Currently the Master would always set an application's initial executor limit to infinity. If the user specified `spark.dynamicAllocation.initialExecutors`, the config would not take effect. This is similar to #11047 but for standalone mode.

Author: Andrew Or <andrew@databricks.com>

Closes #11054 from andrewor14/standalone-da-initial.

Building with scala 2.11 results in the warning trait SynchronizedBuffer in package mutable is deprecated: Synchronization via traits is deprecated as it is inherently unreliable. Consider java.util.concurrent.ConcurrentLinkedQueue as an alternative. Investigation shows we are already using ConcurrentLinkedQueue in other locations so switch our uses of SynchronizedBuffer to ConcurrentLinkedQueue.

Author: Holden Karau <holden@us.ibm.com>

Closes #11059 from holdenk/SPARK-13164-replace-deprecated-synchronized-buffer-in-core.

In the current implementation the mesos coarse scheduler does not wait for the mesos tasks to complete before ending the driver. This causes a race where the task has to finish cleaning up before the mesos driver terminates it with a SIGINT (and SIGKILL after 3 seconds if the SIGINT doesn't work).

This PR causes the mesos coarse scheduler to wait for the mesos tasks to finish (with a timeout defined by `spark.mesos.coarse.shutdown.ms`)

This PR also fixes a regression caused by [SPARK-10987] whereby submitting a shutdown causes a race between the local shutdown procedure and the notification of the scheduler driver disconnection. If the scheduler driver disconnection wins the race, the coarse executor incorrectly exits with status 1 (instead of the proper status 0)

With this patch the mesos coarse scheduler terminates properly, the executors clean up, and the tasks are reported as `FINISHED` in the Mesos console (as opposed to `KILLED` in < 1.6 or `FAILED` in 1.6 and later)

Author: Charles Allen <charles@allen-net.com>

Closes #10319 from drcrallen/SPARK-12330.

This is a small addendum to #10762 to make the code more robust again future changes.

Author: Reynold Xin <rxin@databricks.com>

Closes #11070 from rxin/SPARK-12828-natural-join.

JIRA: https://issues.apache.org/jira/browse/SPARK-13113

As we shift bits right, looks like the bitwise AND operation is unnecessary.

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

Closes #11002 from viirya/improve-decodepagenumber.

minor fix for api link in ml onevsrest

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #11068 from hhbyyh/onevsrestDoc.

Jira:
https://issues.apache.org/jira/browse/SPARK-12828

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #10762 from adrian-wang/naturaljoin.

This is a step towards consolidating `SQLContext` and `HiveContext`.

This patch extends the existing Catalog API added in #10982 to include methods for handling table partitions. In particular, a partition is identified by `PartitionSpec`, which is just a `Map[String, String]`. The Catalog is still not used by anything yet, but its API is now more or less complete and an implementation is fully tested.

About 200 lines are test code.

Author: Andrew Or <andrew@databricks.com>

Closes #11069 from andrewor14/catalog.

Make an internal non-deprecated version of incBytesRead and incRecordsRead so we don't have unecessary deprecation warnings in our build.

Right now incBytesRead and incRecordsRead are marked as deprecated and for internal use only. We should make private[spark] versions which are not deprecated and switch to those internally so as to not clutter up the warning messages when building.

cc andrewor14 who did the initial deprecation

Author: Holden Karau <holden@us.ibm.com>

Closes #11056 from holdenk/SPARK-13152-fix-task-metrics-deprecation-warnings.

Best time is stabler than average time, also added a column for nano seconds per row (which could be used to estimate contributions of each components in a query).

Having best time and average time together for more information (we can see kind of variance).

rate, time per row and relative are all calculated using best time.

The result looks like this:
```
Intel(R) Core(TM) i7-4558U CPU  2.80GHz
rang/filter/sum:                    Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
-------------------------------------------------------------------------------------------
rang/filter/sum codegen=false          14332 / 16646         36.0          27.8       1.0X
rang/filter/sum codegen=true              845 /  940        620.0           1.6      17.0X
```

Author: Davies Liu <davies@databricks.com>

Closes #11018 from davies/gen_bench.

They seem redundant and we can simply use DataFrameReader/Writer. The new usage looks like:

```scala
val df = sqlContext.read.stream("...")
val handle = df.write.stream("...")
handle.stop()
```

Author: Reynold Xin <rxin@databricks.com>

Closes #11062 from rxin/SPARK-13166.

Added a Cores column in the Executors UI

Author: Alex Bozarth <ajbozart@us.ibm.com>

Closes #11039 from ajbozarth/spark3611.

The ```SparkSqlLexer``` currently swallows characters which have not been defined in the grammar. This causes problems with SQL commands, such as: ```add jar file:///tmp/ab/TestUDTF.jar```. In this example the `````` is swallowed.

This PR adds an extra Lexer rule to handle such input, and makes a tiny modification to the ```ASTNode```.

cc davies liancheng

Author: Herman van Hovell <hvanhovell@questtec.nl>

Closes #11052 from hvanhovell/SPARK-13157.

A row from stream side could match multiple rows on build side, the loop for these matched rows should not be interrupted when emitting a row, so we buffer the output rows in a linked list, check the termination condition on producer loop (for example, Range or Aggregate).

Author: Davies Liu <davies@databricks.com>

Closes #10989 from davies/gen_join.

I have clearly prefix the two 'Duration' columns in 'Details of Batch' Streaming tab as 'Output Op Duration' and 'Job Duration'

Author: Mario Briggs <mario.briggs@in.ibm.com>
Author: mariobriggs <mariobriggs@in.ibm.com>

Closes #11022 from mariobriggs/spark-12739.

Based on the semantics of a query, we can derive a number of data constraints on output of each (logical or physical) operator. For instance, if a filter defines `‘a > 10`, we know that the output data of this filter satisfies 2 constraints:

1. `‘a > 10`
2. `isNotNull(‘a)`

This PR proposes a possible way of keeping track of these constraints and propagating them in the logical plan, which can then help us build more advanced optimizations (such as pruning redundant filters, optimizing joins, among others). We define constraints as a set of (implicitly conjunctive) expressions. For e.g., if a filter operator has constraints = `Set(‘a > 10, ‘b < 100)`, it’s implied that the outputs satisfy both individual constraints (i.e., `‘a > 10` AND `‘b < 100`).

Design Document: https://docs.google.com/a/databricks.com/document/d/1WQRgDurUBV9Y6CWOBS75PQIqJwT-6WftVa18xzm7nCo/edit?usp=sharing

Author: Sameer Agarwal <sameer@databricks.com>

Closes #10844 from sameeragarwal/constraints.

1. try to avoid the suffix (unique id)
2. remove the comment if there is no code generated.
3. re-arrange the order of functions
4. trop the new line for inlined blocks.

Author: Davies Liu <davies@databricks.com>

Closes #11032 from davies/better_suffix.

`rpcEnv.awaitTermination()` was not added in #10854 because some Streaming Python tests hung forever.

This patch fixed the hung issue and added rpcEnv.awaitTermination() back to SparkEnv.

Previously, Streaming Kafka Python tests shutdowns the zookeeper server before stopping StreamingContext. Then when stopping StreamingContext, KafkaReceiver may be hung due to https://issues.apache.org/jira/browse/KAFKA-601, hence, some thread of RpcEnv's Dispatcher cannot exit and rpcEnv.awaitTermination is hung.The patch just changed the shutdown order to fix it.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #11031 from zsxwing/awaitTermination.

Fixed the bug in linear regression train for the case when the target variable is constant. The two cases for `fitIntercept=true` or `fitIntercept=false` should be treated differently.

Author: Imran Younus <iyounus@us.ibm.com>

Closes #10702 from iyounus/SPARK-12732_bug_fix_in_linear_regression_train.

This PR add spilling support for generated TungstenAggregate.

If spilling happened, it's not that bad to do the iterator based sort-merge-aggregate (not generated).

The changes will be covered by TungstenAggregationQueryWithControlledFallbackSuite

Author: Davies Liu <davies@databricks.com>

Closes #10998 from davies/gen_spilling.

https://issues.apache.org/jira/browse/SPARK-13122

A race condition can occur in MemoryStore's unrollSafely() method if two threads that
return the same value for currentTaskAttemptId() execute this method concurrently. This
change makes the operation of reading the initial amount of unroll memory used, performing
the unroll, and updating the associated memory maps atomic in order to avoid this race
condition.

Initial proposed fix wraps all of unrollSafely() in a memoryManager.synchronized { } block. A cleaner approach might be introduce a mechanism that synchronizes based on task attempt ID. An alternative option might be to track unroll/pending unroll memory based on block ID rather than task attempt ID.

Author: Adam Budde <budde@amazon.com>

Closes #11012 from budde/master.

This patch implements support for more types when doing the vectorized decode. There are
a few more types remaining but they should be very straightforward after this. This code
has a few copy and paste pieces but they are difficult to eliminate due to performance
considerations.

Specifically, this patch adds support for:
  - String, Long, Byte types
  - Dictionary encoding for those types.

Author: Nong Li <nong@databricks.com>

Closes #10908 from nongli/spark-12992.

when we generate map, we first randomly pick a length, then create a seq of key value pair with the expected length, and finally call `toMap`. However, `toMap` will remove all duplicated keys, which makes the actual map size much less than we expected.

This PR fixes this problem by put keys in a set first, to guarantee we have enough keys to build a map with expected length.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #10930 from cloud-fan/random-generator.

Author: Davies Liu <davies@databricks.com>

Closes #11037 from davies/disable_flaky.

The example will throw error like
<console>:20: error: not found: value StructType

Need to add this line:
import org.apache.spark.sql.types._

Author: Kevin (Sangwoo) Kim <sangwookim.me@gmail.com>

Closes #10141 from swkimme/patch-1.

Already merged into 1.6 branch, this PR is to commit to master the same change

Author: Gabriele Nizzoli <mail@nizzoli.net>

Closes #11028 from gabrielenizzoli/patch-1.

As benchmarked and discussed here: https://github.com/apache/spark/pull/10786/files#r50038294, benefits from codegen, the declarative aggregate function could be much faster than imperative one.

Author: Davies Liu <davies@databricks.com>

Closes #10960 from davies/stddev.

ddl.scala is defined in the execution package, and yet its reference of "UnaryNode" and "Command" are logical. This was fairly confusing when I was trying to understand the ddl code.

Author: Reynold Xin <rxin@databricks.com>

Closes #11021 from rxin/SPARK-13138.

Fixes problem and verifies fix by test suite.
Also - adds optional parameter: nullable (Boolean) to: SchemaUtils.appendColumn
and deduplicates SchemaUtils.appendColumn functions.

Author: Grzegorz Chilkiewicz <grzegorz.chilkiewicz@codilime.com>

Closes #10741 from grzegorz-chilkiewicz/master.

Jira:
https://issues.apache.org/jira/browse/SPARK-13056

Create a map like
{ "a": "somestring", "b": null}
Query like
SELECT col["b"] FROM t1;
NPE would be thrown.

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #10964 from adrian-wang/npewriter.

Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent.  This is for the clustering module.

Author: Bryan Cutler <cutlerb@gmail.com>

Closes #10610 from BryanCutler/param-desc-consistent-cluster-SPARK-12631.

https://issues.apache.org/jira/browse/SPARK-13114

This PR adds a test for tokens more than the fields in schema.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #11020 from HyukjinKwon/SPARK-13114.

Author: Michael Armbrust <michael@databricks.com>

Closes #11014 from marmbrus/seqEncoders.

This is a follow up to 9aadcffa that extends Spark SQL to allow users to _repeatedly_ optimize and execute structured queries.  A `ContinuousQuery` can be expressed using SQL, DataFrames or Datasets.  The purpose of this PR is only to add some initial infrastructure which will be extended in subsequent PRs.

## User-facing API

- `sqlContext.streamFrom` and `df.streamTo` return builder objects that are analogous to the `read/write` interfaces already available to executing queries in a batch-oriented fashion.
- `ContinuousQuery` provides an interface for interacting with a query that is currently executing in the background.

## Internal Interfaces
 - `StreamExecution` - executes streaming queries in micro-batches

The following are currently internal, but public APIs will be provided in a future release.
 - `Source` - an interface for providers of continually arriving data.  A source must have a notion of an `Offset` that monotonically tracks what data has arrived.  For fault tolerance, a source must be able to replay data given a start offset.
 - `Sink` - an interface that accepts the results of a continuously executing query.  Also responsible for tracking the offset that should be resumed from in the case of a failure.

## Testing
 - `MemoryStream` and `MemorySink` - simple implementations of source and sink that keep all data in memory and have methods for simulating durability failures
 - `StreamTest` - a framework for performing actions and checking invariants on a continuous query

Author: Michael Armbrust <michael@databricks.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Josh Rosen <rosenville@gmail.com>

Closes #11006 from marmbrus/structured-streaming.

It is not valid to call `toAttribute` on a `NamedExpression` unless we know for sure that the child produced that `NamedExpression`.  The current code worked fine when the grouping expressions were simple, but when they were a derived value this blew up at execution time.

Author: Michael Armbrust <michael@databricks.com>

Closes #11013 from marmbrus/groupByFunction-master.

1. Use lower case
2. Change long prefixes to something shorter (in this case I am changing only one: TungstenAggregate -> agg).

Author: Reynold Xin <rxin@databricks.com>

Closes #11017 from rxin/SPARK-13130.

Removed isLegacyLogDirectory code path and updated tests
andrewor14

Author: felixcheung <felixcheung_m@hotmail.com>

Closes #10860 from felixcheung/historyserverformat.

Improve printing of StageInfo in onStageCompleted

See also https://github.com/apache/spark/pull/10585

Author: Sean Owen <sowen@cloudera.com>

Closes #10922 from srowen/SPARK-12637.

This pull request creates an internal catalog API. The creation of this API is the first step towards consolidating SQLContext and HiveContext. I envision we will have two different implementations in Spark 2.0: (1) a simple in-memory implementation, and (2) an implementation based on the current HiveClient (ClientWrapper).

I took a look at what Hive's internal metastore interface/implementation, and then created this API based on it. I believe this is the minimal set needed in order to achieve all the needed functionality.

Author: Reynold Xin <rxin@databricks.com>

Closes #10982 from rxin/SPARK-13078.