- Hard-coded Spark SQL sample snippets were moved into source files under examples sub-project.
- Removed the inconsistency between Scala and Java Spark SQL examples
- Scala and Java Spark SQL examples were updated
The work is still in progress. All involved examples were tested manually. An additional round of testing will be done after the code review.
Author: aokolnychyi <okolnychyyanton@gmail.com>
Closes#14119 from aokolnychyi/spark_16303.
## What changes were proposed in this pull request?
RegexExtract and RegexReplace currently crash on non-nullable input due use of a hard-coded local variable name (e.g. compiles fail with `java.lang.Exception: failed to compile: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 85, Column 26: Redefinition of local variable "m" `).
This changes those variables to use fresh names, and also in a few other places.
## How was this patch tested?
Unit tests. rxin
Author: Eric Liang <ekl@databricks.com>
Closes#14168 from ericl/sc-3906.
## What changes were proposed in this pull request?
This patch implements reflect SQL function, which can be used to invoke a Java method in SQL. Slightly different from Hive, this implementation requires the class name and the method name to be literals. This implementation also supports only a smaller number of data types, and requires the function to be static, as suggested by rxin in #13969.
java_method is an alias for reflect, so this should also resolve SPARK-16277.
## How was this patch tested?
Added expression unit tests and an end-to-end test.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14138 from petermaxlee/reflect-static.
This option is used by Hive to directly delete the files instead of
moving them to the trash. This is needed in certain configurations
where moving the files does not work. For non-Hive tables and partitions,
Spark already behaves as if the PURGE option was set, so there's no
need to do anything.
Hive support for PURGE was added in 0.14 (for tables) and 1.2 (for
partitions), so the code reflects that: trying to use the option with
older versions of Hive will cause an exception to be thrown.
The change is a little noisier than I would like, because of the code
to propagate the new flag through all the interfaces and implementations;
the main changes are in the parser and in HiveShim, aside from the tests
(DDLCommandSuite, VersionsSuite).
Tested by running sql and catalyst unit tests, plus VersionsSuite which
has been updated to test the version-specific behavior. I also ran an
internal test suite that uses PURGE and would not pass previously.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#13831 from vanzin/SPARK-16119.
## What changes were proposed in this pull request?
Since externalShuffleService is essential for spark, better monitoring for shuffle service is necessary. In order to do so, we added various metrics in shuffle service and imported into ExternalShuffleServiceSource for metric system.
Metrics added in shuffle service:
* registeredExecutorsSize
* openBlockRequestLatencyMillis
* registerExecutorRequestLatencyMillis
* blockTransferRateBytes
JIRA Issue: https://issues.apache.org/jira/browse/SPARK-16405
## How was this patch tested?
Some test cases are added to verify metrics as expected in metric system. Those unit test cases are shown in `ExternalShuffleBlockHandlerSuite `
Author: Yangyang Liu <yangyangliu@fb.com>
Closes#14080 from lovexi/yangyang-metrics.
## What changes were proposed in this pull request?
The `SparkHadoopUtil` singleton was instantiated before `ApplicationMaster` in `ApplicationMaster.main` when deploying spark on yarn cluster mode, the `conf` in the `SparkHadoopUtil` singleton didn't include user's configuration.
So, we should load the properties file with the Spark configuration and set entries as system properties before `SparkHadoopUtil` first instantiate.
## How was this patch tested?
Add a test case
Author: sharkd <sharkd.tu@gmail.com>
Author: sharkdtu <sharkdtu@tencent.com>
Closes#14088 from sharkdtu/master.
## What changes were proposed in this pull request?
In code generation, it is incorrect for expressions to reuse variable names across different instances of itself. As an example, SPARK-16488 reports a bug in which pmod expression reuses variable name "r".
This patch updates ExpressionEvalHelper test harness to always project two instances of the same expression, which will help us catch variable reuse problems in expression unit tests. This patch also fixes the bug in crc32 expression.
## How was this patch tested?
This is a test harness change, but I also created a new test suite for testing the test harness.
Author: Reynold Xin <rxin@databricks.com>
Closes#14146 from rxin/SPARK-16489.
## What changes were proposed in this pull request?
when query only use metadata (example: partition key), it can return results based on metadata without scanning files. Hive did it in HIVE-1003.
## How was this patch tested?
add unit tests
Author: Lianhui Wang <lianhuiwang09@gmail.com>
Author: Wenchen Fan <wenchen@databricks.com>
Author: Lianhui Wang <lianhuiwang@users.noreply.github.com>
Closes#13494 from lianhuiwang/metadata-only.
## What changes were proposed in this pull request?
In `ml.regression.LinearRegression`, it use breeze `LBFGS` and `OWLQN` optimizer to do data training, but do not check whether breeze's optimizer returned result actually reached convergence.
The `LBFGS` and `OWLQN` optimizer in breeze finish iteration may result the following situations:
1) reach max iteration number
2) function reach value convergence
3) objective function stop improving
4) gradient reach convergence
5) search failed(due to some internal numerical error)
I add warning printing code so that
if the iteration result is (1) or (3) or (5) in above, it will print a warning with respective reason string.
## How was this patch tested?
Manual.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#14122 from WeichenXu123/add_lr_not_convergence_warn.
## What changes were proposed in this pull request?
Currently the input `RDD` of `Dataset` is always serialized to `RDD[InternalRow]` prior to being as `Dataset`, but there is a case that we use `map` or `mapPartitions` just after converted to `Dataset`.
In this case, serialize and then deserialize happens but it would not be needed.
This pr adds `ExistingRDD` logical plan for input with `RDD` to have a chance to eliminate serialize/deserialize.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#13890 from ueshin/issues/SPARK-16189.
## What changes were proposed in this pull request?
In `train` method of `ml.regression.LinearRegression` when handling situation `std(label) == 0`
the code replace `std(label)` with `mean(label)` but the relative comment is inconsistent, I update it.
## How was this patch tested?
N/A
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#14121 from WeichenXu123/update_lr_comment.
## What changes were proposed in this pull request?
It would be useful to support listing the columns that are referenced by a filter. This can help simplify data source planning, because with this we would be able to implement unhandledFilters method in HadoopFsRelation.
This is based on rxin's patch (#13901) and adds unit tests.
## How was this patch tested?
Added a new suite FiltersSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Author: Reynold Xin <rxin@databricks.com>
Closes#14120 from petermaxlee/SPARK-16199.
## What changes were proposed in this pull request?
In order to make it clear which filters are fully handled by the
underlying datasource we will mark them with an *. This will give a
clear visual queue to users that the filter is being treated differently
by catalyst than filters which are just presented to the underlying
DataSource.
Examples from the FilteredScanSuite, in this example `c IN (...)` is handled by the source, `b < ...` is not
### Before
```
//SELECT a FROM oneToTenFiltered WHERE a + b > 9 AND b < 16 AND c IN ('bbbbbBBBBB', 'cccccCCCCC', 'dddddDDDDD', 'foo')
== Physical Plan ==
Project [a#0]
+- Filter (((a#0 + b#1) > 9) && (b#1 < 16))
+- Scan SimpleFilteredScan(1,10)[a#0,b#1] PushedFilters: [LessThan(b,16), In(c, [bbbbbBBBBB,cccccCCCCC,dddddDDDDD,foo]]
```
### After
```
== Physical Plan ==
Project [a#0]
+- Filter (((a#0 + b#1) > 9) && (b#1 < 16))
+- Scan SimpleFilteredScan(1,10)[a#0,b#1] PushedFilters: [LessThan(b,16), *In(c, [bbbbbBBBBB,cccccCCCCC,dddddDDDDD,foo]]
```
## How was the this patch tested?
Manually tested with the Spark Cassandra Connector, a source which fully handles underlying filters. Now fully handled filters appear with an * next to their names. I can add an automated test as well if requested
Post 1.6.1
Tested by modifying the FilteredScanSuite to run explains.
Author: Russell Spitzer <Russell.Spitzer@gmail.com>
Closes#11317 from RussellSpitzer/SPARK-12639-Star.
## What changes were proposed in this pull request?
This patch fixes a variable namespace collision bug in pmod and partitionBy
## How was this patch tested?
Regression test for one possible occurrence. A more general fix in `ExpressionEvalHelper.checkEvaluation` will be in a subsequent PR.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#14144 from sameeragarwal/codegen-bug.
## What changes were proposed in this pull request?
Incorrect list of files were being allocated to a batch. This caused a file to read multiple times in the multiple batches.
## How was this patch tested?
Added unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#14143 from tdas/SPARK-16430-1.
## What changes were proposed in this pull request?
Display `No physical plan. Waiting for data.` instead of `N/A` for StreamingQuery.explain when no data arrives because `N/A` doesn't provide meaningful information.
## How was this patch tested?
Existing unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#14100 from zsxwing/SPARK-16433.
## What changes were proposed in this pull request?
Some minor changes for documentation page "Spark Streaming + Kinesis Integration".
Moved "streaming-kinesis-arch.png" before the bullet list, not in between the bullets.
## How was this patch tested?
Tested manually, on my local machine.
Author: Xin Ren <iamshrek@126.com>
Closes#14097 from keypointt/kinesisDoc.
## What changes were proposed in this pull request?
A structured streaming example with event time windowing.
## How was this patch tested?
Run locally
Author: James Thomas <jamesjoethomas@gmail.com>
Closes#13957 from jjthomas/current.
Some Hadoop classes needed by the Hive metastore client jars are not present
in Spark's packaging (for example, "org/apache/hadoop/mapred/MRVersion"). So
if the parent class loader fails to find a class, try to load it from the
isolated class loader, in case it's available there.
Tested by setting spark.sql.hive.metastore.jars to local paths with Hive/Hadoop
libraries and verifying that Spark can talk to the metastore.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#14020 from vanzin/SPARK-16349.
## What changes were proposed in this pull request?
* Update SparkR ML section to make them consistent with SparkR API docs.
* Since #13972 adds labelling support for the ```include_example``` Jekyll plugin, so that we can split the single ```ml.R``` example file into multiple line blocks with different labels, and include them in different algorithms/models in the generated HTML page.
## How was this patch tested?
Only docs update, manually check the generated docs.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#14011 from yanboliang/r-user-guide-update.
## What changes were proposed in this pull request?
Temporary tables are used frequently, but `spark.catalog.listColumns` does not support those tables. This PR make `SessionCatalog` supports temporary table column listing.
**Before**
```scala
scala> spark.range(10).createOrReplaceTempView("t1")
scala> spark.catalog.listTables().collect()
res1: Array[org.apache.spark.sql.catalog.Table] = Array(Table[name=`t1`, tableType=`TEMPORARY`, isTemporary=`true`])
scala> spark.catalog.listColumns("t1").collect()
org.apache.spark.sql.AnalysisException: Table `t1` does not exist in database `default`.;
```
**After**
```
scala> spark.catalog.listColumns("t1").collect()
res2: Array[org.apache.spark.sql.catalog.Column] = Array(Column[name='id', description='id', dataType='bigint', nullable='false', isPartition='false', isBucket='false'])
```
## How was this patch tested?
Pass the Jenkins tests including a new testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14114 from dongjoon-hyun/SPARK-16458.
## What changes were proposed in this pull request?
After SPARK-16476 (committed earlier today as #14128), we can finally bump the version number.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#14130 from rxin/SPARK-16477.
## What changes were proposed in this pull request?
This PR prevents dropping the current database to avoid errors like the followings.
```scala
scala> sql("create database delete_db")
scala> sql("use delete_db")
scala> sql("drop database delete_db")
scala> sql("create table t as select 1")
org.apache.spark.sql.catalyst.analysis.NoSuchDatabaseException: Database `delete_db` not found;
```
## How was this patch tested?
Pass the Jenkins tests including an updated testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14115 from dongjoon-hyun/SPARK-16459.
#### What changes were proposed in this pull request?
**Issue 1:** When a query containing LIMIT/TABLESAMPLE 0, the statistics could be zero. Results are correct but it could cause a huge performance regression. For example,
```Scala
Seq(("one", 1), ("two", 2), ("three", 3), ("four", 4)).toDF("k", "v")
.createOrReplaceTempView("test")
val df1 = spark.table("test")
val df2 = spark.table("test").limit(0)
val df = df1.join(df2, Seq("k"), "left")
```
The statistics of both `df` and `df2` are zero. The statistics values should never be zero; otherwise `sizeInBytes` of `BinaryNode` will also be zero (product of children). This PR is to increase it to `1` when the num of rows is equal to 0.
**Issue 2:** When a query containing negative LIMIT/TABLESAMPLE, we should issue exceptions. Negative values could break the implementation assumption of multiple parts. For example, statistics calculation. Below is the example query.
```SQL
SELECT * FROM testData TABLESAMPLE (-1 rows)
SELECT * FROM testData LIMIT -1
```
This PR is to issue an appropriate exception in this case.
**Issue 3:** Spark SQL follows the restriction of LIMIT clause in Hive. The argument to the LIMIT clause must evaluate to a constant value. It can be a numeric literal, or another kind of numeric expression involving operators, casts, and function return values. You cannot refer to a column or use a subquery. Currently, we do not detect whether the expression in LIMIT clause is foldable or not. If non-foldable, we might issue a strange error message. For example,
```SQL
SELECT * FROM testData LIMIT rand() > 0.2
```
Then, a misleading error message is issued, like
```
assertion failed: No plan for GlobalLimit (_nondeterministic#203 > 0.2)
+- Project [key#11, value#12, rand(-1441968339187861415) AS _nondeterministic#203]
+- LocalLimit (_nondeterministic#202 > 0.2)
+- Project [key#11, value#12, rand(-1308350387169017676) AS _nondeterministic#202]
+- LogicalRDD [key#11, value#12]
java.lang.AssertionError: assertion failed: No plan for GlobalLimit (_nondeterministic#203 > 0.2)
+- Project [key#11, value#12, rand(-1441968339187861415) AS _nondeterministic#203]
+- LocalLimit (_nondeterministic#202 > 0.2)
+- Project [key#11, value#12, rand(-1308350387169017676) AS _nondeterministic#202]
+- LogicalRDD [key#11, value#12]
```
This PR detects it and then issues a meaningful error message.
#### How was this patch tested?
Added test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14034 from gatorsmile/limit.
## What changes were proposed in this pull request?
This patch implements all remaining xpath functions that Hive supports and not natively supported in Spark: xpath_int, xpath_short, xpath_long, xpath_float, xpath_double, xpath_string, and xpath.
## How was this patch tested?
Added unit tests and end-to-end tests.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#13991 from petermaxlee/SPARK-16318.
## What changes were proposed in this pull request?
It is currently fairly difficult to have proper mima excludes when we cut a version branch. I'm proposing a small change to take the exclude list out of the exclude function, and put it in a variable so we can easily union excludes.
After this change, we can bump pom.xml version to 2.1.0-SNAPSHOT, without bumping the diff base version. Note that I also deleted all the exclude rules for version 1.x, to cut down the size of the file.
## How was this patch tested?
N/A - this is a build infra change.
Author: Reynold Xin <rxin@databricks.com>
Closes#14128 from rxin/SPARK-16476.
#### What changes were proposed in this pull request?
When users try to implement a data source API with extending only `RelationProvider` and `CreatableRelationProvider`, they will hit an error when resolving the relation.
```Scala
spark.read
.format("org.apache.spark.sql.test.DefaultSourceWithoutUserSpecifiedSchema")
.load()
.write.
format("org.apache.spark.sql.test.DefaultSourceWithoutUserSpecifiedSchema")
.save()
```
The error they hit is like
```
org.apache.spark.sql.test.DefaultSourceWithoutUserSpecifiedSchema does not allow user-specified schemas.;
org.apache.spark.sql.AnalysisException: org.apache.spark.sql.test.DefaultSourceWithoutUserSpecifiedSchema does not allow user-specified schemas.;
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:319)
at org.apache.spark.sql.execution.datasources.DataSource.write(DataSource.scala:494)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:211)
```
Actually, the bug fix is simple. [`DataSource.createRelation(sparkSession.sqlContext, mode, options, data)`](dd644f8117/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSource.scala (L429)) already returns a BaseRelation. We should not assign schema to `userSpecifiedSchema`. That schema assignment only makes sense for the data sources that extend `FileFormat`.
#### How was this patch tested?
Added a test case.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14075 from gatorsmile/dataSource.
## What changes were proposed in this pull request?
Documentation changes to indicate that fine-grained mode is now deprecated. No code changes were made, and all fine-grained mode instructions were left in place. We can remove all of that once the deprecation cycle completes (Does Spark have a standard deprecation cycle? One major version?)
Blocked on https://github.com/apache/spark/pull/14059
## How was this patch tested?
Viewed in Github
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#14078 from mgummelt/deprecate-fine-grained.
## What changes were proposed in this pull request?
It's possible to also change the callers to not pass in empty chunks, but it seems cleaner to just allow `ChunkedByteBuffer` to handle empty arrays. cc JoshRosen
## How was this patch tested?
Unit tests, also checked that the original reproduction case in https://github.com/apache/spark/pull/11748#issuecomment-230760283 is resolved.
Author: Eric Liang <ekl@databricks.com>
Closes#14099 from ericl/spark-16432.
## What changes were proposed in this pull request?
Avoid error finding max of empty Seq when stageIds is empty. It does fix the immediate problem; I don't know if it results in meaningful output, but not an error at least.
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#14105 from srowen/SPARK-16376.
## What changes were proposed in this pull request?
Allow for kafka topic subscriptions based on a regex pattern.
## How was this patch tested?
Unit tests, manual tests
Author: cody koeninger <cody@koeninger.org>
Closes#14026 from koeninger/SPARK-13569.
## What changes were proposed in this pull request?
Currently, JDBC Writer uses dialects to get datatypes, but doesn't to quote field names. This PR uses dialects to quote the field names, too.
**Reported Error Scenario (MySQL case)**
```scala
scala> val url="jdbc:mysql://localhost:3306/temp"
scala> val prop = new java.util.Properties
scala> prop.setProperty("user","root")
scala> spark.createDataset(Seq("a","b","c")).toDF("order")
scala> df.write.mode("overwrite").jdbc(url, "temptable", prop)
...MySQLSyntaxErrorException: ... near 'order TEXT )
```
## How was this patch tested?
Pass the Jenkins tests and manually do the above case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14107 from dongjoon-hyun/SPARK-16387.
## What changes were proposed in this pull request?
This PR adds hive-thriftserver profile to scala 2.10 build created by release-build.sh.
Author: Yin Huai <yhuai@databricks.com>
Closes#14108 from yhuai/SPARK-16453.
## What changes were proposed in this pull request?
This PR adds parse_url SQL functions in order to remove Hive fallback.
A new implementation of #13999
## How was this patch tested?
Pass the exist tests including new testcases.
Author: wujian <jan.chou.wu@gmail.com>
Closes#14008 from janplus/SPARK-16281.
## What changes were proposed in this pull request?
This uses the try/finally pattern to ensure streams are closed after use. `UnsafeShuffleWriter` wasn't closing compression streams, causing them to leak resources until garbage collected. This was causing a problem with codecs that use off-heap memory.
## How was this patch tested?
Current tests are sufficient. This should not change behavior.
Author: Ryan Blue <blue@apache.org>
Closes#14093 from rdblue/SPARK-16420-unsafe-shuffle-writer-leak.
## What changes were proposed in this pull request?
Adds an quoteAll option for writing CSV which will quote all fields.
See https://issues.apache.org/jira/browse/SPARK-13638
## How was this patch tested?
Added a test to verify the output columns are quoted for all fields in the Dataframe
Author: Jurriaan Pruis <email@jurriaanpruis.nl>
Closes#13374 from jurriaan/csv-quote-all.
## What changes were proposed in this pull request?
This PR implements `sentences` SQL function.
## How was this patch tested?
Pass the Jenkins tests with a new testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14004 from dongjoon-hyun/SPARK_16285.
## What changes were proposed in this pull request?
This small patch modifies ExpressionEvalHelper. checkEvaluation to support comparing NaN values for floating point comparisons.
## How was this patch tested?
This is a test harness change.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14103 from petermaxlee/SPARK-16436.
## What changes were proposed in this pull request?
An option that limits the file stream source to read 1 file at a time enables rate limiting. It has the additional convenience that a static set of files can be used like a stream for testing as this will allows those files to be considered one at a time.
This PR adds option `maxFilesPerTrigger`.
## How was this patch tested?
New unit test
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#14094 from tdas/SPARK-16430.
## What changes were proposed in this pull request?
This PR prevents ERRORs when `summary(df)` is called for `SparkDataFrame` with not-numeric columns. This failure happens only in `SparkR`.
**Before**
```r
> df <- createDataFrame(faithful)
> df <- withColumn(df, "boolean", df$waiting==79)
> summary(df)
16/07/07 14:15:16 ERROR RBackendHandler: describe on 34 failed
Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
org.apache.spark.sql.AnalysisException: cannot resolve 'avg(`boolean`)' due to data type mismatch: function average requires numeric types, not BooleanType;
```
**After**
```r
> df <- createDataFrame(faithful)
> df <- withColumn(df, "boolean", df$waiting==79)
> summary(df)
SparkDataFrame[summary:string, eruptions:string, waiting:string]
```
## How was this patch tested?
Pass the Jenkins with a updated testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14096 from dongjoon-hyun/SPARK-16425.
## What changes were proposed in this pull request?
Apply default "NA" as null string for R, like R read.csv na.string parameter.
https://stat.ethz.ch/R-manual/R-devel/library/utils/html/read.table.html
na.strings = "NA"
An user passing a csv file with NA value should get the same behavior with SparkR read.df(... source = "csv")
(couldn't open JIRA, will do that later)
## How was this patch tested?
unit tests
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13984 from felixcheung/rcsvnastring.
## What changes were proposed in this pull request?
In #13537 we truncate `simpleString` if it is a long `StructType`. But sometimes we need `catalogString` to reconstruct `TypeInfo`, for example in description of [SPARK-16415 ](https://issues.apache.org/jira/browse/SPARK-16415). So we need to keep the implementation of `catalogString` not affected by our truncate.
## How was this patch tested?
added a test case.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#14089 from adrian-wang/catalogstring.
## What changes were proposed in this pull request?
There are cases where `complete` output mode does not output updated aggregated value; for details please refer to [SPARK-16350](https://issues.apache.org/jira/browse/SPARK-16350).
The cause is that, as we do `data.as[T].foreachPartition { iter => ... }` in `ForeachSink.addBatch()`, `foreachPartition()` does not support incremental planning for now.
This patches makes `foreachPartition()` support incremental planning in `ForeachSink`, by making a special version of `Dataset` with its `rdd()` method supporting incremental planning.
## How was this patch tested?
Added a unit test which failed before the change
Author: Liwei Lin <lwlin7@gmail.com>
Closes#14030 from lw-lin/fix-foreach-complete.
## What changes were proposed in this pull request?
This PR improves `OptimizeIn` optimizer to remove the literal repetitions from SQL `IN` predicates. This optimizer prevents user mistakes and also can optimize some queries like [TPCDS-36](https://github.com/apache/spark/blob/master/sql/core/src/test/resources/tpcds/q36.sql#L19).
**Before**
```scala
scala> sql("select state from (select explode(array('CA','TN')) state) where state in ('TN','TN','TN','TN','TN','TN','TN')").explain
== Physical Plan ==
*Filter state#6 IN (TN,TN,TN,TN,TN,TN,TN)
+- Generate explode([CA,TN]), false, false, [state#6]
+- Scan OneRowRelation[]
```
**After**
```scala
scala> sql("select state from (select explode(array('CA','TN')) state) where state in ('TN','TN','TN','TN','TN','TN','TN')").explain
== Physical Plan ==
*Filter state#6 IN (TN)
+- Generate explode([CA,TN]), false, false, [state#6]
+- Scan OneRowRelation[]
```
## How was this patch tested?
Pass the Jenkins tests (including a new testcase).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13876 from dongjoon-hyun/SPARK-16174.
