## What changes were proposed in this pull request?
This adds analyzer rules for resolving table-valued functions, and adds one builtin implementation for range(). The arguments for range() are the same as those of `spark.range()`.
## How was this patch tested?
Unit tests.
cc hvanhovell
Author: Eric Liang <ekl@databricks.com>
Closes#14656 from ericl/sc-4309.
## What changes were proposed in this pull request?
- adds documentation for https://issues.apache.org/jira/browse/SPARK-11714
## How was this patch tested?
Doc no test needed.
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Closes#14667 from skonto/add_doc.
## What changes were proposed in this pull request?
The `Optimizer` rules `PushThroughSetOperations` and `PushDownPredicate` have a redundant rule to push down `Filter` through `Union`. We should remove it.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#14687 from viirya/remove-extra-pushdown.
## What changes were proposed in this pull request?
This patch introduces a new private ReduceAggregator interface that is a subclass of Aggregator. ReduceAggregator only requires a single associative and commutative reduce function. ReduceAggregator is also used to implement KeyValueGroupedDataset.reduceGroups in order to support partial aggregation.
Note that the pull request was initially done by viirya.
## How was this patch tested?
Covered by original tests for reduceGroups, as well as a new test suite for ReduceAggregator.
Author: Reynold Xin <rxin@databricks.com>
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#14576 from rxin/reduceAggregator.
## What changes were proposed in this pull request?
I was looking at the code for UnresolvedOrdinal and made a few small changes to make it slightly more clear:
1. Rename the rule to SubstituteUnresolvedOrdinals which is more consistent with other rules that start with verbs. Note that this is still inconsistent with CTESubstitution and WindowsSubstitution.
2. Broke the test suite down from a single test case to three test cases.
## How was this patch tested?
This is a minor cleanup.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14672 from petermaxlee/SPARK-17034.
## What changes were proposed in this pull request?
A TreeNodeException is thrown when executing the following minimal example in Spark 2.0.
import spark.implicits._
case class test (x: Int, q: Int)
val d = Seq(1).toDF("x")
d.withColumn("q", lit(0)).as[test].groupByKey(_.x).flatMapGroups{case (x, iter) => List[Int]()}.show
d.withColumn("q", expr("0")).as[test].groupByKey(_.x).flatMapGroups{case (x, iter) => List[Int]()}.show
The problem is at `FoldablePropagation`. The rule will do `transformExpressions` on `LogicalPlan`. The query above contains a `MapGroups` which has a parameter `dataAttributes:Seq[Attribute]`. One attributes in `dataAttributes` will be transformed to an `Alias(literal(0), _)` in `FoldablePropagation`. `Alias` is not an `Attribute` and causes the error.
We can't easily detect such type inconsistency during transforming expressions. A direct approach to this problem is to skip doing `FoldablePropagation` on object operators as they should not contain such expressions.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#14648 from viirya/flat-mapping.
https://issues.apache.org/jira/browse/SPARK-17038
## What changes were proposed in this pull request?
StreamingSource's lastReceivedBatch_submissionTime, lastReceivedBatch_processingTimeStart, and lastReceivedBatch_processingTimeEnd all use data from lastCompletedBatch instead of lastReceivedBatch.
In particular, this makes it impossible to match lastReceivedBatch_records with a batchID/submission time.
This is apparent when looking at StreamingSource.scala, lines 89-94.
## How was this patch tested?
Manually running unit tests on local laptop
Author: Xin Ren <iamshrek@126.com>
Closes#14681 from keypointt/SPARK-17038.
## What changes were proposed in this pull request?
Currently, the stackTrace (as `Array[StackTraceElements]`) reported through StreamingQueryListener.onQueryTerminated is useless as it has the stack trace of where StreamingQueryException is defined, not the stack trace of underlying exception. For example, if a streaming query fails because of a / by zero exception in a task, the `QueryTerminated.stackTrace` will have
```
org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches(StreamExecution.scala:211)
org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:124)
```
This is basically useless, as it is location where the StreamingQueryException was defined. What we want is
Here is the right way to reason about what should be posted as through StreamingQueryListener.onQueryTerminated
- The actual exception could either be a SparkException, or an arbitrary exception.
- SparkException reports the relevant executor stack trace of a failed task as a string in the the exception message. The `Array[StackTraceElements]` returned by `SparkException.stackTrace()` is mostly irrelevant.
- For any arbitrary exception, the `Array[StackTraceElements]` returned by `exception.stackTrace()` may be relevant.
- When there is an error in a streaming query, it's hard to reason whether the `Array[StackTraceElements]` is useful or not. In fact, it is not clear whether it is even useful to report the stack trace as this array of Java objects. It may be sufficient to report the strack trace as a string, along with the message. This is how Spark reported executor stra
- Hence, this PR simplifies the API by removing the array `stackTrace` from `QueryTerminated`. Instead the `exception` returns a string containing the message and the stack trace of the actual underlying exception that failed the streaming query (i.e. not that of the StreamingQueryException). If anyone is interested in the actual stack trace as an array, can always access them through `streamingQuery.exception` which returns the exception object.
With this change, if a streaming query fails because of a / by zero exception in a task, the `QueryTerminated.exception` will be
```
org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 0.0 failed 1 times, most recent failure: Lost task 1.0 in stage 0.0 (TID 1, localhost): java.lang.ArithmeticException: / by zero
at org.apache.spark.sql.streaming.StreamingQueryListenerSuite$$anonfun$5$$anonfun$apply$mcV$sp$4$$anonfun$apply$mcV$sp$5.apply$mcII$sp(StreamingQueryListenerSuite.scala:153)
at org.apache.spark.sql.streaming.StreamingQueryListenerSuite$$anonfun$5$$anonfun$apply$mcV$sp$4$$anonfun$apply$mcV$sp$5.apply(StreamingQueryListenerSuite.scala:153)
at org.apache.spark.sql.streaming.StreamingQueryListenerSuite$$anonfun$5$$anonfun$apply$mcV$sp$4$$anonfun$apply$mcV$sp$5.apply(StreamingQueryListenerSuite.scala:153)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:232)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:226)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:744)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1429)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1417)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1416)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1416)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
...
```
It contains the relevant executor stack trace. In a case non-SparkException, if the streaming source MemoryStream throws an exception, exception message will have the relevant stack trace.
```
java.lang.RuntimeException: this is the exception message
at org.apache.spark.sql.execution.streaming.MemoryStream.getBatch(memory.scala:103)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$5.apply(StreamExecution.scala:316)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$5.apply(StreamExecution.scala:313)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at org.apache.spark.sql.execution.streaming.StreamProgress.foreach(StreamProgress.scala:25)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at org.apache.spark.sql.execution.streaming.StreamProgress.flatMap(StreamProgress.scala:25)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatch(StreamExecution.scala:313)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anonfun$org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches$1.apply$mcZ$sp(StreamExecution.scala:197)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:43)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches(StreamExecution.scala:187)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:124)
```
Note that this change in the public `QueryTerminated` class is okay as the APIs are still experimental.
## How was this patch tested?
Unit tests that test whether the right information is present in the exception message reported through QueryTerminated object.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#14675 from tdas/SPARK-17096.
A review of the code, working back from Hadoop's `FileSystem.exists()` and `FileSystem.isDirectory()` code, then removing uses of the calls when superfluous.
1. delete is harmless if called on a nonexistent path, so don't do any checks before deletes
1. any `FileSystem.exists()` check before `getFileStatus()` or `open()` is superfluous as the operation itself does the check. Instead the `FileNotFoundException` is caught and triggers the downgraded path. When a `FileNotFoundException` was thrown before, the code still creates a new FNFE with the error messages. Though now the inner exceptions are nested, for easier diagnostics.
Initially, relying on Jenkins test runs.
One troublespot here is that some of the codepaths are clearly error situations; it's not clear that they have coverage anyway. Trying to create the failure conditions in tests would be ideal, but it will also be hard.
Author: Steve Loughran <stevel@apache.org>
Closes#14371 from steveloughran/cloud/SPARK-16736-superfluous-fs-calls.
## What changes were proposed in this pull request?
Gaussian Mixture Model wrapper in SparkR, similarly to R's ```mvnormalmixEM```.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#14392 from yanboliang/spark-16446.
There are two narrow races that could cause the ApplicationMaster to miss
when the user application instantiates the SparkContext, which could cause
app failures when nothing was wrong with the app. It was also possible for
a failing application to get stuck in the loop that waits for the context
for a long time, instead of failing quickly.
The change uses a promise to track the SparkContext instance, which gets
rid of the races and allows for some simplification of the code.
Tested with existing unit tests, and a new one being added to test the
timeout code.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#14542 from vanzin/SPARK-16930.
## What changes were proposed in this pull request?
We use reflection to convert `TreeNode` to json string, and currently don't support arbitrary object. `UserDefinedGenerator` takes a function object, so we should skip json format test for it, or the tests can be flacky, e.g. `DataFrameSuite.simple explode`, this test always fail with scala 2.10(branch 1.6 builds with scala 2.10 by default), but pass with scala 2.11(master branch builds with scala 2.11 by default).
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14679 from cloud-fan/json.
## What changes were proposed in this pull request?
The current subquery expression interface contains a little bit of technical debt in the form of a few different access paths to get and set the query contained by the expression. This is confusing to anyone who goes over this code.
This PR unifies these access paths.
## How was this patch tested?
(Existing tests)
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14685 from hvanhovell/SPARK-17106.
## What changes were proposed in this pull request?
This PR splits the generated code for ```SafeProjection.apply``` by using ```ctx.splitExpressions()```. This is because the large code body for ```NewInstance``` may grow beyond 64KB bytecode size for ```apply()``` method.
Here is [the original PR](https://github.com/apache/spark/pull/13243) for SPARK-15285. However, it breaks a build with Scala 2.10 since Scala 2.10 does not a case class with large number of members. Thus, it was reverted by [this commit](fa244e5a90).
## How was this patch tested?
Added new tests by using `DefinedByConstructorParams` instead of case class for scala-2.10
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#14670 from kiszk/SPARK-15285-2.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
Add Isotonic Regression wrapper in SparkR
Wrappers in R and Scala are added.
Unit tests
Documentation
## How was this patch tested?
Manually tested with sudo ./R/run-tests.sh
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#14182 from wangmiao1981/isoR.
## What changes were proposed in this pull request?
Currently methods in `ParserUtils` are tested indirectly, we should add test cases in `ParserUtilsSuite` to verify their integrity directly.
## How was this patch tested?
New test cases in `ParserUtilsSuite`
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#14620 from jiangxb1987/parserUtils.
## What changes were proposed in this pull request?
This should be credited to mvervuurt. The main purpose of this PR is
- simply to include the change for the same instance in `DataFrameReader` just to match up.
- just avoid duplicately verifying the PR (as I already did).
The documentation for both should be the same because both assume the `properties` should be the same `dict` for the same option.
## How was this patch tested?
Manually building Python documentation.
This will produce the output as below:
- `DataFrameReader`
- `DataFrameWriter`
Closes#14624
Author: hyukjinkwon <gurwls223@gmail.com>
Author: mvervuurt <m.a.vervuurt@gmail.com>
Closes#14677 from HyukjinKwon/typo-python.
## What changes were proposed in this pull request?
This PR adds a field to subquery alias in order to make the usage of views in a resolved `LogicalPlan` more visible (and more understandable).
For example, the following view and query:
```sql
create view constants as select 1 as id union all select 1 union all select 42
select * from constants;
```
...now yields the following analyzed plan:
```
Project [id#39]
+- SubqueryAlias c, `default`.`constants`
+- Project [gen_attr_0#36 AS id#39]
+- SubqueryAlias gen_subquery_0
+- Union
:- Union
: :- Project [1 AS gen_attr_0#36]
: : +- OneRowRelation$
: +- Project [1 AS gen_attr_1#37]
: +- OneRowRelation$
+- Project [42 AS gen_attr_2#38]
+- OneRowRelation$
```
## How was this patch tested?
Added tests for the two code paths in `SessionCatalogSuite` (sql/core) and `HiveMetastoreCatalogSuite` (sql/hive)
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14657 from hvanhovell/SPARK-17068.
## What changes were proposed in this pull request?
This PR renames `ParserUtils.assert` to `ParserUtils.validate`. This is done because this method is used to check requirements, and not to check if the program is in an invalid state.
## How was this patch tested?
Simple rename. Compilation should do.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14665 from hvanhovell/SPARK-17084.
## What changes were proposed in this pull request?
Remove the api doc link for mapReduceTriplets operator because in latest api they are remove so when user link to that api they will not get mapReduceTriplets there so its more good to remove than confuse the user.
## How was this patch tested?
Run all the test cases
Author: sandy <phalodi@gmail.com>
Closes#14669 from phalodi/SPARK-17089.
## What changes were proposed in this pull request?
Rename RDD functions for now to avoid CRAN check warnings.
Some RDD functions are sharing generics with DataFrame functions (hence the problem) so after the renames we need to add new generics, for now.
## How was this patch tested?
unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14626 from felixcheung/rrddfunctions.
## What changes were proposed in this pull request?
Fix the issue that ```spark.glm``` ```weightCol``` should in the signature.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#14641 from yanboliang/weightCol.
## What changes were proposed in this pull request?
`PySpark` loses `microsecond` precision for some corner cases during converting `Timestamp` into `Long`. For example, for the following `datetime.max` value should be converted a value whose last 6 digits are '999999'. This PR improves the logic not to lose precision for all cases.
**Corner case**
```python
>>> datetime.datetime.max
datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)
```
**Before**
```python
>>> from datetime import datetime
>>> from pyspark.sql import Row
>>> from pyspark.sql.types import StructType, StructField, TimestampType
>>> schema = StructType([StructField("dt", TimestampType(), False)])
>>> [schema.toInternal(row) for row in [{"dt": datetime.max}]]
[(253402329600000000,)]
```
**After**
```python
>>> [schema.toInternal(row) for row in [{"dt": datetime.max}]]
[(253402329599999999,)]
```
## How was this patch tested?
Pass the Jenkins test with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14631 from dongjoon-hyun/SPARK-17035.
## What changes were proposed in this pull request?
As README.md file is updated over time. Some code snippet outputs are not correct based on new README.md file. For example:
```
scala> textFile.count()
res0: Long = 126
```
should be
```
scala> textFile.count()
res0: Long = 99
```
This pr is to add comments to point out this problem so that new spark learners have a correct reference.
Also, fixed a samll bug, inside current documentation, the outputs of linesWithSpark.count() without and with cache are different (one is 15 and the other is 19)
```
scala> val linesWithSpark = textFile.filter(line => line.contains("Spark"))
linesWithSpark: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[2] at filter at <console>:27
scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"?
res3: Long = 15
...
scala> linesWithSpark.cache()
res7: linesWithSpark.type = MapPartitionsRDD[2] at filter at <console>:27
scala> linesWithSpark.count()
res8: Long = 19
```
## How was this patch tested?
manual test: run `$ SKIP_API=1 jekyll serve --watch`
Author: linbojin <linbojin203@gmail.com>
Closes#14645 from linbojin/quick-start-documentation.
## What changes were proposed in this pull request?
This PR is a small follow-up to https://github.com/apache/spark/pull/14554. This also widens the visibility of a few (similar) Hive classes.
## How was this patch tested?
No test. Only a visibility change.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14654 from hvanhovell/SPARK-16964-hive.
## What changes were proposed in this pull request?
This PR adds expression `UnresolvedOrdinal` to represent the ordinal in GROUP BY or ORDER BY, and fixes the rules when resolving ordinals.
Ordinals in GROUP BY or ORDER BY like `1` in `order by 1` or `group by 1` should be considered as unresolved before analysis. But in current code, it uses `Literal` expression to store the ordinal. This is inappropriate as `Literal` itself is a resolved expression, it gives the user a wrong message that the ordinals has already been resolved.
### Before this change
Ordinal is stored as `Literal` expression
```
scala> sc.setLogLevel("TRACE")
scala> sql("select a from t group by 1 order by 1")
...
'Sort [1 ASC], true
+- 'Aggregate [1], ['a]
+- 'UnresolvedRelation `t
```
For query:
```
scala> Seq(1).toDF("a").createOrReplaceTempView("t")
scala> sql("select count(a), a from t group by 2 having a > 0").show
```
During analysis, the intermediate plan before applying rule `ResolveAggregateFunctions` is:
```
'Filter ('a > 0)
+- Aggregate [2], [count(1) AS count(1)#83L, a#81]
+- LocalRelation [value#7 AS a#9]
```
Before this PR, rule `ResolveAggregateFunctions` believes all expressions of `Aggregate` have already been resolved, and tries to resolve the expressions in `Filter` directly. But this is wrong, as ordinal `2` in Aggregate is not really resolved!
### After this change
Ordinals are stored as `UnresolvedOrdinal`.
```
scala> sc.setLogLevel("TRACE")
scala> sql("select a from t group by 1 order by 1")
...
'Sort [unresolvedordinal(1) ASC], true
+- 'Aggregate [unresolvedordinal(1)], ['a]
+- 'UnresolvedRelation `t`
```
## How was this patch tested?
Unit tests.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14616 from clockfly/spark-16955.
## What changes were proposed in this pull request?
`CatalogStorageFormat.properties` can be used in 2 ways:
1. for hive tables, it stores the serde properties.
2. for data source tables, it stores the data source options, e.g. `path`, `skipHiveMetadata`, etc.
however, both of them have nothing to do with data source properties, e.g. `spark.sql.sources.provider`, so they should not have limitations about data source properties.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14506 from cloud-fan/table-prop.
## What changes were proposed in this pull request?
Add an instruction to ask the user to remove or upgrade the incompatible DataSourceRegister in the error message.
## How was this patch tested?
Test command:
```
build/sbt -Dscala-2.10 package
SPARK_SCALA_VERSION=2.10 bin/spark-shell --packages ai.h2o:sparkling-water-core_2.10:1.6.5
scala> Seq(1).toDS().write.format("parquet").save("foo")
```
Before:
```
java.util.ServiceConfigurationError: org.apache.spark.sql.sources.DataSourceRegister: Provider org.apache.spark.h2o.DefaultSource could not be instantiated
at java.util.ServiceLoader.fail(ServiceLoader.java:232)
at java.util.ServiceLoader.access$100(ServiceLoader.java:185)
at java.util.ServiceLoader$LazyIterator.nextService(ServiceLoader.java:384)
at java.util.ServiceLoader$LazyIterator.next(ServiceLoader.java:404)
at java.util.ServiceLoader$1.next(ServiceLoader.java:480)
...
Caused by: java.lang.NoClassDefFoundError: org/apache/spark/Logging
at java.lang.ClassLoader.defineClass1(Native Method)
at java.lang.ClassLoader.defineClass(ClassLoader.java:760)
at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
at java.net.URLClassLoader.defineClass(URLClassLoader.java:467)
at java.net.URLClassLoader.access$100(URLClassLoader.java:73)
at java.net.URLClassLoader$1.run(URLClassLoader.java:368)
at java.net.URLClassLoader$1.run(URLClassLoader.java:362)
at java.security.AccessController.doPrivileged(Native Method)
...
```
After:
```
java.lang.ClassNotFoundException: Detected an incompatible DataSourceRegister. Please remove the incompatible library from classpath or upgrade it. Error: org.apache.spark.sql.sources.DataSourceRegister: Provider org.apache.spark.h2o.DefaultSource could not be instantiated
at org.apache.spark.sql.execution.datasources.DataSource.lookupDataSource(DataSource.scala:178)
at org.apache.spark.sql.execution.datasources.DataSource.providingClass$lzycompute(DataSource.scala:79)
at org.apache.spark.sql.execution.datasources.DataSource.providingClass(DataSource.scala:79)
at org.apache.spark.sql.execution.datasources.DataSource.write(DataSource.scala:441)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:213)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:196)
...
```
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#14651 from zsxwing/SPARK-17065.
## What changes were proposed in this pull request?
In 2.0, we verify the data type against schema for every row for safety, but with performance cost, this PR make it optional.
When we verify the data type for StructType, it does not support all the types we support in infer schema (for example, dict), this PR fix that to make them consistent.
For Row object which is created using named arguments, the order of fields are sorted by name, they may be not different than the order in provided schema, this PR fix that by ignore the order of fields in this case.
## How was this patch tested?
Created regression tests for them.
Author: Davies Liu <davies@databricks.com>
Closes#14469 from davies/py_dict.
Both core and sql have slightly different code that does variable substitution
of config values. This change refactors that code and encapsulates the logic
of reading config values and expading variables in a new helper class, which
can be configured so that both core and sql can use it without losing existing
functionality, and allows for easier testing and makes it easier to add more
features in the future.
Tested with existing and new unit tests, and by running spark-shell with
some configs referencing variables and making sure it behaved as expected.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#14468 from vanzin/SPARK-16671.
## What changes were proposed in this pull request?
Fix ```LogisticRegression``` typo in error message.
## How was this patch tested?
Docs change, no new tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#14633 from yanboliang/lr-typo.
- Make mesos coarse grained scheduler accept port offers and pre-assign ports
Previous attempt was for fine grained: https://github.com/apache/spark/pull/10808
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Author: Stavros Kontopoulos <stavros.kontopoulos@typesafe.com>
Closes#11157 from skonto/honour_ports_coarse.
## What changes were proposed in this pull request?
* Fixed one typo `"overriden"` as `"overridden"`, also make sure no other same typo.
* Fixed one typo `"lowcase"` as `"lowercase"`, also make sure no other same typo.
## How was this patch tested?
Since the change is very tiny, so I just make sure compilation is successful.
I am new to the spark community, please feel free to let me do other necessary steps.
Thanks in advance!
----
Updated: Found another typo `lowcase` later and fixed then in the same patch
Author: Zhenglai Zhang <zhenglaizhang@hotmail.com>
Closes#14622 from zhenglaizhang/fixtypo.
## What changes were proposed in this pull request?
Replaces custom choose function with o.a.commons.math3.CombinatoricsUtils.binomialCoefficient
## How was this patch tested?
Spark unit tests
Author: zero323 <zero323@users.noreply.github.com>
Closes#14614 from zero323/SPARK-17027.
## What changes were proposed in this pull request?
Don't override app name specified in `SparkConf` with a random app name. Only set it if the conf has no app name even after options have been applied.
See also https://github.com/apache/spark/pull/14602
This is similar to Sherry302 's original proposal in https://github.com/apache/spark/pull/14556
## How was this patch tested?
Jenkins test, with new case reproducing the bug
Author: Sean Owen <sowen@cloudera.com>
Closes#14630 from srowen/SPARK-16966.2.
## What changes were proposed in this pull request?
Update Kafka streaming connector to use Kafka 0.10.0.1 release
## How was this patch tested?
Tested via Spark unit and integration tests
Author: Luciano Resende <lresende@apache.org>
Closes#14606 from lresende/kafka-upgrade.
## What changes were proposed in this pull request?
In the PR, we just allow the user to add additional options when create a new table in JDBC writer.
The options can be table_options or partition_options.
E.g., "CREATE TABLE t (name string) ENGINE=InnoDB DEFAULT CHARSET=utf8"
Here is the usage example:
```
df.write.option("createTableOptions", "ENGINE=InnoDB DEFAULT CHARSET=utf8").jdbc(...)
```
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
will apply test result soon.
Author: GraceH <93113783@qq.com>
Closes#14559 from GraceH/jdbc_options.
## What changes were proposed in this pull request?
Fixed warnings below after scanning through warnings during build:
```
[warn] /home/jenkins/workspace/SparkPullRequestBuilder/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterSchedulerSuite.scala:34: imported `Utils' is permanently hidden by definition of object Utils in package mesos
[warn] import org.apache.spark.scheduler.cluster.mesos.Utils
[warn] ^
```
and
```
[warn] /home/jenkins/workspace/SparkPullRequestBuilder/core/src/test/scala/org/apache/spark/storage/DiskBlockObjectWriterSuite.scala:113: method shuffleBytesWritten in class ShuffleWriteMetrics is deprecated: use bytesWritten instead
[warn] assert(writeMetrics.shuffleBytesWritten === file.length())
[warn] ^
[warn] /home/jenkins/workspace/SparkPullRequestBuilder/core/src/test/scala/org/apache/spark/storage/DiskBlockObjectWriterSuite.scala:119: method shuffleBytesWritten in class ShuffleWriteMetrics is deprecated: use bytesWritten instead
[warn] assert(writeMetrics.shuffleBytesWritten === file.length())
[warn] ^
[warn] /home/jenkins/workspace/SparkPullRequestBuilder/core/src/test/scala/org/apache/spark/storage/DiskBlockObjectWriterSuite.scala:131: method shuffleBytesWritten in class ShuffleWriteMetrics is deprecated: use bytesWritten instead
[warn] assert(writeMetrics.shuffleBytesWritten === file.length())
[warn] ^
[warn] /home/jenkins/workspace/SparkPullRequestBuilder/core/src/test/scala/org/apache/spark/storage/DiskBlockObjectWriterSuite.scala:135: method shuffleBytesWritten in class ShuffleWriteMetrics is deprecated: use bytesWritten instead
[warn] assert(writeMetrics.shuffleBytesWritten === file.length())
[warn] ^
```
## How was this patch tested?
Tested manually on local laptop.
Author: Xin Ren <iamshrek@126.com>
Closes#14609 from keypointt/suiteWarnings.
## What changes were proposed in this pull request?
When documentation is built is should reference examples from the same build. There are times when the docs have links that point to files in the GitHub head which may not be valid on the current release. Changed that in URLs to make them point to the right tag in git using ```SPARK_VERSION_SHORT```
…from its own release version] [Streaming programming guide]
Author: Jagadeesan <as2@us.ibm.com>
Closes#14596 from jagadeesanas2/SPARK-12370.
## What changes were proposed in this pull request?
The configuration doc lost the config option `spark.ui.enabled` (default value is `true`)
I think this option is important because many cases we would like to turn it off.
so I add it.
## How was this patch tested?
N/A
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#14604 from WeichenXu123/add_doc_param_spark_ui_enabled.
## What changes were proposed in this pull request?
This PR changes the CTE resolving rule to use only **forward-declared** tables in order to prevent infinite loops. More specifically, new logic is like the following.
* Resolve CTEs in `WITH` clauses first before replacing the main SQL body.
* When resolving CTEs, only forward-declared CTEs or base tables are referenced.
- Self-referencing is not allowed any more.
- Cross-referencing is not allowed any more.
**Reported Error Scenarios**
```scala
scala> sql("WITH t AS (SELECT 1 FROM t) SELECT * FROM t")
java.lang.StackOverflowError
...
scala> sql("WITH t1 AS (SELECT * FROM t2), t2 AS (SELECT 2 FROM t1) SELECT * FROM t1, t2")
java.lang.StackOverflowError
...
```
Note that `t`, `t1`, and `t2` are not declared in database. Spark falls into infinite loops before resolving table names.
## How was this patch tested?
Pass the Jenkins tests with new two testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14397 from dongjoon-hyun/SPARK-16771-TREENODE.
## What changes were proposed in this pull request?
```GaussianMixture``` should use ```treeAggregate``` rather than ```aggregate``` to improve performance and scalability. In my test of dataset with 200 features and 1M instance, I found there is 20% increased performance.
BTW, we should destroy broadcast variable ```compute``` at the end of each iteration.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#14621 from yanboliang/spark-17033.
#### What changes were proposed in this pull request?
So far, the test cases of `TableIdentifierParserSuite` do not cover the quoted cases. We should add one for avoiding regression.
#### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14244 from gatorsmile/quotedIdentifiers.
## What changes were proposed in this pull request?
In our cluster, sometimes the sql output maybe overrided. When I submit some sql, all insert into the same table, and the sql will cost less one minute, here is the detail,
1 sql1, 11:03 insert into table.
2 sql2, 11:04:11 insert into table.
3 sql3, 11:04:48 insert into table.
4 sql4, 11:05 insert into table.
5 sql5, 11:06 insert into table.
The sql3's output file will override the sql2's output file. here is the log:
```
16/05/04 11:04:11 INFO hive.SparkHiveHadoopWriter: XXfinalPath=hdfs://tl-sng-gdt-nn-tdw.tencent-distribute.com:54310/tmp/assorz/tdw-tdwadmin/20160504/04559505496526517_-1_1204544348/10000/_tmp.p_20160428/attempt_201605041104_0001_m_000000_1
16/05/04 11:04:48 INFO hive.SparkHiveHadoopWriter: XXfinalPath=hdfs://tl-sng-gdt-nn-tdw.tencent-distribute.com:54310/tmp/assorz/tdw-tdwadmin/20160504/04559505496526517_-1_212180468/10000/_tmp.p_20160428/attempt_201605041104_0001_m_000000_1
```
The reason is the output file use SimpleDateFormat("yyyyMMddHHmm"), if two sql insert into the same table in the same minute, the output will be overrite. I think we should change dateFormat to "yyyyMMddHHmmss", in our cluster, we can't finished a sql in one second.
## 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)
Author: hongshen <shenh062326@126.com>
Closes#14574 from shenh062326/SPARK-16985.
## What changes were proposed in this pull request?
This patch updates the SQL parser to parse negative numeric literals as numeric literals, instead of unary minus of positive literals.
This allows the parser to parse the minimal value for each data type, e.g. "-32768S".
## How was this patch tested?
Updated test cases.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14608 from petermaxlee/SPARK-17013.
## What changes were proposed in this pull request?
Currently, Spark ignores path names starting with underscore `_` and `.`. This causes read-failures for the column-partitioned file data sources whose partition column names starts from '_', e.g. `_col`.
**Before**
```scala
scala> spark.range(10).withColumn("_locality_code", $"id").write.partitionBy("_locality_code").save("/tmp/parquet")
scala> spark.read.parquet("/tmp/parquet")
org.apache.spark.sql.AnalysisException: Unable to infer schema for ParquetFormat at /tmp/parquet20. It must be specified manually;
```
**After**
```scala
scala> spark.range(10).withColumn("_locality_code", $"id").write.partitionBy("_locality_code").save("/tmp/parquet")
scala> spark.read.parquet("/tmp/parquet")
res2: org.apache.spark.sql.DataFrame = [id: bigint, _locality_code: int]
```
## How was this patch tested?
Pass the Jenkins with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14585 from dongjoon-hyun/SPARK-16975-PARQUET.
## What changes were proposed in this pull request?
Fix the typo of ```TreeEnsembleModels``` for PySpark, it should ```TreeEnsembleModel``` which will be consistent with Scala. What's more, it represents a tree ensemble model, so ```TreeEnsembleModel``` should be more reasonable. This should not be used public, so it will not involve breaking change.
## How was this patch tested?
No new tests, should pass existing ones.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#14454 from yanboliang/TreeEnsembleModel.
