## What changes were proposed in this pull request?
Improve Avro unit test:
1. use QueryTest/SharedSQLContext/SQLTestUtils, instead of the duplicated test utils.
2. replace deprecated methods
This is a follow up PR for #21760, the PR passes pull request tests but failed in: https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Compile/job/spark-master-compile-maven-hadoop-2.6/7842/
This PR is to fix it.
## How was this patch tested?
Unit test.
Compile with different commands:
```
./build/mvn --force -DzincPort=3643 -DskipTests -Phadoop-2.6 -Phive-thriftserver -Pkinesis-asl -Pspark-ganglia-lgpl -Pmesos -Pyarn compile test-compile
./build/mvn --force -DzincPort=3643 -DskipTests -Phadoop-2.7 -Phive-thriftserver -Pkinesis-asl -Pspark-ganglia-lgpl -Pmesos -Pyarn compile test-compile
./build/mvn --force -DzincPort=3643 -DskipTests -Phadoop-3.1 -Phive-thriftserver -Pkinesis-asl -Pspark-ganglia-lgpl -Pmesos -Pyarn compile test-compile
```
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21768 from gengliangwang/improve_avro_test.
## What changes were proposed in this pull request?
`Timestamp` support pushdown to parquet data source.
Only `TIMESTAMP_MICROS` and `TIMESTAMP_MILLIS` support push down.
## How was this patch tested?
unit tests and benchmark tests
Author: Yuming Wang <yumwang@ebay.com>
Closes#21741 from wangyum/SPARK-24718.
## What changes were proposed in this pull request?
Use longs in calculating min hash to avoid bias due to int overflow.
## How was this patch tested?
Existing tests.
Author: Sean Owen <srowen@gmail.com>
Closes#21750 from srowen/SPARK-24754.
## What changes were proposed in this pull request?
The original pr is: https://github.com/apache/spark/pull/18424
Add a new optimizer rule to convert an IN predicate to an equivalent Parquet filter and add `spark.sql.parquet.pushdown.inFilterThreshold` to control limit thresholds. Different data types have different limit thresholds, this is a copy of data for reference:
Type | limit threshold
-- | --
string | 370
int | 210
long | 285
double | 270
float | 220
decimal | Won't provide better performance before [SPARK-24549](https://issues.apache.org/jira/browse/SPARK-24549)
## How was this patch tested?
unit tests and manual tests
Author: Yuming Wang <yumwang@ebay.com>
Closes#21603 from wangyum/SPARK-17091.
## What changes were proposed in this pull request?
I added integration tests for PySpark ( + checking JVM options + RemoteFileTest) which wasn't properly merged in the initial integration test PR.
## How was this patch tested?
I tested this with integration tests using:
`dev/dev-run-integration-tests.sh --spark-tgz spark-2.4.0-SNAPSHOT-bin-2.7.3.tgz`
Author: Ilan Filonenko <if56@cornell.edu>
Closes#21583 from ifilonenko/master.
## What changes were proposed in this pull request?
Add `org.apache.derby` to `IsolatedClientLoader`, otherwise it may throw an exception:
```scala
...
[info] Cause: java.sql.SQLException: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$12439ab23, see the next exception for details.
[info] at org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)
[info] at org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)
[info] at org.apache.derby.impl.jdbc.Util.seeNextException(Unknown Source)
[info] at org.apache.derby.impl.jdbc.EmbedConnection.bootDatabase(Unknown Source)
[info] at org.apache.derby.impl.jdbc.EmbedConnection.<init>(Unknown Source)
[info] at org.apache.derby.jdbc.InternalDriver$1.run(Unknown Source)
...
```
## How was this patch tested?
unit tests and manual tests
Author: Yuming Wang <yumwang@ebay.com>
Closes#20944 from wangyum/SPARK-23831.
## What changes were proposed in this pull request?
Added the number of iterations in `ClusteringSummary`. This is an helpful information in evaluating how to eventually modify the parameters in order to get a better model.
## How was this patch tested?
modified existing UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20701 from mgaido91/SPARK-23528.
## What changes were proposed in this pull request?
Improve Avro unit test:
1. use QueryTest/SharedSQLContext/SQLTestUtils, instead of the duplicated test utils.
2. replace deprecated methods
## How was this patch tested?
Unit test
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21760 from gengliangwang/improve_avro_test.
## What changes were proposed in this pull request?
When we use a reference from Dataset in filter or sort, which was not used in the prior select, an AnalysisException occurs, e.g.,
```scala
val df = Seq(("test1", 0), ("test2", 1)).toDF("name", "id")
df.select(df("name")).filter(df("id") === 0).show()
```
```scala
org.apache.spark.sql.AnalysisException: Resolved attribute(s) id#6 missing from name#5 in operator !Filter (id#6 = 0).;;
!Filter (id#6 = 0)
+- AnalysisBarrier
+- Project [name#5]
+- Project [_1#2 AS name#5, _2#3 AS id#6]
+- LocalRelation [_1#2, _2#3]
```
This change updates the rule `ResolveMissingReferences` so `Filter` and `Sort` with non-empty `missingInputs` will also be transformed.
## How was this patch tested?
Added tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#21745 from viirya/SPARK-24781.
We have hundreds of kafka topics need to be consumed in one application. The application master will throw OOM exception after hanging for nearly half of an hour.
OOM happens in the env with a lot of topics, and it's not convenient to set up such kind of env in the unit test. So I didn't change/add test case.
Author: Yuanbo Liu <yuanbo@Yuanbos-MacBook-Air.local>
Author: yuanbo <yuanbo@apache.org>
Closes#21690 from yuanboliu/master.
## What changes were proposed in this pull request?
This PR will cache the function name from external catalog, it is used by lookupFunctions in the analyzer, and it is cached for each query plan. The original problem is reported in the [ spark-19737](https://issues.apache.org/jira/browse/SPARK-19737)
## How was this patch tested?
create new test file LookupFunctionsSuite and add test case in SessionCatalogSuite
Author: Kevin Yu <qyu@us.ibm.com>
Closes#20795 from kevinyu98/spark-23486.
## What changes were proposed in this pull request?
Add array_remove / array_zip / map_from_arrays / array_distinct functions in SparkR.
## How was this patch tested?
Add tests in test_sparkSQL.R
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21645 from huaxingao/spark-24537.
## What changes were proposed in this pull request?
Relax the check to allow complex aggregate expressions, like `ceil(sum(col1))` or `sum(col1) + 1`, which roughly means any aggregate expression that could appear in an Aggregate plan except pandas UDF (due to the fact that it is not supported in pivot yet).
## How was this patch tested?
Added 2 tests in pivot.sql
Author: maryannxue <maryannxue@apache.org>
Closes#21753 from maryannxue/pivot-relax-syntax.
## What changes were proposed in this pull request?
The PR is a followup to move the test cases introduced by the original PR in their proper location.
## How was this patch tested?
moved UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21751 from mgaido91/SPARK-24208_followup.
## What changes were proposed in this pull request?
The reader schema is said to be evolved (or projected) when it changed after the data is written. The followings are already supported in file-based data sources. Note that partition columns are not maintained in files. In this PR, `column` means `non-partition column`.
1. Add a column
2. Hide a column
3. Change a column position
4. Change a column type (upcast)
This issue aims to guarantee users a backward-compatible read-schema test coverage on file-based data sources and to prevent future regressions by *adding read schema tests explicitly*.
Here, we consider safe changes without data loss. For example, data type change should be from small types to larger types like `int`-to-`long`, not vice versa.
As of today, in the master branch, file-based data sources have the following coverage.
File Format | Coverage | Note
----------- | ---------- | ------------------------------------------------
TEXT | N/A | Schema consists of a single string column.
CSV | 1, 2, 4 |
JSON | 1, 2, 3, 4 |
ORC | 1, 2, 3, 4 | Native vectorized ORC reader has the widest coverage among ORC formats.
PARQUET | 1, 2, 3 |
## How was this patch tested?
Pass the Jenkins with newly added test suites.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#20208 from dongjoon-hyun/SPARK-SCHEMA-EVOLUTION.
## What changes were proposed in this pull request?
Apache Avro (https://avro.apache.org) is a popular data serialization format. It is widely used in the Spark and Hadoop ecosystem, especially for Kafka-based data pipelines. Using the external package https://github.com/databricks/spark-avro, Spark SQL can read and write the avro data. Making spark-Avro built-in can provide a better experience for first-time users of Spark SQL and structured streaming. We expect the built-in Avro data source can further improve the adoption of structured streaming.
The proposal is to inline code from spark-avro package (https://github.com/databricks/spark-avro). The target release is Spark 2.4.
[Built-in AVRO Data Source In Spark 2.4.pdf](https://github.com/apache/spark/files/2181511/Built-in.AVRO.Data.Source.In.Spark.2.4.pdf)
## How was this patch tested?
Unit test
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21742 from gengliangwang/export_avro.
## What changes were proposed in this pull request?
The `WholeTextFileInputFormat` determines the `maxSplitSize` for the file/s being read using the `wholeTextFiles` method. While this works well for large files, for smaller files where the maxSplitSize is smaller than the defaults being used with configs like hive-site.xml or explicitly passed in the form of `mapreduce.input.fileinputformat.split.minsize.per.node` or `mapreduce.input.fileinputformat.split.minsize.per.rack` , it just throws up an exception.
```java
java.io.IOException: Minimum split size pernode 123456 cannot be larger than maximum split size 9962
at org.apache.hadoop.mapreduce.lib.input.CombineFileInputFormat.getSplits(CombineFileInputFormat.java:200)
at org.apache.spark.rdd.WholeTextFileRDD.getPartitions(WholeTextFileRDD.scala:50)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2096)
at org.apache.spark.rdd.RDD.count(RDD.scala:1158)
... 48 elided
`
This change checks the maxSplitSize against the minSplitSizePerNode and minSplitSizePerRack and set them if `maxSplitSize < minSplitSizePerNode/Rack`
## How was this patch tested?
Test manually setting the conf while launching the job and added unit test.
Author: Dhruve Ashar <dhruveashar@gmail.com>
Closes#21601 from dhruve/bug/SPARK-24610.
Setting the timestamp directly would cause exception on reading stream, it can be set directly only if the mode is not AT_TIMESTAMP
## What changes were proposed in this pull request?
The last patch in the kinesis streaming receiver sets the timestamp for the mode AT_TIMESTAMP, but this mode can only be set via the
`baseClientLibConfiguration.withTimestampAtInitialPositionInStream()
`
and can't be set directly using
`.withInitialPositionInStream()`
This patch fixes the issue.
## How was this patch tested?
Kinesis Receiver doesn't expose the internal state outside, so couldn't find the right way to test this change. Seeking for tips from other contributors here.
Author: Yash Sharma <ysharma@atlassian.com>
Closes#21541 from yashs360/ysharma/fix_kinesis_bug.
## What changes were proposed in this pull request?
With https://github.com/apache/spark/pull/21389, data source schema is validated on driver side before launching read/write tasks.
However,
1. Putting all the validations together in `DataSourceUtils` is tricky and hard to maintain. On second thought after review, I find that the `OrcFileFormat` in hive package is not matched, so that its validation wrong.
2. `DataSourceUtils.verifyWriteSchema` and `DataSourceUtils.verifyReadSchema` is not supposed to be called in every file format. We can move them to some upper entry.
So, I propose we can add a new method `validateDataType` in FileFormat. File format implementation can override the method to specify its supported/non-supported data types.
Although we should focus on data source V2 API, `FileFormat` should remain workable for some time. Adding this new method should be helpful.
## How was this patch tested?
Unit test
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21667 from gengliangwang/refactorSchemaValidate.
## What changes were proposed in this pull request?
The PR adds the SQL function `array_union`. The behavior of the function is based on Presto's one.
This function returns returns an array of the elements in the union of array1 and array2.
Note: The order of elements in the result is not defined.
## How was this patch tested?
Added UTs
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#21061 from kiszk/SPARK-23914.
## What changes were proposed in this pull request?
This PR enables a Java bytecode check tool [spotbugs](https://spotbugs.github.io/) to avoid possible integer overflow at multiplication. When an violation is detected, the build process is stopped.
Due to the tool limitation, some other checks will be enabled. In this PR, [these patterns](http://spotbugs-in-kengo-toda.readthedocs.io/en/lqc-list-detectors/detectors.html#findpuzzlers) in `FindPuzzlers` can be detected.
This check is enabled at `compile` phase. Thus, `mvn compile` or `mvn package` launches this check.
## How was this patch tested?
Existing UTs
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#21542 from kiszk/SPARK-24529.
## What changes were proposed in this pull request?
In the PR, I propose to extend `RuntimeConfig` by new method `isModifiable()` which returns `true` if a config parameter can be modified at runtime (for current session state). For static SQL and core parameters, the method returns `false`.
## How was this patch tested?
Added new test to `RuntimeConfigSuite` for checking Spark core and SQL parameters.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#21730 from MaxGekk/is-modifiable.
## What changes were proposed in this pull request?
The PR simplifies the retrieval of config in `size`, as we can access them from tasks too thanks to SPARK-24250.
## How was this patch tested?
existing UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21736 from mgaido91/SPARK-24605_followup.
## What changes were proposed in this pull request?
In ProgressReporter for streams, we use the `committedOffsets` as the startOffset and `availableOffsets` as the end offset when reporting the status of a trigger in `finishTrigger`. This is a bad pattern that has existed since the beginning of ProgressReporter and it is bad because its super hard to reason about when `availableOffsets` and `committedOffsets` are updated, and when they are recorded. Case in point, this bug silently existed in ContinuousExecution, since before MicroBatchExecution was refactored.
The correct fix it to record the offsets explicitly. This PR adds a simple method which is explicitly called from MicroBatch/ContinuousExecition before updating the `committedOffsets`.
## How was this patch tested?
Added new tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#21744 from tdas/SPARK-24697.
## What changes were proposed in this pull request?
Added check for 404, to avoid json parsing on not found response and to avoid returning malformed or bad request when it was a not found http response.
Not sure if I need to add an additional check on non json response [if(connection.getHeaderField("Content-Type").contains("text/html")) then exception] as non-json is a subset of malformed json and covered in flow.
## How was this patch tested?
./dev/run-tests
Author: Rekha Joshi <rekhajoshm@gmail.com>
Closes#21684 from rekhajoshm/SPARK-24470.
## What changes were proposed in this pull request?
A self-join on a dataset which contains a `FlatMapGroupsInPandas` fails because of duplicate attributes. This happens because we are not dealing with this specific case in our `dedupAttr` rules.
The PR fix the issue by adding the management of the specific case
## How was this patch tested?
added UT + manual tests
Author: Marco Gaido <marcogaido91@gmail.com>
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#21737 from mgaido91/SPARK-24208.
## What changes were proposed in this pull request?
The PR proposes to add support for running the same SQL test input files against different configs leading to the same result.
## How was this patch tested?
Involved UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21568 from mgaido91/SPARK-24562.
## What changes were proposed in this pull request?
Add model predictions for Linear Support Vector Machine (SVM) Classifier, Logistic Regression, GBT, RF and DecisionTree in vignettes.
## How was this patch tested?
Manually ran the test and checked the result.
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21678 from huaxingao/spark-23461.
This PR continues #21095 and intersects with #21238. I've added volume mounts as a separate step and added PersistantVolumeClaim support.
There is a fundamental problem with how we pass the options through spark conf to fabric8. For each volume type and all possible volume options we would have to implement some custom code to map config values to fabric8 calls. This will result in big body of code we would have to support and means that Spark will always be somehow out of sync with k8s.
I think there needs to be a discussion on how to proceed correctly (eg use PodPreset instead)
----
Due to the complications of provisioning and managing actual resources this PR addresses only volume mounting of already present resources.
----
- [x] emptyDir support
- [x] Testing
- [x] Documentation
- [x] KubernetesVolumeUtils tests
Author: Andrew Korzhuev <andrew.korzhuev@klarna.com>
Author: madanadit <adit@alluxio.com>
Closes#21260 from andrusha/k8s-vol.
## What changes were proposed in this pull request?
This PR is proposing a fix for the output data type of ```If``` and ```CaseWhen``` expression. Upon till now, the implementation of exprassions has ignored nullability of nested types from different execution branches and returned the type of the first branch.
This could lead to an unwanted ```NullPointerException``` from other expressions depending on a ```If```/```CaseWhen``` expression.
Example:
```
val rows = new util.ArrayList[Row]()
rows.add(Row(true, ("a", 1)))
rows.add(Row(false, (null, 2)))
val schema = StructType(Seq(
StructField("cond", BooleanType, false),
StructField("s", StructType(Seq(
StructField("val1", StringType, true),
StructField("val2", IntegerType, false)
)), false)
))
val df = spark.createDataFrame(rows, schema)
df
.select(when('cond, struct(lit("x").as("val1"), lit(10).as("val2"))).otherwise('s) as "res")
.select('res.getField("val1"))
.show()
```
Exception:
```
Exception in thread "main" java.lang.NullPointerException
at org.apache.spark.sql.catalyst.expressions.codegen.UnsafeWriter.write(UnsafeWriter.java:109)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
at org.apache.spark.sql.execution.LocalTableScanExec$$anonfun$unsafeRows$1.apply(LocalTableScanExec.scala:44)
at org.apache.spark.sql.execution.LocalTableScanExec$$anonfun$unsafeRows$1.apply(LocalTableScanExec.scala:44)
...
```
Output schema:
```
root
|-- res.val1: string (nullable = false)
```
## How was this patch tested?
New test cases added into
- DataFrameSuite.scala
- conditionalExpressions.scala
Author: Marek Novotny <mn.mikke@gmail.com>
Closes#21687 from mn-mikke/SPARK-24165.
## What changes were proposed in this pull request?
This PR proposes to add `SPHINXPYTHON` environment variable to control the Python version used by Sphinx.
The motivation of this environment variable is, it seems not properly rendering some signatures in the Python documentation when Python 2 is used by Sphinx. See the JIRA's case. It should be encouraged to use Python 3, but looks we will probably live with this problem for a long while in any event.
For the default case of `make html`, it keeps previous behaviour and use `SPHINXBUILD` as it was. If `SPHINXPYTHON` is set, then it forces Sphinx to use the specific Python version.
```
$ SPHINXPYTHON=python3 make html
python3 -msphinx -b html -d _build/doctrees . _build/html
Running Sphinx v1.7.5
...
```
1. if `SPHINXPYTHON` is set, use Python. If `SPHINXBUILD` is set, use sphinx-build.
2. If both are set, `SPHINXBUILD` has a higher priority over `SPHINXPYTHON`
3. By default, `SPHINXBUILD` is used as 'sphinx-build'.
Probably, we can somehow work around this via explicitly setting `SPHINXBUILD` but `sphinx-build` can't be easily distinguished since it (at least in my environment and up to my knowledge) doesn't replace `sphinx-build` when newer Sphinx is installed in different Python version. It confuses and doesn't warn for its Python version.
## How was this patch tested?
Manually tested:
**`python` (Python 2.7) in the path with Sphinx:**
```
$ make html
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.7.5
...
```
**`python` (Python 2.7) in the path without Sphinx:**
```
$ make html
Makefile:8: *** The 'sphinx-build' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the 'sphinx-build' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/. Stop.
```
**`SPHINXPYTHON` set `python` (Python 2.7) with Sphinx:**
```
$ SPHINXPYTHON=python make html
Makefile:35: *** Note that Python 3 is required to generate PySpark documentation correctly for now. Current Python executable was less than Python 3. See SPARK-24530. To force Sphinx to use a specific Python executable, please set SPHINXPYTHON to point to the Python 3 executable.. Stop.
```
**`SPHINXPYTHON` set `python` (Python 2.7) without Sphinx:**
```
$ SPHINXPYTHON=python make html
Makefile:35: *** Note that Python 3 is required to generate PySpark documentation correctly for now. Current Python executable was less than Python 3. See SPARK-24530. To force Sphinx to use a specific Python executable, please set SPHINXPYTHON to point to the Python 3 executable.. Stop.
```
**`SPHINXPYTHON` set `python3` with Sphinx:**
```
$ SPHINXPYTHON=python3 make html
python3 -msphinx -b html -d _build/doctrees . _build/html
Running Sphinx v1.7.5
...
```
**`SPHINXPYTHON` set `python3` without Sphinx:**
```
$ SPHINXPYTHON=python3 make html
Makefile:39: *** Python executable 'python3' did not have Sphinx installed. Make sure you have Sphinx installed, then set the SPHINXPYTHON environment variable to point to the Python executable having Sphinx installed. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/. Stop.
```
**`SPHINXBUILD` set:**
```
$ SPHINXBUILD=sphinx-build make html
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.7.5
...
```
**Both `SPHINXPYTHON` and `SPHINXBUILD` are set:**
```
$ SPHINXBUILD=sphinx-build SPHINXPYTHON=python make html
sphinx-build -b html -d _build/doctrees . _build/html
Running Sphinx v1.7.5
...
```
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21659 from HyukjinKwon/SPARK-24530.
## What changes were proposed in this pull request?
Currently, when a streaming query has multiple watermark, the policy is to choose the min of them as the global watermark. This is safe to do as the global watermark moves with the slowest stream, and is therefore is safe as it does not unexpectedly drop some data as late, etc. While this is indeed the safe thing to do, in some cases, you may want the watermark to advance with the fastest stream, that is, take the max of multiple watermarks. This PR is to add that configuration. It makes the following changes.
- Adds a configuration to specify max as the policy.
- Saves the configuration in OffsetSeqMetadata because changing it in the middle can lead to unpredictable results.
- For old checkpoints without the configuration, it assumes the default policy as min (irrespective of the policy set at the session where the query is being restarted). This is to ensure that existing queries are affected in any way.
TODO
- [ ] Add a test for recovery from existing checkpoints.
## How was this patch tested?
New unit test
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#21701 from tdas/SPARK-24730.
## What changes were proposed in this pull request?
Support the LIMIT operator in structured streaming.
For streams in append or complete output mode, a stream with a LIMIT operator will return no more than the specified number of rows. LIMIT is still unsupported for the update output mode.
This change reverts e4fee395ec as part of it because it is a better and more complete implementation.
## How was this patch tested?
New and existing unit tests.
Author: Mukul Murthy <mukul.murthy@gmail.com>
Closes#21662 from mukulmurthy/SPARK-24662.
## What changes were proposed in this pull request?
This is the first follow-up of https://github.com/apache/spark/pull/21573 , which was only merged to 2.3.
This PR fixes the memory leak in another way: free the `UnsafeExternalMap` when the task ends. All the data buffers in Spark SQL are using `UnsafeExternalMap` and `UnsafeExternalSorter` under the hood, e.g. sort, aggregate, window, SMJ, etc. `UnsafeExternalSorter` registers a task completion listener to free the resource, we should apply the same thing to `UnsafeExternalMap`.
TODO in the next PR:
do not consume all the inputs when having limit in whole stage codegen.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#21738 from cloud-fan/limit.
## What changes were proposed in this pull request?
Currently, `BlockRDD.getPreferredLocations` only get hosts info of blocks, which results in subsequent schedule level is not better than 'NODE_LOCAL'. We can just make a small changes, the schedule level can be improved to 'PROCESS_LOCAL'
## How was this patch tested?
manual test
Author: sharkdtu <sharkdtu@tencent.com>
Closes#21658 from sharkdtu/master.
## What changes were proposed in this pull request?
Not a big deal but this PR adds `sphinx` into `dev/requirements.txt` since we found it needed - https://github.com/apache/spark-website/pull/122#discussion_r200896018
## How was this patch tested?
manually:
```
pip install -r requirements.txt
```
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21735 from HyukjinKwon/minor-dev.
## What changes were proposed in this pull request?
As the implementation of the broadcast hash join is independent of the input hash partitioning, reordering keys is not necessary. Thus, we solve this issue by simply removing the broadcast hash join from the reordering rule in EnsureRequirements.
## How was this patch tested?
N/A
Author: Xiao Li <gatorsmile@gmail.com>
Closes#21728 from gatorsmile/cleanER.
## What changes were proposed in this pull request?
SPARK-22893 tried to unify error messages about dataTypes. Unfortunately, still many places were missing the `simpleString` method in other to have the same representation everywhere.
The PR unified the messages using alway the simpleString representation of the dataTypes in the messages.
## How was this patch tested?
existing/modified UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21321 from mgaido91/SPARK-24268.
## What changes were proposed in this pull request?
Implement map_concat high order function.
This implementation does not pick a winner when the specified maps have overlapping keys. Therefore, this implementation preserves existing duplicate keys in the maps and potentially introduces new duplicates (After discussion with ueshin, we settled on option 1 from [here](https://issues.apache.org/jira/browse/SPARK-23936?focusedCommentId=16464245&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-16464245)).
## How was this patch tested?
New tests
Manual tests
Run all sbt SQL tests
Run all pyspark sql tests
Author: Bruce Robbins <bersprockets@gmail.com>
Closes#21073 from bersprockets/SPARK-23936.
## What changes were proposed in this pull request?
In the case of getting tokens via customized `ServiceCredentialProvider`, it is required that `ServiceCredentialProvider` be available in local spark-submit process classpath. In this case, all the configured remote sources should be forced to download to local.
For the ease of using this configuration, here propose to add wildcard '*' support to `spark.yarn.dist.forceDownloadSchemes`, also clarify the usage of this configuration.
## How was this patch tested?
New UT added.
Author: jerryshao <sshao@hortonworks.com>
Closes#21633 from jerryshao/SPARK-21917-followup.
## What changes were proposed in this pull request?
In the PR, I propose to provide a tip to user how to resolve the issue of timeout expiration for broadcast joins. In particular, they can increase the timeout via **spark.sql.broadcastTimeout** or disable the broadcast at all by setting **spark.sql.autoBroadcastJoinThreshold** to `-1`.
## How was this patch tested?
It tested manually from `spark-shell`:
```
scala> spark.conf.set("spark.sql.broadcastTimeout", 1)
scala> val df = spark.range(100).join(spark.range(15).as[Long].map { x =>
Thread.sleep(5000)
x
}).where("id = value")
scala> df.count()
```
```
org.apache.spark.SparkException: Could not execute broadcast in 1 secs. You can increase the timeout for broadcasts via spark.sql.broadcastTimeout or disable broadcast join by setting spark.sql.autoBroadcastJoinThreshold to -1
at org.apache.spark.sql.execution.exchange.BroadcastExchangeExec.doExecuteBroadcast(BroadcastExchangeExec.scala:150)
```
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#21727 from MaxGekk/broadcast-timeout-error.
## What changes were proposed in this pull request?
We should use `DataType.sameType` to compare element type in `ArrayContains`, otherwise nullability affects comparison result.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#21724 from viirya/SPARK-24749.
## What changes were proposed in this pull request?
SQL `Aggregator` with output type `Option[Boolean]` creates column of type `StructType`. It's not in consistency with a Dataset of similar java class.
This changes the way `definedByConstructorParams` checks given type. For `Option[_]`, it goes to check its type argument.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#21611 from viirya/SPARK-24569.
## What changes were proposed in this pull request?
This pr supported column arguments in timezone of `from_utc_timestamp/to_utc_timestamp` (follow-up of #21693).
## How was this patch tested?
Added tests.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#21723 from maropu/SPARK-24673-FOLLOWUP.
