[jira] [Commented] (SPARK-24486) Slow performance reading ArrayType columns
[
https://issues.apache.org/jira/browse/SPARK-24486?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16790409#comment-16790409
]
Luca Canali commented on SPARK-24486:
-
Thanks [~yumwang] for looking at this. Indeed I confirm that using collect
instead of show is faster. In addition, testing on Spark master (March 12 2019)
I see that show works fast there too (I have not yet looked at which PR fixed
this).
> Slow performance reading ArrayType columns
> --
>
> Key: SPARK-24486
> URL: https://issues.apache.org/jira/browse/SPARK-24486
> Project: Spark
> Issue Type: Bug
> Components: Spark Core, SQL
>Affects Versions: 2.3.0
>Reporter: Luca Canali
>Priority: Minor
>
> We have found an issue of slow performance in one of our applications when
> running on Spark 2.3.0 (the same workload does not have a performance issue
> on Spark 2.2.1). We suspect a regression in the area of handling columns of
> ArrayType. I have built a simplified test case showing a manifestation of the
> issue to help with troubleshooting:
>
>
> {code:java}
> // prepare test data
> val stringListValues=Range(1,3).mkString(",")
> sql(s"select 1 as myid, Array($stringListValues) as myarray from
> range(2)").repartition(1).write.parquet("file:///tmp/deleteme1")
> // run test
> spark.read.parquet("file:///tmp/deleteme1").limit(1).show(){code}
> Performance measurements:
>
> On a desktop-size test system, the test runs in about 2 sec using Spark 2.2.1
> (runtime goes down to subsecond in subsequent runs) and takes close to 20 sec
> on Spark 2.3.0
>
> Additional drill-down using Spark task metrics data, show that in Spark 2.2.1
> only 2 records are read by this workload, while on Spark 2.3.0 all rows in
> the file are read, which appears anomalous.
> Example:
> {code:java}
> bin/spark-shell --master local[*] --driver-memory 2g --packages
> ch.cern.sparkmeasure:spark-measure_2.11:0.11
> val stageMetrics = ch.cern.sparkmeasure.StageMetrics(spark)
> stageMetrics.runAndMeasure(spark.read.parquet("file:///tmp/deleteme1").limit(1).show())
> {code}
>
>
> Selected metrics from Spark 2.3.0 run:
>
> {noformat}
> elapsedTime => 17849 (18 s)
> sum(numTasks) => 11
> sum(recordsRead) => 2
> sum(bytesRead) => 1136448171 (1083.0 MB){noformat}
>
>
> From Spark 2.2.1 run:
>
> {noformat}
> elapsedTime => 1329 (1 s)
> sum(numTasks) => 2
> sum(recordsRead) => 2
> sum(bytesRead) => 269162610 (256.0 MB)
> {noformat}
>
> Note: Using Spark built from master (as I write this, June 7th 2018) shows
> the same behavior as found in Spark 2.3.0
>
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[jira] [Commented] (SPARK-24486) Slow performance reading ArrayType columns
[
https://issues.apache.org/jira/browse/SPARK-24486?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16624723#comment-16624723
]
Yuming Wang commented on SPARK-24486:
-
I can reproduce this issue. I'm working on.
> Slow performance reading ArrayType columns
> --
>
> Key: SPARK-24486
> URL: https://issues.apache.org/jira/browse/SPARK-24486
> Project: Spark
> Issue Type: Bug
> Components: Spark Core, SQL
>Affects Versions: 2.3.0
>Reporter: Luca Canali
>Priority: Minor
>
> We have found an issue of slow performance in one of our applications when
> running on Spark 2.3.0 (the same workload does not have a performance issue
> on Spark 2.2.1). We suspect a regression in the area of handling columns of
> ArrayType. I have built a simplified test case showing a manifestation of the
> issue to help with troubleshooting:
>
>
> {code:java}
> // prepare test data
> val stringListValues=Range(1,3).mkString(",")
> sql(s"select 1 as myid, Array($stringListValues) as myarray from
> range(2)").repartition(1).write.parquet("file:///tmp/deleteme1")
> // run test
> spark.read.parquet("file:///tmp/deleteme1").limit(1).show(){code}
> Performance measurements:
>
> On a desktop-size test system, the test runs in about 2 sec using Spark 2.2.1
> (runtime goes down to subsecond in subsequent runs) and takes close to 20 sec
> on Spark 2.3.0
>
> Additional drill-down using Spark task metrics data, show that in Spark 2.2.1
> only 2 records are read by this workload, while on Spark 2.3.0 all rows in
> the file are read, which appears anomalous.
> Example:
> {code:java}
> bin/spark-shell --master local[*] --driver-memory 2g --packages
> ch.cern.sparkmeasure:spark-measure_2.11:0.11
> val stageMetrics = ch.cern.sparkmeasure.StageMetrics(spark)
> stageMetrics.runAndMeasure(spark.read.parquet("file:///tmp/deleteme1").limit(1).show())
> {code}
>
>
> Selected metrics from Spark 2.3.0 run:
>
> {noformat}
> elapsedTime => 17849 (18 s)
> sum(numTasks) => 11
> sum(recordsRead) => 2
> sum(bytesRead) => 1136448171 (1083.0 MB){noformat}
>
>
> From Spark 2.2.1 run:
>
> {noformat}
> elapsedTime => 1329 (1 s)
> sum(numTasks) => 2
> sum(recordsRead) => 2
> sum(bytesRead) => 269162610 (256.0 MB)
> {noformat}
>
> Note: Using Spark built from master (as I write this, June 7th 2018) shows
> the same behavior as found in Spark 2.3.0
>
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[jira] [Commented] (SPARK-24486) Slow performance reading ArrayType columns
[
https://issues.apache.org/jira/browse/SPARK-24486?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16505192#comment-16505192
]
Luca Canali commented on SPARK-24486:
-
Thanks for your comment.
In all 3 cases (spark 2.2.1, 2.3.0 and latest version from master) I am using a
simple test workload to investigate the issue, that is:
spark.read.parquet("file:///tmp/deleteme1").limit(1).show()
The output is simply the first row of the test table, that is an int value "1"
and an array of 3 int elements.
I'll be happy to provide additional info on the tests and workload. BTW, it
should be straighforward to reproduce this issue in a test environemnt if you
can spare the time.
> Slow performance reading ArrayType columns
> --
>
> Key: SPARK-24486
> URL: https://issues.apache.org/jira/browse/SPARK-24486
> Project: Spark
> Issue Type: Bug
> Components: Spark Core, SQL
>Affects Versions: 2.3.0
>Reporter: Luca Canali
>Priority: Minor
>
> We have found an issue of slow performance in one of our applications when
> running on Spark 2.3.0 (the same workload does not have a performance issue
> on Spark 2.2.1). We suspect a regression in the area of handling columns of
> ArrayType. I have built a simplified test case showing a manifestation of the
> issue to help with troubleshooting:
>
>
> {code:java}
> // prepare test data
> val stringListValues=Range(1,3).mkString(",")
> sql(s"select 1 as myid, Array($stringListValues) as myarray from
> range(2)").repartition(1).write.parquet("file:///tmp/deleteme1")
> // run test
> spark.read.parquet("file:///tmp/deleteme1").limit(1).show(){code}
> Performance measurements:
>
> On a desktop-size test system, the test runs in about 2 sec using Spark 2.2.1
> (runtime goes down to subsecond in subsequent runs) and takes close to 20 sec
> on Spark 2.3.0
>
> Additional drill-down using Spark task metrics data, show that in Spark 2.2.1
> only 2 records are read by this workload, while on Spark 2.3.0 all rows in
> the file are read, which appears anomalous.
> Example:
> {code:java}
> bin/spark-shell --master local[*] --driver-memory 2g --packages
> ch.cern.sparkmeasure:spark-measure_2.11:0.11
> val stageMetrics = ch.cern.sparkmeasure.StageMetrics(spark)
> stageMetrics.runAndMeasure(spark.read.parquet("file:///tmp/deleteme1").limit(1).show())
> {code}
>
>
> Selected metrics from Spark 2.3.0 run:
>
> {noformat}
> elapsedTime => 17849 (18 s)
> sum(numTasks) => 11
> sum(recordsRead) => 2
> sum(bytesRead) => 1136448171 (1083.0 MB){noformat}
>
>
> From Spark 2.2.1 run:
>
> {noformat}
> elapsedTime => 1329 (1 s)
> sum(numTasks) => 2
> sum(recordsRead) => 2
> sum(bytesRead) => 269162610 (256.0 MB)
> {noformat}
>
> Note: Using Spark built from master (as I write this, June 7th 2018) shows
> the same behavior as found in Spark 2.3.0
>
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[jira] [Commented] (SPARK-24486) Slow performance reading ArrayType columns
[
https://issues.apache.org/jira/browse/SPARK-24486?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16504845#comment-16504845
]
Kazuaki Ishizaki commented on SPARK-24486:
--
Thank you for reporting a problem.
Could you please let us know which value is shown for each of three results in
`sum(...)`?
> Slow performance reading ArrayType columns
> --
>
> Key: SPARK-24486
> URL: https://issues.apache.org/jira/browse/SPARK-24486
> Project: Spark
> Issue Type: Bug
> Components: Spark Core, SQL
>Affects Versions: 2.3.0
>Reporter: Luca Canali
>Priority: Minor
>
> We have found an issue of slow performance in one of our applications when
> running on Spark 2.3.0 (the same workload does not have a performance issue
> on Spark 2.2.1). We suspect a regression in the area of handling columns of
> ArrayType. I have built a simplified test case showing a manifestation of the
> issue to help with troubleshooting:
>
>
> {code:java}
> // prepare test data
> val stringListValues=Range(1,3).mkString(",")
> sql(s"select 1 as myid, Array($stringListValues) as myarray from
> range(2)").repartition(1).write.parquet("file:///tmp/deleteme1")
> // run test
> spark.read.parquet("file:///tmp/deleteme1").limit(1).show(){code}
> Performance measurements:
>
> On a desktop-size test system, the test runs in about 2 sec using Spark 2.2.1
> (runtime goes down to subsecond in subsequent runs) and takes close to 20 sec
> on Spark 2.3.0
>
> Additional drill-down using Spark task metrics data, show that in Spark 2.2.1
> only 2 records are read by this workload, while on Spark 2.3.0 all rows in
> the file are read, which appears anomalous.
> Example:
> {code:java}
> bin/spark-shell --master local[*] --driver-memory 2g --packages
> ch.cern.sparkmeasure:spark-measure_2.11:0.11
> val stageMetrics = ch.cern.sparkmeasure.StageMetrics(spark)
> stageMetrics.runAndMeasure(spark.read.parquet("file:///tmp/deleteme1").limit(1).show())
> {code}
>
>
> Selected metrics from Spark 2.3.0 run:
>
> {noformat}
> elapsedTime => 17849 (18 s)
> sum(numTasks) => 11
> sum(recordsRead) => 2
> sum(bytesRead) => 1136448171 (1083.0 MB){noformat}
>
>
> From Spark 2.2.1 run:
>
> {noformat}
> elapsedTime => 1329 (1 s)
> sum(numTasks) => 2
> sum(recordsRead) => 2
> sum(bytesRead) => 269162610 (256.0 MB)
> {noformat}
>
> Note: Using Spark built from master (as I write this, June 7th 2018) shows
> the same behavior as found in Spark 2.3.0
>
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