| Commit message (Collapse) | Author | Age | Files | Lines |
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## What changes were proposed in this pull request?
This is just cleanup. This allows us to remove HiveContext later without inflating the diff too much. This PR fixes the conflicts of https://github.com/apache/spark/pull/12431. It also removes the `def hiveConf` from `HiveSqlParser`. So, we will pass the HiveConf associated with a session explicitly instead of relying on Hive's `SessionState` to pass `HiveConf`.
## How was this patch tested?
Existing tests.
Closes #12431
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes #12449 from yhuai/hiveconf.
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## What changes were proposed in this pull request?
This patch adds a SharedState that groups state shared across multiple SQLContexts. This is analogous to the SessionState added in SPARK-13526 that groups session-specific state. This cleanup makes the constructors of the contexts simpler and ultimately allows us to remove HiveContext in the near future.
## How was this patch tested?
Existing tests.
Author: Yin Huai <yhuai@databricks.com>
Closes #12463 from yhuai/sharedState.
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This reverts commit 5cefecc95a5b8418713516802c416cfde5a94a2d.
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## What changes were proposed in this pull request?
Move the implementation of `hiveContext.analyze` to the command of `AnalyzeTable`.
## How was this patch tested?
Existing tests.
Closes #12429
Author: Yin Huai <yhuai@databricks.com>
Author: Andrew Or <andrew@databricks.com>
Closes #12448 from yhuai/analyzeTable.
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## What changes were proposed in this pull request?
This patch adds a SharedState that groups state shared across multiple SQLContexts. This is analogous to the SessionState added in SPARK-13526 that groups session-specific state. This cleanup makes the constructors of the contexts simpler and ultimately allows us to remove HiveContext in the near future.
## How was this patch tested?
Existing tests.
Closes #12405
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes #12447 from yhuai/sharedState.
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within functional transformations
## What changes were proposed in this pull request?
This PR removes
- Inappropriate type notations
For example, from
```scala
words.foreachRDD { (rdd: RDD[String], time: Time) =>
...
```
to
```scala
words.foreachRDD { (rdd, time) =>
...
```
- Extra anonymous closure within functional transformations.
For example,
```scala
.map(item => {
...
})
```
which can be just simply as below:
```scala
.map { item =>
...
}
```
and corrects some obvious style nits.
## How was this patch tested?
This was tested after adding rules in `scalastyle-config.xml`, which ended up with not finding all perfectly.
The rules applied were below:
- For the first correction,
```xml
<check customId="NoExtraClosure" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
<parameters><parameter name="regex">(?m)\.[a-zA-Z_][a-zA-Z0-9]*\(\s*[^,]+s*=>\s*\{[^\}]+\}\s*\)</parameter></parameters>
</check>
```
```xml
<check customId="NoExtraClosure" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
<parameters><parameter name="regex">\.[a-zA-Z_][a-zA-Z0-9]*\s*[\{|\(]([^\n>,]+=>)?\s*\{([^()]|(?R))*\}^[,]</parameter></parameters>
</check>
```
- For the second correction
```xml
<check customId="TypeNotation" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
<parameters><parameter name="regex">\.[a-zA-Z_][a-zA-Z0-9]*\s*[\{|\(]\s*\([^):]*:R))*\}^[,]</parameter></parameters>
</check>
```
**Those rules were not added**
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #12413 from HyukjinKwon/SPARK-style.
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## What changes were proposed in this pull request?
This PR moves `CurrentDatabase` from sql/hive package to sql/catalyst. It also adds the function description, which looks like the following.
```
scala> sqlContext.sql("describe function extended current_database").collect.foreach(println)
[Function: current_database]
[Class: org.apache.spark.sql.execution.command.CurrentDatabase]
[Usage: current_database() - Returns the current database.]
[Extended Usage:
> SELECT current_database()]
```
## How was this patch tested?
Existing tests
Author: Yin Huai <yhuai@databricks.com>
Closes #12424 from yhuai/SPARK-14668.
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## What changes were proposed in this pull request?
This patch speeds up group-by aggregates by around 3-5x by leveraging an in-memory `AggregateHashMap` (please see https://github.com/apache/spark/pull/12161), an append-only aggregate hash map that can act as a 'cache' for extremely fast key-value lookups while evaluating aggregates (and fall back to the `BytesToBytesMap` if a given key isn't found).
Architecturally, it is backed by a power-of-2-sized array for index lookups and a columnar batch that stores the key-value pairs. The index lookups in the array rely on linear probing (with a small number of maximum tries) and use an inexpensive hash function which makes it really efficient for a majority of lookups. However, using linear probing and an inexpensive hash function also makes it less robust as compared to the `BytesToBytesMap` (especially for a large number of keys or even for certain distribution of keys) and requires us to fall back on the latter for correctness.
## How was this patch tested?
Java HotSpot(TM) 64-Bit Server VM 1.8.0_73-b02 on Mac OS X 10.11.4
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
Aggregate w keys: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------
codegen = F 2124 / 2204 9.9 101.3 1.0X
codegen = T hashmap = F 1198 / 1364 17.5 57.1 1.8X
codegen = T hashmap = T 369 / 600 56.8 17.6 5.8X
Author: Sameer Agarwal <sameer@databricks.com>
Closes #12345 from sameeragarwal/tungsten-aggregate-integration.
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## What changes were proposed in this pull request?
Removing references to assembly jar in documentation.
Adding an additional (previously undocumented) usage of spark-submit to run examples.
## How was this patch tested?
Ran spark-submit usage to ensure formatting was fine. Ran examples using SparkSubmit.
Author: Mark Grover <mark@apache.org>
Closes #12365 from markgrover/spark-14601.
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## What changes were proposed in this pull request?
JIRA: https://issues.apache.org/jira/browse/SPARK-14592
This patch adds native support for DDL command `CREATE TABLE LIKE`.
The SQL syntax is like:
CREATE TABLE table_name LIKE existing_table
CREATE TABLE IF NOT EXISTS table_name LIKE existing_table
## How was this patch tested?
`HiveDDLCommandSuite`. `HiveQuerySuite` already tests `CREATE TABLE LIKE`.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
This patch had conflicts when merged, resolved by
Committer: Andrew Or <andrew@databricks.com>
Closes #12362 from viirya/create-table-like.
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#### What changes were proposed in this pull request?
This PR is to add a test to ensure drop partitions of an external table will not delete data.
cc yhuai andrewor14
#### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
This patch had conflicts when merged, resolved by
Committer: Andrew Or <andrew@databricks.com>
Closes #12350 from gatorsmile/testDropPartition.
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#### What changes were proposed in this pull request?
This PR is to provide a native DDL support for the following three Alter View commands:
Based on the Hive DDL document:
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL
##### 1. ALTER VIEW RENAME
**Syntax:**
```SQL
ALTER VIEW view_name RENAME TO new_view_name
```
- to change the name of a view to a different name
- not allowed to rename a view's name by ALTER TABLE
##### 2. ALTER VIEW SET TBLPROPERTIES
**Syntax:**
```SQL
ALTER VIEW view_name SET TBLPROPERTIES ('comment' = new_comment);
```
- to add metadata to a view
- not allowed to set views' properties by ALTER TABLE
- ignore it if trying to set a view's existing property key when the value is the same
- overwrite the value if trying to set a view's existing key to a different value
##### 3. ALTER VIEW UNSET TBLPROPERTIES
**Syntax:**
```SQL
ALTER VIEW view_name UNSET TBLPROPERTIES [IF EXISTS] ('comment', 'key')
```
- to remove metadata from a view
- not allowed to unset views' properties by ALTER TABLE
- issue an exception if trying to unset a view's non-existent key
#### How was this patch tested?
Added test cases to verify if it works properly.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes #12324 from gatorsmile/alterView.
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#### What changes were proposed in this pull request?
**HQL Syntax**: [Create View](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-Create/Drop/AlterView
)
```SQL
CREATE VIEW [IF NOT EXISTS] [db_name.]view_name [(column_name [COMMENT column_comment], ...) ]
[COMMENT view_comment]
[TBLPROPERTIES (property_name = property_value, ...)]
AS SELECT ...;
```
Add a support for the `[COMMENT view_comment]` clause
#### How was this patch tested?
Modified the existing test cases to verify the correctness.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes #12288 from gatorsmile/addCommentInCreateView.
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## What changes were proposed in this pull request?
This PR removes extra anonymous closure within functional transformations.
For example,
```scala
.map(item => {
...
})
```
which can be just simply as below:
```scala
.map { item =>
...
}
```
## How was this patch tested?
Related unit tests and `sbt scalastyle`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #12382 from HyukjinKwon/minor-extra-closers.
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## What changes were proposed in this pull request?
This patch implements the `CREATE TABLE` command using the `SessionCatalog`. Previously we handled only `CTAS` and `CREATE TABLE ... USING`. This requires us to refactor `CatalogTable` to accept various fields (e.g. bucket and skew columns) and pass them to Hive.
WIP: Note that I haven't verified whether this actually works yet! But I believe it does.
## How was this patch tested?
Tests will come in a future commit.
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes #12271 from andrewor14/create-table-ddl.
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## What changes were proposed in this pull request?
It looks several recent commits for datasources (maybe while removing old `HadoopFsRelation` interface) missed removing some unused imports.
This PR removes some unused imports in datasources.
## How was this patch tested?
`sbt scalastyle` and some unit tests for them.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #12326 from HyukjinKwon/minor-imports.
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## What changes were proposed in this pull request?
Before we are using `AnalysisException`, `ParseException`, `NoSuchFunctionException` etc when a parsing error encounters. I am trying to make it consistent and also **minimum** code impact to the current implementation by changing the class hierarchy.
1. `NoSuchItemException` is removed, since it is an abstract class and it just simply takes a message string.
2. `NoSuchDatabaseException`, `NoSuchTableException`, `NoSuchPartitionException` and `NoSuchFunctionException` now extends `AnalysisException`, as well as `ParseException`, they are all under `AnalysisException` umbrella, but you can also determine how to use them in a granular way.
## How was this patch tested?
The existing test cases should cover this patch.
Author: bomeng <bmeng@us.ibm.com>
Closes #12314 from bomeng/SPARK-14414.
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SELECT" shouldn't create persisted table
## What changes were proposed in this pull request?
When planning logical plan node `CreateTableUsingAsSelect`, we neglected its `temporary` field and always generates a `CreateMetastoreDataSourceAsSelect`. This PR fixes this issue generating `CreateTempTableUsingAsSelect` when `temporary` is true.
This PR also fixes SPARK-14493 since the root cause of SPARK-14493 is that we were `CreateMetastoreDataSourceAsSelect` uses default Hive warehouse location when `PATH` data source option is absent.
## How was this patch tested?
Added a test case to create a temporary table using the target syntax and check whether it's indeed a temporary table.
Author: Cheng Lian <lian@databricks.com>
Closes #12303 from liancheng/spark-14488-fix-ctas-using.
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## What changes were proposed in this pull request?
Now `HadoopFsRelation` with all kinds of file formats can be handled in `FileSourceStrategy`, we can remove the branches for `HadoopFsRelation` in `FileSourceStrategy` and the `buildInternalScan` API from `FileFormat`.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes #12300 from cloud-fan/remove.
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## What changes were proposed in this pull request?
This implements a few alter table partition commands using the `SessionCatalog`. In particular:
```
ALTER TABLE ... ADD PARTITION ...
ALTER TABLE ... DROP PARTITION ...
ALTER TABLE ... RENAME PARTITION ... TO ...
```
The following operations are not supported, and an `AnalysisException` with a helpful error message will be thrown if the user tries to use them:
```
ALTER TABLE ... EXCHANGE PARTITION ...
ALTER TABLE ... ARCHIVE PARTITION ...
ALTER TABLE ... UNARCHIVE PARTITION ...
ALTER TABLE ... TOUCH ...
ALTER TABLE ... COMPACT ...
ALTER TABLE ... CONCATENATE
MSCK REPAIR TABLE ...
```
## How was this patch tested?
`DDLSuite`, `DDLCommandSuite` and `HiveDDLCommandSuite`
Author: Andrew Or <andrew@databricks.com>
Closes #12220 from andrewor14/alter-partition-ddl.
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Drop Table
#### What changes were proposed in this pull request?
This PR is to address the comment: https://github.com/apache/spark/pull/12146#discussion-diff-59092238. It removes the function `isViewSupported` from `SessionCatalog`. After the removal, we still can capture the user errors if users try to drop a table using `DROP VIEW`.
#### How was this patch tested?
Modified the existing test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes #12284 from gatorsmile/followupDropTable.
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## What changes were proposed in this pull request?
Currently, many functions do now show usages like the followings.
```
scala> sql("desc function extended `sin`").collect().foreach(println)
[Function: sin]
[Class: org.apache.spark.sql.catalyst.expressions.Sin]
[Usage: To be added.]
[Extended Usage:
To be added.]
```
This PR adds descriptions for functions and adds a testcase prevent adding function without usage.
```
scala> sql("desc function extended `sin`").collect().foreach(println);
[Function: sin]
[Class: org.apache.spark.sql.catalyst.expressions.Sin]
[Usage: sin(x) - Returns the sine of x.]
[Extended Usage:
> SELECT sin(0);
0.0]
```
The only exceptions are `cube`, `grouping`, `grouping_id`, `rollup`, `window`.
## How was this patch tested?
Pass the Jenkins tests (including new testcases.)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #12185 from dongjoon-hyun/SPARK-14415.
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external tables
## What changes were proposed in this pull request?
For an external table's metadata (in Hive's representation), its table type needs to be EXTERNAL_TABLE. Also, there needs to be a field called EXTERNAL set in the table property with a value of TRUE (for a MANAGED_TABLE it will be FALSE) based on https://github.com/apache/hive/blob/release-1.2.1/metastore/src/java/org/apache/hadoop/hive/metastore/ObjectStore.java#L1095-L1105. HiveClientImpl's toHiveTable misses to set this table property.
## How was this patch tested?
Added a new test.
Author: Yin Huai <yhuai@databricks.com>
Closes #12275 from yhuai/SPARK-14506.
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#### What changes were proposed in this pull request?
This PR is to provide a native support for DDL `DROP VIEW` and `DROP TABLE`. The PR includes native parsing and native analysis.
Based on the HIVE DDL document for [DROP_VIEW_WEB_LINK](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-
DropView
), `DROP VIEW` is defined as,
**Syntax:**
```SQL
DROP VIEW [IF EXISTS] [db_name.]view_name;
```
- to remove metadata for the specified view.
- illegal to use DROP TABLE on a view.
- illegal to use DROP VIEW on a table.
- this command only works in `HiveContext`. In `SQLContext`, we will get an exception.
This PR also handles `DROP TABLE`.
**Syntax:**
```SQL
DROP TABLE [IF EXISTS] table_name [PURGE];
```
- Previously, the `DROP TABLE` command only can drop Hive tables in `HiveContext`. Now, after this PR, this command also can drop temporary table, external table, external data source table in `SQLContext`.
- In `HiveContext`, we will not issue an exception if the to-be-dropped table does not exist and users did not specify `IF EXISTS`. Instead, we just log an error message. If `IF EXISTS` is specified, we will not issue any error message/exception.
- In `SQLContext`, we will issue an exception if the to-be-dropped table does not exist, unless `IF EXISTS` is specified.
- Data will not be deleted if the tables are `external`, unless table type is `managed_table`.
#### How was this patch tested?
For verifying command parsing, added test cases in `spark/sql/hive/HiveDDLCommandSuite.scala`
For verifying command analysis, added test cases in `spark/sql/hive/execution/HiveDDLSuite.scala`
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes #12146 from gatorsmile/dropView.
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#### What changes were proposed in this pull request?
"Not good to slightly ignore all the un-supported options/clauses. We should either support it or throw an exception." A comment from yhuai in another PR https://github.com/apache/spark/pull/12146
- Can `Explain` be an exception? The `Formatted` clause is used in `HiveCompatibilitySuite`.
- Two unsupported clauses in `Drop Table` are handled in a separate PR: https://github.com/apache/spark/pull/12146
#### How was this patch tested?
Test cases are added to verify all the cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes #12255 from gatorsmile/warningToException.
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## What changes were proposed in this pull request?
…because some of built-in functions are not in function registry.
This fix tries to fix issues in `describe function` command where some of the outputs
still shows Hive's function because some built-in functions are not in FunctionRegistry.
The following built-in functions have been added to FunctionRegistry:
```
-
!
*
/
&
%
^
+
<
<=
<=>
=
==
>
>=
|
~
and
in
like
not
or
rlike
when
```
The following listed functions are not added, but hard coded in `commands.scala` (hvanhovell):
```
!=
<>
between
case
```
Below are the existing result of the above functions that have not been added:
```
spark-sql> describe function `!=`;
Function: <>
Class: org.apache.hadoop.hive.ql.udf.generic.GenericUDFOPNotEqual
Usage: a <> b - Returns TRUE if a is not equal to b
```
```
spark-sql> describe function `<>`;
Function: <>
Class: org.apache.hadoop.hive.ql.udf.generic.GenericUDFOPNotEqual
Usage: a <> b - Returns TRUE if a is not equal to b
```
```
spark-sql> describe function `between`;
Function: between
Class: org.apache.hadoop.hive.ql.udf.generic.GenericUDFBetween
Usage: between a [NOT] BETWEEN b AND c - evaluate if a is [not] in between b and c
```
```
spark-sql> describe function `case`;
Function: case
Class: org.apache.hadoop.hive.ql.udf.generic.GenericUDFCase
Usage: CASE a WHEN b THEN c [WHEN d THEN e]* [ELSE f] END - When a = b, returns c; when a = d, return e; else return f
```
## How was this patch tested?
Existing tests passed. Additional test cases added.
Author: Yong Tang <yong.tang.github@outlook.com>
Closes #12128 from yongtang/SPARK-14335.
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This patch updates our custom Hive 1.2.1 fork in order to shade Kryo in Hive. This is a blocker for upgrading Spark to use Kryo 3 (see #12076).
The source for this new fork of Hive can be found at https://github.com/JoshRosen/hive/tree/release-1.2.1-spark2
Here's the complete diff from the official Hive 1.2.1 release: https://github.com/apache/hive/compare/release-1.2.1...JoshRosen:release-1.2.1-spark2
Here's the diff from the sources that pwendell used to publish the current `1.2.1.spark` release of Hive: https://github.com/pwendell/hive/compare/release-1.2.1-spark...JoshRosen:release-1.2.1-spark2. This diff looks large because his branch used a shell script to rewrite the groupId, whereas I had to commit the groupId changes in order to prevent the find-and-replace from affecting the package names in our relocated Kryo classes: https://github.com/pwendell/hive/compare/release-1.2.1-spark...JoshRosen:release-1.2.1-spark2#diff-6ada9aaec70e069df8f2c34c5519dd1e
Using these changes, I was able to publish a local version of Hive and verify that this change fixes the test failures which are blocking #12076. Note that this PR will not compile until we complete the review of the Hive POM changes and stage and publish a release.
/cc vanzin, steveloughran, and pwendell for review.
Author: Josh Rosen <joshrosen@databricks.com>
Closes #12215 from JoshRosen/shade-kryo-in-hive.
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## What changes were proposed in this pull request?
This is a followup to #12117 and addresses some of the TODOs introduced there. In particular, the resolution of database is now pushed into session catalog, which knows about the current database. Further, the logic for checking whether a function exists is pushed into the external catalog.
No change in functionality is expected.
## How was this patch tested?
`SessionCatalogSuite`, `DDLSuite`
Author: Andrew Or <andrew@databricks.com>
Closes #12198 from andrewor14/function-exists.
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## What changes were proposed in this pull request?
This patch removes DirectParquetOutputCommitter. This was initially created by Databricks as a faster way to write Parquet data to S3. However, given how the underlying S3 Hadoop implementation works, this committer only works when there are no failures. If there are multiple attempts of the same task (e.g. speculation or task failures or node failures), the output data can be corrupted. I don't think this performance optimization outweighs the correctness issue.
## How was this patch tested?
Removed the related tests also.
Author: Reynold Xin <rxin@databricks.com>
Closes #12229 from rxin/SPARK-10063.
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### What changes were proposed in this pull request?
This PR adds support for `LEFT ANTI JOIN` to Spark SQL. A `LEFT ANTI JOIN` is the exact opposite of a `LEFT SEMI JOIN` and can be used to identify rows in one dataset that are not in another dataset. Note that `nulls` on the left side of the join cannot match a row on the right hand side of the join; the result is that left anti join will always select a row with a `null` in one or more of its keys.
We currently add support for the following SQL join syntax:
SELECT *
FROM tbl1 A
LEFT ANTI JOIN tbl2 B
ON A.Id = B.Id
Or using a dataframe:
tbl1.as("a").join(tbl2.as("b"), $"a.id" === $"b.id", "left_anti)
This PR provides serves as the basis for implementing `NOT EXISTS` and `NOT IN (...)` correlated sub-queries. It would also serve as good basis for implementing an more efficient `EXCEPT` operator.
The PR has been (losely) based on PR's by both davies (https://github.com/apache/spark/pull/10706) and chenghao-intel (https://github.com/apache/spark/pull/10563); credit should be given where credit is due.
This PR adds supports for `LEFT ANTI JOIN` to `BroadcastHashJoin` (including codegeneration), `ShuffledHashJoin` and `BroadcastNestedLoopJoin`.
### How was this patch tested?
Added tests to `JoinSuite` and ported `ExistenceJoinSuite` from https://github.com/apache/spark/pull/10563.
cc davies chenghao-intel rxin
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes #12214 from hvanhovell/SPARK-12610.
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#### What changes were proposed in this pull request?
This PR is to fix the compilation errors in Scala 2.10 build, as shown in the link:
https://amplab.cs.berkeley.edu/jenkins/job/spark-master-compile-maven-scala-2.10/735/console
```
[error] /home/jenkins/workspace/spark-master-compile-maven-scala-2.10/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDDLCommandSuite.scala:266: value contains is not a member of Option[String]
[error] assert(desc.viewText.contains("SELECT * FROM tab1"))
[error] ^
[error] /home/jenkins/workspace/spark-master-compile-maven-scala-2.10/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDDLCommandSuite.scala:267: value contains is not a member of Option[String]
[error] assert(desc.viewOriginalText.contains("SELECT * FROM tab1"))
[error] ^
[error] /home/jenkins/workspace/spark-master-compile-maven-scala-2.10/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDDLCommandSuite.scala:293: value contains is not a member of Option[String]
[error] assert(desc.viewText.contains("SELECT * FROM tab1"))
[error] ^
[error] /home/jenkins/workspace/spark-master-compile-maven-scala-2.10/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDDLCommandSuite.scala:294: value contains is not a member of Option[String]
[error] assert(desc.viewOriginalText.contains("SELECT * FROM tab1"))
[error] ^
[error] four errors found
[error] Compile failed at Apr 5, 2016 10:59:09 PM [10.502s]
```
#### How was this patch tested?
Not sure how to trigger Scala 2.10 compilation in the test environment.
Author: gatorsmile <gatorsmile@gmail.com>
Closes #12201 from gatorsmile/buildBreak2.10.
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#### What changes were proposed in this pull request?
Because the concept of partitioning is associated with physical tables, we disable all the supports of partitioned views, which are defined in the following three commands in [Hive DDL Manual](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-Create/Drop/AlterView):
```
ALTER VIEW view DROP [IF EXISTS] PARTITION spec1[, PARTITION spec2, ...];
ALTER VIEW view ADD [IF NOT EXISTS] PARTITION spec;
CREATE VIEW [IF NOT EXISTS] [db_name.]view_name [(column_name [COMMENT column_comment], ...) ]
[COMMENT view_comment]
[TBLPROPERTIES (property_name = property_value, ...)]
AS SELECT ...;
```
An exception is thrown when users issue any of these three DDL commands.
#### How was this patch tested?
Added test cases for parsing create view and changed the existing test cases to verify if the exceptions are thrown.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes #12169 from gatorsmile/viewPartition.
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Because SQL keeps track of all known configs, some customization was
needed in SQLConf to allow that, since the core API does not have that
feature.
Tested via existing (and slightly updated) unit tests.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes #11570 from vanzin/SPARK-529-sql.
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## What changes were proposed in this pull request?
In Spark 2.0, we want to handle the most common `ALTER TABLE` commands ourselves instead of passing the entire query text to Hive. This is done using the new `SessionCatalog` API introduced recently.
The commands supported in this patch include:
```
ALTER TABLE ... RENAME TO ...
ALTER TABLE ... SET TBLPROPERTIES ...
ALTER TABLE ... UNSET TBLPROPERTIES ...
ALTER TABLE ... SET LOCATION ...
ALTER TABLE ... SET SERDE ...
```
The commands we explicitly do not support are:
```
ALTER TABLE ... CLUSTERED BY ...
ALTER TABLE ... SKEWED BY ...
ALTER TABLE ... NOT CLUSTERED
ALTER TABLE ... NOT SORTED
ALTER TABLE ... NOT SKEWED
ALTER TABLE ... NOT STORED AS DIRECTORIES
```
For these we throw exceptions complaining that they are not supported.
## How was this patch tested?
`DDLSuite`
Author: Andrew Or <andrew@databricks.com>
Closes #12121 from andrewor14/alter-table-ddl.
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## What changes were proposed in this pull request?
This PR implements CreateFunction and DropFunction commands. Besides implementing these two commands, we also change how to manage functions. Here are the main changes.
* `FunctionRegistry` will be a container to store all functions builders and it will not actively load any functions. Because of this change, we do not need to maintain a separate registry for HiveContext. So, `HiveFunctionRegistry` is deleted.
* SessionCatalog takes care the job of loading a function if this function is not in the `FunctionRegistry` but its metadata is stored in the external catalog. For this case, SessionCatalog will (1) load the metadata from the external catalog, (2) load all needed resources (i.e. jars and files), (3) create a function builder based on the function definition, (4) register the function builder in the `FunctionRegistry`.
* A `UnresolvedGenerator` is created. So, the parser will not need to call `FunctionRegistry` directly during parsing, which is not a good time to create a Hive UDTF. In the analysis phase, we will resolve `UnresolvedGenerator`.
This PR is based on viirya's https://github.com/apache/spark/pull/12036/
## How was this patch tested?
Existing tests and new tests.
## TODOs
[x] Self-review
[x] Cleanup
[x] More tests for create/drop functions (we need to more tests for permanent functions).
[ ] File JIRAs for all TODOs
[x] Standardize the error message when a function does not exist.
Author: Yin Huai <yhuai@databricks.com>
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes #12117 from yhuai/function.
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SQL Context.
#### What changes were proposed in this pull request?
Currently, the weird error messages are issued if we use Hive Context-only operations in SQL Context.
For example,
- When calling `Drop Table` in SQL Context, we got the following message:
```
Expected exception org.apache.spark.sql.catalyst.parser.ParseException to be thrown, but java.lang.ClassCastException was thrown.
```
- When calling `Script Transform` in SQL Context, we got the message:
```
assertion failed: No plan for ScriptTransformation [key#9,value#10], cat, [tKey#155,tValue#156], null
+- LogicalRDD [key#9,value#10], MapPartitionsRDD[3] at beforeAll at BeforeAndAfterAll.scala:187
```
Updates:
Based on the investigation from hvanhovell , the root cause is `visitChildren`, which is the default implementation. It always returns the result of the last defined context child. After merging the code changes from hvanhovell , it works! Thank you hvanhovell !
#### How was this patch tested?
A few test cases are added.
Not sure if the same issue exist for the other operators/DDL/DML. hvanhovell
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Herman van Hovell <hvanhovell@questtec.nl>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes #12134 from gatorsmile/hiveParserCommand.
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## What changes were proposed in this pull request?
This PR adds Native execution of SHOW TBLPROPERTIES command.
Command Syntax:
``` SQL
SHOW TBLPROPERTIES table_name[(property_key_literal)]
```
## How was this patch tested?
Tests added in HiveComandSuiie and DDLCommandSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes #12133 from dilipbiswal/dkb_show_tblproperties.
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## What changes were proposed in this pull request?
Currently we extract Python UDFs into a special logical plan EvaluatePython in analyzer, But EvaluatePython is not part of catalyst, many rules have no knowledge of it , which will break many things (for example, filter push down or column pruning).
We should treat Python UDFs as normal expressions, until we want to evaluate in physical plan, we could extract them in end of optimizer, or physical plan.
This PR extract Python UDFs in physical plan.
Closes #10935
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes #12127 from davies/py_udf.
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analysis results
## What changes were proposed in this pull request?
This PR contains the following 5 types of maintenance fix over 59 files (+94 lines, -93 lines).
- Fix typos(exception/log strings, testcase name, comments) in 44 lines.
- Fix lint-java errors (MaxLineLength) in 6 lines. (New codes after SPARK-14011)
- Use diamond operators in 40 lines. (New codes after SPARK-13702)
- Fix redundant semicolon in 5 lines.
- Rename class `InferSchemaSuite` to `CSVInferSchemaSuite` in CSVInferSchemaSuite.scala.
## How was this patch tested?
Manual and pass the Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #12139 from dongjoon-hyun/SPARK-14355.
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## What changes were proposed in this pull request?
We throw an AnalysisException that looks like this:
```
scala> sqlContext.sql("CREATE TEMPORARY MACRO SIGMOID (x DOUBLE) 1.0 / (1.0 + EXP(-x))")
org.apache.spark.sql.catalyst.parser.ParseException:
Unsupported SQL statement
== SQL ==
CREATE TEMPORARY MACRO SIGMOID (x DOUBLE) 1.0 / (1.0 + EXP(-x))
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.nativeCommand(ParseDriver.scala:66)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser$$anonfun$parsePlan$1.apply(ParseDriver.scala:56)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser$$anonfun$parsePlan$1.apply(ParseDriver.scala:53)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:86)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:53)
at org.apache.spark.sql.SQLContext.parseSql(SQLContext.scala:198)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:749)
... 48 elided
```
## How was this patch tested?
Add test cases in HiveQuerySuite.scala
Author: bomeng <bmeng@us.ibm.com>
Closes #12125 from bomeng/SPARK-14341.
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## What changes were proposed in this pull request?
EXPLAIN output should be in a single cell.
**Before**
```
scala> sql("explain select 1").collect()
res0: Array[org.apache.spark.sql.Row] = Array([== Physical Plan ==], [WholeStageCodegen], [: +- Project [1 AS 1#1]], [: +- INPUT], [+- Scan OneRowRelation[]])
```
**After**
```
scala> sql("explain select 1").collect()
res1: Array[org.apache.spark.sql.Row] =
Array([== Physical Plan ==
WholeStageCodegen
: +- Project [1 AS 1#4]
: +- INPUT
+- Scan OneRowRelation[]])
```
Or,
```
scala> sql("explain select 1").head
res1: org.apache.spark.sql.Row =
[== Physical Plan ==
WholeStageCodegen
: +- Project [1 AS 1#5]
: +- INPUT
+- Scan OneRowRelation[]]
```
Please note that `Spark-shell(Scala-shell)` trims long string output. So, you may need to use `println` to get full strings.
```
scala> println(sql("explain codegen select 'a' as a group by 1").head)
[Found 2 WholeStageCodegen subtrees.
== Subtree 1 / 2 ==
WholeStageCodegen
...
/* 059 */ }
/* 060 */ }
]
```
## How was this patch tested?
Pass the Jenkins tests. (Testcases are updated.)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #12137 from dongjoon-hyun/SPARK-14350.
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## What changes were proposed in this pull request?
Appends s3 specific configurations and spark.hadoop configurations to hive configuration.
## How was this patch tested?
Tested by running a job on cluster.
…figurations to hive configuration.
Author: Sital Kedia <skedia@fb.com>
Closes #11876 from sitalkedia/hiveConf.
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## What changes were proposed in this pull request?
This PR implements `EXPLAIN CODEGEN` SQL command which returns generated codes like `debugCodegen`. In `spark-shell`, we don't need to `import debug` module. In `spark-sql`, we can use this SQL command now.
**Before**
```
scala> import org.apache.spark.sql.execution.debug._
scala> sql("select 'a' as a group by 1").debugCodegen()
Found 2 WholeStageCodegen subtrees.
== Subtree 1 / 2 ==
...
Generated code:
...
== Subtree 2 / 2 ==
...
Generated code:
...
```
**After**
```
scala> sql("explain extended codegen select 'a' as a group by 1").collect().foreach(println)
[Found 2 WholeStageCodegen subtrees.]
[== Subtree 1 / 2 ==]
...
[]
[Generated code:]
...
[]
[== Subtree 2 / 2 ==]
...
[]
[Generated code:]
...
```
## How was this patch tested?
Pass the Jenkins tests (including new testcases)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #12099 from dongjoon-hyun/SPARK-14251.
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side when evaluating window functions
## What changes were proposed in this pull request?
`SizeBasedWindowFunction.n` is a global singleton attribute created for evaluating size based aggregate window functions like `CUME_DIST`. However, this attribute gets different expression IDs when created on both driver side and executor side. This PR adds `withPartitionSize` method to `SizeBasedWindowFunction` so that we can easily rewrite `SizeBasedWindowFunction.n` on executor side.
## How was this patch tested?
A test case is added in `HiveSparkSubmitSuite`, which supports launching multi-process clusters.
Author: Cheng Lian <lian@databricks.com>
Closes #12040 from liancheng/spark-14244-fix-sized-window-function.
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This PR adds the ability to perform aggregations inside of a `ContinuousQuery`. In order to implement this feature, the planning of aggregation has augmented with a new `StatefulAggregationStrategy`. Unlike batch aggregation, stateful-aggregation uses the `StateStore` (introduced in #11645) to persist the results of partial aggregation across different invocations. The resulting physical plan performs the aggregation using the following progression:
- Partial Aggregation
- Shuffle
- Partial Merge (now there is at most 1 tuple per group)
- StateStoreRestore (now there is 1 tuple from this batch + optionally one from the previous)
- Partial Merge (now there is at most 1 tuple per group)
- StateStoreSave (saves the tuple for the next batch)
- Complete (output the current result of the aggregation)
The following refactoring was also performed to allow us to plug into existing code:
- The get/put implementation is taken from #12013
- The logic for breaking down and de-duping the physical execution of aggregation has been move into a new pattern `PhysicalAggregation`
- The `AttributeReference` used to identify the result of an `AggregateFunction` as been moved into the `AggregateExpression` container. This change moves the reference into the same object as the other intermediate references used in aggregation and eliminates the need to pass around a `Map[(AggregateFunction, Boolean), Attribute]`. Further clean up (using a different aggregation container for logical/physical plans) is deferred to a followup.
- Some planning logic is moved from the `SessionState` into the `QueryExecution` to make it easier to override in the streaming case.
- The ability to write a `StreamTest` that checks only the output of the last batch has been added to simulate the future addition of output modes.
Author: Michael Armbrust <michael@databricks.com>
Closes #12048 from marmbrus/statefulAgg.
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## What changes were proposed in this pull request?
This patch enables use of OrcRelation for SQL queries which read data from Hive tables. Changes in this patch:
- Added a new rule `OrcConversions` which would alter the plan to use `OrcRelation`. In this diff, the conversion is done only for reads.
- Added a new config `spark.sql.hive.convertMetastoreOrc` to control the conversion
BEFORE
```
scala> hqlContext.sql("SELECT * FROM orc_table").explain(true)
== Parsed Logical Plan ==
'Project [unresolvedalias(*, None)]
+- 'UnresolvedRelation `orc_table`, None
== Analyzed Logical Plan ==
key: string, value: string
Project [key#171,value#172]
+- MetastoreRelation default, orc_table, None
== Optimized Logical Plan ==
MetastoreRelation default, orc_table, None
== Physical Plan ==
HiveTableScan [key#171,value#172], MetastoreRelation default, orc_table, None
```
AFTER
```
scala> hqlContext.sql("SELECT * FROM orc_table").explain(true)
== Parsed Logical Plan ==
'Project [unresolvedalias(*, None)]
+- 'UnresolvedRelation `orc_table`, None
== Analyzed Logical Plan ==
key: string, value: string
Project [key#76,value#77]
+- SubqueryAlias orc_table
+- Relation[key#76,value#77] ORC part: struct<>, data: struct<key:string,value:string>
== Optimized Logical Plan ==
Relation[key#76,value#77] ORC part: struct<>, data: struct<key:string,value:string>
== Physical Plan ==
WholeStageCodegen
: +- Scan ORC part: struct<>, data: struct<key:string,value:string>[key#76,value#77] InputPaths: file:/user/hive/warehouse/orc_table
```
## How was this patch tested?
- Added a new unit test. Ran existing unit tests
- Ran with production like data
## Performance gains
Ran on a production table in Facebook (note that the data was in DWRF file format which is similar to ORC)
Best case : when there was no matching rows for the predicate in the query (everything is filtered out)
```
CPU time Wall time Total wall time across all tasks
================================================================
Without the change 541_515 sec 25.0 mins 165.8 hours
With change 407 sec 1.5 mins 15 mins
```
Average case: A subset of rows in the data match the query predicate
```
CPU time Wall time Total wall time across all tasks
================================================================
Without the change 624_630 sec 31.0 mins 199.0 h
With change 14_769 sec 5.3 mins 7.7 h
```
Author: Tejas Patil <tejasp@fb.com>
Closes #11891 from tejasapatil/orc_ppd.
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, and lock/unlock operations.
## What changes were proposed in this pull request?
This PR throws Unsupported Operation exception for create index, drop index, alter index , lock table , lock database, unlock table, and unlock database operations that are not supported in Spark SQL. Currently these operations are executed executed by Hive.
Error:
spark-sql> drop index my_index on my_table;
Error in query:
Unsupported operation: drop index(line 1, pos 0)
## How was this patch tested?
Added test cases to HiveQuerySuite
yhuai hvanhovell andrewor14
Author: sureshthalamati <suresh.thalamati@gmail.com>
Closes #12069 from sureshthalamati/unsupported_ddl_spark-14133.
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SHOW TABLE to use table identifier pattern
## What changes were proposed in this pull request?
This PR addresses the following
1. Supports native execution of SHOW DATABASES command
2. Fixes SHOW TABLES to apply the identifier_with_wildcards pattern if supplied.
SHOW TABLE syntax
```
SHOW TABLES [IN database_name] ['identifier_with_wildcards'];
```
SHOW DATABASES syntax
```
SHOW (DATABASES|SCHEMAS) [LIKE 'identifier_with_wildcards'];
```
## How was this patch tested?
Tests added in SQLQuerySuite (both hive and sql contexts) and DDLCommandSuite
Note: Since the table name pattern was not working , tests are added in both SQLQuerySuite to
verify the application of the table pattern.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes #11991 from dilipbiswal/dkb_show_database.
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### What changes were proposed in this pull request?
This PR removes the ANTLR3 based parser, and moves the new ANTLR4 based parser into the `org.apache.spark.sql.catalyst.parser package`.
### How was this patch tested?
Existing unit tests.
cc rxin andrewor14 yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes #12071 from hvanhovell/SPARK-14211.
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## What changes were proposed in this pull request?
Major changes:
1. Implement `FileFormat.buildReader()` for the CSV data source.
1. Add an extra argument to `FileFormat.buildReader()`, `physicalSchema`, which is basically the result of `FileFormat.inferSchema` or user specified schema.
This argument is necessary because the CSV data source needs to know all the columns of the underlying files to read the file.
## How was this patch tested?
Existing tests should do the work.
Author: Cheng Lian <lian@databricks.com>
Closes #12002 from liancheng/spark-14206-csv-build-reader.
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