Administering Hudi Pipelines

Admins/ops can gain visibility into Hudi datasets/pipelines in the following ways

This section provides a glimpse into each of these, with some general guidance on troubleshooting

Admin CLI

Once hudi has been built, the shell can be fired by via cd hudi-cli && ./ A hudi dataset resides on DFS, in a location referred to as the basePath and we would need this location in order to connect to a Hudi dataset. Hudi library effectively manages this dataset internally, using .hoodie subfolder to track all metadata

To initialize a hudi 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
*                                          *
*     _    _           _   _               *
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*    | |__| |       __| |  -               *
*    |  __  ||   | / _` | ||               *
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Welcome to Hoodie CLI. Please type help if you are looking for help.
hudi->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 hudi table, use the command:

18/09/06 15:57:19 INFO timeline.HoodieActiveTimeline: Loaded instants []
    | Property                | Value                        |
    | basePath                | ...                          |
    | metaPath                | ...                          |
    | fileSystem              | hdfs                         |
    |       | hoodie_table_1               |
    | hoodie.table.type       | COPY_ON_WRITE                |
    | hoodie.archivelog.folder|                              |

Following is a sample command to connect to a Hudi 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

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

* ! - 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


Inspecting Commits

The task of upserting or inserting a batch of incoming records is known as a commit in Hudi. 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|

At the start of each write, Hudi 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}]| [] |



Since Hudi directly manages file sizes for DFS 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 Hudi 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 DFS, Hudi 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).


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


    | 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


    | 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 -> DFS 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


Once the Hudi Client is configured with the right datasetname and environment for metrics, it produces the following graphite metrics, that aid in debugging hudi 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.


Troubleshooting Failures

Section below generally aids in debugging Hudi 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 DFS 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

NOTE: As of now, Hudi 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.

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 Hudi, but handed back to the application to decide what to do with it.


First of all, please confirm if you do indeed have duplicates AFTER ensuring the query is accessing the Hudi datasets properly .

  • 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 Hudi 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.


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 (Hudi/Spark), the above knowledge of the DAG can be used to pinpoint the actual issue. The most often encountered failures result from YARN/DFS 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.

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