## What changes were proposed in this pull request?
Fix Java style errors and remove unused imports, which are randomly found
## How was this patch tested?
Tested on my local machine.
Author: Xin Ren <iamshrek@126.com>
Closes#14161 from keypointt/SPARK-16437.
## What changes were proposed in this pull request?
A second form of AssertQuery now actually invokes the condition; avoids a build warning too
## How was this patch tested?
Jenkins; running StreamTest
Author: Sean Owen <sowen@cloudera.com>
Closes#14133 from srowen/SPARK-15889.2.
## 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?
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?
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?
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?
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.
## 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?
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?
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?
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 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?
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?
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?
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 patch removes InSet filter pushdown from Parquet data source, since row-based pushdown is not beneficial to Spark and brings extra complexity to the code base.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#14076 from rxin/SPARK-16400.
#### What changes were proposed in this pull request?
When creating a view, a common user error is the number of columns produced by the `SELECT` clause does not match the number of column names specified by `CREATE VIEW`.
For example, given Table `t1` only has 3 columns
```SQL
create view v1(col2, col4, col3, col5) as select * from t1
```
Currently, Spark SQL reports the following error:
```
requirement failed
java.lang.IllegalArgumentException: requirement failed
at scala.Predef$.require(Predef.scala:212)
at org.apache.spark.sql.execution.command.CreateViewCommand.run(views.scala:90)
```
This error message is very confusing. This PR is to detect the error and issue a meaningful error message.
#### How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14047 from gatorsmile/viewMismatchedColumns.
## What changes were proposed in this pull request?
Currently, Scala API supports to take options with the types, `String`, `Long`, `Double` and `Boolean` and Python API also supports other types.
This PR corrects `tableProperty` rule to support other types (string, boolean, double and integer) so that support the options for data sources in a consistent way. This will affect other rules such as DBPROPERTIES and TBLPROPERTIES (allowing other types as values).
Also, `TODO add bucketing and partitioning.` was removed because it was resolved in 24bea00047
## How was this patch tested?
Unit test in `MetastoreDataSourcesSuite.scala`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#13517 from HyukjinKwon/SPARK-14839.
## What changes were proposed in this pull request?
This is a small follow-up for SPARK-16371:
1. Hide removeMetadata from public API.
2. Add JIRA ticket number to test case name.
## How was this patch tested?
Updated a test comment.
Author: Reynold Xin <rxin@databricks.com>
Closes#14074 from rxin/parquet-filter.
## What changes were proposed in this pull request?
Currently, if there is a schema as below:
```
root
|-- _1: struct (nullable = true)
| |-- _1: integer (nullable = true)
```
and if we execute the codes below:
```scala
df.filter("_1 IS NOT NULL").count()
```
This pushes down a filter although this filter is being applied to `StructType`.(If my understanding is correct, Spark does not pushes down filters for those).
The reason is, `ParquetFilters.getFieldMap` produces results below:
```
(_1,StructType(StructField(_1,IntegerType,true)))
(_1,IntegerType)
```
and then it becomes a `Map`
```
(_1,IntegerType)
```
Now, because of ` ....lift(dataTypeOf(name)).map(_(name, value))`, this pushes down filters for `_1` which Parquet thinks is `IntegerType`. However, it is actually `StructType`.
So, Parquet filter2 produces incorrect results, for example, the codes below:
```
df.filter("_1 IS NOT NULL").count()
```
produces always 0.
This PR prevents this by not finding nested fields.
## How was this patch tested?
Unit test in `ParquetFilterSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#14067 from HyukjinKwon/SPARK-16371.
## What changes were proposed in this pull request?
These two configs should always be true after Spark 2.0. This patch removes them from the config list. Note that ideally this should've gone into branch-2.0, but due to the timing of the release we should only merge this in master for Spark 2.1.
## How was this patch tested?
Updated test cases.
Author: Reynold Xin <rxin@databricks.com>
Closes#14061 from rxin/SPARK-16388.
## What changes were proposed in this pull request?
Currently, `regexp_replace` function supports `Column` arguments in a query. This PR supports that in a `Dataset` operation, too.
## How was this patch tested?
Pass the Jenkins tests with a updated testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14060 from dongjoon-hyun/SPARK-16340.
## What changes were proposed in this pull request?
This PR implements `stack` table generating function.
## How was this patch tested?
Pass the Jenkins tests including new testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14033 from dongjoon-hyun/SPARK-16286.
## What changes were proposed in this pull request?
This patch fixes the bug that the refresh command does not work on temporary views. This patch is based on https://github.com/apache/spark/pull/13989, but removes the public Dataset.refresh() API as well as improved test coverage.
Note that I actually think the public refresh() API is very useful. We can in the future implement it by also invalidating the lazy vals in QueryExecution (or alternatively just create a new QueryExecution).
## How was this patch tested?
Re-enabled a previously ignored test, and added a new test suite for Hive testing behavior of temporary views against MetastoreRelation.
Author: Reynold Xin <rxin@databricks.com>
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14009 from rxin/SPARK-16311.
## What changes were proposed in this pull request?
TypedAggregateExpression sets nullable based on the schema of the outputEncoder
## How was this patch tested?
Add test in DatasetAggregatorSuite
Author: Koert Kuipers <koert@tresata.com>
Closes#13532 from koertkuipers/feat-aggregator-nullable.
## What changes were proposed in this pull request?
This PR implements `inline` table generating function.
## How was this patch tested?
Pass the Jenkins tests with new testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13976 from dongjoon-hyun/SPARK-16288.
## What changes were proposed in this pull request?
This PR adds `map_keys` and `map_values` SQL functions in order to remove Hive fallback.
## How was this patch tested?
Pass the Jenkins tests including new testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13967 from dongjoon-hyun/SPARK-16278.
#### What changes were proposed in this pull request?
Star expansion over a table containing zero column does not work since 1.6. However, it works in Spark 1.5.1. This PR is to fix the issue in the master branch.
For example,
```scala
val rddNoCols = sqlContext.sparkContext.parallelize(1 to 10).map(_ => Row.empty)
val dfNoCols = sqlContext.createDataFrame(rddNoCols, StructType(Seq.empty))
dfNoCols.registerTempTable("temp_table_no_cols")
sqlContext.sql("select * from temp_table_no_cols").show
```
Without the fix, users will get the following the exception:
```
java.lang.IllegalArgumentException: requirement failed
at scala.Predef$.require(Predef.scala:221)
at org.apache.spark.sql.catalyst.analysis.UnresolvedStar.expand(unresolved.scala:199)
```
#### How was this patch tested?
Tests are added
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14007 from gatorsmile/starExpansionTableWithZeroColumn.
## What changes were proposed in this pull request?
In structured streaming, Spark does not report errors when the specified directory does not exist. This is a behavior different from the batch mode. This patch changes the behavior to fail if the directory does not exist (when the path is not a glob pattern).
## How was this patch tested?
Updated unit tests to reflect the new behavior.
Author: Reynold Xin <rxin@databricks.com>
Closes#14002 from rxin/SPARK-16335.
#### What changes were proposed in this pull request?
For JDBC data sources, users can specify `batchsize` for multi-row inserts and `fetchsize` for multi-row fetch. A few issues exist:
- The property keys are case sensitive. Thus, the existing test cases for `fetchsize` use incorrect names, `fetchSize`. Basically, the test cases are broken.
- No test case exists for `batchsize`.
- We do not detect the illegal input values for `fetchsize` and `batchsize`.
For example, when `batchsize` is zero, we got the following exception:
```
Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost): java.lang.ArithmeticException: / by zero
```
when `fetchsize` is less than zero, we got the exception from the underlying JDBC driver:
```
Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost): org.h2.jdbc.JdbcSQLException: Invalid value "-1" for parameter "rows" [90008-183]
```
This PR fixes all the above issues, and issue the appropriate exceptions when detecting the illegal inputs for `fetchsize` and `batchsize`. Also update the function descriptions.
#### How was this patch tested?
Test cases are fixed and added.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13919 from gatorsmile/jdbcProperties.
## What changes were proposed in this pull request?
This patch implements the elt function, as it is implemented in Hive.
## How was this patch tested?
Added expression unit test in StringExpressionsSuite and end-to-end test in StringFunctionsSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#13966 from petermaxlee/SPARK-16276.
## What changes were proposed in this pull request?
Spark silently drops exceptions during file listing. This is a very bad behavior because it can mask legitimate errors and the resulting plan will silently have 0 rows. This patch changes it to not silently drop the errors.
## How was this patch tested?
Manually verified.
Author: Reynold Xin <rxin@databricks.com>
Closes#13987 from rxin/SPARK-16313.
## What changes were proposed in this pull request?
This patch appends a message to suggest users running refresh table or reloading data frames when Spark sees a FileNotFoundException due to stale, cached metadata.
## How was this patch tested?
Added a unit test for this in MetadataCacheSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14003 from petermaxlee/SPARK-16336.
## What changes were proposed in this pull request?
This PR implements `posexplode` table generating function. Currently, master branch raises the following exception for `map` argument. It's different from Hive.
**Before**
```scala
scala> sql("select posexplode(map('a', 1, 'b', 2))").show
org.apache.spark.sql.AnalysisException: No handler for Hive UDF ... posexplode() takes an array as a parameter; line 1 pos 7
```
**After**
```scala
scala> sql("select posexplode(map('a', 1, 'b', 2))").show
+---+---+-----+
|pos|key|value|
+---+---+-----+
| 0| a| 1|
| 1| b| 2|
+---+---+-----+
```
For `array` argument, `after` is the same with `before`.
```
scala> sql("select posexplode(array(1, 2, 3))").show
+---+---+
|pos|col|
+---+---+
| 0| 1|
| 1| 2|
| 2| 3|
+---+---+
```
## How was this patch tested?
Pass the Jenkins tests with newly added testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13971 from dongjoon-hyun/SPARK-16289.
## What changes were proposed in this pull request?
Force the sorter to Spill when number of elements in the pointer array reach a certain size. This is to workaround the issue of timSort failing on large buffer size.
## How was this patch tested?
Tested by running a job which was failing without this change due to TimSort bug.
Author: Sital Kedia <skedia@fb.com>
Closes#13107 from sitalkedia/fix_TimSort.
## What changes were proposed in this pull request?
This patch implements xpath_boolean expression for Spark SQL, a xpath function that returns true or false. The implementation is modelled after Hive's xpath_boolean, except that how the expression handles null inputs. Hive throws a NullPointerException at runtime if either of the input is null. This implementation returns null if either of the input is null.
## How was this patch tested?
Created two new test suites. One for unit tests covering the expression, and the other for end-to-end test in SQL.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#13964 from petermaxlee/SPARK-16274.
## What changes were proposed in this pull request?
After SPARK-15674, `DDLStrategy` prints out the following deprecation messages in the testsuites.
```
12:10:53.284 WARN org.apache.spark.sql.execution.SparkStrategies$DDLStrategy:
CREATE TEMPORARY TABLE normal_orc_source USING... is deprecated,
please use CREATE TEMPORARY VIEW viewName USING... instead
```
Total : 40
- JDBCWriteSuite: 14
- DDLSuite: 6
- TableScanSuite: 6
- ParquetSourceSuite: 5
- OrcSourceSuite: 2
- SQLQuerySuite: 2
- HiveCommandSuite: 2
- JsonSuite: 1
- PrunedScanSuite: 1
- FilteredScanSuite 1
This PR replaces `CREATE TEMPORARY TABLE` with `CREATE TEMPORARY VIEW` in order to remove the deprecation messages in the above testsuites except `DDLSuite`, `SQLQuerySuite`, `HiveCommandSuite`.
The Jenkins results shows only remaining 10 messages.
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/61422/consoleFull
## How was this patch tested?
This is a testsuite-only change.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13956 from dongjoon-hyun/SPARK-16267.
## What changes were proposed in this pull request?
This PR adds 3 optimizer rules for typed filter:
1. push typed filter down through `SerializeFromObject` and eliminate the deserialization in filter condition.
2. pull typed filter up through `SerializeFromObject` and eliminate the deserialization in filter condition.
3. combine adjacent typed filters and share the deserialized object among all the condition expressions.
This PR also adds `TypedFilter` logical plan, to separate it from normal filter, so that the concept is more clear and it's easier to write optimizer rules.
## How was this patch tested?
`TypedFilterOptimizationSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13846 from cloud-fan/filter.
## What changes were proposed in this pull request?
This PR allows `emptyDataFrame.write` since the user didn't specify any partition columns.
**Before**
```scala
scala> spark.emptyDataFrame.write.parquet("/tmp/t1")
org.apache.spark.sql.AnalysisException: Cannot use all columns for partition columns;
scala> spark.emptyDataFrame.write.csv("/tmp/t1")
org.apache.spark.sql.AnalysisException: Cannot use all columns for partition columns;
```
After this PR, there occurs no exceptions and the created directory has only one file, `_SUCCESS`, as expected.
## How was this patch tested?
Pass the Jenkins tests including updated test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13730 from dongjoon-hyun/SPARK-16006.
## What changes were proposed in this pull request?
This PR removes meaningless `StringIteratorReader` for CSV data source.
In `CSVParser.scala`, there is an `Reader` wrapping `Iterator` but there are two problems by this.
Firstly, it was actually not faster than processing line by line with Iterator due to additional logics to wrap `Iterator` to `Reader`.
Secondly, this brought a bit of complexity because it needs additional logics to allow every line to be read bytes by bytes. So, it was pretty difficult to figure out issues about parsing, (eg. SPARK-14103).
A benchmark was performed manually and the results were below:
- Original codes with Reader wrapping Iterator
|End-to-end (ns) | Parse Time (ns) |
|-----------------------|------------------------|
|14116265034 |2008277960 |
- New codes with Iterator
|End-to-end (ns) | Parse Time (ns) |
|-----------------------|------------------------|
|13451699644 | 1549050564 |
For the details for the environment, dataset and methods, please refer the JIRA ticket.
## How was this patch tested?
Existing tests should cover this.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#13808 from HyukjinKwon/SPARK-14480-small.
#### What changes were proposed in this pull request?
Based on the previous discussion with cloud-fan hvanhovell in another related PR https://github.com/apache/spark/pull/13764#discussion_r67994276, it looks reasonable to add convenience methods for users to add `comment` when defining `StructField`.
Currently, the column-related `comment` attribute is stored in `Metadata` of `StructField`. For example, users can add the `comment` attribute using the following way:
```Scala
StructType(
StructField(
"cl1",
IntegerType,
nullable = false,
new MetadataBuilder().putString("comment", "test").build()) :: Nil)
```
This PR is to add more user friendly methods for the `comment` attribute when defining a `StructField`. After the changes, users are provided three different ways to do it:
```Scala
val struct = (new StructType)
.add("a", "int", true, "test1")
val struct = (new StructType)
.add("c", StringType, true, "test3")
val struct = (new StructType)
.add(StructField("d", StringType).withComment("test4"))
```
#### How was this patch tested?
Added test cases:
- `DataTypeSuite` is for testing three types of API changes,
- `DataFrameReaderWriterSuite` is for parquet, json and csv formats - using in-memory catalog
- `OrcQuerySuite.scala` is for orc format using Hive-metastore
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13860 from gatorsmile/newMethodForComment.
## What changes were proposed in this pull request?
`MAX(COUNT(*))` is invalid since aggregate expression can't be nested within another aggregate expression. This case should be captured at analysis phase, but somehow sneaks off to runtime.
The reason is that when checking aggregate expressions in `CheckAnalysis`, a checking branch treats all expressions that reference no input attributes as valid ones. However, `MAX(COUNT(*))` is translated into `MAX(COUNT(1))` at analysis phase and also references no input attribute.
This PR fixes this issue by removing the aforementioned branch.
## How was this patch tested?
New test case added in `AnalysisErrorSuite`.
Author: Cheng Lian <lian@databricks.com>
Closes#13968 from liancheng/spark-16291-nested-agg-functions.
## What changes were proposed in this pull request?
Fixes a couple old references to `DataFrameWriter.startStream` to `DataStreamWriter.start
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#13952 from brkyvz/minor-doc-fix.
## What changes were proposed in this pull request?
The root cause is in `MapObjects`. Its parameter `loopVar` is not declared as child, but sometimes can be same with `lambdaFunction`(e.g. the function that takes `loopVar` and produces `lambdaFunction` may be `identity`), which is a child. This brings trouble when call `withNewChildren`, it may mistakenly treat `loopVar` as a child and cause `IndexOutOfBoundsException: 0` later.
This PR fixes this bug by simply pulling out the paremters from `LambdaVariable` and pass them to `MapObjects` directly.
## How was this patch tested?
new test in `DatasetAggregatorSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13835 from cloud-fan/map-objects.
#### What changes were proposed in this pull request?
koertkuipers identified the PR https://github.com/apache/spark/pull/13727/ changed the behavior of `load` API. After the change, the `load` API does not add the value of `path` into the `options`. Thank you!
This PR is to add the option `path` back to `load()` API in `DataFrameReader`, if and only if users specify one and only one `path` in the `load` API. For example, users can see the `path` option after the following API call,
```Scala
spark.read
.format("parquet")
.load("/test")
```
#### How was this patch tested?
Added test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13933 from gatorsmile/optionPath.
## What changes were proposed in this pull request?
The root cause is: the output attributes of outer join are derived from its children, while they are actually different attributes(outer join can return null).
We have already added some special logic to handle it, e.g. `PushPredicateThroughJoin` won't push down predicates through outer join side, `FixNullability`.
This PR adds one more special logic in `FoldablePropagation`.
## How was this patch tested?
new test in `DataFrameSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13884 from cloud-fan/bug.
## What changes were proposed in this pull request?
Allowing truncate to a specific number of character is convenient at times, especially while operating from the REPL. Sometimes those last few characters make all the difference, and showing everything brings in whole lot of noise.
## How was this patch tested?
Existing tests. + 1 new test in DataFrameSuite.
For SparkR and pyspark, existing tests and manual testing.
Author: Prashant Sharma <prashsh1@in.ibm.com>
Author: Prashant Sharma <prashant@apache.org>
Closes#13839 from ScrapCodes/add_truncateTo_DF.show.
## What changes were proposed in this pull request?
Spark currently shows all functions when issue a `SHOW FUNCTIONS` command. This PR refines the `SHOW FUNCTIONS` command by allowing users to select all functions, user defined function or system functions. The following syntax can be used:
**ALL** (default)
```SHOW FUNCTIONS```
```SHOW ALL FUNCTIONS```
**SYSTEM**
```SHOW SYSTEM FUNCTIONS```
**USER**
```SHOW USER FUNCTIONS```
## How was this patch tested?
Updated tests and added tests to the DDLSuite
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#13929 from hvanhovell/SPARK-16220.
## What changes were proposed in this pull request?
- Fix tests regarding show functions functionality
- Revert `catalog.ListFunctions` and `SHOW FUNCTIONS` to return to `Spark 1.X` functionality.
Cherry picked changes from this PR: https://github.com/apache/spark/pull/13413/files
## How was this patch tested?
Unit tests.
Author: Bill Chambers <bill@databricks.com>
Author: Bill Chambers <wchambers@ischool.berkeley.edu>
Closes#13916 from anabranch/master.
## What changes were proposed in this pull request?
This PR adds a testcase to ensure if `checkAnswer` handles Map type correctly.
## How was this patch tested?
Pass the jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13913 from dongjoon-hyun/SPARK-10591.
## What changes were proposed in this pull request?
One of the most frequent usage patterns for Spark SQL is using **cached tables**. This PR improves `InMemoryTableScanExec` to handle `IN` predicate efficiently by pruning partition batches. Of course, the performance improvement varies over the queries and the datasets. But, for the following simple query, the query duration in Spark UI goes from 9 seconds to 50~90ms. It's about over 100 times faster.
**Before**
```scala
$ bin/spark-shell --driver-memory 6G
scala> val df = spark.range(2000000000)
scala> df.createOrReplaceTempView("t")
scala> spark.catalog.cacheTable("t")
scala> sql("select id from t where id = 1").collect() // About 2 mins
scala> sql("select id from t where id = 1").collect() // less than 90ms
scala> sql("select id from t where id in (1,2,3)").collect() // 9 seconds
```
**After**
```scala
scala> sql("select id from t where id in (1,2,3)").collect() // less than 90ms
```
This PR has impacts over 35 queries of TPC-DS if the tables are cached.
Note that this optimization is applied for `IN`. To apply `IN` predicate having more than 10 items, `spark.sql.optimizer.inSetConversionThreshold` option should be increased.
## How was this patch tested?
Pass the Jenkins tests (including new testcases).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13887 from dongjoon-hyun/SPARK-16186.
## What changes were proposed in this pull request?
`CollectSet` cannot have map-typed data because MapTypeData does not implement `equals`.
So, this pr is to add type checks in `CheckAnalysis`.
## How was this patch tested?
Added tests to check failures when we found map-typed data in `CollectSet`.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#13892 from maropu/SPARK-16192.
## What changes were proposed in this pull request?
Allow to specify empty over clause in window expressions through dataset API
In SQL, its allowed to specify an empty OVER clause in the window expression.
```SQL
select area, sum(product) over () as c from windowData
where product > 3 group by area, product
having avg(month) > 0 order by avg(month), product
```
In this case the analytic function sum is presented based on all the rows of the result set
Currently its not allowed through dataset API and is handled in this PR.
## How was this patch tested?
Added a new test in DataframeWindowSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#13897 from dilipbiswal/spark-empty-over.
## What changes were proposed in this pull request?
One of the most frequent usage patterns for Spark SQL is using **cached tables**. This PR improves `InMemoryTableScanExec` to handle `IN` predicate efficiently by pruning partition batches. Of course, the performance improvement varies over the queries and the datasets. But, for the following simple query, the query duration in Spark UI goes from 9 seconds to 50~90ms. It's about over 100 times faster.
**Before**
```scala
$ bin/spark-shell --driver-memory 6G
scala> val df = spark.range(2000000000)
scala> df.createOrReplaceTempView("t")
scala> spark.catalog.cacheTable("t")
scala> sql("select id from t where id = 1").collect() // About 2 mins
scala> sql("select id from t where id = 1").collect() // less than 90ms
scala> sql("select id from t where id in (1,2,3)").collect() // 9 seconds
```
**After**
```scala
scala> sql("select id from t where id in (1,2,3)").collect() // less than 90ms
```
This PR has impacts over 35 queries of TPC-DS if the tables are cached.
Note that this optimization is applied for `IN`. To apply `IN` predicate having more than 10 items, `spark.sql.optimizer.inSetConversionThreshold` option should be increased.
## How was this patch tested?
Pass the Jenkins tests (including new testcases).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13887 from dongjoon-hyun/SPARK-16186.
## What changes were proposed in this pull request?
This patch fixes an overflow bug in vectorized parquet reader where both off-heap and on-heap variants of `ColumnVector.reserve()` can unfortunately overflow while reserving additional capacity during reads.
## How was this patch tested?
Manual Tests
Author: Sameer Agarwal <sameer@databricks.com>
Closes#13832 from sameeragarwal/negative-array.
## What changes were proposed in this pull request?
Currently, `readBatches` accumulator of `InMemoryTableScanExec` is updated only when `spark.sql.inMemoryColumnarStorage.partitionPruning` is true. Although this metric is used for only testing purpose, we had better have correct metric without considering SQL options.
## How was this patch tested?
Pass the Jenkins tests (including a new testcase).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13870 from dongjoon-hyun/SPARK-16165.
## What changes were proposed in this pull request?
This calculation of statistics is not trivial anymore, it could be very slow on large query (for example, TPC-DS Q64 took several minutes to plan).
During the planning of a query, the statistics of any logical plan should not change (even InMemoryRelation), so we should use `lazy val` to cache the statistics.
For InMemoryRelation, the statistics could be updated after materialization, it's only useful when used in another query (before planning), because once we finished the planning, the statistics will not be used anymore.
## How was this patch tested?
Testsed with TPC-DS Q64, it could be planned in a second after the patch.
Author: Davies Liu <davies@databricks.com>
Closes#13871 from davies/fix_statistics.
## What changes were proposed in this pull request?
When the user uses `ConsoleSink`, we should use a temp location if `checkpointLocation` is not specified.
## How was this patch tested?
The added unit test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13817 from zsxwing/console-checkpoint.
## What changes were proposed in this pull request?
When table is created with column name containing dot, distinct() will fail to run. For example,
```scala
val rowRDD = sparkContext.parallelize(Seq(Row(1), Row(1), Row(2)))
val schema = StructType(Array(StructField("column.with.dot", IntegerType, nullable = false)))
val df = spark.createDataFrame(rowRDD, schema)
```
running the following will have no problem:
```scala
df.select(new Column("`column.with.dot`"))
```
but running the query with additional distinct() will cause exception:
```scala
df.select(new Column("`column.with.dot`")).distinct()
```
The issue is that distinct() will try to resolve the column name, but the column name in the schema does not have backtick with it. So the solution is to add the backtick before passing the column name to resolve().
## How was this patch tested?
Added a new test case.
Author: bomeng <bmeng@us.ibm.com>
Closes#13140 from bomeng/SPARK-15230.
#### What changes were proposed in this pull request?
This PR is to improve test coverage. It verifies whether `Comment` of `Column` can be appropriate handled.
The test cases verify the related parts in Parser, both SQL and DataFrameWriter interface, and both Hive Metastore catalog and In-memory catalog.
#### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13764 from gatorsmile/dataSourceComment.
## What changes were proposed in this pull request?
Although the top level input object can not be null, but when we use `Encoders.tuple` to combine 2 encoders, their input objects are not top level anymore and can be null. We should handle this case.
## How was this patch tested?
new test in DatasetSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13807 from cloud-fan/bug.
## What changes were proposed in this pull request?
Seems the fix of SPARK-14959 breaks the parallel partitioning discovery. This PR fixes the problem
## How was this patch tested?
Tested manually. (This PR also adds a proper test for SPARK-14959)
Author: Yin Huai <yhuai@databricks.com>
Closes#13830 from yhuai/SPARK-16121.
## What changes were proposed in this pull request?
Add a configuration to allow people to set a minimum polling delay when no new data arrives (default is 10ms). This PR also cleans up some INFO logs.
## How was this patch tested?
Existing unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13718 from zsxwing/SPARK-16002.
## What changes were proposed in this pull request?
This PR makes `input_file_name()` function return the file paths not empty strings for external data sources based on `NewHadoopRDD`, such as [spark-redshift](cba5eee1ab/src/main/scala/com/databricks/spark/redshift/RedshiftRelation.scala (L149)) and [spark-xml](https://github.com/databricks/spark-xml/blob/master/src/main/scala/com/databricks/spark/xml/util/XmlFile.scala#L39-L47).
The codes with the external data sources below:
```scala
df.select(input_file_name).show()
```
will produce
- **Before**
```
+-----------------+
|input_file_name()|
+-----------------+
| |
+-----------------+
```
- **After**
```
+--------------------+
| input_file_name()|
+--------------------+
|file:/private/var...|
+--------------------+
```
## How was this patch tested?
Unit tests in `ColumnExpressionSuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#13759 from HyukjinKwon/SPARK-16044.
#### What changes were proposed in this pull request?
This PR is to fix the following bugs:
**Issue 1: Wrong Results when lowerBound is larger than upperBound in Column Partitioning**
```scala
spark.read.jdbc(
url = urlWithUserAndPass,
table = "TEST.seq",
columnName = "id",
lowerBound = 4,
upperBound = 0,
numPartitions = 3,
connectionProperties = new Properties)
```
**Before code changes:**
The returned results are wrong and the generated partitions are wrong:
```
Part 0 id < 3 or id is null
Part 1 id >= 3 AND id < 2
Part 2 id >= 2
```
**After code changes:**
Issue an `IllegalArgumentException` exception:
```
Operation not allowed: the lower bound of partitioning column is larger than the upper bound. lowerBound: 5; higherBound: 1
```
**Issue 2: numPartitions is more than the number of key values between upper and lower bounds**
```scala
spark.read.jdbc(
url = urlWithUserAndPass,
table = "TEST.seq",
columnName = "id",
lowerBound = 1,
upperBound = 5,
numPartitions = 10,
connectionProperties = new Properties)
```
**Before code changes:**
Returned correct results but the generated partitions are very inefficient, like:
```
Partition 0: id < 1 or id is null
Partition 1: id >= 1 AND id < 1
Partition 2: id >= 1 AND id < 1
Partition 3: id >= 1 AND id < 1
Partition 4: id >= 1 AND id < 1
Partition 5: id >= 1 AND id < 1
Partition 6: id >= 1 AND id < 1
Partition 7: id >= 1 AND id < 1
Partition 8: id >= 1 AND id < 1
Partition 9: id >= 1
```
**After code changes:**
Adjust `numPartitions` and can return the correct answers:
```
Partition 0: id < 2 or id is null
Partition 1: id >= 2 AND id < 3
Partition 2: id >= 3 AND id < 4
Partition 3: id >= 4
```
**Issue 3: java.lang.ArithmeticException when numPartitions is zero**
```Scala
spark.read.jdbc(
url = urlWithUserAndPass,
table = "TEST.seq",
columnName = "id",
lowerBound = 0,
upperBound = 4,
numPartitions = 0,
connectionProperties = new Properties)
```
**Before code changes:**
Got the following exception:
```
java.lang.ArithmeticException: / by zero
```
**After code changes:**
Able to return a correct answer by disabling column partitioning when numPartitions is equal to or less than zero
#### How was this patch tested?
Added test cases to verify the results
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13773 from gatorsmile/jdbcPartitioning.
## What changes were proposed in this pull request?
Issues with current reader behavior.
- `text()` without args returns an empty DF with no columns -> inconsistent, its expected that text will always return a DF with `value` string field,
- `textFile()` without args fails with exception because of the above reason, it expected the DF returned by `text()` to have a `value` field.
- `orc()` does not have var args, inconsistent with others
- `json(single-arg)` was removed, but that caused source compatibility issues - [SPARK-16009](https://issues.apache.org/jira/browse/SPARK-16009)
- user specified schema was not respected when `text/csv/...` were used with no args - [SPARK-16007](https://issues.apache.org/jira/browse/SPARK-16007)
The solution I am implementing is to do the following.
- For each format, there will be a single argument method, and a vararg method. For json, parquet, csv, text, this means adding json(string), etc.. For orc, this means adding orc(varargs).
- Remove the special handling of text(), csv(), etc. that returns empty dataframe with no fields. Rather pass on the empty sequence of paths to the datasource, and let each datasource handle it right. For e.g, text data source, should return empty DF with schema (value: string)
- Deduped docs and fixed their formatting.
## How was this patch tested?
Added new unit tests for Scala and Java tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13727 from tdas/SPARK-15982.
## What changes were proposed in this pull request?
ConsoleSinkSuite just collects content from stdout and compare them with the expected string. However, because Spark may not stop some background threads at once, there is a race condition that other threads are outputting logs to **stdout** while ConsoleSinkSuite is running. Then it will make ConsoleSinkSuite fail.
Therefore, I just deleted `ConsoleSinkSuite`. If we want to test ConsoleSinkSuite in future, we should refactoring ConsoleSink to make it testable instead of depending on stdout. Therefore, this test is useless and I just delete it.
## How was this patch tested?
Just removed a flaky test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13776 from zsxwing/SPARK-16050.
## What changes were proposed in this pull request?
This PR adds the static partition support to INSERT statement when the target table is a data source table.
## How was this patch tested?
New tests in InsertIntoHiveTableSuite and DataSourceAnalysisSuite.
**Note: This PR is based on https://github.com/apache/spark/pull/13766. The last commit is the actual change.**
Author: Yin Huai <yhuai@databricks.com>
Closes#13769 from yhuai/SPARK-16030-1.
## What changes were proposed in this pull request?
This patch adds a text-based socket source similar to the one in Spark Streaming for debugging and tutorials. The source is clearly marked as debug-only so that users don't try to run it in production applications, because this type of source cannot provide HA without storing a lot of state in Spark.
## How was this patch tested?
Unit tests and manual tests in spark-shell.
Author: Matei Zaharia <matei@databricks.com>
Closes#13748 from mateiz/socket-source.
## What changes were proposed in this pull request?
`DataFrameWriter` can be used to append data to existing data source tables. It becomes tricky when partition columns used in `DataFrameWriter.partitionBy(columns)` don't match the actual partition columns of the underlying table. This pull request enforces the check so that the partition columns of these two always match.
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13749 from clockfly/SPARK-16034.
## What changes were proposed in this pull request?
The current table insertion has some weird behaviours:
1. inserting into a partitioned table with mismatch columns has confusing error message for hive table, and wrong result for datasource table
2. inserting into a partitioned table without partition list has wrong result for hive table.
This PR fixes these 2 problems.
## How was this patch tested?
new test in hive `SQLQuerySuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13754 from cloud-fan/insert2.
## What changes were proposed in this pull request?
We cannot use `limit` on DataFrame in ConsoleSink because it will use a wrong planner. This PR just collects `DataFrame` and calls `show` on a batch DataFrame based on the result. This is fine since ConsoleSink is only for debugging.
## How was this patch tested?
Manually confirmed ConsoleSink now works with complete mode aggregation.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13740 from zsxwing/complete-console.
## What changes were proposed in this pull request?
This PR fixes the problem that the precedence order is messed when pushing where-clause expression to JDBC layer.
**Case 1:**
For sql `select * from table where (a or b) and c`, the where-clause is wrongly converted to JDBC where-clause `a or (b and c)` after filter push down. The consequence is that JDBC may returns less or more rows than expected.
**Case 2:**
For sql `select * from table where always_false_condition`, the result table may not be empty if the JDBC RDD is partitioned using where-clause:
```
spark.read.jdbc(url, table, predicates = Array("partition 1 where clause", "partition 2 where clause"...)
```
## How was this patch tested?
Unit test.
This PR also close#13640
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13743 from clockfly/SPARK-15916.
## What changes were proposed in this pull request?
My fault -- these 2 conf entries are mysteriously hidden inside the benchmark code and makes it non-obvious to disable whole stage codegen and/or the vectorized parquet reader.
PS: Didn't attach a JIRA as this change should otherwise be a no-op (both these conf are enabled by default in Spark)
## How was this patch tested?
N/A
Author: Sameer Agarwal <sameer@databricks.com>
Closes#13726 from sameeragarwal/tpcds-conf.
## What changes were proposed in this pull request?
Before this patch, after a SparkSession has been created, hadoop conf set directly to SparkContext.hadoopConfiguration will not affect the hadoop conf created by SessionState. This patch makes the change to always use SparkContext.hadoopConfiguration as the base.
This patch also changes the behavior of hive-site.xml support added in https://github.com/apache/spark/pull/12689/. With this patch, we will load hive-site.xml to SparkContext.hadoopConfiguration.
## How was this patch tested?
New test in SparkSessionBuilderSuite.
Author: Yin Huai <yhuai@databricks.com>
Closes#13711 from yhuai/SPARK-15991.
## What changes were proposed in this pull request?
For table test1 (C1 varchar (10), C2 varchar (10)), when I insert a row using
```
sqlContext.sql("insert into test1 values ('abc', 'def', 1)")
```
I got error message
```
Exception in thread "main" java.lang.RuntimeException: RelationC1#0,C2#1 JDBCRelation(test1)
requires that the query in the SELECT clause of the INSERT INTO/OVERWRITE statement
generates the same number of columns as its schema.
```
The error message is a little confusing. In my simple insert statement, it doesn't have a SELECT clause.
I will change the error message to a more general one
```
Exception in thread "main" java.lang.RuntimeException: RelationC1#0,C2#1 JDBCRelation(test1)
requires that the data to be inserted have the same number of columns as the target table.
```
## How was this patch tested?
I tested the patch using my simple unit test, but it's a very trivial change and I don't think I need to check in any test.
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#13492 from huaxingao/spark-15749.
## What changes were proposed in this pull request?
`TRUNCATE TABLE` is currently broken for Spark specific datasource tables (json, csv, ...). This PR correctly sets the location for these datasources which allows them to be truncated.
## How was this patch tested?
Extended the datasources `TRUNCATE TABLE` tests in `DDLSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#13697 from hvanhovell/SPARK-15977.
#### What changes were proposed in this pull request?
~~If the temp table already exists, we should not silently replace it when doing `CACHE TABLE AS SELECT`. This is inconsistent with the behavior of `CREAT VIEW` or `CREATE TABLE`. This PR is to fix this silent drop.~~
~~Maybe, we also can introduce new syntax for replacing the existing one. For example, in Hive, to replace a view, the syntax should be like `ALTER VIEW AS SELECT` or `CREATE OR REPLACE VIEW AS SELECT`~~
The table name in `CACHE TABLE AS SELECT` should NOT contain database prefix like "database.table". Thus, this PR captures this in Parser and outputs a better error message, instead of reporting the view already exists.
In addition, refactoring the `Parser` to generate table identifiers instead of returning the table name string.
#### How was this patch tested?
- Added a test case for caching and uncaching qualified table names
- Fixed a few test cases that do not drop temp table at the end
- Added the related test case for the issue resolved in this PR
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#13572 from gatorsmile/cacheTableAsSelect.
## What changes were proposed in this pull request?
We currently immediately execute `INSERT` commands when they are issued. This is not the case as soon as we use a `WITH` to define common table expressions, for example:
```sql
WITH
tbl AS (SELECT * FROM x WHERE id = 10)
INSERT INTO y
SELECT *
FROM tbl
```
This PR fixes this problem. This PR closes https://github.com/apache/spark/pull/13561 (which fixes the a instance of this problem in the ThriftSever).
## How was this patch tested?
Added a test to `InsertSuite`
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#13678 from hvanhovell/SPARK-15824.
## What changes were proposed in this pull request?
This patch renames various Parquet support classes from CatalystAbc to ParquetAbc. This new naming makes more sense for two reasons:
1. These are not optimizer related (i.e. Catalyst) classes.
2. We are in the Spark code base, and as a result it'd be more clear to call out these are Parquet support classes, rather than some Spark classes.
## How was this patch tested?
Renamed test cases as well.
Author: Reynold Xin <rxin@databricks.com>
Closes#13696 from rxin/parquet-rename.
Renamed for simplicity, so that its obvious that its related to streaming.
Existing unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13673 from tdas/SPARK-15953.
## What changes were proposed in this pull request?
Two issues I've found for "show databases" command:
1. The returned database name list was not sorted, it only works when "like" was used together; (HIVE will always return a sorted list)
2. When it is used as sql("show databases").show, it will output a table with column named as "result", but for sql("show tables").show, it will output the column name as "tableName", so I think we should be consistent and use "databaseName" at least.
## How was this patch tested?
Updated existing test case to test its ordering as well.
Author: bomeng <bmeng@us.ibm.com>
Closes#13671 from bomeng/SPARK-15952.
## What changes were proposed in this pull request?
Currently, the DataFrameReader/Writer has method that are needed for streaming and non-streaming DFs. This is quite awkward because each method in them through runtime exception for one case or the other. So rather having half the methods throw runtime exceptions, its just better to have a different reader/writer API for streams.
- [x] Python API!!
## How was this patch tested?
Existing unit tests + two sets of unit tests for DataFrameReader/Writer and DataStreamReader/Writer.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13653 from tdas/SPARK-15933.
## What changes were proposed in this pull request?
Take the following directory layout as an example:
```
dir/
+- p0=0/
|-_metadata
+- p1=0/
|-part-00001.parquet
|-part-00002.parquet
|-...
```
The `_metadata` file under `p0=0` shouldn't fail partition discovery.
This PR filters output all metadata files whose names start with `_` while doing partition discovery.
## How was this patch tested?
New unit test added in `ParquetPartitionDiscoverySuite`.
Author: Cheng Lian <lian@databricks.com>
Closes#13623 from liancheng/spark-15895-partition-disco-no-metafiles.
#### What changes were proposed in this pull request?
To uncache a table, we have three different ways:
- _SQL interface_: `UNCACHE TABLE`
- _DataSet API_: `sparkSession.catalog.uncacheTable`
- _DataSet API_: `sparkSession.table(tableName).unpersist()`
When the table is not cached,
- _SQL interface_: `UNCACHE TABLE non-cachedTable` -> **no error message**
- _Dataset API_: `sparkSession.catalog.uncacheTable("non-cachedTable")` -> **report a strange error message:**
```requirement failed: Table [a: int] is not cached```
- _Dataset API_: `sparkSession.table("non-cachedTable").unpersist()` -> **no error message**
This PR will make them consistent. No operation if the table has already been uncached.
In addition, this PR also removes `uncacheQuery` and renames `tryUncacheQuery` to `uncacheQuery`, and documents it that it's noop if the table has already been uncached
#### How was this patch tested?
Improved the existing test case for verifying the cases when the table has not been cached.
Also added test cases for verifying the cases when the table does not exist
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#13593 from gatorsmile/uncacheNonCachedTable.
## What changes were proposed in this pull request?
`DataFrame` with plan overriding `sameResult` but not using canonicalized plan to compare can't cacheTable.
The example is like:
```
val localRelation = Seq(1, 2, 3).toDF()
localRelation.createOrReplaceTempView("localRelation")
spark.catalog.cacheTable("localRelation")
assert(
localRelation.queryExecution.withCachedData.collect {
case i: InMemoryRelation => i
}.size == 1)
```
and this will fail as:
```
ArrayBuffer() had size 0 instead of expected size 1
```
The reason is that when do `spark.catalog.cacheTable("localRelation")`, `CacheManager` tries to cache for the plan wrapped by `SubqueryAlias` but when planning for the DataFrame `localRelation`, `CacheManager` tries to find cached table for the not-wrapped plan because the plan for DataFrame `localRelation` is not wrapped.
Some plans like `LocalRelation`, `LogicalRDD`, etc. override `sameResult` method, but not use canonicalized plan to compare so the `CacheManager` can't detect the plans are the same.
This pr modifies them to use canonicalized plan when override `sameResult` method.
## How was this patch tested?
Added a test to check if DataFrame with plan overriding sameResult but not using canonicalized plan to compare can cacheTable.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#13638 from ueshin/issues/SPARK-15915.
## What changes were proposed in this pull request?
Another PR to clean up recent build warnings. This particularly cleans up several instances of the old accumulator API usage in tests that are straightforward to update. I think this qualifies as "minor".
## How was this patch tested?
Jenkins
Author: Sean Owen <sowen@cloudera.com>
Closes#13642 from srowen/BuildWarnings.
## What changes were proposed in this pull request?
SparkSession.catalog.listFunctions currently returns all functions, including the list of built-in functions. This makes the method not as useful because anytime it is run the result set contains over 100 built-in functions.
## How was this patch tested?
CatalogSuite
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#13413 from techaddict/SPARK-15663.
## What changes were proposed in this pull request?
This PR enforces schema check when converting DataFrame to Dataset using Kryo encoder. For example.
**Before the change:**
Schema is NOT checked when converting DataFrame to Dataset using kryo encoder.
```
scala> case class B(b: Int)
scala> implicit val encoder = Encoders.kryo[B]
scala> val df = Seq((1)).toDF("b")
scala> val ds = df.as[B] // Schema compatibility is NOT checked
```
**After the change:**
Report AnalysisException since the schema is NOT compatible.
```
scala> val ds = Seq((1)).toDF("b").as[B]
org.apache.spark.sql.AnalysisException: cannot resolve 'CAST(`b` AS BINARY)' due to data type mismatch: cannot cast IntegerType to BinaryType;
...
```
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13632 from clockfly/spark-15910.
The DataFrameSuite regression tests for SPARK-13774 fail in my environment because they attempt to glob over all of `/mnt` and some of the subdirectories restrictive permissions which cause the test to fail.
This patch rewrites those tests to remove all environment-specific assumptions; the tests now create their own unique temporary paths for use in the tests.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#13649 from JoshRosen/SPARK-15929.
## What changes were proposed in this pull request?
Right now, Spark 2.0 does not load hive-site.xml. Based on users' feedback, it seems make sense to still load this conf file.
This PR adds a `hadoopConf` API in `SharedState`, which is `sparkContext.hadoopConfiguration` by default. When users are under hive context, `SharedState.hadoopConf` will load hive-site.xml and append its configs to `sparkContext.hadoopConfiguration`.
When we need to read hadoop config in spark sql, we should call `SessionState.newHadoopConf`, which contains `sparkContext.hadoopConfiguration`, hive-site.xml and sql configs.
## How was this patch tested?
new test in `HiveDataFrameSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13611 from cloud-fan/hive-site.
## What changes were proposed in this pull request?
ContinuousQueries have names that are unique across all the active ones. However, when queries are rapidly restarted with same name, it causes races conditions with the listener. A listener event from a stopped query can arrive after the query has been restarted, leading to complexities in monitoring infrastructure.
Along with this change, I have also consolidated all the messy code paths to start queries with different sinks.
## How was this patch tested?
Added unit tests, and existing unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13613 from tdas/SPARK-15889.
#### What changes were proposed in this pull request?
When creating a Hive Table (not data source tables), a common error users might make is to specify an existing column name as a partition column. Below is what Hive returns in this case:
```
hive> CREATE TABLE partitioned (id bigint, data string) PARTITIONED BY (data string, part string);
FAILED: SemanticException [Error 10035]: Column repeated in partitioning columns
```
Currently, the error we issued is very confusing:
```
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:For direct MetaStore DB connections, we don't support retries at the client level.);
```
This PR is to fix the above issue by capturing the usage error in `Parser`.
#### How was this patch tested?
Added a test case to `DDLCommandSuite`
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13415 from gatorsmile/partitionColumnsInTableSchema.
## What changes were proposed in this pull request?
This patch does some replacing (as `streaming Datasets/DataFrames` is the term we've chosen in [SPARK-15593](00c310133d)):
- `continuous queries` -> `streaming Datasets/DataFrames`
- `non-continuous queries` -> `non-streaming Datasets/DataFrames`
This patch also adds `test("check foreach() can only be called on streaming Datasets/DataFrames")`.
## How was this patch tested?
N/A
Author: Liwei Lin <lwlin7@gmail.com>
Closes#13595 from lw-lin/continuous-queries-to-streaming-dss-dfs.
## What changes were proposed in this pull request?
It's similar to the bug fixed in https://github.com/apache/spark/pull/13425, we should consider null object and wrap the `CreateStruct` with `If` to do null check.
This PR also improves the test framework to test the objects of `Dataset[T]` directly, instead of calling `toDF` and compare the rows.
## How was this patch tested?
new test in `DatasetAggregatorSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13553 from cloud-fan/agg-null.
# What changes were proposed in this pull request?
This pull request fixes the COUNT bug in the `RewriteCorrelatedScalarSubquery` rule.
After this change, the rule tests the expression at the root of the correlated subquery to determine whether the expression returns `NULL` on empty input. If the expression does not return `NULL`, the rule generates additional logic in the `Project` operator above the rewritten subquery. This additional logic intercepts `NULL` values coming from the outer join and replaces them with the value that the subquery's expression would return on empty input.
This PR takes over https://github.com/apache/spark/pull/13155. It only fixes an issue with `Literal` construction and style issues. All credits should go frreiss.
# How was this patch tested?
Added regression tests to cover all branches of the updated rule (see changes to `SubquerySuite`).
Ran all existing automated regression tests after merging with latest trunk.
Author: frreiss <frreiss@us.ibm.com>
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#13629 from hvanhovell/SPARK-15370-cleanup.
## What changes were proposed in this pull request?
If a cached `DataFrame` executed more than once and then do `uncacheTable` like the following:
```
val selectStar = sql("SELECT * FROM testData WHERE key = 1")
selectStar.createOrReplaceTempView("selectStar")
spark.catalog.cacheTable("selectStar")
checkAnswer(
selectStar,
Seq(Row(1, "1")))
spark.catalog.uncacheTable("selectStar")
checkAnswer(
selectStar,
Seq(Row(1, "1")))
```
, then the uncached `DataFrame` can't execute because of `Task not serializable` exception like:
```
org.apache.spark.SparkException: Task not serializable
at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:298)
at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:288)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:108)
at org.apache.spark.SparkContext.clean(SparkContext.scala:2038)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1897)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1912)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:884)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:357)
at org.apache.spark.rdd.RDD.collect(RDD.scala:883)
at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:290)
...
Caused by: java.lang.UnsupportedOperationException: Accumulator must be registered before send to executor
at org.apache.spark.util.AccumulatorV2.writeReplace(AccumulatorV2.scala:153)
at sun.reflect.GeneratedMethodAccessor2.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at java.io.ObjectStreamClass.invokeWriteReplace(ObjectStreamClass.java:1118)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1136)
at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
...
```
Notice that `DataFrame` uncached with `DataFrame.unpersist()` works, but with `spark.catalog.uncacheTable` doesn't work.
This pr reverts a part of cf38fe0 not to unregister `batchStats` accumulator, which is not needed to be unregistered here because it will be done by `ContextCleaner` after it is collected by GC.
## How was this patch tested?
Added a test to check if DataFrame can execute after uncacheTable and other existing tests.
But I made a test to check if the accumulator was cleared as `ignore` because the test would be flaky.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#13596 from ueshin/issues/SPARK-15870.
## What changes were proposed in this pull request?
Queries with embedded existential sub-query predicates throws exception when building the physical plan.
Example failing query:
```SQL
scala> Seq((1, 1), (2, 2)).toDF("c1", "c2").createOrReplaceTempView("t1")
scala> Seq((1, 1), (2, 2)).toDF("c1", "c2").createOrReplaceTempView("t2")
scala> sql("select c1 from t1 where (case when c2 in (select c2 from t2) then 2 else 3 end) IN (select c2 from t1)").show()
Binding attribute, tree: c2#239
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: c2#239
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:50)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:88)
...
at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:87)
at org.apache.spark.sql.execution.joins.HashJoin$$anonfun$4.apply(HashJoin.scala:66)
at org.apache.spark.sql.execution.joins.HashJoin$$anonfun$4.apply(HashJoin.scala:66)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.immutable.List.map(List.scala:285)
at org.apache.spark.sql.execution.joins.HashJoin$class.org$apache$spark$sql$execution$joins$HashJoin$$x$8(HashJoin.scala:66)
at org.apache.spark.sql.execution.joins.BroadcastHashJoinExec.org$apache$spark$sql$execution$joins$HashJoin$$x$8$lzycompute(BroadcastHashJoinExec.scala:38)
at org.apache.spark.sql.execution.joins.BroadcastHashJoinExec.org$apache$spark$sql$execution$joins$HashJoin$$x$8(BroadcastHashJoinExec.scala:38)
at org.apache.spark.sql.execution.joins.HashJoin$class.buildKeys(HashJoin.scala:63)
at org.apache.spark.sql.execution.joins.BroadcastHashJoinExec.buildKeys$lzycompute(BroadcastHashJoinExec.scala:38)
at org.apache.spark.sql.execution.joins.BroadcastHashJoinExec.buildKeys(BroadcastHashJoinExec.scala:38)
at org.apache.spark.sql.execution.joins.BroadcastHashJoinExec.requiredChildDistribution(BroadcastHashJoinExec.scala:52)
```
**Problem description:**
When the left hand side expression of an existential sub-query predicate contains another embedded sub-query predicate, the RewritePredicateSubquery optimizer rule does not resolve the embedded sub-query expressions into existential joins.For example, the above query has the following optimized plan, which fails during physical plan build.
```SQL
== Optimized Logical Plan ==
Project [_1#224 AS c1#227]
+- Join LeftSemi, (CASE WHEN predicate-subquery#255 [(_2#225 = c2#239)] THEN 2 ELSE 3 END = c2#228#262)
: +- SubqueryAlias predicate-subquery#255 [(_2#225 = c2#239)]
: +- LocalRelation [c2#239]
:- LocalRelation [_1#224, _2#225]
+- LocalRelation [c2#228#262]
== Physical Plan ==
org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: c2#239
```
**Solution:**
In RewritePredicateSubquery, before rewriting the outermost predicate sub-query, resolve any embedded existential sub-queries. The Optimized plan for the above query after the changes looks like below.
```SQL
== Optimized Logical Plan ==
Project [_1#224 AS c1#227]
+- Join LeftSemi, (CASE WHEN exists#285 THEN 2 ELSE 3 END = c2#228#284)
:- Join ExistenceJoin(exists#285), (_2#225 = c2#239)
: :- LocalRelation [_1#224, _2#225]
: +- LocalRelation [c2#239]
+- LocalRelation [c2#228#284]
== Physical Plan ==
*Project [_1#224 AS c1#227]
+- *BroadcastHashJoin [CASE WHEN exists#285 THEN 2 ELSE 3 END], [c2#228#284], LeftSemi, BuildRight
:- *BroadcastHashJoin [_2#225], [c2#239], ExistenceJoin(exists#285), BuildRight
: :- LocalTableScan [_1#224, _2#225]
: +- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)))
: +- LocalTableScan [c2#239]
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)))
+- LocalTableScan [c2#228#284]
+- LocalTableScan [c222#36], [[111],[222]]
```
## How was this patch tested?
Added new test cases in SubquerySuite.scala
Author: Ioana Delaney <ioanamdelaney@gmail.com>
Closes#13570 from ioana-delaney/fixEmbedSubPredV1.
## What changes were proposed in this pull request?
This pull request fixes the COUNT bug in the `RewriteCorrelatedScalarSubquery` rule.
After this change, the rule tests the expression at the root of the correlated subquery to determine whether the expression returns NULL on empty input. If the expression does not return NULL, the rule generates additional logic in the Project operator above the rewritten subquery. This additional logic intercepts NULL values coming from the outer join and replaces them with the value that the subquery's expression would return on empty input.
## How was this patch tested?
Added regression tests to cover all branches of the updated rule (see changes to `SubquerySuite.scala`).
Ran all existing automated regression tests after merging with latest trunk.
Author: frreiss <frreiss@us.ibm.com>
Closes#13155 from frreiss/master.
## What changes were proposed in this pull request?
- Deprecate old Java accumulator API; should use Scala now
- Update Java tests and examples
- Don't bother testing old accumulator API in Java 8 (too)
- (fix a misspelling too)
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#13606 from srowen/SPARK-15086.
## What changes were proposed in this pull request?
This adds support for radix sort of nullable long fields. When a sort field is null and radix sort is enabled, we keep nulls in a separate region of the sort buffer so that radix sort does not need to deal with them. This also has performance benefits when sorting smaller integer types, since the current representation of nulls in two's complement (Long.MIN_VALUE) otherwise forces a full-width radix sort.
This strategy for nulls does mean the sort is no longer stable. cc davies
## How was this patch tested?
Existing randomized sort tests for correctness. I also tested some TPCDS queries and there does not seem to be any significant regression for non-null sorts.
Some test queries (best of 5 runs each).
Before change:
scala> val start = System.nanoTime; spark.range(5000000).selectExpr("if(id > 5, cast(hash(id) as long), NULL) as h").coalesce(1).orderBy("h").collect(); (System.nanoTime - start) / 1e6
start: Long = 3190437233227987
res3: Double = 4716.471091
After change:
scala> val start = System.nanoTime; spark.range(5000000).selectExpr("if(id > 5, cast(hash(id) as long), NULL) as h").coalesce(1).orderBy("h").collect(); (System.nanoTime - start) / 1e6
start: Long = 3190367870952791
res4: Double = 2981.143045
Author: Eric Liang <ekl@databricks.com>
Closes#13161 from ericl/sc-2998.
## What changes were proposed in this pull request?
These were not updated after performance improvements. To make updating them easier, I also moved the results from inline comments out into a file, which is auto-generated when the benchmark is re-run.
Author: Eric Liang <ekl@databricks.com>
Closes#13607 from ericl/sc-3538.
## What changes were proposed in this pull request?
This pr is to add doc for turning off quotations because this behavior is different from `com.databricks.spark.csv`.
## How was this patch tested?
Check behavior to put an empty string in csv options.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#13616 from maropu/SPARK-15585-2.
## What changes were proposed in this pull request?
Spark currently incorrectly continues to use cached data even if the underlying data is overwritten.
Current behavior:
```scala
val dir = "/tmp/test"
sqlContext.range(1000).write.mode("overwrite").parquet(dir)
val df = sqlContext.read.parquet(dir).cache()
df.count() // outputs 1000
sqlContext.range(10).write.mode("overwrite").parquet(dir)
sqlContext.read.parquet(dir).count() // outputs 1000 <---- We are still using the cached dataset
```
This patch fixes this bug by adding support for `REFRESH path` that invalidates and refreshes all the cached data (and the associated metadata) for any dataframe that contains the given data source path.
Expected behavior:
```scala
val dir = "/tmp/test"
sqlContext.range(1000).write.mode("overwrite").parquet(dir)
val df = sqlContext.read.parquet(dir).cache()
df.count() // outputs 1000
sqlContext.range(10).write.mode("overwrite").parquet(dir)
spark.catalog.refreshResource(dir)
sqlContext.read.parquet(dir).count() // outputs 10 <---- We are not using the cached dataset
```
## How was this patch tested?
Unit tests for overwrites and appends in `ParquetQuerySuite` and `CachedTableSuite`.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#13566 from sameeragarwal/refresh-path-2.
## What changes were proposed in this pull request?
Serializer instantiation will consider existing SparkConf
## How was this patch tested?
manual test with `ImmutableList` (Guava) and `kryo-serializers`'s `Immutable*Serializer` implementations.
Added Test Suite.
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: Sela <ansela@paypal.com>
Closes#13424 from amitsela/SPARK-15489.
## What changes were proposed in this pull request?
Currently, we always split the files when it's bigger than maxSplitBytes, but Hadoop LineRecordReader does not respect the splits for compressed files correctly, we should have a API for FileFormat to check whether the file could be splitted or not.
This PR is based on #13442, closes#13442
## How was this patch tested?
add regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#13531 from davies/fix_split.
## What changes were proposed in this pull request?
Code generated `SortMergeJoin` failed with wrong results when using structs as keys. This could (eventually) be traced back to the use of a wrong row reference when comparing structs.
## How was this patch tested?
TBD
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#13589 from hvanhovell/SPARK-15822.
## What changes were proposed in this pull request?
This PR moves `QueryPlanner.planLater()` method into `GenericStrategy` for extra strategies to be able to use `planLater` in its strategy.
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#13147 from ueshin/issues/SPARK-6320.
## What changes were proposed in this pull request?
When saving datasets on storage, `partitionBy` provides an easy way to construct the directory structure. However, if a user choose all columns as partition columns, some exceptions occurs.
- **ORC with all column partitioning**: `AnalysisException` on **future read** due to schema inference failure.
```scala
scala> spark.range(10).write.format("orc").mode("overwrite").partitionBy("id").save("/tmp/data")
scala> spark.read.format("orc").load("/tmp/data").collect()
org.apache.spark.sql.AnalysisException: Unable to infer schema for ORC at /tmp/data. It must be specified manually;
```
- **Parquet with all-column partitioning**: `InvalidSchemaException` on **write execution** due to Parquet limitation.
```scala
scala> spark.range(100).write.format("parquet").mode("overwrite").partitionBy("id").save("/tmp/data")
[Stage 0:> (0 + 8) / 8]16/06/02 16:51:17
ERROR Utils: Aborting task
org.apache.parquet.schema.InvalidSchemaException: A group type can not be empty. Parquet does not support empty group without leaves. Empty group: spark_schema
... (lots of error messages)
```
Although some formats like JSON support all-column partitioning without any problem, it seems not a good idea to make lots of empty directories.
This PR prevents saving with all-column partitioning by consistently raising `AnalysisException` before executing save operation.
## How was this patch tested?
Newly added `PartitioningUtilsSuite`.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13486 from dongjoon-hyun/SPARK-15743.
## What changes were proposed in this pull request?
When the output mode is complete, then the output of a streaming aggregation essentially will contain the complete aggregates every time. So this is not different from a batch dataset within an incremental execution. Other non-streaming operations should be supported on this dataset. In this PR, I am just adding support for sorting, as it is a common useful functionality. Support for other operations will come later.
## How was this patch tested?
Additional unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13549 from tdas/SPARK-15812.
## What changes were proposed in this pull request?
* Add DataFrameWriter.foreach to allow the user consuming data in ContinuousQuery
* ForeachWriter is the interface for the user to consume partitions of data
* Add a type parameter T to DataFrameWriter
Usage
```Scala
val ds = spark.read....stream().as[String]
ds.....write
.queryName(...)
.option("checkpointLocation", ...)
.foreach(new ForeachWriter[Int] {
def open(partitionId: Long, version: Long): Boolean = {
// prepare some resources for a partition
// check `version` if possible and return `false` if this is a duplicated data to skip the data processing.
}
override def process(value: Int): Unit = {
// process data
}
def close(errorOrNull: Throwable): Unit = {
// release resources for a partition
// check `errorOrNull` and handle the error if necessary.
}
})
```
## How was this patch tested?
New unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13342 from zsxwing/foreach.
## What changes were proposed in this pull request?
The fix is pretty simple, just don't make the executedPlan transient in `ScalarSubquery` since it is referenced at execution time.
## How was this patch tested?
I verified the fix manually in non-local mode. It's not clear to me why the problem did not manifest in local mode, any suggestions?
cc davies
Author: Eric Liang <ekl@databricks.com>
Closes#13569 from ericl/fix-scalar-npe.
## What changes were proposed in this pull request?
With very wide tables, e.g. thousands of fields, the plan output is unreadable and often causes OOMs due to inefficient string processing. This truncates all struct and operator field lists to a user configurable threshold to limit performance impact.
It would also be nice to optimize string generation to avoid these sort of O(n^2) slowdowns entirely (i.e. use StringBuilder everywhere including expressions), but this is probably too large of a change for 2.0 at this point, and truncation has other benefits for usability.
## How was this patch tested?
Added a microbenchmark that covers this case particularly well. I also ran the microbenchmark while varying the truncation threshold.
```
numFields = 5
wide shallowly nested struct field r/w: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
2000 wide x 50 rows (write in-mem) 2336 / 2558 0.0 23364.4 0.1X
numFields = 25
wide shallowly nested struct field r/w: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
2000 wide x 50 rows (write in-mem) 4237 / 4465 0.0 42367.9 0.1X
numFields = 100
wide shallowly nested struct field r/w: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
2000 wide x 50 rows (write in-mem) 10458 / 11223 0.0 104582.0 0.0X
numFields = Infinity
wide shallowly nested struct field r/w: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
[info] java.lang.OutOfMemoryError: Java heap space
```
Author: Eric Liang <ekl@databricks.com>
Author: Eric Liang <ekhliang@gmail.com>
Closes#13537 from ericl/truncated-string.
## What changes were proposed in this pull request?
The help function 'toStructType' in the AttributeSeq class doesn't include the metadata when it builds the StructField, so it causes this reported problem https://issues.apache.org/jira/browse/SPARK-15804?jql=project%20%3D%20SPARK when spark writes the the dataframe with the metadata to the parquet datasource.
The code path is when spark writes the dataframe to the parquet datasource through the InsertIntoHadoopFsRelationCommand, spark will build the WriteRelation container, and it will call the help function 'toStructType' to create StructType which contains StructField, it should include the metadata there, otherwise, we will lost the user provide metadata.
## How was this patch tested?
added test case in ParquetQuerySuite.scala
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: Kevin Yu <qyu@us.ibm.com>
Closes#13555 from kevinyu98/spark-15804.
## What changes were proposed in this pull request?
On the SparkUI right now we have this SQLTab that displays accumulator values per operator. However, it only displays metrics updated on the executors, not on the driver. It is useful to also include driver metrics, e.g. broadcast time.
This is a different version from https://github.com/apache/spark/pull/12427. This PR sends driver side accumulator updates right after the updating happens, not at the end of execution, by a new event.
## How was this patch tested?
new test in `SQLListenerSuite`
![qq20160606-0](https://cloud.githubusercontent.com/assets/3182036/15841418/0eb137da-2c06-11e6-9068-5694eeb78530.png)
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13189 from cloud-fan/metrics.
## What changes were proposed in this pull request?
This PR adds ContinuousQueryInfo to make ContinuousQueryListener events serializable in order to support writing events into the event log.
## How was this patch tested?
Jenkins unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13335 from zsxwing/query-info.
## What changes were proposed in this pull request?
The current implementation of "CREATE TEMPORARY TABLE USING datasource..." is NOT creating any intermediate temporary data directory like temporary HDFS folder, instead, it only stores a SQL string in memory. Probably we should use "TEMPORARY VIEW" instead.
This PR assumes a temporary table has to link with some temporary intermediate data. It follows the definition of temporary table like this (from [hortonworks doc](https://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.3.0/bk_dataintegration/content/temp-tables.html)):
> A temporary table is a convenient way for an application to automatically manage intermediate data generated during a complex query
**Example**:
```
scala> spark.sql("CREATE temporary view my_tab7 (c1: String, c2: String) USING org.apache.spark.sql.execution.datasources.csv.CSVFileFormat OPTIONS (PATH '/Users/seanzhong/csv/cars.csv')")
scala> spark.sql("select c1, c2 from my_tab7").show()
+----+-----+
| c1| c2|
+----+-----+
|year| make|
|2012|Tesla|
...
```
It NOW prints a **deprecation warning** if "CREATE TEMPORARY TABLE USING..." is used.
```
scala> spark.sql("CREATE temporary table my_tab7 (c1: String, c2: String) USING org.apache.spark.sql.execution.datasources.csv.CSVFileFormat OPTIONS (PATH '/Users/seanzhong/csv/cars.csv')")
16/05/31 10:39:27 WARN SparkStrategies$DDLStrategy: CREATE TEMPORARY TABLE tableName USING... is deprecated, please use CREATE TEMPORARY VIEW viewName USING... instead
```
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13414 from clockfly/create_temp_view_using.
## What changes were proposed in this pull request?
This PR makes sure the typed Filter doesn't change the Dataset schema.
**Before the change:**
```
scala> val df = spark.range(0,9)
scala> df.schema
res12: org.apache.spark.sql.types.StructType = StructType(StructField(id,LongType,false))
scala> val afterFilter = df.filter(_=>true)
scala> afterFilter.schema // !!! schema is CHANGED!!! Column name is changed from id to value, nullable is changed from false to true.
res13: org.apache.spark.sql.types.StructType = StructType(StructField(value,LongType,true))
```
SerializeFromObject and DeserializeToObject are inserted to wrap the Filter, and these two can possibly change the schema of Dataset.
**After the change:**
```
scala> afterFilter.schema // schema is NOT changed.
res47: org.apache.spark.sql.types.StructType = StructType(StructField(id,LongType,false))
```
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13529 from clockfly/spark-15632.
## What changes were proposed in this pull request?
This pr fixes the behaviour of `format("csv").option("quote", null)` along with one of spark-csv.
Also, it explicitly sets default values for CSV options in python.
## How was this patch tested?
Added tests in CSVSuite.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#13372 from maropu/SPARK-15585.
## What changes were proposed in this pull request?
This change fixes a crash in TungstenAggregate while executing "Dataset complex Aggregator" test case due to IndexOutOfBoundsException.
jira entry for detail: https://issues.apache.org/jira/browse/SPARK-15704
## How was this patch tested?
Using existing unit tests (including DatasetBenchmark)
Author: Hiroshi Inoue <inouehrs@jp.ibm.com>
Closes#13446 from inouehrs/fix_aggregate.
## What changes were proposed in this pull request?
Now Spark SQL can support 'create table src stored as orc/parquet/avro' for orc/parquet/avro table. But Hive can support both commands: ' stored as orc/parquet/avro' and 'stored as orcfile/parquetfile/avrofile'.
So this PR supports these keywords 'orcfile/parquetfile/avrofile' in Spark SQL.
## How was this patch tested?
add unit tests
Author: Lianhui Wang <lianhuiwang09@gmail.com>
Closes#13500 from lianhuiwang/SPARK-15756.
## What changes were proposed in this pull request?
Currently, the memory for temporary buffer used by TimSort is always allocated as on-heap without bookkeeping, it could cause OOM both in on-heap and off-heap mode.
This PR will try to manage that by preallocate it together with the pointer array, same with RadixSort. It both works for on-heap and off-heap mode.
This PR also change the loadFactor of BytesToBytesMap to 0.5 (it was 0.70), it enables use to radix sort also makes sure that we have enough memory for timsort.
## How was this patch tested?
Existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#13318 from davies/fix_timsort.
## What changes were proposed in this pull request?
For input object of non-flat type, we can't encode it to row if it's null, as Spark SQL doesn't allow row to be null, only its columns can be null.
This PR explicitly add this constraint and throw exception if users break it.
## How was this patch tested?
several new tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13469 from cloud-fan/null-object.
## What changes were proposed in this pull request?
Queries with scalar sub-query in the SELECT list run against a local, in-memory relation throw
UnsupportedOperationException exception.
Problem repro:
```SQL
scala> Seq((1, 1), (2, 2)).toDF("c1", "c2").createOrReplaceTempView("t1")
scala> Seq((1, 1), (2, 2)).toDF("c1", "c2").createOrReplaceTempView("t2")
scala> sql("select (select min(c1) from t2) from t1").show()
java.lang.UnsupportedOperationException: Cannot evaluate expression: scalar-subquery#62 []
at org.apache.spark.sql.catalyst.expressions.Unevaluable$class.eval(Expression.scala:215)
at org.apache.spark.sql.catalyst.expressions.ScalarSubquery.eval(subquery.scala:62)
at org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:142)
at org.apache.spark.sql.catalyst.expressions.InterpretedProjection.apply(Projection.scala:45)
at org.apache.spark.sql.catalyst.expressions.InterpretedProjection.apply(Projection.scala:29)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.immutable.List.map(List.scala:285)
at org.apache.spark.sql.catalyst.optimizer.ConvertToLocalRelation$$anonfun$apply$37.applyOrElse(Optimizer.scala:1473)
```
The problem is specific to local, in memory relations. It is caused by rule ConvertToLocalRelation, which attempts to push down
a scalar-subquery expression to the local tables.
The solution prevents the rule to apply if Project references scalar subqueries.
## How was this patch tested?
Added regression tests to SubquerySuite.scala
Author: Ioana Delaney <ioanamdelaney@gmail.com>
Closes#13418 from ioana-delaney/scalarSubV2.
## What changes were proposed in this pull request?
Our encoder framework has been evolved a lot, this PR tries to clean up the code to make it more readable and emphasise the concept that encoder should be used as a container of serde expressions.
1. move validation logic to analyzer instead of encoder
2. only have a `resolveAndBind` method in encoder instead of `resolve` and `bind`, as we don't have the encoder life cycle concept anymore.
3. `Dataset` don't need to keep a resolved encoder, as there is no such concept anymore. bound encoder is still needed to do serialization outside of query framework.
4. Using `BoundReference` to represent an unresolved field in deserializer expression is kind of weird, this PR adds a `GetColumnByOrdinal` for this purpose. (serializer expression still use `BoundReference`, we can replace it with `GetColumnByOrdinal` in follow-ups)
## How was this patch tested?
existing test
Author: Wenchen Fan <wenchen@databricks.com>
Author: Cheng Lian <lian@databricks.com>
Closes#13269 from cloud-fan/clean-encoder.
## What changes were proposed in this pull request?
For consistency, this PR updates some remaining `TungstenAggregation/SortBasedAggregate` after SPARK-15728.
- Update a comment in codegen in `VectorizedHashMapGenerator.scala`.
- `TungstenAggregationQuerySuite` --> `HashAggregationQuerySuite`
- `TungstenAggregationQueryWithControlledFallbackSuite` --> `HashAggregationQueryWithControlledFallbackSuite`
- Update two error messages in `SQLQuerySuite.scala` and `AggregationQuerySuite.scala`.
- Update several comments.
## How was this patch tested?
Manual (Only comment changes and test suite renamings).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13487 from dongjoon-hyun/SPARK-15744.
## What changes were proposed in this pull request?
##### The root cause:
When `DataSource.resolveRelation` is trying to build `ListingFileCatalog` object, `ListLeafFiles` is invoked where a list of `FileStatus` objects are retrieved from the provided path. These FileStatus objects include directories for the partitions (id=0 and id=2 in the jira). However, these directory `FileStatus` objects also try to invoke `getFileBlockLocations` where directory is not allowed for `DistributedFileSystem`, hence the exception happens.
This PR is to remove the block of code that invokes `getFileBlockLocations` for every FileStatus object of the provided path. Instead, we call `HadoopFsRelation.listLeafFiles` directly because this utility method filters out the directories before calling `getFileBlockLocations` for generating `LocatedFileStatus` objects.
## How was this patch tested?
Regtest is run. Manual test:
```
scala> spark.read.format("parquet").load("hdfs://bdavm009.svl.ibm.com:8020/user/spark/SPARK-14959_part").show
+-----+---+
| text| id|
+-----+---+
|hello| 0|
|world| 0|
|hello| 1|
|there| 1|
+-----+---+
spark.read.format("orc").load("hdfs://bdavm009.svl.ibm.com:8020/user/spark/SPARK-14959_orc").show
+-----+---+
| text| id|
+-----+---+
|hello| 0|
|world| 0|
|hello| 1|
|there| 1|
+-----+---+
```
I also tried it with 2 level of partitioning.
I have not found a way to add test case in the unit test bucket that can test a real hdfs file location. Any suggestions will be appreciated.
Author: Xin Wu <xinwu@us.ibm.com>
Closes#13463 from xwu0226/SPARK-14959.
## What changes were proposed in this pull request?
This adds microbenchmarks for tracking performance of queries over very wide or deeply nested DataFrames. It seems performance degrades when DataFrames get thousands of columns wide or hundreds of fields deep.
## How was this patch tested?
Current results included.
cc rxin JoshRosen
Author: Eric Liang <ekl@databricks.com>
Closes#13456 from ericl/sc-3468.
## What changes were proposed in this pull request?
When users create a case class and use java reserved keyword as field name, spark sql will generate illegal java code and throw exception at runtime.
This PR checks the field names when building the encoder, and if illegal field names are used, throw exception immediately with a good error message.
## How was this patch tested?
new test in DatasetSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13485 from cloud-fan/java.
## What changes were proposed in this pull request?
Currently we don't support bucketing for `save` and `insertInto`.
For `save`, we just write the data out into a directory users specified, and it's not a table, we don't keep its metadata. When we read it back, we have no idea if the data is bucketed or not, so it doesn't make sense to use `save` to write bucketed data, as we can't use the bucket information anyway.
We can support it in the future, once we have features like bucket discovery, or we save bucket information in the data directory too, so that we don't need to rely on a metastore.
For `insertInto`, it inserts data into an existing table, so it doesn't make sense to specify bucket information, as we should get the bucket information from the existing table.
This PR improves the error message for the above 2 cases.
## How was this patch tested?
new test in `BukctedWriteSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13452 from cloud-fan/error-msg.
## What changes were proposed in this pull request?
This PR disables writing Parquet summary files by default (i.e., when Hadoop configuration "parquet.enable.summary-metadata" is not set).
Please refer to [SPARK-15719][1] for more details.
## How was this patch tested?
New test case added in `ParquetQuerySuite` to check no summary files are written by default.
[1]: https://issues.apache.org/jira/browse/SPARK-15719
Author: Cheng Lian <lian@databricks.com>
Closes#13455 from liancheng/spark-15719-disable-parquet-summary-files.
## What changes were proposed in this pull request?
This PR bans syntax like `CREATE TEMPORARY TABLE USING AS SELECT`
`CREATE TEMPORARY TABLE ... USING ... AS ...` is not properly implemented, the temporary data is not cleaned up when the session exits. Before a full fix, we probably should ban this syntax.
This PR only impact syntax like `CREATE TEMPORARY TABLE ... USING ... AS ...`.
Other syntax like `CREATE TEMPORARY TABLE .. USING ...` and `CREATE TABLE ... USING ...` are not impacted.
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13451 from clockfly/ban_create_temp_table_using_as.
#### What changes were proposed in this pull request?
This PR is to address the following issues:
- **ISSUE 1:** For ORC source format, we are reporting the strange error message when we did not enable Hive support:
```SQL
SQL Example:
select id from `org.apache.spark.sql.hive.orc`.`file_path`
Error Message:
Table or view not found: `org.apache.spark.sql.hive.orc`.`file_path`
```
Instead, we should issue the error message like:
```
Expected Error Message:
The ORC data source must be used with Hive support enabled
```
- **ISSUE 2:** For the Avro format, we report the strange error message like:
The example query is like
```SQL
SQL Example:
select id from `avro`.`file_path`
select id from `com.databricks.spark.avro`.`file_path`
Error Message:
Table or view not found: `com.databricks.spark.avro`.`file_path`
```
The desired message should be like:
```
Expected Error Message:
Failed to find data source: avro. Please use Spark package http://spark-packages.org/package/databricks/spark-avro"
```
- ~~**ISSUE 3:** Unable to detect incompatibility libraries for Spark 2.0 in Data Source Resolution. We report a strange error message:~~
**Update**: The latest code changes contains
- For JDBC format, we added an extra checking in the rule `ResolveRelations` of `Analyzer`. Without the PR, Spark will return the error message like: `Option 'url' not specified`. Now, we are reporting `Unsupported data source type for direct query on files: jdbc`
- Make data source format name case incensitive so that error handling behaves consistent with the normal cases.
- Added the test cases for all the supported formats.
#### How was this patch tested?
Added test cases to cover all the above issues
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#13283 from gatorsmile/runSQLAgainstFile.
## What changes were proposed in this pull request?
We currently have two physical aggregate operators: TungstenAggregate and SortBasedAggregate. These names don't make a lot of sense from an end-user point of view. This patch renames them HashAggregate and SortAggregate.
## How was this patch tested?
Updated test cases.
Author: Reynold Xin <rxin@databricks.com>
Closes#13465 from rxin/SPARK-15728.
## What changes were proposed in this pull request?
This PR corrects the remaining cases for using old accumulators.
This does not change some old accumulator usages below:
- `ImplicitSuite.scala` - Tests dedicated to old accumulator, for implicits with `AccumulatorParam`
- `AccumulatorSuite.scala` - Tests dedicated to old accumulator
- `JavaSparkContext.scala` - For supporting old accumulators for Java API.
- `debug.package.scala` - Usage with `HashSet[String]`. Currently, it seems no implementation for this. I might be able to write an anonymous class for this but I didn't because I think it is not worth writing a lot of codes only for this.
- `SQLMetricsSuite.scala` - This uses the old accumulator for checking type boxing. It seems new accumulator does not require type boxing for this case whereas the old one requires (due to the use of generic).
## How was this patch tested?
Existing tests cover this.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#13434 from HyukjinKwon/accum.
## What changes were proposed in this pull request?
Currently, `freqItems` raises `UnsupportedOperationException` on `empty.min` usually when its `support` argument is high.
```scala
scala> spark.createDataset(Seq(1, 2, 2, 3, 3, 3)).stat.freqItems(Seq("value"), 2)
16/06/01 11:11:38 ERROR Executor: Exception in task 5.0 in stage 0.0 (TID 5)
java.lang.UnsupportedOperationException: empty.min
...
```
Also, the parameter checking message is wrong.
```
require(support >= 1e-4, s"support ($support) must be greater than 1e-4.")
```
This PR changes the logic to handle the `empty` case and also improves parameter checking.
## How was this patch tested?
Pass the Jenkins tests (with a new testcase).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13449 from dongjoon-hyun/SPARK-15709.
## What changes were proposed in this pull request?
This PR add a rule at the end of analyzer to correct nullable fields of attributes in a logical plan by using nullable fields of the corresponding attributes in its children logical plans (these plans generate the input rows).
This is another approach for addressing SPARK-13484 (the first approach is https://github.com/apache/spark/pull/11371).
Close#113711
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#13290 from yhuai/SPARK-13484.
## What changes were proposed in this pull request?
Join on transformed dataset has attributes conflicts, which make query execution failure, for example:
```
val dataset = Seq(1, 2, 3).toDs
val mappedDs = dataset.map(_ + 1)
mappedDs.as("t1").joinWith(mappedDs.as("t2"), $"t1.value" === $"t2.value").show()
```
will throw exception:
```
org.apache.spark.sql.AnalysisException: cannot resolve '`t1.value`' given input columns: [value];
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:62)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:59)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:287)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:287)
```
## How was this patch tested?
Unit test.
Author: jerryshao <sshao@hortonworks.com>
Closes#13399 from jerryshao/SPARK-15620.
## What changes were proposed in this pull request?
Currently we can't encode top level null object into internal row, as Spark SQL doesn't allow row to be null, only its columns can be null.
This is not a problem before, as we assume the input object is never null. However, for outer join, we do need the semantics of null object.
This PR fixes this problem by making both join sides produce a single column, i.e. nest the logical plan output(by `CreateStruct`), so that we have an extra level to represent top level null obejct.
## How was this patch tested?
new test in `DatasetSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13425 from cloud-fan/outer-join2.
## What changes were proposed in this pull request?
**SPARK-15596**: Even after we renamed a cached table, the plan would remain in the cache with the old table name. If I created a new table using the old name then the old table would return incorrect data. Note that this applies only to Hive tables.
**SPARK-15635**: Renaming a datasource table would render the table not query-able. This is because we store the location of the table in a "path" property, which was not updated to reflect Hive's change in table location following a rename.
## How was this patch tested?
DDLSuite
Author: Andrew Or <andrew@databricks.com>
Closes#13416 from andrewor14/rename-table.
## What changes were proposed in this pull request?
This patch moves all user-facing structured streaming classes into sql.streaming. As part of this, I also added some since version annotation to methods and classes that don't have them.
## How was this patch tested?
Updated tests to reflect the moves.
Author: Reynold Xin <rxin@databricks.com>
Closes#13429 from rxin/SPARK-15686.
## What changes were proposed in this pull request?
Text data source ignores requested schema, and may give wrong result when the only data column is not requested. This may happen when only partitioning column(s) are requested for a partitioned text table.
## How was this patch tested?
New test case added in `TextSuite`.
Author: Cheng Lian <lian@databricks.com>
Closes#13431 from liancheng/spark-14343-partitioned-text-table.
## What changes were proposed in this pull request?
This PR changes function `SparkSession.builder.sparkContext(..)` from **private[sql]** into **private[spark]**, and uses it if applicable like the followings.
```
- val spark = SparkSession.builder().config(sc.getConf).getOrCreate()
+ val spark = SparkSession.builder().sparkContext(sc).getOrCreate()
```
## How was this patch tested?
Pass the existing Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13365 from dongjoon-hyun/SPARK-15618.
## What changes were proposed in this pull request?
Fixes "Can't drop top level columns that contain dots".
This work is based on dilipbiswal's https://github.com/apache/spark/pull/10943.
This PR fixes problems like:
```
scala> Seq((1, 2)).toDF("a.b", "a.c").drop("a.b")
org.apache.spark.sql.AnalysisException: cannot resolve '`a.c`' given input columns: [a.b, a.c];
```
`drop(columnName)` can only be used to drop top level column, so, we should parse the column name literally WITHOUT interpreting dot "."
We should also NOT interpret back tick "`", otherwise it is hard to understand what
```
```aaa```bbb``
```
actually means.
## How was this patch tested?
Unit tests.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13306 from clockfly/fix_drop_column.
## What changes were proposed in this pull request?
This patch does a few things:
1. Adds since version annotation to methods and classes in sql.catalog.
2. Fixed a typo in FilterFunction and a whitespace issue in spark/api/java/function/package.scala
3. Added "database" field to Function class.
## How was this patch tested?
Updated unit test case for "database" field in Function class.
Author: Reynold Xin <rxin@databricks.com>
Closes#13406 from rxin/SPARK-15662.
## What changes were proposed in this pull request?
Currently structured streaming only supports append output mode. This PR adds the following.
- Added support for Complete output mode in the internal state store, analyzer and planner.
- Added public API in Scala and Python for users to specify output mode
- Added checks for unsupported combinations of output mode and DF operations
- Plans with no aggregation should support only Append mode
- Plans with aggregation should support only Update and Complete modes
- Default output mode is Append mode (**Question: should we change this to automatically set to Complete mode when there is aggregation?**)
- Added support for Complete output mode in Memory Sink. So Memory Sink internally supports append and complete, update. But from public API only Complete and Append output modes are supported.
## How was this patch tested?
Unit tests in various test suites
- StreamingAggregationSuite: tests for complete mode
- MemorySinkSuite: tests for checking behavior in Append and Complete modes.
- UnsupportedOperationSuite: tests for checking unsupported combinations of DF ops and output modes
- DataFrameReaderWriterSuite: tests for checking that output mode cannot be called on static DFs
- Python doc test and existing unit tests modified to call write.outputMode.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13286 from tdas/complete-mode.
In this case, the result type of the expression becomes DECIMAL(38, 36) as we promote the individual string literals to DECIMAL(38, 18) when we handle string promotions for `BinaryArthmaticExpression`.
I think we need to cast the string literals to Double type instead. I looked at the history and found that this was changed to use decimal instead of double to avoid potential loss of precision when we cast decimal to double.
To double check i ran the query against hive, mysql. This query returns non NULL result for both the databases and both promote the expression to use double.
Here is the output.
- Hive
```SQL
hive> create table l2 as select (cast(99 as decimal(19,6)) + '2') from l1;
OK
hive> describe l2;
OK
_c0 double
```
- MySQL
```SQL
mysql> create table foo2 as select (cast(99 as decimal(19,6)) + '2') from test;
Query OK, 1 row affected (0.01 sec)
Records: 1 Duplicates: 0 Warnings: 0
mysql> describe foo2;
+-----------------------------------+--------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+-----------------------------------+--------+------+-----+---------+-------+
| (cast(99 as decimal(19,6)) + '2') | double | NO | | 0 | |
+-----------------------------------+--------+------+-----+---------+-------+
```
## How was this patch tested?
Added a new test in SQLQuerySuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#13368 from dilipbiswal/spark-15557.
## What changes were proposed in this pull request?
Right now, we will split the code for expressions into multiple functions when it exceed 64k, which requires that the the expressions are using Row object, but this is not true for whole-state codegen, it will fail to compile after splitted.
This PR will not split the code in whole-stage codegen.
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#13235 from davies/fix_nested_codegen.
## What changes were proposed in this pull request?
This reverts commit c24b6b679c. Sent a PR to run Jenkins tests due to the revert conflicts of `dev/deps/spark-deps-hadoop*`.
## How was this patch tested?
Jenkins unit tests, integration tests, manual tests)
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13417 from zsxwing/revert-SPARK-11753.
## What changes were proposed in this pull request?
When we build serializer for UDT object, we should declare its data type as udt instead of udt.sqlType, or if we deserialize it again, we lose the information that it's a udt object and throw analysis exception.
## How was this patch tested?
new test in `UserDefiendTypeSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13402 from cloud-fan/udt.
#### What changes were proposed in this pull request?
The following condition in the Optimizer rule `OptimizeCodegen` is not right.
```Scala
branches.size < conf.maxCaseBranchesForCodegen
```
- The number of branches in case when clause should be `branches.size + elseBranch.size`.
- `maxCaseBranchesForCodegen` is the maximum boundary for enabling codegen. Thus, we should use `<=` instead of `<`.
This PR is to fix this boundary case and also add missing test cases for verifying the conf `MAX_CASES_BRANCHES`.
#### How was this patch tested?
Added test cases in `SQLConfSuite`
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13392 from gatorsmile/maxCaseWhen.
## What changes were proposed in this pull request?
This patch contains a list of changes as a result of my auditing Dataset, SparkSession, and SQLContext. The patch audits the categorization of experimental APIs, function groups, and deprecations. For the detailed list of changes, please see the diff.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#13370 from rxin/SPARK-15638.
## What changes were proposed in this pull request?
`EmbedSerializerInFilter` implicitly assumes that the plan fragment being optimized doesn't change plan schema, which is reasonable because `Dataset.filter` should never change the schema.
However, due to another issue involving `DeserializeToObject` and `SerializeFromObject`, typed filter *does* change plan schema (see [SPARK-15632][1]). This breaks `EmbedSerializerInFilter` and causes corrupted data.
This PR disables `EmbedSerializerInFilter` when there's a schema change to avoid data corruption. The schema change issue should be addressed in follow-up PRs.
## How was this patch tested?
New test case added in `DatasetSuite`.
[1]: https://issues.apache.org/jira/browse/SPARK-15632
Author: Cheng Lian <lian@databricks.com>
Closes#13362 from liancheng/spark-15112-corrupted-filter.
## What changes were proposed in this pull request?
This change resolves a number of build warnings that have accumulated, before 2.x. It does not address a large number of deprecation warnings, especially related to the Accumulator API. That will happen separately.
## How was this patch tested?
Jenkins
Author: Sean Owen <sowen@cloudera.com>
Closes#13377 from srowen/BuildWarnings.
## What changes were proposed in this pull request?
This is a simple patch that makes package names for Java 8 test suites consistent. I moved everything to test.org.apache.spark to we can test package private APIs properly. Also added "java8" as the package name so we can easily run all the tests related to Java 8.
## How was this patch tested?
This is a test only change.
Author: Reynold Xin <rxin@databricks.com>
Closes#13364 from rxin/SPARK-15633.
## What changes were proposed in this pull request?
These commands ignore the partition spec and change the storage properties of the table itself:
```
ALTER TABLE table_name PARTITION (a=1, b=2) SET SERDE 'my_serde'
ALTER TABLE table_name PARTITION (a=1, b=2) SET SERDEPROPERTIES ('key1'='val1')
```
Now they change the storage properties of the specified partition.
## How was this patch tested?
DDLSuite
Author: Andrew Or <andrew@databricks.com>
Closes#13343 from andrewor14/alter-table-serdeproperties.
## What changes were proposed in this pull request?
This includes minimal changes to get Spark using the current release of Parquet, 1.8.1.
## How was this patch tested?
This uses the existing Parquet tests.
Author: Ryan Blue <blue@apache.org>
Closes#13280 from rdblue/SPARK-9876-update-parquet.
## What changes were proposed in this pull request?
- Refer to the Jira for the problem: jira : https://issues.apache.org/jira/browse/SPARK-14400
- The fix is to check if the process has exited with a non-zero exit code in `hasNext()`. I have moved this and checking of writer thread exception to a separate method.
## How was this patch tested?
- Ran a job which had incorrect transform script command and saw that the job fails
- Existing unit tests for `ScriptTransformationSuite`. Added a new unit test
Author: Tejas Patil <tejasp@fb.com>
Closes#12194 from tejasapatil/script_transform.
## What changes were proposed in this pull request?
This patch adds a new function emptyDataset to SparkSession, for creating an empty dataset.
## How was this patch tested?
Added a test case.
Author: Reynold Xin <rxin@databricks.com>
Closes#13344 from rxin/SPARK-15597.
## What changes were proposed in this pull request?
This PR replaces `spark.sql.sources.` strings with `CreateDataSourceTableUtils.*` constant variables.
## How was this patch tested?
Pass the existing Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13349 from dongjoon-hyun/SPARK-15584.
#### What changes were proposed in this pull request?
The default value of `spark.sql.warehouse.dir` is `System.getProperty("user.dir")/spark-warehouse`. Since `System.getProperty("user.dir")` is a local dir, we should explicitly set the scheme to local filesystem.
cc yhuai
#### How was this patch tested?
Added two test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13348 from gatorsmile/addSchemeToDefaultWarehousePath.
#### What changes were proposed in this pull request?
This PR is to use the new entrance `Sparksession` to replace the existing `SQLContext` and `HiveContext` in SQL test suites.
No change is made in the following suites:
- `ListTablesSuite` is to test the APIs of `SQLContext`.
- `SQLContextSuite` is to test `SQLContext`
- `HiveContextCompatibilitySuite` is to test `HiveContext`
**Update**: Move tests in `ListTableSuite` to `SQLContextSuite`
#### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#13337 from gatorsmile/sparkSessionTest.
## What changes were proposed in this pull request?
Certain table properties (and SerDe properties) are in the protected namespace `spark.sql.sources.`, which we use internally for datasource tables. The user should not be allowed to
(1) Create a Hive table setting these properties
(2) Alter these properties in an existing table
Previously, we threw an exception if the user tried to alter the properties of an existing datasource table. However, this is overly restrictive for datasource tables and does not do anything for Hive tables.
## How was this patch tested?
DDLSuite
Author: Andrew Or <andrew@databricks.com>
Closes#13341 from andrewor14/alter-table-props.
## What changes were proposed in this pull request?
Two more changes:
(1) Fix truncate table for data source tables (only for cases without `PARTITION`)
(2) Disallow truncating external tables or views
## How was this patch tested?
`DDLSuite`
Author: Andrew Or <andrew@databricks.com>
Closes#13315 from andrewor14/truncate-table.
## What changes were proposed in this pull request?
This PR addresses two related issues:
1. `Dataset.showString()` should show case classes/Java beans at all levels as rows, while master code only handles top level ones.
2. `Dataset.showString()` should show full contents produced the underlying query plan
Dataset is only a view of the underlying query plan. Columns not referred by the encoder are still reachable using methods like `Dataset.col`. So it probably makes more sense to show full contents of the query plan.
## How was this patch tested?
Two new test cases are added in `DatasetSuite` to check `.showString()` output.
Author: Cheng Lian <lian@databricks.com>
Closes#13331 from liancheng/spark-15550-ds-show.
## What changes were proposed in this pull request?
Add more verbose error message when order by clause is missed when using Window function.
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13333 from clockfly/spark-13445.
## What changes were proposed in this pull request?
SparkSession has a list of unnecessary private[sql] methods. These methods cause some trouble because private[sql] doesn't apply in Java. In the cases that they are easy to remove, we can simply remove them. This patch does that.
As part of this pull request, I also replaced a bunch of protected[sql] with private[sql], to tighten up visibility.
## How was this patch tested?
Updated test cases to reflect the changes.
Author: Reynold Xin <rxin@databricks.com>
Closes#13319 from rxin/SPARK-15552.
## What changes were proposed in this pull request?
Same as #13302, but for DROP TABLE.
## How was this patch tested?
`DDLSuite`
Author: Andrew Or <andrew@databricks.com>
Closes#13307 from andrewor14/drop-table.
## What changes were proposed in this pull request?
This patch renames various DefaultSources to make their names more self-describing. The choice of "DefaultSource" was from the days when we did not have a good way to specify short names.
They are now named:
- LibSVMFileFormat
- CSVFileFormat
- JdbcRelationProvider
- JsonFileFormat
- ParquetFileFormat
- TextFileFormat
Backward compatibility is maintained through aliasing.
## How was this patch tested?
Updated relevant test cases too.
Author: Reynold Xin <rxin@databricks.com>
Closes#13311 from rxin/SPARK-15543.
## What changes were proposed in this pull request?
Extra strategies does not work for streams because `IncrementalExecution` uses modified planner with stateful operations but it does not include extra strategies.
This pr fixes `IncrementalExecution` to include extra strategies to use them.
## How was this patch tested?
I added a test to check if extra strategies work for streams.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#13261 from ueshin/issues/SPARK-15483.
fixed typos for source code for components [mllib] [streaming] and [SQL]
None and obvious.
Author: lfzCarlosC <lfz.carlos@gmail.com>
Closes#13298 from lfzCarlosC/master.
## What changes were proposed in this pull request?
Override the existing SparkContext is the provided SparkConf is different. PySpark part hasn't been fixed yet, will do that after the first round of review to ensure this is the correct approach.
## How was this patch tested?
Manually verify it in spark-shell.
rxin Please help review it, I think this is a very critical issue for spark 2.0
Author: Jeff Zhang <zjffdu@apache.org>
Closes#13160 from zjffdu/SPARK-15345.
## What changes were proposed in this pull request?
This patch removes the last two commands defined in the catalyst module: DescribeFunction and ShowFunctions. They were unnecessary since the parser could just generate DescribeFunctionCommand and ShowFunctionsCommand directly.
## How was this patch tested?
Created a new SparkSqlParserSuite.
Author: Reynold Xin <rxin@databricks.com>
Closes#13292 from rxin/SPARK-15436.
## What changes were proposed in this pull request?
This PR fixes 3 slow tests:
1. `ParquetQuerySuite.read/write wide table`: This is not a good unit test as it runs more than 5 minutes. This PR removes it and add a new regression test in `CodeGenerationSuite`, which is more "unit".
2. `ParquetQuerySuite.returning batch for wide table`: reduce the threshold and use smaller data size.
3. `DatasetSuite.SPARK-14554: Dataset.map may generate wrong java code for wide table`: Improve `CodeFormatter.format`(introduced at https://github.com/apache/spark/pull/12979) can dramatically speed this it up.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13273 from cloud-fan/test.
## What changes were proposed in this pull request?
Previously, SPARK-8893 added the constraints on positive number of partitions for repartition/coalesce operations in general. This PR adds one missing part for that and adds explicit two testcases.
**Before**
```scala
scala> sc.parallelize(1 to 5).coalesce(0)
java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive.
...
scala> sc.parallelize(1 to 5).repartition(0).collect()
res1: Array[Int] = Array() // empty
scala> spark.sql("select 1").coalesce(0)
res2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [1: int]
scala> spark.sql("select 1").coalesce(0).collect()
java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive.
scala> spark.sql("select 1").repartition(0)
res3: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [1: int]
scala> spark.sql("select 1").repartition(0).collect()
res4: Array[org.apache.spark.sql.Row] = Array() // empty
```
**After**
```scala
scala> sc.parallelize(1 to 5).coalesce(0)
java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive.
...
scala> sc.parallelize(1 to 5).repartition(0)
java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive.
...
scala> spark.sql("select 1").coalesce(0)
java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive.
...
scala> spark.sql("select 1").repartition(0)
java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive.
...
```
## How was this patch tested?
Pass the Jenkins tests with new testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13282 from dongjoon-hyun/SPARK-15512.
## What changes were proposed in this pull request?
If the user relies on the schema to be inferred in file streams can break easily for multiple reasons
- accidentally running on a directory which has no data
- schema changing underneath
- on restart, the query will infer schema again, and may unexpectedly infer incorrect schema, as the file in the directory may be different at the time of the restart.
To avoid these complicated scenarios, for Spark 2.0, we are going to disable schema inferencing by default with a config, so that user is forced to consider explicitly what is the schema it wants, rather than the system trying to infer it and run into weird corner cases.
In this PR, I introduce a SQLConf that determines whether schema inference for file streams is allowed or not. It is disabled by default.
## How was this patch tested?
Updated unit tests that test error behavior with and without schema inference enabled.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13238 from tdas/SPARK-15458.
## What changes were proposed in this pull request?
Jackson suppprts `allowNonNumericNumbers` option to parse non-standard non-numeric numbers such as "NaN", "Infinity", "INF". Currently used Jackson version (2.5.3) doesn't support it all. This patch upgrades the library and make the two ignored tests in `JsonParsingOptionsSuite` passed.
## How was this patch tested?
`JsonParsingOptionsSuite`.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#9759 from viirya/fix-json-nonnumric.