## What changes were proposed in this pull request?
When the watermark is not a column of `dropDuplicates`, right now it will crash. This PR fixed this issue.
## How was this patch tested?
The new unit test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#18822 from zsxwing/SPARK-21546.
The code was failing to account for some cases when setting up log
redirection. For example, if a user redirected only stdout to a file,
the launcher code would leave stderr without redirection, which could
lead to child processes getting stuck because stderr wasn't being
read.
So detect cases where only one of the streams is redirected, and
redirect the other stream to the log as appropriate.
For the old "launch()" API, redirection of the unconfigured stream
only happens if the user has explicitly requested for log redirection.
Log redirection is on by default with "startApplication()".
Most of the change is actually adding new unit tests to make sure the
different cases work as expected. As part of that, I moved some tests
that were in the core/ module to the launcher/ module instead, since
they don't depend on spark-submit.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18696 from vanzin/SPARK-21490.
## What changes were proposed in this pull request?
This PR fixed a potential overflow issue in EventTimeStats.
## How was this patch tested?
The new unit tests
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#18803 from zsxwing/avg.
## What changes were proposed in this pull request?
Python API for Constrained Logistic Regression based on #17922 , thanks for the original contribution from zero323 .
## How was this patch tested?
Unit tests.
Author: zero323 <zero323@users.noreply.github.com>
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18759 from yanboliang/SPARK-20601.
### What changes were proposed in this pull request?
The original error message is pretty confusing. It is unable to tell which number is `number of partitions` and which one is the `RDD ID`. This PR is to improve the checkpoint checking.
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18796 from gatorsmile/improveErrMsgForCheckpoint.
## What changes were proposed in this pull request?
When using PySpark broadcast variables in a multi-threaded environment, `SparkContext._pickled_broadcast_vars` becomes a shared resource. A race condition can occur when broadcast variables that are pickled from one thread get added to the shared ` _pickled_broadcast_vars` and become part of the python command from another thread. This PR introduces a thread-safe pickled registry using thread local storage so that when python command is pickled (causing the broadcast variable to be pickled and added to the registry) each thread will have their own view of the pickle registry to retrieve and clear the broadcast variables used.
## How was this patch tested?
Added a unit test that causes this race condition using another thread.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#18695 from BryanCutler/pyspark-bcast-threadsafe-SPARK-12717.
The --packages option jars are getting added to the classpath with the scheme as "file:///", in Unix it doesn't have problem with this since the scheme contains the Unix Path separator which separates the jar name with location in the classpath. In Windows, the jar file is not getting resolved from the classpath because of the scheme.
Windows : file:///C:/Users/<user>/.ivy2/jars/<jar-name>.jar
Unix : file:///home/<user>/.ivy2/jars/<jar-name>.jar
With this PR, we are avoiding the 'file://' scheme to get added to the packages jar files.
I have verified manually in Windows and Unix environments, with the change it adds the jar to classpath like below,
Windows : C:\Users\<user>\.ivy2\jars\<jar-name>.jar
Unix : /home/<user>/.ivy2/jars/<jar-name>.jar
Author: Devaraj K <devaraj@apache.org>
Closes#18708 from devaraj-kavali/SPARK-21339.
## What changes were proposed in this pull request?
Fix 2 rendering errors on configuration doc page, due to SPARK-21243 and SPARK-15355.
## How was this patch tested?
Manually built and viewed docs with jekyll
Author: Sean Owen <sowen@cloudera.com>
Closes#18793 from srowen/SPARK-21593.
`scala-maven-plugin` in `incremental` mode compiles `Scala` and `Java` classes. There is no need to execute `maven-compiler-plugin` goals to compile (in fact recompile) `Java`.
This change reduces compilation time (over 10% on my machine).
Author: Grzegorz Slowikowski <gslowikowski@gmail.com>
Closes#18750 from gslowikowski/remove-redundant-compilation-from-maven.
The main goal of this change is to avoid the situation described
in the bug, where an AM restart in the middle of a job may cause
no new executors to be allocated because of faulty logic in the
reset path.
The change does two things:
- fixes the executor alloc manager's reset() so that it does not
stop allocation after a reset() in the middle of a job
- re-orders the initialization of the YarnAllocator class so that
it fetches the current executor ID before triggering the reset()
above.
This ensures both that the new allocator gets new requests for executors,
and that it starts from the correct executor id.
Tested with unit tests and by manually causing AM restarts while
running jobs using spark-shell in YARN mode.
Closes#17882
Author: Marcelo Vanzin <vanzin@cloudera.com>
Author: Guoqiang Li <witgo@qq.com>
Closes#18663 from vanzin/SPARK-20079.
Handle the case where the server closes the socket before the full message
has been written by the client.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18727 from vanzin/SPARK-21522.
## What changes were proposed in this pull request?
GBTs inherit from HasStepSize & LInearSVC/Binarizer from HasThreshold
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Author: Ruifeng Zheng <ruifengz@foxmail.com>
Closes#18612 from zhengruifeng/override_HasXXX.
## What changes were proposed in this pull request?
Java's `FileInputStream` and `FileOutputStream` overrides finalize(), even this file input/output stream is closed correctly and promptly, it will still leave some memory footprints which will only get cleaned in Full GC. This will introduce two side effects:
1. Lots of memory footprints regarding to Finalizer will be kept in memory and this will increase the memory overhead. In our use case of external shuffle service, a busy shuffle service will have bunch of this object and potentially lead to OOM.
2. The Finalizer will only be called in Full GC, and this will increase the overhead of Full GC and lead to long GC pause.
https://bugs.openjdk.java.net/browse/JDK-8080225https://www.cloudbees.com/blog/fileinputstream-fileoutputstream-considered-harmful
So to fix this potential issue, here propose to use NIO's Files#newInput/OutputStream instead in some critical paths like shuffle.
Left unchanged FileInputStream in core which I think is not so critical:
```
./core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala:467: val file = new DataInputStream(new FileInputStream(filename))
./core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala:942: val in = new FileInputStream(new File(path))
./core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala:76: val fileIn = new FileInputStream(file)
./core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala:248: val fis = new FileInputStream(file)
./core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala:910: input = new FileInputStream(new File(t))
./core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala:20:import java.io.{FileInputStream, InputStream}
./core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala:132: case Some(f) => new FileInputStream(f)
./core/src/main/scala/org/apache/spark/scheduler/SchedulableBuilder.scala:20:import java.io.{FileInputStream, InputStream}
./core/src/main/scala/org/apache/spark/scheduler/SchedulableBuilder.scala:77: val fis = new FileInputStream(f)
./core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala:27:import org.apache.spark.io.NioBufferedFileInputStream
./core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala:94: new DataInputStream(new NioBufferedFileInputStream(index))
./core/src/main/scala/org/apache/spark/storage/DiskStore.scala:111: val channel = new FileInputStream(file).getChannel()
./core/src/main/scala/org/apache/spark/storage/DiskStore.scala:219: val channel = new FileInputStream(file).getChannel()
./core/src/main/scala/org/apache/spark/TestUtils.scala:20:import java.io.{ByteArrayInputStream, File, FileInputStream, FileOutputStream}
./core/src/main/scala/org/apache/spark/TestUtils.scala:106: val in = new FileInputStream(file)
./core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala:89: inputStream = new FileInputStream(activeFile)
./core/src/main/scala/org/apache/spark/util/Utils.scala:329: if (in.isInstanceOf[FileInputStream] && out.isInstanceOf[FileOutputStream]
./core/src/main/scala/org/apache/spark/util/Utils.scala:332: val inChannel = in.asInstanceOf[FileInputStream].getChannel()
./core/src/main/scala/org/apache/spark/util/Utils.scala:1533: gzInputStream = new GZIPInputStream(new FileInputStream(file))
./core/src/main/scala/org/apache/spark/util/Utils.scala:1560: new GZIPInputStream(new FileInputStream(file))
./core/src/main/scala/org/apache/spark/util/Utils.scala:1562: new FileInputStream(file)
./core/src/main/scala/org/apache/spark/util/Utils.scala:2090: val inReader = new InputStreamReader(new FileInputStream(file), StandardCharsets.UTF_8)
```
Left unchanged FileOutputStream in core:
```
./core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala:957: val out = new FileOutputStream(file)
./core/src/main/scala/org/apache/spark/api/r/RBackend.scala:20:import java.io.{DataOutputStream, File, FileOutputStream, IOException}
./core/src/main/scala/org/apache/spark/api/r/RBackend.scala:131: val dos = new DataOutputStream(new FileOutputStream(f))
./core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala:62: val fileOut = new FileOutputStream(file)
./core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala:160: val outStream = new FileOutputStream(outPath)
./core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala:239: val zipOutputStream = new ZipOutputStream(new FileOutputStream(zipFile, false))
./core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala:949: val out = new FileOutputStream(tempFile)
./core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala:20:import java.io.{File, FileOutputStream, InputStream, IOException}
./core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala:106: val out = new FileOutputStream(file, true)
./core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala:109: * Therefore, for local files, use FileOutputStream instead. */
./core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala:112: new FileOutputStream(uri.getPath)
./core/src/main/scala/org/apache/spark/storage/DiskBlockObjectWriter.scala:20:import java.io.{BufferedOutputStream, File, FileOutputStream, OutputStream}
./core/src/main/scala/org/apache/spark/storage/DiskBlockObjectWriter.scala:71: private var fos: FileOutputStream = null
./core/src/main/scala/org/apache/spark/storage/DiskBlockObjectWriter.scala:102: fos = new FileOutputStream(file, true)
./core/src/main/scala/org/apache/spark/storage/DiskBlockObjectWriter.scala:213: var truncateStream: FileOutputStream = null
./core/src/main/scala/org/apache/spark/storage/DiskBlockObjectWriter.scala:215: truncateStream = new FileOutputStream(file, true)
./core/src/main/scala/org/apache/spark/storage/DiskStore.scala:153: val out = new FileOutputStream(file).getChannel()
./core/src/main/scala/org/apache/spark/TestUtils.scala:20:import java.io.{ByteArrayInputStream, File, FileInputStream, FileOutputStream}
./core/src/main/scala/org/apache/spark/TestUtils.scala:81: val jarStream = new JarOutputStream(new FileOutputStream(jarFile))
./core/src/main/scala/org/apache/spark/TestUtils.scala:96: val jarFileStream = new FileOutputStream(jarFile)
./core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala:20:import java.io.{File, FileOutputStream, InputStream, IOException}
./core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala:31: volatile private var outputStream: FileOutputStream = null
./core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala:97: outputStream = new FileOutputStream(file, true)
./core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala:90: gzOutputStream = new GZIPOutputStream(new FileOutputStream(gzFile))
./core/src/main/scala/org/apache/spark/util/Utils.scala:329: if (in.isInstanceOf[FileInputStream] && out.isInstanceOf[FileOutputStream]
./core/src/main/scala/org/apache/spark/util/Utils.scala:333: val outChannel = out.asInstanceOf[FileOutputStream].getChannel()
./core/src/main/scala/org/apache/spark/util/Utils.scala:527: val out = new FileOutputStream(tempFile)
```
Here in `DiskBlockObjectWriter`, it uses `FileDescriptor` so it is not easy to change to NIO Files API.
For the `FileInputStream` and `FileOutputStream` in common/shuffle* I changed them all.
## How was this patch tested?
Existing tests and manual verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#18684 from jerryshao/SPARK-21475.
## What changes were proposed in this pull request?
This pr added documents about unsupported functions in Hive UDF/UDTF/UDAF.
This pr relates to #18768 and #18527.
## How was this patch tested?
N/A
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18792 from maropu/HOTFIX-20170731.
## What changes were proposed in this pull request?
SPARK-20307 Added handleInvalid option to RFormula for tree-based classification algorithms. We should add this parameter for other classification algorithms in SparkR.
This is a followup PR for SPARK-20307.
## How was this patch tested?
New Unit tests are added.
Author: wangmiao1981 <wm624@hotmail.com>
Closes#18605 from wangmiao1981/class.
## What changes were proposed in this pull request?
Currently, StructType.merge() only reports data types of conflicting fields when merging two incompatible schemas. It would be nice to also report the field names for easier debugging.
## How was this patch tested?
Unit test in DataTypeSuite.
Print exception message when conflict is triggered.
Author: bravo-zhang <mzhang1230@gmail.com>
Closes#16365 from bravo-zhang/spark-18950.
## What changes were proposed in this pull request?
Remove surplus synchronized blocks.
## How was this patch tested?
Unit tests run OK.
Author: iurii.ant <sereneant@gmail.com>
Closes#18775 from SereneAnt/eliminate_unnecessary_synchronization_in_java-R_serialization.
## What changes were proposed in this pull request?
When BytesToBytesMap spills, its longArray should be released. Otherwise, it may not released until the task complete. This array may take a significant amount of memory, which cannot be used by later operator, such as UnsafeShuffleExternalSorter, resulting in more frequent spill in sorter. This patch release the array as destructive iterator will not use this array anymore.
## How was this patch tested?
Manual test in production
Author: Zhan Zhang <zhanzhang@fb.com>
Closes#17180 from zhzhan/memory.
## What changes were proposed in this pull request?
This PR proposes to fix few rather typos in `merge_spark_pr.py`.
- `# usage: ./apache-pr-merge.py (see config env vars below)`
-> `# usage: ./merge_spark_pr.py (see config env vars below)`
- `... have local a Spark ...` -> `... have a local Spark ...`
- `... to Apache.` -> `... to Apache Spark.`
I skimmed this file and these look all I could find.
## How was this patch tested?
pep8 check (`./dev/lint-python`).
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18776 from HyukjinKwon/minor-merge-script.
In programming guide, `numTasks` is used in several places as arguments of Transformations. However, in code, `numPartitions` is used. In this fix, I replace `numTasks` with `numPartitions` in programming guide for consistency.
Author: Cheng Wang <chengwang0511@gmail.com>
Closes#18774 from polarke/replace-numtasks-with-numpartitions-in-doc.
## What changes were proposed in this pull request?
The format of none should be consistent with other compression codec(\`snappy\`, \`lz4\`) as \`none\`.
## How was this patch tested?
This is a typo.
Author: GuoChenzhao <chenzhao.guo@intel.com>
Closes#18758 from gczsjdy/typo.
## What changes were proposed in this pull request?
This pr added parsing rules to support subquery column aliases in FROM clause.
This pr is a sub-task of #18079.
## How was this patch tested?
Added tests in `PlanParserSuite` and `SQLQueryTestSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18185 from maropu/SPARK-20962.
## What changes were proposed in this pull request?
Long values can be passed to `rangeBetween` as range frame boundaries, but we silently convert it to Int values, this can cause wrong results and we should fix this.
Further more, we should accept any legal literal values as range frame boundaries. In this PR, we make it possible for Long values, and make accepting other DataTypes really easy to add.
This PR is mostly based on Herman's previous amazing work: 596f53c339
After this been merged, we can close#16818 .
## How was this patch tested?
Add new tests in `DataFrameWindowFunctionsSuite` and `TypeCoercionSuite`.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#18540 from jiangxb1987/rangeFrame.
## What changes were proposed in this pull request?
When there are aliases (these aliases were added for nested fields) as parameters in `RuntimeReplaceable`, as they are not in the children expression, those aliases can't be cleaned up in analyzer rule `CleanupAliases`.
An expression `nvl(foo.foo1, "value")` can be resolved to two semantically different expressions in a group by query because they contain different aliases.
Because those aliases are not children of `RuntimeReplaceable` which is an `UnaryExpression`. So we can't trim the aliases out by simple transforming the expressions in `CleanupAliases`.
If we want to replace the non-children aliases in `RuntimeReplaceable`, we need to add more codes to `RuntimeReplaceable` and modify all expressions of `RuntimeReplaceable`. It makes the interface ugly IMO.
Consider those aliases will be replaced later at optimization and so they're no harm, this patch chooses to simply override `canonicalized` of `RuntimeReplaceable`.
One concern is about `CleanupAliases`. Because it actually cannot clean up ALL aliases inside a plan. To make caller of this rule notice that, this patch adds a comment to `CleanupAliases`.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18761 from viirya/SPARK-21555.
## What changes were proposed in this pull request?
```DStreams
class FileInputDStream
[line 162] protected[streaming] override def clearMetadata(time: Time) {
batchTimeToSelectedFiles.synchronized {
val oldFiles = batchTimeToSelectedFiles.filter(_._1 < (time - rememberDuration))
batchTimeToSelectedFiles --= oldFiles.keys
```
The above code does not remove the old generatedRDDs. "super.clearMetadata(time)" was added to the beginning of clearMetadata to remove the old generatedRDDs.
## How was this patch tested?
At the end of clearMetadata, the testing code (print the number of generatedRDDs) was added to check the old RDDS were removed manually.
Author: shaofei007 <1427357147@qq.com>
Author: Fei Shao <1427357147@qq.com>
Closes#18718 from shaofei007/master.
## What changes were proposed in this pull request?
This PR proposes `StructType.fieldNames` that returns a copy of a field name list rather than a (undocumented) `StructType.names`.
There are two points here:
- API consistency with Scala/Java
- Provide a safe way to get the field names. Manipulating these might cause unexpected behaviour as below:
```python
from pyspark.sql.types import *
struct = StructType([StructField("f1", StringType(), True)])
names = struct.names
del names[0]
spark.createDataFrame([{"f1": 1}], struct).show()
```
```
...
java.lang.IllegalStateException: Input row doesn't have expected number of values required by the schema. 1 fields are required while 0 values are provided.
at org.apache.spark.sql.execution.python.EvaluatePython$.fromJava(EvaluatePython.scala:138)
at org.apache.spark.sql.SparkSession$$anonfun$6.apply(SparkSession.scala:741)
at org.apache.spark.sql.SparkSession$$anonfun$6.apply(SparkSession.scala:741)
...
```
## How was this patch tested?
Added tests in `python/pyspark/sql/tests.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18618 from HyukjinKwon/SPARK-20090.
If you run a spark job without creating the SparkSession or SparkContext, the spark job logs says it succeeded but yarn says it fails and retries 3 times. Also, since, Application Master unregisters with Resource Manager and exits successfully, it deletes the spark staging directory, so when yarn makes subsequent retries, it fails to find the staging directory and thus, the retries fail.
Added a flag to check whether user has initialized SparkContext. If it is true, we let Application Master unregister with Resource Manager else, we do not let AM unregister with RM.
## How was this patch tested?
Manually tested the fix.
Before:
<img width="1253" alt="screen shot-before" src="https://user-images.githubusercontent.com/22228190/28647214-69bf81e2-722b-11e7-9ed0-d416d2bf23be.png">
After:
<img width="1319" alt="screen shot-after" src="https://user-images.githubusercontent.com/22228190/28647220-70f9eea2-722b-11e7-85c6-e56276b15614.png">
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: pgandhi <pgandhi@yahoo-inc.com>
Author: pgandhi999 <parthkgandhi9@gmail.com>
Closes#18741 from pgandhi999/SPARK-21541.
When I type spark-shell --help, I find that the default value description for the master parameter is missing. The user does not know what the default value is when the master parameter is not included, so we need to add the master parameter default description to the help information.
[https://issues.apache.org/jira/browse/SPARK-21553](https://issues.apache.org/jira/browse/SPARK-21553)
Author: davidxdh <xu.donghui@zte.com.cn>
Author: Donghui Xu <xu.donghui@zte.com.cn>
Closes#18755 from davidxdh/dev_0728.
## What changes were proposed in this pull request?
Fixes current failures in dev/lint-java
## How was this patch tested?
Existing linter, tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#18757 from srowen/LintJava.
## What changes were proposed in this pull request?
add `setWeightCol` method for OneVsRest.
`weightCol` is ignored if classifier doesn't inherit HasWeightCol trait.
## How was this patch tested?
+ [x] add an unit test.
Author: Yan Facai (颜发才) <facai.yan@gmail.com>
Closes#18554 from facaiy/BUG/oneVsRest_missing_weightCol.
## What changes were proposed in this pull request?
This PR contains a tiny update that removes an attribute resolution inconsistency in the Dataset API. The following example is taken from the ticket description:
```
spark.range(1).withColumnRenamed("id", "x").sort(col("id")) // works
spark.range(1).withColumnRenamed("id", "x").sort($"id") // works
spark.range(1).withColumnRenamed("id", "x").sort('id) // works
spark.range(1).withColumnRenamed("id", "x").sort("id") // fails with:
org.apache.spark.sql.AnalysisException: Cannot resolve column name "id" among (x);
```
The above `AnalysisException` happens because the last case calls `Dataset.apply()` to convert strings into columns, which triggers attribute resolution. To make the API consistent between overloaded methods, this PR defers the resolution and constructs columns directly.
Author: aokolnychyi <anton.okolnychyi@sap.com>
Closes#18740 from aokolnychyi/spark-21538.
## What changes were proposed in this pull request?
`UnsafeExternalSorter.recordComparator` can be either `KVComparator` or `RowComparator`, and both of them will keep the reference to the input rows they compared last time.
After sorting, we return the sorted iterator to upstream operators. However, the upstream operators may take a while to consume up the sorted iterator, and `UnsafeExternalSorter` is registered to `TaskContext` at [here](https://github.com/apache/spark/blob/v2.2.0/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java#L159-L161), which means we will keep the `UnsafeExternalSorter` instance and keep the last compared input rows in memory until the sorted iterator is consumed up.
Things get worse if we sort within partitions of a dataset and coalesce all partitions into one, as we will keep a lot of input rows in memory and the time to consume up all the sorted iterators is long.
This PR takes over https://github.com/apache/spark/pull/18543 , the idea is that, we do not keep the record comparator instance in `UnsafeExternalSorter`, but a generator of record comparator.
close#18543
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18679 from cloud-fan/memory-leak.
## What changes were proposed in this pull request?
Add R-like summary table to GLM summary, which includes feature name (if exist), parameter estimate, standard error, t-stat and p-value. This allows scala users to easily gather these commonly used inference results.
srowen yanboliang felixcheung
## How was this patch tested?
New tests. One for testing feature Name, and one for testing the summary Table.
Author: actuaryzhang <actuaryzhang10@gmail.com>
Author: Wayne Zhang <actuaryzhang10@gmail.com>
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16630 from actuaryzhang/glmTable.
## What changes were proposed in this pull request?
This is a refactoring of `ArrowConverters` and related classes.
1. Refactor `ColumnWriter` as `ArrowWriter`.
2. Add `ArrayType` and `StructType` support.
3. Refactor `ArrowConverters` to skip intermediate `ArrowRecordBatch` creation.
## How was this patch tested?
Added some tests and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18655 from ueshin/issues/SPARK-21440.
## What changes were proposed in this pull request?
This PR ensures that `Unsafe.sizeInBytes` must be a multiple of 8. It it is not satisfied. `Unsafe.hashCode` causes the assertion violation.
## How was this patch tested?
Will add test cases
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#18503 from kiszk/SPARK-21271.
## What changes were proposed in this pull request?
Update the description of `spark.shuffle.maxChunksBeingTransferred` to include that the new coming connections will be closed when the max is hit and client should have retry mechanism.
Author: jinxing <jinxing6042@126.com>
Closes#18735 from jinxing64/SPARK-21530.
## What changes were proposed in this pull request?
This generates a documentation for Spark SQL built-in functions.
One drawback is, this requires a proper build to generate built-in function list.
Once it is built, it only takes few seconds by `sql/create-docs.sh`.
Please see https://spark-test.github.io/sparksqldoc/ that I hosted to show the output documentation.
There are few more works to be done in order to make the documentation pretty, for example, separating `Arguments:` and `Examples:` but I guess this should be done within `ExpressionDescription` and `ExpressionInfo` rather than manually parsing it. I will fix these in a follow up.
This requires `pip install mkdocs` to generate HTMLs from markdown files.
## How was this patch tested?
Manually tested:
```
cd docs
jekyll build
```
,
```
cd docs
jekyll serve
```
and
```
cd sql
create-docs.sh
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18702 from HyukjinKwon/SPARK-21485.
## What changes were proposed in this pull request?
This change pulls the `LogisticAggregator` class out of LogisticRegression.scala and makes it extend `DifferentiableLossAggregator`. It also changes logistic regression to use the generic `RDDLossFunction` instead of having its own.
Other minor changes:
* L2Regularization accepts `Option[Int => Double]` for features standard deviation
* L2Regularization uses `Vector` type instead of Array
* Some tests added to LeastSquaresAggregator
## How was this patch tested?
Unit test suites are added.
Author: sethah <shendrickson@cloudera.com>
Closes#18305 from sethah/SPARK-20988.
## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-21524
ValidatorParamsSuiteHelpers.testFileMove() is generating temp dir in the wrong place and does not delete them.
ValidatorParamsSuiteHelpers.testFileMove() is invoked by TrainValidationSplitSuite and crossValidatorSuite. Currently it uses `tempDir` from `TempDirectory`, which unfortunately is never initialized since the `boforeAll()` of `ValidatorParamsSuiteHelpers` is never invoked.
In my system, it leaves some temp directories in the assembly folder each time I run the TrainValidationSplitSuite and crossValidatorSuite.
## How was this patch tested?
unit test fix
Author: Yuhao Yang <yuhao.yang@intel.com>
Closes#18728 from hhbyyh/tempDirFix.
## What changes were proposed in this pull request?
In our production cluster,oom happens when NettyBlockRpcServer receive OpenBlocks message.The reason we observed is below:
When BlockManagerManagedBuffer call ChunkedByteBuffer#toNetty, it will use Unpooled.wrappedBuffer(ByteBuffer... buffers) which use default maxNumComponents=16 in low-level CompositeByteBuf.When our component's number is bigger than 16, it will execute consolidateIfNeeded
int numComponents = this.components.size();
if(numComponents > this.maxNumComponents) {
int capacity = ((CompositeByteBuf.Component)this.components.get(numComponents - 1)).endOffset;
ByteBuf consolidated = this.allocBuffer(capacity);
for(int c = 0; c < numComponents; ++c) {
CompositeByteBuf.Component c1 = (CompositeByteBuf.Component)this.components.get(c);
ByteBuf b = c1.buf;
consolidated.writeBytes(b);
c1.freeIfNecessary();
}
CompositeByteBuf.Component var7 = new CompositeByteBuf.Component(consolidated);
var7.endOffset = var7.length;
this.components.clear();
this.components.add(var7);
}
in CompositeByteBuf which will consume some memory during buffer copy.
We can use another api Unpooled. wrappedBuffer(int maxNumComponents, ByteBuffer... buffers) to avoid this comsuming.
## How was this patch tested?
Test in production cluster.
Author: zhoukang <zhoukang@xiaomi.com>
Closes#18723 from caneGuy/zhoukang/fix-chunkbuffer.
There was some code based on the old SASL handler in the new auth client that
was incorrectly using the SASL user as the user to authenticate against the
external shuffle service. This caused the external service to not be able to
find the correct secret to authenticate the connection, failing the connection.
In the course of debugging, I found that some log messages from the YARN shuffle
service were a little noisy, so I silenced some of them, and also added a couple
of new ones that helped find this issue. On top of that, I found that a check
in the code that records app secrets was wrong, causing more log spam and also
using an O(n) operation instead of an O(1) call.
Also added a new integration suite for the YARN shuffle service with auth on,
and verified it failed before, and passes now.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18706 from vanzin/SPARK-21494.
### What changes were proposed in this pull request?
Like [Hive UDFType](https://hive.apache.org/javadocs/r2.0.1/api/org/apache/hadoop/hive/ql/udf/UDFType.html), we should allow users to add the extra flags for ScalaUDF and JavaUDF too. _stateful_/_impliesOrder_ are not applicable to our Scala UDF. Thus, we only add the following two flags.
- deterministic: Certain optimizations should not be applied if UDF is not deterministic. Deterministic UDF returns same result each time it is invoked with a particular input. This determinism just needs to hold within the context of a query.
When the deterministic flag is not correctly set, the results could be wrong.
For ScalaUDF in Dataset APIs, users can call the following extra APIs for `UserDefinedFunction` to make the corresponding changes.
- `nonDeterministic`: Updates UserDefinedFunction to non-deterministic.
Also fixed the Java UDF name loss issue.
Will submit a separate PR for `distinctLike` for UDAF
### How was this patch tested?
Added test cases for both ScalaUDF
Author: gatorsmile <gatorsmile@gmail.com>
Author: Wenchen Fan <cloud0fan@gmail.com>
Closes#17848 from gatorsmile/udfRegister.
## What changes were proposed in this pull request?
`Traversable.toMap` changed to 'collections.breakOut', that eliminates intermediate tuple collection creation, see [Stack Overflow article](https://stackoverflow.com/questions/1715681/scala-2-8-breakout).
## How was this patch tested?
Unit tests run.
No performance tests performed yet.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: iurii.ant <sereneant@gmail.com>
Closes#18693 from SereneAnt/performance_toMap-breakOut.
inprogress history file in some cases.
Add failure handling for EOFException that can be thrown during
decompression of an inprogress spark history file, treat same as case
where can't parse the last line.
## What changes were proposed in this pull request?
Failure handling for case of EOFException thrown within the ReplayListenerBus.replay method to handle the case analogous to json parse fail case. This path can arise in compressed inprogress history files since an incomplete compression block could be read (not flushed by writer on a block boundary). See the stack trace of this occurrence in the jira ticket (https://issues.apache.org/jira/browse/SPARK-21447)
## How was this patch tested?
Added a unit test that specifically targets validating the failure handling path appropriately when maybeTruncated is true and false.
Author: Eric Vandenberg <ericvandenberg@fb.com>
Closes#18673 from ericvandenbergfb/fix_inprogress_compr_history_file.
When NodeManagers launching Executors,
the `missing` value will exceed the
real value when the launch is slow, this can lead to YARN allocates more resource.
We add the `numExecutorsRunning` when calculate the `missing` to avoid this.
Test by experiment.
Author: DjvuLee <lihu@bytedance.com>
Closes#18651 from djvulee/YarnAllocate.
