## 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?
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?
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?
`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 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?
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?
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>
## 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?
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?
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?
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?
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?
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?
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?
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?
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?
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?
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?
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?
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?
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?
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.
(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?
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?
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?
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?
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?
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?
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?
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 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?
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?
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?
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?
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 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 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 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>
