## What changes were proposed in this pull request?
The `show create table` will show a lot of generated attributes for views that created by older Spark version. This PR will basically revert https://issues.apache.org/jira/browse/SPARK-19272 back, so when you `DESC [FORMATTED|EXTENDED] view` will show the original view DDL text.
## How was this patch tested?
Unit test.
Closes#22458 from zheyuan28/testbranch.
Lead-authored-by: Chris Zhao <chris.zhao@databricks.com>
Co-authored-by: Christopher Zhao <chris.zhao@databricks.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?
There are 2 places we check for problematic `InSubquery`: the rule `ResolveSubquery` and `InSubquery.checkInputDataTypes`. We should unify them.
## How was this patch tested?
existing tests
Closes#22563 from cloud-fan/followup.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
As per the discussion in https://github.com/apache/spark/pull/22553#pullrequestreview-159192221,
override `filterKeys` violates the documented semantics.
This PR is to remove it and add documentation.
Also fix one potential non-serializable map in `FileStreamOptions`.
The only one call of `CaseInsensitiveMap`'s `filterKeys` left is
c3c45cbd76/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveOptions.scala (L88-L90)
But this one is OK.
## How was this patch tested?
Existing unit tests.
Closes#22562 from gengliangwang/SPARK-25541-FOLLOWUP.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PR removes the `InSubquery` expression which was introduced a long time ago and its only usage was removed in 4ce970d714. Hence it is not used anymore.
## How was this patch tested?
existing UTs
Closes#22556 from mgaido91/minor_insubq.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Thanks for bahchis reporting this. It is more like a follow up work for #16581, this PR fix the scenario of Python UDF accessing attributes from both side of join in join condition.
## How was this patch tested?
Add regression tests in PySpark and `BatchEvalPythonExecSuite`.
Closes#22326 from xuanyuanking/SPARK-25314.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In ElementAt, when first argument is MapType, we should coerce the key type and the second argument based on findTightestCommonType. This is not happening currently. We may produce wrong output as we will incorrectly downcast the right hand side double expression to int.
```SQL
spark-sql> select element_at(map(1,"one", 2, "two"), 2.2);
two
```
Also, when the first argument is ArrayType, the second argument should be an integer type or a smaller integral type that can be safely casted to an integer type. Currently we may do an unsafe cast. In the following case, we should fail with an error as 2.2 is not a integer index. But instead we down cast it to int currently and return a result instead.
```SQL
spark-sql> select element_at(array(1,2), 1.24D);
1
```
This PR also supports implicit cast between two MapTypes. I have followed similar logic that exists today to do implicit casts between two array types.
## How was this patch tested?
Added new tests in DataFrameFunctionSuite, TypeCoercionSuite.
Closes#22544 from dilipbiswal/SPARK-25522.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
We have an agreement that the behavior of `from/to_utc_timestamp` is corrected, although the function itself doesn't make much sense in Spark: https://issues.apache.org/jira/browse/SPARK-23715
This PR improves the document.
## How was this patch tested?
N/A
Closes#22543 from cloud-fan/doc.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Refactor `UnsafeProjectionBenchmark` to use main method.
Generate benchmark result:
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "catalyst/test:runMain org.apache.spark.sql.UnsafeProjectionBenchmark"
```
## How was this patch tested?
manual test
Closes#22493 from yucai/SPARK-25485.
Lead-authored-by: yucai <yyu1@ebay.com>
Co-authored-by: Yucai Yu <yucai.yu@foxmail.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?
Refactor `ColumnarBatchBenchmark` to use main method.
Generate benchmark result:
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "sql/test:runMain org.apache.spark.sql.execution.vectorized.ColumnarBatchBenchmark"
```
## How was this patch tested?
manual tests
Closes#22490 from yucai/SPARK-25481.
Lead-authored-by: yucai <yyu1@ebay.com>
Co-authored-by: Yucai Yu <yucai.yu@foxmail.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?
https://github.com/apache/spark/pull/20023 proposed to allow precision lose during decimal operations, to reduce the possibilities of overflow. This is a behavior change and is protected by the DECIMAL_OPERATIONS_ALLOW_PREC_LOSS config. However, that PR introduced another behavior change: pick a minimum precision for integral literals, which is not protected by a config. This PR add a new config for it: `spark.sql.literal.pickMinimumPrecision`.
This can allow users to work around issue in SPARK-25454, which is caused by a long-standing bug of negative scale.
## How was this patch tested?
a new test
Closes#22494 from cloud-fan/decimal.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Describe spark.sql.parquet.writeLegacyFormat property in Spark SQL, DataFrames and Datasets Guide.
## How was this patch tested?
N/A
Closes#22453 from seancxmao/SPARK-20937.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR contains 3 optimizations:
1) it improves significantly the operation `--` on `AttributeSet`. As a benchmark for the `--` operation, the following code has been run
```
test("AttributeSet -- benchmark") {
val attrSetA = AttributeSet((1 to 100).map { i => AttributeReference(s"c$i", IntegerType)() })
val attrSetB = AttributeSet(attrSetA.take(80).toSeq)
val attrSetC = AttributeSet((1 to 100).map { i => AttributeReference(s"c2_$i", IntegerType)() })
val attrSetD = AttributeSet((attrSetA.take(50) ++ attrSetC.take(50)).toSeq)
val attrSetE = AttributeSet((attrSetC.take(50) ++ attrSetA.take(50)).toSeq)
val n_iter = 1000000
val t0 = System.nanoTime()
(1 to n_iter) foreach { _ =>
val r1 = attrSetA -- attrSetB
val r2 = attrSetA -- attrSetC
val r3 = attrSetA -- attrSetD
val r4 = attrSetA -- attrSetE
}
val t1 = System.nanoTime()
val totalTime = t1 - t0
println(s"Average time: ${totalTime / n_iter} us")
}
```
The results are:
```
Before PR - Average time: 67674 us (100 %)
After PR - Average time: 28827 us (42.6 %)
```
2) In `ColumnPruning`, it replaces the occurrences of `(attributeSet1 -- attributeSet2).nonEmpty` with `attributeSet1.subsetOf(attributeSet2)` which is order of magnitudes more efficient (especially where there are many attributes). Running the previous benchmark replacing `--` with `subsetOf` returns:
```
Average time: 67 us (0.1 %)
```
3) Provides a more efficient way of building `AttributeSet`s, which can greatly improve the performance of the methods `references` and `outputSet` of `Expression` and `QueryPlan`. This basically avoids unneeded operations (eg. creating many `AttributeEqual` wrapper classes which could be avoided)
The overall effect of those optimizations has been tested on `ColumnPruning` with the following benchmark:
```
test("ColumnPruning benchmark") {
val attrSetA = (1 to 100).map { i => AttributeReference(s"c$i", IntegerType)() }
val attrSetB = attrSetA.take(80)
val attrSetC = attrSetA.take(20).map(a => Alias(Add(a, Literal(1)), s"${a.name}_1")())
val input = LocalRelation(attrSetA)
val query1 = Project(attrSetB, Project(attrSetA, input)).analyze
val query2 = Project(attrSetC, Project(attrSetA, input)).analyze
val query3 = Project(attrSetA, Project(attrSetA, input)).analyze
val nIter = 100000
val t0 = System.nanoTime()
(1 to nIter).foreach { _ =>
ColumnPruning(query1)
ColumnPruning(query2)
ColumnPruning(query3)
}
val t1 = System.nanoTime()
val totalTime = t1 - t0
println(s"Average time: ${totalTime / nIter} us")
}
```
The output of the test is:
```
Before PR - Average time: 733471 us (100 %)
After PR - Average time: 362455 us (49.4 %)
```
The performance improvement has been evaluated also on the `SQLQueryTestSuite`'s queries:
```
(before) org.apache.spark.sql.catalyst.optimizer.ColumnPruning 518413198 / 1377707172 2756 / 15717
(after) org.apache.spark.sql.catalyst.optimizer.ColumnPruning 415432579 / 1121147950 2756 / 15717
% Running time 80.1% / 81.3%
```
Also other rules benefit especially from (3), despite the impact is lower, eg:
```
(before) org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences 307341442 / 623436806 2154 / 16480
(after) org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences 290511312 / 560962495 2154 / 16480
% Running time 94.5% / 90.0%
```
The reason why the impact on the `SQLQueryTestSuite`'s queries is lower compared to the other benchmark is that the optimizations are more significant when the number of attributes involved is higher. Since in the tests we often have very few attributes, the effect there is lower.
## How was this patch tested?
run benchmarks + existing UTs
Closes#22364 from mgaido91/SPARK-25379.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
`CaseInsensitiveMap` is declared as Serializable. However, it is no serializable after `-` operator or `filterKeys` method.
This PR fix the issue by overriding the operator `-` and method `filterKeys`. So the we can avoid potential `NotSerializableException` on using `CaseInsensitiveMap`.
## How was this patch tested?
New test suite.
Closes#22553 from gengliangwang/fixCaseInsensitiveMap.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Currently, Spark has 7 `withTempPath` and 6 `withSQLConf` functions. This PR aims to remove duplicated and inconsistent code and reduce them to the following meaningful implementations.
**withTempPath**
- `SQLHelper.withTempPath`: The one which was used in `SQLTestUtils`.
**withSQLConf**
- `SQLHelper.withSQLConf`: The one which was used in `PlanTest`.
- `ExecutorSideSQLConfSuite.withSQLConf`: The one which doesn't throw `AnalysisException` on StaticConf changes.
- `SQLTestUtils.withSQLConf`: The one which overrides intentionally to change the active session.
```scala
protected override def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = {
SparkSession.setActiveSession(spark)
super.withSQLConf(pairs: _*)(f)
}
```
## How was this patch tested?
Pass the Jenkins with the existing tests.
Closes#22548 from dongjoon-hyun/SPARK-25534.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
The PR introduces new JSON option `pretty` which allows to turn on `DefaultPrettyPrinter` of `Jackson`'s Json generator. New option is useful in exploring of deep nested columns and in converting of JSON columns in more readable representation (look at the added test).
## How was this patch tested?
Added rount trip test which convert an JSON string to pretty representation via `from_json()` and `to_json()`.
Closes#22534 from MaxGekk/pretty-json.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add the legacy prefix for spark.sql.execution.pandas.groupedMap.assignColumnsByPosition and rename it to spark.sql.legacy.execution.pandas.groupedMap.assignColumnsByName
## How was this patch tested?
The existing tests.
Closes#22540 from gatorsmile/renameAssignColumnsByPosition.
Lead-authored-by: gatorsmile <gatorsmile@gmail.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## 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?
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?
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>
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?
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 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?
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?
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?
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?
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?
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?
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>
PythonForeachWriterSuite was failing because RowQueue now needs to have a handle on a SparkEnv with a SerializerManager, so added a mock env with a serializer manager.
Also fixed a typo in the `finally` that was hiding the real exception.
Tested PythonForeachWriterSuite locally, full tests via jenkins.
Closes#22452 from squito/SPARK-25456.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
SPARK-22333 introduced a regression in the resolution of `CURRENT_DATE` and `CURRENT_TIMESTAMP`. Before that ticket, these 2 functions were resolved in a case insensitive way. After, this depends on the value of `spark.sql.caseSensitive`.
The PR restores the previous behavior and makes their resolution case insensitive anyhow. The PR takes over #21217, therefore it closes#21217 and credit for this patch should be given to jamesthomp.
## How was this patch tested?
added UT
Closes#22440 from mgaido91/SPARK-24151.
Lead-authored-by: James Thompson <jamesthomp@users.noreply.github.com>
Co-authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR makes `GenArrayData.genCodeToCreateArrayData` method simple by using `ArrayData.createArrayData` method.
Before this PR, `genCodeToCreateArrayData` method was complicated
* Generated a temporary Java array to create `ArrayData`
* Had separate code generation path to assign values for `GenericArrayData` and `UnsafeArrayData`
After this PR, the method
* Directly generates `GenericArrayData` or `UnsafeArrayData` without a temporary array
* Has only code generation path to assign values
## How was this patch tested?
Existing UTs
Closes#22439 from kiszk/SPARK-25444.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
The PR takes over #14036 and it introduces a new expression `IntegralDivide` in order to avoid the several unneded cast added previously.
In order to prove the performance gain, the following benchmark has been run:
```
test("Benchmark IntegralDivide") {
val r = new scala.util.Random(91)
val nData = 1000000
val testDataInt = (1 to nData).map(_ => (r.nextInt(), r.nextInt()))
val testDataLong = (1 to nData).map(_ => (r.nextLong(), r.nextLong()))
val testDataShort = (1 to nData).map(_ => (r.nextInt().toShort, r.nextInt().toShort))
// old code
val oldExprsInt = testDataInt.map(x =>
Cast(Divide(Cast(Literal(x._1), DoubleType), Cast(Literal(x._2), DoubleType)), LongType))
val oldExprsLong = testDataLong.map(x =>
Cast(Divide(Cast(Literal(x._1), DoubleType), Cast(Literal(x._2), DoubleType)), LongType))
val oldExprsShort = testDataShort.map(x =>
Cast(Divide(Cast(Literal(x._1), DoubleType), Cast(Literal(x._2), DoubleType)), LongType))
// new code
val newExprsInt = testDataInt.map(x => IntegralDivide(x._1, x._2))
val newExprsLong = testDataLong.map(x => IntegralDivide(x._1, x._2))
val newExprsShort = testDataShort.map(x => IntegralDivide(x._1, x._2))
Seq(("Long", "old", oldExprsLong),
("Long", "new", newExprsLong),
("Int", "old", oldExprsInt),
("Int", "new", newExprsShort),
("Short", "old", oldExprsShort),
("Short", "new", oldExprsShort)).foreach { case (dt, t, ds) =>
val start = System.nanoTime()
ds.foreach(e => e.eval(EmptyRow))
val endNoCodegen = System.nanoTime()
println(s"Running $nData op with $t code on $dt (no-codegen): ${(endNoCodegen - start) / 1000000} ms")
}
}
```
The results on my laptop are:
```
Running 1000000 op with old code on Long (no-codegen): 600 ms
Running 1000000 op with new code on Long (no-codegen): 112 ms
Running 1000000 op with old code on Int (no-codegen): 560 ms
Running 1000000 op with new code on Int (no-codegen): 135 ms
Running 1000000 op with old code on Short (no-codegen): 317 ms
Running 1000000 op with new code on Short (no-codegen): 153 ms
```
Showing a 2-5X improvement. The benchmark doesn't include code generation as it is pretty hard to test the performance there as for such simple operations the most of the time is spent in the code generation/compilation process.
## How was this patch tested?
added UTs
Closes#22395 from mgaido91/SPARK-16323.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Output `dataFilters` in `DataSourceScanExec.metadata`.
## How was this patch tested?
unit tests
Closes#22435 from wangyum/SPARK-25423.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
There are some mistakes in examples of newly added functions. Also the format of the example results are not unified. We should fix them.
## How was this patch tested?
Manually executed the examples.
Closes#22437 from ueshin/issues/SPARK-25431/fix_examples_2.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Spark supports BloomFilter creation for ORC files. This PR aims to add test coverages to prevent accidental regressions like [SPARK-12417](https://issues.apache.org/jira/browse/SPARK-12417).
## How was this patch tested?
Pass the Jenkins with newly added test cases.
Closes#22418 from dongjoon-hyun/SPARK-25427.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
What changes were proposed in this pull request
Updated the Migration guide for the behavior changes done in the JIRA issue SPARK-23425.
How was this patch tested?
Manually verified.
Closes#22396 from sujith71955/master_newtest.
Authored-by: s71955 <sujithchacko.2010@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Fixes TPCH DDL datatype of `customer.c_nationkey` from `STRING` to `BIGINT` according to spec and `nation.nationkey` in `TPCHQuerySuite.scala`. The rest of the keys are OK.
Note, this will lead to **non-comparable previous results** to new runs involving the customer table.
## How was this patch tested?
Manual tests
Author: npoggi <npmnpm@gmail.com>
Closes#22430 from npoggi/SPARK-25439_Fix-TPCH-customer-c_nationkey.
## What changes were proposed in this pull request?
This PR aims to fix three things in `FilterPushdownBenchmark`.
**1. Use the same memory assumption.**
The following configurations are used in ORC and Parquet.
- Memory buffer for writing
- parquet.block.size (default: 128MB)
- orc.stripe.size (default: 64MB)
- Compression chunk size
- parquet.page.size (default: 1MB)
- orc.compress.size (default: 256KB)
SPARK-24692 used 1MB, the default value of `parquet.page.size`, for `parquet.block.size` and `orc.stripe.size`. But, it missed to match `orc.compress.size`. So, the current benchmark shows the result from ORC with 256KB memory for compression and Parquet with 1MB. To compare correctly, we need to be consistent.
**2. Dictionary encoding should not be enforced for all cases.**
SPARK-24206 enforced dictionary encoding for all test cases. This PR recovers the default behavior in general and enforces dictionary encoding only in case of `prepareStringDictTable`.
**3. Generate test result on AWS r3.xlarge**
SPARK-24206 generated the result on AWS in order to reproduce and compare easily. This PR also aims to update the result on the same machine again in the same reason. Specifically, AWS r3.xlarge with Instance Store is used.
## How was this patch tested?
Manual. Enable the test cases and run `FilterPushdownBenchmark` on `AWS r3.xlarge`. It takes about 4 hours 15 minutes.
Closes#22427 from dongjoon-hyun/SPARK-25438.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose overriding session options by extra options in DataSource V2. Extra options are more specific and set via `.option()`, and should overwrite more generic session options. Entries from seconds map overwrites entries with the same key from the first map, for example:
```Scala
scala> Map("option" -> false) ++ Map("option" -> true)
res0: scala.collection.immutable.Map[String,Boolean] = Map(option -> true)
```
## How was this patch tested?
Added a test for checking which option is propagated to a data source in `load()`.
Closes#22413 from MaxGekk/session-options.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
In the dev list, we can still discuss whether the next version is 2.5.0 or 3.0.0. Let us first bump the master branch version to `2.5.0-SNAPSHOT`.
## How was this patch tested?
N/A
Closes#22426 from gatorsmile/bumpVersionMaster.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This pr removed the duplicate fallback logic in `UnsafeProjection`.
This pr comes from #22355.
## How was this patch tested?
Added tests in `CodeGeneratorWithInterpretedFallbackSuite`.
Closes#22417 from maropu/SPARK-25426.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
There are some mistakes in examples of newly added functions. Also the format of the example results are not unified. We should fix and unify them.
## How was this patch tested?
Manually executed the examples.
Closes#22421 from ueshin/issues/SPARK-25431/fix_examples.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
When Hive support enabled, Hive catalog puts extra storage properties into table metadata even for DataSource tables, but we should not have them.
## How was this patch tested?
Modified a test.
Closes#22410 from ueshin/issues/SPARK-25418/hive_metadata.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR ensures to call `super.afterAll()` in `override afterAll()` method for test suites.
* Some suites did not call `super.afterAll()`
* Some suites may call `super.afterAll()` only under certain condition
* Others never call `super.afterAll()`.
This PR also ensures to call `super.beforeAll()` in `override beforeAll()` for test suites.
## How was this patch tested?
Existing UTs
Closes#22337 from kiszk/SPARK-25338.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
(Link to Jira: https://issues.apache.org/jira/browse/SPARK-25406)
## What changes were proposed in this pull request?
The current use of `withSQLConf` in `ParquetSchemaPruningSuite.scala` is incorrect. The desired configuration settings are not being set when running the test cases.
This PR fixes that defective usage and addresses the test failures that were previously masked by that defect.
## How was this patch tested?
I added code to relevant test cases to print the expected SQL configuration settings and found that the settings were not being set as expected. When I changed the order of calls to `test` and `withSQLConf` I found that the configuration settings were being set as expected.
Closes#22394 from mallman/spark-25406-fix_broken_schema_pruning_tests.
Authored-by: Michael Allman <msa@allman.ms>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
This follow-up patch addresses [the review comment](https://github.com/apache/spark/pull/22344/files#r217070658) by adding a helper method to simplify code and fixing style issue.
## How was this patch tested?
Existing unit tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#22409 from viirya/SPARK-25352-followup.
## What changes were proposed in this pull request?
In RuleExecutor, after applying a rule, if the plan has changed, the before and after plan will be logged using level "trace". At times, however, such information can be very helpful for debugging. Hence, making the log level configurable in SQLConf would allow users to turn on the plan change log independently and save the trouble of tweaking log4j settings. Meanwhile, filtering plan change log for specific rules can also be very useful.
So this PR adds two SQL configurations:
1. spark.sql.optimizer.planChangeLog.level - set a specific log level for logging plan changes after a rule is applied.
2. spark.sql.optimizer.planChangeLog.rules - enable plan change logging only for a set of specified rules, separated by commas.
## How was this patch tested?
Added UT.
Closes#22406 from maryannxue/spark-25415.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Field metadata removed from SparkPlanInfo in #18600 . Corresponding, many meta data was also removed from event SparkListenerSQLExecutionStart in Spark event log. If we want to analyze event log to get all input paths, we couldn't get them. Instead, simpleString of SparkPlanInfo JSON only display 100 characters, it won't help.
Before 2.3, the fragment of SparkListenerSQLExecutionStart in event log looks like below (It contains the metadata field which has the intact information):
>{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionStart", Location: InMemoryFileIndex[hdfs://cluster1/sys/edw/test1/test2/test3/test4..., "metadata": {"Location": "InMemoryFileIndex[hdfs://cluster1/sys/edw/test1/test2/test3/test4/test5/snapshot/dt=20180904]","ReadSchema":"struct<snpsht_start_dt:date,snpsht_end_dt:date,am_ntlogin_name:string,am_first_name:string,am_last_name:string,isg_name:string,CRE_DATE:date,CRE_USER:string,UPD_DATE:timestamp,UPD_USER:string>"}
After #18600, metadata field was removed.
>{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionStart", Location: InMemoryFileIndex[hdfs://cluster1/sys/edw/test1/test2/test3/test4...,
So I add this field back to SparkPlanInfo class. Then it will log out the meta data to event log. Intact information in event log is very useful for offline job analysis.
## How was this patch tested?
Unit test
Closes#22353 from LantaoJin/SPARK-25357.
Authored-by: LantaoJin <jinlantao@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PR fixes NPE in `UnivocityParser` caused by malformed CSV input. In some cases, `uniVocity` parser can return `null` for bad input. In the PR, I propose to check result of parsing and not propagate NPE to upper layers.
## How was this patch tested?
I added a test which reproduce the issue and tested by `CSVSuite`.
Closes#22374 from MaxGekk/npe-on-bad-csv.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Schema pruning doesn't work if nested column is used in where clause.
For example,
```
sql("select name.first from contacts where name.first = 'David'")
== Physical Plan ==
*(1) Project [name#19.first AS first#40]
+- *(1) Filter (isnotnull(name#19) && (name#19.first = David))
+- *(1) FileScan parquet [name#19] Batched: false, Format: Parquet, PartitionFilters: [],
PushedFilters: [IsNotNull(name)], ReadSchema: struct<name:struct<first:string,middle:string,last:string>>
```
In above query plan, the scan node reads the entire schema of `name` column.
This issue is reported by:
https://github.com/apache/spark/pull/21320#issuecomment-419290197
The cause is that we infer a root field from expression `IsNotNull(name)`. However, for such expression, we don't really use the nested fields of this root field, so we can ignore the unnecessary nested fields.
## How was this patch tested?
Unit tests.
Closes#22357 from viirya/SPARK-25363.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
We have optimization on global limit to evenly distribute limit rows across all partitions. This optimization doesn't work for ordered results.
For a query ending with sort + limit, in most cases it is performed by `TakeOrderedAndProjectExec`.
But if limit number is bigger than `SQLConf.TOP_K_SORT_FALLBACK_THRESHOLD`, global limit will be used. At this moment, we need to do ordered global limit.
## How was this patch tested?
Unit tests.
Closes#22344 from viirya/SPARK-25352.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR is to fix the null handling in BooleanSimplification. In the rule BooleanSimplification, there are two cases that do not properly handle null values. The optimization is not right if either side is null. This PR is to fix them.
## How was this patch tested?
Added test cases
Closes#22390 from gatorsmile/fixBooleanSimplification.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The leftover state from running a continuous processing streaming job should not affect later microbatch execution jobs. If a continuous processing job runs and the same thread gets reused for a microbatch execution job in the same environment, the microbatch job could get wrong answers because it can attempt to load the wrong version of the state.
## How was this patch tested?
New and existing unit tests
Closes#22386 from mukulmurthy/25399-streamthread.
Authored-by: Mukul Murthy <mukul.murthy@gmail.com>
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
## What changes were proposed in this pull request?
Correct some comparisons between unrelated types to what they seem to… have been trying to do
## How was this patch tested?
Existing tests.
Closes#22384 from srowen/SPARK-25398.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Like `INSERT OVERWRITE DIRECTORY USING` syntax, `INSERT OVERWRITE DIRECTORY STORED AS` should not generate files with duplicate fields because Spark cannot read those files back.
**INSERT OVERWRITE DIRECTORY USING**
```scala
scala> sql("INSERT OVERWRITE DIRECTORY 'file:///tmp/parquet' USING parquet SELECT 'id', 'id2' id")
... ERROR InsertIntoDataSourceDirCommand: Failed to write to directory ...
org.apache.spark.sql.AnalysisException: Found duplicate column(s) when inserting into file:/tmp/parquet: `id`;
```
**INSERT OVERWRITE DIRECTORY STORED AS**
```scala
scala> sql("INSERT OVERWRITE DIRECTORY 'file:///tmp/parquet' STORED AS parquet SELECT 'id', 'id2' id")
// It generates corrupted files
scala> spark.read.parquet("/tmp/parquet").show
18/09/09 22:09:57 WARN DataSource: Found duplicate column(s) in the data schema and the partition schema: `id`;
```
## How was this patch tested?
Pass the Jenkins with newly added test cases.
Closes#22378 from dongjoon-hyun/SPARK-25389.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose new CSV option `emptyValue` and an update in the SQL Migration Guide which describes how to revert previous behavior when empty strings were not written at all. Since Spark 2.4, empty strings are saved as `""` to distinguish them from saved `null`s.
Closes#22234Closes#22367
## How was this patch tested?
It was tested by `CSVSuite` and new tests added in the PR #22234Closes#22389 from MaxGekk/csv-empty-value-master.
Lead-authored-by: Mario Molina <mmolimar@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
It turns out it's a bug that a `DataSourceV2ScanExec` instance may be referred to in the execution plan multiple times. This bug is fixed by https://github.com/apache/spark/pull/22284 and now we have corrected SQL metrics for batch queries.
Thus we don't need the hack in `ProgressReporter` anymore, which fixes the same metrics problem for streaming queries.
## How was this patch tested?
existing tests
Closes#22380 from cloud-fan/followup.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
SPARK-21281 introduced a check for the inputs of `CreateStructLike` to be non-empty. This means that `struct()`, which was previously considered valid, now throws an Exception. This behavior change was introduced in 2.3.0. The change may break users' application on upgrade and it causes `VectorAssembler` to fail when an empty `inputCols` is defined.
The PR removes the added check making `struct()` valid again.
## How was this patch tested?
added UT
Closes#22373 from mgaido91/SPARK-25371.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the Planner, we collect the placeholder which need to be substituted in the query execution plan and once we plan them, we substitute the placeholder with the effective plan.
In this second phase, we rely on the `==` comparison, ie. the `equals` method. This means that if two placeholder plans - which are different instances - have the same attributes (so that they are equal, according to the equal method) they are both substituted with their corresponding new physical plans. So, in such a situation, the first time we substitute both them with the first of the 2 new generated plan and the second time we substitute nothing.
This is usually of no harm for the execution of the query itself, as the 2 plans are identical. But since they are the same instance, now, the local variables are shared (which is unexpected). This causes issues for the metrics collected, as the same node is executed 2 times, so the metrics are accumulated 2 times, wrongly.
The PR proposes to use the `eq` method in checking which placeholder needs to be substituted,; thus in the previous situation, actually both the two different physical nodes which are created (one for each time the logical plan appears in the query plan) are used and the metrics are collected properly for each of them.
## How was this patch tested?
added UT
Closes#22284 from mgaido91/SPARK-25278.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Add the version number for the new APIs.
## How was this patch tested?
N/A
Closes#22377 from gatorsmile/followup24849.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR is to solve the CodeGen code generated by fast hash, and there is no need to apply for a block of memory for every new entry, because unsafeRow's memory can be reused.
## How was this patch tested?
the existed test cases.
Closes#21968 from heary-cao/updateNewMemory.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
How to reproduce:
```scala
spark.sql("CREATE TABLE tbl(id long)")
spark.sql("INSERT OVERWRITE TABLE tbl VALUES 4")
spark.sql("CREATE VIEW view1 AS SELECT id FROM tbl")
spark.sql(s"INSERT OVERWRITE LOCAL DIRECTORY '/tmp/spark/parquet' " +
"STORED AS PARQUET SELECT ID FROM view1")
spark.read.parquet("/tmp/spark/parquet").schema
scala> spark.read.parquet("/tmp/spark/parquet").schema
res10: org.apache.spark.sql.types.StructType = StructType(StructField(id,LongType,true))
```
The schema should be `StructType(StructField(ID,LongType,true))` as we `SELECT ID FROM view1`.
This pr fix this issue.
## How was this patch tested?
unit tests
Closes#22359 from wangyum/SPARK-25313-FOLLOW-UP.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Apache Spark doesn't create Hive table with duplicated fields in both case-sensitive and case-insensitive mode. However, if Spark creates ORC files in case-sensitive mode first and create Hive table on that location, where it's created. In this situation, field resolution should fail in case-insensitive mode. Otherwise, we don't know which columns will be returned or filtered. Previously, SPARK-25132 fixed the same issue in Parquet.
Here is a simple example:
```
val data = spark.range(5).selectExpr("id as a", "id * 2 as A")
spark.conf.set("spark.sql.caseSensitive", true)
data.write.format("orc").mode("overwrite").save("/user/hive/warehouse/orc_data")
sql("CREATE TABLE orc_data_source (A LONG) USING orc LOCATION '/user/hive/warehouse/orc_data'")
spark.conf.set("spark.sql.caseSensitive", false)
sql("select A from orc_data_source").show
+---+
| A|
+---+
| 3|
| 2|
| 4|
| 1|
| 0|
+---+
```
See #22148 for more details about parquet data source reader.
## How was this patch tested?
Unit tests added.
Closes#22262 from seancxmao/SPARK-25175.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
How to reproduce:
```scala
val df1 = spark.createDataFrame(Seq(
(1, 1)
)).toDF("a", "b").withColumn("c", lit(null).cast("int"))
val df2 = df1.union(df1).withColumn("d", spark_partition_id).filter($"c".isNotNull)
df2.show
+---+---+----+---+
| a| b| c| d|
+---+---+----+---+
| 1| 1|null| 0|
| 1| 1|null| 1|
+---+---+----+---+
```
`filter($"c".isNotNull)` was transformed to `(null <=> c#10)` before https://github.com/apache/spark/pull/19201, but it is transformed to `(c#10 = null)` since https://github.com/apache/spark/pull/20155. This pr revert it to `(null <=> c#10)` to fix this issue.
## How was this patch tested?
unit tests
Closes#22368 from wangyum/SPARK-25368.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
When running TPC-DS benchmarks on 2.4 release, npoggi and winglungngai saw more than 10% performance regression on the following queries: q67, q24a and q24b. After we applying the PR https://github.com/apache/spark/pull/22338, the performance regression still exists. If we revert the changes in https://github.com/apache/spark/pull/19222, npoggi and winglungngai found the performance regression was resolved. Thus, this PR is to revert the related changes for unblocking the 2.4 release.
In the future release, we still can continue the investigation and find out the root cause of the regression.
## How was this patch tested?
The existing test cases
Closes#22361 from gatorsmile/revertMemoryBlock.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Add new optimization rule to eliminate unnecessary shuffling by flipping adjacent Window expressions.
## How was this patch tested?
Tested with unit tests, integration tests, and manual tests.
Closes#17899 from ptkool/adjacent_window_optimization.
Authored-by: ptkool <michael.styles@shopify.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
At Spark 2.0.0, SPARK-14335 adds some [commented-out test coverages](https://github.com/apache/spark/pull/12117/files#diff-dd4b39a56fac28b1ced6184453a47358R177
). This PR enables them because it's supported since 2.0.0.
## How was this patch tested?
Pass the Jenkins with re-enabled test coverage.
Closes#22363 from dongjoon-hyun/SPARK-25375.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This took me a while to debug and find out. Looks we better at least leave a debug log that SQL text for a view will be used.
Here's how I got there:
**Hive:**
```
CREATE TABLE emp AS SELECT 'user' AS name, 'address' as address;
CREATE DATABASE d100;
CREATE FUNCTION d100.udf100 AS 'org.apache.hadoop.hive.ql.udf.generic.GenericUDFUpper';
CREATE VIEW testview AS SELECT d100.udf100(name) FROM default.emp;
```
**Spark:**
```
sql("SELECT * FROM testview").show()
```
```
scala> sql("SELECT * FROM testview").show()
org.apache.spark.sql.AnalysisException: Undefined function: 'd100.udf100'. This function is neither a registered temporary function nor a permanent function registered in the database 'default'.; line 1 pos 7
```
Under the hood, it actually makes sense since the view is defined as `SELECT d100.udf100(name) FROM default.emp;` and Hive API:
```
org.apache.hadoop.hive.ql.metadata.Table.getViewExpandedText()
```
This returns a wrongly qualified SQL string for the view as below:
```
SELECT `d100.udf100`(`emp`.`name`) FROM `default`.`emp`
```
which works fine in Hive but not in Spark.
## How was this patch tested?
Manually:
```
18/09/06 19:32:48 DEBUG HiveSessionCatalog: 'SELECT `d100.udf100`(`emp`.`name`) FROM `default`.`emp`' will be used for the view(testview).
```
Closes#22351 from HyukjinKwon/minor-debug.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Before Apache Spark 2.3, table properties were ignored when writing data to a hive table(created with STORED AS PARQUET/ORC syntax), because the compression configurations were not passed to the FileFormatWriter in hadoopConf. Then it was fixed in #20087. But actually for CTAS with USING PARQUET/ORC syntax, table properties were ignored too when convertMastore, so the test case for CTAS not supported.
Now it has been fixed in #20522 , the test case should be enabled too.
## How was this patch tested?
This only re-enables the test cases of previous PR.
Closes#22302 from fjh100456/compressionCodec.
Authored-by: fjh100456 <fu.jinhua6@zte.com.cn>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Add test cases for fromString
## How was this patch tested?
N/A
Closes#22345 from gatorsmile/addTest.
Authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In SharedSparkSession and TestHive, we need to disable the rule ConvertToLocalRelation for better test case coverage.
## How was this patch tested?
Identify the failures after excluding "ConvertToLocalRelation" rule.
Closes#22270 from dilipbiswal/SPARK-25267-final.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This pr removed the method `updateBytesReadWithFileSize` in `FileScanRDD` because it computes input metrics by file size supported in Hadoop 2.5 and earlier. The current Spark does not support the versions, so it causes wrong input metric numbers.
This is rework from #22232.
Closes#22232
## How was this patch tested?
Added tests in `FileBasedDataSourceSuite`.
Closes#22324 from maropu/pr22232-2.
Lead-authored-by: dujunling <dujunling@huawei.com>
Co-authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This is a follow-up pr of #22200.
When casting to decimal type, if `Cast.canNullSafeCastToDecimal()`, overflow won't happen, so we don't need to check the result of `Decimal.changePrecision()`.
## How was this patch tested?
Existing tests.
Closes#22352 from ueshin/issues/SPARK-25208/reduce_code_size.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This is not a perfect solution. It is designed to minimize complexity on the basis of solving problems.
It is effective for English, Chinese characters, Japanese, Korean and so on.
```scala
before:
+---+---------------------------+-------------+
|id |中国 |s2 |
+---+---------------------------+-------------+
|1 |ab |[a] |
|2 |null |[中国, abc] |
|3 |ab1 |[hello world]|
|4 |か行 きゃ(kya) きゅ(kyu) きょ(kyo) |[“中国] |
|5 |中国（你好）a |[“中（国）, 312] |
|6 |中国山(东)服务区 |[“中(国）] |
|7 |中国山东服务区 |[中(国)] |
|8 | |[中国] |
+---+---------------------------+-------------+
after:
+---+-----------------------------------+----------------+
|id |中国 |s2 |
+---+-----------------------------------+----------------+
|1 |ab |[a] |
|2 |null |[中国, abc] |
|3 |ab1 |[hello world] |
|4 |か行 きゃ(kya) きゅ(kyu) きょ(kyo) |[“中国] |
|5 |中国（你好）a |[“中（国）, 312]|
|6 |中国山(东)服务区 |[“中(国）] |
|7 |中国山东服务区 |[中(国)] |
|8 | |[中国] |
+---+-----------------------------------+----------------+
```
## What changes were proposed in this pull request?
When there are wide characters such as Chinese characters or Japanese characters in the data, the show method has a alignment problem.
Try to fix this problem.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22048 from xuejianbest/master.
Authored-by: xuejianbest <384329882@qq.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to extended `to_json` and support any types as element types of input arrays. It should allow converting arrays of primitive types and arrays of arrays. For example:
```
select to_json(array('1','2','3'))
> ["1","2","3"]
select to_json(array(array(1,2,3),array(4)))
> [[1,2,3],[4]]
```
## How was this patch tested?
Added a couple sql tests for arrays of primitive type and of arrays. Also I added round trip test `from_json` -> `to_json`.
Closes#22226 from MaxGekk/to_json-array.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
`HiveExternalCatalogVersionsSuite` Scala-2.12 test has been failing due to class path issue. It is marked as `ABORTED` because it fails at `beforeAll` during data population stage.
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.7-ubuntu-scala-2.12/
```
org.apache.spark.sql.hive.HiveExternalCatalogVersionsSuite *** ABORTED ***
Exception encountered when invoking run on a nested suite - spark-submit returned with exit code 1.
```
The root cause of the failure is that `runSparkSubmit` mixes 2.4.0-SNAPSHOT classes and old Spark (2.1.3/2.2.2/2.3.1) together during `spark-submit`. This PR aims to provide `non-test` mode execution mode to `runSparkSubmit` by removing the followings.
- SPARK_TESTING
- SPARK_SQL_TESTING
- SPARK_PREPEND_CLASSES
- SPARK_DIST_CLASSPATH
Previously, in the class path, new Spark classes are behind the old Spark classes. So, new ones are unseen. However, Spark 2.4.0 reveals this bug due to the recent data source class changes.
## How was this patch tested?
Manual test. After merging, it will be tested via Jenkins.
```scala
$ dev/change-scala-version.sh 2.12
$ build/mvn -DskipTests -Phive -Pscala-2.12 clean package
$ build/mvn -Phive -Pscala-2.12 -Dtest=none -DwildcardSuites=org.apache.spark.sql.hive.HiveExternalCatalogVersionsSuite test
...
HiveExternalCatalogVersionsSuite:
- backward compatibility
...
Tests: succeeded 1, failed 0, canceled 0, ignored 0, pending 0
All tests passed.
```
Closes#22340 from dongjoon-hyun/SPARK-25337.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Let's see the follow example:
```
val location = "/tmp/t"
val df = spark.range(10).toDF("id")
df.write.format("parquet").saveAsTable("tbl")
spark.sql("CREATE VIEW view1 AS SELECT id FROM tbl")
spark.sql(s"CREATE TABLE tbl2(ID long) USING parquet location $location")
spark.sql("INSERT OVERWRITE TABLE tbl2 SELECT ID FROM view1")
println(spark.read.parquet(location).schema)
spark.table("tbl2").show()
```
The output column name in schema will be `id` instead of `ID`, thus the last query shows nothing from `tbl2`.
By enabling the debug message we can see that the output naming is changed from `ID` to `id`, and then the `outputColumns` in `InsertIntoHadoopFsRelationCommand` is changed in `RemoveRedundantAliases`.
**To guarantee correctness**, we should change the output columns from `Seq[Attribute]` to `Seq[String]` to avoid its names being replaced by optimizer.
I will fix project elimination related rules in https://github.com/apache/spark/pull/22311 after this one.
## How was this patch tested?
Unit test.
Closes#22320 from gengliangwang/fixOutputSchema.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
An alternative fix for https://github.com/apache/spark/pull/21698
When Spark rerun tasks for an RDD, there are 3 different behaviors:
1. determinate. Always return the same result with same order when rerun.
2. unordered. Returns same data set in random order when rerun.
3. indeterminate. Returns different result when rerun.
Normally Spark doesn't need to care about it. Spark runs stages one by one, when a task is failed, just rerun it. Although the rerun task may return a different result, users will not be surprised.
However, Spark may rerun a finished stage when seeing fetch failures. When this happens, Spark needs to rerun all the tasks of all the succeeding stages if the RDD output is indeterminate, because the input of the succeeding stages has been changed.
If the RDD output is determinate, we only need to rerun the failed tasks of the succeeding stages, because the input doesn't change.
If the RDD output is unordered, it's same as determinate, because shuffle partitioner is always deterministic(round-robin partitioner is not a shuffle partitioner that extends `org.apache.spark.Partitioner`), so the reducers will still get the same input data set.
This PR fixed the failure handling for `repartition`, to avoid correctness issues.
For `repartition`, it applies a stateful map function to generate a round-robin id, which is order sensitive and makes the RDD's output indeterminate. When the stage contains `repartition` reruns, we must also rerun all the tasks of all the succeeding stages.
**future improvement:**
1. Currently we can't rollback and rerun a shuffle map stage, and just fail. We should fix it later. https://issues.apache.org/jira/browse/SPARK-25341
2. Currently we can't rollback and rerun a result stage, and just fail. We should fix it later. https://issues.apache.org/jira/browse/SPARK-25342
3. We should provide public API to allow users to tag the random level of the RDD's computing function.
## How is this pull request tested?
a new test case
Closes#22112 from cloud-fan/repartition.
Lead-authored-by: Wenchen Fan <wenchen@databricks.com>
Co-authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This is a follow-up of #22313 and aim to ignore the micro benchmark test which takes over 2 minutes in Jenkins.
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-sbt-hadoop-2.6/4939/consoleFull
## How was this patch tested?
The test case should be ignored in Jenkins.
```
[info] FilterPushdownBenchmark:
...
[info] - Pushdown benchmark with many filters !!! IGNORED !!!
```
Closes#22336 from dongjoon-hyun/SPARK-25306-2.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/22259 .
Scala case class has a wide surface: apply, unapply, accessors, copy, etc.
In https://github.com/apache/spark/pull/22259 , we change the type of `UserDefinedFunction.inputTypes` from `Option[Seq[DataType]]` to `Option[Seq[Schema]]`. This breaks backward compatibility.
This PR changes the type back, and use a `var` to keep the new nullable info.
## How was this patch tested?
N/A
Closes#22319 from cloud-fan/revert.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Revert SPARK-24863 (#21819) and SPARK-24748 (#21721) as per discussion in #21721. We will revisit them when the data source v2 APIs are out.
## How was this patch tested?
Jenkins
Closes#22334 from zsxwing/revert-SPARK-24863-SPARK-24748.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In both ORC data sources, `createFilter` function has exponential time complexity due to its skewed filter tree generation. This PR aims to improve it by using new `buildTree` function.
**REPRODUCE**
```scala
// Create and read 1 row table with 1000 columns
sql("set spark.sql.orc.filterPushdown=true")
val selectExpr = (1 to 1000).map(i => s"id c$i")
spark.range(1).selectExpr(selectExpr: _*).write.mode("overwrite").orc("/tmp/orc")
print(s"With 0 filters, ")
spark.time(spark.read.orc("/tmp/orc").count)
// Increase the number of filters
(20 to 30).foreach { width =>
val whereExpr = (1 to width).map(i => s"c$i is not null").mkString(" and ")
print(s"With $width filters, ")
spark.time(spark.read.orc("/tmp/orc").where(whereExpr).count)
}
```
**RESULT**
```scala
With 0 filters, Time taken: 653 ms
With 20 filters, Time taken: 962 ms
With 21 filters, Time taken: 1282 ms
With 22 filters, Time taken: 1982 ms
With 23 filters, Time taken: 3855 ms
With 24 filters, Time taken: 6719 ms
With 25 filters, Time taken: 12669 ms
With 26 filters, Time taken: 25032 ms
With 27 filters, Time taken: 49585 ms
With 28 filters, Time taken: 98980 ms // over 1 min 38 seconds
With 29 filters, Time taken: 198368 ms // over 3 mins
With 30 filters, Time taken: 393744 ms // over 6 mins
```
**AFTER THIS PR**
```scala
With 0 filters, Time taken: 774 ms
With 20 filters, Time taken: 601 ms
With 21 filters, Time taken: 399 ms
With 22 filters, Time taken: 679 ms
With 23 filters, Time taken: 363 ms
With 24 filters, Time taken: 342 ms
With 25 filters, Time taken: 336 ms
With 26 filters, Time taken: 352 ms
With 27 filters, Time taken: 322 ms
With 28 filters, Time taken: 302 ms
With 29 filters, Time taken: 307 ms
With 30 filters, Time taken: 301 ms
```
## How was this patch tested?
Pass the Jenkins with newly added test cases.
Closes#22313 from dongjoon-hyun/SPARK-25306.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
I made one pass over barrier APIs added to Spark 2.4 and updates some scopes and docs. I will update Python docs once Scala doc was reviewed.
One major issue is that `BarrierTaskContext` implements `TaskContextImpl` that exposes some public methods. And internally there were several direct references to `TaskContextImpl` methods instead of `TaskContext`. This PR moved some methods from `TaskContextImpl` to `TaskContext`, remaining package private, and used delegate methods to avoid inheriting `TaskContextImp` and exposing unnecessary APIs.
TODOs:
- [x] scala doc
- [x] python doc (#22261 ).
Closes#22240 from mengxr/SPARK-25248.
Authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
Previously in `TakeOrderedAndProjectSuite` the SparkSession will not get recycled when the test suite finishes.
## How was this patch tested?
N/A
Closes#22330 from jiangxb1987/SPARK-19355.
Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR integrates handling of `UnsafeArrayData` and `GenericArrayData` into one. The current `CodeGenerator.createUnsafeArray` handles only allocation of `UnsafeArrayData`.
This PR introduces a new method `createArrayData` that returns a code to allocate `UnsafeArrayData` or `GenericArrayData` and to assign a value into the allocated array.
This PR also reduce the size of generated code by calling a runtime helper.
This PR replaced `createArrayData` with `createUnsafeArray`. This PR also refactor `ArraySetLike` that can be used for `ArrayDistinct`, too.
This PR also refactors`ArrayDistinct` to use `ArraryBuilder`.
## How was this patch tested?
Existing tests
Closes#21912 from kiszk/SPARK-24962.
Lead-authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Co-authored-by: Takuya UESHIN <ueshin@happy-camper.st>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR fixes a problem that `ArraysOverlap` function throws a `CompilationException` with non-nullable array type.
The following is the stack trace of the original problem:
```
Code generation of arrays_overlap([1,2,3], [4,5,3]) failed:
java.util.concurrent.ExecutionException: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 56, Column 11: failed to compile: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 56, Column 11: Expression "isNull_0" is not an rvalue
java.util.concurrent.ExecutionException: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 56, Column 11: failed to compile: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 56, Column 11: Expression "isNull_0" is not an rvalue
at com.google.common.util.concurrent.AbstractFuture$Sync.getValue(AbstractFuture.java:306)
at com.google.common.util.concurrent.AbstractFuture$Sync.get(AbstractFuture.java:293)
at com.google.common.util.concurrent.AbstractFuture.get(AbstractFuture.java:116)
at com.google.common.util.concurrent.Uninterruptibles.getUninterruptibly(Uninterruptibles.java:135)
at com.google.common.cache.LocalCache$Segment.getAndRecordStats(LocalCache.java:2410)
at com.google.common.cache.LocalCache$Segment.loadSync(LocalCache.java:2380)
at com.google.common.cache.LocalCache$Segment.lockedGetOrLoad(LocalCache.java:2342)
at com.google.common.cache.LocalCache$Segment.get(LocalCache.java:2257)
at com.google.common.cache.LocalCache.get(LocalCache.java:4000)
at com.google.common.cache.LocalCache.getOrLoad(LocalCache.java:4004)
at com.google.common.cache.LocalCache$LocalLoadingCache.get(LocalCache.java:4874)
at org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$.compile(CodeGenerator.scala:1305)
at org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection$.create(GenerateMutableProjection.scala:143)
at org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection$.create(GenerateMutableProjection.scala:48)
at org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection$.create(GenerateMutableProjection.scala:32)
at org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator.generate(CodeGenerator.scala:1260)
```
## How was this patch tested?
Added test in `CollectionExpressionSuite`.
Closes#22317 from kiszk/SPARK-25310.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
Invoking ArrayContains function with non nullable array type throws the following error in the code generation phase. Below is the error snippet.
```SQL
Code generation of array_contains([1,2,3], 1) failed:
java.util.concurrent.ExecutionException: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 40, Column 11: failed to compile: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 40, Column 11: Expression "isNull_0" is not an rvalue
java.util.concurrent.ExecutionException: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 40, Column 11: failed to compile: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 40, Column 11: Expression "isNull_0" is not an rvalue
at com.google.common.util.concurrent.AbstractFuture$Sync.getValue(AbstractFuture.java:306)
at com.google.common.util.concurrent.AbstractFuture$Sync.get(AbstractFuture.java:293)
at com.google.common.util.concurrent.AbstractFuture.get(AbstractFuture.java:116)
at com.google.common.util.concurrent.Uninterruptibles.getUninterruptibly(Uninterruptibles.java:135)
at com.google.common.cache.LocalCache$Segment.getAndRecordStats(LocalCache.java:2410)
at com.google.common.cache.LocalCache$Segment.loadSync(LocalCache.java:2380)
at com.google.common.cache.LocalCache$Segment.lockedGetOrLoad(LocalCache.java:2342)
at com.google.common.cache.LocalCache$Segment.get(LocalCache.java:2257)
at com.google.common.cache.LocalCache.get(LocalCache.java:4000)
at com.google.common.cache.LocalCache.getOrLoad(LocalCache.java:4004)
at com.google.common.cache.LocalCache$LocalLoadingCache.get(LocalCache.java:4874)
at org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$.compile(CodeGenerator.scala:1305)
```
## How was this patch tested?
Added test in CollectionExpressionSuite.
Closes#22315 from dilipbiswal/SPARK-25308.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
remove test-2.10.jar and add test-2.12.jar.
## How was this patch tested?
```
$ sbt -Dscala-2.12
> ++ 2.12.6
> project hive
> testOnly *HiveSparkSubmitSuite -- -z "8489"
```
Closes#22308 from sadhen/SPARK-8489-FOLLOWUP.
Authored-by: Darcy Shen <sadhen@zoho.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR works for one of TODOs in `GenerateUnsafeProjection` "if the nullability of field is correct, we can use it to save null check" to simplify generated code.
When `nullable=false` in `DataType`, `GenerateUnsafeProjection` removed code for null checks in the generated Java code.
## How was this patch tested?
Added new test cases into `GenerateUnsafeProjectionSuite`
Closes#20637 from kiszk/SPARK-23466.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
Move the output verification of Explain test cases to a new suite ExplainSuite.
## How was this patch tested?
N/A
Closes#22300 from gatorsmile/test3200.
Authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Currently, filter pushdown will not work if Parquet schema and Hive metastore schema are in different letter cases even spark.sql.caseSensitive is false.
Like the below case:
```scala
spark.sparkContext.hadoopConfiguration.setInt("parquet.block.size", 8 * 1024 * 1024)
spark.range(1, 40 * 1024 * 1024, 1, 1).sortWithinPartitions("id").write.parquet("/tmp/t")
sql("CREATE TABLE t (ID LONG) USING parquet LOCATION '/tmp/t'")
sql("select * from t where id < 100L").write.csv("/tmp/id")
```
Although filter "ID < 100L" is generated by Spark, it fails to pushdown into parquet actually, Spark still does the full table scan when reading.
This PR provides a case-insensitive field resolution to make it work.
Before - "ID < 100L" fail to pushedown:
<img width="273" alt="screen shot 2018-08-23 at 10 08 26 pm" src="https://user-images.githubusercontent.com/2989575/44530558-40ef8b00-a721-11e8-8abc-7f97671590d3.png">
After - "ID < 100L" pushedown sucessfully:
<img width="267" alt="screen shot 2018-08-23 at 10 08 40 pm" src="https://user-images.githubusercontent.com/2989575/44530567-44831200-a721-11e8-8634-e9f664b33d39.png">
## How was this patch tested?
Added UTs.
Closes#22197 from yucai/SPARK-25207.
Authored-by: yucai <yyu1@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Switch `org.apache.hive.service.server.HiveServer2` to register its shutdown callback with Spark's `ShutdownHookManager`, rather than direct with the Java Runtime callback.
This avoids race conditions in shutdown where the filesystem is shutdown before the flush/write/rename of the event log is completed, particularly on object stores where the write and rename can be slow.
## How was this patch tested?
There's no explicit unit for test this, which is consistent with every other shutdown hook in the codebase.
* There's an implicit test when the scalatest process is halted.
* More manual/integration testing is needed.
HADOOP-15679 has added the ability to explicitly execute the hadoop shutdown hook sequence which spark uses; that could be stabilized for testing if desired, after which all the spark hooks could be tested. Until then: external system tests only.
Author: Steve Loughran <stevel@hortonworks.com>
Closes#22186 from steveloughran/BUG/SPARK-25183-shutdown.
## What changes were proposed in this pull request?
### For `SPARK-5775 read array from partitioned_parquet_with_key_and_complextypes`:
scala2.12
```
scala> (1 to 10).toString
res4: String = Range 1 to 10
```
scala2.11
```
scala> (1 to 10).toString
res2: String = Range(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
```
And
```
def prepareAnswer(answer: Seq[Row], isSorted: Boolean): Seq[Row] = {
val converted: Seq[Row] = answer.map(prepareRow)
if (!isSorted) converted.sortBy(_.toString()) else converted
}
```
sortBy `_.toString` is not a good idea.
### Other failures are caused by
```
Array(Int.box(1)).toSeq == Array(Double.box(1.0)).toSeq
```
It is false in 2.12.2 + and is true in 2.11.x , 2.12.0, 2.12.1
## How was this patch tested?
This is a patch on a specific unit test.
Closes#22264 from sadhen/SPARK25256.
Authored-by: 忍冬 <rendong@wacai.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
`JavaColumnExpressionSuite.java` was added and `org.apache.spark.sql.ColumnExpressionSuite#test("isInCollection: Java Collection")` was removed.
It provides native Java tests for the method `org.apache.spark.sql.Column#isInCollection`.
Closes#22253 from aai95/isInCollectionJavaTest.
Authored-by: aai95 <aai95@yandex.ru>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
This PR is an follow-up PR of #21087 based on [a discussion thread](https://github.com/apache/spark/pull/21087#discussion_r211080067]. Since #21087 changed a condition of `if` statement, the message in an exception is not consistent of the current behavior.
This PR updates the exception message.
## How was this patch tested?
Existing UTs
Closes#22269 from kiszk/SPARK-23997-followup.
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?
Since https://github.com/apache/spark/pull/21696. Spark uses Parquet schema instead of Hive metastore schema to do pushdown.
That change can avoid wrong records returned when Hive metastore schema and parquet schema are in different letter cases. This pr add a test case for it.
More details:
https://issues.apache.org/jira/browse/SPARK-25206
## How was this patch tested?
unit tests
Closes#22267 from wangyum/SPARK-24716-TESTS.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Introduced by #21320 and #11744
```
$ sbt
> ++2.12.6
> project sql
> compile
...
[error] [warn] spark/sql/core/src/main/scala/org/apache/spark/sql/execution/ProjectionOverSchema.scala:41: match may not be exhaustive.
[error] It would fail on the following inputs: (_, ArrayType(_, _)), (_, _)
[error] [warn] getProjection(a.child).map(p => (p, p.dataType)).map {
[error] [warn]
[error] [warn] spark/sql/core/src/main/scala/org/apache/spark/sql/execution/ProjectionOverSchema.scala:52: match may not be exhaustive.
[error] It would fail on the following input: (_, _)
[error] [warn] getProjection(child).map(p => (p, p.dataType)).map {
[error] [warn]
...
```
And
```
$ sbt
> ++2.12.6
> project hive
> testOnly *ParquetMetastoreSuite
...
[error] /Users/rendong/wdi/spark/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala:22: object tools is not a member of package scala
[error] import scala.tools.nsc.Properties
[error] ^
[error] /Users/rendong/wdi/spark/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala:146: not found: value Properties
[error] val version = Properties.versionNumberString match {
[error] ^
[error] two errors found
...
```
## How was this patch tested?
Existing tests.
Closes#22260 from sadhen/fix_exhaustive_match.
Authored-by: 忍冬 <rendong@wacai.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
`fromId` is the child, and `toId` is the parent, see line 127 in `buildSparkPlanGraphNode` above.
The edges in Spark UI also go from child to parent.
## How was this patch tested?
Comment change only. Inspected code above. Inspected how the edges in Spark UI look like.
Closes#22268 from juliuszsompolski/sparkplangraphedgedoc.
Authored-by: Juliusz Sompolski <julek@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Alternative take on https://github.com/apache/spark/pull/22063 that does not introduce udfInternal.
Resolve issue with inferring func types in 2.12 by instead using info captured when UDF is registered -- capturing which types are nullable (i.e. not primitive)
## How was this patch tested?
Existing tests.
Closes#22259 from srowen/SPARK-25044.2.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This changes the calls of `toPandas()` and `createDataFrame()` to use the Arrow stream format, when Arrow is enabled. Previously, Arrow data was written to byte arrays where each chunk is an output of the Arrow file format. This was mainly due to constraints at the time, and caused some overhead by writing the schema/footer on each chunk of data and then having to read multiple Arrow file inputs and concat them together.
Using the Arrow stream format has improved these by increasing performance, lower memory overhead for the average case, and simplified the code. Here are the details of this change:
**toPandas()**
_Before:_
Spark internal rows are converted to Arrow file format, each group of records is a complete Arrow file which contains the schema and other metadata. Next a collect is done and an Array of Arrow files is the result. After that each Arrow file is sent to Python driver which then loads each file and concats them to a single Arrow DataFrame.
_After:_
Spark internal rows are converted to ArrowRecordBatches directly, which is the simplest Arrow component for IPC data transfers. The driver JVM then immediately starts serving data to Python as an Arrow stream, sending the schema first. It then starts a Spark job with a custom handler that sends Arrow RecordBatches to Python. Partitions arriving in order are sent immediately, and out-of-order partitions are buffered until the ones that precede it come in. This improves performance, simplifies memory usage on executors, and improves the average memory usage on the JVM driver. Since the order of partitions must be preserved, the worst case is that the first partition will be the last to arrive all data must be buffered in memory until then. This case is no worse that before when doing a full collect.
**createDataFrame()**
_Before:_
A Pandas DataFrame is split into parts and each part is made into an Arrow file. Then each file is prefixed by the buffer size and written to a temp file. The temp file is read and each Arrow file is parallelized as a byte array.
_After:_
A Pandas DataFrame is split into parts, then an Arrow stream is written to a temp file where each part is an ArrowRecordBatch. The temp file is read as a stream and the Arrow messages are examined. If the message is an ArrowRecordBatch, the data is saved as a byte array. After reading the file, each ArrowRecordBatch is parallelized as a byte array. This has slightly more processing than before because we must look each Arrow message to extract the record batches, but performance ends up a litle better. It is cleaner in the sense that IPC from Python to JVM is done over a single Arrow stream.
## How was this patch tested?
Added new unit tests for the additions to ArrowConverters in Scala, existing tests for Python.
## Performance Tests - toPandas
Tests run on a 4 node standalone cluster with 32 cores total, 14.04.1-Ubuntu and OpenJDK 8
measured wall clock time to execute `toPandas()` and took the average best time of 5 runs/5 loops each.
Test code
```python
df = spark.range(1 << 25, numPartitions=32).toDF("id").withColumn("x1", rand()).withColumn("x2", rand()).withColumn("x3", rand()).withColumn("x4", rand())
for i in range(5):
start = time.time()
_ = df.toPandas()
elapsed = time.time() - start
```
Current Master | This PR
---------------------|------------
5.803557 | 5.16207
5.409119 | 5.133671
5.493509 | 5.147513
5.433107 | 5.105243
5.488757 | 5.018685
Avg Master | Avg This PR
------------------|--------------
5.5256098 | 5.1134364
Speedup of **1.08060595**
## Performance Tests - createDataFrame
Tests run on a 4 node standalone cluster with 32 cores total, 14.04.1-Ubuntu and OpenJDK 8
measured wall clock time to execute `createDataFrame()` and get the first record. Took the average best time of 5 runs/5 loops each.
Test code
```python
def run():
pdf = pd.DataFrame(np.random.rand(10000000, 10))
spark.createDataFrame(pdf).first()
for i in range(6):
start = time.time()
run()
elapsed = time.time() - start
gc.collect()
print("Run %d: %f" % (i, elapsed))
```
Current Master | This PR
--------------------|----------
6.234608 | 5.665641
6.32144 | 5.3475
6.527859 | 5.370803
6.95089 | 5.479151
6.235046 | 5.529167
Avg Master | Avg This PR
---------------|----------------
6.4539686 | 5.4784524
Speedup of **1.178064192**
## Memory Improvements
**toPandas()**
The most significant improvement is reduction of the upper bound space complexity in the JVM driver. Before, the entire dataset was collected in the JVM first before sending it to Python. With this change, as soon as a partition is collected, the result handler immediately sends it to Python, so the upper bound is the size of the largest partition. Also, using the Arrow stream format is more efficient because the schema is written once per stream, followed by record batches. The schema is now only send from driver JVM to Python. Before, multiple Arrow file formats were used that each contained the schema. This duplicated schema was created in the executors, sent to the driver JVM, and then Python where all but the first one received are discarded.
I verified the upper bound limit by running a test that would collect data that would exceed the amount of driver JVM memory available. Using these settings on a standalone cluster:
```
spark.driver.memory 1g
spark.executor.memory 5g
spark.sql.execution.arrow.enabled true
spark.sql.execution.arrow.fallback.enabled false
spark.sql.execution.arrow.maxRecordsPerBatch 0
spark.driver.maxResultSize 2g
```
Test code:
```python
from pyspark.sql.functions import rand
df = spark.range(1 << 25, numPartitions=32).toDF("id").withColumn("x1", rand()).withColumn("x2", rand()).withColumn("x3", rand())
df.toPandas()
```
This makes total data size of 33554432×8×4 = 1073741824
With the current master, it fails with OOM but passes using this PR.
**createDataFrame()**
No significant change in memory except that using the stream format instead of separate file formats avoids duplicated the schema, similar to toPandas above. The process of reading the stream and parallelizing the batches does cause the record batch message metadata to be copied, but it's size is insignificant.
Closes#21546 from BryanCutler/arrow-toPandas-stream-SPARK-23030.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Most of `HigherOrderFunction`s have the same `nullable` definition, ie. they are nullable when one of their arguments is nullable. The PR refactors it in order to avoid code duplication.
## How was this patch tested?
NA
Closes#22243 from mgaido91/MINOR_nullable_hof.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Support Filter in ConvertToLocalRelation, similar to how Project works.
Additionally, in Optimizer, run ConvertToLocalRelation earlier to simplify the plan. This is good for very short queries which often are queries on local relations.
## How was this patch tested?
New test. Manual benchmark.
Author: Bogdan Raducanu <bogdan@databricks.com>
Author: Shixiong Zhu <zsxwing@gmail.com>
Author: Yinan Li <ynli@google.com>
Author: Li Jin <ice.xelloss@gmail.com>
Author: s71955 <sujithchacko.2010@gmail.com>
Author: DB Tsai <d_tsai@apple.com>
Author: jaroslav chládek <mastermism@gmail.com>
Author: Huangweizhe <huangweizhe@bbdservice.com>
Author: Xiangrui Meng <meng@databricks.com>
Author: hyukjinkwon <gurwls223@apache.org>
Author: Kent Yao <yaooqinn@hotmail.com>
Author: caoxuewen <cao.xuewen@zte.com.cn>
Author: liuxian <liu.xian3@zte.com.cn>
Author: Adam Bradbury <abradbury@users.noreply.github.com>
Author: Jose Torres <torres.joseph.f+github@gmail.com>
Author: Yuming Wang <yumwang@ebay.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#22205 from bogdanrdc/local-relation-filter.
## What changes were proposed in this pull request?
This adds `spark.executor.pyspark.memory` to configure Python's address space limit, [`resource.RLIMIT_AS`](https://docs.python.org/3/library/resource.html#resource.RLIMIT_AS). Limiting Python's address space allows Python to participate in memory management. In practice, we see fewer cases of Python taking too much memory because it doesn't know to run garbage collection. This results in YARN killing fewer containers. This also improves error messages so users know that Python is consuming too much memory:
```
File "build/bdist.linux-x86_64/egg/package/library.py", line 265, in fe_engineer
fe_eval_rec.update(f(src_rec_prep, mat_rec_prep))
File "build/bdist.linux-x86_64/egg/package/library.py", line 163, in fe_comp
comparisons = EvaluationUtils.leven_list_compare(src_rec_prep.get(item, []), mat_rec_prep.get(item, []))
File "build/bdist.linux-x86_64/egg/package/evaluationutils.py", line 25, in leven_list_compare
permutations = sorted(permutations, reverse=True)
MemoryError
```
The new pyspark memory setting is used to increase requested YARN container memory, instead of sharing overhead memory between python and off-heap JVM activity.
## How was this patch tested?
Tested memory limits in our YARN cluster and verified that MemoryError is thrown.
Author: Ryan Blue <blue@apache.org>
Closes#21977 from rdblue/SPARK-25004-add-python-memory-limit.
## What changes were proposed in this pull request?
In the PR, I propose to not perform recursive parallel listening of files in the `scanPartitions` method because it can cause a deadlock. Instead of that I propose to do `scanPartitions` in parallel for top level partitions only.
## How was this patch tested?
I extended an existing test to trigger the deadlock.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#22233 from MaxGekk/fix-recover-partitions.
## What changes were proposed in this pull request?
This PR implements the possibility of the user to override the maximum number of buckets when saving to a table.
Currently the limit is a hard-coded 100k, which might be insufficient for large workloads.
A new configuration entry is proposed: `spark.sql.bucketing.maxBuckets`, which defaults to the previous 100k.
## How was this patch tested?
Added unit tests in the following spark.sql test suites:
- CreateTableAsSelectSuite
- BucketedWriteSuite
Author: Fernando Pereira <fernando.pereira@epfl.ch>
Closes#21087 from ferdonline/enh/configurable_bucket_limit.
## What changes were proposed in this pull request?
The PR excludes Python UDFs filters in FileSourceStrategy so that they don't ExtractPythonUDF rule to throw exception. It doesn't make sense to pass Python UDF filters in FileSourceStrategy anyway because they cannot be used as push down filters.
## How was this patch tested?
Add a new regression test
Closes#22104 from icexelloss/SPARK-24721-udf-filter.
Authored-by: Li Jin <ice.xelloss@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
this pr add a configuration parameter to configure the capacity of fast aggregation.
Performance comparison:
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_60-b27 on Windows 7 6.1
Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
Aggregate w multiple keys: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
fasthash = default 5612 / 5882 3.7 267.6 1.0X
fasthash = config 3586 / 3595 5.8 171.0 1.6X
```
## How was this patch tested?
the existed test cases.
Closes#21931 from heary-cao/FastHashCapacity.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is based on the discussion https://github.com/apache/spark/pull/16677/files#r212805327.
As SQL standard doesn't mandate that a nested order by followed by a limit has to respect that ordering clause, this patch removes the `child.outputOrdering` check.
## How was this patch tested?
Unit tests.
Closes#22239 from viirya/improve-global-limit-parallelism-followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Improved the documentation for the datetime functions in `org.apache.spark.sql.functions` by adding details about the supported column input types, the column return type, behaviour on invalid input, supporting examples and clarifications.
## How was this patch tested?
Manually testing each of the datetime functions with different input to ensure that the corresponding Javadoc/Scaladoc matches the behaviour of the function. Successfully ran the `unidoc` SBT process.
Closes#20901 from abradbury/SPARK-23792.
Authored-by: Adam Bradbury <abradbury@users.noreply.github.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR generates the code that to refer a `StructType` generated in the scala code instead of generating `StructType` in Java code.
The original code has two issues.
1. Avoid to used the field name such as `key.name`
1. Support complicated schema (e.g. nested DataType)
At first, [the JIRA entry](https://issues.apache.org/jira/browse/SPARK-25178) proposed to change the generated field name of the keySchema / valueSchema to a dummy name in `RowBasedHashMapGenerator` and `VectorizedHashMapGenerator.scala`. This proposal can addresse issue 1.
Ueshin suggested an approach to refer to a `StructType` generated in the scala code using `ctx.addReferenceObj()`. This approach can address issues 1 and 2. Finally, this PR uses this approach.
## How was this patch tested?
Existing UTs
Closes#22187 from kiszk/SPARK-25178.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
Update to janino 3.0.9 to address Java 8 + Scala 2.12 incompatibility. The error manifests as test failures like this in `ExpressionEncoderSuite`:
```
- encode/decode for seq of string: List(abc, xyz) *** FAILED ***
java.lang.RuntimeException: Error while encoding: org.codehaus.janino.InternalCompilerException: failed to compile: org.codehaus.janino.InternalCompilerException: Compiling "GeneratedClass": Two non-abstract methods "public int scala.collection.TraversableOnce.size()" have the same parameter types, declaring type and return type
```
It comes up pretty immediately in any generated code that references Scala collections, and virtually always concerning the `size()` method.
## How was this patch tested?
Existing tests
Closes#22203 from srowen/SPARK-25029.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
(Link to Jira: https://issues.apache.org/jira/browse/SPARK-4502)
_N.B. This is a restart of PR #16578 which includes a subset of that code. Relevant review comments from that PR should be considered incorporated by reference. Please avoid duplication in review by reviewing that PR first. The summary below is an edited copy of the summary of the previous PR._
## What changes were proposed in this pull request?
One of the hallmarks of a column-oriented data storage format is the ability to read data from a subset of columns, efficiently skipping reads from other columns. Spark has long had support for pruning unneeded top-level schema fields from the scan of a parquet file. For example, consider a table, `contacts`, backed by parquet with the following Spark SQL schema:
```
root
|-- name: struct
| |-- first: string
| |-- last: string
|-- address: string
```
Parquet stores this table's data in three physical columns: `name.first`, `name.last` and `address`. To answer the query
```SQL
select address from contacts
```
Spark will read only from the `address` column of parquet data. However, to answer the query
```SQL
select name.first from contacts
```
Spark will read `name.first` and `name.last` from parquet.
This PR modifies Spark SQL to support a finer-grain of schema pruning. With this patch, Spark reads only the `name.first` column to answer the previous query.
### Implementation
There are two main components of this patch. First, there is a `ParquetSchemaPruning` optimizer rule for gathering the required schema fields of a `PhysicalOperation` over a parquet file, constructing a new schema based on those required fields and rewriting the plan in terms of that pruned schema. The pruned schema fields are pushed down to the parquet requested read schema. `ParquetSchemaPruning` uses a new `ProjectionOverSchema` extractor for rewriting a catalyst expression in terms of a pruned schema.
Second, the `ParquetRowConverter` has been patched to ensure the ordinals of the parquet columns read are correct for the pruned schema. `ParquetReadSupport` has been patched to address a compatibility mismatch between Spark's built in vectorized reader and the parquet-mr library's reader.
### Limitation
Among the complex Spark SQL data types, this patch supports parquet column pruning of nested sequences of struct fields only.
## How was this patch tested?
Care has been taken to ensure correctness and prevent regressions. A more advanced version of this patch incorporating optimizations for rewriting queries involving aggregations and joins has been running on a production Spark cluster at VideoAmp for several years. In that time, one bug was found and fixed early on, and we added a regression test for that bug.
We forward-ported this patch to Spark master in June 2016 and have been running this patch against Spark 2.x branches on ad-hoc clusters since then.
Closes#21320 from mallman/spark-4502-parquet_column_pruning-foundation.
Lead-authored-by: Michael Allman <msa@allman.ms>
Co-authored-by: Adam Jacques <adam@technowizardry.net>
Co-authored-by: Michael Allman <michael@videoamp.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Dataset.apply calls dataset.deserializer (to provide an early error) which ends up calling the full Analyzer on the deserializer. This can take tens of milliseconds, depending on how big the plan is.
Since Dataset.apply is called for many Dataset operations such as Dataset.where it can be a significant overhead for short queries.
According to a comment in the PR that introduced this check, we can at least remove this check for DataFrames: https://github.com/apache/spark/pull/20402#discussion_r164338267
## How was this patch tested?
Existing tests + manual benchmark
Author: Bogdan Raducanu <bogdan@databricks.com>
Closes#22201 from bogdanrdc/deserializer-fix.
## What changes were proposed in this pull request?
**Problem statement**
load data command with hdfs file paths consists of wild card strings like * are not working
eg:
"load data inpath 'hdfs://hacluster/user/ext* into table t1"
throws Analysis exception while executing this query
**Analysis -**
Currently fs.exists() API which is used for path validation in load command API cannot resolve the path with wild card pattern, To mitigate this problem i am using globStatus() API another api which can resolve the paths with hdfs supported wildcards like *,? etc(inline with hive wildcard support).
**Improvement identified as part of this issue -**
Currently system wont support wildcard character to be used for folder level path in a local file system. This PR has handled this scenario, the same globStatus API will unify the validation logic of local and non local file systems, this will ensure the behavior consistency between the hdfs and local file path in load command.
with this improvement user will be able to use a wildcard character in folder level path of a local file system in load command inline with hive behaviour, in older versions user can use wildcards only in file path of the local file system if they use in folder path system use to give an error by mentioning that not supported.
eg: load data local inpath '/localfilesystem/folder* into table t1
## How was this patch tested?
a) Manually tested by executing test-cases in HDFS yarn cluster. Reports is been attached in below section.
b) Existing test-case can verify the impact and functionality for local file path scenarios
c) A test-case is been added for verifying the functionality when wild card is been used in folder level path of a local file system
## Test Results
Note: all ip's were updated to localhost for security reasons.
HDFS path details
```
vm1:/opt/ficlient # hadoop fs -ls /user/data/sujith1
Found 2 items
-rw-r--r-- 3 shahid hadoop 4802 2018-03-26 15:45 /user/data/sujith1/typeddata60.txt
-rw-r--r-- 3 shahid hadoop 4883 2018-03-26 15:45 /user/data/sujith1/typeddata61.txt
vm1:/opt/ficlient # hadoop fs -ls /user/data/sujith2
Found 2 items
-rw-r--r-- 3 shahid hadoop 4802 2018-03-26 15:45 /user/data/sujith2/typeddata60.txt
-rw-r--r-- 3 shahid hadoop 4883 2018-03-26 15:45 /user/data/sujith2/typeddata61.txt
```
positive scenario by specifying complete file path to know about record size
```
0: jdbc:hive2://localhost:22550/default> create table wild_spark (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) row format delimited fields terminated by ',';
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (1.217 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujith1/typeddata60.txt' into table wild_spark;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (4.236 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujith1/typeddata61.txt' into table wild_spark;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (0.602 seconds)
0: jdbc:hive2://localhost:22550/default> select count(*) from wild_spark;
+-----------+--+
| count(1) |
+-----------+--+
| 121 |
+-----------+--+
1 row selected (18.529 seconds)
0: jdbc:hive2://localhost:22550/default>
```
With wild card character in file path
```
0: jdbc:hive2://localhost:22550/default> create table spark_withWildChar (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) row format delimited fields terminated by ',';
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (0.409 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujith1/type*' into table spark_withWildChar;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (1.502 seconds)
0: jdbc:hive2://localhost:22550/default> select count(*) from spark_withWildChar;
+-----------+--+
| count(1) |
+-----------+--+
| 121 |
+-----------+--+
```
with ? wild card scenario
```
0: jdbc:hive2://localhost:22550/default> create table spark_withWildChar_DiffChar (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) row format delimited fields terminated by ',';
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (0.489 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujith1/?ypeddata60.txt' into table spark_withWildChar_DiffChar;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (1.152 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujith1/?ypeddata61.txt' into table spark_withWildChar_DiffChar;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (0.644 seconds)
0: jdbc:hive2://localhost:22550/default> select count(*) from spark_withWildChar_DiffChar;
+-----------+--+
| count(1) |
+-----------+--+
| 121 |
+-----------+--+
1 row selected (16.078 seconds)
```
with folder level wild card scenario
```
0: jdbc:hive2://localhost:22550/default> create table spark_withWildChar_folderlevel (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) row format delimited fields terminated by ',';
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (0.489 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/suji*/*' into table spark_withWildChar_folderlevel;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (1.152 seconds)
0: jdbc:hive2://localhost:22550/default> select count(*) from spark_withWildChar_folderlevel;
+-----------+--+
| count(1) |
+-----------+--+
| 242 |
+-----------+--+
1 row selected (16.078 seconds)
```
Negative scenario invalid path
```
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujiinvalid*/*' into table spark_withWildChar_folder;
Error: org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: /user/data/sujiinvalid*/*; (state=,code=0)
0: jdbc:hive2://localhost:22550/default>
```
Hive Test results- file level
```
0: jdbc:hive2://localhost:21066/> create table hive_withWildChar_files (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) stored as TEXTFILE;
No rows affected (0.723 seconds)
0: jdbc:hive2://localhost:21066/> load data inpath '/user/data/sujith1/type*' into table hive_withWildChar_files;
INFO : Loading data to table default.hive_withwildchar_files from hdfs://hacluster/user/sujith1/type*
No rows affected (0.682 seconds)
0: jdbc:hive2://localhost:21066/> select count(*) from hive_withWildChar_files;
+------+--+
| _c0 |
+------+--+
| 121 |
+------+--+
1 row selected (50.832 seconds)
```
Hive Test results- folder level
```
0: jdbc:hive2://localhost:21066/> create table hive_withWildChar_folder (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) stored as TEXTFILE;
No rows affected (0.459 seconds)
0: jdbc:hive2://localhost:21066/> load data inpath '/user/data/suji*/*' into table hive_withWildChar_folder;
INFO : Loading data to table default.hive_withwildchar_folder from hdfs://hacluster/user/data/suji*/*
No rows affected (0.76 seconds)
0: jdbc:hive2://localhost:21066/> select count(*) from hive_withWildChar_folder;
+------+--+
| _c0 |
+------+--+
| 242 |
+------+--+
1 row selected (46.483 seconds)
```
Closes#20611 from sujith71955/master_wldcardsupport.
Lead-authored-by: s71955 <sujithchacko.2010@gmail.com>
Co-authored-by: sujith71955 <sujithchacko.2010@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Fix a race in the rate source tests. We need a better way of testing restart behavior.
## How was this patch tested?
unit test
Closes#22191 from jose-torres/racetest.
Authored-by: Jose Torres <torres.joseph.f+github@gmail.com>
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
## What changes were proposed in this pull request?
Casting to `DecimalType` is not always needed to force nullable.
If the decimal type to cast is wider than original type, or only truncating or precision loss, the casted value won't be `null`.
## How was this patch tested?
Added and modified tests.
Closes#22200 from ueshin/issues/SPARK-25208/cast_nullable_decimal.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
[SPARK-25126] (https://issues.apache.org/jira/browse/SPARK-25126)
reports loading a large number of orc files consumes a lot of memory
in both 2.0 and 2.3. The issue is caused by creating a Reader for every
orc file in order to infer the schema.
In OrFileOperator.ReadSchema, a Reader is created for every file
although only the first valid one is used. This uses significant
amount of memory when there `paths` have a lot of files. In 2.3
a different code path (OrcUtils.readSchema) is used for inferring
schema for orc files. This commit changes both functions to create
Reader lazily.
## How was this patch tested?
Pass the Jenkins with a newly added test case by dongjoon-hyun
Closes#22157 from raofu/SPARK-25126.
Lead-authored-by: Rao Fu <rao@coupang.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Co-authored-by: Rao Fu <raofu04@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
VectorizedParquetRecordReader::initializeInternal rebuilds the column list and path list once for each column. Therefore, it indirectly iterates 2\*colCount\*colCount times for each parquet file.
This inefficiency impacts jobs that read parquet-backed tables with many columns and many files. Jobs that read tables with few columns or few files are not impacted.
This PR changes initializeInternal so that it builds each list only once.
I ran benchmarks on my laptop with 1 worker thread, running this query:
<pre>
sql("select * from parquet_backed_table where id1 = 1").collect
</pre>
There are roughly one matching row for every 425 rows, and the matching rows are sprinkled pretty evenly throughout the table (that is, every page for column <code>id1</code> has at least one matching row).
6000 columns, 1 million rows, 67 32M files:
master | branch | improvement
-------|---------|-----------
10.87 min | 6.09 min | 44%
6000 columns, 1 million rows, 23 98m files:
master | branch | improvement
-------|---------|-----------
7.39 min | 5.80 min | 21%
600 columns 10 million rows, 67 32M files:
master | branch | improvement
-------|---------|-----------
1.95 min | 1.96 min | -0.5%
60 columns, 100 million rows, 67 32M files:
master | branch | improvement
-------|---------|-----------
0.55 min | 0.55 min | 0%
## How was this patch tested?
- sql unit tests
- pyspark-sql tests
Closes#22188 from bersprockets/SPARK-25164.
Authored-by: Bruce Robbins <bersprockets@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This pr proposed to show RDD/relation names in RDD/Hive table scan nodes.
This change made these names show up in the webUI and explain results.
For example;
```
scala> sql("CREATE TABLE t(c1 int) USING hive")
scala> sql("INSERT INTO t VALUES(1)")
scala> spark.table("t").explain()
== Physical Plan ==
Scan hive default.t [c1#8], HiveTableRelation `default`.`t`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [c1#8]
^^^^^^^^^^^
```
<img width="212" alt="spark-pr-hive" src="https://user-images.githubusercontent.com/692303/44501013-51264c80-a6c6-11e8-94f8-0704aee83bb6.png">
Closes#20226
## How was this patch tested?
Added tests in `DataFrameSuite`, `DatasetSuite`, and `HiveExplainSuite`
Closes#22153 from maropu/pr20226.
Lead-authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Co-authored-by: Tejas Patil <tejasp@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a follow-up pr of #22031 which added `zip_with` function to fix an example.
## How was this patch tested?
Existing tests.
Closes#22194 from ueshin/issues/SPARK-23932/fix_examples.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
They depend on internal Expression APIs. Let's see how far we can get without it.
## How was this patch tested?
Just some code removal. There's no existing tests as far as I can tell so it's easy to remove.
Closes#22185 from rxin/SPARK-25127.
Authored-by: Reynold Xin <rxin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The race condition that caused test failure is between 2 threads.
- The MicrobatchExecution thread that processes inputs to produce answers and then generates progress events.
- The test thread that generates some input data, checked the answer and then verified the query generated progress event.
The synchronization structure between these threads is as follows
1. MicrobatchExecution thread, in every batch, does the following in order.
a. Processes batch input to generate answer.
b. Signals `awaitProgressLockCondition` to wake up threads waiting for progress using `awaitOffset`
c. Generates progress event
2. Test execution thread
a. Calls `awaitOffset` to wait for progress, which waits on `awaitProgressLockCondition`.
b. As soon as `awaitProgressLockCondition` is signaled, it would move on the in the test to check answer.
c. Finally, it would verify the last generated progress event.
What can happen is the following sequence of events: 2a -> 1a -> 1b -> 2b -> 2c -> 1c.
In other words, the progress event may be generated after the test tries to verify it.
The solution has two steps.
1. Signal the waiting thread after the progress event has been generated, that is, after `finishTrigger()`.
2. Increase the timeout of `awaitProgressLockCondition.await(100 ms)` to a large value.
This latter is to ensure that test thread for keeps waiting on `awaitProgressLockCondition`until the MicroBatchExecution thread explicitly signals it. With the existing small timeout of 100ms the following sequence can occur.
- MicroBatchExecution thread updates committed offsets
- Test thread waiting on `awaitProgressLockCondition` accidentally times out after 100 ms, finds that the committed offsets have been updated, therefore returns from `awaitOffset` and moves on to the progress event tests.
- MicroBatchExecution thread then generates progress event and signals. But the test thread has already attempted to verify the event and failed.
By increasing the timeout to large (e.g., `streamingTimeoutMs = 60 seconds`, similar to `awaitInitialization`), this above type of race condition is also avoided.
## How was this patch tested?
Ran locally many times.
Closes#22182 from tdas/SPARK-25184.
Authored-by: Tathagata Das <tathagata.das1565@gmail.com>
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
## What changes were proposed in this pull request?
Fix issues arising from the fact that builtins __file__, __long__, __raw_input()__, __unicode__, __xrange()__, etc. were all removed from Python 3. __Undefined names__ have the potential to raise [NameError](https://docs.python.org/3/library/exceptions.html#NameError) at runtime.
## How was this patch tested?
* $ __python2 -m flake8 . --count --select=E9,F82 --show-source --statistics__
* $ __python3 -m flake8 . --count --select=E9,F82 --show-source --statistics__
holdenk
flake8 testing of https://github.com/apache/spark on Python 3.6.3
$ __python3 -m flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics__
```
./dev/merge_spark_pr.py:98:14: F821 undefined name 'raw_input'
result = raw_input("\n%s (y/n): " % prompt)
^
./dev/merge_spark_pr.py:136:22: F821 undefined name 'raw_input'
primary_author = raw_input(
^
./dev/merge_spark_pr.py:186:16: F821 undefined name 'raw_input'
pick_ref = raw_input("Enter a branch name [%s]: " % default_branch)
^
./dev/merge_spark_pr.py:233:15: F821 undefined name 'raw_input'
jira_id = raw_input("Enter a JIRA id [%s]: " % default_jira_id)
^
./dev/merge_spark_pr.py:278:20: F821 undefined name 'raw_input'
fix_versions = raw_input("Enter comma-separated fix version(s) [%s]: " % default_fix_versions)
^
./dev/merge_spark_pr.py:317:28: F821 undefined name 'raw_input'
raw_assignee = raw_input(
^
./dev/merge_spark_pr.py:430:14: F821 undefined name 'raw_input'
pr_num = raw_input("Which pull request would you like to merge? (e.g. 34): ")
^
./dev/merge_spark_pr.py:442:18: F821 undefined name 'raw_input'
result = raw_input("Would you like to use the modified title? (y/n): ")
^
./dev/merge_spark_pr.py:493:11: F821 undefined name 'raw_input'
while raw_input("\n%s (y/n): " % pick_prompt).lower() == "y":
^
./dev/create-release/releaseutils.py:58:16: F821 undefined name 'raw_input'
response = raw_input("%s [y/n]: " % msg)
^
./dev/create-release/releaseutils.py:152:38: F821 undefined name 'unicode'
author = unidecode.unidecode(unicode(author, "UTF-8")).strip()
^
./python/setup.py:37:11: F821 undefined name '__version__'
VERSION = __version__
^
./python/pyspark/cloudpickle.py:275:18: F821 undefined name 'buffer'
dispatch[buffer] = save_buffer
^
./python/pyspark/cloudpickle.py:807:18: F821 undefined name 'file'
dispatch[file] = save_file
^
./python/pyspark/sql/conf.py:61:61: F821 undefined name 'unicode'
if not isinstance(obj, str) and not isinstance(obj, unicode):
^
./python/pyspark/sql/streaming.py:25:21: F821 undefined name 'long'
intlike = (int, long)
^
./python/pyspark/streaming/dstream.py:405:35: F821 undefined name 'long'
return self._sc._jvm.Time(long(timestamp * 1000))
^
./sql/hive/src/test/resources/data/scripts/dumpdata_script.py:21:10: F821 undefined name 'xrange'
for i in xrange(50):
^
./sql/hive/src/test/resources/data/scripts/dumpdata_script.py:22:14: F821 undefined name 'xrange'
for j in xrange(5):
^
./sql/hive/src/test/resources/data/scripts/dumpdata_script.py:23:18: F821 undefined name 'xrange'
for k in xrange(20022):
^
20 F821 undefined name 'raw_input'
20
```
Closes#20838 from cclauss/fix-undefined-names.
Authored-by: cclauss <cclauss@bluewin.ch>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Improve the data source v2 API according to the [design doc](https://docs.google.com/document/d/1DDXCTCrup4bKWByTalkXWgavcPdvur8a4eEu8x1BzPM/edit?usp=sharing)
summary of the changes
1. rename `ReadSupport` -> `DataSourceReader` -> `InputPartition` -> `InputPartitionReader` to `BatchReadSupportProvider` -> `BatchReadSupport` -> `InputPartition`/`PartitionReaderFactory` -> `PartitionReader`. Similar renaming also happens at streaming and write APIs.
2. create `ScanConfig` to store query specific information like operator pushdown result, streaming offsets, etc. This makes batch and streaming `ReadSupport`(previouslly named `DataSourceReader`) immutable. All other methods take `ScanConfig` as input, which implies applying operator pushdown and getting streaming offsets happen before all other things(get input partitions, report statistics, etc.).
3. separate `InputPartition` to `InputPartition` and `PartitionReaderFactory`. This is a natural separation, data splitting and reading are orthogonal and we should not mix them in one interfaces. This also makes the naming consistent between read and write API: `PartitionReaderFactory` vs `DataWriterFactory`.
4. separate the batch and streaming interfaces. Sometimes it's painful to force the streaming interface to extend batch interface, as we may need to override some batch methods to return false, or even leak the streaming concept to batch API(e.g. `DataWriterFactory#createWriter(partitionId, taskId, epochId)`)
Some follow-ups we should do after this PR (tracked by https://issues.apache.org/jira/browse/SPARK-25186 ):
1. Revisit the life cycle of `ReadSupport` instances. Currently I keep it same as the previous `DataSourceReader`, i.e. the life cycle is bound to the batch/stream query. This fits streaming very well but may not be perfect for batch source. We can also consider to let `ReadSupport.newScanConfigBuilder` take `DataSourceOptions` as parameter, if we decide to change the life cycle.
2. Add `WriteConfig`. This is similar to `ScanConfig` and makes the write API more flexible. But it's only needed when we add the `replaceWhere` support, and it needs to change the streaming execution engine for this new concept, which I think is better to be done in another PR.
3. Refine the document. This PR adds/changes a lot of document and it's very likely that some people may have better ideas.
4. Figure out the life cycle of `CustomMetrics`. It looks to me that it should be bound to a `ScanConfig`, but we need to change `ProgressReporter` to get the `ScanConfig`. Better to be done in another PR.
5. Better operator pushdown API. This PR keeps the pushdown API as it was, i.e. using the `SupportsPushdownXYZ` traits. We can design a better API using build pattern, but this is a complicated design and deserves an individual JIRA ticket and design doc.
6. Improve the continuous streaming engine to only create a new `ScanConfig` when re-configuring.
7. Remove `SupportsPushdownCatalystFilter`. This is actually not a must-have for file source, we can change the hive partition pruning to use the public `Filter`.
## How was this patch tested?
existing tests.
Closes#22009 from cloud-fan/redesign.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The PR moves the compilation of the regexp for code formatting outside the method which is called for each code block when splitting expressions, in order to avoid recompiling the regexp every time.
Credit should be given to Izek Greenfield.
## How was this patch tested?
existing UTs
Closes#22135 from mgaido91/SPARK-25093.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This fixes a perf regression caused by https://github.com/apache/spark/pull/21376 .
We should not use `RDD#toLocalIterator`, which triggers one Spark job per RDD partition. This is very bad for RDDs with a lot of small partitions.
To fix it, this PR introduces a way to access SQLConf in the scheduler event loop thread, so that we don't need to use `RDD#toLocalIterator` anymore in `JsonInferSchema`.
## How was this patch tested?
a new test
Closes#22152 from cloud-fan/conf.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This pr is to fix bugs when expr codegen fails; we need to catch `java.util.concurrent.ExecutionException` instead of `InternalCompilerException` and `CompileException` . This handling is the same with the `WholeStageCodegenExec ` one: 60af2501e1/sql/core/src/main/scala/org/apache/spark/sql/execution/WholeStageCodegenExec.scala (L585)
## How was this patch tested?
Added tests in `CodeGeneratorWithInterpretedFallbackSuite`
Closes#22154 from maropu/SPARK-25140.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Two back to PRs implicitly conflicted by one PR removing an existing import that the other PR needed. This did not cause explicit conflict as the import already existed, but not used.
https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Compile/job/spark-master-compile-maven-hadoop-2.7/8226/consoleFull
```
[info] Compiling 342 Scala sources and 97 Java sources to /home/jenkins/workspace/spark-master-compile-maven-hadoop-2.7/sql/core/target/scala-2.11/classes...
[warn] /home/jenkins/workspace/spark-master-compile-maven-hadoop-2.7/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala:128: value ENABLE_JOB_SUMMARY in object ParquetOutputFormat is deprecated: see corresponding Javadoc for more information.
[warn] && conf.get(ParquetOutputFormat.ENABLE_JOB_SUMMARY) == null) {
[warn] ^
[error] /home/jenkins/workspace/spark-master-compile-maven-hadoop-2.7/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/statefulOperators.scala:95: value asJava is not a member of scala.collection.immutable.Map[String,Long]
[error] new java.util.HashMap(customMetrics.mapValues(long2Long).asJava)
[error] ^
[warn] one warning found
[error] one error found
[error] Compile failed at Aug 21, 2018 4:04:35 PM [12.827s]
```
## How was this patch tested?
It compiles!
Closes#22175 from tdas/fix-build.
Authored-by: Tathagata Das <tathagata.das1565@gmail.com>
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
## What changes were proposed in this pull request?
This patch exposes the estimation of size of cache (loadedMaps) in HDFSBackedStateStoreProvider as a custom metric of StateStore.
The rationalize of the patch is that state backed by HDFSBackedStateStoreProvider will consume more memory than the number what we can get from query status due to caching multiple versions of states. The memory footprint to be much larger than query status reports in situations where the state store is getting a lot of updates: while shallow-copying map incurs additional small memory usages due to the size of map entities and references, but row objects will still be shared across the versions. If there're lots of updates between batches, less row objects will be shared and more row objects will exist in memory consuming much memory then what we expect.
While HDFSBackedStateStore refers loadedMaps in HDFSBackedStateStoreProvider directly, there would be only one `StateStoreWriter` which refers a StateStoreProvider, so the value is not exposed as well as being aggregated multiple times. Current state metrics are safe to aggregate for the same reason.
## How was this patch tested?
Tested manually. Below is the snapshot of UI page which is reflected by the patch:
<img width="601" alt="screen shot 2018-06-05 at 10 16 16 pm" src="https://user-images.githubusercontent.com/1317309/40978481-b46ad324-690e-11e8-9b0f-e80528612a62.png">
Please refer "estimated size of states cache in provider total" as well as "count of versions in state cache in provider".
Closes#21469 from HeartSaVioR/SPARK-24441.
Authored-by: Jungtaek Lim <kabhwan@gmail.com>
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
## What changes were proposed in this pull request?
In https://issues.apache.org/jira/browse/SPARK-24924, the data source provider com.databricks.spark.avro is mapped to the new package org.apache.spark.sql.avro .
As per the discussion in the [Jira](https://issues.apache.org/jira/browse/SPARK-24924) and PR #22119, we should make the mapping configurable.
This PR also improve the error message when data source of Avro/Kafka is not found.
## How was this patch tested?
Unit test
Closes#22133 from gengliangwang/configurable_avro_mapping.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This patch proposes a new flag option for stateful aggregation: remove redundant key data from value.
Enabling new option runs similar with current, and uses less memory for state according to key/value fields of state operator.
Please refer below link to see detailed perf. test result:
https://issues.apache.org/jira/browse/SPARK-24763?focusedCommentId=16536539&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-16536539
Since the state between enabling the option and disabling the option is not compatible, the option is set to 'disable' by default (to ensure backward compatibility), and OffsetSeqMetadata would prevent modifying the option after executing query.
## How was this patch tested?
Modify unit tests to cover both disabling option and enabling option.
Also did manual tests to see whether propose patch improves state memory usage.
Closes#21733 from HeartSaVioR/SPARK-24763.
Authored-by: Jungtaek Lim <kabhwan@gmail.com>
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/22079#discussion_r209705612 It is possible for two objects to be unequal and yet we consider them as equal with this code, if the long values are separated by Int.MaxValue.
This PR fixes the issue.
## How was this patch tested?
Add new test cases in `RecordBinaryComparatorSuite`.
Closes#22101 from jiangxb1987/fix-rbc.
Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Spark SQL returns NULL for a column whose Hive metastore schema and Parquet schema are in different letter cases, regardless of spark.sql.caseSensitive set to true or false. This PR aims to add case-insensitive field resolution for ParquetFileFormat.
* Do case-insensitive resolution only if Spark is in case-insensitive mode.
* Field resolution should fail if there is ambiguity, i.e. more than one field is matched.
## How was this patch tested?
Unit tests added.
Closes#22148 from seancxmao/SPARK-25132-Parquet.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
When column pruning is turned on the checking of headers in the csv should only be for the fields in the requiredSchema, not the dataSchema, because column pruning means only requiredSchema is read.
## How was this patch tested?
Added 2 unit tests where column pruning is turned on/off and csv headers are checked againt schema
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22123 from koertkuipers/feat-csv-column-pruning-and-check-header.
Authored-by: Koert Kuipers <koert@tresata.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
[SPARK-25144](https://issues.apache.org/jira/browse/SPARK-25144) reports memory leaks on Apache Spark 2.0.2 ~ 2.3.2-RC5. The bug is already fixed via #21738 as a part of SPARK-21743. This PR only adds a test case to prevent any future regression.
```scala
scala> case class Foo(bar: Option[String])
scala> val ds = List(Foo(Some("bar"))).toDS
scala> val result = ds.flatMap(_.bar).distinct
scala> result.rdd.isEmpty
18/08/19 23:01:54 WARN Executor: Managed memory leak detected; size = 8650752 bytes, TID = 125
res0: Boolean = false
```
## How was this patch tested?
Pass the Jenkins with a new added test case.
Closes#22155 from dongjoon-hyun/SPARK-25144-2.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR for supporting logical timestamp types https://github.com/apache/spark/pull/21935, a SQL configuration spark.sql.avro.outputTimestampType is added, so that user can specify the output timestamp precision they want.
With PR https://github.com/apache/spark/pull/21847, the output file can be written with user specified types.
So there is no need to have such trivial configuration. Otherwise to make it consistent we need to add configuration for all the Catalyst types that can be converted into different Avro types.
This PR also add a test case for user specified output schema with different timestamp types.
## How was this patch tested?
Unit test
Closes#22151 from gengliangwang/removeOutputTimestampType.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
We should also check `HigherOrderFunction.bind` method passes expected parameters.
This pr modifies tests for higher-order functions to check `bind` method.
## How was this patch tested?
Modified tests.
Closes#22131 from ueshin/issues/SPARK-25141/bind_test.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to skip invoking of the CSV/JSON parser per each line in the case if the required schema is empty. Added benchmarks for `count()` shows performance improvement up to **3.5 times**.
Before:
```
Count a dataset with 10 columns: Best/Avg Time(ms) Rate(M/s) Per Row(ns)
--------------------------------------------------------------------------------------
JSON count() 7676 / 7715 1.3 767.6
CSV count() 3309 / 3363 3.0 330.9
```
After:
```
Count a dataset with 10 columns: Best/Avg Time(ms) Rate(M/s) Per Row(ns)
--------------------------------------------------------------------------------------
JSON count() 2104 / 2156 4.8 210.4
CSV count() 2332 / 2386 4.3 233.2
```
## How was this patch tested?
It was tested by `CSVSuite` and `JSONSuite` as well as on added benchmarks.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Author: Maxim Gekk <max.gekk@gmail.com>
Closes#21909 from MaxGekk/empty-schema-optimization.
## What changes were proposed in this pull request?
Put annotation args in one line, or API doc generation will fail.
~~~
[error] /Users/meng/src/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala:1559: annotation argument needs to be a constant; found: "_FUNC_(expr) - Returns the character length of string data or number of bytes of ".+("binary data. The length of string data includes the trailing spaces. The length of binary ").+("data includes binary zeros.")
[error] "binary data. The length of string data includes the trailing spaces. The length of binary " +
[error] ^
[info] No documentation generated with unsuccessful compiler run
[error] one error found
[error] (catalyst/compile:doc) Scaladoc generation failed
[error] Total time: 27 s, completed Aug 17, 2018 3:20:08 PM
~~~
## How was this patch tested?
sbt catalyst/compile:doc passed
Closes#22137 from mengxr/minor-doc-fix.
Authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This is a follow-up pr of #22017 which added `map_zip_with` function.
In the test, when creating a lambda function, we use the `valueContainsNull` values for the nullabilities of the value arguments, but we should've used `true` as the same as `bind` method because the values might be `null` if the keys don't match.
## How was this patch tested?
Added small tests and existing tests.
Closes#22126 from ueshin/issues/SPARK-23938/fix_tests.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
The method ```*supportEquals``` determining whether elements of a data type could be used as items in a hash set or as keys in a hash map is duplicated across multiple collection and higher-order functions.
This PR suggests to deduplicate the method.
## How was this patch tested?
Run tests in:
- DataFrameFunctionsSuite
- CollectionExpressionsSuite
- HigherOrderExpressionsSuite
Closes#22110 from mn-mikke/SPARK-25122.
Authored-by: Marek Novotny <mn.mikke@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This pr adds `transform_values` function which applies the function to each entry of the map and transforms the values.
```javascript
> SELECT transform_values(map(array(1, 2, 3), array(1, 2, 3)), (k,v) -> v + 1);
map(1->2, 2->3, 3->4)
> SELECT transform_values(map(array(1, 2, 3), array(1, 2, 3)), (k,v) -> k + v);
map(1->2, 2->4, 3->6)
```
## How was this patch tested?
New Tests added to
`DataFrameFunctionsSuite`
`HigherOrderFunctionsSuite`
`SQLQueryTestSuite`
Closes#22045 from codeatri/SPARK-23940.
Authored-by: codeatri <nehapatil6@gmail.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
Add RewriteCorrelatedScalarSubquery in the list of nonExcludableRules since its used to transform correlated scalar subqueries to joins.
## How was this patch tested?
Added test in OptimizerRuleExclusionSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#22108 from dilipbiswal/scalar_exclusion.
## What changes were proposed in this pull request?
Merges the two given arrays, element-wise, into a single array using function. If one array is shorter, nulls are appended at the end to match the length of the longer array, before applying function:
```
SELECT zip_with(ARRAY[1, 3, 5], ARRAY['a', 'b', 'c'], (x, y) -> (y, x)); -- [ROW('a', 1), ROW('b', 3), ROW('c', 5)]
SELECT zip_with(ARRAY[1, 2], ARRAY[3, 4], (x, y) -> x + y); -- [4, 6]
SELECT zip_with(ARRAY['a', 'b', 'c'], ARRAY['d', 'e', 'f'], (x, y) -> concat(x, y)); -- ['ad', 'be', 'cf']
SELECT zip_with(ARRAY['a'], ARRAY['d', null, 'f'], (x, y) -> coalesce(x, y)); -- ['a', null, 'f']
```
## How was this patch tested?
Added tests
Closes#22031 from techaddict/SPARK-23932.
Authored-by: Sandeep Singh <sandeep@techaddict.me>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
This pr adds transform_keys function which applies the function to each entry of the map and transforms the keys.
```javascript
> SELECT transform_keys(map(array(1, 2, 3), array(1, 2, 3)), (k,v) -> k + 1);
map(2->1, 3->2, 4->3)
> SELECT transform_keys(map(array(1, 2, 3), array(1, 2, 3)), (k,v) -> k + v);
map(2->1, 4->2, 6->3)
```
## How was this patch tested?
Added tests.
Closes#22013 from codeatri/SPARK-23939.
Authored-by: codeatri <nehapatil6@gmail.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
Correct the javadoc for expm1() function.
## How was this patch tested?
None. It is a minor issue.
Closes#22115 from bomeng/25082.
Authored-by: Bo Meng <bo.meng@jd.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This is split from #21520. This includes changes of `BoundAttribute` and `Cast`.
This patch also adds few convenient APIs:
```scala
CodeGenerator.freshVariable(name: String, dt: DataType): VariableValue
CodeGenerator.freshVariable(name: String, javaClass: Class[_]): VariableValue
JavaCode.javaType(javaClass: Class[_]): Inline
JavaCode.javaType(dataType: DataType): Inline
JavaCode.boxedType(dataType: DataType): Inline
```
## How was this patch tested?
Existing tests.
Closes#21537 from viirya/SPARK-24505-1.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Upgrade Apache Arrow to 0.10.0
Version 0.10.0 has a number of bug fixes and improvements with the following pertaining directly to usage in Spark:
* Allow for adding BinaryType support ARROW-2141
* Bug fix related to array serialization ARROW-1973
* Python2 str will be made into an Arrow string instead of bytes ARROW-2101
* Python bytearrays are supported in as input to pyarrow ARROW-2141
* Java has common interface for reset to cleanup complex vectors in Spark ArrowWriter ARROW-1962
* Cleanup pyarrow type equality checks ARROW-2423
* ArrowStreamWriter should not hold references to ArrowBlocks ARROW-2632, ARROW-2645
* Improved low level handling of messages for RecordBatch ARROW-2704
## How was this patch tested?
existing tests
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#21939 from BryanCutler/arrow-upgrade-010.
## What changes were proposed in this pull request?
Add logging for all generated methods from the `CodeGenerator` whose bytecode size goes above 8000 bytes.
This is to help with gathering stats on how often Spark is generating methods too big to be JIT'd. It covers all codegen scenarios, include whole-stage codegen and also individual expression codegen, e.g. unsafe projection, mutable projection, etc.
## How was this patch tested?
Manually tested that logging did happen when generated method was above 8000 bytes.
Also added a new unit test case to `CodeGenerationSuite` to verify that the logging did happen.
Author: Kris Mok <kris.mok@databricks.com>
Closes#22103 from rednaxelafx/codegen-8k-logging.
## What changes were proposed in this pull request?
A small change to print the master and appId from spark-sql as with logging turned down all the way (`log4j.logger.org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver=WARN`), we may not know this information easily. This adds the following string before the `spark-sql>` prompt shows on the screen.
`Spark master: yarn, Application Id: application_123456789_12345`
## How was this patch tested?
I ran spark-sql locally and saw the appId displayed as expected.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22025 from abellina/SPARK-25043_print_master_and_app_id_from_sparksql.
Lead-authored-by: Alessandro Bellina <abellina@gmail.com>
Co-authored-by: Alessandro Bellina <abellina@yahoo-inc.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
## What changes were proposed in this pull request?
This PR adds a new SQL function called ```map_zip_with```. It merges the two given maps into a single map by applying function to the pair of values with the same key.
## How was this patch tested?
Added new tests into:
- DataFrameFunctionsSuite.scala
- HigherOrderFunctionsSuite.scala
Closes#22017 from mn-mikke/SPARK-23938.
Authored-by: Marek Novotny <mn.mikke@gmail.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
This PR fixes the an example for `to_json` in doc and function description.
- http://spark.apache.org/docs/2.3.0/api/sql/#to_json
- `describe function extended`
## How was this patch tested?
Pass the Jenkins with the updated test.
Closes#22096 from dongjoon-hyun/minor_json.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
`ANALYZE TABLE ... PARTITION(...) COMPUTE STATISTICS` can fail with a NPE if a partition column contains a NULL value.
The PR avoids the NPE, replacing the `NULL` values with the default partition placeholder.
## How was this patch tested?
added UT
Closes#22036 from mgaido91/SPARK-25028.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a follow-up pr of #21954 to address comments.
- Rename ambiguous name `inputs` to `arguments`.
- Add argument type check and remove hacky workaround.
- Address other small comments.
## How was this patch tested?
Existing tests and some additional tests.
Closes#22075 from ueshin/issues/SPARK-23908/fup1.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PR removes a restriction for element types of array type which exists in `from_json` for the root type. Currently, the function can handle only arrays of structs. Even array of primitive types is disallowed. The PR allows arrays of any types currently supported by JSON datasource. Here is an example of an array of a primitive type:
```
scala> import org.apache.spark.sql.functions._
scala> val df = Seq("[1, 2, 3]").toDF("a")
scala> val schema = new ArrayType(IntegerType, false)
scala> val arr = df.select(from_json($"a", schema))
scala> arr.printSchema
root
|-- jsontostructs(a): array (nullable = true)
| |-- element: integer (containsNull = true)
```
and result of converting of the json string to the `ArrayType`:
```
scala> arr.show
+----------------+
|jsontostructs(a)|
+----------------+
| [1, 2, 3]|
+----------------+
```
## How was this patch tested?
I added a few positive and negative tests:
- array of primitive types
- array of arrays
- array of structs
- array of maps
Closes#21439 from MaxGekk/from_json-array.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In type coercion for complex types, if the found type is force-nullable to cast, we should loosen the nullability to be able to cast. Also for map key type, we can't use the type.
## How was this patch tested?
Added some test.
Closes#22086 from ueshin/issues/SPARK-25096/fix_type_coercion.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Support Avro logical date type:
https://avro.apache.org/docs/1.8.2/spec.html#Decimal
## How was this patch tested?
Unit test
Closes#22037 from gengliangwang/avro_decimal.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Fix scaladoc in Column
## How was this patch tested?
None
Closes#22069 from sadhen/fix_doc_minor.
Authored-by: 忍冬 <rendong@wacai.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
This PR adds codes to ``"Test `spark.sql.parquet.compression.codec` config"` in `ParquetCompressionCodecPrecedenceSuite`.
## How was this patch tested?
Existing UTs
Closes#22083 from kiszk/ParquetCompressionCodecPrecedenceSuite.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add RewriteExceptAll and RewriteIntersectAll in the list of nonExcludableRules as the rewrites are essential for the functioning of EXCEPT ALL and INTERSECT ALL feature.
## How was this patch tested?
Added test in OptimizerRuleExclusionSuite.
Closes#22080 from dilipbiswal/exceptall_rewrite_exclusion.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Fixing typos is sometimes very hard. It's not so easy to visually review them. Recently, I discovered a very useful tool for it, [misspell](https://github.com/client9/misspell).
This pull request fixes minor typos detected by [misspell](https://github.com/client9/misspell) except for the false positives. If you would like me to work on other files as well, let me know.
## How was this patch tested?
### before
```
$ misspell . | grep -v '.js'
R/pkg/R/SQLContext.R:354:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:424:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:445:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:495:43: "definiton" is a misspelling of "definition"
NOTICE-binary:454:16: "containd" is a misspelling of "contained"
R/pkg/R/context.R:46:43: "definiton" is a misspelling of "definition"
R/pkg/R/context.R:74:43: "definiton" is a misspelling of "definition"
R/pkg/R/DataFrame.R:591:48: "persistance" is a misspelling of "persistence"
R/pkg/R/streaming.R:166:44: "occured" is a misspelling of "occurred"
R/pkg/inst/worker/worker.R:65:22: "ouput" is a misspelling of "output"
R/pkg/tests/fulltests/test_utils.R:106:25: "environemnt" is a misspelling of "environment"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/InMemoryStoreSuite.java:38:39: "existant" is a misspelling of "existent"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/LevelDBSuite.java:83:39: "existant" is a misspelling of "existent"
common/network-common/src/main/java/org/apache/spark/network/crypto/TransportCipher.java:243:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:234:19: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:238:63: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:244:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:276:39: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
common/unsafe/src/test/scala/org/apache/spark/unsafe/types/UTF8StringPropertyCheckSuite.scala:195:15: "orgin" is a misspelling of "origin"
core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala:621:39: "gauranteed" is a misspelling of "guaranteed"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/main/scala/org/apache/spark/storage/DiskStore.scala:282:18: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/util/ListenerBus.scala:64:17: "overriden" is a misspelling of "overridden"
core/src/test/scala/org/apache/spark/ShuffleSuite.scala:211:7: "substracted" is a misspelling of "subtracted"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:2468:84: "truely" is a misspelling of "truly"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:25:18: "persistance" is a misspelling of "persistence"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:26:69: "persistance" is a misspelling of "persistence"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
dev/run-pip-tests:55:28: "enviroments" is a misspelling of "environments"
dev/run-pip-tests:91:37: "virutal" is a misspelling of "virtual"
dev/merge_spark_pr.py:377:72: "accross" is a misspelling of "across"
dev/merge_spark_pr.py:378:66: "accross" is a misspelling of "across"
dev/run-pip-tests:126:25: "enviroments" is a misspelling of "environments"
docs/configuration.md:1830:82: "overriden" is a misspelling of "overridden"
docs/structured-streaming-programming-guide.md:525:45: "processs" is a misspelling of "processes"
docs/structured-streaming-programming-guide.md:1165:61: "BETWEN" is a misspelling of "BETWEEN"
docs/sql-programming-guide.md:1891:810: "behaivor" is a misspelling of "behavior"
examples/src/main/python/sql/arrow.py:98:8: "substract" is a misspelling of "subtract"
examples/src/main/python/sql/arrow.py:103:27: "substract" is a misspelling of "subtract"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala:230:24: "inital" is a misspelling of "initial"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala:237:26: "descripiton" is a misspelling of "descriptions"
python/pyspark/find_spark_home.py:30:13: "enviroment" is a misspelling of "environment"
python/pyspark/context.py:937:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:938:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:939:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:940:12: "supress" is a misspelling of "suppress"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:713:8: "probabilty" is a misspelling of "probability"
python/pyspark/ml/clustering.py:1038:8: "Currenlty" is a misspelling of "Currently"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/ml/regression.py:1378:20: "paramter" is a misspelling of "parameter"
python/pyspark/mllib/stat/_statistics.py:262:8: "probabilty" is a misspelling of "probability"
python/pyspark/rdd.py:1363:32: "paramter" is a misspelling of "parameter"
python/pyspark/streaming/tests.py:825:42: "retuns" is a misspelling of "returns"
python/pyspark/sql/tests.py:768:29: "initalization" is a misspelling of "initialization"
python/pyspark/sql/tests.py:3616:31: "initalize" is a misspelling of "initialize"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala:120:39: "arbitary" is a misspelling of "arbitrary"
resource-managers/mesos/src/test/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArgumentsSuite.scala:26:45: "sucessfully" is a misspelling of "successfully"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala:358:27: "constaints" is a misspelling of "constraints"
resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala:111:24: "senstive" is a misspelling of "sensitive"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala:1063:5: "overwirte" is a misspelling of "overwrite"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala:1348:17: "compatability" is a misspelling of "compatibility"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala:77:36: "paramter" is a misspelling of "parameter"
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:1374:22: "precendence" is a misspelling of "precedence"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala:238:27: "unnecassary" is a misspelling of "unnecessary"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala:212:17: "whn" is a misspelling of "when"
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinHelper.scala:147:60: "timestmap" is a misspelling of "timestamp"
sql/core/src/test/scala/org/apache/spark/sql/TPCDSQuerySuite.scala:150:45: "precentage" is a misspelling of "percentage"
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchemaSuite.scala:135:29: "infered" is a misspelling of "inferred"
sql/hive/src/test/resources/golden/udf_instr-1-2e76f819563dbaba4beb51e3a130b922:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_instr-2-32da357fc754badd6e3898dcc8989182:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-1-6e41693c9c6dceea4d7fab4c02884e4e:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-2-d9b5934457931447874d6bb7c13de478:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:9:79: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:13:110: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/annotate_stats_join.q:46:105: "distint" is a misspelling of "distinct"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/auto_sortmerge_join_11.q:29:3: "Currenly" is a misspelling of "Currently"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/avro_partitioned.q:72:15: "existant" is a misspelling of "existent"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/decimal_udf.q:25:3: "substraction" is a misspelling of "subtraction"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby2_map_multi_distinct.q:16:51: "funtion" is a misspelling of "function"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby_sort_8.q:15:30: "issueing" is a misspelling of "issuing"
sql/hive/src/test/scala/org/apache/spark/sql/sources/HadoopFsRelationTest.scala:669:52: "wiht" is a misspelling of "with"
sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/session/HiveSessionImpl.java:474:9: "Refering" is a misspelling of "Referring"
```
### after
```
$ misspell . | grep -v '.js'
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
```
Closes#22070 from seratch/fix-typo.
Authored-by: Kazuhiro Sera <seratch@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
"distribute by" on multiple columns (wrap in brackets) may lead to codegen issue.
Simple way to reproduce:
```scala
val df = spark.range(1000)
val columns = (0 until 400).map{ i => s"id as id$i" }
val distributeExprs = (0 until 100).map(c => s"id$c").mkString(",")
df.selectExpr(columns : _*).createTempView("test")
spark.sql(s"select * from test distribute by ($distributeExprs)").count()
```
## How was this patch tested?
Add UT.
Closes#22066 from yucai/SPARK-25084.
Authored-by: yucai <yyu1@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Parquet file provides six codecs: "snappy", "gzip", "lzo", "lz4", "brotli", "zstd".
This pr add missing compression codec :"lz4", "brotli", "zstd" .
## How was this patch tested?
N/A
Closes#22068 from 10110346/nosupportlz4.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
A logical `Limit` is performed physically by two operations `LocalLimit` and `GlobalLimit`.
Most of time, we gather all data into a single partition in order to run `GlobalLimit`. If we use a very big limit number, shuffling data causes performance issue also reduces parallelism.
We can avoid shuffling into single partition if we don't care data ordering. This patch implements this idea by doing a map stage during global limit. It collects the info of row numbers at each partition. For each partition, we locally retrieves limited data without any shuffling to finish this global limit.
For example, we have three partitions with rows (100, 100, 50) respectively. In global limit of 100 rows, we may take (34, 33, 33) rows for each partition locally. After global limit we still have three partitions.
If the data partition has certain ordering, we can't distribute required rows evenly to each partitions because it could change data ordering. But we still can avoid shuffling.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16677 from viirya/improve-global-limit-parallelism.
## What changes were proposed in this pull request?
Support for text socket stream in spark structured streaming "continuous" mode. This is roughly based on the idea of ContinuousMemoryStream where the executor queries the data from driver over an RPC endpoint.
This makes it possible to create Structured streaming continuous pipeline to ingest data via "nc" and run examples.
## How was this patch tested?
Unit test and ran spark examples in structured streaming continuous mode.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#21199 from arunmahadevan/SPARK-24127.
Authored-by: Arun Mahadevan <arunm@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR simplified code generation for `ArrayDistinct`. #21966 enabled code generation only if the type can be specialized by the hash set. This PR follows this strategy.
Optimization of null handling will be implemented in #21912.
## How was this patch tested?
Existing UTs
Closes#22044 from kiszk/SPARK-23912-follow.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
This is a follow-up to #21305 that adds a test suite for AppendData analysis.
This also fixes the following problems uncovered by these tests:
* Incorrect order of data types passed to `canWrite` is fixed
* The field check calls `canWrite` first to ensure all errors are found
* `AppendData#resolved` must check resolution of the query's attributes
* Column names are quoted to show empty names
## How was this patch tested?
This PR adds a test suite for AppendData analysis.
Closes#22043 from rdblue/SPARK-24251-add-append-data-analysis-tests.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This pr adds `exists` function which tests whether a predicate holds for one or more elements in the array.
```sql
> SELECT exists(array(1, 2, 3), x -> x % 2 == 0);
true
```
## How was this patch tested?
Added tests.
Closes#22052 from ueshin/issues/SPARK-25068/exists.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
When a `SparkSession` is stopped, `SQLConf.get` should use the fallback conf to avoid weird issues like
```
sbt.ForkMain$ForkError: java.lang.IllegalStateException: LiveListenerBus is stopped.
at org.apache.spark.scheduler.LiveListenerBus.addToQueue(LiveListenerBus.scala:97)
at org.apache.spark.scheduler.LiveListenerBus.addToStatusQueue(LiveListenerBus.scala:80)
at org.apache.spark.sql.internal.SharedState.<init>(SharedState.scala:93)
at org.apache.spark.sql.SparkSession$$anonfun$sharedState$1.apply(SparkSession.scala:120)
at org.apache.spark.sql.SparkSession$$anonfun$sharedState$1.apply(SparkSession.scala:120)
at scala.Option.getOrElse(Option.scala:121)
...
```
## How was this patch tested?
a new test suite
Closes#22056 from cloud-fan/session.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Just delete the unused variable `inputFields` in WindowExec, avoid making others confused while reading the code.
## How was this patch tested?
Existing UT.
Closes#22057 from xuanyuanking/SPARK-25077.
Authored-by: liyuanjian <liyuanjian@baidu.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Currently, Analyze table calculates table size sequentially for each partition. We can parallelize size calculations over partitions.
Results : Tested on a table with 100 partitions and data stored in S3.
With changes :
- 10.429s
- 10.557s
- 10.439s
- 9.893s
Without changes :
- 110.034s
- 99.510s
- 100.743s
- 99.106s
## How was this patch tested?
Simple unit test.
Closes#21608 from Achuth17/improveAnalyze.
Lead-authored-by: Achuth17 <Achuth.narayan@gmail.com>
Co-authored-by: arajagopal17 <arajagopal@qubole.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Correct the class name typo checked in through SPARK-24891
## How was this patch tested?
Passed all existing tests.
Closes#22049 from maryannxue/known-not-null.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR addresses two issues in `BufferHolderSparkSubmitSuite`.
1. While `BufferHolderSparkSubmitSuite` tried to allocate a large object several times, it actually allocated an object once and reused the object.
2. `BufferHolderSparkSubmitSuite` may fail due to timeout
To assign a small object before allocating a large object each time solved issue 1 by avoiding reuse.
To increasing heap size from 4g to 7g solved issue 2. It can also avoid OOM after fixing issue 1.
## How was this patch tested?
Updated existing `BufferHolderSparkSubmitSuite`
Closes#20636 from kiszk/SPARK-23415.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR fixes typo regarding `auxiliary verb + verb[s]`. This is a follow-on of #21956.
## How was this patch tested?
N/A
Closes#22040 from kiszk/spellcheck1.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
We should use `Block.isEmpty/nonEmpty` instead of comparing with empty string to check whether the code is empty or not.
```
[error] [warn] /.../sql/core/src/main/scala/org/apache/spark/sql/execution/WholeStageCodegenExec.scala:278: org.apache.spark.sql.catalyst.expressions.codegen.Block and String are unrelated: they will most likely always compare unequal
[error] [warn] if (ev.code != "" && required.contains(attributes(i))) {
[error] [warn]
[error] [warn] /.../sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoinExec.scala:323: org.apache.spark.sql.catalyst.expressions.codegen.Block and String are unrelated: they will most likely never compare equal
[error] [warn] | ${buildVars.filter(_.code == "").map(v => s"${v.isNull} = true;").mkString("\n")}
[error] [warn]
```
## How was this patch tested?
Existing tests.
Closes#22041 from ueshin/issues/SPARK-25058/fix_comparison.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
We have completed a significant subset of the object related Expressions to provide an interpreted fallback. This PR is going to modify the tests to also test the interpreted code paths.
One concern right now is that by testing the interpreted code paths too, we will double current test time or more. Otherwise, we can only choose to test the interpreted code paths for just few test suites such as encoder related.
## How was this patch tested?
Existing tests.
Closes#21535 from viirya/SPARK-23596.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This is a follow-up pr of #22014.
We still have some more compilation errors in scala-2.12 with sbt:
```
[error] [warn] /.../sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala:493: match may not be exhaustive.
[error] It would fail on the following input: (_, _)
[error] [warn] val typeMatches = (targetType, f.dataType) match {
[error] [warn]
[error] [warn] /.../sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MicroBatchExecution.scala:393: match may not be exhaustive.
[error] It would fail on the following input: (_, _)
[error] [warn] prevBatchOff.get.toStreamProgress(sources).foreach {
[error] [warn]
[error] [warn] /.../sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/AggUtils.scala:173: match may not be exhaustive.
[error] It would fail on the following input: AggregateExpression(_, _, false, _)
[error] [warn] val rewrittenDistinctFunctions = functionsWithDistinct.map {
[error] [warn]
[error] [warn] /.../sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/SymmetricHashJoinStateManager.scala:271: match may not be exhaustive.
[error] It would fail on the following input: (_, _)
[error] [warn] keyWithIndexToValueMetrics.customMetrics.map {
[error] [warn]
[error] [warn] /.../sql/core/src/main/scala/org/apache/spark/sql/execution/command/tables.scala:959: match may not be exhaustive.
[error] It would fail on the following input: CatalogTableType(_)
[error] [warn] val tableTypeString = metadata.tableType match {
[error] [warn]
[error] [warn] /.../sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala:923: match may not be exhaustive.
[error] It would fail on the following input: CatalogTableType(_)
[error] [warn] hiveTable.setTableType(table.tableType match {
[error] [warn]
```
## How was this patch tested?
Manually build with Scala-2.12.
Closes#22039 from ueshin/issues/SPARK-25036/fix_match.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
This pr is a follow-up pr of #21982 and fixes the examples.
## How was this patch tested?
Existing tests.
Closes#22035 from ueshin/issues/SPARK-23911/fup1.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
The PR remove the following compilation error using scala-2.12 with sbt by adding a default case to `match`.
```
/home/ishizaki/Spark/PR/scala212/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/ValueInterval.scala:63: match may not be exhaustive.
[error] It would fail on the following inputs: (NumericValueInterval(_, _), _), (_, NumericValueInterval(_, _)), (_, _)
[error] [warn] def isIntersected(r1: ValueInterval, r2: ValueInterval): Boolean = (r1, r2) match {
[error] [warn]
[error] [warn] /home/ishizaki/Spark/PR/scala212/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/ValueInterval.scala:79: match may not be exhaustive.
[error] It would fail on the following inputs: (NumericValueInterval(_, _), _), (_, NumericValueInterval(_, _)), (_, _)
[error] [warn] (r1, r2) match {
[error] [warn]
[error] [warn] /home/ishizaki/Spark/PR/scala212/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproxCountDistinctForIntervals.scala:67: match may not be exhaustive.
[error] It would fail on the following inputs: (ArrayType(_, _), _), (_, ArrayData()), (_, _)
[error] [warn] (endpointsExpression.dataType, endpointsExpression.eval()) match {
[error] [warn]
[error] [warn] /home/ishizaki/Spark/PR/scala212/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala:470: match may not be exhaustive.
[error] It would fail on the following inputs: NewFunctionSpec(_, None, Some(_)), NewFunctionSpec(_, Some(_), None)
[error] [warn] newFunction match {
[error] [warn]
[error] [warn] [error] [warn] /home/ishizaki/Spark/PR/scala212/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala:709: match may not be exhaustive.
[error] It would fail on the following input: Schema((x: org.apache.spark.sql.types.DataType forSome x not in org.apache.spark.sql.types.StructType), _)
[error] [warn] def attributesFor[T: TypeTag]: Seq[Attribute] = schemaFor[T] match {
[error] [warn]
```
## How was this patch tested?
Existing UTs with Scala-2.11.
Manually build with Scala-2.12
Closes#22014 from kiszk/SPARK-25036b.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR fixes an exception during the compilation of generated code of `mapEntry`. This error occurs since the current code uses `key` type to store a `value` when `key` and `value` types are primitive type.
```
val mid0 = Literal.create(Map(1 -> 1.1, 2 -> 2.2), MapType(IntegerType, DoubleType))
checkEvaluation(MapEntries(mid0), Seq(r(1, 1.1), r(2, 2.2)))
```
```
[info] Code generation of map_entries(keys: [1,2], values: [1.1,2.2]) failed:
[info] java.util.concurrent.ExecutionException: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 80, Column 20: failed to compile: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 80, Column 20: No applicable constructor/method found for actual parameters "int, double"; candidates are: "public void org.apache.spark.sql.catalyst.expressions.UnsafeRow.setInt(int, int)", "public void org.apache.spark.sql.catalyst.InternalRow.setInt(int, int)"
[info] java.util.concurrent.ExecutionException: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 80, Column 20: failed to compile: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 80, Column 20: No applicable constructor/method found for actual parameters "int, double"; candidates are: "public void org.apache.spark.sql.catalyst.expressions.UnsafeRow.setInt(int, int)", "public void org.apache.spark.sql.catalyst.InternalRow.setInt(int, int)"
[info] at com.google.common.util.concurrent.AbstractFuture$Sync.getValue(AbstractFuture.java:306)
[info] at com.google.common.util.concurrent.AbstractFuture$Sync.get(AbstractFuture.java:293)
[info] at com.google.common.util.concurrent.AbstractFuture.get(AbstractFuture.java:116)
[info] at com.google.common.util.concurrent.Uninterruptibles.getUninterruptibly(Uninterruptibles.java:135)
[info] at com.google.common.cache.LocalCache$Segment.getAndRecordStats(LocalCache.java:2410)
[info] at com.google.common.cache.LocalCache$Segment.loadSync(LocalCache.java:2380)
[info] at com.google.common.cache.LocalCache$Segment.lockedGetOrLoad(LocalCache.java:2342)
[info] at com.google.common.cache.LocalCache$Segment.get(LocalCache.java:2257)
[info] at com.google.common.cache.LocalCache.get(LocalCache.java:4000)
[info] at com.google.common.cache.LocalCache.getOrLoad(LocalCache.java:4004)
[info] at com.google.common.cache.LocalCache$LocalLoadingCache.get(LocalCache.java:4874)
[info] at org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$.compile(CodeGenerator.scala:1290)
...
```
## How was this patch tested?
Added a new test to `CollectionExpressionsSuite`
Closes#22033 from kiszk/SPARK-23935-followup.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
This is a follow-up pr of #21980.
`Shuffle` can also be `ExpressionWithRandomSeed` to produce different values for each execution in streaming query.
## How was this patch tested?
Added a test.
Closes#22027 from ueshin/issues/SPARK-25010/random_seed.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This adds a new logical plan, AppendData, that was proposed in SPARK-23521: Standardize SQL logical plans.
* DataFrameWriter uses the new AppendData plan for DataSourceV2 appends
* AppendData is resolved if its output columns match the incoming data frame
* A new analyzer rule, ResolveOutputColumns, validates data before it is appended. This rule will add safe casts, rename columns, and checks nullability
## How was this patch tested?
Existing tests for v2 appends. Will add AppendData tests to validate logical plan analysis.
Closes#21305 from rdblue/SPARK-24251-add-append-data.
Lead-authored-by: Ryan Blue <blue@apache.org>
Co-authored-by: Ryan Blue <rdblue@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Alter View can excute sql like "ALTER VIEW ... AS INSERT INTO" . We should throw ParseException(s"Operation not allowed: $message", ctx) as Create View does.
```
override def visitCreateView(ctx: CreateViewContext): LogicalPlan = withOrigin(ctx) {
if (ctx.identifierList != null) {
operationNotAllowed("CREATE VIEW ... PARTITIONED ON", ctx)
} else {
// CREATE VIEW ... AS INSERT INTO is not allowed.
ctx.query.queryNoWith match {
case s: SingleInsertQueryContext if s.insertInto != null =>
operationNotAllowed("CREATE VIEW ... AS INSERT INTO", ctx)
case _: MultiInsertQueryContext =>
operationNotAllowed("CREATE VIEW ... AS FROM ... [INSERT INTO ...]+", ctx)
case _ => // OK
}
```
```
override def visitAlterViewQuery(ctx: AlterViewQueryContext): LogicalPlan = withOrigin(ctx) {
// ALTER VIEW ... AS INSERT INTO is not allowed.
ctx.query.queryNoWith match {
case s: SingleInsertQueryContext if s.insertInto != null =>
operationNotAllowed("ALTER VIEW ... AS INSERT INTO", ctx)
case _: MultiInsertQueryContext =>
operationNotAllowed("ALTER VIEW ... AS FROM ... [INSERT INTO ...]+", ctx)
case _ => // OK
}
AlterViewAsCommand(
name = visitTableIdentifier(ctx.tableIdentifier),
originalText = source(ctx.query),
query = plan(ctx.query))
}
```
## How was this patch tested?
UT has been added in SparkSqlParserSuite
Closes#22028 from sddyljsx/SPARK-25046.
Lead-authored-by: Neal Song <neal_song@126.com>
Co-authored-by: neal <neal_song@126.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The PR fix the bug in `buildFormattedString` function in `MapType`, which makes the printed schema misleading.
## How was this patch tested?
Added UT
Closes#22006 from invkrh/fix-map-schema-print.
Authored-by: invkrh <invkrh@gmail.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The PR adds the high order function `map_filter`, which filters the entries of a map and returns a new map which contains only the entries which satisfied the filter function.
## How was this patch tested?
added UTs
Closes#21986 from mgaido91/SPARK-23937.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/21822
Similar to `TreeNode`, `AnalysisHelper` should also provide 3 versions of transformations: `resolveOperatorsUp`, `resolveOperatorsDown` and `resolveOperators`.
This PR adds the missing `resolveOperatorsUp`, and also fixes some code style which is missed in #21822
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#21932 from cloud-fan/follow.
## What changes were proposed in this pull request?
The tests for shuffle can be flaky (eg. https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/94355/testReport/). This happens because we have not set the seed for `Random`.
## How was this patch tested?
running 10000 times the UT (validated that with a different seed eg. 12345 the test fails).
Closes#22023 from mgaido91/SPARK-23928_followup.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The design details is attached to the JIRA issue [here](https://drive.google.com/file/d/1zKm3aNZ3DpsqIuoMvRsf0kkDkXsAasxH/view)
High level overview of the changes are:
- Enhance the qualifier to be more than one string
- Add support to store the qualifier. Enhance the lookupRelation to keep the qualifier appropriately.
- Enhance the table matching column resolution algorithm to account for qualifier being more than a string.
- Enhance the table matching algorithm in UnresolvedStar.expand
- Ensure that we continue to support select t1.i1 from db1.t1
## How was this patch tested?
- New tests are added.
- Several test scenarios were added in a separate [test pr 17067](https://github.com/apache/spark/pull/17067). The tests that were not supported earlier are marked with TODO markers and those are now supported with the code changes here.
- Existing unit tests ( hive, catalyst and sql) were run successfully.
Closes#17185 from skambha/colResolution.
Authored-by: Sunitha Kambhampati <skambha@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to replace Scala parallel collections by new methods `parmap()`. The methods use futures to transform a sequential collection by applying a lambda function to each element in parallel. The result of `parmap` is another regular (sequential) collection.
The proposed `parmap` method aims to solve the problem of impossibility to interrupt parallel Scala collection. This possibility is needed for reliable task preemption.
## How was this patch tested?
A test was added to `ThreadUtilsSuite`
Closes#21913 from MaxGekk/par-map.
Authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Using struct types in subqueries with the `IN` clause can generate invalid plans in `RewritePredicateSubquery`. Indeed, we are not handling clearly the cases when the outer value is a struct or the output of the inner subquery is a struct.
The PR aims to make Spark's behavior the same as the one of the other RDBMS - namely Oracle and Postgres behavior were checked. So we consider valid only queries having the same number of fields in the outer value and in the subquery. This means that:
- `(a, b) IN (select c, d from ...)` is a valid query;
- `(a, b) IN (select (c, d) from ...)` throws an AnalysisException, as in the subquery we have only one field of type struct while in the outer value we have 2 fields;
- `a IN (select (c, d) from ...)` - where `a` is a struct - is a valid query.
## How was this patch tested?
Added UT
Closes#21403 from mgaido91/SPARK-24313.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Like Uuid in SPARK-24896, Rand and Randn expressions now produce the same results for each execution in streaming query. It doesn't make too much sense for streaming queries. We should make them produce different results as Uuid.
In this change, similar to Uuid, we assign new random seeds to Rand/Randn when returning optimized plan from `IncrementalExecution`.
Note: Different to Uuid, Rand/Randn can be created with initial seed. Because we replace this initial seed at `IncrementalExecution`, it doesn't use the initial seed anymore. For now it seems to me not a big issue for streaming query. But need to confirm with others. cc zsxwing cloud-fan
## How was this patch tested?
Added test.
Closes#21980 from viirya/SPARK-25010.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR refactors `ArrayUnion` based on [this suggestion](https://github.com/apache/spark/pull/21103#discussion_r205668821).
1. Generate optimized code for all of the primitive types except `boolean`
1. Generate code using `ArrayBuilder` or `ArrayBuffer`
1. Leave only a generic path in the interpreted path
## How was this patch tested?
Existing tests
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#21937 from kiszk/SPARK-23914-follow.
## What changes were proposed in this pull request?
Currently the Structured Streaming sources and sinks does not have a way to report custom metrics. Providing an option to report custom metrics and making it available via Streaming Query progress can enable sources and sinks to report custom progress information (E.g. the lag metrics for Kafka source).
Similar metrics can be reported for Sinks as well, but would like to get initial feedback before proceeding further.
## How was this patch tested?
New and existing unit tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#21721 from arunmahadevan/SPARK-24748.
Authored-by: Arun Mahadevan <arunm@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The patch adds metrics regarding state and watermark to dropwizard metrics, so that watermark and state rows/size can be tracked via time-series manner.
## How was this patch tested?
Manually tested with CSV metric sink.
Closes#21622 from HeartSaVioR/SPARK-24637.
Authored-by: Jungtaek Lim <kabhwan@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The PR refactors the aggregate expressions which were not using DSL in order to simplify them.
## How was this patch tested?
NA
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21970 from mgaido91/SPARK-24996.
## What changes were proposed in this pull request?
This PR fixes a comparison of `ExprValue.isNull` with `String`. `ExprValue.isNull` should be compared with `LiteralTrue` or `LiteralFalse`.
This causes the following compilation error using scala-2.12 with sbt. In addition, this code may also generate incorrect code in Spark 2.3.
```
/home/ishizaki/Spark/PR/scala212/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala:94: org.apache.spark.sql.catalyst.expressions.codegen.ExprValue and String are unrelated: they will most likely always compare unequal
[error] [warn] if (eval.isNull != "true") {
[error] [warn]
[error] [warn] /home/ishizaki/Spark/PR/scala212/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala:126: org.apache.spark.sql.catalyst.expressions.codegen.ExprValue and String are unrelated: they will most likely never compare equal
[error] [warn] if (eval.isNull == "true") {
[error] [warn]
[error] [warn] /home/ishizaki/Spark/PR/scala212/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala:133: org.apache.spark.sql.catalyst.expressions.codegen.ExprValue and String are unrelated: they will most likely never compare equal
[error] [warn] if (eval.isNull == "true") {
[error] [warn]
[error] [warn] /home/ishizaki/Spark/PR/scala212/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala:90: org.apache.spark.sql.catalyst.expressions.codegen.ExprValue and String are unrelated: they will most likely never compare equal
[error] [warn] if (inputs.map(_.isNull).forall(_ == "false")) {
[error] [warn]
```
## How was this patch tested?
Existing UTs
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#22012 from kiszk/SPARK-25036a.
## What changes were proposed in this pull request?
Currently, debug package has a implicit class "DebugQuery" which matches Dataset to provide debug features on Dataset class. It doesn't work with structured streaming: it requires query is already started, and the information can be retrieved from StreamingQuery, not Dataset. I guess that's why "explain" had to be placed to StreamingQuery whereas it already exists on Dataset.
This patch adds a new implicit class "DebugStreamQuery" which matches StreamingQuery to provide similar debug features on StreamingQuery class.
## How was this patch tested?
Added relevant unit tests.
Author: Jungtaek Lim <kabhwan@gmail.com>
Closes#21222 from HeartSaVioR/SPARK-24161.
## What changes were proposed in this pull request?
During upgrading Apache ORC to 1.5.2 ([SPARK-24576](https://issues.apache.org/jira/browse/SPARK-24576)), `sql/core` module overrides the exclusion rules of parent pom file and it causes published `spark-sql_2.1X` artifacts have incomplete exclusion rules ([SPARK-25019](https://issues.apache.org/jira/browse/SPARK-25019)). This PR fixes it by moving the newly added exclusion rule to the parent pom. This also fixes the sbt build hack introduced at that time.
## How was this patch tested?
Pass the existing dependency check and the tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#22003 from dongjoon-hyun/SPARK-25019.
## What changes were proposed in this pull request?
The PR adds the SQL function `array_intersect`. The behavior of the function is based on Presto's one.
This function returns returns an array of the elements in the intersection 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#21102 from kiszk/SPARK-23913.
## What changes were proposed in this pull request?
Having the default value of isAll in the logical plan nodes INTERSECT/EXCEPT could introduce bugs when the callers are not aware of it. This PR removes the default value and makes caller explicitly specify them.
## How was this patch tested?
This is a refactoring change. Existing tests test the functionality already.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#22000 from dilipbiswal/SPARK-25025.
## What changes were proposed in this pull request?
Follow up to fix an unmerged review comment.
## How was this patch tested?
Unit test ResolveHintsSuite.
Author: John Zhuge <jzhuge@apache.org>
Closes#21998 from jzhuge/SPARK-24940.
## What changes were proposed in this pull request?
A follow up of #21118
Since we use `InternalRow` in the read API of data source v2, we should do the same thing for the write API.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#21948 from cloud-fan/row-write.
## What changes were proposed in this pull request?
This pr adds `aggregate` function which applies a binary operator to an initial state and all elements in the array, and reduces this to a single state. The final state is converted into the final result by applying a finish function.
```sql
> SELECT aggregate(array(1, 2, 3), (acc, x) -> acc + x);
6
> SELECT aggregate(array(1, 2, 3), (acc, x) -> acc + x, acc -> acc * 10);
60
```
## How was this patch tested?
Added tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#21982 from ueshin/issues/SPARK-23911/aggregate.
## What changes were proposed in this pull request?
There are many warnings in the current build (for instance see https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7/4734/console).
**common**:
```
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDB.java:237: warning: [rawtypes] found raw type: LevelDBIterator
[warn] void closeIterator(LevelDBIterator it) throws IOException {
[warn] ^
[warn] missing type arguments for generic class LevelDBIterator<T>
[warn] where T is a type-variable:
[warn] T extends Object declared in class LevelDBIterator
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:151: warning: [deprecation] group() in AbstractBootstrap has been deprecated
[warn] if (bootstrap != null && bootstrap.group() != null) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:152: warning: [deprecation] group() in AbstractBootstrap has been deprecated
[warn] bootstrap.group().shutdownGracefully();
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:154: warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
[warn] if (bootstrap != null && bootstrap.childGroup() != null) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:155: warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
[warn] bootstrap.childGroup().shutdownGracefully();
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/util/NettyUtils.java:112: warning: [deprecation] PooledByteBufAllocator(boolean,int,int,int,int,int,int,int) in PooledByteBufAllocator has been deprecated
[warn] return new PooledByteBufAllocator(
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportClient.java:321: warning: [rawtypes] found raw type: Future
[warn] public void operationComplete(Future future) throws Exception {
[warn] ^
[warn] missing type arguments for generic class Future<V>
[warn] where V is a type-variable:
[warn] V extends Object declared in interface Future
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215: warning: [rawtypes] found raw type: StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, resp.streamId, resp.byteCount,
[warn] ^
[warn] missing type arguments for generic class StreamInterceptor<T>
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215: warning: [rawtypes] found raw type: StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, resp.streamId, resp.byteCount,
[warn] ^
[warn] missing type arguments for generic class StreamInterceptor<T>
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215: warning: [unchecked] unchecked call to StreamInterceptor(MessageHandler<T>,String,long,StreamCallback) as a member of the raw type StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, resp.streamId, resp.byteCount,
[warn] ^
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java:255: warning: [rawtypes] found raw type: StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, wrappedCallback.getID(),
[warn] ^
[warn] missing type arguments for generic class StreamInterceptor<T>
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java:255: warning: [rawtypes] found raw type: StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, wrappedCallback.getID(),
[warn] ^
[warn] missing type arguments for generic class StreamInterceptor<T>
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java:255: warning: [unchecked] unchecked call to StreamInterceptor(MessageHandler<T>,String,long,StreamCallback) as a member of the raw type StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, wrappedCallback.getID(),
[warn] ^
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/crypto/TransportCipher.java:270: warning: [deprecation] transfered() in FileRegion has been deprecated
[warn] region.transferTo(byteRawChannel, region.transfered());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:304: warning: [deprecation] transfered() in FileRegion has been deprecated
[warn] region.transferTo(byteChannel, region.transfered());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/test/java/org/apache/spark/network/ProtocolSuite.java:119: warning: [deprecation] transfered() in FileRegion has been deprecated
[warn] while (in.transfered() < in.count()) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/test/java/org/apache/spark/network/ProtocolSuite.java:120: warning: [deprecation] transfered() in FileRegion has been deprecated
[warn] in.transferTo(channel, in.transfered());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java:80: warning: [static] static method should be qualified by type name, Murmur3_x86_32, instead of by an expression
[warn] Assert.assertEquals(-300363099, hasher.hashUnsafeWords(bytes, offset, 16, 42));
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java:84: warning: [static] static method should be qualified by type name, Murmur3_x86_32, instead of by an expression
[warn] Assert.assertEquals(-1210324667, hasher.hashUnsafeWords(bytes, offset, 16, 42));
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java:88: warning: [static] static method should be qualified by type name, Murmur3_x86_32, instead of by an expression
[warn] Assert.assertEquals(-634919701, hasher.hashUnsafeWords(bytes, offset, 16, 42));
[warn] ^
```
**launcher**:
```
[warn] Pruning sources from previous analysis, due to incompatible CompileSetup.
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/launcher/src/main/java/org/apache/spark/launcher/AbstractLauncher.java:31: warning: [rawtypes] found raw type: AbstractLauncher
[warn] public abstract class AbstractLauncher<T extends AbstractLauncher> {
[warn] ^
[warn] missing type arguments for generic class AbstractLauncher<T>
[warn] where T is a type-variable:
[warn] T extends AbstractLauncher declared in class AbstractLauncher
```
**core**:
```
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/main/scala/org/apache/spark/api/r/RBackend.scala:99: method group in class AbstractBootstrap is deprecated: see corresponding Javadoc for more information.
[warn] if (bootstrap != null && bootstrap.group() != null) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/main/scala/org/apache/spark/api/r/RBackend.scala💯 method group in class AbstractBootstrap is deprecated: see corresponding Javadoc for more information.
[warn] bootstrap.group().shutdownGracefully()
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/main/scala/org/apache/spark/api/r/RBackend.scala:102: method childGroup in class ServerBootstrap is deprecated: see corresponding Javadoc for more information.
[warn] if (bootstrap != null && bootstrap.childGroup() != null) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/main/scala/org/apache/spark/api/r/RBackend.scala:103: method childGroup in class ServerBootstrap is deprecated: see corresponding Javadoc for more information.
[warn] bootstrap.childGroup().shutdownGracefully()
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala:151: reflective access of structural type member method getData should be enabled
[warn] by making the implicit value scala.language.reflectiveCalls visible.
[warn] This can be achieved by adding the import clause 'import scala.language.reflectiveCalls'
[warn] or by setting the compiler option -language:reflectiveCalls.
[warn] See the Scaladoc for value scala.language.reflectiveCalls for a discussion
[warn] why the feature should be explicitly enabled.
[warn] val rdd = sc.parallelize(1 to 1).map(concreteObject.getData)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala:175: reflective access of structural type member value innerObject2 should be enabled
[warn] by making the implicit value scala.language.reflectiveCalls visible.
[warn] val rdd = sc.parallelize(1 to 1).map(concreteObject.innerObject2.getData)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala:175: reflective access of structural type member method getData should be enabled
[warn] by making the implicit value scala.language.reflectiveCalls visible.
[warn] val rdd = sc.parallelize(1 to 1).map(concreteObject.innerObject2.getData)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/LocalSparkContext.scala:32: constructor Slf4JLoggerFactory in class Slf4JLoggerFactory is deprecated: see corresponding Javadoc for more information.
[warn] InternalLoggerFactory.setDefaultFactory(new Slf4JLoggerFactory())
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:218: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] assert(wrapper.stageAttemptId === stages.head.attemptId)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:261: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] stageAttemptId = stages.head.attemptId))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:287: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] stageAttemptId = stages.head.attemptId))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:471: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] stageAttemptId = stages.last.attemptId))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:966: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] listener.onTaskStart(SparkListenerTaskStart(dropped.stageId, dropped.attemptId, task))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:972: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] listener.onTaskEnd(SparkListenerTaskEnd(dropped.stageId, dropped.attemptId,
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:976: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] .taskSummary(dropped.stageId, dropped.attemptId, Array(0.25d, 0.50d, 0.75d))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:1146: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] SparkListenerTaskEnd(stage1.stageId, stage1.attemptId, "taskType", Success, tasks(1), null))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:1150: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] SparkListenerTaskEnd(stage1.stageId, stage1.attemptId, "taskType", Success, tasks(0), null))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/storage/DiskStoreSuite.scala:197: method transfered in trait FileRegion is deprecated: see corresponding Javadoc for more information.
[warn] while (region.transfered() < region.count()) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/storage/DiskStoreSuite.scala:198: method transfered in trait FileRegion is deprecated: see corresponding Javadoc for more information.
[warn] region.transferTo(byteChannel, region.transfered())
[warn] ^
```
**sql**:
```
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala:534: abstract type T is unchecked since it is eliminated by erasure
[warn] assert(partitioning.isInstanceOf[T])
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala:534: abstract type T is unchecked since it is eliminated by erasure
[warn] assert(partitioning.isInstanceOf[T])
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ObjectExpressionsSuite.scala:323: inferred existential type Option[Class[_$1]]( forSome { type _$1 }), which cannot be expressed by wildcards, should be enabled
[warn] by making the implicit value scala.language.existentials visible.
[warn] This can be achieved by adding the import clause 'import scala.language.existentials'
[warn] or by setting the compiler option -language:existentials.
[warn] See the Scaladoc for value scala.language.existentials for a discussion
[warn] why the feature should be explicitly enabled.
[warn] val optClass = Option(collectionCls)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java:226: warning: [deprecation] ParquetFileReader(Configuration,FileMetaData,Path,List<BlockMetaData>,List<ColumnDescriptor>) in ParquetFileReader has been deprecated
[warn] this.reader = new ParquetFileReader(
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:178: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] (descriptor.getType() == PrimitiveType.PrimitiveTypeName.INT32 ||
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:179: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] (descriptor.getType() == PrimitiveType.PrimitiveTypeName.INT64 &&
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:181: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] descriptor.getType() == PrimitiveType.PrimitiveTypeName.FLOAT ||
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:182: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] descriptor.getType() == PrimitiveType.PrimitiveTypeName.DOUBLE ||
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:183: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] descriptor.getType() == PrimitiveType.PrimitiveTypeName.BINARY))) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:198: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] switch (descriptor.getType()) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:221: warning: [deprecation] getTypeLength() in ColumnDescriptor has been deprecated
[warn] readFixedLenByteArrayBatch(rowId, num, column, descriptor.getTypeLength());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:224: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] throw new IOException("Unsupported type: " + descriptor.getType());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:246: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] descriptor.getType().toString(),
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:258: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] switch (descriptor.getType()) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:384: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] throw new UnsupportedOperationException("Unsupported type: " + descriptor.getType());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/vectorized/ArrowColumnVector.java:458: warning: [static] static variable should be qualified by type name, BaseRepeatedValueVector, instead of by an expression
[warn] int index = rowId * accessor.OFFSET_WIDTH;
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/vectorized/ArrowColumnVector.java:460: warning: [static] static variable should be qualified by type name, BaseRepeatedValueVector, instead of by an expression
[warn] int end = offsets.getInt(index + accessor.OFFSET_WIDTH);
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/BenchmarkQueryTest.scala:57: a pure expression does nothing in statement position; you may be omitting necessary parentheses
[warn] case s => s
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetInteroperabilitySuite.scala:182: inferred existential type org.apache.parquet.column.statistics.Statistics[?0]( forSome { type ?0 <: Comparable[?0] }), which cannot be expressed by wildcards, should be enabled
[warn] by making the implicit value scala.language.existentials visible.
[warn] This can be achieved by adding the import clause 'import scala.language.existentials'
[warn] or by setting the compiler option -language:existentials.
[warn] See the Scaladoc for value scala.language.existentials for a discussion
[warn] why the feature should be explicitly enabled.
[warn] val columnStats = oneBlockColumnMeta.getStatistics
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/sources/ForeachBatchSinkSuite.scala:146: implicit conversion method conv should be enabled
[warn] by making the implicit value scala.language.implicitConversions visible.
[warn] This can be achieved by adding the import clause 'import scala.language.implicitConversions'
[warn] or by setting the compiler option -language:implicitConversions.
[warn] See the Scaladoc for value scala.language.implicitConversions for a discussion
[warn] why the feature should be explicitly enabled.
[warn] implicit def conv(x: (Int, Long)): KV = KV(x._1, x._2)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/streaming/continuous/shuffle/ContinuousShuffleSuite.scala:48: implicit conversion method unsafeRow should be enabled
[warn] by making the implicit value scala.language.implicitConversions visible.
[warn] private implicit def unsafeRow(value: Int) = {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetInteroperabilitySuite.scala:178: method getType in class ColumnDescriptor is deprecated: see corresponding Javadoc for more information.
[warn] assert(oneFooter.getFileMetaData.getSchema.getColumns.get(0).getType() ===
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala:154: method readAllFootersInParallel in object ParquetFileReader is deprecated: see corresponding Javadoc for more information.
[warn] ParquetFileReader.readAllFootersInParallel(configuration, fs.getFileStatus(path)).asScala.toSeq
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/hive/src/test/java/org/apache/spark/sql/hive/test/Complex.java:679: warning: [cast] redundant cast to Complex
[warn] Complex typedOther = (Complex)other;
[warn] ^
```
**mllib**:
```
[warn] Pruning sources from previous analysis, due to incompatible CompileSetup.
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala:597: match may not be exhaustive.
[warn] It would fail on the following inputs: None, Some((x: Tuple2[?, ?] forSome x not in (?, ?)))
[warn] val df = dfs.find {
[warn] ^
```
This PR does not target fix all of them since some look pretty tricky to fix and there look too many warnings including false positive (like deprecated API but it's used in its test, etc.)
## How was this patch tested?
Existing tests should cover this.
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21975 from HyukjinKwon/remove-build-warnings.
## What changes were proposed in this pull request?
simplify the codegen:
1. only do real codegen if the type can be specialized by the hash set
2. change the null handling. Before: track the nullElementIndex, and create a new ArrayData to insert the null in the middle. After: track the nullElementIndex, put a null placeholder in the ArrayBuilder, at the end create ArrayData from ArrayBuilder directly.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#21966 from cloud-fan/minor2.
## What changes were proposed in this pull request?
This pr adds `filter` function which filters the input array using the given predicate.
```sql
> SELECT filter(array(1, 2, 3), x -> x % 2 == 1);
array(1, 3)
```
## How was this patch tested?
Added tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#21965 from ueshin/issues/SPARK-23909/filter.
## What changes were proposed in this pull request?
Many Spark SQL users in my company have asked for a way to control the number of output files in Spark SQL. The users prefer not to use function repartition(n) or coalesce(n, shuffle) that require them to write and deploy Scala/Java/Python code. We propose adding the following Hive-style Coalesce and Repartition Hint to Spark SQL:
```
... SELECT /*+ COALESCE(numPartitions) */ ...
... SELECT /*+ REPARTITION(numPartitions) */ ...
```
Multiple such hints are allowed. Multiple nodes are inserted into the logical plan, and the optimizer will pick the leftmost hint.
```
INSERT INTO s SELECT /*+ REPARTITION(100), COALESCE(500), COALESCE(10) */ * FROM t
== Logical Plan ==
'InsertIntoTable 'UnresolvedRelation `s`, false, false
+- 'UnresolvedHint REPARTITION, [100]
+- 'UnresolvedHint COALESCE, [500]
+- 'UnresolvedHint COALESCE, [10]
+- 'Project [*]
+- 'UnresolvedRelation `t`
== Optimized Logical Plan ==
InsertIntoHadoopFsRelationCommand ...
+- Repartition 100, true
+- HiveTableRelation ...
```
## How was this patch tested?
All unit tests. Manual tests using explain.
Author: John Zhuge <jzhuge@apache.org>
Closes#21911 from jzhuge/SPARK-24940.
## What changes were proposed in this pull request?
In the PR, I propose column-based API for the `pivot()` function. It allows using of any column expressions as the pivot column. Also this makes it consistent with how groupBy() works.
## How was this patch tested?
I added new tests to `DataFramePivotSuite` and updated PySpark examples for the `pivot()` function.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#21699 from MaxGekk/pivot-column.
## What changes were proposed in this pull request?
Enable support for MINUS ALL which was gated at AstBuilder.
## How was this patch tested?
Added tests in SQLQueryTestSuite and modify PlanParserSuite.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#21963 from dilipbiswal/minus-all.
## What changes were proposed in this pull request?
In the current master, `toString` throws an exception when `RelationalGroupedDataset` has unresolved expressions;
```
scala> spark.range(0, 10).groupBy("id")
res4: org.apache.spark.sql.RelationalGroupedDataset = RelationalGroupedDataset: [grouping expressions: [id: bigint], value: [id: bigint], type: GroupBy]
scala> spark.range(0, 10).groupBy('id)
org.apache.spark.sql.catalyst.analysis.UnresolvedException: Invalid call to dataType on unresolved object, tree: 'id
at org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute.dataType(unresolved.scala:105)
at org.apache.spark.sql.RelationalGroupedDataset$$anonfun$12.apply(RelationalGroupedDataset.scala:474)
at org.apache.spark.sql.RelationalGroupedDataset$$anonfun$12.apply(RelationalGroupedDataset.scala:473)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.AbstractTraversable.map(Traversable.scala:104)
at org.apache.spark.sql.RelationalGroupedDataset.toString(RelationalGroupedDataset.scala:473)
at scala.runtime.ScalaRunTime$.scala$runtime$ScalaRunTime$$inner$1(ScalaRunTime.scala:332)
at scala.runtime.ScalaRunTime$.stringOf(ScalaRunTime.scala:337)
at scala.runtime.ScalaRunTime$.replStringOf(ScalaRunTime.scala:345)
```
This pr fixed code to handle the unresolved case in `RelationalGroupedDataset.toString`.
Closes#21752
## How was this patch tested?
Added tests in `DataFrameAggregateSuite`.
Author: Chris Horn <chorn4033@gmail.com>
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#21964 from maropu/SPARK-24788.
## What changes were proposed in this pull request?
Currently the set operations INTERSECT, UNION and EXCEPT are assigned the same precedence. This PR fixes the problem by giving INTERSECT higher precedence than UNION and EXCEPT. UNION and EXCEPT operators are evaluated in the order in which they appear in the query from left to right.
This results in change in behavior because of the change in order of evaluations of set operators in a query. The old behavior is still preserved under a newly added config parameter.
Query `:`
```
SELECT * FROM t1
UNION
SELECT * FROM t2
EXCEPT
SELECT * FROM t3
INTERSECT
SELECT * FROM t4
```
Parsed plan before the change `:`
```
== Parsed Logical Plan ==
'Intersect false
:- 'Except false
: :- 'Distinct
: : +- 'Union
: : :- 'Project [*]
: : : +- 'UnresolvedRelation `t1`
: : +- 'Project [*]
: : +- 'UnresolvedRelation `t2`
: +- 'Project [*]
: +- 'UnresolvedRelation `t3`
+- 'Project [*]
+- 'UnresolvedRelation `t4`
```
Parsed plan after the change `:`
```
== Parsed Logical Plan ==
'Except false
:- 'Distinct
: +- 'Union
: :- 'Project [*]
: : +- 'UnresolvedRelation `t1`
: +- 'Project [*]
: +- 'UnresolvedRelation `t2`
+- 'Intersect false
:- 'Project [*]
: +- 'UnresolvedRelation `t3`
+- 'Project [*]
+- 'UnresolvedRelation `t4`
```
## How was this patch tested?
Added tests in PlanParserSuite, SQLQueryTestSuite.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#21941 from dilipbiswal/SPARK-24966.
## What changes were proposed in this pull request?
Support reading/writing Avro logical timestamp type with different precisions
https://avro.apache.org/docs/1.8.2/spec.html#Timestamp+%28millisecond+precision%29
To specify the output timestamp type, use Dataframe option `outputTimestampType` or SQL config `spark.sql.avro.outputTimestampType`. The supported values are
* `TIMESTAMP_MICROS`
* `TIMESTAMP_MILLIS`
The default output type is `TIMESTAMP_MICROS`
## How was this patch tested?
Unit test
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21935 from gengliangwang/avro_timestamp.
## What changes were proposed in this pull request?
This PR refactors code to get a value for "spark.sql.codegen.comments" by avoiding `SparkEnv.get.conf`. This PR uses `SQLConf.get.codegenComments` since `SQLConf.get` always returns an instance of `SQLConf`.
## How was this patch tested?
Added test case to `DebuggingSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19449 from kiszk/SPARK-22219.
## What changes were proposed in this pull request?
`Uuid`'s results depend on random seed given during analysis. Thus under streaming query, we will have the same uuids in each execution. This seems to be incorrect for streaming query execution.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#21854 from viirya/uuid_in_streaming.
## What changes were proposed in this pull request?
In the current master, `EnsureRequirements` sets the number of exchanges in `ExchangeCoordinator` before `ReuseExchange`. Then, `ReuseExchange` removes some duplicate exchange and the actual number of registered exchanges changes. Finally, the assertion in `ExchangeCoordinator` fails because the logical number of exchanges and the actual number of registered exchanges become different;
https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/ExchangeCoordinator.scala#L201
This pr fixed the issue and the code to reproduce this is as follows;
```
scala> sql("SET spark.sql.adaptive.enabled=true")
scala> sql("SET spark.sql.autoBroadcastJoinThreshold=-1")
scala> val df = spark.range(1).selectExpr("id AS key", "id AS value")
scala> val resultDf = df.join(df, "key").join(df, "key")
scala> resultDf.show
...
at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec$$anonfun$doExecute$1.apply(ShuffleExchangeExec.scala:119)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52)
... 101 more
Caused by: java.lang.AssertionError: assertion failed
at scala.Predef$.assert(Predef.scala:156)
at org.apache.spark.sql.execution.exchange.ExchangeCoordinator.doEstimationIfNecessary(ExchangeCoordinator.scala:201)
at org.apache.spark.sql.execution.exchange.ExchangeCoordinator.postShuffleRDD(ExchangeCoordinator.scala:259)
at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec$$anonfun$doExecute$1.apply(ShuffleExchangeExec.scala:124)
at org.apache.spark.sql.execution.exchange.ShuffleExchangeExec$$anonfun$doExecute$1.apply(ShuffleExchangeExec.scala:119)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52)
...
```
## How was this patch tested?
Added tests in `ExchangeCoordinatorSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#21754 from maropu/SPARK-24705-2.
## What changes were proposed in this pull request?
This pr adds `transform` function which transforms elements in an array using the function.
Optionally we can take the index of each element as the second argument.
```sql
> SELECT transform(array(1, 2, 3), x -> x + 1);
array(2, 3, 4)
> SELECT transform(array(1, 2, 3), (x, i) -> x + i);
array(1, 3, 5)
```
## How was this patch tested?
Added tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#21954 from ueshin/issues/SPARK-23908/transform.
## What changes were proposed in this pull request?
This pull request provides a fix for SPARK-24742: SQL Field MetaData was throwing an Exception in the hashCode method when a "null" Metadata was added via "putNull"
## How was this patch tested?
A new unittest is provided in org/apache/spark/sql/types/MetadataSuite.scala
Author: Kaya Kupferschmidt <k.kupferschmidt@dimajix.de>
Closes#21722 from kupferk/SPARK-24742.
## What changes were proposed in this pull request?
Remove the AnalysisBarrier LogicalPlan node, which is useless now.
## How was this patch tested?
N/A
Author: Xiao Li <gatorsmile@gmail.com>
Closes#21962 from gatorsmile/refactor2.
## What changes were proposed in this pull request?
This PR addresses issues 2,3 in this [document](https://docs.google.com/document/d/1fbkjEL878witxVQpOCbjlvOvadHtVjYXeB-2mgzDTvk).
* We modified the closure cleaner to identify closures that are implemented via the LambdaMetaFactory mechanism (serializedLambdas) (issue2).
* We also fix the issue due to scala/bug#11016. There are two options for solving the Unit issue, either add () at the end of the closure or use the trick described in the doc. Otherwise overloading resolution does not work (we are not going to eliminate either of the methods) here. Compiler tries to adapt to Unit and makes these two methods candidates for overloading, when there is polymorphic overloading there is no ambiguity (that is the workaround implemented). This does not look that good but it serves its purpose as we need to support two different uses for method: `addTaskCompletionListener`. One that passes a TaskCompletionListener and one that passes a closure that is wrapped with a TaskCompletionListener later on (issue3).
Note: regarding issue 1 in the doc the plan is:
> Do Nothing. Don’t try to fix this as this is only a problem for Java users who would want to use 2.11 binaries. In that case they can cast to MapFunction to be able to utilize lambdas. In Spark 3.0.0 the API should be simplified so that this issue is removed.
## How was this patch tested?
This was manually tested:
```./dev/change-scala-version.sh 2.12
./build/mvn -DskipTests -Pscala-2.12 clean package
./build/mvn -Pscala-2.12 clean package -DwildcardSuites=org.apache.spark.serializer.ProactiveClosureSerializationSuite -Dtest=None
./build/mvn -Pscala-2.12 clean package -DwildcardSuites=org.apache.spark.util.ClosureCleanerSuite -Dtest=None
./build/mvn -Pscala-2.12 clean package -DwildcardSuites=org.apache.spark.streaming.DStreamClosureSuite -Dtest=None```
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Closes#21930 from skonto/scala2.12-sup.
## What changes were proposed in this pull request?
This PR is to refactor the code in AVERAGE by dsl.
## How was this patch tested?
N/A
Author: Xiao Li <gatorsmile@gmail.com>
Closes#21951 from gatorsmile/refactor1.
## What changes were proposed in this pull request?
Regarding user-specified schema, data sources may have 3 different behaviors:
1. must have a user-specified schema
2. can't have a user-specified schema
3. can accept the user-specified if it's given, or infer the schema.
I added `ReadSupportWithSchema` to support these behaviors, following data source v1. But it turns out we don't need this extra interface. We can just add a `createReader(schema, options)` to `ReadSupport` and make it call `createReader(options)` by default.
TODO: also fix the streaming API in followup PRs.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#21946 from cloud-fan/ds-schema.
## What changes were proposed in this pull request?
How to reproduce:
```sql
spark-sql> CREATE TABLE tbl AS SELECT 1;
spark-sql> CREATE TABLE tbl1 (c1 BIGINT, day STRING, hour STRING)
> USING parquet
> PARTITIONED BY (day, hour);
spark-sql> INSERT INTO TABLE tbl1 PARTITION (day = '2018-07-25', hour='01') SELECT * FROM tbl where 1=0;
spark-sql> SHOW PARTITIONS tbl1;
spark-sql> CREATE TABLE tbl2 (c1 BIGINT)
> PARTITIONED BY (day STRING, hour STRING);
spark-sql> INSERT INTO TABLE tbl2 PARTITION (day = '2018-07-25', hour='01') SELECT * FROM tbl where 1=0;
spark-sql> SHOW PARTITIONS tbl2;
day=2018-07-25/hour=01
spark-sql>
```
1. Users will be confused about whether the partition data of `tbl1` is generated.
2. Inconsistent with Hive table behavior.
This pr fix this issues.
## How was this patch tested?
unit tests
Author: Yuming Wang <yumwang@ebay.com>
Closes#21883 from wangyum/SPARK-24937.
## What changes were proposed in this pull request?
The PR adds the SQL function `array_except`. The behavior of the function is based on Presto's one.
This function returns returns an array of the elements in array1 but not in 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#21103 from kiszk/SPARK-23915.
## What changes were proposed in this pull request?
This is a follow up of https://github.com/apache/spark/pull/21118 .
In https://github.com/apache/spark/pull/21118 we added `SupportsDeprecatedScanRow`. Ideally data source should produce `InternalRow` instead of `Row` for better performance. We should remove `SupportsDeprecatedScanRow` and encourage data sources to produce `InternalRow`, which is also very easy to build.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#21921 from cloud-fan/row.
