This patch allows `Repartition` to support UnsafeRows. This is accomplished by implementing the logical `Repartition` operator in terms of `Exchange` and a new `RoundRobinPartitioning`.

Author: Josh Rosen <joshrosen@databricks.com>
Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #8083 from JoshRosen/SPARK-9702.

The created decimal is wrong if using `Decimal(unscaled, precision, scale)` with unscaled > 1e18 and and precision > 18 and scale > 0.

This bug exists since the beginning.

Author: Davies Liu <davies@databricks.com>

Closes #9014 from davies/fix_decimal.

Consolidate the Cholesky solvers in WeightedLeastSquares and ALS.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8936 from yanboliang/spark-10490.

DeclarativeAggregate matches more closely with ImperativeAggregate we already have.

Author: Reynold Xin <rxin@databricks.com>

Closes #9013 from rxin/SPARK-10982.

Provide initialModel param for pyspark.mllib.clustering.KMeans

Author: Evan Chen <chene@us.ibm.com>

Closes #8967 from evanyc15/SPARK-10779-pyspark-mllib.

HadoopRDD throws exception in executor, something like below.
{noformat}
5/09/17 18:51:21 INFO metastore.HiveMetaStore: 0: Opening raw store with implemenation class:org.apache.hadoop.hive.metastore.ObjectStore
15/09/17 18:51:21 INFO metastore.ObjectStore: ObjectStore, initialize called
15/09/17 18:51:21 WARN metastore.HiveMetaStore: Retrying creating default database after error: Class org.datanucleus.api.jdo.JDOPersistenceManagerFactory was not found.
javax.jdo.JDOFatalUserException: Class org.datanucleus.api.jdo.JDOPersistenceManagerFactory was not found.
	at javax.jdo.JDOHelper.invokeGetPersistenceManagerFactoryOnImplementation(JDOHelper.java:1175)
	at javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:808)
	at javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:701)
	at org.apache.hadoop.hive.metastore.ObjectStore.getPMF(ObjectStore.java:365)
	at org.apache.hadoop.hive.metastore.ObjectStore.getPersistenceManager(ObjectStore.java:394)
	at org.apache.hadoop.hive.metastore.ObjectStore.initialize(ObjectStore.java:291)
	at org.apache.hadoop.hive.metastore.ObjectStore.setConf(ObjectStore.java:258)
	at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:73)
	at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:133)
	at org.apache.hadoop.hive.metastore.RawStoreProxy.<init>(RawStoreProxy.java:57)
	at org.apache.hadoop.hive.metastore.RawStoreProxy.getProxy(RawStoreProxy.java:66)
	at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.newRawStore(HiveMetaStore.java:593)
	at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.getMS(HiveMetaStore.java:571)
	at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:620)
	at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.init(HiveMetaStore.java:461)
	at org.apache.hadoop.hive.metastore.RetryingHMSHandler.<init>(RetryingHMSHandler.java:66)
	at org.apache.hadoop.hive.metastore.RetryingHMSHandler.getProxy(RetryingHMSHandler.java:72)
	at org.apache.hadoop.hive.metastore.HiveMetaStore.newRetryingHMSHandler(HiveMetaStore.java:5762)
	at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:199)
	at org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient.<init>(SessionHiveMetaStoreClient.java:74)
	at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
	at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:57)
	at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
	at java.lang.reflect.Constructor.newInstance(Constructor.java:526)
	at org.apache.hadoop.hive.metastore.MetaStoreUtils.newInstance(MetaStoreUtils.java:1521)
	at org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.<init>(RetryingMetaStoreClient.java:86)
	at org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:132)
	at org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:104)
	at org.apache.hadoop.hive.ql.metadata.Hive.createMetaStoreClient(Hive.java:3005)
	at org.apache.hadoop.hive.ql.metadata.Hive.getMSC(Hive.java:3024)
	at org.apache.hadoop.hive.ql.metadata.Hive.getAllDatabases(Hive.java:1234)
	at org.apache.hadoop.hive.ql.metadata.Hive.reloadFunctions(Hive.java:174)
	at org.apache.hadoop.hive.ql.metadata.Hive.<clinit>(Hive.java:166)
	at org.apache.hadoop.hive.ql.plan.PlanUtils.configureJobPropertiesForStorageHandler(PlanUtils.java:803)
	at org.apache.hadoop.hive.ql.plan.PlanUtils.configureInputJobPropertiesForStorageHandler(PlanUtils.java:782)
	at org.apache.spark.sql.hive.HadoopTableReader$.initializeLocalJobConfFunc(TableReader.scala:298)
	at org.apache.spark.sql.hive.HadoopTableReader$$anonfun$12.apply(TableReader.scala:274)
	at org.apache.spark.sql.hive.HadoopTableReader$$anonfun$12.apply(TableReader.scala:274)
	at org.apache.spark.rdd.HadoopRDD$$anonfun$getJobConf$6.apply(HadoopRDD.scala:176)
	at org.apache.spark.rdd.HadoopRDD$$anonfun$getJobConf$6.apply(HadoopRDD.scala:176)
	at scala.Option.map(Option.scala:145)
	at org.apache.spark.rdd.HadoopRDD.getJobConf(HadoopRDD.scala:176)
	at org.apache.spark.rdd.HadoopRDD$$anon$1.<init>(HadoopRDD.scala:220)
	at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:216)
	at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:101)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
	at org.apache.spark.rdd.UnionRDD.compute(UnionRDD.scala:87)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
	at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
	at org.apache.spark.scheduler.Task.run(Task.scala:88)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
	at java.lang.Thread.run(Thread.java:745)
{noformat}

Author: navis.ryu <navis@apache.org>

Closes #8804 from navis/SPARK-10679.

JIRA: https://issues.apache.org/jira/browse/SPARK-10856

For Microsoft SQL Server, TimestampType should be mapped to DATETIME instead of TIMESTAMP.

Related information for the datatype mapping: https://msdn.microsoft.com/en-us/library/ms378878(v=sql.110).aspx

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

Closes #8978 from viirya/mysql-jdbc-timestamp.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8775 from vanzin/SPARK-10300.

[SPARK-10941] [SQL] Refactor AggregateFunction2 and AlgebraicAggregate interfaces to improve code clarity

This patch refactors several of the Aggregate2 interfaces in order to improve code clarity.

The biggest change is a refactoring of the `AggregateFunction2` class hierarchy. In the old code, we had a class named `AlgebraicAggregate` that inherited from `AggregateFunction2`, added a new set of methods, then banned the use of the inherited methods. I found this to be fairly confusing because.

If you look carefully at the existing code, you'll see that subclasses of `AggregateFunction2` fall into two disjoint categories: imperative aggregation functions which directly extended `AggregateFunction2` and declarative, expression-based aggregate functions which extended `AlgebraicAggregate`. In order to make this more explicit, this patch refactors things so that `AggregateFunction2` is a sealed abstract class with two subclasses, `ImperativeAggregateFunction` and `ExpressionAggregateFunction`. The superclass, `AggregateFunction2`, now only contains methods and fields that are common to both subclasses.

After making this change, I updated the various AggregationIterator classes to comply with this new naming scheme. I also performed several small renamings in the aggregate interfaces themselves in order to improve clarity and rewrote or expanded a number of comments.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8973 from JoshRosen/tungsten-agg-comments.

It is currently impossible to clear Param values once set. It would be helpful to be able to.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8619 from holdenk/SPARK-9841-params-clear-needs-to-be-public.

The `self` method returns null when called from the constructor;
instead, registration should happen in the `onStart` method, at
which point the `self` reference has already been initialized.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #9005 from vanzin/SPARK-10964.

A recent change to fix the referenced bug caused this exception in
the `SparkContext.stop()` path:

org.apache.spark.SparkException: YarnSparkHadoopUtil is not available in non-YARN mode!
        at org.apache.spark.deploy.yarn.YarnSparkHadoopUtil$.get(YarnSparkHadoopUtil.scala:167)
        at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.stop(YarnClientSchedulerBackend.scala:182)
        at org.apache.spark.scheduler.TaskSchedulerImpl.stop(TaskSchedulerImpl.scala:440)
        at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1579)
        at org.apache.spark.SparkContext$$anonfun$stop$7.apply$mcV$sp(SparkContext.scala:1730)
        at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1185)
        at org.apache.spark.SparkContext.stop(SparkContext.scala:1729)

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8996 from vanzin/SPARK-10812.

This PR is mostly cosmetic and cleans up some warts in codegen (nearly all of which were inherited from the original quasiquote version).
 - Add lines numbers to errors (in stacktraces when debug logging is on, and always for compile fails)
 - Use a variable for input row instead of hardcoding "i" everywhere
 - rename `primitive` -> `value` (since its often actually an object)

Author: Michael Armbrust <michael@databricks.com>

Closes #9006 from marmbrus/codegen-cleanup.

Currently if it isn't set it scans `/lib/*` and adds every dir to the
classpath which makes the env too large and every command called
afterwords fails.

Author: Kevin Cox <kevincox@kevincox.ca>

Closes #8994 from kevincox/kevincox-only-add-hive-to-classpath-if-var-is-set.

Author: Sun Rui <rui.sun@intel.com>

Closes #8869 from sun-rui/SPARK-10752.

In the Markdown docs for the spark.mllib Programming Guide, we have code examples with codetabs for each language. We should link to each language's API docs within the corresponding codetab, but we are inconsistent about this. For an example of what we want to do, see the "ChiSqSelector" section in https://github.com/apache/spark/blob/64743870f23bffb8d96dcc8a0181c1452782a151/docs/mllib-feature-extraction.md
This JIRA is just for spark.mllib, not spark.ml.

Please let me know if more work is needed, thanks a lot.

Author: Xin Ren <iamshrek@126.com>

Closes #8977 from keypointt/SPARK-10669.

This PR implements the following features for both `master` and `branch-1.5`.
1. Display the failed output op count in the batch list
2. Display the failure reason of output op in the batch detail page

Screenshots:
<img width="1356" alt="1" src="https://cloud.githubusercontent.com/assets/1000778/10198387/5b2b97ec-67ce-11e5-81c2-f818b9d2f3ad.png">
<img width="1356" alt="2" src="https://cloud.githubusercontent.com/assets/1000778/10198388/5b76ac14-67ce-11e5-8c8b-de2683c5b485.png">

There are still two remaining problems in the UI.
1. If an output operation doesn't run any spark job, we cannot get the its duration since now it's the sum of all jobs' durations.
2. If an output operation doesn't run any spark job, we cannot get the description since it's the latest job's call site.

We need to add new `StreamingListenerEvent` about output operations to fix them. So I'd like to fix them only for `master` in another PR.

Author: zsxwing <zsxwing@gmail.com>

Closes #8950 from zsxwing/batch-failure.

[SPARK-10957] [ML] setParams changes quantileProbabilities unexpectly in PySpark's AFTSurvivalRegression

If user doesn't specify `quantileProbs` in `setParams`, it will get reset to the default value. We don't need special handling here. vectorijk yanboliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #9001 from mengxr/SPARK-10957.

Implement Python API for AFTSurvivalRegression

Author: vectorijk <jiangkai@gmail.com>

Closes #8926 from vectorijk/spark-10688.

This should go into 1.5.2 also.

The issue is we were no longer adding the __app__.jar to the system classpath.

Author: Thomas Graves <tgraves@staydecay.corp.gq1.yahoo.com>
Author: Tom Graves <tgraves@yahoo-inc.com>

Closes #8959 from tgravescs/SPARK-10901.

This makes YARN containers behave like all other processes launched by
Spark, which launch with a default perm gen size of 256m unless
overridden by the user (or not needed by the vm).

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8970 from vanzin/SPARK-10916.

This PR remove the typeId in columnar cache, it's not needed anymore, it also remove DATE and TIMESTAMP (use INT/LONG instead).

Author: Davies Liu <davies@databricks.com>

Closes #8989 from davies/refactor_cache.

These code was left there to produce clear diff for https://github.com/apache/spark/pull/8747

Author: Wenchen Fan <cloud0fan@163.com>

Closes #8991 from cloud-fan/clean.

Add output operation events to StreamingListener so as to implement the following UI features:

1. Progress bar of a batch in the batch list.
2. Be able to display output operation `description` and `duration` when there is no spark job in a Streaming job.

Author: zsxwing <zsxwing@gmail.com>

Closes #8958 from zsxwing/output-operation-events.

`Murmur3_x86_32.hashUnsafeWords` only accepts word-aligned bytes, but unsafe array is not.

Author: Wenchen Fan <cloud0fan@163.com>

Closes #8987 from cloud-fan/hash.

This PR is a completely rewritten of GenerateUnsafeProjection, to accomplish the goal of copying data only once. The old code of GenerateUnsafeProjection is still there to reduce review difficulty.

Instead of creating unsafe conversion code for struct, array and map, we create code of writing the content to the global row buffer.

Author: Wenchen Fan <cloud0fan@163.com>
Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8747 from cloud-fan/copy-once.

I don't believe the API changed at all.

Author: Avrohom Katz <iambpentameter@gmail.com>

Closes #8957 from akatz/kcl-upgrade.

[SPARK-9570] [DOCS] Consistent recommendation for submitting spark apps to YARN, -master yarn --deploy-mode x vs -master yarn-x'.

Recommend `--master yarn --deploy-mode {cluster,client}` consistently in docs.
Follow-on to https://github.com/apache/spark/pull/8385
CC nssalian

Author: Sean Owen <sowen@cloudera.com>

Closes #8968 from srowen/SPARK-9570.

The fix is to coerce `c("a", "b")` into a list such that it could be serialized to call JVM with.

Author: felixcheung <felixcheung_m@hotmail.com>

Closes #8961 from felixcheung/rselect.

I was reading throught the scheduler and found this small mistake.

Author: Guillaume Poulin <guillaume@hopper.com>

Closes #8966 from gpoulin/remember_duration_typo.

This PR just reverted https://github.com/apache/spark/commit/02144d6745ec0a6d8877d969feb82139bd22437f to remerge #6457 and also included the commits in #8905.

Author: zsxwing <zsxwing@gmail.com>

Closes #8944 from zsxwing/SPARK-6028.

Given LogicalRelation (and other classes) were moved from sources package to execution.sources package, removed private[sql] to make LogicalRelation public to facilitate access for data sources.

Author: gweidner <gweidner@us.ibm.com>

Closes #8965 from gweidner/SPARK-7275.

Compatibility between history server script and functionality

The history server has its argument parsing class in HistoryServerArguments. However, this doesn't get involved in the start-history-server.sh codepath where the $0 arg is assigned to spark.history.fs.logDirectory and all other arguments discarded (e.g --property-file.)
This stops the other options being usable from this script

Author: Joshi <rekhajoshm@gmail.com>
Author: Rekha Joshi <rekhajoshm@gmail.com>

Closes #8758 from rekhajoshm/SPARK-10317.

https://github.com/apache/spark/pull/8882 broke our build.

Author: Yin Huai <yhuai@databricks.com>

Closes #8964 from yhuai/fixStyle.

See JIRA [here](https://issues.apache.org/jira/browse/SPARK-6530).

Author: Xusen Yin <yinxusen@gmail.com>

Closes #5742 from yinxusen/SPARK-6530.

JIRA issue [here](https://issues.apache.org/jira/browse/SPARK-5890).

I borrow the code of `findSplits` from `RandomForest`. I don't think it's good to call it from `RandomForest` directly.

Author: Xusen Yin <yinxusen@gmail.com>

Closes #5779 from yinxusen/SPARK-5890.

Document CrossValidatorModel members: bestModel and avgMetrics

Author: Rerngvit Yanggratoke <rerngvit@kth.se>

Closes #8882 from rerngvit/Spark-9798.

The utilities such as Substring#substringBinarySQL and BinaryPrefixComparator#computePrefix for binary data are put together in ByteArray for easy-to-read.

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

Closes #8122 from maropu/CleanUpForBinaryType.

We introduced SQL option `spark.sql.parquet.followParquetFormatSpec` while working on implementing Parquet backwards-compatibility rules in SPARK-6777. It indicates whether we should use legacy Parquet format adopted by Spark 1.4 and prior versions or the standard format defined in parquet-format spec to write Parquet files.

This option defaults to `false` and is marked as a non-public option (`isPublic = false`) because we haven't finished refactored Parquet write path. The problem is, the name of this option is somewhat confusing, because it's not super intuitive why we shouldn't follow the spec. Would be nice to rename it to `spark.sql.parquet.writeLegacyFormat`, and invert its default value (the two option names have opposite meanings).

Although this option is private in 1.5, we'll make it public in 1.6 after refactoring Parquet write path. So that users can decide whether to write Parquet files in standard format or legacy format.

Author: Cheng Lian <lian@databricks.com>

Closes #8566 from liancheng/spark-10400/deprecate-follow-parquet-format-spec.