Commits · dd6b7dbe7043f3fa3d2e3993d2e13f87231a59ca · cs525-sp18-g07 / spark

Jun 28, 2016

[SPARK-15863][SQL][DOC][FOLLOW-UP] Update SQL programming guide. · dd6b7dbe

Yin Huai authored 8 years ago

## What changes were proposed in this pull request?
This PR makes several updates to SQL programming guide.

Author: Yin Huai <yhuai@databricks.com>

Closes #13938 from yhuai/doc.

dd6b7dbe

[SPARK-16221][SQL] Redirect Parquet JUL logger via SLF4J for WRITE operations · a0da854f

Dongjoon Hyun authored 8 years ago

## What changes were proposed in this pull request?

[SPARK-8118](https://github.com/apache/spark/pull/8196) implements redirecting Parquet JUL logger via SLF4J, but it is currently applied only when READ operations occurs. If users use only WRITE operations, there occurs many Parquet logs.

This PR makes the redirection work on WRITE operations, too.

**Before**
```scala
scala> spark.range(10).write.format("parquet").mode("overwrite").save("/tmp/p")
SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.
Jun 26, 2016 9:04:38 PM INFO: org.apache.parquet.hadoop.codec.CodecConfig: Compression: SNAPPY
............ about 70 lines Parquet Log .............
scala> spark.range(10).write.format("parquet").mode("overwrite").save("/tmp/p")
............ about 70 lines Parquet Log .............
```

**After**
```scala
scala> spark.range(10).write.format("parquet").mode("overwrite").save("/tmp/p")
SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.
scala> spark.range(10).write.format("parquet").mode("overwrite").save("/tmp/p")
```

This PR also fixes some typos.

## How was this patch tested?

Manual.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #13918 from dongjoon-hyun/SPARK-16221.

a0da854f

Jun 27, 2016

[SPARK-16111][SQL][DOC] Hide SparkOrcNewRecordReader in API docs · 50fdd866

Dongjoon Hyun authored 8 years ago

## What changes were proposed in this pull request?

Currently, Spark Scala/Java API documents shows **org.apache.hadoop.hive.ql.io.orc** package at the top.

http://spark.apache.org/docs/2.0.0-preview/api/scala/index.html#org.apache.spark.package
http://spark.apache.org/docs/2.0.0-preview/api/java/index.html

This PR hides `SparkOrcNewRecordReader` from API docs.

## How was this patch tested?

Manual. (`build/sbt unidoc`).

The following is the screenshot after this PR.

**Scala API doc**

![Scala API doc](https://app.box.com/representation/file_version_75673952621/image_2048/1.png?shared_name=2mdqydygs8le6q9x00356898662zjwz6)

**Java API doc**

![Java API doc](https://app.box.com/representation/file_version_75673951725/image_2048/1.png?shared_name=iv23eeqy3avvkqz203v9ygfaqeyml85j)

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #13914 from dongjoon-hyun/SPARK-16111.

50fdd866

[SPARK-16143][R] group AFT survival regression methods docs in a single Rd · 1b7fc581

Junyang Qian authored 8 years ago

## What changes were proposed in this pull request?

This PR groups `spark.survreg`, `summary(AFT)`, `predict(AFT)`, `write.ml(AFT)` for survival regression into a single Rd.

## How was this patch tested?

Manually checked generated HTML doc. See attached screenshots.

![screen shot 2016-06-27 at 10 28 20 am](https://cloud.githubusercontent.com/assets/15318264/16392008/a14cf472-3c5e-11e6-9ce5-490ed1a52249.png)
![screen shot 2016-06-27 at 10 28 35 am](https://cloud.githubusercontent.com/assets/15318264/16392009/a14e333c-3c5e-11e6-8bd7-c2e9ba71f8e2.png)

Author: Junyang Qian <junyangq@databricks.com>

Closes #13927 from junyangq/SPARK-16143.

1b7fc581

[SPARK-16220][SQL] Add scope to show functions · 02a029df

Herman van Hovell authored 8 years ago

## What changes were proposed in this pull request?
Spark currently shows all functions when issue a `SHOW FUNCTIONS` command. This PR refines the `SHOW FUNCTIONS` command by allowing users to select all functions, user defined function or system functions. The following syntax can be used:

**ALL** (default)
```SHOW FUNCTIONS```
```SHOW ALL FUNCTIONS```

**SYSTEM**
```SHOW SYSTEM FUNCTIONS```

**USER**
```SHOW USER FUNCTIONS```
## How was this patch tested?
Updated tests and added tests to the DDLSuite

Author: Herman van Hovell <hvanhovell@databricks.com>

Closes #13929 from hvanhovell/SPARK-16220.

02a029df

[SPARK-16106][CORE] TaskSchedulerImpl should properly track executors added to existing hosts · c15b552d

Imran Rashid authored 8 years ago

## What changes were proposed in this pull request?

TaskSchedulerImpl used to only set `newExecAvailable` when a new *host* was added, not when a new executor was added to an existing host.  It also didn't update some internal state tracking live executors until a task was scheduled on the executor.  This patch changes it to properly update as soon as it knows about a new executor.

## How was this patch tested?

added a unit test, ran everything via jenkins.

Author: Imran Rashid <irashid@cloudera.com>

Closes #13826 from squito/SPARK-16106_executorByHosts.

c15b552d

[SPARK-16136][CORE] Fix flaky TaskManagerSuite · 28215891

Imran Rashid authored 8 years ago

## What changes were proposed in this pull request?

TaskManagerSuite "Kill other task attempts when one attempt belonging to the same task succeeds" was flaky.  When checking whether a task is speculatable, at least one millisecond must pass since the task was submitted.  Use a manual clock to avoid the problem.

I noticed these tests were leaving lots of threads lying around as well (which prevented me from running the test repeatedly), so I fixed that too.

## How was this patch tested?

Ran the test 1k times on my laptop, passed every time (it failed about 20% of the time before this).

Author: Imran Rashid <irashid@cloudera.com>

Closes #13848 from squito/fix_flaky_taskmanagersuite.

28215891

[SPARK-16231][PYSPARK][ML][EXAMPLES] dataframe_example.py fails to convert ML style vectors · 1aa191e5

Bryan Cutler authored 8 years ago

## What changes were proposed in this pull request?
Need to convert ML Vectors to the old MLlib style before doing Statistics.colStats operations on the DataFrame

## How was this patch tested?
Ran example, local tests

Author: Bryan Cutler <cutlerb@gmail.com>

Closes #13928 from BryanCutler/pyspark-ml-example-vector-conv-SPARK-16231.

1aa191e5

[SPARK-16187][ML] Implement util method for ML Matrix conversion in scala/java · c17b1abf

Yuhao Yang authored 8 years ago

## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-16187
This is to provide conversion utils between old/new vector columns in a DataFrame. So users can use it to migrate their datasets and pipelines manually.

## How was this patch tested?

java and scala ut

Author: Yuhao Yang <yuhao.yang@intel.com>

Closes #13888 from hhbyyh/matComp.

c17b1abf

[SPARK-16220][SQL] Revert Change to Bring Back SHOW FUNCTIONS Functionality · c48c8ebc

Bill Chambers authored 8 years ago

## What changes were proposed in this pull request?

- Fix tests regarding show functions functionality
- Revert `catalog.ListFunctions` and `SHOW FUNCTIONS` to return to `Spark 1.X` functionality.

Cherry picked changes from this PR: https://github.com/apache/spark/pull/13413/files

## How was this patch tested?

Unit tests.

Author: Bill Chambers <bill@databricks.com>
Author: Bill Chambers <wchambers@ischool.berkeley.edu>

Closes #13916 from anabranch/master.

c48c8ebc

[SPARK-16135][SQL] Remove hashCode and euqals in ArrayBasedMapData · 3e4e868c

Takeshi YAMAMURO authored 8 years ago

## What changes were proposed in this pull request?
This pr is to remove `hashCode` and `equals` in `ArrayBasedMapData` because the type cannot be used as join keys, grouping keys, or in equality tests.

## How was this patch tested?
Add a new test suite `MapDataSuite` for comparison tests.

Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>

Closes #13847 from maropu/UnsafeMapTest.

3e4e868c

[SPARK-10591][SQL][TEST] Add a testcase to ensure if `checkAnswer` handles map correctly · 11f420b4

Dongjoon Hyun authored 8 years ago

## What changes were proposed in this pull request?

This PR adds a testcase to ensure if `checkAnswer` handles Map type correctly.

## How was this patch tested?

Pass the jenkins tests.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #13913 from dongjoon-hyun/SPARK-10591.

11f420b4

[MINOR][CORE] Fix display wrong free memory size in the log · 52d4fe05

jerryshao authored 8 years ago

## What changes were proposed in this pull request?

Free memory size displayed in the log is wrong (used memory), fix to make it correct.

## How was this patch tested?

N/A

Author: jerryshao <sshao@hortonworks.com>

Closes #13804 from jerryshao/memory-log-fix.

52d4fe05

[SPARK-16214][EXAMPLES] fix the denominator of SparkPi · b4520263

杨浩 authored 8 years ago

## What changes were proposed in this pull request?

reduce the denominator of SparkPi by 1

## How was this patch tested?

  integration tests

Author: 杨浩 <yanghaogn@163.com>

Closes #13910 from yanghaogn/patch-1.

b4520263

Jun 26, 2016

[SPARK-16184][SPARKR] conf API for SparkSession · 30b182bc

Felix Cheung authored 8 years ago

## What changes were proposed in this pull request?

Add `conf` method to get Runtime Config from SparkSession

## How was this patch tested?

unit tests, manual tests

This is how it works in sparkR shell:
```
 SparkSession available as 'spark'.
> conf()
$hive.metastore.warehouse.dir
[1] "file:/opt/spark-2.0.0-bin-hadoop2.6/R/spark-warehouse"

$spark.app.id
[1] "local-1466749575523"

$spark.app.name
[1] "SparkR"

$spark.driver.host
[1] "10.0.2.1"

$spark.driver.port
[1] "45629"

$spark.executorEnv.LD_LIBRARY_PATH
[1] "$LD_LIBRARY_PATH:/usr/lib/R/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/default-java/jre/lib/amd64/server"

$spark.executor.id
[1] "driver"

$spark.home
[1] "/opt/spark-2.0.0-bin-hadoop2.6"

$spark.master
[1] "local[*]"

$spark.sql.catalogImplementation
[1] "hive"

$spark.submit.deployMode
[1] "client"

> conf("spark.master")
$spark.master
[1] "local[*]"

```

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #13885 from felixcheung/rconf.

30b182bc

Jun 25, 2016

[SPARK-16193][TESTS] Address flaky ExternalAppendOnlyMapSuite spilling tests · e8774158

Sean Owen authored 8 years ago

## What changes were proposed in this pull request?

Make spill tests wait until job has completed before returning the number of stages that spilled

## How was this patch tested?

Existing Jenkins tests.

Author: Sean Owen <sowen@cloudera.com>

Closes #13896 from srowen/SPARK-16193.

e8774158

[SPARK-1301][WEB UI] Added anchor links to Accumulators and Tasks on StagePage · 3ee9695d

Alex Bozarth authored 8 years ago

## What changes were proposed in this pull request?

Sometimes the "Aggregated Metrics by Executor" table on the Stage page can get very long so actor links to the Accumulators and Tasks tables below it have been added to the summary at the top of the page. This has been done in the same way as the Jobs and Stages pages. Note: the Accumulators link only displays when the table exists.

## How was this patch tested?

Manually Tested and dev/run-tests

![justtasks](https://cloud.githubusercontent.com/assets/13952758/15165269/6e8efe8c-16c9-11e6-9784-cffe966fdcf0.png)
![withaccumulators](https://cloud.githubusercontent.com/assets/13952758/15165270/7019ec9e-16c9-11e6-8649-db69ed7a317d.png)

Author: Alex Bozarth <ajbozart@us.ibm.com>

Closes #13037 from ajbozarth/spark1301.

3ee9695d

[SPARK-15958] Make initial buffer size for the Sorter configurable · bf665a95

Sital Kedia authored 8 years ago

## What changes were proposed in this pull request?

Currently the initial buffer size in the sorter is hard coded inside the code and is too small for large workload. As a result, the sorter spends significant time expanding the buffer size and copying the data. It would be useful to have it configurable.

## How was this patch tested?

Tested by running a job on the cluster.

Author: Sital Kedia <skedia@fb.com>

Closes #13699 from sitalkedia/config_sort_buffer_upstream.

bf665a95

[MLLIB] org.apache.spark.mllib.util.SVMDataGenerator generates... · a3c7b418

José Antonio authored 8 years ago

[MLLIB] org.apache.spark.mllib.util.SVMDataGenerator generates ArrayIndexOutOfBoundsException. I have found the bug and tested the solution.

## What changes were proposed in this pull request?

Just adjust the size of an array in line 58 so it does not cause an ArrayOutOfBoundsException in line 66.

## How was this patch tested?

Manual tests. I have recompiled the entire project with the fix, it has been built successfully and I have run the code, also with good results.

line 66: val yD = blas.ddot(trueWeights.length, x, 1, trueWeights, 1) + rnd.nextGaussian() * 0.1
crashes because trueWeights has length "nfeatures + 1" while "x" has length "features", and they should have the same length.

To fix this just make trueWeights be the same length as x.

I have recompiled the project with the change and it is working now:
[spark-1.6.1]$ spark-submit --master local[*] --class org.apache.spark.mllib.util.SVMDataGenerator mllib/target/spark-mllib_2.11-1.6.1.jar local /home/user/test

And it generates the data successfully now in the specified folder.

Author: José Antonio <joseanmunoz@gmail.com>

Closes #13895 from j4munoz/patch-2.

a3c7b418

[SPARK-16186] [SQL] Support partition batch pruning with `IN` predicate in InMemoryTableScanExec · a7d29499

Dongjoon Hyun authored 8 years ago

## What changes were proposed in this pull request?

One of the most frequent usage patterns for Spark SQL is using **cached tables**. This PR improves `InMemoryTableScanExec` to handle `IN` predicate efficiently by pruning partition batches. Of course, the performance improvement varies over the queries and the datasets. But, for the following simple query, the query duration in Spark UI goes from 9 seconds to 50~90ms. It's about over 100 times faster.

**Before**
```scala
$ bin/spark-shell --driver-memory 6G
scala> val df = spark.range(2000000000)
scala> df.createOrReplaceTempView("t")
scala> spark.catalog.cacheTable("t")
scala> sql("select id from t where id = 1").collect()    // About 2 mins
scala> sql("select id from t where id = 1").collect()    // less than 90ms
scala> sql("select id from t where id in (1,2,3)").collect()  // 9 seconds
```

**After**
```scala
scala> sql("select id from t where id in (1,2,3)").collect() // less than 90ms
```

This PR has impacts over 35 queries of TPC-DS if the tables are cached.
Note that this optimization is applied for `IN`.  To apply `IN` predicate having more than 10 items, `spark.sql.optimizer.inSetConversionThreshold` option should be increased.

## How was this patch tested?

Pass the Jenkins tests (including new testcases).

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #13887 from dongjoon-hyun/SPARK-16186.

a7d29499

Jun 24, 2016

[SPARK-16192][SQL] Add type checks in CollectSet · d2e44d7d

Takeshi YAMAMURO authored 8 years ago

## What changes were proposed in this pull request?
`CollectSet` cannot have map-typed data because MapTypeData does not implement `equals`.
So, this pr is to add type checks in `CheckAnalysis`.

## How was this patch tested?
Added tests to check failures when we found map-typed data in `CollectSet`.

Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>

Closes #13892 from maropu/SPARK-16192.

d2e44d7d

[SPARK-16195][SQL] Allow users to specify empty over clause in window... · 9053054c

Dilip Biswal authored 8 years ago

[SPARK-16195][SQL] Allow users to specify empty over clause in window expressions through dataset API

## What changes were proposed in this pull request?
Allow to specify empty over clause in window expressions through dataset API

In SQL, its allowed to specify an empty OVER clause in the window expression.

```SQL
select area, sum(product) over () as c from windowData
where product > 3 group by area, product
having avg(month) > 0 order by avg(month), product
```
In this case the analytic function sum is presented based on all the rows of the result set

Currently its not allowed through dataset API and is handled in this PR.

## How was this patch tested?

Added a new test in DataframeWindowSuite

Author: Dilip Biswal <dbiswal@us.ibm.com>

Closes #13897 from dilipbiswal/spark-empty-over.

9053054c

[SPARK-16173] [SQL] Can't join describe() of DataFrame in Scala 2.10 · e5d0928e

Dongjoon Hyun authored 8 years ago

## What changes were proposed in this pull request?

This PR fixes `DataFrame.describe()` by forcing materialization to make the `Seq` serializable. Currently, `describe()` of DataFrame throws `Task not serializable` Spark exceptions when joining in Scala 2.10.

## How was this patch tested?

Manual. (After building with Scala 2.10, test on `bin/spark-shell` and `bin/pyspark`.)

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #13900 from dongjoon-hyun/SPARK-16173.

e5d0928e

Revert "[SPARK-16186] [SQL] Support partition batch pruning with `IN`... · 20768dad

Davies Liu authored 8 years ago

Revert "[SPARK-16186] [SQL] Support partition batch pruning with `IN` predicate in InMemoryTableScanExec"

This reverts commit a65bcbc2.

20768dad

[SPARK-16186] [SQL] Support partition batch pruning with `IN` predicate in InMemoryTableScanExec · a65bcbc2

Dongjoon Hyun authored 8 years ago

## What changes were proposed in this pull request?

One of the most frequent usage patterns for Spark SQL is using **cached tables**. This PR improves `InMemoryTableScanExec` to handle `IN` predicate efficiently by pruning partition batches. Of course, the performance improvement varies over the queries and the datasets. But, for the following simple query, the query duration in Spark UI goes from 9 seconds to 50~90ms. It's about over 100 times faster.

**Before**
```scala
$ bin/spark-shell --driver-memory 6G
scala> val df = spark.range(2000000000)
scala> df.createOrReplaceTempView("t")
scala> spark.catalog.cacheTable("t")
scala> sql("select id from t where id = 1").collect()    // About 2 mins
scala> sql("select id from t where id = 1").collect()    // less than 90ms
scala> sql("select id from t where id in (1,2,3)").collect()  // 9 seconds
```

**After**
```scala
scala> sql("select id from t where id in (1,2,3)").collect() // less than 90ms
```

This PR has impacts over 35 queries of TPC-DS if the tables are cached.
Note that this optimization is applied for `IN`.  To apply `IN` predicate having more than 10 items, `spark.sql.optimizer.inSetConversionThreshold` option should be increased.

## How was this patch tested?

Pass the Jenkins tests (including new testcases).

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #13887 from dongjoon-hyun/SPARK-16186.

a65bcbc2

[SPARK-16179][PYSPARK] fix bugs for Python udf in generate · 4435de1b

Davies Liu authored 8 years ago

## What changes were proposed in this pull request?

This PR fix the bug when Python UDF is used in explode (generator), GenerateExec requires that all the attributes in expressions should be resolvable from children when creating, we should replace the children first, then replace it's expressions.

```
>>> df.select(explode(f(*df))).show()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/vlad/dev/spark/python/pyspark/sql/dataframe.py", line 286, in show
    print(self._jdf.showString(n, truncate))
  File "/home/vlad/dev/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 933, in __call__
  File "/home/vlad/dev/spark/python/pyspark/sql/utils.py", line 63, in deco
    return f(*a, **kw)
  File "/home/vlad/dev/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line 312, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o52.showString.
: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: makeCopy, tree:
Generate explode(<lambda>(_1#0L)), false, false, [col#15L]
+- Scan ExistingRDD[_1#0L]

	at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:50)
	at org.apache.spark.sql.catalyst.trees.TreeNode.makeCopy(TreeNode.scala:387)
	at org.apache.spark.sql.execution.SparkPlan.makeCopy(SparkPlan.scala:69)
	at org.apache.spark.sql.execution.SparkPlan.makeCopy(SparkPlan.scala:45)
	at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsDown(QueryPlan.scala:177)
	at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressions(QueryPlan.scala:144)
	at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract(ExtractPythonUDFs.scala:153)
	at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:114)
	at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:113)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301)
	at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
	at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:300)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:321)
	at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179)
	at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:319)
	at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:298)
	at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.apply(ExtractPythonUDFs.scala:113)
	at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.apply(ExtractPythonUDFs.scala:93)
	at org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:95)
	at org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:95)
	at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
	at scala.collection.immutable.List.foldLeft(List.scala:84)
	at org.apache.spark.sql.execution.QueryExecution.prepareForExecution(QueryExecution.scala:95)
	at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:85)
	at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:85)
	at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2557)
	at org.apache.spark.sql.Dataset.head(Dataset.scala:1923)
	at org.apache.spark.sql.Dataset.take(Dataset.scala:2138)
	at org.apache.spark.sql.Dataset.showString(Dataset.scala:239)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:280)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:211)
	at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.reflect.InvocationTargetException
	at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
	at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
	at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
	at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$makeCopy$1$$anonfun$apply$13.apply(TreeNode.scala:413)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$makeCopy$1$$anonfun$apply$13.apply(TreeNode.scala:413)
	at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$makeCopy$1.apply(TreeNode.scala:412)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$makeCopy$1.apply(TreeNode.scala:387)
	at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49)
	... 42 more
Caused by: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: pythonUDF0#20
	at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:50)
	at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:88)
	at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:87)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:279)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:279)
	at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
	at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:278)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:284)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:284)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:321)
	at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179)
	at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:319)
	at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:284)
	at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:268)
	at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:87)
	at org.apache.spark.sql.execution.GenerateExec.<init>(GenerateExec.scala:63)
	... 52 more
Caused by: java.lang.RuntimeException: Couldn't find pythonUDF0#20 in [_1#0L]
	at scala.sys.package$.error(package.scala:27)
	at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:94)
	at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:88)
	at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49)
	... 67 more
```

## How was this patch tested?

Added regression tests.

Author: Davies Liu <davies@databricks.com>

Closes #13883 from davies/udf_in_generate.

4435de1b

[SQL][MINOR] Simplify data source predicate filter translation. · 5f8de216

Reynold Xin authored 8 years ago

## What changes were proposed in this pull request?
This is a small patch to rewrite the predicate filter translation in DataSourceStrategy. The original code used excessive functional constructs (e.g. unzip) and was very difficult to understand.

## How was this patch tested?
Should be covered by existing tests.

Author: Reynold Xin <rxin@databricks.com>

Closes #13889 from rxin/simplify-predicate-filter.

5f8de216

[SPARK-16077] [PYSPARK] catch the exception from pickle.whichmodule() · d4893540

Davies Liu authored 8 years ago

## What changes were proposed in this pull request?

In the case that we don't know which module a object came from, will call pickle.whichmodule() to go throught all the loaded modules to find the object, which could fail because some modules, for example, six, see https://bitbucket.org/gutworth/six/issues/63/importing-six-breaks-pickling

We should ignore the exception here, use `__main__` as the module name (it means we can't find the module).

## How was this patch tested?

Manual tested. Can't have a unit test for this.

Author: Davies Liu <davies@databricks.com>

Closes #13788 from davies/whichmodule.

d4893540

[SPARK-15963][CORE] Catch `TaskKilledException` correctly in Executor.TaskRunner · a4851ed0

Liwei Lin authored 8 years ago

## The problem

Before this change, if either of the following cases happened to a task , the task would be marked as `FAILED` instead of `KILLED`:
- the task was killed before it was deserialized
- `executor.kill()` marked `taskRunner.killed`, but before calling `task.killed()` the worker thread threw the `TaskKilledException`

The reason is, in the `catch` block of the current [Executor.TaskRunner](https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/executor/Executor.scala#L362)'s implementation, we are mistakenly catching:
```scala
case _: TaskKilledException | _: InterruptedException if task.killed => ...
```
the semantics of which is:
- **(**`TaskKilledException` **OR** `InterruptedException`**)** **AND** `task.killed`

Then when `TaskKilledException` is thrown but `task.killed` is not marked, we would mark the task as `FAILED` (which should really be `KILLED`).

## What changes were proposed in this pull request?

This patch alters the catch condition's semantics from:
- **(**`TaskKilledException` **OR** `InterruptedException`**)** **AND** `task.killed`

to

- `TaskKilledException` **OR** **(**`InterruptedException` **AND** `task.killed`**)**

so that we can catch `TaskKilledException` correctly and mark the task as `KILLED` correctly.

## How was this patch tested?

Added unit test which failed before the change, ran new test 1000 times manually

Author: Liwei Lin <lwlin7@gmail.com>

Closes #13685 from lw-lin/fix-task-killed.

a4851ed0

[SPARK-15997][DOC][ML] Update user guide for HashingTF, QuantileVectorizer and CountVectorizer · be88383e

GayathriMurali authored 8 years ago

## What changes were proposed in this pull request?

Made changes to HashingTF,QuantileVectorizer and CountVectorizer

Author: GayathriMurali <gayathri.m@intel.com>

Closes #13745 from GayathriMurali/SPARK-15997.

be88383e

[SPARK-16129][CORE][SQL] Eliminate direct use of commons-lang classes in favor of commons-lang3 · 158af162

Sean Owen authored 8 years ago

## What changes were proposed in this pull request?

Replace use of `commons-lang` in favor of `commons-lang3` and forbid the former via scalastyle; remove `NotImplementedException` from `comons-lang` in favor of JDK `UnsupportedOperationException`

## How was this patch tested?

Jenkins tests

Author: Sean Owen <sowen@cloudera.com>

Closes #13843 from srowen/SPARK-16129.

158af162

[SPARK-16125][YARN] Fix not test yarn cluster mode correctly in YarnClusterSuite · f4fd7432

peng.zhang authored 8 years ago

## What changes were proposed in this pull request?

Since SPARK-13220(Deprecate "yarn-client" and "yarn-cluster"), YarnClusterSuite doesn't test "yarn cluster" mode correctly.
This pull request fixes it.

## How was this patch tested?
Unit test

(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)

Author: peng.zhang <peng.zhang@xiaomi.com>

Closes #13836 from renozhang/SPARK-16125-test-yarn-cluster-mode.

f4fd7432

[SPARK-13709][SQL] Initialize deserializer with both table and partition... · 2d2f607b

Cheng Lian authored 8 years ago

[SPARK-13709][SQL] Initialize deserializer with both table and partition properties when reading partitioned tables

## What changes were proposed in this pull request?

When reading partitions of a partitioned Hive SerDe table, we only initializes the deserializer using partition properties. However, for SerDes like `AvroSerDe`, essential properties (e.g. Avro schema information) may be defined in table properties. We should merge both table properties and partition properties before initializing the deserializer.

Note that an individual partition may have different properties than the one defined in the table properties (e.g. partitions within a table can have different SerDes). Thus, for any property key defined in both partition and table properties, the value set in partition properties wins.

## How was this patch tested?

New test case added in `QueryPartitionSuite`.

Author: Cheng Lian <lian@databricks.com>

Closes #13865 from liancheng/spark-13709-partitioned-avro-table.

2d2f607b

Jun 23, 2016

[SPARK-16133][ML] model loading backward compatibility for ml.feature · cc6778ee

Yuhao Yang authored 8 years ago

## What changes were proposed in this pull request?

model loading backward compatibility for ml.feature,

## How was this patch tested?

existing ut and manual test for loading 1.6 models.

Author: Yuhao Yang <yuhao.yang@intel.com>
Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #13844 from hhbyyh/featureComp.

cc6778ee

[SPARK-16142][R] group naiveBayes method docs in a single Rd · 4a40d43b

Xiangrui Meng authored 8 years ago

## What changes were proposed in this pull request?

This PR groups `spark.naiveBayes`, `summary(NB)`, `predict(NB)`, and `write.ml(NB)` into a single Rd.

## How was this patch tested?

Manually checked generated HTML doc. See attached screenshots.

![screen shot 2016-06-23 at 2 11 00 pm](https://cloud.githubusercontent.com/assets/829644/16320452/a5885e92-394c-11e6-994f-2ab5cddad86f.png)

![screen shot 2016-06-23 at 2 11 15 pm](https://cloud.githubusercontent.com/assets/829644/16320455/aad1f6d8-394c-11e6-8ef4-13bee989f52f.png)

Author: Xiangrui Meng <meng@databricks.com>

Closes #13877 from mengxr/SPARK-16142.

4a40d43b

[SPARK-16177][ML] model loading backward compatibility for ml.regression · 14bc5a7f

Yuhao Yang authored 8 years ago

## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-16177
model loading backward compatibility for ml.regression

## How was this patch tested?

existing ut and manual test for loading 1.6 models.

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #13879 from hhbyyh/regreComp.

14bc5a7f

[SQL][MINOR] ParserUtils.operationNotAllowed should throw exception directly · 6a3c6276

Wenchen Fan authored 8 years ago

## What changes were proposed in this pull request?

It's weird that `ParserUtils.operationNotAllowed` returns an exception and the caller throw it.

## How was this patch tested?

N/A

Author: Wenchen Fan <wenchen@databricks.com>

Closes #13874 from cloud-fan/style.

6a3c6276

[SPARK-16123] Avoid NegativeArraySizeException while reserving additional... · cc71d4fa

Sameer Agarwal authored 8 years ago

[SPARK-16123] Avoid NegativeArraySizeException while reserving additional capacity in VectorizedColumnReader

## What changes were proposed in this pull request?

This patch fixes an overflow bug in vectorized parquet reader where both off-heap and on-heap variants of `ColumnVector.reserve()` can unfortunately overflow while reserving additional capacity during reads.

## How was this patch tested?

Manual Tests

Author: Sameer Agarwal <sameer@databricks.com>

Closes #13832 from sameeragarwal/negative-array.

cc71d4fa

[SPARK-16165][SQL] Fix the update logic for InMemoryTableScanExec.readBatches · 264bc636

Dongjoon Hyun authored 8 years ago

## What changes were proposed in this pull request?

Currently, `readBatches` accumulator of `InMemoryTableScanExec` is updated only when `spark.sql.inMemoryColumnarStorage.partitionPruning` is true. Although this metric is used for only testing purpose, we had better have correct metric without considering SQL options.

## How was this patch tested?

Pass the Jenkins tests (including a new testcase).

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #13870 from dongjoon-hyun/SPARK-16165.

264bc636

[SPARK-15443][SQL] Fix 'explain' for streaming Dataset · 0e4bdebe

Shixiong Zhu authored 8 years ago

## What changes were proposed in this pull request?

- Fix the `explain` command for streaming Dataset/DataFrame. E.g.,
```
== Parsed Logical Plan ==
'SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7]
+- 'MapElements <function1>, obj#6: java.lang.String
   +- 'DeserializeToObject unresolveddeserializer(createexternalrow(getcolumnbyordinal(0, StringType).toString, StructField(value,StringType,true))), obj#5: org.apache.spark.sql.Row
      +- Filter <function1>.apply
         +- StreamingRelation FileSource[/Users/zsx/stream], [value#0]

== Analyzed Logical Plan ==
value: string
SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7]
+- MapElements <function1>, obj#6: java.lang.String
   +- DeserializeToObject createexternalrow(value#0.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row
      +- Filter <function1>.apply
         +- StreamingRelation FileSource[/Users/zsx/stream], [value#0]

== Optimized Logical Plan ==
SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7]
+- MapElements <function1>, obj#6: java.lang.String
   +- DeserializeToObject createexternalrow(value#0.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row
      +- Filter <function1>.apply
         +- StreamingRelation FileSource[/Users/zsx/stream], [value#0]

== Physical Plan ==
*SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7]
+- *MapElements <function1>, obj#6: java.lang.String
   +- *DeserializeToObject createexternalrow(value#0.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row
      +- *Filter <function1>.apply
         +- StreamingRelation FileSource[/Users/zsx/stream], [value#0]
```

- Add `StreamingQuery.explain` to display the last execution plan. E.g.,
```
== Parsed Logical Plan ==
SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7]
+- MapElements <function1>, obj#6: java.lang.String
   +- DeserializeToObject createexternalrow(value#12.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row
      +- Filter <function1>.apply
         +- Relation[value#12] text

== Analyzed Logical Plan ==
value: string
SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7]
+- MapElements <function1>, obj#6: java.lang.String
   +- DeserializeToObject createexternalrow(value#12.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row
      +- Filter <function1>.apply
         +- Relation[value#12] text

== Optimized Logical Plan ==
SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7]
+- MapElements <function1>, obj#6: java.lang.String
   +- DeserializeToObject createexternalrow(value#12.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row
      +- Filter <function1>.apply
         +- Relation[value#12] text

== Physical Plan ==
*SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7]
+- *MapElements <function1>, obj#6: java.lang.String
   +- *DeserializeToObject createexternalrow(value#12.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row
      +- *Filter <function1>.apply
         +- *Scan text [value#12] Format: org.apache.spark.sql.execution.datasources.text.TextFileFormat1836ab91, InputPaths: file:/Users/zsx/stream/a.txt, file:/Users/zsx/stream/b.txt, file:/Users/zsx/stream/c.txt, PushedFilters: [], ReadSchema: struct<value:string>
```

## How was this patch tested?

The added unit tests.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #13815 from zsxwing/sdf-explain.

0e4bdebe