This adds an extra filter for private or protected classes. We only filter for package private right now.

Author: Timothy Hunter <timhunter@databricks.com>

Closes #9697 from thunterdb/spark-11732.

Currently the size of cached batch in only controlled by `batchSize` (default value is 10000), which does not work well with the size of serialized columns (for example, complex types). The memory used to build the batch is not accounted, it's easy to OOM (especially after unified memory management).

This PR introduce a hard limit as 4M for total columns (up to 50 columns of uncompressed primitive columns).

This also change the way to grow buffer, double it each time, then trim it once finished.

cc liancheng

Author: Davies Liu <davies@databricks.com>

Closes #9760 from davies/cache_limit.

This excludes Estimators and ones which include Vector and other non-basic types for Params or data.  This adds:
* Bucketizer
* DCT
* HashingTF
* Interaction
* NGram
* Normalizer
* OneHotEncoder
* PolynomialExpansion
* QuantileDiscretizer
* RFormula
* SQLTransformer
* StopWordsRemover
* StringIndexer
* Tokenizer
* VectorAssembler
* VectorSlicer

CC: mengxr

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

Closes #9755 from jkbradley/transformer-io.

Add ARRAY support to `PostgresDialect`.

Nested ARRAY is not allowed for now because it's hard to get the array dimension info. See http://stackoverflow.com/questions/16619113/how-to-get-array-base-type-in-postgres-via-jdbc

Thanks for the initial work from mariusvniekerk !

Close https://github.com/apache/spark/pull/9137

Author: Wenchen Fan <wenchen@databricks.com>

Closes #9662 from cloud-fan/postgre.

Based on the comment of cloud-fan in https://github.com/apache/spark/pull/9216, update the AttributeReference's hashCode function by including the hashCode of the other attributes including name, nullable and qualifiers.

Here, I am not 100% sure if we should include name in the hashCode calculation, since the original hashCode calculation does not include it.

marmbrus cloud-fan Please review if the changes are good.

Author: gatorsmile <gatorsmile@gmail.com>

Closes #9761 from gatorsmile/hashCodeNamedExpression.

This PR adds a new option `spark.sql.hive.thriftServer.singleSession` for disabling multi-session support in the Thrift server.

Note that this option is added as a Spark configuration (retrieved from `SparkConf`) rather than Spark SQL configuration (retrieved from `SQLConf`). This is because all SQL configurations are session-ized. Since multi-session support is by default on, no JDBC connection can modify global configurations like the newly added one.

Author: Cheng Lian <lian@databricks.com>

Closes #9740 from liancheng/spark-11089.single-session-option.

[SPARK-11679][SQL] Invoking method " apply(fields: java.util.List[StructField])" in "StructType" gets ClassCastException

In the previous method, fields.toArray will cast java.util.List[StructField] into Array[Object] which can not cast into Array[StructField], thus when invoking this method will throw "java.lang.ClassCastException: [Ljava.lang.Object; cannot be cast to [Lorg.apache.spark.sql.types.StructField;"
I directly cast java.util.List[StructField] into Array[StructField]  in this patch.

Author: mayuanwen <mayuanwen@qiyi.com>

Closes #9649 from jackieMaKing/Spark-11679.

This is to support JSON serialization of Param[Vector] in the pipeline API. It could be used for other purposes too. The schema is the same as `VectorUDT`. jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #9751 from mengxr/SPARK-11766.

Set s3a credentials when creating a new default hadoop configuration.

Author: Chris Bannister <chris.bannister@swiftkey.com>

Closes #9663 from Zariel/set-s3a-creds.

Exception details can be seen here (https://issues.apache.org/jira/browse/SPARK-11744).

Author: jerryshao <sshao@hortonworks.com>

Closes #9721 from jerryshao/SPARK-11744.

MESOS_NATIVE_LIBRARY was renamed in favor of MESOS_NATIVE_JAVA_LIBRARY. This commit fixes the reference in the documentation.

Author: Philipp Hoffmann <mail@philipphoffmann.de>

Closes #9768 from philipphoffmann/patch-2.

In the **[Task Launching Overheads](http://spark.apache.org/docs/latest/streaming-programming-guide.html#task-launching-overheads)** section,
>Task Serialization: Using Kryo serialization for serializing tasks can reduce the task sizes, and therefore reduce the time taken to send them to the slaves.

as we known **Task Serialization** is configuration by **spark.closure.serializer** parameter, but currently only the Java serializer is supported. If we set **spark.closure.serializer** to **org.apache.spark.serializer.KryoSerializer**, then this will throw a exception.

Author: yangping.wu <wyphao.2007@163.com>

Closes #9734 from 397090770/397090770-patch-1.

According to discussion in PR #9664, the anonymous `HiveFunctionRegistry` in `HiveContext` can be removed now.

Author: Cheng Lian <lian@databricks.com>

Closes #9737 from liancheng/spark-11191.follow-up.

The randomly generated ArrayData used for the UDT `ExamplePoint` in `RowEncoderSuite` sometimes doesn't have enough elements. In this case, this test will fail. This patch is to fix it.

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

Closes #9757 from viirya/fix-randomgenerated-udt.

[SPARK-11447][SQL] change NullType to StringType during binaryComparison between NullType and StringType

During executing PromoteStrings rule, if one side of binaryComparison is StringType and the other side is not StringType, the current code will promote(cast) the StringType to DoubleType, and if the StringType doesn't contain the numbers, it will get null value. So if it is doing <=> (NULL-safe equal) with Null, it will not filter anything, caused the problem reported by this jira.

I proposal to the changes through this PR, can you review my code changes ?

This problem only happen for <=>, other operators works fine.

scala> val filteredDF = df.filter(df("column") > (new Column(Literal(null))))
filteredDF: org.apache.spark.sql.DataFrame = [column: string]

scala> filteredDF.show
+------+
|column|
+------+
+------+

scala> val filteredDF = df.filter(df("column") === (new Column(Literal(null))))
filteredDF: org.apache.spark.sql.DataFrame = [column: string]

scala> filteredDF.show
+------+
|column|
+------+
+------+

scala> df.registerTempTable("DF")

scala> sqlContext.sql("select * from DF where 'column' = NULL")
res27: org.apache.spark.sql.DataFrame = [column: string]

scala> res27.show
+------+
|column|
+------+
+------+

Author: Kevin Yu <qyu@us.ibm.com>

Closes #9720 from kevinyu98/working_on_spark-11447.

[SPARK-11694][FOLLOW-UP] Clean up imports, use a common function for metadata and add a test for FIXED_LEN_BYTE_ARRAY

As discussed https://github.com/apache/spark/pull/9660 https://github.com/apache/spark/pull/9060, I cleaned up unused imports, added a test for fixed-length byte array and used a common function for writing metadata for Parquet.

For the test for fixed-length byte array, I have tested and checked the encoding types with [parquet-tools](https://github.com/Parquet/parquet-mr/tree/master/parquet-tools).

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #9754 from HyukjinKwon/SPARK-11694-followup.

This patch adds an alias for current_timestamp (now function).

Also fixes SPARK-9196 to re-enable the test case for current_timestamp.

Author: Reynold Xin <rxin@databricks.com>

Closes #9753 from rxin/SPARK-11768.

The code was using the wrong API to add data to the internal composite
buffer, causing buffers to leak in certain situations. Use the right
API and enhance the tests to catch memory leaks.

Also, avoid reusing the composite buffers when downstream handlers keep
references to them; this seems to cause a few different issues even though
the ref counting code seems to be correct, so instead pay the cost of copying
a few bytes when that situation happens.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #9619 from vanzin/SPARK-11617.

Pipeline and PipelineModel extend Readable and Writable.  Persistence succeeds only when all stages are Writable.

Note: This PR reinstates tests for other read/write functionality.  It should probably not get merged until [https://issues.apache.org/jira/browse/SPARK-11672] gets fixed.

CC: mengxr

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

Closes #9674 from jkbradley/pipeline-io.

Author: jerryshao <sshao@hortonworks.com>

Closes #9730 from jerryshao/clickstream-fix.

Author: Andrew Or <andrew@databricks.com>

Closes #9676 from andrewor14/memory-management-docs.

When we call AsyncRDDActions#takeAsync, actually another DAGScheduler#runJob is called from another thread so we cannot get proper callsite infomation.

Following screenshots are before this patch applied and after.

Before:
<img width="1268" alt="2015-11-04 1 26 40" src="https://cloud.githubusercontent.com/assets/4736016/10914069/0ffc1306-8294-11e5-8e89-c4fadf58dd12.png">
<img width="1258" alt="2015-11-04 1 26 52" src="https://cloud.githubusercontent.com/assets/4736016/10914070/0ffe84ce-8294-11e5-8b2a-69d36276bedb.png">

After:
<img width="1268" alt="2015-11-04 0 48 07" src="https://cloud.githubusercontent.com/assets/4736016/10914080/1d8cfb7a-8294-11e5-9e09-ede25c2563e8.png">
<img width="1269" alt="2015-11-04 0 48 26" src="https://cloud.githubusercontent.com/assets/4736016/10914081/1d934e3a-8294-11e5-8b5e-e3dc37aaced3.png">

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #9437 from sarutak/SPARK-11480.

See https://github.com/apache/spark/pull/9390#issuecomment-157160063 and https://gist.github.com/shivaram/3a2fecce60768a603dac for more information

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #9744 from shivaram/sparkr-package-test-disable.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #9591 from cloud-fan/agg-test.

This fix is to change the equals method to check all of the specified fields for equality of AttributeReference.

Author: gatorsmile <gatorsmile@gmail.com>

Closes #9216 from gatorsmile/namedExpressEqual.

Invocation of getters for type extending AnyVal returns default value (if field value is null) instead of throwing NPE. Please check comments for SPARK-11553 issue for more details.

Author: Bartlomiej Alberski <bartlomiej.alberski@allegrogroup.com>

Closes #9642 from alberskib/bugfix/SPARK-11553.

<img width="1365" alt="screen shot 2015-11-13 at 9 57 43 pm" src="https://cloud.githubusercontent.com/assets/1000778/11162322/9b88e204-8a51-11e5-8c57-a44889cab713.png">

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #9711 from zsxwing/failure-info.

Revert "[SPARK-11271][SPARK-11016][CORE] Use Spark BitSet instead of RoaringBitmap to reduce memory usage"

This reverts commit e209fa27.

…ishable

Propagate pushed filters to PhyicalRDD in DataSourceStrategy.apply

Author: Zee Chen <zeechen@us.ibm.com>

Closes #9679 from zeocio/spark-11390.

These 2 are very similar, we can consolidate them into one.

Also add tests for it and fix a bug.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #9729 from cloud-fan/tuple.

Currently if dynamic allocation is enabled, explicitly killing executor will not get response, so the executor metadata is wrong in driver side. Which will make dynamic allocation on Yarn fail to work.

The problem is  `disableExecutor` returns false for pending killing executors when `onDisconnect` is detected, so no further implementation is done.

One solution is to bypass these explicitly killed executors to use `super.onDisconnect` to remove executor. This is simple.

Another solution is still querying the loss reason for these explicitly kill executors. Since executor may get killed and informed in the same AM-RM communication, so current way of adding pending loss reason request is not worked (container complete is already processed), here we should store this loss reason for later query.

Here this PR chooses solution 2.

Please help to review. vanzin I think this part is changed by you previously, would you please help to review? Thanks a lot.

Author: jerryshao <sshao@hortonworks.com>

Closes #9684 from jerryshao/SPARK-11718.

Author: Daniel Jalova <djalova@us.ibm.com>

Closes #9186 from djalova/SPARK-6328.

Using batching on the driver for the WriteAheadLog should be an improvement for all environments and use cases. Users will be able to scale to much higher number of receivers with the BatchedWriteAheadLog. Therefore we should turn it on by default, and QA it in the QA period.

I've also added some tests to make sure the default configurations are correct regarding recent additions:
 - batching on by default
 - closeFileAfterWrite off by default
 - parallelRecovery off by default

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #9695 from brkyvz/enable-batch-wal.

JIRA: https://issues.apache.org/jira/browse/SPARK-11743

RowEncoder doesn't support UserDefinedType now. We should add the support for it.

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

Closes #9712 from viirya/rowencoder-udt.

code snippet to reproduce it:
```
TimeZone.setDefault(TimeZone.getTimeZone("Asia/Shanghai"))
val t = Timestamp.valueOf("1900-06-11 12:14:50.789")
val us = fromJavaTimestamp(t)
assert(getSeconds(us) === t.getSeconds)
```

it will be good to add a regression test for it, but the reproducing code need to change the default timezone, and even we change it back, the `lazy val defaultTimeZone` in `DataTimeUtils` is fixed.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #9728 from cloud-fan/seconds.

When computing partition for non-parquet relation, `HadoopRDD.compute` is used. but it does not set the thread local variable `inputFileName` in `NewSqlHadoopRDD`, like `NewSqlHadoopRDD.compute` does.. Yet, when getting the `inputFileName`, `NewSqlHadoopRDD.inputFileName` is exptected, which is empty now.
Adding the setting inputFileName in HadoopRDD.compute resolves this issue.

Author: xin Wu <xinwu@us.ibm.com>

Closes #9542 from xwu0226/SPARK-11522.

Parquet supports some JSON and BSON datatypes. They are represented as binary for BSON and string (UTF-8) for JSON internally.

I searched a bit and found Apache drill also supports both in this way, [link](https://drill.apache.org/docs/parquet-format/).

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

Closes #9658 from HyukjinKwon/SPARK-11692.

https://issues.apache.org/jira/browse/SPARK-11044

Spark writes a parquet file only with writer version1 ignoring the writer version given by user.

So, in this PR, it keeps the writer version if given or sets version1 as default.

Author: hyukjinkwon <gurwls223@gmail.com>
Author: HyukjinKwon <gurwls223@gmail.com>

Closes #9060 from HyukjinKwon/SPARK-11044.

This patch adds the following options to the JSON data source, for dealing with non-standard JSON files:
* `allowComments` (default `false`): ignores Java/C++ style comment in JSON records
* `allowUnquotedFieldNames` (default `false`): allows unquoted JSON field names
* `allowSingleQuotes` (default `true`): allows single quotes in addition to double quotes
* `allowNumericLeadingZeros` (default `false`): allows leading zeros in numbers (e.g. 00012)

To avoid passing a lot of options throughout the json package, I introduced a new JSONOptions case class to define all JSON config options.

Also updated documentation to explain these options.

Scala

![screen shot 2015-11-15 at 6 12 12 pm](https://cloud.githubusercontent.com/assets/323388/11172965/e3ace6ec-8bc4-11e5-805e-2d78f80d0ed6.png)

Python

![screen shot 2015-11-15 at 6 11 28 pm](https://cloud.githubusercontent.com/assets/323388/11172964/e23ed6ee-8bc4-11e5-8216-312f5983acd5.png)

Author: Reynold Xin <rxin@databricks.com>

Closes #9724 from rxin/SPARK-11745.

This reverts commit 3e0a6cf1.