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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 Tue, 08 Jun 2021 20:59:00 GMT

     [ https://issues.apache.org/jira/browse/MAPREDUCE-7341?focusedWorklogId=608746&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-608746
]

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

                Author: ASF GitHub Bot
            Created on: 08/Jun/21 20:58
            Start Date: 08/Jun/21 20:58
    Worklog Time Spent: 10m 
      Work Description: steveloughran commented on a change in pull request #2971:
URL: https://github.com/apache/hadoop/pull/2971#discussion_r647788743



##########
File path: hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/lib/output/committer/manifest/CommitJobStage.java
##########
@@ -0,0 +1,112 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.hadoop.mapreduce.lib.output.committer.manifest;
+
+import java.io.IOException;
+import java.util.List;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import org.apache.commons.lang3.tuple.Pair;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.fs.statistics.impl.IOStatisticsStore;
+import org.apache.hadoop.mapreduce.lib.output.committer.manifest.files.ManifestSuccessData;
+import org.apache.hadoop.mapreduce.lib.output.committer.manifest.files.TaskManifest;
+
+import static org.apache.commons.io.FileUtils.byteCountToDisplaySize;
+import static org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterStatisticNames.OP_JOB_COMMITTED_BYTES;
+import static org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterStatisticNames.OP_JOB_COMMITTED_FILES;
+import static org.apache.hadoop.mapreduce.lib.output.committer.manifest.ManifestCommitterStatisticNames.OP_STAGE_JOB_COMMIT;
+
+/**
+ * Commit the Job.
+ * Arguments (save manifest, validate output)
+ */

Review comment:
       I agree: will do




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

    Worklog Id:     (was: 608746)
    Time Spent: 3h 50m  (was: 3h 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: 3h 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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