### What changes were proposed in this pull request?
This PR aims to add `io.netty.tryReflectionSetAccessible=true` to the testing configuration for JDK11 because this is an officially documented requirement of Apache Arrow.
Apache Arrow community documented this requirement at `0.15.0` ([ARROW-6206](https://github.com/apache/arrow/pull/5078)).
> #### For java 9 or later, should set "-Dio.netty.tryReflectionSetAccessible=true".
> This fixes `java.lang.UnsupportedOperationException: sun.misc.Unsafe or java.nio.DirectByteBuffer.(long, int) not available`. thrown by netty.
### Why are the changes needed?
After ARROW-3191, Arrow Java library requires the property `io.netty.tryReflectionSetAccessible` to be set to true for JDK >= 9. After https://github.com/apache/spark/pull/26133, JDK11 Jenkins job seem to fail.
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-3.2-jdk-11/676/
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-3.2-jdk-11/677/
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-3.2-jdk-11/678/
```scala
Previous exception in task:
sun.misc.Unsafe or java.nio.DirectByteBuffer.<init>(long, int) not available

io.netty.util.internal.PlatformDependent.directBuffer(PlatformDependent.java:473)

io.netty.buffer.NettyArrowBuf.getDirectBuffer(NettyArrowBuf.java:243)

io.netty.buffer.NettyArrowBuf.nioBuffer(NettyArrowBuf.java:233)

io.netty.buffer.ArrowBuf.nioBuffer(ArrowBuf.java:245)

org.apache.arrow.vector.ipc.message.ArrowRecordBatch.computeBodyLength(ArrowRecordBatch.java:222)

```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass the Jenkins with JDK11.
Closes#26552 from dongjoon-hyun/SPARK-ARROW-JDK11.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Hive support STORED AS new file format syntax:
```sql
CREATE TABLE tbl(a int) STORED AS TEXTFILE;
CREATE TABLE tbl2 LIKE tbl STORED AS PARQUET;
```
We add a similar syntax for Spark. Here we separate to two features:
1. specify a different table provider in CREATE TABLE LIKE
2. Hive compatibility
In this PR, we address the first one:
- [ ] Using `USING provider` to specify a different table provider in CREATE TABLE LIKE.
- [ ] Using `STORED AS file_format` in CREATE TABLE LIKE to address Hive compatibility.
### Why are the changes needed?
Use CREATE TABLE tb1 LIKE tb2 command to create an empty table tb1 based on the definition of table tb2. The most user case is to create tb1 with the same schema of tb2. But an inconvenient case here is this command also copies the FileFormat from tb2, it cannot change the input/output format and serde. Add the ability of changing file format is useful for some scenarios like upgrading a table from a low performance file format to a high performance one (parquet, orc).
### Does this PR introduce any user-facing change?
Add a new syntax based on current CTL:
```sql
CREATE TABLE tbl2 LIKE tbl [USING parquet];
```
### How was this patch tested?
Modify some exist UTs.
Closes#26097 from LantaoJin/SPARK-29421.
Authored-by: lajin <lajin@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This aims to exclude the `preview` release to recover `HiveExternalCatalogVersionsSuite`. Currently, new preview release breaks `branch-2.4` PRBuilder since yesterday. New release (especially `preview`) should not affect `branch-2.4`.
- https://github.com/apache/spark/pull/26417 (Failed 4 times)
### Why are the changes needed?
**BEFORE**
```scala
scala> scala.io.Source.fromURL("https://dist.apache.org/repos/dist/release/spark/").mkString.split("\n").filter(_.contains("""<li><a href="spark-""")).map("""<a href="spark-(\d.\d.\d)/">""".r.findFirstMatchIn(_).get.group(1))
java.util.NoSuchElementException: None.get
```
**AFTER**
```scala
scala> scala.io.Source.fromURL("https://dist.apache.org/repos/dist/release/spark/").mkString.split("\n").filter(_.contains("""<li><a href="spark-""")).filterNot(_.contains("preview")).map("""<a href="spark-(\d.\d.\d)/">""".r.findFirstMatchIn(_).get.group(1))
res5: Array[String] = Array(2.3.4, 2.4.4)
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
This should pass the PRBuilder.
Closes#26428 from dongjoon-hyun/SPARK-HiveExternalCatalogVersionsSuite.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
To push the built jars to maven release repository, we need to remove the 'SNAPSHOT' tag from the version name.
Made the following changes in this PR:
* Update all the `3.0.0-SNAPSHOT` version name to `3.0.0-preview`
* Update the sparkR version number check logic to allow jvm version like `3.0.0-preview`
**Please note those changes were generated by the release script in the past, but this time since we manually add tags on master branch, we need to manually apply those changes too.**
We shall revert the changes after 3.0.0-preview release passed.
### Why are the changes needed?
To make the maven release repository to accept the built jars.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
### What changes were proposed in this pull request?
To push the built jars to maven release repository, we need to remove the 'SNAPSHOT' tag from the version name.
Made the following changes in this PR:
* Update all the `3.0.0-SNAPSHOT` version name to `3.0.0-preview`
* Update the PySpark version from `3.0.0.dev0` to `3.0.0`
**Please note those changes were generated by the release script in the past, but this time since we manually add tags on master branch, we need to manually apply those changes too.**
We shall revert the changes after 3.0.0-preview release passed.
### Why are the changes needed?
To make the maven release repository to accept the built jars.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#26243 from jiangxb1987/3.0.0-preview-prepare.
Lead-authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
### What changes were proposed in this pull request?
There're some issues observed in `HiveUserDefinedTypeSuite."Support UDT in Hive UDF"`:
1) Neither function (TestUDF) nor test take "nullable" point column into account.
2) ExamplePointUDT. sqlType is ArrayType which doesn't provide information how many elements are expected. RandomDataGenerator may provide less elements than needed.
This patch fixes `HiveUserDefinedTypeSuite."Support UDT in Hive UDF"` to change the type of "point" column to be non-nullable, as well as not use RandomDataGenerator to create row for UDT backed by ArrayType.
### Why are the changes needed?
CI builds are failing in high occurrences.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually tested by running tests locally multiple times.
Closes#26287 from HeartSaVioR/SPARK-28158-FOLLOWUP.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
After this PR, we can create and register Hive UDFs to accept UDT type, like `VectorUDT` and `MatrixUDT`. These UDTs are widely used in Spark machine learning.
## How was this patch tested?
add new ut
Closes#24961 from uncleGen/SPARK-28158.
Authored-by: uncleGen <hustyugm@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Only use antlr4 to parse the interval string, and remove the duplicated parsing logic from `CalendarInterval`.
### Why are the changes needed?
Simplify the code and fix inconsistent behaviors.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Pass the Jenkins with the updated test cases.
Closes#26190 from cloud-fan/parser.
Lead-authored-by: Wenchen Fan <wenchen@databricks.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add CacheTableStatement and make CACHE TABLE go through the same catalog/table resolution framework of v2 commands.
### Why are the changes needed?
It's important to make all the commands have the same table resolution behavior, to avoid confusing end-users. e.g.
```
USE my_catalog
DESC t // success and describe the table t from my_catalog
CACHE TABLE t // report table not found as there is no table t in the session catalog
```
### Does this PR introduce any user-facing change?
yes. When running CACHE TABLE, Spark fails the command if the current catalog is set to a v2 catalog, or the table name specified a v2 catalog.
### How was this patch tested?
Unit tests.
Closes#26179 from viirya/SPARK-29522.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`CatalogTable` to `HiveTable` will change the table's ownership. How to reproduce:
```scala
import org.apache.spark.sql.catalyst.TableIdentifier
import org.apache.spark.sql.catalyst.catalog.{CatalogStorageFormat, CatalogTable, CatalogTableType}
import org.apache.spark.sql.types.{LongType, StructType}
val identifier = TableIdentifier("spark_29498", None)
val owner = "SPARK-29498"
val newTable = CatalogTable(
identifier,
tableType = CatalogTableType.EXTERNAL,
storage = CatalogStorageFormat(
locationUri = None,
inputFormat = None,
outputFormat = None,
serde = None,
compressed = false,
properties = Map.empty),
owner = owner,
schema = new StructType().add("i", LongType, false),
provider = Some("hive"))
spark.sessionState.catalog.createTable(newTable, false)
// The owner is not SPARK-29498
println(spark.sessionState.catalog.getTableMetadata(identifier).owner)
```
This PR makes it set the `HiveTable`'s owner to `CatalogTable`'s owner if it's owner is not empty when converting `CatalogTable` to `HiveTable`.
### Why are the changes needed?
We should not change the ownership of the table when converting `CatalogTable` to `HiveTable`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
unit test
Closes#26160 from wangyum/SPARK-29498.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Current Spark SQL `SHOW FUNCTIONS` don't show `!=`, `<>`, `between`, `case`
But these expressions is truly functions. We should show it in SQL `SHOW FUNCTIONS`
### Why are the changes needed?
SHOW FUNCTIONS show '!=', '<>' , 'between', 'case'
### Does this PR introduce any user-facing change?
SHOW FUNCTIONS show '!=', '<>' , 'between', 'case'
### How was this patch tested?
UT
Closes#26053 from AngersZhuuuu/SPARK-29379.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch proposes to delete old Hive external partition directory even the partition does not exist in Hive, when insert overwrite Hive external table partition.
### Why are the changes needed?
When insert overwrite to a Hive external table partition, if the partition does not exist, Hive will not check if the external partition directory exists or not before copying files. So if users drop the partition, and then do insert overwrite to the same partition, the partition will have both old and new data.
For example:
```scala
withSQLConf(HiveUtils.CONVERT_METASTORE_PARQUET.key -> "false") {
// test is an external Hive table.
sql("INSERT OVERWRITE TABLE test PARTITION(name='n1') SELECT 1")
sql("ALTER TABLE test DROP PARTITION(name='n1')")
sql("INSERT OVERWRITE TABLE test PARTITION(name='n1') SELECT 2")
sql("SELECT id FROM test WHERE name = 'n1' ORDER BY id") // Got both 1 and 2.
}
```
### Does this PR introduce any user-facing change?
Yes. This fix a correctness issue when users drop partition on a Hive external table partition and then insert overwrite it.
### How was this patch tested?
Added test.
Closes#25979 from viirya/SPARK-29295.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In this change, we give preference to the original table's owner if it is not empty.
### Why are the changes needed?
When executing 'insert into/overwrite ...' DML, or 'alter table set tblproperties ...' DDL, spark would change the ownership of the table the one who runs the spark application.
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
Compare with the behavior of Apache Hive
Closes#26068 from yaooqinn/SPARK-29405.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes a few typos:
1. Sparks => Spark's
2. parallize => parallelize
3. doesnt => doesn't
Closes#26140 from plusplusjiajia/fix-typos.
Authored-by: Jiajia Li <jiajia.li@intel.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Change Literal.sql to output CAST('fpValue' AS FLOAT) instead of CAST(fpValue AS FLOAT) as the SQL for a floating point literal.
### Why are the changes needed?
The old version doesn't work for very small floating point numbers; the value will fail to parse if it doesn't fit in a DECIMAL(38).
This doesn't apply to doubles because they have special literal syntax.
### Does this PR introduce any user-facing change?
Not really.
### How was this patch tested?
New unit tests.
Closes#26114 from jose-torres/fpliteral.
Authored-by: Jose Torres <joseph.torres@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
When inserting a value into a column with the different data type, Spark performs type coercion. Currently, we support 3 policies for the store assignment rules: ANSI, legacy and strict, which can be set via the option "spark.sql.storeAssignmentPolicy":
1. ANSI: Spark performs the type coercion as per ANSI SQL. In practice, the behavior is mostly the same as PostgreSQL. It disallows certain unreasonable type conversions such as converting `string` to `int` and `double` to `boolean`. It will throw a runtime exception if the value is out-of-range(overflow).
2. Legacy: Spark allows the type coercion as long as it is a valid `Cast`, which is very loose. E.g., converting either `string` to `int` or `double` to `boolean` is allowed. It is the current behavior in Spark 2.x for compatibility with Hive. When inserting an out-of-range value to a integral field, the low-order bits of the value is inserted(the same as Java/Scala numeric type casting). For example, if 257 is inserted to a field of Byte type, the result is 1.
3. Strict: Spark doesn't allow any possible precision loss or data truncation in store assignment, e.g., converting either `double` to `int` or `decimal` to `double` is allowed. The rules are originally for Dataset encoder. As far as I know, no mainstream DBMS is using this policy by default.
Currently, the V1 data source uses "Legacy" policy by default, while V2 uses "Strict". This proposal is to use "ANSI" policy by default for both V1 and V2 in Spark 3.0.
### Why are the changes needed?
Following the ANSI SQL standard is most reasonable among the 3 policies.
### Does this PR introduce any user-facing change?
Yes.
The default store assignment policy is ANSI for both V1 and V2 data sources.
### How was this patch tested?
Unit test
Closes#26107 from gengliangwang/ansiPolicyAsDefault.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
move the statement logical plans that were created for v2 commands to a new file `statements.scala`, under the same package of `v2Commands.scala`.
This PR also includes some minor cleanups:
1. remove `private[sql]` from `ParsedStatement` as it's in the private package.
2. remove unnecessary override of `output` and `children`.
3. add missing classdoc.
### Why are the changes needed?
Similar to https://github.com/apache/spark/pull/26111 , this is to better organize the logical plans of data source v2.
It's a bit weird to put the statements in the package `org.apache.spark.sql.catalyst.plans.logical.sql` as `sql` is not a good sub-package name in Spark SQL.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#26125 from cloud-fan/statement.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR is a very minor follow-up to become robust because `spark.sql.additionalRemoteRepositories` is a configuration which has a comma-separated value.
### Why are the changes needed?
This makes sure that `getHiveContribJar` will not fail on the configuration changes.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manual. Change the default value with multiple repositories and run the following.
```
build/sbt -Phive "project hive" "test-only org.apache.spark.sql.hive.HiveSparkSubmitSuite"
```
Closes#26096 from dongjoon-hyun/SPARK-27831.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
Syntax like `'foo` is deprecated in Scala 2.13. Replace usages with `Symbol("foo")`
### Why are the changes needed?
Avoids ~50 deprecation warnings when attempting to build with 2.13.
### Does this PR introduce any user-facing change?
None, should be no functional change at all.
### How was this patch tested?
Existing tests.
Closes#26061 from srowen/SPARK-29392.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Currently we deal with different `ParsedStatement` in many places and write duplicated catalog/table lookup logic. In general the lookup logic is
1. try look up the catalog by name. If no such catalog, and default catalog is not set, convert `ParsedStatement` to v1 command like `ShowDatabasesCommand`. Otherwise, convert `ParsedStatement` to v2 command like `ShowNamespaces`.
2. try look up the table by name. If no such table, fail. If the table is a `V1Table`, convert `ParsedStatement` to v1 command like `CreateTable`. Otherwise, convert `ParsedStatement` to v2 command like `CreateV2Table`.
However, since the code is duplicated we don't apply this lookup logic consistently. For example, we forget to consider the v2 session catalog in several places.
This PR centralizes the catalog/table lookup logic by 3 rules.
1. `ResolveCatalogs` (in catalyst). This rule resolves v2 catalog from the multipart identifier in SQL statements, and convert the statement to v2 command if the resolved catalog is not session catalog. If the command needs to resolve the table (e.g. ALTER TABLE), put an `UnresolvedV2Table` in the command.
2. `ResolveTables` (in catalyst). It resolves `UnresolvedV2Table` to `DataSourceV2Relation`.
3. `ResolveSessionCatalog` (in sql/core). This rule is only effective if the resolved catalog is session catalog. For commands that don't need to resolve the table, this rule converts the statement to v1 command directly. Otherwise, it converts the statement to v1 command if the resolved table is v1 table, and convert to v2 command if the resolved table is v2 table. Hopefully we can remove this rule eventually when v1 fallback is not needed anymore.
### Why are the changes needed?
Reduce duplicated code and make the catalog/table lookup logic consistent.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#25747 from cloud-fan/lookup.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
LOAD DATA command resolves the partition column name as case sensitive manner,
where as in insert commandthe partition column name will be resolved using
the SQLConf resolver where the names will be resolved based on `spark.sql.caseSensitive` property. Same logic can be applied for resolving the partition column names in LOAD COMMAND.
### Why are the changes needed?
It's to handle the partition column name correctly according to the configuration.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT and manual testing.
Closes#24903 from sujith71955/master_paritionColName.
Lead-authored-by: s71955 <sujithchacko.2010@gmail.com>
Co-authored-by: sujith71955 <sujithchacko.2010@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR exposes USE CATALOG/USE SQL commands as described in this [SPIP](https://docs.google.com/document/d/1jEcvomPiTc5GtB9F7d2RTVVpMY64Qy7INCA_rFEd9HQ/edit#)
It also exposes `currentCatalog` in `CatalogManager`.
Finally, it changes `SHOW NAMESPACES` and `SHOW TABLES` to use the current catalog if no catalog is specified (instead of default catalog).
### Why are the changes needed?
There is currently no mechanism to change current catalog/namespace thru SQL commands.
### Does this PR introduce any user-facing change?
Yes, you can perform the following:
```scala
// Sets the current catalog to 'testcat'
spark.sql("USE CATALOG testcat")
// Sets the current catalog to 'testcat' and current namespace to 'ns1.ns2'.
spark.sql("USE ns1.ns2 IN testcat")
// Now, the following will use 'testcat' as the current catalog and 'ns1.ns2' as the current namespace.
spark.sql("SHOW NAMESPACES")
```
### How was this patch tested?
Added new unit tests.
Closes#25771 from imback82/use_namespace.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Scala 2.13 emits a deprecation warning for procedure-like declarations:
```
def foo() {
...
```
This is equivalent to the following, so should be changed to avoid a warning:
```
def foo(): Unit = {
...
```
### Why are the changes needed?
It will avoid about a thousand compiler warnings when we start to support Scala 2.13. I wanted to make the change in 3.0 as there are less likely to be back-ports from 3.0 to 2.4 than 3.1 to 3.0, for example, minimizing that downside to touching so many files.
Unfortunately, that makes this quite a big change.
### Does this PR introduce any user-facing change?
No behavior change at all.
### How was this patch tested?
Existing tests.
Closes#25968 from srowen/SPARK-29291.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
HiveClientImpl may be log sensitive information. e.g. url, secret and token:
```scala
logDebug(
s"""
|Applying Hadoop/Hive/Spark and extra properties to Hive Conf:
|$k=${if (k.toLowerCase(Locale.ROOT).contains("password")) "xxx" else v}
""".stripMargin)
```
So redact it. Use SQLConf.get.redactOptions.
I add a new overloading function to fit this situation for one by one kv pair situation.
### Why are the changes needed?
Redact sensitive information when construct HiveClientImpl
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
MT
Run command
` /sbin/start-thriftserver.sh`
In log we can get
```
19/09/28 08:27:02 main DEBUG HiveClientImpl:
Applying Hadoop/Hive/Spark and extra properties to Hive Conf:
hive.druid.metadata.password=*********(redacted)
```
Closes#25954 from AngersZhuuuu/SPARK-29247.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Support the syntax of `ALTER (DATABASE|SCHEMA) database_name SET LOCATION` path. Please note that only Hive 3.x metastore support this syntax.
Ref:
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDLhttps://issues.apache.org/jira/browse/HIVE-8472
### Why are the changes needed?
Support more syntax.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#25883 from wangyum/SPARK-28476.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
This PR moves Hive test jars(`hive-contrib-*.jar` and `hive-hcatalog-core-*.jar`) from maven dependency to local file.
### Why are the changes needed?
`--jars` can't be tested since `hive-contrib-*.jar` and `hive-hcatalog-core-*.jar` are already in classpath.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
manual test
Closes#25690 from wangyum/SPARK-27831-revert.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
Hive 2.3 will set a new UDFClassLoader to hiveConf.classLoader when initializing SessionState since HIVE-11878, and
1. ADDJarCommand will add jars to clientLoader.classLoader.
2. --jar passed jar will be added to clientLoader.classLoader
3. jar passed by hive conf `hive.aux.jars` [SPARK-28954](https://github.com/apache/spark/pull/25653) [SPARK-28840](https://github.com/apache/spark/pull/25542) will be added to clientLoader.classLoader too
For these reason we cannot load the jars added by ADDJarCommand because of class loader got changed. We reset it to clientLoader.ClassLoader here.
### Why are the changes needed?
support for jdk11
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
UT
```
export JAVA_HOME=/usr/lib/jdk-11.0.3
export PATH=$JAVA_HOME/bin:$PATH
build/sbt -Phive-thriftserver -Phadoop-3.2
hive/test-only *HiveSparkSubmitSuite -- -z "SPARK-8368: includes jars passed in through --jars"
hive-thriftserver/test-only *HiveThriftBinaryServerSuite -- -z "test add jar"
```
Closes#25775 from AngersZhuuuu/SPARK-29015-STS-JDK11.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Added a new config "spark.sql.additionalRemoteRepositories", a comma-delimited string config of the optional additional remote maven mirror.
### Why are the changes needed?
We need to connect the Maven repositories in IsolatedClientLoader for downloading Hive jars,
end-users can set this config if the default maven central repo is unreachable.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing UT.
Closes#25849 from xuanyuanking/SPARK-29175.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Copy any "spark.hive.foo=bar" spark properties into hadoop conf as "hive.foo=bar"
### Why are the changes needed?
Providing spark side config entry for hive configurations.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
UT.
Closes#25661 from WeichenXu123/add_hive_conf.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Change the remote repo used in IsolatedClientLoader from datanucleus to google mirror.
### Why are the changes needed?
We need to connect the Maven repositories in IsolatedClientLoader for downloading Hive jars. The repository currently used is "http://www.datanucleus.org/downloads/maven2", which is [no longer maintained](http://www.datanucleus.org:15080/downloads/maven2/README.txt). This will cause downloading failure and make hive test cases flaky while Jenkins host is blocked by maven central repo.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing UT.
Closes#25915 from xuanyuanking/SPARK-29229.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch fixes the issue brought by [SPARK-21870](http://issues.apache.org/jira/browse/SPARK-21870): when generating code for parameter type, it doesn't consider array type in javaType. At least we have one, Spark should generate code for BinaryType as `byte[]`, but Spark create the code for BinaryType as `[B` and generated code fails compilation.
Below is the generated code which failed compilation (Line 380):
```
/* 380 */ private void agg_doAggregate_count_0([B agg_expr_1_1, boolean agg_exprIsNull_1_1, org.apache.spark.sql.catalyst.InternalRow agg_unsafeRowAggBuffer_1) throws java.io.IOException {
/* 381 */ // evaluate aggregate function for count
/* 382 */ boolean agg_isNull_26 = false;
/* 383 */ long agg_value_28 = -1L;
/* 384 */ if (!false && agg_exprIsNull_1_1) {
/* 385 */ long agg_value_31 = agg_unsafeRowAggBuffer_1.getLong(1);
/* 386 */ agg_isNull_26 = false;
/* 387 */ agg_value_28 = agg_value_31;
/* 388 */ } else {
/* 389 */ long agg_value_33 = agg_unsafeRowAggBuffer_1.getLong(1);
/* 390 */
/* 391 */ long agg_value_32 = -1L;
/* 392 */
/* 393 */ agg_value_32 = agg_value_33 + 1L;
/* 394 */ agg_isNull_26 = false;
/* 395 */ agg_value_28 = agg_value_32;
/* 396 */ }
/* 397 */ // update unsafe row buffer
/* 398 */ agg_unsafeRowAggBuffer_1.setLong(1, agg_value_28);
/* 399 */ }
```
There wasn't any test for HashAggregateExec specifically testing this, but randomized test in ObjectHashAggregateSuite could encounter this and that's why ObjectHashAggregateSuite is flaky.
### Why are the changes needed?
Without the fix, generated code from HashAggregateExec may fail compilation.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added new UT. Without the fix, newly added UT fails.
Closes#25830 from HeartSaVioR/SPARK-29140.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This pr is to propagate all the SQL configurations to executors in `SQLQueryTestSuite`. When the propagation enabled in the tests, a potential bug below becomes apparent;
```
CREATE TABLE num_data (id int, val decimal(38,10)) USING parquet;
....
select sum(udf(CAST(null AS Decimal(38,0)))) from range(1,4): QueryOutput(select sum(udf(CAST(null AS Decimal(38,0)))) from range(1,4),struct<>,java.lang.IllegalArgumentException
[info] requirement failed: MutableProjection cannot use UnsafeRow for output data types: decimal(38,0)) (SQLQueryTestSuite.scala:380)
```
The root culprit is that `InterpretedMutableProjection` has incorrect validation in the interpreter mode: `validExprs.forall { case (e, _) => UnsafeRow.isFixedLength(e.dataType) }`. This validation should be the same with the condition (`isMutable`) in `HashAggregate.supportsAggregate`: https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala#L1126
### Why are the changes needed?
Bug fixes.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added tests in `AggregationQuerySuite`
Closes#25831 from maropu/SPARK-29122.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR aims to use `tryWithResource` for ORC file.
### Why are the changes needed?
This is a follow-up to address https://github.com/apache/spark/pull/25006#discussion_r298788206 .
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass the Jenkins with the existing tests.
Closes#25842 from dongjoon-hyun/SPARK-28208.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Refactored SQL-related benchmark and made them depend on `SqlBasedBenchmark`. In particular, creation of Spark session are moved into `override def getSparkSession: SparkSession`.
### Why are the changes needed?
This should simplify maintenance of SQL-based benchmarks by reducing the number of dependencies. In the future, it should be easier to refactor & extend all SQL benchmarks by changing only one trait. Finally, all SQL-based benchmarks will look uniformly.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By running the modified benchmarks.
Closes#25828 from MaxGekk/sql-benchmarks-refactoring.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
It upgrades ORC from 1.5.5 to 1.5.6 and adds closes the ORC readers when they aren't used to
create RecordReaders.
## How was this patch tested?
The changed unit tests were run.
Closes#25006 from omalley/spark-28208.
Lead-authored-by: Owen O'Malley <omalley@apache.org>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
**What changes were proposed in this pull request?**
Issue 1 : modifications not required as these are different formats for the same info. In the case of a Spark DataFrame, null is correct.
Issue 2 mentioned in JIRA Spark SQL "desc formatted tablename" is not showing the header # col_name,data_type,comment , seems to be the header has been removed knowingly as part of SPARK-20954.
Issue 3:
Corrected the Last Access time, the value shall display 'UNKNOWN' as currently system wont support the last access time evaluation, since hive was setting Last access time as '0' in metastore even though spark CatalogTable last access time value set as -1. this will make the validation logic of LasAccessTime where spark sets 'UNKNOWN' value if last access time value set as -1 (means not evaluated).
**Does this PR introduce any user-facing change?**
No
**How was this patch tested?**
Locally and corrected a ut.
Attaching the test report below
Closes#25720 from sujith71955/master_describe_info.
Authored-by: s71955 <sujithchacko.2010@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch proposes to add new tests to test the username of HiveClient to prevent changing the semantic unintentionally. The owner of Hive table has been changed back-and-forth, principal -> username -> principal, and looks like the change is not intentional. (Please refer [SPARK-28996](https://issues.apache.org/jira/browse/SPARK-28996) for more details.) This patch intends to prevent this.
This patch also renames previous HiveClientSuite(s) to HivePartitionFilteringSuite(s) as it was commented as TODO, as well as previous tests are too narrowed to test only partition filtering.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Newly added UTs.
Closes#25696 from HeartSaVioR/SPARK-28996.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
…create table through spark-sql or beeline
## What changes were proposed in this pull request?
fix table owner use user instead of principal when create table through spark-sql
private val userName = conf.getUser will get ugi's userName which is principal info, and i copy the source code into HiveClientImpl, and use ugi.getShortUserName() instead of ugi.getUserName(). The owner display correctly.
## How was this patch tested?
1. create a table in kerberos cluster
2. use "desc formatted tbName" check owner
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23952 from hddong/SPARK-26929-fix-table-owner.
Lead-authored-by: hongdd <jn_hdd@163.com>
Co-authored-by: hongdongdong <hongdongdong@cmss.chinamobile.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
JIRA :https://issues.apache.org/jira/browse/SPARK-29050
'a hdfs' change into 'an hdfs'
'an unique' change into 'a unique'
'an url' change into 'a url'
'a error' change into 'an error'
Closes#25756 from dengziming/feature_fix_typos.
Authored-by: dengziming <dengziming@growingio.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Remove `InsertIntoTable` and replace it's usage by `InsertIntoStatement`
### Why are the changes needed?
`InsertIntoTable` and `InsertIntoStatement` are almost identical (except some namings). It doesn't make sense to keep 2 identical plans. After the removal of `InsertIntoTable`, the analysis process becomes:
1. parser creates `InsertIntoStatement`
2. v2 rule `ResolveInsertInto` converts `InsertIntoStatement` to v2 commands.
3. v1 rules like `DataSourceAnalysis` and `HiveAnalysis` convert `InsertIntoStatement` to v1 commands.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing tests
Closes#25763 from cloud-fan/remove.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
- Remove SQLContext.createExternalTable and Catalog.createExternalTable, deprecated in favor of createTable since 2.2.0, plus tests of deprecated methods
- Remove HiveContext, deprecated in 2.0.0, in favor of `SparkSession.builder.enableHiveSupport`
- Remove deprecated KinesisUtils.createStream methods, plus tests of deprecated methods, deprecate in 2.2.0
- Remove deprecated MLlib (not Spark ML) linear method support, mostly utility constructors and 'train' methods, and associated docs. This includes methods in LinearRegression, LogisticRegression, Lasso, RidgeRegression. These have been deprecated since 2.0.0
- Remove deprecated Pyspark MLlib linear method support, including LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD
- Remove 'runs' argument in KMeans.train() method, which has been a no-op since 2.0.0
- Remove deprecated ChiSqSelector isSorted protected method
- Remove deprecated 'yarn-cluster' and 'yarn-client' master argument in favor of 'yarn' and deploy mode 'cluster', etc
Notes:
- I was not able to remove deprecated DataFrameReader.json(RDD) in favor of DataFrameReader.json(Dataset); the former was deprecated in 2.2.0, but, it is still needed to support Pyspark's .json() method, which can't use a Dataset.
- Looks like SQLContext.createExternalTable was not actually deprecated in Pyspark, but, almost certainly was meant to be? Catalog.createExternalTable was.
- I afterwards noted that the toDegrees, toRadians functions were almost removed fully in SPARK-25908, but Felix suggested keeping just the R version as they hadn't been technically deprecated. I'd like to revisit that. Do we really want the inconsistency? I'm not against reverting it again, but then that implies leaving SQLContext.createExternalTable just in Pyspark too, which seems weird.
- I *kept* LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD, RidgeRegressionWithSGD in Pyspark, though deprecated, as it is hard to remove them (still used by StreamingLogisticRegressionWithSGD?) and they are not fully removed in Scala. Maybe should not have been deprecated.
### Why are the changes needed?
Deprecated items are easiest to remove in a major release, so we should do so as much as possible for Spark 3. This does not target items deprecated 'recently' as of Spark 2.3, which is still 18 months old.
### Does this PR introduce any user-facing change?
Yes, in that deprecated items are removed from some public APIs.
### How was this patch tested?
Existing tests.
Closes#25684 from srowen/SPARK-28980.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The `V2SessionCatalog` has 2 functionalities:
1. work as an adapter: provide v2 APIs and translate calls to the `SessionCatalog`.
2. allow users to extend it, so that they can add hooks to apply custom logic before calling methods of the builtin catalog (session catalog).
To leverage the second functionality, users must extend `V2SessionCatalog` which is an internal class. There is no doc to explain this usage.
This PR does 2 things:
1. refine the document of the config `spark.sql.catalog.session`.
2. add a public abstract class `CatalogExtension` for users to write implementations.
TODOs for followup PRs:
1. discuss if we should allow users to completely overwrite the v2 session catalog with a new one.
2. discuss to change the name of session catalog, so that it's less likely to conflict with existing namespace names.
## How was this patch tested?
existing tests
Closes#25104 from cloud-fan/session-catalog.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR disables schema verification and allows schema auto-creation in the Derby database, in case the config for the Metastore is set otherwise.
## How was this patch tested?
NA
Closes#25663 from bogdanghit/hive-schema.
Authored-by: Bogdan Ghit <bogdan.ghit@databricks.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
merge the `V2WriteSupportCheck` and `V2StreamingScanSupportCheck` to one rule: `TableCapabilityCheck`.
### Why are the changes needed?
It's a little confusing to have 2 rules to check DS v2 table capability, while one rule says it checks write and another rule says it checks streaming scan. We can clearly tell it from the rule names that the batch scan check is missing.
It's better to have a centralized place for this check, with a name that clearly says it checks table capability.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing tests
Closes#25679 from cloud-fan/dsv2-check.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Datasource table now supports partition tables long ago. This commit adds the ability to translate
the InsertIntoTable(HiveTableRelation) to datasource table insertion.
## How was this patch tested?
Existing tests with some modification
Closes#25306 from advancedxy/SPARK-28573.
Authored-by: Xianjin YE <advancedxy@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The Experimental and Evolving annotations are both (like Unstable) used to express that a an API may change. However there are many things in the code that have been marked that way since even Spark 1.x. Per the dev thread, anything introduced at or before Spark 2.3.0 is pretty much 'stable' in that it would not change without a deprecation cycle. Therefore I'd like to remove most of these annotations. And, remove the `:: Experimental ::` scaladoc tag too. And likewise for Python, R.
The changes below can be summarized as:
- Generally, anything introduced at or before Spark 2.3.0 has been unmarked as neither Evolving nor Experimental
- Obviously experimental items like DSv2, Barrier mode, ExperimentalMethods are untouched
- I _did_ unmark a few MLlib classes introduced in 2.4, as I am quite confident they're not going to change (e.g. KolmogorovSmirnovTest, PowerIterationClustering)
It's a big change to review, so I'd suggest scanning the list of _files_ changed to see if any area seems like it should remain partly experimental and examine those.
### Why are the changes needed?
Many of these annotations are incorrect; the APIs are de facto stable. Leaving them also makes legitimate usages of the annotations less meaningful.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#25558 from srowen/SPARK-28855.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Hive 3.1.2 has been released. This PR upgrades the Hive Metastore Client to 3.1.2 for Hive 3.1.
Hive 3.1.2 release notes:
https://issues.apache.org/jira/secure/ReleaseNote.jspa?version=12344397&styleName=Html&projectId=12310843
### Why are the changes needed?
This is an improvement to support a newly release 3.1.2. Otherwise, it will throws `UnsupportedOperationException` if user `set spark.sql.hive.metastore.version=3.1.2`:
```scala
Exception in thread "main" java.lang.UnsupportedOperationException: Unsupported Hive Metastore version (3.1.2). Please set spark.sql.hive.metastore.version with a valid version.
at org.apache.spark.sql.hive.client.IsolatedClientLoader$.hiveVersion(IsolatedClientLoader.scala:109)
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT
Closes#25604 from wangyum/SPARK-28890.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR makes `spark.sql.statistics.fallBackToHdfs` not support Hive partitioned tables.
### Why are the changes needed?
The current implementation is incorrect for external partitions and it is expensive to support partitioned table with external partitions.
### Does this PR introduce any user-facing change?
Yes. But I think it will not change the join strategy because partitioned table usually very large.
### How was this patch tested?
unit test
Closes#25584 from wangyum/SPARK-28876.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
