## What changes were proposed in this pull request?
Make the integration test script build all modules.
In order to not run all the non-Kubernetes integration tests in the build, support specifying tags and tag all integration tests specifically with "k8s". Supply the k8s tag in the dev/dev-run-integration-tests.sh script.
## How was this patch tested?
The build system will test this.
Author: mcheah <mcheah@palantir.com>
Closes#21800 from mccheah/k8s-integration-tests-maven-fix.
## What changes were proposed in this pull request?
The example wants to create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0)), but the list is given as [1, 2, 3, 4, 5, 6]. Now it is changed as [1, 3, 5, 2, 4, 6].
And the example wants to create an RDD of coordinate entries like:
entries = sc.parallelize([(0, 0, 1.2), (1, 0, 2.1), (2, 1, 3.7)]).
However, it is done with the MatrixEntry class like:
entries = sc.parallelize([MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7)]),
where the third MatrixEntry has a different row index.
Now it is changed as MatrixEntry(2, 1, 3.7).
## How was this patch tested?
This is trivial enough that it should not affect tests.
Author: Weizhe Huang <huangweizhebbdservice.com>
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Huangweizhe <huangweizhe@bbdservice.com>
Closes#21612 from huangweizhe123/my_change.
## What changes were proposed in this pull request?
In DatasetSuite.scala, in the 1299 line,
test("SPARK-19896: cannot have circular references in in case class") ,
there are duplicate words "in in". We can get rid of one.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: 韩田田00222924 <han.tiantian@zte.com.cn>
Closes#21767 from httfighter/inin.
## What changes were proposed in this pull request?
While looking through the codebase, it appeared that the scala code for RDD.cartesian does not have any tests for correctness. This adds a couple basic tests to verify cartesian yields correct values. While the implementation for RDD.cartesian is pretty simple, it always helps to have a few tests!
## How was this patch tested?
The new test cases pass, and the scala style tests from running dev/run-tests all pass.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Nihar Sheth <niharrsheth@gmail.com>
Closes#21765 from NiharS/cartesianTests.
## What changes were proposed in this pull request?
This pr fixes lint-java and Scala 2.12 build.
lint-java:
```
[ERROR] src/test/resources/log4j.properties:[0] (misc) NewlineAtEndOfFile: File does not end with a newline.
```
Scala 2.12 build:
```
[error] /.../sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousCoalesceRDD.scala:121: overloaded method value addTaskCompletionListener with alternatives:
[error] (f: org.apache.spark.TaskContext => Unit)org.apache.spark.TaskContext <and>
[error] (listener: org.apache.spark.util.TaskCompletionListener)org.apache.spark.TaskContext
[error] cannot be applied to (org.apache.spark.TaskContext => java.util.List[Runnable])
[error] context.addTaskCompletionListener { ctx =>
[error] ^
```
## How was this patch tested?
Manually executed lint-java and Scala 2.12 build in my local environment.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#21801 from ueshin/issues/SPARK-24386_24768/fix_build.
## What changes were proposed in this pull request?
This issue aims to upgrade Apache ORC library from 1.4.4 to 1.5.2 in order to bring the following benefits into Apache Spark.
- [ORC-91](https://issues.apache.org/jira/browse/ORC-91) Support for variable length blocks in HDFS (The current space wasted in ORC to padding is known to be 5%.)
- [ORC-344](https://issues.apache.org/jira/browse/ORC-344) Support for using Decimal64ColumnVector
In addition to that, Apache Hive 3.1 and 3.2 will use ORC 1.5.1 ([HIVE-19669](https://issues.apache.org/jira/browse/HIVE-19465)) and 1.5.2 ([HIVE-19792](https://issues.apache.org/jira/browse/HIVE-19792)) respectively. This will improve the compatibility between Apache Spark and Apache Hive by sharing the common library.
## How was this patch tested?
Pass the Jenkins with all existing tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#21582 from dongjoon-hyun/SPARK-24576.
## What changes were proposed in this pull request?
Two new rules in the logical plan optimizers are added.
1. When there is only one element in the **`Collection`**, the
physical plan will be optimized to **`EqualTo`**, so predicate
pushdown can be used.
```scala
profileDF.filter( $"profileID".isInCollection(Set(6))).explain(true)
"""
|== Physical Plan ==
|*(1) Project [profileID#0]
|+- *(1) Filter (isnotnull(profileID#0) && (profileID#0 = 6))
| +- *(1) FileScan parquet [profileID#0] Batched: true, Format: Parquet,
| PartitionFilters: [],
| PushedFilters: [IsNotNull(profileID), EqualTo(profileID,6)],
| ReadSchema: struct<profileID:int>
""".stripMargin
```
2. When the **`Collection`** is empty, and the input is nullable, the
logical plan will be simplified to
```scala
profileDF.filter( $"profileID".isInCollection(Set())).explain(true)
"""
|== Optimized Logical Plan ==
|Filter if (isnull(profileID#0)) null else false
|+- Relation[profileID#0] parquet
""".stripMargin
```
TODO:
1. For multiple conditions with numbers less than certain thresholds,
we should still allow predicate pushdown.
2. Optimize the **`In`** using **`tableswitch`** or **`lookupswitch`**
when the numbers of the categories are low, and they are **`Int`**,
**`Long`**.
3. The default immutable hash trees set is slow for query, and we
should do benchmark for using different set implementation for faster
query.
4. **`filter(if (condition) null else false)`** can be optimized to false.
## How was this patch tested?
Couple new tests are added.
Author: DB Tsai <d_tsai@apple.com>
Closes#21797 from dbtsai/optimize-in.
## What changes were proposed in this pull request?
This PR updates the Instrumentation class to make it more flexible and a little bit easier to use. When these APIs are merged, I'll followup with a PR to update the training code to use these new APIs so we can remove the old APIs. These changes are all to private APIs so this PR doesn't make any user facing changes.
## How was this patch tested?
Existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Bago Amirbekian <bago@databricks.com>
Closes#21719 from MrBago/new-instrumentation-apis.
## What changes were proposed in this pull request?
Remove the non-negative checks of window start time to make window support negative start time, and add a check to guarantee the absolute value of start time is less than slide duration.
## How was this patch tested?
New unit tests.
Author: HanShuliang <kevinzwx1992@gmail.com>
Closes#18903 from KevinZwx/dev.
## What changes were proposed in this pull request?
Test HiveExternalCatalogVersionsSuite vs only current Spark releases
## How was this patch tested?
`HiveExternalCatalogVersionsSuite`
Author: Sean Owen <srowen@gmail.com>
Closes#21793 from srowen/SPARK-24813.3.
## What changes were proposed in this pull request?
The PR tries to avoid serialization of private fields of already added collection functions and follows up on comments in [SPARK-23922](https://github.com/apache/spark/pull/21028) and [SPARK-23935](https://github.com/apache/spark/pull/21236)
## How was this patch tested?
Run tests from:
- CollectionExpressionSuite.scala
- DataFrameFunctionsSuite.scala
Author: Marek Novotny <mn.mikke@gmail.com>
Closes#21352 from mn-mikke/SPARK-24305.
## What changes were proposed in this pull request?
The thrift scheduling pool configuration was removed from a previous release. Adding this back to the job scheduling configuration docs.
This PR takes over #17536 and handle some comments here.
## How was this patch tested?
Manually.
Closes#17536
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21778 from HyukjinKwon/SPARK-20220.
## What changes were proposed in this pull request?
Three legacy statements are removed by this patch:
- in HiveExternalCatalog: The withClient wrapper is not necessary for the private method getRawTable.
- in HiveClientImpl: There are some redundant code in both the tableExists and getTableOption method.
This PR takes over https://github.com/apache/spark/pull/20425
## How was this patch tested?
Existing tests
Closes#20425
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21780 from HyukjinKwon/SPARK-23259.
## What changes were proposed in this pull request?
Two new rules in the logical plan optimizers are added.
1. When there is only one element in the **`Collection`**, the
physical plan will be optimized to **`EqualTo`**, so predicate
pushdown can be used.
```scala
profileDF.filter( $"profileID".isInCollection(Set(6))).explain(true)
"""
|== Physical Plan ==
|*(1) Project [profileID#0]
|+- *(1) Filter (isnotnull(profileID#0) && (profileID#0 = 6))
| +- *(1) FileScan parquet [profileID#0] Batched: true, Format: Parquet,
| PartitionFilters: [],
| PushedFilters: [IsNotNull(profileID), EqualTo(profileID,6)],
| ReadSchema: struct<profileID:int>
""".stripMargin
```
2. When the **`Collection`** is empty, and the input is nullable, the
logical plan will be simplified to
```scala
profileDF.filter( $"profileID".isInCollection(Set())).explain(true)
"""
|== Optimized Logical Plan ==
|Filter if (isnull(profileID#0)) null else false
|+- Relation[profileID#0] parquet
""".stripMargin
```
TODO:
1. For multiple conditions with numbers less than certain thresholds,
we should still allow predicate pushdown.
2. Optimize the **`In`** using **`tableswitch`** or **`lookupswitch`**
when the numbers of the categories are low, and they are **`Int`**,
**`Long`**.
3. The default immutable hash trees set is slow for query, and we
should do benchmark for using different set implementation for faster
query.
4. **`filter(if (condition) null else false)`** can be optimized to false.
## How was this patch tested?
Couple new tests are added.
Author: DB Tsai <d_tsai@apple.com>
Closes#21442 from dbtsai/optimize-in.
## What changes were proposed in this pull request?
In the PR, I propose to change default behaviour of AVRO datasource which currently ignores files without `.avro` extension in read by default. This PR sets the default value for `avro.mapred.ignore.inputs.without.extension` to `false` in the case if the parameter is not set by an user.
## How was this patch tested?
Added a test file without extension in AVRO format, and new test for reading the file with and wihout specified schema.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Author: Maxim Gekk <max.gekk@gmail.com>
Closes#21769 from MaxGekk/avro-without-extension.
## What changes were proposed in this pull request?
We have some functions which need to aware the nullabilities of all children, such as `CreateArray`, `CreateMap`, `Concat`, and so on. Currently we add casts to fix the nullabilities, but the casts might be removed during the optimization phase.
After the discussion, we decided to not add extra casts for just fixing the nullabilities of the nested types, but handle them by functions themselves.
## How was this patch tested?
Modified and added some tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#21704 from ueshin/issues/SPARK-24734/concat_containsnull.
When invoking MatrixFactorizationModel.recommendProducts(Int, Int) with a non-existing user, a java.util.NoSuchElementException is thrown:
> java.util.NoSuchElementException: next on empty iterator
at scala.collection.Iterator$$anon$2.next(Iterator.scala:39)
at scala.collection.Iterator$$anon$2.next(Iterator.scala:37)
at scala.collection.IndexedSeqLike$Elements.next(IndexedSeqLike.scala:63)
at scala.collection.IterableLike$class.head(IterableLike.scala:107)
at scala.collection.mutable.WrappedArray.scala$collection$IndexedSeqOptimized$$super$head(WrappedArray.scala:35)
at scala.collection.IndexedSeqOptimized$class.head(IndexedSeqOptimized.scala:126)
at scala.collection.mutable.WrappedArray.head(WrappedArray.scala:35)
at org.apache.spark.mllib.recommendation.MatrixFactorizationModel.recommendProducts(MatrixFactorizationModel.scala:169)
## What changes were proposed in this pull request?
Throw a better exception, like "user-id/product-id doesn't found in the model", for a non-existent user/product
## How was this patch tested?
Added UT
Author: Shahid <shahidki31@gmail.com>
Closes#21740 from shahidki31/checkInvalidUserProduct.
## What changes were proposed in this pull request?
Support Decimal type push down to the parquet data sources.
The Decimal comparator used is: [`BINARY_AS_SIGNED_INTEGER_COMPARATOR`](c6764c4a08/parquet-column/src/main/java/org/apache/parquet/schema/PrimitiveComparator.java (L224-L292)).
## How was this patch tested?
unit tests and manual tests.
**manual tests**:
```scala
spark.range(10000000).selectExpr("id", "cast(id as decimal(9)) as d1", "cast(id as decimal(9, 2)) as d2", "cast(id as decimal(18)) as d3", "cast(id as decimal(18, 4)) as d4", "cast(id as decimal(38)) as d5", "cast(id as decimal(38, 18)) as d6").coalesce(1).write.option("parquet.block.size", 1048576).parquet("/tmp/spark/parquet/decimal")
val df = spark.read.parquet("/tmp/spark/parquet/decimal/")
spark.sql("set spark.sql.parquet.filterPushdown.decimal=true")
// Only read about 1 MB data
df.filter("d2 = 10000").show
// Only read about 1 MB data
df.filter("d4 = 10000").show
spark.sql("set spark.sql.parquet.filterPushdown.decimal=false")
// Read 174.3 MB data
df.filter("d2 = 10000").show
// Read 174.3 MB data
df.filter("d4 = 10000").show
```
Author: Yuming Wang <yumwang@ebay.com>
Closes#21556 from wangyum/SPARK-24549.
It is corrected as per the configuration.
## What changes were proposed in this pull request?
IdleTimeout info used to print in the logs is taken based on the cacheBlock. If it is cacheBlock then cachedExecutorIdleTimeoutS is considered else executorIdleTimeoutS
## How was this patch tested?
Manual Test
spark-sql> cache table sample;
2018-05-15 14:44:02 INFO DAGScheduler:54 - Submitting 3 missing tasks from ShuffleMapStage 0 (MapPartitionsRDD[8] at processCmd at CliDriver.java:376) (first 15 tasks are for partitions Vector(0, 1, 2))
2018-05-15 14:44:02 INFO YarnScheduler:54 - Adding task set 0.0 with 3 tasks
2018-05-15 14:44:03 INFO ExecutorAllocationManager:54 - Requesting 1 new executor because tasks are backlogged (new desired total will be 1)
...
...
2018-05-15 14:46:10 INFO YarnClientSchedulerBackend:54 - Actual list of executor(s) to be killed is 1
2018-05-15 14:46:10 INFO **ExecutorAllocationManager:54 - Removing executor 1 because it has been idle for 120 seconds (new desired total will be 0)**
2018-05-15 14:46:11 INFO YarnSchedulerBackend$YarnDriverEndpoint:54 - Disabling executor 1.
2018-05-15 14:46:11 INFO DAGScheduler:54 - Executor lost: 1 (epoch 1)
Author: sandeep-katta <sandeep.katta2007@gmail.com>
Closes#21565 from sandeep-katta/loginfoBug.
## What changes were proposed in this pull request?
In the PR, I propose to move `testFile()` to the common trait `SQLTestUtilsBase` and wrap test files in `AvroSuite` by the method `testFile()` which returns full paths to test files in the resource folder.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#21773 from MaxGekk/test-file.
## What changes were proposed in this pull request?
This pr modified code to project required data from CSV parsed data when column pruning disabled.
In the current master, an exception below happens if `spark.sql.csv.parser.columnPruning.enabled` is false. This is because required formats and CSV parsed formats are different from each other;
```
./bin/spark-shell --conf spark.sql.csv.parser.columnPruning.enabled=false
scala> val dir = "/tmp/spark-csv/csv"
scala> spark.range(10).selectExpr("id % 2 AS p", "id").write.mode("overwrite").partitionBy("p").csv(dir)
scala> spark.read.csv(dir).selectExpr("sum(p)").collect()
18/06/25 13:48:46 ERROR Executor: Exception in task 2.0 in stage 2.0 (TID 7)
java.lang.ClassCastException: org.apache.spark.unsafe.types.UTF8String cannot be cast to java.lang.Integer
at scala.runtime.BoxesRunTime.unboxToInt(BoxesRunTime.java:101)
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow$class.getInt(rows.scala:41)
...
```
## How was this patch tested?
Added tests in `CSVSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#21657 from maropu/SPARK-24676.
## What changes were proposed in this pull request?
unpersist `instances` after training
## How was this patch tested?
existing tests
Author: 郑瑞峰 <zhengruifeng@ZBMAC-C02VX5XWH.local>
Closes#21562 from zhengruifeng/gmm_unpersist.
## What changes were proposed in this pull request?
Try only unique ASF mirrors to download Spark release; fall back to Apache archive if no mirrors available or release is not mirrored
## How was this patch tested?
Existing HiveExternalCatalogVersionsSuite
Author: Sean Owen <srowen@gmail.com>
Closes#21776 from srowen/SPARK-24813.
## What changes were proposed in this pull request?
Currently the Avro Deserializer converts input Avro format data to `Row`, and then convert the `Row` to `InternalRow`.
While the Avro Serializer converts `InternalRow` to `Row`, and then output Avro format data.
This PR allows direct conversion between `InternalRow` and Avro format data.
## How was this patch tested?
Unit test
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21762 from gengliangwang/avro_io.
## What changes were proposed in this pull request?
In the PR, I propose to output an warning if the `addFile()` or `addJar()` methods are callled more than once for the same path. Currently, overwriting of already added files is not supported. New comments and warning are reflected the existing behaviour.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#21771 from MaxGekk/warning-on-adding-file.
## What changes were proposed in this pull request?
Improve Avro unit test:
1. use QueryTest/SharedSQLContext/SQLTestUtils, instead of the duplicated test utils.
2. replace deprecated methods
This is a follow up PR for #21760, the PR passes pull request tests but failed in: https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Compile/job/spark-master-compile-maven-hadoop-2.6/7842/
This PR is to fix it.
## How was this patch tested?
Unit test.
Compile with different commands:
```
./build/mvn --force -DzincPort=3643 -DskipTests -Phadoop-2.6 -Phive-thriftserver -Pkinesis-asl -Pspark-ganglia-lgpl -Pmesos -Pyarn compile test-compile
./build/mvn --force -DzincPort=3643 -DskipTests -Phadoop-2.7 -Phive-thriftserver -Pkinesis-asl -Pspark-ganglia-lgpl -Pmesos -Pyarn compile test-compile
./build/mvn --force -DzincPort=3643 -DskipTests -Phadoop-3.1 -Phive-thriftserver -Pkinesis-asl -Pspark-ganglia-lgpl -Pmesos -Pyarn compile test-compile
```
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21768 from gengliangwang/improve_avro_test.
## What changes were proposed in this pull request?
`Timestamp` support pushdown to parquet data source.
Only `TIMESTAMP_MICROS` and `TIMESTAMP_MILLIS` support push down.
## How was this patch tested?
unit tests and benchmark tests
Author: Yuming Wang <yumwang@ebay.com>
Closes#21741 from wangyum/SPARK-24718.
## What changes were proposed in this pull request?
Use longs in calculating min hash to avoid bias due to int overflow.
## How was this patch tested?
Existing tests.
Author: Sean Owen <srowen@gmail.com>
Closes#21750 from srowen/SPARK-24754.
## What changes were proposed in this pull request?
The original pr is: https://github.com/apache/spark/pull/18424
Add a new optimizer rule to convert an IN predicate to an equivalent Parquet filter and add `spark.sql.parquet.pushdown.inFilterThreshold` to control limit thresholds. Different data types have different limit thresholds, this is a copy of data for reference:
Type | limit threshold
-- | --
string | 370
int | 210
long | 285
double | 270
float | 220
decimal | Won't provide better performance before [SPARK-24549](https://issues.apache.org/jira/browse/SPARK-24549)
## How was this patch tested?
unit tests and manual tests
Author: Yuming Wang <yumwang@ebay.com>
Closes#21603 from wangyum/SPARK-17091.
## What changes were proposed in this pull request?
I added integration tests for PySpark ( + checking JVM options + RemoteFileTest) which wasn't properly merged in the initial integration test PR.
## How was this patch tested?
I tested this with integration tests using:
`dev/dev-run-integration-tests.sh --spark-tgz spark-2.4.0-SNAPSHOT-bin-2.7.3.tgz`
Author: Ilan Filonenko <if56@cornell.edu>
Closes#21583 from ifilonenko/master.
## What changes were proposed in this pull request?
Add `org.apache.derby` to `IsolatedClientLoader`, otherwise it may throw an exception:
```scala
...
[info] Cause: java.sql.SQLException: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$12439ab23, see the next exception for details.
[info] at org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)
[info] at org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)
[info] at org.apache.derby.impl.jdbc.Util.seeNextException(Unknown Source)
[info] at org.apache.derby.impl.jdbc.EmbedConnection.bootDatabase(Unknown Source)
[info] at org.apache.derby.impl.jdbc.EmbedConnection.<init>(Unknown Source)
[info] at org.apache.derby.jdbc.InternalDriver$1.run(Unknown Source)
...
```
## How was this patch tested?
unit tests and manual tests
Author: Yuming Wang <yumwang@ebay.com>
Closes#20944 from wangyum/SPARK-23831.
## What changes were proposed in this pull request?
Added the number of iterations in `ClusteringSummary`. This is an helpful information in evaluating how to eventually modify the parameters in order to get a better model.
## How was this patch tested?
modified existing UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20701 from mgaido91/SPARK-23528.
## What changes were proposed in this pull request?
Improve Avro unit test:
1. use QueryTest/SharedSQLContext/SQLTestUtils, instead of the duplicated test utils.
2. replace deprecated methods
## How was this patch tested?
Unit test
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21760 from gengliangwang/improve_avro_test.
## What changes were proposed in this pull request?
When we use a reference from Dataset in filter or sort, which was not used in the prior select, an AnalysisException occurs, e.g.,
```scala
val df = Seq(("test1", 0), ("test2", 1)).toDF("name", "id")
df.select(df("name")).filter(df("id") === 0).show()
```
```scala
org.apache.spark.sql.AnalysisException: Resolved attribute(s) id#6 missing from name#5 in operator !Filter (id#6 = 0).;;
!Filter (id#6 = 0)
+- AnalysisBarrier
+- Project [name#5]
+- Project [_1#2 AS name#5, _2#3 AS id#6]
+- LocalRelation [_1#2, _2#3]
```
This change updates the rule `ResolveMissingReferences` so `Filter` and `Sort` with non-empty `missingInputs` will also be transformed.
## How was this patch tested?
Added tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#21745 from viirya/SPARK-24781.
We have hundreds of kafka topics need to be consumed in one application. The application master will throw OOM exception after hanging for nearly half of an hour.
OOM happens in the env with a lot of topics, and it's not convenient to set up such kind of env in the unit test. So I didn't change/add test case.
Author: Yuanbo Liu <yuanbo@Yuanbos-MacBook-Air.local>
Author: yuanbo <yuanbo@apache.org>
Closes#21690 from yuanboliu/master.
## What changes were proposed in this pull request?
This PR will cache the function name from external catalog, it is used by lookupFunctions in the analyzer, and it is cached for each query plan. The original problem is reported in the [ spark-19737](https://issues.apache.org/jira/browse/SPARK-19737)
## How was this patch tested?
create new test file LookupFunctionsSuite and add test case in SessionCatalogSuite
Author: Kevin Yu <qyu@us.ibm.com>
Closes#20795 from kevinyu98/spark-23486.
## What changes were proposed in this pull request?
Add array_remove / array_zip / map_from_arrays / array_distinct functions in SparkR.
## How was this patch tested?
Add tests in test_sparkSQL.R
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21645 from huaxingao/spark-24537.
## What changes were proposed in this pull request?
Relax the check to allow complex aggregate expressions, like `ceil(sum(col1))` or `sum(col1) + 1`, which roughly means any aggregate expression that could appear in an Aggregate plan except pandas UDF (due to the fact that it is not supported in pivot yet).
## How was this patch tested?
Added 2 tests in pivot.sql
Author: maryannxue <maryannxue@apache.org>
Closes#21753 from maryannxue/pivot-relax-syntax.
## What changes were proposed in this pull request?
The PR is a followup to move the test cases introduced by the original PR in their proper location.
## How was this patch tested?
moved UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21751 from mgaido91/SPARK-24208_followup.
## What changes were proposed in this pull request?
The reader schema is said to be evolved (or projected) when it changed after the data is written. The followings are already supported in file-based data sources. Note that partition columns are not maintained in files. In this PR, `column` means `non-partition column`.
1. Add a column
2. Hide a column
3. Change a column position
4. Change a column type (upcast)
This issue aims to guarantee users a backward-compatible read-schema test coverage on file-based data sources and to prevent future regressions by *adding read schema tests explicitly*.
Here, we consider safe changes without data loss. For example, data type change should be from small types to larger types like `int`-to-`long`, not vice versa.
As of today, in the master branch, file-based data sources have the following coverage.
File Format | Coverage | Note
----------- | ---------- | ------------------------------------------------
TEXT | N/A | Schema consists of a single string column.
CSV | 1, 2, 4 |
JSON | 1, 2, 3, 4 |
ORC | 1, 2, 3, 4 | Native vectorized ORC reader has the widest coverage among ORC formats.
PARQUET | 1, 2, 3 |
## How was this patch tested?
Pass the Jenkins with newly added test suites.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#20208 from dongjoon-hyun/SPARK-SCHEMA-EVOLUTION.
## What changes were proposed in this pull request?
Apache Avro (https://avro.apache.org) is a popular data serialization format. It is widely used in the Spark and Hadoop ecosystem, especially for Kafka-based data pipelines. Using the external package https://github.com/databricks/spark-avro, Spark SQL can read and write the avro data. Making spark-Avro built-in can provide a better experience for first-time users of Spark SQL and structured streaming. We expect the built-in Avro data source can further improve the adoption of structured streaming.
The proposal is to inline code from spark-avro package (https://github.com/databricks/spark-avro). The target release is Spark 2.4.
[Built-in AVRO Data Source In Spark 2.4.pdf](https://github.com/apache/spark/files/2181511/Built-in.AVRO.Data.Source.In.Spark.2.4.pdf)
## How was this patch tested?
Unit test
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21742 from gengliangwang/export_avro.
## What changes were proposed in this pull request?
The `WholeTextFileInputFormat` determines the `maxSplitSize` for the file/s being read using the `wholeTextFiles` method. While this works well for large files, for smaller files where the maxSplitSize is smaller than the defaults being used with configs like hive-site.xml or explicitly passed in the form of `mapreduce.input.fileinputformat.split.minsize.per.node` or `mapreduce.input.fileinputformat.split.minsize.per.rack` , it just throws up an exception.
```java
java.io.IOException: Minimum split size pernode 123456 cannot be larger than maximum split size 9962
at org.apache.hadoop.mapreduce.lib.input.CombineFileInputFormat.getSplits(CombineFileInputFormat.java:200)
at org.apache.spark.rdd.WholeTextFileRDD.getPartitions(WholeTextFileRDD.scala:50)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2096)
at org.apache.spark.rdd.RDD.count(RDD.scala:1158)
... 48 elided
`
This change checks the maxSplitSize against the minSplitSizePerNode and minSplitSizePerRack and set them if `maxSplitSize < minSplitSizePerNode/Rack`
## How was this patch tested?
Test manually setting the conf while launching the job and added unit test.
Author: Dhruve Ashar <dhruveashar@gmail.com>
Closes#21601 from dhruve/bug/SPARK-24610.
Setting the timestamp directly would cause exception on reading stream, it can be set directly only if the mode is not AT_TIMESTAMP
## What changes were proposed in this pull request?
The last patch in the kinesis streaming receiver sets the timestamp for the mode AT_TIMESTAMP, but this mode can only be set via the
`baseClientLibConfiguration.withTimestampAtInitialPositionInStream()
`
and can't be set directly using
`.withInitialPositionInStream()`
This patch fixes the issue.
## How was this patch tested?
Kinesis Receiver doesn't expose the internal state outside, so couldn't find the right way to test this change. Seeking for tips from other contributors here.
Author: Yash Sharma <ysharma@atlassian.com>
Closes#21541 from yashs360/ysharma/fix_kinesis_bug.
## What changes were proposed in this pull request?
With https://github.com/apache/spark/pull/21389, data source schema is validated on driver side before launching read/write tasks.
However,
1. Putting all the validations together in `DataSourceUtils` is tricky and hard to maintain. On second thought after review, I find that the `OrcFileFormat` in hive package is not matched, so that its validation wrong.
2. `DataSourceUtils.verifyWriteSchema` and `DataSourceUtils.verifyReadSchema` is not supposed to be called in every file format. We can move them to some upper entry.
So, I propose we can add a new method `validateDataType` in FileFormat. File format implementation can override the method to specify its supported/non-supported data types.
Although we should focus on data source V2 API, `FileFormat` should remain workable for some time. Adding this new method should be helpful.
## How was this patch tested?
Unit test
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21667 from gengliangwang/refactorSchemaValidate.
