---
title: Quickstart
keywords: quickstart
tags: [quickstart]
sidebar: mydoc_sidebar
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
```

{% include callout.html content="You might want to add your spark assembly jar 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" %}



## Generate a Hoodie Dataset

Create the output folder on your local HDFS
```
hdfs dfs -mkdir -p /tmp/hoodie/sample-table
```

You can run the __HoodieClientExample__ class, to place a two commits (commit 1 => 100 inserts, commit 2 => 100 updates to previously inserted 100 records) onto your HDFS at /tmp/hoodie/sample-table


## Register Dataset to Hive Metastore

Add in the hoodie-hadoop-mr jar so, Hive can read the Hoodie dataset and answer the query.

```
hive> add jar file:///tmp/hoodie-hadoop-mr-0.2.7.jar;
Added [file:///tmp/hoodie-hadoop-mr-0.2.7.jar] to class path
Added resources: [file:///tmp/hoodie-hadoop-mr-0.2.7.jar]
```

Then, you need to create a ReadOptimized 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
   'com.uber.hoodie.hadoop.HoodieOutputFormat'
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';
```

## Querying The Dataset

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
$ export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
$ spark-shell --jars /tmp/hoodie-hadoop-mr-0.2.7.jar --driver-class-path $HADOOP_CONF_DIR --conf spark.sql.hive.convertMetastoreParquet=false


scala> sqlContext.sql("show tables").show(10000)
scala> sqlContext.sql("describe hoodie_test").show(10000)
scala> sqlContext.sql("select count(*) from hoodie_test").show(10000)
```


### Presto

Checkout the 'master' branch on OSS Presto, build it, and place your installation somewhere.

* Copy the hoodie-hadoop-mr-0.2.7 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

Let's now perform a query, to obtain the __ONLY__ changed rows since a commit in the past.

```
hive> set hoodie.scan.mode=INCREMENTAL;
hive> set hoodie.last.commitTs=001;
hive> select `_hoodie_commit_time`, rider, driver from hoodie_test 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>
```