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2127 commits

Author SHA1 Message Date
Sameer Agarwal b5c60bcdca [SPARK-14447][SQL] Speed up TungstenAggregate w/ keys using VectorizedHashMap
## 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.
2016-04-14 20:57:03 -07:00
Mark Grover ff9ae61a3b [SPARK-14601][DOC] Minor doc/usage changes related to removal of Spark assembly
## 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.
2016-04-14 18:51:43 -07:00
Liang-Chi Hsieh 28efdd3fd7 [SPARK-14592][SQL] Native support for CREATE TABLE LIKE DDL command
## 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.
2016-04-14 11:08:08 -07:00
Reynold Xin dac40b68dc [SPARK-14619] Track internal accumulators (metrics) by stage attempt
## What changes were proposed in this pull request?
When there are multiple attempts for a stage, we currently only reset internal accumulator values if all the tasks are resubmitted. It would make more sense to reset the accumulator values for each stage attempt. This will allow us to eventually get rid of the internal flag in the Accumulator class. This is part of my bigger effort to simplify accumulators and task metrics.

## How was this patch tested?
Covered by existing tests.

Author: Reynold Xin <rxin@databricks.com>

Closes #12378 from rxin/SPARK-14619.
2016-04-14 10:54:57 -07:00
Liwei Lin 3e27940a19 [SPARK-14630][BUILD][CORE][SQL][STREAMING] Code style: public abstract methods should have explicit return types
## What changes were proposed in this pull request?

Currently many public abstract methods (in abstract classes as well as traits) don't declare return types explicitly, such as in [o.a.s.streaming.dstream.InputDStream](https://github.com/apache/spark/blob/master/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala#L110):
```scala
def start() // should be: def start(): Unit
def stop()  // should be: def stop(): Unit
```

These methods exist in core, sql, streaming; this PR fixes them.

## How was this patch tested?

N/A

## Which piece of scala style rule led to the changes?

the rule was added separately in https://github.com/apache/spark/pull/12396

Author: Liwei Lin <lwlin7@gmail.com>

Closes #12389 from lw-lin/public-abstract-methods.
2016-04-14 10:14:38 -07:00
gatorsmile 0d22092cd9 [SPARK-14125][SQL] Native DDL Support: Alter View
#### 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.
2016-04-14 08:34:11 -07:00
hyukjinkwon 6fc3dc8839 [MINOR][SQL] Remove extra anonymous closure within functional transformations
## 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.
2016-04-14 09:43:41 +01:00
hyukjinkwon b4819404a6 [SPARK-14596][SQL] Remove not used SqlNewHadoopRDD and some more unused imports
## What changes were proposed in this pull request?

Old `HadoopFsRelation` API includes `buildInternalScan()` which uses `SqlNewHadoopRDD` in `ParquetRelation`.
Because now the old API is removed, `SqlNewHadoopRDD` is not used anymore.

So, this PR removes `SqlNewHadoopRDD` and several unused imports.

This was discussed in https://github.com/apache/spark/pull/12326.

## How was this patch tested?

Several related existing unit tests and `sbt scalastyle`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #12354 from HyukjinKwon/SPARK-14596.
2016-04-14 15:43:44 +08:00
Davies Liu 62b7f306fb [SPARK-14607] [SPARK-14484] [SQL] fix case-insensitive predicates in FileSourceStrategy
## What changes were proposed in this pull request?

When prune the partitions or push down predicates, case-sensitivity is not respected. In order to make it work with case-insensitive, this PR update the AttributeReference inside predicate to use the name from schema.

## How was this patch tested?

Add regression tests for case-insensitive.

Author: Davies Liu <davies@databricks.com>

Closes #12371 from davies/case_insensi.
2016-04-13 17:17:19 -07:00
Andrew Or 7d2ed8cc03 [SPARK-14388][SQL] Implement CREATE TABLE
## 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.
2016-04-13 11:08:34 -07:00
Wenchen Fan a5f8c9b15b [SPARK-14554][SQL][FOLLOW-UP] use checkDataset to check the result
## What changes were proposed in this pull request?

address this comment: https://github.com/apache/spark/pull/12322#discussion_r59417359

## How was this patch tested?

N/A

Author: Wenchen Fan <wenchen@databricks.com>

Closes #12346 from cloud-fan/tmp.
2016-04-13 11:41:09 +08:00
hyukjinkwon 587cd554af [MINOR][SQL] Remove some unused imports in datasources.
## 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.
2016-04-13 10:20:03 +08:00
Shixiong Zhu 768b3d623c [SPARK-14579][SQL] Fix a race condition in StreamExecution.processAllAvailable
## What changes were proposed in this pull request?

There is a race condition in `StreamExecution.processAllAvailable`. Here is an execution order to reproduce it.

| Time        |Thread 1           | MicroBatchThread  |
|:-------------:|:-------------:|:-----:|
| 1 | |  `dataAvailable in constructNextBatch` returns false  |
| 2 | addData(newData)      |   |
| 3 | `noNewData = false` in  processAllAvailable |  |
| 4 | | noNewData = true |
| 5 | `noNewData` is true so just return | |

The root cause is that `checking dataAvailable and change noNewData to true` is not atomic. This PR puts these two actions into `synchronized` to make sure they are atomic.

In addition, this PR also has the following changes:

- Make `committedOffsets` and `availableOffsets` volatile to make sure they can be seen in other threads.
- Copy the reference of `availableOffsets` to a local variable so that `sourceStatuses` can use a snapshot of `availableOffsets`.

## How was this patch tested?

Existing unit tests.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #12339 from zsxwing/race-condition.
2016-04-12 17:31:47 -07:00
Davies Liu 372baf0479 [SPARK-14578] [SQL] Fix codegen for CreateExternalRow with nested wide schema
## What changes were proposed in this pull request?

The wide schema, the expression of fields will be splitted into multiple functions, but the variable for loopVar can't be accessed in splitted functions, this PR change them as class member.

## How was this patch tested?

Added regression test.

Author: Davies Liu <davies@databricks.com>

Closes #12338 from davies/nested_row.
2016-04-12 17:26:37 -07:00
Davies Liu 1ef5f8cfa6 [SPARK-14544] [SQL] improve performance of SQL UI tab
## What changes were proposed in this pull request?

This PR improve the performance of SQL UI by:

1) remove the details column in all executions page (the first page in SQL tab). We can check the details by enter the execution page.
2) break-all is super slow in Chrome recently, so switch to break-word.
3) Using "display: none" to hide a block.
4) using one js closure for  for all the executions, not one for each.
5) remove the height limitation of details, don't need to scroll it in the tiny window.

## How was this patch tested?

Exists tests.

![ui](https://cloud.githubusercontent.com/assets/40902/14445712/68d7b258-0004-11e6-9b48-5d329b05d165.png)

Author: Davies Liu <davies@databricks.com>

Closes #12311 from davies/ui_perf.
2016-04-12 15:03:00 -07:00
bomeng bcd2076274 [SPARK-14414][SQL] improve the error message class hierarchy
## 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.
2016-04-12 13:43:39 -07:00
Liwei Lin 852bbc6c00 [SPARK-14556][SQL] Code clean-ups for package o.a.s.sql.execution.streaming.state
## What changes were proposed in this pull request?

- `StateStoreConf.**max**DeltasForSnapshot` was renamed to `StateStoreConf.**min**DeltasForSnapshot`
- some state switch checks were added
- improved consistency between method names and string literals
- other comments & typo fix

## How was this patch tested?

N/A

Author: Liwei Lin <lwlin7@gmail.com>

Closes #12323 from lw-lin/streaming-state-clean-up.
2016-04-12 11:50:51 -07:00
Shixiong Zhu 6bf692147c [SPARK-14474][SQL] Move FileSource offset log into checkpointLocation
## What changes were proposed in this pull request?

Now that we have a single location for storing checkpointed state. This PR just propagates the checkpoint location into FileStreamSource so that we don't have one random log off on its own.

## How was this patch tested?

test("metadataPath should be in checkpointLocation")

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #12247 from zsxwing/file-source-log-location.
2016-04-12 10:46:28 -07:00
Dongjoon Hyun b0f5497e95 [SPARK-14508][BUILD] Add a new ScalaStyle Rule OmitBracesInCase
## What changes were proposed in this pull request?

According to the [Spark Code Style Guide](https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide) and [Scala Style Guide](http://docs.scala-lang.org/style/control-structures.html#curlybraces), we had better enforce the following rule.
  ```
  case: Always omit braces in case clauses.
  ```
This PR makes a new ScalaStyle rule, 'OmitBracesInCase', and enforces it to the code.

## How was this patch tested?

Pass the Jenkins tests (including Scala style checking)

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #12280 from dongjoon-hyun/SPARK-14508.
2016-04-12 00:43:28 -07:00
Wenchen Fan 678b96e77b [SPARK-14535][SQL] Remove buildInternalScan from FileFormat
## 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.
2016-04-11 22:59:42 -07:00
Wenchen Fan 52a801124f [SPARK-14554][SQL] disable whole stage codegen if there are too many input columns
## What changes were proposed in this pull request?

In https://github.com/apache/spark/pull/12047/files#diff-94a1f59bcc9b6758c4ca874652437634R529, we may split field expressions codes in `CreateExternalRow` to support wide table. However, the whole stage codegen framework doesn't support it, because the input for expressions is not always the input row, but can be `CodeGenContext.currentVars`, which doesn't work well with `CodeGenContext.splitExpressions`.

Actually we do have a check to guard against this cases, but it's incomplete, it only checks output fields.

This PR improves the whole stage codegen support check, to disable it if there are too many input fields, so that we can avoid splitting field expressions codes in `CreateExternalRow` for whole stage codegen.

TODO: Is it a better solution if we can make `CodeGenContext.currentVars` work well with `CodeGenContext.splitExpressions`?

## How was this patch tested?

new test in DatasetSuite.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #12322 from cloud-fan/codegen.
2016-04-11 22:58:35 -07:00
gatorsmile 2d81ba542e [SPARK-14362][SPARK-14406][SQL][FOLLOW-UP] DDL Native Support: Drop View and Drop Table
#### What changes were proposed in this pull request?
In this PR, we are trying to address the comment in the original PR: dfce9665c4 (commitcomment-17057030)

In this PR, we checks if table/view exists at the beginning and then does not need to capture the exceptions, including `NoSuchTableException` and `InvalidTableException`. We still capture the NonFatal exception when doing `sqlContext.cacheManager.tryUncacheQuery`.

#### How was this patch tested?
The existing test cases should cover the code changes of this PR.

Author: gatorsmile <gatorsmile@gmail.com>

Closes #12321 from gatorsmile/dropViewFollowup.
2016-04-11 22:33:05 -07:00
Andrew Or 83fb96403b [SPARK-14132][SPARK-14133][SQL] Alter table partition DDLs
## 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.
2016-04-11 20:59:45 -07:00
Liang-Chi Hsieh 26d7af9119 [SPARK-14520][SQL] Use correct return type in VectorizedParquetInputFormat
## What changes were proposed in this pull request?
JIRA: https://issues.apache.org/jira/browse/SPARK-14520

`VectorizedParquetInputFormat` inherits `ParquetInputFormat` and overrides `createRecordReader`. However, its overridden `createRecordReader` returns a `ParquetRecordReader`. It should return a `RecordReader`. Otherwise, `ClassCastException` will be thrown.

## How was this patch tested?

Existing tests.

Author: Liang-Chi Hsieh <simonh@tw.ibm.com>

Closes #12292 from viirya/fix-vectorized-input-format.
2016-04-11 19:06:38 -07:00
Eric Liang 6f27027d96 [SPARK-14475] Propagate user-defined context from driver to executors
## What changes were proposed in this pull request?

This adds a new API call `TaskContext.getLocalProperty` for getting properties set in the driver from executors. These local properties are automatically propagated from the driver to executors. For streaming, the context for streaming tasks will be the initial driver context when ssc.start() is called.

## How was this patch tested?

Unit tests.

cc JoshRosen

Author: Eric Liang <ekl@databricks.com>

Closes #12248 from ericl/sc-2813.
2016-04-11 18:33:54 -07:00
Shixiong Zhu 2dacc81ec3 [SPARK-14494][SQL] Fix the race conditions in MemoryStream and MemorySink
## What changes were proposed in this pull request?

Make sure accessing mutable variables in MemoryStream and MemorySink are protected by `synchronized`.
This is probably why MemorySinkSuite failed here: https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.2/650/testReport/junit/org.apache.spark.sql.streaming/MemorySinkSuite/registering_as_a_table/

## How was this patch tested?
Existing unit tests.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #12261 from zsxwing/memory-race-condition.
2016-04-11 10:42:51 -07:00
Rekha Joshi e82d95bf63 [SPARK-14372][SQL] Dataset.randomSplit() needs a Java version
## What changes were proposed in this pull request?

1.Added method randomSplitAsList() in Dataset for java
for https://issues.apache.org/jira/browse/SPARK-14372

## How was this patch tested?

TestSuite

Author: Rekha Joshi <rekhajoshm@gmail.com>
Author: Joshi <rekhajoshm@gmail.com>

Closes #12184 from rekhajoshm/SPARK-14372.
2016-04-11 17:13:30 +08:00
gatorsmile 9f838bd242 [SPARK-14362][SPARK-14406][SQL][FOLLOW-UP] DDL Native Support: Drop View and 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.
2016-04-10 20:46:15 -07:00
Davies Liu fbf8d00883 [SPARK-14419] [MINOR] coding style cleanup
## What changes were proposed in this pull request?

Making them more consistent.

## How was this patch tested?

Existing tests.

Author: Davies Liu <davies@databricks.com>

Closes #12289 from davies/cleanup_style.
2016-04-10 18:10:44 -07:00
Dongjoon Hyun a7ce473bd0 [SPARK-14415][SQL] All functions should show usages by command DESC FUNCTION
## 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.
2016-04-10 11:46:45 -07:00
Dongjoon Hyun aea30a1a9b [SPARK-14465][BUILD] Checkstyle should check all Java files
## What changes were proposed in this pull request?

Currently, `checkstyle` is configured to check the files under `src/main/java`. However, Spark has Java files in `src/main/scala`, too. This PR fixes the following configuration in `pom.xml` and the unchecked-so-far violations on those files.
```xml
-<sourceDirectory>${basedir}/src/main/java</sourceDirectory>
+<sourceDirectories>${basedir}/src/main/java,${basedir}/src/main/scala</sourceDirectories>
```

## How was this patch tested?

After passing the Jenkins build and manually `dev/lint-java`. (Note that Jenkins does not run `lint-java`)

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #12242 from dongjoon-hyun/SPARK-14465.
2016-04-09 21:31:20 -07:00
Nong Li 5989c85b53 [SPARK-14217] [SQL] Fix bug if parquet data has columns that use dictionary encoding for some of the data
## What changes were proposed in this pull request?

This PR is based on #12017

Currently, this causes batches where some values are dictionary encoded and some
which are not. The non-dictionary encoded values cause us to remove the dictionary
from the batch causing the first values to return garbage.

This patch fixes the issue by first decoding the dictionary for the values that are
already dictionary encoded before switching. A similar thing is done for the reverse
case where the initial values are not dictionary encoded.

## How was this patch tested?

This is difficult to test but replicated on a test cluster using a large tpcds data set.

Author: Nong Li <nong@databricks.com>
Author: Davies Liu <davies@databricks.com>

Closes #12279 from davies/fix_dict.
2016-04-09 17:45:10 -07:00
Davies Liu 5cb5edaf9c [SPARK-14419] [SQL] Improve HashedRelation for key fit within Long
## What changes were proposed in this pull request?

Currently, we use java HashMap for HashedRelation if the key could fit within a Long. The java HashMap and CompactBuffer are not memory efficient, the memory used by them is also accounted accurately.

This PR introduce a LongToUnsafeRowMap (similar to BytesToBytesMap) for better memory efficiency and performance.

This PR reopen #12190 to fix bugs.

## How was this patch tested?

Existing tests.

Author: Davies Liu <davies@databricks.com>

Closes #12278 from davies/long_map3.
2016-04-09 17:44:38 -07:00
gatorsmile dfce9665c4 [SPARK-14362][SPARK-14406][SQL] DDL Native Support: Drop View and Drop Table
#### 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.
2016-04-09 17:40:36 -07:00
gatorsmile 9be5558e00 [SPARK-14481][SQL] Issue Exceptions for All Unsupported Options during Parsing
#### 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.
2016-04-09 14:10:44 -07:00
Yong Tang cd2fed7012 [SPARK-14335][SQL] Describe function command returns wrong output
## 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.
2016-04-09 13:54:30 -07:00
Davies Liu f7ec854f1b Revert "[SPARK-14419] [SQL] Improve HashedRelation for key fit within Long"
This reverts commit 90c0a04506.
2016-04-09 13:51:28 -07:00
bomeng 10a95781ee [SPARK-14496][SQL] fix some javadoc typos
## What changes were proposed in this pull request?

Minor issues. Found 2 typos while browsing the code.

## How was this patch tested?
None.

Author: bomeng <bmeng@us.ibm.com>

Closes #12264 from bomeng/SPARK-14496.
2016-04-09 22:30:54 +09:00
Davies Liu 90c0a04506 [SPARK-14419] [SQL] Improve HashedRelation for key fit within Long
## What changes were proposed in this pull request?

Currently, we use java HashMap for HashedRelation if the key could fit within a Long. The java HashMap and CompactBuffer are not memory efficient, the memory used by them is also accounted accurately.

This PR introduce a LongToUnsafeRowMap (similar to BytesToBytesMap) for better memory efficiency and performance.

## How was this patch tested?

Updated existing tests.

Author: Davies Liu <davies@databricks.com>

Closes #12190 from davies/long_map2.
2016-04-09 00:37:55 -07:00
Reynold Xin 520dde48d0 [SPARK-14451][SQL] Move encoder definition into Aggregator interface
## What changes were proposed in this pull request?
When we first introduced Aggregators, we required the user of Aggregators to (implicitly) specify the encoders. It would actually make more sense to have the encoders be specified by the implementation of Aggregators, since each implementation should have the most state about how to encode its own data type.

Note that this simplifies the Java API because Java users no longer need to explicitly specify encoders for aggregators.

## How was this patch tested?
Updated unit tests.

Author: Reynold Xin <rxin@databricks.com>

Closes #12231 from rxin/SPARK-14451.
2016-04-09 00:00:39 -07:00
Reynold Xin 2f0b882e5c [SPARK-14482][SQL] Change default Parquet codec from gzip to snappy
## What changes were proposed in this pull request?
Based on our tests, gzip decompression is very slow (< 100MB/s), making queries decompression bound. Snappy can decompress at ~ 500MB/s on a single core.

This patch changes the default compression codec for Parquet output from gzip to snappy, and also introduces a ParquetOptions class to be more consistent with other data sources (e.g. CSV, JSON).

## How was this patch tested?
Should be covered by existing unit tests.

Author: Reynold Xin <rxin@databricks.com>

Closes #12256 from rxin/SPARK-14482.
2016-04-08 23:52:04 -07:00
Joseph K. Bradley d7af736b2c [SPARK-14498][ML][PYTHON][SQL] Many cleanups to ML and ML-related docs
## What changes were proposed in this pull request?

Cleanups to documentation.  No changes to code.
* GBT docs: Move Scala doc for private object GradientBoostedTrees to public docs for GBTClassifier,Regressor
* GLM regParam: needs doc saying it is for L2 only
* TrainValidationSplitModel: add .. versionadded:: 2.0.0
* Rename “_transformer_params_from_java” to “_transfer_params_from_java”
* LogReg Summary classes: “probability” col should not say “calibrated”
* LR summaries: coefficientStandardErrors —> document that intercept stderr comes last.  Same for t,p-values
* approxCountDistinct: Document meaning of “rsd" argument.
* LDA: note which params are for online LDA only

## How was this patch tested?

Doc build

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #12266 from jkbradley/ml-doc-cleanups.
2016-04-08 20:15:44 -07:00
Sameer Agarwal 813e96e6fa [SPARK-14454] Better exception handling while marking tasks as failed
## What changes were proposed in this pull request?

This patch adds support for better handling of exceptions inside catch blocks if the code within the block throws an exception. For instance here is the code in a catch block before this change in `WriterContainer.scala`:

```scala
logError("Aborting task.", cause)
// call failure callbacks first, so we could have a chance to cleanup the writer.
TaskContext.get().asInstanceOf[TaskContextImpl].markTaskFailed(cause)
if (currentWriter != null) {
  currentWriter.close()
}
abortTask()
throw new SparkException("Task failed while writing rows.", cause)
```

If `markTaskFailed` or `currentWriter.close` throws an exception, we currently lose the original cause. This PR fixes this problem by implementing a utility function `Utils.tryWithSafeCatch` that suppresses (`Throwable.addSuppressed`) the exception that are thrown within the catch block and rethrowing the original exception.

## How was this patch tested?

No new functionality added

Author: Sameer Agarwal <sameer@databricks.com>

Closes #12234 from sameeragarwal/fix-exception.
2016-04-08 17:23:32 -07:00
Sameer Agarwal f8c9beca38 [SPARK-14394][SQL] Generate AggregateHashMap class for LongTypes during TungstenAggregate codegen
## What changes were proposed in this pull request?

This PR adds support for generating the `AggregateHashMap` class in `TungstenAggregate` if the aggregate group by keys/value are of `LongType`. Note that currently this generate aggregate is not actually used.

NB: This currently only supports `LongType` keys/values (please see `isAggregateHashMapSupported` in `TungstenAggregate`) and will be generalized to other data types in a subsequent PR.

## How was this patch tested?

Manually inspected the generated code. This is what the generated map looks like for 2 keys:

```java
/* 068 */   public class agg_GeneratedAggregateHashMap {
/* 069 */     private org.apache.spark.sql.execution.vectorized.ColumnarBatch batch;
/* 070 */     private int[] buckets;
/* 071 */     private int numBuckets;
/* 072 */     private int maxSteps;
/* 073 */     private int numRows = 0;
/* 074 */     private org.apache.spark.sql.types.StructType schema =
/* 075 */     new org.apache.spark.sql.types.StructType()
/* 076 */     .add("k1", org.apache.spark.sql.types.DataTypes.LongType)
/* 077 */     .add("k2", org.apache.spark.sql.types.DataTypes.LongType)
/* 078 */     .add("sum", org.apache.spark.sql.types.DataTypes.LongType);
/* 079 */
/* 080 */     public agg_GeneratedAggregateHashMap(int capacity, double loadFactor, int maxSteps) {
/* 081 */       assert (capacity > 0 && ((capacity & (capacity - 1)) == 0));
/* 082 */       this.maxSteps = maxSteps;
/* 083 */       numBuckets = (int) (capacity / loadFactor);
/* 084 */       batch = org.apache.spark.sql.execution.vectorized.ColumnarBatch.allocate(schema,
/* 085 */         org.apache.spark.memory.MemoryMode.ON_HEAP, capacity);
/* 086 */       buckets = new int[numBuckets];
/* 087 */       java.util.Arrays.fill(buckets, -1);
/* 088 */     }
/* 089 */
/* 090 */     public agg_GeneratedAggregateHashMap() {
/* 091 */       new agg_GeneratedAggregateHashMap(1 << 16, 0.25, 5);
/* 092 */     }
/* 093 */
/* 094 */     public org.apache.spark.sql.execution.vectorized.ColumnarBatch.Row findOrInsert(long agg_key, long agg_key1) {
/* 095 */       long h = hash(agg_key, agg_key1);
/* 096 */       int step = 0;
/* 097 */       int idx = (int) h & (numBuckets - 1);
/* 098 */       while (step < maxSteps) {
/* 099 */         // Return bucket index if it's either an empty slot or already contains the key
/* 100 */         if (buckets[idx] == -1) {
/* 101 */           batch.column(0).putLong(numRows, agg_key);
/* 102 */           batch.column(1).putLong(numRows, agg_key1);
/* 103 */           batch.column(2).putLong(numRows, 0);
/* 104 */           buckets[idx] = numRows++;
/* 105 */           return batch.getRow(buckets[idx]);
/* 106 */         } else if (equals(idx, agg_key, agg_key1)) {
/* 107 */           return batch.getRow(buckets[idx]);
/* 108 */         }
/* 109 */         idx = (idx + 1) & (numBuckets - 1);
/* 110 */         step++;
/* 111 */       }
/* 112 */       // Didn't find it
/* 113 */       return null;
/* 114 */     }
/* 115 */
/* 116 */     private boolean equals(int idx, long agg_key, long agg_key1) {
/* 117 */       return batch.column(0).getLong(buckets[idx]) == agg_key && batch.column(1).getLong(buckets[idx]) == agg_key1;
/* 118 */     }
/* 119 */
/* 120 */     // TODO: Improve this Hash Function
/* 121 */     private long hash(long agg_key, long agg_key1) {
/* 122 */       return agg_key ^ agg_key1;
/* 123 */     }
/* 124 */
/* 125 */   }
```

Author: Sameer Agarwal <sameer@databricks.com>

Closes #12161 from sameeragarwal/tungsten-aggregate.
2016-04-08 13:52:28 -07:00
tedyu 02757535b5 [SPARK-14448] Improvements to ColumnVector
## What changes were proposed in this pull request?

In this PR, two changes are proposed for ColumnVector :
1. ColumnVector should be declared as implementing AutoCloseable - it already has close() method
2. In OnHeapColumnVector#reserveInternal(), we only need to allocate new array when existing array is null or the length of existing array is shorter than the newCapacity.

## How was this patch tested?

Existing unit tests.

Author: tedyu <yuzhihong@gmail.com>

Closes #12225 from tedyu/master.
2016-04-08 12:25:36 -07:00
hyukjinkwon 73b56a3c6c [SPARK-14189][SQL] JSON data sources find compatible types even if inferred decimal type is not capable of the others
## What changes were proposed in this pull request?

https://issues.apache.org/jira/browse/SPARK-14189

When inferred types in the same field during finding compatible `DataType`, are `IntegralType` and `DecimalType` but `DecimalType` is not capable of the given `IntegralType`, JSON data source simply fails to find a compatible type resulting in `StringType`.

This can be observed when `prefersDecimal` is enabled.

```scala
def mixedIntegerAndDoubleRecords: RDD[String] =
  sqlContext.sparkContext.parallelize(
    """{"a": 3, "b": 1.1}""" ::
    """{"a": 3.1, "b": 1}""" :: Nil)

val jsonDF = sqlContext.read
  .option("prefersDecimal", "true")
  .json(mixedIntegerAndDoubleRecords)
  .printSchema()
```

- **Before**

```
root
 |-- a: string (nullable = true)
 |-- b: string (nullable = true)
```

- **After**

```
root
 |-- a: decimal(21, 1) (nullable = true)
 |-- b: decimal(21, 1) (nullable = true)
```
(Note that integer is inferred as `LongType` which becomes `DecimalType(20, 0)`)

## How was this patch tested?

unit tests were used and style tests by `dev/run_tests`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #11993 from HyukjinKwon/SPARK-14189.
2016-04-08 00:30:26 -07:00
hyukjinkwon 725b860e2b [SPARK-14103][SQL] Parse unescaped quotes in CSV data source.
## What changes were proposed in this pull request?

This PR resolves the problem during parsing unescaped quotes in input data. For example, currently the data below:

```
"a"b,ccc,ddd
e,f,g
```

produces a data below:

- **Before**

```bash
["a"b,ccc,ddd[\n]e,f,g]  <- as a value.
```

- **After**

```bash
["a"b], [ccc], [ddd]
[e], [f], [g]
```

This PR bumps up the Univocity parser's version. This was fixed in `2.0.2`, https://github.com/uniVocity/univocity-parsers/issues/60.

## How was this patch tested?

Unit tests in `CSVSuite` and `sbt/sbt scalastyle`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #12226 from HyukjinKwon/SPARK-14103-quote.
2016-04-08 00:28:59 -07:00
Reynold Xin 04fb7dba70 Replace getLocalizedMessage with just normal toString in exception handling in WriterContainer. 2016-04-07 21:41:41 -07:00
Wenchen Fan 49fb237081 [SPARK-14270][SQL] whole stage codegen support for typed filter
## What changes were proposed in this pull request?

We implement typed filter by `MapPartitions`, which doesn't work well with whole stage codegen. This PR use `Filter` to implement typed filter and we can get the whole stage codegen support for free.

This PR also introduced `DeserializeToObject` and `SerializeFromObject`, to seperate serialization logic from object operator, so that it's eaiser to write optimization rules for adjacent object operators.

## How was this patch tested?

existing tests.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #12061 from cloud-fan/whole-stage-codegen.
2016-04-07 17:23:34 -07:00
Andrew Or ae1db91d15 [SPARK-14410][SQL] Push functions existence check into catalog
## 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.
2016-04-07 16:23:17 -07:00