## What changes were proposed in this pull request?
Currently Spark SQL doesn't support sorting columns in descending order. However, the parser accepts the syntax and silently drops sorting directions. This PR fixes this by throwing an exception if `DESC` is specified as sorting direction of a sorting column.
## How was this patch tested?
A test case is added to test the invalid sorting order by checking exception message.
Author: Cheng Lian <lian@databricks.com>
Closes#12759 from liancheng/spark-14981.
## What changes were proposed in this pull request?
We recently inlined Hive's thrift server code in SPARK-15004. This patch removes the code related to zookeeper service discovery, Tez, and Hive on Spark, since they are irrelevant.
## How was this patch tested?
N/A - removing dead code
Author: Reynold Xin <rxin@databricks.com>
Closes#12780 from rxin/SPARK-15004.
This test always fail with sbt's hadoop 2.3 and 2.4 tests. Let'e disable it for now and investigate the problem.
Author: Yin Huai <yhuai@databricks.com>
Closes#12783 from yhuai/SPARK-15011-ignore.
## What changes were proposed in this pull request?
The `catalog` and `conf` APIs were exposed in `SparkSession` in #12713 and #12669. This patch adds those to the python API.
## How was this patch tested?
Python tests.
Author: Andrew Or <andrew@databricks.com>
Closes#12765 from andrewor14/python-spark-session-more.
## What changes were proposed in this pull request?
This PR copy the thrift-server from hive-service-1.2 (including TCLIService.thrift and generated Java source code) into sql/hive-thriftserver, so we can do further cleanup and improvements.
## How was this patch tested?
Existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#12764 from davies/thrift_server.
## What changes were proposed in this pull request?
This patch removes executionHive from HiveSessionState and HiveSharedState.
## How was this patch tested?
Updated test cases.
Author: Reynold Xin <rxin@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#12770 from rxin/SPARK-14994.
## What changes were proposed in this pull request?
This PR adds support for easily running and benchmarking a set of common TPCDS queries locally in SparkSQL.
## How was this patch tested?
N/A
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12771 from sameeragarwal/tpcds-2.
#### What changes were proposed in this pull request?
Replaces a logical `Except` operator with a `Left-anti Join` operator. This way, we can take advantage of all the benefits of join implementations (e.g. managed memory, code generation, broadcast joins).
```SQL
SELECT a1, a2 FROM Tab1 EXCEPT SELECT b1, b2 FROM Tab2
==> SELECT DISTINCT a1, a2 FROM Tab1 LEFT ANTI JOIN Tab2 ON a1<=>b1 AND a2<=>b2
```
Note:
1. This rule is only applicable to EXCEPT DISTINCT. Do not use it for EXCEPT ALL.
2. This rule has to be done after de-duplicating the attributes; otherwise, the enerated
join conditions will be incorrect.
This PR also corrects the existing behavior in Spark. Before this PR, the behavior is like
```SQL
test("except") {
val df_left = Seq(1, 2, 2, 3, 3, 4).toDF("id")
val df_right = Seq(1, 3).toDF("id")
checkAnswer(
df_left.except(df_right),
Row(2) :: Row(2) :: Row(4) :: Nil
)
}
```
After this PR, the result is corrected. We strictly follow the SQL compliance of `Except Distinct`.
#### How was this patch tested?
Modified and added a few test cases to verify the optimization rule and the results of operators.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12736 from gatorsmile/exceptByAntiJoin.
## What changes were proposed in this pull request?
Minor typo fixes
## How was this patch tested?
local build
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#12755 from zhengruifeng/fix_doc_dataset.
## What changes were proposed in this pull request?
This patch removes HiveNativeCommand, so we can continue to remove the dependency on Hive. This pull request also removes the ability to generate golden result file using Hive.
## How was this patch tested?
Updated tests to reflect this.
Author: Reynold Xin <rxin@databricks.com>
Closes#12769 from rxin/SPARK-14991.
## What changes were proposed in this pull request?
`AccumulatorContext` is not thread-safe, that's why all of its methods are synchronized. However, there is one exception: the `AccumulatorContext.originals`. `NewAccumulator` use it to check if it's registered, which is wrong as it's not synchronized.
This PR mark `AccumulatorContext.originals` as `private` and now all access to `AccumulatorContext` is synchronized.
## How was this patch tested?
I verified it locally. To be safe, we can let jenkins test it many times to make sure this problem is gone.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12773 from cloud-fan/debug.
## What changes were proposed in this pull request?
This PR adds `since` tag into the matrix and vector classes in spark-mllib-local.
## How was this patch tested?
Scala-style checks passed.
Author: Pravin Gadakh <prgadakh@in.ibm.com>
Closes#12416 from pravingadakh/SPARK-14613.
## What changes were proposed in this pull request?
The FileCatalog object gets created even if the user specifies schema, which means files in the directory is enumerated even thought its not necessary. For large directories this is very slow. User would want to specify schema in such scenarios of large dirs, and this defeats the purpose quite a bit.
## How was this patch tested?
Hard to test this with unit test.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#12748 from tdas/SPARK-14970.
## What changes were proposed in this pull request?
Fix to ScalaDoc for StructType.
## How was this patch tested?
Built locally.
Author: Gregory Hart <greg.hart@thinkbiganalytics.com>
Closes#12758 from freastro/hotfix/SPARK-14965.
## What changes were proposed in this pull request?
Currently we use `SQLUserDefinedType` annotation to register UDTs for user classes. However, by doing this, we add Spark dependency to user classes.
For some user classes, it is unnecessary to add such dependency that will increase deployment difficulty.
We should provide alternative approach to register UDTs for user classes without `SQLUserDefinedType` annotation.
## How was this patch tested?
`UserDefinedTypeSuite`
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#12259 from viirya/improve-sql-usertype.
## What changes were proposed in this pull request?
This PR introduces a new accumulator API which is much simpler than before:
1. the type hierarchy is simplified, now we only have an `Accumulator` class
2. Combine `initialValue` and `zeroValue` concepts into just one concept: `zeroValue`
3. there in only one `register` method, the accumulator registration and cleanup registration are combined.
4. the `id`,`name` and `countFailedValues` are combined into an `AccumulatorMetadata`, and is provided during registration.
`SQLMetric` is a good example to show the simplicity of this new API.
What we break:
1. no `setValue` anymore. In the new API, the intermedia type can be different from the result type, it's very hard to implement a general `setValue`
2. accumulator can't be serialized before registered.
Problems need to be addressed in follow-ups:
1. with this new API, `AccumulatorInfo` doesn't make a lot of sense, the partial output is not partial updates, we need to expose the intermediate value.
2. `ExceptionFailure` should not carry the accumulator updates. Why do users care about accumulator updates for failed cases? It looks like we only use this feature to update the internal metrics, how about we sending a heartbeat to update internal metrics after the failure event?
3. the public event `SparkListenerTaskEnd` carries a `TaskMetrics`. Ideally this `TaskMetrics` don't need to carry external accumulators, as the only method of `TaskMetrics` that can access external accumulators is `private[spark]`. However, `SQLListener` use it to retrieve sql metrics.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12612 from cloud-fan/acc.
## What changes were proposed in this pull request?
Currently, LongToUnsafeRowMap use byte array as the underlying page, which can't be larger 1G.
This PR improves LongToUnsafeRowMap to scale up to 8G bytes by using array of Long instead of array of byte.
## How was this patch tested?
Manually ran a test to confirm that both UnsafeHashedRelation and LongHashedRelation could build a map that larger than 2G.
Author: Davies Liu <davies@databricks.com>
Closes#12740 from davies/larger_broadcast.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-12143
This PR adds the support for conversion between `SparkRow` in Spark and `RowSet` in Hive for `BinaryType` as `Array[Byte]` (JDBC)
## How was this patch tested?
Unittests in `HiveThriftBinaryServerSuite` (regression test)
Closes#10139
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#12733 from HyukjinKwon/SPARK-12143.
## What changes were proposed in this pull request?
`interfaces.scala` was getting big. This just moves the biggest class in there to a new file for cleanliness.
## How was this patch tested?
Just moving things around.
Author: Andrew Or <andrew@databricks.com>
Closes#12721 from andrewor14/move-external-catalog.
Currently, we can only create persisted partitioned and/or bucketed data source tables using the Dataset API but not using SQL DDL. This PR implements the following syntax to add partitioning and bucketing support to the SQL DDL:
```
CREATE TABLE <table-name>
USING <provider> [OPTIONS (<key1> <value1>, <key2> <value2>, ...)]
[PARTITIONED BY (col1, col2, ...)]
[CLUSTERED BY (col1, col2, ...) [SORTED BY (col1, col2, ...)] INTO <n> BUCKETS]
AS SELECT ...
```
Test cases are added in `MetastoreDataSourcesSuite` to check the newly added syntax.
Author: Cheng Lian <lian@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#12734 from liancheng/spark-14954.
## What changes were proposed in this pull request?
This PR aims to implement decimal aggregation optimization for window queries by improving existing `DecimalAggregates`. Historically, `DecimalAggregates` optimizer is designed to transform general `sum/avg(decimal)`, but it breaks recently added windows queries like the followings. The following queries work well without the current `DecimalAggregates` optimizer.
**Sum**
```scala
scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").head
java.lang.RuntimeException: Unsupported window function: MakeDecimal((sum(UnscaledValue(a#31)),mode=Complete,isDistinct=false),12,1)
scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#23]
: +- INPUT
+- Window [MakeDecimal((sum(UnscaledValue(a#21)),mode=Complete,isDistinct=false),12,1) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#23]
+- Exchange SinglePartition, None
+- Generate explode([1.0,2.0]), false, false, [a#21]
+- Scan OneRowRelation[]
```
**Average**
```scala
scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").head
java.lang.RuntimeException: Unsupported window function: cast(((avg(UnscaledValue(a#40)),mode=Complete,isDistinct=false) / 10.0) as decimal(6,5))
scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#44]
: +- INPUT
+- Window [cast(((avg(UnscaledValue(a#42)),mode=Complete,isDistinct=false) / 10.0) as decimal(6,5)) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#44]
+- Exchange SinglePartition, None
+- Generate explode([1.0,2.0]), false, false, [a#42]
+- Scan OneRowRelation[]
```
After this PR, those queries work fine and new optimized physical plans look like the followings.
**Sum**
```scala
scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#35]
: +- INPUT
+- Window [MakeDecimal((sum(UnscaledValue(a#33)),mode=Complete,isDistinct=false) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING),12,1) AS sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#35]
+- Exchange SinglePartition, None
+- Generate explode([1.0,2.0]), false, false, [a#33]
+- Scan OneRowRelation[]
```
**Average**
```scala
scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#47]
: +- INPUT
+- Window [cast(((avg(UnscaledValue(a#45)),mode=Complete,isDistinct=false) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) / 10.0) as decimal(6,5)) AS avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#47]
+- Exchange SinglePartition, None
+- Generate explode([1.0,2.0]), false, false, [a#45]
+- Scan OneRowRelation[]
```
In this PR, *SUM over window* pattern matching is based on the code of hvanhovell ; he should be credited for the work he did.
## How was this patch tested?
Pass the Jenkins tests (with newly added testcases)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12421 from dongjoon-hyun/SPARK-14664.
## What changes were proposed in this pull request?
The `Batch` class, which had been used to indicate progress in a stream, was abandoned by [[SPARK-13985][SQL] Deterministic batches with ids](caea152145) and then became useless.
This patch:
- removes the `Batch` class
- ~~does some related renaming~~ (update: this has been reverted)
- fixes some related comments
## How was this patch tested?
N/A
Author: Liwei Lin <lwlin7@gmail.com>
Closes#12638 from lw-lin/remove-batch.
### What changes were proposed in this pull request?
Anti-Joins using BroadcastHashJoin's unique key code path are broken; it currently returns Semi Join results . This PR fixes this bug.
### How was this patch tested?
Added tests cases to `ExistenceJoinSuite`.
cc davies gatorsmile
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12730 from hvanhovell/SPARK-14950.
## What changes were proposed in this pull request?
This patch removes the use of HiveConf from InsertIntoHiveTable. I think this is the last major use of HiveConf and after this we can try to remove the execution HiveConf.
## How was this patch tested?
Internal refactoring and should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12728 from rxin/SPARK-14949.
## What changes were proposed in this pull request?
This PR will make Spark SQL not allow ALTER TABLE ADD/REPLACE/CHANGE COLUMN, ALTER TABLE SET FILEFORMAT, DFS, and transaction related commands.
## How was this patch tested?
Existing tests. For those tests that I put in the blacklist, I am adding the useful parts back to SQLQuerySuite.
Author: Yin Huai <yhuai@databricks.com>
Closes#12714 from yhuai/banNativeCommand.
## What changes were proposed in this pull request?
This patch removes HiveConf from HiveTableScanExec and HiveTableReader and instead just uses our own configuration system. I'm splitting the large change of removing HiveConf into multiple independent pull requests because it is very difficult to debug test failures when they are all combined in one giant one.
## How was this patch tested?
Should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12727 from rxin/SPARK-14944.
## What changes were proposed in this pull request?
We currently expose both Hadoop configuration and Spark SQL configuration in RuntimeConfig. I think we can remove the Hadoop configuration part, and simply generate Hadoop Configuration on the fly by passing all the SQL configurations into it. This way, there is a single interface (in Java/Scala/Python/SQL) for end-users.
As part of this patch, I also removed some config options deprecated in Spark 1.x.
## How was this patch tested?
Updated relevant tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12689 from rxin/SPARK-14913.
## What changes were proposed in this pull request?
#12625 exposed a new user-facing conf interface in `SparkSession`. This patch adds a catalog interface.
## How was this patch tested?
See `CatalogSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#12713 from andrewor14/user-facing-catalog.
## What changes were proposed in this pull request?
This PR adds Native execution of SHOW COLUMNS and SHOW PARTITION commands.
Command Syntax:
``` SQL
SHOW COLUMNS (FROM | IN) table_identifier [(FROM | IN) database]
```
``` SQL
SHOW PARTITIONS [db_name.]table_name [PARTITION(partition_spec)]
```
## How was this patch tested?
Added test cases in HiveCommandSuite to verify execution and DDLCommandSuite
to verify plans.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#12222 from dilipbiswal/dkb_show_columns.
## What changes were proposed in this pull request?
While the vectorized hash map in `TungstenAggregate` is currently supported for all primitive data types during partial aggregation, this patch only enables the hash map for a subset of cases that've been verified to show performance improvements on our benchmarks subject to an internal conf that sets an upper limit on the maximum length of the aggregate key/value schema. This list of supported use-cases should be expanded over time.
## How was this patch tested?
This is no new change in functionality so existing tests should suffice. Performance tests were done on TPCDS benchmarks.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12710 from sameeragarwal/vectorized-enable.
## What changes were proposed in this pull request?
This PR update SortMergeJoinExec to support LeftSemi/LeftAnti, so it could support all the join types, same as other three join implementations: BroadcastHashJoinExec, ShuffledHashJoinExec,and BroadcastNestedLoopJoinExec.
This PR also simplify the join selection in SparkStrategy.
## How was this patch tested?
Added new tests.
Author: Davies Liu <davies@databricks.com>
Closes#12668 from davies/smj_semi.
## What changes were proposed in this pull request?
That patch mistakenly widened the visibility from `private[x]` to `protected[x]`. This patch reverts those changes.
Author: Andrew Or <andrew@databricks.com>
Closes#12686 from andrewor14/visibility.
## What changes were proposed in this pull request?
We currently have no way for users to propagate options to the underlying library that rely in Hadoop configurations to work. For example, there are various options in parquet-mr that users might want to set, but the data source API does not expose a per-job way to set it. This patch propagates the user-specified options also into Hadoop Configuration.
## How was this patch tested?
Used a mock data source implementation to test both the read path and the write path.
Author: Reynold Xin <rxin@databricks.com>
Closes#12688 from rxin/SPARK-14912.
#### What changes were proposed in this pull request?
The existing `Describe Function` only support the function name in `identifier`. This is different from what Hive behaves. That is why many test cases `udf_abc` in `HiveCompatibilitySuite` are not using our native DDL support. For example,
- udf_not.q
- udf_bitwise_not.q
This PR is to resolve the issues. Now, we can support the command of `Describe Function` whose function names are in the following format:
- `qualifiedName` (e.g., `db.func1`)
- `STRING` (e.g., `'func1'`)
- `comparisonOperator` (e.g,. `<`)
- `arithmeticOperator` (e.g., `+`)
- `predicateOperator` (e.g., `or`)
Note, before this PR, we only have a native command support when the function name is in the format of `qualifiedName`.
#### How was this patch tested?
Added test cases in `DDLSuite.scala`. Also manually verified all the related test cases in `HiveCompatibilitySuite` passed.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12679 from gatorsmile/descFunction.
## What changes were proposed in this pull request?
Minor typo fixes (too minor to deserve separate a JIRA)
## How was this patch tested?
local build
Author: Jacek Laskowski <jacek@japila.pl>
Closes#12469 from jaceklaskowski/minor-typo-fixes.
## What changes were proposed in this pull request?
Use Long.parseLong which returns a primative.
Use a series of appends() reduces the creation of an extra StringBuilder type
## How was this patch tested?
Unit tests
Author: Azeem Jiva <azeemj@gmail.com>
Closes#12520 from javawithjiva/minor.
## What changes were proposed in this pull request?
In Spark 2.0, `SparkSession` is the new thing. Internally we should stop using `SQLContext` everywhere since that's supposed to be not the main user-facing API anymore.
In this patch I took care to not break any public APIs. The one place that's suspect is `o.a.s.ml.source.libsvm.DefaultSource`, but according to mengxr it's not supposed to be public so it's OK to change the underlying `FileFormat` trait.
**Reviewers**: This is a big patch that may be difficult to review but the changes are actually really straightforward. If you prefer I can break it up into a few smaller patches, but it will delay the progress of this issue a little.
## How was this patch tested?
No change in functionality intended.
Author: Andrew Or <andrew@databricks.com>
Closes#12625 from andrewor14/spark-session-refactor.
## What changes were proposed in this pull request?
This is for users who can't upgrade and need to continue to use HiveContext.
## How was this patch tested?
Added some basic tests for sanity check.
This is based on #12672 and closes#12672.
Author: Andrew Or <andrew@databricks.com>
Author: Reynold Xin <rxin@databricks.com>
Closes#12682 from rxin/add-back-hive-context.
## What changes were proposed in this pull request?
Minor followup to https://github.com/apache/spark/pull/12651
## How was this patch tested?
Test-only change
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12674 from sameeragarwal/tpcds-fix-2.
## What changes were proposed in this pull request?
`RuntimeConfig` is the new user-facing API in 2.0 added in #11378. Until now, however, it's been dead code. This patch uses `RuntimeConfig` in `SessionState` and exposes that through the `SparkSession`.
## How was this patch tested?
New test in `SQLContextSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#12669 from andrewor14/use-runtime-conf.
## What changes were proposed in this pull request?
This patch changes UnresolvedFunction and UnresolvedGenerator to use a FunctionIdentifier rather than just a String for function name. Also changed SessionCatalog to accept FunctionIdentifier in lookupFunction.
## How was this patch tested?
Updated related unit tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12659 from rxin/SPARK-14888.
## What changes were proposed in this pull request?
```
Spark context available as 'sc' (master = local[*], app id = local-1461283768192).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 2.0.0-SNAPSHOT
/_/
Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_51)
Type in expressions to have them evaluated.
Type :help for more information.
scala> sql("SHOW TABLES").collect()
16/04/21 17:09:39 WARN ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 1.2.0
16/04/21 17:09:39 WARN ObjectStore: Failed to get database default, returning NoSuchObjectException
res0: Array[org.apache.spark.sql.Row] = Array([src,false])
scala> sql("SHOW TABLES").collect()
res1: Array[org.apache.spark.sql.Row] = Array([src,false])
scala> spark.createDataFrame(Seq((1, 1), (2, 2), (3, 3)))
res2: org.apache.spark.sql.DataFrame = [_1: int, _2: int]
```
Hive things are loaded lazily.
## How was this patch tested?
Manual.
Author: Andrew Or <andrew@databricks.com>
Closes#12589 from andrewor14/spark-session-repl.
#### What changes were proposed in this pull request?
For performance, predicates can be pushed through Window if and only if the following conditions are satisfied:
1. All the expressions are part of window partitioning key. The expressions can be compound.
2. Deterministic
#### How was this patch tested?
TODO:
- [X] DSL needs to be modified for window
- [X] more tests will be added.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#11635 from gatorsmile/pushPredicateThroughWindow.
## What changes were proposed in this pull request?
This removes the class `HiveContext` itself along with all code usages associated with it. The bulk of the work was already done in #12485. This is mainly just code cleanup and actually removing the class.
Note: A couple of things will break after this patch. These will be fixed separately.
- the python HiveContext
- all the documentation / comments referencing HiveContext
- there will be no more HiveContext in the REPL (fixed by #12589)
## How was this patch tested?
No change in functionality.
Author: Andrew Or <andrew@databricks.com>
Closes#12585 from andrewor14/delete-hive-context.
#### What changes were proposed in this pull request?
Disable the test case involving INPUTDRIVER and OUTPUTDRIVER, which are not supported
#### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12662 from gatorsmile/disableInOutDriver.
## What changes were proposed in this pull request?
This method was accidentally made `private[sql]` in Spark 2.0. This PR makes it public again, since 3rd party data sources like spark-avro depend on it.
## How was this patch tested?
N/A
Author: Cheng Lian <lian@databricks.com>
Closes#12652 from liancheng/spark-14875.
