Please help review it. Thanks

Author: Jeff Zhang <zjffdu@apache.org>

Closes #9114 from zjffdu/SPARK-11099.

[SPARK-11093] [CORE] ChildFirstURLClassLoader#getResources should return all found resources, not just those in the child classloader

Author: Adam Lewandowski <alewandowski@ipcoop.com>

Closes #9106 from alewando/childFirstFix.

Actually all of the `UnaryMathExpression` doens't support the Decimal, will create follow ups for supporing it. This is the first PR which will be good to review the approach I am taking.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #9086 from chenghao-intel/ceiling.

This patch extends TungstenAggregate to support ImperativeAggregate functions. The existing TungstenAggregate operator only supported DeclarativeAggregate functions, which are defined in terms of Catalyst expressions and can be evaluated via generated projections. ImperativeAggregate functions, on the other hand, are evaluated by calling their `initialize`, `update`, `merge`, and `eval` methods.

The basic strategy here is similar to how SortBasedAggregate evaluates both types of aggregate functions: use a generated projection to evaluate the expression-based declarative aggregates with dummy placeholder expressions inserted in place of the imperative aggregate function output, then invoke the imperative aggregate functions and target them against the aggregation buffer. The bulk of the diff here consists of code that was copied and adapted from SortBasedAggregate, with some key changes to handle TungstenAggregate's sort fallback path.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #9038 from JoshRosen/support-interpreted-in-tungsten-agg-final.

```scala
withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED.key -> "true") {
      withTempPath { dir =>
        val path = s"${dir.getCanonicalPath}/part=1"
        (1 to 3).map(i => (i, i.toString)).toDF("a", "b").write.parquet(path)

        // If the "part = 1" filter gets pushed down, this query will throw an exception since
        // "part" is not a valid column in the actual Parquet file
        checkAnswer(
          sqlContext.read.parquet(path).filter("a > 0 and (part = 0 or a > 1)"),
          (2 to 3).map(i => Row(i, i.toString, 1)))
      }
    }
```

We expect the result to be:
```
2,1
3,1
```
But got
```
1,1
2,1
3,1
```

Author: Cheng Hao <hao.cheng@intel.com>

Closes #8916 from chenghao-intel/partition_filter.

o.a.s.sql.catalyst and o.a.s.sql.execution are supposed to be private.

Author: Reynold Xin <rxin@databricks.com>

Closes #9121 from rxin/SPARK-11113.

Right now, we have QualifiedTableName, TableIdentifier, and Seq[String] to represent table identifiers. We should only have one form and TableIdentifier is the best one because it provides methods to get table name, database name, return unquoted string, and return quoted string.

Author: Wenchen Fan <wenchen@databricks.com>
Author: Wenchen Fan <cloud0fan@163.com>

Closes #8453 from cloud-fan/table-name.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #9119 from cloud-fan/callback.

A few more changes:

1. Renamed IDVerifier -> RpcEndpointVerifier
2. Renamed NettyRpcAddress -> RpcEndpointAddress
3. Simplified NettyRpcHandler a bit by removing the connection count tracking. This is OK because I now force spark.shuffle.io.numConnectionsPerPeer to 1
4. Reduced spark.rpc.connect.threads to 64. It would be great to eventually remove this extra thread pool.
5. Minor cleanup & documentation.

Author: Reynold Xin <rxin@databricks.com>

Closes #9112 from rxin/SPARK-11096.

Close #9064
Close #9063
Close #9062

These pull requests were merged into branch-1.5, branch-1.4, and branch-1.3.

the fix is for jira https://issues.apache.org/jira/browse/SPARK-8386

Author: Huaxin Gao <huaxing@us.ibm.com>

Closes #9042 from huaxingao/spark8386.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #9053 from vanzin/SPARK-11040.

should pick into spark 1.5.2 also.

https://issues.apache.org/jira/browse/SPARK-10619

looks like this was broken by commit: https://github.com/apache/spark/commit/fb1d06fc242ec00320f1a3049673fbb03c4a6eb9#diff-b8adb646ef90f616c34eb5c98d1ebd16
It looks like somethings were change to use the UIUtils.listingTable but executor page wasn't converted so when it removed sortable from the UIUtils. TABLE_CLASS_NOT_STRIPED it broke this page.

Simply add the sortable tag back in and it fixes both active UI and the history server UI.

Author: Tom Graves <tgraves@yahoo-inc.com>

Closes #9101 from tgravescs/SPARK-10619.

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

Closes #9023 from sun-rui/SPARK-10996.

I was having issues with collect() and orderBy() in Spark 1.5.0 so I used the DataFrame.R file and test_sparkSQL.R file from the Spark 1.5.1 download. I only modified the join() function in DataFrame.R to include "full", "fullouter", "left", "right", and "leftsemi" and added corresponding test cases in the test for join() and merge() in test_sparkSQL.R file.
Pull request because I filed this JIRA bug report:
https://issues.apache.org/jira/browse/SPARK-10981

Author: Monica Liu <liu.monica.f@gmail.com>

Closes #9029 from mfliu/master.

https://issues.apache.org/jira/browse/SPARK-11091

Author: Yin Huai <yhuai@databricks.com>

Closes #9103 from yhuai/SPARK-11091.

With this feature, we can track the query plan, time cost, exception during query execution for spark users.

Author: Wenchen Fan <cloud0fan@163.com>

Closes #9078 from cloud-fan/callback.

We should not stop resolving having when the having condtion is resolved, or something like `count(1)` will crash.

Author: Wenchen Fan <cloud0fan@163.com>

Closes #9105 from cloud-fan/having.

This is a first draft of the ability to construct expressions that will take a catalyst internal row and construct a Product (case class or tuple) that has fields with the correct names.  Support include:
 - Nested classes
 - Maps
 - Efficiently handling of arrays of primitive types

Not yet supported:
 - Case classes that require custom collection types (i.e. List instead of Seq).

Author: Michael Armbrust <michael@databricks.com>

Closes #9100 from marmbrus/productContructor.

Value of the quantile probabilities array should be in the range (0, 1) instead of [0,1]
 in `AFTSurvivalRegression.scala` according to [Discussion] (https://github.com/apache/spark/pull/8926#discussion-diff-40698242)

Author: vectorijk <jiangkai@gmail.com>

Closes #9083 from vectorijk/spark-11059.

Spark's release packaging scripts used to live in a separate repository. Although these scripts are now part of the Spark repo, there are some minor patches made against the old repos that are missing in Spark's copy of the script. This PR ports those changes.

/cc shivaram, who originally submitted these changes against https://github.com/rxin/spark-utils

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8986 from JoshRosen/port-release-build-fixes-from-rxin-repo.

In the current implementation of named expressions' `ExprIds`, we rely on a per-JVM AtomicLong to ensure that expression ids are unique within a JVM. However, these expression ids will not be _globally_ unique. This opens the potential for id collisions if new expression ids happen to be created inside of tasks rather than on the driver.

There are currently a few cases where tasks allocate expression ids, which happen to be safe because those expressions are never compared to expressions created on the driver. In order to guard against the introduction of invalid comparisons between driver-created and executor-created expression ids, this patch extends `ExprId` to incorporate a UUID to identify the JVM that created the id, which prevents collisions.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #9093 from JoshRosen/SPARK-11080.

…n script

Author: trystanleftwich <trystan@atscale.com>

Closes #9065 from trystanleftwich/SPARK-11052.

This patch unifies the memory management of the storage and execution regions such that either side can borrow memory from each other. When memory pressure arises, storage will be evicted in favor of execution. To avoid regressions in cases where storage is crucial, we dynamically allocate a fraction of space for storage that execution cannot evict. Several configurations are introduced:

- **spark.memory.fraction (default 0.75)**: ​fraction of the heap space used for execution and storage. The lower this is, the more frequently spills and cached data eviction occur. The purpose of this config is to set aside memory for internal metadata, user data structures, and imprecise size estimation in the case of sparse, unusually large records.

- **spark.memory.storageFraction (default 0.5)**: size of the storage region within the space set aside by `s​park.memory.fraction`. ​Cached data may only be evicted if total storage exceeds this region.

- **spark.memory.useLegacyMode (default false)**: whether to use the memory management that existed in Spark 1.5 and before. This is mainly for backward compatibility.

For a detailed description of the design, see [SPARK-10000](https://issues.apache.org/jira/browse/SPARK-10000). This patch builds on top of the `MemoryManager` interface introduced in #9000.

Author: Andrew Or <andrew@databricks.com>

Closes #9084 from andrewor14/unified-memory-manager.

This PR implements the JSON SerDe for the following param types: `Boolean`, `Int`, `Long`, `Float`, `Double`, `String`, `Array[Int]`, `Array[Double]`, and `Array[String]`. The implementation of `Float`, `Double`, and `Array[Double]` are specialized to handle `NaN` and `Inf`s. This will be used in pipeline persistence. jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #9090 from mengxr/SPARK-7402.

Output list of supported modules for python tests in error message when given bad module name.

CC: davies

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

Closes #9088 from jkbradley/python-tests-modules.

Bring the change code up to date.

Author: Adrian Zhuang <adrian555@users.noreply.github.com>
Author: adrian555 <wzhuang@us.ibm.com>

Closes #9031 from adrian555/attach2.

as.DataFrame is more a R-style like signature.
Also, I'd like to know if we could make the context, e.g. sqlContext global, so that we do not have to specify it as an argument, when we each time create a dataframe.

Author: Narine Kokhlikyan <narine.kokhlikyan@gmail.com>

Closes #8952 from NarineK/sparkrasDataFrame.

Two points in this PR:

1.    Originally thought was that a named R list is assumed to be a struct in SerDe. But this is problematic because some R functions will implicitly generate named lists that are not intended to be a struct when transferred by SerDe. So SerDe clients have to explicitly mark a names list as struct by changing its class from "list" to "struct".

2.    SerDe is in the Spark Core module, and data of StructType is represented as GenricRow which is defined in Spark SQL module. SerDe can't import GenricRow as in maven build  Spark SQL module depends on Spark Core module. So this PR adds a registration hook in SerDe to allow SQLUtils in Spark SQL module to register its functions for serialization and deserialization of StructType.

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

Closes #8794 from sun-rui/SPARK-10051.

The SQLTab will be shared by multiple sessions.

If we create multiple independent SQLContexts (not using newSession()), will still see multiple SQLTabs in the Spark UI.

Author: Davies Liu <davies@databricks.com>

Closes #9048 from davies/sqlui.

I'm going through the implementation right now for post-doc review. Adding more comments and renaming things as I go through them.

I also want to write higher level documentation about how the whole thing works -- but those will come in other pull requests.

Author: Reynold Xin <rxin@databricks.com>

Closes #9091 from rxin/rpc-review.

Currently, All windows function could generate wrong result in cluster sometimes.

The root cause is that AttributeReference is called in executor, then id of it may not be unique than others created in driver.

Here is the script that could reproduce the problem (run in local cluster):
```
from pyspark import SparkContext, HiveContext
from pyspark.sql.window import Window
from pyspark.sql.functions import rowNumber

sqlContext = HiveContext(SparkContext())
sqlContext.setConf("spark.sql.shuffle.partitions", "3")
df =  sqlContext.range(1<<20)
df2 = df.select((df.id % 1000).alias("A"), (df.id / 1000).alias('B'))
ws = Window.partitionBy(df2.A).orderBy(df2.B)
df3 = df2.select("client", "date", rowNumber().over(ws).alias("rn")).filter("rn < 0")
assert df3.count() == 0
```

Author: Davies Liu <davies@databricks.com>
Author: Yin Huai <yhuai@databricks.com>

Closes #9050 from davies/wrong_window.

[SPARK-11026] [YARN] spark.yarn.user.classpath.first does work for 'spark-submit --jars hdfs://user/foo.jar'

when spark.yarn.user.classpath.first=true and using 'spark-submit --jars hdfs://user/foo.jar', it can not put foo.jar to system classpath. so we need to put yarn's linkNames of jars to the system classpath. vanzin tgravescs

Author: Lianhui Wang <lianhuiwang09@gmail.com>

Closes #9045 from lianhuiwang/spark-11026.

This PR improve the unrolling and read of complex types in columnar cache:
1) Using UnsafeProjection to do serialization of complex types, so they will not be serialized three times (two for actualSize)
2) Copy the bytes from UnsafeRow/UnsafeArrayData to ByteBuffer directly, avoiding the immediate byte[]
3) Using the underlying array in ByteBuffer to create UTF8String/UnsafeRow/UnsafeArrayData without copy.

Combine these optimizations,  we can reduce the unrolling time from 25s to 21s (20% less), reduce the scanning time from 3.5s to 2.5s (28% less).

```
df = sqlContext.read.parquet(path)
t = time.time()
df.cache()
df.count()
print 'unrolling', time.time() - t

for i in range(10):
    t = time.time()
    print df.select("*")._jdf.queryExecution().toRdd().count()
    print time.time() - t
```

The schema is
```
root
 |-- a: struct (nullable = true)
 |    |-- b: long (nullable = true)
 |    |-- c: string (nullable = true)
 |-- d: array (nullable = true)
 |    |-- element: long (containsNull = true)
 |-- e: map (nullable = true)
 |    |-- key: long
 |    |-- value: string (valueContainsNull = true)
```

Now the columnar cache depends on that UnsafeProjection support all the data types (including UDT), this PR also fix that.

Author: Davies Liu <davies@databricks.com>

Closes #9016 from davies/complex2.

Add application attempt window for Spark on Yarn to ignore old out of window failures, this is useful for long running applications to recover from failures.

Author: jerryshao <sshao@hortonworks.com>

Closes #8857 from jerryshao/SPARK-10739 and squashes the following commits:

36eabdc [jerryshao] change the doc
7f9b77d [jerryshao] Style change
1c9afd0 [jerryshao] Address the comments
caca695 [jerryshao] Add application attempt window for Spark on Yarn

This commit improves the documentation around building Spark to
(1) recommend using SBT interactive mode to avoid the overhead of
launching SBT and (2) refer to the wiki page that documents using
SPARK_PREPEND_CLASSES to avoid creating the assembly jar for each
compile.

cc srowen

Author: Kay Ousterhout <kayousterhout@gmail.com>

Closes #9068 from kayousterhout/SPARK-11056.

https://issues.apache.org/jira/browse/SPARK-11042

Author: Yin Huai <yhuai@databricks.com>

Closes #9058 from yhuai/SPARK-11042.

This patch adds a signal handler to trap Ctrl-C and cancels running job.

Author: Ashwin Shankar <ashankar@netflix.com>

Closes #9033 from ashwinshankar77/master.

The issue is that local paths on Windows, when provided with drive
letters or backslashes, are not valid URIs.

Instead of trying to figure out whether paths are URIs or not, use
Utils.resolveURI() which does that for us.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #9049 from vanzin/SPARK-11023 and squashes the following commits:

77021f2 [Marcelo Vanzin] [SPARK-11023] [yarn] Avoid creating URIs from local paths directly.

For Parquet decimal columns that are encoded using plain-dictionary encoding, we can make the upper level converter aware of the dictionary, so that we can pre-instantiate all the decimals to avoid duplicated instantiation.

Note that plain-dictionary encoding isn't available for `FIXED_LEN_BYTE_ARRAY` for Parquet writer version `PARQUET_1_0`. So currently only decimals written as `INT32` and `INT64` can benefit from this optimization.

Author: Cheng Lian <lian@databricks.com>

Closes #9040 from liancheng/spark-11007.decimal-converter-dict-support.