---
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

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
    ________________________________________________________________________________________________________________________________________________________________________
    | CommitTime    | Total Written (B)| 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
    __________________________________________________________________________________________________________________________________________
    | 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
    ________________________________________________________________________________________________________________________________________________________
    | Partition Path| File ID                             | Previous Commit| Total Records Updated| Total Records Written| Total Bytes Written| Total Errors|
    |=======================================================================================================================================================|
    ....
    ....
```

#### 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
    ________________________________________________________________________________________________
    | CommitTime    | Min     | 10th    | 50th    | avg     | 95th    | Max     | NumFiles| StdDev  |
    |===============================================================================================|
    | 20161004211210| 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).

## 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.