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https://issues.apache.org/jira/browse/MAPREDUCE-7341?focusedWorklogId=741632&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-741632
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ASF GitHub Bot logged work on MAPREDUCE-7341:
---------------------------------------------
Author: ASF GitHub Bot
Created on: 15/Mar/22 13:52
Start Date: 15/Mar/22 13:52
Worklog Time Spent: 10m
Work Description: steveloughran edited a comment on pull request #2971:
URL: https://github.com/apache/hadoop/pull/2971#issuecomment-1068012871
@mukund-thakur,
I have addressed all the little nits in the code -thank you for reviewing
the text and Java docs so thoroughly.
I have also improved that rejection of filesystem by schema, adding wasb to
the set of unsupported stores,
and a test for it.
Once we add a better rename api to the filesystem/filecontext, we can add
path capabilities for renames
to probe for.
I have also tried to clarify in the Java docs and comments places where
there was ambiguity.
This includes comments in the hadoop-azure pom where the references to the
mapreduce jars are imported with scopes of `provided` and `test`.
Everyone reviewing this patch needs to understand what these scopes mean, so
they will understand why I have no intention of changing those declarations or
adding anything to hadoop-common.
| scope name | classpath of | transitive? |
|------------|--------------|-------------|
| `provided` | src/main and src/test builds | *no* |
| `test` | src/test builds | *no* |
It is not a requirement to use the file system, nor is it exported as a new
dependency. I am 100% confident of this because these same dependencies were
added to hadoop-aws in HADOOP-13786, _Add S3A committers for zero-rename
commits to S3 endpoints_ -and nobody has ever reported the filesystem not
instantiating.
note, cloudstore storediag doesn't put these dependencies on the cp when
invoked via `hadoop jar cloudstore.jar storediag` so verifying my statements is
straightforward.
tested: azure cardiff
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Issue Time Tracking
-------------------
Worklog Id: (was: 741632)
Time Spent: 35h 50m (was: 35h 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: 35h 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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