## What changes were proposed in this pull request?

The `trait GroupingAnalytics` only has one implementation, it's an unnecessary abstraction. This PR removes it, and does some code simplification when resolving `GroupingSet`.

## How was this patch tested?

existing tests

Author: Wenchen Fan <wenchen@databricks.com>

Closes #11469 from cloud-fan/groupingset.

## What changes were proposed in this pull request?
This pr to make the short names of compression codecs in `ParquetRelation` consistent against other ones. This pr comes from #11324.

## How was this patch tested?
Add more tests in `TextSuite`.

Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>

Closes #11408 from maropu/SPARK-13528.

## What changes were proposed in this pull request?

Also updated the other benchmarks when the default to use vectorized decode was flipped.

Author: Nong Li <nong@databricks.com>

Closes #11454 from nongli/benchmark.

## What changes were proposed in this pull request?

In order to tell OutputStream that the task has failed or not, we should call the failure callbacks BEFORE calling writer.close().

## How was this patch tested?

Added new unit tests.

Author: Davies Liu <davies@databricks.com>

Closes #11450 from davies/callback.

#### What changes were proposed in this pull request?
```SQL
FROM
(FROM test SELECT TRANSFORM(key, value) USING 'cat' AS (`thing1` int, thing2 string)) t
SELECT thing1 + 1
```
This query returns an analysis error, like:
```
Failed to analyze query: org.apache.spark.sql.AnalysisException: cannot resolve '`thing1`' given input columns: [`thing1`, thing2]; line 3 pos 7
'Project [unresolvedalias(('thing1 + 1), None)]
+- SubqueryAlias t
   +- ScriptTransformation [key#2,value#3], cat, [`thing1`#6,thing2#7], HiveScriptIOSchema(List(),List(),Some(org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe),Some(org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe),List((field.delim,	)),List((field.delim,	)),Some(org.apache.hadoop.hive.ql.exec.TextRecordReader),Some(org.apache.hadoop.hive.ql.exec.TextRecordWriter),false)
      +- SubqueryAlias test
         +- Project [_1#0 AS key#2,_2#1 AS value#3]
            +- LocalRelation [_1#0,_2#1], [[1,1],[2,2],[3,3],[4,4],[5,5]]
```

The backpacks of \`thing1\` should be cleaned before entering Parser/Analyzer. This PR fixes this issue.

#### How was this patch tested?

Added a test case and modified an existing test case

Author: gatorsmile <gatorsmile@gmail.com>

Closes #11415 from gatorsmile/scriptTransform.

#### What changes were proposed in this pull request?

This PR is to prune unnecessary columns when the operator is  `MapPartitions`. The solution is to add an extra `Project` in the child node.

For the other two operators `AppendColumns` and `MapGroups`, it sounds doable. More discussions are required. The major reason is the current implementation of the `inputPlan` of `groupBy` is based on the child of `AppendColumns`. It might be a bug? Thus, will submit a separate PR.

#### How was this patch tested?

Added a test case in ColumnPruningSuite to verify the rule. Added another test case in DatasetSuite.scala to verify the data.

Author: gatorsmile <gatorsmile@gmail.com>

Closes #11460 from gatorsmile/datasetPruningNew.

## What changes were proposed in this pull request?

Change in class FormatNumber to make it work irrespective of locale.

## How was this patch tested?

Unit tests.

Author: lgieron <lgieron@gmail.com>

Closes #11396 from lgieron/SPARK-13515_Fix_Format_Number.

[SPARK-13167][SQL] Include rows with null values for partition column when reading from JDBC datasources.

Rows with null values in partition column are not included in the results because none of the partition
where clause specify is null predicate on the partition column. This fix adds is null predicate on the partition column  to the first JDBC partition where clause.

Example:
JDBCPartition(THEID < 1 or THEID is null, 0),JDBCPartition(THEID >= 1 AND THEID < 2,1),
JDBCPartition(THEID >= 2, 2)

Author: sureshthalamati <suresh.thalamati@gmail.com>

Closes #11063 from sureshthalamati/nullable_jdbc_part_col_spark-13167.

## What changes were proposed in this pull request?

Broadcast left semi join without joining keys is already supported in BroadcastNestedLoopJoin, it has the same implementation as LeftSemiJoinBNL, we should remove that.

## How was this patch tested?

Updated unit tests.

Author: Davies Liu <davies@databricks.com>

Closes #11448 from davies/remove_bnl.

## What changes were proposed in this pull request?

This PR defer the resolution from a id of dictionary to value until the column is actually accessed (inside getInt/getLong), this is very useful for those columns and rows that are filtered out. It's also useful for binary type, we will not need to copy all the byte arrays.

This PR also change the underlying type for small decimal that could be fit within a Int, in order to use getInt() to lookup the value from IntDictionary.

## How was this patch tested?

Manually test TPCDS Q7 with scale factor 10, saw about 30% improvements (after PR #11274).

Author: Davies Liu <davies@databricks.com>

Closes #11437 from davies/decode_dict.

JIRA: https://issues.apache.org/jira/browse/SPARK-13511

## What changes were proposed in this pull request?

Current limit operator doesn't support wholestage codegen. This is open to add support for it.

In the `doConsume` of `GlobalLimit` and `LocalLimit`, we use a count term to count the processed rows. Once the row numbers catches the limit number, we set the variable `stopEarly` of `BufferedRowIterator` newly added in this pr to `true` that indicates we want to stop processing remaining rows. Then when the wholestage codegen framework checks `shouldStop()`, it will stop the processing of the row iterator.

Before this, the executed plan for a query `sqlContext.range(N).limit(100).groupBy().sum()` is:

    TungstenAggregate(key=[], functions=[(sum(id#5L),mode=Final,isDistinct=false)], output=[sum(id)#6L])
    +- TungstenAggregate(key=[], functions=[(sum(id#5L),mode=Partial,isDistinct=false)], output=[sum#9L])
       +- GlobalLimit 100
          +- Exchange SinglePartition, None
             +- LocalLimit 100
                +- Range 0, 1, 1, 524288000, [id#5L]

After add wholestage codegen support:

    WholeStageCodegen
    :  +- TungstenAggregate(key=[], functions=[(sum(id#40L),mode=Final,isDistinct=false)], output=[sum(id)#41L])
    :     +- TungstenAggregate(key=[], functions=[(sum(id#40L),mode=Partial,isDistinct=false)], output=[sum#44L])
    :        +- GlobalLimit 100
    :           +- INPUT
    +- Exchange SinglePartition, None
       +- WholeStageCodegen
          :  +- LocalLimit 100
          :     +- Range 0, 1, 1, 524288000, [id#40L]

## How was this patch tested?

A test is added into BenchmarkWholeStageCodegen.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #11391 from viirya/wholestage-limit.

## What changes were proposed in this pull request?
This PR adds support for implementing whole state codegen for sort. Builds heaving on nongli 's PR: https://github.com/apache/spark/pull/11008 (which actually implements the feature), and adds the following changes on top:

- [x]  Generated code updates peak execution memory metrics
- [x]  Unit tests in `WholeStageCodegenSuite` and `SQLMetricsSuite`

## How was this patch tested?

New unit tests in `WholeStageCodegenSuite` and `SQLMetricsSuite`. Further, all existing sort tests should pass.

Author: Sameer Agarwal <sameer@databricks.com>
Author: Nong Li <nong@databricks.com>

Closes #11359 from sameeragarwal/sort-codegen.

#### What changes were proposed in this pull request?

After analysis by Analyzer, two operators could have alias. They are `Project` and `Aggregate`. So far, we only rewrite and propagate constraints if `Alias` is defined in `Project`. This PR is to resolve this issue in `Aggregate`.

#### How was this patch tested?

Added a test case for `Aggregate` in `ConstraintPropagationSuite`.

marmbrus sameeragarwal

Author: gatorsmile <gatorsmile@gmail.com>

Closes #11422 from gatorsmile/validConstraintsInUnaryNodes.

https://issues.apache.org/jira/browse/SPARK-13507
https://issues.apache.org/jira/browse/SPARK-13509

## What changes were proposed in this pull request?
This PR adds the support to write CSV data directly by a single call to the given path.

Several unitests were added for each functionality.
## How was this patch tested?

This was tested with unittests and with `dev/run_tests` for coding style

Author: hyukjinkwon <gurwls223@gmail.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>

Closes #11389 from HyukjinKwon/SPARK-13507-13509.

## What changes were proposed in this pull request?

Nested classes defined within Scala objects are translated into Java static nested classes. Unlike inner classes, they don't need outer scopes. But the analyzer still thinks that an outer scope is required.

This PR fixes this issue simply by checking whether a nested class is static before looking up its outer scope.

## How was this patch tested?

A test case is added to `DatasetSuite`. It checks contents of a Dataset whose element type is a nested class declared in a Scala object.

Author: Cheng Lian <lian@databricks.com>

Closes #11421 from liancheng/spark-13540-object-as-outer-scope.

Fix type inference issue for sparse CSV data - https://issues.apache.org/jira/browse/SPARK-13309

Author: Rahul Tanwani <rahul@Rahuls-MacBook-Pro.local>

Closes #11194 from tanwanirahul/master.

JIRA: https://issues.apache.org/jira/browse/SPARK-13537

## What changes were proposed in this pull request?

In readBytes of VectorizedPlainValuesReader, we use buffer[offset] to access bytes in buffer. It is incorrect because offset is added with Platform.BYTE_ARRAY_OFFSET when initialization. We should fix it.

## How was this patch tested?

`ParquetHadoopFsRelationSuite` sometimes (depending on the randomly generated data) will be [failed](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/52136/consoleFull) by this bug. After applying this, the test can be passed.

I added a test to `ParquetHadoopFsRelationSuite` with the data which will fail without this patch.

The error exception:

    [info] ParquetHadoopFsRelationSuite:
    [info] - test all data types - StringType (440 milliseconds)
    [info] - test all data types - BinaryType (434 milliseconds)
    [info] - test all data types - BooleanType (406 milliseconds)
    20:59:38.618 ERROR org.apache.spark.executor.Executor: Exception in task 0.0 in stage 2597.0 (TID 67966)
    java.lang.ArrayIndexOutOfBoundsException: 46
	at org.apache.spark.sql.execution.datasources.parquet.VectorizedPlainValuesReader.readBytes(VectorizedPlainValuesReader.java:88)

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #11418 from viirya/fix-readbytes.

## What changes were proposed in this pull request?

This creates a `SessionState`, which groups a few fields that existed in `SQLContext`. Because `HiveContext` extends `SQLContext` we also need to make changes there. This is mainly a cleanup task that will soon pave the way for merging the two contexts.

## How was this patch tested?

Existing unit tests; this patch introduces no change in behavior.

Author: Andrew Or <andrew@databricks.com>

Closes #11405 from andrewor14/refactor-session.

## What changes were proposed in this pull request?

Fix readBytes in VectorizedPlainValuesReader. This fixes a copy and paste issue.

## How was this patch tested?

Ran ParquetHadoopFsRelationSuite which failed before this.

Author: Nong Li <nong@databricks.com>

Closes #11414 from nongli/spark-13533.

JIRA: https://issues.apache.org/jira/browse/SPARK-13530

## What changes were proposed in this pull request?

By enabling vectorized parquet scanner by default, the unit test `ParquetHadoopFsRelationSuite` based on `HadoopFsRelationTest` will be failed due to the lack of short type support in `UnsafeRowParquetRecordReader`. We should fix it.

The error exception:

    [info] ParquetHadoopFsRelationSuite:
    [info] - test all data types - StringType (499 milliseconds)
    [info] - test all data types - BinaryType (447 milliseconds)
    [info] - test all data types - BooleanType (520 milliseconds)
    [info] - test all data types - ByteType (418 milliseconds)
    00:22:58.920 ERROR org.apache.spark.executor.Executor: Exception in task 0.0 in stage 124.0 (TID 1949)
    org.apache.commons.lang.NotImplementedException: Unimplemented type: ShortType
	at org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader$ColumnReader.readIntBatch(UnsafeRowParquetRecordReader.java:769)
	at org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader$ColumnReader.readBatch(UnsafeRowParquetRecordReader.java:640)
	at org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader$ColumnReader.access$000(UnsafeRowParquetRecordReader.java:461)
	at org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader.nextBatch(UnsafeRowParquetRecordReader.java:224)
## How was this patch tested?

The unit test `ParquetHadoopFsRelationSuite` based on `HadoopFsRelationTest` will be [failed](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/52110/consoleFull) due to the lack of short type support in UnsafeRowParquetRecordReader. By adding this support, the test can be passed.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #11412 from viirya/add-shorttype-support.

## What changes were proposed in this pull request?

Change the default of the flag to enable this feature now that the implementation is complete.

## How was this patch tested?

The new parquet reader should be a drop in, so will be exercised by the existing tests.

Author: Nong Li <nong@databricks.com>

Closes #11397 from nongli/spark-13518.

## What changes were proposed in this pull request?

This patch includes these performance fixes:
  - Remove unnecessary setNotNull() calls. The NULL bits are cleared already.
  - Speed up RLE group decoding
  - Speed up dictionary decoding by decoding NULLs directly into the result.

## How was this patch tested?

(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)

In addition to the updated benchmarks, on TPCDS, the result of these changes
running Q55 (sf40) is:

```
TPCDS:                             Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)
---------------------------------------------------------------------------------
q55 (Before)                             6398 / 6616         18.0          55.5
q55 (After)                              4983 / 5189         23.1          43.3
```

Author: Nong Li <nong@databricks.com>

Closes #11375 from nongli/spark-13499.

## What changes were proposed in this pull request?

Currently, BroadcastNestedLoopJoin is implemented for worst case, it's too slow, very easy to hang forever. This PR will create fast path for some joinType and buildSide, also improve the worst case (will use much less memory than before).

Before this PR, one task requires O(N*K) + O(K) in worst cases, N is number of rows from one partition of streamed table, it could hang the job (because of GC).

In order to workaround this for InnerJoin, we have to disable auto-broadcast, switch to CartesianProduct: This could be workaround for InnerJoin, see https://forums.databricks.com/questions/6747/how-do-i-get-a-cartesian-product-of-a-huge-dataset.html

In this PR, we will have fast path for these joins :

 InnerJoin with BuildLeft or BuildRight
 LeftOuterJoin with BuildRight
 RightOuterJoin with BuildLeft
 LeftSemi with BuildRight

These fast paths are all stream based (take one pass on streamed table), required O(1) memory.

All other join types and build types will take two pass on streamed table, one pass to find the matched rows that includes streamed part, which require O(1) memory, another pass to find the rows from build table that does not have a matched row from streamed table, which required O(K) memory, K is the number rows from build side, one bit per row, should be much smaller than the memory for broadcast. The following join types work in this way:

LeftOuterJoin with BuildLeft
RightOuterJoin with BuildRight
FullOuterJoin with BuildLeft or BuildRight
LeftSemi with BuildLeft

This PR also added tests for all the join types for BroadcastNestedLoopJoin.

After this PR, for InnerJoin with one small table, BroadcastNestedLoopJoin should be faster than CartesianProduct, we don't need that workaround anymore.

## How was the this patch tested?

Added unit tests.

Author: Davies Liu <davies@databricks.com>

Closes #11328 from davies/nested_loop.

## What changes were proposed in this pull request?

This is another try of PR #11323.

This PR removes DataFrame RDD operations except for `foreach` and `foreachPartitions` (they are actions rather than transformations). Original calls are now replaced by calls to methods of `DataFrame.rdd`.

PR #11323 was reverted because it introduced a regression: both `DataFrame.foreach` and `DataFrame.foreachPartitions` wrap underlying RDD operations with `withNewExecutionId` to track Spark jobs. But they are removed in #11323.

## How was the this patch tested?

No extra tests are added. Existing tests should do the work.

Author: Cheng Lian <lian@databricks.com>

Closes #11388 from liancheng/remove-df-rdd-ops.

## What changes were proposed in this pull request?

https://issues.apache.org/jira/browse/SPARK-13503
This PR makes the TEXT datasource can compress output by option instead of manually setting Hadoop configurations.
For reflecting codec by names, it is similar with https://github.com/apache/spark/pull/10805 and https://github.com/apache/spark/pull/10858.

## How was this patch tested?

This was tested with unittests and with `dev/run_tests` for coding style

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #11384 from HyukjinKwon/SPARK-13503.

## What changes were proposed in this pull request?
This patch creates the public API for runtime configuration and an implementation for it. The public runtime configuration includes configs for existing SQL, as well as Hadoop Configuration.

This new interface is currently dead code. It will be added to SQLContext and a session entry point to Spark when we add that.

## How was this patch tested?
a new unit test suite

Author: Reynold Xin <rxin@databricks.com>

Closes #11378 from rxin/SPARK-13487.

[SPARK-12941][SQL][MASTER] Spark-SQL JDBC Oracle dialect fails to map string datatypes to Oracle VARCHAR datatype

## What changes were proposed in this pull request?

This Pull request is used for the fix SPARK-12941, creating a data type mapping to Oracle for the corresponding data type"Stringtype" from dataframe. This PR is for the master branch fix, where as another PR is already tested with the branch 1.4

## How was the this patch tested?

(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
This patch was tested using the Oracle docker .Created a new integration suite for the same.The oracle.jdbc jar was to be downloaded from the maven repository.Since there was no jdbc jar available in the maven repository, the jar was downloaded from oracle site manually and installed in the local; thus tested. So, for SparkQA test case run, the ojdbc jar might be manually placed in the local maven repository(com/oracle/ojdbc6/11.2.0.2.0) while Spark QA test run.

Author: thomastechs <thomas.sebastian@tcs.com>

Closes #11306 from thomastechs/master.

This pr added benchmark codes for Encoder#compress().
Also, it replaced the benchmark results with new ones because the output format of `Benchmark` changed.

Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>

Closes #11236 from maropu/CompressionSpike.

## Motivation

As a pre-requisite to off-heap caching of blocks, we need a mechanism to prevent pages / blocks from being evicted while they are being read. With on-heap objects, evicting a block while it is being read merely leads to memory-accounting problems (because we assume that an evicted block is a candidate for garbage-collection, which will not be true during a read), but with off-heap memory this will lead to either data corruption or segmentation faults.

## Changes

### BlockInfoManager and reader/writer locks

This patch adds block-level read/write locks to the BlockManager. It introduces a new `BlockInfoManager` component, which is contained within the `BlockManager`, holds the `BlockInfo` objects that the `BlockManager` uses for tracking block metadata, and exposes APIs for locking blocks in either shared read or exclusive write modes.

`BlockManager`'s `get*()` and `put*()` methods now implicitly acquire the necessary locks. After a `get()` call successfully retrieves a block, that block is locked in a shared read mode. A `put()` call will block until it acquires an exclusive write lock. If the write succeeds, the write lock will be downgraded to a shared read lock before returning to the caller. This `put()` locking behavior allows us store a block and then immediately turn around and read it without having to worry about it having been evicted between the write and the read, which will allow us to significantly simplify `CacheManager` in the future (see #10748).

See `BlockInfoManagerSuite`'s test cases for a more detailed specification of the locking semantics.

### Auto-release of locks at the end of tasks

Our locking APIs support explicit release of locks (by calling `unlock()`), but it's not always possible to guarantee that locks will be released prior to the end of the task. One reason for this is our iterator interface: since our iterators don't support an explicit `close()` operator to signal that no more records will be consumed, operations like `take()` or `limit()` don't have a good means to release locks on their input iterators' blocks. Another example is broadcast variables, whose block locks can only be released at the end of the task.

To address this, `BlockInfoManager` uses a pair of maps to track the set of locks acquired by each task. Lock acquisitions automatically record the current task attempt id by obtaining it from `TaskContext`. When a task finishes, code in `Executor` calls `BlockInfoManager.unlockAllLocksForTask(taskAttemptId)` to free locks.

### Locking and the MemoryStore

In order to prevent in-memory blocks from being evicted while they are being read, the `MemoryStore`'s `evictBlocksToFreeSpace()` method acquires write locks on blocks which it is considering as candidates for eviction. These lock acquisitions are non-blocking, so a block which is being read will not be evicted. By holding write locks until the eviction is performed or skipped (in case evicting the blocks would not free enough memory), we avoid a race where a new reader starts to read a block after the block has been marked as an eviction candidate but before it has been removed.

### Locking and remote block transfer

This patch makes small changes to to block transfer and network layer code so that locks acquired by the BlockTransferService are released as soon as block transfer messages are consumed and released by Netty. This builds on top of #11193, a bug fix related to freeing of network layer ManagedBuffers.

## FAQ

- **Why not use Java's built-in [`ReadWriteLock`](https://docs.oracle.com/javase/7/docs/api/java/util/concurrent/locks/ReadWriteLock.html)?**

  Our locks operate on a per-task rather than per-thread level. Under certain circumstances a task may consist of multiple threads, so using `ReadWriteLock` would mean that we might call `unlock()` from a thread which didn't hold the lock in question, an operation which has undefined semantics. If we could rely on Java 8 classes, we might be able to use [`StampedLock`](https://docs.oracle.com/javase/8/docs/api/java/util/concurrent/locks/StampedLock.html) to work around this issue.

- **Why not detect "leaked" locks in tests?**:

  See above notes about `take()` and `limit`.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #10705 from JoshRosen/pin-pages.

This reverts commit 157fe64f.

## What changes were proposed in this pull request?

This PR removes DataFrame RDD operations. Original calls are now replaced by calls to methods of `DataFrame.rdd`.

## How was the this patch tested?

No extra tests are added. Existing tests should do the work.

Author: Cheng Lian <lian@databricks.com>

Closes #11323 from liancheng/remove-df-rdd-ops.

## What changes were proposed in this pull request?

Predicates shouldn't be pushed through project with nondeterministic field(s).

See https://github.com/graphframes/graphframes/pull/23 and SPARK-13473 for more details.

This PR targets master, branch-1.6, and branch-1.5.

## How was this patch tested?

A test case is added in `FilterPushdownSuite`. It constructs a query plan where a filter is over a project with a nondeterministic field. Optimized query plan shouldn't change in this case.

Author: Cheng Lian <lian@databricks.com>

Closes #11348 from liancheng/spark-13473-no-ppd-through-nondeterministic-project-field.

## What changes were proposed in this pull request?
This patch moves SQLConf into org.apache.spark.sql.internal package to make it very explicit that it is internal. Soon I will also submit more API work that creates implementations of interfaces in this internal package.

## How was this patch tested?
If it compiles, then the refactoring should work.

Author: Reynold Xin <rxin@databricks.com>

Closes #11363 from rxin/SPARK-13486.

## What changes were proposed in this pull request?

This PR mostly rewrite the ColumnPruning rule to support most of the SQL logical plans (except those for Dataset).

This PR also fix a bug in Generate, it should always output UnsafeRow, added an regression test for that.

## How was this patch tested?

This is test by unit tests, also manually test with TPCDS Q78, which could prune all unused columns successfully, improved the performance by 78% (from 22s to 12s).

Author: Davies Liu <davies@databricks.com>

Closes #11354 from davies/fix_column_pruning.

## What changes were proposed in this pull request?

* Scala DataFrameStatFunctions: Added version of approxQuantile taking a List instead of an Array, for Python compatbility
* Python DataFrame and DataFrameStatFunctions: Added approxQuantile

## How was this patch tested?

* unit test in sql/tests.py

Documentation was copied from the existing approxQuantile exactly.

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

Closes #11356 from jkbradley/approx-quantile-python.

This PR adds a new abstraction called an `ExpressionSet` which attempts to canonicalize expressions to remove cosmetic differences.  Deterministic expressions that are in the set after canonicalization will always return the same answer given the same input (i.e. false positives should not be possible). However, it is possible that two canonical expressions that are not equal will in fact return the same answer given any input (i.e. false negatives are possible).

```scala
val set = AttributeSet('a + 1 :: 1 + 'a :: Nil)

set.iterator => Iterator('a + 1)
set.contains('a + 1) => true
set.contains(1 + 'a) => true
set.contains('a + 2) => false
```

Other relevant changes include:
 - Since this concept overlaps with the existing `semanticEquals` and `semanticHash`, those functions are also ported to this new infrastructure.
 - A memoized `canonicalized` version of the expression is added as a `lazy val` to `Expression` and is used by both `semanticEquals` and `ExpressionSet`.
 - A set of unit tests for `ExpressionSet` are added
 - Tests which expect `semanticEquals` to be less intelligent than it now is are updated.

As a followup, we should consider auditing the places where we do `O(n)` `semanticEquals` operations and replace them with `ExpressionSet`.  We should also consider consolidating `AttributeSet` as a specialized factory for an `ExpressionSet.`

Author: Michael Armbrust <michael@databricks.com>

Closes #11338 from marmbrus/expressionSet.

Some parts of the engine rely on UnsafeRow which the vectorized parquet scanner does not want
to produce. This add a conversion in Physical RDD. In the case where codegen is used (and the
scan is the start of the pipeline), there is no requirement to use UnsafeRow. This patch adds
update PhysicallRDD to support codegen, which eliminates the need for the UnsafeRow conversion
in all cases.

The result of these changes for TPCDS-Q19 at the 10gb sf reduces the query time from 9.5 seconds
to 6.5 seconds.

Author: Nong Li <nong@databricks.com>

Closes #11141 from nongli/spark-13250.

## What changes were proposed in this pull request?

Reverting SPARK-13376 (https://github.com/apache/spark/commit/d563c8fa01cfaebb5899ff7970115d0f2e64e8d5) affects the test added by SPARK-13383. So, I am fixing the test.

Author: Yin Huai <yhuai@databricks.com>

Closes #11355 from yhuai/SPARK-13383-fix-test.

[SPARK-13475][TESTS][SQL] HiveCompatibilitySuite should still run in PR builder even if a PR only changes sql/core

## What changes were proposed in this pull request?

`HiveCompatibilitySuite` should still run in PR build even if a PR only changes sql/core. So, I am going to remove `ExtendedHiveTest` annotation from `HiveCompatibilitySuite`.

https://issues.apache.org/jira/browse/SPARK-13475

Author: Yin Huai <yhuai@databricks.com>

Closes #11351 from yhuai/SPARK-13475.

## What changes were proposed in this pull request?
Since "[SPARK-13321][SQL] Support nested UNION in parser" is reverted, we need to disable the test case that requires this PR. Thanks!

rxin yhuai marmbrus

## How was this patch tested?

N/A

Author: gatorsmile <gatorsmile@gmail.com>

Closes #11352 from gatorsmile/disableTestCase.