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The code convertToCanonicalEdges is such that srcIds are smaller than dstIds but the scaladoc suggested otherwise. Have fixed the same. Author: Gaurav Kumar <gauravkumar37@gmail.com> Closes #9666 from gauravkumar37/patch-1.
…lient When looking up Hive temporary functions, we should always use the `SessionState` within the execution Hive client, since temporary functions are registered there. Author: Cheng Lian <lian@databricks.com> Closes #9664 from liancheng/spark-11191.fix-temp-function.
…l types Parquet supports some unsigned datatypes. However, Since Spark does not support unsigned datatypes, it needs to emit an exception with a clear message rather then with the one saying illegal datatype. Author: hyukjinkwon <gurwls223@gmail.com> Closes #9646 from HyukjinKwon/SPARK-10113.
switched stddev support from DeclarativeAggregate to ImperativeAggregate. Author: JihongMa <linlin200605@gmail.com> Closes #9380 from JihongMA/SPARK-11420.
The stop() callback was trying to close the launcher connection in the same thread that handles connection data, which ended up causing a deadlock. So avoid that by dispatching the stop() request in its own thread. On top of that, add some exception safety to a few parts of the code, and use "destroyForcibly" from Java 8 if it's available, to force kill the child process. The flip side is that "kill()" may not actually work if running Java 7. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #9633 from vanzin/SPARK-11655.
Example for sqlContext.createDataDrame from pandas.DataFrame has a typo Author: Chris Snow <chsnow123@gmail.com> Closes #9639 from snowch/patch-2.
Author: Jean-Baptiste Onofré <jbonofre@apache.org> Closes #9487 from jbonofre/SPARK-2533-2.
<img width="931" alt="screen shot 2015-11-11 at 1 53 21 pm" src="https://cloud.githubusercontent.com/assets/2133137/11108261/35d183d4-889a-11e5-9572-85e9d6cebd26.png"> Author: Andrew Or <andrew@databricks.com> Closes #9638 from andrewor14/fix-kryo-docs.
…ster managers Author: Andrew Or <andrew@databricks.com> Closes #9637 from andrewor14/update-da-docs.
Author: Chris Snow <chsnow123@gmail.com> Closes #9640 from snowch/patch-3.
…pped error message This helps debug issues caused by multiple SparkContext instances. JoshRosen andrewor14 ~~~ scala> sc.stop() scala> sc.parallelize(0 until 10) java.lang.IllegalStateException: Cannot call methods on a stopped SparkContext. This stopped SparkContext was created at: org.apache.spark.SparkContext.<init>(SparkContext.scala:82) org.apache.spark.repl.SparkILoop.createSparkContext(SparkILoop.scala:1017) $iwC$$iwC.<init>(<console>:9) $iwC.<init>(<console>:18) <init>(<console>:20) .<init>(<console>:24) .<clinit>(<console>) .<init>(<console>:7) .<clinit>(<console>) $print(<console>) sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) java.lang.reflect.Method.invoke(Method.java:606) org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065) org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1340) org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840) org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871) org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819) org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:857) The active context was created at: (No active SparkContext.) ~~~ Author: Xiangrui Meng <meng@databricks.com> Closes #9675 from mengxr/SPARK-11709.
Per discussion in the initial Pipelines LDA PR [#9513], we should make LDAModel abstract and create a LocalLDAModel. This code simplification should be done before the 1.6 release to ensure API compatibility in future releases. CC feynmanliang mengxr Author: Joseph K. Bradley <joseph@databricks.com> Closes #9678 from jkbradley/lda-pipelines-2.
This PR adds a new method, `reduce`, to `GroupedDataset`, which allows similar operations to `reduceByKey` on a traditional `PairRDD`.
```scala
val ds = Seq("abc", "xyz", "hello").toDS()
ds.groupBy(_.length).reduce(_ + _).collect() // not actually commutative :P
res0: Array(3 -> "abcxyz", 5 -> "hello")
```
While implementing this method and its test cases several more deficiencies were found in our encoder handling. Specifically, in order to support positional resolution, named resolution and tuple composition, it is important to keep the unresolved encoder around and to use it when constructing new `Datasets` with the same object type but different output attributes. We now divide the encoder lifecycle into three phases (that mirror the lifecycle of standard expressions) and have checks at various boundaries:
- Unresoved Encoders: all users facing encoders (those constructed by implicits, static methods, or tuple composition) are unresolved, meaning they have only `UnresolvedAttributes` for named fields and `BoundReferences` for fields accessed by ordinal.
- Resolved Encoders: internal to a `[Grouped]Dataset` the encoder is resolved, meaning all input has been resolved to a specific `AttributeReference`. Any encoders that are placed into a logical plan for use in object construction should be resolved.
- BoundEncoder: Are constructed by physical plans, right before actual conversion from row -> object is performed.
It is left to future work to add explicit checks for resolution and provide good error messages when it fails. We might also consider enforcing the above constraints in the type system (i.e. `fromRow` only exists on a `ResolvedEncoder`), but we should probably wait before spending too much time on this.
Author: Michael Armbrust <michael@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes #9673 from marmbrus/pr/9628.
TODO - [x] Add Java API - [x] Add API tests - [x] Add a function test Author: Shixiong Zhu <shixiong@databricks.com> Closes #9636 from zsxwing/java-track.
… + minor recovery tweaks The support for closing WriteAheadLog files after writes was just merged in. Closing every file after a write is a very expensive operation as it creates many small files on S3. It's not necessary to enable it on HDFS anyway. However, when you have many small files on S3, recovery takes very long. In addition, files start stacking up pretty quickly, and deletes may not be able to keep up, therefore deletes can also be parallelized. This PR adds support for the two parallelization steps mentioned above, in addition to a couple more failures I encountered during recovery. Author: Burak Yavuz <brkyvz@gmail.com> Closes #9373 from brkyvz/par-recovery.
…tate is not updated Bug: Timestamp is not updated if there is data but the corresponding state is not updated. This is wrong, and timeout is defined as "no data for a while", not "not state update for a while". Fix: Update timestamp when timestamp when timeout is specified, otherwise no need. Also refactored the code for better testability and added unit tests. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #9648 from tdas/SPARK-11681.
We set `sqlContext = null` in `afterAll`. However, this doesn't change `SQLContext.activeContext` and then `SQLContext.getOrCreate` might use the `SparkContext` from previous test suite and hence causes the error. This PR calls `clearActive` in `beforeAll` and `afterAll` to avoid using an old context from other test suites. cc: yhuai Author: Xiangrui Meng <meng@databricks.com> Closes #9677 from mengxr/SPARK-11672.2.
…mentation Clean out hundreds of `style: Commented code should be removed.` from lintr Like these: ``` /opt/spark-1.6.0-bin-hadoop2.6/R/pkg/R/DataFrame.R:513:3: style: Commented code should be removed. # sc <- sparkR.init() ^~~~~~~~~~~~~~~~~~~ /opt/spark-1.6.0-bin-hadoop2.6/R/pkg/R/DataFrame.R:514:3: style: Commented code should be removed. # sqlContext <- sparkRSQL.init(sc) ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ /opt/spark-1.6.0-bin-hadoop2.6/R/pkg/R/DataFrame.R:515:3: style: Commented code should be removed. # path <- "path/to/file.json" ^~~~~~~~~~~~~~~~~~~~~~~~~~~ ``` tried without export or rdname, neither work instead, added this `#' noRd` to suppress .Rd file generation also updated `family` for DataFrame functions for longer descriptive text instead of `dataframe_funcs`  this covers *most* of 'Commented code' but I left out a few that looks legitimate. Author: felixcheung <felixcheung_m@hotmail.com> Closes #9463 from felixcheung/rlintr.
see: https://issues.apache.org/jira/browse/SPARK-11717 SparkR generates R session data and history files under current directory. It might be useful to ignore these files even running SparkR on spark directory for test or development. Author: Lewuathe <lewuathe@me.com> Closes #9681 from Lewuathe/SPARK-11717.
…rceptron Classification Add Python example code for Multilayer Perceptron Classification, and make example code in user guide document testable. mengxr Author: Yanbo Liang <ybliang8@gmail.com> Closes #9594 from yanboliang/spark-11629.
Currently, all the shuffle writer will write to target path directly, the file could be corrupted by other attempt of the same partition on the same executor. They should write to temporary file then rename to target path, as what we do in output committer. In order to make the rename atomic, the temporary file should be created in the same local directory (FileSystem). This PR is based on #9214 , thanks to squito . Closes #9214 Author: Davies Liu <davies@databricks.com> Closes #9610 from davies/safe_shuffle.
…ot report failures This PR just checks the test results and returns 1 if the test fails, so that `run-tests.py` can mark it fail. Author: Shixiong Zhu <shixiong@databricks.com> Closes #9669 from zsxwing/streaming-python-tests.
…f the table. https://issues.apache.org/jira/browse/SPARK-11678 The change of this PR is to pass root paths of table to the partition discovery logic. So, the process of partition discovery stops at those root paths instead of going all the way to the root path of the file system. Author: Yin Huai <yhuai@databricks.com> Closes #9651 from yhuai/SPARK-11678.
… include_example I have made the required changes and tested. Kindly review the changes. Author: Rishabh Bhardwaj <rbnext29@gmail.com> Closes #9407 from rishabhbhardwaj/SPARK-11445.
…dLibSVMFile to load DataFrame Use LibSVM data source rather than MLUtils.loadLibSVMFile to load DataFrame, include: * Use libSVM data source for all example codes under examples/ml, and remove unused import. * Use libSVM data source for user guides under ml-*** which were omitted by #8697. * Fix bug: We should use ```sqlContext.read().format("libsvm").load(path)``` at Java side, but the API doc and user guides misuse as ```sqlContext.read.format("libsvm").load(path)```. * Code cleanup. mengxr Author: Yanbo Liang <ybliang8@gmail.com> Closes #9690 from yanboliang/spark-11723.
This PR adds pivot to the python api of GroupedData with the same syntax as Scala/Java. Author: Andrew Ray <ray.andrew@gmail.com> Closes #9653 from aray/sql-pivot-python.
* rename `AppendColumn` to `AppendColumns` to be consistent with the physical plan name. * clean up stale comments. * always pass in resolved encoder to `TypedColumn.withInputType`(test added) * enable a mistakenly disabled java test. Author: Wenchen Fan <wenchen@databricks.com> Closes #9688 from cloud-fan/follow.
…ctEncoder also add more tests for encoders, and fix bugs that I found: * when convert array to catalyst array, we can only skip element conversion for native types(e.g. int, long, boolean), not `AtomicType`(String is AtomicType but we need to convert it) * we should also handle scala `BigDecimal` when convert from catalyst `Decimal`. * complex map type should be supported other issues that still in investigation: * encode java `BigDecimal` and decode it back, seems we will loss precision info. * when encode case class that defined inside a object, `ClassNotFound` exception will be thrown. I'll remove unused code in a follow-up PR. Author: Wenchen Fan <wenchen@databricks.com> Closes #9693 from cloud-fan/split.
…reAll Still saw some error messages caused by `SQLContext.getOrCreate`: https://amplab.cs.berkeley.edu/jenkins/job/Spark-Master-SBT/3997/AMPLAB_JENKINS_BUILD_PROFILE=hadoop2.3,label=spark-test/testReport/junit/org.apache.spark.ml.util/JavaDefaultReadWriteSuite/testDefaultReadWrite/ This PR sets the active SQLContext in beforeAll, which is not automatically set in `new SQLContext`. This makes `SQLContext.getOrCreate` return the right SQLContext. cc: yhuai Author: Xiangrui Meng <meng@databricks.com> Closes #9694 from mengxr/SPARK-11672.3.
`SessionManager` will set the `operationLog` if the configuration `hive.server2.logging.operation.enabled` is true in version of hive 1.2.1. But the spark did not adapt to this change, so no matter enabled the configuration or not, spark thrift server will always log the warn message. PS: if `hive.server2.logging.operation.enabled` is false, it should log the warn message (the same as hive thrift server). Author: huangzhaowei <carlmartinmax@gmail.com> Closes #9056 from SaintBacchus/SPARK-11043.
Currently, `spark-sql` would not flush command history when exiting. Author: Daoyuan Wang <daoyuan.wang@intel.com> Closes #9563 from adrian-wang/jline.
…parent class loader Without patch, two additional tests of ExecutorClassLoaderSuite fails. - "resource from parent" - "resources from parent" Detailed explanation is here, https://issues.apache.org/jira/browse/SPARK-11818?focusedCommentId=15011202&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15011202 Author: Jungtaek Lim <kabhwan@gmail.com> Closes #9812 from HeartSaVioR/SPARK-11818.
we should pass in resolved encodera to logical `CoGroup` and bind them in physical `CoGroup` Author: Wenchen Fan <wenchen@databricks.com> Closes #9928 from cloud-fan/cogroup.
Remove duplicate ml examples (only for ml). mengxr Author: Yanbo Liang <ybliang8@gmail.com> Closes #9933 from yanboliang/SPARK-11685.
…aries ignore weight col Doc for 1.6 that the summaries mostly ignore the weight column. To be corrected for 1.7 CC: mengxr thunterdb Author: Joseph K. Bradley <joseph@databricks.com> Closes #9927 from jkbradley/linregsummary-doc.
Add read/write support to LDA, similar to ALS. save/load for ml.LocalLDAModel is done. For DistributedLDAModel, I'm not sure if we can invoke save on the mllib.DistributedLDAModel directly. I'll send update after some test. Author: Yuhao Yang <hhbyyh@gmail.com> Closes #9894 from hhbyyh/ldaMLsave.
Author: Wenchen Fan <wenchen@databricks.com> Closes #9909 from cloud-fan/get-struct.
… bus's thread This is continuation of SPARK-11761 Andrew suggested adding this protection. See tail of #9741 Author: tedyu <yuzhihong@gmail.com> Closes #9852 from tedyu/master.
Currently pivot's signature looks like ```scala scala.annotation.varargs def pivot(pivotColumn: Column, values: Column*): GroupedData scala.annotation.varargs def pivot(pivotColumn: String, values: Any*): GroupedData ``` I think we can remove the one that takes "Column" types, since callers should always be passing in literals. It'd also be more clear if the values are not varargs, but rather Seq or java.util.List. I also made similar changes for Python. Author: Reynold Xin <rxin@databricks.com> Closes #9929 from rxin/SPARK-11946.
…ot logger. In the default Spark distribution, there are currently two separate log4j config files, with different default values for the root logger, so that when running the shell you have a different default log level. This makes the shell more usable, since the logs don't overwhelm the output. But if you install a custom log4j.properties, you lose that, because then it's going to be used no matter whether you're running a regular app or the shell. With this change, the overriding of the log level is done differently; the log level repl's main class (org.apache.spark.repl.Main) is used to define the root logger's level when running the shell, defaulting to WARN if it's not set explicitly. On a somewhat related change, the shell output about the "sc" variable was changed a bit to contain a little more useful information about the application, since when the root logger's log level is WARN, that information is never shown to the user. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #9816 from vanzin/shell-logging.
After calling spill() on SortedIterator, the array inside InMemorySorter is not needed, it should be freed during spilling, this could help to join multiple tables with limited memory. Author: Davies Liu <davies@databricks.com> Closes #9793 from davies/free_array.
… metastore When using remote Hive metastore, `hive.metastore.uris` is set to the metastore URI. However, it overrides `javax.jdo.option.ConnectionURL` unexpectedly, thus the execution Hive client connects to the actual remote Hive metastore instead of the Derby metastore created in the temporary directory. Cleaning this configuration for the execution Hive client fixes this issue. Author: Cheng Lian <lian@databricks.com> Closes #9895 from liancheng/spark-11783.clean-remote-metastore-config.
This PR is to provide two common `coalesce` and `repartition` in Dataset APIs. After reading the comments of SPARK-9999, I am unclear about the plan for supporting re-partitioning in Dataset APIs. Currently, both RDD APIs and Dataframe APIs provide users such a flexibility to control the number of partitions. In most traditional RDBMS, they expose the number of partitions, the partitioning columns, the table partitioning methods to DBAs for performance tuning and storage planning. Normally, these parameters could largely affect the query performance. Since the actual performance depends on the workload types, I think it is almost impossible to automate the discovery of the best partitioning strategy for all the scenarios. I am wondering if Dataset APIs are planning to hide these APIs from users? Feel free to reject my PR if it does not match the plan. Thank you for your answers. marmbrus rxin cloud-fan Author: gatorsmile <gatorsmile@gmail.com> Closes #9899 from gatorsmile/coalesce.
…taFrameReader This patch makes it consistent to use varargs in all DataFrameReader methods, including Parquet, JSON, text, and the generic load function. Also added a few more API tests for the Java API. Author: Reynold Xin <rxin@databricks.com> Closes #9945 from rxin/SPARK-11967.
… in Spark 2.0." Also fixed some documentation as I saw them. Author: Reynold Xin <rxin@databricks.com> Closes #9930 from rxin/SPARK-11947.
…leBasedWriteAheadLog.close()` There is a race condition in `FileBasedWriteAheadLog.close()`, where if delete's of old log files are in progress, the write ahead log may close, and result in a `RejectedExecutionException`. This is okay, and should be handled gracefully. Example test failures: https://amplab.cs.berkeley.edu/jenkins/job/Spark-1.6-SBT/AMPLAB_JENKINS_BUILD_PROFILE=hadoop1.0,label=spark-test/95/testReport/junit/org.apache.spark.streaming.util/BatchedWriteAheadLogWithCloseFileAfterWriteSuite/BatchedWriteAheadLog___clean_old_logs/ The reason the test fails is in `afterEach`, `writeAheadLog.close` is called, and there may still be async deletes in flight. tdas zsxwing Author: Burak Yavuz <brkyvz@gmail.com> Closes #9953 from brkyvz/flaky-ss.
Author: Reynold Xin <rxin@databricks.com> Closes #9948 from rxin/SPARK-10621.
…nd recovered from checkpoint file This solves the following exception caused when empty state RDD is checkpointed and recovered. The root cause is that an empty OpenHashMapBasedStateMap cannot be deserialized as the initialCapacity is set to zero. ``` Job aborted due to stage failure: Task 0 in stage 6.0 failed 1 times, most recent failure: Lost task 0.0 in stage 6.0 (TID 20, localhost): java.lang.IllegalArgumentException: requirement failed: Invalid initial capacity at scala.Predef$.require(Predef.scala:233) at org.apache.spark.streaming.util.OpenHashMapBasedStateMap.<init>(StateMap.scala:96) at org.apache.spark.streaming.util.OpenHashMapBasedStateMap.<init>(StateMap.scala:86) at org.apache.spark.streaming.util.OpenHashMapBasedStateMap.readObject(StateMap.scala:291) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:606) at java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1017) at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1893) at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798) at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990) at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915) at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798) at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) at java.io.ObjectInputStream.readObject(ObjectInputStream.java:370) at org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:76) at org.apache.spark.serializer.DeserializationStream$$anon$1.getNext(Serializer.scala:181) at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73) at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) at scala.collection.Iterator$class.foreach(Iterator.scala:727) at scala.collection.AbstractIterator.foreach(Iterator.scala:1157) at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47) at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273) at scala.collection.AbstractIterator.to(Iterator.scala:1157) at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265) at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157) at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252) at scala.collection.AbstractIterator.toArray(Iterator.scala:1157) at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:921) at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:921) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) at org.apache.spark.scheduler.Task.run(Task.scala:88) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) 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) ``` Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #9958 from tdas/SPARK-11979.
…n Dataset API Except inner join, maybe the other join types are also useful when users are using the joinWith function. Thus, added the joinType into the existing joinWith call in Dataset APIs. Also providing another joinWith interface for the cartesian-join-like functionality. Please provide your opinions. marmbrus rxin cloud-fan Thank you! Author: gatorsmile <gatorsmile@gmail.com> Closes #9921 from gatorsmile/joinWith.
…from Queryable Also added show methods to Dataset. Author: Reynold Xin <rxin@databricks.com> Closes #9964 from rxin/SPARK-11981.
…e to spark.dynamicAllocation.enabled and spark.executor.instances both set Changed the log type to a 'warning' instead of 'info' as required. Author: Ashwin Swaroop <Ashwin Swaroop> Closes #9926 from ashwinswaroop/master.
…for registerFunction [Python] Straightforward change on the python doc Author: Jeff Zhang <zjffdu@apache.org> Closes #9901 from zjffdu/SPARK-11860.
…ted with a Stage This issue was addressed in #5494, but the fix in that PR, while safe in the sense that it will prevent the SparkContext from shutting down, misses the actual bug. The intent of `submitMissingTasks` should be understood as "submit the Tasks that are missing for the Stage, and run them as part of the ActiveJob identified by jobId". Because of a long-standing bug, the `jobId` parameter was never being used. Instead, we were trying to use the jobId with which the Stage was created -- which may no longer exist as an ActiveJob, hence the crash reported in SPARK-6880. The correct fix is to use the ActiveJob specified by the supplied jobId parameter, which is guaranteed to exist at the call sites of submitMissingTasks. This fix should be applied to all maintenance branches, since it has existed since 1.0. kayousterhout pankajarora12 Author: Mark Hamstra <markhamstra@gmail.com> Author: Imran Rashid <irashid@cloudera.com> Closes #6291 from markhamstra/SPARK-6880.
- NettyRpcEnv::openStream() now correctly propagates errors to the read side of the pipe. - NettyStreamManager now throws if the file being transferred does not exist. - The network library now correctly handles zero-sized streams. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #9941 from vanzin/SPARK-11956.
…ython Author: felixcheung <felixcheung_m@hotmail.com> Closes #9967 from felixcheung/pypivotdoc.
…VM exits
deleting the temp dir like that
```
scala> import scala.collection.mutable
import scala.collection.mutable
scala> val a = mutable.Set(1,2,3,4,7,0,8,98,9)
a: scala.collection.mutable.Set[Int] = Set(0, 9, 1, 2, 3, 7, 4, 8, 98)
scala> a.foreach(x => {a.remove(x) })
scala> a.foreach(println(_))
98
```
You may not modify a collection while traversing or iterating over it.This can not delete all element of the collection
Author: Zhongshuai Pei <peizhongshuai@huawei.com>
Closes #9951 from DoingDone9/Bug_RemainDir.
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pull request from apache/master
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pull request from apache/master