## What changes were proposed in this pull request?
Refactor SortBenchmark to use main method.
Generate benchmark result:
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.SortBenchmark"
```
## How was this patch tested?
manual tests
Closes#22495 from yucai/SPARK-25486.
Authored-by: yucai <yyu1@ebay.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This patch reverts entirely all the regr_* functions added in SPARK-23907. These were added by mgaido91 (and proposed by gatorsmile) to improve compatibility with other database systems, without any actual use cases. However, they are very rarely used, and in Spark there are much better ways to compute these functions, due to Spark's flexibility in exposing real programming APIs.
I'm going through all the APIs added in Spark 2.4 and I think we should revert these. If there are strong enough demands and more use cases, we can add them back in the future pretty easily.
## How was this patch tested?
Reverted test cases also.
Closes#22541 from rxin/SPARK-23907.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In ArrayRemove, we currently cast the right hand side expression to match the element type of the left hand side Array. This may result in down casting and may return wrong result or questionable result.
Example :
```SQL
spark-sql> select array_remove(array(1,2,3), 1.23D);
[2,3]
```
```SQL
spark-sql> select array_remove(array(1,2,3), 'foo');
NULL
```
We should safely coerce both left and right hand side expressions.
## How was this patch tested?
Added tests in DataFrameFunctionsSuite
Closes#22542 from dilipbiswal/SPARK-25519.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Test steps :
1) bin/spark-shell --conf spark.ui.retainedTasks=200
```
val rdd = sc.parallelize(1 to 1000, 1000)
rdd.count
```
Stage tab in the UI will display 10 pages with 100 tasks per page. But number of retained tasks is only 200. So, from the 3rd page onwards will display nothing.
We have to calculate total pages based on the number of tasks need display in the UI.
**Before fix:**
**After fix:**
## How was this patch tested?
Manually tested
Closes#22526 from shahidki31/SPARK-25502.
Authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
In ArrayPosition, we currently cast the right hand side expression to match the element type of the left hand side Array. This may result in down casting and may return wrong result or questionable result.
Example :
```SQL
spark-sql> select array_position(array(1), 1.34);
1
```
```SQL
spark-sql> select array_position(array(1), 'foo');
null
```
We should safely coerce both left and right hand side expressions.
## How was this patch tested?
Added tests in DataFrameFunctionsSuite
Closes#22407 from dilipbiswal/SPARK-25416.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The function actually exists in current selected database, and it's failed to init during `lookupFunciton`, but the exception message is:
```
This function is neither a registered temporary function nor a permanent function registered in the database 'default'.
```
This is not conducive to positioning problems. This PR fix the problem.
## How was this patch tested?
new test case + manual tests
Closes#18544 from stanzhai/fix-udf-error-message.
Authored-by: Stan Zhai <mail@stanzhai.site>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Refactor `CompressionSchemeBenchmark` to use main method.
Generate benchmark result:
```sh
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.columnar.compression.CompressionSchemeBenchmark"
```
## How was this patch tested?
manual tests
Closes#22486 from wangyum/SPARK-25478.
Lead-authored-by: Yuming Wang <yumwang@ebay.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Add `Locale.ROOT` when `toUpperCase`.
## How was this patch tested?
manual tests
Closes#22531 from wangyum/SPARK-25415.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Refactor `UDTSerializationBenchmark` to use main method and print the output as a separate file.
Run blow command to generate benchmark results:
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "mllib/test:runMain org.apache.spark.mllib.linalg.UDTSerializationBenchmark"
```
## How was this patch tested?
Manual tests.
Closes#22499 from seancxmao/SPARK-25489.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This PR does not fix the problem itself but just target to add few comments to run PySpark tests on Python 3.6 and macOS High Serria since it actually blocks to run tests on this enviornment.
it does not target to fix the problem yet.
The problem here looks because we fork python workers and the forked workers somehow call Objective-C libraries in some codes at CPython's implementation. After debugging a while, I suspect `pickle` in Python 3.6 has some changes:
58419b9267/python/pyspark/serializers.py (L577)
in particular, it looks also related to which objects are serialized or not as well.
This link (http://sealiesoftware.com/blog/archive/2017/6/5/Objective-C_and_fork_in_macOS_1013.html) and this link (https://blog.phusion.nl/2017/10/13/why-ruby-app-servers-break-on-macos-high-sierra-and-what-can-be-done-about-it/) were helpful for me to understand this.
I am still debugging this but my guts say it's difficult to fix or workaround within Spark side.
## How was this patch tested?
Manually tested:
Before `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`:
```
/usr/local/Cellar/python/3.6.5/Frameworks/Python.framework/Versions/3.6/lib/python3.6/subprocess.py:766: ResourceWarning: subprocess 27563 is still running
ResourceWarning, source=self)
[Stage 0:> (0 + 1) / 1]objc[27586]: +[__NSPlaceholderDictionary initialize] may have been in progress in another thread when fork() was called.
objc[27586]: +[__NSPlaceholderDictionary initialize] may have been in progress in another thread when fork() was called. We cannot safely call it or ignore it in the fork() child process. Crashing instead. Set a breakpoint on objc_initializeAfterForkError to debug.
ERROR
======================================================================
ERROR: test_streaming_foreach_with_simple_function (pyspark.sql.tests.SQLTests)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/.../spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/.../spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling o54.processAllAvailable.
: org.apache.spark.sql.streaming.StreamingQueryException: Writing job aborted.
=== Streaming Query ===
Identifier: [id = f508d634-407c-4232-806b-70e54b055c42, runId = 08d1435b-5358-4fb6-b167-811584a3163e]
Current Committed Offsets: {}
Current Available Offsets: {FileStreamSource[file:/var/folders/71/484zt4z10ks1vydt03bhp6hr0000gp/T/tmpolebys1s]: {"logOffset":0}}
Current State: ACTIVE
Thread State: RUNNABLE
Logical Plan:
FileStreamSource[file:/var/folders/71/484zt4z10ks1vydt03bhp6hr0000gp/T/tmpolebys1s]
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:295)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)
Caused by: org.apache.spark.SparkException: Writing job aborted.
at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.doExecute(WriteToDataSourceV2Exec.scala:91)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
```
After `OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES`:
```
test_streaming_foreach_with_simple_function (pyspark.sql.tests.SQLTests) ...
ok
```
Closes#22480 from HyukjinKwon/SPARK-25473.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Always close the tempFile after `serializer.dump_stream(data, tempFile)` in _serialize_to_jvm
## How was this patch tested?
N/A
Closes#22523 from gatorsmile/fixMinor.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Current the file [parquetSuites.scala](f29c2b5287/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala) is not recognizable.
When I tried to find test suites for built-in Parquet conversions for Hive serde, I can only find [HiveParquetSuite](f29c2b5287/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala) in the first few minutes.
This PR is to:
1. Rename `ParquetMetastoreSuite` to `HiveParquetMetastoreSuite`, and create a single file for it.
2. Rename `ParquetSourceSuite` to `HiveParquetSourceSuite`, and create a single file for it.
3. Create a single file for `ParquetPartitioningTest`.
4. Delete `parquetSuites.scala` .
## How was this patch tested?
Unit test
Closes#22467 from gengliangwang/refactor_parquet_suites.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
change back the constructor to:
```
class LocalLDAModel private[ml] (
uid: String,
vocabSize: Int,
private[clustering] val oldLocalModel : OldLocalLDAModel,
sparkSession: SparkSession)
```
Although it is marked `private[ml]`, it is used in `mleap` and the master change breaks `mleap` building.
See mleap code [here](c7860af328/mleap-spark/src/main/scala/org/apache/spark/ml/bundle/ops/clustering/LDAModelOp.scala (L57))
## How was this patch tested?
Manual.
Closes#22510 from WeichenXu123/LDA_fix.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
One more legacy config to go ...
Closes#22515 from rxin/allowCreatingManagedTableUsingNonemptyLocation.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Currently there are two classes with the same naming BenchmarkBase:
1. `org.apache.spark.util.BenchmarkBase`
2. `org.apache.spark.sql.execution.benchmark.BenchmarkBase`
This is very confusing. And the benchmark object `org.apache.spark.sql.execution.benchmark.FilterPushdownBenchmark` is using the one in `org.apache.spark.util.BenchmarkBase`, while there is another class `BenchmarkBase` in the same package of it...
Here I propose:
1. the package `org.apache.spark.util.BenchmarkBase` should be in test package of core module. Move it to package `org.apache.spark.benchmark` .
2. Move `org.apache.spark.util.Benchmark` to test package of core module. Move it to package `org.apache.spark.benchmark` .
3. Rename the class `org.apache.spark.sql.execution.benchmark.BenchmarkBase` as `BenchmarkWithCodegen`
## How was this patch tested?
Unit test
Closes#22513 from gengliangwang/refactorBenchmarkBase.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Description:
Right now, the default server side netty handler threads is 2 * # cores, and can be further configured with parameter spark.shuffle.io.serverThreads.
In order to process a client request, it would require one available server netty handler thread.
However, when the server netty handler threads start to process ChunkFetchRequests, they will be blocked on disk I/O, mostly due to disk contentions from the random read operations initiated by all the ChunkFetchRequests received from clients.
As a result, when the shuffle server is serving many concurrent ChunkFetchRequests, the server side netty handler threads could all be blocked on reading shuffle files, thus leaving no handler thread available to process other types of requests which should all be very quick to process.
This issue could potentially be fixed by limiting the number of netty handler threads that could get blocked when processing ChunkFetchRequest. We have a patch to do this by using a separate EventLoopGroup with a dedicated ChannelHandler to process ChunkFetchRequest. This enables shuffle server to reserve netty handler threads for non-ChunkFetchRequest, thus enabling consistent processing time for these requests which are fast to process. After deploying the patch in our infrastructure, we no longer see timeout issues with either executor registration with local shuffle server or shuffle client establishing connection with remote shuffle server.
(Please fill in changes proposed in this fix)
For Original PR please refer here
https://github.com/apache/spark/pull/21402
## How was this patch tested?
Unit tests and stress testing.
(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.
Closes#22173 from redsanket/SPARK-24335.
Authored-by: Sanket Chintapalli <schintap@yahoo-inc.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
The PR proposes to avoid usage of pattern matching for each call of ```eval``` method within:
- ```Concat```
- ```Reverse```
- ```ElementAt```
## How was this patch tested?
Run the existing tests for ```Concat```, ```Reverse``` and ```ElementAt``` expression classes.
Closes#22471 from mn-mikke/SPARK-25470.
Authored-by: Marek Novotny <mn.mikke@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
## What changes were proposed in this pull request?
See title. Makes our legacy backward compatibility configs more consistent.
## How was this patch tested?
Make sure all references have been updated:
```
> git grep compareDateTimestampInTimestamp
docs/sql-programming-guide.md: - Since Spark 2.4, Spark compares a DATE type with a TIMESTAMP type after promotes both sides to TIMESTAMP. To set `false` to `spark.sql.legacy.compareDateTimestampInTimestamp` restores the previous behavior. This option will be removed in Spark 3.0.
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala: // if conf.compareDateTimestampInTimestamp is true
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala: => if (conf.compareDateTimestampInTimestamp) Some(TimestampType) else Some(StringType)
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala: => if (conf.compareDateTimestampInTimestamp) Some(TimestampType) else Some(StringType)
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala: buildConf("spark.sql.legacy.compareDateTimestampInTimestamp")
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala: def compareDateTimestampInTimestamp : Boolean = getConf(COMPARE_DATE_TIMESTAMP_IN_TIMESTAMP)
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercionSuite.scala: "spark.sql.legacy.compareDateTimestampInTimestamp" -> convertToTS.toString) {
```
Closes#22508 from rxin/SPARK-23549.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
See above. This should go into the 2.4 release.
Closes#22509 from rxin/SPARK-25384.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Refactor PrimitiveArrayBenchmark to use main method and print the output as a separate file.
Run blow command to generate benchmark results:
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.PrimitiveArrayBenchmark"
```
## How was this patch tested?
Manual tests.
Closes#22497 from seancxmao/SPARK-25487.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
## What changes were proposed in this pull request?
This PR upgrades Spark's use of Janino from 3.0.9 to 3.0.10.
Note that 3.0.10 is a out-of-band release specifically for fixing an integer overflow issue in Janino's `ClassFile` reader. It is otherwise exactly the same as 3.0.9, so it's a low risk and compatible upgrade.
The integer overflow issue affects Spark SQL's codegen stats collection: when a generated Class file is huge, especially when the constant pool size is above `Short.MAX_VALUE`, Janino's `ClassFile reader` will throw an exception when Spark wants to parse the generated Class file to collect stats. So we'll miss the stats of some huge Class files.
The related Janino issue is: https://github.com/janino-compiler/janino/issues/58
## How was this patch tested?
Existing codegen tests.
Closes#22506 from rednaxelafx/upgrade-janino.
Authored-by: Kris Mok <kris.mok@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This reverts commit 417ad92502.
We decided to keep the current behaviors unchanged and will consider whether we will deprecate the these functions in 3.0. For more details, see the discussion in https://issues.apache.org/jira/browse/SPARK-23715
## How was this patch tested?
The existing tests.
Closes#22505 from gatorsmile/revertSpark-23715.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Refactor `DataSourceWriteBenchmark` and add write benchmark for AVRO.
## How was this patch tested?
Build and run the benchmark.
Closes#22451 from gengliangwang/avroWriteBenchmark.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Legitimate stops of streams may actually cause an exception to be captured by stream execution, because the job throws a SparkException regarding job cancellation during a stop. This PR makes the stop more graceful by swallowing this cancellation error.
## How was this patch tested?
This is pretty hard to test. The existing tests should make sure that we're not swallowing other specific SparkExceptions. I've also run the `KafkaSourceStressForDontFailOnDataLossSuite`100 times, and it didn't fail, whereas it used to be flaky.
Closes#22478 from brkyvz/SPARK-25472.
Authored-by: Burak Yavuz <brkyvz@gmail.com>
Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
## What changes were proposed in this pull request?
Hadoop2.6 and hadoop2.7 do not contain zstd and brotli compressioncodec ,hadoop 3.1 also contains only zstd compressioncodec .
So I think we should remove zstd and brotil for the time being.
**set `spark.sql.parquet.compression.codec=brotli`:**
Caused by: org.apache.parquet.hadoop.BadConfigurationException: Class org.apache.hadoop.io.compress.BrotliCodec was not found
at org.apache.parquet.hadoop.CodecFactory.getCodec(CodecFactory.java:235)
at org.apache.parquet.hadoop.CodecFactory$HeapBytesCompressor.<init>(CodecFactory.java:142)
at org.apache.parquet.hadoop.CodecFactory.createCompressor(CodecFactory.java:206)
at org.apache.parquet.hadoop.CodecFactory.getCompressor(CodecFactory.java:189)
at org.apache.parquet.hadoop.ParquetRecordWriter.<init>(ParquetRecordWriter.java:153)
at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:411)
at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:349)
at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetOutputWriter.scala:37)
at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anon$1.newInstance(ParquetFileFormat.scala:161)
**set `spark.sql.parquet.compression.codec=zstd`:**
Caused by: org.apache.parquet.hadoop.BadConfigurationException: Class org.apache.hadoop.io.compress.ZStandardCodec was not found
at org.apache.parquet.hadoop.CodecFactory.getCodec(CodecFactory.java:235)
at org.apache.parquet.hadoop.CodecFactory$HeapBytesCompressor.<init>(CodecFactory.java:142)
at org.apache.parquet.hadoop.CodecFactory.createCompressor(CodecFactory.java:206)
at org.apache.parquet.hadoop.CodecFactory.getCompressor(CodecFactory.java:189)
at org.apache.parquet.hadoop.ParquetRecordWriter.<init>(ParquetRecordWriter.java:153)
at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:411)
at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:349)
at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetOutputWriter.scala:37)
at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anon$1.newInstance(ParquetFileFormat.scala:161)
## How was this patch tested?
Exist unit test
Closes#22358 from 10110346/notsupportzstdandbrotil.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
A continuation of squito's executor plugin task. By his request I took his code and added testing and moved the plugin initialization to a separate thread.
Executor plugins now run on one separate thread, so the executor does not wait on them. Added testing.
## How was this patch tested?
Added test cases that test using a sample plugin.
Closes#22192 from NiharS/executorPlugin.
Lead-authored-by: Nihar Sheth <niharrsheth@gmail.com>
Co-authored-by: NiharS <niharrsheth@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
In the PR, I propose to add an overloaded method for `sampleBy` which accepts the first argument of the `Column` type. This will allow to sample by any complex columns as well as sampling by multiple columns. For example:
```Scala
spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 17),
("Alice", 10))).toDF("name", "age")
.stat
.sampleBy(struct($"name", $"age"), Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0), 36L)
.show()
+-----+---+
| name|age|
+-----+---+
| Nico| 8|
|Alice| 10|
+-----+---+
```
## How was this patch tested?
Added new test for sampling by multiple columns for Scala and test for Java, Python to check that `sampleBy` is able to sample by `Column` type argument.
Closes#22365 from MaxGekk/sample-by-column.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The problem was cause by the PushProjectThroughUnion rule, which, when creating new Project for each child of Union, uses the same exprId for expressions of the same position. This is wrong because, for each child of Union, the expressions are all independent, and it can lead to a wrong result if other rules like FoldablePropagation kicks in, taking two different expressions as the same.
This fix is to create new expressions in the new Project for each child of Union.
## How was this patch tested?
Added UT.
Closes#22447 from maryannxue/push-project-thru-union-bug.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR proposes to use add a helper in `PythonUtils` instead of direct accessing Scala package.
## How was this patch tested?
Jenkins tests.
Closes#22483 from HyukjinKwon/minor-refactoring.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In ArrayContains, we currently cast the right hand side expression to match the element type of the left hand side Array. This may result in down casting and may return wrong result or questionable result.
Example :
```SQL
spark-sql> select array_contains(array(1), 1.34);
true
```
```SQL
spark-sql> select array_contains(array(1), 'foo');
null
```
We should safely coerce both left and right hand side expressions.
## How was this patch tested?
Added tests in DataFrameFunctionsSuite
Closes#22408 from dilipbiswal/SPARK-25417.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR proposes to respect `SessionConfigSupport` in SS datasources as well. Currently these are only respected in batch sources:
e06da95cd9/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala (L198-L203)e06da95cd9/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala (L244-L249)
If a developer makes a datasource V2 that supports both structured streaming and batch jobs, batch jobs respect a specific configuration, let's say, URL to connect and fetch data (which end users might not be aware of); however, structured streaming ends up with not supporting this (and should explicitly be set into options).
## How was this patch tested?
Unit tests were added.
Closes#22462 from HyukjinKwon/SPARK-25460.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This goes to revert sequential PRs based on some discussion and comments at https://github.com/apache/spark/pull/16677#issuecomment-422650759.
#22344#22330#22239#16677
## How was this patch tested?
Existing tests.
Closes#22481 from viirya/revert-SPARK-19355-1.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Refactor `FilterPushdownBenchmark` use `main` method. we can use 3 ways to run this test now:
1. bin/spark-submit --class org.apache.spark.sql.execution.benchmark.FilterPushdownBenchmark spark-sql_2.11-2.5.0-SNAPSHOT-tests.jar
2. build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.FilterPushdownBenchmark"
3. SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.benchmark.FilterPushdownBenchmark"
The method 2 and the method 3 do not need to compile the `spark-sql_*-tests.jar` package. So these two methods are mainly for developers to quickly do benchmark.
## How was this patch tested?
manual tests
Closes#22443 from wangyum/SPARK-25339.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Addition of numeric and list hints for SparkR.
## How was this patch tested?
Add test in test_sparkSQL.R
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21649 from huaxingao/spark-23648.
## What changes were proposed in this pull request?
This patch adds an "optimizer" prefix to nested schema pruning.
## How was this patch tested?
Should be covered by existing tests.
Closes#22475 from rxin/SPARK-4502.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The PR proposes to return the data type of the operands as a result for the `div` operator. Before the PR, `bigint` is always returned. It introduces also a `spark.sql.legacy.integralDivide.returnBigint` config in order to let the users restore the legacy behavior.
## How was this patch tested?
added UTs
Closes#22465 from mgaido91/SPARK-25457.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PR updates the migration guide in order to explain the changes introduced in the behavior of the IN operator with subqueries, in particular, the improved handling of struct attributes in these situations.
## How was this patch tested?
NA
Closes#22469 from mgaido91/SPARK-24341_followup.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This patch changes the config option `spark.sql.streaming.noDataMicroBatchesEnabled` to `spark.sql.streaming.noDataMicroBatches.enabled` to be more consistent with rest of the configs. Unfortunately there is one streaming config called `spark.sql.streaming.metricsEnabled`. For that one we should just use a fallback config and change it in a separate patch.
## How was this patch tested?
Made sure no other references to this config are in the code base:
```
> git grep "noDataMicro"
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala: buildConf("spark.sql.streaming.noDataMicroBatches.enabled")
```
Closes#22476 from rxin/SPARK-24157.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: Reynold Xin <rxin@databricks.com>
## What changes were proposed in this pull request?
Fix test that constructs a Pandas DataFrame by specifying the column order. Previously this test assumed the columns would be sorted alphabetically, however when using Python 3.6 with Pandas 0.23 or higher, the original column order is maintained. This causes the columns to get mixed up and the test errors.
Manually tested with `python/run-tests` using Python 3.6.6 and Pandas 0.23.4
Closes#22477 from BryanCutler/pyspark-tests-py36-pd23-SPARK-25471.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Improve testcase "image datasource test: read non image" to tolerate different schema representation.
Because file:/path and file:///path are both valid URI-ifications so in some environment the testcase will fail.
## How was this patch tested?
Manual.
Closes#22449 from WeichenXu123/image_url.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
For self-join/self-union, Spark will produce a physical plan which has multiple `DataSourceV2ScanExec` instances referring to the same `ReadSupport` instance. In this case, the streaming source is indeed scanned multiple times, and the `numInputRows` metrics should be counted for each scan.
Actually we already have 2 test cases to verify the behavior:
1. `StreamingQuerySuite.input row calculation with same V2 source used twice in self-join`
2. `KafkaMicroBatchSourceSuiteBase.ensure stream-stream self-join generates only one offset in log and correct metrics`.
However, in these 2 tests, the expected result is different, which is super confusing. It turns out that, the first test doesn't trigger exchange reuse, so the source is scanned twice. The second test triggers exchange reuse, and the source is scanned only once.
This PR proposes to improve these 2 tests, to test with/without exchange reuse.
## How was this patch tested?
test only change
Closes#22402 from cloud-fan/bug.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In SPARK-23711, `UnsafeProjection` supports fallback to an interpreted mode. Therefore, this pr fixed code to support the same fallback mode in `MutableProjection` based on `CodeGeneratorWithInterpretedFallback`.
## How was this patch tested?
Added tests in `CodeGeneratorWithInterpretedFallbackSuite`.
Closes#22355 from maropu/SPARK-25358.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a follow up for #22441.
1. Remove flag "-Pkafka-0-8" for Scala 2.12 build.
2. Clean up the script, simpler logic.
3. Switch to Scala version to 2.11 before script exit.
## How was this patch tested?
Manual test.
Closes#22454 from gengliangwang/revise_release_build.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
To be more consistent with other statistics based configs.
## How was this patch tested?
N/A - straightforward rename of config option. Used `git grep` to make sure there are no mention of it.
Closes#22457 from rxin/SPARK-24626.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
`spark.sql.fromJsonForceNullableSchema` -> `spark.sql.function.fromJson.forceNullable`
## How was this patch tested?
Made sure there are no more references to `spark.sql.fromJsonForceNullableSchema`.
Closes#22459 from rxin/SPARK-23173.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
