## 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.
## What changes were proposed in this pull request?
A shuffle service can serves blocks from multiple apps/tasks. Thus the shuffle service can suffers high memory usage when lots of shuffle-reads happen at the same time. In my cluster, OOM always happens on shuffle service. Analyzing heap dump, memory cost by Netty(ChannelOutboundBufferEntry) can be up to 2~3G. It might make sense to reject "open blocks" request when memory usage is high on shuffle service.
93dd0c518d and 85c6ce6193 tried to alleviate the memory pressure on shuffle service but cannot solve the root cause. This pr proposes to control currency of shuffle read.
## How was this patch tested?
Added unit test.
Author: jinxing <jinxing6042@126.com>
Closes#18388 from jinxing64/SPARK-21175.
I find a bug about 'quick start',and created a new issues,Sean Owen let
me to make a pull request, and I do
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
## 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)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Trueman <lizhaoch@users.noreply.github.com>
Author: lizhaoch <lizhaoc@163.com>
Closes#18722 from lizhaoch/master.
## What changes were proposed in this pull request?
The examples and docs for Spark-Kinesis integrations use the deprecated KinesisUtils. We should update the docs to use the KinesisInputDStream builder to create DStreams.
## How was this patch tested?
The patch primarily updates the documents. The patch will also need to make changes to the Spark-Kinesis examples. The examples need to be tested.
Author: Yash Sharma <ysharma@atlassian.com>
Closes#18071 from yssharma/ysharma/kinesis_docs.
## What changes were proposed in this pull request?
This PR ensures to call `super.afterEach()` in overriding `afterEach()` method in `DatasetCacheSuite`. When we override `afterEach()` method in Testsuite, we have to call `super.afterEach()`.
This is a follow-up of #18719 and SPARK-21512.
## How was this patch tested?
Used the existing test suite
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#18721 from kiszk/SPARK-21516.
## What changes were proposed in this pull request?
With supervise enabled for a driver, re-launching it was failing because the driver had the same framework Id. This patch creates a new driver framework id every time we re-launch a driver, but we keep the driver submission id the same since that is the same with the task id the driver was launched with on mesos and retry state and other info within Dispatcher's data structures uses that as a key.
We append a "-retry-%4d" string as a suffix to the framework id passed by the dispatcher to the driver and the same value to the app_id created by each driver, except the first time where we dont need the retry suffix.
The previous format for the frameworkId was 'DispactherFId-DriverSubmissionId'.
We also detect the case where we have multiple spark contexts started from within the same driver and we do set proper names to their corresponding app-ids. The old practice was to unset the framework id passed from the dispatcher after the driver framework was started for the first time and let mesos decide the framework ID for subsequent spark contexts. The decided fId was passed as an appID.
This patch affects heavily the history server. Btw we dont have the issues of the standalone case where driver id must be different since the dispatcher will re-launch a driver(mesos task) only if it gets an update that it is dead and this is verified by mesos implicitly. We also dont fix the fine grained mode which is deprecated and of no use.
## How was this patch tested?
This task was manually tested on dc/os. Launched a driver, stoped its container and verified the expected behavior.
Initial retry of the driver, driver in pending state:
Driver re-launched:
Another re-try:
The resulted entries in history server at the bottom:
Regarding multiple spark contexts here is the end result regarding the spark history server, for the second spark context we add an increasing number as a suffix:
Author: Stavros Kontopoulos <st.kontopoulos@gmail.com>
Closes#18705 from skonto/fix_supervise_flag.
## What changes were proposed in this pull request?
In #18483 , we fixed the data copy bug when saving into `InternalRow`, and removed all workarounds for this bug in the aggregate code path. However, the object hash aggregate was missed, this PR fixes it.
This patch is also a requirement for #17419 , which shows that DataFrame version is slower than RDD version because of this issue.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18712 from cloud-fan/minor.
## What changes were proposed in this pull request?
This PR avoids to reuse unpersistent dataset among test cases by making dataset unpersistent at the end of each test case.
In `DatasetCacheSuite`, the test case `"get storage level"` does not make dataset unpersisit after make the dataset persisitent. The same dataset will be made persistent by the test case `"persist and then rebind right encoder when join 2 datasets"` Thus, we run these test cases, the second case does not perform to make dataset persistent. This is because in
When we run only the second case, it performs to make dataset persistent. It is not good to change behavior of the second test suite. The first test case should correctly make dataset unpersistent.
```
Testing started at 17:52 ...
01:52:15.053 WARN org.apache.hadoop.util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
01:52:48.595 WARN org.apache.spark.sql.execution.CacheManager: Asked to cache already cached data.
01:52:48.692 WARN org.apache.spark.sql.execution.CacheManager: Asked to cache already cached data.
01:52:50.864 WARN org.apache.spark.storage.RandomBlockReplicationPolicy: Expecting 1 replicas with only 0 peer/s.
01:52:50.864 WARN org.apache.spark.storage.RandomBlockReplicationPolicy: Expecting 1 replicas with only 0 peer/s.
01:52:50.868 WARN org.apache.spark.storage.BlockManager: Block rdd_8_1 replicated to only 0 peer(s) instead of 1 peers
01:52:50.868 WARN org.apache.spark.storage.BlockManager: Block rdd_8_0 replicated to only 0 peer(s) instead of 1 peers
```
After this PR, these messages do not appear
```
Testing started at 18:14 ...
02:15:05.329 WARN org.apache.hadoop.util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Process finished with exit code 0
```
## How was this patch tested?
Used the existing test
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#18719 from kiszk/SPARK-21512.
## What changes were proposed in this pull request?
This patch removes the `****` string from test names in FlatMapGroupsWithStateSuite. `***` is a common string developers grep for when using Scala test (because it immediately shows the failing test cases). The existence of the `****` in test names disrupts that workflow.
## How was this patch tested?
N/A - test only change.
Author: Reynold Xin <rxin@databricks.com>
Closes#18715 from rxin/FlatMapGroupsWithStateStar.
## What changes were proposed in this pull request?
When the code that is generated is greater than 64k, then Janino compile will fail and CodeGenerator.scala will log the entire code at Error level.
SPARK-20871 suggests only logging the code at Debug level.
Since, the code is already logged at debug level, this Pull Request proposes not including the formatted code in the Error logging and exception message at all.
When an exception occurs, the code will be logged at Info level but truncated if it is more than 1000 lines long.
## How was this patch tested?
Existing tests were run.
An extra test test case was added to CodeFormatterSuite to test the new maxLines parameter,
Author: pj.fanning <pj.fanning@workday.com>
Closes#18658 from pjfanning/SPARK-20871.
Executors run a thread pool with daemon threads to run tasks. This means
that those threads remain active when the JVM is shutting down, meaning
those tasks are affected by code that runs in shutdown hooks.
So if a shutdown hook messes with something that the task is using (e.g.
an HDFS connection), the task will fail and will report that failure to
the driver. That will make the driver mark the task as failed regardless
of what caused the executor to shut down. So, for example, if YARN pre-empted
that executor, the driver would consider that task failed when it should
instead ignore the failure.
This change avoids reporting failures to the driver when shutdown hooks
are executing; this fixes the YARN preemption accounting, and doesn't really
change things much for other scenarios, other than reporting a more generic
error ("Executor lost") when the executor shuts down unexpectedly - which
is arguably more correct.
Tested with a hacky app running on spark-shell that tried to cause failures
only when shutdown hooks were running, verified that preemption didn't cause
the app to fail because of task failures exceeding the threshold.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18594 from vanzin/SPARK-20904.
## What changes were proposed in this pull request?
It's a follow-up of https://github.com/apache/spark/pull/18689 , which forgot to remove a useless test.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18716 from cloud-fan/test.
## What changes were proposed in this pull request?
Update the Quickstart and RDD programming guides to mention pip.
## How was this patch tested?
Built docs locally.
Author: Holden Karau <holden@us.ibm.com>
Closes#18698 from holdenk/SPARK-21434-add-pyspark-pip-documentation.
## What changes were proposed in this pull request?
This is a follow-up of #18680.
In some environment, a compile error happens saying:
```
.../sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/ArrowColumnVector.java:243:
error: not found: type Array
public void loadBytes(Array array) {
^
```
This pr fixes it.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18701 from ueshin/issues/SPARK-21472_fup1.
## What changes were proposed in this pull request?
Minor change to kafka integration document for structured streaming.
## How was this patch tested?
N/A, doc change only.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18550 from viirya/minor-ss-kafka-doc.
## What changes were proposed in this pull request?
DirectParquetOutputCommitter was removed from Spark as it was deemed unsafe to use. We however still have some code to generate warning. This patch removes those code as well.
This is kind of a follow-up of https://github.com/apache/spark/pull/16796
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18689 from cloud-fan/minor.
## What changes were proposed in this pull request?
Currently spark-streaming-kafka-0-10 has a dependency on the full kafka distribution (but only uses and requires the kafka-clients library).
The PR fixes that (the library only depends on kafka-clients), and the tests depend on the full kafka.
## How was this patch tested?
All existing tests still pass.
Author: Tim Van Wassenhove <github@timvw.be>
Closes#18353 from timvw/master.
## What changes were proposed in this pull request?
During writing to the .inprogress file (stored on the HDFS) Hadoop doesn't update file length until close and therefor Spark's history server can't detect any changes. We have to send UPDATE_LENGTH manually.
Author: Oleg Danilov <oleg.danilov@wandisco.com>
Closes#16924 from dosoft/SPARK-19531.
## What changes were proposed in this pull request?
Introducing `ArrowColumnVector` as a reader for Arrow vectors.
It extends `ColumnVector`, so we will be able to use it with `ColumnarBatch` and its functionalities.
Currently it supports primitive types and `StringType`, `ArrayType` and `StructType`.
## How was this patch tested?
Added tests for `ArrowColumnVector` and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18680 from ueshin/issues/SPARK-21472.
## What changes were proposed in this pull request?
There are mainly two reasons for this reorg:
* Some params are placed in ```RFormulaBase```, while others are placed in ```RFormula```, this is disordered.
* ```RFormulaModel``` should have params ```handleInvalid```, ```formula``` and ```forceIndexLabel```, that users can get invalid values handling policy, formula or whether to force index label if they only have a ```RFormulaModel```. So we need move these params to ```RFormulaBase``` which is also inherited by ```RFormulaModel```.
* ```RFormulaModel``` should support set different ```handleInvalid``` when cross validation.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18681 from yanboliang/rformula-reorg.
## What changes were proposed in this pull request?
This PR is to mark the parameter `rows` and `unsafeRow` of LocalTableScanExec transient. It can avoid serializing the unneeded objects.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18686 from gatorsmile/LocalTableScanExec.
## What changes were proposed in this pull request?
After SPARK-12661, I guess we officially dropped Python 2.6 support. It looks there are few places missing this notes.
I grepped "Python 2.6" and "python 2.6" and the results were below:
```
./core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala: // Unpickle array.array generated by Python 2.6
./docs/index.md:Note that support for Java 7, Python 2.6 and old Hadoop versions before 2.6.5 were removed as of Spark 2.2.0.
./docs/rdd-programming-guide.md:Spark {{site.SPARK_VERSION}} works with Python 2.6+ or Python 3.4+. It can use the standard CPython interpreter,
./docs/rdd-programming-guide.md:Note that support for Python 2.6 is deprecated as of Spark 2.0.0, and may be removed in Spark 2.2.0.
./python/pyspark/context.py: warnings.warn("Support for Python 2.6 is deprecated as of Spark 2.0.0")
./python/pyspark/ml/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/mllib/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/serializers.py: # On Python 2.6, we can't write bytearrays to streams, so we need to convert them
./python/pyspark/sql/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/streaming/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/tests.py: # NOTE: dict is used instead of collections.Counter for Python 2.6
./python/pyspark/tests.py: # NOTE: dict is used instead of collections.Counter for Python 2.6
```
This PR only proposes to change visible changes as below:
```
./docs/rdd-programming-guide.md:Spark {{site.SPARK_VERSION}} works with Python 2.6+ or Python 3.4+. It can use the standard CPython interpreter,
./docs/rdd-programming-guide.md:Note that support for Python 2.6 is deprecated as of Spark 2.0.0, and may be removed in Spark 2.2.0.
./python/pyspark/context.py: warnings.warn("Support for Python 2.6 is deprecated as of Spark 2.0.0")
```
This one is already correct:
```
./docs/index.md:Note that support for Java 7, Python 2.6 and old Hadoop versions before 2.6.5 were removed as of Spark 2.2.0.
```
## How was this patch tested?
```bash
grep -r "Python 2.6" .
grep -r "python 2.6" .
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18682 from HyukjinKwon/minor-python.26.
## What changes were proposed in this pull request?
This is the reopen of https://github.com/apache/spark/pull/14198, with merge conflicts resolved.
ueshin Could you please take a look at my code?
Fix bugs about types that result an array of null when creating DataFrame using python.
Python's array.array have richer type than python itself, e.g. we can have `array('f',[1,2,3])` and `array('d',[1,2,3])`. Codes in spark-sql and pyspark didn't take this into consideration which might cause a problem that you get an array of null values when you have `array('f')` in your rows.
A simple code to reproduce this bug is:
```
from pyspark import SparkContext
from pyspark.sql import SQLContext,Row,DataFrame
from array import array
sc = SparkContext()
sqlContext = SQLContext(sc)
row1 = Row(floatarray=array('f',[1,2,3]), doublearray=array('d',[1,2,3]))
rows = sc.parallelize([ row1 ])
df = sqlContext.createDataFrame(rows)
df.show()
```
which have output
```
+---------------+------------------+
| doublearray| floatarray|
+---------------+------------------+
|[1.0, 2.0, 3.0]|[null, null, null]|
+---------------+------------------+
```
## How was this patch tested?
New test case added
Author: Xiang Gao <qasdfgtyuiop@gmail.com>
Author: Gao, Xiang <qasdfgtyuiop@gmail.com>
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#18444 from zasdfgbnm/fix_array_infer.
## What changes were proposed in this pull request?
When using the MetadataLogFileIndex to read back a table, we don't respect the user provided schema as the proper column types. This can lead to issues when trying to read strings that look like dates that get truncated to DateType, or longs being truncated to IntegerType, just because a long value doesn't exist.
## How was this patch tested?
Unit tests and manual tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#18676 from brkyvz/stream-partitioning.
## What changes were proposed in this pull request?
Two invalid join types were mistakenly listed in the javadoc for joinWith, in the Dataset class. I presume these were copied from the javadoc of join, but since joinWith returns a Dataset\<Tuple2\>, left_semi and left_anti are invalid, as they only return values from one of the datasets, instead of from both
## How was this patch tested?
I ran the following code :
```
public static void main(String[] args) {
SparkSession spark = new SparkSession(new SparkContext("local[*]", "Test"));
Dataset<Row> one = spark.createDataFrame(Arrays.asList(new Bean(1), new Bean(2), new Bean(3), new Bean(4), new Bean(5)), Bean.class);
Dataset<Row> two = spark.createDataFrame(Arrays.asList(new Bean(4), new Bean(5), new Bean(6), new Bean(7), new Bean(8), new Bean(9)), Bean.class);
try {two.joinWith(one, one.col("x").equalTo(two.col("x")), "inner").show();} catch (Exception e) {e.printStackTrace();}
try {two.joinWith(one, one.col("x").equalTo(two.col("x")), "cross").show();} catch (Exception e) {e.printStackTrace();}
try {two.joinWith(one, one.col("x").equalTo(two.col("x")), "outer").show();} catch (Exception e) {e.printStackTrace();}
try {two.joinWith(one, one.col("x").equalTo(two.col("x")), "full").show();} catch (Exception e) {e.printStackTrace();}
try {two.joinWith(one, one.col("x").equalTo(two.col("x")), "full_outer").show();} catch (Exception e) {e.printStackTrace();}
try {two.joinWith(one, one.col("x").equalTo(two.col("x")), "left").show();} catch (Exception e) {e.printStackTrace();}
try {two.joinWith(one, one.col("x").equalTo(two.col("x")), "left_outer").show();} catch (Exception e) {e.printStackTrace();}
try {two.joinWith(one, one.col("x").equalTo(two.col("x")), "right").show();} catch (Exception e) {e.printStackTrace();}
try {two.joinWith(one, one.col("x").equalTo(two.col("x")), "right_outer").show();} catch (Exception e) {e.printStackTrace();}
try {two.joinWith(one, one.col("x").equalTo(two.col("x")), "left_semi").show();} catch (Exception e) {e.printStackTrace();}
try {two.joinWith(one, one.col("x").equalTo(two.col("x")), "left_anti").show();} catch (Exception e) {e.printStackTrace();}
}
```
which tests all the different join types, and the last two (left_semi and left_anti) threw exceptions. The same code using join instead of joinWith did fine. The Bean class was just a java bean with a single int field, x.
Author: Corey Woodfield <coreywoodfield@gmail.com>
Closes#18462 from coreywoodfield/master.
## What changes were proposed in this pull request?
Current behavior: in Mesos cluster mode, the driver failover_timeout is set to zero. If the driver temporarily loses connectivity with the Mesos master, the framework will be torn down and all executors killed.
Proposed change: make the failover_timeout configurable via a new option, spark.mesos.driver.failoverTimeout. The default value is still zero.
Note: with non-zero failover_timeout, an explicit teardown is needed in some cases. This is captured in https://issues.apache.org/jira/browse/SPARK-21458
## How was this patch tested?
Added a unit test to make sure the config option is set while creating the scheduler driver.
Ran an integration test with mesosphere/spark showing that with a non-zero failover_timeout the Spark job finishes after a driver is disconnected from the master.
Author: Susan X. Huynh <xhuynh@mesosphere.com>
Closes#18674 from susanxhuynh/sh-mesos-failover-timeout.
## What changes were proposed in this pull request?
JIRA Issue: https://issues.apache.org/jira/browse/SPARK-21446
options.asConnectionProperties can not have fetchsize，because fetchsize belongs to Spark-only options, and Spark-only options have been excluded in connection properities.
So change properties of beforeFetch from options.asConnectionProperties.asScala.toMap to options.asProperties.asScala.toMap
## How was this patch tested?
Author: DFFuture <albert.zhang23@gmail.com>
Closes#18665 from DFFuture/sparksql_pg.
## What changes were proposed in this pull request?
For configurations with external shuffle enabled, we have observed that if a very large no. of blocks are being fetched from a remote host, it puts the NM under extra pressure and can crash it. This change introduces a configuration `spark.reducer.maxBlocksInFlightPerAddress` , to limit the no. of map outputs being fetched from a given remote address. The changes applied here are applicable for both the scenarios - when external shuffle is enabled as well as disabled.
## How was this patch tested?
Ran the job with the default configuration which does not change the existing behavior and ran it with few configurations of lower values -10,20,50,100. The job ran fine and there is no change in the output. (I will update the metrics related to NM in some time.)
Author: Dhruve Ashar <dhruveashar@gmail.com>
Closes#18487 from dhruve/impr/SPARK-21243.
## What changes were proposed in this pull request?
Use of `ProcessingTime` class was deprecated in favor of `Trigger.ProcessingTime` in Spark 2.2. However interval uses to ProcessingTime causes deprecation warnings during compilation. This cannot be avoided entirely as even though it is deprecated as a public API, ProcessingTime instances are used internally in TriggerExecutor. This PR is to minimize the warning by removing its uses from tests as much as possible.
## How was this patch tested?
Existing tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#18678 from tdas/SPARK-21464.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/projects/SPARK/issues/SPARK-21441
This issue can be reproduced by the following example:
```
val spark = SparkSession
.builder()
.appName("smj-codegen")
.master("local")
.config("spark.sql.autoBroadcastJoinThreshold", "1")
.getOrCreate()
val df1 = spark.createDataFrame(Seq((1, 1), (2, 2), (3, 3))).toDF("key", "int")
val df2 = spark.createDataFrame(Seq((1, "1"), (2, "2"), (3, "3"))).toDF("key", "str")
val df = df1.join(df2, df1("key") === df2("key"))
.filter("int = 2 or reflect('java.lang.Integer', 'valueOf', str) = 1")
.select("int")
df.show()
```
To conclude, the issue happens when:
(1) SortMergeJoin condition contains CodegenFallback expressions.
(2) In PhysicalPlan tree, SortMergeJoin node is the child of root node, e.g., the Project in above example.
This patch fixes the logic in `CollapseCodegenStages` rule.
## How was this patch tested?
Unit test and manual verification in our cluster.
Author: donnyzone <wellfengzhu@gmail.com>
Closes#18656 from DonnyZone/Fix_SortMergeJoinExec.
## What changes were proposed in this pull request?
In `SlidingWindowFunctionFrame`, it is now adding all rows to the buffer for which the input row value is equal to or less than the output row upper bound, then drop all rows from the buffer for which the input row value is smaller than the output row lower bound.
This could result in the buffer is very big though the window is small.
For example:
```
select a, b, sum(a)
over (partition by b order by a range between 1000000 following and 1000001 following)
from table
```
We can refine the logic and just add the qualified rows into buffer.
## How was this patch tested?
Manual test:
Run sql
`select shop, shopInfo, district, sum(revenue) over(partition by district order by revenue range between 100 following and 200 following) from revenueList limit 10`
against a table with 4 columns(shop: String, shopInfo: String, district: String, revenue: Int). The biggest partition is around 2G bytes, containing 200k lines.
Configure the executor with 2G bytes memory.
With the change in this pr, it works find. Without this change, below exception will be thrown.
```
MemoryError: Java heap space
at org.apache.spark.sql.catalyst.expressions.UnsafeRow.copy(UnsafeRow.java:504)
at org.apache.spark.sql.catalyst.expressions.UnsafeRow.copy(UnsafeRow.java:62)
at org.apache.spark.sql.execution.window.SlidingWindowFunctionFrame.write(WindowFunctionFrame.scala:201)
at org.apache.spark.sql.execution.window.WindowExec$$anonfun$14$$anon$1.next(WindowExec.scala:365)
at org.apache.spark.sql.execution.window.WindowExec$$anonfun$14$$anon$1.next(WindowExec.scala:289)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$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:395)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:231)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:225)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:341)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
```
Author: jinxing <jinxing6042@126.com>
Closes#18634 from jinxing64/SPARK-21414.
## What changes were proposed in this pull request?
The most of BoundedPriorityQueue usages in ML/MLLIB are:
Get the value of BoundedPriorityQueue, then sort it.
For example, in Word2Vec: pq.toSeq.sortBy(-_._2)
in ALS, pq.toArray.sorted()
The test results show using pq.poll is much faster than sort the value.
It is good to add the poll function for BoundedPriorityQueue.
## How was this patch tested?
The existing UT
Author: Peng <peng.meng@intel.com>
Author: Peng Meng <peng.meng@intel.com>
Closes#18620 from mpjlu/add-poll.
## What changes were proposed in this pull request?
Add EmptyDirectoryWriteTask for empty task while writing files. Fix the empty result for parquet format by leaving the first partition for meta writing.
## How was this patch tested?
Add new test in `FileFormatWriterSuite `
Author: xuanyuanking <xyliyuanjian@gmail.com>
Closes#18654 from xuanyuanking/SPARK-21435.
## What changes were proposed in this pull request?
- Added batchId to StreamingQueryProgress.json as that was missing from the generated json.
- Also, removed recently added numPartitions from StatefulOperatorProgress as this value does not change through the query run, and there are other ways to find that.
## How was this patch tested?
Updated unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#18675 from tdas/SPARK-21462.
## What changes were proposed in this pull request?
When we list partitions from hive metastore with a partial partition spec, we are expecting exact matching according to the partition values. However, hive treats dot specially and match any single character for dot. We should do an extra filter to drop unexpected partitions.
## How was this patch tested?
new regression test.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18671 from cloud-fan/hive.
Instead of using the host's cpu count, use the number of cores allocated
for the Spark process when sizing the RPC dispatch thread pool. This avoids
creating large thread pools on large machines when the number of allocated
cores is small.
Tested by verifying number of threads with spark.executor.cores set
to 1 and 4; same thing for YARN AM.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18639 from vanzin/SPARK-21408.
## What changes were proposed in this pull request?
In the current `YARNHadoopDelegationTokenManager`, `FileSystem` to which to get tokens are created out of KDC logged UGI, using these `FileSystem` to get new tokens will lead to exception. The main thing is that Spark code trying to get new tokens from the FS created with token auth-ed UGI, but Hadoop can only grant new tokens in kerberized UGI. To fix this issue, we should lazily create these FileSystem within KDC logged UGI.
## How was this patch tested?
Manual verification in secure cluster.
CC vanzin mgummelt please help to review, thanks!
Author: jerryshao <sshao@hortonworks.com>
Closes#18633 from jerryshao/SPARK-21411.
## What changes were proposed in this pull request?
Address scapegoat warnings for:
- BigDecimal double constructor
- Catching NPE
- Finalizer without super
- List.size is O(n)
- Prefer Seq.empty
- Prefer Set.empty
- reverse.map instead of reverseMap
- Type shadowing
- Unnecessary if condition.
- Use .log1p
- Var could be val
In some instances like Seq.empty, I avoided making the change even where valid in test code to keep the scope of the change smaller. Those issues are concerned with performance and it won't matter for tests.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#18635 from srowen/Scapegoat1.
## What changes were proposed in this pull request?
Making those two classes will avoid Serialization issues like below:
```
Caused by: java.io.NotSerializableException: org.apache.spark.unsafe.types.UTF8String$IntWrapper
Serialization stack:
- object not serializable (class: org.apache.spark.unsafe.types.UTF8String$IntWrapper, value: org.apache.spark.unsafe.types.UTF8String$IntWrapper326450e)
- field (class: org.apache.spark.sql.catalyst.expressions.Cast$$anonfun$castToInt$1, name: result$2, type: class org.apache.spark.unsafe.types.UTF8String$IntWrapper)
- object (class org.apache.spark.sql.catalyst.expressions.Cast$$anonfun$castToInt$1, <function1>)
```
## How was this patch tested?
- [x] Manual testing
- [ ] Unit test
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#18660 from brkyvz/serializableutf8.
