[jira] [Resolved] (SPARK-24997) Support MINUS ALL
[ https://issues.apache.org/jira/browse/SPARK-24997?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li resolved SPARK-24997. - Resolution: Fixed Assignee: Dilip Biswal Fix Version/s: 2.4.0 > Support MINUS ALL > - > > Key: SPARK-24997 > URL: https://issues.apache.org/jira/browse/SPARK-24997 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 2.3.1 >Reporter: Dilip Biswal >Assignee: Dilip Biswal >Priority: Major > Fix For: 2.4.0 > > > MINUS is synonym for EXCEPT. We have added support for EXCEPT ALL. We need to > enable support for MINUS ALL as well. -- 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-24788) RelationalGroupedDataset.toString throws errors when grouping by UnresolvedAttribute
[
https://issues.apache.org/jira/browse/SPARK-24788?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiao Li resolved SPARK-24788.
-
Resolution: Fixed
Assignee: Chris Horn
Fix Version/s: 2.4.0
> RelationalGroupedDataset.toString throws errors when grouping by
> UnresolvedAttribute
>
>
> Key: SPARK-24788
> URL: https://issues.apache.org/jira/browse/SPARK-24788
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.1
>Reporter: Chris Horn
>Assignee: Chris Horn
>Priority: Minor
> Fix For: 2.4.0
>
>
> This causes references to the RelationalGroupedDataset to break on the shell
> because of the toString call:
> {code:java}
> scala> spark.range(0, 10).groupBy("id")
> res4: org.apache.spark.sql.RelationalGroupedDataset =
> RelationalGroupedDataset: [grouping expressions: [id: bigint], value: [id:
> bigint], type: GroupBy]
> scala> spark.range(0, 10).groupBy('id)
> org.apache.spark.sql.catalyst.analysis.UnresolvedException: Invalid call to
> dataType on unresolved object, tree: 'id
> at
> org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute.dataType(unresolved.scala:105)
> at
> org.apache.spark.sql.RelationalGroupedDataset$$anonfun$12.apply(RelationalGroupedDataset.scala:474)
> at
> org.apache.spark.sql.RelationalGroupedDataset$$anonfun$12.apply(RelationalGroupedDataset.scala:473)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.AbstractTraversable.map(Traversable.scala:104)
> at
> org.apache.spark.sql.RelationalGroupedDataset.toString(RelationalGroupedDataset.scala:473)
> at
> scala.runtime.ScalaRunTime$.scala$runtime$ScalaRunTime$$inner$1(ScalaRunTime.scala:332)
> at scala.runtime.ScalaRunTime$.stringOf(ScalaRunTime.scala:337)
> at scala.runtime.ScalaRunTime$.replStringOf(ScalaRunTime.scala:345)
> {code}
>
> I will create a PR.
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[jira] [Updated] (SPARK-24948) SHS filters wrongly some applications due to permission check
[ https://issues.apache.org/jira/browse/SPARK-24948?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Saisai Shao updated SPARK-24948: Priority: Blocker (was: Major) > SHS filters wrongly some applications due to permission check > - > > Key: SPARK-24948 > URL: https://issues.apache.org/jira/browse/SPARK-24948 > Project: Spark > Issue Type: Bug > Components: Web UI >Affects Versions: 2.3.1 >Reporter: Marco Gaido >Priority: Blocker > > SHS filters the event logs it doesn't have permissions to read. > Unfortunately, this check is quite naive, as it takes into account only the > base permissions (ie. user, group, other permissions). For instance, if ACL > are enabled, they are ignored in this check; moreover, each filesystem may > have different policies (eg. they can consider spark as a superuser who can > access everything). > This results in some applications not being displayed in the SHS, despite the > Spark user (or whatever user the SHS is started with) can actually read their > ent logs. -- 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-25002) Avro: revise the output record namespace
[ https://issues.apache.org/jira/browse/SPARK-25002?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Wenchen Fan resolved SPARK-25002. - Resolution: Fixed Fix Version/s: 2.4.0 Issue resolved by pull request 21974 [https://github.com/apache/spark/pull/21974] > Avro: revise the output record namespace > > > Key: SPARK-25002 > URL: https://issues.apache.org/jira/browse/SPARK-25002 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.4.0 >Reporter: Gengliang Wang >Assignee: Gengliang Wang >Priority: Major > Fix For: 2.4.0 > > > Currently the output namespace is starting with "." > Although it is valid according to Avro spec, we should remove the starting > dot in case of failures when the output file is read by other lib: > https://github.com/linkedin/goavro/pull/96 -- 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-25002) Avro: revise the output record namespace
[ https://issues.apache.org/jira/browse/SPARK-25002?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Wenchen Fan reassigned SPARK-25002: --- Assignee: Gengliang Wang > Avro: revise the output record namespace > > > Key: SPARK-25002 > URL: https://issues.apache.org/jira/browse/SPARK-25002 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.4.0 >Reporter: Gengliang Wang >Assignee: Gengliang Wang >Priority: Major > Fix For: 2.4.0 > > > Currently the output namespace is starting with "." > Although it is valid according to Avro spec, we should remove the starting > dot in case of failures when the output file is read by other lib: > https://github.com/linkedin/goavro/pull/96 -- 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-24948) SHS filters wrongly some applications due to permission check
[ https://issues.apache.org/jira/browse/SPARK-24948?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Saisai Shao updated SPARK-24948: Target Version/s: 2.2.3, 2.3.2, 2.4.0 > SHS filters wrongly some applications due to permission check > - > > Key: SPARK-24948 > URL: https://issues.apache.org/jira/browse/SPARK-24948 > Project: Spark > Issue Type: Bug > Components: Web UI >Affects Versions: 2.3.1 >Reporter: Marco Gaido >Priority: Major > > SHS filters the event logs it doesn't have permissions to read. > Unfortunately, this check is quite naive, as it takes into account only the > base permissions (ie. user, group, other permissions). For instance, if ACL > are enabled, they are ignored in this check; moreover, each filesystem may > have different policies (eg. they can consider spark as a superuser who can > access everything). > This results in some applications not being displayed in the SHS, despite the > Spark user (or whatever user the SHS is started with) can actually read their > ent logs. -- 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-23911) High-order function: reduce(array, initialState S, inputFunction, outputFunction) → R
[
https://issues.apache.org/jira/browse/SPARK-23911?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567791#comment-16567791
]
Takuya Ueshin commented on SPARK-23911:
---
[~smilegator] [~hvanhovell] I'd use {{aggregate}} instead of {{reduce}} for
this function name because {{aggregate}} better fits the functionality of the
function. WDYT?
> High-order function: reduce(array, initialState S, inputFunction,
> outputFunction) → R
> ---
>
> Key: SPARK-23911
> URL: https://issues.apache.org/jira/browse/SPARK-23911
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Xiao Li
>Assignee: Herman van Hovell
>Priority: Major
>
> Ref: https://prestodb.io/docs/current/functions/array.html
> Returns a single value reduced from array. inputFunction will be invoked for
> each element in array in order. In addition to taking the element,
> inputFunction takes the current state, initially initialState, and returns
> the new state. outputFunction will be invoked to turn the final state into
> the result value. It may be the identity function (i -> i).
> {noformat}
> SELECT reduce(ARRAY [], 0, (s, x) -> s + x, s -> s); -- 0
> SELECT reduce(ARRAY [5, 20, 50], 0, (s, x) -> s + x, s -> s); -- 75
> SELECT reduce(ARRAY [5, 20, NULL, 50], 0, (s, x) -> s + x, s -> s); -- NULL
> SELECT reduce(ARRAY [5, 20, NULL, 50], 0, (s, x) -> s + COALESCE(x, 0), s ->
> s); -- 75
> SELECT reduce(ARRAY [5, 20, NULL, 50], 0, (s, x) -> IF(x IS NULL, s, s + x),
> s -> s); -- 75
> SELECT reduce(ARRAY [2147483647, 1], CAST (0 AS BIGINT), (s, x) -> s + x, s
> -> s); -- 2147483648
> SELECT reduce(ARRAY [5, 6, 10, 20], -- calculates arithmetic average: 10.25
> CAST(ROW(0.0, 0) AS ROW(sum DOUBLE, count INTEGER)),
> (s, x) -> CAST(ROW(x + s.sum, s.count + 1) AS ROW(sum DOUBLE,
> count INTEGER)),
> s -> IF(s.count = 0, NULL, s.sum / s.count));
> {noformat}
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[jira] [Created] (SPARK-25011) Add PrefixSpan to __all__
yuhao yang created SPARK-25011: -- Summary: Add PrefixSpan to __all__ Key: SPARK-25011 URL: https://issues.apache.org/jira/browse/SPARK-25011 Project: Spark Issue Type: Bug Components: ML Affects Versions: 2.4.0 Reporter: yuhao yang Add PrefixSpan to __all__ in fpm.py -- 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-24966) Fix the precedence rule for set operations.
[ https://issues.apache.org/jira/browse/SPARK-24966?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li resolved SPARK-24966. - Resolution: Fixed Fix Version/s: 2.4.0 > Fix the precedence rule for set operations. > --- > > Key: SPARK-24966 > URL: https://issues.apache.org/jira/browse/SPARK-24966 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 2.3.1 >Reporter: Dilip Biswal >Priority: Major > Fix For: 2.4.0 > > > Currently the set operations INTERSECT, UNION and EXCEPT are assigned the > same precedence. We need to change to make sure INTERSECT is given higher > precedence than UNION and EXCEPT. UNION and EXCEPT should be evaluated in the > order they appear in the query from left to right. > Given this will result in a change in behavior, we need to keep it under a > config. > Here is a reference : > https://docs.microsoft.com/en-us/sql/t-sql/language-elements/set-operators-except-and-intersect-transact-sql?view=sql-server-2017 -- 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-24966) Fix the precedence rule for set operations.
[ https://issues.apache.org/jira/browse/SPARK-24966?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li reassigned SPARK-24966: --- Assignee: Dilip Biswal > Fix the precedence rule for set operations. > --- > > Key: SPARK-24966 > URL: https://issues.apache.org/jira/browse/SPARK-24966 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 2.3.1 >Reporter: Dilip Biswal >Assignee: Dilip Biswal >Priority: Major > Fix For: 2.4.0 > > > Currently the set operations INTERSECT, UNION and EXCEPT are assigned the > same precedence. We need to change to make sure INTERSECT is given higher > precedence than UNION and EXCEPT. UNION and EXCEPT should be evaluated in the > order they appear in the query from left to right. > Given this will result in a change in behavior, we need to keep it under a > config. > Here is a reference : > https://docs.microsoft.com/en-us/sql/t-sql/language-elements/set-operators-except-and-intersect-transact-sql?view=sql-server-2017 -- 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-24924) Add mapping for built-in Avro data source
[
https://issues.apache.org/jira/browse/SPARK-24924?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567708#comment-16567708
]
Hyukjin Kwon commented on SPARK-24924:
--
Similar discussion was made in SPARK-20590 when we port CSV. in my experience,
users really don't know if {{com.databricks.spark.avro}} or {{avro}} mean
external Avro jar or internal jar (same thing happened in CSV -
I was active in that Spark CSV (databricks) package FWIW).
if users were using the external avro, they will likely meet the error if they
directly upgrade Spark. Otherwise, users will see the release note that Avro
package is included in 2.4.0, and they will not provide the external jar.
If users miss the release note, then they will try to explicitly provide the
thirdparty jar, which will now give the error message like:
{code}
17/05/10 09:47:44 WARN DataSource: Multiple sources found for csv
(org.apache.spark.sql.execution.datasources.csv.CSVFileFormat,
com.databricks.spark.csv.DefaultSource15), defaulting to the internal
datasource (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat).
{code}
Encouraging to use builtin's one might better be preferred since the behaviours
will kept same at its best for now.
Otherwise, If external Avro must be used, I think it can be still used if the
source is specified by fully qualified path in theory.
> Add mapping for built-in Avro data source
> -
>
> Key: SPARK-24924
> URL: https://issues.apache.org/jira/browse/SPARK-24924
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
>Affects Versions: 2.4.0
>Reporter: Dongjoon Hyun
>Assignee: Dongjoon Hyun
>Priority: Minor
> Fix For: 2.4.0
>
>
> This issue aims to the followings.
> # Like `com.databricks.spark.csv` mapping, we had better map
> `com.databricks.spark.avro` to built-in Avro data source.
> # Remove incorrect error message, `Please find an Avro package at ...`.
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[jira] [Resolved] (SPARK-24977) input_file_name() result can't save and use for partitionBy()
[ https://issues.apache.org/jira/browse/SPARK-24977?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Sean Owen resolved SPARK-24977. --- Resolution: Not A Problem This isn't nearly sufficient detail for a JIRA, and evidence it isn't a problem. > input_file_name() result can't save and use for partitionBy() > - > > Key: SPARK-24977 > URL: https://issues.apache.org/jira/browse/SPARK-24977 > Project: Spark > Issue Type: Bug > Components: PySpark, Spark Core, SQL >Affects Versions: 2.3.1 >Reporter: Srinivasarao Padala >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-24977) input_file_name() result can't save and use for partitionBy()
[ https://issues.apache.org/jira/browse/SPARK-24977?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Sean Owen updated SPARK-24977: -- Target Version/s: (was: 2.2.1) > input_file_name() result can't save and use for partitionBy() > - > > Key: SPARK-24977 > URL: https://issues.apache.org/jira/browse/SPARK-24977 > Project: Spark > Issue Type: Bug > Components: PySpark, Spark Core, SQL >Affects Versions: 2.3.1 >Reporter: Srinivasarao Padala >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-24910) Spark Bloom Filter Closure Serialization improvement for very high volume of Data
[
https://issues.apache.org/jira/browse/SPARK-24910?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Sean Owen updated SPARK-24910:
--
Shepherd: (was: Sean Owen)
Flags: (was: Patch)
Labels: (was: bloom-filter)
Priority: Minor (was: Major)
> Spark Bloom Filter Closure Serialization improvement for very high volume of
> Data
> -
>
> Key: SPARK-24910
> URL: https://issues.apache.org/jira/browse/SPARK-24910
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core, SQL
>Affects Versions: 2.3.1
>Reporter: Himangshu Ranjan Borah
>Priority: Minor
>
> I am proposing an improvement to the Bloom Filter Generation logic being used
> in the DataFrameStatFunctions' Bloom Filter API using mapPartitions() instead
> of aggregate() to avoid closure serialization which fails for huge BitArrays.
> Spark's Stat Functions' Bloom Filter Implementation uses
> aggregate/treeAggregate operations which uses a closure with a dependency on
> the bloom filter that is created in the driver. Since Spark hard codes the
> closure serializer to Java Serializer it fails in closure cleanup for very
> big sizes of Bloom Filters (Typically with num items ~ Billions and with fpp
> ~ 0.001). Kryo serializer work's fine in such a scale but seems like there
> were some issues using Kryo for closure serialization due to which Spark 2.0
> hardcoded it to Java. The call-stack that we get typically looks like,
> {{{color:#f79232}java.lang.OutOfMemoryError{color}}}
> {{{color:#f79232} at
> java.io.ByteArrayOutputStream.hugeCapacity(ByteArrayOutputStream.java:123){color}}}
> {{{color:#f79232} at
> java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:117){color}}}
> {{{color:#f79232} at
> java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93){color}}}
> {{{color:#f79232} at
> java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153){color}}}
> {{{color:#f79232} at
> org.apache.spark.util.ByteBufferOutputStream.write(ByteBufferOutputStream.scala:41){color}}}
> {{{color:#f79232} at
> java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877){color}}}
> {{{color:#f79232} at
> java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786){color}}}
> {{{color:#f79232} at
> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1189){color}}}
> {{{color:#f79232} at
> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548){color}}}
> {{{color:#f79232} at
> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509){color}}}
> {{{color:#f79232} at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432){color}}}
> {{{color:#f79232} at
> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178){color}}}
> {{{color:#f79232} at
> java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348){color}}}
> {{{color:#f79232} at
> org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:43){color}}}
> {{{color:#f79232} at
> org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:100){color}}}
> {{{color:#f79232} at
> org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:342){color}}}
> {{{color:#f79232} at
> org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:335){color}}}
> {{{color:#f79232} at
> org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:159){color}}}
> {{{color:#f79232} at
> org.apache.spark.SparkContext.clean(SparkContext.scala:2292){color}}}
> {{{color:#f79232} at
> org.apache.spark.SparkContext.runJob(SparkContext.scala:2022){color}}}
> {{{color:#f79232} at
> org.apache.spark.SparkContext.runJob(SparkContext.scala:2124){color}}}
> {{{color:#f79232} at
> org.apache.spark.rdd.RDD$$anonfun$fold$1.apply(RDD.scala:1092){color}}}
> {{{color:#f79232} at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151){color}}}
> {{{color:#f79232} at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112){color}}}
> {{{color:#f79232} at
> org.apache.spark.rdd.RDD.withScope(RDD.scala:363){color}}}
> {{{color:#f79232} at org.apache.spark.rdd.RDD.fold(RDD.scala:1086){color}}}
> {{{color:#f79232} at
> org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1.apply(RDD.scala:1155){color}}}
> {{{color:#f79232} at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151){color}}}
> {{{color:#f79232} at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112){color}}}
> {{{color:#f79232} at
> org.apache.spark.rdd.RDD.withScope(RDD.scala:363){color}}}
> {{{color:#f79232} at
> org
[jira] [Commented] (SPARK-10413) ML models should support prediction on single instances
[
https://issues.apache.org/jira/browse/SPARK-10413?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567683#comment-16567683
]
zhengruifeng commented on SPARK-10413:
--
Is there a plan to expose predict in clustering algs? [~mengxr]
I just encounter a case in which this feature is needed.
> ML models should support prediction on single instances
> ---
>
> Key: SPARK-10413
> URL: https://issues.apache.org/jira/browse/SPARK-10413
> Project: Spark
> Issue Type: Umbrella
> Components: ML
>Reporter: Xiangrui Meng
>Priority: Critical
>
> Currently models in the pipeline API only implement transform(DataFrame). It
> would be quite useful to support prediction on single instance.
> UPDATE: This issue is for making predictions with single models. We can make
> methods like {{def predict(features: Vector): Double}} public.
> * This issue is *not* for single-instance prediction for full Pipelines,
> which would require making predictions on {{Row}}s.
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[jira] [Commented] (SPARK-25009) Standalone Cluster mode application submit is not working
[ https://issues.apache.org/jira/browse/SPARK-25009?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567677#comment-16567677 ] Imran Rashid commented on SPARK-25009: -- [~devaraj.k] I don't think SPARK-22941 is in 2.3.1, so I changed the Affects Versions, please let me know if I'm mistaken. > Standalone Cluster mode application submit is not working > - > > Key: SPARK-25009 > URL: https://issues.apache.org/jira/browse/SPARK-25009 > Project: Spark > Issue Type: Bug > Components: Spark Core >Affects Versions: 2.4.0 >Reporter: Devaraj K >Priority: Critical > > It is not showing any error while submitting but the app is not running and > as well as not showing in the web UI. -- 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-25009) Standalone Cluster mode application submit is not working
[ https://issues.apache.org/jira/browse/SPARK-25009?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Imran Rashid updated SPARK-25009: - Affects Version/s: (was: 2.3.1) 2.4.0 > Standalone Cluster mode application submit is not working > - > > Key: SPARK-25009 > URL: https://issues.apache.org/jira/browse/SPARK-25009 > Project: Spark > Issue Type: Bug > Components: Spark Core >Affects Versions: 2.4.0 >Reporter: Devaraj K >Priority: Critical > > It is not showing any error while submitting but the app is not running and > as well as not showing in the web UI. -- 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-25009) Standalone Cluster mode application submit is not working
[ https://issues.apache.org/jira/browse/SPARK-25009?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Imran Rashid updated SPARK-25009: - Priority: Critical (was: Blocker) > Standalone Cluster mode application submit is not working > - > > Key: SPARK-25009 > URL: https://issues.apache.org/jira/browse/SPARK-25009 > Project: Spark > Issue Type: Bug > Components: Spark Core >Affects Versions: 2.3.1 >Reporter: Devaraj K >Priority: Critical > > It is not showing any error while submitting but the app is not running and > as well as not showing in the web UI. -- 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-25009) Standalone Cluster mode application submit is not working
[ https://issues.apache.org/jira/browse/SPARK-25009?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Imran Rashid updated SPARK-25009: - Priority: Blocker (was: Major) > Standalone Cluster mode application submit is not working > - > > Key: SPARK-25009 > URL: https://issues.apache.org/jira/browse/SPARK-25009 > Project: Spark > Issue Type: Bug > Components: Spark Core >Affects Versions: 2.3.1 >Reporter: Devaraj K >Priority: Blocker > > It is not showing any error while submitting but the app is not running and > as well as not showing in the web UI. -- 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-24945) Switch to uniVocity >= 2.7.2
[ https://issues.apache.org/jira/browse/SPARK-24945?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Hyukjin Kwon reassigned SPARK-24945: Assignee: Maxim Gekk > Switch to uniVocity >= 2.7.2 > > > Key: SPARK-24945 > URL: https://issues.apache.org/jira/browse/SPARK-24945 > Project: Spark > Issue Type: Improvement > Components: SQL >Affects Versions: 2.3.1 >Reporter: Maxim Gekk >Assignee: Maxim Gekk >Priority: Minor > Fix For: 2.4.0 > > > The recent version 2.7.2 of uniVocity parser includes the fix: > https://github.com/uniVocity/univocity-parsers/issues/250 . And the recent > version has better 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] [Resolved] (SPARK-24945) Switch to uniVocity >= 2.7.2
[ https://issues.apache.org/jira/browse/SPARK-24945?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Hyukjin Kwon resolved SPARK-24945. -- Resolution: Fixed Fix Version/s: 2.4.0 Issue resolved by pull request 21969 [https://github.com/apache/spark/pull/21969] > Switch to uniVocity >= 2.7.2 > > > Key: SPARK-24945 > URL: https://issues.apache.org/jira/browse/SPARK-24945 > Project: Spark > Issue Type: Improvement > Components: SQL >Affects Versions: 2.3.1 >Reporter: Maxim Gekk >Assignee: Maxim Gekk >Priority: Minor > Fix For: 2.4.0 > > > The recent version 2.7.2 of uniVocity parser includes the fix: > https://github.com/uniVocity/univocity-parsers/issues/250 . And the recent > version has better 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] [Assigned] (SPARK-24773) support reading AVRO logical types - Timestamp with different precisions
[ https://issues.apache.org/jira/browse/SPARK-24773?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Hyukjin Kwon reassigned SPARK-24773: Assignee: Gengliang Wang > support reading AVRO logical types - Timestamp with different precisions > > > Key: SPARK-24773 > URL: https://issues.apache.org/jira/browse/SPARK-24773 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.4.0 >Reporter: Gengliang Wang >Assignee: Gengliang Wang >Priority: Major > Fix For: 2.4.0 > > -- 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] [Resolved] (SPARK-24773) support reading AVRO logical types - Timestamp with different precisions
[ https://issues.apache.org/jira/browse/SPARK-24773?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Hyukjin Kwon resolved SPARK-24773. -- Resolution: Fixed Fix Version/s: 2.4.0 Issue resolved by pull request 21935 [https://github.com/apache/spark/pull/21935] > support reading AVRO logical types - Timestamp with different precisions > > > Key: SPARK-24773 > URL: https://issues.apache.org/jira/browse/SPARK-24773 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.4.0 >Reporter: Gengliang Wang >Assignee: Gengliang Wang >Priority: Major > Fix For: 2.4.0 > > -- 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-25010) Rand/Randn should produce different values for each execution in streaming query
[ https://issues.apache.org/jira/browse/SPARK-25010?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567592#comment-16567592 ] Apache Spark commented on SPARK-25010: -- User 'viirya' has created a pull request for this issue: https://github.com/apache/spark/pull/21980 > Rand/Randn should produce different values for each execution in streaming > query > > > Key: SPARK-25010 > URL: https://issues.apache.org/jira/browse/SPARK-25010 > Project: Spark > Issue Type: Bug > Components: SQL, Structured Streaming >Affects Versions: 2.4.0 >Reporter: Liang-Chi Hsieh >Priority: Major > > Like Uuid in SPARK-24896, Rand and Randn expressions now produce the same > results for each execution in streaming query. It doesn't make too much sense > for streaming queries. We should make them produce different results as Uuid. -- 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-25010) Rand/Randn should produce different values for each execution in streaming query
[ https://issues.apache.org/jira/browse/SPARK-25010?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-25010: Assignee: (was: Apache Spark) > Rand/Randn should produce different values for each execution in streaming > query > > > Key: SPARK-25010 > URL: https://issues.apache.org/jira/browse/SPARK-25010 > Project: Spark > Issue Type: Bug > Components: SQL, Structured Streaming >Affects Versions: 2.4.0 >Reporter: Liang-Chi Hsieh >Priority: Major > > Like Uuid in SPARK-24896, Rand and Randn expressions now produce the same > results for each execution in streaming query. It doesn't make too much sense > for streaming queries. We should make them produce different results as Uuid. -- 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-25010) Rand/Randn should produce different values for each execution in streaming query
[ https://issues.apache.org/jira/browse/SPARK-25010?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-25010: Assignee: Apache Spark > Rand/Randn should produce different values for each execution in streaming > query > > > Key: SPARK-25010 > URL: https://issues.apache.org/jira/browse/SPARK-25010 > Project: Spark > Issue Type: Bug > Components: SQL, Structured Streaming >Affects Versions: 2.4.0 >Reporter: Liang-Chi Hsieh >Assignee: Apache Spark >Priority: Major > > Like Uuid in SPARK-24896, Rand and Randn expressions now produce the same > results for each execution in streaming query. It doesn't make too much sense > for streaming queries. We should make them produce different results as Uuid. -- 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-21961) Filter out BlockStatuses Accumulators during replaying history logs in Spark History Server
[ https://issues.apache.org/jira/browse/SPARK-21961?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Ye Zhou resolved SPARK-21961. - Resolution: Won't Fix > Filter out BlockStatuses Accumulators during replaying history logs in Spark > History Server > --- > > Key: SPARK-21961 > URL: https://issues.apache.org/jira/browse/SPARK-21961 > Project: Spark > Issue Type: Improvement > Components: Spark Core >Affects Versions: 2.1.0, 2.2.0 >Reporter: Ye Zhou >Priority: Major > Attachments: Objects_Count_in_Heap.png, One_Thread_Took_24GB.png > > > As described in SPARK-20923, TaskMetrics._updatedBlockStatuses uses a lot of > memory in Driver. Recently we also noticed the same issue in Spark History > Server. Even though in SPARK-20084, those event logs are getting removed from > history log. But multiple versions of Spark including 1.6.x and 2.1.0 > versions are deployed in our production cluster, none of them have these two > patches included. > In this case, those event logs will still be in shown up in logs and Spark > History Server will replay them. Spark History Server continuously get severe > Full GCs even though we tried to limit cache size as well as enlarge the > heapsize to 40GB. We also tried with different GC tuning parameters, like > using CMS or G1GC. None of them works. > We made a heap dump, and found that the top memory consumer objects is > BlockStatus. There was even one thread that took 23GB heap which was > replaying one log file. > Since the former two tickets has resolved related issues in both driver and > writing to history logs, we should also consider add this filter to Spark > History Server in order to decrease the memory consumption for replaying one > history log. For use cases like us, where we have multiple older versions of > Spark deployed, this filter should be pretty useful. > We have deployed our Spark History Server with this filter which works fine > in our production cluster, which has processed thousands of logs and only got > several full GC in total. > !https://issues.apache.org/jira/secure/attachment/12886191/Objects_Count_in_Heap.png! > !https://issues.apache.org/jira/secure/attachment/12886190/One_Thread_Took_24GB.png! -- 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-24928) spark sql cross join running time too long
[
https://issues.apache.org/jira/browse/SPARK-24928?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567578#comment-16567578
]
Matthew Normyle commented on SPARK-24928:
-
{color:#cc7832}val {color}largeRDD =
sc.parallelize({color:#9876aa}Seq{color}.fill({color:#6897bb}1000{color})(Random.nextInt))
{color:#cc7832}val {color}smallRDD =
sc.parallelize({color:#9876aa}Seq{color}.fill({color:#6897bb}1{color})(Random.nextInt))
*(1)* largeRDD.cartesian(smallRDD).count()
*(2)* smallRDD.cartesian(largeRDD).count()
Building from master, I can see that (1) consistently takes about twice as long
as (2) on my machine.
> spark sql cross join running time too long
> --
>
> Key: SPARK-24928
> URL: https://issues.apache.org/jira/browse/SPARK-24928
> Project: Spark
> Issue Type: Bug
> Components: Optimizer
>Affects Versions: 1.6.2
>Reporter: LIFULONG
>Priority: Minor
>
> spark sql running time is too long while input left table and right table is
> small hdfs text format data,
> the sql is: select * from t1 cross join t2
> the line of t1 is 49, three column
> the line of t2 is 1, one column only
> running more than 30mins and then failed
>
>
> spark CartesianRDD also has the same problem, example test code is:
> val ones = sc.textFile("hdfs://host:port/data/cartesian_data/t1b") //1 line
> 1 column
> val twos = sc.textFile("hdfs://host:port/data/cartesian_data/t2b") //49
> line 3 column
> val cartesian = new CartesianRDD(sc, twos, ones)
> cartesian.count()
> running more than 5 mins,while use CartesianRDD(sc, ones, twos) , it only use
> less than 10 seconds
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[jira] [Created] (SPARK-25010) Rand/Randn should produce different values for each execution in streaming query
Liang-Chi Hsieh created SPARK-25010: --- Summary: Rand/Randn should produce different values for each execution in streaming query Key: SPARK-25010 URL: https://issues.apache.org/jira/browse/SPARK-25010 Project: Spark Issue Type: Bug Components: SQL, Structured Streaming Affects Versions: 2.4.0 Reporter: Liang-Chi Hsieh Like Uuid in SPARK-24896, Rand and Randn expressions now produce the same results for each execution in streaming query. It doesn't make too much sense for streaming queries. We should make them produce different results as Uuid. -- 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-22219) Refector "spark.sql.codegen.comments"
[
https://issues.apache.org/jira/browse/SPARK-22219?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Sean Owen resolved SPARK-22219.
---
Resolution: Fixed
Fix Version/s: 2.4.0
Issue resolved by pull request 19449
[https://github.com/apache/spark/pull/19449]
> Refector "spark.sql.codegen.comments"
> -
>
> Key: SPARK-22219
> URL: https://issues.apache.org/jira/browse/SPARK-22219
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 2.2.0
>Reporter: Kazuaki Ishizaki
>Assignee: Kazuaki Ishizaki
>Priority: Minor
> Fix For: 2.4.0
>
>
> To get a value for {{"spark.sql.codegen.comments"}} is not the latest
> approach. This refactoring is to use better approach to get a value for
> {{"spark.sql.codegen.comments"}} .
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[jira] [Assigned] (SPARK-22219) Refector "spark.sql.codegen.comments"
[
https://issues.apache.org/jira/browse/SPARK-22219?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Sean Owen reassigned SPARK-22219:
-
Assignee: Kazuaki Ishizaki
> Refector "spark.sql.codegen.comments"
> -
>
> Key: SPARK-22219
> URL: https://issues.apache.org/jira/browse/SPARK-22219
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 2.2.0
>Reporter: Kazuaki Ishizaki
>Assignee: Kazuaki Ishizaki
>Priority: Minor
> Fix For: 2.4.0
>
>
> To get a value for {{"spark.sql.codegen.comments"}} is not the latest
> approach. This refactoring is to use better approach to get a value for
> {{"spark.sql.codegen.comments"}} .
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[jira] [Updated] (SPARK-24909) Spark scheduler can hang when fetch failures, executor lost, task running on lost executor, and multiple stage attempts
[
https://issues.apache.org/jira/browse/SPARK-24909?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Thomas Graves updated SPARK-24909:
--
Target Version/s: 2.4.0
> Spark scheduler can hang when fetch failures, executor lost, task running on
> lost executor, and multiple stage attempts
> ---
>
> Key: SPARK-24909
> URL: https://issues.apache.org/jira/browse/SPARK-24909
> Project: Spark
> Issue Type: Bug
> Components: Scheduler
>Affects Versions: 2.3.1
>Reporter: Thomas Graves
>Priority: Critical
>
> The DAGScheduler can hang if the executor was lost (due to fetch failure) and
> all the tasks in the tasks sets are marked as completed.
> ([https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala#L1265)]
> It never creates new task attempts in the task scheduler but the dag
> scheduler still has pendingPartitions.
> {code:java}
> 8/07/22 08:30:00 INFO scheduler.TaskSetManager: Starting task 55769.0 in
> stage 44.0 (TID 970752, host1.com, executor 33, partition 55769,
> PROCESS_LOCAL, 7874 bytes)
> 18/07/22 08:30:29 INFO scheduler.DAGScheduler: Marking ShuffleMapStage 44
> (repartition at Lift.scala:191) as failed due to a fetch failure from
> ShuffleMapStage 42 (map at foo.scala:27)
> 18/07/22 08:30:29 INFO scheduler.DAGScheduler: Resubmitting ShuffleMapStage
> 42 (map at foo.scala:27) and ShuffleMapStage 44 (repartition at
> bar.scala:191) due to fetch failure
>
> 18/07/22 08:30:56 INFO scheduler.DAGScheduler: Executor lost: 33 (epoch 18)
> 18/07/22 08:30:56 INFO schedulerDAGScheduler: Shuffle files lost for
> executor: 33 (epoch 18)
> 18/07/22 08:31:20 INFO scheduler.DAGScheduler: Submitting ShuffleMapStage 44
> (MapPartitionsRDD[70] at repartition at bar.scala:191), which has no missing
> parents
> 18/07/22 08:31:21 INFO cluster.YarnClusterScheduler: Adding task set 44.1
> with 59955 tasks
> 18/07/22 08:31:41 INFO scheduler.TaskSetManager: Finished task 55769.0 in
> stage 44.0 (TID 970752) in 101505 ms on host1.com (executor 33) (15081/73320)
> 8/07/22 08:31:41 INFO scheduler.DAGScheduler: Ignoring possibly bogus
> ShuffleMapTask(44, 55769) completion from executor 33{code}
>
>
> In the logs above you will see that task 55769.0 finished after the executor
> was lost and a new task set was started. The DAG scheduler says "Ignoring
> possibly bogus".. but in the TaskSetManager side it has marked those tasks as
> completed for all stage attempts. The DAGScheduler gets hung here. I did a
> heap dump on the process and can see that 55769 is still in the DAGScheduler
> pendingPartitions list but the tasksetmanagers are all complete
>
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[jira] [Resolved] (SPARK-24896) Uuid expression should produce different values in each execution under streaming query
[ https://issues.apache.org/jira/browse/SPARK-24896?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Shixiong Zhu resolved SPARK-24896. -- Resolution: Fixed Assignee: Liang-Chi Hsieh Fix Version/s: 2.4.0 > Uuid expression should produce different values in each execution under > streaming query > --- > > Key: SPARK-24896 > URL: https://issues.apache.org/jira/browse/SPARK-24896 > Project: Spark > Issue Type: Bug > Components: SQL, Structured Streaming >Affects Versions: 2.3.0, 2.3.1 >Reporter: Liang-Chi Hsieh >Assignee: Liang-Chi Hsieh >Priority: Major > Fix For: 2.4.0 > > > Uuid's results depend on random seed given during analysis. Thus under > streaming query, we will have the same uuids in each execution. -- 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-24896) Uuid expression should produce different values in each execution under streaming query
[ https://issues.apache.org/jira/browse/SPARK-24896?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Shixiong Zhu updated SPARK-24896: - Affects Version/s: (was: 2.4.0) 2.3.0 2.3.1 > Uuid expression should produce different values in each execution under > streaming query > --- > > Key: SPARK-24896 > URL: https://issues.apache.org/jira/browse/SPARK-24896 > Project: Spark > Issue Type: Bug > Components: SQL, Structured Streaming >Affects Versions: 2.3.0, 2.3.1 >Reporter: Liang-Chi Hsieh >Assignee: Liang-Chi Hsieh >Priority: Major > Fix For: 2.4.0 > > > Uuid's results depend on random seed given during analysis. Thus under > streaming query, we will have the same uuids in each execution. -- 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-24909) Spark scheduler can hang when fetch failures, executor lost, task running on lost executor, and multiple stage attempts
[
https://issues.apache.org/jira/browse/SPARK-24909?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-24909:
Assignee: Apache Spark
> Spark scheduler can hang when fetch failures, executor lost, task running on
> lost executor, and multiple stage attempts
> ---
>
> Key: SPARK-24909
> URL: https://issues.apache.org/jira/browse/SPARK-24909
> Project: Spark
> Issue Type: Bug
> Components: Scheduler
>Affects Versions: 2.3.1
>Reporter: Thomas Graves
>Assignee: Apache Spark
>Priority: Critical
>
> The DAGScheduler can hang if the executor was lost (due to fetch failure) and
> all the tasks in the tasks sets are marked as completed.
> ([https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala#L1265)]
> It never creates new task attempts in the task scheduler but the dag
> scheduler still has pendingPartitions.
> {code:java}
> 8/07/22 08:30:00 INFO scheduler.TaskSetManager: Starting task 55769.0 in
> stage 44.0 (TID 970752, host1.com, executor 33, partition 55769,
> PROCESS_LOCAL, 7874 bytes)
> 18/07/22 08:30:29 INFO scheduler.DAGScheduler: Marking ShuffleMapStage 44
> (repartition at Lift.scala:191) as failed due to a fetch failure from
> ShuffleMapStage 42 (map at foo.scala:27)
> 18/07/22 08:30:29 INFO scheduler.DAGScheduler: Resubmitting ShuffleMapStage
> 42 (map at foo.scala:27) and ShuffleMapStage 44 (repartition at
> bar.scala:191) due to fetch failure
>
> 18/07/22 08:30:56 INFO scheduler.DAGScheduler: Executor lost: 33 (epoch 18)
> 18/07/22 08:30:56 INFO schedulerDAGScheduler: Shuffle files lost for
> executor: 33 (epoch 18)
> 18/07/22 08:31:20 INFO scheduler.DAGScheduler: Submitting ShuffleMapStage 44
> (MapPartitionsRDD[70] at repartition at bar.scala:191), which has no missing
> parents
> 18/07/22 08:31:21 INFO cluster.YarnClusterScheduler: Adding task set 44.1
> with 59955 tasks
> 18/07/22 08:31:41 INFO scheduler.TaskSetManager: Finished task 55769.0 in
> stage 44.0 (TID 970752) in 101505 ms on host1.com (executor 33) (15081/73320)
> 8/07/22 08:31:41 INFO scheduler.DAGScheduler: Ignoring possibly bogus
> ShuffleMapTask(44, 55769) completion from executor 33{code}
>
>
> In the logs above you will see that task 55769.0 finished after the executor
> was lost and a new task set was started. The DAG scheduler says "Ignoring
> possibly bogus".. but in the TaskSetManager side it has marked those tasks as
> completed for all stage attempts. The DAGScheduler gets hung here. I did a
> heap dump on the process and can see that 55769 is still in the DAGScheduler
> pendingPartitions list but the tasksetmanagers are all complete
>
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[jira] [Assigned] (SPARK-24909) Spark scheduler can hang when fetch failures, executor lost, task running on lost executor, and multiple stage attempts
[
https://issues.apache.org/jira/browse/SPARK-24909?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-24909:
Assignee: (was: Apache Spark)
> Spark scheduler can hang when fetch failures, executor lost, task running on
> lost executor, and multiple stage attempts
> ---
>
> Key: SPARK-24909
> URL: https://issues.apache.org/jira/browse/SPARK-24909
> Project: Spark
> Issue Type: Bug
> Components: Scheduler
>Affects Versions: 2.3.1
>Reporter: Thomas Graves
>Priority: Critical
>
> The DAGScheduler can hang if the executor was lost (due to fetch failure) and
> all the tasks in the tasks sets are marked as completed.
> ([https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala#L1265)]
> It never creates new task attempts in the task scheduler but the dag
> scheduler still has pendingPartitions.
> {code:java}
> 8/07/22 08:30:00 INFO scheduler.TaskSetManager: Starting task 55769.0 in
> stage 44.0 (TID 970752, host1.com, executor 33, partition 55769,
> PROCESS_LOCAL, 7874 bytes)
> 18/07/22 08:30:29 INFO scheduler.DAGScheduler: Marking ShuffleMapStage 44
> (repartition at Lift.scala:191) as failed due to a fetch failure from
> ShuffleMapStage 42 (map at foo.scala:27)
> 18/07/22 08:30:29 INFO scheduler.DAGScheduler: Resubmitting ShuffleMapStage
> 42 (map at foo.scala:27) and ShuffleMapStage 44 (repartition at
> bar.scala:191) due to fetch failure
>
> 18/07/22 08:30:56 INFO scheduler.DAGScheduler: Executor lost: 33 (epoch 18)
> 18/07/22 08:30:56 INFO schedulerDAGScheduler: Shuffle files lost for
> executor: 33 (epoch 18)
> 18/07/22 08:31:20 INFO scheduler.DAGScheduler: Submitting ShuffleMapStage 44
> (MapPartitionsRDD[70] at repartition at bar.scala:191), which has no missing
> parents
> 18/07/22 08:31:21 INFO cluster.YarnClusterScheduler: Adding task set 44.1
> with 59955 tasks
> 18/07/22 08:31:41 INFO scheduler.TaskSetManager: Finished task 55769.0 in
> stage 44.0 (TID 970752) in 101505 ms on host1.com (executor 33) (15081/73320)
> 8/07/22 08:31:41 INFO scheduler.DAGScheduler: Ignoring possibly bogus
> ShuffleMapTask(44, 55769) completion from executor 33{code}
>
>
> In the logs above you will see that task 55769.0 finished after the executor
> was lost and a new task set was started. The DAG scheduler says "Ignoring
> possibly bogus".. but in the TaskSetManager side it has marked those tasks as
> completed for all stage attempts. The DAGScheduler gets hung here. I did a
> heap dump on the process and can see that 55769 is still in the DAGScheduler
> pendingPartitions list but the tasksetmanagers are all complete
>
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[jira] [Assigned] (SPARK-25009) Standalone Cluster mode application submit is not working
[ https://issues.apache.org/jira/browse/SPARK-25009?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-25009: Assignee: (was: Apache Spark) > Standalone Cluster mode application submit is not working > - > > Key: SPARK-25009 > URL: https://issues.apache.org/jira/browse/SPARK-25009 > Project: Spark > Issue Type: Bug > Components: Spark Core >Affects Versions: 2.3.1 >Reporter: Devaraj K >Priority: Major > > It is not showing any error while submitting but the app is not running and > as well as not showing in the web UI. -- 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-25009) Standalone Cluster mode application submit is not working
[ https://issues.apache.org/jira/browse/SPARK-25009?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-25009: Assignee: Apache Spark > Standalone Cluster mode application submit is not working > - > > Key: SPARK-25009 > URL: https://issues.apache.org/jira/browse/SPARK-25009 > Project: Spark > Issue Type: Bug > Components: Spark Core >Affects Versions: 2.3.1 >Reporter: Devaraj K >Assignee: Apache Spark >Priority: Major > > It is not showing any error while submitting but the app is not running and > as well as not showing in the web UI. -- 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-25009) Standalone Cluster mode application submit is not working
[ https://issues.apache.org/jira/browse/SPARK-25009?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567536#comment-16567536 ] Apache Spark commented on SPARK-25009: -- User 'devaraj-kavali' has created a pull request for this issue: https://github.com/apache/spark/pull/21979 > Standalone Cluster mode application submit is not working > - > > Key: SPARK-25009 > URL: https://issues.apache.org/jira/browse/SPARK-25009 > Project: Spark > Issue Type: Bug > Components: Spark Core >Affects Versions: 2.3.1 >Reporter: Devaraj K >Priority: Major > > It is not showing any error while submitting but the app is not running and > as well as not showing in the web UI. -- 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-25009) Standalone Cluster mode application submit is not working
Devaraj K created SPARK-25009: - Summary: Standalone Cluster mode application submit is not working Key: SPARK-25009 URL: https://issues.apache.org/jira/browse/SPARK-25009 Project: Spark Issue Type: Bug Components: Spark Core Affects Versions: 2.3.1 Reporter: Devaraj K It is not showing any error while submitting but the app is not running and as well as not showing in the web UI. -- 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-25001) Fix build miscellaneous warnings
[
https://issues.apache.org/jira/browse/SPARK-25001?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-25001:
Assignee: (was: Apache Spark)
> Fix build miscellaneous warnings
>
>
> Key: SPARK-25001
> URL: https://issues.apache.org/jira/browse/SPARK-25001
> Project: Spark
> Issue Type: Improvement
> Components: Build
>Affects Versions: 2.4.0
>Reporter: Hyukjin Kwon
>Priority: Major
>
> There are many warnings in the current build (for instance see
> https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7/4734/console).
> {code}
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDB.java:237:
> warning: [rawtypes] found raw type: LevelDBIterator
> [warn] void closeIterator(LevelDBIterator it) throws IOException {
> [warn] ^
> [warn] missing type arguments for generic class LevelDBIterator
> [warn] where T is a type-variable:
> [warn] T extends Object declared in class LevelDBIterator
> [warn] 1 warning
> [warn] Pruning sources from previous analysis, due to incompatible
> CompileSetup.
> [warn] Pruning sources from previous analysis, due to incompatible
> CompileSetup.
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:151:
> warning: [deprecation] group() in AbstractBootstrap has been deprecated
> [warn] if (bootstrap != null && bootstrap.group() != null) {
> [warn] ^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:152:
> warning: [deprecation] group() in AbstractBootstrap has been deprecated
> [warn] bootstrap.group().shutdownGracefully();
> [warn]^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:154:
> warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
> [warn] if (bootstrap != null && bootstrap.childGroup() != null) {
> [warn] ^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:155:
> warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
> [warn] bootstrap.childGroup().shutdownGracefully();
> [warn]^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/util/NettyUtils.java:112:
> warning: [deprecation]
> PooledByteBufAllocator(boolean,int,int,int,int,int,int,int) in
> PooledByteBufAllocator has been deprecated
> [warn] return new PooledByteBufAllocator(
> [warn]^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportClient.java:321:
> warning: [rawtypes] found raw type: Future
> [warn] public void operationComplete(Future future) throws Exception {
> [warn] ^
> [warn] missing type arguments for generic class Future
> [warn] where V is a type-variable:
> [warn] V extends Object declared in interface Future
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215:
> warning: [rawtypes] found raw type: StreamInterceptor
> [warn] StreamInterceptor interceptor = new StreamInterceptor(this,
> resp.streamId, resp.byteCount,
> [warn] ^
> [warn] missing type arguments for generic class StreamInterceptor
> [warn] where T is a type-variable:
> [warn] T extends Message declared in class StreamInterceptor
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215:
> warning: [rawtypes] found raw type: StreamInterceptor
> [warn] StreamInterceptor interceptor = new StreamInterceptor(this,
> resp.streamId, resp.byteCount,
> [warn] ^
> [warn] missing type arguments for generic class StreamInterceptor
> [warn] where T is a type-variable:
> [warn] T extends Message declared in class StreamInterceptor
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215:
> warning: [unchecked] unchecke
[jira] [Assigned] (SPARK-25001) Fix build miscellaneous warnings
[
https://issues.apache.org/jira/browse/SPARK-25001?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-25001:
Assignee: Apache Spark
> Fix build miscellaneous warnings
>
>
> Key: SPARK-25001
> URL: https://issues.apache.org/jira/browse/SPARK-25001
> Project: Spark
> Issue Type: Improvement
> Components: Build
>Affects Versions: 2.4.0
>Reporter: Hyukjin Kwon
>Assignee: Apache Spark
>Priority: Major
>
> There are many warnings in the current build (for instance see
> https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7/4734/console).
> {code}
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDB.java:237:
> warning: [rawtypes] found raw type: LevelDBIterator
> [warn] void closeIterator(LevelDBIterator it) throws IOException {
> [warn] ^
> [warn] missing type arguments for generic class LevelDBIterator
> [warn] where T is a type-variable:
> [warn] T extends Object declared in class LevelDBIterator
> [warn] 1 warning
> [warn] Pruning sources from previous analysis, due to incompatible
> CompileSetup.
> [warn] Pruning sources from previous analysis, due to incompatible
> CompileSetup.
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:151:
> warning: [deprecation] group() in AbstractBootstrap has been deprecated
> [warn] if (bootstrap != null && bootstrap.group() != null) {
> [warn] ^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:152:
> warning: [deprecation] group() in AbstractBootstrap has been deprecated
> [warn] bootstrap.group().shutdownGracefully();
> [warn]^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:154:
> warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
> [warn] if (bootstrap != null && bootstrap.childGroup() != null) {
> [warn] ^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:155:
> warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
> [warn] bootstrap.childGroup().shutdownGracefully();
> [warn]^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/util/NettyUtils.java:112:
> warning: [deprecation]
> PooledByteBufAllocator(boolean,int,int,int,int,int,int,int) in
> PooledByteBufAllocator has been deprecated
> [warn] return new PooledByteBufAllocator(
> [warn]^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportClient.java:321:
> warning: [rawtypes] found raw type: Future
> [warn] public void operationComplete(Future future) throws Exception {
> [warn] ^
> [warn] missing type arguments for generic class Future
> [warn] where V is a type-variable:
> [warn] V extends Object declared in interface Future
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215:
> warning: [rawtypes] found raw type: StreamInterceptor
> [warn] StreamInterceptor interceptor = new StreamInterceptor(this,
> resp.streamId, resp.byteCount,
> [warn] ^
> [warn] missing type arguments for generic class StreamInterceptor
> [warn] where T is a type-variable:
> [warn] T extends Message declared in class StreamInterceptor
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215:
> warning: [rawtypes] found raw type: StreamInterceptor
> [warn] StreamInterceptor interceptor = new StreamInterceptor(this,
> resp.streamId, resp.byteCount,
> [warn] ^
> [warn] missing type arguments for generic class StreamInterceptor
> [warn] where T is a type-variable:
> [warn] T extends Message declared in class StreamInterceptor
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215:
> warn
[jira] [Created] (SPARK-25008) Add memory mode info to showMemoryUsage in TaskMemoryManager
Ankur Gupta created SPARK-25008:
---
Summary: Add memory mode info to showMemoryUsage in
TaskMemoryManager
Key: SPARK-25008
URL: https://issues.apache.org/jira/browse/SPARK-25008
Project: Spark
Issue Type: Task
Components: Spark Core
Affects Versions: 2.3.0
Reporter: Ankur Gupta
TaskMemoryManager prints the current memory usage information before throwing
an OOM exception which is helpful in debugging issues. This log does not have
the memory mode information which can be also useful to quickly determine which
memory users need to increase.
This JIRA is to add that information to showMemoryUsage method of
TaskMemoryManager.
Current logs:
{code}
18/07/03 17:57:16 INFO memory.TaskMemoryManager: Memory used in task 318
18/07/03 17:57:16 INFO memory.TaskMemoryManager: Acquired by
org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter@7f084d1b:
1024.0 KB
18/07/03 17:57:16 INFO memory.TaskMemoryManager: Acquired by
org.apache.spark.shuffle.sort.ShuffleExternalSorter@713d50f2: 32.0 KB
18/07/03 17:57:16 INFO memory.TaskMemoryManager: 0 bytes of memory were used by
task 318 but are not associated with specific consumers
18/07/03 17:57:16 INFO memory.TaskMemoryManager: 1081344 bytes of memory are
used for execution and 306201016 bytes of memory are used for storage
18/07/03 17:57:16 ERROR executor.Executor: Exception in task 86.0 in stage 49.0
(TID 318)
java.lang.OutOfMemoryError: Unable to acquire 326284160 bytes of memory, got
3112960
at org.apache.spark.memory.MemoryConsumer.allocatePage(MemoryConsumer.java:127)
at
org.apache.spark.shuffle.sort.ShuffleExternalSorter.acquireNewPageIfNecessary(ShuffleExternalSorter.java:359)
at
org.apache.spark.shuffle.sort.ShuffleExternalSorter.insertRecord(ShuffleExternalSorter.java:382)
at
org.apache.spark.shuffle.sort.UnsafeShuffleWriter.insertRecordIntoSorter(UnsafeShuffleWriter.java:246)
at
org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:167)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
{code}
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[jira] [Commented] (SPARK-25007) Add transform / array_except /array_union / array_shuffle to SparkR
[ https://issues.apache.org/jira/browse/SPARK-25007?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567521#comment-16567521 ] Huaxin Gao commented on SPARK-25007: I will work on this. Thanks! > Add transform / array_except /array_union / array_shuffle to SparkR > --- > > Key: SPARK-25007 > URL: https://issues.apache.org/jira/browse/SPARK-25007 > Project: Spark > Issue Type: Sub-task > Components: SparkR >Affects Versions: 2.4.0 >Reporter: Huaxin Gao >Priority: Major > > Add R version of > * transform -SPARK-23908- > * array_except -SPARK-23915- > * array_union -SPARK-23914- > * array_shuffle -SPARK-23928- -- 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-25007) Add transform / array_except /array_union / array_shuffle to SparkR
Huaxin Gao created SPARK-25007: -- Summary: Add transform / array_except /array_union / array_shuffle to SparkR Key: SPARK-25007 URL: https://issues.apache.org/jira/browse/SPARK-25007 Project: Spark Issue Type: Sub-task Components: SparkR Affects Versions: 2.4.0 Reporter: Huaxin Gao Add R version of * transform -SPARK-23908- * array_except -SPARK-23915- * array_union -SPARK-23914- * array_shuffle -SPARK-23928- -- 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-25006) Add optional catalog to TableIdentifier
[ https://issues.apache.org/jira/browse/SPARK-25006?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-25006: Assignee: Apache Spark > Add optional catalog to TableIdentifier > --- > > Key: SPARK-25006 > URL: https://issues.apache.org/jira/browse/SPARK-25006 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 2.3.0 >Reporter: Ryan Blue >Assignee: Apache Spark >Priority: Major > > For multi-catalog support, Spark table identifiers need to identify the > catalog for a table. -- 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-25006) Add optional catalog to TableIdentifier
[ https://issues.apache.org/jira/browse/SPARK-25006?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567513#comment-16567513 ] Apache Spark commented on SPARK-25006: -- User 'rdblue' has created a pull request for this issue: https://github.com/apache/spark/pull/21978 > Add optional catalog to TableIdentifier > --- > > Key: SPARK-25006 > URL: https://issues.apache.org/jira/browse/SPARK-25006 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 2.3.0 >Reporter: Ryan Blue >Priority: Major > > For multi-catalog support, Spark table identifiers need to identify the > catalog for a table. -- 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-25006) Add optional catalog to TableIdentifier
[ https://issues.apache.org/jira/browse/SPARK-25006?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-25006: Assignee: (was: Apache Spark) > Add optional catalog to TableIdentifier > --- > > Key: SPARK-25006 > URL: https://issues.apache.org/jira/browse/SPARK-25006 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 2.3.0 >Reporter: Ryan Blue >Priority: Major > > For multi-catalog support, Spark table identifiers need to identify the > catalog for a table. -- 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-23908) High-order function: transform(array, function) → array
[
https://issues.apache.org/jira/browse/SPARK-23908?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Herman van Hovell reassigned SPARK-23908:
-
Assignee: Takuya Ueshin (was: Herman van Hovell)
> High-order function: transform(array, function) → array
> ---
>
> Key: SPARK-23908
> URL: https://issues.apache.org/jira/browse/SPARK-23908
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Xiao Li
>Assignee: Takuya Ueshin
>Priority: Major
> Fix For: 2.4.0
>
>
> Ref: https://prestodb.io/docs/current/functions/array.html
> Returns an array that is the result of applying function to each element of
> array:
> {noformat}
> SELECT transform(ARRAY [], x -> x + 1); -- []
> SELECT transform(ARRAY [5, 6], x -> x + 1); -- [6, 7]
> SELECT transform(ARRAY [5, NULL, 6], x -> COALESCE(x, 0) + 1); -- [6, 1, 7]
> SELECT transform(ARRAY ['x', 'abc', 'z'], x -> x || '0'); -- ['x0', 'abc0',
> 'z0']
> SELECT transform(ARRAY [ARRAY [1, NULL, 2], ARRAY[3, NULL]], a -> filter(a, x
> -> x IS NOT NULL)); -- [[1, 2], [3]]
> {noformat}
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[jira] [Created] (SPARK-25006) Add optional catalog to TableIdentifier
Ryan Blue created SPARK-25006: - Summary: Add optional catalog to TableIdentifier Key: SPARK-25006 URL: https://issues.apache.org/jira/browse/SPARK-25006 Project: Spark Issue Type: Bug Components: SQL Affects Versions: 2.3.0 Reporter: Ryan Blue For multi-catalog support, Spark table identifiers need to identify the catalog for a table. -- 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-25005) Structured streaming doesn't support kafka transaction (creating empty offset with abort & markers)
Quentin Ambard created SPARK-25005: -- Summary: Structured streaming doesn't support kafka transaction (creating empty offset with abort & markers) Key: SPARK-25005 URL: https://issues.apache.org/jira/browse/SPARK-25005 Project: Spark Issue Type: Bug Components: Structured Streaming Affects Versions: 2.3.1 Reporter: Quentin Ambard Structured streaming can't consume kafka transaction. We could try to apply SPARK-24720 (DStream) logic to Structured Streaming source -- 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-24720) kafka transaction creates Non-consecutive Offsets (due to transaction offset) making streaming fail when failOnDataLoss=true
[ https://issues.apache.org/jira/browse/SPARK-24720?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Quentin Ambard updated SPARK-24720: --- Component/s: (was: Structured Streaming) DStreams > kafka transaction creates Non-consecutive Offsets (due to transaction offset) > making streaming fail when failOnDataLoss=true > > > Key: SPARK-24720 > URL: https://issues.apache.org/jira/browse/SPARK-24720 > Project: Spark > Issue Type: Bug > Components: DStreams >Affects Versions: 2.3.1 >Reporter: Quentin Ambard >Priority: Major > > When kafka transactions are used, sending 1 message to kafka will result to 1 > offset for the data + 1 offset to mark the transaction. > When kafka connector for spark streaming read a topic with non-consecutive > offset, it leads to a failure. SPARK-17147 fixed this issue for compacted > topics. > However, SPARK-17147 doesn't fix this issue for kafka transactions: if 1 > message + 1 transaction commit are in a partition, spark will try to read > offsets [0 2[. offset 0 (containing the message) will be read, but offset 1 > won't return a value and buffer.hasNext() will be false even after a poll > since no data are present for offset 1 (it's the transaction commit) > -- 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-24705) Spark.sql.adaptive.enabled=true is enabled and self-join query
[ https://issues.apache.org/jira/browse/SPARK-24705?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li resolved SPARK-24705. - Resolution: Fixed Assignee: Takeshi Yamamuro Fix Version/s: 2.4.0 > Spark.sql.adaptive.enabled=true is enabled and self-join query > -- > > Key: SPARK-24705 > URL: https://issues.apache.org/jira/browse/SPARK-24705 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 2.2.1, 2.3.1 >Reporter: cheng >Assignee: Takeshi Yamamuro >Priority: Minor > Fix For: 2.4.0 > > Attachments: Error stack.txt > > > [~smilegator] > When loading data using jdbc and enabling spark.sql.adaptive.enabled=true , > for example loading a tableA table, unexpected results can occur when you use > the following query. > For example: > device_loc table comes from the jdbc data source > select tv_a.imei > from ( select a.imei,a.speed from device_loc a) tv_a > inner join ( select a.imei,a.speed from device_loc a ) tv_b on tv_a.imei = > tv_b.imei > group by tv_a.imei > When the cache tabel device_loc is executed before this query is executed, > everything is fine,However, if you do not execute cache table, unexpected > results will occur, resulting in failure to execute. > Remarks：Attachment records the stack when the error occurred -- 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-20964) Make some keywords reserved along with the ANSI/SQL standard
[ https://issues.apache.org/jira/browse/SPARK-20964?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li updated SPARK-20964: Target Version/s: 3.0.0 > Make some keywords reserved along with the ANSI/SQL standard > > > Key: SPARK-20964 > URL: https://issues.apache.org/jira/browse/SPARK-20964 > Project: Spark > Issue Type: Improvement > Components: SQL >Affects Versions: 2.1.1 >Reporter: Takeshi Yamamuro >Priority: Minor > > The current Spark has many non-reserved words that are essentially reserved > in the ANSI/SQL standard > (http://developer.mimer.se/validator/sql-reserved-words.tml). > https://github.com/apache/spark/blob/master/sql/catalyst/src/main/antlr4/org/apache/spark/sql/catalyst/parser/SqlBase.g4#L709 > This is because there are many datasources (for instance twitter4j) that > unfortunately use reserved keywords for column names (See [~hvanhovell]'s > comments: https://github.com/apache/spark/pull/18079#discussion_r118842186). > We might fix this issue in future major releases. -- 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-23908) High-order function: transform(array, function) → array
[
https://issues.apache.org/jira/browse/SPARK-23908?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiao Li resolved SPARK-23908.
-
Resolution: Fixed
Fix Version/s: 2.4.0
> High-order function: transform(array, function) → array
> ---
>
> Key: SPARK-23908
> URL: https://issues.apache.org/jira/browse/SPARK-23908
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Xiao Li
>Assignee: Herman van Hovell
>Priority: Major
> Fix For: 2.4.0
>
>
> Ref: https://prestodb.io/docs/current/functions/array.html
> Returns an array that is the result of applying function to each element of
> array:
> {noformat}
> SELECT transform(ARRAY [], x -> x + 1); -- []
> SELECT transform(ARRAY [5, 6], x -> x + 1); -- [6, 7]
> SELECT transform(ARRAY [5, NULL, 6], x -> COALESCE(x, 0) + 1); -- [6, 1, 7]
> SELECT transform(ARRAY ['x', 'abc', 'z'], x -> x || '0'); -- ['x0', 'abc0',
> 'z0']
> SELECT transform(ARRAY [ARRAY [1, NULL, 2], ARRAY[3, NULL]], a -> filter(a, x
> -> x IS NOT NULL)); -- [[1, 2], [3]]
> {noformat}
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[jira] [Assigned] (SPARK-25004) Add spark.executor.pyspark.memory config to set resource.RLIMIT_AS
[
https://issues.apache.org/jira/browse/SPARK-25004?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-25004:
Assignee: (was: Apache Spark)
> Add spark.executor.pyspark.memory config to set resource.RLIMIT_AS
> --
>
> Key: SPARK-25004
> URL: https://issues.apache.org/jira/browse/SPARK-25004
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 2.3.0
>Reporter: Ryan Blue
>Priority: Major
>
> Some platforms support limiting Python's addressable memory space by limiting
> [{{resource.RLIMIT_AS}}|https://docs.python.org/3/library/resource.html#resource.RLIMIT_AS].
> We've found that adding a limit is very useful when running in YARN because
> when Python doesn't know about memory constraints, it doesn't know when to
> garbage collect and will continue using memory when it doesn't need to.
> Adding a limit reduces PySpark memory consumption and avoids YARN killing
> containers because Python hasn't cleaned up memory.
> This also improves error messages for users, allowing them to see when Python
> is allocating too much memory instead of YARN killing the container:
> {code:lang=python}
> File "build/bdist.linux-x86_64/egg/package/library.py", line 265, in
> fe_engineer
> fe_eval_rec.update(f(src_rec_prep, mat_rec_prep))
> File "build/bdist.linux-x86_64/egg/package/library.py", line 163, in fe_comp
> comparisons = EvaluationUtils.leven_list_compare(src_rec_prep.get(item,
> []), mat_rec_prep.get(item, []))
> File "build/bdist.linux-x86_64/egg/package/evaluationutils.py", line 25, in
> leven_list_compare
> permutations = sorted(permutations, reverse=True)
> MemoryError
> {code}
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[jira] [Commented] (SPARK-25004) Add spark.executor.pyspark.memory config to set resource.RLIMIT_AS
[
https://issues.apache.org/jira/browse/SPARK-25004?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567396#comment-16567396
]
Apache Spark commented on SPARK-25004:
--
User 'rdblue' has created a pull request for this issue:
https://github.com/apache/spark/pull/21977
> Add spark.executor.pyspark.memory config to set resource.RLIMIT_AS
> --
>
> Key: SPARK-25004
> URL: https://issues.apache.org/jira/browse/SPARK-25004
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 2.3.0
>Reporter: Ryan Blue
>Priority: Major
>
> Some platforms support limiting Python's addressable memory space by limiting
> [{{resource.RLIMIT_AS}}|https://docs.python.org/3/library/resource.html#resource.RLIMIT_AS].
> We've found that adding a limit is very useful when running in YARN because
> when Python doesn't know about memory constraints, it doesn't know when to
> garbage collect and will continue using memory when it doesn't need to.
> Adding a limit reduces PySpark memory consumption and avoids YARN killing
> containers because Python hasn't cleaned up memory.
> This also improves error messages for users, allowing them to see when Python
> is allocating too much memory instead of YARN killing the container:
> {code:lang=python}
> File "build/bdist.linux-x86_64/egg/package/library.py", line 265, in
> fe_engineer
> fe_eval_rec.update(f(src_rec_prep, mat_rec_prep))
> File "build/bdist.linux-x86_64/egg/package/library.py", line 163, in fe_comp
> comparisons = EvaluationUtils.leven_list_compare(src_rec_prep.get(item,
> []), mat_rec_prep.get(item, []))
> File "build/bdist.linux-x86_64/egg/package/evaluationutils.py", line 25, in
> leven_list_compare
> permutations = sorted(permutations, reverse=True)
> MemoryError
> {code}
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[jira] [Assigned] (SPARK-25004) Add spark.executor.pyspark.memory config to set resource.RLIMIT_AS
[
https://issues.apache.org/jira/browse/SPARK-25004?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-25004:
Assignee: Apache Spark
> Add spark.executor.pyspark.memory config to set resource.RLIMIT_AS
> --
>
> Key: SPARK-25004
> URL: https://issues.apache.org/jira/browse/SPARK-25004
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 2.3.0
>Reporter: Ryan Blue
>Assignee: Apache Spark
>Priority: Major
>
> Some platforms support limiting Python's addressable memory space by limiting
> [{{resource.RLIMIT_AS}}|https://docs.python.org/3/library/resource.html#resource.RLIMIT_AS].
> We've found that adding a limit is very useful when running in YARN because
> when Python doesn't know about memory constraints, it doesn't know when to
> garbage collect and will continue using memory when it doesn't need to.
> Adding a limit reduces PySpark memory consumption and avoids YARN killing
> containers because Python hasn't cleaned up memory.
> This also improves error messages for users, allowing them to see when Python
> is allocating too much memory instead of YARN killing the container:
> {code:lang=python}
> File "build/bdist.linux-x86_64/egg/package/library.py", line 265, in
> fe_engineer
> fe_eval_rec.update(f(src_rec_prep, mat_rec_prep))
> File "build/bdist.linux-x86_64/egg/package/library.py", line 163, in fe_comp
> comparisons = EvaluationUtils.leven_list_compare(src_rec_prep.get(item,
> []), mat_rec_prep.get(item, []))
> File "build/bdist.linux-x86_64/egg/package/evaluationutils.py", line 25, in
> leven_list_compare
> permutations = sorted(permutations, reverse=True)
> MemoryError
> {code}
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[jira] [Commented] (SPARK-24615) Accelerator-aware task scheduling for Spark
[ https://issues.apache.org/jira/browse/SPARK-24615?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567393#comment-16567393 ] Mingjie Tang commented on SPARK-24615: -- >From user's perspective, user only concern about the GPU resource for RDD, and >do not understand the stage or partitions of RDD. Therefore, underline >resource allocation mechanism would assign the resources to executor >automatically. Similar as cache or persistence to different level, maybe we can provide different configuration to users. Then, resource allocation to follow the predefined policy to allocate resource. > Accelerator-aware task scheduling for Spark > --- > > Key: SPARK-24615 > URL: https://issues.apache.org/jira/browse/SPARK-24615 > Project: Spark > Issue Type: Improvement > Components: Spark Core >Affects Versions: 2.4.0 >Reporter: Saisai Shao >Assignee: Saisai Shao >Priority: Major > Labels: Hydrogen, SPIP > > In the machine learning area, accelerator card (GPU, FPGA, TPU) is > predominant compared to CPUs. To make the current Spark architecture to work > with accelerator cards, Spark itself should understand the existence of > accelerators and know how to schedule task onto the executors where > accelerators are equipped. > Current Spark’s scheduler schedules tasks based on the locality of the data > plus the available of CPUs. This will introduce some problems when scheduling > tasks with accelerators required. > # CPU cores are usually more than accelerators on one node, using CPU cores > to schedule accelerator required tasks will introduce the mismatch. > # In one cluster, we always assume that CPU is equipped in each node, but > this is not true of accelerator cards. > # The existence of heterogeneous tasks (accelerator required or not) > requires scheduler to schedule tasks with a smart way. > So here propose to improve the current scheduler to support heterogeneous > tasks (accelerator requires or not). This can be part of the work of Project > hydrogen. > Details is attached in google doc. It doesn't cover all the implementation > details, just highlight the parts should be changed. > > CC [~yanboliang] [~merlintang] -- 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-25004) Add spark.executor.pyspark.memory config to set resource.RLIMIT_AS
[
https://issues.apache.org/jira/browse/SPARK-25004?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ryan Blue updated SPARK-25004:
--
Description:
Some platforms support limiting Python's addressable memory space by limiting
[{{resource.RLIMIT_AS}}|https://docs.python.org/3/library/resource.html#resource.RLIMIT_AS].
We've found that adding a limit is very useful when running in YARN because
when Python doesn't know about memory constraints, it doesn't know when to
garbage collect and will continue using memory when it doesn't need to. Adding
a limit reduces PySpark memory consumption and avoids YARN killing containers
because Python hasn't cleaned up memory.
This also improves error messages for users, allowing them to see when Python
is allocating too much memory instead of YARN killing the container:
{code:lang=python}
File "build/bdist.linux-x86_64/egg/package/library.py", line 265, in
fe_engineer
fe_eval_rec.update(f(src_rec_prep, mat_rec_prep))
File "build/bdist.linux-x86_64/egg/package/library.py", line 163, in fe_comp
comparisons = EvaluationUtils.leven_list_compare(src_rec_prep.get(item,
[]), mat_rec_prep.get(item, []))
File "build/bdist.linux-x86_64/egg/package/evaluationutils.py", line 25, in
leven_list_compare
permutations = sorted(permutations, reverse=True)
MemoryError
{code}
was:
Some platforms support limiting Python's addressable memory space by limiting
[{{resource.RLIMIT_AS}}|https://docs.python.org/3/library/resource.html#resource.RLIMIT_AS].
We've found that adding a limit is very useful when running in YARN because
when Python doesn't know about memory constraints, it doesn't know when to
garbage collect and will continue using memory when it doesn't need to. Adding
a limit reduces PySpark memory consumption and avoids YARN killing containers
because Python hasn't cleaned up memory.
This also improves error messages for users, allowing them to see when Python
is allocating too much memory instead of YARN killing the container:
{code:lang=python}
File "build/bdist.linux-x86_64/egg/package/library.py", line 265, in
fe_engineer
fe_eval_rec.update(f(src_rec_prep, mat_rec_prep))
File "build/bdist.linux-x86_64/egg/package/library.py", line 163, in fe_comp
comparisons = EvaluationUtils.leven_list_compare(src_rec_prep.get(item,
[]), mat_rec_prep.get(item, []))
File "build/bdist.linux-x86_64/egg/package/evaluationutils.py", line 25, in
leven_list_compare
permutations = sorted(permutations, reverse=True)
MemoryError
{code}
> Add spark.executor.pyspark.memory config to set resource.RLIMIT_AS
> --
>
> Key: SPARK-25004
> URL: https://issues.apache.org/jira/browse/SPARK-25004
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 2.3.0
>Reporter: Ryan Blue
>Priority: Major
>
> Some platforms support limiting Python's addressable memory space by limiting
> [{{resource.RLIMIT_AS}}|https://docs.python.org/3/library/resource.html#resource.RLIMIT_AS].
> We've found that adding a limit is very useful when running in YARN because
> when Python doesn't know about memory constraints, it doesn't know when to
> garbage collect and will continue using memory when it doesn't need to.
> Adding a limit reduces PySpark memory consumption and avoids YARN killing
> containers because Python hasn't cleaned up memory.
> This also improves error messages for users, allowing them to see when Python
> is allocating too much memory instead of YARN killing the container:
> {code:lang=python}
> File "build/bdist.linux-x86_64/egg/package/library.py", line 265, in
> fe_engineer
> fe_eval_rec.update(f(src_rec_prep, mat_rec_prep))
> File "build/bdist.linux-x86_64/egg/package/library.py", line 163, in fe_comp
> comparisons = EvaluationUtils.leven_list_compare(src_rec_prep.get(item,
> []), mat_rec_prep.get(item, []))
> File "build/bdist.linux-x86_64/egg/package/evaluationutils.py", line 25, in
> leven_list_compare
> permutations = sorted(permutations, reverse=True)
> MemoryError
> {code}
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[jira] [Created] (SPARK-25004) Add spark.executor.pyspark.memory config to set resource.RLIMIT_AS
Ryan Blue created SPARK-25004:
-
Summary: Add spark.executor.pyspark.memory config to set
resource.RLIMIT_AS
Key: SPARK-25004
URL: https://issues.apache.org/jira/browse/SPARK-25004
Project: Spark
Issue Type: Bug
Components: PySpark
Affects Versions: 2.3.0
Reporter: Ryan Blue
Some platforms support limiting Python's addressable memory space by limiting
[{{resource.RLIMIT_AS}}|https://docs.python.org/3/library/resource.html#resource.RLIMIT_AS].
We've found that adding a limit is very useful when running in YARN because
when Python doesn't know about memory constraints, it doesn't know when to
garbage collect and will continue using memory when it doesn't need to. Adding
a limit reduces PySpark memory consumption and avoids YARN killing containers
because Python hasn't cleaned up memory.
This also improves error messages for users, allowing them to see when Python
is allocating too much memory instead of YARN killing the container:
{code:lang=python}
File "build/bdist.linux-x86_64/egg/package/library.py", line 265, in
fe_engineer
fe_eval_rec.update(f(src_rec_prep, mat_rec_prep))
File "build/bdist.linux-x86_64/egg/package/library.py", line 163, in fe_comp
comparisons = EvaluationUtils.leven_list_compare(src_rec_prep.get(item,
[]), mat_rec_prep.get(item, []))
File "build/bdist.linux-x86_64/egg/package/evaluationutils.py", line 25, in
leven_list_compare
permutations = sorted(permutations, reverse=True)
MemoryError
{code}
--
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[jira] [Commented] (SPARK-14220) Build and test Spark against Scala 2.12
[ https://issues.apache.org/jira/browse/SPARK-14220?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567383#comment-16567383 ] Reynold Xin commented on SPARK-14220: - This is awesome! Congrats! > Build and test Spark against Scala 2.12 > --- > > Key: SPARK-14220 > URL: https://issues.apache.org/jira/browse/SPARK-14220 > Project: Spark > Issue Type: Umbrella > Components: Build, Project Infra >Affects Versions: 2.1.0 >Reporter: Josh Rosen >Priority: Blocker > Labels: release-notes > Fix For: 2.4.0 > > > This umbrella JIRA tracks the requirements for building and testing Spark > against the current Scala 2.12 milestone. -- 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-24434) Support user-specified driver and executor pod templates
[ https://issues.apache.org/jira/browse/SPARK-24434?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567367#comment-16567367 ] Stavros Kontopoulos commented on SPARK-24434: - Btw I have started working on a PR on this so I expect many more things to come up. > Support user-specified driver and executor pod templates > > > Key: SPARK-24434 > URL: https://issues.apache.org/jira/browse/SPARK-24434 > Project: Spark > Issue Type: New Feature > Components: Kubernetes >Affects Versions: 2.4.0 >Reporter: Yinan Li >Priority: Major > > With more requests for customizing the driver and executor pods coming, the > current approach of adding new Spark configuration options has some serious > drawbacks: 1) it means more Kubernetes specific configuration options to > maintain, and 2) it widens the gap between the declarative model used by > Kubernetes and the configuration model used by Spark. We should start > designing a solution that allows users to specify pod templates as central > places for all customization needs for the driver and executor pods. -- 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-24817) Implement BarrierTaskContext.barrier()
[
https://issues.apache.org/jira/browse/SPARK-24817?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567358#comment-16567358
]
Erik Erlandson commented on SPARK-24817:
I have been looking at the use cases for barrier-mode on the design doc. The
primary story seems to be along the lines of using {{mapPartitions}} to:
# write out any partitioned data (and sync)
# execute some kind of ML logic (TF, etc) (possibly syncing on stages here?)
# optionally move back into "normal" spark executions
My mental model has been that the value proposition for Hydrogen is primarily a
convergence argument: it is easier to not have to leave a Spark workflow and
execute something like TF using some other toolchain. But OTOH, given that the
Spark programmer has to write out the partitioned data and then invoke ML
tooling like TF regardless, does the increase to convenience pay for the cost
in complexity for absorbing new clustering & scheduling models into Spark,
along with other consequences, for example SPARK-24615, compared to the "null
hypothesis" of writing partition data, then using ML-specific clustering
toolchains (kubeflow, for example), and consuming the resulting products in
Spark afterward.
> Implement BarrierTaskContext.barrier()
> --
>
> Key: SPARK-24817
> URL: https://issues.apache.org/jira/browse/SPARK-24817
> Project: Spark
> Issue Type: New Feature
> Components: Spark Core
>Affects Versions: 2.4.0
>Reporter: Jiang Xingbo
>Priority: Major
>
> Implement BarrierTaskContext.barrier(), to support global sync between all
> the tasks in a barrier stage. The global sync shall finish immediately once
> all tasks in the same barrier stage reaches the same barrier.
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[jira] [Commented] (SPARK-24434) Support user-specified driver and executor pod templates
[ https://issues.apache.org/jira/browse/SPARK-24434?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567335#comment-16567335 ] Stavros Kontopoulos commented on SPARK-24434: - Thanks [~rvesse] I will have a look. > Support user-specified driver and executor pod templates > > > Key: SPARK-24434 > URL: https://issues.apache.org/jira/browse/SPARK-24434 > Project: Spark > Issue Type: New Feature > Components: Kubernetes >Affects Versions: 2.4.0 >Reporter: Yinan Li >Priority: Major > > With more requests for customizing the driver and executor pods coming, the > current approach of adding new Spark configuration options has some serious > drawbacks: 1) it means more Kubernetes specific configuration options to > maintain, and 2) it widens the gap between the declarative model used by > Kubernetes and the configuration model used by Spark. We should start > designing a solution that allows users to specify pod templates as central > places for all customization needs for the driver and executor pods. -- 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-24817) Implement BarrierTaskContext.barrier()
[ https://issues.apache.org/jira/browse/SPARK-24817?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567301#comment-16567301 ] Erik Erlandson commented on SPARK-24817: Thanks [~jiangxb] - I'd expect that design to work out-of-box on the k8s backend. ML-specific code seems like it will have needs that are harder to predict, by definition. If it can use IP addresses in the cluster space, it should work regardless. If it wants fqdn, then perhaps additional pod configurations will be required. > Implement BarrierTaskContext.barrier() > -- > > Key: SPARK-24817 > URL: https://issues.apache.org/jira/browse/SPARK-24817 > Project: Spark > Issue Type: New Feature > Components: Spark Core >Affects Versions: 2.4.0 >Reporter: Jiang Xingbo >Priority: Major > > Implement BarrierTaskContext.barrier(), to support global sync between all > the tasks in a barrier stage. The global sync shall finish immediately once > all tasks in the same barrier stage reaches the same barrier. -- 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-24988) Add a castBySchema method which casts all the values of a DataFrame based on the DataTypes of a StructType
[
https://issues.apache.org/jira/browse/SPARK-24988?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hyukjin Kwon resolved SPARK-24988.
--
Resolution: Won't Fix
> Add a castBySchema method which casts all the values of a DataFrame based on
> the DataTypes of a StructType
> --
>
> Key: SPARK-24988
> URL: https://issues.apache.org/jira/browse/SPARK-24988
> Project: Spark
> Issue Type: New Feature
> Components: SQL
>Affects Versions: 2.4.0
>Reporter: mahmoud mehdi
>Priority: Minor
>
> The main goal of this User Story is to extend the Dataframe methods in order
> to add a method which casts all the values of a Dataframe, based on the
> DataTypes of a StructType.
> This feature can be useful when we have a large dataframe, and that we need
> to make multiple casts. In that case, we won't have to cast each value
> independently, all we have to do is to pass a StructType to the method
> castBySchema with the types we need (In real world examples, this schema is
> generally provided by the client, which was my case).
> I'll explain the new feature via an example, let's create a dataframe of
> strings :
> {code:java}
> val df = Seq(("test1", "0"), ("test2", "1")).toDF("name", "id")
> {code}
> Let's suppose that we want to cast the second column's values of the
> dataframe to integers, all we have to do is the following :
> {code:java}
> val schema = StructType( Seq( StructField("name", StringType, true),
> StructField("id", IntegerType, true))){code}
> {code:java}
> df.castBySchema(schema)
> {code}
> I made sure that castBySchema works also with nested StructTypes by adding
> several tests.
>
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[jira] [Created] (SPARK-25003) Pyspark Does not use Spark Sql Extensions
Russell Spitzer created SPARK-25003:
---
Summary: Pyspark Does not use Spark Sql Extensions
Key: SPARK-25003
URL: https://issues.apache.org/jira/browse/SPARK-25003
Project: Spark
Issue Type: Bug
Components: PySpark
Affects Versions: 2.3.1, 2.2.2
Reporter: Russell Spitzer
When creating a SparkSession here
[https://github.com/apache/spark/blob/v2.2.2/python/pyspark/sql/session.py#L216]
{code:python}
if jsparkSession is None:
jsparkSession = self._jvm.SparkSession(self._jsc.sc())
self._jsparkSession = jsparkSession
{code}
I believe it ends up calling the constructor here
https://github.com/apache/spark/blob/v2.2.2/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala#L85-L87
{code:scala}
private[sql] def this(sc: SparkContext) {
this(sc, None, None, new SparkSessionExtensions)
}
{code}
Which creates a new SparkSessionsExtensions object and does not pick up new
extensions that could have been set in the config like the companion
getOrCreate does.
https://github.com/apache/spark/blob/v2.2.2/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala#L928-L944
{code:scala}
//in getOrCreate
// Initialize extensions if the user has defined a configurator class.
val extensionConfOption =
sparkContext.conf.get(StaticSQLConf.SPARK_SESSION_EXTENSIONS)
if (extensionConfOption.isDefined) {
val extensionConfClassName = extensionConfOption.get
try {
val extensionConfClass = Utils.classForName(extensionConfClassName)
val extensionConf = extensionConfClass.newInstance()
.asInstanceOf[SparkSessionExtensions => Unit]
extensionConf(extensions)
} catch {
// Ignore the error if we cannot find the class or when the class
has the wrong type.
case e @ (_: ClassCastException |
_: ClassNotFoundException |
_: NoClassDefFoundError) =>
logWarning(s"Cannot use $extensionConfClassName to configure
session extensions.", e)
}
}
{code}
I think a quick fix would be to use the getOrCreate method from the companion
object instead of calling the constructor from the SparkContext. Or we could
fix this by ensuring that all constructors attempt to pick up custom extensions
if they are set.
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[jira] [Commented] (SPARK-25001) Fix build miscellaneous warnings
[
https://issues.apache.org/jira/browse/SPARK-25001?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567136#comment-16567136
]
Apache Spark commented on SPARK-25001:
--
User 'HyukjinKwon' has created a pull request for this issue:
https://github.com/apache/spark/pull/21975
> Fix build miscellaneous warnings
>
>
> Key: SPARK-25001
> URL: https://issues.apache.org/jira/browse/SPARK-25001
> Project: Spark
> Issue Type: Improvement
> Components: Build
>Affects Versions: 2.4.0
>Reporter: Hyukjin Kwon
>Priority: Major
>
> There are many warnings in the current build (for instance see
> https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7/4734/console).
> {code}
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDB.java:237:
> warning: [rawtypes] found raw type: LevelDBIterator
> [warn] void closeIterator(LevelDBIterator it) throws IOException {
> [warn] ^
> [warn] missing type arguments for generic class LevelDBIterator
> [warn] where T is a type-variable:
> [warn] T extends Object declared in class LevelDBIterator
> [warn] 1 warning
> [warn] Pruning sources from previous analysis, due to incompatible
> CompileSetup.
> [warn] Pruning sources from previous analysis, due to incompatible
> CompileSetup.
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:151:
> warning: [deprecation] group() in AbstractBootstrap has been deprecated
> [warn] if (bootstrap != null && bootstrap.group() != null) {
> [warn] ^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:152:
> warning: [deprecation] group() in AbstractBootstrap has been deprecated
> [warn] bootstrap.group().shutdownGracefully();
> [warn]^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:154:
> warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
> [warn] if (bootstrap != null && bootstrap.childGroup() != null) {
> [warn] ^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:155:
> warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
> [warn] bootstrap.childGroup().shutdownGracefully();
> [warn]^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/util/NettyUtils.java:112:
> warning: [deprecation]
> PooledByteBufAllocator(boolean,int,int,int,int,int,int,int) in
> PooledByteBufAllocator has been deprecated
> [warn] return new PooledByteBufAllocator(
> [warn]^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportClient.java:321:
> warning: [rawtypes] found raw type: Future
> [warn] public void operationComplete(Future future) throws Exception {
> [warn] ^
> [warn] missing type arguments for generic class Future
> [warn] where V is a type-variable:
> [warn] V extends Object declared in interface Future
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215:
> warning: [rawtypes] found raw type: StreamInterceptor
> [warn] StreamInterceptor interceptor = new StreamInterceptor(this,
> resp.streamId, resp.byteCount,
> [warn] ^
> [warn] missing type arguments for generic class StreamInterceptor
> [warn] where T is a type-variable:
> [warn] T extends Message declared in class StreamInterceptor
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215:
> warning: [rawtypes] found raw type: StreamInterceptor
> [warn] StreamInterceptor interceptor = new StreamInterceptor(this,
> resp.streamId, resp.byteCount,
> [warn] ^
> [warn] missing type arguments for generic class StreamInterceptor
> [warn] where T is a type-variable:
> [warn] T extends Message declared in class StreamInterceptor
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-co
[jira] [Assigned] (SPARK-25002) Avro: revise the output record namespace
[ https://issues.apache.org/jira/browse/SPARK-25002?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-25002: Assignee: (was: Apache Spark) > Avro: revise the output record namespace > > > Key: SPARK-25002 > URL: https://issues.apache.org/jira/browse/SPARK-25002 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.4.0 >Reporter: Gengliang Wang >Priority: Major > > Currently the output namespace is starting with "." > Although it is valid according to Avro spec, we should remove the starting > dot in case of failures when the output file is read by other lib: > https://github.com/linkedin/goavro/pull/96 -- 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-25002) Avro: revise the output record namespace
[ https://issues.apache.org/jira/browse/SPARK-25002?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-25002: Assignee: Apache Spark > Avro: revise the output record namespace > > > Key: SPARK-25002 > URL: https://issues.apache.org/jira/browse/SPARK-25002 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.4.0 >Reporter: Gengliang Wang >Assignee: Apache Spark >Priority: Major > > Currently the output namespace is starting with "." > Although it is valid according to Avro spec, we should remove the starting > dot in case of failures when the output file is read by other lib: > https://github.com/linkedin/goavro/pull/96 -- 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-25002) Avro: revise the output record namespace
[ https://issues.apache.org/jira/browse/SPARK-25002?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567134#comment-16567134 ] Apache Spark commented on SPARK-25002: -- User 'gengliangwang' has created a pull request for this issue: https://github.com/apache/spark/pull/21974 > Avro: revise the output record namespace > > > Key: SPARK-25002 > URL: https://issues.apache.org/jira/browse/SPARK-25002 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.4.0 >Reporter: Gengliang Wang >Priority: Major > > Currently the output namespace is starting with "." > Although it is valid according to Avro spec, we should remove the starting > dot in case of failures when the output file is read by other lib: > https://github.com/linkedin/goavro/pull/96 -- 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-25001) Fix build miscellaneous warnings
[
https://issues.apache.org/jira/browse/SPARK-25001?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hyukjin Kwon updated SPARK-25001:
-
Summary: Fix build miscellaneous warnings (was: Handle build warnings in
common, core, launcher, mllib, sql)
> Fix build miscellaneous warnings
>
>
> Key: SPARK-25001
> URL: https://issues.apache.org/jira/browse/SPARK-25001
> Project: Spark
> Issue Type: Improvement
> Components: Build
>Affects Versions: 2.4.0
>Reporter: Hyukjin Kwon
>Priority: Major
>
> There are many warnings in the current build (for instance see
> https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7/4734/console).
> {code}
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDB.java:237:
> warning: [rawtypes] found raw type: LevelDBIterator
> [warn] void closeIterator(LevelDBIterator it) throws IOException {
> [warn] ^
> [warn] missing type arguments for generic class LevelDBIterator
> [warn] where T is a type-variable:
> [warn] T extends Object declared in class LevelDBIterator
> [warn] 1 warning
> [warn] Pruning sources from previous analysis, due to incompatible
> CompileSetup.
> [warn] Pruning sources from previous analysis, due to incompatible
> CompileSetup.
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:151:
> warning: [deprecation] group() in AbstractBootstrap has been deprecated
> [warn] if (bootstrap != null && bootstrap.group() != null) {
> [warn] ^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:152:
> warning: [deprecation] group() in AbstractBootstrap has been deprecated
> [warn] bootstrap.group().shutdownGracefully();
> [warn]^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:154:
> warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
> [warn] if (bootstrap != null && bootstrap.childGroup() != null) {
> [warn] ^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:155:
> warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
> [warn] bootstrap.childGroup().shutdownGracefully();
> [warn]^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/util/NettyUtils.java:112:
> warning: [deprecation]
> PooledByteBufAllocator(boolean,int,int,int,int,int,int,int) in
> PooledByteBufAllocator has been deprecated
> [warn] return new PooledByteBufAllocator(
> [warn]^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportClient.java:321:
> warning: [rawtypes] found raw type: Future
> [warn] public void operationComplete(Future future) throws Exception {
> [warn] ^
> [warn] missing type arguments for generic class Future
> [warn] where V is a type-variable:
> [warn] V extends Object declared in interface Future
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215:
> warning: [rawtypes] found raw type: StreamInterceptor
> [warn] StreamInterceptor interceptor = new StreamInterceptor(this,
> resp.streamId, resp.byteCount,
> [warn] ^
> [warn] missing type arguments for generic class StreamInterceptor
> [warn] where T is a type-variable:
> [warn] T extends Message declared in class StreamInterceptor
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215:
> warning: [rawtypes] found raw type: StreamInterceptor
> [warn] StreamInterceptor interceptor = new StreamInterceptor(this,
> resp.streamId, resp.byteCount,
> [warn] ^
> [warn] missing type arguments for generic class StreamInterceptor
> [warn] where T is a type-variable:
> [warn] T extends Message declared in class StreamInterceptor
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/clie
[jira] [Updated] (SPARK-25002) Avro: revise the output record namespace
[ https://issues.apache.org/jira/browse/SPARK-25002?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Gengliang Wang updated SPARK-25002: --- Summary: Avro: revise the output record namespace (was: Avro: revise the output namespace) > Avro: revise the output record namespace > > > Key: SPARK-25002 > URL: https://issues.apache.org/jira/browse/SPARK-25002 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.4.0 >Reporter: Gengliang Wang >Priority: Major > > Currently the output namespace is starting with "." > Although it is valid according to Avro spec, we should remove the starting > dot in case of failures when the output file is read by other lib: > https://github.com/linkedin/goavro/pull/96 -- 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-25002) Avro: revise the output namespace
Gengliang Wang created SPARK-25002: -- Summary: Avro: revise the output namespace Key: SPARK-25002 URL: https://issues.apache.org/jira/browse/SPARK-25002 Project: Spark Issue Type: Sub-task Components: SQL Affects Versions: 2.4.0 Reporter: Gengliang Wang Currently the output namespace is starting with "." Although it is valid according to Avro spec, we should remove the starting dot in case of failures when the output file is read by other lib: https://github.com/linkedin/goavro/pull/96 -- 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-25001) Handle build warnings in common, core, launcher, mllib, sql
[
https://issues.apache.org/jira/browse/SPARK-25001?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hyukjin Kwon updated SPARK-25001:
-
Summary: Handle build warnings in common, core, launcher, mllib, sql (was:
Remove build warnings in common, core, launcher, mllib, sql)
> Handle build warnings in common, core, launcher, mllib, sql
> ---
>
> Key: SPARK-25001
> URL: https://issues.apache.org/jira/browse/SPARK-25001
> Project: Spark
> Issue Type: Improvement
> Components: Build
>Affects Versions: 2.4.0
>Reporter: Hyukjin Kwon
>Priority: Major
>
> There are many warnings in the current build (for instance see
> https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7/4734/console).
> {code}
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDB.java:237:
> warning: [rawtypes] found raw type: LevelDBIterator
> [warn] void closeIterator(LevelDBIterator it) throws IOException {
> [warn] ^
> [warn] missing type arguments for generic class LevelDBIterator
> [warn] where T is a type-variable:
> [warn] T extends Object declared in class LevelDBIterator
> [warn] 1 warning
> [warn] Pruning sources from previous analysis, due to incompatible
> CompileSetup.
> [warn] Pruning sources from previous analysis, due to incompatible
> CompileSetup.
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:151:
> warning: [deprecation] group() in AbstractBootstrap has been deprecated
> [warn] if (bootstrap != null && bootstrap.group() != null) {
> [warn] ^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:152:
> warning: [deprecation] group() in AbstractBootstrap has been deprecated
> [warn] bootstrap.group().shutdownGracefully();
> [warn]^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:154:
> warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
> [warn] if (bootstrap != null && bootstrap.childGroup() != null) {
> [warn] ^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:155:
> warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
> [warn] bootstrap.childGroup().shutdownGracefully();
> [warn]^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/util/NettyUtils.java:112:
> warning: [deprecation]
> PooledByteBufAllocator(boolean,int,int,int,int,int,int,int) in
> PooledByteBufAllocator has been deprecated
> [warn] return new PooledByteBufAllocator(
> [warn]^
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportClient.java:321:
> warning: [rawtypes] found raw type: Future
> [warn] public void operationComplete(Future future) throws Exception {
> [warn] ^
> [warn] missing type arguments for generic class Future
> [warn] where V is a type-variable:
> [warn] V extends Object declared in interface Future
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215:
> warning: [rawtypes] found raw type: StreamInterceptor
> [warn] StreamInterceptor interceptor = new StreamInterceptor(this,
> resp.streamId, resp.byteCount,
> [warn] ^
> [warn] missing type arguments for generic class StreamInterceptor
> [warn] where T is a type-variable:
> [warn] T extends Message declared in class StreamInterceptor
> [warn]
> /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215:
> warning: [rawtypes] found raw type: StreamInterceptor
> [warn] StreamInterceptor interceptor = new StreamInterceptor(this,
> resp.streamId, resp.byteCount,
> [warn] ^
> [warn] missing type arguments for generic class StreamInterceptor
> [warn] where T is a type-variable:
> [warn] T extends Message declared in class StreamInterceptor
> [warn]
> /home/jenkins/workspace/spark-master-test-m
[jira] [Created] (SPARK-25001) Remove build warnings in common, core, launcher, mllib, sql
Hyukjin Kwon created SPARK-25001:
Summary: Remove build warnings in common, core, launcher, mllib,
sql
Key: SPARK-25001
URL: https://issues.apache.org/jira/browse/SPARK-25001
Project: Spark
Issue Type: Improvement
Components: Build
Affects Versions: 2.4.0
Reporter: Hyukjin Kwon
There are many warnings in the current build (for instance see
https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7/4734/console).
{code}
[warn]
/home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDB.java:237:
warning: [rawtypes] found raw type: LevelDBIterator
[warn] void closeIterator(LevelDBIterator it) throws IOException {
[warn] ^
[warn] missing type arguments for generic class LevelDBIterator
[warn] where T is a type-variable:
[warn] T extends Object declared in class LevelDBIterator
[warn] 1 warning
[warn] Pruning sources from previous analysis, due to incompatible CompileSetup.
[warn] Pruning sources from previous analysis, due to incompatible CompileSetup.
[warn]
/home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:151:
warning: [deprecation] group() in AbstractBootstrap has been deprecated
[warn] if (bootstrap != null && bootstrap.group() != null) {
[warn] ^
[warn]
/home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:152:
warning: [deprecation] group() in AbstractBootstrap has been deprecated
[warn] bootstrap.group().shutdownGracefully();
[warn]^
[warn]
/home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:154:
warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
[warn] if (bootstrap != null && bootstrap.childGroup() != null) {
[warn] ^
[warn]
/home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:155:
warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
[warn] bootstrap.childGroup().shutdownGracefully();
[warn]^
[warn]
/home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/util/NettyUtils.java:112:
warning: [deprecation]
PooledByteBufAllocator(boolean,int,int,int,int,int,int,int) in
PooledByteBufAllocator has been deprecated
[warn] return new PooledByteBufAllocator(
[warn]^
[warn]
/home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportClient.java:321:
warning: [rawtypes] found raw type: Future
[warn] public void operationComplete(Future future) throws Exception {
[warn] ^
[warn] missing type arguments for generic class Future
[warn] where V is a type-variable:
[warn] V extends Object declared in interface Future
[warn]
/home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215:
warning: [rawtypes] found raw type: StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this,
resp.streamId, resp.byteCount,
[warn] ^
[warn] missing type arguments for generic class StreamInterceptor
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn]
/home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215:
warning: [rawtypes] found raw type: StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this,
resp.streamId, resp.byteCount,
[warn] ^
[warn] missing type arguments for generic class StreamInterceptor
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn]
/home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215:
warning: [unchecked] unchecked call to
StreamInterceptor(MessageHandler,String,long,StreamCallback) as a member of
the raw type StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this,
resp.streamId, resp.byteCount,
[warn] ^
[warn] where T is a type-variable:
[warn] T extends Me
[jira] [Updated] (SPARK-24940) Coalesce Hint for SQL Queries
[
https://issues.apache.org/jira/browse/SPARK-24940?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiao Li updated SPARK-24940:
Target Version/s: 2.4.0
> Coalesce Hint for SQL Queries
> -
>
> Key: SPARK-24940
> URL: https://issues.apache.org/jira/browse/SPARK-24940
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 2.1.1
>Reporter: John Zhuge
>Priority: Major
>
> Many Spark SQL users in my company have asked for a way to control the number
> of output files in Spark SQL. The users prefer not to use function
> repartition\(n\) or coalesce(n, shuffle) that require them to write and
> deploy Scala/Java/Python code.
>
> There are use cases to either reduce or increase the number.
>
> The DataFrame API has repartition/coalesce for a long time. However, we do
> not have an equivalent functionality in SQL queries. We propose adding the
> following Hive-style Coalesce hint to Spark SQL.
> {noformat}
> /*+ COALESCE(n, shuffle) */
> /*+ REPARTITION(n) */
> {noformat}
> REPARTITION\(n\) is equal to COALESCE(n, shuffle=true).
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[jira] [Commented] (SPARK-14220) Build and test Spark against Scala 2.12
[ https://issues.apache.org/jira/browse/SPARK-14220?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567062#comment-16567062 ] Kildiev Rustam commented on SPARK-14220: Hurra > Build and test Spark against Scala 2.12 > --- > > Key: SPARK-14220 > URL: https://issues.apache.org/jira/browse/SPARK-14220 > Project: Spark > Issue Type: Umbrella > Components: Build, Project Infra >Affects Versions: 2.1.0 >Reporter: Josh Rosen >Priority: Blocker > Labels: release-notes > Fix For: 2.4.0 > > > This umbrella JIRA tracks the requirements for building and testing Spark > against the current Scala 2.12 milestone. -- 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-24924) Add mapping for built-in Avro data source
[ https://issues.apache.org/jira/browse/SPARK-24924?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567045#comment-16567045 ] Thomas Graves commented on SPARK-24924: --- why are we doing this? If a user ships the spark-avro databricks jar and references the com.databricks.spark.avro class, why do we want to map that to our built in version which might be different? > Add mapping for built-in Avro data source > - > > Key: SPARK-24924 > URL: https://issues.apache.org/jira/browse/SPARK-24924 > Project: Spark > Issue Type: Sub-task > Components: SQL >Affects Versions: 2.4.0 >Reporter: Dongjoon Hyun >Assignee: Dongjoon Hyun >Priority: Minor > Fix For: 2.4.0 > > > This issue aims to the followings. > # Like `com.databricks.spark.csv` mapping, we had better map > `com.databricks.spark.avro` to built-in Avro data source. > # Remove incorrect error message, `Please find an Avro package at ...`. -- 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-24821) Fail fast when submitted job compute on a subset of all the partitions for a barrier stage
[ https://issues.apache.org/jira/browse/SPARK-24821?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiangrui Meng resolved SPARK-24821. --- Resolution: Fixed Fix Version/s: 2.4.0 Issue resolved by pull request 21927 [https://github.com/apache/spark/pull/21927] > Fail fast when submitted job compute on a subset of all the partitions for a > barrier stage > -- > > Key: SPARK-24821 > URL: https://issues.apache.org/jira/browse/SPARK-24821 > Project: Spark > Issue Type: New Feature > Components: Spark Core >Affects Versions: 2.4.0 >Reporter: Jiang Xingbo >Assignee: Jiang Xingbo >Priority: Major > Fix For: 2.4.0 > > > Detect SparkContext.runJob() launch a barrier stage with a subset of all the > partitions, one example is the `first()` operation. -- 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-24821) Fail fast when submitted job compute on a subset of all the partitions for a barrier stage
[ https://issues.apache.org/jira/browse/SPARK-24821?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiangrui Meng reassigned SPARK-24821: - Assignee: Jiang Xingbo > Fail fast when submitted job compute on a subset of all the partitions for a > barrier stage > -- > > Key: SPARK-24821 > URL: https://issues.apache.org/jira/browse/SPARK-24821 > Project: Spark > Issue Type: New Feature > Components: Spark Core >Affects Versions: 2.4.0 >Reporter: Jiang Xingbo >Assignee: Jiang Xingbo >Priority: Major > Fix For: 2.4.0 > > > Detect SparkContext.runJob() launch a barrier stage with a subset of all the > partitions, one example is the `first()` operation. -- 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-24820) Fail fast when submitted job contains PartitionPruningRDD in a barrier stage
[ https://issues.apache.org/jira/browse/SPARK-24820?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiangrui Meng reassigned SPARK-24820: - Assignee: Jiang Xingbo > Fail fast when submitted job contains PartitionPruningRDD in a barrier stage > > > Key: SPARK-24820 > URL: https://issues.apache.org/jira/browse/SPARK-24820 > Project: Spark > Issue Type: New Feature > Components: Spark Core >Affects Versions: 2.4.0 >Reporter: Jiang Xingbo >Assignee: Jiang Xingbo >Priority: Major > Fix For: 2.4.0 > > > Detect SparkContext.runJob() launch a barrier stage including > PartitionPruningRDD. -- 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-24820) Fail fast when submitted job contains PartitionPruningRDD in a barrier stage
[ https://issues.apache.org/jira/browse/SPARK-24820?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiangrui Meng resolved SPARK-24820. --- Resolution: Fixed Fix Version/s: 2.4.0 Issue resolved by pull request 21927 [https://github.com/apache/spark/pull/21927] > Fail fast when submitted job contains PartitionPruningRDD in a barrier stage > > > Key: SPARK-24820 > URL: https://issues.apache.org/jira/browse/SPARK-24820 > Project: Spark > Issue Type: New Feature > Components: Spark Core >Affects Versions: 2.4.0 >Reporter: Jiang Xingbo >Assignee: Jiang Xingbo >Priority: Major > Fix For: 2.4.0 > > > Detect SparkContext.runJob() launch a barrier stage including > PartitionPruningRDD. -- 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-24598) SPARK SQL:Datatype overflow conditions gives incorrect result
[ https://issues.apache.org/jira/browse/SPARK-24598?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xiao Li resolved SPARK-24598. - Resolution: Done Assignee: Marco Gaido Fix Version/s: 2.4.0 > SPARK SQL:Datatype overflow conditions gives incorrect result > - > > Key: SPARK-24598 > URL: https://issues.apache.org/jira/browse/SPARK-24598 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 2.2.0 >Reporter: navya >Assignee: Marco Gaido >Priority: Major > Fix For: 2.4.0 > > > Execute an sql query, so that it results in overflow conditions. > EX - SELECT 9223372036854775807 + 1 result = -9223372036854776000 > > Expected result - Error should be throw like mysql. > mysql> SELECT 9223372036854775807 + 1; > ERROR 1690 (22003): BIGINT value is out of range in '(9223372036854775807 + > 1)' -- 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-24434) Support user-specified driver and executor pod templates
[ https://issues.apache.org/jira/browse/SPARK-24434?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567017#comment-16567017 ] Rob Vesse commented on SPARK-24434: --- [~skonto] Added a couple more comments based on some issues I've run into during ongoing development > Support user-specified driver and executor pod templates > > > Key: SPARK-24434 > URL: https://issues.apache.org/jira/browse/SPARK-24434 > Project: Spark > Issue Type: New Feature > Components: Kubernetes >Affects Versions: 2.4.0 >Reporter: Yinan Li >Priority: Major > > With more requests for customizing the driver and executor pods coming, the > current approach of adding new Spark configuration options has some serious > drawbacks: 1) it means more Kubernetes specific configuration options to > maintain, and 2) it widens the gap between the declarative model used by > Kubernetes and the configuration model used by Spark. We should start > designing a solution that allows users to specify pod templates as central > places for all customization needs for the driver and executor pods. -- 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-18057) Update structured streaming kafka from 0.10.0.1 to 2.0.0
[ https://issues.apache.org/jira/browse/SPARK-18057?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567015#comment-16567015 ] Sean Owen commented on SPARK-18057: --- [~zsxwing] hm, it seems weird that Spark is then using two incompatible versions of Kafka at once. An app that used Spark Streaming and SQL with Kafka wouldn't work, it seem? I also think it's probably pretty easy to update the tests – I got it maybe 80% of the way there already, just not sure how to use the internal Log API. > Update structured streaming kafka from 0.10.0.1 to 2.0.0 > > > Key: SPARK-18057 > URL: https://issues.apache.org/jira/browse/SPARK-18057 > Project: Spark > Issue Type: Improvement > Components: Structured Streaming >Reporter: Cody Koeninger >Assignee: Ted Yu >Priority: Blocker > > 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-18057) Update structured streaming kafka from 0.10.0.1 to 2.0.0
[ https://issues.apache.org/jira/browse/SPARK-18057?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16567010#comment-16567010 ] Shixiong Zhu commented on SPARK-18057: -- [~srowen] Could you create a new ticket for DStreams Kafka? IMO, they are two different modules and don't have to upgrade in the same version. > Update structured streaming kafka from 0.10.0.1 to 2.0.0 > > > Key: SPARK-18057 > URL: https://issues.apache.org/jira/browse/SPARK-18057 > Project: Spark > Issue Type: Improvement > Components: Structured Streaming >Reporter: Cody Koeninger >Assignee: Ted Yu >Priority: Blocker > > 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] [Comment Edited] (SPARK-24826) Self-Join not working in Apache Spark 2.2.2
[ https://issues.apache.org/jira/browse/SPARK-24826?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16566981#comment-16566981 ] Joseph Fourny edited comment on SPARK-24826 at 8/2/18 3:57 PM: --- I was able to reproduce this defect with an inner-join of two temp views that refer to equivalent local relations. I started by creating 2 datasets (in Java) from a List of GenericRow and registered them as separate views. As far as the optimizer is concerned, the contents of the local relations are the same. If you update one of the datasets to make them distinct, then the assertion is not longer triggered. Note: I have to force a SortMergeJoin to trigger the issue in ExchangeCoordinator. was (Author: josephfourny): I was able to reproduce this defect with an inner-join of two temp views that refer to equivalent local relations. I started by creating 2 datasets (in Java) from a List of GenericRow and registered them as separate views. As far as the optimizer is concerned, the contents of the local relations are the same. Note: I have to force a SortMergeJoin to trigger the issue in ExchangeCoordinator. > Self-Join not working in Apache Spark 2.2.2 > --- > > Key: SPARK-24826 > URL: https://issues.apache.org/jira/browse/SPARK-24826 > Project: Spark > Issue Type: Bug > Components: Optimizer >Affects Versions: 2.2.2 >Reporter: Michail Giannakopoulos >Priority: Major > Attachments: > part-0-48210471-3088-4cee-8670-a332444bae66-c000.gz.parquet > > > Running a self-join against a table derived from a parquet file with many > columns fails during the planning phase with the following stack-trace: > org.apache.spark.sql.catalyst.errors.package$TreeNodeException: execute, tree: > Exchange(coordinator id: 331918455) hashpartitioning(_row_id#0L, 2), > coordinator[target post-shuffle partition size: 67108864] > +- Project [_row_id#0L, id#1L, member_id#2L, loan_amnt#3L, funded_amnt#4L, > funded_amnt_inv#5L, term#6, int_rate#7, installment#8, grade#9, sub_grade#10, > emp_title#11, emp_length#12, home_ownership#13, annual_inc#14, > verification_status#15, issue_d#16, loan_status#17, pymnt_plan#18, url#19, > desc_#20, purpose#21, title#22, zip_code#23, ... 92 more fields|#0L, id#1L, > member_id#2L, loan_amnt#3L, funded_amnt#4L, funded_amnt_inv#5L, term#6, > int_rate#7, installment#8, grade#9, sub_grade#10, emp_title#11, > emp_length#12, home_ownership#13, annual_inc#14, verification_status#15, > issue_d#16, loan_status#17, pymnt_plan#18, url#19, desc_#20, purpose#21, > title#22, zip_code#23, ... 92 more fields] > +- Filter isnotnull(_row_id#0L) > +- FileScan parquet > [_row_id#0L,id#1L,member_id#2L,loan_amnt#3L,funded_amnt#4L,funded_amnt_inv#5L,term#6,int_rate#7,installment#8,grade#9,sub_grade#10,emp_title#11,emp_length#12,home_ownership#13,annual_inc#14,verification_status#15,issue_d#16,loan_status#17,pymnt_plan#18,url#19,desc_#20,purpose#21,title#22,zip_code#23,... > 92 more > fields|#0L,id#1L,member_id#2L,loan_amnt#3L,funded_amnt#4L,funded_amnt_inv#5L,term#6,int_rate#7,installment#8,grade#9,sub_grade#10,emp_title#11,emp_length#12,home_ownership#13,annual_inc#14,verification_status#15,issue_d#16,loan_status#17,pymnt_plan#18,url#19,desc_#20,purpose#21,title#22,zip_code#23,... > 92 more fields] Batched: false, Format: Parquet, Location: > InMemoryFileIndex[file:/c:/Users/gianna/Desktop/alpha.parquet/part-0-48210471-3088-4cee-8670-..., > PartitionFilters: [], PushedFilters: [IsNotNull(_row_id)], ReadSchema: > struct<_row_id:bigint,id:bigint,member_id:bigint,loan_amnt:bigint,funded_amnt:bigint,funded_amnt_... > at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56) > at > org.apache.spark.sql.execution.exchange.ShuffleExchange.doExecute(ShuffleExchange.scala:115) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) > at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135) > at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116) > at org.apache.spark.sql.execution.SortExec.doExecute(SortExec.scala:101) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperat
[jira] [Commented] (SPARK-24826) Self-Join not working in Apache Spark 2.2.2
[ https://issues.apache.org/jira/browse/SPARK-24826?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16566981#comment-16566981 ] Joseph Fourny commented on SPARK-24826: --- I was able to reproduce this defect with an inner-join of two temp views that refer to equivalent local relations. I started by creating 2 datasets (in Java) from a List of GenericRow and registered them as separate views. As far as the optimizer is concerned, the contents of the local relations are the same. Note: I have to force a SortMergeJoin to trigger the issue in ExchangeCoordinator. > Self-Join not working in Apache Spark 2.2.2 > --- > > Key: SPARK-24826 > URL: https://issues.apache.org/jira/browse/SPARK-24826 > Project: Spark > Issue Type: Bug > Components: Optimizer >Affects Versions: 2.2.2 >Reporter: Michail Giannakopoulos >Priority: Major > Attachments: > part-0-48210471-3088-4cee-8670-a332444bae66-c000.gz.parquet > > > Running a self-join against a table derived from a parquet file with many > columns fails during the planning phase with the following stack-trace: > org.apache.spark.sql.catalyst.errors.package$TreeNodeException: execute, tree: > Exchange(coordinator id: 331918455) hashpartitioning(_row_id#0L, 2), > coordinator[target post-shuffle partition size: 67108864] > +- Project [_row_id#0L, id#1L, member_id#2L, loan_amnt#3L, funded_amnt#4L, > funded_amnt_inv#5L, term#6, int_rate#7, installment#8, grade#9, sub_grade#10, > emp_title#11, emp_length#12, home_ownership#13, annual_inc#14, > verification_status#15, issue_d#16, loan_status#17, pymnt_plan#18, url#19, > desc_#20, purpose#21, title#22, zip_code#23, ... 92 more fields|#0L, id#1L, > member_id#2L, loan_amnt#3L, funded_amnt#4L, funded_amnt_inv#5L, term#6, > int_rate#7, installment#8, grade#9, sub_grade#10, emp_title#11, > emp_length#12, home_ownership#13, annual_inc#14, verification_status#15, > issue_d#16, loan_status#17, pymnt_plan#18, url#19, desc_#20, purpose#21, > title#22, zip_code#23, ... 92 more fields] > +- Filter isnotnull(_row_id#0L) > +- FileScan parquet > [_row_id#0L,id#1L,member_id#2L,loan_amnt#3L,funded_amnt#4L,funded_amnt_inv#5L,term#6,int_rate#7,installment#8,grade#9,sub_grade#10,emp_title#11,emp_length#12,home_ownership#13,annual_inc#14,verification_status#15,issue_d#16,loan_status#17,pymnt_plan#18,url#19,desc_#20,purpose#21,title#22,zip_code#23,... > 92 more > fields|#0L,id#1L,member_id#2L,loan_amnt#3L,funded_amnt#4L,funded_amnt_inv#5L,term#6,int_rate#7,installment#8,grade#9,sub_grade#10,emp_title#11,emp_length#12,home_ownership#13,annual_inc#14,verification_status#15,issue_d#16,loan_status#17,pymnt_plan#18,url#19,desc_#20,purpose#21,title#22,zip_code#23,... > 92 more fields] Batched: false, Format: Parquet, Location: > InMemoryFileIndex[file:/c:/Users/gianna/Desktop/alpha.parquet/part-0-48210471-3088-4cee-8670-..., > PartitionFilters: [], PushedFilters: [IsNotNull(_row_id)], ReadSchema: > struct<_row_id:bigint,id:bigint,member_id:bigint,loan_amnt:bigint,funded_amnt:bigint,funded_amnt_... > at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56) > at > org.apache.spark.sql.execution.exchange.ShuffleExchange.doExecute(ShuffleExchange.scala:115) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) > at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135) > at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116) > at org.apache.spark.sql.execution.SortExec.doExecute(SortExec.scala:101) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) > at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135) > at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116) > at > org.apache.spark.sql.execution.joins.SortMergeJoinExec.doExecute(SortMergeJoinExec.scala:141) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138) > at
[jira] [Commented] (SPARK-24630) SPIP: Support SQLStreaming in Spark
[ https://issues.apache.org/jira/browse/SPARK-24630?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16566954#comment-16566954 ] Jackey Lee commented on SPARK-24630: [~uncleGen] Are you willing to assist in the code review? I can submit some of the implementation. > SPIP: Support SQLStreaming in Spark > --- > > Key: SPARK-24630 > URL: https://issues.apache.org/jira/browse/SPARK-24630 > Project: Spark > Issue Type: Improvement > Components: Structured Streaming >Affects Versions: 2.2.0, 2.2.1 >Reporter: Jackey Lee >Priority: Minor > Labels: SQLStreaming > Attachments: SQLStreaming SPIP.pdf > > > At present, KafkaSQL, Flink SQL(which is actually based on Calcite), > SQLStream, StormSQL all provide a stream type SQL interface, with which users > with little knowledge about streaming, can easily develop a flow system > processing model. In Spark, we can also support SQL API based on > StructStreamig. > To support for SQL Streaming, there are two key points: > 1, Analysis should be able to parse streaming type SQL. > 2, Analyzer should be able to map metadata information to the corresponding > Relation. -- 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-14540) Support Scala 2.12 closures and Java 8 lambdas in ClosureCleaner
[
https://issues.apache.org/jira/browse/SPARK-14540?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16566948#comment-16566948
]
Simeon H.K. Fitch commented on SPARK-14540:
---
Congratulations! A long, difficult haul... Cheers all around!
> Support Scala 2.12 closures and Java 8 lambdas in ClosureCleaner
>
>
> Key: SPARK-14540
> URL: https://issues.apache.org/jira/browse/SPARK-14540
> Project: Spark
> Issue Type: Sub-task
> Components: Spark Core
>Reporter: Josh Rosen
>Assignee: Stavros Kontopoulos
>Priority: Major
> Labels: release-notes
> Fix For: 2.4.0
>
>
> Using https://github.com/JoshRosen/spark/tree/build-for-2.12, I tried running
> ClosureCleanerSuite with Scala 2.12 and ran into two bad test failures:
> {code}
> [info] - toplevel return statements in closures are identified at cleaning
> time *** FAILED *** (32 milliseconds)
> [info] Expected exception
> org.apache.spark.util.ReturnStatementInClosureException to be thrown, but no
> exception was thrown. (ClosureCleanerSuite.scala:57)
> {code}
> and
> {code}
> [info] - user provided closures are actually cleaned *** FAILED *** (56
> milliseconds)
> [info] Expected ReturnStatementInClosureException, but got
> org.apache.spark.SparkException: Job aborted due to stage failure: Task not
> serializable: java.io.NotSerializableException: java.lang.Object
> [info]- element of array (index: 0)
> [info]- array (class "[Ljava.lang.Object;", size: 1)
> [info]- field (class "java.lang.invoke.SerializedLambda", name:
> "capturedArgs", type: "class [Ljava.lang.Object;")
> [info]- object (class "java.lang.invoke.SerializedLambda",
> SerializedLambda[capturingClass=class
> org.apache.spark.util.TestUserClosuresActuallyCleaned$,
> functionalInterfaceMethod=scala/runtime/java8/JFunction1$mcII$sp.apply$mcII$sp:(I)I,
> implementation=invokeStatic
> org/apache/spark/util/TestUserClosuresActuallyCleaned$.org$apache$spark$util$TestUserClosuresActuallyCleaned$$$anonfun$69:(Ljava/lang/Object;I)I,
> instantiatedMethodType=(I)I, numCaptured=1])
> [info]- element of array (index: 0)
> [info]- array (class "[Ljava.lang.Object;", size: 1)
> [info]- field (class "java.lang.invoke.SerializedLambda", name:
> "capturedArgs", type: "class [Ljava.lang.Object;")
> [info]- object (class "java.lang.invoke.SerializedLambda",
> SerializedLambda[capturingClass=class org.apache.spark.rdd.RDD,
> functionalInterfaceMethod=scala/Function3.apply:(Ljava/lang/Object;Ljava/lang/Object;Ljava/lang/Object;)Ljava/lang/Object;,
> implementation=invokeStatic
> org/apache/spark/rdd/RDD.org$apache$spark$rdd$RDD$$$anonfun$20$adapted:(Lscala/Function1;Lorg/apache/spark/TaskContext;Ljava/lang/Object;Lscala/collection/Iterator;)Lscala/collection/Iterator;,
>
> instantiatedMethodType=(Lorg/apache/spark/TaskContext;Ljava/lang/Object;Lscala/collection/Iterator;)Lscala/collection/Iterator;,
> numCaptured=1])
> [info]- field (class "org.apache.spark.rdd.MapPartitionsRDD", name:
> "f", type: "interface scala.Function3")
> [info]- object (class "org.apache.spark.rdd.MapPartitionsRDD",
> MapPartitionsRDD[2] at apply at Transformer.scala:22)
> [info]- field (class "scala.Tuple2", name: "_1", type: "class
> java.lang.Object")
> [info]- root object (class "scala.Tuple2", (MapPartitionsRDD[2] at
> apply at
> Transformer.scala:22,org.apache.spark.SparkContext$$Lambda$957/431842435@6e803685)).
> [info] This means the closure provided by user is not actually cleaned.
> (ClosureCleanerSuite.scala:78)
> {code}
> We'll need to figure out a closure cleaning strategy which works for 2.12
> lambdas.
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[jira] [Commented] (SPARK-24980) add support for pandas/arrow etc for python2.7 and pypy builds
[ https://issues.apache.org/jira/browse/SPARK-24980?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16566949#comment-16566949 ] shane knapp commented on SPARK-24980: - looking pretty good (you have to go to the very bottom of the 16M build log to get to the output) : [https://amplab.cs.berkeley.edu/jenkins/view/RISELab%20Infra/job/spark-master-test-sbt-hadoop-2.6-ubuntu-test/896/consoleFull] summary: * py27 and py34 pandas and pyarrow tests run * pypy doesn't have any additional libs installed, plus the version we're running is pretty old – 2.5.1. the following tests were skipped: pandas, pyarrow, numpy, scipy * i will need to bump the unittest py2.7 lib to >= 3.3 to support mocks * we should really think about bumping python3 from 3.4 to 3.5 at some point in the not-too-distant-future > add support for pandas/arrow etc for python2.7 and pypy builds > -- > > Key: SPARK-24980 > URL: https://issues.apache.org/jira/browse/SPARK-24980 > Project: Spark > Issue Type: Improvement > Components: Build, PySpark >Affects Versions: 2.3.1 >Reporter: shane knapp >Assignee: shane knapp >Priority: Major > > since we have full support for python3.4 via anaconda, it's time to create > similar environments for 2.7 and pypy 2.5.1. > -- 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-14220) Build and test Spark against Scala 2.12
[ https://issues.apache.org/jira/browse/SPARK-14220?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16566945#comment-16566945 ] Simeon H.K. Fitch commented on SPARK-14220: --- (flag)(*)(*r)(*g)(*b)(*y):D(*y)(*b)(*g)(*r)(*)(flag) Way to go! This is amazing. > Build and test Spark against Scala 2.12 > --- > > Key: SPARK-14220 > URL: https://issues.apache.org/jira/browse/SPARK-14220 > Project: Spark > Issue Type: Umbrella > Components: Build, Project Infra >Affects Versions: 2.1.0 >Reporter: Josh Rosen >Priority: Blocker > Labels: release-notes > Fix For: 2.4.0 > > > This umbrella JIRA tracks the requirements for building and testing Spark > against the current Scala 2.12 milestone. -- 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] [Issue Comment Deleted] (SPARK-14220) Build and test Spark against Scala 2.12
[ https://issues.apache.org/jira/browse/SPARK-14220?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Simeon H.K. Fitch updated SPARK-14220: -- Comment: was deleted (was: (flag)(*)(*r)(*g)(*b)(*y):D(*y)(*b)(*g)(*r)(*)(flag) Way to go! This is amazing.) > Build and test Spark against Scala 2.12 > --- > > Key: SPARK-14220 > URL: https://issues.apache.org/jira/browse/SPARK-14220 > Project: Spark > Issue Type: Umbrella > Components: Build, Project Infra >Affects Versions: 2.1.0 >Reporter: Josh Rosen >Priority: Blocker > Labels: release-notes > Fix For: 2.4.0 > > > This umbrella JIRA tracks the requirements for building and testing Spark > against the current Scala 2.12 milestone. -- 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-14220) Build and test Spark against Scala 2.12
[ https://issues.apache.org/jira/browse/SPARK-14220?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16566942#comment-16566942 ] Simeon H.K. Fitch commented on SPARK-14220: --- (flag)(*)(*r)(*g)(*b)(*y):D(*y)(*b)(*g)(*r)(*)(flag) Way to go! This is amazing. > Build and test Spark against Scala 2.12 > --- > > Key: SPARK-14220 > URL: https://issues.apache.org/jira/browse/SPARK-14220 > Project: Spark > Issue Type: Umbrella > Components: Build, Project Infra >Affects Versions: 2.1.0 >Reporter: Josh Rosen >Priority: Blocker > Labels: release-notes > Fix For: 2.4.0 > > > This umbrella JIRA tracks the requirements for building and testing Spark > against the current Scala 2.12 milestone. -- 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-24795) Implement barrier execution mode
[ https://issues.apache.org/jira/browse/SPARK-24795?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16566935#comment-16566935 ] Apache Spark commented on SPARK-24795: -- User 'jiangxb1987' has created a pull request for this issue: https://github.com/apache/spark/pull/21972 > Implement barrier execution mode > > > Key: SPARK-24795 > URL: https://issues.apache.org/jira/browse/SPARK-24795 > Project: Spark > Issue Type: New Feature > Components: Spark Core >Affects Versions: 2.4.0 >Reporter: Jiang Xingbo >Assignee: Jiang Xingbo >Priority: Major > Fix For: 2.4.0 > > > Implement barrier execution mode, as described in SPARK-24582 > Include all the API changes and basic implementation (except for > BarrierTaskContext.barrier()) -- 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-24947) aggregateAsync and foldAsync for RDD
[
https://issues.apache.org/jira/browse/SPARK-24947?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-24947:
Assignee: (was: Apache Spark)
> aggregateAsync and foldAsync for RDD
>
>
> Key: SPARK-24947
> URL: https://issues.apache.org/jira/browse/SPARK-24947
> Project: Spark
> Issue Type: New Feature
> Components: Spark Core
>Affects Versions: 2.3.1
>Reporter: Cody Allen
>Priority: Minor
>
> {{AsyncRDDActions}} contains {{collectAsync}}, {{countAsync}},
> {{foreachAsync}}, etc; but it doesn't provide general mechanisms for reducing
> datasets asynchronously. If I want to aggregate some statistics on a large
> dataset and it's going to take an hour, I shouldn't need to completely block
> a thread for the hour to wait for the result.
>
> I propose the following methods be added to {{AsyncRDDActions}}:
>
> {{def aggregateAsync[U](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) =>
> U): FutureAction[U]}}
> {{def foldAsync(zeroValue: T)(op: (T, T) => T): FutureAction[T]}}
>
> Locally I have a version of {{aggregateAsync}} implemented based on
> {{submitJob}} (similar to how {{countAsync}} is implemented), and a
> {{foldAsync}} implementation that simply delegates through to
> {{aggregateAsync}}. I haven't yet written unit tests for these, but I can do
> so if this is a contribution that would be accepted. Please let me know.
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[jira] [Assigned] (SPARK-24947) aggregateAsync and foldAsync for RDD
[
https://issues.apache.org/jira/browse/SPARK-24947?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-24947:
Assignee: Apache Spark
> aggregateAsync and foldAsync for RDD
>
>
> Key: SPARK-24947
> URL: https://issues.apache.org/jira/browse/SPARK-24947
> Project: Spark
> Issue Type: New Feature
> Components: Spark Core
>Affects Versions: 2.3.1
>Reporter: Cody Allen
>Assignee: Apache Spark
>Priority: Minor
>
> {{AsyncRDDActions}} contains {{collectAsync}}, {{countAsync}},
> {{foreachAsync}}, etc; but it doesn't provide general mechanisms for reducing
> datasets asynchronously. If I want to aggregate some statistics on a large
> dataset and it's going to take an hour, I shouldn't need to completely block
> a thread for the hour to wait for the result.
>
> I propose the following methods be added to {{AsyncRDDActions}}:
>
> {{def aggregateAsync[U](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) =>
> U): FutureAction[U]}}
> {{def foldAsync(zeroValue: T)(op: (T, T) => T): FutureAction[T]}}
>
> Locally I have a version of {{aggregateAsync}} implemented based on
> {{submitJob}} (similar to how {{countAsync}} is implemented), and a
> {{foldAsync}} implementation that simply delegates through to
> {{aggregateAsync}}. I haven't yet written unit tests for these, but I can do
> so if this is a contribution that would be accepted. Please let me know.
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[jira] [Commented] (SPARK-24947) aggregateAsync and foldAsync for RDD
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https://issues.apache.org/jira/browse/SPARK-24947?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16566867#comment-16566867
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Apache Spark commented on SPARK-24947:
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User 'ceedubs' has created a pull request for this issue:
https://github.com/apache/spark/pull/21971
> aggregateAsync and foldAsync for RDD
>
>
> Key: SPARK-24947
> URL: https://issues.apache.org/jira/browse/SPARK-24947
> Project: Spark
> Issue Type: New Feature
> Components: Spark Core
>Affects Versions: 2.3.1
>Reporter: Cody Allen
>Priority: Minor
>
> {{AsyncRDDActions}} contains {{collectAsync}}, {{countAsync}},
> {{foreachAsync}}, etc; but it doesn't provide general mechanisms for reducing
> datasets asynchronously. If I want to aggregate some statistics on a large
> dataset and it's going to take an hour, I shouldn't need to completely block
> a thread for the hour to wait for the result.
>
> I propose the following methods be added to {{AsyncRDDActions}}:
>
> {{def aggregateAsync[U](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) =>
> U): FutureAction[U]}}
> {{def foldAsync(zeroValue: T)(op: (T, T) => T): FutureAction[T]}}
>
> Locally I have a version of {{aggregateAsync}} implemented based on
> {{submitJob}} (similar to how {{countAsync}} is implemented), and a
> {{foldAsync}} implementation that simply delegates through to
> {{aggregateAsync}}. I haven't yet written unit tests for these, but I can do
> so if this is a contribution that would be accepted. Please let me know.
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