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Posted to common-issues@hadoop.apache.org by "Steve Loughran (Jira)" <ji...@apache.org> on 2021/07/29 09:25:00 UTC
[jira] [Updated] (HADOOP-17789) S3 CSV read performance with Spark
with Hadoop 3.3.1 is slower than older Hadoop
[ https://issues.apache.org/jira/browse/HADOOP-17789?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Steve Loughran updated HADOOP-17789:
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Summary: S3 CSV read performance with Spark with Hadoop 3.3.1 is slower than older Hadoop (was: S3 read performance with Spark with Hadoop 3.3.1 is slower than older Hadoop)
> S3 CSV read performance with Spark with Hadoop 3.3.1 is slower than older Hadoop
> --------------------------------------------------------------------------------
>
> Key: HADOOP-17789
> URL: https://issues.apache.org/jira/browse/HADOOP-17789
> Project: Hadoop Common
> Issue Type: Improvement
> Components: fs/s3
> Affects Versions: 3.3.1
> Reporter: Arghya Saha
> Priority: Minor
> Attachments: storediag.log
>
>
> This is issue is continuation to https://issues.apache.org/jira/browse/HADOOP-17755
> The input data reported by Spark(Hadoop 3.3.1) was almost double and read runtime also increased (around 20%) compared to Spark(Hadoop 3.2.0) with same exact amount of resource and same configuration. And this is happening with other jobs as well which was not impacted by read fully error as stated above.
> *I was having the same exact issue when I was using the workaround fs.s3a.readahead.range = 1G with Hadoop 3.2.0*
> Below is further details :
>
> |Hadoop Version|Actual size of the files(in SQL Tab)|Reported size of the file(In Stages)|Time to complete the Stage|fs.s3a.readahead.range|
> |Hadoop 3.2.0|29.3 GiB|29.3 GiB|23 min|64K|
> |Hadoop 3.3.1|29.3 GiB|*{color:#ff0000}58.7 GiB{color}*|*{color:#ff0000}27 min{color}*|{color:#172b4d}64K{color}|
> |Hadoop 3.2.0|29.3 GiB|*{color:#ff0000}58.7 GiB{color}*|*{color:#ff0000}~27 min{color}*|{color:#172b4d}1G{color}|
> * *Shuffle Write* is same (95.9 GiB) for all the above three cases
> I was expecting some improvement(or same as 3.2.0) with Hadoop 3.3.1 with read operations, please suggest how to approach this and resolve this.
> I have used the default s3a config along with below and also using EKS cluster
> {code:java}
> spark.hadoop.fs.s3a.committer.magic.enabled: 'true'
> spark.hadoop.fs.s3a.committer.name: magic
> spark.hadoop.mapreduce.outputcommitter.factory.scheme.s3a: org.apache.hadoop.fs.s3a.commit.S3ACommitterFactory
> spark.hadoop.fs.s3a.downgrade.syncable.exceptions: "true"{code}
> * I did not use
> {code:java}
> spark.hadoop.fs.s3a.experimental.input.fadvise=random{code}
> And as already mentioned I have used same Spark, same amount of resources and same config. Only change is Hadoop 3.2.0 to Hadoop 3.3.1 (Built with Spark using ./dev/make-distribution.sh --name spark-patched --pip -Pkubernetes -Phive -Phive-thriftserver -Dhadoop.version="3.3.1")
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