In Spark on YARN, when configuring "spark.yarn.jars" with local jars (jars started with "local" scheme), we will get inaccurate classpath for AM and containers. This is because we don't remove "local" scheme when concatenating classpath. It is OK to run because classpath is separated with ":" and java treat "local" as a separate jar. But we could improve it to remove the scheme.
Updated `ClientSuite` to check "local" is not in the classpath.
cc jerryshao
Author: Li Yichao <lyc@zhihu.com>
Author: Li Yichao <liyichao.good@gmail.com>
Closes#18129 from liyichao/SPARK-20365.
### What changes were proposed in this pull request?
Before this PR, Subquery reuse does not work. Below are three issues:
- Subquery reuse does not work.
- It is sharing the same `SQLConf` (`spark.sql.exchange.reuse`) with the one for Exchange Reuse.
- No test case covers the rule Subquery reuse.
This PR is to fix the above three issues.
- Ignored the physical operator `SubqueryExec` when comparing two plans.
- Added a dedicated conf `spark.sql.subqueries.reuse` for controlling Subquery Reuse
- Added a test case for verifying the behavior
### How was this patch tested?
N/A
Author: Xiao Li <gatorsmile@gmail.com>
Closes#18169 from gatorsmile/subqueryReuse.
## What changes were proposed in this pull request?
- ~~I added the method `toBlockMatrixDense` to the IndexedRowMatrix class. The current implementation of `toBlockMatrix` is insufficient for users with relatively dense IndexedRowMatrix objects, since it assumes sparsity.~~
EDIT: Ended up deciding that there should be just a single `toBlockMatrix` method, which creates a BlockMatrix whose blocks may be dense or sparse depending on the sparsity of the rows. This method will work better on any current use case of `toBlockMatrix` and doesn't go through `CoordinateMatrix` like the old method.
## How was this patch tested?
~~I used the same tests already written for `toBlockMatrix()` to test this method. I also added a new additional unit test for an edge case that was not adequately tested by current test suite.~~
I ran the original `IndexedRowMatrix` tests, plus wrote more to better handle edge cases ignored by original tests.
Author: John Compitello <johnc@broadinstitute.org>
Closes#17459 from johnc1231/johnc-fix-ir-to-block.
## What changes were proposed in this pull request?
Add build-int SQL function - UUID.
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18136 from wangyum/SPARK-20910.
## What changes were proposed in this pull request?
Hadoop FileSystem's statistics in based on thread local variables, this is ok if the RDD computation chain is running in the same thread. But if child RDD creates another thread to consume the iterator got from Hadoop RDDs, the bytesRead computation will be error, because now the iterator's `next()` and `close()` may run in different threads. This could be happened when using PySpark with PythonRDD.
So here building a map to track the `bytesRead` for different thread and add them together. This method will be used in three RDDs, `HadoopRDD`, `NewHadoopRDD` and `FileScanRDD`. I assume `FileScanRDD` cannot be called directly, so I only fixed `HadoopRDD` and `NewHadoopRDD`.
## How was this patch tested?
Unit test and local cluster verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#17617 from jerryshao/SPARK-20244.
## What changes were proposed in this pull request?
`IllegalAccessError` is a fatal error (a subclass of LinkageError) and its meaning is `Thrown if an application attempts to access or modify a field, or to call a method that it does not have access to`. Throwing a fatal error for AccumulatorV2 is not necessary and is pretty bad because it usually will just kill executors or SparkContext ([SPARK-20666](https://issues.apache.org/jira/browse/SPARK-20666) is an example of killing SparkContext due to `IllegalAccessError`). I think the correct type of exception in AccumulatorV2 should be `IllegalStateException`.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#18168 from zsxwing/SPARK-20940.
## What changes were proposed in this pull request?
When the user runs a Structured Streaming query in a cluster, if the driver uses the local file system, StateStore running in executors will throw a file-not-found exception. However, the current error is not obvious.
This PR makes StreamExecution resolve the path in driver and uses the full path including the scheme part (such as `hdfs:/`, `file:/`) in StateStore.
Then if the above error happens, StateStore will throw an error with this full path which starts with `file:/`, and it makes this error obvious: the checkpoint location is on the local file system.
One potential minor issue is that the user cannot use different default file system settings in driver and executors (e.g., use a public HDFS address in driver and a private HDFS address in executors) after this change. However, since the batch query also has this issue (See 4bb6a53ebd/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala (L402)), it doesn't make things worse.
## How was this patch tested?
The new added test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#18149 from zsxwing/SPARK-20894.
### What changes were proposed in this pull request?
This PR does the following tasks:
- Added since
- Added the Python API
- Added test cases
### How was this patch tested?
Added test cases to both Scala and Python
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18147 from gatorsmile/createOrReplaceGlobalTempView.
## What changes were proposed in this pull request?
Explicitly handle the FetchFailedException in FileFormatWriter, so it does not get wrapped.
Note that this is no longer strictly necessary after SPARK-19276, but it improves error messages and also will help avoid others stumbling across this in the future.
## How was this patch tested?
Existing unit tests.
Closes https://github.com/apache/spark/pull/17893
Author: Liu Shaohui <liushaohui@xiaomi.com>
Closes#18145 from squito/SPARK-20633.
## What changes were proposed in this pull request?
ShuffleId is determined before job submitted. But it's hard to predict stageId by shuffleId.
Stage is created in DAGScheduler(
https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala#L381), but the order is n
ot determined in `HashSet`.
I added a log(println(s"Creating ShufflMapStage-$id on shuffle-${shuffleDep.shuffleId}")) after (https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala#L331), when testing BasicSchedulerIntegrationSuite:"multi-stage job". It will print:
Creating ShufflMapStage-0 on shuffle-0
Creating ShufflMapStage-1 on shuffle-2
Creating ShufflMapStage-2 on shuffle-1
Creating ShufflMapStage-3 on shuffle-3
or
Creating ShufflMapStage-0 on shuffle-1
Creating ShufflMapStage-1 on shuffle-3
Creating ShufflMapStage-2 on shuffle-0
Creating ShufflMapStage-3 on shuffle-2
It might be better to avoid generating the MapStatus by stageId.
Author: jinxing <jinxing6042@126.com>
Closes#17603 from jinxing64/SPARK-20288.
## What changes were proposed in this pull request?
Revert the handling of negative values in ALS with implicit feedback, so that the confidence is the absolute value of the rating and the preference is 0 for negative ratings. This was the original behavior.
## How was this patch tested?
This patch was tested with the existing unit tests and an added unit test to ensure that negative ratings are not ignored.
mengxr
Author: David Eis <deis@bloomberg.net>
Closes#18022 from davideis/bugfix/negative-rating.
## What changes were proposed in this pull request?
Minor changes to scaladoc
## How was this patch tested?
Local build
Author: Jacek Laskowski <jacek@japila.pl>
Closes#18074 from jaceklaskowski/scaladoc-fixes.
## What changes were proposed in this pull request?
to investigate how long they run
## How was this patch tested?
Jenkins, AppVeyor
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#18104 from felixcheung/rtimetest.
## What changes were proposed in this pull request?
Current HistoryServer will display completed date of in-progress application as `1969-12-31 23:59:59`, which is not so meaningful. Instead of unnecessarily showing this incorrect completed date, here propose to make this column invisible for in-progress applications.
The purpose of only making this column invisible rather than deleting this field is that: this data is fetched through REST API, and in the REST API the format is like below shows, in which `endTime` matches `endTimeEpoch`. So instead of changing REST API to break backward compatibility, here choosing a simple solution to only make this column invisible.
```
[ {
"id" : "local-1491805439678",
"name" : "Spark shell",
"attempts" : [ {
"startTime" : "2017-04-10T06:23:57.574GMT",
"endTime" : "1969-12-31T23:59:59.999GMT",
"lastUpdated" : "2017-04-10T06:23:57.574GMT",
"duration" : 0,
"sparkUser" : "",
"completed" : false,
"startTimeEpoch" : 1491805437574,
"endTimeEpoch" : -1,
"lastUpdatedEpoch" : 1491805437574
} ]
} ]%
```
Here is UI before changed:
<img width="1317" alt="screen shot 2017-04-10 at 3 45 57 pm" src="https://cloud.githubusercontent.com/assets/850797/24851938/17d46cc0-1e08-11e7-84c7-90120e171b41.png">
And after:
<img width="1281" alt="screen shot 2017-04-10 at 4 02 35 pm" src="https://cloud.githubusercontent.com/assets/850797/24851945/1fe9da58-1e08-11e7-8d0d-9262324f9074.png">
## How was this patch tested?
Manual verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#17588 from jerryshao/SPARK-20275.
## What changes were proposed in this pull request?
Currently the `DataFrameWriter` operations have several problems:
1. non-file-format data source writing action doesn't show up in the SQL tab in Spark UI
2. file-format data source writing action shows a scan node in the SQL tab, without saying anything about writing. (streaming also have this issue, but not fixed in this PR)
3. Spark SQL CLI actions don't show up in the SQL tab.
This PR fixes all of them, by refactoring the `ExecuteCommandExec` to make it have children.
close https://github.com/apache/spark/pull/17540
## How was this patch tested?
existing tests.
Also test the UI manually. For a simple command: `Seq(1 -> "a").toDF("i", "j").write.parquet("/tmp/qwe")`
before this PR:
<img width="266" alt="qq20170523-035840 2x" src="https://cloud.githubusercontent.com/assets/3182036/26326050/24e18ba2-3f6c-11e7-8817-6dd275bf6ac5.png">
after this PR:
<img width="287" alt="qq20170523-035708 2x" src="https://cloud.githubusercontent.com/assets/3182036/26326054/2ad7f460-3f6c-11e7-8053-d68325beb28f.png">
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18064 from cloud-fan/execution.
## What changes were proposed in this pull request?
A bunch of changes to the StateStore APIs and implementation.
Current state store API has a bunch of problems that causes too many transient objects causing memory pressure.
- `StateStore.get(): Option` forces creation of Some/None objects for every get. Changed this to return the row or null.
- `StateStore.iterator(): (UnsafeRow, UnsafeRow)` forces creation of new tuple for each record returned. Changed this to return a UnsafeRowTuple which can be reused across records.
- `StateStore.updates()` requires the implementation to keep track of updates, while this is used minimally (only by Append mode in streaming aggregations). Removed updates() and updated StateStoreSaveExec accordingly.
- `StateStore.filter(condition)` and `StateStore.remove(condition)` has been merge into a single API `getRange(start, end)` which allows a state store to do optimized range queries (i.e. avoid full scans). Stateful operators have been updated accordingly.
- Removed a lot of unnecessary row copies Each operator copied rows before calling StateStore.put() even if the implementation does not require it to be copied. It is left up to the implementation on whether to copy the row or not.
Additionally,
- Added a name to the StateStoreId so that each operator+partition can use multiple state stores (different names)
- Added a configuration that allows the user to specify which implementation to use.
- Added new metrics to understand the time taken to update keys, remove keys and commit all changes to the state store. These metrics will be visible on the plan diagram in the SQL tab of the UI.
- Refactored unit tests such that they can be reused to test any implementation of StateStore.
## How was this patch tested?
Old and new unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#18107 from tdas/SPARK-20376.
### What changes were proposed in this pull request?
We are unable to call the function registered in the not-current database.
```Scala
sql("CREATE DATABASE dAtABaSe1")
sql(s"CREATE FUNCTION dAtABaSe1.test_avg AS '${classOf[GenericUDAFAverage].getName}'")
sql("SELECT dAtABaSe1.test_avg(1)")
```
The above code returns an error:
```
Undefined function: 'dAtABaSe1.test_avg'. This function is neither a registered temporary function nor a permanent function registered in the database 'default'.; line 1 pos 7
```
This PR is to fix the above issue.
### How was this patch tested?
Added test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#18146 from gatorsmile/qualifiedFunction.
## What changes were proposed in this pull request?
Fix test
"don't submit stage until its dependencies map outputs are registered (SPARK-5259)" ,
"run trivial shuffle with out-of-band executor failure and retry",
"reduce tasks should be placed locally with map output"
in DAGSchedulerSuite.
Author: jinxing <jinxing6042@126.com>
Closes#17634 from jinxing64/SPARK-20333.
## What changes were proposed in this pull request?
Added the createOrReplaceGlobalTempView method for dataset
Author: Arman <arman.yazdani.10@gmail.com>
Closes#16598 from arman1371/patch-1.
## What changes were proposed in this pull request?
PySpark supports stringIndexerOrderType in RFormula as in #17967.
## How was this patch tested?
docstring test
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes#18122 from actuaryzhang/PythonRFormula.
## What changes were proposed in this pull request?
We changed the parser to reject unaliased subqueries in the FROM clause in SPARK-20690. However, the error message that we now give isn't very helpful:
scala> sql("""SELECT x FROM (SELECT 1 AS x)""")
org.apache.spark.sql.catalyst.parser.ParseException:
mismatched input 'FROM' expecting {<EOF>, 'WHERE', 'GROUP', 'ORDER', 'HAVING', 'LIMIT', 'LATERAL', 'WINDOW', 'UNION', 'EXCEPT', 'MINUS', 'INTERSECT', 'SORT', 'CLUSTER', 'DISTRIBUTE'}(line 1, pos 9)
We should modify the parser to throw a more clear error for such queries:
scala> sql("""SELECT x FROM (SELECT 1 AS x)""")
org.apache.spark.sql.catalyst.parser.ParseException:
The unaliased subqueries in the FROM clause are not supported.(line 1, pos 14)
## How was this patch tested?
Modified existing tests to reflect this change.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18141 from viirya/SPARK-20916.
## What changes were proposed in this pull request?
Fix some indent issues.
## How was this patch tested?
existing tests.
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18133 from wangyum/IndentIssues.
## What changes were proposed in this pull request?
Add build-int SQL function - DAYOFWEEK
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18134 from wangyum/SPARK-20909.
## What changes were proposed in this pull request?
In summary, cost of recreating a KafkaProducer for writing every batch is high as it starts a lot threads and make connections and then closes them. A KafkaProducer instance is promised to be thread safe in Kafka docs. Reuse of KafkaProducer instance while writing via multiple threads is encouraged.
Furthermore, I have performance improvement of 10x in latency, with this patch.
### These are times that addBatch took in ms. Without applying this patch
### These are times that addBatch took in ms. After applying this patch
## How was this patch tested?
Running distributed benchmarks comparing runs with this patch and without it.
Added relevant unit tests.
Author: Prashant Sharma <prashsh1@in.ibm.com>
Closes#17308 from ScrapCodes/cached-kafka-producer.
## What changes were proposed in this pull request?
Supress console output by using `testQuietly` in test suites
## How was this patch tested?
Tested by `"SPARK-19372: Filter can be executed w/o generated code due to JVM code size limit"` in `DataFrameSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#18135 from kiszk/SPARK-20907.
## What changes were proposed in this pull request?
This PR adds built-in SQL function `(REPLACE(<string_expression>, <search_string> [, <replacement_string>])`
`REPLACE()` return that string that is replaced all occurrences with given string.
## How was this patch tested?
added new test suites
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#18047 from kiszk/SPARK-20750.
## What changes were proposed in this pull request?
See class doc of `ConstantPropagation` for the approach used.
## How was this patch tested?
- Added unit tests
Author: Tejas Patil <tejasp@fb.com>
Closes#17993 from tejasapatil/SPARK-20758_const_propagation.
## What changes were proposed in this pull request?
Now, we have two sources of statistics, i.e. Spark's stats and Hive's stats. Spark's stats is generated by running "analyze" command in Spark. Once it's available, we respect this stats over Hive's.
This pr is to clearly document in related code the mechanism to choose between these two sources of stats.
## How was this patch tested?
Not related.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#18105 from wzhfy/cboSwitchStats.
## What changes were proposed in this pull request?
This pr added parsing rules to support table column aliases in FROM clause.
## How was this patch tested?
Added tests in `PlanParserSuite`, `SQLQueryTestSuite`, and `PlanParserSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18079 from maropu/SPARK-20841.
### What changes were proposed in this pull request?
In Cache manager, the plan matching should ignore Hint.
```Scala
val df1 = spark.range(10).join(broadcast(spark.range(10)))
df1.cache()
spark.range(10).join(spark.range(10)).explain()
```
The output plan of the above query shows that the second query is not using the cached data of the first query.
```
BroadcastNestedLoopJoin BuildRight, Inner
:- *Range (0, 10, step=1, splits=2)
+- BroadcastExchange IdentityBroadcastMode
+- *Range (0, 10, step=1, splits=2)
```
After the fix, the plan becomes
```
InMemoryTableScan [id#20L, id#23L]
+- InMemoryRelation [id#20L, id#23L], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
+- BroadcastNestedLoopJoin BuildRight, Inner
:- *Range (0, 10, step=1, splits=2)
+- BroadcastExchange IdentityBroadcastMode
+- *Range (0, 10, step=1, splits=2)
```
### How was this patch tested?
Added a test.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#18131 from gatorsmile/HintCache.
## What changes were proposed in this pull request?
spark-sql>SELECT ceil(cast(12345.1233 as float));
spark-sql>12345
For this case, the result we expected is `12346`
spark-sql>SELECT floor(cast(-12345.1233 as float));
spark-sql>-12345
For this case, the result we expected is `-12346`
Because in `Ceil` or `Floor`, `inputTypes` has no FloatType, so it is converted to LongType.
## How was this patch tested?
After the modification:
spark-sql>SELECT ceil(cast(12345.1233 as float));
spark-sql>12346
spark-sql>SELECT floor(cast(-12345.1233 as float));
spark-sql>-12346
Author: liuxian <liu.xian3@zte.com.cn>
Closes#18103 from 10110346/wip-lx-0525-1.
## What changes were proposed in this pull request?
The failed test case is, we have a `SortMergeJoinExec` for a self-join, which means we have a `ReusedExchange` node in the query plan. It works fine without caching, but throws an exception in `SortMergeJoinExec.outputPartitioning` if we cache it.
The root cause is, `ReusedExchange` doesn't propagate the output partitioning from its child, so in `SortMergeJoinExec.outputPartitioning` we create `PartitioningCollection` with a hash partitioning and an unknown partitioning, and fail.
This bug is mostly fine, because inserting the `ReusedExchange` is the last step to prepare the physical plan, we won't call `SortMergeJoinExec.outputPartitioning` anymore after this.
However, if the dataframe is cached, the physical plan of it becomes `InMemoryTableScanExec`, which contains another physical plan representing the cached query, and it has gone through the entire planning phase and may have `ReusedExchange`. Then the planner call `InMemoryTableScanExec.outputPartitioning`, which then calls `SortMergeJoinExec.outputPartitioning` and trigger this bug.
## How was this patch tested?
a new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18121 from cloud-fan/bug.
[https://issues.apache.org/jira/browse/SPARK-20875](https://issues.apache.org/jira/browse/SPARK-20875)
When the "deleteRecursively" method is invoked,spark doesn't print any log if the path was deleted.For example,spark only print "Removing directory" when the worker began cleaning spark.work.dir,but didn't print any log about "the path has been delete".So, I can't judge whether the path was deleted form the worker's logfile,If there is any accidents about Linux.
Author: liuzhaokun <liu.zhaokun@zte.com.cn>
Closes#18102 from liu-zhaokun/master_log.
## What changes were proposed in this pull request?
Add a `worker` configuration to set how long to wait before forcibly killing driver.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#18126 from zsxwing/SPARK-20843.
## What changes were proposed in this pull request?
Add built-in SQL function `CH[A]R`:
For `CHR(bigint|double n)`, returns the ASCII character having the binary equivalent to `n`. If n is larger than 256 the result is equivalent to CHR(n % 256)
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18019 from wangyum/SPARK-20748.
## What changes were proposed in this pull request?
This PR includes some minor improvement for the comments and tests in https://github.com/apache/spark/pull/16989
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18117 from cloud-fan/follow.
## What changes were proposed in this pull request?
This PR makes spark-submit script download remote files to local file system for local/standalone client mode.
## How was this patch tested?
- Unit tests
- Manual tests by adding s3a jar and testing against file on s3.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Yu Peng <loneknightpy@gmail.com>
Closes#18078 from loneknightpy/download-jar-in-spark-submit.
## What changes were proposed in this pull request?
Upon encountering an invalid columntype, the column type object is printed, rather than the type.
This change improves this by outputting its name.
## How was this patch tested?
Added a simple unit test to verify the contents of the raised exception
Author: setjet <rubenljanssen@gmail.com>
Closes#18097 from setjet/spark-20873.
## What changes were proposed in this pull request?
- Add Scala, Python and Java examples for `partitionBy`, `sortBy` and `bucketBy`.
- Add _Bucketing, Sorting and Partitioning_ section to SQL Programming Guide
- Remove bucketing from Unsupported Hive Functionalities.
## How was this patch tested?
Manual tests, docs build.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17938 from zero323/DOCS-BUCKETING-AND-PARTITIONING.
## What changes were proposed in this pull request?
When the individual partition size in a spill is small, mergeSpillsWithTransferTo method does many small disk ios which is really inefficient. One way to improve the performance will be to use mergeSpillsWithFileStream method by turning off transfer to and using buffered file read/write to improve the io throughput.
However, the current implementation of mergeSpillsWithFileStream does not do a buffer read/write of the files and in addition to that it unnecessarily flushes the output files for each partitions.
## How was this patch tested?
Tested this change by running a job on the cluster and the map stage run time was reduced by around 20%.
Author: Sital Kedia <skedia@fb.com>
Closes#17343 from sitalkedia/upstream_mergeSpillsWithFileStream.
Now that Structured Streaming has been out for several Spark release and has large production use cases, the `Experimental` label is no longer appropriate. I've left `InterfaceStability.Evolving` however, as I think we may make a few changes to the pluggable Source & Sink API in Spark 2.3.
Author: Michael Armbrust <michael@databricks.com>
Closes#18065 from marmbrus/streamingGA.
## What changes were proposed in this pull request?
In my test, the submitted app running with out an error when the --total-executor-cores less than 0
and given the warnings:
"2017-05-22 17:19:36,319 WARN org.apache.spark.scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources";
It should exit directly when the --total-executor-cores parameter is setted less than 0 when submit a application
(Please fill in changes proposed in this fix)
## How was this patch tested?
Run the ut tests
(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: 10129659 <chen.yanshan@zte.com.cn>
Closes#18060 from eatoncys/totalcores.
## What changes were proposed in this pull request?
`ConfigBuilder` builds `ConfigEntry` which can only read value with one key, if we wanna change the config name but still keep the old one, it's hard to do.
This PR introduce `ConfigBuilder.withAlternative`, to support reading config value with alternative keys. And also rename `spark.scheduler.listenerbus.eventqueue.size` to `spark.scheduler.listenerbus.eventqueue.capacity` with this feature, according to https://github.com/apache/spark/pull/14269#discussion_r118432313
## How was this patch tested?
a new test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18110 from cloud-fan/config.
## What changes were proposed in this pull request?
Include documentation of the fact that the updateFunc is sometimes called with no new values. This is documented in the main documentation here: https://spark.apache.org/docs/latest/streaming-programming-guide.html#updatestatebykey-operation however from the docs included with the code it is not clear that this is the case.
## How was this patch tested?
PR only changes comments. Confirmed code still builds.
Author: Wil Selwood <wil.selwood@sa.catapult.org.uk>
Closes#18088 from wselwood/note-edge-case-in-docs.
## What changes were proposed in this pull request?
Long time ago we fixed a [bug](https://issues.apache.org/jira/browse/SPARK-3948) in shuffle writer about `FileChannel.transferTo`. We were not very confident about that fix, so we added a position check after the writing, try to discover the bug earlier.
However this checking is missing in the new `UnsafeShuffleWriter`, this PR adds it.
https://issues.apache.org/jira/browse/SPARK-18105 maybe related to that `FileChannel.transferTo` bug, hopefully we can find out the root cause after adding this position check.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18091 from cloud-fan/shuffle.
