[jira] [Work logged] (MAPREDUCE-7341) Add a task-manifest output committer for Azure and GCS

["ASF GitHub Bot (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MAPREDUCE-7341?focusedWorklogId=592117&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-592117 ]

ASF GitHub Bot logged work on MAPREDUCE-7341:
---------------------------------------------

                Author: ASF GitHub Bot
            Created on: 03/May/21 11:22
            Start Date: 03/May/21 11:22
    Worklog Time Spent: 10m 
      Work Description: steveloughran opened a new pull request #2971:
URL: https://github.com/apache/hadoop/pull/2971


   
   File output committer which uses a manifest file to pass the lists of directories to create and files to rename from task attempts to job committer; job commit does dir creation and file rename across a pool of threads. Based on lessons/code from the S3A committer, 
   
   Status:
   * file formats done.
   * implementation architecture "stages you can chain" done
   
   Testing: todo. Will take the S3A commit protocol test as a baseline and make an abstract contract test from it; have one for the local FS and add one to Azure too. 


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Issue Time Tracking
-------------------

            Worklog Id:     (was: 592117)
    Remaining Estimate: 0h
            Time Spent: 10m

> Add a task-manifest output committer for Azure and GCS
> ------------------------------------------------------
>
>                 Key: MAPREDUCE-7341
>                 URL: https://issues.apache.org/jira/browse/MAPREDUCE-7341
>             Project: Hadoop Map/Reduce
>          Issue Type: New Feature
>          Components: client
>    Affects Versions: 3.3.1
>            Reporter: Steve Loughran
>            Assignee: Steve Loughran
>            Priority: Major
>          Time Spent: 10m
>  Remaining Estimate: 0h
>
> Add a task-manifest output committer for Azure and GCS
> The S3A committers are very popular in Spark on S3, as they are both correct and fast.
> The classic FileOutputCommitter v1 and v2 algorithms are all that is available for Azure ABFS and Google GCS, and they have limitations. 
> The v2 algorithm isn't safe in the presence of failed task attempt commits, so we
> recommend the v1 algorithm for Azure. But that is slow because it sequentially lists
> then renames files and directories, one-by-one. The latencies of list
> and rename make things slow.
> Google GCS lacks the atomic directory rename required for v1 correctness;
> v2 can be used (which doesn't have the job commit performance limitations),
> but it's not safe.
> Proposed
> * Add a new FileOutputFormat committer which uses an intermediate manifest to
>   pass the list of files created by a TA to the job committer.
> * Job committer to parallelise reading these task manifests and submit all the
>   rename operations into a pool of worker threads. (also: mkdir, directory deletions on cleanup)
> * Use the committer plugin mechanism added for s3a to make this the default committer for ABFS
>   (i.e. no need to make any changes to FileOutputCommitter)
> * Add lots of IOStatistics instrumentation + logging of operations in the JobCommit
>   for visibility of where delays are occurring.
> * Reuse the S3A committer _SUCCESS JSON structure to publish IOStats & other data
>   for testing/support.  
> This committer will be faster than the V1 algorithm because of the parallelisation, and
> because a manifest written by create-and-rename will be exclusive to a single task
> attempt, delivers the isolation which the v2 committer lacks.
> This is not an attempt to do an iceberg/hudi/delta-lake style manifest-only format
> for describing the contents of a table; the final output is still a directory tree
> which must be scanned during query planning.
> As such the format is still suboptimal for cloud storage -but at least we will have
> faster job execution during the commit phases.
>   
> Note: this will also work on HDFS, where again, it should be faster than
> the v1 committer. However the target is very much Spark with ABFS and GCS; no plans to worry about MR as that simplifies the challenge of dealing with job restart (i.e. you don't have to)



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