docs/api_docs.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: API Docs
-keywords: usecases
-sidebar: mydoc_sidebar
-permalink: api_docs.html
----
-
-Work In Progress
-
-
diff --git a/docs/code_and_design.md b/docs/code_and_design.md
deleted file mode 100644
index 3baaa97b4..000000000
--- a/docs/code_and_design.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-title: Code Structure
-keywords: usecases
-sidebar: mydoc_sidebar
-permalink: code_and_design.html
----
-
-## Code & Project Structure
-
- * hoodie-client     : Spark client library to take a bunch of inserts + updates and apply them to a Hoodie table
- * hoodie-common     : Common code shared between different artifacts of Hoodie
-
- ## HoodieLogFormat
-
- The following diagram depicts the LogFormat for Hoodie MergeOnRead. Each logfile consists of one or more log blocks.
- Each logblock follows the format shown below.
-
- | Field  | Description |
- |-------------- |------------------|
- | MAGIC    | A magic header that marks the start of a block |
- | VERSION  | The version of the LogFormat, this helps define how to switch between different log format as it evolves |
- | TYPE     | The type of the log block |
- | HEADER LENGTH | The length of the headers, 0 if no headers |
- | HEADER        | Metadata needed for a log block. For eg. INSTANT_TIME, TARGET_INSTANT_TIME, SCHEMA etc. |
- | CONTENT LENGTH |  The length of the content of the log block |
- | CONTENT        | The content of the log block, for example, for a DATA_BLOCK, the content is (number of records + actual records) in byte [] |
- | FOOTER LENGTH  | The length of the footers, 0 if no footers |
- | FOOTER         | Metadata needed for a log block. For eg. index entries, a bloom filter for records in a DATA_BLOCK etc. |
- | LOGBLOCK LENGTH | The total number of bytes written for a log block, typically the SUM(everything_above). This is a LONG. This acts as a reverse pointer to be able to traverse the log in reverse.|
-
-
- {% include image.html file="hoodie_log_format_v2.png" alt="hoodie_log_format_v2.png" %}
-
-
-
-
-
-
diff --git a/docs/community.md b/docs/community.md
deleted file mode 100644
index 89f50de01..000000000
--- a/docs/community.md
+++ /dev/null
@@ -1,22 +0,0 @@
----
-title: Community
-keywords: usecases
-sidebar: mydoc_sidebar
-toc: false
-permalink: community.html
----
-
-## Contributing
-We :heart: contributions. If you find a bug in the library or would like to add new features, go ahead and open
-issues or pull requests against this repo. Before you do so, please sign the
-[Uber CLA](https://docs.google.com/a/uber.com/forms/d/1pAwS_-dA1KhPlfxzYLBqK6rsSWwRwH95OCCZrcsY5rk/viewform).
-Also, be sure to write unit tests for your bug fix or feature to show that it works as expected.
-If the reviewer feels this contributions needs to be in the release notes, please add it to CHANGELOG.md as well.
-
-If you want to participate in day-day conversations, please join our [slack group](https://join.slack.com/t/apache-hudi/signup).
-If you are from select pre-listed email domains, you can self signup. Others, please add your email onto this [issue](https://github.com/uber/hoodie/issues/143)
-
-## Becoming a Committer 
-
-Hoodie has adopted a lot of guidelines set forth in [Google Chromium project](https://www.chromium.org/getting-involved/become-a-committer), to determine committership proposals. However, given this is a much younger project, we would have the contribution bar to be 10-15 non-trivial patches instead.
-Additionally, we expect active engagement with the community over a few months, in terms of conference/meetup talks, helping out with issues/questions on slack/github.
diff --git a/docs/comparison.md b/docs/comparison.md
deleted file mode 100644
index 0120bcb2e..000000000
--- a/docs/comparison.md
+++ /dev/null
@@ -1,61 +0,0 @@
----
-title: Comparison
-keywords: usecases
-sidebar: mydoc_sidebar
-permalink: comparison.html
-toc: false
----
-
-Hoodie fills a big void for processing data on top of HDFS, and thus mostly co-exists nicely with these technologies. However,
-it would be useful to understand how Hoodie fits into the current big data ecosystem, contrasting it with a few related systems
-and bring out the different tradeoffs these systems have accepted in their design.
-
-## Kudu
-
-[Apache Kudu](https://kudu.apache.org) is a storage system that has similar goals as Hoodie, which is to bring real-time analytics on petabytes of data via first
-class support for `upserts`. A key differentiator is that Kudu also attempts to serve as a datastore for OLTP workloads, something that Hoodie does not aspire to be.
-Consequently, Kudu does not support incremental pulling (as of early 2017), something Hoodie does to enable incremental processing use cases.
-
-
-Kudu diverges from a distributed file system abstraction and HDFS altogether, with its own set of storage servers talking to each  other via RAFT.
-Hoodie, on the other hand, is designed to work with an underlying Hadoop compatible filesystem (HDFS,S3 or Ceph) and does not have its own fleet of storage servers,
-instead relying on Apache Spark to do the heavy-lifting. Thu, Hoodie can be scaled easily, just like other Spark jobs, while Kudu would require hardware
-& operational support, typical to datastores like HBase or Vertica. We have not at this point, done any head to head benchmarks against Kudu (given RTTable is WIP).
-But, if we were to go with results shared by [CERN](https://db-blog.web.cern.ch/blog/zbigniew-baranowski/2017-01-performance-comparison-different-file-formats-and-storage-engines) ,
-we expect Hoodie to positioned at something that ingests parquet with superior performance.
-
-
-## Hive Transactions
-
-[Hive Transactions/ACID](https://cwiki.apache.org/confluence/display/Hive/Hive+Transactions) is another similar effort, which tries to implement storage like
-`merge-on-read`, on top of ORC file format. Understandably, this feature is heavily tied to Hive and other efforts like [LLAP](https://cwiki.apache.org/confluence/display/Hive/LLAP).
-Hive transactions does not offer the read-optimized storage option or the incremental pulling, that Hoodie does. In terms of implementation choices, Hoodie leverages
-the full power of a processing framework like Spark, while Hive transactions feature is implemented underneath by Hive tasks/queries kicked off by user or the Hive metastore.
-Based on our production experience, embedding Hoodie as a library into existing Spark pipelines was much easier and less operationally heavy, compared with the other approach.
-Hoodie is also designed to work with non-hive enginers like Presto/Spark and will incorporate file formats other than parquet over time.
-
-## HBase
-
-Even though [HBase](https://hbase.apache.org) is ultimately a key-value store for OLTP workloads, users often tend to associate HBase with analytics given the proximity to Hadoop.
-Given HBase is heavily write-optimized, it supports sub-second upserts out-of-box and Hive-on-HBase lets users query that data. However, in terms of actual performance for analytical workloads,
-hybrid columnar storage formats like Parquet/ORC handily beat HBase, since these workloads are predominantly read-heavy. Hoodie bridges this gap between faster data and having
-analytical storage formats. From an operational perspective, arming users with a library that provides faster data, is more scalable, than managing a big farm of HBase region servers,
-just for analytics. Finally, HBase does not support incremental processing primitives like `commit times`, `incremental pull` as first class citizens like Hoodie.
-
-## Stream Processing
-
-A popular question, we get is : "How does Hoodie relate to stream processing systems?", which we will try to answer here. Simply put, Hoodie can integrate with
-batch (`copy-on-write storage`) and streaming (`merge-on-read storage`) jobs of today, to store the computed results in Hadoop. For Spark apps, this can happen via direct
-integration of Hoodie library with Spark/Spark streaming DAGs. In case of Non-Spark processing systems (eg: Flink, Hive), the processing can be done in the respective systems
-and later sent into a Hoodie table via a Kafka topic/HDFS intermediate file. In more conceptual level, data processing
-pipelines just consist of three components : `source`, `processing`, `sink`, with users ultimately running queries against the sink to use the results of the pipeline.
-Hoodie can act as either a source or sink, that stores data on HDFS. Applicability of Hoodie to a given stream processing pipeline ultimately boils down to suitability
-of Presto/SparkSQL/Hive for your queries.
-
-More advanced use cases revolve around the concepts of [incremental processing](https://www.oreilly.com/ideas/ubers-case-for-incremental-processing-on-hadoop), which effectively
-uses Hoodie even inside the `processing` engine to speed up typical batch pipelines. For e.g: Hoodie can be used as a state store inside a processing DAG (similar
-to how [rocksDB](https://ci.apache.org/projects/flink/flink-docs-release-1.2/ops/state_backends.html#the-rocksdbstatebackend) is used by Flink). This is an item on the roadmap
-and will eventually happen as a [Beam Runner](https://github.com/uber/hoodie/issues/8)
-
-
-
diff --git a/docs/concepts.md b/docs/concepts.md
deleted file mode 100644
index e757a8944..000000000
--- a/docs/concepts.md
+++ /dev/null
@@ -1,159 +0,0 @@
----
-title: Concepts
-keywords: concepts
-sidebar: mydoc_sidebar
-permalink: concepts.html
-toc: false
-summary: "Here we introduce some basic concepts & give a broad technical overview of Hoodie"
----
-
-Hoodie provides the following primitives over datasets on HDFS
-
- * Upsert                     (how do I change the dataset?)
- * Incremental consumption    (how do I fetch data that changed?)
-
-
-In order to achieve this, Hoodie maintains a `timeline` of all activity performed on the dataset, that helps provide `instantaenous` views of the dataset,
-while also efficiently supporting retrieval of data in the order of arrival into the dataset.
-Such key activities include
-
- * `COMMITS` - A single commit captures information about an **atomic write** of a batch of records into a dataset.
-       Commits are identified by a monotonically increasing timestamp, denoting the start of the write operation.
- * `CLEANS` - Background activity that gets rid of older versions of files in the dataset, that are no longer needed.
- * `DELTA_COMMITS` - A single commit captures information about an **atomic write** of a batch of records into a 
- MergeOnRead storage type of dataset
- * `COMPACTIONS` - Background activity to reconcile differential data structures within Hoodie e.g: moving updates from row based log files to columnar formats.
-
-
-{% include image.html file="hoodie_timeline.png" alt="hoodie_timeline.png" %}
-
-Example above shows upserts happenings between 10:00 and 10:20 on a Hoodie dataset, roughly every 5 mins, leaving commit metadata on the hoodie timeline, along
-with other background cleaning/compactions. One key observation to make is that the commit time indicates the `arrival time` of the data (10:20AM), while the actual data
-organization reflects the actual time or `event time`, the data was intended for (hourly buckets from 07:00). These are two key concepts when reasoning about tradeoffs between latency and completeness of data.
-
-When there is late arriving data (data intended for 9:00 arriving >1 hr late at 10:20), we can see the upsert producing new data into even older time buckets/folders.
-With the help of the timeline, an incremental query attempting to get all new data that was committed successfully since 10:00 hours, is able to very efficiently consume
-only the changed files without say scanning all the time buckets > 07:00.
-
-## Terminologies
-
- * `Hudi Dataset` 
-    A structured hive/spark dataset managed by Hudi. Hudi supports both partitioned and non-partitioned Hive tables. 
- * `Commit` 
-    A commit marks a new batch of data applied to a dataset. Hudi maintains  monotonically increasing timestamps to track commits and guarantees that a commit is atomically 
-    published.
- * `Commit Timeline`
-    Commit Timeline refers to the sequence of Commits that was applied in order on a dataset over its lifetime. 
- * `File Slice` 
-    Hudi provides efficient handling of updates by having a fixed mapping between record key to a logical file Id. 
-    Hudi uses MVCC to provide atomicity and isolation of readers from a writer. This means that a logical fileId will
-    have many physical versions of it. Each of these physical version of a file represents a complete view of the
-    file as of a commit and is called File Slice
- * `File Group`
-    A file-group is a file-slice timeline. It is a list of file-slices in commit order. It is identified by `file id`
-
-
-## Storage Types
-
-Hoodie storage types capture how data is indexed & laid out on the filesystem, and how the above primitives and timeline activities are implemented on top of
-such organization (i.e how data is written). This is not to be confused with the notion of Read Optimized & Near-Real time tables, which are merely how the underlying data is exposed
-to the queries (i.e how data is read).
-
-Hoodie (will) supports the following storage types.
-
-| Storage Type  | Supported Tables |
-|-------------- |------------------|
-| Copy On Write | Read Optimized   |
-| Merge On Read | Read Optimized + Near Real-time |
-
-  - Copy On Write : A heavily read optimized storage type, that simply creates new versions of files corresponding to the records that changed.
-  - Merge On Read : Also provides a near-real time datasets in the order of 5 mins, by shifting some of the write cost, to the reads and merging incoming and on-disk data on-the-fly
-
-{% include callout.html content="Hoodie is a young project. merge-on-read is currently underway. Get involved [here](https://github.com/uber/hoodie/projects/1)" type="info" %}
-
-Regardless of the storage type, Hoodie organizes a datasets into a directory structure under a `basepath`,
-very similar to Hive tables. Dataset is broken up into partitions, which are folders containing files for that partition.
-Each partition uniquely identified by its `partitionpath`, which is relative to the basepath.
-
-Within each partition, records are distributed into multiple files. Each file is identified by an unique `file id` and the `commit` that
-produced the file. Multiple files can share the same file id but written at different commits, in case of updates.
-
-Each record is uniquely identified by a `record key` and mapped to a file id forever. This mapping between record key
-and file id, never changes once the first version of a record has been written to a file. In short, the
- `file id` identifies a group of files, that contain all versions of a group of records.
-
-
-## Copy On Write
-
-As mentioned above, each commit on Copy On Write storage, produces new versions of files. In other words, we implicitly compact every
-commit, such that only columnar data exists. As a result, the write amplification (number of bytes written for 1 byte of incoming data)
- is much higher, where read amplification is close to zero. This is a much desired property for a system like Hadoop, which is predominantly read-heavy.
-
-Following illustrates how this works conceptually, when  data written into copy-on-write storage  and two queries running on top of it.
-
-
-{% include image.html file="hoodie_cow.png" alt="hoodie_cow.png" %}
-
-
-As data gets written, updates to existing file ids, produce a new version for that file id stamped with the commit and
-inserts allocate a new file id and write its first version for that file id. These file versions and their commits are color coded above.
-Normal SQL queries running against such dataset (eg: select count(*) counting the total records in that partition), first checks the timeline for latest commit
-and filters all but latest versions of each file id. As you can see, an old query does not see the current inflight commit's files colored in pink,
-but a new query starting after the commit picks up the new data. Thus queries are immune to any write failures/partial writes and only run on committed data.
-
-The intention of copy on write storage, is to fundamentally improve how datasets are managed today on Hadoop through
-
-  - First class support for atomically updating data at file-level, instead of rewriting whole tables/partitions
-  - Ability to incremental consume changes, as opposed to wasteful scans or fumbling with heuristical approaches
-  - Tight control file sizes to keep query performance excellent (small files hurt query performance considerably).
-
-
-## Merge On Read
-
-Merge on read storage is a superset of copy on write, in the sense it still provides a read optimized view of the dataset via the Read Optmized table.
-But, additionally stores incoming upserts for each file id, onto a `row based append log`, that enables providing near real-time data to the queries
- by applying the append log, onto the latest version of each file id on-the-fly during query time. Thus, this storage type attempts to balance read and write amplication intelligently, to provide near real-time queries.
-The most significant change here, would be to the compactor, which now carefully chooses which append logs need to be compacted onto
-their columnar base data, to keep the query performance in check (larger append logs would incur longer merge times with merge data on query side)
-
-Following illustrates how the storage works, and shows queries on both near-real time table and read optimized table.
-
-{% include image.html file="hoodie_mor.png" alt="hoodie_mor.png" max-width="1000" %}
-
-
-There are lot of interesting things happening in this example, which bring out the subleties in the approach.
-
- - We now have commits every 1 minute or so, something we could not do in the other storage type.
- - Within each file id group, now there is an append log, which holds incoming updates to records in the base columnar files. In the example, the append logs hold
- all the data from 10:05 to 10:10. The base columnar files are still versioned with the commit, as before.
- Thus, if one were to simply look at base files alone, then the storage layout looks exactly like a copy on write table.
- - A periodic compaction process reconciles these changes from the append log and produces a new version of base file, just like what happened at 10:05 in the example.
- - There are two ways of querying the same underlying storage: ReadOptimized (RO) Table and Near-Realtime (RT) table, depending on whether we chose query performance or freshness of data.
- - The semantics around when data from a commit is available to a query changes in a subtle way for the RO table. Note, that such a query
- running at 10:10, wont see data after 10:05 above, while a query on the RT table always sees the freshest data.
- - When we trigger compaction & what it decides to compact hold all the key to solving these hard problems. By implementing a compacting
- strategy, where we aggressively compact the latest partitions compared to older partitions, we could ensure the RO Table sees data
- published within X minutes in a consistent fashion.
-
-{% include callout.html content="Hoodie is a young project. merge-on-read is currently underway. Get involved [here](https://github.com/uber/hoodie/projects/1)" type="info" %}
-
-The intention of merge on read storage, is to enable near real-time processing directly on top of Hadoop, as opposed to copying
-data out to specialized systems, which may not be able to handle the data volume.
-
-## Trade offs when choosing different storage types and views
-
-### Storage Types
-
-| Trade-off | CopyOnWrite | MergeOnRead |
-|-------------- |------------------| ------------------|
-| Data Latency | Higher   | Lower |
-| Update cost (I/O) | Higher (rewrite entire parquet) | Lower (append to delta file) |
-| Parquet File Size | Smaller (high update(I/0) cost) | Larger (low update cost) |
-| Write Amplification | Higher | Lower (depending on compaction strategy) |
-
-### Hudi Views
-
-| Trade-off | ReadOptimized | RealTime |
-|-------------- |------------------| ------------------|
-| Data Latency | Higher   | Lower |
-| Query Latency | Lower (raw columnar performance) | Higher (merge columnar + row based delta) |
\ No newline at end of file
diff --git a/docs/configurations.md b/docs/configurations.md
deleted file mode 100644
index 8c3ebe9d1..000000000
--- a/docs/configurations.md
+++ /dev/null
@@ -1,203 +0,0 @@
----
-title: Configurations
-keywords: configurations
-sidebar: mydoc_sidebar
-permalink: configurations.html
-toc: false
-summary: "Here we list all possible configurations and what they mean"
----
-
-### Configuration
-
-* [HoodieWriteConfig](#HoodieWriteConfig) <br/>
-<span style="color:grey">Top Level Config which is passed in when HoodieWriteClent is created.</span>
-    - [withPath](#withPath) (hoodie_base_path) <br/>
-    <span style="color:grey">Base HDFS path under which all the data partitions are created. Hoodie stores all the main meta-data about commits, savepoints, cleaning audit logs etc in .hoodie directory under the base directory. </span>
-    - [withSchema](#withSchema) (schema_str) <br/>
-    <span style="color:grey">This is the current reader avro schema for the Hoodie Dataset. This is a string of the entire schema. HoodieWriteClient uses this schema to pass on to implementations of HoodieRecordPayload to convert from the source format to avro record. This is also used when re-writing records during an update. </span>
-    - [withParallelism](#withParallelism) (insert_shuffle_parallelism = 200, upsert_shuffle_parallelism = 200) <br/>
-    <span style="color:grey">Insert DAG uses the insert_parallelism in every shuffle. Upsert DAG uses the upsert_parallelism in every shuffle. Typical workload is profiled and once a min parallelism is established, trade off between latency and cluster usage optimizations this is tuned and have a conservatively high number to optimize for latency and  </span>
-    - [combineInput](#combineInput) (on_insert = false, on_update=true) <br/>
-    <span style="color:grey">Flag which first combines the input RDD and merges multiple partial records into a single record before inserting or updating in HDFS</span>
-    - [withWriteStatusStorageLevel](#withWriteStatusStorageLevel) (level = MEMORY_AND_DISK_SER) <br/>
-    <span style="color:grey">HoodieWriteClient.insert and HoodieWriteClient.upsert returns a persisted RDD[WriteStatus], this is because the Client can choose to inspect the WriteStatus and choose and commit or not based on the failures. This is a configuration for the storage level for this RDD </span>
-    - [withAutoCommit](#withAutoCommit) (autoCommit = true) <br/>
-    <span style="color:grey">Should HoodieWriteClient autoCommit after insert and upsert. The client can choose to turn off auto-commit and commit on a "defined success condition"</span>
-    - [withAssumeDatePartitioning](#withAssumeDatePartitioning) (assumeDatePartitioning = false) <br/>
-        <span style="color:grey">Should HoodieWriteClient assume the data is partitioned by dates, i.e three levels from base path. This is a stop-gap to support tables created by versions < 0.3.1. Will be removed eventually </span>
-    - [withConsistencyCheckEnabled](#withConsistencyCheckEnabled) (enabled = false) <br/>
-        <span style="color:grey">Should HoodieWriteClient perform additional checks to ensure written files' are listable on the underlying filesystem/storage. Set this to true, to workaround S3's eventual consistency model and ensure all data written as a part of a commit is faithfully available for queries. </span>
-
-    - [withIndexConfig](#withIndexConfig) (HoodieIndexConfig) <br/>
-    <span style="color:grey">Hoodie uses a index to help find the FileID which contains an incoming record key. This is pluggable to have a external index (HBase) or use the default bloom filter stored in the Parquet files</span>
-        - [withIndexType](#withIndexType) (indexType = BLOOM) <br/>
-        <span style="color:grey">Type of index to use. Default is Bloom filter. Possible options are [BLOOM | HBASE | INMEMORY]. Bloom filters removes the dependency on a external system and is stored in the footer of the Parquet Data Files</span>
-        - [bloomFilterNumEntries](#bloomFilterNumEntries) (60000) <br/>
-        <span style="color:grey">Only applies if index type is BLOOM. <br/>This is the number of entries to be stored in the bloom filter. We assume the maxParquetFileSize is 128MB and averageRecordSize is 1024B and hence we approx a total of 130K records in a file. The default (60000) is roughly half of this approximation. [#70](https://github.com/uber/hoodie/issues/70) tracks computing this dynamically. Warning: Setting this very low, will generate a lot of false positives and index lookup will have to scan a lot more files than it has to and Setting this to a very high number will increase the size every data file linearly (roughly 4KB for every 50000 entries).</span>
-        - [bloomFilterFPP](#bloomFilterFPP) (0.000000001) <br/>
-        <span style="color:grey">Only applies if index type is BLOOM. <br/> Error rate allowed given the number of entries. This is used to calculate how many bits should be assigned for the bloom filter and the number of hash functions. This is usually set very low (default: 0.000000001), we like to tradeoff disk space for lower false positives</span>
-        - [bloomIndexPruneByRanges](#bloomIndexPruneByRanges) (true) <br/>
-        <span style="color:grey">Only applies if index type is BLOOM. <br/> When true, range information from files to leveraged speed up index lookups. Particularly helpful, if the key has a monotonously increasing prefix, such as timestamp.</span>
-        - [bloomIndexUseCaching](#bloomIndexUseCaching) (true) <br/>
-        <span style="color:grey">Only applies if index type is BLOOM. <br/> When true, the input RDD will cached to speed up index lookup by reducing IO for computing parallelism or affected partitions</span>
-        - [bloomIndexParallelism](#bloomIndexParallelism) (0) <br/>
-        <span style="color:grey">Only applies if index type is BLOOM. <br/> This is the amount of parallelism for index lookup, which involves a Spark Shuffle. By default, this is auto computed based on input workload characteristics</span>
-        - [hbaseZkQuorum](#hbaseZkQuorum) (zkString) <br/>
-        <span style="color:grey">Only application if index type is HBASE. HBase ZK Quorum url to connect to.</span>
-        - [hbaseZkPort](#hbaseZkPort) (port) <br/>
-        <span style="color:grey">Only application if index type is HBASE. HBase ZK Quorum port to connect to.</span>
-        - [hbaseTableName](#hbaseTableName) (tableName) <br/>
-        <span style="color:grey">Only application if index type is HBASE. HBase Table name to use as the index. Hoodie stores the row_key and [partition_path, fileID, commitTime] mapping in the table.</span>
-
-    - [withStorageConfig](#withStorageConfig) (HoodieStorageConfig) <br/>
-    <span style="color:grey">Storage related configs</span>
-        - [limitFileSize](#limitFileSize) (size = 120MB) <br/>
-        <span style="color:grey">Hoodie re-writes a single file during update (copy_on_write) or a compaction (merge_on_read). This is fundamental unit of parallelism. It is important that this is aligned with the underlying filesystem block size. </span>
-        - [parquetBlockSize](#parquetBlockSize) (rowgroupsize = 120MB) <br/>
-        <span style="color:grey">Parquet RowGroup size. Its better than this is aligned with the file size, so that a single column within a file is stored continuously on disk</span>
-        - [parquetPageSize](#parquetPageSize) (pagesize = 1MB) <br/>
-        <span style="color:grey">Parquet page size. Page is the unit of read within a parquet file. Within a block, pages are compressed seperately. </span>
-        - [logFileMaxSize](#logFileMaxSize) (logFileSize = 1GB) <br/>
-        <span style="color:grey">LogFile max size. This is the maximum size allowed for a log file before it is rolled over to the next version. </span>
-        - [logFileDataBlockMaxSize](#logFileDataBlockMaxSize) (dataBlockSize = 256MB) <br/>
-        <span style="color:grey">LogFile Data block max size. This is the maximum size allowed for a single data block to be appended to a log file. This helps to make sure the data appended to the log file is broken up into sizable blocks to prevent from OOM errors. This size should be greater than the JVM memory. </span>
-
-    - [withCompactionConfig](#withCompactionConfig) (HoodieCompactionConfig) <br/>
-    <span style="color:grey">Cleaning and configurations related to compaction techniques</span>
-        - [withCleanerPolicy](#withCleanerPolicy) (policy = KEEP_LATEST_COMMITS) <br/>
-        <span style="color:grey">Hoodie Cleaning policy. Hoodie will delete older versions of parquet files to re-claim space. Any Query/Computation referring to this version of the file will fail. It is good to make sure that the data is retained for more than the maximum query execution time.</span>
-        - [retainCommits](#retainCommits) (no_of_commits_to_retain = 24) <br/>
-        <span style="color:grey">Number of commits to retain. So data will be retained for num_of_commits * time_between_commits (scheduled). This also directly translates into how much you can incrementally pull on this dataset</span>
-        - [archiveCommitsWith](#archiveCommitsWith) (minCommits = 96, maxCommits = 128) <br/>
-        <span style="color:grey">Each commit is a small file in the .hoodie directory. Since HDFS is not designed to handle multiple small files, hoodie archives older commits into a sequential log. A commit is published atomically by a rename of the commit file.</span>
-        - [compactionSmallFileSize](#compactionSmallFileSize) (size = 0) <br/>
-        <span style="color:grey">Small files can always happen because of the number of insert records in a paritition in a batch. Hoodie has an option to auto-resolve small files by masking inserts into this partition as updates to existing small files. The size here is the minimum file size considered as a "small file size". This should be less < maxFileSize and setting it to 0, turns off this feature. </span>
-        - [insertSplitSize](#insertSplitSize) (size = 500000) <br/>
-        <span style="color:grey">Insert Write Parallelism. Number of inserts grouped for a single partition. Writing out 100MB files, with atleast 1kb records, means 100K records per file. Default is to overprovision to 500K. To improve insert latency, tune this to match the number of records in a single file. Setting this to a low number, will result in small files (particularly when compactionSmallFileSize is 0)</span>
-        - [autoTuneInsertSplits](#autoTuneInsertSplits) (true) <br/>
-        <span style="color:grey">Should hoodie dynamically compute the insertSplitSize based on the last 24 commit's metadata. Turned off by default. </span>
-        - [approxRecordSize](#approxRecordSize) () <br/>
-        <span style="color:grey">The average record size. If specified, hoodie will use this and not compute dynamically based on the last 24 commit's metadata. No value set as default. This is critical in computing the insert parallelism and bin-packing inserts into small files. See above.</span>
-        - [withCompactionLazyBlockReadEnabled](#withCompactionLazyBlockReadEnabled) (true) <br/>
-        <span style="color:grey">When a CompactedLogScanner merges all log files, this config helps to choose whether the logblocks should be read lazily or not. Choose true to use I/O intensive lazy block reading (low memory usage) or false for Memory intensive immediate block read (high memory usage)</span>
-        - [withMaxNumDeltaCommitsBeforeCompaction](#withMaxNumDeltaCommitsBeforeCompaction) (maxNumDeltaCommitsBeforeCompaction = 10) <br/>
-        <span style="color:grey">Number of max delta commits to keep before triggering an inline compaction</span>
-        - [withCompactionReverseLogReadEnabled](#withCompactionReverseLogReadEnabled) (false) <br/>
-        <span style="color:grey">HoodieLogFormatReader reads a logfile in the forward direction starting from pos=0 to pos=file_length. If this config is set to true, the Reader reads the logfile in reverse direction, from pos=file_length to pos=0</span>
-
-    - [withMetricsConfig](#withMetricsConfig) (HoodieMetricsConfig) <br/>
-    <span style="color:grey">Hoodie publishes metrics on every commit, clean, rollback etc.</span>
-        - [on](#on) (true) <br/>
-        <span style="color:grey">Turn sending metrics on/off. on by default.</span>
-        - [withReporterType](#withReporterType) (GRAPHITE) <br/>
-        <span style="color:grey">Type of metrics reporter. Graphite is the default and the only value suppported.</span>
-        - [toGraphiteHost](#toGraphiteHost) () <br/>
-        <span style="color:grey">Graphite host to connect to</span>
-        - [onGraphitePort](#onGraphitePort) () <br/>
-        <span style="color:grey">Graphite port to connect to</span>
-        - [usePrefix](#usePrefix) () <br/>
-        <span style="color:grey">Standard prefix for all metrics</span>
-
-    - [withMemoryConfig](#withMemoryConfig) (HoodieMemoryConfig) <br/>
-    <span style="color:grey">Memory related configs</span>
-        - [withMaxMemoryFractionPerPartitionMerge](#withMaxMemoryFractionPerPartitionMerge) (maxMemoryFractionPerPartitionMerge = 0.6) <br/>
-        <span style="color:grey">This fraction is multiplied with the user memory fraction (1 - spark.memory.fraction) to get a final fraction of heap space to use during merge </span>
-        - [withMaxMemorySizePerCompactionInBytes](#withMaxMemorySizePerCompactionInBytes) (maxMemorySizePerCompactionInBytes = 1GB) <br/>
-        <span style="color:grey">HoodieCompactedLogScanner reads logblocks, converts records to HoodieRecords and then merges these log blocks and records. At any point, the number of entries in a log block can be less than or equal to the number of entries in the corresponding parquet file. This can lead to OOM in the Scanner. Hence, a spillable map helps alleviate the memory pressure. Use this config to set the max allowable inMemory footprint of the spillable map.</span>
-
-    - [S3Configs](s3_hoodie.html) (Hoodie S3 Configs) <br/>
-    <span style="color:grey">Configurations required for S3 and Hoodie co-operability.</span>
-
-    - [GCSConfigs](gcs_hoodie.html) (Hoodie GCS Configs) <br/>
-    <span style="color:grey">Configurations required for GCS and Hoodie co-operability.</span>
-
-* [Hoodie Datasource](#datasource) <br/>
-<span style="color:grey">Configs for datasource</span>
-    - [write options](#writeoptions) (write.format.option(...)) <br/>
-    <span style="color:grey"> Options useful for writing datasets </span>
-        - [OPERATION_OPT_KEY](#OPERATION_OPT_KEY) (Default: upsert) <br/>
-        <span style="color:grey">whether to do upsert, insert or bulkinsert for the write operation</span>
-        - [STORAGE_TYPE_OPT_KEY](#STORAGE_TYPE_OPT_KEY) (Default: COPY_ON_WRITE) <br/>
-        <span style="color:grey">The storage type for the underlying data, for this write. This can't change between writes.</span>
-        - [TABLE_NAME_OPT_KEY](#TABLE_NAME_OPT_KEY) (Default: None (mandatory)) <br/>
-        <span style="color:grey">Hive table name, to register the dataset into.</span>
-        - [PRECOMBINE_FIELD_OPT_KEY](#PRECOMBINE_FIELD_OPT_KEY) (Default: ts) <br/>
-        <span style="color:grey">Field used in preCombining before actual write. When two records have the same key value,
-        we will pick the one with the largest value for the precombine field, determined by Object.compareTo(..)</span>
-        - [PAYLOAD_CLASS_OPT_KEY](#PAYLOAD_CLASS_OPT_KEY) (Default: com.uber.hoodie.OverwriteWithLatestAvroPayload) <br/>
-        <span style="color:grey">Payload class used. Override this, if you like to roll your own merge logic, when upserting/inserting.
-        This will render any value set for `PRECOMBINE_FIELD_OPT_VAL` in-effective</span>
-        - [RECORDKEY_FIELD_OPT_KEY](#RECORDKEY_FIELD_OPT_KEY) (Default: uuid) <br/>
-        <span style="color:grey">Record key field. Value to be used as the `recordKey` component of `HoodieKey`. Actual value
-        will be obtained by invoking .toString() on the field value. Nested fields can be specified using
-        the dot notation eg: `a.b.c`</span>
-        - [PARTITIONPATH_FIELD_OPT_KEY](#PARTITIONPATH_FIELD_OPT_KEY) (Default: partitionpath) <br/>
-        <span style="color:grey">Partition path field. Value to be used at the `partitionPath` component of `HoodieKey`.
-        Actual value ontained by invoking .toString()</span>
-        - [KEYGENERATOR_CLASS_OPT_KEY](#KEYGENERATOR_CLASS_OPT_KEY) (Default: com.uber.hoodie.SimpleKeyGenerator) <br/>
-        <span style="color:grey">Key generator class, that implements will extract the key out of incoming `Row` object</span>
-        - [COMMIT_METADATA_KEYPREFIX_OPT_KEY](#COMMIT_METADATA_KEYPREFIX_OPT_KEY) (Default: _) <br/>
-        <span style="color:grey">Option keys beginning with this prefix, are automatically added to the commit/deltacommit metadata.
-        This is useful to store checkpointing information, in a consistent way with the hoodie timeline</span>
-
-    - [read options](#readoptions) (read.format.option(...)) <br/>
-    <span style="color:grey">Options useful for reading datasets</span>
-        - [VIEW_TYPE_OPT_KEY](#VIEW_TYPE_OPT_KEY) (Default:  = read_optimized) <br/>
-        <span style="color:grey">Whether data needs to be read, in incremental mode (new data since an instantTime)
-        (or) Read Optimized mode (obtain latest view, based on columnar data)
-        (or) Real time mode (obtain latest view, based on row & columnar data)</span>
-        - [BEGIN_INSTANTTIME_OPT_KEY](#BEGIN_INSTANTTIME_OPT_KEY) (Default: None (Mandatory in incremental mode)) <br/>
-        <span style="color:grey">Instant time to start incrementally pulling data from. The instanttime here need not
-        necessarily correspond to an instant on the timeline. New data written with an
-         `instant_time > BEGIN_INSTANTTIME` are fetched out. For e.g: '20170901080000' will get
-         all new data written after Sep 1, 2017 08:00AM.</span>
-        - [END_INSTANTTIME_OPT_KEY](#END_INSTANTTIME_OPT_KEY) (Default: latest instant (i.e fetches all new data since begin instant time)) <br/>
-        <span style="color:grey"> Instant time to limit incrementally fetched data to. New data written with an
-        `instant_time <= END_INSTANTTIME` are fetched out.</span>
-
-
-### Tuning
-
-Writing data via Hoodie happens as a Spark job and thus general rules of spark debugging applies here too. Below is a list of things to keep in mind, if you are looking to improving performance or reliability.
-
- - **Write operations** : Use `bulkinsert` to load new data into a table, and there on use `upsert`/`insert`. 
- Difference between them is that bulk insert uses a disk based write path to scale to load large inputs without need to cache it.
- - **Input Parallelism** : By default, Hoodie tends to over-partition input (i.e `withParallelism(1500)`), to ensure each Spark partition stays within the 2GB limit for inputs upto 500GB. Bump this up accordingly if you have larger inputs. We recommend having shuffle parallelism `hoodie.[insert|upsert|bulkinsert].shuffle.parallelism` such that its atleast input_data_size/500MB
- - **Off-heap memory** : Hoodie writes parquet files and that needs good amount of off-heap memory proportional to schema width. Consider setting something like `spark.yarn.executor.memoryOverhead` or `spark.yarn.driver.memoryOverhead`, if you are running into such failures.
- - **Spark Memory** : Typically, hoodie needs to be able to read a single file into memory to perform merges or compactions and thus the executor memory should be sufficient to accomodate this. In addition, Hoodie caches the input to be able to intelligently place data and thus leaving some `spark.storage.memoryFraction` will generally help boost performance.
- - **Sizing files** : Set `limitFileSize` above judiciously, to balance ingest/write latency vs number of files & consequently metadata overhead associated with it.
- - **Timeseries/Log data** : Default configs are tuned for database/nosql changelogs where individual record sizes are large. Another very popular class of data is timeseries/event/log data that tends to be more volumnious with lot more records per partition. In such cases
-    - Consider tuning the bloom filter accuracy via `.bloomFilterFPP()/bloomFilterNumEntries()` to achieve your target index look up time
-    - Consider making a key that is prefixed with time of the event, which will enable range pruning & significantly speeding up index lookup.
- - **GC Tuning** : Please be sure to follow garbage collection tuning tips from Spark tuning guide to avoid OutOfMemory errors
-    - [Must] Use G1/CMS Collector. Sample CMS Flags to add to spark.executor.extraJavaOptions : ``-XX:NewSize=1g -XX:SurvivorRatio=2 -XX:+UseCompressedOops -XX:+UseConcMarkSweepGC -XX:+UseParNewGC -XX:CMSInitiatingOccupancyFraction=70 -XX:+PrintTenuringDistribution -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -XX:+PrintGCDateStamps -XX:+PrintGCApplicationStoppedTime -XX:+PrintGCApplicationConcurrentTime -XX:+PrintTenuringDistribution -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/tmp/hoodie-heapdump.hprof`
-    - If it keeps OOMing still, reduce spark memory conservatively: `spark.memory.fraction=0.2, spark.memory.storageFraction=0.2` allowing it to spill rather than OOM. (reliably slow vs crashing intermittently)
-
- Below is a full working production config
-
- ```
- spark.driver.extraClassPath    /etc/hive/conf
- spark.driver.extraJavaOptions    -XX:+PrintTenuringDistribution -XX:+PrintGCDetails -XX:+PrintGCDateStamps -XX:+PrintGCApplicationStoppedTime -XX:+PrintGCApplicationConcurrentTime -XX:+PrintGCTimeStamps -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/tmp/hoodie-heapdump.hprof
- spark.driver.maxResultSize    2g
- spark.driver.memory    4g
- spark.executor.cores    1
- spark.executor.extraJavaOptions    -XX:+PrintFlagsFinal -XX:+PrintReferenceGC -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -XX:+PrintAdaptiveSizePolicy -XX:+UnlockDiagnosticVMOptions -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/tmp/hoodie-heapdump.hprof
- spark.executor.id    driver
- spark.executor.instances    300
- spark.executor.memory    6g
- spark.rdd.compress true
-
- spark.kryoserializer.buffer.max    512m
- spark.serializer    org.apache.spark.serializer.KryoSerializer
- spark.shuffle.memoryFraction    0.2
- spark.shuffle.service.enabled    true
- spark.sql.hive.convertMetastoreParquet    false
- spark.storage.memoryFraction    0.6
- spark.submit.deployMode    cluster
- spark.task.cpus    1
- spark.task.maxFailures    4
-
- spark.yarn.driver.memoryOverhead    1024
- spark.yarn.executor.memoryOverhead    3072
- spark.yarn.max.executor.failures    100
- ```
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diff --git a/docs/css/customstyles.css b/docs/css/customstyles.css
deleted file mode 100644
index 676c80469..000000000
--- a/docs/css/customstyles.css
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diff --git a/docs/css/font-awesome.min.css b/docs/css/font-awesome.min.css
deleted file mode 100644
index 08e68ec0c..000000000
--- a/docs/css/font-awesome.min.css
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diff --git a/docs/css/modern-business.css b/docs/css/modern-business.css
deleted file mode 100755
index 58dc4a2d6..000000000
--- a/docs/css/modern-business.css
+++ /dev/null
@@ -1,93 +0,0 @@
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\ No newline at end of file
diff --git a/docs/css/printstyles.css b/docs/css/printstyles.css
deleted file mode 100644
index 5e55e5765..000000000
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diff --git a/docs/css/syntax.css b/docs/css/syntax.css
deleted file mode 100644
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diff --git a/docs/css/theme-blue.css b/docs/css/theme-blue.css
deleted file mode 100644
index 3b99929ad..000000000
--- a/docs/css/theme-blue.css
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diff --git a/docs/css/theme-green.css b/docs/css/theme-green.css
deleted file mode 100644
index ac3d459dc..000000000
--- a/docs/css/theme-green.css
+++ /dev/null
@@ -1,99 +0,0 @@
-.summary {
-    color: #808080;
-    border-left: 5px solid #E50E51;
-    font-size:16px;
-}
-
-
-h3 {color: #E50E51; }
-h4 {color: #808080; }
-
-.nav-tabs > li.active > a, .nav-tabs > li.active > a:hover, .nav-tabs > li.active > a:focus {
-    background-color: #248ec2;
-    color: white;
-}
-
-.nav > li.active > a {
-    background-color: #72ac4a;
-}
-
-.nav > li > a:hover {
-    background-color: #72ac4a;
-}
-
-div.navbar-collapse .dropdown-menu > li > a:hover {
-    background-color: #72ac4a;
-}
-
-.nav li.thirdlevel > a {
-    background-color: #FAFAFA !important;
-    color: #72ac4a;
-    font-weight: bold;
-}
-
-a[data-toggle="tooltip"] {
-    color: #649345;
-    font-style: italic;
-    cursor: default;
-}
-
-.navbar-inverse {
-    background-color: #72ac4a;
-    border-color: #5b893c;
-}
-
-.navbar-inverse .navbar-nav > .open > a, .navbar-inverse .navbar-nav > .open > a:hover, .navbar-inverse .navbar-nav > .open > a:focus {
-    color: #5b893c;
-}
-
-.navbar-inverse .navbar-nav > .open > a, .navbar-inverse .navbar-nav > .open > a:hover, .navbar-inverse .navbar-nav > .open > a:focus {
-    background-color: #5b893c;
-    color: #ffffff;
-}
-
-/* not sure if using this ...*/
-.navbar-inverse .navbar-collapse, .navbar-inverse .navbar-form {
-    border-color: #72ac4a !important;
-}
-
-.btn-primary {
-    color: #ffffff;
-    background-color: #5b893c;
-    border-color: #5b893c;
-}
-
-.btn-primary:hover,
-.btn-primary:focus,
-.btn-primary:active,
-.btn-primary.active,
-.open .dropdown-toggle.btn-primary {
-    background-color: #72ac4a;
-    border-color: #5b893c;
-}
-
-.printTitle {
-    color: #5b893c !important;
-}
-
-body.print h1 {color: #5b893c !important; font-size:28px;}
-body.print h2 {color: #595959 !important; font-size:24px;}
-body.print h3 {color: #E50E51 !important; font-size:14px;}
-body.print h4 {color: #679DCE !important; font-size:14px; font-style: italic;}
-
-.anchorjs-link:hover {
-    color: #333;
-}
-
-div.sidebarTitle {
-    color: #333;
-}
-
-li.sidebarTitle {
-    margin-top:40px;
-    font-weight:normal;
-    font-size:130%;
-    color: #ED1951;
-    margin-bottom:10px;
-    margin-left: 5px;
-
-}
diff --git a/docs/dev_setup.md b/docs/dev_setup.md
deleted file mode 100644
index 1bdeec793..000000000
--- a/docs/dev_setup.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Developer Setup
-keywords: developer setup
-sidebar: mydoc_sidebar
-permalink: dev_setup.html
----
-
-### Code Style
-
- We have embraced the code style largely based on [google format](https://google.github.io/styleguide/javaguide.html).
- Please setup your IDE with style files from [here](../style/)
- We also recommend setting up the [Save Action Plugin](https://plugins.jetbrains.com/plugin/7642-save-actions) to auto format & organize imports on save.
- The Maven Compilation life-cycle will fail if there are checkstyle violations.
diff --git a/docs/feed.xml b/docs/feed.xml
deleted file mode 100644
index d8d6ac999..000000000
--- a/docs/feed.xml
+++ /dev/null
@@ -1,32 +0,0 @@
----
-search: exclude
-layout: none
----
-
-<?xml version="1.0" encoding="UTF-8"?>
-<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
-    <channel>
-        <title>{{ site.title | xml_escape }}</title>
-        <description>{{ site.description | xml_escape }}</description>
-        <link>{{ site.url }}/</link>
-        <atom:link href="{{ "/feed.xml" | prepend: site.url }}" rel="self" type="application/rss+xml"/>
-        <pubDate>{{ site.time | date_to_rfc822 }}</pubDate>
-        <lastBuildDate>{{ site.time | date_to_rfc822 }}</lastBuildDate>
-        <generator>Jekyll v{{ jekyll.version }}</generator>
-        {% for post in site.posts limit:10 %}
-        <item>
-            <title>{{ post.title | xml_escape }}</title>
-            <description>{{ post.content | xml_escape }}</description>
-            <pubDate>{{ post.date | date_to_rfc822 }}</pubDate>
-            <link>{{ post.url | prepend: site.url }}</link>
-            <guid isPermaLink="true">{{ post.url | prepend: site.url }}</guid>
-            {% for tag in post.tags %}
-            <category>{{ tag | xml_escape }}</category>
-            {% endfor %}
-            {% for tag in page.tags %}
-            <category>{{ cat | xml_escape }}</category>
-            {% endfor %}
-        </item>
-        {% endfor %}
-    </channel>
-</rss>
diff --git a/docs/fonts/FontAwesome.otf b/docs/fonts/FontAwesome.otf
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diff --git a/docs/gcs_filesystem.md b/docs/gcs_filesystem.md
deleted file mode 100644
index 7f9a0227b..000000000
--- a/docs/gcs_filesystem.md
+++ /dev/null
@@ -1,62 +0,0 @@
----
-title: GCS Filesystem (experimental)
-keywords: sql hive gcs spark presto
-sidebar: mydoc_sidebar
-permalink: gcs_hoodie.html
-toc: false
-summary: In this page, we go over how to configure hoodie with Google Cloud Storage.
----
-Hoodie works with HDFS by default and GCS **regional** buckets provide an HDFS API with strong consistency.
-
-## GCS Configs
-
-There are two configurations required for Hoodie GCS compatibility:
-
-- Adding GCS Credentials for Hoodie
-- Adding required jars to classpath
-
-### GCS Credentials
-
-Add the required configs in your core-site.xml from where Hoodie can fetch them. Replace the `fs.defaultFS` with your GCS bucket name and Hoodie should be able to read/write from the bucket.
-
-```xml
-  <property>
-    <name>fs.defaultFS</name>
-    <value>gs://hoodie-bucket</value>
-  </property>
-
-  <property>
-    <name>fs.gs.impl</name>
-    <value>com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem</value>
-    <description>The FileSystem for gs: (GCS) uris.</description>
-  </property>
-
-  <property>
-    <name>fs.AbstractFileSystem.gs.impl</name>
-    <value>com.google.cloud.hadoop.fs.gcs.GoogleHadoopFS</value>
-    <description>The AbstractFileSystem for gs: (GCS) uris.</description>
-  </property>
-
-  <property>
-    <name>fs.gs.project.id</name>
-    <value>GCS_PROJECT_ID</value>
-  </property>
-  <property>
-    <name>google.cloud.auth.service.account.enable</name>
-    <value>true</value>
-  </property>
-  <property>
-    <name>google.cloud.auth.service.account.email</name>
-    <value>GCS_SERVICE_ACCOUNT_EMAIL</value>
-  </property>
-  <property>
-    <name>google.cloud.auth.service.account.keyfile</name>
-    <value>GCS_SERVICE_ACCOUNT_KEYFILE</value>
-  </property>
-```
-
-### GCS Libs
-
-GCS hadoop libraries to add to our classpath
-
-- com.google.cloud.bigdataoss:gcs-connector:1.6.0-hadoop2
diff --git a/docs/images/androidsdkmanagericon.png b/docs/images/androidsdkmanagericon.png
deleted file mode 100644
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GIT binary patch
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diff --git a/docs/implementation.md b/docs/implementation.md
deleted file mode 100644
index 0e1508380..000000000
--- a/docs/implementation.md
+++ /dev/null
@@ -1,275 +0,0 @@
----
-title: Implementation
-keywords: implementation
-sidebar: mydoc_sidebar
-toc: false
-permalink: implementation.html
----
-
-Hoodie is implemented as a Spark library, which makes it easy to integrate into existing data pipelines or ingestion
-libraries (which we will refer to as `hoodie clients`). Hoodie Clients prepare an `RDD[HoodieRecord]` that contains the data to be upserted and
-Hoodie upsert/insert is merely a Spark DAG, that can be broken into two big pieces.
-
- - **Indexing** :  A big part of Hoodie's efficiency comes from indexing the mapping from record keys to the file ids, to which they belong to.
- This index also helps the `HoodieWriteClient` separate upserted records into inserts and updates, so they can be treated differently.
- `HoodieReadClient` supports operations such as `filterExists` (used for de-duplication of table) and an efficient batch `read(keys)` api, that
- can read out the records corresponding to the keys using the index much quickly, than a typical scan via a query. The index is also atomically
- updated each commit, and is also rolled back when commits are rolled back.
-
- - **Storage** : The storage part of the DAG is responsible for taking an `RDD[HoodieRecord]`, that has been tagged as
- an insert or update via index lookup, and writing it out efficiently onto storage.
-
-## Index
-
-Hoodie currently provides two choices for indexes : `BloomIndex` and `HBaseIndex` to map a record key into the file id to which it belongs to. This enables
-us to speed up upserts significantly, without scanning over every record in the dataset. Hoodie Indices can be classified based on
-their ability to lookup records across partition. A `global` index does not need partition information for finding the file-id for a record key 
-but a `non-global` does.
-
-#### HBase Index (global)
-
-Here, we just use HBase in a straightforward way to store the mapping above. The challenge with using HBase (or any external key-value store
- for that matter) is performing rollback of a commit and handling partial index updates.
- Since the HBase table is indexed by record key and not commit Time, we would have to scan all the entries which will be prohibitively expensive.
- Insteead, we store the commit time with the value and discard its value if it does not belong to a valid commit.
-
-#### Bloom Index (non-global)
-
-This index is built by adding bloom filters with a very high false positive tolerance (e.g: 1/10^9), to the parquet file footers.
-The advantage of this index over HBase is the obvious removal of a big external dependency, and also nicer handling of rollbacks & partial updates
-since the index is part of the data file itself.
-
-At runtime, checking the Bloom Index for a given set of record keys effectively amounts to checking all the bloom filters within a given
-partition, against the incoming records, using a Spark join. Much of the engineering effort towards the Bloom index has gone into scaling this join
-by caching the incoming RDD[HoodieRecord] and dynamically tuning join parallelism, to avoid hitting Spark limitations like 2GB maximum
-for partition size. As a result, Bloom Index implementation has been able to handle single upserts upto 5TB, in a reliable manner.
-
-
-## Storage
-
-The implementation specifics of the two storage types, introduced in [concepts](concepts.html) section, are detailed below.
-
-
-#### Copy On Write
-
-The Spark DAG for this storage, is relatively simpler. The key goal here is to group the tagged hoodie record RDD, into a series of
-updates and inserts, by using a partitioner. To achieve the goals of maintaining file sizes, we first sample the input to obtain a `workload profile`
-that understands the spread of inserts vs updates, their distribution among the partitions etc. With this information, we bin-pack the
-records such that
-
- - For updates, the latest version of the that file id, is rewritten once, with new values for all records that have changed
- - For inserts, the records are first packed onto the smallest file in each partition path, until it reaches the configured maximum size.
-   Any remaining records after that, are again packed into new file id groups, again meeting the size requirements.
-
-In this storage, index updation is a no-op, since the bloom filters are already written as a part of committing data.
-
-In the case of Copy-On-Write, a single parquet file constitutes one `file slice` which contains one complete version of 
-the file 
-
-{% include image.html file="hoodie_log_format_v2.png" alt="hoodie_log_format_v2.png" max-width="1000" %}
-
-#### Merge On Read
-
-In the Merge-On-Read storage model, there are 2 logical components - one for ingesting data (both inserts/updates) into the dataset
- and another for creating compacted views. The former is hereby referred to as `Writer` while the later
- is referred as `Compactor`.
- 
-##### Merge On Read Writer
- 
- At a high level, Merge-On-Read Writer goes through same stages as Copy-On-Write writer in ingesting data.
- The key difference here is that updates are appended to latest log (delta) file belonging to the latest file slice 
- without merging. For inserts, Hudi supports 2 modes:
-
-   1. Inserts to Log Files - This is done for datasets that have an indexable log files (for eg global index)
-   2. Inserts to parquet files - This is done for datasets that do not have indexable log files, for eg bloom index
-      embedded in parquer files. Hudi treats writing new records in the same way as inserting to Copy-On-Write files.
-
-As in the case of Copy-On-Write, the input tagged records are partitioned such that all upserts destined to 
-a `file id` are grouped together. This upsert-batch is written as one or more log-blocks written to log-files.
-Hudi allows clients to control log file sizes (See [Storage Configs](../configurations))
-
-The WriteClient API is same for both Copy-On-Write and Merge-On-Read writers.
- 
-With Merge-On-Read, several rounds of data-writes would have resulted in accumulation of one or more log-files.
-All these log-files along with base-parquet (if exists) constitute a `file slice` which represents one complete version
-of the file. 
-  
-#### Compactor
-
-Realtime Readers will perform in-situ merge of these delta log-files to provide the most recent (committed) view of
-the dataset. To keep the query-performance in check and eventually achieve read-optimized performance, Hudi supports
-compacting these log-files asynchronously to create read-optimized views.
-
-Asynchronous Compaction involves 2 steps:
-
-  * `Compaction Schedule` : Hudi Write Client exposes API to create Compaction plans which contains the list of `file slice`
-    to be compacted atomically in a single compaction commit. Hudi allows pluggable strategies for choosing
-    file slices for each compaction runs. This step is typically done inline by Writer process as Hudi expects
-    only one schedule is being generated at a time which allows Hudi to enforce the constraint that pending compaction
-    plans do not step on each other file-slices. This constraint allows for multiple concurrent `Compactors` to run at 
-    the same time. Some of the common strategies used for choosing `file slice` for compaction are:
-    * BoundedIO - Limit the number of file slices chosen for a compaction plan by expected total IO (read + write) 
-    needed to complete compaction run 
-    * Log File Size - Prefer file-slices with larger amounts of delta log data to be merged
-    * Day Based - Prefer file slice belonging to latest day partitions
-    ```
-        API for scheduling compaction
-          /**
-           * Schedules a new compaction instant
-           * @param extraMetadata
-           * @return Compaction Instant timestamp if a new compaction plan is scheduled
-           */
-           Optional<String> scheduleCompaction(Optional<Map<String, String>> extraMetadata) throws IOException;
-     ```
-  * `Compactor` : Hudi provides a separate API in Write Client to execute a compaction plan. The compaction
-    plan (just like a commit) is identified by a timestamp. Most of the design and implementation complexities for Async
-    Compaction is for guaranteeing snapshot isolation to readers and writer when
-    multiple concurrent compactors are running. Typical compactor deployment involves launching a separate
-    spark application which executes pending compactions when they become available. The core logic of compacting
-    file slices in the Compactor is very similar to that of merging updates in a Copy-On-Write table. The only
-    difference being in the case of compaction, there is an additional step of merging the records in delta log-files. 
-    
-    Here are the main API to lookup and execute a compaction plan.
-    ```
-      Main API in HoodieWriteClient for running Compaction:
-       /**
-        * Performs Compaction corresponding to instant-time
-        * @param compactionInstantTime   Compaction Instant Time
-        * @return
-        * @throws IOException
-        */
-        public JavaRDD<WriteStatus> compact(String compactionInstantTime) throws IOException;
-    
-      To lookup all pending compactions, use the API defined in HoodieReadClient
-    
-      /**
-       * Return all pending compactions with instant time for clients to decide what to compact next.
-       * @return
-       */
-      public List<Pair<String, HoodieCompactionPlan>> getPendingCompactions();
-    ```
-
-Refer to  __hoodie-client/src/test/java/HoodieClientExample.java__ class for an example of how compaction
-is scheduled and executed.
-
-##### Deployment Models
-
-These are typical Hoodie Writer and Compaction deployment models
-
-  * `Inline Compaction` : At each round, a single spark application ingests new batch to dataset. It then optionally decides to schedule
-   a compaction run and executes it in sequence.
-  * `Single Dedicated Async Compactor` :  The Spark application which brings in new changes to dataset (writer) periodically
-     schedules compaction. The Writer application does not run compaction inline. A separate spark applications periodically
-     probes for pending compaction and executes the compaction.
-  * ` Multi Async Compactors` : This mode is similar to `Single Dedicated Async Compactor` mode. The main difference being
-     now there can be more than one spark application picking different compactions and executing them in parallel.
-     In order to ensure compactors do not step on each other, they use coordination service like zookeeper to pickup unique
-     pending compaction instants and run them.
-
-The Compaction process requires one executor per file-slice in the compaction plan. So, the best resource allocation
-strategy (both in terms of speed and resource usage) for clusters supporting dynamic allocation is to lookup the compaction
-plan to be run to figure out the number of file slices being compacted and choose that many number of executors.
-
-## Async Compaction Design Deep-Dive (Optional)
-
-For the purpose of this section, it is important to distinguish between 2 types of commits as pertaining to the file-group: 
-
-A commit which generates a merged and read-optimized file-slice is called `snapshot commit` (SC) with respect to that file-group.
-A commit which merely appended the new/updated records assigned to the file-group into a new log block is called `delta commit` (DC) 
-with respect to that file-group.
-
-### Algorithm
-
-The algorithm is described with an illustration. Let us assume a scenario where there are commits SC1, DC2, DC3 that have
-already completed on a data-set. Commit DC4 is currently ongoing with the writer (ingestion) process using it to upsert data. 
-Let us also imagine there are a set of file-groups (FG1 … FGn) in the data-set whose latest version (`File-Slice`) 
-contains the base file created by commit SC1 (snapshot-commit in columnar format) and a log file containing row-based 
-log blocks of 2 delta-commits (DC2 and DC3). 
-
-{% include image.html file="async_compac_1.png" alt="async_compac_1.png" max-width="1000" %}
-
- * Writer (Ingestion) that is going to commit "DC4" starts. The record updates in this batch are grouped by file-groups 
-   and appended in row formats to the corresponding log file as delta commit. Let us imagine a subset of file-groups has 
-   this new log block (delta commit) DC4 added.
- * Before the writer job completes, it runs the compaction strategy to decide which file-group to compact by compactor 
-   and creates a new compaction-request commit SC5. This commit file is marked as “requested” with metadata denoting 
-   which fileIds to compact (based on selection policy). Writer completes without running compaction (will be run async). 
- 
-   {% include image.html file="async_compac_2.png" alt="async_compac_2.png" max-width="1000" %}
- 
- * Writer job runs again ingesting next batch. It starts with commit DC6. It reads the earliest inflight compaction 
-   request marker commit in timeline order and collects the (fileId, Compaction Commit Id “CcId” ) pairs from meta-data. 
-   Ingestion DC6 ensures a new file-slice with base-commit “CcId” gets allocated for the file-group. 
-   The Writer will simply append records in row-format to the first log-file (as delta-commit) assuming the 
-   base-file (“Phantom-Base-File”) will be created eventually by the compactor.
-   
-   {% include image.html file="async_compac_3.png" alt="async_compac_3.png" max-width="1000" %}
- 
- * Compactor runs at some time  and commits at “Tc” (concurrently or before/after Ingestion DC6). It reads the commit-timeline 
-   and finds the first unprocessed compaction request marker commit. Compactor reads the commit’s metadata finding the 
-   file-slices to be compacted. It compacts the file-slice and creates the missing base-file (“Phantom-Base-File”) 
-   with “CCId” as the commit-timestamp. Compactor then marks the compaction commit timestamp as completed. 
-   It is important to realize that at data-set level, there could be different file-groups requesting compaction at 
-   different commit timestamps.
- 
-    {% include image.html file="async_compac_4.png" alt="async_compac_4.png" max-width="1000" %}
-
- * Near Real-time reader interested in getting the latest snapshot will have 2 cases. Let us assume that the 
-   incremental ingestion (writer at DC6) happened before the compaction (some time “Tc”’).  
-   The below description is with regards to compaction from file-group perspective. 
-   * `Reader querying at time between ingestion completion time for DC6 and compaction finish “Tc”`: 
-     Hoodie’s implementation will be changed to become aware of file-groups currently waiting for compaction and 
-     merge log-files corresponding to DC2-DC6 with the base-file corresponding to SC1. In essence, Hoodie will create 
-     a pseudo file-slice by combining the 2 file-slices starting at base-commits SC1 and SC5 to one. 
-     For file-groups not waiting for compaction, the reader behavior is essentially the same - read latest file-slice 
-     and merge on the fly.
-   * `Reader querying at time after compaction finished (> “Tc”)` : In this case, reader will not find any pending 
-     compactions in the timeline and will simply have the current behavior of reading the latest file-slice and 
-     merging on-the-fly.
-     
- * Read-Optimized View readers will query against the latest columnar base-file for each file-groups. 
-
-The above algorithm explains Async compaction w.r.t a single compaction run on a single file-group. It is important
-to note that multiple compaction plans can be run concurrently as they are essentially operating on different 
-file-groups.
-
-## Performance
-
-In this section, we go over some real world performance numbers for Hoodie upserts, incremental pull and compare them against
-the conventional alternatives for achieving these tasks.
-
-#### Upsert vs Bulk Loading
-
-Following shows the speed up obtained for NoSQL ingestion, by switching from bulk loads off HBase to Parquet to incrementally upserting
-on a Hoodie dataset, on 5 tables ranging from small to huge.
-
-{% include image.html file="hoodie_upsert_perf1.png" alt="hoodie_upsert_perf1.png" max-width="1000" %}
-
-
-Given Hoodie can build the dataset incrementally, it opens doors for also scheduling ingesting more frequently thus reducing latency, with
-significant savings on the overall compute cost.
-
-
-{% include image.html file="hoodie_upsert_perf2.png" alt="hoodie_upsert_perf2.png" max-width="1000" %}
-
-Hoodie upserts have been stress tested upto 4TB in a single commit across the t1 table.
-
-
-
-#### Copy On Write Regular Query Performance
-
-The major design goal for copy-on-write storage was to achieve the latency reduction & efficiency gains in previous section,
-with no impact on queries. Following charts compare the hoodie vs non-hoodie datasets across Hive/Presto/Spark queries.
-
-**Hive**
-
-{% include image.html file="hoodie_query_perf_hive.png" alt="hoodie_query_perf_hive.png" max-width="800" %}
-
-**Spark**
-
-{% include image.html file="hoodie_query_perf_spark.png" alt="hoodie_query_perf_spark.png" max-width="1000" %}
-
-**Presto**
-
-{% include image.html file="hoodie_query_perf_presto.png" alt="hoodie_query_perf_presto.png" max-width="1000" %}
-
diff --git a/docs/incremental_processing.md b/docs/incremental_processing.md
deleted file mode 100644
index 9a65ccfe1..000000000
--- a/docs/incremental_processing.md
+++ /dev/null
@@ -1,237 +0,0 @@
----
-title: Incremental Processing
-keywords: incremental processing
-sidebar: mydoc_sidebar
-permalink: incremental_processing.html
-toc: false
-summary: In this page, we will discuss some available tools for ingesting data incrementally & consuming the changes.
----
-
-As discussed in the concepts section, the two basic primitives needed for [incrementally processing](https://www.oreilly.com/ideas/ubers-case-for-incremental-processing-on-hadoop),
-data using Hoodie are `upserts` (to apply changes to a dataset) and `incremental pulls` (to obtain a change stream/log from a dataset). This section
-discusses a few tools that can be used to achieve these on different contexts.
-
-## Incremental Ingestion
-
-Following means can be used to apply a delta or an incremental change to a Hoodie dataset. For e.g, the incremental changes could be from a Kafka topic or files uploaded to HDFS or
-even changes pulled from another Hoodie dataset.
-
-#### DeltaStreamer Tool
-
-The `HoodieDeltaStreamer` utility provides the way to achieve all of these, by using the capabilities of `HoodieWriteClient`, and support simply row-row ingestion (no transformations)
-from different sources such as DFS or Kafka.
-
-The tool is a spark job (part of hoodie-utilities), that provides the following functionality
-
- - Ability to consume new events from Kafka, incremental imports from Sqoop or output of `HiveIncrementalPuller` or files under a folder on HDFS
- - Support json, avro or a custom payload types for the incoming data
- - New data is written to a Hoodie dataset, with support for checkpointing & schemas and registered onto Hive
-
-Command line options describe capabilities in more detail (first build hoodie-utilities using `mvn clean package`).
-
-```
-[hoodie]$ spark-submit --class com.uber.hoodie.utilities.deltastreamer.HoodieDeltaStreamer `ls hoodie-utilities/target/hoodie-utilities-*-SNAPSHOT.jar` --help
-Usage: <main class> [options]
-  Options:
-    --help, -h
-
-    --key-generator-class
-      Subclass of com.uber.hoodie.KeyGenerator to generate a HoodieKey from
-      the given avro record. Built in: SimpleKeyGenerator (uses provided field
-      names as recordkey & partitionpath. Nested fields specified via dot
-      notation, e.g: a.b.c)
-      Default: com.uber.hoodie.SimpleKeyGenerator
-    --op
-      Takes one of these values : UPSERT (default), INSERT (use when input is
-      purely new data/inserts to gain speed)
-      Default: UPSERT
-      Possible Values: [UPSERT, INSERT, BULK_INSERT]
-    --payload-class
-      subclass of HoodieRecordPayload, that works off a GenericRecord.
-      Implement your own, if you want to do something other than overwriting
-      existing value
-      Default: com.uber.hoodie.OverwriteWithLatestAvroPayload
-    --props
-      path to properties file on localfs or dfs, with configurations for
-      hoodie client, schema provider, key generator and data source. For
-      hoodie client props, sane defaults are used, but recommend use to
-      provide basic things like metrics endpoints, hive configs etc. For
-      sources, referto individual classes, for supported properties.
-      Default: file:///Users/vinoth/bin/hoodie/src/test/resources/delta-streamer-config/dfs-source.properties
-    --schemaprovider-class
-      subclass of com.uber.hoodie.utilities.schema.SchemaProvider to attach
-      schemas to input & target table data, built in options:
-      FilebasedSchemaProvider
-      Default: com.uber.hoodie.utilities.schema.FilebasedSchemaProvider
-    --source-class
-      Subclass of com.uber.hoodie.utilities.sources to read data. Built-in
-      options: com.uber.hoodie.utilities.sources.{JsonDFSSource (default),
-      AvroDFSSource, JsonKafkaSource, AvroKafkaSource, HiveIncrPullSource}
-      Default: com.uber.hoodie.utilities.sources.JsonDFSSource
-    --source-limit
-      Maximum amount of data to read from source. Default: No limit For e.g:
-      DFSSource => max bytes to read, KafkaSource => max events to read
-      Default: 9223372036854775807
-    --source-ordering-field
-      Field within source record to decide how to break ties between records
-      with same key in input data. Default: 'ts' holding unix timestamp of
-      record
-      Default: ts
-    --spark-master
-      spark master to use.
-      Default: local[2]
-  * --target-base-path
-      base path for the target hoodie dataset. (Will be created if did not
-      exist first time around. If exists, expected to be a hoodie dataset)
-  * --target-table
-      name of the target table in Hive
-    --transformer-class
-      subclass of com.uber.hoodie.utilities.transform.Transformer. UDF to 
-      transform raw source dataset to a target dataset (conforming to target 
-      schema) before writing. Default : Not set. E:g - 
-      com.uber.hoodie.utilities.transform.SqlQueryBasedTransformer (which 
-      allows a SQL query template to be passed as a transformation function)
-```
-
-
-The tool takes a hierarchically composed property file and has pluggable interfaces for extracting data, key generation and providing schema. Sample configs for ingesting from kafka and dfs are
-provided under `hoodie-utilities/src/test/resources/delta-streamer-config`.
-
-For e.g: once you have Confluent Kafka, Schema registry up & running, produce some test data using ([impressions.avro](https://docs.confluent.io/current/ksql/docs/tutorials/generate-custom-test-data.html) provided by schema-registry repo)
-
-```
-[confluent-5.0.0]$ bin/ksql-datagen schema=../impressions.avro format=avro topic=impressions key=impressionid
-```
-
-and then ingest it as follows.
-
-```
-[hoodie]$ spark-submit --class com.uber.hoodie.utilities.deltastreamer.HoodieDeltaStreamer `ls hoodie-utilities/target/hoodie-utilities-*-SNAPSHOT.jar` \
-  --props file://${PWD}/hoodie-utilities/src/test/resources/delta-streamer-config/kafka-source.properties \
-  --schemaprovider-class com.uber.hoodie.utilities.schema.SchemaRegistryProvider \
-  --source-class com.uber.hoodie.utilities.sources.AvroKafkaSource \
-  --source-ordering-field impresssiontime \
-  --target-base-path file:///tmp/hoodie-deltastreamer-op --target-table uber.impressions \
-  --op BULK_INSERT
-```
-
-In some cases, you may want to convert your existing dataset into Hoodie, before you can begin ingesting new data. This can be accomplished using the `hdfsparquetimport` command on the `hoodie-cli`.
-Currently, there is support for converting parquet datasets.
-
-#### Via Custom Spark Job
-
-The `hoodie-spark` module offers the DataSource API to write any data frame into a Hoodie dataset. Following is how we can upsert a dataframe, while specifying the field names that need to be used
-for `recordKey => _row_key`, `partitionPath => partition` and `precombineKey => timestamp`
-
-
-```
-inputDF.write()
-       .format("com.uber.hoodie")
-       .options(clientOpts) // any of the hoodie client opts can be passed in as well
-       .option(DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY(), "_row_key")
-       .option(DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY(), "partition")
-       .option(DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY(), "timestamp")
-       .option(HoodieWriteConfig.TABLE_NAME, tableName)
-       .mode(SaveMode.Append)
-       .save(basePath);
-```
-
-Please refer to [configurations](configurations.html) section, to view all datasource options.
-
-#### Syncing to Hive
-
-Once new data is written to a Hoodie dataset, via tools like above, we need the ability to sync with Hive and reflect the table schema such that queries can pick up new columns and partitions. To do this, Hoodie provides a `HiveSyncTool`, which can be
-invoked as below, once you have built the hoodie-hive module.
-
-```
- [hoodie-hive]$ java -cp target/hoodie-hive-0.3.6-SNAPSHOT-jar-with-dependencies.jar:target/jars/* com.uber.hoodie.hive.HiveSyncTool --help
-Usage: <main class> [options]
-  Options:
-  * --base-path
-       Basepath of hoodie dataset to sync
-  * --database
-       name of the target database in Hive
-    --help, -h
-
-       Default: false
-  * --jdbc-url
-       Hive jdbc connect url
-  * --pass
-       Hive password
-  * --table
-       name of the target table in Hive
-  * --user
-       Hive username
-
-
-```
-
-{% include callout.html content="Note that for now, due to jar mismatches between Spark & Hive, its recommended to run this as a separate Java task in your workflow manager/cron. This is getting fix [here](https://github.com/uber/hoodie/issues/123)" type="info" %}
-
-
-## Incrementally Pulling
-
-Hoodie datasets can be pulled incrementally, which means you can get ALL and ONLY the updated & new rows since a specified commit timestamp.
-This, together with upserts, are particularly useful for building data pipelines where 1 or more source hoodie tables are incrementally pulled (streams/facts),
-joined with other tables (datasets/dimensions), to produce deltas to a target hoodie dataset. Then, using the delta streamer tool these deltas can be upserted into the
-target hoodie dataset to complete the pipeline.
-
-#### Via Spark Job
-The `hoodie-spark` module offers the DataSource API, offers a more elegant way to pull data from Hoodie dataset (plus more) and process it via Spark.
-This class can be used within existing Spark jobs and offers the following functionality.
-
-A sample incremental pull, that will obtain all records written since `beginInstantTime`, looks like below.
-
-```
- Dataset<Row> hoodieIncViewDF = spark.read()
-     .format("com.uber.hoodie")
-     .option(DataSourceReadOptions.VIEW_TYPE_OPT_KEY(),
-             DataSourceReadOptions.VIEW_TYPE_INCREMENTAL_OPT_VAL())
-     .option(DataSourceReadOptions.BEGIN_INSTANTTIME_OPT_KEY(),
-            <beginInstantTime>)
-     .load(tablePath); // For incremental view, pass in the root/base path of dataset
-```
-
-Please refer to [configurations](configurations.html) section, to view all datasource options.
-
-
-Additionally, `HoodieReadClient` offers the following functionality using Hoodie's implicit indexing.
-
-| **API** | **Description** |
-| read(keys) | Read out the data corresponding to the keys as a DataFrame, using Hoodie's own index for faster lookup |
-| filterExists() | Filter out already existing records from the provided RDD[HoodieRecord]. Useful for de-duplication |
-| checkExists(keys) | Check if the provided keys exist in a Hoodie dataset |
-
-
-#### HiveIncrementalPuller Tool
-`HiveIncrementalPuller` allows the above to be done via HiveQL, combining the benefits of Hive (reliably process complex SQL queries) and incremental primitives
-(speed up query by pulling tables incrementally instead of scanning fully). The tool uses Hive JDBC to run the Hive query saving its results in a temp table.
-that can later be upserted. Upsert utility (`HoodieDeltaStreamer`) has all the state it needs from the directory structure to know what should be the commit time on the target table.
-e.g: `/app/incremental-hql/intermediate/{source_table_name}_temp/{last_commit_included}`.The Delta Hive table registered will be of the form `{tmpdb}.{source_table}_{last_commit_included}`.
-
-The following are the configuration options for HiveIncrementalPuller
-
-| **Config** | **Description** | **Default** |
-|hiveUrl| Hive Server 2 URL to connect to |  |
-|hiveUser| Hive Server 2 Username |  |
-|hivePass| Hive Server 2 Password |  |
-|queue| YARN Queue name |  |
-|tmp| Directory where the temporary delta data is stored in HDFS. The directory structure will follow conventions. Please see the below section.  |  |
-|extractSQLFile| The SQL to execute on the source table to extract the data. The data extracted will be all the rows that changed since a particular point in time. |  |
-|sourceTable| Source Table Name. Needed to set hive environment properties. |  |
-|targetTable| Target Table Name. Needed for the intermediate storage directory structure.  |  |
-|sourceDataPath| Source HDFS Base Path. This is where the hoodie metadata will be read. |  |
-|targetDataPath| Target HDFS Base path. This is needed to compute the fromCommitTime. This is not needed if fromCommitTime is specified explicitly. |  |
-|tmpdb| The database to which the intermediate temp delta table will be created | hoodie_temp |
-|fromCommitTime| This is the most important parameter. This is the point in time from which the changed records are pulled from.  |  |
-|maxCommits| Number of commits to include in the pull. Setting this to -1 will include all the commits from fromCommitTime. Setting this to a value > 0, will include records that ONLY changed in the specified number of commits after fromCommitTime. This may be needed if you need to catch up say 2 commits at a time. | 3 |
-|help| Utility Help |  |
-
-
-Setting the fromCommitTime=0 and maxCommits=-1 will pull in the entire source dataset and can be used to initiate backfills. If the target dataset is a hoodie dataset,
-then the utility can determine if the target dataset has no commits or is behind more than 24 hour (this is configurable),
-it will automatically use the backfill configuration, since applying the last 24 hours incrementally could take more time than doing a backfill. The current limitation of the tool
-is the lack of support for self-joining the same table in mixed mode (normal and incremental modes).
-
-
-
diff --git a/docs/index.md b/docs/index.md
deleted file mode 100644
index b5e9e5aea..000000000
--- a/docs/index.md
+++ /dev/null
@@ -1,26 +0,0 @@
----
-title: Hoodie Overview
-keywords: homepage
-tags: [getting_started]
-sidebar: mydoc_sidebar
-permalink: index.html
-summary: "Hoodie lowers data latency across the board, while simultaneously achieving orders of magnitude of efficiency over traditional batch processing."
----
-
-
-
-
-Hoodie manages storage of large analytical datasets on [HDFS](http://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html) and serve them out via two types of tables
-
- * **Read Optimized Table** - Provides excellent query performance via purely columnar storage (e.g. [Parquet](https://parquet.apache.org/))
- * **Near-Real time Table** - Provides queries on real-time data, using a combination of columnar & row based storage (e.g Parquet + [Avro](http://avro.apache.org/docs/current/mr.html))
-
-
-{% include image.html file="hoodie_intro_1.png" alt="hoodie_intro_1.png" %}
-
-By carefully managing how data is laid out in storage & how it’s exposed to queries, Hoodie is able to power a rich data ecosystem where external sources can be ingested into Hadoop in near real-time. The ingested data is then available for interactive SQL Engines like [Presto](https://prestodb.io) & [Spark](https://spark.apache.org/sql/), while at the same time capable of being consumed incrementally from processing/ETL frameworks like [Hive](https://hive.apache.org/) & [Spark](https://spark.apache.org/docs/latest/) to build derived (Hoodie) datasets.
-
-Hoodie broadly consists of a self contained Spark library to build datasets and integrations with existing query engines for data access.
-
-{% include callout.html content="Hoodie is a new project. Near Real-Time  Table implementation is currently underway. Get involved [here](https://github.com/uber/hoodie/projects/1)" type="info" %}
-
diff --git a/docs/js/customscripts.js b/docs/js/customscripts.js
deleted file mode 100644
index 8c9fb4cb9..000000000
--- a/docs/js/customscripts.js
+++ /dev/null
@@ -1,55 +0,0 @@
-
-$('#mysidebar').height($(".nav").height());
-
-
-$( document ).ready(function() {
-
-    //this script says, if the height of the viewport is greater than 800px, then insert affix class, which makes the nav bar float in a fixed
-    // position as your scroll. if you have a lot of nav items, this height may not work for you.
-    var h = $(window).height();
-    //console.log (h);
-    if (h > 800) {
-        $( "#mysidebar" ).attr("class", "nav affix");
-    }
-    // activate tooltips. although this is a bootstrap js function, it must be activated this way in your theme.
-    $('[data-toggle="tooltip"]').tooltip({
-        placement : 'top'
-    });
-
-    /**
-     * AnchorJS
-     */
-    anchors.add('h2,h3,h4,h5');
-
-});
-
-// needed for nav tabs on pages. See Formatting > Nav tabs for more details.
-// script from http://stackoverflow.com/questions/10523433/how-do-i-keep-the-current-tab-active-with-twitter-bootstrap-after-a-page-reload
-$(function() {
-    var json, tabsState;
-    $('a[data-toggle="pill"], a[data-toggle="tab"]').on('shown.bs.tab', function(e) {
-        var href, json, parentId, tabsState;
-
-        tabsState = localStorage.getItem("tabs-state");
-        json = JSON.parse(tabsState || "{}");
-        parentId = $(e.target).parents("ul.nav.nav-pills, ul.nav.nav-tabs").attr("id");
-        href = $(e.target).attr('href');
-        json[parentId] = href;
-
-        return localStorage.setItem("tabs-state", JSON.stringify(json));
-    });
-
-    tabsState = localStorage.getItem("tabs-state");
-    json = JSON.parse(tabsState || "{}");
-
-    $.each(json, function(containerId, href) {
-        return $("#" + containerId + " a[href=" + href + "]").tab('show');
-    });
-
-    $("ul.nav.nav-pills, ul.nav.nav-tabs").each(function() {
-        var $this = $(this);
-        if (!json[$this.attr("id")]) {
-            return $this.find("a[data-toggle=tab]:first, a[data-toggle=pill]:first").tab("show");
-        }
-    });
-});
diff --git a/docs/js/jekyll-search.js b/docs/js/jekyll-search.js
deleted file mode 100644
index 04d6a0d3a..000000000
--- a/docs/js/jekyll-search.js
+++ /dev/null
@@ -1 +0,0 @@
-!function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a="function"==typeof require&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);throw new Error("Cannot find module '"+o+"'")}var f=n[o]={exports:{}};t[o][0].call(f.exports,function(e){var n=t[o][1][e];return s(n?n:e)},f,f.exports,e,t,n,r)}return n[o].exports}for(var i="function"==typeof require&&require,o=0;o<r.length;o++)s(r[o]);return s}({1:[function(require,module){module.exports=function(){function receivedResponse(xhr){return 200==xhr.status&&4==xhr.readyState}function handleResponse(xhr,callback){xhr.onreadystatechange=function(){if(receivedResponse(xhr))try{callback(null,JSON.parse(xhr.responseText))}catch(err){callback(err,null)}}}var self=this;self.load=function(location,callback){var xhr=window.XMLHttpRequest?new XMLHttpRequest:new ActiveXObject("Microsoft.XMLHTTP");xhr.open("GET",location,!0),handleResponse(xhr,callback),xhr.send()}}},{}],2:[function(require,module){function FuzzySearchStrategy(){function 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\ No newline at end of file
diff --git a/docs/js/jquery.ba-throttle-debounce.min.js b/docs/js/jquery.ba-throttle-debounce.min.js
deleted file mode 100644
index 07205508e..000000000
--- a/docs/js/jquery.ba-throttle-debounce.min.js
+++ /dev/null
@@ -1,9 +0,0 @@
-/*
- * jQuery throttle / debounce - v1.1 - 3/7/2010
- * http://benalman.com/projects/jquery-throttle-debounce-plugin/
- * 
- * Copyright (c) 2010 "Cowboy" Ben Alman
- * Dual licensed under the MIT and GPL licenses.
- * http://benalman.com/about/license/
- */
-(function(b,c){var $=b.jQuery||b.Cowboy||(b.Cowboy={}),a;$.throttle=a=function(e,f,j,i){var h,d=0;if(typeof f!=="boolean"){i=j;j=f;f=c}function g(){var o=this,m=+new Date()-d,n=arguments;function l(){d=+new Date();j.apply(o,n)}function k(){h=c}if(i&&!h){l()}h&&clearTimeout(h);if(i===c&&m>e){l()}else{if(f!==true){h=setTimeout(i?k:l,i===c?e-m:e)}}}if($.guid){g.guid=j.guid=j.guid||$.guid++}return g};$.debounce=function(d,e,f){return f===c?a(d,e,false):a(d,f,e!==false)}})(this);
\ No newline at end of file
diff --git a/docs/js/jquery.localScroll.min.js b/docs/js/jquery.localScroll.min.js
deleted file mode 100644
index 48a6e168f..000000000
--- a/docs/js/jquery.localScroll.min.js
+++ /dev/null
@@ -1,7 +0,0 @@
-/**
- * Copyright (c) 2007-2014 Ariel Flesler - aflesler<a>gmail<d>com | http://flesler.blogspot.com
- * Licensed under MIT
- * @author Ariel Flesler
- * @version 1.3.5
- */
-;(function(a){if(typeof define==='function'&&define.amd){define(['jquery'],a)}else{a(jQuery)}}(function($){var g=location.href.replace(/#.*/,'');var h=$.localScroll=function(a){$('body').localScroll(a)};h.defaults={duration:1000,axis:'y',event:'click',stop:true,target:window};$.fn.localScroll=function(a){a=$.extend({},h.defaults,a);if(a.hash&&location.hash){if(a.target)window.scrollTo(0,0);scroll(0,location,a)}return a.lazy?this.on(a.event,'a,area',function(e){if(filter.call(this)){scroll(e,this,a)}}):this.find('a,area').filter(filter).bind(a.event,function(e){scroll(e,this,a)}).end().end();function filter(){return!!this.href&&!!this.hash&&this.href.replace(this.hash,'')==g&&(!a.filter||$(this).is(a.filter))}};h.hash=function(){};function scroll(e,a,b){var c=a.hash.slice(1),elem=document.getElementById(c)||document.getElementsByName(c)[0];if(!elem)return;if(e)e.preventDefault();var d=$(b.target);if(b.lock&&d.is(':animated')||b.onBefore&&b.onBefore(e,elem,d)===false)return;if(b.stop)d._scrollable().stop(true);if(b.hash){var f=elem.id===c?'id':'name',$a=$('<a> </a>').attr(f,c).css({position:'absolute',top:$(window).scrollTop(),left:$(window).scrollLeft()});elem[f]='';$('body').prepend($a);location.hash=a.hash;$a.remove();elem[f]=c}d.scrollTo(elem,b).trigger('notify.serialScroll',[elem])};return h}));
\ No newline at end of file
diff --git a/docs/js/jquery.navgoco.min.js b/docs/js/jquery.navgoco.min.js
deleted file mode 100755
index 4ba447533..000000000
--- a/docs/js/jquery.navgoco.min.js
+++ /dev/null
@@ -1,8 +0,0 @@
-/*
- * jQuery Navgoco Menus Plugin v0.2.1 (2014-04-11)
- * https://github.com/tefra/navgoco
- *
- * Copyright (c) 2014 Chris T (@tefra)
- * BSD - https://github.com/tefra/navgoco/blob/master/LICENSE-BSD
- */
-!function(a){"use strict";var b=function(b,c,d){return this.el=b,this.$el=a(b),this.options=c,this.uuid=this.$el.attr("id")?this.$el.attr("id"):d,this.state={},this.init(),this};b.prototype={init:function(){var b=this;b._load(),b.$el.find("ul").each(function(c){var d=a(this);d.attr("data-index",c),b.options.save&&b.state.hasOwnProperty(c)?(d.parent().addClass(b.options.openClass),d.show()):d.parent().hasClass(b.options.openClass)?(d.show(),b.state[c]=1):d.hide()});var c=a("<span></span>").prepend(b.options.caretHtml),d=b.$el.find("li > a");b._trigger(c,!1),b._trigger(d,!0),b.$el.find("li:has(ul) > a").prepend(c)},_trigger:function(b,c){var d=this;b.on("click",function(b){b.stopPropagation();var e=c?a(this).next():a(this).parent().next(),f=!1;if(c){var g=a(this).attr("href");f=void 0===g||""===g||"#"===g}if(e=e.length>0?e:!1,d.options.onClickBefore.call(this,b,e),!c||e&&f)b.preventDefault(),d._toggle(e,e.is(":hidden")),d._save();else if(d.options.accordion){var h=d.state=d._parents(a(this));d.$el.find("ul").filter(":visible").each(function(){var b=a(this),c=b.attr("data-index");h.hasOwnProperty(c)||d._toggle(b,!1)}),d._save()}d.options.onClickAfter.call(this,b,e)})},_toggle:function(b,c){var d=this,e=b.attr("data-index"),f=b.parent();if(d.options.onToggleBefore.call(this,b,c),c){if(f.addClass(d.options.openClass),b.slideDown(d.options.slide),d.state[e]=1,d.options.accordion){var g=d.state=d._parents(b);g[e]=d.state[e]=1,d.$el.find("ul").filter(":visible").each(function(){var b=a(this),c=b.attr("data-index");g.hasOwnProperty(c)||d._toggle(b,!1)})}}else f.removeClass(d.options.openClass),b.slideUp(d.options.slide),d.state[e]=0;d.options.onToggleAfter.call(this,b,c)},_parents:function(b,c){var d={},e=b.parent(),f=e.parents("ul");return f.each(function(){var b=a(this),e=b.attr("data-index");return e?void(d[e]=c?b:1):!1}),d},_save:function(){if(this.options.save){var b={};for(var d in this.state)1===this.state[d]&&(b[d]=1);c[this.uuid]=this.state=b,a.cookie(this.options.cookie.name,JSON.stringify(c),this.options.cookie)}},_load:function(){if(this.options.save){if(null===c){var b=a.cookie(this.options.cookie.name);c=b?JSON.parse(b):{}}this.state=c.hasOwnProperty(this.uuid)?c[this.uuid]:{}}},toggle:function(b){var c=this,d=arguments.length;if(1>=d)c.$el.find("ul").each(function(){var d=a(this);c._toggle(d,b)});else{var e,f={},g=Array.prototype.slice.call(arguments,1);d--;for(var h=0;d>h;h++){e=g[h];var i=c.$el.find('ul[data-index="'+e+'"]').first();if(i&&(f[e]=i,b)){var j=c._parents(i,!0);for(var k in j)f.hasOwnProperty(k)||(f[k]=j[k])}}for(e in f)c._toggle(f[e],b)}c._save()},destroy:function(){a.removeData(this.$el),this.$el.find("li:has(ul) > a").unbind("click"),this.$el.find("li:has(ul) > a > span").unbind("click")}},a.fn.navgoco=function(c){if("string"==typeof c&&"_"!==c.charAt(0)&&"init"!==c)var d=!0,e=Array.prototype.slice.call(arguments,1);else c=a.extend({},a.fn.navgoco.defaults,c||{}),a.cookie||(c.save=!1);return this.each(function(f){var g=a(this),h=g.data("navgoco");h||(h=new b(this,d?a.fn.navgoco.defaults:c,f),g.data("navgoco",h)),d&&h[c].apply(h,e)})};var c=null;a.fn.navgoco.defaults={caretHtml:"",accordion:!1,openClass:"open",save:!0,cookie:{name:"navgoco",expires:!1,path:"/"},slide:{duration:400,easing:"swing"},onClickBefore:a.noop,onClickAfter:a.noop,onToggleBefore:a.noop,onToggleAfter:a.noop}}(jQuery);
\ No newline at end of file
diff --git a/docs/js/jquery.scrollTo.min.js b/docs/js/jquery.scrollTo.min.js
deleted file mode 100644
index 5f5e01e8e..000000000
--- a/docs/js/jquery.scrollTo.min.js
+++ /dev/null
@@ -1,7 +0,0 @@
-/**
- * Copyright (c) 2007-2014 Ariel Flesler - aflesler<a>gmail<d>com | http://flesler.blogspot.com
- * Licensed under MIT
- * @author Ariel Flesler
- * @version 1.4.14
- */
-;(function(k){'use strict';k(['jquery'],function($){var j=$.scrollTo=function(a,b,c){return $(window).scrollTo(a,b,c)};j.defaults={axis:'xy',duration:0,limit:!0};j.window=function(a){return $(window)._scrollable()};$.fn._scrollable=function(){return this.map(function(){var a=this,isWin=!a.nodeName||$.inArray(a.nodeName.toLowerCase(),['iframe','#document','html','body'])!=-1;if(!isWin)return a;var b=(a.contentWindow||a).document||a.ownerDocument||a;return/webkit/i.test(navigator.userAgent)||b.compatMode=='BackCompat'?b.body:b.documentElement})};$.fn.scrollTo=function(f,g,h){if(typeof g=='object'){h=g;g=0}if(typeof h=='function')h={onAfter:h};if(f=='max')f=9e9;h=$.extend({},j.defaults,h);g=g||h.duration;h.queue=h.queue&&h.axis.length>1;if(h.queue)g/=2;h.offset=both(h.offset);h.over=both(h.over);return this._scrollable().each(function(){if(f==null)return;var d=this,$elem=$(d),targ=f,toff,attr={},win=$elem.is('html,body');switch(typeof targ){case'number':case'string':if(/^([+-]=?)?\d+(\.\d+)?(px|%)?$/.test(targ)){targ=both(targ);break}targ=win?$(targ):$(targ,this);if(!targ.length)return;case'object':if(targ.is||targ.style)toff=(targ=$(targ)).offset()}var e=$.isFunction(h.offset)&&h.offset(d,targ)||h.offset;$.each(h.axis.split(''),function(i,a){var b=a=='x'?'Left':'Top',pos=b.toLowerCase(),key='scroll'+b,old=d[key],max=j.max(d,a);if(toff){attr[key]=toff[pos]+(win?0:old-$elem.offset()[pos]);if(h.margin){attr[key]-=parseInt(targ.css('margin'+b))||0;attr[key]-=parseInt(targ.css('border'+b+'Width'))||0}attr[key]+=e[pos]||0;if(h.over[pos])attr[key]+=targ[a=='x'?'width':'height']()*h.over[pos]}else{var c=targ[pos];attr[key]=c.slice&&c.slice(-1)=='%'?parseFloat(c)/100*max:c}if(h.limit&&/^\d+$/.test(attr[key]))attr[key]=attr[key]<=0?0:Math.min(attr[key],max);if(!i&&h.queue){if(old!=attr[key])animate(h.onAfterFirst);delete attr[key]}});animate(h.onAfter);function animate(a){$elem.animate(attr,g,h.easing,a&&function(){a.call(this,targ,h)})}}).end()};j.max=function(a,b){var c=b=='x'?'Width':'Height',scroll='scroll'+c;if(!$(a).is('html,body'))return a[scroll]-$(a)[c.toLowerCase()]();var d='client'+c,html=a.ownerDocument.documentElement,body=a.ownerDocument.body;return Math.max(html[scroll],body[scroll])-Math.min(html[d],body[d])};function both(a){return $.isFunction(a)||$.isPlainObject(a)?a:{top:a,left:a}}return j})}(typeof define==='function'&&define.amd?define:function(a,b){if(typeof module!=='undefined'&&module.exports){module.exports=b(require('jquery'))}else{b(jQuery)}}));
\ No newline at end of file
diff --git a/docs/js/jquery.shuffle.min.js b/docs/js/jquery.shuffle.min.js
deleted file mode 100644
index d10312719..000000000
--- a/docs/js/jquery.shuffle.min.js
+++ /dev/null
@@ -1,1588 +0,0 @@
-/*!
- * Shuffle.js by @Vestride
- * Categorize, sort, and filter a responsive grid of items.
- * Dependencies: jQuery 1.9+, Modernizr 2.6.2+
- * @license MIT license
- * @version 3.0.0
- */
-
-/* Modernizr 2.6.2 (Custom Build) | MIT & BSD
- * Build: http://modernizr.com/download/#-csstransforms-csstransforms3d-csstransitions-cssclasses-prefixed-teststyles-testprop-testallprops-prefixes-domprefixes
- */
-window.Modernizr=function(a,b,c){function z(a){j.cssText=a}function A(a,b){return z(m.join(a+";")+(b||""))}function B(a,b){return typeof a===b}function C(a,b){return!!~(""+a).indexOf(b)}function D(a,b){for(var d in a){var e=a[d];if(!C(e,"-")&&j[e]!==c)return b=="pfx"?e:!0}return!1}function E(a,b,d){for(var e in a){var f=b[a[e]];if(f!==c)return d===!1?a[e]:B(f,"function")?f.bind(d||b):f}return!1}function F(a,b,c){var d=a.charAt(0).toUpperCase()+a.slice(1),e=(a+" "+o.join(d+" ")+d).split(" ");return B(b,"string")||B(b,"undefined")?D(e,b):(e=(a+" "+p.join(d+" ")+d).split(" "),E(e,b,c))}var d="2.6.2",e={},f=!0,g=b.documentElement,h="modernizr",i=b.createElement(h),j=i.style,k,l={}.toString,m=" -webkit- -moz- -o- -ms- ".split(" "),n="Webkit Moz O ms",o=n.split(" "),p=n.toLowerCase().split(" "),q={},r={},s={},t=[],u=t.slice,v,w=function(a,c,d,e){var f,i,j,k,l=b.createElement("div"),m=b.body,n=m||b.createElement("body");if(parseInt(d,10))while(d--)j=b.createElement("div"),j.id=e?e[d]:h+(d+1),l.appendChild(j);return f=["&#173;",'<style id="s',h,'">',a,"</style>"].join(""),l.id=h,(m?l:n).innerHTML+=f,n.appendChild(l),m||(n.style.background="",n.style.overflow="hidden",k=g.style.overflow,g.style.overflow="hidden",g.appendChild(n)),i=c(l,a),m?l.parentNode.removeChild(l):(n.parentNode.removeChild(n),g.style.overflow=k),!!i},x={}.hasOwnProperty,y;!B(x,"undefined")&&!B(x.call,"undefined")?y=function(a,b){return x.call(a,b)}:y=function(a,b){return b in a&&B(a.constructor.prototype[b],"undefined")},Function.prototype.bind||(Function.prototype.bind=function(b){var c=this;if(typeof c!="function")throw new TypeError;var d=u.call(arguments,1),e=function(){if(this instanceof e){var a=function(){};a.prototype=c.prototype;var f=new a,g=c.apply(f,d.concat(u.call(arguments)));return Object(g)===g?g:f}return c.apply(b,d.concat(u.call(arguments)))};return e}),q.csstransforms=function(){return!!F("transform")},q.csstransforms3d=function(){var a=!!F("perspective");return a&&"webkitPerspective"in g.style&&w("@media (transform-3d),(-webkit-transform-3d){#modernizr{left:9px;position:absolute;height:3px;}}",function(b,c){a=b.offsetLeft===9&&b.offsetHeight===3}),a},q.csstransitions=function(){return F("transition")};for(var G in q)y(q,G)&&(v=G.toLowerCase(),e[v]=q[G](),t.push((e[v]?"":"no-")+v));return e.addTest=function(a,b){if(typeof a=="object")for(var d in a)y(a,d)&&e.addTest(d,a[d]);else{a=a.toLowerCase();if(e[a]!==c)return e;b=typeof b=="function"?b():b,typeof f!="undefined"&&f&&(g.className+=" "+(b?"":"no-")+a),e[a]=b}return e},z(""),i=k=null,e._version=d,e._prefixes=m,e._domPrefixes=p,e._cssomPrefixes=o,e.testProp=function(a){return D([a])},e.testAllProps=F,e.testStyles=w,e.prefixed=function(a,b,c){return b?F(a,b,c):F(a,"pfx")},g.className=g.className.replace(/(^|\s)no-js(\s|$)/,"$1$2")+(f?" js "+t.join(" "):""),e}(this,this.document);
-
-(function (factory) {
-  if (typeof define === 'function' && define.amd) {
-    define(['jquery', 'modernizr'], factory);
-  } else {
-    window.Shuffle = factory(window.jQuery, window.Modernizr);
-  }
-})(function($, Modernizr, undefined) {
-
-'use strict';
-
-
-// Validate Modernizr exists.
-// Shuffle requires `csstransitions`, `csstransforms`, `csstransforms3d`,
-// and `prefixed` to exist on the Modernizr object.
-if (typeof Modernizr !== 'object') {
-  throw new Error('Shuffle.js requires Modernizr.\n' +
-      'http://vestride.github.io/Shuffle/#dependencies');
-}
-
-
-/**
- * Returns css prefixed properties like `-webkit-transition` or `box-sizing`
- * from `transition` or `boxSizing`, respectively.
- * @param {(string|boolean)} prop Property to be prefixed.
- * @return {string} The prefixed css property.
- */
-function dashify( prop ) {
-  if (!prop) {
-    return '';
-  }
-
-  // Replace upper case with dash-lowercase,
-  // then fix ms- prefixes because they're not capitalized.
-  return prop.replace(/([A-Z])/g, function( str, m1 ) {
-    return '-' + m1.toLowerCase();
-  }).replace(/^ms-/,'-ms-');
-}
-
-// Constant, prefixed variables.
-var TRANSITION = Modernizr.prefixed('transition');
-var TRANSITION_DELAY = Modernizr.prefixed('transitionDelay');
-var TRANSITION_DURATION = Modernizr.prefixed('transitionDuration');
-
-// Note(glen): Stock Android 4.1.x browser will fail here because it wrongly
-// says it supports non-prefixed transitions.
-// https://github.com/Modernizr/Modernizr/issues/897
-var TRANSITIONEND = {
-  'WebkitTransition' : 'webkitTransitionEnd',
-  'transition' : 'transitionend'
-}[ TRANSITION ];
-
-var TRANSFORM = Modernizr.prefixed('transform');
-var CSS_TRANSFORM = dashify(TRANSFORM);
-
-// Constants
-var CAN_TRANSITION_TRANSFORMS = Modernizr.csstransforms && Modernizr.csstransitions;
-var HAS_TRANSFORMS_3D = Modernizr.csstransforms3d;
-var SHUFFLE = 'shuffle';
-var COLUMN_THRESHOLD = 0.3;
-
-// Configurable. You can change these constants to fit your application.
-// The default scale and concealed scale, however, have to be different values.
-var ALL_ITEMS = 'all';
-var FILTER_ATTRIBUTE_KEY = 'groups';
-var DEFAULT_SCALE = 1;
-var CONCEALED_SCALE = 0.001;
-
-
-// Underscore's throttle function.
-function throttle(func, wait, options) {
-  var context, args, result;
-  var timeout = null;
-  var previous = 0;
-  options = options || {};
-  var later = function() {
-    previous = options.leading === false ? 0 : $.now();
-    timeout = null;
-    result = func.apply(context, args);
-    context = args = null;
-  };
-  return function() {
-    var now = $.now();
-    if (!previous && options.leading === false) {
-      previous = now;
-    }
-    var remaining = wait - (now - previous);
-    context = this;
-    args = arguments;
-    if (remaining <= 0 || remaining > wait) {
-      clearTimeout(timeout);
-      timeout = null;
-      previous = now;
-      result = func.apply(context, args);
-      context = args = null;
-    } else if (!timeout && options.trailing !== false) {
-      timeout = setTimeout(later, remaining);
-    }
-    return result;
-  };
-}
-
-function each(obj, iterator, context) {
-  for (var i = 0, length = obj.length; i < length; i++) {
-    if (iterator.call(context, obj[i], i, obj) === {}) {
-      return;
-    }
-  }
-}
-
-function defer(fn, context, wait) {
-  return setTimeout( $.proxy( fn, context ), wait );
-}
-
-function arrayMax( array ) {
-  return Math.max.apply( Math, array );
-}
-
-function arrayMin( array ) {
-  return Math.min.apply( Math, array );
-}
-
-
-/**
- * Always returns a numeric value, given a value.
- * @param {*} value Possibly numeric value.
- * @return {number} `value` or zero if `value` isn't numeric.
- * @private
- */
-function getNumber(value) {
-  return $.isNumeric(value) ? value : 0;
-}
-
-
-/**
- * Represents a coordinate pair.
- * @param {number} [x=0] X.
- * @param {number} [y=0] Y.
- */
-var Point = function(x, y) {
-  this.x = getNumber( x );
-  this.y = getNumber( y );
-};
-
-
-/**
- * Whether two points are equal.
- * @param {Point} a Point A.
- * @param {Point} b Point B.
- * @return {boolean}
- */
-Point.equals = function(a, b) {
-  return a.x === b.x && a.y === b.y;
-};
-
-
-// Used for unique instance variables
-var id = 0;
-var $window = $( window );
-
-
-/**
- * Categorize, sort, and filter a responsive grid of items.
- *
- * @param {Element} element An element which is the parent container for the grid items.
- * @param {Object} [options=Shuffle.options] Options object.
- * @constructor
- */
-var Shuffle = function( element, options ) {
-  options = options || {};
-  $.extend( this, Shuffle.options, options, Shuffle.settings );
-
-  this.$el = $(element);
-  this.element = element;
-  this.unique = 'shuffle_' + id++;
-
-  this._fire( Shuffle.EventType.LOADING );
-  this._init();
-
-  // Dispatch the done event asynchronously so that people can bind to it after
-  // Shuffle has been initialized.
-  defer(function() {
-    this.initialized = true;
-    this._fire( Shuffle.EventType.DONE );
-  }, this, 16);
-};
-
-
-/**
- * Events the container element emits with the .shuffle namespace.
- * For example, "done.shuffle".
- * @enum {string}
- */
-Shuffle.EventType = {
-  LOADING: 'loading',
-  DONE: 'done',
-  LAYOUT: 'layout',
-  REMOVED: 'removed'
-};
-
-
-/** @enum {string} */
-Shuffle.ClassName = {
-  BASE: SHUFFLE,
-  SHUFFLE_ITEM: 'shuffle-item',
-  FILTERED: 'filtered',
-  CONCEALED: 'concealed'
-};
-
-
-// Overrideable options
-Shuffle.options = {
-  group: ALL_ITEMS, // Initial filter group.
-  speed: 250, // Transition/animation speed (milliseconds).
-  easing: 'ease-out', // CSS easing function to use.
-  itemSelector: '', // e.g. '.picture-item'.
-  sizer: null, // Sizer element. Use an element to determine the size of columns and gutters.
-  gutterWidth: 0, // A static number or function that tells the plugin how wide the gutters between columns are (in pixels).
-  columnWidth: 0, // A static number or function that returns a number which tells the plugin how wide the columns are (in pixels).
-  delimeter: null, // If your group is not json, and is comma delimeted, you could set delimeter to ','.
-  buffer: 0, // Useful for percentage based heights when they might not always be exactly the same (in pixels).
-  initialSort: null, // Shuffle can be initialized with a sort object. It is the same object given to the sort method.
-  throttle: throttle, // By default, shuffle will throttle resize events. This can be changed or removed.
-  throttleTime: 300, // How often shuffle can be called on resize (in milliseconds).
-  sequentialFadeDelay: 150, // Delay between each item that fades in when adding items.
-  supported: CAN_TRANSITION_TRANSFORMS // Whether to use transforms or absolute positioning.
-};
-
-
-// Not overrideable
-Shuffle.settings = {
-  useSizer: false,
-  itemCss : { // default CSS for each item
-    position: 'absolute',
-    top: 0,
-    left: 0,
-    visibility: 'visible'
-  },
-  revealAppendedDelay: 300,
-  lastSort: {},
-  lastFilter: ALL_ITEMS,
-  enabled: true,
-  destroyed: false,
-  initialized: false,
-  _animations: [],
-  styleQueue: []
-};
-
-
-// Expose for testing.
-Shuffle.Point = Point;
-
-
-/**
- * Static methods.
- */
-
-/**
- * If the browser has 3d transforms available, build a string with those,
- * otherwise use 2d transforms.
- * @param {Point} point X and Y positions.
- * @param {number} scale Scale amount.
- * @return {string} A normalized string which can be used with the transform style.
- * @private
- */
-Shuffle._getItemTransformString = function(point, scale) {
-  if ( HAS_TRANSFORMS_3D ) {
-    return 'translate3d(' + point.x + 'px, ' + point.y + 'px, 0) scale3d(' + scale + ', ' + scale + ', 1)';
-  } else {
-    return 'translate(' + point.x + 'px, ' + point.y + 'px) scale(' + scale + ')';
-  }
-};
-
-
-/**
- * Retrieve the computed style for an element, parsed as a float. This should
- * not be used for width or height values because jQuery mangles them and they
- * are not precise enough.
- * @param {Element} element Element to get style for.
- * @param {string} style Style property.
- * @return {number} The parsed computed value or zero if that fails because IE
- *     will return 'auto' when the element doesn't have margins instead of
- *     the computed style.
- * @private
- */
-Shuffle._getNumberStyle = function( element, style ) {
-  return Shuffle._getFloat( $( element ).css( style )  );
-};
-
-
-/**
- * Parse a string as an integer.
- * @param {string} value String integer.
- * @return {number} The string as an integer or zero.
- * @private
- */
-Shuffle._getInt = function(value) {
-  return getNumber( parseInt( value, 10 ) );
-};
-
-/**
- * Parse a string as an float.
- * @param {string} value String float.
- * @return {number} The string as an float or zero.
- * @private
- */
-Shuffle._getFloat = function(value) {
-  return getNumber( parseFloat( value ) );
-};
-
-
-/**
- * Returns the outer width of an element, optionally including its margins.
- * The `offsetWidth` property must be used because having a scale transform
- * on the element affects the bounding box. Sadly, Firefox doesn't return an
- * integer value for offsetWidth (yet).
- * @param {Element} element The element.
- * @param {boolean} [includeMargins] Whether to include margins. Default is false.
- * @return {number} The width.
- */
-Shuffle._getOuterWidth = function( element, includeMargins ) {
-  var width = element.offsetWidth;
-
-  // Use jQuery here because it uses getComputedStyle internally and is
-  // cross-browser. Using the style property of the element will only work
-  // if there are inline styles.
-  if ( includeMargins ) {
-    var marginLeft = Shuffle._getNumberStyle( element, 'marginLeft');
-    var marginRight = Shuffle._getNumberStyle( element, 'marginRight');
-    width += marginLeft + marginRight;
-  }
-
-  return width;
-};
-
-
-/**
- * Returns the outer height of an element, optionally including its margins.
- * @param {Element} element The element.
- * @param {boolean} [includeMargins] Whether to include margins. Default is false.
- * @return {number} The height.
- */
-Shuffle._getOuterHeight = function( element, includeMargins ) {
-  var height = element.offsetHeight;
-
-  if ( includeMargins ) {
-    var marginTop = Shuffle._getNumberStyle( element, 'marginTop');
-    var marginBottom = Shuffle._getNumberStyle( element, 'marginBottom');
-    height += marginTop + marginBottom;
-  }
-
-  return height;
-};
-
-
-/**
- * Change a property or execute a function which will not have a transition
- * @param {Element} element DOM element that won't be transitioned
- * @param {Function} callback A function which will be called while transition
- *     is set to 0ms.
- * @param {Object} [context] Optional context for the callback function.
- * @private
- */
-Shuffle._skipTransition = function( element, callback, context ) {
-  var duration = element.style[ TRANSITION_DURATION ];
-
-  // Set the duration to zero so it happens immediately
-  element.style[ TRANSITION_DURATION ] = '0ms'; // ms needed for firefox!
-
-  callback.call( context );
-
-  // Force reflow
-  var reflow = element.offsetWidth;
-  // Avoid jshint warnings: unused variables and expressions.
-  reflow = null;
-
-  // Put the duration back
-  element.style[ TRANSITION_DURATION ] = duration;
-};
-
-
-/**
- * Instance methods.
- */
-
-Shuffle.prototype._init = function() {
-  this.$items = this._getItems();
-
-  this.sizer = this._getElementOption( this.sizer );
-
-  if ( this.sizer ) {
-    this.useSizer = true;
-  }
-
-  // Add class and invalidate styles
-  this.$el.addClass( Shuffle.ClassName.BASE );
-
-  // Set initial css for each item
-  this._initItems();
-
-  // Bind resize events
-  // http://stackoverflow.com/questions/1852751/window-resize-event-firing-in-internet-explorer
-  $window.on('resize.' + SHUFFLE + '.' + this.unique, this._getResizeFunction());
-
-  // Get container css all in one request. Causes reflow
-  var containerCSS = this.$el.css(['position', 'overflow']);
-  var containerWidth = Shuffle._getOuterWidth( this.element );
-
-  // Add styles to the container if it doesn't have them.
-  this._validateStyles( containerCSS );
-
-  // We already got the container's width above, no need to cause another reflow getting it again...
-  // Calculate the number of columns there will be
-  this._setColumns( containerWidth );
-
-  // Kick off!
-  this.shuffle( this.group, this.initialSort );
-
-  // The shuffle items haven't had transitions set on them yet
-  // so the user doesn't see the first layout. Set them now that the first layout is done.
-  if ( this.supported ) {
-    defer(function() {
-      this._setTransitions();
-      this.element.style[ TRANSITION ] = 'height ' + this.speed + 'ms ' + this.easing;
-    }, this);
-  }
-};
-
-
-/**
- * Returns a throttled and proxied function for the resize handler.
- * @return {Function}
- * @private
- */
-Shuffle.prototype._getResizeFunction = function() {
-  var resizeFunction = $.proxy( this._onResize, this );
-  return this.throttle ?
-      this.throttle( resizeFunction, this.throttleTime ) :
-      resizeFunction;
-};
-
-
-/**
- * Retrieve an element from an option.
- * @param {string|jQuery|Element} option The option to check.
- * @return {?Element} The plain element or null.
- * @private
- */
-Shuffle.prototype._getElementOption = function( option ) {
-  // If column width is a string, treat is as a selector and search for the
-  // sizer element within the outermost container
-  if ( typeof option === 'string' ) {
-    return this.$el.find( option )[0] || null;
-
-  // Check for an element
-  } else if ( option && option.nodeType && option.nodeType === 1 ) {
-    return option;
-
-  // Check for jQuery object
-  } else if ( option && option.jquery ) {
-    return option[0];
-  }
-
-  return null;
-};
-
-
-/**
- * Ensures the shuffle container has the css styles it needs applied to it.
- * @param {Object} styles Key value pairs for position and overflow.
- * @private
- */
-Shuffle.prototype._validateStyles = function(styles) {
-  // Position cannot be static.
-  if ( styles.position === 'static' ) {
-    this.element.style.position = 'relative';
-  }
-
-  // Overflow has to be hidden
-  if ( styles.overflow !== 'hidden' ) {
-    this.element.style.overflow = 'hidden';
-  }
-};
-
-
-/**
- * Filter the elements by a category.
- * @param {string} [category] Category to filter by. If it's given, the last
- *     category will be used to filter the items.
- * @param {ArrayLike} [$collection] Optionally filter a collection. Defaults to
- *     all the items.
- * @return {jQuery} Filtered items.
- * @private
- */
-Shuffle.prototype._filter = function( category, $collection ) {
-  category = category || this.lastFilter;
-  $collection = $collection || this.$items;
-
-  var set = this._getFilteredSets( category, $collection );
-
-  // Individually add/remove concealed/filtered classes
-  this._toggleFilterClasses( set.filtered, set.concealed );
-
-  // Save the last filter in case elements are appended.
-  this.lastFilter = category;
-
-  // This is saved mainly because providing a filter function (like searching)
-  // will overwrite the `lastFilter` property every time its called.
-  if ( typeof category === 'string' ) {
-    this.group = category;
-  }
-
-  return set.filtered;
-};
-
-
-/**
- * Returns an object containing the filtered and concealed elements.
- * @param {string|Function} category Category or function to filter by.
- * @param {ArrayLike.<Element>} $items A collection of items to filter.
- * @return {!{filtered: jQuery, concealed: jQuery}}
- * @private
- */
-Shuffle.prototype._getFilteredSets = function( category, $items ) {
-  var $filtered = $();
-  var $concealed = $();
-
-  // category === 'all', add filtered class to everything
-  if ( category === ALL_ITEMS ) {
-    $filtered = $items;
-
-  // Loop through each item and use provided function to determine
-  // whether to hide it or not.
-  } else {
-    each($items, function( el ) {
-      var $item = $(el);
-      if ( this._doesPassFilter( category, $item ) ) {
-        $filtered = $filtered.add( $item );
-      } else {
-        $concealed = $concealed.add( $item );
-      }
-    }, this);
-  }
-
-  return {
-    filtered: $filtered,
-    concealed: $concealed
-  };
-};
-
-
-/**
- * Test an item to see if it passes a category.
- * @param {string|Function} category Category or function to filter by.
- * @param {jQuery} $item A single item, wrapped with jQuery.
- * @return {boolean} Whether it passes the category/filter.
- * @private
- */
-Shuffle.prototype._doesPassFilter = function( category, $item ) {
-  if ( $.isFunction( category ) ) {
-    return category.call( $item[0], $item, this );
-
-  // Check each element's data-groups attribute against the given category.
-  } else {
-    var groups = $item.data( FILTER_ATTRIBUTE_KEY );
-    var keys = this.delimeter && !$.isArray( groups ) ?
-        groups.split( this.delimeter ) :
-        groups;
-    return $.inArray(category, keys) > -1;
-  }
-};
-
-
-/**
- * Toggles the filtered and concealed class names.
- * @param {jQuery} $filtered Filtered set.
- * @param {jQuery} $concealed Concealed set.
- * @private
- */
-Shuffle.prototype._toggleFilterClasses = function( $filtered, $concealed ) {
-  $filtered
-    .removeClass( Shuffle.ClassName.CONCEALED )
-    .addClass( Shuffle.ClassName.FILTERED );
-  $concealed
-    .removeClass( Shuffle.ClassName.FILTERED )
-    .addClass( Shuffle.ClassName.CONCEALED );
-};
-
-
-/**
- * Set the initial css for each item
- * @param {jQuery} [$items] Optionally specifiy at set to initialize
- */
-Shuffle.prototype._initItems = function( $items ) {
-  $items = $items || this.$items;
-  $items.addClass([
-    Shuffle.ClassName.SHUFFLE_ITEM,
-    Shuffle.ClassName.FILTERED
-  ].join(' '));
-  $items.css( this.itemCss ).data('point', new Point()).data('scale', DEFAULT_SCALE);
-};
-
-
-/**
- * Updates the filtered item count.
- * @private
- */
-Shuffle.prototype._updateItemCount = function() {
-  this.visibleItems = this._getFilteredItems().length;
-};
-
-
-/**
- * Sets css transform transition on a an element.
- * @param {Element} element Element to set transition on.
- * @private
- */
-Shuffle.prototype._setTransition = function( element ) {
-  element.style[ TRANSITION ] = CSS_TRANSFORM + ' ' + this.speed + 'ms ' +
-    this.easing + ', opacity ' + this.speed + 'ms ' + this.easing;
-};
-
-
-/**
- * Sets css transform transition on a group of elements.
- * @param {ArrayLike.<Element>} $items Elements to set transitions on.
- * @private
- */
-Shuffle.prototype._setTransitions = function( $items ) {
-  $items = $items || this.$items;
-  each($items, function( el ) {
-    this._setTransition( el );
-  }, this);
-};
-
-
-/**
- * Sets a transition delay on a collection of elements, making each delay
- * greater than the last.
- * @param {ArrayLike.<Element>} $collection Array to iterate over.
- */
-Shuffle.prototype._setSequentialDelay = function( $collection ) {
-  if ( !this.supported ) {
-    return;
-  }
-
-  // $collection can be an array of dom elements or jquery object
-  each($collection, function( el, i ) {
-    // This works because the transition-property: transform, opacity;
-    el.style[ TRANSITION_DELAY ] = '0ms,' + ((i + 1) * this.sequentialFadeDelay) + 'ms';
-  }, this);
-};
-
-
-Shuffle.prototype._getItems = function() {
-  return this.$el.children( this.itemSelector );
-};
-
-
-Shuffle.prototype._getFilteredItems = function() {
-  return this.$items.filter('.' + Shuffle.ClassName.FILTERED);
-};
-
-
-Shuffle.prototype._getConcealedItems = function() {
-  return this.$items.filter('.' + Shuffle.ClassName.CONCEALED);
-};
-
-
-/**
- * Returns the column size, based on column width and sizer options.
- * @param {number} containerWidth Size of the parent container.
- * @param {number} gutterSize Size of the gutters.
- * @return {number}
- * @private
- */
-Shuffle.prototype._getColumnSize = function( containerWidth, gutterSize ) {
-  var size;
-
-  // If the columnWidth property is a function, then the grid is fluid
-  if ( $.isFunction( this.columnWidth ) ) {
-    size = this.columnWidth(containerWidth);
-
-  // columnWidth option isn't a function, are they using a sizing element?
-  } else if ( this.useSizer ) {
-    size = Shuffle._getOuterWidth(this.sizer);
-
-  // if not, how about the explicitly set option?
-  } else if ( this.columnWidth ) {
-    size = this.columnWidth;
-
-  // or use the size of the first item
-  } else if ( this.$items.length > 0 ) {
-    size = Shuffle._getOuterWidth(this.$items[0], true);
-
-  // if there's no items, use size of container
-  } else {
-    size = containerWidth;
-  }
-
-  // Don't let them set a column width of zero.
-  if ( size === 0 ) {
-    size = containerWidth;
-  }
-
-  return size + gutterSize;
-};
-
-
-/**
- * Returns the gutter size, based on gutter width and sizer options.
- * @param {number} containerWidth Size of the parent container.
- * @return {number}
- * @private
- */
-Shuffle.prototype._getGutterSize = function( containerWidth ) {
-  var size;
-  if ( $.isFunction( this.gutterWidth ) ) {
-    size = this.gutterWidth(containerWidth);
-  } else if ( this.useSizer ) {
-    size = Shuffle._getNumberStyle(this.sizer, 'marginLeft');
-  } else {
-    size = this.gutterWidth;
-  }
-
-  return size;
-};
-
-
-/**
- * Calculate the number of columns to be used. Gets css if using sizer element.
- * @param {number} [theContainerWidth] Optionally specify a container width if it's already available.
- */
-Shuffle.prototype._setColumns = function( theContainerWidth ) {
-  var containerWidth = theContainerWidth || Shuffle._getOuterWidth( this.element );
-  var gutter = this._getGutterSize( containerWidth );
-  var columnWidth = this._getColumnSize( containerWidth, gutter );
-  var calculatedColumns = (containerWidth + gutter) / columnWidth;
-
-  // Widths given from getComputedStyle are not precise enough...
-  if ( Math.abs(Math.round(calculatedColumns) - calculatedColumns) < COLUMN_THRESHOLD ) {
-    // e.g. calculatedColumns = 11.998876
-    calculatedColumns = Math.round( calculatedColumns );
-  }
-
-  this.cols = Math.max( Math.floor(calculatedColumns), 1 );
-  this.containerWidth = containerWidth;
-  this.colWidth = columnWidth;
-};
-
-/**
- * Adjust the height of the grid
- */
-Shuffle.prototype._setContainerSize = function() {
-  this.$el.css( 'height', this._getContainerSize() );
-};
-
-
-/**
- * Based on the column heights, it returns the biggest one.
- * @return {number}
- * @private
- */
-Shuffle.prototype._getContainerSize = function() {
-  return arrayMax( this.positions );
-};
-
-
-/**
- * Fire events with .shuffle namespace
- */
-Shuffle.prototype._fire = function( name, args ) {
-  this.$el.trigger( name + '.' + SHUFFLE, args && args.length ? args : [ this ] );
-};
-
-
-/**
- * Zeros out the y columns array, which is used to determine item placement.
- * @private
- */
-Shuffle.prototype._resetCols = function() {
-  var i = this.cols;
-  this.positions = [];
-  while (i--) {
-    this.positions.push( 0 );
-  }
-};
-
-
-/**
- * Loops through each item that should be shown and calculates the x, y position.
- * @param {Array.<Element>} items Array of items that will be shown/layed out in order in their array.
- *     Because jQuery collection are always ordered in DOM order, we can't pass a jq collection.
- * @param {boolean} [isOnlyPosition=false] If true this will position the items with zero opacity.
- */
-Shuffle.prototype._layout = function( items, isOnlyPosition ) {
-  each(items, function( item ) {
-    this._layoutItem( item, !!isOnlyPosition );
-  }, this);
-
-  // `_layout` always happens after `_shrink`, so it's safe to process the style
-  // queue here with styles from the shrink method.
-  this._processStyleQueue();
-
-  // Adjust the height of the container.
-  this._setContainerSize();
-};
-
-
-/**
- * Calculates the position of the item and pushes it onto the style queue.
- * @param {Element} item Element which is being positioned.
- * @param {boolean} isOnlyPosition Whether to position the item, but with zero
- *     opacity so that it can fade in later.
- * @private
- */
-Shuffle.prototype._layoutItem = function( item, isOnlyPosition ) {
-  var $item = $(item);
-  var itemData = $item.data();
-  var currPos = itemData.point;
-  var currScale = itemData.scale;
-  var itemSize = {
-    width: Shuffle._getOuterWidth( item, true ),
-    height: Shuffle._getOuterHeight( item, true )
-  };
-  var pos = this._getItemPosition( itemSize );
-
-  // If the item will not change its position, do not add it to the render
-  // queue. Transitions don't fire when setting a property to the same value.
-  if ( Point.equals(currPos, pos) && currScale === DEFAULT_SCALE ) {
-    return;
-  }
-
-  // Save data for shrink
-  itemData.point = pos;
-  itemData.scale = DEFAULT_SCALE;
-
-  this.styleQueue.push({
-    $item: $item,
-    point: pos,
-    scale: DEFAULT_SCALE,
-    opacity: isOnlyPosition ? 0 : 1,
-    skipTransition: isOnlyPosition,
-    callfront: function() {
-      if ( !isOnlyPosition ) {
-        $item.css( 'visibility', 'visible' );
-      }
-    },
-    callback: function() {
-      if ( isOnlyPosition ) {
-        $item.css( 'visibility', 'hidden' );
-      }
-    }
-  });
-};
-
-
-/**
- * Determine the location of the next item, based on its size.
- * @param {{width: number, height: number}} itemSize Object with width and height.
- * @return {Point}
- * @private
- */
-Shuffle.prototype._getItemPosition = function( itemSize ) {
-  var columnSpan = this._getColumnSpan( itemSize.width, this.colWidth, this.cols );
-
-  var setY = this._getColumnSet( columnSpan, this.cols );
-
-  // Finds the index of the smallest number in the set.
-  var shortColumnIndex = this._getShortColumn( setY, this.buffer );
-
-  // Position the item
-  var point = new Point(
-    Math.round( this.colWidth * shortColumnIndex ),
-    Math.round( setY[shortColumnIndex] ));
-
-  // Update the columns array with the new values for each column.
-  // e.g. before the update the columns could be [250, 0, 0, 0] for an item
-  // which spans 2 columns. After it would be [250, itemHeight, itemHeight, 0].
-  var setHeight = setY[shortColumnIndex] + itemSize.height;
-  var setSpan = this.cols + 1 - setY.length;
-  for ( var i = 0; i < setSpan; i++ ) {
-    this.positions[ shortColumnIndex + i ] = setHeight;
-  }
-
-  return point;
-};
-
-
-/**
- * Determine the number of columns an items spans.
- * @param {number} itemWidth Width of the item.
- * @param {number} columnWidth Width of the column (includes gutter).
- * @param {number} columns Total number of columns
- * @return {number}
- * @private
- */
-Shuffle.prototype._getColumnSpan = function( itemWidth, columnWidth, columns ) {
-  var columnSpan = itemWidth / columnWidth;
-
-  // If the difference between the rounded column span number and the
-  // calculated column span number is really small, round the number to
-  // make it fit.
-  if ( Math.abs(Math.round( columnSpan ) - columnSpan ) < COLUMN_THRESHOLD ) {
-    // e.g. columnSpan = 4.0089945390298745
-    columnSpan = Math.round( columnSpan );
-  }
-
-  // Ensure the column span is not more than the amount of columns in the whole layout.
-  return Math.min( Math.ceil( columnSpan ), columns );
-};
-
-
-/**
- * Retrieves the column set to use for placement.
- * @param {number} columnSpan The number of columns this current item spans.
- * @param {number} columns The total columns in the grid.
- * @return {Array.<number>} An array of numbers represeting the column set.
- * @private
- */
-Shuffle.prototype._getColumnSet = function( columnSpan, columns ) {
-  // The item spans only one column.
-  if ( columnSpan === 1 ) {
-    return this.positions;
-
-  // The item spans more than one column, figure out how many different
-  // places it could fit horizontally.
-  // The group count is the number of places within the positions this block
-  // could fit, ignoring the current positions of items.
-  // Imagine a 2 column brick as the second item in a 4 column grid with
-  // 10px height each. Find the places it would fit:
-  // [10, 0, 0, 0]
-  //  |   |  |
-  //  *   *  *
-  //
-  // Then take the places which fit and get the bigger of the two:
-  // max([10, 0]), max([0, 0]), max([0, 0]) = [10, 0, 0]
-  //
-  // Next, find the first smallest number (the short column).
-  // [10, 0, 0]
-  //      |
-  //      *
-  //
-  // And that's where it should be placed!
-  } else {
-    var groupCount = columns + 1 - columnSpan;
-    var groupY = [];
-
-    // For how many possible positions for this item there are.
-    for ( var i = 0; i < groupCount; i++ ) {
-      // Find the bigger value for each place it could fit.
-      groupY[i] = arrayMax( this.positions.slice( i, i + columnSpan ) );
-    }
-
-    return groupY;
-  }
-};
-
-
-/**
- * Find index of short column, the first from the left where this item will go.
- *
- * @param {Array.<number>} positions The array to search for the smallest number.
- * @param {number} buffer Optional buffer which is very useful when the height
- *     is a percentage of the width.
- * @return {number} Index of the short column.
- * @private
- */
-Shuffle.prototype._getShortColumn = function( positions, buffer ) {
-  var minPosition = arrayMin( positions );
-  for (var i = 0, len = positions.length; i < len; i++) {
-    if ( positions[i] >= minPosition - buffer && positions[i] <= minPosition + buffer ) {
-      return i;
-    }
-  }
-  return 0;
-};
-
-
-/**
- * Hides the elements that don't match our filter.
- * @param {jQuery} $collection jQuery collection to shrink.
- * @private
- */
-Shuffle.prototype._shrink = function( $collection ) {
-  var $concealed = $collection || this._getConcealedItems();
-
-  each($concealed, function( item ) {
-    var $item = $(item);
-    var itemData = $item.data();
-
-    // Continuing would add a transitionend event listener to the element, but
-    // that listener would not execute because the transform and opacity would
-    // stay the same.
-    if ( itemData.scale === CONCEALED_SCALE ) {
-      return;
-    }
-
-    itemData.scale = CONCEALED_SCALE;
-
-    this.styleQueue.push({
-      $item: $item,
-      point: itemData.point,
-      scale : CONCEALED_SCALE,
-      opacity: 0,
-      callback: function() {
-        $item.css( 'visibility', 'hidden' );
-      }
-    });
-  }, this);
-};
-
-
-/**
- * Resize handler.
- * @private
- */
-Shuffle.prototype._onResize = function() {
-  // If shuffle is disabled, destroyed, don't do anything
-  if ( !this.enabled || this.destroyed || this.isTransitioning ) {
-    return;
-  }
-
-  // Will need to check height in the future if it's layed out horizontaly
-  var containerWidth = Shuffle._getOuterWidth( this.element );
-
-  // containerWidth hasn't changed, don't do anything
-  if ( containerWidth === this.containerWidth ) {
-    return;
-  }
-
-  this.update();
-};
-
-
-/**
- * Returns styles for either jQuery animate or transition.
- * @param {Object} opts Transition options.
- * @return {!Object} Transforms for transitions, left/top for animate.
- * @private
- */
-Shuffle.prototype._getStylesForTransition = function( opts ) {
-  var styles = {
-    opacity: opts.opacity
-  };
-
-  if ( this.supported ) {
-    styles[ TRANSFORM ] = Shuffle._getItemTransformString( opts.point, opts.scale );
-  } else {
-    styles.left = opts.point.x;
-    styles.top = opts.point.y;
-  }
-
-  return styles;
-};
-
-
-/**
- * Transitions an item in the grid
- *
- * @param {Object} opts options.
- * @param {jQuery} opts.$item jQuery object representing the current item.
- * @param {Point} opts.point A point object with the x and y coordinates.
- * @param {number} opts.scale Amount to scale the item.
- * @param {number} opts.opacity Opacity of the item.
- * @param {Function} opts.callback Complete function for the animation.
- * @param {Function} opts.callfront Function to call before transitioning.
- * @private
- */
-Shuffle.prototype._transition = function( opts ) {
-  var styles = this._getStylesForTransition( opts );
-  this._startItemAnimation( opts.$item, styles, opts.callfront || $.noop, opts.callback || $.noop );
-};
-
-
-Shuffle.prototype._startItemAnimation = function( $item, styles, callfront, callback ) {
-  // Transition end handler removes its listener.
-  function handleTransitionEnd( evt ) {
-    // Make sure this event handler has not bubbled up from a child.
-    if ( evt.target === evt.currentTarget ) {
-      $( evt.target ).off( TRANSITIONEND, handleTransitionEnd );
-      callback();
-    }
-  }
-
-  callfront();
-
-  // Transitions are not set until shuffle has loaded to avoid the initial transition.
-  if ( !this.initialized ) {
-    $item.css( styles );
-    callback();
-    return;
-  }
-
-  // Use CSS Transforms if we have them
-  if ( this.supported ) {
-    $item.css( styles );
-    $item.on( TRANSITIONEND, handleTransitionEnd );
-
-  // Use jQuery to animate left/top
-  } else {
-    // Save the deferred object which jQuery returns.
-    var anim = $item.stop( true ).animate( styles, this.speed, 'swing', callback );
-    // Push the animation to the list of pending animations.
-    this._animations.push( anim.promise() );
-  }
-};
-
-
-/**
- * Execute the styles gathered in the style queue. This applies styles to elements,
- * triggering transitions.
- * @param {boolean} noLayout Whether to trigger a layout event.
- * @private
- */
-Shuffle.prototype._processStyleQueue = function( noLayout ) {
-  var $transitions = $();
-
-  // Iterate over the queue and keep track of ones that use transitions.
-  each(this.styleQueue, function( transitionObj ) {
-    if ( transitionObj.skipTransition ) {
-      this._styleImmediately( transitionObj );
-    } else {
-      $transitions = $transitions.add( transitionObj.$item );
-      this._transition( transitionObj );
-    }
-  }, this);
-
-
-  if ( $transitions.length > 0 && this.initialized ) {
-    // Set flag that shuffle is currently in motion.
-    this.isTransitioning = true;
-
-    if ( this.supported ) {
-      this._whenCollectionDone( $transitions, TRANSITIONEND, this._movementFinished );
-
-    // The _transition function appends a promise to the animations array.
-    // When they're all complete, do things.
-    } else {
-      this._whenAnimationsDone( this._movementFinished );
-    }
-
-  // A call to layout happened, but none of the newly filtered items will
-  // change position. Asynchronously fire the callback here.
-  } else if ( !noLayout ) {
-    defer( this._layoutEnd, this );
-  }
-
-  // Remove everything in the style queue
-  this.styleQueue.length = 0;
-};
-
-
-/**
- * Apply styles without a transition.
- * @param {Object} opts Transitions options object.
- * @private
- */
-Shuffle.prototype._styleImmediately = function( opts ) {
-  Shuffle._skipTransition(opts.$item[0], function() {
-    opts.$item.css( this._getStylesForTransition( opts ) );
-  }, this);
-};
-
-Shuffle.prototype._movementFinished = function() {
-  this.isTransitioning = false;
-  this._layoutEnd();
-};
-
-Shuffle.prototype._layoutEnd = function() {
-  this._fire( Shuffle.EventType.LAYOUT );
-};
-
-Shuffle.prototype._addItems = function( $newItems, addToEnd, isSequential ) {
-  // Add classes and set initial positions.
-  this._initItems( $newItems );
-
-  // Add transition to each item.
-  this._setTransitions( $newItems );
-
-  // Update the list of
-  this.$items = this._getItems();
-
-  // Shrink all items (without transitions).
-  this._shrink( $newItems );
-  each(this.styleQueue, function( transitionObj ) {
-    transitionObj.skipTransition = true;
-  });
-
-  // Apply shrink positions, but do not cause a layout event.
-  this._processStyleQueue( true );
-
-  if ( addToEnd ) {
-    this._addItemsToEnd( $newItems, isSequential );
-  } else {
-    this.shuffle( this.lastFilter );
-  }
-};
-
-
-Shuffle.prototype._addItemsToEnd = function( $newItems, isSequential ) {
-  // Get ones that passed the current filter
-  var $passed = this._filter( null, $newItems );
-  var passed = $passed.get();
-
-  // How many filtered elements?
-  this._updateItemCount();
-
-  this._layout( passed, true );
-
-  if ( isSequential && this.supported ) {
-    this._setSequentialDelay( passed );
-  }
-
-  this._revealAppended( passed );
-};
-
-
-/**
- * Triggers appended elements to fade in.
- * @param {ArrayLike.<Element>} $newFilteredItems Collection of elements.
- * @private
- */
-Shuffle.prototype._revealAppended = function( newFilteredItems ) {
-  defer(function() {
-    each(newFilteredItems, function( el ) {
-      var $item = $( el );
-      this._transition({
-        $item: $item,
-        opacity: 1,
-        point: $item.data('point'),
-        scale: DEFAULT_SCALE
-      });
-    }, this);
-
-    this._whenCollectionDone($(newFilteredItems), TRANSITIONEND, function() {
-      $(newFilteredItems).css( TRANSITION_DELAY, '0ms' );
-      this._movementFinished();
-    });
-  }, this, this.revealAppendedDelay);
-};
-
-
-/**
- * Execute a function when an event has been triggered for every item in a collection.
- * @param {jQuery} $collection Collection of elements.
- * @param {string} eventName Event to listen for.
- * @param {Function} callback Callback to execute when they're done.
- * @private
- */
-Shuffle.prototype._whenCollectionDone = function( $collection, eventName, callback ) {
-  var done = 0;
-  var items = $collection.length;
-  var self = this;
-
-  function handleEventName( evt ) {
-    if ( evt.target === evt.currentTarget ) {
-      $( evt.target ).off( eventName, handleEventName );
-      done++;
-
-      // Execute callback if all items have emitted the correct event.
-      if ( done === items ) {
-        callback.call( self );
-      }
-    }
-  }
-
-  // Bind the event to all items.
-  $collection.on( eventName, handleEventName );
-};
-
-
-/**
- * Execute a callback after jQuery `animate` for a collection has finished.
- * @param {Function} callback Callback to execute when they're done.
- * @private
- */
-Shuffle.prototype._whenAnimationsDone = function( callback ) {
-  $.when.apply( null, this._animations ).always( $.proxy( function() {
-    this._animations.length = 0;
-    callback.call( this );
-  }, this ));
-};
-
-
-/**
- * Public Methods
- */
-
-/**
- * The magic. This is what makes the plugin 'shuffle'
- * @param {string|Function} [category] Category to filter by. Can be a function
- * @param {Object} [sortObj] A sort object which can sort the filtered set
- */
-Shuffle.prototype.shuffle = function( category, sortObj ) {
-  if ( !this.enabled || this.isTransitioning ) {
-    return;
-  }
-
-  if ( !category ) {
-    category = ALL_ITEMS;
-  }
-
-  this._filter( category );
-
-  // How many filtered elements?
-  this._updateItemCount();
-
-  // Shrink each concealed item
-  this._shrink();
-
-  // Update transforms on .filtered elements so they will animate to their new positions
-  this.sort( sortObj );
-};
-
-
-/**
- * Gets the .filtered elements, sorts them, and passes them to layout.
- * @param {Object} opts the options object for the sorted plugin
- */
-Shuffle.prototype.sort = function( opts ) {
-  if ( this.enabled && !this.isTransitioning ) {
-    this._resetCols();
-
-    var sortOptions = opts || this.lastSort;
-    var items = this._getFilteredItems().sorted( sortOptions );
-
-    this._layout( items );
-
-    this.lastSort = sortOptions;
-  }
-};
-
-
-/**
- * Reposition everything.
- * @param {boolean} isOnlyLayout If true, column and gutter widths won't be
- *     recalculated.
- */
-Shuffle.prototype.update = function( isOnlyLayout ) {
-  if ( this.enabled && !this.isTransitioning ) {
-
-    if ( !isOnlyLayout ) {
-      // Get updated colCount
-      this._setColumns();
-    }
-
-    // Layout items
-    this.sort();
-  }
-};
-
-
-/**
- * Use this instead of `update()` if you don't need the columns and gutters updated
- * Maybe an image inside `shuffle` loaded (and now has a height), which means calculations
- * could be off.
- */
-Shuffle.prototype.layout = function() {
-  this.update( true );
-};
-
-
-/**
- * New items have been appended to shuffle. Fade them in sequentially
- * @param {jQuery} $newItems jQuery collection of new items
- * @param {boolean} [addToEnd=false] If true, new items will be added to the end / bottom
- *     of the items. If not true, items will be mixed in with the current sort order.
- * @param {boolean} [isSequential=true] If false, new items won't sequentially fade in
- */
-Shuffle.prototype.appended = function( $newItems, addToEnd, isSequential ) {
-  this._addItems( $newItems, addToEnd === true, isSequential !== false );
-};
-
-
-/**
- * Disables shuffle from updating dimensions and layout on resize
- */
-Shuffle.prototype.disable = function() {
-  this.enabled = false;
-};
-
-
-/**
- * Enables shuffle again
- * @param {boolean} [isUpdateLayout=true] if undefined, shuffle will update columns and gutters
- */
-Shuffle.prototype.enable = function( isUpdateLayout ) {
-  this.enabled = true;
-  if ( isUpdateLayout !== false ) {
-    this.update();
-  }
-};
-
-
-/**
- * Remove 1 or more shuffle items
- * @param {jQuery} $collection A jQuery object containing one or more element in shuffle
- * @return {Shuffle} The shuffle object
- */
-Shuffle.prototype.remove = function( $collection ) {
-
-  // If this isn't a jquery object, exit
-  if ( !$collection.length || !$collection.jquery ) {
-    return;
-  }
-
-  function handleRemoved() {
-    // Remove the collection in the callback
-    $collection.remove();
-
-    // Update things now that elements have been removed.
-    this.$items = this._getItems();
-    this._updateItemCount();
-
-    this._fire( Shuffle.EventType.REMOVED, [ $collection, this ] );
-
-    // Let it get garbage collected
-    $collection = null;
-  }
-
-  // Hide collection first.
-  this._toggleFilterClasses( $(), $collection );
-  this._shrink( $collection );
-
-  this.sort();
-
-  this.$el.one( Shuffle.EventType.LAYOUT + '.' + SHUFFLE, $.proxy( handleRemoved, this ) );
-};
-
-
-/**
- * Destroys shuffle, removes events, styles, and classes
- */
-Shuffle.prototype.destroy = function() {
-  // If there is more than one shuffle instance on the page,
-  // removing the resize handler from the window would remove them
-  // all. This is why a unique value is needed.
-  $window.off('.' + this.unique);
-
-  // Reset container styles
-  this.$el
-      .removeClass( SHUFFLE )
-      .removeAttr('style')
-      .removeData( SHUFFLE );
-
-  // Reset individual item styles
-  this.$items
-      .removeAttr('style')
-      .removeData('point')
-      .removeData('scale')
-      .removeClass([
-        Shuffle.ClassName.CONCEALED,
-        Shuffle.ClassName.FILTERED,
-        Shuffle.ClassName.SHUFFLE_ITEM
-      ].join(' '));
-
-  // Null DOM references
-  this.$items = null;
-  this.$el = null;
-  this.sizer = null;
-  this.element = null;
-
-  // Set a flag so if a debounced resize has been triggered,
-  // it can first check if it is actually destroyed and not doing anything
-  this.destroyed = true;
-};
-
-
-// Plugin definition
-$.fn.shuffle = function( opts ) {
-  var args = Array.prototype.slice.call( arguments, 1 );
-  return this.each(function() {
-    var $this = $( this );
-    var shuffle = $this.data( SHUFFLE );
-
-    // If we don't have a stored shuffle, make a new one and save it
-    if ( !shuffle ) {
-      shuffle = new Shuffle( this, opts );
-      $this.data( SHUFFLE, shuffle );
-    } else if ( typeof opts === 'string' && shuffle[ opts ] ) {
-      shuffle[ opts ].apply( shuffle, args );
-    }
-  });
-};
-
-
-// http://stackoverflow.com/a/962890/373422
-function randomize( array ) {
-  var tmp, current;
-  var top = array.length;
-
-  if ( !top ) {
-    return array;
-  }
-
-  while ( --top ) {
-    current = Math.floor( Math.random() * (top + 1) );
-    tmp = array[ current ];
-    array[ current ] = array[ top ];
-    array[ top ] = tmp;
-  }
-
-  return array;
-}
-
-
-// You can return `undefined` from the `by` function to revert to DOM order
-// This plugin does NOT return a jQuery object. It returns a plain array because
-// jQuery sorts everything in DOM order.
-$.fn.sorted = function(options) {
-  var opts = $.extend({}, $.fn.sorted.defaults, options);
-  var arr = this.get();
-  var revert = false;
-
-  if ( !arr.length ) {
-    return [];
-  }
-
-  if ( opts.randomize ) {
-    return randomize( arr );
-  }
-
-  // Sort the elements by the opts.by function.
-  // If we don't have opts.by, default to DOM order
-  if ( $.isFunction( opts.by ) ) {
-    arr.sort(function(a, b) {
-
-      // Exit early if we already know we want to revert
-      if ( revert ) {
-        return 0;
-      }
-
-      var valA = opts.by($(a));
-      var valB = opts.by($(b));
-
-      // If both values are undefined, use the DOM order
-      if ( valA === undefined && valB === undefined ) {
-        revert = true;
-        return 0;
-      }
-
-      if ( valA < valB || valA === 'sortFirst' || valB === 'sortLast' ) {
-        return -1;
-      }
-
-      if ( valA > valB || valA === 'sortLast' || valB === 'sortFirst' ) {
-        return 1;
-      }
-
-      return 0;
-    });
-  }
-
-  // Revert to the original array if necessary
-  if ( revert ) {
-    return this.get();
-  }
-
-  if ( opts.reverse ) {
-    arr.reverse();
-  }
-
-  return arr;
-};
-
-
-$.fn.sorted.defaults = {
-  reverse: false, // Use array.reverse() to reverse the results
-  by: null, // Sorting function
-  randomize: false // If true, this will skip the sorting and return a randomized order in the array
-};
-
-return Shuffle;
-
-});
\ No newline at end of file
diff --git a/docs/js/mydoc_scroll.html b/docs/js/mydoc_scroll.html
deleted file mode 100644
index 39fa55e99..000000000
--- a/docs/js/mydoc_scroll.html
+++ /dev/null
@@ -1,240 +0,0 @@
----
-title: Scroll layout
-type: scroll
-keywords: json, scrolling, scrollto, jquery plugin
-tags: special_layouts
-last_updated: November 30, 2015
-summary: "This page demonstrates how you the integration of a script called ScrollTo, which is used here to link definitions of a JSON code sample to a list of definitions for that particular term. The scenario here is that the JSON blocks are really long, with extensive nesting and subnesting, which makes it difficult for tables below the JSON to adequately explain the term in a usable way."
----
-
-{% if site.output == "pdf" %}
-{{site.data.alerts.note}} The content on this page doesn't display well on PDF, but I included it anyway so you could see the problems this layout poses if you're including it in PDF.  {{site.data.alerts.end}}
-{% endif %}
-
-{% if site.output == "web" %}
- <script src="../js/jquery.scrollTo.min.js"></script>
-    <!-- local scroll must come after scrollTo, because localScroll is based on scrollTo, which is based on jQuery -->
-<script src="../js/jquery.localScroll.min.js"></script>
-
-<script>
-
-$( document ).ready(function() {
-$('#small-box-links').localScroll({
-           target:'#definition-box-container',
-           showSpeed: 1000,
-           hash: true,
-           easing: 'swing',
-           onAfter:function(){
-          $("#definition-box-container div a").addClass("active");
-          $("#definition-box-container div a").removeClass("active");
-          $("#definition-box-container div").removeClass("active");
-           $(window.location.hash).addClass("active");
-  }
-});
-
-
-});
-</script>
-
-<!-- documentation for localScroll function: http://flesler.blogspot.com/2007/10/jquerylocalscroll-10.html -->
-    <style type="text/css">
- 
-   #definition-box-container {
-        border: 0px solid #dedede;
-        border-radius: 4px;
-        max-height: 500px;
-        overflow: scroll;
-        padding-left:5px;
-        border-radius:5px;
-    }
-
-    #json-box-container {
-
-      border: 1px solid #dedede;
-      border-radius: 4px;
-      max-height:500px;
-      overflow: scroll;
-    }
-
-    #small-box-links {
-      float: left;
-    }
-
-    #definition-box-container .active {
-     background-color: #FFFBCC;
-    }
-    
-    div#definition-box-container div {
-      padding: 10px;
-}
-
-    @media (min-height: 700px)  {
-       #json-box-container {
-           max-height:500px;
-         }
-           #definition-box-container  {
-          max-height: 500px;
-  }
-    }
-
-     @media (min-height: 800px)  {
-       #json-box-container {
-           max-height:600px;
-    }
-        #definition-box-container  {
-          max-height: 600px;
-  }
-  }
-
-    @media (min-height: 900px)  {
-       #json-box-container {
-           max-height:700px;
-    }
-        #definition-box-container  {
-          max-height: 700px;
-  }
-  }
-
-     @media (min-height: 1000px)  {
-       #json-box-container {
-           max-height:800px;
-    }
-        #definition-box-container  {
-          max-height: 800px;
-  }
-  }
-
-    @media (min-height: 1100px)  {
-       #json-box-container {
-           max-height:900px;
-    }
-        #definition-box-container  {
-          max-height: 900px;
-  }
-  }
-
-
-    </style>
-
-
-<div class="container">
-<div class="row">
- 
-<div id="small-box-links">
-
-<div class="col-md-6">
-<div id="json-box-container">
-<pre>
-{
-  <a href="#apples">"apples"</a>: "red fruit at the store",
-  <a href="#bananas">"bananas"</a>: "yellow bananas in a bunch",
-  <a href="#carrots">"carrots"</a>: "orange vegetables that grow in the ground",
-  <a href="#dingbats">"dingbats"</a>: "a type of character symbol on a computer",
-  <a href="#eggs">"eggs"</a>: "chickens lay them, and people eat them",
-  <a href="#falafel">"falafel"</a>: "a Mediterranean sandwich consisting of lots of different stuff i don't know much about",
-  <a href="#giraffe">"giraffe"</a>: "tall animal, has purple tongue",
-  <a href="#hippo">"hippo"</a>: "surprisingly dangerous amphibian",
-  <a href="#igloo">"igloo"</a>: "an ice shelter made by eskimos",
-  <a href="#jeep">"jeep</a>: "the only car that starts with a j",
-  <a href="#kilt">"kilt"</a>: "something worn by scottish people, not a dress",
-  <a href="#lamp">"lamp"</a>: "you use it to read by your bedside at night"
-  <a href="#manifold">"manifold"</a>: "an intake mechanism on a car, like a valve, i think",
-  <a href="#octopus">"octopus"</a>: "eight tentacles, shoots ink, lives in dark caves, very mysterious",
-  <a href="#paranoia">"paranoia"</a>: "the constant feeling that others are out to get you, conspiring against your success",
-  <a href="#qui">"qui"</a>: "a life force that runs through your body",
-  <a href="#radical">"radical"</a>: "someone who opposes the status quo in major ways",
-  <a href="#silly">"silly"</a>: "how I feel writing this dummy copy",
-  <a href="#taffy">"taffy"</a>: "the sweets children like the most and dentists hate the worst",
-  <a href="#umbrella">"umbrella"</a>: "an invention that has not had any advancements in 200 years",
-  <a href="#vampire">"vampire"</a>: "a paranormal figure that is surprisingly in vogue despite its basic nature",
-  <a href="#washington">"washington"</a>: "the place where tom was born",
-  <a href="#xylophone">"xylophone"</a>: "some kind of pinging instrument used to sound chime-like notes",
-  <a href="#yahoo">"yahoo"</a>: "an expression of exuberance, said under breath when something works right",
-  <a href="#zeta">"zeta"</a>: "the way british people pronounce z",
-  <a href="#alpha">"alpha"</a>: "the original letter of the alphabet, which has since come to mean the first. however, i think the original symbol of alpha is actually an ox. it is somewhat of a mystery to linquists as to the exact origin of the letter alpha, but it basically represents the dawn of the alphabet, which proved to be a huge step forward for human thought and expression.",
-  <a href="#beta">"beta"</a>: "the period of time when something is finished but undergoing testing by a group of people.",
-  <a href="#cappa">"cappa"</a>: "how italians refer to their baseball caps",
-  <a href="#dunno">"dunno"</a>: "informal expression for 'don't know'"
-  }
-      
-</pre>
-
-</div><!-- end json-box-container-->
-</div> <!-- end col-md-6-->
-
-<div class="col-md-3">
-
-<div id="definition-box-container">
-
-
-    <div id="apples"><h5>apples</h5>Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer magna massa, euismod sed rutrum at, ullamcorper quis tellus. Vestibulum erat purus, aliquet sit amet pellentesque eget, tempus at ante. Nulla justo nisi, elementum nec nisi eget, consectetur varius tortor. </div>
-
-    <div id="bananas"><h5>bananas</h5>Curabitur quis nibh sed eros viverra tempus et quis lorem. Nulla convallis sit amet risus vitae rutrum. Nulla at faucibus lectus. Pellentesque tortor nisl, interdum ac quam non, egestas congue massa. Vestibulum non porttitor lacus. Nam tincidunt arcu lectus. Donec eget ornare neque, hendrerit ornare lectus. In ac pretium odio.</div>
-
-    <div id="carrots"><h5>carrots</h5>Vivamus pulvinar vestibulum pharetra. Vivamus vitae diam iaculis, posuere mi sed, dignissim massa. Nunc vitae aliquet urna. Proin sed pulvinar ex. Maecenas nisl lorem, rutrum sit amet hendrerit sed, posuere at odio. Sed consectetur semper tristique. Vivamus finibus varius felis at convallis. Fusce in dictum nunc.</div>
-
-    <div id="dingbats"><h5>dingbats</h5>Curabitur feugiat lorem eget elit ullamcorper tincidunt. In euismod diam aliquet tortor fermentum tempor. Fusce quam felis, commodo viverra orci vitae, scelerisque aliquet risus. </div>
-
-    <div id="eggs"><h5>eggs</h5>Duis est nunc, fringilla eu ligula et, varius dignissim dui. Vivamus in tellus vitae ipsum vehicula fermentum at congue tellus. Suspendisse fermentum, magna vitae aliquet sodales, tellus nisi rutrum arcu, vitae auctor dolor quam ac tellus. Cras posuere augue erat, in sagittis quam lacinia id.</div>
-
-    <div id="falafel"><h5>falafel</h5>Praesent auctor a enim non lacinia. Integer sodales aliquet mi vel dapibus. Donec consequat justo eget nisi lacinia, eu sodales ligula molestie. Sed sapien nulla, rhoncus at elementum a, </div>
-
-    <div id="giraffe"><h5>giraffe</h5>Nullam venenatis at lectus sed pharetra. Sed hendrerit ligula lectus, non pellentesque diam faucibus sit amet. Aliquam dictum hendrerit pellentesque. Cras eu nisl sagittis, faucibus velit sit amet, sagittis odio. Donec vulputate ex vitae purus</div>
-
-    <div id="hippo"><h5>hippo</h5>Cras nec pretium nulla. Suspendisse tempus tortor vel venenatis pulvinar. Integer varius tempor enim fringilla tincidunt. Phasellus magna turpis, auctor vitae elit eget, fringilla pellentesque est. Phasellus ut porta risus. Curabitur iaculis sapien sed venenatis auctor. Integer eu orci at lectus eleifend auctor id rutrum urna.</div>
-
-    <div id="igloo"><h5>igloo</h5>Suspendisse tempus tortor vel venenatis pulvinar. Integer varius tempor enim fringilla tincidunt. Phasellus magna turpis, auctor vitae elit eget, fringilla pellentesque est. Phasellus ut porta risus.  </div>
-
-    <div id="jeep"><h5>jeep</h5>Nulla vitae metus rutrum, condimentum orci nec, maximus est. Aenean sit amet ante nec elit dignissim faucibus eget quis quam. </div>
-
-    <div id="kilt"><h5>kilt</h5>Morbi maximus, erat vel rhoncus sagittis, dolor purus dignissim ante, sit amet pharetra ex justo vitae ipsum. Nulla consequat interdum neque</div>
-
-    <div id="lamp"><h5>lamp</h5>Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Mauris aliquam dapibus blandit. Donec porta, enim hendrerit venenatis vulputate, orci diam lacinia nibh, faucibus rutrum dolor dui ut quam.</div>
-
-    <div id="manifold"><h5>manifold</h5>Donec finibus massa vel nisi ullamcorper, vitae ornare enim euismod. Aliquam auctor quam erat. Duis interdum rutrum orci, ac interdum urna pharetra eget.</div>
-
-    <div id="octopus"><h5>octopus</h5>Nulla id egestas enim. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse potenti. Curabitur eu lobortis ligula.</div>
-
-    <div id="paranoia"><h5>paranoia</h5>Aenean hendrerit mauris ipsum, non laoreet ipsum luctus vel. Curabitur tristique auctor elit ut pulvinar. Quisque arcu arcu, condimentum aliquam sodales nec, dignissim nec justo. Nunc tristique sem felis, pharetra euismod lorem volutpat sed. Ut porttitor metus sit amet elit rhoncus semper.</div>
-
-    <div id="qui"><h5>qui</h5>Quisque rhoncus cursus felis vel elementum. Vestibulum dignissim molestie tortor nec facilisis. Praesent a nibh condimentum, porta nulla egestas, auctor eros</div>
-
-    <div id="radical"><h5>radical</h5>Etiam hendrerit interdum tellus, at aliquet sapien egestas in. Aenean eu urna nisl. Cras vitae risus pharetra, elementum mauris nec, auctor lectus. Fusce pellentesque venenatis dictum. Proin at augue at mauris finibus semper ultricies sed eros.</div>
-
-    <div id="silly"><h5>silly</h5>Praesent pulvinar consequat posuere. Morbi egestas rhoncus felis, id fermentum metus lobortis in. Vestibulum nibh orci, euismod eget vestibulum nec, vehicula vitae tortor. Aenean ullamcorper enim nunc, eu auctor ligula auctor eget.</div>
-
-    <div id="taffy"><h5>taffy</h5>Etiam et arcu vel lacus aliquet lobortis in in massa. Nunc non mollis elit. Aenean accumsan orci quis risus aliquam, non gravida nulla molestie. Mauris pharetra libero et magna aliquam aliquam. Integer quis luctus dolor. </div>
-
-    <div id="umbrella"><h5>umbrella</h5>Fusce molestie finibus malesuada. Nullam ac egestas quam, id venenatis ligula. Pellentesque pulvinar elit et vestibulum fringilla. Cras volutpat sed quam ornare scelerisque. Vivamus volutpat ante pretium scelerisque tempus. Etiam venenatis tempor nisl dignissim sollicitudin. Curabitur ac risus vitae dolor pretium posuere vel vitae diam. Donec in odio arcu.</div>
-
-    <div id="vampire"><h5>vampire</h5>Vestibulum pretium condimentum commodo. Integer placerat leo non ipsum ultrices, ac convallis elit varius. Vestibulum ultricies, justo eu rutrum molestie, quam arcu euismod sapien, vel gravida ipsum nulla eget erat. </div>
-
-    <div id="washington"><h5>washington</h5>Nunc ac quam eu risus dictum sodales. Nam ac risus iaculis, aliquet sem eu, mollis mauris. Curabitur pretium facilisis orci ut lacinia. Sed fermentum leo a odio blandit rutrum. Phasellus at nibh vel odio interdum vulputate ac eget urna. Nam eu arcu dapibus, sodales ligula nec, volutpat ipsum. Suspendisse auctor tellus vitae libero euismod venenatis. </div>
-
-    <div id="xylophone"><h5>xylophone</h5>Sed molestie lobortis ante sit amet hendrerit. Sed pharetra nisi sed interdum pulvinar. Nunc efficitur erat non aliquam mattis. Sed id nisl mattis lacus vehicula volutpat vitae vel massa. Curabitur interdum velit odio, vitae sollicitudin nunc rutrum non. </div>
-
-    <div id="yahoo"><h5>yahoo</h5>Nunc commodo consectetur scelerisque. Proin fermentum ligula ac quam finibus tincidunt. Aenean venenatis nisi et semper semper. Nunc sodales velit ipsum, ac pellentesque augue placerat eu.</div>
-
-    <div id="zeta"><h5>zeta</h5>Nullam ac suscipit odio. Curabitur viverra arcu ut egestas sollicitudin. Fusce sodales varius lectus ut tristique. Etiam eget nunc ornare, aliquet tortor eget, consequat mauris. Integer sit amet fermentum augue. </div>
-
-    <div id="alpha"><h5>alpha</h5>Praesent nec neque ac tellus sodales eleifend nec vel ipsum. Cras et semper risus. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Integer mattis leo nisl, a tincidunt lectus tristique eget. Donec finibus lobortis viverra. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Vivamus egestas pulvinar odio non vehicula. Morbi malesuada leo eget nisl sagittis aliquet.</div>
-
-
-    <div id="beta"><h5>beta</h5>Mauris a libero vel enim pharetra interdum non a quam. Sed tincidunt ut elit sed dignissim. Suspendisse vitae tellus dapibus, fermentum lacus ac, fermentum lacus. Nam ante odio, fringilla ac elementum a, mollis sed tellus.</div>
-
-    <div id="cappa"><h5>cappa</h5>Nam molestie semper nulla et molestie. Ut facilisis, ipsum sed convallis posuere, mi mauris bibendum erat, nec egestas ipsum est nec dolor. </div>
-
-    <div id="dunno"><h5>dunno</h5>Etiam et metus congue, commodo libero et, accumsan sem. Aliquam erat volutpat. Quisque tincidunt, tortor non blandit ullamcorper, orci mauris dignissim augue, eget vehicula nulla justo sed dolor. Nunc ac urna quis nisi maximus pharetra in a mauris. Proin metus mi, venenatis vitae tristique sed, fermentum at purus. Aliquam erat volutpat. Maecenas efficitur sodales nibh, ac hendrerit felis facilisis et. Interdum et malesuada fames ac ante ipsum primis in faucibus.</div>
-
-
-</div> <!-- end definition-box-container -->
-</div> <!-- end col-md-3-->
-</div> <!-- end small-box-links -->
-</div> <!-- end row -->
-</div> <!-- end container -->
-
-
-{{site.data.alerts.note}} This was mostly an experiment to see if there was a better way to document a long JSON code example. I haven't actually used this approach in my own documentation.{{site.data.alerts.end}}
-
-{% endif %}
-
diff --git a/docs/js/toc.js b/docs/js/toc.js
deleted file mode 100644
index a54d172ef..000000000
--- a/docs/js/toc.js
+++ /dev/null
@@ -1,82 +0,0 @@
-// https://github.com/ghiculescu/jekyll-table-of-contents
-(function($){
-  $.fn.toc = function(options) {
-    var defaults = {
-      noBackToTopLinks: false,
-      title: '',
-      minimumHeaders: 3,
-      headers: 'h1, h2, h3, h4',
-      listType: 'ol', // values: [ol|ul]
-      showEffect: 'show', // values: [show|slideDown|fadeIn|none]
-      showSpeed: 'slow' // set to 0 to deactivate effect
-    },
-    settings = $.extend(defaults, options);
-
-    var headers = $(settings.headers).filter(function() {
-      // get all headers with an ID
-      var previousSiblingName = $(this).prev().attr( "name" );
-      if (!this.id && previousSiblingName) {
-        this.id = $(this).attr( "id", previousSiblingName.replace(/\./g, "-") );
-      }
-      return this.id;
-    }), output = $(this);
-    if (!headers.length || headers.length < settings.minimumHeaders || !output.length) {
-      return;
-    }
-
-    if (0 === settings.showSpeed) {
-      settings.showEffect = 'none';
-    }
-
-    var render = {
-      show: function() { output.hide().html(html).show(settings.showSpeed); },
-      slideDown: function() { output.hide().html(html).slideDown(settings.showSpeed); },
-      fadeIn: function() { output.hide().html(html).fadeIn(settings.showSpeed); },
-      none: function() { output.html(html); }
-    };
-
-    var get_level = function(ele) { return parseInt(ele.nodeName.replace("H", ""), 10); }
-    var highest_level = headers.map(function(_, ele) { return get_level(ele); }).get().sort()[0];
-    var return_to_top = '<i class="icon-arrow-up back-to-top"> </i>';
-
-    var level = get_level(headers[0]),
-      this_level,
-      html = settings.title + " <"+settings.listType+">";
-    headers.on('click', function() {
-      if (!settings.noBackToTopLinks) {
-        window.location.hash = this.id;
-      }
-    })
-    .addClass('clickable-header')
-    .each(function(_, header) {
-      this_level = get_level(header);
-      if (!settings.noBackToTopLinks && this_level === highest_level) {
-        $(header).addClass('top-level-header').after(return_to_top);
-      }
-      if (this_level === level) // same level as before; same indenting
-        html += "<li><a href='#" + header.id + "'>" + header.innerHTML + "</a>";
-      else if (this_level <= level){ // higher level than before; end parent ol
-        for(i = this_level; i < level; i++) {
-          html += "</li></"+settings.listType+">"
-        }
-        html += "<li><a href='#" + header.id + "'>" + header.innerHTML + "</a>";
-      }
-      else if (this_level > level) { // lower level than before; expand the previous to contain a ol
-        for(i = this_level; i > level; i--) {
-          html += "<"+settings.listType+"><li>"
-        }
-        html += "<a href='#" + header.id + "'>" + header.innerHTML + "</a>";
-      }
-      level = this_level; // update for the next one
-    });
-    html += "</"+settings.listType+">";
-    if (!settings.noBackToTopLinks) {
-      $(document).on('click', '.back-to-top', function() {
-        $(window).scrollTop(0);
-        window.location.hash = '';
-      });
-    }
-
-    render[settings.showEffect]();
-  };
-})(jQuery);
\ No newline at end of file
diff --git a/docs/licenses/LICENSE b/docs/licenses/LICENSE
deleted file mode 100644
index e04b3d02e..000000000
--- a/docs/licenses/LICENSE
+++ /dev/null
@@ -1,21 +0,0 @@
-The MIT License (MIT)
-
-Copyright (c) 2016 Tom Johnson
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/docs/licenses/LICENSE-BSD-NAVGOCO.txt b/docs/licenses/LICENSE-BSD-NAVGOCO.txt
deleted file mode 100644
index 7fdefc390..000000000
--- a/docs/licenses/LICENSE-BSD-NAVGOCO.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-/* This license pertains to the Navgoco jQuery component used for the sidebar. */
-
-Copyright (c) 2013, Christodoulos Tsoulloftas, http://www.komposta.net
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without modification,
-are permitted provided that the following conditions are met:
-
-   * Redistributions of source code must retain the above copyright notice,
-      this list of conditions and the following disclaimer.
-   * Redistributions in binary form must reproduce the above copyright notice,
-      this list of conditions and the following disclaimer in the documentation
-      and/or other materials provided with the distribution.
-   * Neither the name of the <Christodoulos Tsoulloftas> nor the names of its
-      contributors may be used to endorse or promote products derived from this
-      software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
-IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
-INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
-LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
-OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
-OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/docs/migration_guide.md b/docs/migration_guide.md
deleted file mode 100644
index a5d5506c4..000000000
--- a/docs/migration_guide.md
+++ /dev/null
@@ -1,71 +0,0 @@
----
-title: Migration Guide
-keywords: migration guide
-sidebar: mydoc_sidebar
-permalink: migration_guide.html
-toc: false
-summary: In this page, we will discuss some available tools for migrating your existing dataset into a Hudi managed 
-dataset
-
-
-Hudi maintains metadata such as commit timeline and indexes to manage a dataset. The commit timelines helps to understand the actions happening on a dataset as well as the current state of a dataset. Indexes are used by Hudi to maintain a record key to file id mapping to efficiently locate a record. At the moment, Hudi supports writing only parquet columnar formats.
-To be able to start using Hudi for your existing dataset, you will need to migrate your existing dataset into a Hudi managed dataset. There are a couple of ways to achieve this.
-
-
-## Approaches
-
-
-### Approach 1
-
-Hudi can be used to manage an existing dataset without affecting/altering the historical data already present in the 
-dataset. Hudi has been implemented to be compatible with such a mixed dataset with a caveat that either the complete 
-Hive partition is Hudi managed or not. Thus the lowest granularity at which Hudi manages a dataset is a Hive 
-partition. Start using the datasource API or the WriteClient to write to the dataset and make sure you start writing 
-to a new partition or convert your last N partitions into Hudi instead of the entire table. Note, since the historical
- partitions are not managed by HUDI, none of the primitives provided by HUDI work on the data in those partitions. More concretely, one cannot perform upserts or incremental pull on such older partitions not managed by the HUDI dataset. 
-Take this approach if your dataset is an append only type of dataset and you do not expect to perform any updates to existing (or non Hudi managed) partitions.
-
-
-### Approach 2
-
-Import your existing dataset into a Hudi managed dataset. Since all the data is Hudi managed, none of the limitations
- of Approach 1 apply here. Updates spanning any partitions can be applied to this dataset and Hudi will efficiently 
- make the update available to queries. Note that not only do you get to use all Hoodie primitives on this dataset, 
- there are other additional advantages of doing this. Hudi automatically manages file sizes of a Hudi managed dataset
- . You can define the desired file size when converting this dataset and Hudi will ensure it writes out files 
- adhering to the config. It will also ensure that smaller files later get corrected by routing some new inserts into 
- small files rather than writing new small ones thus maintaining the health of your cluster.
-
-There are a few options when choosing this approach.
-#### Option 1
-Use the HDFSParquetImporter tool. As the name suggests, this only works if your existing dataset is in 
-parquet file 
-format. This tool essentially starts a Spark Job to read the existing parquet dataset and converts it into a HUDI managed dataset by re-writing all the data. 
-#### Option 2 
-For huge datasets, this could be as simple as : for partition in [list of partitions in source dataset] {
-        val inputDF = spark.read.format("any_input_format").load("partition_path")
-        inputDF.write.format("com.uber.hoodie").option()....save("basePath")
-        }      
-#### Option 3
-Write your own custom logic of how to load an existing dataset into a Hudi managed one. Please read about the RDD API
- [here](quickstart.md).
-
-```
-Using the HDFSParquetImporter Tool. Once hoodie has been built via `mvn clean install -DskipTests`, the shell can be 
-fired by via `cd hoodie-cli && ./hoodie-cli.sh`.
-
-hoodie->hdfsparquetimport 
-        --upsert false 
-        --srcPath /user/parquet/dataset/basepath 
-        --targetPath 
-        /user/hoodie/dataset/basepath 
-        --tableName hoodie_table 
-        --tableType COPY_ON_WRITE 
-        --rowKeyField _row_key 
-        --partitionPathField partitionStr 
-        --parallelism 1500 
-        --schemaFilePath /user/table/schema 
-        --format parquet 
-        --sparkMemory 6g 
-        --retry 2
-```
\ No newline at end of file
diff --git a/docs/pages/news/news.html b/docs/pages/news/news.html
deleted file mode 100644
index cb3d5d023..000000000
--- a/docs/pages/news/news.html
+++ /dev/null
@@ -1,32 +0,0 @@
----
-title: News
-sidebar: home_sidebar
-keywords: news, blog, updates, release notes, announcements
-permalink: news.html
-toc: false
-folder: news
----
-<div class="home">
-
-    <div class="post-list">
-        {% for post in site.posts limit:10 %}
-
-
-    <h2><a class="post-link" href="{{ post.url | remove: "/" }}">{{ post.title }}</a></h2>
-        <span class="post-meta">{{ post.date | date: "%b %-d, %Y" }} /
-            {% for tag in post.tags %}
-
-                <a href="{{ "tag_" | append: tag | append: ".html"}}">{{tag}}</a>{% unless forloop.last %}, {% endunless%}
-
-                {% endfor %}</span>
-        <p>{% if page.summary %} {{ page.summary | strip_html | strip_newlines | truncate: 160 }} {% else %} {{ post.content | truncatewords: 50 | strip_html }} {% endif %}</p>
-
-        {% endfor %}
-
-        <p><a href="feed.xml"" class="btn btn-primary navbar-btn cursorNorm" role="button">RSS Subscribe{{tag}}</a></p>
-
-<hr />
-        <p>See more posts from the <a href="news_archive.html">News Archive</a>. </p>
-
-
-</div>
diff --git a/docs/pages/news/news_archive.html b/docs/pages/news/news_archive.html
deleted file mode 100644
index 41e164d13..000000000
--- a/docs/pages/news/news_archive.html
+++ /dev/null
@@ -1,41 +0,0 @@
----
-title: News
-sidebar: mydoc_sidebar
-keywords: news, blog, updates, release notes, announcements
-permalink: news_archive.html
-toc: false
-folder: news
----
-
-<div class="home">
-
-    <div class="post-list">
-
-
-    <section id="archive">
-        <h3>This year's posts</h3>
-        {%for post in site.posts %}
-        {% unless post.next %}
-        <ul class="this">
-            {% else %}
-            {% capture year %}{{ post.date | date: '%Y' }}{% endcapture %}
-            {% capture nyear %}{{ post.next.date | date: '%Y' }}{% endcapture %}
-            {% if year != nyear %}
-        </ul>
-        <h3>{{ post.date | date: '%Y' }}</h3>
-        <ul class="past">
-            {% endif %}
-            {% endunless %}
-            <li><time>{{ post.date | date:"%d %b" }}</time><a href="{{ post.url | remove: "/"}}">{{ post.title }}</a></li>
-            {% endfor %}
-        </ul>
-    </section>
-
-
-    <hr/>
-
-
-
-
-
-    </div>
diff --git a/docs/powered_by.md b/docs/powered_by.md
deleted file mode 100644
index d4d6fe1bf..000000000
--- a/docs/powered_by.md
+++ /dev/null
@@ -1,40 +0,0 @@
----
-title: Talks & Powered By
-keywords: talks
-sidebar: mydoc_sidebar
-permalink: powered_by.html
-toc: false
----
-
-## Adoption
-
-#### Uber
-
-Hoodie was originally developed at [Uber](https://uber.com), to achieve [low latency database ingestion, with high efficiency](http://www.slideshare.net/vinothchandar/hadoop-strata-talk-uber-your-hadoop-has-arrived/32).
-It has been in production since Aug 2016, powering ~100 highly business critical tables on Hadoop, worth 100s of TBs(including top 10 including trips,riders,partners).
-It also powers several incremental Hive ETL pipelines and being currently integrated into Uber's data dispersal system.
-
-## Talks & Presentations
-
-1. ["Hoodie: Incremental processing on Hadoop at Uber"](https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/56511) -  By Vinoth Chandar & Prasanna Rajaperumal
-   Mar 2017, Strata + Hadoop World, San Jose, CA
-
-2. ["Hoodie: An Open Source Incremental Processing Framework From Uber"](http://www.dataengconf.com/hoodie-an-open-source-incremental-processing-framework-from-uber) - By Vinoth Chandar.
-   Apr 2017, DataEngConf, San Francisco, CA [Slides](https://www.slideshare.net/vinothchandar/hoodie-dataengconf-2017) [Video](https://www.youtube.com/watch?v=7Wudjc-v7CA)
-
-
-3. ["Incremental Processing on Large Analytical Datasets"](https://spark-summit.org/2017/events/incremental-processing-on-large-analytical-datasets/) - By Prasanna Rajaperumal
-   June 2017, Spark Summit 2017, San Francisco, CA. [Slides](https://www.slideshare.net/databricks/incremental-processing-on-large-analytical-datasets-with-prasanna-rajaperumal-and-vinoth-chandar) [Video](https://www.youtube.com/watch?v=3HS0lQX-cgo&feature=youtu.be)
-
-4. ["Hudi: Unifying storage and serving for batch and near-real-time analytics"](https://conferences.oreilly.com/strata/strata-ny/public/schedule/detail/70937) - By Nishith Agarwal & Balaji Vardarajan
-   September 2018, Strata Data Conference, New York, NY
-
-5. ["Hudi: Large-Scale, Near Real-Time Pipelines at Uber"](https://databricks
-.com/session/hudi-near-real-time-spark-pipelines-at-petabyte-scale) - By Vinoth Chander & Nishith Agarwal
-   October 2018, Spark+AI Summit Europe, London, UK
-   
-## Articles
-
-1. ["The Case for incremental processing on Hadoop"](https://www.oreilly.com/ideas/ubers-case-for-incremental-processing-on-hadoop) - O'reilly Ideas article by Vinoth Chandar
-2. ["Hoodie: Uber Engineering's Incremental Processing Framework on Hadoop"](https://eng.uber.com/hoodie/) - Engineering Blog By Prasanna Rajaperumal
-
diff --git a/docs/quickstart.md b/docs/quickstart.md
deleted file mode 100644
index 882e660de..000000000
--- a/docs/quickstart.md
+++ /dev/null
@@ -1,1248 +0,0 @@
----
-title: Quickstart
-keywords: quickstart
-tags: [quickstart]
-sidebar: mydoc_sidebar
-toc: false
-permalink: quickstart.html
----
-
-
-## Download Hoodie
-
-Check out code and pull it into Intellij as a normal maven project.
-
-Normally build the maven project, from command line
-```
-$ mvn clean install -DskipTests -DskipITs
-
-To work with older version of Hive (pre Hive-1.2.1), use
-
-$ mvn clean install -DskipTests -DskipITs -Dhive11
-
-```
-
-{% include callout.html content="You might want to add your spark jars folder to project dependencies under 'Module Setttings', to be able to run Spark from IDE" type="info" %}
-
-{% include note.html content="Setup your local hadoop/hive test environment, so you can play with entire ecosystem. See [this](http://www.bytearray.io/2016/05/setting-up-hadoopyarnsparkhive-on-mac.html) for reference" %}
-
-
-## Version Compatibility
-
-Hoodie requires Java 8 to be installed. Hoodie works with Spark-2.x versions. We have verified that hoodie works with the following combination of Hadoop/Hive/Spark.
-
-| Hadoop | Hive  | Spark | Instructions to Build Hoodie | 
-| ---- | ----- | ---- | ---- |
-| 2.6.0-cdh5.7.2 | 1.1.0-cdh5.7.2 | spark-2.[1-3].x | Use "mvn clean install -DskipTests -Dhadoop.version=2.6.0-cdh5.7.2 -Dhive.version=1.1.0-cdh5.7.2" |
-| Apache hadoop-2.8.4 | Apache hive-2.3.3 | spark-2.[1-3].x | Use "mvn clean install -DskipTests" |
-| Apache hadoop-2.7.3 | Apache hive-1.2.1 | spark-2.[1-3].x | Use "mvn clean install -DskipTests" |
-
-If your environment has other versions of hadoop/hive/spark, please try out hoodie and let us know if there are any issues. We are limited by our bandwidth to certify other combinations. 
-It would be of great help if you can reach out to us with your setup and experience with hoodie.
-
-## Generate a Hoodie Dataset
-
-### Requirements & Environment Variable
-
-Please set the following environment variablies according to your setup. We have given an example setup with CDH version
-
-```
-export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/jre/
-export HIVE_HOME=/var/hadoop/setup/apache-hive-1.1.0-cdh5.7.2-bin
-export HADOOP_HOME=/var/hadoop/setup/hadoop-2.6.0-cdh5.7.2
-export HADOOP_INSTALL=/var/hadoop/setup/hadoop-2.6.0-cdh5.7.2
-export HADOOP_CONF_DIR=$HADOOP_INSTALL/etc/hadoop
-export SPARK_HOME=/var/hadoop/setup/spark-2.3.1-bin-hadoop2.7
-export SPARK_INSTALL=$SPARK_HOME
-export SPARK_CONF_DIR=$SPARK_HOME/conf
-export PATH=$JAVA_HOME/bin:$HIVE_HOME/bin:$HADOOP_HOME/bin:$SPARK_INSTALL/bin:$PATH
-```
-
-### Supported API's
-
-Use the DataSource API to quickly start reading or writing hoodie datasets in few lines of code. Ideal for most 
-ingestion use-cases.
-Use the RDD API to perform more involved actions on a hoodie dataset
-
-#### DataSource API
-
-Run __hoodie-spark/src/test/java/HoodieJavaApp.java__ class, to place a two commits (commit 1 => 100 inserts, commit 2 => 100 updates to previously inserted 100 records) onto your HDFS/local filesystem. Use the wrapper script
-to run from command-line
-
-```
-cd hoodie-spark
-./run_hoodie_app.sh --help
-Usage: <main class> [options]
-  Options:
-    --help, -h
-       Default: false
-    --table-name, -n
-       table name for Hoodie sample table
-       Default: hoodie_rt
-    --table-path, -p
-       path for Hoodie sample table
-       Default: file:///tmp/hoodie/sample-table
-    --table-type, -t
-       One of COPY_ON_WRITE or MERGE_ON_READ
-       Default: COPY_ON_WRITE
-
-
-```
-
-The class lets you choose table names, output paths and one of the storage types. In your own applications, be sure to include the `hoodie-spark` module as dependency
-and follow a similar pattern to write/read datasets via the datasource.
-
-#### RDD API
-
-RDD level APIs give you more power and control over things, via the `hoodie-client` module .
-Refer to  __hoodie-client/src/test/java/HoodieClientExample.java__ class for an example.
-
-
-
-## Query a Hoodie dataset
-
-### Register Dataset to Hive Metastore
-
-Now, lets see how we can publish this data into Hive.
-
-#### Starting up Hive locally
-
-```
-hdfs namenode # start name node
-hdfs datanode # start data node
-
-bin/hive --service metastore  # start metastore
-bin/hiveserver2 \
-  --hiveconf hive.root.logger=INFO,console \
-  --hiveconf hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat \
-  --hiveconf ive.stats.autogather=false \
-  --hiveconf hive.aux.jars.path=hoodie/packaging/hoodie-hadoop-mr-bundle/target/hoodie-hadoop-mr-bundle-0.4.3-SNAPSHOT.jar
-
-```
-
-
-#### Hive Sync Tool
-
-Hive Sync Tool will update/create the necessary metadata(schema and partitions) in hive metastore.
-This allows for schema evolution and incremental addition of new partitions written to.
-It uses an incremental approach by storing the last commit time synced in the TBLPROPERTIES and only syncing the commits from the last sync commit time stored.
-This can be run as frequently as the ingestion pipeline to make sure new partitions and schema evolution changes are reflected immediately.
-
-```
-cd hoodie-hive
-./run_sync_tool.sh
-  --user hive
-  --pass hive 
-  --database default 
-  --jdbc-url "jdbc:hive2://localhost:10010/" 
-  --base-path tmp/hoodie/sample-table/ 
-  --table hoodie_test 
-  --partitioned-by field1,field2
-
-```
-
-
-
-#### Manually via Beeline
-Add in the hoodie-hadoop-mr-bundler jar so, Hive can read the Hoodie dataset and answer the query.
-Also, For reading hoodie tables using hive, the following configs needs to be setup
-
-```
-hive> set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
-hive> set hive.stats.autogather=false;
-hive> add jar file:///tmp/hoodie-hadoop-mr-bundle-0.4.3.jar;
-Added [file:///tmp/hoodie-hadoop-mr-bundle-0.4.3.jar] to class path
-Added resources: [file:///tmp/hoodie-hadoop-mr-bundle-0.4.3.jar]
-```
-
-Then, you need to create a __ReadOptimized__ Hive table as below (only type supported as of now)and register the sample partitions
-
-```
-drop table hoodie_test;
-CREATE EXTERNAL TABLE hoodie_test(`_row_key`  string,
-`_hoodie_commit_time` string,
-`_hoodie_commit_seqno` string,
- rider string,
- driver string,
- begin_lat double,
- begin_lon double,
- end_lat double,
- end_lon double,
- fare double)
-PARTITIONED BY (`datestr` string)
-ROW FORMAT SERDE
-   'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
-STORED AS INPUTFORMAT
-   'com.uber.hoodie.hadoop.HoodieInputFormat'
-OUTPUTFORMAT
-   'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
-LOCATION
-   'hdfs:///tmp/hoodie/sample-table';
-
-ALTER TABLE `hoodie_test` ADD IF NOT EXISTS PARTITION (datestr='2016-03-15') LOCATION 'hdfs:///tmp/hoodie/sample-table/2016/03/15';
-ALTER TABLE `hoodie_test` ADD IF NOT EXISTS PARTITION (datestr='2015-03-16') LOCATION 'hdfs:///tmp/hoodie/sample-table/2015/03/16';
-ALTER TABLE `hoodie_test` ADD IF NOT EXISTS PARTITION (datestr='2015-03-17') LOCATION 'hdfs:///tmp/hoodie/sample-table/2015/03/17';
-
-set mapreduce.framework.name=yarn;
-```
-
-And you can generate a __Realtime__ Hive table, as below
-
-```
-DROP TABLE hoodie_rt;
-CREATE EXTERNAL TABLE hoodie_rt(
-`_hoodie_commit_time` string,
-`_hoodie_commit_seqno` string,
-`_hoodie_record_key` string,
-`_hoodie_partition_path` string,
-`_hoodie_file_name` string,
- timestamp double,
- `_row_key` string,
- rider string,
- driver string,
- begin_lat double,
- begin_lon double,
- end_lat double,
- end_lon double,
- fare double)
-PARTITIONED BY (`datestr` string)
-ROW FORMAT SERDE
-   'com.uber.hoodie.hadoop.realtime.HoodieParquetSerde'
-STORED AS INPUTFORMAT
-   'com.uber.hoodie.hadoop.realtime.HoodieRealtimeInputFormat'
-OUTPUTFORMAT
-   'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
-LOCATION
-   'file:///tmp/hoodie/sample-table';
-
-ALTER TABLE `hoodie_rt` ADD IF NOT EXISTS PARTITION (datestr='2016-03-15') LOCATION 'file:///tmp/hoodie/sample-table/2016/03/15';
-ALTER TABLE `hoodie_rt` ADD IF NOT EXISTS PARTITION (datestr='2015-03-16') LOCATION 'file:///tmp/hoodie/sample-table/2015/03/16';
-ALTER TABLE `hoodie_rt` ADD IF NOT EXISTS PARTITION (datestr='2015-03-17') LOCATION 'file:///tmp/hoodie/sample-table/2015/03/17';
-
-```
-
-
-
-### Using different query engines
-
-Now, we can proceed to query the dataset, as we would normally do across all the three query engines supported.
-
-#### HiveQL
-
-Let's first perform a query on the latest committed snapshot of the table
-
-```
-hive> select count(*) from hoodie_test;
-...
-OK
-100
-Time taken: 18.05 seconds, Fetched: 1 row(s)
-hive>
-```
-
-#### SparkSQL
-
-Spark is super easy, once you get Hive working as above. Just spin up a Spark Shell as below
-
-```
-$ cd $SPARK_INSTALL
-$ spark-shell --jars $HUDI_SRC/packaging/hoodie-spark-bundle/target/hoodie-spark-bundle-0.4.3-SNAPSHOT.jar --driver-class-path $HADOOP_CONF_DIR  --conf spark.sql.hive.convertMetastoreParquet=false --packages com.databricks:spark-avro_2.11:4.0.0
-
-scala> val sqlContext = new org.apache.spark.sql.SQLContext(sc)
-scala> sqlContext.sql("show tables").show(10000)
-scala> sqlContext.sql("describe hoodie_test").show(10000)
-scala> sqlContext.sql("describe hoodie_rt").show(10000)
-scala> sqlContext.sql("select count(*) from hoodie_test").show(10000)
-```
-
-You can also use the sample queries in __hoodie-utilities/src/test/java/HoodieSparkSQLExample.java__ for running on `hoodie_rt`
-
-#### Presto
-
-Checkout the 'master' branch on OSS Presto, build it, and place your installation somewhere.
-
-* Copy the hudi/packaging/hoodie-presto-bundle/target/hoodie-presto-bundle-*.jar into $PRESTO_INSTALL/plugin/hive-hadoop2/
-* Startup your server and you should be able to query the same Hive table via Presto
-
-```
-show columns from hive.default.hoodie_test;
-select count(*) from hive.default.hoodie_test
-```
-
-
-
-## Incremental Queries of a Hoodie dataset
-
-Let's now perform a query, to obtain the __ONLY__ changed rows since a commit in the past.
-
-```
-hive> set hoodie.hoodie_test.consume.mode=INCREMENTAL;
-hive> set hoodie.hoodie_test.consume.start.timestamp=001;
-hive> set hoodie.hoodie_test.consume.max.commits=10;
-hive> select `_hoodie_commit_time`, rider, driver from hoodie_test where `_hoodie_commit_time` > '001' limit 10;
-OK
-All commits :[001, 002]
-002	rider-001	driver-001
-002	rider-001	driver-001
-002	rider-002	driver-002
-002	rider-001	driver-001
-002	rider-001	driver-001
-002	rider-002	driver-002
-002	rider-001	driver-001
-002	rider-002	driver-002
-002	rider-002	driver-002
-002	rider-001	driver-001
-Time taken: 0.056 seconds, Fetched: 10 row(s)
-hive>
-hive>
-```
-
-
-{% include note.html content="This is only supported for Read-optimized tables for now." %}
-
-
-## A Demo using docker containers
-
-Lets use a real world example to see how hudi works end to end. For this purpose, a self contained
-data infrastructure is brought up in a local docker cluster within your computer. 
-
-The steps assume you are using Mac laptop
-
-### Prerequisites
-
-  * Docker Setup :  For Mac, Please follow the steps as defined in [https://docs.docker.com/v17.12/docker-for-mac/install/]. For running Spark-SQL queries, please ensure atleast 6 GB and 4 CPUs are allocated to Docker (See Docker -> Preferences -> Advanced). Otherwise, spark-SQL queries could be killed because of memory issues.
-  * kafkacat : A command-line utility to publish/consume from kafka topics. Use `brew install kafkacat` to install kafkacat
-  * /etc/hosts : The demo references many services running in container by the hostname. Add the following settings to /etc/hosts
-  
-  ```
-   127.0.0.1 adhoc-1
-   127.0.0.1 adhoc-2
-   127.0.0.1 namenode
-   127.0.0.1 datanode1
-   127.0.0.1 hiveserver
-   127.0.0.1 hivemetastore
-   127.0.0.1 kafkabroker
-   127.0.0.1 sparkmaster
-   127.0.0.1 zookeeper
-  ```
-
-### Setting up Docker Cluster
-
-
-#### Build Hoodie
-
-The first step is to build hoodie
-```
-cd <HUDI_WORKSPACE>
-mvn package -DskipTests
-```
-
-#### Bringing up Demo Cluster
-
-The next step is to run the docker compose script and setup configs for bringing up the cluster.
-This should pull the docker images from docker hub and setup docker cluster.
-
-```
-cd docker
-./setup_demo.sh
-....
-....
-....
-Stopping spark-worker-1            ... done
-Stopping hiveserver                ... done
-Stopping hivemetastore             ... done
-Stopping historyserver             ... done
-.......
-......
-Creating network "hudi_demo" with the default driver
-Creating hive-metastore-postgresql ... done
-Creating namenode                  ... done
-Creating zookeeper                 ... done
-Creating kafkabroker               ... done
-Creating hivemetastore             ... done
-Creating historyserver             ... done
-Creating hiveserver                ... done
-Creating datanode1                 ... done
-Creating sparkmaster               ... done
-Creating adhoc-1                   ... done
-Creating adhoc-2                   ... done
-Creating spark-worker-1            ... done
-Copying spark default config and setting up configs
-Copying spark default config and setting up configs
-Copying spark default config and setting up configs
-varadarb-C02SG7Q3G8WP:docker varadarb$ docker ps
-```
-
-At this point, the docker cluster will be up and running. The demo cluster brings up the following services
-
-   * HDFS Services (NameNode, DataNode)
-   * Spark Master and Worker
-   * Hive Services (Metastore, HiveServer2 along with PostgresDB)
-   * Kafka Broker and a Zookeeper Node (Kakfa will be used as upstream source for the demo) 
-   * Adhoc containers to run Hudi/Hive CLI commands
-
-### Demo
-
-Stock Tracker data will be used to showcase both different Hudi Views and the effects of Compaction. 
-
-Take a look at the directory `docker/demo/data`. There are 2 batches of stock data - each at 1 minute granularity. 
-The first batch contains stocker tracker data for some stock symbols during the first hour of trading window 
-(9:30 a.m to 10:30 a.m). The second batch contains tracker data for next 30 mins (10:30 - 11 a.m). Hudi will
-be used to ingest these batches to a dataset which will contain the latest stock tracker data at hour level granularity.
-The batches are windowed intentionally so that the second batch contains updates to some of the rows in the first batch.
-
-#### Step 1 : Publish the first batch to Kafka
-
-Upload the first batch to Kafka topic 'stock ticks'
-
-```
-cat docker/demo/data/batch_1.json | kafkacat -b kafkabroker -t stock_ticks -P 
-
-To check if the new topic shows up, use
-kafkacat -b kafkabroker -L -J | jq .
-{
-  "originating_broker": {
-    "id": 1001,
-    "name": "kafkabroker:9092/1001"
-  },
-  "query": {
-    "topic": "*"
-  },
-  "brokers": [
-    {
-      "id": 1001,
-      "name": "kafkabroker:9092"
-    }
-  ],
-  "topics": [
-    {
-      "topic": "stock_ticks",
-      "partitions": [
-        {
-          "partition": 0,
-          "leader": 1001,
-          "replicas": [
-            {
-              "id": 1001
-            }
-          ],
-          "isrs": [
-            {
-              "id": 1001
-            }
-          ]
-        }
-      ]
-    }
-  ]
-}
-
-```
-
-#### Step 2: Incrementally ingest data from Kafka topic
-
-Hudi comes with a tool named DeltaStreamer. This tool can connect to variety of data sources (including Kafka) to
-pull changes and apply to Hudi dataset using upsert/insert primitives. Here, we will use the tool to download
-json data from kafka topic and ingest to both COW and MOR tables we initialized in the previous step. This tool 
-automatically initializes the datasets in the file-system if they do not exist yet.
-
-```
-docker exec -it adhoc-2 /bin/bash
-
-# Run the following spark-submit command to execute the delta-streamer and ingest to stock_ticks_cow dataset in HDFS
-spark-submit --class com.uber.hoodie.utilities.deltastreamer.HoodieDeltaStreamer $HUDI_UTILITIES_BUNDLE --storage-type COPY_ON_WRITE --source-class com.uber.hoodie.utilities.sources.JsonKafkaSource --source-ordering-field ts  --target-base-path /user/hive/warehouse/stock_ticks_cow --target-table stock_ticks_cow --schemaprovider-class com.uber.hoodie.utilities.schema.FilebasedSchemaProvider --props /var/demo/config/kafka-source.properties
-....
-....
-2018-09-24 22:20:00 INFO  OutputCommitCoordinator$OutputCommitCoordinatorEndpoint:54 - OutputCommitCoordinator stopped!
-2018-09-24 22:20:00 INFO  SparkContext:54 - Successfully stopped SparkContext
-
-# Run the following spark-submit command to execute the delta-streamer and ingest to stock_ticks_mor dataset in HDFS
-spark-submit --class com.uber.hoodie.utilities.deltastreamer.HoodieDeltaStreamer $HUDI_UTILITIES_BUNDLE --storage-type MERGE_ON_READ --source-class com.uber.hoodie.utilities.sources.JsonKafkaSource --source-ordering-field ts  --target-base-path /user/hive/warehouse/stock_ticks_mor --target-table stock_ticks_mor --schemaprovider-class com.uber.hoodie.utilities.schema.FilebasedSchemaProvider --props /var/demo/config/kafka-source.properties
-....
-2018-09-24 22:22:01 INFO  OutputCommitCoordinator$OutputCommitCoordinatorEndpoint:54 - OutputCommitCoordinator stopped!
-2018-09-24 22:22:01 INFO  SparkContext:54 - Successfully stopped SparkContext
-....
-
-# As part of the setup (Look at setup_demo.sh), the configs needed for DeltaStreamer is uploaded to HDFS. The configs
-# contain mostly Kafa connectivity settings, the avro-schema to be used for ingesting along with key and partitioning fields.
-
-exit
-```
-
-You can use HDFS web-browser to look at the datasets 
-`http://namenode:50070/explorer.html#/user/hive/warehouse/stock_ticks_cow`. 
-
-You can explore the new partition folder created in the dataset along with a "deltacommit"
-file under .hoodie which signals a successful commit.
-
-There will be a similar setup when you browse the MOR dataset
-`http://namenode:50070/explorer.html#/user/hive/warehouse/stock_ticks_mor`
-
-
-#### Step 3: Sync with Hive
-
-At this step, the datasets are available in HDFS. We need to sync with Hive to create new Hive tables and add partitions
-inorder to run Hive queries against those datasets.
-
-```
-docker exec -it adhoc-2 /bin/bash
-
-# THis command takes in HIveServer URL and COW Hudi Dataset location in HDFS and sync the HDFS state to Hive
-/var/hoodie/ws/hoodie-hive/run_sync_tool.sh  --jdbc-url jdbc:hive2://hiveserver:10000 --user hive --pass hive --partitioned-by dt --base-path /user/hive/warehouse/stock_ticks_cow --database default --table stock_ticks_cow
-.....
-2018-09-24 22:22:45,568 INFO  [main] hive.HiveSyncTool (HiveSyncTool.java:syncHoodieTable(112)) - Sync complete for stock_ticks_cow
-.....
-
-# Now run hive-sync for the second data-set in HDFS using Merge-On-Read (MOR storage)
-/var/hoodie/ws/hoodie-hive/run_sync_tool.sh  --jdbc-url jdbc:hive2://hiveserver:10000 --user hive --pass hive --partitioned-by dt --base-path /user/hive/warehouse/stock_ticks_mor --database default --table stock_ticks_mor
-...
-2018-09-24 22:23:09,171 INFO  [main] hive.HiveSyncTool (HiveSyncTool.java:syncHoodieTable(112)) - Sync complete for stock_ticks_mor
-...
-2018-09-24 22:23:09,559 INFO  [main] hive.HiveSyncTool (HiveSyncTool.java:syncHoodieTable(112)) - Sync complete for stock_ticks_mor_rt
-....
-exit
-```
-After executing the above command, you will notice 
-
-1. A hive table named `stock_ticks_cow` created which provides Read-Optimized view for the Copy On Write dataset.
-2. Two new tables `stock_ticks_mor` and `stock_ticks_mor_rt` created for the Merge On Read dataset. The former
-provides the ReadOptimized view for the Hudi dataset and the later provides the realtime-view for the dataset.
-
-
-#### Step 4 (a): Run Hive Queries
-
-Run a hive query to find the latest timestamp ingested for stock symbol 'GOOG'. You will notice that both read-optimized
-(for both COW and MOR dataset)and realtime views (for MOR dataset)give the same value "10:29 a.m" as Hudi create a 
-parquet file for the first batch of data.
-
-```
-docker exec -it adhoc-2 /bin/bash
-beeline -u jdbc:hive2://hiveserver:10000 --hiveconf hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat --hiveconf hive.stats.autogather=false
-# List Tables
-0: jdbc:hive2://hiveserver:10000> show tables;
-+---------------------+--+
-|      tab_name       |
-+---------------------+--+
-| stock_ticks_cow     |
-| stock_ticks_mor     |
-| stock_ticks_mor_rt  |
-+---------------------+--+
-2 rows selected (0.801 seconds)
-0: jdbc:hive2://hiveserver:10000>
-
-
-# Look at partitions that were added
-0: jdbc:hive2://hiveserver:10000> show partitions stock_ticks_mor_rt;
-+----------------+--+
-|   partition    |
-+----------------+--+
-| dt=2018-08-31  |
-+----------------+--+
-1 row selected (0.24 seconds)
-
-
-# COPY-ON-WRITE Queries:
-=========================
-
-
-0: jdbc:hive2://hiveserver:10000> select symbol, max(ts) from stock_ticks_cow group by symbol HAVING symbol = 'GOOG';
-+---------+----------------------+--+
-| symbol  |         _c1          |
-+---------+----------------------+--+
-| GOOG    | 2018-08-31 10:29:00  |
-+---------+----------------------+--+
-
-Now, run a projection query:
-
-0: jdbc:hive2://hiveserver:10000> select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_cow where  symbol = 'GOOG';
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924221953       | GOOG    | 2018-08-31 09:59:00  | 6330    | 1230.5     | 1230.02   |
-| 20180924221953       | GOOG    | 2018-08-31 10:29:00  | 3391    | 1230.1899  | 1230.085  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-
-
-# Merge-On-Read Queries:
-==========================
-
-Lets run similar queries against M-O-R dataset. Lets look at both 
-ReadOptimized and Realtime views supported by M-O-R dataset
-
-# Run against ReadOptimized View. Notice that the latest timestamp is 10:29
-0: jdbc:hive2://hiveserver:10000> select symbol, max(ts) from stock_ticks_mor group by symbol HAVING symbol = 'GOOG';
-WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.
-+---------+----------------------+--+
-| symbol  |         _c1          |
-+---------+----------------------+--+
-| GOOG    | 2018-08-31 10:29:00  |
-+---------+----------------------+--+
-1 row selected (6.326 seconds)
-
-
-# Run against Realtime View. Notice that the latest timestamp is again 10:29
-
-0: jdbc:hive2://hiveserver:10000> select symbol, max(ts) from stock_ticks_mor_rt group by symbol HAVING symbol = 'GOOG';
-WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.
-+---------+----------------------+--+
-| symbol  |         _c1          |
-+---------+----------------------+--+
-| GOOG    | 2018-08-31 10:29:00  |
-+---------+----------------------+--+
-1 row selected (1.606 seconds)
-
-
-# Run projection query against Read Optimized and Realtime tables
-
-0: jdbc:hive2://hiveserver:10000> select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor where  symbol = 'GOOG';
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924222155       | GOOG    | 2018-08-31 09:59:00  | 6330    | 1230.5     | 1230.02   |
-| 20180924222155       | GOOG    | 2018-08-31 10:29:00  | 3391    | 1230.1899  | 1230.085  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-
-0: jdbc:hive2://hiveserver:10000> select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor_rt where  symbol = 'GOOG';
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924222155       | GOOG    | 2018-08-31 09:59:00  | 6330    | 1230.5     | 1230.02   |
-| 20180924222155       | GOOG    | 2018-08-31 10:29:00  | 3391    | 1230.1899  | 1230.085  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-
-exit
-exit
-```
-
-#### Step 4 (b): Run Spark-SQL Queries
-Hudi support Spark as query processor just like Hive. Here are the same hive queries
-running in spark-sql
-
-```
-docker exec -it adhoc-1 /bin/bash
-$SPARK_INSTALL/bin/spark-shell --jars $HUDI_SPARK_BUNDLE --master local[2] --driver-class-path $HADOOP_CONF_DIR --conf spark.sql.hive.convertMetastoreParquet=false --deploy-mode client  --driver-memory 1G --executor-memory 3G --num-executors 1  --packages com.databricks:spark-avro_2.11:4.0.0
-...
-
-Welcome to
-      ____              __
-     / __/__  ___ _____/ /__
-    _\ \/ _ \/ _ `/ __/  '_/
-   /___/ .__/\_,_/_/ /_/\_\   version 2.3.1
-      /_/
-
-Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_181)
-Type in expressions to have them evaluated.
-Type :help for more information.
-
-scala>
-scala> spark.sql("show tables").show(100, false)
-+--------+------------------+-----------+
-|database|tableName         |isTemporary|
-+--------+------------------+-----------+
-|default |stock_ticks_cow   |false      |
-|default |stock_ticks_mor   |false      |
-|default |stock_ticks_mor_rt|false      |
-+--------+------------------+-----------+
-
-# Copy-On-Write Table
-
-## Run max timestamp query against COW table
-
-scala> spark.sql("select symbol, max(ts) from stock_ticks_cow group by symbol HAVING symbol = 'GOOG'").show(100, false)
-[Stage 0:>                                                          (0 + 1) / 1]SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
-SLF4J: Defaulting to no-operation (NOP) logger implementation
-SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.
-+------+-------------------+
-|symbol|max(ts)            |
-+------+-------------------+
-|GOOG  |2018-08-31 10:29:00|
-+------+-------------------+
-
-## Projection Query
-
-scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_cow where  symbol = 'GOOG'").show(100, false)
-+-------------------+------+-------------------+------+---------+--------+
-|_hoodie_commit_time|symbol|ts                 |volume|open     |close   |
-+-------------------+------+-------------------+------+---------+--------+
-|20180924221953     |GOOG  |2018-08-31 09:59:00|6330  |1230.5   |1230.02 |
-|20180924221953     |GOOG  |2018-08-31 10:29:00|3391  |1230.1899|1230.085|
-+-------------------+------+-------------------+------+---------+--------+
-
-# Merge-On-Read Queries:
-==========================
-
-Lets run similar queries against M-O-R dataset. Lets look at both 
-ReadOptimized and Realtime views supported by M-O-R dataset
-
-# Run against ReadOptimized View. Notice that the latest timestamp is 10:29
-scala> spark.sql("select symbol, max(ts) from stock_ticks_mor group by symbol HAVING symbol = 'GOOG'").show(100, false)
-+------+-------------------+
-|symbol|max(ts)            |
-+------+-------------------+
-|GOOG  |2018-08-31 10:29:00|
-+------+-------------------+
-
-
-# Run against Realtime View. Notice that the latest timestamp is again 10:29
-
-scala> spark.sql("select symbol, max(ts) from stock_ticks_mor_rt group by symbol HAVING symbol = 'GOOG'").show(100, false)
-+------+-------------------+
-|symbol|max(ts)            |
-+------+-------------------+
-|GOOG  |2018-08-31 10:29:00|
-+------+-------------------+
-
-# Run projection query against Read Optimized and Realtime tables
-
-scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor where  symbol = 'GOOG'").show(100, false)
-+-------------------+------+-------------------+------+---------+--------+
-|_hoodie_commit_time|symbol|ts                 |volume|open     |close   |
-+-------------------+------+-------------------+------+---------+--------+
-|20180924222155     |GOOG  |2018-08-31 09:59:00|6330  |1230.5   |1230.02 |
-|20180924222155     |GOOG  |2018-08-31 10:29:00|3391  |1230.1899|1230.085|
-+-------------------+------+-------------------+------+---------+--------+
-
-scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor_rt where  symbol = 'GOOG'").show(100, false)
-+-------------------+------+-------------------+------+---------+--------+
-|_hoodie_commit_time|symbol|ts                 |volume|open     |close   |
-+-------------------+------+-------------------+------+---------+--------+
-|20180924222155     |GOOG  |2018-08-31 09:59:00|6330  |1230.5   |1230.02 |
-|20180924222155     |GOOG  |2018-08-31 10:29:00|3391  |1230.1899|1230.085|
-+-------------------+------+-------------------+------+---------+--------+
-
-```
-
-
-#### Step 5: Upload second batch to Kafka and run DeltaStreamer to ingest
-
-Upload the second batch of data and ingest this batch using delta-streamer. As this batch does not bring in any new
-partitions, there is no need to run hive-sync
-
-```
-cat docker/demo/data/batch_2.json | kafkacat -b kafkabroker -t stock_ticks -P 
-
-# Within Docker container, run the ingestion command
-docker exec -it adhoc-2 /bin/bash
-
-# Run the following spark-submit command to execute the delta-streamer and ingest to stock_ticks_cow dataset in HDFS
-spark-submit --class com.uber.hoodie.utilities.deltastreamer.HoodieDeltaStreamer $HUDI_UTILITIES_BUNDLE --storage-type COPY_ON_WRITE --source-class com.uber.hoodie.utilities.sources.JsonKafkaSource --source-ordering-field ts  --target-base-path /user/hive/warehouse/stock_ticks_cow --target-table stock_ticks_cow --schemaprovider-class com.uber.hoodie.utilities.schema.FilebasedSchemaProvider --props /var/demo/config/kafka-source.properties 
-
-# Run the following spark-submit command to execute the delta-streamer and ingest to stock_ticks_mor dataset in HDFS
-spark-submit --class com.uber.hoodie.utilities.deltastreamer.HoodieDeltaStreamer $HUDI_UTILITIES_BUNDLE --storage-type MERGE_ON_READ --source-class com.uber.hoodie.utilities.sources.JsonKafkaSource --source-ordering-field ts  --target-base-path /user/hive/warehouse/stock_ticks_mor --target-table stock_ticks_mor --schemaprovider-class com.uber.hoodie.utilities.schema.FilebasedSchemaProvider --props /var/demo/config/kafka-source.properties
-
-exit
-```
-With Copy-On-Write table, the second ingestion by DeltaStreamer resulted in a new version of Parquet file getting created.
-See `http://namenode:50070/explorer.html#/user/hive/warehouse/stock_ticks_cow/2018/08/31`
-
-With Merge-On-Read table, the second ingestion merely appended the batch to an unmerged delta (log) file. 
-Take a look at the HDFS filesystem to get an idea: `http://namenode:50070/explorer.html#/user/hive/warehouse/stock_ticks_mor/2018/08/31`
-
-#### Step 6(a): Run Hive Queries
-
-With Copy-On-Write table, the read-optimized view immediately sees the changes as part of second batch once the batch 
-got committed as each ingestion creates newer versions of parquet files. 
-
-With Merge-On-Read table, the second ingestion merely appended the batch to an unmerged delta (log) file. 
-This is the time, when ReadOptimized and Realtime views will provide different results. ReadOptimized view will still
-return "10:29 am" as it will only read from the Parquet file. Realtime View will do on-the-fly merge and return
-latest committed data which is "10:59 a.m".
-
-```
-docker exec -it adhoc-2 /bin/bash
-beeline -u jdbc:hive2://hiveserver:10000 --hiveconf hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat --hiveconf hive.stats.autogather=false
-
-# Copy On Write Table:
-
-0: jdbc:hive2://hiveserver:10000> select symbol, max(ts) from stock_ticks_cow group by symbol HAVING symbol = 'GOOG';
-WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.
-+---------+----------------------+--+
-| symbol  |         _c1          |
-+---------+----------------------+--+
-| GOOG    | 2018-08-31 10:59:00  |
-+---------+----------------------+--+
-1 row selected (1.932 seconds)
-
-0: jdbc:hive2://hiveserver:10000> select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_cow where  symbol = 'GOOG';
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924221953       | GOOG    | 2018-08-31 09:59:00  | 6330    | 1230.5     | 1230.02   |
-| 20180924224524       | GOOG    | 2018-08-31 10:59:00  | 9021    | 1227.1993  | 1227.215  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-
-As you can notice, the above queries now reflect the changes that came as part of ingesting second batch.
-
-
-# Merge On Read Table: 
-
-# Read Optimized View
-0: jdbc:hive2://hiveserver:10000> select symbol, max(ts) from stock_ticks_mor group by symbol HAVING symbol = 'GOOG';
-WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.
-+---------+----------------------+--+
-| symbol  |         _c1          |
-+---------+----------------------+--+
-| GOOG    | 2018-08-31 10:29:00  |
-+---------+----------------------+--+
-1 row selected (1.6 seconds)
-
-0: jdbc:hive2://hiveserver:10000> select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor where  symbol = 'GOOG';
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924222155       | GOOG    | 2018-08-31 09:59:00  | 6330    | 1230.5     | 1230.02   |
-| 20180924222155       | GOOG    | 2018-08-31 10:29:00  | 3391    | 1230.1899  | 1230.085  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-
-# Realtime View
-0: jdbc:hive2://hiveserver:10000> select symbol, max(ts) from stock_ticks_mor_rt group by symbol HAVING symbol = 'GOOG';
-WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.
-+---------+----------------------+--+
-| symbol  |         _c1          |
-+---------+----------------------+--+
-| GOOG    | 2018-08-31 10:59:00  |
-+---------+----------------------+--+
-
-0: jdbc:hive2://hiveserver:10000> select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor_rt where  symbol = 'GOOG';
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924222155       | GOOG    | 2018-08-31 09:59:00  | 6330    | 1230.5     | 1230.02   |
-| 20180924224537       | GOOG    | 2018-08-31 10:59:00  | 9021    | 1227.1993  | 1227.215  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-
-exit
-exit
-```
-
-#### Step 6(b): Run Spark SQL Queries
-
-Running the same queries in Spark-SQL:
-
-```
-docker exec -it adhoc-1 /bin/bash
-bash-4.4# $SPARK_INSTALL/bin/spark-shell --jars $HUDI_SPARK_BUNDLE --driver-class-path $HADOOP_CONF_DIR --conf spark.sql.hive.convertMetastoreParquet=false --deploy-mode client  --driver-memory 1G --master local[2] --executor-memory 3G --num-executors 1  --packages com.databricks:spark-avro_2.11:4.0.0
-
-# Copy On Write Table:
-
-scala> spark.sql("select symbol, max(ts) from stock_ticks_cow group by symbol HAVING symbol = 'GOOG'").show(100, false)
-+------+-------------------+
-|symbol|max(ts)            |
-+------+-------------------+
-|GOOG  |2018-08-31 10:59:00|
-+------+-------------------+
-
-scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_cow where  symbol = 'GOOG'").show(100, false)
-
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924221953       | GOOG    | 2018-08-31 09:59:00  | 6330    | 1230.5     | 1230.02   |
-| 20180924224524       | GOOG    | 2018-08-31 10:59:00  | 9021    | 1227.1993  | 1227.215  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-
-As you can notice, the above queries now reflect the changes that came as part of ingesting second batch.
-
-
-# Merge On Read Table: 
-
-# Read Optimized View
-scala> spark.sql("select symbol, max(ts) from stock_ticks_mor group by symbol HAVING symbol = 'GOOG'").show(100, false)
-+---------+----------------------+--+
-| symbol  |         _c1          |
-+---------+----------------------+--+
-| GOOG    | 2018-08-31 10:29:00  |
-+---------+----------------------+--+
-1 row selected (1.6 seconds)
-
-scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor where  symbol = 'GOOG'").show(100, false)
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924222155       | GOOG    | 2018-08-31 09:59:00  | 6330    | 1230.5     | 1230.02   |
-| 20180924222155       | GOOG    | 2018-08-31 10:29:00  | 3391    | 1230.1899  | 1230.085  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-
-# Realtime View
-scala> spark.sql("select symbol, max(ts) from stock_ticks_mor_rt group by symbol HAVING symbol = 'GOOG'").show(100, false)
-+---------+----------------------+--+
-| symbol  |         _c1          |
-+---------+----------------------+--+
-| GOOG    | 2018-08-31 10:59:00  |
-+---------+----------------------+--+
-
-scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor_rt where  symbol = 'GOOG'").show(100, false)
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924222155       | GOOG    | 2018-08-31 09:59:00  | 6330    | 1230.5     | 1230.02   |
-| 20180924224537       | GOOG    | 2018-08-31 10:59:00  | 9021    | 1227.1993  | 1227.215  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-
-exit
-exit
-```
-
-#### Step 7 : Incremental Query for COPY-ON-WRITE Table
-
-With 2 batches of data ingested, lets showcase the support for incremental queries in Hudi Copy-On-Write datasets
-
-Lets take the same projection query example
-```
-docker exec -it adhoc-2 /bin/bash
-beeline -u jdbc:hive2://hiveserver:10000 --hiveconf hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat --hiveconf hive.stats.autogather=false
-
-0: jdbc:hive2://hiveserver:10000> select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_cow where  symbol = 'GOOG';
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924064621       | GOOG    | 2018-08-31 09:59:00  | 6330    | 1230.5     | 1230.02   |
-| 20180924065039       | GOOG    | 2018-08-31 10:59:00  | 9021    | 1227.1993  | 1227.215  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-
-```
-
-As you notice from the above queries, there are 2 commits - 20180924064621 and 20180924065039 in timeline order.
-When you follow the steps, you will be getting different timestamps for commits. Substitute them
-in place of the above timestamps.
-
-To show the effects of incremental-query, let us assume that a reader has already seen the changes as part of
-ingesting first batch. Now, for the reader to see effect of the second batch, he/she has to keep the start timestamp to
-the commit time of the first batch (20180924064621) and run incremental query
-
-`Hudi incremental mode` provides efficient scanning for incremental queries by filtering out files that do not have any 
-candidate rows using hudi-managed metadata. 
-
-```
-docker exec -it adhoc-2 /bin/bash
-beeline -u jdbc:hive2://hiveserver:10000 --hiveconf hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat --hiveconf hive.stats.autogather=false
-0: jdbc:hive2://hiveserver:10000> set hoodie.stock_ticks_cow.consume.mode=INCREMENTAL;
-No rows affected (0.009 seconds)
-0: jdbc:hive2://hiveserver:10000>  set hoodie.stock_ticks_cow.consume.max.commits=3;
-No rows affected (0.009 seconds)
-0: jdbc:hive2://hiveserver:10000> set hoodie.stock_ticks_cow.consume.start.timestamp=20180924064621;
-
-# With the above setting, file-ids that do not have any updates from the commit 20180924065039 is filtered out without scanning.
-# Here is the incremental query :
-
-0: jdbc:hive2://hiveserver:10000>
-0: jdbc:hive2://hiveserver:10000> select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_cow where  symbol = 'GOOG' and `_hoodie_commit_time` > '20180924064621';
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924065039       | GOOG    | 2018-08-31 10:59:00  | 9021    | 1227.1993  | 1227.215  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-1 row selected (0.83 seconds)
-0: jdbc:hive2://hiveserver:10000>
-```
-
-##### Incremental Query with Spark SQL:
-```
-docker exec -it adhoc-1 /bin/bash
-bash-4.4# $SPARK_INSTALL/bin/spark-shell --jars $HUDI_SPARK_BUNDLE --driver-class-path $HADOOP_CONF_DIR --conf spark.sql.hive.convertMetastoreParquet=false --deploy-mode client  --driver-memory 1G --master local[2] --executor-memory 3G --num-executors 1  --packages com.databricks:spark-avro_2.11:4.0.0
-Welcome to
-      ____              __
-     / __/__  ___ _____/ /__
-    _\ \/ _ \/ _ `/ __/  '_/
-   /___/ .__/\_,_/_/ /_/\_\   version 2.3.1
-      /_/
-
-Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_181)
-Type in expressions to have them evaluated.
-Type :help for more information.
-
-scala> import com.uber.hoodie.DataSourceReadOptions
-import com.uber.hoodie.DataSourceReadOptions
-
-# In the below query, 20180925045257 is the first commit's timestamp
-scala> val hoodieIncViewDF =  spark.read.format("com.uber.hoodie").option(DataSourceReadOptions.VIEW_TYPE_OPT_KEY, DataSourceReadOptions.VIEW_TYPE_INCREMENTAL_OPT_VAL).option(DataSourceReadOptions.BEGIN_INSTANTTIME_OPT_KEY, "20180924064621").load("/user/hive/warehouse/stock_ticks_cow")
-SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
-SLF4J: Defaulting to no-operation (NOP) logger implementation
-SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.
-hoodieIncViewDF: org.apache.spark.sql.DataFrame = [_hoodie_commit_time: string, _hoodie_commit_seqno: string ... 15 more fields]
-
-scala> hoodieIncViewDF.registerTempTable("stock_ticks_cow_incr_tmp1")
-warning: there was one deprecation warning; re-run with -deprecation for details
-
-scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_cow_incr_tmp1 where  symbol = 'GOOG'").show(100, false);
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924065039       | GOOG    | 2018-08-31 10:59:00  | 9021    | 1227.1993  | 1227.215  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-
-```
-
-
-#### Step 8: Schedule and Run Compaction for Merge-On-Read dataset
-
-Lets schedule and run a compaction to create a new version of columnar  file so that read-optimized readers will see fresher data.
-Again, You can use Hudi CLI to manually schedule and run compaction
-
-```
-docker exec -it adhoc-1 /bin/bash
-^[[Aroot@adhoc-1:/opt#   /var/hoodie/ws/hoodie-cli/hoodie-cli.sh
-============================================
-*                                          *
-*     _    _                 _ _           *
-*    | |  | |               | (_)          *
-*    | |__| | ___   ___   __| |_  ___      *
-*    |  __  |/ _ \ / _ \ / _` | |/ _ \     *
-*    | |  | | (_) | (_) | (_| | |  __/     *
-*    |_|  |_|\___/ \___/ \__,_|_|\___|     *
-*                                          *
-============================================
-
-Welcome to Hoodie CLI. Please type help if you are looking for help.
-hoodie->connect --path /user/hive/warehouse/stock_ticks_mor
-18/09/24 06:59:34 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
-18/09/24 06:59:35 INFO table.HoodieTableMetaClient: Loading HoodieTableMetaClient from /user/hive/warehouse/stock_ticks_mor
-18/09/24 06:59:35 INFO util.FSUtils: Hadoop Configuration: fs.defaultFS: [hdfs://namenode:8020], Config:[Configuration: core-default.xml, core-site.xml, mapred-default.xml, mapred-site.xml, yarn-default.xml, yarn-site.xml, hdfs-default.xml, hdfs-site.xml], FileSystem: [DFS[DFSClient[clientName=DFSClient_NONMAPREDUCE_-1261652683_11, ugi=root (auth:SIMPLE)]]]
-18/09/24 06:59:35 INFO table.HoodieTableConfig: Loading dataset properties from /user/hive/warehouse/stock_ticks_mor/.hoodie/hoodie.properties
-18/09/24 06:59:36 INFO table.HoodieTableMetaClient: Finished Loading Table of type MERGE_ON_READ from /user/hive/warehouse/stock_ticks_mor
-Metadata for table stock_ticks_mor loaded
-
-# Ensure no compactions are present
-
-hoodie:stock_ticks_mor->compactions show all
-18/09/24 06:59:54 INFO timeline.HoodieActiveTimeline: Loaded instants [[20180924064636__clean__COMPLETED], [20180924064636__deltacommit__COMPLETED], [20180924065057__clean__COMPLETED], [20180924065057__deltacommit__COMPLETED]]
-    ___________________________________________________________________
-    | Compaction Instant Time| State    | Total FileIds to be Compacted|
-    |==================================================================|
-    
-# Schedule a compaction. This will use Spark Launcher to schedule compaction
-hoodie:stock_ticks_mor->compaction schedule
-....
-Compaction successfully completed for 20180924070031
-
-# Now refresh and check again. You will see that there is a new compaction requested
-
-hoodie:stock_ticks->connect --path /user/hive/warehouse/stock_ticks_mor
-18/09/24 07:01:16 INFO table.HoodieTableMetaClient: Loading HoodieTableMetaClient from /user/hive/warehouse/stock_ticks_mor
-18/09/24 07:01:16 INFO util.FSUtils: Hadoop Configuration: fs.defaultFS: [hdfs://namenode:8020], Config:[Configuration: core-default.xml, core-site.xml, mapred-default.xml, mapred-site.xml, yarn-default.xml, yarn-site.xml, hdfs-default.xml, hdfs-site.xml], FileSystem: [DFS[DFSClient[clientName=DFSClient_NONMAPREDUCE_-1261652683_11, ugi=root (auth:SIMPLE)]]]
-18/09/24 07:01:16 INFO table.HoodieTableConfig: Loading dataset properties from /user/hive/warehouse/stock_ticks_mor/.hoodie/hoodie.properties
-18/09/24 07:01:16 INFO table.HoodieTableMetaClient: Finished Loading Table of type MERGE_ON_READ from /user/hive/warehouse/stock_ticks_mor
-Metadata for table stock_ticks_mor loaded
-
-hoodie:stock_ticks_mor->compactions show all
-18/09/24 06:34:12 INFO timeline.HoodieActiveTimeline: Loaded instants [[20180924041125__clean__COMPLETED], [20180924041125__deltacommit__COMPLETED], [20180924042735__clean__COMPLETED], [20180924042735__deltacommit__COMPLETED], [==>20180924063245__compaction__REQUESTED]]
-    ___________________________________________________________________
-    | Compaction Instant Time| State    | Total FileIds to be Compacted|
-    |==================================================================|
-    | 20180924070031         | REQUESTED| 1                            | 
-
-# Execute the compaction. The compaction instant value passed below must be the one displayed in the above "compactions show all" query
-hoodie:stock_ticks_mor->compaction run --compactionInstant  20180924070031 --parallelism 2 --sparkMemory 1G  --schemaFilePath /var/demo/config/schema.avsc --retry 1  
-....
-Compaction successfully completed for 20180924070031
-
-
-## Now check if compaction is completed
-
-hoodie:stock_ticks_mor->connect --path /user/hive/warehouse/stock_ticks_mor
-18/09/24 07:03:00 INFO table.HoodieTableMetaClient: Loading HoodieTableMetaClient from /user/hive/warehouse/stock_ticks_mor
-18/09/24 07:03:00 INFO util.FSUtils: Hadoop Configuration: fs.defaultFS: [hdfs://namenode:8020], Config:[Configuration: core-default.xml, core-site.xml, mapred-default.xml, mapred-site.xml, yarn-default.xml, yarn-site.xml, hdfs-default.xml, hdfs-site.xml], FileSystem: [DFS[DFSClient[clientName=DFSClient_NONMAPREDUCE_-1261652683_11, ugi=root (auth:SIMPLE)]]]
-18/09/24 07:03:00 INFO table.HoodieTableConfig: Loading dataset properties from /user/hive/warehouse/stock_ticks_mor/.hoodie/hoodie.properties
-18/09/24 07:03:00 INFO table.HoodieTableMetaClient: Finished Loading Table of type MERGE_ON_READ from /user/hive/warehouse/stock_ticks_mor
-Metadata for table stock_ticks_mor loaded
-
-hoodie:stock_ticks->compactions show all
-18/09/24 07:03:15 INFO timeline.HoodieActiveTimeline: Loaded instants [[20180924064636__clean__COMPLETED], [20180924064636__deltacommit__COMPLETED], [20180924065057__clean__COMPLETED], [20180924065057__deltacommit__COMPLETED], [20180924070031__commit__COMPLETED]]
-    ___________________________________________________________________
-    | Compaction Instant Time| State    | Total FileIds to be Compacted|
-    |==================================================================|
-    | 20180924070031         | COMPLETED| 1                            |
-
-``` 
-
-#### Step 9: Run Hive Queries including incremental queries
-
-You will see that both ReadOptimized and Realtime Views will show the latest committed data.
-Lets also run the incremental query for MOR table.
-From looking at the below query output, it will be clear that the fist commit time for the MOR table is 20180924064636
-and the second commit time is 20180924070031
-
-```
-docker exec -it adhoc-2 /bin/bash
-beeline -u jdbc:hive2://hiveserver:10000 --hiveconf hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat --hiveconf hive.stats.autogather=false
-
-# Read Optimized View
-0: jdbc:hive2://hiveserver:10000> select symbol, max(ts) from stock_ticks_mor group by symbol HAVING symbol = 'GOOG';
-WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.
-+---------+----------------------+--+
-| symbol  |         _c1          |
-+---------+----------------------+--+
-| GOOG    | 2018-08-31 10:59:00  |
-+---------+----------------------+--+
-1 row selected (1.6 seconds)
-
-0: jdbc:hive2://hiveserver:10000> select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor where  symbol = 'GOOG';
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924064636       | GOOG    | 2018-08-31 09:59:00  | 6330    | 1230.5     | 1230.02   |
-| 20180924070031       | GOOG    | 2018-08-31 10:59:00  | 9021    | 1227.1993  | 1227.215  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-
-# Realtime View
-0: jdbc:hive2://hiveserver:10000> select symbol, max(ts) from stock_ticks_mor_rt group by symbol HAVING symbol = 'GOOG';
-WARNING: Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive 1.X releases.
-+---------+----------------------+--+
-| symbol  |         _c1          |
-+---------+----------------------+--+
-| GOOG    | 2018-08-31 10:59:00  |
-+---------+----------------------+--+
-
-0: jdbc:hive2://hiveserver:10000> select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor_rt where  symbol = 'GOOG';
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924064636       | GOOG    | 2018-08-31 09:59:00  | 6330    | 1230.5     | 1230.02   |
-| 20180924070031       | GOOG    | 2018-08-31 10:59:00  | 9021    | 1227.1993  | 1227.215  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-
-# Incremental View:
-
-0: jdbc:hive2://hiveserver:10000> set hoodie.stock_ticks_mor.consume.mode=INCREMENTAL;
-No rows affected (0.008 seconds)
-# Max-Commits covers both second batch and compaction commit
-0: jdbc:hive2://hiveserver:10000> set hoodie.stock_ticks_mor.consume.max.commits=3;
-No rows affected (0.007 seconds)
-0: jdbc:hive2://hiveserver:10000> set hoodie.stock_ticks_mor.consume.start.timestamp=20180924064636;
-No rows affected (0.013 seconds)
-# Query:
-0: jdbc:hive2://hiveserver:10000> select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor where  symbol = 'GOOG' and `_hoodie_commit_time` > '20180924064636';
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924070031       | GOOG    | 2018-08-31 10:59:00  | 9021    | 1227.1993  | 1227.215  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-exit
-exit
-```
-
-##### Read Optimized and Realtime Views for MOR with Spark-SQL after compaction
-
-```
-docker exec -it adhoc-1 /bin/bash
-bash-4.4# $SPARK_INSTALL/bin/spark-shell --jars $HUDI_SPARK_BUNDLE --driver-class-path $HADOOP_CONF_DIR --conf spark.sql.hive.convertMetastoreParquet=false --deploy-mode client  --driver-memory 1G --master local[2] --executor-memory 3G --num-executors 1  --packages com.databricks:spark-avro_2.11:4.0.0
-
-# Read Optimized View
-scala> spark.sql("select symbol, max(ts) from stock_ticks_mor group by symbol HAVING symbol = 'GOOG'").show(100, false)
-+---------+----------------------+--+
-| symbol  |         _c1          |
-+---------+----------------------+--+
-| GOOG    | 2018-08-31 10:59:00  |
-+---------+----------------------+--+
-1 row selected (1.6 seconds)
-
-scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor where  symbol = 'GOOG'").show(100, false)
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924064636       | GOOG    | 2018-08-31 09:59:00  | 6330    | 1230.5     | 1230.02   |
-| 20180924070031       | GOOG    | 2018-08-31 10:59:00  | 9021    | 1227.1993  | 1227.215  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-
-# Realtime View
-scala> spark.sql("select symbol, max(ts) from stock_ticks_mor_rt group by symbol HAVING symbol = 'GOOG'").show(100, false)
-+---------+----------------------+--+
-| symbol  |         _c1          |
-+---------+----------------------+--+
-| GOOG    | 2018-08-31 10:59:00  |
-+---------+----------------------+--+
-
-scala> spark.sql("select `_hoodie_commit_time`, symbol, ts, volume, open, close  from stock_ticks_mor_rt where  symbol = 'GOOG'").show(100, false)
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| _hoodie_commit_time  | symbol  |          ts          | volume  |    open    |   close   |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-| 20180924064636       | GOOG    | 2018-08-31 09:59:00  | 6330    | 1230.5     | 1230.02   |
-| 20180924070031       | GOOG    | 2018-08-31 10:59:00  | 9021    | 1227.1993  | 1227.215  |
-+----------------------+---------+----------------------+---------+------------+-----------+--+
-```
-
-
-This brings the demo to an end.
-
-## Testing Hoodie in Local Docker environment
-
-You can bring up a hadoop docker environment containing Hadoop, Hive and Spark services with support for hoodie.
-```
-$ mvn pre-integration-test -DskipTests
-```
-The above command builds docker images for all the services with 
-current hoodie source installed at /var/hoodie/ws and also brings up the services using a compose file. We 
-currently use Hadoop (v2.8.4), Hive (v2.3.3) and Spark (v2.3.1) in docker images. 
-
-To bring down the containers
-```
-$ cd hoodie-integ-test
-$ mvn docker-compose:down
-```
-
-If you want to bring up the docker containers, use
-```
-$ cd hoodie-integ-test
-$  mvn docker-compose:up -DdetachedMode=true
-```
-
-Hoodie is a library that is operated in a broader data analytics/ingestion environment 
-involving Hadoop, Hive and Spark. Interoperability with all these systems is a key objective for us. We are
-actively adding integration-tests under __hoodie-integ-test/src/test/java__ that makes use of this 
-docker environment (See __hoodie-integ-test/src/test/java/com/uber/hoodie/integ/ITTestHoodieSanity.java__ )
-
-
-#### Building Local Docker Containers:
-
-The docker images required for demo and running integration test are already in docker-hub. The docker images
-and compose scripts are carefully implemented so that they serve dual-purpose
-
-1. The docker images have inbuilt hudi jar files with environment variable pointing to those jars (HUDI_HADOOP_BUNDLE, ...)
-2. For running integration-tests, we need the jars generated locally to be used for running services within docker. The
-   docker-compose scripts (see `docker/compose/docker-compose_hadoop284_hive233_spark231.yml`) ensures local jars override
-   inbuilt jars by mounting local HUDI workspace over the docker location
-
-This helps avoid maintaining separate docker images and avoids the costly step of building HUDI docker images locally.
-But if users want to test hudi from locations with lower network bandwidth, they can still build local images 
-run the script 
-`docker/build_local_docker_images.sh` to build local docker images before running `docker/setup_demo.sh`
- 
-Here are the commands:
-
-```
-cd docker
-./build_local_docker_images.sh
-.....
-
-[INFO] Reactor Summary:
-[INFO]
-[INFO] Hoodie ............................................. SUCCESS [  1.709 s]
-[INFO] hoodie-common ...................................... SUCCESS [  9.015 s]
-[INFO] hoodie-hadoop-mr ................................... SUCCESS [  1.108 s]
-[INFO] hoodie-client ...................................... SUCCESS [  4.409 s]
-[INFO] hoodie-hive ........................................ SUCCESS [  0.976 s]
-[INFO] hoodie-spark ....................................... SUCCESS [ 26.522 s]
-[INFO] hoodie-utilities ................................... SUCCESS [ 16.256 s]
-[INFO] hoodie-cli ......................................... SUCCESS [ 11.341 s]
-[INFO] hoodie-hadoop-mr-bundle ............................ SUCCESS [  1.893 s]
-[INFO] hoodie-hive-bundle ................................. SUCCESS [ 14.099 s]
-[INFO] hoodie-spark-bundle ................................ SUCCESS [ 58.252 s]
-[INFO] hoodie-hadoop-docker ............................... SUCCESS [  0.612 s]
-[INFO] hoodie-hadoop-base-docker .......................... SUCCESS [04:04 min]
-[INFO] hoodie-hadoop-namenode-docker ...................... SUCCESS [  6.142 s]
-[INFO] hoodie-hadoop-datanode-docker ...................... SUCCESS [  7.763 s]
-[INFO] hoodie-hadoop-history-docker ....................... SUCCESS [  5.922 s]
-[INFO] hoodie-hadoop-hive-docker .......................... SUCCESS [ 56.152 s]
-[INFO] hoodie-hadoop-sparkbase-docker ..................... SUCCESS [01:18 min]
-[INFO] hoodie-hadoop-sparkmaster-docker ................... SUCCESS [  2.964 s]
-[INFO] hoodie-hadoop-sparkworker-docker ................... SUCCESS [  3.032 s]
-[INFO] hoodie-hadoop-sparkadhoc-docker .................... SUCCESS [  2.764 s]
-[INFO] hoodie-integ-test .................................. SUCCESS [  1.785 s]
-[INFO] ------------------------------------------------------------------------
-[INFO] BUILD SUCCESS
-[INFO] ------------------------------------------------------------------------
-[INFO] Total time: 09:15 min
-[INFO] Finished at: 2018-09-10T17:47:37-07:00
-[INFO] Final Memory: 236M/1848M
-[INFO] ------------------------------------------------------------------------
-```
diff --git a/docs/roadmap.md b/docs/roadmap.md
deleted file mode 100644
index 692240c92..000000000
--- a/docs/roadmap.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Roadmap
-keywords: usecases
-sidebar: mydoc_sidebar
-permalink: roadmap.html
----
-
-## Planned Features
-
-* Support for Self Joins - As of now, you cannot incrementally consume the same table more than once, since the InputFormat does not understand the QueryPlan.
-* Hoodie Spark Datasource -  Allows for reading and writing data back using Apache Spark natively (without falling back to InputFormat), which can be more performant
-* Hoodie Presto Connector - Allows for querying data managed by Hoodie using Presto natively, which can again boost [performance](https://prestodb.io/docs/current/release/release-0.138.html)
-
-
diff --git a/docs/s3_filesystem.md b/docs/s3_filesystem.md
deleted file mode 100644
index 09d70e625..000000000
--- a/docs/s3_filesystem.md
+++ /dev/null
@@ -1,79 +0,0 @@
----
-title: S3 Filesystem (experimental)
-keywords: sql hive s3 spark presto
-sidebar: mydoc_sidebar
-permalink: s3_hoodie.html
-toc: false
-summary: In this page, we go over how to configure hoodie with S3 filesystem.
----
-Hoodie works with HDFS by default. There is an experimental work going on Hoodie-S3 compatibility.
-
-## AWS configs
-
-There are two configurations required for Hoodie-S3 compatibility:
-
-- Adding AWS Credentials for Hoodie
-- Adding required Jars to classpath
-
-### AWS Credentials
-
-Simplest way to use Hoodie with S3, is to configure your `SparkSession` or `SparkContext` with S3 credentials. Hoodie will automatically pick this up and talk to S3.
-
-Alternatively, add the required configs in your core-site.xml from where Hoodie can fetch them. Replace the `fs.defaultFS` with your S3 bucket name and Hoodie should be able to read/write from the bucket.
-
-```
-  <property>
-      <name>fs.defaultFS</name>
-      <value>s3://ysharma</value>
-  </property>
-
-  <property>
-      <name>fs.s3.impl</name>
-      <value>org.apache.hadoop.fs.s3native.NativeS3FileSystem</value>
-  </property>
-
-  <property>
-      <name>fs.s3.awsAccessKeyId</name>
-      <value>AWS_KEY</value>
-  </property>
-
-  <property>
-       <name>fs.s3.awsSecretAccessKey</name>
-       <value>AWS_SECRET</value>
-  </property>
-
-  <property>
-       <name>fs.s3n.awsAccessKeyId</name>
-       <value>AWS_KEY</value>
-  </property>
-
-  <property>
-       <name>fs.s3n.awsSecretAccessKey</name>
-       <value>AWS_SECRET</value>
-  </property>
-```
-
-
-Utilities such as hoodie-cli or deltastreamer tool, can pick up s3 creds via environmental variable prefixed with `HOODIE_ENV_`. For e.g below is a bash snippet to setup
-such variables and then have cli be able to work on datasets stored in s3
-
-```
-export HOODIE_ENV_fs_DOT_s3a_DOT_access_DOT_key=$accessKey
-export HOODIE_ENV_fs_DOT_s3a_DOT_secret_DOT_key=$secretKey
-export HOODIE_ENV_fs_DOT_s3_DOT_awsAccessKeyId=$accessKey
-export HOODIE_ENV_fs_DOT_s3_DOT_awsSecretAccessKey=$secretKey
-export HOODIE_ENV_fs_DOT_s3n_DOT_awsAccessKeyId=$accessKey
-export HOODIE_ENV_fs_DOT_s3n_DOT_awsSecretAccessKey=$secretKey
-export HOODIE_ENV_fs_DOT_s3n_DOT_impl=org.apache.hadoop.fs.s3a.S3AFileSystem
-```
-
-
-
-### AWS Libs
-
-AWS hadoop libraries to add to our classpath
-
- - com.amazonaws:aws-java-sdk:1.10.34
- - org.apache.hadoop:hadoop-aws:2.7.3
-
-
diff --git a/docs/search.json b/docs/search.json
deleted file mode 100644
index b61ffb72d..000000000
--- a/docs/search.json
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: search
-layout: none
-search: exclude
----
-
-[
-{% for page in site.pages %}
-{% unless page.search == "exclude" %}
-{
-"title": "{{ page.title | escape }}",
-"tags": "{{ page.tags }}",
-"keywords": "{{page.keywords}}",
-"url": "{{ page.url | remove: "/"}}",
-"summary": "{{page.summary | strip }}"
-}
-{% unless forloop.last and site.posts.size < 1 %},{% endunless %}
-{% endunless %}
-{% endfor %}
-
-{% for post in site.posts %}
-
-{
-"title": "{{ post.title | escape }}",
-"tags": "{{ post.tags }}",
-"keywords": "{{post.keywords}}",
-"url": "{{ post.url }}",
-"summary": "{{post.summary | strip }}"
-}
-{% unless forloop.last %},{% endunless %}
-{% endfor %}
-
-]
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
deleted file mode 100644
index 38a04d6c4..000000000
--- a/docs/sitemap.xml
+++ /dev/null
@@ -1,24 +0,0 @@
----
-layout: none
-search: exclude
----
-
-<?xml version="1.0" encoding="UTF-8"?>
-<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
-  {% for post in site.posts %}
-  {% unless post.search == "exclude" %}
-  <url>
-    <loc>{{site.url}}{{post.url}}</loc>
-  </url>
-  {% endunless %}
-  {% endfor %}
-
-
-  {% for page in site.pages %}
-  {% unless page.search == "exclude" %}
-  <url>
-    <loc>{{site.url}}{{ page.url}}</loc>
-  </url>
-  {% endunless %}
-  {% endfor %}
-</urlset>
\ No newline at end of file
diff --git a/docs/sql_queries.md b/docs/sql_queries.md
deleted file mode 100644
index f133a85bb..000000000
--- a/docs/sql_queries.md
+++ /dev/null
@@ -1,68 +0,0 @@
----
-title: SQL Queries
-keywords: sql hive spark presto
-sidebar: mydoc_sidebar
-permalink: sql_queries.html
-toc: false
-summary: In this page, we go over how to enable SQL queries on Hoodie built tables.
----
-
-Hoodie registers the dataset into the Hive metastore backed by `HoodieInputFormat`. This makes the data accessible to
-Hive & Spark & Presto automatically. To be able to perform normal SQL queries on such a dataset, we need to get the individual query engines
-to call `HoodieInputFormat.getSplits()`, during query planning such that the right versions of files are exposed to it.
-
-
-In the following sections, we cover the configs needed across different query engines to achieve this.
-
-{% include callout.html content="Instructions are currently only for Copy-on-write storage" type="info" %}
-
-
-## Hive
-
-For HiveServer2 access, [install](https://www.cloudera.com/documentation/enterprise/5-6-x/topics/cm_mc_hive_udf.html#concept_nc3_mms_lr)
-the hoodie-hadoop-mr-bundle-x.y.z-SNAPSHOT.jar into the aux jars path and we should be able to recognize the Hoodie tables and query them correctly.
-
-For beeline access, the `hive.input.format` variable needs to be set to the fully qualified path name of the inputformat `com.uber.hoodie.hadoop.HoodieInputFormat`
-For Tez, additionally the `hive.tez.input.format` needs to be set to `org.apache.hadoop.hive.ql.io.HiveInputFormat`
-
-## Spark
-
-There are two ways of running Spark SQL on Hoodie datasets.
-
-First method involves, setting `spark.sql.hive.convertMetastoreParquet=false`, forcing Spark to fallback
-to using the Hive Serde to read the data (planning/executions is still Spark). This turns off optimizations in Spark
-towards Parquet reading, which we will address in the next method based on path filters.
-However benchmarks have not revealed any real performance degradation with Hoodie & SparkSQL, compared to native support.
-
-{% include callout.html content="Get involved to improve this integration [here](https://github.com/uber/hoodie/issues/7) and [here](https://issues.apache.org/jira/browse/SPARK-19351) " type="info" %}
-
-Sample command is provided below to spin up Spark Shell
-
-```
-$ spark-shell --jars hoodie-spark-bundle-x.y.z-SNAPSHOT.jar --driver-class-path /etc/hive/conf  --packages com.databricks:spark-avro_2.11:4.0.0 --conf spark.sql.hive.convertMetastoreParquet=false --num-executors 10 --driver-memory 7g --executor-memory 2g  --master yarn-client
-
-scala> sqlContext.sql("select count(*) from uber.trips where datestr = '2016-10-02'").show()
-
-```
-
-
-For scheduled Spark jobs, a dependency to [hoodie-hadoop-mr](https://mvnrepository.com/artifact/com.uber.hoodie/hoodie-hadoop-mr) and [hoodie-client](https://mvnrepository.com/artifact/com.uber.hoodie/hoodie-client) modules needs to be added
-and the same config needs to be set on `SparkConf` or conveniently via `HoodieReadClient.addHoodieSupport(conf)`
-
-{% include callout.html content="Don't instantiate a HoodieWriteClient against a table you don't own. Hoodie is a single writer & multiple reader system as of now. You may accidentally cause incidents otherwise.
-" type="warning" %}
-
-The second method uses a new feature in Spark 2.x, which allows for the work of HoodieInputFormat to be done via a path filter as below. This method uses Spark built-in optimizations for
-reading Parquet files, just like queries on non-hoodie tables.
-
-```
-spark.sparkContext.hadoopConfiguration.setClass("mapreduce.input.pathFilter.class", classOf[com.uber.hoodie.hadoop.HoodieROTablePathFilter], classOf[org.apache.hadoop.fs.PathFilter]);
-```
-
-
-## Presto
-
-Presto requires a [patch](https://github.com/prestodb/presto/pull/7002) (until the PR is merged) and the hoodie-hadoop-mr-bundle jar to be placed
-into `<presto_install>/plugin/hive-hadoop2/`.
-
-{% include callout.html content="Get involved to improve this integration [here](https://github.com/uber/hoodie/issues/81)" type="info" %}
diff --git a/docs/use_cases.md b/docs/use_cases.md
deleted file mode 100644
index 717ec12e4..000000000
--- a/docs/use_cases.md
+++ /dev/null
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-title: Use Cases
-keywords: usecases
-sidebar: mydoc_sidebar
-permalink: use_cases.html
-toc: false
-summary: "Following are some sample use-cases for Hoodie, which illustrate the benefits in terms of faster processing & increased efficiency"
-
----
-
-
-
-## Near Real-Time Ingestion
-
-Ingesting data from external sources like (event logs, databases, external sources) into a [Hadoop Data Lake](http://martinfowler.com/bliki/DataLake.html) is a well known problem.
-In most (if not all) Hadoop deployments, it is unfortunately solved in a piecemeal fashion, using a medley of ingestion tools,
-even though this data is arguably the most valuable for the entire organization.
-
-
-For RDBMS ingestion, Hoodie provides __faster loads via Upserts__, as opposed costly & inefficient bulk loads. For e.g, you can read the MySQL BIN log or [Sqoop Incremental Import](https://sqoop.apache.org/docs/1.4.2/SqoopUserGuide.html#_incremental_imports) and apply them to an
-equivalent Hoodie table on HDFS. This would be much faster/efficient than a [bulk merge job](https://sqoop.apache.org/docs/1.4.0-incubating/SqoopUserGuide.html#id1770457)
-or [complicated handcrafted merge workflows](http://hortonworks.com/blog/four-step-strategy-incremental-updates-hive/)
-
-
-For NoSQL datastores like [Cassandra](http://cassandra.apache.org/) / [Voldemort](http://www.project-voldemort.com/voldemort/) / [HBase](https://hbase.apache.org/), even moderately big installations store billions of rows.
-It goes without saying that __full bulk loads are simply infeasible__ and more efficient approaches are needed if ingestion is to keep up with the typically high update volumes.
-
-
-Even for immutable data sources like [Kafka](kafka.apache.org) , Hoodie helps __enforces a minimum file size on HDFS__, which improves NameNode health by solving one of the [age old problems in Hadoop land](https://blog.cloudera.com/blog/2009/02/the-small-files-problem/) in a holistic way. This is all the more important for event streams, since typically its higher volume (eg: click streams) and if not managed well, can cause serious damage to your Hadoop cluster.
-
-Across all sources, Hoodie adds the much needed ability to atomically publish new data to consumers via notion of commits, shielding them from partial ingestion failures
-
-
-## Near Real-time Analytics
-
-Typically, real-time [datamarts](https://en.wikipedia.org/wiki/Data_mart) are powered by specialized analytical stores such as [Druid](http://druid.io/) or [Memsql](http://www.memsql.com/) or [even OpenTSDB](http://opentsdb.net/) .
-This is absolutely perfect for lower scale ([relative to Hadoop installations like this](https://blog.twitter.com/2015/hadoop-filesystem-at-twitter)) data, that needs sub-second query responses such as system monitoring or interactive real-time analysis.
-But, typically these systems end up getting abused for less interactive queries also since data on Hadoop is intolerably stale. This leads to under utilization & wasteful hardware/license costs.
-
-
-On the other hand, interactive SQL solutions on Hadoop such as Presto & SparkSQL excel in __queries that finish within few seconds__.
-By bringing __data freshness to a few minutes__, Hoodie can provide a much efficient alternative, as well unlock real-time analytics on __several magnitudes larger datasets__ stored in HDFS.
-Also, Hoodie has no external dependencies (like a dedicated HBase cluster, purely used for real-time analytics) and thus enables faster analytics on much fresher analytics, without increasing the operational overhead.
-
-
-## Incremental Processing Pipelines
-
-One fundamental ability Hadoop provides is to build a chain of datasets derived from each other via DAGs expressed as workflows.
-Workflows often depend on new data being output by multiple upstream workflows and traditionally, availability of new data is indicated by a new HDFS Folder/Hive Partition.
-Let's take a concrete example to illustrate this. An upstream workflow `U` can create a Hive partition for every hour, with data for that hour (event_time) at the end of each hour (processing_time), providing effective freshness of 1 hour.
-Then, a downstream workflow `D`, kicks off immediately after `U` finishes, and does its own processing for the next hour, increasing the effective latency to 2 hours.
-
-The above paradigm simply ignores late arriving data i.e when `processing_time` and `event_time` drift apart.
-Unfortunately, in today's post-mobile & pre-IoT world, __late data from intermittently connected mobile devices & sensors are the norm, not an anomaly__.
-In such cases, the only remedy to guarantee correctness is to [reprocess the last few hours](https://falcon.apache.org/FalconDocumentation.html#Handling_late_input_data) worth of data,
-over and over again each hour, which can significantly hurt the efficiency across the entire ecosystem. For e.g; imagine reprocessing TBs worth of data every hour across hundreds of workflows.
-
-
-Hoodie comes to the rescue again, by providing a way to consume new data (including late data) from an upsteam Hoodie dataset `HU` at a record granularity (not folders/partitions),
-apply the processing logic, and efficiently update/reconcile late data with a downstream Hoodie dataset `HD`. Here, `HU` and `HD` can be continuously scheduled at a much more frequent schedule
-like 15 mins, and providing an end-end latency of 30 mins at `HD`.
-
-
-{% include callout.html content="To achieve this, Hoodie has embraced similar concepts from stream processing frameworks like [Spark Streaming](https://spark.apache.org/docs/latest/streaming-programming-guide.html#join-operations) , Pub/Sub systems like [Kafka](http://kafka.apache.org/documentation/#theconsumer)
-or database replication technologies like [Oracle XStream](https://docs.oracle.com/cd/E11882_01/server.112/e16545/xstrm_cncpt.htm#XSTRM187).
-For the more curious, a more detailed explanation of the benefits of Incremetal Processing (compared to Stream Processing & Batch Processing) can be found [here](https://www.oreilly.com/ideas/ubers-case-for-incremental-processing-on-hadoop)" type="info" %}
-
-
-## Data Dispersal From Hadoop
-
-A popular use-case for Hadoop, is to crunch data and then disperse it back to an online serving store, to be used by an application.
-For e.g, a Spark Pipeline can [determine hard braking events on Hadoop](https://eng.uber.com/telematics/) and load them into a serving store like ElasticSearch, to be used by the Uber application to increase safe driving. Typical architectures for this employ a `queue` between Hadoop and serving store, to prevent overwhelming the target serving store.
-A popular choice for this queue is Kafka and this model often results in __redundant storage of same data on HDFS (for offline analysis on computed results) and Kafka (for dispersal)__
-
-Once again Hoodie can efficiently solve this problem, by having the Spark Pipeline upsert output from
-each run into a Hoodie dataset, which can then be incrementally tailed (just like a Kafka topic) for new data & written into the serving store.
-