Replace `JavaConversions` implicits with `JavaConverters`

Most occurrences I've seen so far are necessary conversions; a few have been avoidable. None are in critical code as far as I see, yet.

Author: Sean Owen <sowen@cloudera.com>

Closes #8033 from srowen/SPARK-9613.

Author: ehnalis <zoltan.zvara@gmail.com>

Closes #8308 from ehnalis/master.

Author: Zhang, Liye <liye.zhang@intel.com>

Closes #8412 from liyezhang556520/minorDoc.

https://issues.apache.org/jira/browse/SPARK-10197

Author: Yin Huai <yhuai@databricks.com>

Closes #8407 from yhuai/ORCSPARK-10197.

Spark SQL's data sources API exposes Catalyst's internal types through its Filter interfaces. This is a problem because types like UTF8String are not stable developer APIs and should not be exposed to third-parties.

This issue caused incompatibilities when upgrading our `spark-redshift` library to work against Spark 1.5.0.  To avoid these issues in the future we should only expose public types through these Filter objects. This patch accomplishes this by using CatalystTypeConverters to add the appropriate conversions.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8403 from JoshRosen/datasources-internal-vs-external-types.

We misunderstood the Julian days and nanoseconds of the day in parquet (as TimestampType) from Hive/Impala, they are overlapped, so can't be added together directly.

In order to avoid the confusing rounding when do the converting, we use `2440588` as the Julian Day of epoch of unix timestamp (which should be 2440587.5).

Author: Davies Liu <davies@databricks.com>
Author: Cheng Lian <lian@databricks.com>

Closes #8400 from davies/timestamp_parquet.

When write ahead log is not enabled, a recovered streaming driver still tries to run jobs using pre-failure block ids, and fails as the block do not exists in-memory any more (and cannot be recovered as receiver WAL is not enabled).

This occurs because the driver-side WAL of ReceivedBlockTracker is recovers that past block information, and ReceiveInputDStream creates BlockRDDs even if those blocks do not exist.

The solution in this PR is to filter out block ids that do not exist before creating the BlockRDD. In addition, it adds unit tests to verify other logic in ReceiverInputDStream.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8405 from tdas/SPARK-10210.

Follow up to https://github.com/apache/spark/pull/7047

pwendell mentioned that MapR should use `hadoop-provided` now, and indeed the new build script does not produce `mapr3`/`mapr4` artifacts anymore. Hence the action seems to be to remove the profiles, which are now not used.

CC trystanleftwich

Author: Sean Owen <sowen@cloudera.com>

Closes #8338 from srowen/SPARK-6196.

cc: shivaram

## Summary

- Add name tags to each methods in DataFrame.R and column.R
- Replace `rdname column` with `rdname {each_func}`. i.e. alias method : `rdname column` =>  `rdname alias`

## Generated PDF File
https://drive.google.com/file/d/0B9biIZIU47lLNHN2aFpnQXlSeGs/view?usp=sharing

## JIRA
[[SPARK-10214] Improve SparkR Column, DataFrame API docs - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10214)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8414 from yu-iskw/SPARK-10214.

[SPARK-9293] [SPARK-9813] Analysis should check that set operations are only performed on tables with equal numbers of columns

This patch adds an analyzer rule to ensure that set operations (union, intersect, and except) are only applied to tables with the same number of columns. Without this rule, there are scenarios where invalid queries can return incorrect results instead of failing with error messages; SPARK-9813 provides one example of this problem. In other cases, the invalid query can crash at runtime with extremely confusing exceptions.

I also performed a bit of cleanup to refactor some of those logical operators' code into a common `SetOperation` base class.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7631 from JoshRosen/SPARK-9293.

PR #8341 is a valid fix for SPARK-10136, but it didn't catch the real root cause.  The real problem can be rather tricky to explain, and requires audiences to be pretty familiar with parquet-format spec, especially details of `LIST` backwards-compatibility rules.  Let me have a try to give an explanation here.

The structure of the problematic Parquet schema generated by parquet-avro is something like this:

```
message m {
  <repetition> group f (LIST) {         // Level 1
    repeated group array (LIST) {       // Level 2
      repeated <primitive-type> array;  // Level 3
    }
  }
}
```

(The schema generated by parquet-thrift is structurally similar, just replace the `array` at level 2 with `f_tuple`, and the other one at level 3 with `f_tuple_tuple`.)

This structure consists of two nested legacy 2-level `LIST`-like structures:

1. The repeated group type at level 2 is the element type of the outer array defined at level 1

   This group should map to an `CatalystArrayConverter.ElementConverter` when building converters.

2. The repeated primitive type at level 3 is the element type of the inner array defined at level 2

   This group should also map to an `CatalystArrayConverter.ElementConverter`.

The root cause of SPARK-10136 is that, the group at level 2 isn't properly recognized as the element type of level 1.  Thus, according to parquet-format spec, the repeated primitive at level 3 is left as a so called "unannotated repeated primitive type", and is recognized as a required list of required primitive type, thus a `RepeatedPrimitiveConverter` instead of a `CatalystArrayConverter.ElementConverter` is created for it.

According to  parquet-format spec, unannotated repeated type shouldn't appear in a `LIST`- or `MAP`-annotated group.  PR #8341 fixed this issue by allowing such unannotated repeated type appear in `LIST`-annotated groups, which is a non-standard, hacky, but valid fix.  (I didn't realize this when authoring #8341 though.)

As for the reason why level 2 isn't recognized as a list element type, it's because of the following `LIST` backwards-compatibility rule defined in the parquet-format spec:

> If the repeated field is a group with one field and is named either `array` or uses the `LIST`-annotated group's name with `_tuple` appended then the repeated type is the element type and elements are required.

(The `array` part is for parquet-avro compatibility, while the `_tuple` part is for parquet-thrift.)

This rule is implemented in [`CatalystSchemaConverter.isElementType`] [1], but neglected in [`CatalystRowConverter.isElementType`] [2].  This PR delivers a more robust fix by adding this rule in the latter method.

Note that parquet-avro 1.7.0 also suffers from this issue. Details can be found at [PARQUET-364] [3].

[1]: https://github.com/apache/spark/blob/85f9a61357994da5023b08b0a8a2eb09388ce7f8/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala#L259-L305
[2]: https://github.com/apache/spark/blob/85f9a61357994da5023b08b0a8a2eb09388ce7f8/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala#L456-L463
[3]: https://issues.apache.org/jira/browse/PARQUET-364

Author: Cheng Lian <lian@databricks.com>

Closes #8361 from liancheng/spark-10136/proper-version.

https://issues.apache.org/jira/browse/SPARK-10196

Author: Yin Huai <yhuai@databricks.com>

Closes #8408 from yhuai/DecimalJsonSPARK-10196.

This PR fixes the following cases for `ReceiverSchedulingPolicy`.

1) Assume there are 4 executors: host1, host2, host3, host4, and 5 receivers: r1, r2, r3, r4, r5. Then `ReceiverSchedulingPolicy.scheduleReceivers` will return (r1 -> host1, r2 -> host2, r3 -> host3, r4 -> host4, r5 -> host1).
Let's assume r1 starts at first on `host1` as `scheduleReceivers` suggested,  and try to register with ReceiverTracker. But the previous `ReceiverSchedulingPolicy.rescheduleReceiver` will return (host2, host3, host4) according to the current executor weights (host1 -> 1.0, host2 -> 0.5, host3 -> 0.5, host4 -> 0.5), so ReceiverTracker will reject `r1`. This is unexpected since r1 is starting exactly where `scheduleReceivers` suggested.

This case can be fixed by ignoring the information of the receiver that is rescheduling in `receiverTrackingInfoMap`.

2) Assume there are 3 executors (host1, host2, host3) and each executors has 3 cores, and 3 receivers: r1, r2, r3. Assume r1 is running on host1. Now r2 is restarting, the previous `ReceiverSchedulingPolicy.rescheduleReceiver` will always return (host1, host2, host3). So it's possible that r2 will be scheduled to host1 by TaskScheduler. r3 is similar. Then at last, it's possible that there are 3 receivers running on host1, while host2 and host3 are idle.

This issue can be fixed by returning only executors that have the minimum wight rather than returning at least 3 executors.

Author: zsxwing <zsxwing@gmail.com>

Closes #8340 from zsxwing/fix-receiver-scheduling.

…ult maxRatePerPartition setting of 0

Author: cody koeninger <cody@koeninger.org>

Closes #8413 from koeninger/backpressure-testing-master.

In `HiveComparisionTest`s it is possible to fail a query of the form `SELECT * FROM dest1`, where `dest1` is the query that is actually computing the incorrect results.  To aid debugging this patch improves the harness to also print these query plans and their results.

Author: Michael Armbrust <michael@databricks.com>

Closes #8388 from marmbrus/generatedTables.

[SPARK-10121] [SQL] Thrift server always use the latest class loader provided by the conf of executionHive's state

https://issues.apache.org/jira/browse/SPARK-10121

Looks like the problem is that if we add a jar through another thread, the thread handling the JDBC session will not get the latest classloader.

Author: Yin Huai <yhuai@databricks.com>

Closes #8368 from yhuai/SPARK-10121.

 * Makes `SQLImplicits.rddToDataFrameHolder` scaladoc consistent with `SQLContext.createDataFrame[A <: Product](rdd: RDD[A])` since the former is essentially a wrapper for the latter
 * Clarifies `createDataFrame[A <: Product]` scaladoc to apply for any `RDD[Product]`, not just case classes

Author: Feynman Liang <fliang@databricks.com>

Closes #8406 from feynmanliang/sql-doc-fixes.

cc: shivaram

## Summary

- Modify `tdname` of expression functions. i.e. `ascii`: `rdname functions` => `rdname ascii`
- Replace the dynamical function definitions to the static ones because of thir documentations.

## Generated PDF File
https://drive.google.com/file/d/0B9biIZIU47lLX2t6ZjRoRnBTSEU/view?usp=sharing

## JIRA
[[SPARK-10118] Improve SparkR API docs for 1.5 release - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10118)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Author: Yuu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8386 from yu-iskw/SPARK-10118.

Currently, we eagerly attempt to resolve functions, even before their children are resolved.  However, this is not valid in cases where we need to know the types of the input arguments (i.e. when resolving Hive UDFs).

As a fix, this PR delays function resolution until the functions children are resolved.  This change also necessitates a change to the way we resolve aggregate expressions that are not in aggregate operators (e.g., in `HAVING` or `ORDER BY` clauses).  Specifically, we can't assume that these misplaced functions will be resolved, allowing us to differentiate aggregate functions from normal functions.  To compensate for this change we now attempt to resolve these unresolved expressions in the context of the aggregate operator, before checking to see if any aggregate expressions are present.

Author: Michael Armbrust <michael@databricks.com>

Closes #8371 from marmbrus/hiveUDFResolution.

This adds a missing null check to the Decimal `toScala` converter in `CatalystTypeConverters`, fixing an NPE.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8401 from JoshRosen/SPARK-10190.

User guide for spark.ml GBTs and Random Forests.
The examples are copied from the decision tree guide and modified to run.

I caught some issues I had somehow missed in the tree guide as well.

I have run all examples, including Java ones.  (Of course, I thought I had previously as well...)

CC: mengxr manishamde yanboliang

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

Closes #8369 from jkbradley/ml-ensemble-docs.

Move `test.org.apache.spark.sql.hive` package tests to apparent intended `org.apache.spark.sql.hive` as they don't intend to test behavior from outside org.apache.spark.*

Alternate take, per discussion at https://github.com/apache/spark/pull/8051
I think this is what vanzin and I had in mind but also CC rxin to cross-check, as this does indeed depend on whether these tests were accidentally in this package or not. Testing from a `test.org.apache.spark` package is legitimate but didn't seem to be the intent here.

Author: Sean Owen <sowen@cloudera.com>

Closes #8307 from srowen/SPARK-9758.

This PR refactors `ParquetHiveCompatibilitySuite` so that it's easier to add new test cases.

Hit two bugs, SPARK-10177 and HIVE-11625, while working on this, added test cases for them and marked as ignored for now. SPARK-10177 will be addressed in a separate PR.

Author: Cheng Lian <lian@databricks.com>

Closes #8392 from liancheng/spark-8580/parquet-hive-compat-tests.

The peak execution memory metric was introduced in SPARK-8735. That was before Tungsten was enabled by default, so it assumed that `spark.sql.unsafe.enabled` must be explicitly set to true. The result is that the memory is not displayed by default.

Author: Andrew Or <andrew@databricks.com>

Closes #8345 from andrewor14/show-memory-default.

This PR contains examples on how to use some of the Stat Functions available for DataFrames under `df.stat`.

rxin

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #8378 from brkyvz/update-sql-docs.

[SPARK-9791] [PACKAGE] Change private class to private class to prevent unnecessary classes from showing up in the docs

In addition, some random cleanup of import ordering

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8387 from tdas/SPARK-9791 and squashes the following commits:

67f3ee9 [Tathagata Das] Change private class to private[package] class to prevent them from showing up in the docs

This PR removed the `outputFile` configuration from pom.xml and updated `tests.py` to search jars for both sbt build and maven build.

I ran ` mvn -Pkinesis-asl -DskipTests clean install` locally, and verified the jars in my local repository were correct. I also checked Python tests for maven build, and it passed all tests.

Author: zsxwing <zsxwing@gmail.com>

Closes #8373 from zsxwing/SPARK-10168 and squashes the following commits:

e0b5818 [zsxwing] Fix the sbt build
c697627 [zsxwing] Add the jar pathes to the exception message
be1d8a5 [zsxwing] Fix the issue that maven publishes wrong artifact jars

[SPARK-10142] [STREAMING] Made python checkpoint recovery handle non-local checkpoint paths and existing SparkContexts

The current code only checks checkpoint files in local filesystem, and always tries to create a new Python SparkContext (even if one already exists). The solution is to do the following:
1. Use the same code path as Java to check whether a valid checkpoint exists
2. Create a new Python SparkContext only if there no active one.

There is not test for the path as its hard to test with distributed filesystem paths in a local unit test. I am going to test it with a distributed file system manually to verify that this patch works.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8366 from tdas/SPARK-10142 and squashes the following commits:

3afa666 [Tathagata Das] Added tests
2dd4ae5 [Tathagata Das] Added the check to not create a context if one already exists
9bf151b [Tathagata Das] Made python checkpoint recovery use java to find the checkpoint files

GaussianMixture now distributes matrix decompositions for certain problem sizes. Distributed computation actually fails, but this was not tested in unit tests.

This PR adds a unit test which checks this.  It failed previously but works with this fix.

CC: mengxr

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

Closes #8370 from jkbradley/gmm-fix.

Added the active and inactive receiver numbers in the summary section of Streaming page.

<img width="1074" alt="screen shot 2015-08-21 at 2 08 54 pm" src="https://cloud.githubusercontent.com/assets/1000778/9402437/ff2806a2-480f-11e5-8f8e-efdf8e5d514d.png">

Author: zsxwing <zsxwing@gmail.com>

Closes #8351 from zsxwing/receiver-number.

Update `See the Scala example` to `See the Java example`.

Author: Keiji Yoshida <yoshida.keiji.84@gmail.com>

Closes #8376 from yosssi/patch-1.

This PR adds full codegen support for ConcatWs, is a substitute of #7782

JIRA: https://issues.apache.org/jira/browse/SPARK-9401

cc davies

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #8353 from yjshen/concatws.

Update `lineLengths.persist();` to `lineLengths.persist(StorageLevel.MEMORY_ONLY());` because `JavaRDD#persist` needs a parameter of `StorageLevel`.

Author: Keiji Yoshida <yoshida.keiji.84@gmail.com>

Closes #8372 from yosssi/patch-1.

Add user guide for `VectorSlicer`, with Java test suite and Python version VectorSlicer.

Note that Python version does not support selecting by names now.

Author: Xusen Yin <yinxusen@gmail.com>

Closes #8267 from yinxusen/SPARK-9893.

Removed categorical feature info validation since no longer needed

This is needed to make the ML user guide examples work (in another current PR).

CC: mengxr

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

Closes #8367 from jkbradley/gbt-single-cat.

[SPARK-10143] [SQL] Use parquet's block size (row group size) setting as the min split size if necessary.

https://issues.apache.org/jira/browse/SPARK-10143

With this PR, we will set min split size to parquet's block size (row group size) set in the conf if the min split size is smaller. So, we can avoid have too many tasks and even useless tasks for reading parquet data.

I tested it locally. The table I have has 343MB and it is in my local FS. Because I did not set any min/max split size, the default split size was 32MB and the map stage had 11 tasks. But there were only three tasks that actually read data. With my PR, there were only three tasks in the map stage. Here is the difference.

Without this PR:
![image](https://cloud.githubusercontent.com/assets/2072857/9399179/8587dba6-4765-11e5-9189-7ebba52a2b6d.png)

With this PR:
![image](https://cloud.githubusercontent.com/assets/2072857/9399185/a4735d74-4765-11e5-8848-1f1e361a6b4b.png)

Even if the block size setting does match the actual block size of parquet file, I think it is still generally good to use parquet's block size setting if min split size is smaller than this block size.

Tested it on a cluster using
```
val count = sqlContext.table("""store_sales""").groupBy().count().queryExecution.executedPlan(3).execute().count
```
Basically, it reads 0 column of table `store_sales`. My table has 1824 parquet files with size from 80MB to 280MB (1 to 3 row group sizes). Without this patch, in a 16 worker cluster, the job had 5023 tasks and spent 102s. With this patch, the job had 2893 tasks and spent 64s. It is still not as good as using one mapper per file (1824 tasks and 42s), but it is much better than our master.

Author: Yin Huai <yhuai@databricks.com>

Closes #8346 from yhuai/parquetMinSplit.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #8352 from MechCoder/since.

Details of the bug and explanations can be seen in [SPARK-10122](https://issues.apache.org/jira/browse/SPARK-10122).

tdas , please help to review.

Author: jerryshao <sshao@hortonworks.com>

Closes #8347 from jerryshao/SPARK-10122 and squashes the following commits:

4039b16 [jerryshao] Fix getOffsetRanges in transform() bug

Type coercion for IF should have children resolved first, or we could meet unresolved exception.

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

Closes #8331 from adrian-wang/spark10130.

https://issues.apache.org/jira/browse/SPARK-9439

In general, Yarn apps should be robust to NodeManager restarts.  However, if you run spark with the external shuffle service on, after a NM restart all shuffles fail, b/c the shuffle service has lost some state with info on each executor.  (Note the shuffle data is perfectly fine on disk across a NM restart, the problem is we've lost the small bit of state that lets us *find* those files.)

The solution proposed here is that the external shuffle service can write out its state to leveldb (backed by a local file) every time an executor is added.  When running with yarn, that file is in the NM's local dir.  Whenever the service is started, it looks for that file, and if it exists, it reads the file and re-registers all executors there.

Nothing is changed in non-yarn modes with this patch.  The service is not given a place to save the state to, so it operates the same as before.  This should make it easy to update other cluster managers as well, by just supplying the right file & the equivalent of yarn's `initializeApplication` -- I'm not familiar enough with those modes to know how to do that.

Author: Imran Rashid <irashid@cloudera.com>

Closes #7943 from squito/leveldb_external_shuffle_service_NM_restart and squashes the following commits:

0d285d3 [Imran Rashid] review feedback
70951d6 [Imran Rashid] Merge branch 'master' into leveldb_external_shuffle_service_NM_restart
5c71c8c [Imran Rashid] save executor to db before registering; style
2499c8c [Imran Rashid] explicit dependency on jackson-annotations
795d28f [Imran Rashid] review feedback
81f80e2 [Imran Rashid] Merge branch 'master' into leveldb_external_shuffle_service_NM_restart
594d520 [Imran Rashid] use json to serialize application executor info
1a7980b [Imran Rashid] version
8267d2a [Imran Rashid] style
e9f99e8 [Imran Rashid] cleanup the handling of bad dbs a little
9378ba3 [Imran Rashid] fail gracefully on corrupt leveldb files
acedb62 [Imran Rashid] switch to writing out one record per executor
79922b7 [Imran Rashid] rely on yarn to call stopApplication; assorted cleanup
12b6a35 [Imran Rashid] save registered executors when apps are removed; add tests
c878fbe [Imran Rashid] better explanation of shuffle service port handling
694934c [Imran Rashid] only open leveldb connection once per service
d596410 [Imran Rashid] store executor data in leveldb
59800b7 [Imran Rashid] Files.move in case renaming is unsupported
32fe5ae [Imran Rashid] Merge branch 'master' into external_shuffle_service_NM_restart
d7450f0 [Imran Rashid] style
f729e2b [Imran Rashid] debugging
4492835 [Imran Rashid] lol, dont use a PrintWriter b/c of scalastyle checks
0a39b98 [Imran Rashid] Merge branch 'master' into external_shuffle_service_NM_restart
55f49fc [Imran Rashid] make sure the service doesnt die if the registered executor file is corrupt; add tests
245db19 [Imran Rashid] style
62586a6 [Imran Rashid] just serialize the whole executors map
bdbbf0d [Imran Rashid] comments, remove some unnecessary changes
857331a [Imran Rashid] better tests & comments
bb9d1e6 [Imran Rashid] formatting
bdc4b32 [Imran Rashid] rename
86e0cb9 [Imran Rashid] for tests, shuffle service finds an open port
23994ff [Imran Rashid] style
7504de8 [Imran Rashid] style
a36729c [Imran Rashid] cleanup
efb6195 [Imran Rashid] proper unit test, and no longer leak if apps stop during NM restart
dd93dc0 [Imran Rashid] test for shuffle service w/ NM restarts
d596969 [Imran Rashid] cleanup imports
0e9d69b [Imran Rashid] better names
9eae119 [Imran Rashid] cleanup lots of duplication
1136f44 [Imran Rashid] test needs to have an actual shuffle
0b588bd [Imran Rashid] more fixes ...
ad122ef [Imran Rashid] more fixes
5e5a7c3 [Imran Rashid] fix build
c69f46b [Imran Rashid] maybe working version, needs tests & cleanup ...
bb3ba49 [Imran Rashid] minor cleanup
36127d3 [Imran Rashid] wip
b9d2ced [Imran Rashid] incomplete setup for external shuffle service tests