[jira] [Assigned] (SPARK-22692) Reduce the number of generated mutable states
[ https://issues.apache.org/jira/browse/SPARK-22692?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li reassigned SPARK-22692: --- Assignee: Marco Gaido > Reduce the number of generated mutable states > - > > Key: SPARK-22692 > URL: https://issues.apache.org/jira/browse/SPARK-22692 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 2.3.0 >Reporter: Marco Gaido >Assignee: Marco Gaido >Priority: Major > > A large number of mutable states can cause a error during code generation due > to reaching the constant pool limit. There is an ongoing effort on > SPARK-18016 to fix the problem, nonetheless we can also alleviate it avoiding > to create a global variables when they are not needed. > Therefore I am creating this umbrella ticket to track the elimination of > usage of global variables where not needed. This is not a duplicate or an > alternative to SPARK-18016: this is a complementary effort which can help > together with it to support wider datasets. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-22939) Support Spark UDF in registerFunction
[
https://issues.apache.org/jira/browse/SPARK-22939?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiao Li updated SPARK-22939:
Issue Type: Improvement (was: Bug)
> Support Spark UDF in registerFunction
> -
>
> Key: SPARK-22939
> URL: https://issues.apache.org/jira/browse/SPARK-22939
> Project: Spark
> Issue Type: Improvement
> Components: PySpark, SQL
>Affects Versions: 2.3.0
>Reporter: Xiao Li
>Assignee: Xiao Li
>Priority: Major
> Fix For: 2.3.0
>
>
> {noformat}
> import random
> from pyspark.sql.functions import udf
> from pyspark.sql.types import IntegerType, StringType
> random_udf = udf(lambda: int(random.random() * 100),
> IntegerType()).asNondeterministic()
> spark.catalog.registerFunction("random_udf", random_udf, StringType())
> spark.sql("SELECT random_udf()").collect()
> {noformat}
> We will get the following error.
> {noformat}
> Py4JError: An error occurred while calling o29.__getnewargs__. Trace:
> py4j.Py4JException: Method __getnewargs__([]) does not exist
> at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:318)
> at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:326)
> at py4j.Gateway.invoke(Gateway.java:274)
> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
> at py4j.commands.CallCommand.execute(CallCommand.java:79)
> at py4j.GatewayConnection.run(GatewayConnection.java:214)
> at java.lang.Thread.run(Thread.java:745)
> {noformat}
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[jira] [Updated] (SPARK-22961) Constant columns no longer picked as constraints in 2.3
[
https://issues.apache.org/jira/browse/SPARK-22961?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiao Li updated SPARK-22961:
Issue Type: Bug (was: Improvement)
> Constant columns no longer picked as constraints in 2.3
> ---
>
> Key: SPARK-22961
> URL: https://issues.apache.org/jira/browse/SPARK-22961
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Adrian Ionescu
>Assignee: Adrian Ionescu
>Priority: Major
> Labels: constraints, optimizer, regression
> Fix For: 2.3.0
>
>
> We're no longer picking up {{x = 2}} as a constraint from something like
> {{df.withColumn("x", lit(2))}}
> The unit test below succeeds in {{branch-2.2}}:
> {code}
> test("constraints should be inferred from aliased literals") {
> val originalLeft = testRelation.subquery('left).as("left")
> val optimizedLeft = testRelation.subquery('left).where(IsNotNull('a) &&
> 'a <=> 2).as("left")
> val right = Project(Seq(Literal(2).as("two")),
> testRelation.subquery('right)).as("right")
> val condition = Some("left.a".attr === "right.two".attr)
> val original = originalLeft.join(right, Inner, condition)
> val correct = optimizedLeft.join(right, Inner, condition)
> comparePlans(Optimize.execute(original.analyze), correct.analyze)
> }
> {code}
> but fails in {{branch-2.3}} with:
> {code}
> == FAIL: Plans do not match ===
> 'Join Inner, (two#0 = a#0) 'Join Inner, (two#0 = a#0)
> !:- Filter isnotnull(a#0) :- Filter ((2 <=> a#0) &&
> isnotnull(a#0))
> : +- LocalRelation , [a#0, b#0, c#0] : +- LocalRelation ,
> [a#0, b#0, c#0]
> +- Project [2 AS two#0]+- Project [2 AS two#0]
> +- LocalRelation , [a#0, b#0, c#0] +- LocalRelation ,
> [a#0, b#0, c#0]
> {code}
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[jira] [Updated] (SPARK-16060) Vectorized Orc reader
[ https://issues.apache.org/jira/browse/SPARK-16060?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-16060: Labels: release-notes releasenotes (was: release-notes) > Vectorized Orc reader > - > > Key: SPARK-16060 > URL: https://issues.apache.org/jira/browse/SPARK-16060 > Project: Spark > Issue Type: New Feature > Components: SQL >Affects Versions: 1.6.3, 2.0.2, 2.1.2, 2.2.1 >Reporter: Liang-Chi Hsieh >Assignee: Dongjoon Hyun >Priority: Major > Labels: release-notes, releasenotes > Fix For: 2.3.0 > > > Currently Orc reader in Spark SQL doesn't support vectorized reading. As Hive > Orc already support vectorization, we should add this support to improve Orc > reading performance. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-22510) Exceptions caused by 64KB JVM bytecode or 64K constant pool entry limit
[ https://issues.apache.org/jira/browse/SPARK-22510?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-22510: Labels: releasenotes (was: ) > Exceptions caused by 64KB JVM bytecode or 64K constant pool entry limit > > > Key: SPARK-22510 > URL: https://issues.apache.org/jira/browse/SPARK-22510 > Project: Spark > Issue Type: Umbrella > Components: SQL >Affects Versions: 2.2.0 >Reporter: Xiao Li >Assignee: Kazuaki Ishizaki >Priority: Major > Labels: releasenotes > > Codegen can throw an exception due to the 64KB JVM bytecode or 64K constant > pool entry limit. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-22510) Exceptions caused by 64KB JVM bytecode or 64K constant pool entry limit
[ https://issues.apache.org/jira/browse/SPARK-22510?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li reassigned SPARK-22510: --- Assignee: Kazuaki Ishizaki > Exceptions caused by 64KB JVM bytecode or 64K constant pool entry limit > > > Key: SPARK-22510 > URL: https://issues.apache.org/jira/browse/SPARK-22510 > Project: Spark > Issue Type: Umbrella > Components: SQL >Affects Versions: 2.2.0 >Reporter: Xiao Li >Assignee: Kazuaki Ishizaki >Priority: Major > Labels: releasenotes > > Codegen can throw an exception due to the 64KB JVM bytecode or 64K constant > pool entry limit. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-22510) Exceptions caused by 64KB JVM bytecode or 64K constant pool entry limit
[ https://issues.apache.org/jira/browse/SPARK-22510?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-22510: Fix Version/s: (was: 2.3.0) > Exceptions caused by 64KB JVM bytecode or 64K constant pool entry limit > > > Key: SPARK-22510 > URL: https://issues.apache.org/jira/browse/SPARK-22510 > Project: Spark > Issue Type: Umbrella > Components: SQL >Affects Versions: 2.2.0 >Reporter: Xiao Li >Priority: Major > > Codegen can throw an exception due to the 64KB JVM bytecode or 64K constant > pool entry limit. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-22510) Exceptions caused by 64KB JVM bytecode or 64K constant pool entry limit
[ https://issues.apache.org/jira/browse/SPARK-22510?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-22510: Fix Version/s: 2.3.0 > Exceptions caused by 64KB JVM bytecode or 64K constant pool entry limit > > > Key: SPARK-22510 > URL: https://issues.apache.org/jira/browse/SPARK-22510 > Project: Spark > Issue Type: Umbrella > Components: SQL >Affects Versions: 2.2.0 >Reporter: Xiao Li >Priority: Major > > Codegen can throw an exception due to the 64KB JVM bytecode or 64K constant > pool entry limit. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-20392) Slow performance when calling fit on ML pipeline for dataset with many columns but few rows
[
https://issues.apache.org/jira/browse/SPARK-20392?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiao Li updated SPARK-20392:
Component/s: SQL
> Slow performance when calling fit on ML pipeline for dataset with many
> columns but few rows
> ---
>
> Key: SPARK-20392
> URL: https://issues.apache.org/jira/browse/SPARK-20392
> Project: Spark
> Issue Type: Improvement
> Components: ML, SQL
>Affects Versions: 2.1.0
>Reporter: Barry Becker
>Assignee: Liang-Chi Hsieh
>Priority: Major
> Fix For: 2.3.0
>
> Attachments: blockbuster.csv, blockbuster_fewCols.csv,
> giant_query_plan_for_fitting_pipeline.txt, model_9754.zip, model_9756.zip
>
>
> This started as a [question on stack
> overflow|http://stackoverflow.com/questions/43484006/why-is-it-slow-to-apply-a-spark-pipeline-to-dataset-with-many-columns-but-few-ro],
> but it seems like a bug.
> I am testing spark pipelines using a simple dataset (attached) with 312
> (mostly numeric) columns, but only 421 rows. It is small, but it takes 3
> minutes to apply my ML pipeline to it on a 24 core server with 60G of memory.
> This seems much to long for such a tiny dataset. Similar pipelines run
> quickly on datasets that have fewer columns and more rows. It's something
> about the number of columns that is causing the slow performance.
> Here are a list of the stages in my pipeline:
> {code}
> 000_strIdx_5708525b2b6c
> 001_strIdx_ec2296082913
> 002_bucketizer_3cbc8811877b
> 003_bucketizer_5a01d5d78436
> 004_bucketizer_bf290d11364d
> 005_bucketizer_c3296dfe94b2
> 006_bucketizer_7071ca50eb85
> 007_bucketizer_27738213c2a1
> 008_bucketizer_bd728fd89ba1
> 009_bucketizer_e1e716f51796
> 010_bucketizer_38be665993ba
> 011_bucketizer_5a0e41e5e94f
> 012_bucketizer_b5a3d5743aaa
> 013_bucketizer_4420f98ff7ff
> 014_bucketizer_777cc4fe6d12
> 015_bucketizer_f0f3a3e5530e
> 016_bucketizer_218ecca3b5c1
> 017_bucketizer_0b083439a192
> 018_bucketizer_4520203aec27
> 019_bucketizer_462c2c346079
> 020_bucketizer_47435822e04c
> 021_bucketizer_eb9dccb5e6e8
> 022_bucketizer_b5f63dd7451d
> 023_bucketizer_e0fd5041c841
> 024_bucketizer_ffb3b9737100
> 025_bucketizer_e06c0d29273c
> 026_bucketizer_36ee535a425f
> 027_bucketizer_ee3a330269f1
> 028_bucketizer_094b58ea01c0
> 029_bucketizer_e93ea86c08e2
> 030_bucketizer_4728a718bc4b
> 031_bucketizer_08f6189c7fcc
> 032_bucketizer_11feb74901e6
> 033_bucketizer_ab4add4966c7
> 034_bucketizer_4474f7f1b8ce
> 035_bucketizer_90cfa5918d71
> 036_bucketizer_1a9ff5e4eccb
> 037_bucketizer_38085415a4f4
> 038_bucketizer_9b5e5a8d12eb
> 039_bucketizer_082bb650ecc3
> 040_bucketizer_57e1e363c483
> 041_bucketizer_337583fbfd65
> 042_bucketizer_73e8f6673262
> 043_bucketizer_0f9394ed30b8
> 044_bucketizer_8530f3570019
> 045_bucketizer_c53614f1e507
> 046_bucketizer_8fd99e6ec27b
> 047_bucketizer_6a8610496d8a
> 048_bucketizer_888b0055c1ad
> 049_bucketizer_974e0a1433a6
> 050_bucketizer_e848c0937cb9
> 051_bucketizer_95611095a4ac
> 052_bucketizer_660a6031acd9
> 053_bucketizer_aaffe5a3140d
> 054_bucketizer_8dc569be285f
> 055_bucketizer_83d1bffa07bc
> 056_bucketizer_0c6180ba75e6
> 057_bucketizer_452f265a000d
> 058_bucketizer_38e02ddfb447
> 059_bucketizer_6fa4ad5d3ebd
> 060_bucketizer_91044ee766ce
> 061_bucketizer_9a9ef04a173d
> 062_bucketizer_3d98eb15f206
> 063_bucketizer_c4915bb4d4ed
> 064_bucketizer_8ca2b6550c38
> 065_bucketizer_417ee9b760bc
> 066_bucketizer_67f3556bebe8
> 067_bucketizer_0556deb652c6
> 068_bucketizer_067b4b3d234c
> 069_bucketizer_30ba55321538
> 070_bucketizer_ad826cc5d746
> 071_bucketizer_77676a898055
> 072_bucketizer_05c37a38ce30
> 073_bucketizer_6d9ae54163ed
> 074_bucketizer_8cd668b2855d
> 075_bucketizer_d50ea1732021
> 076_bucketizer_c68f467c9559
> 077_bucketizer_ee1dfc840db1
> 078_bucketizer_83ec06a32519
> 079_bucketizer_741d08c1b69e
> 080_bucketizer_b7402e4829c7
> 081_bucketizer_8adc590dc447
> 082_bucketizer_673be99bdace
> 083_bucketizer_77693b45f94c
> 084_bucketizer_53529c6b1ac4
> 085_bucketizer_6a3ca776a81e
> 086_bucketizer_6679d9588ac1
> 087_bucketizer_6c73af456f65
> 088_bucketizer_2291b2c5ab51
> 089_bucketizer_cb3d0fe669d8
> 090_bucketizer_e71f913c1512
> 091_bucketizer_156528f65ce7
> 092_bucketizer_f3ec5dae079b
> 093_bucketizer_809fab77eee1
> 094_bucketizer_6925831511e6
> 095_bucketizer_c5d853b95707
> 096_bucketizer_e677659ca253
> 097_bucketizer_396e35548c72
> 098_bucketizer_78a6410d7a84
> 099_bucketizer_e3ae6e54bca1
> 100_bucketizer_9fed5923fe8a
> 101_bucketizer_8925ba4c3ee2
> 102_bucketizer_95750b6942b8
> 103_bucketizer_6e8b50a1918b
> 104_bucketizer_36cfcc13d4ba
> 105_bucketizer_2716d0455512
> 106_bucketizer_9bcf2891652f
> 107_bucketizer_8c3d352915f7
> 108_bucketizer_0786c17d5ef9
> 109_bucketizer_f22df23ef56f
> 110_bucketizer_bad04578bd20
> 111_bucketizer_35cfbde7e28f
>
[jira] [Updated] (SPARK-20682) Add new ORCFileFormat based on Apache ORC
[ https://issues.apache.org/jira/browse/SPARK-20682?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-20682: Labels: releasenotes (was: ) > Add new ORCFileFormat based on Apache ORC > - > > Key: SPARK-20682 > URL: https://issues.apache.org/jira/browse/SPARK-20682 > Project: Spark > Issue Type: Improvement > Components: SQL >Affects Versions: 1.4.1, 1.5.2, 1.6.3, 2.1.1, 2.2.0 >Reporter: Dongjoon Hyun >Assignee: Dongjoon Hyun >Priority: Major > Labels: releasenotes > Fix For: 2.3.0 > > > Since SPARK-2883, Apache Spark supports Apache ORC inside `sql/hive` module > with Hive dependency. This issue aims to add a new and faster ORC data source > inside `sql/core` and to replace the old ORC data source eventually. In this > issue, the latest Apache ORC 1.4.0 (released yesterday) is used. > There are four key benefits. > - Speed: Use both Spark `ColumnarBatch` and ORC `RowBatch` together. This is > faster than the current implementation in Spark. > - Stability: Apache ORC 1.4.0 has many fixes and we can depend on ORC > community more. > - Usability: User can use `ORC` data sources without hive module, i.e, > `-Phive`. > - Maintainability: Reduce the Hive dependency and can remove old legacy code > later. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-23219) Rename ReadTask to DataReaderFactory
[ https://issues.apache.org/jira/browse/SPARK-23219?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-23219: Parent Issue: SPARK-15689 (was: SPARK-22386) > Rename ReadTask to DataReaderFactory > > > Key: SPARK-23219 > URL: https://issues.apache.org/jira/browse/SPARK-23219 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.2.1 >Reporter: Gengliang Wang >Assignee: Gengliang Wang >Priority: Major > Fix For: 2.3.0 > > -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-23280) add map type support to ColumnVector
[ https://issues.apache.org/jira/browse/SPARK-23280?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-23280: Assignee: Wenchen Fan (was: Apache Spark) > add map type support to ColumnVector > > > Key: SPARK-23280 > URL: https://issues.apache.org/jira/browse/SPARK-23280 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.3.0 >Reporter: Wenchen Fan >Assignee: Wenchen Fan >Priority: Major > -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-23280) add map type support to ColumnVector
[ https://issues.apache.org/jira/browse/SPARK-23280?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16346372#comment-16346372 ] Apache Spark commented on SPARK-23280: -- User 'cloud-fan' has created a pull request for this issue: https://github.com/apache/spark/pull/20450 > add map type support to ColumnVector > > > Key: SPARK-23280 > URL: https://issues.apache.org/jira/browse/SPARK-23280 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.3.0 >Reporter: Wenchen Fan >Assignee: Wenchen Fan >Priority: Major > -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-23280) add map type support to ColumnVector
[ https://issues.apache.org/jira/browse/SPARK-23280?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-23280: Assignee: Apache Spark (was: Wenchen Fan) > add map type support to ColumnVector > > > Key: SPARK-23280 > URL: https://issues.apache.org/jira/browse/SPARK-23280 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.3.0 >Reporter: Wenchen Fan >Assignee: Apache Spark >Priority: Major > -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-22400) rename some APIs and classes to make their meaning clearer
[ https://issues.apache.org/jira/browse/SPARK-22400?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-22400: Parent Issue: SPARK-15689 (was: SPARK-22386) > rename some APIs and classes to make their meaning clearer > -- > > Key: SPARK-22400 > URL: https://issues.apache.org/jira/browse/SPARK-22400 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.3.0 >Reporter: Zhenhua Wang >Assignee: Zhenhua Wang >Priority: Major > Fix For: 2.3.0 > > > Both `ReadSupport` and `ReadTask` have a method called `createReader`, but > they create different things. This could cause some confusion for data source > developers. The same issue exists between `WriteSupport` and > `DataWriterFactory`, both of which have a method called `createWriter`. > Besides, the name of `RowToInternalRowDataWriterFactory` is not correct, > because it actually converts `InternalRow`s to `Row`s. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-22452) DataSourceV2Options should have getInt, getBoolean, etc.
[ https://issues.apache.org/jira/browse/SPARK-22452?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-22452: Parent Issue: SPARK-15689 (was: SPARK-22386) > DataSourceV2Options should have getInt, getBoolean, etc. > > > Key: SPARK-22452 > URL: https://issues.apache.org/jira/browse/SPARK-22452 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.3.0 >Reporter: Wenchen Fan >Assignee: Sunitha Kambhampati >Priority: Major > Fix For: 2.3.0 > > -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-22392) columnar reader interface
[ https://issues.apache.org/jira/browse/SPARK-22392?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-22392: Parent Issue: SPARK-15689 (was: SPARK-22386) > columnar reader interface > -- > > Key: SPARK-22392 > URL: https://issues.apache.org/jira/browse/SPARK-22392 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.3.0 >Reporter: Wenchen Fan >Assignee: Wenchen Fan >Priority: Major > Fix For: 2.3.0 > > -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-22389) partitioning reporting
[ https://issues.apache.org/jira/browse/SPARK-22389?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-22389: Fix Version/s: (was: 2.3.1) 2.3.0 > partitioning reporting > -- > > Key: SPARK-22389 > URL: https://issues.apache.org/jira/browse/SPARK-22389 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.3.0 >Reporter: Wenchen Fan >Assignee: Wenchen Fan >Priority: Major > Fix For: 2.3.0 > > > We should allow data source to report partitioning and avoid shuffle at Spark > side -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-22389) partitioning reporting
[ https://issues.apache.org/jira/browse/SPARK-22389?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-22389: Parent Issue: SPARK-15689 (was: SPARK-22386) > partitioning reporting > -- > > Key: SPARK-22389 > URL: https://issues.apache.org/jira/browse/SPARK-22389 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.3.0 >Reporter: Wenchen Fan >Assignee: Wenchen Fan >Priority: Major > Fix For: 2.3.0 > > > We should allow data source to report partitioning and avoid shuffle at Spark > side -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-22387) propagate session configs to data source read/write options
[
https://issues.apache.org/jira/browse/SPARK-22387?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiao Li updated SPARK-22387:
Parent Issue: SPARK-15689 (was: SPARK-22386)
> propagate session configs to data source read/write options
> ---
>
> Key: SPARK-22387
> URL: https://issues.apache.org/jira/browse/SPARK-22387
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Wenchen Fan
>Assignee: Jiang Xingbo
>Priority: Major
> Fix For: 2.3.0
>
>
> This is an open discussion. The general idea is we should allow users to set
> some common configs in session conf so that they don't need to type them
> again and again for each data source operations.
> Proposal 1:
> propagate every session config which starts with {{spark.datasource.config.}}
> to data source options. The downside is, users may only want to set some
> common configs for a specific data source.
> Proposal 2:
> propagate session config which starts with
> {{spark.datasource.config.myDataSource.}} only to {{myDataSource}}
> operations. One downside is, some data source may not have a short name and
> makes the config key pretty long, e.g.
> {{spark.datasource.config.com.company.foo.bar.key1}}.
> Proposal 3:
> Introduce a trait `WithSessionConfig` which defines session config key
> prefix. Then we can pick session configs with this key-prefix and propagate
> it to this particular data source.
> One another thing also worth to think: sometimes it's really annoying if
> users have a typo in the config key and spend a lot of time to figure out why
> things don't work as expected. We should allow data source to validate the
> given options and throw exception if an option can't be recognized.
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[jira] [Updated] (SPARK-22386) Data Source V2 improvements
[ https://issues.apache.org/jira/browse/SPARK-22386?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-22386: Labels: releasenotes (was: ) > Data Source V2 improvements > --- > > Key: SPARK-22386 > URL: https://issues.apache.org/jira/browse/SPARK-22386 > Project: Spark > Issue Type: Improvement > Components: SQL >Affects Versions: 2.3.0 >Reporter: Wenchen Fan >Priority: Major > Labels: releasenotes > -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Created] (SPARK-23280) add map type support to ColumnVector
Wenchen Fan created SPARK-23280: --- Summary: add map type support to ColumnVector Key: SPARK-23280 URL: https://issues.apache.org/jira/browse/SPARK-23280 Project: Spark Issue Type: Sub-task Components: SQL Affects Versions: 2.3.0 Reporter: Wenchen Fan Assignee: Wenchen Fan -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-23260) remove V2 from the class name of data source reader/writer
[ https://issues.apache.org/jira/browse/SPARK-23260?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-23260: Issue Type: Sub-task (was: Improvement) Parent: SPARK-15689 > remove V2 from the class name of data source reader/writer > -- > > Key: SPARK-23260 > URL: https://issues.apache.org/jira/browse/SPARK-23260 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.3.0 >Reporter: Wenchen Fan >Assignee: Wenchen Fan >Priority: Major > Fix For: 2.3.0 > > -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-23262) mix-in interface should extend the interface it aimed to mix in
[ https://issues.apache.org/jira/browse/SPARK-23262?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-23262: Issue Type: Sub-task (was: Bug) Parent: SPARK-15689 > mix-in interface should extend the interface it aimed to mix in > --- > > Key: SPARK-23262 > URL: https://issues.apache.org/jira/browse/SPARK-23262 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.3.0 >Reporter: Wenchen Fan >Assignee: Wenchen Fan >Priority: Major > Fix For: 2.3.0 > > -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-20960) make ColumnVector public
[ https://issues.apache.org/jira/browse/SPARK-20960?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-20960: Labels: releasenotes (was: ) > make ColumnVector public > > > Key: SPARK-20960 > URL: https://issues.apache.org/jira/browse/SPARK-20960 > Project: Spark > Issue Type: New Feature > Components: SQL >Affects Versions: 2.3.0 >Reporter: Wenchen Fan >Assignee: Wenchen Fan >Priority: Major > Labels: releasenotes > Fix For: 2.3.0 > > > ColumnVector is an internal interface in Spark SQL, which is only used for > vectorized parquet reader to represent the in-memory columnar format. > In Spark 2.3 we want to make ColumnVector public, so that we can provide a > more efficient way for data exchanges between Spark and external systems. For > example, we can use ColumnVector to build the columnar read API in data > source framework, we can use ColumnVector to build a more efficient UDF API, > etc. > We also want to introduce a new ColumnVector implementation based on Apache > Arrow(basically just a wrapper over Arrow), so that external systems(like > Python Pandas DataFrame) can build ColumnVector very easily. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Resolved] (SPARK-22969) aggregateByKey with aggregator compression
[
https://issues.apache.org/jira/browse/SPARK-22969?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
zhengruifeng resolved SPARK-22969.
--
Resolution: Not A Problem
> aggregateByKey with aggregator compression
> --
>
> Key: SPARK-22969
> URL: https://issues.apache.org/jira/browse/SPARK-22969
> Project: Spark
> Issue Type: Question
> Components: Spark Core
>Affects Versions: 2.4.0
>Reporter: zhengruifeng
>Priority: Minor
>
> I encounter a special case that the aggregator can be represented as two
> types:
> a) high memory-footprint, but fast {{update}}
> b) compact, but must be converted to type a before calling {{update}} and
> {{merge}}.
> I wonder whether it is possible to compress the fat aggregators in
> {{aggregateByKey}} before shuffle, how can I impl it? [~cloud_fan]
> One similar case maybe:
> Using {{aggregateByKey}}/{{reduceByKey}} to compute the nnz vector (number of
> non-zero value) for different keys on a large sparse dataset.
> We can use {{DenseVector}} as the aggregators to count the nnz, and then
> compress it by call {{Vector#compressed}} before send it to the network.
> Another similar case maybe calling {{QuantileSummaries#compress}} before
> communication.
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[jira] [Resolved] (SPARK-23272) add calendar interval type support to ColumnVector
[ https://issues.apache.org/jira/browse/SPARK-23272?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Wenchen Fan resolved SPARK-23272. - Resolution: Fixed Fix Version/s: 2.3.0 Issue resolved by pull request 20438 [https://github.com/apache/spark/pull/20438] > add calendar interval type support to ColumnVector > -- > > Key: SPARK-23272 > URL: https://issues.apache.org/jira/browse/SPARK-23272 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.3.0 >Reporter: Wenchen Fan >Assignee: Wenchen Fan >Priority: Major > Fix For: 2.3.0 > > -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-22969) aggregateByKey with aggregator compression
[
https://issues.apache.org/jira/browse/SPARK-22969?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16346359#comment-16346359
]
zhengruifeng commented on SPARK-22969:
--
[~srowen] Mailing list is a better place to discuss. Thanks.
> aggregateByKey with aggregator compression
> --
>
> Key: SPARK-22969
> URL: https://issues.apache.org/jira/browse/SPARK-22969
> Project: Spark
> Issue Type: Question
> Components: Spark Core
>Affects Versions: 2.4.0
>Reporter: zhengruifeng
>Priority: Minor
>
> I encounter a special case that the aggregator can be represented as two
> types:
> a) high memory-footprint, but fast {{update}}
> b) compact, but must be converted to type a before calling {{update}} and
> {{merge}}.
> I wonder whether it is possible to compress the fat aggregators in
> {{aggregateByKey}} before shuffle, how can I impl it? [~cloud_fan]
> One similar case maybe:
> Using {{aggregateByKey}}/{{reduceByKey}} to compute the nnz vector (number of
> non-zero value) for different keys on a large sparse dataset.
> We can use {{DenseVector}} as the aggregators to count the nnz, and then
> compress it by call {{Vector#compressed}} before send it to the network.
> Another similar case maybe calling {{QuantileSummaries#compress}} before
> communication.
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[jira] [Updated] (SPARK-22971) OneVsRestModel should use temporary RawPredictionCol
[
https://issues.apache.org/jira/browse/SPARK-22971?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
zhengruifeng updated SPARK-22971:
-
Affects Version/s: (was: 2.3.0)
2.4.0
> OneVsRestModel should use temporary RawPredictionCol
>
>
> Key: SPARK-22971
> URL: https://issues.apache.org/jira/browse/SPARK-22971
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 2.4.0
>Reporter: zhengruifeng
>Priority: Minor
>
> Issue occurs when I transform one dataframe with two different classification
> models, first by a {{RandomForestClassificationModel}}, then a
> {{OneVsRestModel}}.
> The first transform generate a new colum "rawPrediction", which will be
> internally used in {{OneVsRestModel#transform}} and cause failure.
> {code}
> scala> val df =
> spark.read.format("libsvm").load("/Users/zrf/Dev/OpenSource/spark/data/mllib/sample_multiclass_classification_data.txt")
> 18/01/05 17:08:18 WARN ObjectStore: Failed to get database global_temp,
> returning NoSuchObjectException
> df: org.apache.spark.sql.DataFrame = [label: double, features: vector]
> scala> val rf = new RandomForestClassifier()
> rf: org.apache.spark.ml.classification.RandomForestClassifier =
> rfc_c11b1e1e1f7f
> scala> val rfm = rf.fit(df)
> rfm: org.apache.spark.ml.classification.RandomForestClassificationModel =
> RandomForestClassificationModel (uid=rfc_c11b1e1e1f7f) with 20 trees
> scala> val lr = new LogisticRegression().setMaxIter(1)
> lr: org.apache.spark.ml.classification.LogisticRegression =
> logreg_f5a5285eba06
> scala> val ovr = new OneVsRest().setClassifier(lr)
> ovr: org.apache.spark.ml.classification.OneVsRest = oneVsRest_8f5584190634
> scala> val ovrModel = ovr.fit(df)
> ovrModel: org.apache.spark.ml.classification.OneVsRestModel =
> oneVsRest_8f5584190634
> scala> val df2 = rfm.setPredictionCol("rfPred").transform(df)
> df2: org.apache.spark.sql.DataFrame = [label: double, features: vector ... 3
> more fields]
> scala> val df3 = ovrModel.setPredictionCol("ovrPred").transform(df2)
> java.lang.IllegalArgumentException: requirement failed: Column rawPrediction
> already exists.
> at scala.Predef$.require(Predef.scala:224)
> at org.apache.spark.ml.util.SchemaUtils$.appendColumn(SchemaUtils.scala:101)
> at org.apache.spark.ml.util.SchemaUtils$.appendColumn(SchemaUtils.scala:91)
> at
> org.apache.spark.ml.classification.ClassifierParams$class.validateAndTransformSchema(Classifier.scala:43)
> at
> org.apache.spark.ml.classification.ProbabilisticClassificationModel.org$apache$spark$ml$classification$ProbabilisticClassifierParams$$super$validateAndTransformSchema(ProbabilisticClassifier.scala:77)
> at
> org.apache.spark.ml.classification.ProbabilisticClassifierParams$class.validateAndTransformSchema(ProbabilisticClassifier.scala:37)
> at
> org.apache.spark.ml.classification.LogisticRegressionModel.org$apache$spark$ml$classification$LogisticRegressionParams$$super$validateAndTransformSchema(LogisticRegression.scala:904)
> at
> org.apache.spark.ml.classification.LogisticRegressionParams$class.validateAndTransformSchema(LogisticRegression.scala:265)
> at
> org.apache.spark.ml.classification.LogisticRegressionModel.validateAndTransformSchema(LogisticRegression.scala:904)
> at org.apache.spark.ml.PredictionModel.transformSchema(Predictor.scala:192)
> at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:74)
> at
> org.apache.spark.ml.classification.ProbabilisticClassificationModel.transform(ProbabilisticClassifier.scala:104)
> at
> org.apache.spark.ml.classification.OneVsRestModel$$anonfun$7.apply(OneVsRest.scala:184)
> at
> org.apache.spark.ml.classification.OneVsRestModel$$anonfun$7.apply(OneVsRest.scala:173)
> at
> scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:57)
> at
> scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:66)
> at scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:186)
> at
> org.apache.spark.ml.classification.OneVsRestModel.transform(OneVsRest.scala:173)
> ... 50 elided
> {code}
> {{OneVsRestModel#transform}} only generates a new prediction column, and
> should not fail by other columns.
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[jira] [Assigned] (SPARK-23040) BlockStoreShuffleReader's return Iterator isn't interruptible if aggregator or ordering is specified
[
https://issues.apache.org/jira/browse/SPARK-23040?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-23040:
Assignee: (was: Apache Spark)
> BlockStoreShuffleReader's return Iterator isn't interruptible if aggregator
> or ordering is specified
>
>
> Key: SPARK-23040
> URL: https://issues.apache.org/jira/browse/SPARK-23040
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
>Affects Versions: 2.0.0, 2.0.1, 2.0.2, 2.1.0, 2.1.1, 2.1.2, 2.2.0, 2.2.1
>Reporter: Xianjin YE
>Priority: Minor
>
> For example, if ordering is specified, the returned iterator is an
> CompletionIterator
> {code:java}
> dep.keyOrdering match {
> case Some(keyOrd: Ordering[K]) =>
> // Create an ExternalSorter to sort the data.
> val sorter =
> new ExternalSorter[K, C, C](context, ordering = Some(keyOrd),
> serializer = dep.serializer)
> sorter.insertAll(aggregatedIter)
> context.taskMetrics().incMemoryBytesSpilled(sorter.memoryBytesSpilled)
> context.taskMetrics().incDiskBytesSpilled(sorter.diskBytesSpilled)
>
> context.taskMetrics().incPeakExecutionMemory(sorter.peakMemoryUsedBytes)
> CompletionIterator[Product2[K, C], Iterator[Product2[K,
> C]]](sorter.iterator, sorter.stop())
> case None =>
> aggregatedIter
> }
> {code}
> However the sorter would consume(in sorter.insertAll) the
> aggregatedIter(which may be interruptible), then creates an iterator which
> isn't interruptible.
> The problem with this is that Spark task cannot be cancelled due to stage
> fail(without interruptThread enabled, which is disabled by default), which
> wasting executor resource.
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[jira] [Assigned] (SPARK-23040) BlockStoreShuffleReader's return Iterator isn't interruptible if aggregator or ordering is specified
[
https://issues.apache.org/jira/browse/SPARK-23040?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-23040:
Assignee: Apache Spark
> BlockStoreShuffleReader's return Iterator isn't interruptible if aggregator
> or ordering is specified
>
>
> Key: SPARK-23040
> URL: https://issues.apache.org/jira/browse/SPARK-23040
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
>Affects Versions: 2.0.0, 2.0.1, 2.0.2, 2.1.0, 2.1.1, 2.1.2, 2.2.0, 2.2.1
>Reporter: Xianjin YE
>Assignee: Apache Spark
>Priority: Minor
>
> For example, if ordering is specified, the returned iterator is an
> CompletionIterator
> {code:java}
> dep.keyOrdering match {
> case Some(keyOrd: Ordering[K]) =>
> // Create an ExternalSorter to sort the data.
> val sorter =
> new ExternalSorter[K, C, C](context, ordering = Some(keyOrd),
> serializer = dep.serializer)
> sorter.insertAll(aggregatedIter)
> context.taskMetrics().incMemoryBytesSpilled(sorter.memoryBytesSpilled)
> context.taskMetrics().incDiskBytesSpilled(sorter.diskBytesSpilled)
>
> context.taskMetrics().incPeakExecutionMemory(sorter.peakMemoryUsedBytes)
> CompletionIterator[Product2[K, C], Iterator[Product2[K,
> C]]](sorter.iterator, sorter.stop())
> case None =>
> aggregatedIter
> }
> {code}
> However the sorter would consume(in sorter.insertAll) the
> aggregatedIter(which may be interruptible), then creates an iterator which
> isn't interruptible.
> The problem with this is that Spark task cannot be cancelled due to stage
> fail(without interruptThread enabled, which is disabled by default), which
> wasting executor resource.
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[jira] [Commented] (SPARK-23040) BlockStoreShuffleReader's return Iterator isn't interruptible if aggregator or ordering is specified
[
https://issues.apache.org/jira/browse/SPARK-23040?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16346325#comment-16346325
]
Apache Spark commented on SPARK-23040:
--
User 'advancedxy' has created a pull request for this issue:
https://github.com/apache/spark/pull/20449
> BlockStoreShuffleReader's return Iterator isn't interruptible if aggregator
> or ordering is specified
>
>
> Key: SPARK-23040
> URL: https://issues.apache.org/jira/browse/SPARK-23040
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
>Affects Versions: 2.0.0, 2.0.1, 2.0.2, 2.1.0, 2.1.1, 2.1.2, 2.2.0, 2.2.1
>Reporter: Xianjin YE
>Priority: Minor
>
> For example, if ordering is specified, the returned iterator is an
> CompletionIterator
> {code:java}
> dep.keyOrdering match {
> case Some(keyOrd: Ordering[K]) =>
> // Create an ExternalSorter to sort the data.
> val sorter =
> new ExternalSorter[K, C, C](context, ordering = Some(keyOrd),
> serializer = dep.serializer)
> sorter.insertAll(aggregatedIter)
> context.taskMetrics().incMemoryBytesSpilled(sorter.memoryBytesSpilled)
> context.taskMetrics().incDiskBytesSpilled(sorter.diskBytesSpilled)
>
> context.taskMetrics().incPeakExecutionMemory(sorter.peakMemoryUsedBytes)
> CompletionIterator[Product2[K, C], Iterator[Product2[K,
> C]]](sorter.iterator, sorter.stop())
> case None =>
> aggregatedIter
> }
> {code}
> However the sorter would consume(in sorter.insertAll) the
> aggregatedIter(which may be interruptible), then creates an iterator which
> isn't interruptible.
> The problem with this is that Spark task cannot be cancelled due to stage
> fail(without interruptThread enabled, which is disabled by default), which
> wasting executor resource.
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[jira] [Resolved] (SPARK-23279) Avoid triggering distributed job for Console sink
[ https://issues.apache.org/jira/browse/SPARK-23279?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Saisai Shao resolved SPARK-23279. - Resolution: Fixed Assignee: Saisai Shao Fix Version/s: 2.3.0 > Avoid triggering distributed job for Console sink > - > > Key: SPARK-23279 > URL: https://issues.apache.org/jira/browse/SPARK-23279 > Project: Spark > Issue Type: Improvement > Components: Structured Streaming >Affects Versions: 2.3.0 >Reporter: Saisai Shao >Assignee: Saisai Shao >Priority: Minor > Fix For: 2.3.0 > > > Console sink will redistribute collected local data and trigger a distributed > job in each batch, this is not necessary, so here change to local job. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-23279) Avoid triggering distributed job for Console sink
[ https://issues.apache.org/jira/browse/SPARK-23279?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16346301#comment-16346301 ] Saisai Shao commented on SPARK-23279: - Issue resolved by pull request 20447 https://github.com/apache/spark/pull/20447 > Avoid triggering distributed job for Console sink > - > > Key: SPARK-23279 > URL: https://issues.apache.org/jira/browse/SPARK-23279 > Project: Spark > Issue Type: Improvement > Components: Structured Streaming >Affects Versions: 2.3.0 >Reporter: Saisai Shao >Priority: Minor > > Console sink will redistribute collected local data and trigger a distributed > job in each batch, this is not necessary, so here change to local job. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-23202) Break down DataSourceV2Writer.commit into two phase
[
https://issues.apache.org/jira/browse/SPARK-23202?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Sameer Agarwal updated SPARK-23202:
---
Target Version/s: 2.3.0
> Break down DataSourceV2Writer.commit into two phase
> ---
>
> Key: SPARK-23202
> URL: https://issues.apache.org/jira/browse/SPARK-23202
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Gengliang Wang
>Priority: Blocker
>
> Currently, the api DataSourceV2Writer#commit(WriterCommitMessage[]) commits a
> writing job with a list of commit messages.
> It makes sense in some scenarios, e.g. MicroBatchExecution.
> However, on receiving commit message, driver can start processing
> messages(e.g. persist messages into files) before all the messages are
> collected.
> The proposal is to Break down DataSourceV2Writer.commit into two phase:
> # add(WriterCommitMessage message): Handles a commit message produced by
> \{@link DataWriter#commit()}.
> # commit(): Commits the writing job.
> This should make the API more flexible, and more reasonable for implementing
> some datasources.
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[jira] [Updated] (SPARK-23202) Break down DataSourceV2Writer.commit into two phase
[
https://issues.apache.org/jira/browse/SPARK-23202?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Gengliang Wang updated SPARK-23202:
---
Affects Version/s: (was: 2.2.1)
2.3.0
> Break down DataSourceV2Writer.commit into two phase
> ---
>
> Key: SPARK-23202
> URL: https://issues.apache.org/jira/browse/SPARK-23202
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Gengliang Wang
>Priority: Blocker
>
> Currently, the api DataSourceV2Writer#commit(WriterCommitMessage[]) commits a
> writing job with a list of commit messages.
> It makes sense in some scenarios, e.g. MicroBatchExecution.
> However, on receiving commit message, driver can start processing
> messages(e.g. persist messages into files) before all the messages are
> collected.
> The proposal is to Break down DataSourceV2Writer.commit into two phase:
> # add(WriterCommitMessage message): Handles a commit message produced by
> \{@link DataWriter#commit()}.
> # commit(): Commits the writing job.
> This should make the API more flexible, and more reasonable for implementing
> some datasources.
--
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[jira] [Updated] (SPARK-23203) DataSourceV2 should use immutable trees.
[ https://issues.apache.org/jira/browse/SPARK-23203?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-23203: Priority: Blocker (was: Major) > DataSourceV2 should use immutable trees. > > > Key: SPARK-23203 > URL: https://issues.apache.org/jira/browse/SPARK-23203 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.3.0 >Reporter: Ryan Blue >Priority: Blocker > > The DataSourceV2 integration doesn't use [immutable > trees|https://databricks.com/blog/2015/04/13/deep-dive-into-spark-sqls-catalyst-optimizer.html], > which is a basic requirement of Catalyst. The v2 relation should not wrap a > mutable reader and change the logical plan by pushing projections and filters. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-23203) DataSourceV2 should use immutable trees.
[ https://issues.apache.org/jira/browse/SPARK-23203?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16346253#comment-16346253 ] Apache Spark commented on SPARK-23203: -- User 'cloud-fan' has created a pull request for this issue: https://github.com/apache/spark/pull/20448 > DataSourceV2 should use immutable trees. > > > Key: SPARK-23203 > URL: https://issues.apache.org/jira/browse/SPARK-23203 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.3.0 >Reporter: Ryan Blue >Priority: Major > > The DataSourceV2 integration doesn't use [immutable > trees|https://databricks.com/blog/2015/04/13/deep-dive-into-spark-sqls-catalyst-optimizer.html], > which is a basic requirement of Catalyst. The v2 relation should not wrap a > mutable reader and change the logical plan by pushing projections and filters. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Comment Edited] (SPARK-23251) ClassNotFoundException: scala.Any when there's a missing implicit Map encoder
[
https://issues.apache.org/jira/browse/SPARK-23251?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16346246#comment-16346246
]
Bruce Robbins edited comment on SPARK-23251 at 1/31/18 4:35 AM:
[~srowen] This also occurs with compiled apps submitted via spark-submit.
For example, this app:
{code:java}
object Implicit1 {
def main(args: Array[String]) {
if (args.length < 1) {
Console.err.println("No input file specified")
System.exit(1)
}
val inputFilename = args(0)
val spark = SparkSession.builder().appName("Implicit1").getOrCreate()
import spark.implicits._
val df = spark.read.json(inputFilename)
//implicit val mapEncoder = org.apache.spark.sql.Encoders.kryo[Map[String,
Any]]
val results = df.map(row => row.getValuesMap[Any](List("stationName",
"year"))).take(15)
results.foreach(println)
}
}{code}
When run on Spark 2.3 (via spark-submit), I get the same exception as I see
with spark-shell.
With the implicit mapEncoder line uncommented, this compiles and runs fine on
both 2.2 and 2.3.
Here's the exception from spark-submit on spark 2.3:
{noformat}
bash-3.2$ ./bin/spark-submit --version
Welcome to
__
/ _/_ ___ / /_
\ \/ _ \/ _ `/ __/ '/
/__/ ./_,// //_\ version 2.3.1-SNAPSHOT
/_/
Using Scala version 2.11.8, Java HotSpot(TM) 64-Bit Server VM, 1.8.0_161
Branch branch-2.3
Compiled by user brobbins on 2018-01-28T01:25:18Z
Revision 3b6fc286d105ae7de737c46e50cf941e6831ab98
Url https://github.com/apache/spark.git
Type --help for more information.
bash-3.2$ ./bin/spark-submit --class "Implicit1"
~/github/sparkAppPlay/target/scala-2.11/temps_2.11-1.0.jar
~/ncdc_gsod_short.jsonl
..
Exception in thread "main" java.lang.ClassNotFoundException: scala.Any
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:348)
at
scala.reflect.runtime.JavaMirrors$JavaMirror.javaClass(JavaMirrors.scala:555)
at
scala.reflect.runtime.JavaMirrors$JavaMirror$$anonfun$classToJava$1.apply(JavaMirrors.scala:1211)
at
scala.reflect.runtime.JavaMirrors$JavaMirror$$anonfun$classToJava$1.apply(JavaMirrors.scala:1203)
at
scala.reflect.runtime.TwoWayCaches$TwoWayCache$$anonfun$toJava$1.apply(TwoWayCaches.scala:49)
at scala.reflect.runtime.Gil$class.gilSynchronized(Gil.scala:19)
at scala.reflect.runtime.JavaUniverse.gilSynchronized(JavaUniverse.scala:16)
at scala.reflect.runtime.TwoWayCaches$TwoWayCache.toJava(TwoWayCaches.scala:44)
at
scala.reflect.runtime.JavaMirrors$JavaMirror.classToJava(JavaMirrors.scala:1203)
at
scala.reflect.runtime.JavaMirrors$JavaMirror.runtimeClass(JavaMirrors.scala:194)
at
scala.reflect.runtime.JavaMirrors$JavaMirror.runtimeClass(JavaMirrors.scala:54)
at
org.apache.spark.sql.catalyst.ScalaReflection$.getClassFromType(ScalaReflection.scala:700)
at
org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$dataTypeFor$1.apply(ScalaReflection.scala:84)
at
org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$dataTypeFor$1.apply(ScalaReflection.scala:65)
at
scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:56)
at
org.apache.spark.sql.catalyst.ScalaReflection$class.cleanUpReflectionObjects(ScalaReflection.scala:824)
at
org.apache.spark.sql.catalyst.ScalaReflection$.cleanUpReflectionObjects(ScalaReflection.scala:39)
at
org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$dataTypeFor(ScalaReflection.scala:64)
at
org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:512)
at
org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:445)
at
scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:56)
at
org.apache.spark.sql.catalyst.ScalaReflection$class.cleanUpReflectionObjects(ScalaReflection.scala:824)
at
org.apache.spark.sql.catalyst.ScalaReflection$.cleanUpReflectionObjects(ScalaReflection.scala:39)
at
org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor(ScalaReflection.scala:445)
at
org.apache.spark.sql.catalyst.ScalaReflection$.serializerFor(ScalaReflection.scala:434)
at
org.apache.spark.sql.catalyst.encoders.ExpressionEncoder$.apply(ExpressionEncoder.scala:71)
at org.apache.spark.sql.SQLImplicits.newMapEncoder(SQLImplicits.scala:172)
at Implicit1$.main(Implicit1.scala:17)
at Implicit1.main(Implicit1.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
[jira] [Commented] (SPARK-23251) ClassNotFoundException: scala.Any when there's a missing implicit Map encoder
[
https://issues.apache.org/jira/browse/SPARK-23251?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16346246#comment-16346246
]
Bruce Robbins commented on SPARK-23251:
---
[~srowen] This also occurs with compiled apps submitted via spark-submit.
For example, this app:
{code:java}
object Implicit1 {
def main(args: Array[String]) {
if (args.length < 1) {
Console.err.println("No input file specified")
System.exit(1)
}
val inputFilename = args(0)
val spark = SparkSession.builder().appName("Implicit1").getOrCreate()
import spark.implicits._
val df = spark.read.json(inputFilename)
//implicit val mapEncoder = org.apache.spark.sql.Encoders.kryo[Map[String,
Any]]
val results = df.map(row => row.getValuesMap[Any](List("stationName",
"year"))).take(15)
results.foreach(println)
}
}{code}
When run on Spark 2.3 (via spark-submit), I get the same exception as I see
with spark-shell.
With the implicit mapEncoder line uncommented, this compiles and runs fine on
both 2.2 and 2.3.
Here's the exception from spark-submit on spark 2.3:
{noformat}
bash-3.2$ ./bin/spark-submit --version
Welcome to
__
/ __/__ ___ _/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.3.1-SNAPSHOT
/_/
Using Scala version 2.11.8, Java HotSpot(TM) 64-Bit Server VM, 1.8.0_161
Branch branch-2.3
Compiled by user brobbins on 2018-01-28T01:25:18Z
Revision 3b6fc286d105ae7de737c46e50cf941e6831ab98
Url https://github.com/apache/spark.git
Type --help for more information.
bash-3.2$ ./bin/spark-submit --class "Implicit1"
~/github/sparkAppPlay/target/scala-2.11/temps_2.11-1.0.jar
~/ncdc_gsod_short.jsonl
..
Exception in thread "main" java.lang.ClassNotFoundException: scala.Any
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:348)
at
scala.reflect.runtime.JavaMirrors$JavaMirror.javaClass(JavaMirrors.scala:555)
at
scala.reflect.runtime.JavaMirrors$JavaMirror$$anonfun$classToJava$1.apply(JavaMirrors.scala:1211)
at
scala.reflect.runtime.JavaMirrors$JavaMirror$$anonfun$classToJava$1.apply(JavaMirrors.scala:1203)
at
scala.reflect.runtime.TwoWayCaches$TwoWayCache$$anonfun$toJava$1.apply(TwoWayCaches.scala:49)
at scala.reflect.runtime.Gil$class.gilSynchronized(Gil.scala:19)
at scala.reflect.runtime.JavaUniverse.gilSynchronized(JavaUniverse.scala:16)
at scala.reflect.runtime.TwoWayCaches$TwoWayCache.toJava(TwoWayCaches.scala:44)
at
scala.reflect.runtime.JavaMirrors$JavaMirror.classToJava(JavaMirrors.scala:1203)
at
scala.reflect.runtime.JavaMirrors$JavaMirror.runtimeClass(JavaMirrors.scala:194)
at
scala.reflect.runtime.JavaMirrors$JavaMirror.runtimeClass(JavaMirrors.scala:54)
at
org.apache.spark.sql.catalyst.ScalaReflection$.getClassFromType(ScalaReflection.scala:700)
at
org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$dataTypeFor$1.apply(ScalaReflection.scala:84)
at
org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$dataTypeFor$1.apply(ScalaReflection.scala:65)
at
scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:56)
at
org.apache.spark.sql.catalyst.ScalaReflection$class.cleanUpReflectionObjects(ScalaReflection.scala:824)
at
org.apache.spark.sql.catalyst.ScalaReflection$.cleanUpReflectionObjects(ScalaReflection.scala:39)
at
org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$dataTypeFor(ScalaReflection.scala:64)
at
org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:512)
at
org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:445)
at
scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:56)
at
org.apache.spark.sql.catalyst.ScalaReflection$class.cleanUpReflectionObjects(ScalaReflection.scala:824)
at
org.apache.spark.sql.catalyst.ScalaReflection$.cleanUpReflectionObjects(ScalaReflection.scala:39)
at
org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor(ScalaReflection.scala:445)
at
org.apache.spark.sql.catalyst.ScalaReflection$.serializerFor(ScalaReflection.scala:434)
at
org.apache.spark.sql.catalyst.encoders.ExpressionEncoder$.apply(ExpressionEncoder.scala:71)
at org.apache.spark.sql.SQLImplicits.newMapEncoder(SQLImplicits.scala:172)
at Implicit1$.main(Implicit1.scala:17)
at Implicit1.main(Implicit1.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
[jira] [Resolved] (SPARK-23274) ReplaceExceptWithFilter fails on dataframes filtered on same column
[
https://issues.apache.org/jira/browse/SPARK-23274?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiao Li resolved SPARK-23274.
-
Resolution: Fixed
Fix Version/s: 2.3.0
> ReplaceExceptWithFilter fails on dataframes filtered on same column
> ---
>
> Key: SPARK-23274
> URL: https://issues.apache.org/jira/browse/SPARK-23274
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Onur Satici
>Assignee: Xiao Li
>Priority: Blocker
> Fix For: 2.3.0
>
>
> Currently affects:
> {code:java}
> $ git tag --contains 01f6ba0e7a
> v2.3.0-rc1
> v2.3.0-rc2
> {code}
> Steps to reproduce:
> {code:java}
> $ cat test.csv
> a,b
> 1,2
> 1,3
> 2,2
> 2,4
> {code}
> {code:java}
> val df = spark.read.format("csv").option("header", "true").load("test.csv")
> val df1 = df.filter($"a" === 1)
> val df2 = df.filter($"a" === 2)
> df1.select("b").except(df2.select("b")).show
> {code}
> results in:
> {code:java}
> java.util.NoSuchElementException: key not found: a
> at scala.collection.MapLike$class.default(MapLike.scala:228)
> at scala.collection.AbstractMap.default(Map.scala:59)
> at scala.collection.MapLike$class.apply(MapLike.scala:141)
> at scala.collection.AbstractMap.apply(Map.scala:59)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition$1.applyOrElse(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition$1.applyOrElse(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$.org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$apply$1.applyOrElse(ReplaceExceptWithFilter.scala:50)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$apply$1.applyOrElse(ReplaceExceptWithFilter.scala:48)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
>
[jira] [Commented] (SPARK-18016) Code Generation: Constant Pool Past Limit for Wide/Nested Dataset
[
https://issues.apache.org/jira/browse/SPARK-18016?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16346200#comment-16346200
]
Gaurav Garg commented on SPARK-18016:
-
Thanks [~kiszk] for helping me out. I have attached the logs of my test code
which throws constant pool exception. Please find attached log file.
[^910825_9.zip]
> Code Generation: Constant Pool Past Limit for Wide/Nested Dataset
> -
>
> Key: SPARK-18016
> URL: https://issues.apache.org/jira/browse/SPARK-18016
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.1.0
>Reporter: Aleksander Eskilson
>Assignee: Kazuaki Ishizaki
>Priority: Major
> Fix For: 2.3.0
>
> Attachments: 910825_9.zip
>
>
> When attempting to encode collections of large Java objects to Datasets
> having very wide or deeply nested schemas, code generation can fail, yielding:
> {code}
> Caused by: org.codehaus.janino.JaninoRuntimeException: Constant pool for
> class
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection
> has grown past JVM limit of 0x
> at
> org.codehaus.janino.util.ClassFile.addToConstantPool(ClassFile.java:499)
> at
> org.codehaus.janino.util.ClassFile.addConstantNameAndTypeInfo(ClassFile.java:439)
> at
> org.codehaus.janino.util.ClassFile.addConstantMethodrefInfo(ClassFile.java:358)
> at
> org.codehaus.janino.UnitCompiler.writeConstantMethodrefInfo(UnitCompiler.java:4)
> at org.codehaus.janino.UnitCompiler.compileGet2(UnitCompiler.java:4547)
> at org.codehaus.janino.UnitCompiler.access$7500(UnitCompiler.java:206)
> at
> org.codehaus.janino.UnitCompiler$12.visitMethodInvocation(UnitCompiler.java:3774)
> at
> org.codehaus.janino.UnitCompiler$12.visitMethodInvocation(UnitCompiler.java:3762)
> at org.codehaus.janino.Java$MethodInvocation.accept(Java.java:4328)
> at org.codehaus.janino.UnitCompiler.compileGet(UnitCompiler.java:3762)
> at
> org.codehaus.janino.UnitCompiler.compileGetValue(UnitCompiler.java:4933)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:3180)
> at org.codehaus.janino.UnitCompiler.access$5000(UnitCompiler.java:206)
> at
> org.codehaus.janino.UnitCompiler$9.visitMethodInvocation(UnitCompiler.java:3151)
> at
> org.codehaus.janino.UnitCompiler$9.visitMethodInvocation(UnitCompiler.java:3139)
> at org.codehaus.janino.Java$MethodInvocation.accept(Java.java:4328)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:3139)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:2112)
> at org.codehaus.janino.UnitCompiler.access$1700(UnitCompiler.java:206)
> at
> org.codehaus.janino.UnitCompiler$6.visitExpressionStatement(UnitCompiler.java:1377)
> at
> org.codehaus.janino.UnitCompiler$6.visitExpressionStatement(UnitCompiler.java:1370)
> at org.codehaus.janino.Java$ExpressionStatement.accept(Java.java:2558)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:1370)
> at
> org.codehaus.janino.UnitCompiler.compileStatements(UnitCompiler.java:1450)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:2811)
> at
> org.codehaus.janino.UnitCompiler.compileDeclaredMethods(UnitCompiler.java:1262)
> at
> org.codehaus.janino.UnitCompiler.compileDeclaredMethods(UnitCompiler.java:1234)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:538)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:890)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:894)
> at org.codehaus.janino.UnitCompiler.access$600(UnitCompiler.java:206)
> at
> org.codehaus.janino.UnitCompiler$2.visitMemberClassDeclaration(UnitCompiler.java:377)
> at
> org.codehaus.janino.UnitCompiler$2.visitMemberClassDeclaration(UnitCompiler.java:369)
> at
> org.codehaus.janino.Java$MemberClassDeclaration.accept(Java.java:1128)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:369)
> at
> org.codehaus.janino.UnitCompiler.compileDeclaredMemberTypes(UnitCompiler.java:1209)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:564)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:420)
> at org.codehaus.janino.UnitCompiler.access$400(UnitCompiler.java:206)
> at
> org.codehaus.janino.UnitCompiler$2.visitPackageMemberClassDeclaration(UnitCompiler.java:374)
> at
> org.codehaus.janino.UnitCompiler$2.visitPackageMemberClassDeclaration(UnitCompiler.java:369)
> at
> org.codehaus.janino.Java$AbstractPackageMemberClassDeclaration.accept(Java.java:1309)
> at
[jira] [Updated] (SPARK-18016) Code Generation: Constant Pool Past Limit for Wide/Nested Dataset
[
https://issues.apache.org/jira/browse/SPARK-18016?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Gaurav Garg updated SPARK-18016:
Attachment: 910825_9.zip
> Code Generation: Constant Pool Past Limit for Wide/Nested Dataset
> -
>
> Key: SPARK-18016
> URL: https://issues.apache.org/jira/browse/SPARK-18016
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.1.0
>Reporter: Aleksander Eskilson
>Assignee: Kazuaki Ishizaki
>Priority: Major
> Fix For: 2.3.0
>
> Attachments: 910825_9.zip
>
>
> When attempting to encode collections of large Java objects to Datasets
> having very wide or deeply nested schemas, code generation can fail, yielding:
> {code}
> Caused by: org.codehaus.janino.JaninoRuntimeException: Constant pool for
> class
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection
> has grown past JVM limit of 0x
> at
> org.codehaus.janino.util.ClassFile.addToConstantPool(ClassFile.java:499)
> at
> org.codehaus.janino.util.ClassFile.addConstantNameAndTypeInfo(ClassFile.java:439)
> at
> org.codehaus.janino.util.ClassFile.addConstantMethodrefInfo(ClassFile.java:358)
> at
> org.codehaus.janino.UnitCompiler.writeConstantMethodrefInfo(UnitCompiler.java:4)
> at org.codehaus.janino.UnitCompiler.compileGet2(UnitCompiler.java:4547)
> at org.codehaus.janino.UnitCompiler.access$7500(UnitCompiler.java:206)
> at
> org.codehaus.janino.UnitCompiler$12.visitMethodInvocation(UnitCompiler.java:3774)
> at
> org.codehaus.janino.UnitCompiler$12.visitMethodInvocation(UnitCompiler.java:3762)
> at org.codehaus.janino.Java$MethodInvocation.accept(Java.java:4328)
> at org.codehaus.janino.UnitCompiler.compileGet(UnitCompiler.java:3762)
> at
> org.codehaus.janino.UnitCompiler.compileGetValue(UnitCompiler.java:4933)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:3180)
> at org.codehaus.janino.UnitCompiler.access$5000(UnitCompiler.java:206)
> at
> org.codehaus.janino.UnitCompiler$9.visitMethodInvocation(UnitCompiler.java:3151)
> at
> org.codehaus.janino.UnitCompiler$9.visitMethodInvocation(UnitCompiler.java:3139)
> at org.codehaus.janino.Java$MethodInvocation.accept(Java.java:4328)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:3139)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:2112)
> at org.codehaus.janino.UnitCompiler.access$1700(UnitCompiler.java:206)
> at
> org.codehaus.janino.UnitCompiler$6.visitExpressionStatement(UnitCompiler.java:1377)
> at
> org.codehaus.janino.UnitCompiler$6.visitExpressionStatement(UnitCompiler.java:1370)
> at org.codehaus.janino.Java$ExpressionStatement.accept(Java.java:2558)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:1370)
> at
> org.codehaus.janino.UnitCompiler.compileStatements(UnitCompiler.java:1450)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:2811)
> at
> org.codehaus.janino.UnitCompiler.compileDeclaredMethods(UnitCompiler.java:1262)
> at
> org.codehaus.janino.UnitCompiler.compileDeclaredMethods(UnitCompiler.java:1234)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:538)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:890)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:894)
> at org.codehaus.janino.UnitCompiler.access$600(UnitCompiler.java:206)
> at
> org.codehaus.janino.UnitCompiler$2.visitMemberClassDeclaration(UnitCompiler.java:377)
> at
> org.codehaus.janino.UnitCompiler$2.visitMemberClassDeclaration(UnitCompiler.java:369)
> at
> org.codehaus.janino.Java$MemberClassDeclaration.accept(Java.java:1128)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:369)
> at
> org.codehaus.janino.UnitCompiler.compileDeclaredMemberTypes(UnitCompiler.java:1209)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:564)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:420)
> at org.codehaus.janino.UnitCompiler.access$400(UnitCompiler.java:206)
> at
> org.codehaus.janino.UnitCompiler$2.visitPackageMemberClassDeclaration(UnitCompiler.java:374)
> at
> org.codehaus.janino.UnitCompiler$2.visitPackageMemberClassDeclaration(UnitCompiler.java:369)
> at
> org.codehaus.janino.Java$AbstractPackageMemberClassDeclaration.accept(Java.java:1309)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:369)
> at org.codehaus.janino.UnitCompiler.compileUnit(UnitCompiler.java:345)
> at
>
[jira] [Commented] (SPARK-23279) Avoid triggering distributed job for Console sink
[ https://issues.apache.org/jira/browse/SPARK-23279?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16346158#comment-16346158 ] Apache Spark commented on SPARK-23279: -- User 'jerryshao' has created a pull request for this issue: https://github.com/apache/spark/pull/20447 > Avoid triggering distributed job for Console sink > - > > Key: SPARK-23279 > URL: https://issues.apache.org/jira/browse/SPARK-23279 > Project: Spark > Issue Type: Improvement > Components: Structured Streaming >Affects Versions: 2.3.0 >Reporter: Saisai Shao >Priority: Minor > > Console sink will redistribute collected local data and trigger a distributed > job in each batch, this is not necessary, so here change to local job. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-23279) Avoid triggering distributed job for Console sink
[ https://issues.apache.org/jira/browse/SPARK-23279?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-23279: Assignee: (was: Apache Spark) > Avoid triggering distributed job for Console sink > - > > Key: SPARK-23279 > URL: https://issues.apache.org/jira/browse/SPARK-23279 > Project: Spark > Issue Type: Improvement > Components: Structured Streaming >Affects Versions: 2.3.0 >Reporter: Saisai Shao >Priority: Minor > > Console sink will redistribute collected local data and trigger a distributed > job in each batch, this is not necessary, so here change to local job. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-23279) Avoid triggering distributed job for Console sink
[ https://issues.apache.org/jira/browse/SPARK-23279?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-23279: Assignee: Apache Spark > Avoid triggering distributed job for Console sink > - > > Key: SPARK-23279 > URL: https://issues.apache.org/jira/browse/SPARK-23279 > Project: Spark > Issue Type: Improvement > Components: Structured Streaming >Affects Versions: 2.3.0 >Reporter: Saisai Shao >Assignee: Apache Spark >Priority: Minor > > Console sink will redistribute collected local data and trigger a distributed > job in each batch, this is not necessary, so here change to local job. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Resolved] (SPARK-23277) Spark ALS : param coldStartStrategy does not exist.
[ https://issues.apache.org/jira/browse/SPARK-23277?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Sean Owen resolved SPARK-23277. --- Resolution: Invalid > Spark ALS : param coldStartStrategy does not exist. > --- > > Key: SPARK-23277 > URL: https://issues.apache.org/jira/browse/SPARK-23277 > Project: Spark > Issue Type: Bug > Components: ML >Affects Versions: 2.1.0 >Reporter: Surya Prakash Reddy >Priority: Major > -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Created] (SPARK-23279) Avoid triggering distributed job for Console sink
Saisai Shao created SPARK-23279: --- Summary: Avoid triggering distributed job for Console sink Key: SPARK-23279 URL: https://issues.apache.org/jira/browse/SPARK-23279 Project: Spark Issue Type: Improvement Components: Structured Streaming Affects Versions: 2.3.0 Reporter: Saisai Shao Console sink will redistribute collected local data and trigger a distributed job in each batch, this is not necessary, so here change to local job. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-23273) Spark Dataset withColumn - schema column order isn't the same as case class paramether order
[
https://issues.apache.org/jira/browse/SPARK-23273?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16346148#comment-16346148
]
Liang-Chi Hsieh commented on SPARK-23273:
-
The {{name}} column will be added after {{age}} in {{ds2}}. So the schema of
{{ds2}} doesn't match {{ds1}} in the order of columns. You can change column
order with a projection before union:
{code:java}
scala> ds1.union(ds2.select("name", "age").as[NameAge]).show
+-+---+
| name|age|
+-+---+
|henriquedsg89| 1|
+-+---+
{code}
Since 2.3.0, there is an API {{unionByName}} can be used for this kind of cases:
{code:java}
scala> ds1.unionByName(ds2).show
+-+---+
| name|age|
+-+---+
|henriquedsg89| 1|
+-+---+
{code}
> Spark Dataset withColumn - schema column order isn't the same as case class
> paramether order
>
>
> Key: SPARK-23273
> URL: https://issues.apache.org/jira/browse/SPARK-23273
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.2.1
>Reporter: Henrique dos Santos Goulart
>Priority: Major
>
> {code:java}
> case class OnlyAge(age: Int)
> case class NameAge(name: String, age: Int)
> val ds1 = spark.emptyDataset[NameAge]
> val ds2 = spark
> .createDataset(Seq(OnlyAge(1)))
> .withColumn("name", lit("henriquedsg89"))
> .as[NameAge]
> ds1.show()
> ds2.show()
> ds1.union(ds2)
> {code}
>
> It's going to raise this error:
> {noformat}
> Cannot up cast `age` from string to int as it may truncate
> The type path of the target object is:
> - field (class: "scala.Int", name: "age")
> - root class: "dw.NameAge"{noformat}
> It seems that .as[CaseClass] doesn't keep the order of paramethers that is
> typed on case class.
> If I change the case class paramether order, it's going to work... like:
> {code:java}
> case class NameAge(age: Int, name: String){code}
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[jira] [Commented] (SPARK-23254) Add user guide entry for DataFrame multivariate summary
[ https://issues.apache.org/jira/browse/SPARK-23254?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16346118#comment-16346118 ] Apache Spark commented on SPARK-23254: -- User 'WeichenXu123' has created a pull request for this issue: https://github.com/apache/spark/pull/20446 > Add user guide entry for DataFrame multivariate summary > --- > > Key: SPARK-23254 > URL: https://issues.apache.org/jira/browse/SPARK-23254 > Project: Spark > Issue Type: Documentation > Components: Documentation, ML >Affects Versions: 2.3.0 >Reporter: Nick Pentreath >Priority: Minor > > SPARK-19634 added a DataFrame API for vector summary statistics. The [ML user > guide|http://spark.apache.org/docs/latest/ml-statistics.html] should be > updated, with the relevant example (to be in parity with the [MLlib user > guide|http://spark.apache.org/docs/latest/mllib-statistics.html#summary-statistics]). > -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-23254) Add user guide entry for DataFrame multivariate summary
[ https://issues.apache.org/jira/browse/SPARK-23254?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-23254: Assignee: Apache Spark > Add user guide entry for DataFrame multivariate summary > --- > > Key: SPARK-23254 > URL: https://issues.apache.org/jira/browse/SPARK-23254 > Project: Spark > Issue Type: Documentation > Components: Documentation, ML >Affects Versions: 2.3.0 >Reporter: Nick Pentreath >Assignee: Apache Spark >Priority: Minor > > SPARK-19634 added a DataFrame API for vector summary statistics. The [ML user > guide|http://spark.apache.org/docs/latest/ml-statistics.html] should be > updated, with the relevant example (to be in parity with the [MLlib user > guide|http://spark.apache.org/docs/latest/mllib-statistics.html#summary-statistics]). > -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-23254) Add user guide entry for DataFrame multivariate summary
[ https://issues.apache.org/jira/browse/SPARK-23254?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-23254: Assignee: (was: Apache Spark) > Add user guide entry for DataFrame multivariate summary > --- > > Key: SPARK-23254 > URL: https://issues.apache.org/jira/browse/SPARK-23254 > Project: Spark > Issue Type: Documentation > Components: Documentation, ML >Affects Versions: 2.3.0 >Reporter: Nick Pentreath >Priority: Minor > > SPARK-19634 added a DataFrame API for vector summary statistics. The [ML user > guide|http://spark.apache.org/docs/latest/ml-statistics.html] should be > updated, with the relevant example (to be in parity with the [MLlib user > guide|http://spark.apache.org/docs/latest/mllib-statistics.html#summary-statistics]). > -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-23092) Migrate MemoryStream to DataSource V2
[ https://issues.apache.org/jira/browse/SPARK-23092?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16346110#comment-16346110 ] Apache Spark commented on SPARK-23092: -- User 'tdas' has created a pull request for this issue: https://github.com/apache/spark/pull/20445 > Migrate MemoryStream to DataSource V2 > - > > Key: SPARK-23092 > URL: https://issues.apache.org/jira/browse/SPARK-23092 > Project: Spark > Issue Type: Task > Components: Structured Streaming >Affects Versions: 2.3.0 >Reporter: Burak Yavuz >Priority: Major > > We should migrate the MemoryStream for Structured Streaming to DataSourceV2 -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Resolved] (SPARK-23276) Enable UDT tests in (Hive)OrcHadoopFsRelationSuite
[ https://issues.apache.org/jira/browse/SPARK-23276?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li resolved SPARK-23276. - Resolution: Fixed Assignee: Dongjoon Hyun Fix Version/s: 2.3.0 > Enable UDT tests in (Hive)OrcHadoopFsRelationSuite > -- > > Key: SPARK-23276 > URL: https://issues.apache.org/jira/browse/SPARK-23276 > Project: Spark > Issue Type: Improvement > Components: SQL, Tests >Affects Versions: 2.3.0 >Reporter: Dongjoon Hyun >Assignee: Dongjoon Hyun >Priority: Minor > Fix For: 2.3.0 > > > Like Parquet, ORC test suite should enable UDT tests. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-23261) Rename Pandas UDFs
[ https://issues.apache.org/jira/browse/SPARK-23261?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-23261: Description: Rename the public APIs of pandas udfs from - PANDAS SCALAR UDF -> SCALAR PANDAS UDF - PANDAS GROUP MAP UDF -> GROUPED MAP PANDAS UDF - PANDAS GROUP AGG UDF -> PANDAS UDAF [Only 2.4] was: Rename the public APIs of pandas udfs from - PANDAS SCALAR UDF -> SCALAR PANDAS UDF - PANDAS GROUP MAP UDF -> GROUPED MAP PANDAS UDF - PANDAS GROUP AGG UDF -> PANDAS UDAF > Rename Pandas UDFs > -- > > Key: SPARK-23261 > URL: https://issues.apache.org/jira/browse/SPARK-23261 > Project: Spark > Issue Type: Sub-task > Components: PySpark >Affects Versions: 2.3.0 >Reporter: Xiao Li >Assignee: Xiao Li >Priority: Major > Fix For: 2.3.0 > > > Rename the public APIs of pandas udfs from > - PANDAS SCALAR UDF -> SCALAR PANDAS UDF > - PANDAS GROUP MAP UDF -> GROUPED MAP PANDAS UDF > - PANDAS GROUP AGG UDF -> PANDAS UDAF [Only 2.4] -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-23261) Rename Pandas UDFs
[ https://issues.apache.org/jira/browse/SPARK-23261?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-23261: Fix Version/s: (was: 2.4.0) 2.3.0 > Rename Pandas UDFs > -- > > Key: SPARK-23261 > URL: https://issues.apache.org/jira/browse/SPARK-23261 > Project: Spark > Issue Type: Sub-task > Components: PySpark >Affects Versions: 2.3.0 >Reporter: Xiao Li >Assignee: Xiao Li >Priority: Major > Fix For: 2.3.0 > > > Rename the public APIs of pandas udfs from > - PANDAS SCALAR UDF -> SCALAR PANDAS UDF > - PANDAS GROUP MAP UDF -> GROUPED MAP PANDAS UDF > - PANDAS GROUP AGG UDF -> PANDAS UDAF -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-23202) Break down DataSourceV2Writer.commit into two phase
[
https://issues.apache.org/jira/browse/SPARK-23202?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wenchen Fan updated SPARK-23202:
Priority: Blocker (was: Major)
> Break down DataSourceV2Writer.commit into two phase
> ---
>
> Key: SPARK-23202
> URL: https://issues.apache.org/jira/browse/SPARK-23202
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 2.2.1
>Reporter: Gengliang Wang
>Priority: Blocker
>
> Currently, the api DataSourceV2Writer#commit(WriterCommitMessage[]) commits a
> writing job with a list of commit messages.
> It makes sense in some scenarios, e.g. MicroBatchExecution.
> However, on receiving commit message, driver can start processing
> messages(e.g. persist messages into files) before all the messages are
> collected.
> The proposal is to Break down DataSourceV2Writer.commit into two phase:
> # add(WriterCommitMessage message): Handles a commit message produced by
> \{@link DataWriter#commit()}.
> # commit(): Commits the writing job.
> This should make the API more flexible, and more reasonable for implementing
> some datasources.
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[jira] [Commented] (SPARK-23251) ClassNotFoundException: scala.Any when there's a missing implicit Map encoder
[
https://issues.apache.org/jira/browse/SPARK-23251?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16346075#comment-16346075
]
Sean Owen commented on SPARK-23251:
---
Hm. I don't know is this is related to Encoders and the mechanism you cite, not
directly. The error is that {{scala.Any}} can't be found, which of course must
certainly be available. This is typically a classloader issue, and in
{{spark-shell}} the classloader situation is complicated.
It may be a real problem still, or at least a symptom of a known class of
problems. But can you confirm that this doesn't happen without the shell?
> ClassNotFoundException: scala.Any when there's a missing implicit Map encoder
> -
>
> Key: SPARK-23251
> URL: https://issues.apache.org/jira/browse/SPARK-23251
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.1
> Environment: mac os high sierra, centos 7
>Reporter: Bruce Robbins
>Priority: Minor
>
> In branch-2.2, when you attempt to use row.getValuesMap[Any] without an
> implicit Map encoder, you get a nice descriptive compile-time error:
> {noformat}
> scala> df.map(row => row.getValuesMap[Any](List("stationName",
> "year"))).collect
> :26: error: Unable to find encoder for type stored in a Dataset.
> Primitive types (Int, String, etc) and Product types (case classes) are
> supported by importing spark.implicits._ Support for serializing other types
> will be added in future releases.
> df.map(row => row.getValuesMap[Any](List("stationName",
> "year"))).collect
> ^
> scala> implicit val mapEncoder =
> org.apache.spark.sql.Encoders.kryo[Map[String, Any]]
> mapEncoder: org.apache.spark.sql.Encoder[Map[String,Any]] = class[value[0]:
> binary]
> scala> df.map(row => row.getValuesMap[Any](List("stationName",
> "year"))).collect
> res1: Array[Map[String,Any]] = Array(Map(stationName -> 007026 9, year ->
> 2014), Map(stationName -> 007026 9, year -> 2014), Map(stationName ->
> 007026 9, year -> 2014),
> etc...
> {noformat}
>
> On the latest master and also on branch-2.3, the transformation compiles (at
> least on spark-shell), but throws a ClassNotFoundException:
>
> {noformat}
> scala> df.map(row => row.getValuesMap[Any](List("stationName",
> "year"))).collect
> java.lang.ClassNotFoundException: scala.Any
> at
> scala.reflect.internal.util.AbstractFileClassLoader.findClass(AbstractFileClassLoader.scala:62)
> at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
> at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
> at java.lang.Class.forName0(Native Method)
> at java.lang.Class.forName(Class.java:348)
> at
> scala.reflect.runtime.JavaMirrors$JavaMirror.javaClass(JavaMirrors.scala:555)
> at
> scala.reflect.runtime.JavaMirrors$JavaMirror$$anonfun$classToJava$1.apply(JavaMirrors.scala:1211)
> at
> scala.reflect.runtime.JavaMirrors$JavaMirror$$anonfun$classToJava$1.apply(JavaMirrors.scala:1203)
> at
> scala.reflect.runtime.TwoWayCaches$TwoWayCache$$anonfun$toJava$1.apply(TwoWayCaches.scala:49)
> at scala.reflect.runtime.Gil$class.gilSynchronized(Gil.scala:19)
> at scala.reflect.runtime.JavaUniverse.gilSynchronized(JavaUniverse.scala:16)
> at
> scala.reflect.runtime.TwoWayCaches$TwoWayCache.toJava(TwoWayCaches.scala:44)
> at
> scala.reflect.runtime.JavaMirrors$JavaMirror.classToJava(JavaMirrors.scala:1203)
> at
> scala.reflect.runtime.JavaMirrors$JavaMirror.runtimeClass(JavaMirrors.scala:194)
> at
> scala.reflect.runtime.JavaMirrors$JavaMirror.runtimeClass(JavaMirrors.scala:54)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$.getClassFromType(ScalaReflection.scala:700)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$dataTypeFor$1.apply(ScalaReflection.scala:84)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$dataTypeFor$1.apply(ScalaReflection.scala:65)
> at
> scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:56)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$class.cleanUpReflectionObjects(ScalaReflection.scala:824)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$.cleanUpReflectionObjects(ScalaReflection.scala:39)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$dataTypeFor(ScalaReflection.scala:64)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:512)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:445)
> at
>
[jira] [Assigned] (SPARK-23274) ReplaceExceptWithFilter fails on dataframes filtered on same column
[
https://issues.apache.org/jira/browse/SPARK-23274?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-23274:
Assignee: Xiao Li (was: Apache Spark)
> ReplaceExceptWithFilter fails on dataframes filtered on same column
> ---
>
> Key: SPARK-23274
> URL: https://issues.apache.org/jira/browse/SPARK-23274
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Onur Satici
>Assignee: Xiao Li
>Priority: Blocker
>
> Currently affects:
> {code:java}
> $ git tag --contains 01f6ba0e7a
> v2.3.0-rc1
> v2.3.0-rc2
> {code}
> Steps to reproduce:
> {code:java}
> $ cat test.csv
> a,b
> 1,2
> 1,3
> 2,2
> 2,4
> {code}
> {code:java}
> val df = spark.read.format("csv").option("header", "true").load("test.csv")
> val df1 = df.filter($"a" === 1)
> val df2 = df.filter($"a" === 2)
> df1.select("b").except(df2.select("b")).show
> {code}
> results in:
> {code:java}
> java.util.NoSuchElementException: key not found: a
> at scala.collection.MapLike$class.default(MapLike.scala:228)
> at scala.collection.AbstractMap.default(Map.scala:59)
> at scala.collection.MapLike$class.apply(MapLike.scala:141)
> at scala.collection.AbstractMap.apply(Map.scala:59)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition$1.applyOrElse(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition$1.applyOrElse(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$.org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$apply$1.applyOrElse(ReplaceExceptWithFilter.scala:50)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$apply$1.applyOrElse(ReplaceExceptWithFilter.scala:48)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
>
[jira] [Commented] (SPARK-23274) ReplaceExceptWithFilter fails on dataframes filtered on same column
[
https://issues.apache.org/jira/browse/SPARK-23274?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16346071#comment-16346071
]
Apache Spark commented on SPARK-23274:
--
User 'gatorsmile' has created a pull request for this issue:
https://github.com/apache/spark/pull/20444
> ReplaceExceptWithFilter fails on dataframes filtered on same column
> ---
>
> Key: SPARK-23274
> URL: https://issues.apache.org/jira/browse/SPARK-23274
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Onur Satici
>Assignee: Xiao Li
>Priority: Blocker
>
> Currently affects:
> {code:java}
> $ git tag --contains 01f6ba0e7a
> v2.3.0-rc1
> v2.3.0-rc2
> {code}
> Steps to reproduce:
> {code:java}
> $ cat test.csv
> a,b
> 1,2
> 1,3
> 2,2
> 2,4
> {code}
> {code:java}
> val df = spark.read.format("csv").option("header", "true").load("test.csv")
> val df1 = df.filter($"a" === 1)
> val df2 = df.filter($"a" === 2)
> df1.select("b").except(df2.select("b")).show
> {code}
> results in:
> {code:java}
> java.util.NoSuchElementException: key not found: a
> at scala.collection.MapLike$class.default(MapLike.scala:228)
> at scala.collection.AbstractMap.default(Map.scala:59)
> at scala.collection.MapLike$class.apply(MapLike.scala:141)
> at scala.collection.AbstractMap.apply(Map.scala:59)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition$1.applyOrElse(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition$1.applyOrElse(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$.org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$apply$1.applyOrElse(ReplaceExceptWithFilter.scala:50)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$apply$1.applyOrElse(ReplaceExceptWithFilter.scala:48)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
>
[jira] [Assigned] (SPARK-23274) ReplaceExceptWithFilter fails on dataframes filtered on same column
[
https://issues.apache.org/jira/browse/SPARK-23274?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-23274:
Assignee: Apache Spark (was: Xiao Li)
> ReplaceExceptWithFilter fails on dataframes filtered on same column
> ---
>
> Key: SPARK-23274
> URL: https://issues.apache.org/jira/browse/SPARK-23274
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Onur Satici
>Assignee: Apache Spark
>Priority: Blocker
>
> Currently affects:
> {code:java}
> $ git tag --contains 01f6ba0e7a
> v2.3.0-rc1
> v2.3.0-rc2
> {code}
> Steps to reproduce:
> {code:java}
> $ cat test.csv
> a,b
> 1,2
> 1,3
> 2,2
> 2,4
> {code}
> {code:java}
> val df = spark.read.format("csv").option("header", "true").load("test.csv")
> val df1 = df.filter($"a" === 1)
> val df2 = df.filter($"a" === 2)
> df1.select("b").except(df2.select("b")).show
> {code}
> results in:
> {code:java}
> java.util.NoSuchElementException: key not found: a
> at scala.collection.MapLike$class.default(MapLike.scala:228)
> at scala.collection.AbstractMap.default(Map.scala:59)
> at scala.collection.MapLike$class.apply(MapLike.scala:141)
> at scala.collection.AbstractMap.apply(Map.scala:59)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition$1.applyOrElse(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition$1.applyOrElse(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$.org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$apply$1.applyOrElse(ReplaceExceptWithFilter.scala:50)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$apply$1.applyOrElse(ReplaceExceptWithFilter.scala:48)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
>
[jira] [Commented] (SPARK-23251) ClassNotFoundException: scala.Any when there's a missing implicit Map encoder
[
https://issues.apache.org/jira/browse/SPARK-23251?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16346050#comment-16346050
]
Bruce Robbins commented on SPARK-23251:
---
I commented out the following line in
sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala and the problem
went away:
{code:java}
implicit def newMapEncoder[T <: Map[_, _] : TypeTag]: Encoder[T] =
ExpressionEncoder()
{code}
By "went away", I mean I now had to specify a Map encoder for my map function
to compile (rather than have it compile and then throw an exception).
Checking with [~michalsenkyr], who will know more than I do.
> ClassNotFoundException: scala.Any when there's a missing implicit Map encoder
> -
>
> Key: SPARK-23251
> URL: https://issues.apache.org/jira/browse/SPARK-23251
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.1
> Environment: mac os high sierra, centos 7
>Reporter: Bruce Robbins
>Priority: Minor
>
> In branch-2.2, when you attempt to use row.getValuesMap[Any] without an
> implicit Map encoder, you get a nice descriptive compile-time error:
> {noformat}
> scala> df.map(row => row.getValuesMap[Any](List("stationName",
> "year"))).collect
> :26: error: Unable to find encoder for type stored in a Dataset.
> Primitive types (Int, String, etc) and Product types (case classes) are
> supported by importing spark.implicits._ Support for serializing other types
> will be added in future releases.
> df.map(row => row.getValuesMap[Any](List("stationName",
> "year"))).collect
> ^
> scala> implicit val mapEncoder =
> org.apache.spark.sql.Encoders.kryo[Map[String, Any]]
> mapEncoder: org.apache.spark.sql.Encoder[Map[String,Any]] = class[value[0]:
> binary]
> scala> df.map(row => row.getValuesMap[Any](List("stationName",
> "year"))).collect
> res1: Array[Map[String,Any]] = Array(Map(stationName -> 007026 9, year ->
> 2014), Map(stationName -> 007026 9, year -> 2014), Map(stationName ->
> 007026 9, year -> 2014),
> etc...
> {noformat}
>
> On the latest master and also on branch-2.3, the transformation compiles (at
> least on spark-shell), but throws a ClassNotFoundException:
>
> {noformat}
> scala> df.map(row => row.getValuesMap[Any](List("stationName",
> "year"))).collect
> java.lang.ClassNotFoundException: scala.Any
> at
> scala.reflect.internal.util.AbstractFileClassLoader.findClass(AbstractFileClassLoader.scala:62)
> at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
> at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
> at java.lang.Class.forName0(Native Method)
> at java.lang.Class.forName(Class.java:348)
> at
> scala.reflect.runtime.JavaMirrors$JavaMirror.javaClass(JavaMirrors.scala:555)
> at
> scala.reflect.runtime.JavaMirrors$JavaMirror$$anonfun$classToJava$1.apply(JavaMirrors.scala:1211)
> at
> scala.reflect.runtime.JavaMirrors$JavaMirror$$anonfun$classToJava$1.apply(JavaMirrors.scala:1203)
> at
> scala.reflect.runtime.TwoWayCaches$TwoWayCache$$anonfun$toJava$1.apply(TwoWayCaches.scala:49)
> at scala.reflect.runtime.Gil$class.gilSynchronized(Gil.scala:19)
> at scala.reflect.runtime.JavaUniverse.gilSynchronized(JavaUniverse.scala:16)
> at
> scala.reflect.runtime.TwoWayCaches$TwoWayCache.toJava(TwoWayCaches.scala:44)
> at
> scala.reflect.runtime.JavaMirrors$JavaMirror.classToJava(JavaMirrors.scala:1203)
> at
> scala.reflect.runtime.JavaMirrors$JavaMirror.runtimeClass(JavaMirrors.scala:194)
> at
> scala.reflect.runtime.JavaMirrors$JavaMirror.runtimeClass(JavaMirrors.scala:54)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$.getClassFromType(ScalaReflection.scala:700)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$dataTypeFor$1.apply(ScalaReflection.scala:84)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$dataTypeFor$1.apply(ScalaReflection.scala:65)
> at
> scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:56)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$class.cleanUpReflectionObjects(ScalaReflection.scala:824)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$.cleanUpReflectionObjects(ScalaReflection.scala:39)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$dataTypeFor(ScalaReflection.scala:64)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:512)
> at
> org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor$1.apply(ScalaReflection.scala:445)
[jira] [Commented] (SPARK-18057) Update structured streaming kafka from 10.0.1 to 10.2.0
[ https://issues.apache.org/jira/browse/SPARK-18057?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16345963#comment-16345963 ] John Cheng commented on SPARK-18057: Apache Kafka is now at version 1.0. For people who want to use Spark streaming against Kafka brokers on 1.0.0, it is preferable to use the `org.apache.kafka:kafka-clients:jar:1.0.0` client. "Most of the discussion on the performance impact of [upgrading to the 0.10.0 message format|https://kafka.apache.org/0110/documentation.html#upgrade_10_performance_impact] remains pertinent to the 0.11.0 upgrade. This mainly affects clusters that are not secured with TLS since "zero-copy" transfer is already not possible in that case. In order to avoid the cost of down-conversion, you should ensure that consumer applications are upgraded to the latest 0.11.0 client." > Update structured streaming kafka from 10.0.1 to 10.2.0 > --- > > Key: SPARK-18057 > URL: https://issues.apache.org/jira/browse/SPARK-18057 > Project: Spark > Issue Type: Improvement > Components: Structured Streaming >Reporter: Cody Koeninger >Priority: Major > > There are a couple of relevant KIPs here, > https://archive.apache.org/dist/kafka/0.10.1.0/RELEASE_NOTES.html -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-23157) withColumn fails for a column that is a result of mapped DataSet
[
https://issues.apache.org/jira/browse/SPARK-23157?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16345949#comment-16345949
]
Apache Spark commented on SPARK-23157:
--
User 'henryr' has created a pull request for this issue:
https://github.com/apache/spark/pull/20443
> withColumn fails for a column that is a result of mapped DataSet
>
>
> Key: SPARK-23157
> URL: https://issues.apache.org/jira/browse/SPARK-23157
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.2.1
>Reporter: Tomasz Bartczak
>Priority: Minor
>
> Having
> {code:java}
> case class R(id: String)
> val ds = spark.createDataset(Seq(R("1")))
> {code}
> This works:
> {code}
> scala> ds.withColumn("n", ds.col("id"))
> res16: org.apache.spark.sql.DataFrame = [id: string, n: string]
> {code}
> but when we map over ds it fails:
> {code}
> scala> ds.withColumn("n", ds.map(a => a).col("id"))
> org.apache.spark.sql.AnalysisException: resolved attribute(s) id#55 missing
> from id#4 in operator !Project [id#4, id#55 AS n#57];;
> !Project [id#4, id#55 AS n#57]
> +- LocalRelation [id#4]
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:39)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:91)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:347)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:78)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:78)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:91)
> at
> org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:52)
> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:67)
> at
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2884)
> at org.apache.spark.sql.Dataset.select(Dataset.scala:1150)
> at org.apache.spark.sql.Dataset.withColumn(Dataset.scala:1905)
> ... 48 elided
> {code}
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[jira] [Resolved] (SPARK-23275) hive/tests have been failing when run locally on the laptop (Mac) with OOM
[
https://issues.apache.org/jira/browse/SPARK-23275?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiao Li resolved SPARK-23275.
-
Resolution: Fixed
Assignee: Dilip Biswal
Fix Version/s: 2.3.0
> hive/tests have been failing when run locally on the laptop (Mac) with OOM
> ---
>
> Key: SPARK-23275
> URL: https://issues.apache.org/jira/browse/SPARK-23275
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.1
>Reporter: Dilip Biswal
>Assignee: Dilip Biswal
>Priority: Major
> Fix For: 2.3.0
>
>
> hive tests have been failing when they are run locally (Mac Os) after a
> recent change in the trunk. After running the tests for some time, the test
> fails with OOM with Error: unable to create new native thread.
> I noticed the thread count goes all the way up to 2000+ after which we start
> getting these OOM errors. Most of the threads seem to be related to the
> connection pool in hive metastore (BoneCP-x- ). This behaviour change
> is happening after we made the following change to HiveClientImpl.reset()
> {code}
> def reset(): Unit = withHiveState {
> try {
> // code
> } finally {
> runSqlHive("USE default") ===> this is causing the issue
> }
> {code}
> I am proposing to temporarily back-out part of a fix made to address
> SPARK-23000 to resolve this issue while we work-out the exact reason for this
> sudden increase in thread counts.
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[jira] [Commented] (SPARK-23236) Make it easier to find the rest API, especially in local mode
[ https://issues.apache.org/jira/browse/SPARK-23236?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16345827#comment-16345827 ] Alex Bozarth commented on SPARK-23236: -- For #1, a REST API endpoint shouldn't return html, but we could return a custom html response (such as 405 or another response code) that includes a short "maybe you meant..." description. For #2 I would be ok with a "maybe you meant..." response but with a link to the Spark REST API Doc to aid the user. > Make it easier to find the rest API, especially in local mode > - > > Key: SPARK-23236 > URL: https://issues.apache.org/jira/browse/SPARK-23236 > Project: Spark > Issue Type: Improvement > Components: Web UI >Affects Versions: 2.3.0 >Reporter: Imran Rashid >Priority: Trivial > Labels: newbie > > This is really minor, but it always takes me a little bit to figure out how > to get from the UI to the rest api. Its especially a pain in local-mode, > where you need the app-id, though in general I don't know the app-id, so have > to either look in logs or go to another endpoint first in the ui just to find > the app-id. While it wouldn't really help anybody accessing the endpoints > programmatically, we could make it easier for someone doing exploration via > their browser. > Some things which could be improved: > * /api/v1 just provides a link to "/api/v1/applications" > * /api provides a link to "/api/v1/applications" > * /api/v1/applications/[app-id] gives a list of links for the other endpoints > * on the UI, there is a link to at least /api/v1/applications/[app-id] -- > better still if each UI page links to the corresponding endpoint, eg. the all > jobs page would link to /api/v1/applications/[app-id]/jobs -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-23237) Add UI / endpoint for threaddumps for executors with active tasks
[
https://issues.apache.org/jira/browse/SPARK-23237?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16345818#comment-16345818
]
Alex Bozarth commented on SPARK-23237:
--
I would rather keep it to an api endpoint, but what I'm worried about is having
an end point that returns a specific threadDump decided by some unknown
algorithm. Again, I'm willing to look at a PR to see if the exact impl will
change my mind.
> Add UI / endpoint for threaddumps for executors with active tasks
> -
>
> Key: SPARK-23237
> URL: https://issues.apache.org/jira/browse/SPARK-23237
> Project: Spark
> Issue Type: New Feature
> Components: Web UI
>Affects Versions: 2.3.0
>Reporter: Imran Rashid
>Priority: Major
>
> Frequently, when there are a handful of straggler tasks, users want to know
> what is going on in those executors running the stragglers. Currently, that
> is a bit of a pain to do: you have to go to the page for your active stage,
> find the task, figure out which executor its on, then go to the executors
> page, and get the thread dump. Or maybe you just go to the executors page,
> find the executor with an active task, and then click on that, but that
> doesn't work if you've got multiple stages running.
> Users could figure this by extracting the info from the stage rest endpoint,
> but it's such a common thing to do that we should make it easy.
> I realize that figuring out a good way to do this is a little tricky. We
> don't want to make it easy to end up pulling thread dumps from 1000 executors
> back to the driver. So we've got to come up with a reasonable heuristic for
> choosing which executors to poll. And we've also got to find a suitable
> place to put this.
> My suggestion is that the stage page always has a link to the thread dumps
> for the *one* executor with the longest running task. And there would be a
> corresponding endpoint in the rest api with the same info, maybe at
> {{/applications/[app-id]/stages/[stage-id]/[stage-attempt-id]/slowestTaskThreadDump}}.
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[jira] [Updated] (SPARK-23274) ReplaceExceptWithFilter fails on dataframes filtered on same column
[
https://issues.apache.org/jira/browse/SPARK-23274?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Josh Rosen updated SPARK-23274:
---
Labels: (was: correctness)
> ReplaceExceptWithFilter fails on dataframes filtered on same column
> ---
>
> Key: SPARK-23274
> URL: https://issues.apache.org/jira/browse/SPARK-23274
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Onur Satici
>Assignee: Xiao Li
>Priority: Blocker
>
> Currently affects:
> {code:java}
> $ git tag --contains 01f6ba0e7a
> v2.3.0-rc1
> v2.3.0-rc2
> {code}
> Steps to reproduce:
> {code:java}
> $ cat test.csv
> a,b
> 1,2
> 1,3
> 2,2
> 2,4
> {code}
> {code:java}
> val df = spark.read.format("csv").option("header", "true").load("test.csv")
> val df1 = df.filter($"a" === 1)
> val df2 = df.filter($"a" === 2)
> df1.select("b").except(df2.select("b")).show
> {code}
> results in:
> {code:java}
> java.util.NoSuchElementException: key not found: a
> at scala.collection.MapLike$class.default(MapLike.scala:228)
> at scala.collection.AbstractMap.default(Map.scala:59)
> at scala.collection.MapLike$class.apply(MapLike.scala:141)
> at scala.collection.AbstractMap.apply(Map.scala:59)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition$1.applyOrElse(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition$1.applyOrElse(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$.org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$apply$1.applyOrElse(ReplaceExceptWithFilter.scala:50)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$apply$1.applyOrElse(ReplaceExceptWithFilter.scala:48)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
>
[jira] [Resolved] (SPARK-23278) Spark ALS : param coldStartStrategy does not exist.
[ https://issues.apache.org/jira/browse/SPARK-23278?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Sean Owen resolved SPARK-23278. --- Resolution: Duplicate You opened this twice, so I closed it. Please don't reopen JIRAs. Your description here is just a stack trace. Without anything more I'd close the other one too. > Spark ALS : param coldStartStrategy does not exist. > --- > > Key: SPARK-23278 > URL: https://issues.apache.org/jira/browse/SPARK-23278 > Project: Spark > Issue Type: Bug > Components: ML >Affects Versions: 2.1.0 >Reporter: Surya Prakash Reddy >Priority: Major > > An error occurred while calling o105.getParam. : > java.util.NoSuchElementException: Param coldStartStrategy does not exist. at > org.apache.spark.ml.param.Params$$anonfun$getParam$2.apply(params.scala:601) > at > org.apache.spark.ml.param.Params$$anonfun$getParam$2.apply(params.scala:601) > at scala.Option.getOrElse(Option.scala:121) at > org.apache.spark.ml.param.Params$class.getParam(params.scala:600) at > org.apache.spark.ml.PipelineStage.getParam(Pipeline.scala:42) at > sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:606) at > py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at > py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at > py4j.Gateway.invoke(Gateway.java:280) at > py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at > py4j.commands.CallCommand.execute(CallCommand.java:79) at > py4j.GatewayConnection.run(GatewayConnection.java:214) at > java.lang.Thread.run(Thread.java:744) -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Closed] (SPARK-23278) Spark ALS : param coldStartStrategy does not exist.
[ https://issues.apache.org/jira/browse/SPARK-23278?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Sean Owen closed SPARK-23278. - > Spark ALS : param coldStartStrategy does not exist. > --- > > Key: SPARK-23278 > URL: https://issues.apache.org/jira/browse/SPARK-23278 > Project: Spark > Issue Type: Bug > Components: ML >Affects Versions: 2.1.0 >Reporter: Surya Prakash Reddy >Priority: Major > > An error occurred while calling o105.getParam. : > java.util.NoSuchElementException: Param coldStartStrategy does not exist. at > org.apache.spark.ml.param.Params$$anonfun$getParam$2.apply(params.scala:601) > at > org.apache.spark.ml.param.Params$$anonfun$getParam$2.apply(params.scala:601) > at scala.Option.getOrElse(Option.scala:121) at > org.apache.spark.ml.param.Params$class.getParam(params.scala:600) at > org.apache.spark.ml.PipelineStage.getParam(Pipeline.scala:42) at > sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:606) at > py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at > py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at > py4j.Gateway.invoke(Gateway.java:280) at > py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at > py4j.commands.CallCommand.execute(CallCommand.java:79) at > py4j.GatewayConnection.run(GatewayConnection.java:214) at > java.lang.Thread.run(Thread.java:744) -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Reopened] (SPARK-23278) Spark ALS : param coldStartStrategy does not exist.
[ https://issues.apache.org/jira/browse/SPARK-23278?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Surya Prakash Reddy reopened SPARK-23278: - > Spark ALS : param coldStartStrategy does not exist. > --- > > Key: SPARK-23278 > URL: https://issues.apache.org/jira/browse/SPARK-23278 > Project: Spark > Issue Type: Bug > Components: ML >Affects Versions: 2.1.0 >Reporter: Surya Prakash Reddy >Priority: Major > > An error occurred while calling o105.getParam. : > java.util.NoSuchElementException: Param coldStartStrategy does not exist. at > org.apache.spark.ml.param.Params$$anonfun$getParam$2.apply(params.scala:601) > at > org.apache.spark.ml.param.Params$$anonfun$getParam$2.apply(params.scala:601) > at scala.Option.getOrElse(Option.scala:121) at > org.apache.spark.ml.param.Params$class.getParam(params.scala:600) at > org.apache.spark.ml.PipelineStage.getParam(Pipeline.scala:42) at > sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:606) at > py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at > py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at > py4j.Gateway.invoke(Gateway.java:280) at > py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at > py4j.commands.CallCommand.execute(CallCommand.java:79) at > py4j.GatewayConnection.run(GatewayConnection.java:214) at > java.lang.Thread.run(Thread.java:744) -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Resolved] (SPARK-23278) Spark ALS : param coldStartStrategy does not exist.
[ https://issues.apache.org/jira/browse/SPARK-23278?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Sean Owen resolved SPARK-23278. --- Resolution: Duplicate > Spark ALS : param coldStartStrategy does not exist. > --- > > Key: SPARK-23278 > URL: https://issues.apache.org/jira/browse/SPARK-23278 > Project: Spark > Issue Type: Bug > Components: ML >Affects Versions: 2.1.0 >Reporter: Surya Prakash Reddy >Priority: Major > > An error occurred while calling o105.getParam. : > java.util.NoSuchElementException: Param coldStartStrategy does not exist. at > org.apache.spark.ml.param.Params$$anonfun$getParam$2.apply(params.scala:601) > at > org.apache.spark.ml.param.Params$$anonfun$getParam$2.apply(params.scala:601) > at scala.Option.getOrElse(Option.scala:121) at > org.apache.spark.ml.param.Params$class.getParam(params.scala:600) at > org.apache.spark.ml.PipelineStage.getParam(Pipeline.scala:42) at > sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:606) at > py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at > py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at > py4j.Gateway.invoke(Gateway.java:280) at > py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at > py4j.commands.CallCommand.execute(CallCommand.java:79) at > py4j.GatewayConnection.run(GatewayConnection.java:214) at > java.lang.Thread.run(Thread.java:744) -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-23265) Update multi-column error handling logic in QuantileDiscretizer
[
https://issues.apache.org/jira/browse/SPARK-23265?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-23265:
Assignee: (was: Apache Spark)
> Update multi-column error handling logic in QuantileDiscretizer
> ---
>
> Key: SPARK-23265
> URL: https://issues.apache.org/jira/browse/SPARK-23265
> Project: Spark
> Issue Type: Improvement
> Components: ML
>Affects Versions: 2.3.0
>Reporter: Nick Pentreath
>Priority: Major
>
> SPARK-22397 added support for multiple columns to {{QuantileDiscretizer}}. If
> both single- and mulit-column params are set (specifically {{inputCol}} /
> {{inputCols}}) an error is thrown.
> However, SPARK-22799 added more comprehensive error logic for {{Bucketizer}}.
> The logic for {{QuantileDiscretizer}} should be updated to match. *Note* that
> for this transformer, it is acceptable to set the single-column param for
> {{numBuckets }}when transforming multiple columns, since that is then applied
> to all columns.
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[jira] [Assigned] (SPARK-23265) Update multi-column error handling logic in QuantileDiscretizer
[
https://issues.apache.org/jira/browse/SPARK-23265?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-23265:
Assignee: Apache Spark
> Update multi-column error handling logic in QuantileDiscretizer
> ---
>
> Key: SPARK-23265
> URL: https://issues.apache.org/jira/browse/SPARK-23265
> Project: Spark
> Issue Type: Improvement
> Components: ML
>Affects Versions: 2.3.0
>Reporter: Nick Pentreath
>Assignee: Apache Spark
>Priority: Major
>
> SPARK-22397 added support for multiple columns to {{QuantileDiscretizer}}. If
> both single- and mulit-column params are set (specifically {{inputCol}} /
> {{inputCols}}) an error is thrown.
> However, SPARK-22799 added more comprehensive error logic for {{Bucketizer}}.
> The logic for {{QuantileDiscretizer}} should be updated to match. *Note* that
> for this transformer, it is acceptable to set the single-column param for
> {{numBuckets }}when transforming multiple columns, since that is then applied
> to all columns.
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[jira] [Commented] (SPARK-23265) Update multi-column error handling logic in QuantileDiscretizer
[
https://issues.apache.org/jira/browse/SPARK-23265?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16345723#comment-16345723
]
Apache Spark commented on SPARK-23265:
--
User 'huaxingao' has created a pull request for this issue:
https://github.com/apache/spark/pull/20442
> Update multi-column error handling logic in QuantileDiscretizer
> ---
>
> Key: SPARK-23265
> URL: https://issues.apache.org/jira/browse/SPARK-23265
> Project: Spark
> Issue Type: Improvement
> Components: ML
>Affects Versions: 2.3.0
>Reporter: Nick Pentreath
>Priority: Major
>
> SPARK-22397 added support for multiple columns to {{QuantileDiscretizer}}. If
> both single- and mulit-column params are set (specifically {{inputCol}} /
> {{inputCols}}) an error is thrown.
> However, SPARK-22799 added more comprehensive error logic for {{Bucketizer}}.
> The logic for {{QuantileDiscretizer}} should be updated to match. *Note* that
> for this transformer, it is acceptable to set the single-column param for
> {{numBuckets }}when transforming multiple columns, since that is then applied
> to all columns.
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[jira] [Updated] (SPARK-23278) Spark ALS : param coldStartStrategy does not exist.
[ https://issues.apache.org/jira/browse/SPARK-23278?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Surya Prakash Reddy updated SPARK-23278: Description: An error occurred while calling o105.getParam. : java.util.NoSuchElementException: Param coldStartStrategy does not exist. at org.apache.spark.ml.param.Params$$anonfun$getParam$2.apply(params.scala:601) at org.apache.spark.ml.param.Params$$anonfun$getParam$2.apply(params.scala:601) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.ml.param.Params$class.getParam(params.scala:600) at org.apache.spark.ml.PipelineStage.getParam(Pipeline.scala:42) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:606) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:280) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:214) at java.lang.Thread.run(Thread.java:744) > Spark ALS : param coldStartStrategy does not exist. > --- > > Key: SPARK-23278 > URL: https://issues.apache.org/jira/browse/SPARK-23278 > Project: Spark > Issue Type: Bug > Components: ML >Affects Versions: 2.1.0 >Reporter: Surya Prakash Reddy >Priority: Major > > An error occurred while calling o105.getParam. : > java.util.NoSuchElementException: Param coldStartStrategy does not exist. at > org.apache.spark.ml.param.Params$$anonfun$getParam$2.apply(params.scala:601) > at > org.apache.spark.ml.param.Params$$anonfun$getParam$2.apply(params.scala:601) > at scala.Option.getOrElse(Option.scala:121) at > org.apache.spark.ml.param.Params$class.getParam(params.scala:600) at > org.apache.spark.ml.PipelineStage.getParam(Pipeline.scala:42) at > sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:606) at > py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at > py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at > py4j.Gateway.invoke(Gateway.java:280) at > py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at > py4j.commands.CallCommand.execute(CallCommand.java:79) at > py4j.GatewayConnection.run(GatewayConnection.java:214) at > java.lang.Thread.run(Thread.java:744) -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Created] (SPARK-23278) Spark ALS : param coldStartStrategy does not exist.
Surya Prakash Reddy created SPARK-23278: --- Summary: Spark ALS : param coldStartStrategy does not exist. Key: SPARK-23278 URL: https://issues.apache.org/jira/browse/SPARK-23278 Project: Spark Issue Type: Bug Components: ML Affects Versions: 2.1.0 Reporter: Surya Prakash Reddy -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Created] (SPARK-23277) Spark ALS : param coldStartStrategy does not exist.
Surya Prakash Reddy created SPARK-23277: --- Summary: Spark ALS : param coldStartStrategy does not exist. Key: SPARK-23277 URL: https://issues.apache.org/jira/browse/SPARK-23277 Project: Spark Issue Type: Bug Components: ML Affects Versions: 2.1.0 Reporter: Surya Prakash Reddy -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-23275) hive/tests have been failing when run locally on the laptop (Mac) with OOM
[
https://issues.apache.org/jira/browse/SPARK-23275?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16345686#comment-16345686
]
Apache Spark commented on SPARK-23275:
--
User 'dilipbiswal' has created a pull request for this issue:
https://github.com/apache/spark/pull/20441
> hive/tests have been failing when run locally on the laptop (Mac) with OOM
> ---
>
> Key: SPARK-23275
> URL: https://issues.apache.org/jira/browse/SPARK-23275
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.1
>Reporter: Dilip Biswal
>Priority: Major
>
> hive tests have been failing when they are run locally (Mac Os) after a
> recent change in the trunk. After running the tests for some time, the test
> fails with OOM with Error: unable to create new native thread.
> I noticed the thread count goes all the way up to 2000+ after which we start
> getting these OOM errors. Most of the threads seem to be related to the
> connection pool in hive metastore (BoneCP-x- ). This behaviour change
> is happening after we made the following change to HiveClientImpl.reset()
> {code}
> def reset(): Unit = withHiveState {
> try {
> // code
> } finally {
> runSqlHive("USE default") ===> this is causing the issue
> }
> {code}
> I am proposing to temporarily back-out part of a fix made to address
> SPARK-23000 to resolve this issue while we work-out the exact reason for this
> sudden increase in thread counts.
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[jira] [Assigned] (SPARK-23275) hive/tests have been failing when run locally on the laptop (Mac) with OOM
[
https://issues.apache.org/jira/browse/SPARK-23275?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-23275:
Assignee: (was: Apache Spark)
> hive/tests have been failing when run locally on the laptop (Mac) with OOM
> ---
>
> Key: SPARK-23275
> URL: https://issues.apache.org/jira/browse/SPARK-23275
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.1
>Reporter: Dilip Biswal
>Priority: Major
>
> hive tests have been failing when they are run locally (Mac Os) after a
> recent change in the trunk. After running the tests for some time, the test
> fails with OOM with Error: unable to create new native thread.
> I noticed the thread count goes all the way up to 2000+ after which we start
> getting these OOM errors. Most of the threads seem to be related to the
> connection pool in hive metastore (BoneCP-x- ). This behaviour change
> is happening after we made the following change to HiveClientImpl.reset()
> {code}
> def reset(): Unit = withHiveState {
> try {
> // code
> } finally {
> runSqlHive("USE default") ===> this is causing the issue
> }
> {code}
> I am proposing to temporarily back-out part of a fix made to address
> SPARK-23000 to resolve this issue while we work-out the exact reason for this
> sudden increase in thread counts.
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[jira] [Assigned] (SPARK-23275) hive/tests have been failing when run locally on the laptop (Mac) with OOM
[
https://issues.apache.org/jira/browse/SPARK-23275?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-23275:
Assignee: Apache Spark
> hive/tests have been failing when run locally on the laptop (Mac) with OOM
> ---
>
> Key: SPARK-23275
> URL: https://issues.apache.org/jira/browse/SPARK-23275
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.1
>Reporter: Dilip Biswal
>Assignee: Apache Spark
>Priority: Major
>
> hive tests have been failing when they are run locally (Mac Os) after a
> recent change in the trunk. After running the tests for some time, the test
> fails with OOM with Error: unable to create new native thread.
> I noticed the thread count goes all the way up to 2000+ after which we start
> getting these OOM errors. Most of the threads seem to be related to the
> connection pool in hive metastore (BoneCP-x- ). This behaviour change
> is happening after we made the following change to HiveClientImpl.reset()
> {code}
> def reset(): Unit = withHiveState {
> try {
> // code
> } finally {
> runSqlHive("USE default") ===> this is causing the issue
> }
> {code}
> I am proposing to temporarily back-out part of a fix made to address
> SPARK-23000 to resolve this issue while we work-out the exact reason for this
> sudden increase in thread counts.
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[jira] [Updated] (SPARK-23274) ReplaceExceptWithFilter fails on dataframes filtered on same column
[
https://issues.apache.org/jira/browse/SPARK-23274?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Josh Rosen updated SPARK-23274:
---
Labels: correctness (was: )
> ReplaceExceptWithFilter fails on dataframes filtered on same column
> ---
>
> Key: SPARK-23274
> URL: https://issues.apache.org/jira/browse/SPARK-23274
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Onur Satici
>Assignee: Xiao Li
>Priority: Blocker
> Labels: correctness
>
> Currently affects:
> {code:java}
> $ git tag --contains 01f6ba0e7a
> v2.3.0-rc1
> v2.3.0-rc2
> {code}
> Steps to reproduce:
> {code:java}
> $ cat test.csv
> a,b
> 1,2
> 1,3
> 2,2
> 2,4
> {code}
> {code:java}
> val df = spark.read.format("csv").option("header", "true").load("test.csv")
> val df1 = df.filter($"a" === 1)
> val df2 = df.filter($"a" === 2)
> df1.select("b").except(df2.select("b")).show
> {code}
> results in:
> {code:java}
> java.util.NoSuchElementException: key not found: a
> at scala.collection.MapLike$class.default(MapLike.scala:228)
> at scala.collection.AbstractMap.default(Map.scala:59)
> at scala.collection.MapLike$class.apply(MapLike.scala:141)
> at scala.collection.AbstractMap.apply(Map.scala:59)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition$1.applyOrElse(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition$1.applyOrElse(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$.org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$apply$1.applyOrElse(ReplaceExceptWithFilter.scala:50)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$apply$1.applyOrElse(ReplaceExceptWithFilter.scala:48)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
>
[jira] [Assigned] (SPARK-23276) Enable UDT tests in (Hive)OrcHadoopFsRelationSuite
[ https://issues.apache.org/jira/browse/SPARK-23276?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-23276: Assignee: (was: Apache Spark) > Enable UDT tests in (Hive)OrcHadoopFsRelationSuite > -- > > Key: SPARK-23276 > URL: https://issues.apache.org/jira/browse/SPARK-23276 > Project: Spark > Issue Type: Improvement > Components: SQL, Tests >Affects Versions: 2.3.0 >Reporter: Dongjoon Hyun >Priority: Minor > > Like Parquet, ORC test suite should enable UDT tests. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-23276) Enable UDT tests in (Hive)OrcHadoopFsRelationSuite
[ https://issues.apache.org/jira/browse/SPARK-23276?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-23276: Assignee: Apache Spark > Enable UDT tests in (Hive)OrcHadoopFsRelationSuite > -- > > Key: SPARK-23276 > URL: https://issues.apache.org/jira/browse/SPARK-23276 > Project: Spark > Issue Type: Improvement > Components: SQL, Tests >Affects Versions: 2.3.0 >Reporter: Dongjoon Hyun >Assignee: Apache Spark >Priority: Minor > > Like Parquet, ORC test suite should enable UDT tests. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-23276) Enable UDT tests in (Hive)OrcHadoopFsRelationSuite
[ https://issues.apache.org/jira/browse/SPARK-23276?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16345679#comment-16345679 ] Apache Spark commented on SPARK-23276: -- User 'dongjoon-hyun' has created a pull request for this issue: https://github.com/apache/spark/pull/20440 > Enable UDT tests in (Hive)OrcHadoopFsRelationSuite > -- > > Key: SPARK-23276 > URL: https://issues.apache.org/jira/browse/SPARK-23276 > Project: Spark > Issue Type: Improvement > Components: SQL, Tests >Affects Versions: 2.3.0 >Reporter: Dongjoon Hyun >Priority: Minor > > Like Parquet, ORC test suite should enable UDT tests. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-23276) Enable UDT tests in (Hive)OrcHadoopFsRelationSuite
[ https://issues.apache.org/jira/browse/SPARK-23276?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Dongjoon Hyun updated SPARK-23276: -- Component/s: Tests > Enable UDT tests in (Hive)OrcHadoopFsRelationSuite > -- > > Key: SPARK-23276 > URL: https://issues.apache.org/jira/browse/SPARK-23276 > Project: Spark > Issue Type: Improvement > Components: SQL, Tests >Affects Versions: 2.3.0 >Reporter: Dongjoon Hyun >Priority: Minor > > Like Parquet, ORC test suite should enable UDT tests. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-23276) Enable UDT tests in (Hive)OrcHadoopFsRelationSuite
[ https://issues.apache.org/jira/browse/SPARK-23276?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Dongjoon Hyun updated SPARK-23276: -- Description: Like Parquet, ORC test suite should enable UDT tests. > Enable UDT tests in (Hive)OrcHadoopFsRelationSuite > -- > > Key: SPARK-23276 > URL: https://issues.apache.org/jira/browse/SPARK-23276 > Project: Spark > Issue Type: Improvement > Components: SQL, Tests >Affects Versions: 2.3.0 >Reporter: Dongjoon Hyun >Priority: Minor > > Like Parquet, ORC test suite should enable UDT tests. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Created] (SPARK-23276) Enable UDT tests in (Hive)OrcHadoopFsRelationSuite
Dongjoon Hyun created SPARK-23276: - Summary: Enable UDT tests in (Hive)OrcHadoopFsRelationSuite Key: SPARK-23276 URL: https://issues.apache.org/jira/browse/SPARK-23276 Project: Spark Issue Type: Improvement Components: SQL Affects Versions: 2.3.0 Reporter: Dongjoon Hyun -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Resolved] (SPARK-23267) Increase spark.sql.codegen.hugeMethodLimit to 65535
[ https://issues.apache.org/jira/browse/SPARK-23267?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li resolved SPARK-23267. - Resolution: Fixed Fix Version/s: 2.3.0 > Increase spark.sql.codegen.hugeMethodLimit to 65535 > --- > > Key: SPARK-23267 > URL: https://issues.apache.org/jira/browse/SPARK-23267 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 2.3.0 >Reporter: Xiao Li >Assignee: Xiao Li >Priority: Blocker > Fix For: 2.3.0 > > > Still saw the performance regression introduced by > `spark.sql.codegen.hugeMethodLimit` in our internal workloads. There are two > major issues in the current solution. > * The size of the complied byte code is not identical to the bytecode size > of the method. The detection is still not accurate. > * The bytecode size of a single operator (e.g., `SerializeFromObject`) could > still exceed 8K limit. We saw the performance regression in such scenario. > Since it is close to the release of 2.3, we decide to increase it to 64K for > avoiding the perf regression. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Created] (SPARK-23275) hive/tests have been failing when run locally on the laptop (Mac) with OOM
Dilip Biswal created SPARK-23275:
Summary: hive/tests have been failing when run locally on the
laptop (Mac) with OOM
Key: SPARK-23275
URL: https://issues.apache.org/jira/browse/SPARK-23275
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 2.3.1
Reporter: Dilip Biswal
hive tests have been failing when they are run locally (Mac Os) after a recent
change in the trunk. After running the tests for some time, the test fails with
OOM with Error: unable to create new native thread.
I noticed the thread count goes all the way up to 2000+ after which we start
getting these OOM errors. Most of the threads seem to be related to the
connection pool in hive metastore (BoneCP-x- ). This behaviour change
is happening after we made the following change to HiveClientImpl.reset()
{code}
def reset(): Unit = withHiveState {
try {
// code
} finally {
runSqlHive("USE default") ===> this is causing the issue
}
{code}
I am proposing to temporarily back-out part of a fix made to address
SPARK-23000 to resolve this issue while we work-out the exact reason for this
sudden increase in thread counts.
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[jira] [Commented] (SPARK-23254) Add user guide entry for DataFrame multivariate summary
[ https://issues.apache.org/jira/browse/SPARK-23254?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16345651#comment-16345651 ] Weichen Xu commented on SPARK-23254: I will work on this. Thanks! > Add user guide entry for DataFrame multivariate summary > --- > > Key: SPARK-23254 > URL: https://issues.apache.org/jira/browse/SPARK-23254 > Project: Spark > Issue Type: Documentation > Components: Documentation, ML >Affects Versions: 2.3.0 >Reporter: Nick Pentreath >Priority: Minor > > SPARK-19634 added a DataFrame API for vector summary statistics. The [ML user > guide|http://spark.apache.org/docs/latest/ml-statistics.html] should be > updated, with the relevant example (to be in parity with the [MLlib user > guide|http://spark.apache.org/docs/latest/mllib-statistics.html#summary-statistics]). > -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-23261) Rename Pandas UDFs
[ https://issues.apache.org/jira/browse/SPARK-23261?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16345641#comment-16345641 ] Apache Spark commented on SPARK-23261: -- User 'gatorsmile' has created a pull request for this issue: https://github.com/apache/spark/pull/20439 > Rename Pandas UDFs > -- > > Key: SPARK-23261 > URL: https://issues.apache.org/jira/browse/SPARK-23261 > Project: Spark > Issue Type: Sub-task > Components: PySpark >Affects Versions: 2.3.0 >Reporter: Xiao Li >Assignee: Xiao Li >Priority: Major > Fix For: 2.4.0 > > > Rename the public APIs of pandas udfs from > - PANDAS SCALAR UDF -> SCALAR PANDAS UDF > - PANDAS GROUP MAP UDF -> GROUPED MAP PANDAS UDF > - PANDAS GROUP AGG UDF -> PANDAS UDAF -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18085) SPIP: Better History Server scalability for many / large applications
[ https://issues.apache.org/jira/browse/SPARK-18085?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16345615#comment-16345615 ] Marcelo Vanzin commented on SPARK-18085: If you want the short and dirty description: these changes decouple the application status data storage from the UI code, and allow different ways for storing the status data. Shipped with 2.3 are in-memory and disk storage options. This allows the SHS to use disk-based storage to use less memory and serve data more quickly when restarted. > SPIP: Better History Server scalability for many / large applications > - > > Key: SPARK-18085 > URL: https://issues.apache.org/jira/browse/SPARK-18085 > Project: Spark > Issue Type: Umbrella > Components: Spark Core, Web UI >Affects Versions: 2.0.0 >Reporter: Marcelo Vanzin >Priority: Major > Labels: SPIP > Fix For: 2.3.0 > > Attachments: screenshot-1.png, screenshot-2.png, spark_hs_next_gen.pdf > > > It's a known fact that the History Server currently has some annoying issues > when serving lots of applications, and when serving large applications. > I'm filing this umbrella to track work related to addressing those issues. > I'll be attaching a document shortly describing the issues and suggesting a > path to how to solve them. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-23020) Re-enable Flaky Test: org.apache.spark.launcher.SparkLauncherSuite.testInProcessLauncher
[ https://issues.apache.org/jira/browse/SPARK-23020?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-23020: Assignee: Marcelo Vanzin (was: Apache Spark) > Re-enable Flaky Test: > org.apache.spark.launcher.SparkLauncherSuite.testInProcessLauncher > > > Key: SPARK-23020 > URL: https://issues.apache.org/jira/browse/SPARK-23020 > Project: Spark > Issue Type: Bug > Components: Tests >Affects Versions: 2.4.0 >Reporter: Sameer Agarwal >Assignee: Marcelo Vanzin >Priority: Blocker > > https://amplab.cs.berkeley.edu/jenkins/job/spark-branch-2.3-test-maven-hadoop-2.7/42/testReport/junit/org.apache.spark.launcher/SparkLauncherSuite/testInProcessLauncher/history/ -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-23020) Re-enable Flaky Test: org.apache.spark.launcher.SparkLauncherSuite.testInProcessLauncher
[ https://issues.apache.org/jira/browse/SPARK-23020?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-23020: Assignee: Apache Spark (was: Marcelo Vanzin) > Re-enable Flaky Test: > org.apache.spark.launcher.SparkLauncherSuite.testInProcessLauncher > > > Key: SPARK-23020 > URL: https://issues.apache.org/jira/browse/SPARK-23020 > Project: Spark > Issue Type: Bug > Components: Tests >Affects Versions: 2.4.0 >Reporter: Sameer Agarwal >Assignee: Apache Spark >Priority: Blocker > > https://amplab.cs.berkeley.edu/jenkins/job/spark-branch-2.3-test-maven-hadoop-2.7/42/testReport/junit/org.apache.spark.launcher/SparkLauncherSuite/testInProcessLauncher/history/ -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-12394) Support writing out pre-hash-partitioned data and exploit that in join optimizations to avoid shuffle (i.e. bucketing in Hive)
[ https://issues.apache.org/jira/browse/SPARK-12394?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16345598#comment-16345598 ] Thomas Bünger commented on SPARK-12394: --- Any news on this issue? Is it really fixed? I also can't find a corresponding pull request. > Support writing out pre-hash-partitioned data and exploit that in join > optimizations to avoid shuffle (i.e. bucketing in Hive) > -- > > Key: SPARK-12394 > URL: https://issues.apache.org/jira/browse/SPARK-12394 > Project: Spark > Issue Type: New Feature > Components: SQL >Reporter: Reynold Xin >Assignee: Nong Li >Priority: Major > Fix For: 2.0.0 > > Attachments: BucketedTables.pdf > > > In many cases users know ahead of time the columns that they will be joining > or aggregating on. Ideally they should be able to leverage this information > and pre-shuffle the data so that subsequent queries do not require a shuffle. > Hive supports this functionality by allowing the user to define buckets, > which are hash partitioning of the data based on some key. > - Allow the user to specify a set of columns when caching or writing out data > - Allow the user to specify some parallelism > - Shuffle the data when writing / caching such that its distributed by these > columns > - When planning/executing a query, use this distribution to avoid another > shuffle when reading, assuming the join or aggregation is compatible with the > columns specified > - Should work with existing save modes: append, overwrite, etc > - Should work at least with all Hadoops FS data sources > - Should work with any data source when caching -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-23265) Update multi-column error handling logic in QuantileDiscretizer
[
https://issues.apache.org/jira/browse/SPARK-23265?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16345585#comment-16345585
]
Huaxin Gao commented on SPARK-23265:
I am working on it. Will submit a PR today.
> Update multi-column error handling logic in QuantileDiscretizer
> ---
>
> Key: SPARK-23265
> URL: https://issues.apache.org/jira/browse/SPARK-23265
> Project: Spark
> Issue Type: Improvement
> Components: ML
>Affects Versions: 2.3.0
>Reporter: Nick Pentreath
>Priority: Major
>
> SPARK-22397 added support for multiple columns to {{QuantileDiscretizer}}. If
> both single- and mulit-column params are set (specifically {{inputCol}} /
> {{inputCols}}) an error is thrown.
> However, SPARK-22799 added more comprehensive error logic for {{Bucketizer}}.
> The logic for {{QuantileDiscretizer}} should be updated to match. *Note* that
> for this transformer, it is acceptable to set the single-column param for
> {{numBuckets }}when transforming multiple columns, since that is then applied
> to all columns.
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[jira] [Comment Edited] (SPARK-18016) Code Generation: Constant Pool Past Limit for Wide/Nested Dataset
[
https://issues.apache.org/jira/browse/SPARK-18016?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16345551#comment-16345551
]
Kazuaki Ishizaki edited comment on SPARK-18016 at 1/30/18 6:27 PM:
---
[~gaurav.garg] Thank you for your confirmation. I am running a program that has
{{Statistics.corr(vec, "pearson")}}. It takes some time until the program stops.
I would appreciate it if you could share a log file that includes the generated
Java code.
was (Author: kiszk):
[~gaurav.garg] Thank you for your confirmation. I am running a program that has
\{{Statistics.corr(vec, "pearson"})}}. It takes some time until the program
stops.
I would appreciate it if you could share a log file that includes the generated
Java code.
> Code Generation: Constant Pool Past Limit for Wide/Nested Dataset
> -
>
> Key: SPARK-18016
> URL: https://issues.apache.org/jira/browse/SPARK-18016
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.1.0
>Reporter: Aleksander Eskilson
>Assignee: Kazuaki Ishizaki
>Priority: Major
> Fix For: 2.3.0
>
>
> When attempting to encode collections of large Java objects to Datasets
> having very wide or deeply nested schemas, code generation can fail, yielding:
> {code}
> Caused by: org.codehaus.janino.JaninoRuntimeException: Constant pool for
> class
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection
> has grown past JVM limit of 0x
> at
> org.codehaus.janino.util.ClassFile.addToConstantPool(ClassFile.java:499)
> at
> org.codehaus.janino.util.ClassFile.addConstantNameAndTypeInfo(ClassFile.java:439)
> at
> org.codehaus.janino.util.ClassFile.addConstantMethodrefInfo(ClassFile.java:358)
> at
> org.codehaus.janino.UnitCompiler.writeConstantMethodrefInfo(UnitCompiler.java:4)
> at org.codehaus.janino.UnitCompiler.compileGet2(UnitCompiler.java:4547)
> at org.codehaus.janino.UnitCompiler.access$7500(UnitCompiler.java:206)
> at
> org.codehaus.janino.UnitCompiler$12.visitMethodInvocation(UnitCompiler.java:3774)
> at
> org.codehaus.janino.UnitCompiler$12.visitMethodInvocation(UnitCompiler.java:3762)
> at org.codehaus.janino.Java$MethodInvocation.accept(Java.java:4328)
> at org.codehaus.janino.UnitCompiler.compileGet(UnitCompiler.java:3762)
> at
> org.codehaus.janino.UnitCompiler.compileGetValue(UnitCompiler.java:4933)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:3180)
> at org.codehaus.janino.UnitCompiler.access$5000(UnitCompiler.java:206)
> at
> org.codehaus.janino.UnitCompiler$9.visitMethodInvocation(UnitCompiler.java:3151)
> at
> org.codehaus.janino.UnitCompiler$9.visitMethodInvocation(UnitCompiler.java:3139)
> at org.codehaus.janino.Java$MethodInvocation.accept(Java.java:4328)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:3139)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:2112)
> at org.codehaus.janino.UnitCompiler.access$1700(UnitCompiler.java:206)
> at
> org.codehaus.janino.UnitCompiler$6.visitExpressionStatement(UnitCompiler.java:1377)
> at
> org.codehaus.janino.UnitCompiler$6.visitExpressionStatement(UnitCompiler.java:1370)
> at org.codehaus.janino.Java$ExpressionStatement.accept(Java.java:2558)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:1370)
> at
> org.codehaus.janino.UnitCompiler.compileStatements(UnitCompiler.java:1450)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:2811)
> at
> org.codehaus.janino.UnitCompiler.compileDeclaredMethods(UnitCompiler.java:1262)
> at
> org.codehaus.janino.UnitCompiler.compileDeclaredMethods(UnitCompiler.java:1234)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:538)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:890)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:894)
> at org.codehaus.janino.UnitCompiler.access$600(UnitCompiler.java:206)
> at
> org.codehaus.janino.UnitCompiler$2.visitMemberClassDeclaration(UnitCompiler.java:377)
> at
> org.codehaus.janino.UnitCompiler$2.visitMemberClassDeclaration(UnitCompiler.java:369)
> at
> org.codehaus.janino.Java$MemberClassDeclaration.accept(Java.java:1128)
> at org.codehaus.janino.UnitCompiler.compile(UnitCompiler.java:369)
> at
> org.codehaus.janino.UnitCompiler.compileDeclaredMemberTypes(UnitCompiler.java:1209)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:564)
> at org.codehaus.janino.UnitCompiler.compile2(UnitCompiler.java:420)
> at
[jira] [Updated] (SPARK-23274) ReplaceExceptWithFilter fails on dataframes filtered on same column
[
https://issues.apache.org/jira/browse/SPARK-23274?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiao Li updated SPARK-23274:
Target Version/s: 2.3.0
> ReplaceExceptWithFilter fails on dataframes filtered on same column
> ---
>
> Key: SPARK-23274
> URL: https://issues.apache.org/jira/browse/SPARK-23274
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Onur Satici
>Assignee: Xiao Li
>Priority: Blocker
>
> Currently affects:
> {code:java}
> $ git tag --contains 01f6ba0e7a
> v2.3.0-rc1
> v2.3.0-rc2
> {code}
> Steps to reproduce:
> {code:java}
> $ cat test.csv
> a,b
> 1,2
> 1,3
> 2,2
> 2,4
> {code}
> {code:java}
> val df = spark.read.format("csv").option("header", "true").load("test.csv")
> val df1 = df.filter($"a" === 1)
> val df2 = df.filter($"a" === 2)
> df1.select("b").except(df2.select("b")).show
> {code}
> results in:
> {code:java}
> java.util.NoSuchElementException: key not found: a
> at scala.collection.MapLike$class.default(MapLike.scala:228)
> at scala.collection.AbstractMap.default(Map.scala:59)
> at scala.collection.MapLike$class.apply(MapLike.scala:141)
> at scala.collection.AbstractMap.apply(Map.scala:59)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition$1.applyOrElse(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition$1.applyOrElse(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$.org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$apply$1.applyOrElse(ReplaceExceptWithFilter.scala:50)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$apply$1.applyOrElse(ReplaceExceptWithFilter.scala:48)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
>
[jira] [Commented] (SPARK-23274) ReplaceExceptWithFilter fails on dataframes filtered on same column
[
https://issues.apache.org/jira/browse/SPARK-23274?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16345554#comment-16345554
]
Xiao Li commented on SPARK-23274:
-
Since this is a regression, I will try to fix it ASAP
> ReplaceExceptWithFilter fails on dataframes filtered on same column
> ---
>
> Key: SPARK-23274
> URL: https://issues.apache.org/jira/browse/SPARK-23274
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Onur Satici
>Assignee: Xiao Li
>Priority: Blocker
>
> Currently affects:
> {code:java}
> $ git tag --contains 01f6ba0e7a
> v2.3.0-rc1
> v2.3.0-rc2
> {code}
> Steps to reproduce:
> {code:java}
> $ cat test.csv
> a,b
> 1,2
> 1,3
> 2,2
> 2,4
> {code}
> {code:java}
> val df = spark.read.format("csv").option("header", "true").load("test.csv")
> val df1 = df.filter($"a" === 1)
> val df2 = df.filter($"a" === 2)
> df1.select("b").except(df2.select("b")).show
> {code}
> results in:
> {code:java}
> java.util.NoSuchElementException: key not found: a
> at scala.collection.MapLike$class.default(MapLike.scala:228)
> at scala.collection.AbstractMap.default(Map.scala:59)
> at scala.collection.MapLike$class.apply(MapLike.scala:141)
> at scala.collection.AbstractMap.apply(Map.scala:59)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition$1.applyOrElse(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition$1.applyOrElse(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$.org$apache$spark$sql$catalyst$optimizer$ReplaceExceptWithFilter$$transformCondition(ReplaceExceptWithFilter.scala:60)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$apply$1.applyOrElse(ReplaceExceptWithFilter.scala:50)
> at
> org.apache.spark.sql.catalyst.optimizer.ReplaceExceptWithFilter$$anonfun$apply$1.applyOrElse(ReplaceExceptWithFilter.scala:48)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
> at
>
