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From "ASF GitHub Bot (Jira)" <j...@apache.org>
Subject [jira] [Work logged] (MAPREDUCE-7341) Add a task-manifest output committer for Azure and GCS
Date Mon, 14 Jun 2021 07:36:01 GMT

     [ https://issues.apache.org/jira/browse/MAPREDUCE-7341?focusedWorklogId=610290&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-610290

ASF GitHub Bot logged work on MAPREDUCE-7341:

                Author: ASF GitHub Bot
            Created on: 14/Jun/21 07:36
            Start Date: 14/Jun/21 07:36
    Worklog Time Spent: 10m 
      Work Description: steveloughran commented on pull request #2971:
URL: https://github.com/apache/hadoop/pull/2971#issuecomment-859727811

   @mukund-thakur  -thanks, addressed your comments.
   I've been thinking about that ManifestSuccessData, more specifically: there's no reporting
of the result and hence IOStats after job/task failure.
   * I'm going to have task commit and job commit log the IOStats @ info.
   * I'm wondering whether it'd be useful to have an option to save a manifest after success/failure
to some path as $jobID.json?
   saving those stats would make it possible to collect/correlate results after test runs
where the output dirs keep being overwritten, and get stats on failures too. If we think this
is good I'd add some more options (including any exception message/stack trace on a failure)
so that further work could load them in and report.

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Issue Time Tracking

    Worklog Id:     (was: 610290)
    Time Spent: 4h 50m  (was: 4h 40m)

> 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
>              Labels: pull-request-available
>          Time Spent: 4h 50m
>  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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