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Posted to issues@spark.apache.org by "Jungtaek Lim (Jira)" <ji...@apache.org> on 2020/07/01 00:18:00 UTC
[jira] [Commented] (SPARK-32130) Spark 3.0 json load performance is
unacceptable in comparison of Spark 2.4
[ https://issues.apache.org/jira/browse/SPARK-32130?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17149019#comment-17149019 ]
Jungtaek Lim commented on SPARK-32130:
--------------------------------------
There might be some tricks to make type inference for timestamps fail faster, but that would depend on the value and will be same (even worse for adding overhead on applying tricks) for worst case. Unless the performance becomes on par or similar on every cases, turning it on by default doesn't seem to be ideal.
[~dongjoon] [~cloud_fan] [~hyukjin.kwon] [~maxgekk] Could we please revisit this?
> Spark 3.0 json load performance is unacceptable in comparison of Spark 2.4
> --------------------------------------------------------------------------
>
> Key: SPARK-32130
> URL: https://issues.apache.org/jira/browse/SPARK-32130
> Project: Spark
> Issue Type: Bug
> Components: Input/Output
> Affects Versions: 3.0.0
> Environment: 20/06/29 07:52:19 WARN Utils: Your hostname, sanjeevs-MacBook-Pro-2.local resolves to a loopback address: 127.0.0.1; using 10.0.0.8 instead (on interface en0)
> 20/06/29 07:52:19 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address
> 20/06/29 07:52:19 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
> Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
> Setting default log level to "WARN".
> To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
> 20/06/29 07:52:26 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
> Spark context Web UI available at http://10.0.0.8:4041
> Spark context available as 'sc' (master = local[*], app id = local-1593442346864).
> Spark session available as 'spark'.
> Welcome to
> ____ __
> / __/__ ___ _____/ /__
> _\ \/ _ \/ _ `/ __/ '_/
> /___/ .__/\_,_/_/ /_/\_\ version 3.0.0
> /_/
> Using Scala version 2.12.10 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_251)
> Type in expressions to have them evaluated.
> Type :help for more information.
> Reporter: Sanjeev Mishra
> Priority: Critical
> Attachments: SPARK 32130 - replication and findings.ipynb, small-anon.tar
>
>
> We are planning to move to Spark 3 but the read performance of our json files is unacceptable. Following is the performance numbers when compared to Spark 2.4
>
> Spark 2.4
> scala> spark.time(spark.read.json("/data/20200528"))
> Time taken: {color:#ff0000}19691 ms{color}
> res61: org.apache.spark.sql.DataFrame = [created: bigint, id: string ... 5 more fields]
> scala> spark.time(res61.count())
> Time taken: {color:#0000ff}7113 ms{color}
> res64: Long = 2605349
> Spark 3.0
> scala> spark.time(spark.read.json("/data/20200528"))
> 20/06/29 08:06:53 WARN package: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.sql.debug.maxToStringFields'.
> Time taken: {color:#ff0000}849652 ms{color}
> res0: org.apache.spark.sql.DataFrame = [created: bigint, id: string ... 5 more fields]
> scala> spark.time(res0.count())
> Time taken: {color:#0000ff}8201 ms{color}
> res2: Long = 2605349
>
>
> I am attaching a sample data (please delete is once you are able to reproduce the issue) that is much smaller than the actual size but the performance comparison can still be verified.
> The sample tar contains bunch of json.gz files, each line of the file is self contained json doc as shown below
> To reproduce the issue please untar the attachment - it will have multiple .json.gz files whose contents will look similar to following
>
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> {quote}
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> {quote}
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> {quote}
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