From mapreduce-issues-return-95471-archive-asf-public=cust-asf.ponee.io@hadoop.apache.org Mon Jun 14 07:36:18 2021 Return-Path: X-Original-To: archive-asf-public@cust-asf.ponee.io Delivered-To: archive-asf-public@cust-asf.ponee.io Received: from mxout1-ec2-va.apache.org (mxout1-ec2-va.apache.org [3.227.148.255]) by mx-eu-01.ponee.io (Postfix) with ESMTPS id 4C696180181 for ; Mon, 14 Jun 2021 09:36:18 +0200 (CEST) Received: from mail.apache.org (mailroute1-lw-us.apache.org [207.244.88.153]) by mxout1-ec2-va.apache.org (ASF Mail Server at mxout1-ec2-va.apache.org) with SMTP id 4AE633F5E5 for ; Mon, 14 Jun 2021 07:36:14 +0000 (UTC) Received: (qmail 19509 invoked by uid 500); 14 Jun 2021 07:36:07 -0000 Mailing-List: contact mapreduce-issues-help@hadoop.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Delivered-To: mailing list mapreduce-issues@hadoop.apache.org Received: (qmail 19303 invoked by uid 99); 14 Jun 2021 07:36:06 -0000 Received: from mailrelay1-he-de.apache.org (HELO mailrelay1-he-de.apache.org) (116.203.21.61) by apache.org (qpsmtpd/0.29) with ESMTP; Mon, 14 Jun 2021 07:36:06 +0000 Received: from jira2-he-de.apache.org (unknown [IPv6:2a01:4f8:242:1f49::2]) by mailrelay1-he-de.apache.org (ASF Mail Server at mailrelay1-he-de.apache.org) with ESMTPS id EA4083E8EA for ; Mon, 14 Jun 2021 07:36:02 +0000 (UTC) Received: from jira2-he-de.apache.org (localhost.localdomain [127.0.0.1]) by jira2-he-de.apache.org (ASF Mail Server at jira2-he-de.apache.org) with ESMTP id ADAC6C81EE9 for ; Mon, 14 Jun 2021 07:36:01 +0000 (UTC) Date: Mon, 14 Jun 2021 07:36:01 +0000 (UTC) From: "ASF GitHub Bot (Jira)" To: mapreduce-issues@hadoop.apache.org Message-ID: In-Reply-To: References: Subject: [jira] [Work logged] (MAPREDUCE-7341) Add a task-manifest output committer for Azure and GCS MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 7bit X-JIRA-FingerPrint: 30527f35849b9dde25b450d4833f0394 [ 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. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: users@infra.apache.org 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) -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: mapreduce-issues-unsubscribe@hadoop.apache.org For additional commands, e-mail: mapreduce-issues-help@hadoop.apache.org