SPARK-3093 : masterLock in Worker is no longer need

there's no need to use masterLock in Worker now since all communications are within Akka actor

Author: CrazyJvm <crazyjvm@gmail.com>

Closes #2008 from CrazyJvm/no-need-master-lock and squashes the following commits:

dd39e20 [CrazyJvm] fix format
58e7fa5 [CrazyJvm] there's no need to use masterLock now since all communications are within Akka actor
[SPARK-2862] histogram method fails on some choices of bucketCount

Author: Chandan Kumar <chandan.kumar@imaginea.com>

Closes #1787 from nrchandan/spark-2862 and squashes the following commits:

a76bbf6 [Chandan Kumar] [SPARK-2862] Fix for a broken test case and add new test cases
4211eea [Chandan Kumar] [SPARK-2862] Add Scala bug id
13854f1 [Chandan Kumar] [SPARK-2862] Use shorthand range notation to avoid Scala bug
SPARK-3096: Include parquet hive serde by default in build

A small change - we should just add this dependency. It doesn't have any recursive deps and it's needed for reading have parquet tables.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #2009 from pwendell/parquet and squashes the following commits:

e411f9f [Patrick Wendell] SPARk-309: Include parquet hive serde by default in build
[SPARK-3084] [SQL] Collect broadcasted tables in parallel in joins

BroadcastHashJoin has a broadcastFuture variable that tries to collect
the broadcasted table in a separate thread, but this doesn't help
because it's a lazy val that only gets initialized when you attempt to
build the RDD. Thus queries that broadcast multiple tables would collect
and broadcast them sequentially. I changed this to a val to let it start
collecting right when the operator is created.

Author: Matei Zaharia <matei@databricks.com>

Closes #1990 from mateiz/spark-3084 and squashes the following commits:

f468766 [Matei Zaharia] [SPARK-3084] Collect broadcasted tables in parallel in joins
[SPARK-3085] [SQL] Use compact data structures in SQL joins

This reuses the CompactBuffer from Spark Core to save memory and pointer
dereferences. I also tried AppendOnlyMap instead of java.util.HashMap
but unfortunately that slows things down because it seems to do more
equals() calls and the equals on GenericRow, and especially JoinedRow,
is pretty expensive.

Author: Matei Zaharia <matei@databricks.com>

Closes #1993 from mateiz/spark-3085 and squashes the following commits:

188221e [Matei Zaharia] Remove unneeded import
5f903ee [Matei Zaharia] [SPARK-3085] [SQL] Use compact data structures in SQL joins
SPARK-3025 [SQL]: Allow JDBC clients to set a fair scheduler pool

This definitely needs review as I am not familiar with this part of Spark.
I tested this locally and it did seem to work.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #1937 from pwendell/scheduler and squashes the following commits:

b858e33 [Patrick Wendell] SPARK-3025: Allow JDBC clients to set a fair scheduler pool
[SPARK-3091] [SQL] Add support for caching metadata on Parquet files

For larger Parquet files, reading the file footers (which is done in parallel on up to 5 threads) and HDFS block locations (which is serial) can take multiple seconds. We can add an option to cache this data within FilteringParquetInputFormat. Unfortunately ParquetInputFormat only caches footers within each instance of ParquetInputFormat, not across them.

Note: this PR leaves this turned off by default for 1.1, but I believe it's safe to turn it on after. The keys in the hash maps are FileStatus objects that include a modification time, so this will work fine if files are modified. The location cache could become invalid if files have moved within HDFS, but that's rare so I just made it invalidate entries every 15 minutes.

Author: Matei Zaharia <matei@databricks.com>

Closes #2005 from mateiz/parquet-cache and squashes the following commits:

dae8efe [Matei Zaharia] Bug fix
c71e9ed [Matei Zaharia] Handle empty statuses directly
22072b0 [Matei Zaharia] Use Guava caches and add a config option for caching metadata
8fb56ce [Matei Zaharia] Cache file block locations too
453bd21 [Matei Zaharia] Bug fix
4094df6 [Matei Zaharia] First attempt at caching Parquet footers
[SPARK-2406][SQL] Initial support for using ParquetTableScan to read HiveMetaStore tables.

This PR adds an experimental flag `spark.sql.hive.convertMetastoreParquet` that when true causes the planner to detects tables that use Hive's Parquet SerDe and instead plans them using Spark SQL's native `ParquetTableScan`.

Author: Michael Armbrust <michael@databricks.com>
Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1819 from marmbrus/parquetMetastore and squashes the following commits:

1620079 [Michael Armbrust] Revert "remove hive parquet bundle"
cc30430 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into parquetMetastore
4f3d54f [Michael Armbrust] fix style
41ebc5f [Michael Armbrust] remove hive parquet bundle
a43e0da [Michael Armbrust] Merge remote-tracking branch 'origin/master' into parquetMetastore
4c4dc19 [Michael Armbrust] Fix bug with tree splicing.
ebb267e [Michael Armbrust] include parquet hive to tests pass (Remove this later).
c0d9b72 [Michael Armbrust] Avoid creating a HadoopRDD per partition.  Add dirty hacks to retrieve partition values from the InputSplit.
8cdc93c [Michael Armbrust] Merge pull request #8 from yhuai/parquetMetastore
a0baec7 [Yin Huai] Partitioning columns can be resolved.
1161338 [Michael Armbrust] Add a test to make sure conversion is actually happening
212d5cd [Michael Armbrust] Initial support for using ParquetTableScan to read HiveMetaStore tables.
Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check