Spark 2.0 error in Intellij #15020
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Spark 2.0 error in Intellij #15020
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… doc ## What changes were proposed in this pull request? Fixed several inline formatting in ml features doc. Before: <img width="475" alt="screen shot 2016-07-14 at 12 24 57 pm" src="https://cloud.githubusercontent.com/assets/717363/16827974/1e1b6e04-49be-11e6-8aa9-4a0cb6cd3b4e.png"> After: <img width="404" alt="screen shot 2016-07-14 at 12 25 48 pm" src="https://cloud.githubusercontent.com/assets/717363/16827976/2576510a-49be-11e6-96dd-92a1fa464d36.png"> ## How was this patch tested? Genetate the docs locally by `SKIP_API=1 jekyll build` and view it in the browser. Author: Shuai Lin <linshuai2012@gmail.com> Closes #14194 from lins05/fix-docs-formatting. (cherry picked from commit 3b6e1d0) Signed-off-by: Sean Owen <sowen@cloudera.com>
…nEliminationSuite."Semantic equals and hash" ## What changes were proposed in this pull request? SubexpressionEliminationSuite."Semantic equals and hash" assumes the default AttributeReference's exprId wont' be "ExprId(1)". However, that depends on when this test runs. It may happen to use "ExprId(1)". This PR detects the conflict and makes sure we create a different ExprId when the conflict happens. ## How was this patch tested? Jenkins unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes #14350 from zsxwing/SPARK-16715. (cherry picked from commit 12f490b) Signed-off-by: Shixiong Zhu <shixiong@databricks.com>
…locks in HDFSMetadataLog ## What changes were proposed in this pull request? Current fix for deadlock disables interrupts in the StreamExecution which getting offsets for all sources, and when writing to any metadata log, to avoid potential deadlocks in HDFSMetadataLog(see JIRA for more details). However, disabling interrupts can have unintended consequences in other sources. So I am making the fix more narrow, by disabling interrupt it only in the HDFSMetadataLog. This is a narrower fix for something risky like disabling interrupt. ## How was this patch tested? Existing tests. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #14292 from tdas/SPARK-14131. (cherry picked from commit c979c8b) Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
…uite when eventually fails ## What changes were proposed in this pull request? This PR moves `ssc.stop()` into `finally` for `StreamingContextSuite.createValidCheckpoint` to avoid leaking a StreamingContext since leaking a StreamingContext will fail a lot of tests and make us hard to find the real failure one. ## How was this patch tested? Jenkins unit tests Author: Shixiong Zhu <shixiong@databricks.com> Closes #14354 from zsxwing/ssc-leak. (cherry picked from commit e164a04) Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
## What changes were proposed in this pull request? Minor doc fix regarding the spark.speculation.quantile configuration parameter. It incorrectly states it should be a percentage, when it should be a fraction. ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) I tried building the documentation but got some unidoc errors. I also got them when building off origin/master, so I don't think I caused that problem. I did run the web app and saw the changes reflected as expected. Author: Nicholas Brown <nbrown@adroitdigital.com> Closes #14352 from nwbvt/master. (cherry picked from commit ba0aade) Signed-off-by: Reynold Xin <rxin@databricks.com>
…ueries
## What changes were proposed in this pull request?
Currently, `SQLBuilder` raises `empty.reduceLeft` exceptions on *unoptimized* `EXISTS` queries. We had better prevent this.
```scala
scala> sql("CREATE TABLE t1(a int)")
scala> val df = sql("select * from t1 b where exists (select * from t1 a)")
scala> new org.apache.spark.sql.catalyst.SQLBuilder(df).toSQL
java.lang.UnsupportedOperationException: empty.reduceLeft
```
## How was this patch tested?
Pass the Jenkins tests with a new test suite.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #14307 from dongjoon-hyun/SPARK-16672.
(cherry picked from commit 8a8d26f)
Signed-off-by: Reynold Xin <rxin@databricks.com>
We don't generally make things in catalyst/execution private. Instead they are just undocumented due to their lack of stability guarantees. Author: Michael Armbrust <michael@databricks.com> Closes #14356 from marmbrus/patch-1. (cherry picked from commit f99e34e) Signed-off-by: Reynold Xin <rxin@databricks.com>
…ed to lead and lag functions ## What changes were proposed in this pull request? This PR contains three changes. First, this PR changes the behavior of lead/lag back to Spark 1.6's behavior, which is described as below: 1. lead/lag respect null input values, which means that if the offset row exists and the input value is null, the result will be null instead of the default value. 2. If the offset row does not exist, the default value will be used. 3. OffsetWindowFunction's nullable setting also considers the nullability of its input (because of the first change). Second, this PR fixes the evaluation of lead/lag when the input expression is a literal. This fix is a result of the first change. In current master, if a literal is used as the input expression of a lead or lag function, the result will be this literal even if the offset row does not exist. Third, this PR makes ResolveWindowFrame not fire if a window function is not resolved. ## How was this patch tested? New tests in SQLWindowFunctionSuite Author: Yin Huai <yhuai@databricks.com> Closes #14284 from yhuai/lead-lag. (cherry picked from commit 815f3ee) Signed-off-by: Yin Huai <yhuai@databricks.com>
## What changes were proposed in this pull request? The current test is incorrect, because - The expected number of messages does not take into account that the topic has 2 partitions, and rate is set per partition. - Also in some cases, the test ran out of data in Kafka while waiting for the right amount of data per batch. The PR - Reduces the number of partitions to 1 - Adds more data to Kafka - Runs with 0.5 second so that batches are created slowly ## How was this patch tested? Ran many times locally, going to run it many times in Jenkins (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #14361 from tdas/kafka-rate-test-fix. (cherry picked from commit 03c2743) Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
Currently, the generated SQLs have not-stable IDs for generated attributes.
The stable generated SQL will give more benefit for understanding or testing the queries.
This PR provides stable SQL generation by the followings.
- Provide unique ids for generated subqueries, `gen_subquery_xxx`.
- Provide unique and stable ids for generated attributes, `gen_attr_xxx`.
**Before**
```scala
scala> new org.apache.spark.sql.catalyst.SQLBuilder(sql("select 1")).toSQL
res0: String = SELECT `gen_attr_0` AS `1` FROM (SELECT 1 AS `gen_attr_0`) AS gen_subquery_0
scala> new org.apache.spark.sql.catalyst.SQLBuilder(sql("select 1")).toSQL
res1: String = SELECT `gen_attr_4` AS `1` FROM (SELECT 1 AS `gen_attr_4`) AS gen_subquery_0
```
**After**
```scala
scala> new org.apache.spark.sql.catalyst.SQLBuilder(sql("select 1")).toSQL
res1: String = SELECT `gen_attr_0` AS `1` FROM (SELECT 1 AS `gen_attr_0`) AS gen_subquery_0
scala> new org.apache.spark.sql.catalyst.SQLBuilder(sql("select 1")).toSQL
res2: String = SELECT `gen_attr_0` AS `1` FROM (SELECT 1 AS `gen_attr_0`) AS gen_subquery_0
```
Pass the existing Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #14257 from dongjoon-hyun/SPARK-16621.
(cherry picked from commit 5b8e848)
Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request? Fix some mistake in ```LinearRegression``` formula. ## How was this patch tested? Documents change, no tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #14369 from yanboliang/LiR-formula. (cherry picked from commit 3c3371b) Signed-off-by: Sean Owen <sowen@cloudera.com>
Spark currently throws exceptions for invalid casts for all other data types except date type. Somehow date type returns null. It should be consistent and throws analysis exception as well. Added a unit test case in CastSuite. Author: petermaxlee <petermaxlee@gmail.com> Closes #14358 from petermaxlee/SPARK-16729. (cherry picked from commit ef0ccbc) Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request? added missing keyword for java example ## How was this patch tested? wasn't Author: Bartek Wiśniewski <wedi@Ava.local> Closes #14381 from wedi-dev/quickfix/missing_keyword. (cherry picked from commit bc4851a) Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request? Spark 1.x supports using the Hive type name as function names for doing casts, e.g. ```sql SELECT int(1.0); SELECT string(2.0); ``` The above query would work in Spark 1.x because Spark 1.x fail back to Hive for unimplemented functions, and break in Spark 2.0 because the fall back was removed. This patch implements function aliases using an analyzer rule for the following cast functions: - boolean - tinyint - smallint - int - bigint - float - double - decimal - date - timestamp - binary - string ## How was this patch tested? Added end-to-end tests in SQLCompatibilityFunctionSuite. Author: petermaxlee <petermaxlee@gmail.com> Closes #14364 from petermaxlee/SPARK-16730-2. (cherry picked from commit 11d427c) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
…LSuite ## What changes were proposed in this pull request? We currently test subquery SQL building using the `HiveCompatibilitySuite`. The is not desired since SQL building is actually a part of `sql/core` and because we are slowly reducing our dependency on Hive. This PR adds the same tests from the whitelist of `HiveCompatibilitySuite` into `LogicalPlanToSQLSuite`. ## How was this patch tested? This adds more testcases. Pass the Jenkins tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14383 from dongjoon-hyun/SPARK-15232. (cherry picked from commit 5c2ae79) Signed-off-by: Reynold Xin <rxin@databricks.com>
…ping by column should work
## What changes were proposed in this pull request?
The query with having condition that contains grouping by column will be failed during analysis. E.g.,
create table tbl(a int, b string);
select count(b) from tbl group by a + 1 having a + 1 = 2;
Having condition should be able to use grouping by column.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes #14296 from viirya/having-contains-grouping-column.
(cherry picked from commit 9ade77c)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
Avoid overflow of Long type causing a NegativeArraySizeException a few lines later. Unit tests for HashedRelationSuite still pass. I can confirm the python script I included in https://issues.apache.org/jira/browse/SPARK-16740 works fine with this patch. Unfortunately I don't have the knowledge/time to write a Scala test case for HashedRelationSuite right now. As the patch is pretty obvious I hope it can be included without this. Thanks! Author: Sylvain Zimmer <sylvain@sylvainzimmer.com> Closes #14373 from sylvinus/master. (cherry picked from commit 1178d61) Signed-off-by: Reynold Xin <rxin@databricks.com>
…utOfMemoryError ## What changes were proposed in this pull request? We currently don't bound or manage the data array size used by column vectors in the vectorized reader (they're just bound by INT.MAX) which may lead to OOMs while reading data. As a short term fix, this patch intercepts the OutOfMemoryError exception and suggest the user to disable the vectorized parquet reader. ## How was this patch tested? Existing Tests Author: Sameer Agarwal <sameerag@cs.berkeley.edu> Closes #14387 from sameeragarwal/oom. (cherry picked from commit 3fd39b8) Signed-off-by: Reynold Xin <rxin@databricks.com>
…ing fixes ## What's Been Changed The PR corrects several broken or missing class references in the Python API docs. It also correct formatting problems. For example, you can see [here](http://spark.apache.org/docs/2.0.0/api/python/pyspark.sql.html#pyspark.sql.SQLContext.registerFunction) how Sphinx is not picking up the reference to `DataType`. That's because the reference is relative to the current module, whereas `DataType` is in a different module. You can also see [here](http://spark.apache.org/docs/2.0.0/api/python/pyspark.sql.html#pyspark.sql.SQLContext.createDataFrame) how the formatting for byte, tinyint, and so on is italic instead of monospace. That's because in ReST single backticks just make things italic, unlike in Markdown. ## Testing I tested this PR by [building the Python docs](https://github.com/apache/spark/tree/master/docs#generating-the-documentation-html) and reviewing the results locally in my browser. I confirmed that the broken or missing class references were resolved, and that the formatting was corrected. Author: Nicholas Chammas <nicholas.chammas@gmail.com> Closes #14393 from nchammas/python-docstring-fixes. (cherry picked from commit 274f3b9) Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request? f12f11e introduced this bug, missed foreach as map ## How was this patch tested? Test added Author: Wesley Tang <tangmingjun@mininglamp.com> Closes #14324 from breakdawn/master. (cherry picked from commit d1d5069) Signed-off-by: Sean Owen <sowen@cloudera.com>
…column type mistake ## What changes were proposed in this pull request? ML ```GaussianMixture``` training failed due to feature column type mistake. The feature column type should be ```ml.linalg.VectorUDT``` but got ```mllib.linalg.VectorUDT``` by mistake. See [SPARK-16750](https://issues.apache.org/jira/browse/SPARK-16750) for how to reproduce this bug. Why the unit tests did not complain this errors? Because some estimators/transformers missed calling ```transformSchema(dataset.schema)``` firstly during ```fit``` or ```transform```. I will also add this function to all estimators/transformers who missed in this PR. ## How was this patch tested? No new tests, should pass existing ones. Author: Yanbo Liang <ybliang8@gmail.com> Closes #14378 from yanboliang/spark-16750. (cherry picked from commit 0557a45) Signed-off-by: Sean Owen <sowen@cloudera.com>
Version of derby upgraded based on important security info at VersionEye. Test scope added so we don't include it in our final package anyway. NB: I think this should be backported to all previous releases as it is a security problem https://www.versioneye.com/java/org.apache.derby:derby/10.11.1.1 The CVE number is 2015-1832. I also suggest we add a SECURITY tag for JIRAs Existing tests with the change making sure that we see no new failures. I checked derby 10.12.x and not derby 10.11.x is downloaded to our ~/.m2 folder. I then used dev/make-distribution.sh and checked the dist/jars folder for Spark 2.0: no derby jar is present. I don't know if this would also remove it from the assembly jar in our 1.x branches. Author: Adam Roberts <aroberts@uk.ibm.com> Closes #14379 from a-roberts/patch-4. (cherry picked from commit 04a2c07) Signed-off-by: Sean Owen <sowen@cloudera.com>
## What changes were proposed in this pull request? Fix the link at http://spark.apache.org/docs/latest/ml-guide.html. ## How was this patch tested? None Author: Sun Dapeng <sdp@apache.org> Closes #14386 from sundapeng/doclink. (cherry picked from commit 2c15323) Signed-off-by: Sean Owen <sowen@cloudera.com>
… in TreeNodeException ## What changes were proposed in this pull request? We do not want SparkExceptions from job failures in the planning phase to create TreeNodeException. Hence do not wrap SparkException in TreeNodeException. ## How was this patch tested? New unit test Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #14395 from tdas/SPARK-16748. (cherry picked from commit bbc2475) Signed-off-by: Yin Huai <yhuai@databricks.com>
… exception ## What changes were proposed in this pull request? Some Java examples are using mllib.linalg.Vectors instead of ml.linalg.Vectors and causes an exception when run. Also there are some Java examples that incorrectly specify data types in the schema, also causing an exception. ## How was this patch tested? Ran corrected examples locally Author: Bryan Cutler <cutlerb@gmail.com> Closes #14405 from BryanCutler/java-examples-ml.Vectors-fix-SPARK-16800. (cherry picked from commit a6290e5) Signed-off-by: Sean Owen <sowen@cloudera.com>
## What changes were proposed in this pull request? This patch makes SparkILoop.getAddedJars a public developer API. It is a useful function to get the list of jars added. ## How was this patch tested? N/A - this is a simple visibility change. Author: Reynold Xin <rxin@databricks.com> Closes #14417 from rxin/SPARK-16812. (cherry picked from commit 7c27d07) Signed-off-by: Reynold Xin <rxin@databricks.com>
…st package The catalyst package is meant to be internal, and as a result it does not make sense to mark things as private[sql] or private[spark]. It simply makes debugging harder when Spark developers need to inspect the plans at runtime. This patch removes all private[sql] and private[spark] visibility modifiers in org.apache.spark.sql.catalyst. N/A - just visibility changes. Author: Reynold Xin <rxin@databricks.com> Closes #14418 from rxin/SPARK-16813. (cherry picked from commit 064d91f) Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request? It is useful to log the timezone when query result does not match, especially on build machines that have different timezone from AMPLab Jenkins. ## How was this patch tested? This is a test-only change. Author: Reynold Xin <rxin@databricks.com> Closes #14413 from rxin/SPARK-16805. (cherry picked from commit 579fbcf) Signed-off-by: Yin Huai <yhuai@databricks.com>
## What changes were proposed in this pull request? Change to non-deprecated constructor for SQLContext. ## How was this patch tested? Existing tests Author: Holden Karau <holden@us.ibm.com> Closes #14406 from holdenk/SPARK-16778-fix-use-of-deprecated-SQLContext-constructor. (cherry picked from commit 1e9b59b) Signed-off-by: Sean Owen <sowen@cloudera.com>
…n spaces ## What changes were proposed in this pull request? TRIM/LTRIM/RTRIM should not strips characters other than spaces, we were trimming all chars small than ASCII 0x20(space) ## How was this patch tested? fixed existing tests. Author: Sandeep Singh <sandeep@techaddict.me> Closes #14924 from techaddict/SPARK-17299. (cherry picked from commit 7775d9f) Signed-off-by: Sean Owen <sowen@cloudera.com>
…Values() ## What changes were proposed in this pull request? This patch fixes a `java.io.StreamCorruptedException` error affecting remote reads of cached values when certain data types are used. The problem stems from #11801 / SPARK-13990, a patch to have Spark automatically pick the "best" serializer when caching RDDs. If PySpark cached a PythonRDD, then this would be cached as an `RDD[Array[Byte]]` and the automatic serializer selection would pick KryoSerializer for replication and block transfer. However, the `getRemoteValues()` / `getRemoteBytes()` code path did not pass proper class tags in order to enable the same serializer to be used during deserialization, causing Java to be inappropriately used instead of Kryo, leading to the StreamCorruptedException. We already fixed a similar bug in #14311, which dealt with similar issues in block replication. Prior to that patch, it seems that we had no tests to ensure that block replication actually succeeded. Similarly, prior to this bug fix patch it looks like we had no tests to perform remote reads of cached data, which is why this bug was able to remain latent for so long. This patch addresses the bug by modifying `BlockManager`'s `get()` and `getRemoteValues()` methods to accept ClassTags, allowing the proper class tag to be threaded in the `getOrElseUpdate` code path (which is used by `rdd.iterator`) ## How was this patch tested? Extended the caching tests in `DistributedSuite` to exercise the `getRemoteValues` path, plus manual testing to verify that the PySpark bug reproduction in SPARK-17110 is fixed. Author: Josh Rosen <joshrosen@databricks.com> Closes #14952 from JoshRosen/SPARK-17110. (cherry picked from commit 29cfab3) Signed-off-by: Josh Rosen <joshrosen@databricks.com>
## What changes were proposed in this pull request? Fix the 'ask' type parameter in 'removeExecutor' to eliminate a lot of error logs `Cannot cast java.lang.Boolean to scala.runtime.Nothing$` ## How was this patch tested? Jenkins Author: Shixiong Zhu <shixiong@databricks.com> Closes #14983 from zsxwing/SPARK-17316-3. (cherry picked from commit 175b434) Signed-off-by: Shixiong Zhu <shixiong@databricks.com>
…F execution ## What changes were proposed in this pull request? If `ScalaUDF` throws exceptions during executing user code, sometimes it's hard for users to figure out what's wrong, especially when they use Spark shell. An example ``` org.apache.spark.SparkException: Job aborted due to stage failure: Task 12 in stage 325.0 failed 4 times, most recent failure: Lost task 12.3 in stage 325.0 (TID 35622, 10.0.207.202): java.lang.NullPointerException at line8414e872fb8b42aba390efc153d1611a12.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(<console>:40) at line8414e872fb8b42aba390efc153d1611a12.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(<console>:40) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source) ... ``` We should catch these exceptions and rethrow them with better error message, to say that the exception is happened in scala udf. This PR also does some clean up for `ScalaUDF` and add a unit test suite for it. ## How was this patch tested? the new test suite Author: Wenchen Fan <wenchen@databricks.com> Closes #14850 from cloud-fan/npe. (cherry picked from commit 8d08f43) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
…ays to save file names in FileStreamSource ## What changes were proposed in this pull request? When we create a filestream on a directory that has partitioned subdirs (i.e. dir/x=y/), then ListingFileCatalog.allFiles returns the files in the dir as Seq[String] which internally is a Stream[String]. This is because of this [line](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningAwareFileCatalog.scala#L93), where a LinkedHashSet.values.toSeq returns Stream. Then when the [FileStreamSource](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FileStreamSource.scala#L79) filters this Stream[String] to remove the seen files, it creates a new Stream[String], which has a filter function that has a $outer reference to the FileStreamSource (in Scala 2.10). Trying to serialize this Stream[String] causes NotSerializableException. This will happened even if there is just one file in the dir. Its important to note that this behavior is different in Scala 2.11. There is no $outer reference to FileStreamSource, so it does not throw NotSerializableException. However, with a large sequence of files (tested with 10000 files), it throws StackOverflowError. This is because how Stream class is implemented. Its basically like a linked list, and attempting to serialize a long Stream requires *recursively* going through linked list, thus resulting in StackOverflowError. In short, across both Scala 2.10 and 2.11, serialization fails when both the following conditions are true. - file stream defined on a partitioned directory - directory has 10k+ files The right solution is to convert the seq to an array before writing to the log. This PR implements this fix in two ways. - Changing all uses for HDFSMetadataLog to ensure Array is used instead of Seq - Added a `require` in HDFSMetadataLog such that it is never used with type Seq ## How was this patch tested? Added unit test that test that ensures the file stream source can handle with 10000 files. This tests fails in both Scala 2.10 and 2.11 with different failures as indicated above. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #14987 from tdas/SPARK-17372. (cherry picked from commit eb1ab88) Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
Fixed bug in `dapplyCollect` by changing the `compute` function of `worker.R` to explicitly handle raw (binary) vectors. cc shivaram Unit tests Author: Clark Fitzgerald <clarkfitzg@gmail.com> Closes #14783 from clarkfitzg/SPARK-16785. (cherry picked from commit 9fccde4) Signed-off-by: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
## What changes were proposed in this pull request? This PR backports #14867 to branch-2.0. It fixes a number of join ordering bugs. ## How was this patch tested? Added tests to `PlanParserSuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #14984 from hvanhovell/SPARK-17296-branch-2.0.
## What changes were proposed in this pull request? Fixing the typo in the unit test of CodeGenerationSuite.scala ## How was this patch tested? Ran the unit test after fixing the typo and it passes Author: Srinivasa Reddy Vundela <vsr@cloudera.com> Closes #14989 from vundela/typo_fix. (cherry picked from commit 76ad89e) Signed-off-by: Sean Owen <sowen@cloudera.com>
## What changes were proposed in this pull request? DAGScheduler invalidates shuffle files when an executor loss event occurs, but not when the external shuffle service is enabled. This is because when shuffle service is on, the shuffle file lifetime can exceed the executor lifetime. However, it also doesn't invalidate shuffle files when the shuffle service itself is lost (due to whole slave loss). This can cause long hangs when slaves are lost since the file loss is not detected until a subsequent stage attempts to read the shuffle files. The proposed fix is to also invalidate shuffle files when an executor is lost due to a `SlaveLost` event. ## How was this patch tested? Unit tests, also verified on an actual cluster that slave loss invalidates shuffle files immediately as expected. cc mateiz Author: Eric Liang <ekl@databricks.com> Closes #14931 from ericl/sc-4439. (cherry picked from commit 649fa4b) Signed-off-by: Josh Rosen <joshrosen@databricks.com>
## What changes were proposed in this pull request? Backport changes from #14710 and #14925 to 2.0 Author: Marcelo Vanzin <vanzin@cloudera.com> Author: Angus Gerry <angolon@gmail.com> Closes #14933 from angolon/SPARK-16533-2.0.
…n hadoopFile and newHadoopFile APIs ## What changes were proposed in this pull request? This PR backports #14960 ## How was this patch tested? AppVeyor - https://ci.appveyor.com/project/HyukjinKwon/spark/build/86-backport-SPARK-17339-r Author: hyukjinkwon <gurwls223@gmail.com> Closes #15008 from HyukjinKwon/backport-SPARK-17339.
…ling upgrade branch-2.0 version of this patch. The differences are in the YarnShuffleService for finding the location to put the DB. branch-2.0 does not use the yarn nm recovery path like master does. Tested in manually on 8 node yarn cluster and ran unit tests. Manually tests verified DB created properly and it found them if already existed. Verified that during rolling upgrade credentials were reloaded and running application was not affected. Author: Thomas Graves <tgraves@staydecay.corp.gq1.yahoo.com> Closes #14997 from tgravescs/SPARK-16711-branch2.0.
… to data source ## What changes were proposed in this pull request? additional options were not passed down in write.df. ## How was this patch tested? unit tests falaki shivaram Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #15010 from felixcheung/testreadoptions. (cherry picked from commit f0d21b7) Signed-off-by: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
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Can one of the admins verify this patch? |
Member
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Maybe you'd better close this PR and ask this to user-mailing list. I think we can have a better answer. Please refer http://spark.apache.org/community.html to subscribe. |
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You can find : File -> Project Structure -> Modules -> Dependcies , change the provided to compile. As HyukjinKwon say , you'd better ask this to user-mailing list . |
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## What changes were proposed in this pull request? This patch adds methods for extracting major and minor versions as Int types in Scala from a Spark version string. Motivation: There are many hacks within Spark's codebase to identify and compare Spark versions. We should add a simple utility to standardize these code paths, especially since there have been mistakes made in the past. This will let us add unit tests as well. Currently, I want this functionality to check Spark versions to provide backwards compatibility for ML model persistence. ## How was this patch tested? Unit tests Author: Joseph K. Bradley <joseph@databricks.com> Closes #15017 from jkbradley/version-parsing. (cherry picked from commit 65b814b) Signed-off-by: Joseph K. Bradley <joseph@databricks.com>
… Parquet vectorized reader This PR fixes `ColumnVectorUtils.populate` so that Parquet vectorized reader can read partitioned table with dates/timestamps. This works fine with Parquet normal reader. This is being only called within [VectorizedParquetRecordReader.java#L185](https://github.com/apache/spark/blob/master/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedParquetRecordReader.java#L185). When partition column types are explicitly given to `DateType` or `TimestampType` (rather than inferring the type of partition column), this fails with the exception below: ``` 16/09/01 10:30:07 ERROR Executor: Exception in task 0.0 in stage 5.0 (TID 6) java.lang.ClassCastException: java.lang.Integer cannot be cast to java.sql.Date at org.apache.spark.sql.execution.vectorized.ColumnVectorUtils.populate(ColumnVectorUtils.java:89) at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.initBatch(VectorizedParquetRecordReader.java:185) at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.initBatch(VectorizedParquetRecordReader.java:204) at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anonfun$buildReader$1.apply(ParquetFileFormat.scala:362) ... ``` Unit tests in `SQLQuerySuite`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #14919 from HyukjinKwon/SPARK-17354. (cherry picked from commit f7d2143) Signed-off-by: Davies Liu <davies.liu@gmail.com>
## What changes were proposed in this pull request? Share the ForkJoinTaskSupport between UnionRDD instances to avoid creating a huge number of threads if lots of RDDs are created at the same time. ## How was this patch tested? This uses existing UnionRDD tests. Author: Ryan Blue <blue@apache.org> Closes #14985 from rdblue/SPARK-17396-use-shared-pool. (cherry picked from commit 6ea5055) Signed-off-by: Sean Owen <sowen@cloudera.com>
…es and adding more tests This PR build on #14976 and fixes a correctness bug that would cause the wrong quantile to be returned for small target errors. This PR adds 8 unit tests that were failing without the fix. Author: Timothy Hunter <timhunter@databricks.com> Author: Sean Owen <sowen@cloudera.com> Closes #15002 from thunterdb/ml-1783. (cherry picked from commit 180796e) Signed-off-by: Sean Owen <sowen@cloudera.com>
…m spark-config.sh ## What changes were proposed in this pull request? During startup of Spark standalone, the script file spark-config.sh appends to the PYTHONPATH and can be sourced many times, causing duplicates in the path. This change adds a env flag that is set when the PYTHONPATH is appended so it will happen only one time. ## How was this patch tested? Manually started standalone master/worker and verified PYTHONPATH has no duplicate entries. Author: Bryan Cutler <cutlerb@gmail.com> Closes #15028 from BryanCutler/fix-duplicate-pythonpath-SPARK-17336. (cherry picked from commit c76baff) Signed-off-by: Sean Owen <sowen@cloudera.com>
The `TaskMetricsUIData.updatedBlockStatuses` field is assigned to but never read, increasing the memory consumption of the web UI. We should remove this field. Author: Josh Rosen <joshrosen@databricks.com> Closes #15038 from JoshRosen/remove-updated-block-statuses-from-TaskMetricsUIData. (cherry picked from commit 72eec70) Signed-off-by: Shixiong Zhu <shixiong@databricks.com>
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…che the whole RDD in memory ## What changes were proposed in this pull request? MemoryStore may throw OutOfMemoryError when trying to cache a super big RDD that cannot fit in memory. ``` scala> sc.parallelize(1 to 1000000000, 100).map(x => new Array[Long](1000)).cache().count() java.lang.OutOfMemoryError: Java heap space at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:24) at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:23) at scala.collection.Iterator$$anon$11.next(Iterator.scala:409) at scala.collection.Iterator$JoinIterator.next(Iterator.scala:232) at org.apache.spark.storage.memory.PartiallyUnrolledIterator.next(MemoryStore.scala:683) at org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43) at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1684) at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134) at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1915) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1915) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70) at org.apache.spark.scheduler.Task.run(Task.scala:86) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) 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) ``` Spark MemoryStore uses SizeTrackingVector as a temporary unrolling buffer to store all input values that it has read so far before transferring the values to storage memory cache. The problem is that when the input RDD is too big for caching in memory, the temporary unrolling memory SizeTrackingVector is not garbage collected in time. As SizeTrackingVector can occupy all available storage memory, it may cause the executor JVM to run out of memory quickly. More info can be found at https://issues.apache.org/jira/browse/SPARK-17503 ## How was this patch tested? Unit test and manual test. ### Before change Heap memory consumption <img width="702" alt="screen shot 2016-09-12 at 4 16 15 pm" src="https://cloud.githubusercontent.com/assets/2595532/18429524/60d73a26-7906-11e6-9768-6f286f5c58c8.png"> Heap dump <img width="1402" alt="screen shot 2016-09-12 at 4 34 19 pm" src="https://cloud.githubusercontent.com/assets/2595532/18429577/cbc1ef20-7906-11e6-847b-b5903f450b3b.png"> ### After change Heap memory consumption <img width="706" alt="screen shot 2016-09-12 at 4 29 10 pm" src="https://cloud.githubusercontent.com/assets/2595532/18429503/4abe9342-7906-11e6-844a-b2f815072624.png"> Author: Sean Zhong <seanzhong@databricks.com> Closes #15056 from clockfly/memory_store_leak. (cherry picked from commit 1742c3a) Signed-off-by: Josh Rosen <joshrosen@databricks.com>
This patch makes a handful of post-Spark-2.0 MiMa exclusion and build updates. It should be merged to master and a subset of it should be picked into branch-2.0 in order to test Spark 2.0.1-SNAPSHOT. - Remove the ` sketch`, `mllibLocal`, and `streamingKafka010` from the list of excluded subprojects so that MiMa checks them. - Remove now-unnecessary special-case handling of the Kafka 0.8 artifact in `mimaSettings`. - Move the exclusion added in SPARK-14743 from `v20excludes` to `v21excludes`, since that patch was only merged into master and not branch-2.0. - Add exclusions for an API change introduced by SPARK-17096 / #14675. - Add missing exclusions for the `o.a.spark.internal` and `o.a.spark.sql.internal` packages. Author: Josh Rosen <joshrosen@databricks.com> Closes #15061 from JoshRosen/post-2.0-mima-changes. (cherry picked from commit 7c51b99) Signed-off-by: Josh Rosen <joshrosen@databricks.com>
…ng entire job ## What changes were proposed in this pull request? In Spark's `RDD.getOrCompute` we first try to read a local copy of a cached RDD block, then a remote copy, and only fall back to recomputing the block if no cached copy (local or remote) can be read. This logic works correctly in the case where no remote copies of the block exist, but if there _are_ remote copies and reads of those copies fail (due to network issues or internal Spark bugs) then the BlockManager will throw a `BlockFetchException` that will fail the task (and which could possibly fail the whole job if the read failures keep occurring). In the cases of TorrentBroadcast and task result fetching we really do want to fail the entire job in case no remote blocks can be fetched, but this logic is inappropriate for reads of cached RDD blocks because those can/should be recomputed in case cached blocks are unavailable. Therefore, I think that the `BlockManager.getRemoteBytes()` method should never throw on remote fetch errors and, instead, should handle failures by returning `None`. ## How was this patch tested? Block manager changes should be covered by modified tests in `BlockManagerSuite`: the old tests expected exceptions to be thrown on failed remote reads, while the modified tests now expect `None` to be returned from the `getRemote*` method. I also manually inspected all usages of `BlockManager.getRemoteValues()`, `getRemoteBytes()`, and `get()` to verify that they correctly pattern-match on the result and handle `None`. Note that these `None` branches are already exercised because the old `getRemoteBytes` returned `None` when no remote locations for the block could be found (which could occur if an executor died and its block manager de-registered with the master). Author: Josh Rosen <joshrosen@databricks.com> Closes #15037 from JoshRosen/SPARK-17485. (cherry picked from commit f9c580f) Signed-off-by: Josh Rosen <joshrosen@databricks.com>
## What changes were proposed in this pull request? When there is any Python UDF in the Project between Sort and Limit, it will be collected into TakeOrderedAndProjectExec, ExtractPythonUDFs failed to pull the Python UDFs out because QueryPlan.expressions does not include the expression inside Option[Seq[Expression]]. Ideally, we should fix the `QueryPlan.expressions`, but tried with no luck (it always run into infinite loop). In PR, I changed the TakeOrderedAndProjectExec to no use Option[Seq[Expression]] to workaround it. cc JoshRosen ## How was this patch tested? Added regression test. Author: Davies Liu <davies@databricks.com> Closes #15030 from davies/all_expr. (cherry picked from commit a91ab70) Signed-off-by: Davies Liu <davies.liu@gmail.com>
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