[jira] [Commented] (SPARK-45282) Join loses records for cached datasets
[
https://issues.apache.org/jira/browse/SPARK-45282?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17808393#comment-17808393
]
Rob Russo commented on SPARK-45282:
---
Is it possible that this also affects spark 3.3.2? I have an application that
has been running on spark 3.3.2 and with AQE enabled. When I upgraded to 3.5.0
I immediately ran into the issue in this ticket. However when I started looking
more closely I found that for 1 particular type of report the issue was still
present even after rolling back to 3.3.2 with AQE enabled.
Either way on 3.3.2 or 3.5.0, disabling AQE fixed the problem.
> Join loses records for cached datasets
> --
>
> Key: SPARK-45282
> URL: https://issues.apache.org/jira/browse/SPARK-45282
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.4.1, 3.5.0
> Environment: spark 3.4.1 on apache hadoop 3.3.6 or kubernetes 1.26 or
> databricks 13.3
>Reporter: koert kuipers
>Assignee: Emil Ejbyfeldt
>Priority: Blocker
> Labels: CorrectnessBug, correctness, pull-request-available
> Fix For: 3.4.2
>
>
> we observed this issue on spark 3.4.1 but it is also present on 3.5.0. it is
> not present on spark 3.3.1.
> it only shows up in distributed environment. i cannot replicate in unit test.
> however i did get it to show up on hadoop cluster, kubernetes, and on
> databricks 13.3
> the issue is that records are dropped when two cached dataframes are joined.
> it seems in spark 3.4.1 in queryplan some Exchanges are dropped as an
> optimization while in spark 3.3.1 these Exhanges are still present. it seems
> to be an issue with AQE with canChangeCachedPlanOutputPartitioning=true.
> to reproduce on distributed cluster these settings needed are:
> {code:java}
> spark.sql.adaptive.advisoryPartitionSizeInBytes 33554432
> spark.sql.adaptive.coalescePartitions.parallelismFirst false
> spark.sql.adaptive.enabled true
> spark.sql.optimizer.canChangeCachedPlanOutputPartitioning true {code}
> code using scala to reproduce is:
> {code:java}
> import java.util.UUID
> import org.apache.spark.sql.functions.col
> import spark.implicits._
> val data = (1 to 100).toDS().map(i =>
> UUID.randomUUID().toString).persist()
> val left = data.map(k => (k, 1))
> val right = data.map(k => (k, k)) // if i change this to k => (k, 1) it works!
> println("number of left " + left.count())
> println("number of right " + right.count())
> println("number of (left join right) " +
> left.toDF("key", "value1").join(right.toDF("key", "value2"), "key").count()
> )
> val left1 = left
> .toDF("key", "value1")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of left1 " + left1.count())
> val right1 = right
> .toDF("key", "value2")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of right1 " + right1.count())
> println("number of (left1 join right1) " + left1.join(right1,
> "key").count()) // this gives incorrect result{code}
> this produces the following output:
> {code:java}
> number of left 100
> number of right 100
> number of (left join right) 100
> number of left1 100
> number of right1 100
> number of (left1 join right1) 859531 {code}
> note that the last number (the incorrect one) actually varies depending on
> settings and cluster size etc.
>
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[jira] [Commented] (SPARK-45282) Join loses records for cached datasets
[
https://issues.apache.org/jira/browse/SPARK-45282?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17784320#comment-17784320
]
Emil Ejbyfeldt commented on SPARK-45282:
Created this [https://github.com/apache/spark/pull/43729] to backport the fix
to 3.4 from my manual test it solved the reproduction in this ticket.
> Join loses records for cached datasets
> --
>
> Key: SPARK-45282
> URL: https://issues.apache.org/jira/browse/SPARK-45282
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.4.1, 3.5.0
> Environment: spark 3.4.1 on apache hadoop 3.3.6 or kubernetes 1.26 or
> databricks 13.3
>Reporter: koert kuipers
>Priority: Blocker
> Labels: CorrectnessBug, correctness, pull-request-available
>
> we observed this issue on spark 3.4.1 but it is also present on 3.5.0. it is
> not present on spark 3.3.1.
> it only shows up in distributed environment. i cannot replicate in unit test.
> however i did get it to show up on hadoop cluster, kubernetes, and on
> databricks 13.3
> the issue is that records are dropped when two cached dataframes are joined.
> it seems in spark 3.4.1 in queryplan some Exchanges are dropped as an
> optimization while in spark 3.3.1 these Exhanges are still present. it seems
> to be an issue with AQE with canChangeCachedPlanOutputPartitioning=true.
> to reproduce on distributed cluster these settings needed are:
> {code:java}
> spark.sql.adaptive.advisoryPartitionSizeInBytes 33554432
> spark.sql.adaptive.coalescePartitions.parallelismFirst false
> spark.sql.adaptive.enabled true
> spark.sql.optimizer.canChangeCachedPlanOutputPartitioning true {code}
> code using scala to reproduce is:
> {code:java}
> import java.util.UUID
> import org.apache.spark.sql.functions.col
> import spark.implicits._
> val data = (1 to 100).toDS().map(i =>
> UUID.randomUUID().toString).persist()
> val left = data.map(k => (k, 1))
> val right = data.map(k => (k, k)) // if i change this to k => (k, 1) it works!
> println("number of left " + left.count())
> println("number of right " + right.count())
> println("number of (left join right) " +
> left.toDF("key", "value1").join(right.toDF("key", "value2"), "key").count()
> )
> val left1 = left
> .toDF("key", "value1")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of left1 " + left1.count())
> val right1 = right
> .toDF("key", "value2")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of right1 " + right1.count())
> println("number of (left1 join right1) " + left1.join(right1,
> "key").count()) // this gives incorrect result{code}
> this produces the following output:
> {code:java}
> number of left 100
> number of right 100
> number of (left join right) 100
> number of left1 100
> number of right1 100
> number of (left1 join right1) 859531 {code}
> note that the last number (the incorrect one) actually varies depending on
> settings and cluster size etc.
>
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[jira] [Commented] (SPARK-45282) Join loses records for cached datasets
[
https://issues.apache.org/jira/browse/SPARK-45282?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17784115#comment-17784115
]
koert kuipers commented on SPARK-45282:
---
it does look like same issue
and partitioning being the cause makes sense too
> Join loses records for cached datasets
> --
>
> Key: SPARK-45282
> URL: https://issues.apache.org/jira/browse/SPARK-45282
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.4.1, 3.5.0
> Environment: spark 3.4.1 on apache hadoop 3.3.6 or kubernetes 1.26 or
> databricks 13.3
>Reporter: koert kuipers
>Priority: Blocker
> Labels: CorrectnessBug, correctness
>
> we observed this issue on spark 3.4.1 but it is also present on 3.5.0. it is
> not present on spark 3.3.1.
> it only shows up in distributed environment. i cannot replicate in unit test.
> however i did get it to show up on hadoop cluster, kubernetes, and on
> databricks 13.3
> the issue is that records are dropped when two cached dataframes are joined.
> it seems in spark 3.4.1 in queryplan some Exchanges are dropped as an
> optimization while in spark 3.3.1 these Exhanges are still present. it seems
> to be an issue with AQE with canChangeCachedPlanOutputPartitioning=true.
> to reproduce on distributed cluster these settings needed are:
> {code:java}
> spark.sql.adaptive.advisoryPartitionSizeInBytes 33554432
> spark.sql.adaptive.coalescePartitions.parallelismFirst false
> spark.sql.adaptive.enabled true
> spark.sql.optimizer.canChangeCachedPlanOutputPartitioning true {code}
> code using scala to reproduce is:
> {code:java}
> import java.util.UUID
> import org.apache.spark.sql.functions.col
> import spark.implicits._
> val data = (1 to 100).toDS().map(i =>
> UUID.randomUUID().toString).persist()
> val left = data.map(k => (k, 1))
> val right = data.map(k => (k, k)) // if i change this to k => (k, 1) it works!
> println("number of left " + left.count())
> println("number of right " + right.count())
> println("number of (left join right) " +
> left.toDF("key", "value1").join(right.toDF("key", "value2"), "key").count()
> )
> val left1 = left
> .toDF("key", "value1")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of left1 " + left1.count())
> val right1 = right
> .toDF("key", "value2")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of right1 " + right1.count())
> println("number of (left1 join right1) " + left1.join(right1,
> "key").count()) // this gives incorrect result{code}
> this produces the following output:
> {code:java}
> number of left 100
> number of right 100
> number of (left join right) 100
> number of left1 100
> number of right1 100
> number of (left1 join right1) 859531 {code}
> note that the last number (the incorrect one) actually varies depending on
> settings and cluster size etc.
>
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[jira] [Commented] (SPARK-45282) Join loses records for cached datasets
[
https://issues.apache.org/jira/browse/SPARK-45282?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17784109#comment-17784109
]
Emil Ejbyfeldt commented on SPARK-45282:
The code reproducing the bug looks quite similar to
https://issues.apache.org/jira/browse/SPARK-45592 I wonder if the fix for that
might also have solved this bug as I could not reproduce this issue on a build
from the master branch.
> Join loses records for cached datasets
> --
>
> Key: SPARK-45282
> URL: https://issues.apache.org/jira/browse/SPARK-45282
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.4.1, 3.5.0
> Environment: spark 3.4.1 on apache hadoop 3.3.6 or kubernetes 1.26 or
> databricks 13.3
>Reporter: koert kuipers
>Priority: Blocker
> Labels: CorrectnessBug, correctness
>
> we observed this issue on spark 3.4.1 but it is also present on 3.5.0. it is
> not present on spark 3.3.1.
> it only shows up in distributed environment. i cannot replicate in unit test.
> however i did get it to show up on hadoop cluster, kubernetes, and on
> databricks 13.3
> the issue is that records are dropped when two cached dataframes are joined.
> it seems in spark 3.4.1 in queryplan some Exchanges are dropped as an
> optimization while in spark 3.3.1 these Exhanges are still present. it seems
> to be an issue with AQE with canChangeCachedPlanOutputPartitioning=true.
> to reproduce on distributed cluster these settings needed are:
> {code:java}
> spark.sql.adaptive.advisoryPartitionSizeInBytes 33554432
> spark.sql.adaptive.coalescePartitions.parallelismFirst false
> spark.sql.adaptive.enabled true
> spark.sql.optimizer.canChangeCachedPlanOutputPartitioning true {code}
> code using scala to reproduce is:
> {code:java}
> import java.util.UUID
> import org.apache.spark.sql.functions.col
> import spark.implicits._
> val data = (1 to 100).toDS().map(i =>
> UUID.randomUUID().toString).persist()
> val left = data.map(k => (k, 1))
> val right = data.map(k => (k, k)) // if i change this to k => (k, 1) it works!
> println("number of left " + left.count())
> println("number of right " + right.count())
> println("number of (left join right) " +
> left.toDF("key", "value1").join(right.toDF("key", "value2"), "key").count()
> )
> val left1 = left
> .toDF("key", "value1")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of left1 " + left1.count())
> val right1 = right
> .toDF("key", "value2")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of right1 " + right1.count())
> println("number of (left1 join right1) " + left1.join(right1,
> "key").count()) // this gives incorrect result{code}
> this produces the following output:
> {code:java}
> number of left 100
> number of right 100
> number of (left join right) 100
> number of left1 100
> number of right1 100
> number of (left1 join right1) 859531 {code}
> note that the last number (the incorrect one) actually varies depending on
> settings and cluster size etc.
>
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[jira] [Commented] (SPARK-45282) Join loses records for cached datasets
[
https://issues.apache.org/jira/browse/SPARK-45282?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17781464#comment-17781464
]
Dongjoon Hyun commented on SPARK-45282:
---
Thank you for sharing, [~koert].
> Join loses records for cached datasets
> --
>
> Key: SPARK-45282
> URL: https://issues.apache.org/jira/browse/SPARK-45282
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.4.1, 3.5.0
> Environment: spark 3.4.1 on apache hadoop 3.3.6 or kubernetes 1.26 or
> databricks 13.3
>Reporter: koert kuipers
>Priority: Blocker
> Labels: CorrectnessBug, correctness
>
> we observed this issue on spark 3.4.1 but it is also present on 3.5.0. it is
> not present on spark 3.3.1.
> it only shows up in distributed environment. i cannot replicate in unit test.
> however i did get it to show up on hadoop cluster, kubernetes, and on
> databricks 13.3
> the issue is that records are dropped when two cached dataframes are joined.
> it seems in spark 3.4.1 in queryplan some Exchanges are dropped as an
> optimization while in spark 3.3.1 these Exhanges are still present. it seems
> to be an issue with AQE with canChangeCachedPlanOutputPartitioning=true.
> to reproduce on distributed cluster these settings needed are:
> {code:java}
> spark.sql.adaptive.advisoryPartitionSizeInBytes 33554432
> spark.sql.adaptive.coalescePartitions.parallelismFirst false
> spark.sql.adaptive.enabled true
> spark.sql.optimizer.canChangeCachedPlanOutputPartitioning true {code}
> code using scala to reproduce is:
> {code:java}
> import java.util.UUID
> import org.apache.spark.sql.functions.col
> import spark.implicits._
> val data = (1 to 100).toDS().map(i =>
> UUID.randomUUID().toString).persist()
> val left = data.map(k => (k, 1))
> val right = data.map(k => (k, k)) // if i change this to k => (k, 1) it works!
> println("number of left " + left.count())
> println("number of right " + right.count())
> println("number of (left join right) " +
> left.toDF("key", "value1").join(right.toDF("key", "value2"), "key").count()
> )
> val left1 = left
> .toDF("key", "value1")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of left1 " + left1.count())
> val right1 = right
> .toDF("key", "value2")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of right1 " + right1.count())
> println("number of (left1 join right1) " + left1.join(right1,
> "key").count()) // this gives incorrect result{code}
> this produces the following output:
> {code:java}
> number of left 100
> number of right 100
> number of (left join right) 100
> number of left1 100
> number of right1 100
> number of (left1 join right1) 859531 {code}
> note that the last number (the incorrect one) actually varies depending on
> settings and cluster size etc.
>
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[jira] [Commented] (SPARK-45282) Join loses records for cached datasets
[
https://issues.apache.org/jira/browse/SPARK-45282?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17781171#comment-17781171
]
koert kuipers commented on SPARK-45282:
---
last time i checked this issue was still present in 3.4/3.5/master
> Join loses records for cached datasets
> --
>
> Key: SPARK-45282
> URL: https://issues.apache.org/jira/browse/SPARK-45282
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.4.1, 3.5.0
> Environment: spark 3.4.1 on apache hadoop 3.3.6 or kubernetes 1.26 or
> databricks 13.3
>Reporter: koert kuipers
>Priority: Blocker
> Labels: CorrectnessBug, correctness
>
> we observed this issue on spark 3.4.1 but it is also present on 3.5.0. it is
> not present on spark 3.3.1.
> it only shows up in distributed environment. i cannot replicate in unit test.
> however i did get it to show up on hadoop cluster, kubernetes, and on
> databricks 13.3
> the issue is that records are dropped when two cached dataframes are joined.
> it seems in spark 3.4.1 in queryplan some Exchanges are dropped as an
> optimization while in spark 3.3.1 these Exhanges are still present. it seems
> to be an issue with AQE with canChangeCachedPlanOutputPartitioning=true.
> to reproduce on distributed cluster these settings needed are:
> {code:java}
> spark.sql.adaptive.advisoryPartitionSizeInBytes 33554432
> spark.sql.adaptive.coalescePartitions.parallelismFirst false
> spark.sql.adaptive.enabled true
> spark.sql.optimizer.canChangeCachedPlanOutputPartitioning true {code}
> code using scala to reproduce is:
> {code:java}
> import java.util.UUID
> import org.apache.spark.sql.functions.col
> import spark.implicits._
> val data = (1 to 100).toDS().map(i =>
> UUID.randomUUID().toString).persist()
> val left = data.map(k => (k, 1))
> val right = data.map(k => (k, k)) // if i change this to k => (k, 1) it works!
> println("number of left " + left.count())
> println("number of right " + right.count())
> println("number of (left join right) " +
> left.toDF("key", "value1").join(right.toDF("key", "value2"), "key").count()
> )
> val left1 = left
> .toDF("key", "value1")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of left1 " + left1.count())
> val right1 = right
> .toDF("key", "value2")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of right1 " + right1.count())
> println("number of (left1 join right1) " + left1.join(right1,
> "key").count()) // this gives incorrect result{code}
> this produces the following output:
> {code:java}
> number of left 100
> number of right 100
> number of (left join right) 100
> number of left1 100
> number of right1 100
> number of (left1 join right1) 859531 {code}
> note that the last number (the incorrect one) actually varies depending on
> settings and cluster size etc.
>
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[jira] [Commented] (SPARK-45282) Join loses records for cached datasets
[
https://issues.apache.org/jira/browse/SPARK-45282?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17781168#comment-17781168
]
Dongjoon Hyun commented on SPARK-45282:
---
Let me increase the priority to `Blocker` for now.
> Join loses records for cached datasets
> --
>
> Key: SPARK-45282
> URL: https://issues.apache.org/jira/browse/SPARK-45282
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.4.1, 3.5.0
> Environment: spark 3.4.1 on apache hadoop 3.3.6 or kubernetes 1.26 or
> databricks 13.3
>Reporter: koert kuipers
>Priority: Blocker
> Labels: CorrectnessBug, correctness
>
> we observed this issue on spark 3.4.1 but it is also present on 3.5.0. it is
> not present on spark 3.3.1.
> it only shows up in distributed environment. i cannot replicate in unit test.
> however i did get it to show up on hadoop cluster, kubernetes, and on
> databricks 13.3
> the issue is that records are dropped when two cached dataframes are joined.
> it seems in spark 3.4.1 in queryplan some Exchanges are dropped as an
> optimization while in spark 3.3.1 these Exhanges are still present. it seems
> to be an issue with AQE with canChangeCachedPlanOutputPartitioning=true.
> to reproduce on distributed cluster these settings needed are:
> {code:java}
> spark.sql.adaptive.advisoryPartitionSizeInBytes 33554432
> spark.sql.adaptive.coalescePartitions.parallelismFirst false
> spark.sql.adaptive.enabled true
> spark.sql.optimizer.canChangeCachedPlanOutputPartitioning true {code}
> code using scala to reproduce is:
> {code:java}
> import java.util.UUID
> import org.apache.spark.sql.functions.col
> import spark.implicits._
> val data = (1 to 100).toDS().map(i =>
> UUID.randomUUID().toString).persist()
> val left = data.map(k => (k, 1))
> val right = data.map(k => (k, k)) // if i change this to k => (k, 1) it works!
> println("number of left " + left.count())
> println("number of right " + right.count())
> println("number of (left join right) " +
> left.toDF("key", "value1").join(right.toDF("key", "value2"), "key").count()
> )
> val left1 = left
> .toDF("key", "value1")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of left1 " + left1.count())
> val right1 = right
> .toDF("key", "value2")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of right1 " + right1.count())
> println("number of (left1 join right1) " + left1.join(right1,
> "key").count()) // this gives incorrect result{code}
> this produces the following output:
> {code:java}
> number of left 100
> number of right 100
> number of (left join right) 100
> number of left1 100
> number of right1 100
> number of (left1 join right1) 859531 {code}
> note that the last number (the incorrect one) actually varies depending on
> settings and cluster size etc.
>
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[jira] [Commented] (SPARK-45282) Join loses records for cached datasets
[
https://issues.apache.org/jira/browse/SPARK-45282?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17781167#comment-17781167
]
Dongjoon Hyun commented on SPARK-45282:
---
Hi, All
Is this correctness issue still valid in branch-3.4/3.5/master? Or, I'm
wondering if this exists in the reported platform, Databricks 13.3?
> Join loses records for cached datasets
> --
>
> Key: SPARK-45282
> URL: https://issues.apache.org/jira/browse/SPARK-45282
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.4.1, 3.5.0
> Environment: spark 3.4.1 on apache hadoop 3.3.6 or kubernetes 1.26 or
> databricks 13.3
>Reporter: koert kuipers
>Priority: Major
> Labels: CorrectnessBug, correctness
>
> we observed this issue on spark 3.4.1 but it is also present on 3.5.0. it is
> not present on spark 3.3.1.
> it only shows up in distributed environment. i cannot replicate in unit test.
> however i did get it to show up on hadoop cluster, kubernetes, and on
> databricks 13.3
> the issue is that records are dropped when two cached dataframes are joined.
> it seems in spark 3.4.1 in queryplan some Exchanges are dropped as an
> optimization while in spark 3.3.1 these Exhanges are still present. it seems
> to be an issue with AQE with canChangeCachedPlanOutputPartitioning=true.
> to reproduce on distributed cluster these settings needed are:
> {code:java}
> spark.sql.adaptive.advisoryPartitionSizeInBytes 33554432
> spark.sql.adaptive.coalescePartitions.parallelismFirst false
> spark.sql.adaptive.enabled true
> spark.sql.optimizer.canChangeCachedPlanOutputPartitioning true {code}
> code using scala to reproduce is:
> {code:java}
> import java.util.UUID
> import org.apache.spark.sql.functions.col
> import spark.implicits._
> val data = (1 to 100).toDS().map(i =>
> UUID.randomUUID().toString).persist()
> val left = data.map(k => (k, 1))
> val right = data.map(k => (k, k)) // if i change this to k => (k, 1) it works!
> println("number of left " + left.count())
> println("number of right " + right.count())
> println("number of (left join right) " +
> left.toDF("key", "value1").join(right.toDF("key", "value2"), "key").count()
> )
> val left1 = left
> .toDF("key", "value1")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of left1 " + left1.count())
> val right1 = right
> .toDF("key", "value2")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of right1 " + right1.count())
> println("number of (left1 join right1) " + left1.join(right1,
> "key").count()) // this gives incorrect result{code}
> this produces the following output:
> {code:java}
> number of left 100
> number of right 100
> number of (left join right) 100
> number of left1 100
> number of right1 100
> number of (left1 join right1) 859531 {code}
> note that the last number (the incorrect one) actually varies depending on
> settings and cluster size etc.
>
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[jira] [Commented] (SPARK-45282) Join loses records for cached datasets
[
https://issues.apache.org/jira/browse/SPARK-45282?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17769893#comment-17769893
]
koert kuipers commented on SPARK-45282:
---
yes i can reproduce it.
master branch on commit:
{code:java}
commit 7e8aafd2c0f1f6fcd03a69afe2b85fd3fda95d20 (HEAD -> master,
upstream/master)
Author: lanmengran1
Date: Tue Sep 26 21:01:02 2023 -0500 [SPARK-45334][SQL] Remove misleading
comment in parquetSchemaConverter {code}
i build spark for k8s using:
{code:java}
$ dev/make-distribution.sh --name kubernetes --tgz -Pkubernetes -Phadoop-cloud
{code}
created docker container using Dockerfile provided in
resource-managers/kubernetes/docker/src/main/dockerfiles/spark/Dockerfile
launch pod and shell inside:
{code:java}
185@proxy:~/work-dir$ export SPARK_LOCAL_HOSTNAME=$(hostname -i
185@proxy:~/work-dir$ export SPARK_PUBLIC_DNS=$(hostname -i)
185@proxy:~/work-dir$ /opt/spark/bin/spark-shell --master
k8s://https://kubernetes.default:443 --deploy-mode client --num-executors 4
--executor-memory 2G --conf
spark.serializer=org.apache.spark.serializer.KryoSerializer --conf
spark.kubernetes.namespace=default --conf
spark.sql.adaptive.coalescePartitions.parallelismFirst=false --conf
spark.sql.adaptive.enabled=true --conf
spark.sql.adaptive.advisoryPartitionSizeInBytes=33554432 --conf
spark.sql.optimizer.canChangeCachedPlanOutputPartitioning=true --conf
spark.kubernetes.container.image=.dkr.ecr.us-east-1.amazonaws.com/spark:4.0.0-SNAPSHOT
23/09/28 03:44:57 WARN NativeCodeLoader: Unable to load native-hadoop library
for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use
setLogLevel(newLevel).
Welcome to
__
/ __/__ ___ _/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 4.0.0-SNAPSHOT
/_/
Using Scala version 2.13.11 (OpenJDK 64-Bit Server VM, Java 21)
Type in expressions to have them evaluated.
Type :help for more information.
Spark context Web UI available at http://10.177.71.94:4040
Spark context available as 'sc' (master = k8s://https://kubernetes.default:443,
app id = spark-5ab0957571944828866a2f23068ff180).
Spark session available as 'spark'.scala> :paste
// Entering paste mode (ctrl-D to finish)import java.util.UUID
import org.apache.spark.sql.functions.col
import spark.implicits._
val data = (1 to 100).toDS().map(i => UUID.randomUUID().toString).persist()
val left = data.map(k => (k, 1))
val right = data.map(k => (k, k)) // if i change this to k => (k, 1) it works!
println("number of left " + left.count())
println("number of right " + right.count())
println("number of (left join right) " +
left.toDF("key", "vertex").join(right.toDF("key", "state"), "key").count()
)
val left1 = left
.toDF("key", "vertex")
.repartition(col("key")) // comment out this line to make it work
.persist()
println("number of left1 " + left1.count())
val right1 = right
.toDF("key", "state")
.repartition(col("key")) // comment out this line to make it work
.persist()
println("number of right1 " + right1.count())
println("number of (left1 join right1) " + left1.join(right1, "key").count())
// this gives incorrect result
// Exiting paste mode, now interpreting.
23/09/28 03:45:30 WARN TaskSetManager: Stage 0 contains a task of very large
size (6631 KiB). The maximum recommended task size is 1000 KiB.
23/09/28 03:45:34 WARN TaskSetManager: Stage 1 contains a task of very large
size (6631 KiB). The maximum recommended task size is 1000 KiB.
number of left 100
23/09/28 03:45:36 WARN TaskSetManager: Stage 4 contains a task of very large
size (6631 KiB). The maximum recommended task size is 1000 KiB.
number of right 100
23/09/28 03:45:39 WARN TaskSetManager: Stage 7 contains a task of very large
size (6631 KiB). The maximum recommended task size is 1000 KiB.
23/09/28 03:45:40 WARN TaskSetManager: Stage 8 contains a task of very large
size (6631 KiB). The maximum recommended task size is 1000 KiB.
number of (left join right) 100
23/09/28 03:45:45 WARN TaskSetManager: Stage 16 contains a task of very large
size (6631 KiB). The maximum recommended task size is 1000 KiB.
number of left1 100
23/09/28 03:45:48 WARN TaskSetManager: Stage 24 contains a task of very large
size (6631 KiB). The maximum recommended task size is 1000 KiB.
number of right1 100
number of (left1 join right1) 850735
import java.util.UUID
import
[jira] [Commented] (SPARK-45282) Join loses records for cached datasets
[
https://issues.apache.org/jira/browse/SPARK-45282?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17769383#comment-17769383
]
XiDuo You commented on SPARK-45282:
---
I can not re-produce this issue in master branch (4.0.0), [~koert] have you
tried master branch ?
> Join loses records for cached datasets
> --
>
> Key: SPARK-45282
> URL: https://issues.apache.org/jira/browse/SPARK-45282
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.4.1, 3.5.0
> Environment: spark 3.4.1 on apache hadoop 3.3.6 or kubernetes 1.26 or
> databricks 13.3
>Reporter: koert kuipers
>Priority: Major
> Labels: CorrectnessBug, correctness
>
> we observed this issue on spark 3.4.1 but it is also present on 3.5.0. it is
> not present on spark 3.3.1.
> it only shows up in distributed environment. i cannot replicate in unit test.
> however i did get it to show up on hadoop cluster, kubernetes, and on
> databricks 13.3
> the issue is that records are dropped when two cached dataframes are joined.
> it seems in spark 3.4.1 in queryplan some Exchanges are dropped as an
> optimization while in spark 3.3.1 these Exhanges are still present. it seems
> to be an issue with AQE with canChangeCachedPlanOutputPartitioning=true.
> to reproduce on distributed cluster these settings needed are:
> {code:java}
> spark.sql.adaptive.advisoryPartitionSizeInBytes 33554432
> spark.sql.adaptive.coalescePartitions.parallelismFirst false
> spark.sql.adaptive.enabled true
> spark.sql.optimizer.canChangeCachedPlanOutputPartitioning true {code}
> code using scala to reproduce is:
> {code:java}
> import java.util.UUID
> import org.apache.spark.sql.functions.col
> import spark.implicits._
> val data = (1 to 100).toDS().map(i =>
> UUID.randomUUID().toString).persist()
> val left = data.map(k => (k, 1))
> val right = data.map(k => (k, k)) // if i change this to k => (k, 1) it works!
> println("number of left " + left.count())
> println("number of right " + right.count())
> println("number of (left join right) " +
> left.toDF("key", "value1").join(right.toDF("key", "value2"), "key").count()
> )
> val left1 = left
> .toDF("key", "value1")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of left1 " + left1.count())
> val right1 = right
> .toDF("key", "value2")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of right1 " + right1.count())
> println("number of (left1 join right1) " + left1.join(right1,
> "key").count()) // this gives incorrect result{code}
> this produces the following output:
> {code:java}
> number of left 100
> number of right 100
> number of (left join right) 100
> number of left1 100
> number of right1 100
> number of (left1 join right1) 859531 {code}
> note that the last number (the incorrect one) actually varies depending on
> settings and cluster size etc.
>
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[jira] [Commented] (SPARK-45282) Join loses records for cached datasets
[
https://issues.apache.org/jira/browse/SPARK-45282?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17768399#comment-17768399
]
Yuming Wang commented on SPARK-45282:
-
cc [~ulysses] [~cloud_fan]
> Join loses records for cached datasets
> --
>
> Key: SPARK-45282
> URL: https://issues.apache.org/jira/browse/SPARK-45282
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.4.1, 3.5.0
> Environment: spark 3.4.1 on apache hadoop 3.3.6 or kubernetes 1.26 or
> databricks 13.3
>Reporter: koert kuipers
>Priority: Major
> Labels: CorrectnessBug, correctness
>
> we observed this issue on spark 3.4.1 but it is also present on 3.5.0. it is
> not present on spark 3.3.1.
> it only shows up in distributed environment. i cannot replicate in unit test.
> however i did get it to show up on hadoop cluster, kubernetes, and on
> databricks 13.3
> the issue is that records are dropped when two cached dataframes are joined.
> it seems in spark 3.4.1 in queryplan some Exchanges are dropped as an
> optimization while in spark 3.3.1 these Exhanges are still present. it seems
> to be an issue with AQE with canChangeCachedPlanOutputPartitioning=true.
> to reproduce on distributed cluster these settings needed are:
> {code:java}
> spark.sql.adaptive.advisoryPartitionSizeInBytes 33554432
> spark.sql.adaptive.coalescePartitions.parallelismFirst false
> spark.sql.adaptive.enabled true
> spark.sql.optimizer.canChangeCachedPlanOutputPartitioning true {code}
> code using scala to reproduce is:
> {code:java}
> import java.util.UUID
> import org.apache.spark.sql.functions.col
> import spark.implicits._
> val data = (1 to 100).toDS().map(i =>
> UUID.randomUUID().toString).persist()
> val left = data.map(k => (k, 1))
> val right = data.map(k => (k, k)) // if i change this to k => (k, 1) it works!
> println("number of left " + left.count())
> println("number of right " + right.count())
> println("number of (left join right) " +
> left.toDF("key", "value1").join(right.toDF("key", "value2"), "key").count()
> )
> val left1 = left
> .toDF("key", "value1")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of left1 " + left1.count())
> val right1 = right
> .toDF("key", "value2")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of right1 " + right1.count())
> println("number of (left1 join right1) " + left1.join(right1,
> "key").count()) // this gives incorrect result{code}
> this produces the following output:
> {code:java}
> number of left 100
> number of right 100
> number of (left join right) 100
> number of left1 100
> number of right1 100
> number of (left1 join right1) 859531 {code}
> note that the last number (the incorrect one) actually varies depending on
> settings and cluster size etc.
>
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[jira] [Commented] (SPARK-45282) Join loses records for cached datasets
[
https://issues.apache.org/jira/browse/SPARK-45282?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17768123#comment-17768123
]
koert kuipers commented on SPARK-45282:
---
after reverting SPARK-41048 the issue went away. so i think this is the cause.
> Join loses records for cached datasets
> --
>
> Key: SPARK-45282
> URL: https://issues.apache.org/jira/browse/SPARK-45282
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.4.1, 3.5.0
> Environment: spark 3.4.1 on apache hadoop 3.3.6 or kubernetes 1.26 or
> databricks 13.3
>Reporter: koert kuipers
>Priority: Major
> Labels: CorrectnessBug, correctness
>
> we observed this issue on spark 3.4.1 but it is also present on 3.5.0. it is
> not present on spark 3.3.1.
> it only shows up in distributed environment. i cannot replicate in unit test.
> however i did get it to show up on hadoop cluster, kubernetes, and on
> databricks 13.3
> the issue is that records are dropped when two cached dataframes are joined.
> it seems in spark 3.4.1 in queryplan some Exchanges are dropped as an
> optimization while in spark 3.3.1 these Exhanges are still present. it seems
> to be an issue with AQE with canChangeCachedPlanOutputPartitioning=true.
> to reproduce on distributed cluster these settings needed are:
> {code:java}
> spark.sql.adaptive.advisoryPartitionSizeInBytes 33554432
> spark.sql.adaptive.coalescePartitions.parallelismFirst false
> spark.sql.adaptive.enabled true
> spark.sql.optimizer.canChangeCachedPlanOutputPartitioning true {code}
> code using scala 2.13 to reproduce is:
> {code:java}
> import java.util.UUID
> import org.apache.spark.sql.functions.col
> import spark.implicits._
> val data = (1 to 100).toDS().map(i =>
> UUID.randomUUID().toString).persist()
> val left = data.map(k => (k, 1))
> val right = data.map(k => (k, k)) // if i change this to k => (k, 1) it works!
> println("number of left " + left.count())
> println("number of right " + right.count())
> println("number of (left join right) " +
> left.toDF("key", "vertex").join(right.toDF("key", "state"), "key").count()
> )
> val left1 = left
> .toDF("key", "vertex")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of left1 " + left1.count())
> val right1 = right
> .toDF("key", "state")
> .repartition(col("key")) // comment out this line to make it work
> .persist()
> println("number of right1 " + right1.count())
> println("number of (left1 join right1) " + left1.join(right1,
> "key").count()) // this gives incorrect result{code}
> this produces the following output:
> {code:java}
> number of left 100
> number of right 100
> number of (left join right) 100
> number of left1 100
> number of right1 100
> number of (left1 join right1) 859531 {code}
> note that the last number (the incorrect one) actually varies depending on
> settings and cluster size etc.
>
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