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