## What changes were proposed in this pull request?
Added Launcher support for monitoring Mesos apps in Client mode. SPARK-11033 can handle the support for Mesos/Cluster mode since the Standalone/Cluster and Mesos/Cluster modes use the same code at client side.
## How was this patch tested?
I verified it manually by running launcher application, able to launch, stop and kill the mesos applications and also can invoke other launcher API's.
Author: Devaraj K <devaraj@apache.org>
Closes#19385 from devaraj-kavali/SPARK-11034.
## What changes were proposed in this pull request?
This PR changes `keyWithIndexToNumValues` to `keyWithIndexToValue`.
There will be directories on HDFS named with this `keyWithIndexToNumValues`. So if we ever want to fix this, let's fix it now.
## How was this patch tested?
existing unit test cases.
Author: Liwei Lin <lwlin7@gmail.com>
Closes#19435 from lw-lin/keyWithIndex.
## What changes were proposed in this pull request?
This is a minor folllowup of #19474 .
#19474 partially reverted #18064 but accidentally introduced a behavior change. `Command` extended `LogicalPlan` before #18064 , but #19474 made it extend `LeafNode`. This is an internal behavior change as now all `Command` subclasses can't define children, and they have to implement `computeStatistic` method.
This PR fixes this by making `Command` extend `LogicalPlan`
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19493 from cloud-fan/minor.
## What changes were proposed in this pull request?
This is an effort to reduce the difference between Hive and Spark. Spark supports case-sensitivity in columns. Especially, for Struct types, with `spark.sql.caseSensitive=true`, the following is supported.
```scala
scala> sql("select named_struct('a', 1, 'A', 2).a").show
+--------------------------+
|named_struct(a, 1, A, 2).a|
+--------------------------+
| 1|
+--------------------------+
scala> sql("select named_struct('a', 1, 'A', 2).A").show
+--------------------------+
|named_struct(a, 1, A, 2).A|
+--------------------------+
| 2|
+--------------------------+
```
And vice versa, with `spark.sql.caseSensitive=false`, the following is supported.
```scala
scala> sql("select named_struct('a', 1).A, named_struct('A', 1).a").show
+--------------------+--------------------+
|named_struct(a, 1).A|named_struct(A, 1).a|
+--------------------+--------------------+
| 1| 1|
+--------------------+--------------------+
```
However, types are considered different. For example, SET operations fail.
```scala
scala> sql("SELECT named_struct('a',1) union all (select named_struct('A',2))").show
org.apache.spark.sql.AnalysisException: Union can only be performed on tables with the compatible column types. struct<A:int> <> struct<a:int> at the first column of the second table;;
'Union
:- Project [named_struct(a, 1) AS named_struct(a, 1)#57]
: +- OneRowRelation$
+- Project [named_struct(A, 2) AS named_struct(A, 2)#58]
+- OneRowRelation$
```
This PR aims to support case-insensitive type equality. For example, in Set operation, the above operation succeed when `spark.sql.caseSensitive=false`.
```scala
scala> sql("SELECT named_struct('a',1) union all (select named_struct('A',2))").show
+------------------+
|named_struct(a, 1)|
+------------------+
| [1]|
| [2]|
+------------------+
```
## How was this patch tested?
Pass the Jenkins with a newly add test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#18460 from dongjoon-hyun/SPARK-21247.
## What changes were proposed in this pull request?
Before Hive 2.0, ORC File schema has invalid column names like `_col1` and `_col2`. This is a well-known limitation and there are several Apache Spark issues with `spark.sql.hive.convertMetastoreOrc=true`. This PR ignores ORC File schema and use Spark schema.
## How was this patch tested?
Pass the newly added test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19470 from dongjoon-hyun/SPARK-18355.
## What changes were proposed in this pull request?
For non-deterministic expressions, they should be considered as not contained in the [[ExpressionSet]].
This is consistent with how we define `semanticEquals` between two expressions.
Otherwise, combining expressions will remove non-deterministic expressions which should be reserved.
E.g.
Combine filters of
```scala
testRelation.where(Rand(0) > 0.1).where(Rand(0) > 0.1)
```
should result in
```scala
testRelation.where(Rand(0) > 0.1 && Rand(0) > 0.1)
```
## How was this patch tested?
Unit test
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#19475 from gengliangwang/non-deterministic-expressionSet.
## What changes were proposed in this pull request?
Due to optimizer removing some unnecessary aliases, the logical and physical plan may have different output attribute ids. FileFormatWriter should handle this when creating the physical sort node.
## How was this patch tested?
new regression test.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19483 from cloud-fan/bug2.
## What changes were proposed in this pull request?
The method `deterministic` is frequently called in optimizer.
Refactor `deterministic` as lazy value, in order to avoid redundant computations.
## How was this patch tested?
Simple benchmark test over TPC-DS queries, run time from query string to optimized plan(continuous 20 runs, and get the average of last 5 results):
Before changes: 12601 ms
After changes: 11993ms
This is 4.8% performance improvement.
Also run test with Unit test.
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#19478 from gengliangwang/deterministicAsLazyVal.
## What changes were proposed in this pull request?
`ParquetFileFormat` to relax its requirement of output committer class from `org.apache.parquet.hadoop.ParquetOutputCommitter` or subclass thereof (and so implicitly Hadoop `FileOutputCommitter`) to any committer implementing `org.apache.hadoop.mapreduce.OutputCommitter`
This enables output committers which don't write to the filesystem the way `FileOutputCommitter` does to save parquet data from a dataframe: at present you cannot do this.
Before a committer which isn't a subclass of `ParquetOutputCommitter`, it checks to see if the context has requested summary metadata by setting `parquet.enable.summary-metadata`. If true, and the committer class isn't a parquet committer, it raises a RuntimeException with an error message.
(It could downgrade, of course, but raising an exception makes it clear there won't be an summary. It also makes the behaviour testable.)
Note that `SQLConf` already states that any `OutputCommitter` can be used, but that typically it's a subclass of ParquetOutputCommitter. That's not currently true. This patch will make the code consistent with the docs, adding tests to verify,
## How was this patch tested?
The patch includes a test suite, `ParquetCommitterSuite`, with a new committer, `MarkingFileOutputCommitter` which extends `FileOutputCommitter` and writes a marker file in the destination directory. The presence of the marker file can be used to verify the new committer was used. The tests then try the combinations of Parquet committer summary/no-summary and marking committer summary/no-summary.
| committer | summary | outcome |
|-----------|---------|---------|
| parquet | true | success |
| parquet | false | success |
| marking | false | success with marker |
| marking | true | exception |
All tests are happy.
Author: Steve Loughran <stevel@hortonworks.com>
Closes#19448 from steveloughran/cloud/SPARK-22217-committer.
## What changes were proposed in this pull request?
Adding the code for setting 'aggregate time' metric to non-codegen path in HashAggregateExec and to ObjectHashAggregateExces.
## How was this patch tested?
Tested manually.
Author: Ala Luszczak <ala@databricks.com>
Closes#19473 from ala/fix-agg-time.
## What changes were proposed in this pull request?
As we discussed in https://github.com/apache/spark/pull/19136#discussion_r137023744 , we should push down operators to data source before planning, so that data source can report statistics more accurate.
This PR also includes some cleanup for the read path.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19424 from cloud-fan/follow.
## What changes were proposed in this pull request?
We only need request `bbos.size - unrollMemoryUsedByThisBlock` after unrolled the block.
## How was this patch tested?
Existing UT.
Author: Xianyang Liu <xianyang.liu@intel.com>
Closes#19316 from ConeyLiu/putIteratorAsBytes.
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/18064, we allowed `RunnableCommand` to have children in order to fix some UI issues. Then we made `InsertIntoXXX` commands take the input `query` as a child, when we do the actual writing, we just pass the physical plan to the writer(`FileFormatWriter.write`).
However this is problematic. In Spark SQL, optimizer and planner are allowed to change the schema names a little bit. e.g. `ColumnPruning` rule will remove no-op `Project`s, like `Project("A", Scan("a"))`, and thus change the output schema from "<A: int>" to `<a: int>`. When it comes to writing, especially for self-description data format like parquet, we may write the wrong schema to the file and cause null values at the read path.
Fortunately, in https://github.com/apache/spark/pull/18450 , we decided to allow nested execution and one query can map to multiple executions in the UI. This releases the major restriction in #18604 , and now we don't have to take the input `query` as child of `InsertIntoXXX` commands.
So the fix is simple, this PR partially revert #18064 and make `InsertIntoXXX` commands leaf nodes again.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19474 from cloud-fan/bug.
## What changes were proposed in this pull request?
Added documentation for loading csv files into Dataframes
## How was this patch tested?
/dev/run-tests
Author: Jorge Machado <jorge.w.machado@hotmail.com>
Closes#19429 from jomach/master.
## What changes were proposed in this pull request?
Implement StreamingRelation.computeStats to fix explain
## How was this patch tested?
- unit tests: `StreamingRelation.computeStats` and `StreamingExecutionRelation.computeStats`.
- regression tests: `explain join with a normal source` and `explain join with MemoryStream`.
Author: Shixiong Zhu <zsxwing@gmail.com>
Closes#19465 from zsxwing/SPARK-21988.
## What changes were proposed in this pull request?
Currently percentile_approx never returns the first element when percentile is in (relativeError, 1/N], where relativeError default 1/10000, and N is the total number of elements. But ideally, percentiles in [0, 1/N] should all return the first element as the answer.
For example, given input data 1 to 10, if a user queries 10% (or even less) percentile, it should return 1, because the first value 1 already reaches 10%. Currently it returns 2.
Based on the paper, targetError is not rounded up, and searching index should start from 0 instead of 1. By following the paper, we should be able to fix the cases mentioned above.
## How was this patch tested?
Added a new test case and fix existing test cases.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#19438 from wzhfy/improve_percentile_approx.
## What changes were proposed in this pull request?
Current `CodeGeneraor.splitExpressions` splits statements into methods if the total length of statements is more than 1024 characters. The length may include comments or empty line.
This PR excludes comment or empty line from the length to reduce the number of generated methods in a class, by using `CodeFormatter.stripExtraNewLinesAndComments()` method.
## How was this patch tested?
Existing tests
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#18966 from kiszk/SPARK-21751.
This change adds a new SQL config key that is equivalent to SparkContext's
"spark.extraListeners", allowing users to register QueryExecutionListener
instances through the Spark configuration system instead of having to
explicitly do it in code.
The code used by SparkContext to implement the feature was refactored into
a helper method in the Utils class, and SQL's ExecutionListenerManager was
modified to use it to initialize listener declared in the configuration.
Unit tests were added to verify all the new functionality.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19309 from vanzin/SPARK-19558.
## What changes were proposed in this pull request?
This PR adds an apply() function on df.groupby(). apply() takes a pandas udf that is a transformation on `pandas.DataFrame` -> `pandas.DataFrame`.
Static schema
-------------------
```
schema = df.schema
pandas_udf(schema)
def normalize(df):
df = df.assign(v1 = (df.v1 - df.v1.mean()) / df.v1.std()
return df
df.groupBy('id').apply(normalize)
```
Dynamic schema
-----------------------
**This use case is removed from the PR and we will discuss this as a follow up. See discussion https://github.com/apache/spark/pull/18732#pullrequestreview-66583248**
Another example to use pd.DataFrame dtypes as output schema of the udf:
```
sample_df = df.filter(df.id == 1).toPandas()
def foo(df):
ret = # Some transformation on the input pd.DataFrame
return ret
foo_udf = pandas_udf(foo, foo(sample_df).dtypes)
df.groupBy('id').apply(foo_udf)
```
In interactive use case, user usually have a sample pd.DataFrame to test function `foo` in their notebook. Having been able to use `foo(sample_df).dtypes` frees user from specifying the output schema of `foo`.
Design doc: https://github.com/icexelloss/spark/blob/pandas-udf-doc/docs/pyspark-pandas-udf.md
## How was this patch tested?
* Added GroupbyApplyTest
Author: Li Jin <ice.xelloss@gmail.com>
Author: Takuya UESHIN <ueshin@databricks.com>
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#18732 from icexelloss/groupby-apply-SPARK-20396.
## What changes were proposed in this pull request?
1. a test reproducing [SPARK-21907](https://issues.apache.org/jira/browse/SPARK-21907)
2. a fix for the root cause of the issue.
`org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter.spill` calls `org.apache.spark.util.collection.unsafe.sort.UnsafeInMemorySorter.reset` which may trigger another spill,
when this happens the `array` member is already de-allocated but still referenced by the code, this causes the nested spill to fail with an NPE in `org.apache.spark.memory.TaskMemoryManager.getPage`.
This patch introduces a reproduction in a test case and a fix, the fix simply sets the in-mem sorter's array member to an empty array before actually performing the allocation. This prevents the spilling code from 'touching' the de-allocated array.
## How was this patch tested?
introduced a new test case: `org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorterSuite#testOOMDuringSpill`.
Author: Eyal Farago <eyal@nrgene.com>
Closes#19181 from eyalfa/SPARK-21907__oom_during_spill.
## What changes were proposed in this pull request?
In a bare metal system with No DNS setup, spark may be configured with SPARK_LOCAL* for IP and host properties.
During a driver failover, in cluster deployment mode. SPARK_LOCAL* should be ignored while restarting on another node and should be picked up from target system's local environment.
## How was this patch tested?
Distributed deployment against a spark standalone cluster of 6 Workers. Tested by killing JVM's running driver and verified the restarted JVMs have right configurations on them.
Author: Prashant Sharma <prashsh1@in.ibm.com>
Author: Prashant Sharma <prashant@apache.org>
Closes#17357 from ScrapCodes/driver-failover-fix.
## What changes were proposed in this pull request?
The number of cores assigned to each executor is configurable. When this is not explicitly set, multiple executors from the same application may be launched on the same worker too.
## How was this patch tested?
N/A
Author: liuxian <liu.xian3@zte.com.cn>
Closes#18711 from 10110346/executorcores.
## What changes were proposed in this pull request?
Fix probabilisticClassificationModel corner case: normalization of all-zero raw predictions, throw IllegalArgumentException with description.
## How was this patch tested?
Test case added.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#19106 from WeichenXu123/SPARK-21770.
## What changes were proposed in this pull request?
This is a follow-up of #19384.
In the previous pr, only definitions of the config names were modified, but we also need to modify the names in runtime or tests specified as string literal.
## How was this patch tested?
Existing tests but modified the config names.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#19462 from ueshin/issues/SPARK-22159/fup1.
## What changes were proposed in this pull request?
We should not break the assumption that the length of the allocated byte array is word rounded:
https://github.com/apache/spark/blob/master/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java#L170
So we want to use `Integer.MAX_VALUE - 15` instead of `Integer.MAX_VALUE - 8` as the upper bound of an allocated byte array.
cc: srowen gatorsmile
## How was this patch tested?
Since the Spark unit test JVM has less than 1GB heap, here we run the test code as a submit job, so it can run on a JVM has 4GB memory.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Feng Liu <fengliu@databricks.com>
Closes#19460 from liufengdb/fix_array_max.
## What changes were proposed in this pull request?
In state store restore, for each row, put the saved state before the row in the iterator instead of after.
This fixes an issue where agg(last('attr)) will forever return the last value of 'attr from the first microbatch.
## How was this patch tested?
new unit test
Author: Jose Torres <jose@databricks.com>
Closes#19461 from joseph-torres/SPARK-22230.
## What changes were proposed in this pull request?
This updates the broadcast join code path to lazily decompress pages and
iterate through UnsafeRows to prevent all rows from being held in memory
while the broadcast table is being built.
## How was this patch tested?
Existing tests.
Author: Ryan Blue <blue@apache.org>
Closes#19394 from rdblue/broadcast-driver-memory.
Fix misleading error message when argument is expected.
## What changes were proposed in this pull request?
Change message to be accurate.
## How was this patch tested?
Messaging change, was tested manually.
Author: Pavel Sakun <pavel.sakun@gmail.com>
Closes#19457 from pavel-sakun/patch-1.
This patch fixes application re-attempts when running spark on yarn using the external shuffle service with security on. Currently executors will fail to launch on any application re-attempt when launched on a nodemanager that had an executor from the first attempt. The reason for this is because we aren't updating the secret key after the first application attempt. The fix here is to just remove the containskey check to see if it already exists. In this way, we always add it and make sure its the most recent secret. Similarly remove the check for containsKey on the remove since its just adding extra check that isn't really needed.
Note this worked before spark 2.2 because the check used to be contains (which was looking for the value) rather then containsKey, so that never matched and it was just always adding the new secret.
Patch was tested on a 10 node cluster as well as added the unit test.
The test ran was a wordcount where the output directory already existed. With the bug present the application attempt failed with max number of executor Failures which were all saslExceptions. With the fix present the application re-attempts fail with directory already exists or when you remove the directory between attempts the re-attemps succeed.
Author: Thomas Graves <tgraves@unharmedunarmed.corp.ne1.yahoo.com>
Closes#19450 from tgravescs/SPARK-22218.
## What changes were proposed in this pull request?
This PR disables console progress bar feature in non-shell environment by overriding the configuration.
## How was this patch tested?
Manual. Run the following examples with and without `spark.ui.showConsoleProgress` in order to see progress bar on master branch and this PR.
**Scala Shell**
```scala
spark.range(1000000000).map(_ + 1).count
```
**PySpark**
```python
spark.range(10000000).rdd.map(lambda x: len(x)).count()
```
**Spark Submit**
```python
from pyspark.sql import SparkSession
if __name__ == "__main__":
spark = SparkSession.builder.getOrCreate()
spark.range(2000000).rdd.map(lambda row: len(row)).count()
spark.stop()
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19061 from dongjoon-hyun/SPARK-21568.
This PR adds methods `recommendForUserSubset` and `recommendForItemSubset` to `ALSModel`. These allow recommending for a specified set of user / item ids rather than for every user / item (as in the `recommendForAllX` methods).
The subset methods take a `DataFrame` as input, containing ids in the column specified by the param `userCol` or `itemCol`. The model will generate recommendations for each _unique_ id in this input dataframe.
## How was this patch tested?
New unit tests in `ALSSuite` and Python doctests in `ALS`. Ran updated examples locally.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#18748 from MLnick/als-recommend-df.
## What changes were proposed in this pull request?
As the detail scenario described in [SPARK-22074](https://issues.apache.org/jira/browse/SPARK-22074), unnecessary resubmitted may cause stage hanging in currently release versions. This patch add a new var in TaskInfo to mark this task killed by other attempt or not.
## How was this patch tested?
Add a new UT `[SPARK-22074] Task killed by other attempt task should not be resubmitted` in TaskSetManagerSuite, this UT recreate the scenario in JIRA description, it failed without the changes in this PR and passed conversely.
Author: Yuanjian Li <xyliyuanjian@gmail.com>
Closes#19287 from xuanyuanking/SPARK-22074.
## What changes were proposed in this pull request?
Prior to this commit BlockId.hashCode and BlockId.equals were defined
in terms of BlockId.name. This allowed the subclasses to be concise and
enforced BlockId.name as a single unique identifier for a block. All
subclasses override BlockId.name with an expression involving an
allocation of StringBuilder and ultimatelly String. This is suboptimal
since it induced unnecessary GC pressure on the dirver, see
BlockManagerMasterEndpoint.
The commit removes the definition of hashCode and equals from the base
class. No other change is necessary since all subclasses are in fact
case classes and therefore have auto-generated hashCode and equals. No
change of behaviour is expected.
Sidenote: you might be wondering, why did the subclasses use the base
implementation and the auto-generated one? Apparently, this behaviour
is documented in the spec. See this SO answer for details
https://stackoverflow.com/a/44990210/262432.
## How was this patch tested?
BlockIdSuite
Author: Sergei Lebedev <s.lebedev@criteo.com>
Closes#19369 from superbobry/blockid-equals-hashcode.
## What changes were proposed in this pull request?
Current equation of learning rate is incorrect when `numIterations` > `1`.
This PR is based on [original C code](https://github.com/tmikolov/word2vec/blob/master/word2vec.c#L393).
cc: mengxr
## How was this patch tested?
manual tests
I modified [this example code](https://spark.apache.org/docs/2.1.1/mllib-feature-extraction.html#example).
### `numIteration=1`
#### Code
```scala
import org.apache.spark.mllib.feature.{Word2Vec, Word2VecModel}
val input = sc.textFile("data/mllib/sample_lda_data.txt").map(line => line.split(" ").toSeq)
val word2vec = new Word2Vec()
val model = word2vec.fit(input)
val synonyms = model.findSynonyms("1", 5)
for((synonym, cosineSimilarity) <- synonyms) {
println(s"$synonym $cosineSimilarity")
}
```
#### Result
```
2 0.175856813788414
0 0.10971353203058243
4 0.09818313270807266
3 0.012947646901011467
9 -0.09881238639354706
```
### `numIteration=5`
#### Code
```scala
import org.apache.spark.mllib.feature.{Word2Vec, Word2VecModel}
val input = sc.textFile("data/mllib/sample_lda_data.txt").map(line => line.split(" ").toSeq)
val word2vec = new Word2Vec()
word2vec.setNumIterations(5)
val model = word2vec.fit(input)
val synonyms = model.findSynonyms("1", 5)
for((synonym, cosineSimilarity) <- synonyms) {
println(s"$synonym $cosineSimilarity")
}
```
#### Result
```
0 0.9898583889007568
2 0.9808019399642944
4 0.9794934391975403
3 0.9506527781486511
9 -0.9065656661987305
```
Author: Kento NOZAWA <k_nzw@klis.tsukuba.ac.jp>
Closes#19372 from nzw0301/master.
## What changes were proposed in this pull request?
`monotonically_increasing_id` doesn't work in Structured Streaming. We should throw an exception if a streaming query uses it.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19336 from viirya/SPARK-21947.
## What changes were proposed in this pull request?
In this PR we make a few changes to the list hive partitions code, to make the code more extensible.
The following changes are made:
1. In `HiveClientImpl.getPartitions()`, call `client.getPartitions` instead of `shim.getAllPartitions` when `spec` is empty;
2. In `HiveTableScanExec`, previously we always call `listPartitionsByFilter` if the config `metastorePartitionPruning` is enabled, but actually, we'd better call `listPartitions` if `partitionPruningPred` is empty;
3. We should use sessionCatalog instead of SharedState.externalCatalog in `HiveTableScanExec`.
## How was this patch tested?
Tested by existing test cases since this is code refactor, no regression or behavior change is expected.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#19444 from jiangxb1987/hivePartitions.
## What changes were proposed in this pull request?
All the windows command scripts can not handle quotes in parameter.
Run a windows command shell with parameter which has quotes can reproduce the bug:
```
C:\Users\meng\software\spark-2.2.0-bin-hadoop2.7> bin\spark-shell --driver-java-options " -Dfile.encoding=utf-8 "
'C:\Users\meng\software\spark-2.2.0-bin-hadoop2.7\bin\spark-shell2.cmd" --driver-java-options "' is not recognized as an internal or external command,
operable program or batch file.
```
Windows recognize "--driver-java-options" as part of the command.
All the Windows command script has the following code have the bug.
```
cmd /V /E /C "<other command>" %*
```
We should quote command and parameters like
```
cmd /V /E /C ""<other command>" %*"
```
## How was this patch tested?
Test manually on Windows 10 and Windows 7
We can verify it by the following demo:
```
C:\Users\meng\program\demo>cat a.cmd
echo off
cmd /V /E /C "b.cmd" %*
C:\Users\meng\program\demo>cat b.cmd
echo off
echo %*
C:\Users\meng\program\demo>cat c.cmd
echo off
cmd /V /E /C ""b.cmd" %*"
C:\Users\meng\program\demo>a.cmd "123"
'b.cmd" "123' is not recognized as an internal or external command,
operable program or batch file.
C:\Users\meng\program\demo>c.cmd "123"
"123"
```
With the spark-shell.cmd example, change it to the following code will make the command execute succeed.
```
cmd /V /E /C ""%~dp0spark-shell2.cmd" %*"
```
```
C:\Users\meng\software\spark-2.2.0-bin-hadoop2.7> bin\spark-shell --driver-java-options " -Dfile.encoding=utf-8 "
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
...
```
Author: minixalpha <xkzalpha@gmail.com>
Closes#19090 from minixalpha/master.
## What changes were proposed in this pull request?
Move flume behind a profile, take 2. See https://github.com/apache/spark/pull/19365 for most of the back-story.
This change should fix the problem by removing the examples module dependency and moving Flume examples to the module itself. It also adds deprecation messages, per a discussion on dev about deprecating for 2.3.0.
## How was this patch tested?
Existing tests, which still enable flume integration.
Author: Sean Owen <sowen@cloudera.com>
Closes#19412 from srowen/SPARK-22142.2.
## What changes were proposed in this pull request?
When exceeding `spark.sql.codegen.hugeMethodLimit`, the runtime fallbacks to the Volcano iterator solution. This could cause an infinite loop when `FileSourceScanExec` can use the columnar batch to read the data. This PR is to fix the issue.
## How was this patch tested?
Added a test
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19440 from gatorsmile/testt.
## What changes were proposed in this pull request?
Looks like `FlatMapGroupsInRExec.requiredChildDistribution` didn't consider empty grouping attributes. It should be a problem when running `EnsureRequirements` and `gapply` in R can't work on empty grouping columns.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19436 from viirya/fix-flatmapinr-distribution.
## What changes were proposed in this pull request?
Currently, the group state of user-defined-type is encoded as top-level columns in the UnsafeRows stores in the state store. The timeout timestamp is also saved as (when needed) as the last top-level column. Since the group state is serialized to top-level columns, you cannot save "null" as a value of state (setting null in all the top-level columns is not equivalent). So we don't let the user set the timeout without initializing the state for a key. Based on user experience, this leads to confusion.
This PR is to change the row format such that the state is saved as nested columns. This would allow the state to be set to null, and avoid these confusing corner cases.
## How was this patch tested?
Refactored tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#19416 from tdas/SPARK-22187.
## What changes were proposed in this pull request?
By definition the table name in Spark can be something like `123x`, `25a`, etc., with exceptions for literals like `12L`, `23BD`, etc. However, Spark SQL has a special byte length literal, which stops users to use digits followed by `b`, `k`, `m`, `g` as identifiers.
byte length literal is not a standard sql literal and is only used in the `tableSample` parser rule. This PR move the parsing of byte length literal from lexer to parser, so that users can use it as identifiers.
## How was this patch tested?
regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19392 from cloud-fan/parser-bug.
## What changes were proposed in this pull request?
This pr added code to check actual bytecode size when compiling generated code. In #18810, we added code to give up code compilation and use interpreter execution in `SparkPlan` if the line number of generated functions goes over `maxLinesPerFunction`. But, we already have code to collect metrics for compiled bytecode size in `CodeGenerator` object. So,we could easily reuse the code for this purpose.
## How was this patch tested?
Added tests in `WholeStageCodegenSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#19083 from maropu/SPARK-21871.
## What changes were proposed in this pull request?
This PR abstracts data compressed by `CompressibleColumnAccessor` using `ColumnVector` in batch method. When `ColumnAccessor.decompress` is called, `ColumnVector` will have uncompressed data. This batch decompress does not use `InternalRow` to reduce the number of memory accesses.
As first step of this implementation, this JIRA supports primitive data types. Another PR will support array and other data types.
This implementation decompress data in batch into uncompressed column batch, as rxin suggested at [here](https://github.com/apache/spark/pull/18468#issuecomment-316914076). Another implementation uses adapter approach [as cloud-fan suggested](https://github.com/apache/spark/pull/18468).
## How was this patch tested?
Added test suites
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#18704 from kiszk/SPARK-20783a.
## What changes were proposed in this pull request?
[SPARK-22193][SQL] Minor typo fix in SortMergeJoinExec. Nothing major, but it bothered me going into.Hence fixing
## How was this patch tested?
existing tests
Author: Rekha Joshi <rekhajoshm@gmail.com>
Author: rjoshi2 <rekhajoshm@gmail.com>
Closes#19422 from rekhajoshm/SPARK-22193.
## What changes were proposed in this pull request?
Allow one-sided outer joins between two streams when a watermark is defined.
## How was this patch tested?
new unit tests
Author: Jose Torres <jose@databricks.com>
Closes#19327 from joseph-torres/outerjoin.
