### What changes were proposed in this pull request?
Make r file extension check case insensitive for spark-submit.
### Why are the changes needed?
spark-submit does not accept `.r` files as R scripts. Some codebases have r files that end with lowercase file extensions. It is inconvenient to use spark-submit with lowercase extension R files. The error is not very clear (https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala#L232).
```
$ ./bin/spark-submit examples/src/main/r/dataframe.r
Exception in thread "main" org.apache.spark.SparkException: Cannot load main class from JAR file:/Users/dongjoon/APACHE/spark-release/spark-2.4.4-bin-hadoop2.7/examples/src/main/r/dataframe.r
```
### Does this PR introduce any user-facing change?
Yes. spark-submit can now be used to run R scripts with `.r` file extension.
### How was this patch tested?
Manual.
```
$ mv examples/src/main/r/dataframe.R examples/src/main/r/dataframe.r
$ ./bin/spark-submit examples/src/main/r/dataframe.r
```
Closes#25778 from Loquats/r-case.
Authored-by: Andy Zhang <yue.zhang@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
During Spark History Server startup, there are two things happening simultaneously that call into `java.nio.file.FileSystems.getDefault()` and we sometime hit [JDK-8194653](https://bugs.openjdk.java.net/browse/JDK-8194653).
1) start jetty server
2) start ApplicationHistoryProvider (which reads files from HDFS)
We should do these two things sequentially instead of in parallel.
We introduce a start() method in ApplicationHistoryProvider (and its subclass FsHistoryProvider), and we do initialize inside the start() method instead of the constructor.
In HistoryServer, we explicitly call provider.start() after we call bind() which starts the Jetty server.
### Why are the changes needed?
It is a bug that occasionally starting Spark History Server results in process hang due to deadlock among threads.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
I stress tested this PR with a bash script to stop and start Spark History Server more than 1000 times, it worked fine. Previously I can only do the stop/start loop less than 10 times before I hit the deadlock issue.
Closes#25705 from shanyu/shanyu-29003.
Authored-by: Shanyu Zhao <shzhao@microsoft.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR enables GitHub Action on PRs.
### Why are the changes needed?
So far, we detect JDK11 compilation error after merging.
This PR aims to prevent JDK11 compilation error at PR stage.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manual. See the GitHub Action on this PR.
Closes#25786 from dongjoon-hyun/SPARK-29079.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
This PR aims to add a new enforcer rule to ban duplicated pom dependency during build stage.
### Why are the changes needed?
This will help us by preventing the extra effort like the followings.
```
e63098b287 [SPARK-29007][MLLIB][FOLLOWUP] Remove duplicated dependency
39e044e3d8 [MINOR][BUILD] Remove duplicate test-jar:test spark-sql dependency from Hive module
d8fefab4d8 [HOTFIX][BUILD][TEST-MAVEN] Remove duplicate dependency
e9445b187e [SPARK-6866][Build] Remove duplicated dependency in launcher/pom.xml
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually.
If we have something like e63098b287, it will fail at building phase at PR like the following.
```
[WARNING] Rule 0: org.apache.maven.plugins.enforcer.BanDuplicatePomDependencyVersions failed with message:
Found 1 duplicate dependency declaration in this project:
- dependencies.dependency[org.apache.spark:spark-streaming_${scala.binary.version}:test-jar] ( 2 times )
...
[INFO] ------------------------------------------------------------------------
[INFO] BUILD FAILURE
[ERROR] Failed to execute goal org.apache.maven.plugins:maven-enforcer-plugin:3.0.0-M2:enforce (enforce-no-duplicate-dependencies) on project spark-mllib_2.12: Some Enforcer rules have failed. Look above for specific messages explaining why the rule failed. -> [Help 1]
```
Closes#25784 from dongjoon-hyun/SPARK-29075.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
We already have found and fixed the correctness issue before when RDD output is INDETERMINATE. One missing part is sampling-based RDD. This kind of RDDs is order sensitive to its input. A sampling-based RDD with unordered input, should be INDETERMINATE.
### Why are the changes needed?
A sampling-based RDD with unordered input is just like MapPartitionsRDD with isOrderSensitive parameter as true. The RDD output can be different after a rerun.
It is a problem in ML applications.
In ML, sample is used to prepare training data. ML algorithm fits the model based on the sampled data. If rerun tasks of sample produce different output during model fitting, ML results will be unreliable and also buggy.
Each sample is random output, but once you sampled, the output should be determinate.
### Does this PR introduce any user-facing change?
Previously, a sampling-based RDD can possibly come with different output after a rerun.
After this patch, sampling-based RDD is INDETERMINATE. For an INDETERMINATE map stage, currently Spark scheduler will re-try all the tasks of the failed stage.
### How was this patch tested?
Added test.
Closes#25751 from viirya/sample-order-sensitive.
Authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.com>
### What changes were proposed in this pull request?
This removes the duplicated dependency which is added by [SPARK-29007](b62ef8f793/mllib/pom.xml (L58-L64)).
### Why are the changes needed?
Maven complains this kind of duplications. We had better be safe in the future Maven versions.
```
$ cd mllib
$ mvn clean package -DskipTests
[INFO] Scanning for projects...
[WARNING]
[WARNING] Some problems were encountered while building the effective model for org.apache.spark:spark-mllib_2.12🫙3.0.0-SNAPSHOT
[WARNING] 'dependencies.dependency.(groupId:artifactId:type:classifier)' must be unique: org.apache.spark:spark-streaming_${scala.binary.version}:test-jar -> duplicate declaration of version ${project.version} line 119, column 17
[WARNING]
[WARNING] It is highly recommended to fix these problems because they threaten the stability of your build.
[WARNING]
[WARNING] For this reason, future Maven versions might no longer support building such malformed projects.
[WARNING]
...
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manual check since this is a warning.
```
$ cd mllib
$ mvn clean package -DskipTests
```
Closes#25783 from dongjoon-hyun/SPARK-29007.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This update adds support for Kafka Headers functionality in Structured Streaming.
## How was this patch tested?
With following unit tests:
- KafkaRelationSuite: "default starting and ending offsets with headers" (new)
- KafkaSinkSuite: "batch - write to kafka" (updated)
Closes#22282 from dongjinleekr/feature/SPARK-23539.
Lead-authored-by: Lee Dongjin <dongjin@apache.org>
Co-authored-by: Jungtaek Lim <kabhwan@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Follow the scala ```OneVsRestParams``` implementation, move ```setClassifier``` from ```OneVsRestParams``` to ```OneVsRest``` in Pyspark
### Why are the changes needed?
1. Maintain the parity between scala and python code.
2. ```Classifier``` can only be set in the estimator.
### Does this PR introduce any user-facing change?
Yes.
Previous behavior: ```OneVsRestModel``` has method ```setClassifier```
Current behavior: ```setClassifier``` is removed from ```OneVsRestModel```. ```classifier``` can only be set in ```OneVsRest```.
### How was this patch tested?
Use existing tests
Closes#25715 from huaxingao/spark-28969.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
ThriftServerSessionPage displays timestamp 0 (1970/01/01) instead of nothing if query finish time and close time are not set.
Change it to display nothing, like ThriftServerPage.
### Why are the changes needed?
Obvious bug.
### Does this PR introduce any user-facing change?
Finish time and Close time will be displayed correctly on ThriftServerSessionPage in JDBC/ODBC Spark UI.
### How was this patch tested?
Manual test.
Closes#25762 from juliuszsompolski/SPARK-29056.
Authored-by: Juliusz Sompolski <julek@databricks.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
Added document for CREATE VIEW command.
### Why are the changes needed?
As a reference to syntax and examples of CREATE VIEW command.
### How was this patch tested?
Documentation update. Verified manually.
Closes#25543 from amanomer/spark-28795.
Lead-authored-by: aman_omer <amanomer1996@gmail.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Co-authored-by: Aman Omer <amanomer1996@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Document DROP DATABASE statement in SQL Reference
### Why are the changes needed?
Currently from spark there is no complete sql guide is present, so it is better to document all the sql commands, this jira is sub part of this task.
### Does this PR introduce any user-facing change?
Yes, Before there was no documentation about drop database syntax
After Fix
### How was this patch tested?
tested with jenkyll build
Closes#25554 from sandeep-katta/dropDbDoc.
Authored-by: sandeep katta <sandeep.katta2007@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
Document REFRESH TABLE statement in the SQL Reference Guide.
### Why are the changes needed?
Currently there is no documentation in the SPARK SQL to describe how to use this command, it is to address this issue.
### Does this PR introduce any user-facing change?
Yes.
#### Before:
There is no documentation for this.
#### After:
<img width="826" alt="Screen Shot 2019-09-12 at 11 39 21 AM" src="https://user-images.githubusercontent.com/7550280/64811385-01752600-d552-11e9-876d-91ebb005b851.png">
### How was this patch tested?
Using jykll build --serve
Closes#25549 from kevinyu98/spark-28828-refreshTable.
Authored-by: Kevin Yu <qyu@us.ibm.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
The `Column.isInCollection()` with a large size collection will generate an expression with large size children expressions. This make analyzer and optimizer take a long time to run.
In this PR, in `isInCollection()` function, directly generate `InSet` expression, avoid generating too many children expressions.
### Why are the changes needed?
`Column.isInCollection()` with a large size collection sometimes become a bottleneck when running sql.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually benchmark it in spark-shell:
```
def testExplainTime(collectionSize: Int) = {
val df = spark.range(10).withColumn("id2", col("id") + 1)
val list = Range(0, collectionSize).toList
val startTime = System.currentTimeMillis()
df.where(col("id").isInCollection(list)).where(col("id2").isInCollection(list)).explain()
val elapsedTime = System.currentTimeMillis() - startTime
println(s"cost time: ${elapsedTime}ms")
}
```
Then test on collection size 5, 10, 100, 1000, 10000, test result is:
collection size | explain time (before) | explain time (after)
------ | ------ | ------
5 | 26ms | 29ms
10 | 30ms | 48ms
100 | 104ms | 50ms
1000 | 1202ms | 58ms
10000 | 10012ms | 523ms
Closes#25754 from WeichenXu123/improve_in_collection.
Lead-authored-by: WeichenXu <weichen.xu@databricks.com>
Co-authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
This PR adds a utility class `AdaptiveSparkPlanHelper` which provides methods related to tree traversal of an `AdaptiveSparkPlanExec` plan. Unlike their counterparts in `TreeNode` or
`QueryPlan`, these methods traverse down leaf nodes of adaptive plans, i.e., `AdaptiveSparkPlanExec` and `QueryStageExec`.
### Why are the changes needed?
This utility class can greatly simplify tree traversal code for adaptive spark plans.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Refined `AdaptiveQueryExecSuite` with the help of the new utility methods.
Closes#25764 from maryannxue/aqe-utils.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to extend `ExtractBenchmark` and add new ones for:
- `EXTRACT` and `DATE` as input column
- the `DATE_PART` function and `DATE`/`TIMESTAMP` input column
### Why are the changes needed?
The `EXTRACT` expression is rebased on the `DATE_PART` expression by the PR https://github.com/apache/spark/pull/25410 where some of sub-expressions take `DATE` column as the input (`Millennium`, `Year` and etc.) but others require `TIMESTAMP` column (`Hour`, `Minute`). Separate benchmarks for `DATE` should exclude overhead of implicit conversions `DATE` <-> `TIMESTAMP`.
### Does this PR introduce any user-facing change?
No, it doesn't.
### How was this patch tested?
- Regenerated results of `ExtractBenchmark`
Closes#25772 from MaxGekk/date_part-benchmark.
Authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
reorganize the packages of DS v2 interfaces/classes:
1. `org.spark.sql.connector.catalog`: put `TableCatalog`, `Table` and other related interfaces/classes
2. `org.spark.sql.connector.expression`: put `Expression`, `Transform` and other related interfaces/classes
3. `org.spark.sql.connector.read`: put `ScanBuilder`, `Scan` and other related interfaces/classes
4. `org.spark.sql.connector.write`: put `WriteBuilder`, `BatchWrite` and other related interfaces/classes
### Why are the changes needed?
Data Source V2 has evolved a lot. It's a bit weird that `Expression` is in `org.spark.sql.catalog.v2` and `Table` is in `org.spark.sql.sources.v2`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing tests
Closes#25700 from cloud-fan/package.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In method `SQLMetricsTestUtils.testMetricsDynamicPartition()`, there is a CREATE TABLE sentence without `withTable` block. It causes test failure if use same table name in other unit tests.
### Why are the changes needed?
To avoid "table already exists" in tests.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Exist UT
Closes#25752 from LantaoJin/SPARK-29045.
Authored-by: LantaoJin <jinlantao@gmail.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
# What changes were proposed in this pull request?
This patch fixes the bug regarding NPE in SQLConf.get, which is only possible when SparkContext._dagScheduler is null due to stopping SparkContext. The logic doesn't seem to consider active SparkContext could be in progress of stopping.
Note that it can't be encountered easily as `SparkContext.stop()` blocks the main thread, but there're many cases which SQLConf.get is accessed concurrently while SparkContext.stop() is executing - users run another threads, or listener is accessing SQLConf.get after dagScheduler is set to null (this is the case what I encountered.)
### Why are the changes needed?
The bug brings NPE.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Added new UT to verify NPE doesn't occur. Without patch, the test fails with throwing NPE.
Closes#25753 from HeartSaVioR/SPARK-29046.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
JIRA :https://issues.apache.org/jira/browse/SPARK-29050
'a hdfs' change into 'an hdfs'
'an unique' change into 'a unique'
'an url' change into 'a url'
'a error' change into 'an error'
Closes#25756 from dengziming/feature_fix_typos.
Authored-by: dengziming <dengziming@growingio.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Remove `InsertIntoTable` and replace it's usage by `InsertIntoStatement`
### Why are the changes needed?
`InsertIntoTable` and `InsertIntoStatement` are almost identical (except some namings). It doesn't make sense to keep 2 identical plans. After the removal of `InsertIntoTable`, the analysis process becomes:
1. parser creates `InsertIntoStatement`
2. v2 rule `ResolveInsertInto` converts `InsertIntoStatement` to v2 commands.
3. v1 rules like `DataSourceAnalysis` and `HiveAnalysis` convert `InsertIntoStatement` to v1 commands.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing tests
Closes#25763 from cloud-fan/remove.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR proposes to allow `bytes` as an acceptable type for binary type for `createDataFrame`.
### Why are the changes needed?
`bytes` is a standard type for binary in Python. This should be respected in PySpark side.
### Does this PR introduce any user-facing change?
Yes, _when specified type is binary_, we will allow `bytes` as a binary type. Previously this was not allowed in both Python 2 and Python 3 as below:
```python
spark.createDataFrame([[b"abcd"]], "col binary")
```
in Python 3
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/session.py", line 787, in createDataFrame
rdd, schema = self._createFromLocal(map(prepare, data), schema)
File "/.../spark/python/pyspark/sql/session.py", line 442, in _createFromLocal
data = list(data)
File "/.../spark/python/pyspark/sql/session.py", line 769, in prepare
verify_func(obj)
File "/.../forked/spark/python/pyspark/sql/types.py", line 1403, in verify
verify_value(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1384, in verify_struct
verifier(v)
File "/.../spark/python/pyspark/sql/types.py", line 1403, in verify
verify_value(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1397, in verify_default
verify_acceptable_types(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1282, in verify_acceptable_types
% (dataType, obj, type(obj))))
TypeError: field col: BinaryType can not accept object b'abcd' in type <class 'bytes'>
```
in Python 2:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../spark/python/pyspark/sql/session.py", line 787, in createDataFrame
rdd, schema = self._createFromLocal(map(prepare, data), schema)
File "/.../spark/python/pyspark/sql/session.py", line 442, in _createFromLocal
data = list(data)
File "/.../spark/python/pyspark/sql/session.py", line 769, in prepare
verify_func(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1403, in verify
verify_value(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1384, in verify_struct
verifier(v)
File "/.../spark/python/pyspark/sql/types.py", line 1403, in verify
verify_value(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1397, in verify_default
verify_acceptable_types(obj)
File "/.../spark/python/pyspark/sql/types.py", line 1282, in verify_acceptable_types
% (dataType, obj, type(obj))))
TypeError: field col: BinaryType can not accept object 'abcd' in type <type 'str'>
```
So, it won't break anything.
### How was this patch tested?
Unittests were added and also manually tested as below.
```bash
./run-tests --python-executables=python2,python3 --testnames "pyspark.sql.tests.test_serde"
```
Closes#25749 from HyukjinKwon/SPARK-29041.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch fixes the flaky test failure from StreamingContextSuite "stop slow receiver gracefully", via putting flag whether initializing slow receiver is completed, and wait for such flag to be true. As receiver should be submitted via job and initialized in executor, 500ms might not be enough for covering all cases.
### Why are the changes needed?
We got some reports for test failure on this test. Please refer [SPARK-24663](https://issues.apache.org/jira/browse/SPARK-24663)
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Modified UT. I've artificially made delay on handling job submission via adding below code in `DAGScheduler.submitJob`:
```
if (rdd != null && rdd.name != null && rdd.name.startsWith("Receiver")) {
println(s"Receiver Job! rdd name: ${rdd.name}")
Thread.sleep(1000)
}
```
and the test "stop slow receiver gracefully" failed on current master and passed on the patch.
Closes#25725 from HeartSaVioR/SPARK-24663.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
This patch enforces tests to prevent leaking newly created SparkContext while is created via initializing StreamingContext. Leaking SparkContext in test would make most of following tests being failed as well, so this patch applies defensive programming, trying its best to ensure SparkContext is cleaned up.
### Why are the changes needed?
We got some case in CI build where SparkContext is being leaked and other tests are affected by leaked SparkContext. Ideally we should isolate the environment among tests if possible.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Modified UTs.
Closes#25709 from HeartSaVioR/SPARK-29007.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
This patch ensures accessing recorded values in listener is always after letting listeners fully process all events. To ensure this, this patch adds new class to hide these values and access with methods which will ensure above condition. Without this guard, two threads are running concurrently - 1) listeners process thread 2) test main thread - and race condition would occur.
That's why we also see very odd thing, error message saying condition is met but test failed:
```
- Barrier task failures from the same stage attempt don't trigger multiple stage retries *** FAILED ***
ArrayBuffer(0) did not equal List(0) (DAGSchedulerSuite.scala:2656)
```
which means verification failed, and condition is met just before constructing error message.
The guard is properly placed in many spots, but missed in some places. This patch enforces that it can't be missed.
### Why are the changes needed?
UT fails intermittently and this patch will address the flakyness.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Modified UT.
Also made the flaky tests artificially failing via applying 50ms of sleep on each onXXX method.
I found 3 methods being failed. (They've marked as X. Just ignore ! as they failed on waiting listener in given timeout and these tests don't deal with these recorded values - it uses other timeout value 1000ms than 10000ms for this listener so affected via side-effect.)
When I applied same in this patch all tests marked as X passed.
Closes#25706 from HeartSaVioR/SPARK-26989.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
PR #22112 fixed the todo added by PR #20393(SPARK-23207). We can remove it now.
### Why are the changes needed?
In order not to confuse developers.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
no need to test
Closes#25755 from LinhongLiu/remove-todo.
Authored-by: Liu,Linhong <liulinhong@baidu.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Document the resource scheduling feature - https://issues.apache.org/jira/browse/SPARK-24615
Add general docs, yarn, kubernetes, and standalone cluster specific ones.
### Why are the changes needed?
Help users understand the feature
### Does this PR introduce any user-facing change?
docs
### How was this patch tested?
N/A
Closes#25698 from tgravescs/SPARK-27492-gpu-sched-docs.
Authored-by: Thomas Graves <tgraves@nvidia.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
This PR allows `bin/spark-submit --version` to show the correct information while the previous versions, which were created by `dev/create-release/do-release-docker.sh`, show incorrect information.
There are two root causes to show incorrect information:
1. Did not pass `USER` environment variable to the docker container
1. Did not keep `.git` directory in the work directory
### Why are the changes needed?
The information is missing while the previous versions show the correct information.
### Does this PR introduce any user-facing change?
Yes, the following is the console output in branch-2.3
```
$ bin/spark-submit --version
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.3.4
/_/
Using Scala version 2.11.8, OpenJDK 64-Bit Server VM, 1.8.0_212
Branch HEAD
Compiled by user ishizaki on 2019-09-02T02:18:10Z
Revision 8c6f8150f3
Url https://gitbox.apache.org/repos/asf/spark.git
Type --help for more information.
```
Without this PR, the console output is as follows
```
$ spark-submit --version
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.3.4
/_/
Using Scala version 2.11.8, OpenJDK 64-Bit Server VM, 1.8.0_212
Branch
Compiled by user on 2019-08-26T08:29:39Z
Revision
Url
Type --help for more information.
```
### How was this patch tested?
After building the package, I manually executed `bin/spark-submit --version`
Closes#25655 from kiszk/SPARK-28906.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Uses the APIs introduced in SPARK-28209 in the UnsafeShuffleWriter.
## How was this patch tested?
Since this is just a refactor, existing unit tests should cover the relevant code paths. Micro-benchmarks from the original fork where this code was built show no degradation in performance.
Closes#25304 from mccheah/shuffle-writer-refactor-unsafe-writer.
Lead-authored-by: mcheah <mcheah@palantir.com>
Co-authored-by: mccheah <mcheah@palantir.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
- For trait without companion object constructor, currently the method to get constructor parameters `constructParams` in `ScalaReflection` will throw exception.
```
scala.ScalaReflectionException: <none> is not a term
at scala.reflect.api.Symbols$SymbolApi.asTerm(Symbols.scala:211)
at scala.reflect.api.Symbols$SymbolApi.asTerm$(Symbols.scala:211)
at scala.reflect.internal.Symbols$SymbolContextApiImpl.asTerm(Symbols.scala:106)
at org.apache.spark.sql.catalyst.ScalaReflection.getCompanionConstructor(ScalaReflection.scala:909)
at org.apache.spark.sql.catalyst.ScalaReflection.constructParams(ScalaReflection.scala:914)
at org.apache.spark.sql.catalyst.ScalaReflection.constructParams$(ScalaReflection.scala:912)
at org.apache.spark.sql.catalyst.ScalaReflection$.constructParams(ScalaReflection.scala:47)
at org.apache.spark.sql.catalyst.ScalaReflection.getConstructorParameters(ScalaReflection.scala:890)
at org.apache.spark.sql.catalyst.ScalaReflection.getConstructorParameters$(ScalaReflection.scala:886)
at org.apache.spark.sql.catalyst.ScalaReflection$.getConstructorParameters(ScalaReflection.scala:47)
```
- Instead this PR would throw exception:
```
Unable to find constructor for type [XXX]. This could happen if [XXX] is an interface or a trait without companion object constructor
UnsupportedOperationException:
```
In the normal usage of ExpressionEncoder, this can happen if the type is interface extending `scala.Product`. Also, since this is a protected method, this could have been other arbitrary types without constructor.
### Why are the changes needed?
- The error message `<none> is not a term` isn't helpful for users to understand the problem.
### Does this PR introduce any user-facing change?
- The exception would be thrown instead of runtime exception from the `scala.ScalaReflectionException`.
### How was this patch tested?
- Added a unit test to illustrate the `type` where expression encoder will fail and trigger the proposed error message.
Closes#25736 from mickjermsurawong-stripe/SPARK-29026.
Authored-by: Mick Jermsurawong <mickjermsurawong@stripe.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Current Spark Thrift Server return TypeInfo includes
1. INTERVAL_YEAR_MONTH
2. INTERVAL_DAY_TIME
3. UNION
4. USER_DEFINED
Spark doesn't support INTERVAL_YEAR_MONTH, INTERVAL_YEAR_MONTH, UNION
and won't return USER)DEFINED type.
This PR overwrite GetTypeInfoOperation with SparkGetTypeInfoOperation to exclude types which we don't need.
In hive-1.2.1 Type class is `org.apache.hive.service.cli.Type`
In hive-2.3.x Type class is `org.apache.hadoop.hive.serde2.thrift.Type`
Use ThrifrserverShimUtils to fit version problem and exclude types we don't need
### Why are the changes needed?
We should return type info of Spark's own type info
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Manuel test & Added UT
Closes#25694 from AngersZhuuuu/SPARK-28982.
Lead-authored-by: angerszhu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
Add links to IBM Cloud Storage connector in cloud-integration.md
### Why are the changes needed?
This page mentions the connectors to cloud providers. Currently connector to
IBM cloud storage is not specified. This PR adds the necessary links for
completeness.
### Does this PR introduce any user-facing change?
Yes.
**Before:**
<img width="1234" alt="Screen Shot 2019-09-09 at 3 52 44 PM" src="https://user-images.githubusercontent.com/14225158/64571863-11a2c080-d31a-11e9-82e3-78c02675adb9.png">
**After.**
<img width="1234" alt="Screen Shot 2019-09-10 at 8 16 49 AM" src="https://user-images.githubusercontent.com/14225158/64626857-663e4e00-d3a3-11e9-8fa3-15ebf52ea832.png">
### How was this patch tested?
Tested using jykyll build --serve
Closes#25737 from dilipbiswal/ibm-cloud-storage.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Implement the SHOW DATABASES logical and physical plans for data source v2 tables.
### Why are the changes needed?
To support `SHOW DATABASES` SQL commands for v2 tables.
### Does this PR introduce any user-facing change?
`spark.sql("SHOW DATABASES")` will return namespaces if the default catalog is set:
```
+---------------+
| namespace|
+---------------+
| ns1|
| ns1.ns1_1|
|ns1.ns1_1.ns1_2|
+---------------+
```
### How was this patch tested?
Added unit tests to `DataSourceV2SQLSuite`.
Closes#25601 from imback82/show_databases.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Removes useless `Properties` created according to hvanhovell 's suggestion.
### Why are the changes needed?
Avoid useless code.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
existing UTs
Closes#25742 from mgaido91/SPARK-28939_followup.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
The intent to use the --hiveconf/--hivevar parameter is just an initialization value, so setting it once in ```SparkSQLSessionManager#openSession``` is sufficient, and each time the ```SparkExecuteStatementOperation``` setting causes the variable to not be modified.
### Why are the changes needed?
It is wrong to set the --hivevar/--hiveconf variable in every ```SparkExecuteStatementOperation```, which prevents variable updates.
### Does this PR introduce any user-facing change?
```
cat <<EOF > test.sql
select '\${a}', '\${b}';
set b=bvalue_MOD_VALUE;
set b;
EOF
beeline -u jdbc:hive2://localhost:10000 --hiveconf a=avalue --hivevar b=bvalue -f test.sql
```
current result:
```
+-----------------+-----------------+--+
| avalue | bvalue |
+-----------------+-----------------+--+
| avalue | bvalue |
+-----------------+-----------------+--+
+-----------------+-----------------+--+
| key | value |
+-----------------+-----------------+--+
| b | bvalue |
+-----------------+-----------------+--+
1 row selected (0.022 seconds)
```
after modification:
```
+-----------------+-----------------+--+
| avalue | bvalue |
+-----------------+-----------------+--+
| avalue | bvalue |
+-----------------+-----------------+--+
+-----------------+-----------------+--+
| key | value |
+-----------------+-----------------+--+
| b | bvalue_MOD_VALUE|
+-----------------+-----------------+--+
1 row selected (0.022 seconds)
```
### How was this patch tested?
modified the existing unit test
Closes#25722 from cxzl25/fix_SPARK-26598.
Authored-by: sychen <sychen@ctrip.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
This PR aims to recover the JDK11 compilation with a workaround.
For now, the master branch is broken like the following due to a [Scala bug](https://github.com/scala/bug/issues/10418) which is fixed in `2.13.0-RC2`.
```
[ERROR] [Error] /spark/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecutionRDD.scala:42: ambiguous reference to overloaded definition,
both method putAll in class Properties of type (x$1: java.util.Map[_, _])Unit
and method putAll in class Hashtable of type (x$1: java.util.Map[_ <: Object, _ <: Object])Unit
match argument types (java.util.Map[String,String])
```
- https://github.com/apache/spark/actions (JDK11 build monitoring)
### Why are the changes needed?
This workaround recovers JDK11 compilation.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manual build with JDK11 because this is JDK11 compilation fix.
- Jenkins builds with JDK8 and tests with JDK11.
- GitHub action will verify this after merging.
Closes#25738 from dongjoon-hyun/SPARK-28939.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Remove the project node if the streaming scan is columnar
### Why are the changes needed?
This is a followup of https://github.com/apache/spark/pull/25586. Batch and streaming share the same DS v2 read API so both can support columnar reads. We should apply #25586 to streaming scan as well.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#25727 from cloud-fan/follow.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
SPARK-22799 added more comprehensive error logic for Bucketizer. This PR is to update QuantileDiscretizer match the new error logic in Bucketizer.
## How was this patch tested?
Add new unit test.
Closes#20442 from huaxingao/spark-23265.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.com>
## What changes were proposed in this pull request?
This is a ANSI SQL and feature id is `T312`
```
<binary overlay function> ::=
OVERLAY <left paren> <binary value expression> PLACING <binary value expression>
FROM <start position> [ FOR <string length> ] <right paren>
```
This PR related to https://github.com/apache/spark/pull/24918 and support treat byte array.
ref: https://www.postgresql.org/docs/11/functions-binarystring.html
## How was this patch tested?
new UT.
There are some show of the PR on my production environment.
```
spark-sql> select overlay(encode('Spark SQL', 'utf-8') PLACING encode('_', 'utf-8') FROM 6);
Spark_SQL
Time taken: 0.285 s
spark-sql> select overlay(encode('Spark SQL', 'utf-8') PLACING encode('CORE', 'utf-8') FROM 7);
Spark CORE
Time taken: 0.202 s
spark-sql> select overlay(encode('Spark SQL', 'utf-8') PLACING encode('ANSI ', 'utf-8') FROM 7 FOR 0);
Spark ANSI SQL
Time taken: 0.165 s
spark-sql> select overlay(encode('Spark SQL', 'utf-8') PLACING encode('tructured', 'utf-8') FROM 2 FOR 4);
Structured SQL
Time taken: 0.141 s
```
Closes#25172 from beliefer/ansi-overlay-byte-array.
Lead-authored-by: gengjiaan <gengjiaan@360.cn>
Co-authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
## What changes were proposed in this pull request?
Running spark on yarn, I got
```
java.lang.ClassCastException: org.apache.hadoop.ipc.CallerContext$Builder cannot be cast to scala.runtime.Nothing$
```
Utils.classForName return Class[Nothing], I think it should be defind as Class[_] to resolve this issue
## How was this patch tested?
not need
Closes#25389 from hddong/SPARK-28657-fix-currentContext-Instance-failed.
Lead-authored-by: hongdd <jn_hdd@163.com>
Co-authored-by: hongdongdong <hongdongdong@cmss.chinamobile.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
At the moment there are 3 places where communication protocol with Kafka cluster has to be set when delegation token used:
* On delegation token
* On source
* On sink
Most of the time users are using the same protocol on all these places (within one Kafka cluster). It would be better to declare it in one place (delegation token side) and Kafka sources/sinks can take this config over.
In this PR I've I've modified the code in a way that Kafka sources/sinks are taking over delegation token side `security.protocol` configuration when the token and the source/sink matches in `bootstrap.servers` configuration. This default configuration can be overwritten on each source/sink independently by using `kafka.security.protocol` configuration.
### Why are the changes needed?
The actual configuration's default behavior represents the minority of the use-cases and inconvenient.
### Does this PR introduce any user-facing change?
Yes, with this change users need to provide less configuration parameters by default.
### How was this patch tested?
Existing + additional unit tests.
Closes#25631 from gaborgsomogyi/SPARK-28928.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
This patch fixes the bug regarding accessing `DStreamCheckpointData.currentCheckpointFiles` without guarding which makes the test `basic rdd checkpoints + dstream graph checkpoint recovery` being flaky.
There're two possible points to make test failing:
1. checkpoint logic is too slow so that checkpoint cannot be handled within real delay
2. There's multithreads-unsafe point in `DStreamCheckpointData.update`: it clears `currentCheckpointFiles` and adds new checkpointFiles. Race condition can happen between main thread for test and JobGenerator's event loop thread.
`lastProcessedBatch` guarantees that all events for given time are processed, as commented:
`// last batch whose completion,checkpointing and metadata cleanup has been completed`. That means, if we wait for time for exactly same amount as advanced the time in test (multiply of checkpoint interval as well as batch duration) we can expect nothing will happen in DStreamCheckpointData afterwards unless we advance the clock.
This patch applies the observation above.
### Why are the changes needed?
The test is reported as flaky as [SPARK-28214](https://issues.apache.org/jira/browse/SPARK-28214), and the test code seems unsafe.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Modified UT. I've added some debug messages and confirmed no method in DStreamCheckpointData is being called between "after waiting lastProcessedBatch" and "advancing clock" even I added huge amount of sleep between twos, which avoids race-condition.
I was also able to make existing test artificially failing (not 100% consistently but high likely) via adding sleep between `currentCheckpointFiles.clear()` and `currentCheckpointFiles ++= checkpointFiles` in `DStreamCheckpointData.update`, and confirmed modified test doesn't fail the test multiple times.
Closes#25731 from HeartSaVioR/SPARK-28214.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
Document CLEAR CACHE statement in SQL Reference
### Why are the changes needed?
To complete SQL Reference
### Does this PR introduce any user-facing change?
Yes
After change:
### How was this patch tested?
Tested using jykyll build --serve
Closes#25541 from huaxingao/spark-28831-n.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
- Remove SQLContext.createExternalTable and Catalog.createExternalTable, deprecated in favor of createTable since 2.2.0, plus tests of deprecated methods
- Remove HiveContext, deprecated in 2.0.0, in favor of `SparkSession.builder.enableHiveSupport`
- Remove deprecated KinesisUtils.createStream methods, plus tests of deprecated methods, deprecate in 2.2.0
- Remove deprecated MLlib (not Spark ML) linear method support, mostly utility constructors and 'train' methods, and associated docs. This includes methods in LinearRegression, LogisticRegression, Lasso, RidgeRegression. These have been deprecated since 2.0.0
- Remove deprecated Pyspark MLlib linear method support, including LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD
- Remove 'runs' argument in KMeans.train() method, which has been a no-op since 2.0.0
- Remove deprecated ChiSqSelector isSorted protected method
- Remove deprecated 'yarn-cluster' and 'yarn-client' master argument in favor of 'yarn' and deploy mode 'cluster', etc
Notes:
- I was not able to remove deprecated DataFrameReader.json(RDD) in favor of DataFrameReader.json(Dataset); the former was deprecated in 2.2.0, but, it is still needed to support Pyspark's .json() method, which can't use a Dataset.
- Looks like SQLContext.createExternalTable was not actually deprecated in Pyspark, but, almost certainly was meant to be? Catalog.createExternalTable was.
- I afterwards noted that the toDegrees, toRadians functions were almost removed fully in SPARK-25908, but Felix suggested keeping just the R version as they hadn't been technically deprecated. I'd like to revisit that. Do we really want the inconsistency? I'm not against reverting it again, but then that implies leaving SQLContext.createExternalTable just in Pyspark too, which seems weird.
- I *kept* LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD, RidgeRegressionWithSGD in Pyspark, though deprecated, as it is hard to remove them (still used by StreamingLogisticRegressionWithSGD?) and they are not fully removed in Scala. Maybe should not have been deprecated.
### Why are the changes needed?
Deprecated items are easiest to remove in a major release, so we should do so as much as possible for Spark 3. This does not target items deprecated 'recently' as of Spark 2.3, which is still 18 months old.
### Does this PR introduce any user-facing change?
Yes, in that deprecated items are removed from some public APIs.
### How was this patch tested?
Existing tests.
Closes#25684 from srowen/SPARK-28980.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
