[SPARK-3006] Failed to execute spark-shell in Windows OS

Modified the order of the options and arguments in spark-shell.cmd

Author: Masayoshi TSUZUKI <tsudukim@oss.nttdata.co.jp>

Closes #1918 from tsudukim/feature/SPARK-3006 and squashes the following commits:

8bba494 [Masayoshi TSUZUKI] [SPARK-3006] Failed to execute spark-shell in Windows OS
1a32410 [Masayoshi TSUZUKI] [SPARK-3006] Failed to execute spark-shell in Windows OS

(cherry picked from commit 9497b12d429cf9d075807896637e40e205175203)
Signed-off-by: Andrew Or <andrewor14@gmail.com>
[Docs] Add missing <code> tags (minor)

These configs looked inconsistent from the rest.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1936 from andrewor14/docs-code and squashes the following commits:

15f578a [Andrew Or] Add <code> tag

(cherry picked from commit e4245656438d00714ebd59e89c4de3fdaae83494)
Signed-off-by: Reynold Xin <rxin@apache.org>
[SPARK-2995][MLLIB] add ALS.setIntermediateRDDStorageLevel

As mentioned in SPARK-2465, using `MEMORY_AND_DISK_SER` for user/product in/out links together with `spark.rdd.compress=true` can help reduce the space requirement by a lot, at the cost of speed. It might be useful to add this option so people can run ALS on much bigger datasets.

Another option for the method name is `setIntermediateRDDStorageLevel`.

Author: Xiangrui Meng <meng@databricks.com>

Closes #1913 from mengxr/als-storagelevel and squashes the following commits:

d942017 [Xiangrui Meng] rename to setIntermediateRDDStorageLevel
7550029 [Xiangrui Meng] add ALS.setIntermediateDataStorageLevel

(cherry picked from commit 69a57a18ee35af1cc5a00b67a80837ea317cd330)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
[SPARK-3029] Disable local execution of Spark jobs by default

Currently, local execution of Spark jobs is only used by take(), and it can be problematic as it can load a significant amount of data onto the driver. The worst case scenarios occur if the RDD is cached (guaranteed to load whole partition), has very large elements, or the partition is just large and we apply a filter with high selectivity or computational overhead.

Additionally, jobs that run locally in this manner do not show up in the web UI, and are thus harder to track or understand what is occurring.

This PR adds a flag to disable local execution, which is turned OFF by default, with the intention of perhaps eventually removing this functionality altogether. Removing it now is a tougher proposition since it is part of the public runJob API. An alternative solution would be to limit the flag to take()/first() to avoid impacting any external users of this API, but such usage (or, at least, reliance upon the feature) is hopefully minimal.

Author: Aaron Davidson <aaron@databricks.com>

Closes #1321 from aarondav/allowlocal and squashes the following commits:

136b253 [Aaron Davidson] Fix DAGSchedulerSuite
5599d55 [Aaron Davidson] [RFC] Disable local execution of Spark jobs by default

(cherry picked from commit d069c5d9d2f6ce06389ca2ddf0b3ae4db72c5797)
Signed-off-by: Reynold Xin <rxin@apache.org>
SPARK-2893: Do not swallow Exceptions when running a custom kryo registrator

The previous behaviour of swallowing ClassNotFound exceptions when running a custom Kryo registrator could lead to difficult to debug problems later on at serialisation / deserialisation time, see SPARK-2878.  Instead it is better to fail fast.

Added test case.

Author: Graham Dennis <graham.dennis@gmail.com>

Closes #1827 from GrahamDennis/feature/spark-2893 and squashes the following commits:

fbe4cb6 [Graham Dennis] [SPARK-2878]: Update the test case to match the updated exception message
65e53c5 [Graham Dennis] [SPARK-2893]: Improve message when a spark.kryo.registrator fails.
f480d85 [Graham Dennis] [SPARK-2893] Fix typo.
b59d2c2 [Graham Dennis] SPARK-2893: Do not swallow Exceptions when running a custom spark.kryo.registrator

(cherry picked from commit 6b8de0e36c7548046c3b8a57f2c8e7e788dde8cc)
Signed-off-by: Reynold Xin <rxin@apache.org>
[SPARK-3011][SQL] _temporary directory should be filtered out by sqlContext.parquetFile

Author: Chia-Yung Su <chiayung@appier.com>

Closes #1924 from joesu/bugfix-spark3011 and squashes the following commits:

c7e44f2 [Chia-Yung Su] match syntax
f8fc32a [Chia-Yung Su] filter out tmp dir

(cherry picked from commit 078f3fbda860e2f5de34153c55dfc3fecb4256e9)
Signed-off-by: Michael Armbrust <michael@databricks.com>
[SPARK-2927][SQL] Add a conf to configure if we always read Binary columns stored in Parquet as String columns

This PR adds a new conf flag `spark.sql.parquet.binaryAsString`. When it is `true`, if there is no parquet metadata file available to provide the schema of the data, we will always treat binary fields stored in parquet as string fields. This conf is used to provide a way to read string fields generated without UTF8 decoration.

JIRA: https://issues.apache.org/jira/browse/SPARK-2927

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1855 from yhuai/parquetBinaryAsString and squashes the following commits:

689ffa9 [Yin Huai] Add missing "=".
80827de [Yin Huai] Unit test.
1765ca4 [Yin Huai] Use .toBoolean.
9d3f199 [Yin Huai] Merge remote-tracking branch 'upstream/master' into parquetBinaryAsString
5d436a1 [Yin Huai] The initial support of adding a conf to treat binary columns stored in Parquet as string columns.

(cherry picked from commit add75d4831fdc35712bf8b737574ea0bc677c37c)
Signed-off-by: Michael Armbrust <michael@databricks.com>
[SQL] Python JsonRDD UTF8 Encoding Fix

Only encode unicode objects to UTF-8, and not strings

Author: Ahir Reddy <ahirreddy@gmail.com>

Closes #1914 from ahirreddy/json-rdd-unicode-fix1 and squashes the following commits:

ca4e9ba [Ahir Reddy] Encoding Fix

(cherry picked from commit fde692b361773110c262abe219e7c8128bd76419)
Signed-off-by: Michael Armbrust <michael@databricks.com>
[SPARK-2925] [sql]fix spark-sql and start-thriftserver shell bugs when set --driver-java-options

https://issues.apache.org/jira/browse/SPARK-2925

Run cmd like this will get the error
bin/spark-sql --driver-java-options '-Xdebug -Xnoagent -Xrunjdwp:transport=dt_socket,address=8788,server=y,suspend=y'

Error: Unrecognized option '-Xnoagent'.
Run with --help for usage help or --verbose for debug output

Author: wangfei <wangfei_hello@126.com>
Author: wangfei <wangfei1@huawei.com>

Closes #1851 from scwf/patch-2 and squashes the following commits:

516554d [wangfei] quote variables to fix this issue
8bd40f2 [wangfei] quote variables to fix this problem
e6d79e3 [wangfei] fix start-thriftserver bug when set driver-java-options
948395d [wangfei] fix spark-sql error when set --driver-java-options

(cherry picked from commit 267fdffe2743bc2dc706c8ac8af0ae33a358a5d3)
Signed-off-by: Michael Armbrust <michael@databricks.com>
Minor cleanup of metrics.Source

- Added override.
- Marked some variables as private.

Author: Reynold Xin <rxin@apache.org>

Closes #1943 from rxin/metricsSource and squashes the following commits:

fbfa943 [Reynold Xin] Minor cleanup of metrics.Source. - Added override. - Marked some variables as private.

(cherry picked from commit eaeb0f76fa0f103c7db0f3975cb8562715410973)
Signed-off-by: Reynold Xin <rxin@apache.org>
[SPARK-2979][MLlib] Improve the convergence rate by minimizing the condition number

In theory, the scale of your inputs are irrelevant to logistic regression.
You can "theoretically" multiply X1 by 1E6 and the estimate for β1 will
adjust accordingly. It will be 1E-6 times smaller than the original β1, due
to the invariance property of MLEs.

However, during the optimization process, the convergence (rate)
depends on the condition number of the training dataset. Scaling
the variables often reduces this condition number, thus improving
the convergence rate.

Without reducing the condition number, some training datasets
mixing the columns with different scales may not be able to converge.

GLMNET and LIBSVM packages perform the scaling to reduce
the condition number, and return the weights in the original scale.
See page 9 in http://cran.r-project.org/web/packages/glmnet/glmnet.pdf

Here, if useFeatureScaling is enabled, we will standardize the training
features by dividing the variance of each column (without subtracting
the mean to densify the sparse vector), and train the model in the
scaled space. Then we transform the coefficients from the scaled space
to the original scale as GLMNET and LIBSVM do.

Currently, it's only enabled in LogisticRegressionWithLBFGS.

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #1897 from dbtsai/dbtsai-feature-scaling and squashes the following commits:

f19fc02 [DB Tsai] Added more comments
1d85289 [DB Tsai] Improve the convergence rate by minimize the condition number in LOR with LBFGS

(cherry picked from commit 96221067572e5955af1a7710b0cca33a73db4bd5)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
Revert  [SPARK-3011][SQL] _temporary directory should be filtered out by sqlContext.parquetFile

Reverts #1924 due to build failures with hadoop 0.23.

Author: Michael Armbrust <michael@databricks.com>

Closes #1949 from marmbrus/revert1924 and squashes the following commits:

6bff940 [Michael Armbrust] Revert "[SPARK-3011][SQL] _temporary directory should be filtered out by sqlContext.parquetFile"

(cherry picked from commit a7f8a4f5ee757450ce8d4028021441435081cf53)
Signed-off-by: Michael Armbrust <michael@databricks.com>
SPARK-3009: Reverted readObject method in ApplicationInfo so that Applic...

...ationInfo is initialized properly after deserialization

Author: Jacek Lewandowski <lewandowski.jacek@gmail.com>

Closes #1947 from jacek-lewandowski/master and squashes the following commits:

713b2f1 [Jacek Lewandowski] SPARK-3009: Reverted readObject method in ApplicationInfo so that ApplicationInfo is initialized properly after deserialization

(cherry picked from commit a75bc7a21db07258913d038bf604c0a3c1e55b46)
Signed-off-by: Andrew Or <andrewor14@gmail.com>
Make dev/mima runnable on Mac OS X.

Mac OS X's find is from the BSD variant that doesn't have -printf option.

Author: Reynold Xin <rxin@apache.org>

Closes #1953 from rxin/mima and squashes the following commits:

e284afe [Reynold Xin] Make dev/mima runnable on Mac OS X.

(cherry picked from commit fa5a08e67d1086045ac249c2090c5e4d0a17b828)
Signed-off-by: Reynold Xin <rxin@apache.org>
[SPARK-3027] TaskContext: tighten visibility and provide Java friendly callback API

Note this also passes the TaskContext itself to the TaskCompletionListener. In the future we can mark TaskContext with the exception object if exception occurs during task execution.

Author: Reynold Xin <rxin@apache.org>

Closes #1938 from rxin/TaskContext and squashes the following commits:

145de43 [Reynold Xin] Added JavaTaskCompletionListenerImpl for Java API friendly guarantee.
f435ea5 [Reynold Xin] Added license header for TaskCompletionListener.
dc4ed27 [Reynold Xin] [SPARK-3027] TaskContext: tighten the visibility and provide Java friendly callback API

(cherry picked from commit 655699f8b7156e8216431393436368e80626cdb2)
Signed-off-by: Reynold Xin <rxin@apache.org>
[SPARK-2736] PySpark converter and example script for reading Avro files

JIRA: https://issues.apache.org/jira/browse/SPARK-2736

This patch includes:
1. An Avro converter that converts Avro data types to Python. It handles all 3 Avro data mappings (Generic, Specific and Reflect).
2. An example Python script for reading Avro files using AvroKeyInputFormat and the converter.
3. Fixing a classloading issue.

cc @MLnick @JoshRosen @mateiz

Author: Kan Zhang <kzhang@apache.org>

Closes #1916 from kanzhang/SPARK-2736 and squashes the following commits:

02443f8 [Kan Zhang] [SPARK-2736] Adding .avsc files to .rat-excludes
f74e9a9 [Kan Zhang] [SPARK-2736] nit: clazz -> className
82cc505 [Kan Zhang] [SPARK-2736] Update data sample
0be7761 [Kan Zhang] [SPARK-2736] Example pyspark script and data files
c8e5881 [Kan Zhang] [SPARK-2736] Trying to work with all 3 Avro data models
2271a5b [Kan Zhang] [SPARK-2736] Using the right class loader to find Avro classes
536876b [Kan Zhang] [SPARK-2736] Adding Avro to Java converter

(cherry picked from commit 9422a9b084e3fd5b2b9be2752013588adfb430d0)
Signed-off-by: Matei Zaharia <matei@databricks.com>
[SPARK-2936] Migrate Netty network module from Java to Scala

The Netty network module was originally written when Scala 2.9.x had a bug that prevents a pure Scala implementation, and a subset of the files were done in Java. We have since upgraded to Scala 2.10, and can migrate all Java files now to Scala.

https://github.com/netty/netty/issues/781

https://github.com/mesos/spark/pull/522

Author: Reynold Xin <rxin@apache.org>

Closes #1865 from rxin/netty and squashes the following commits:

332422f [Reynold Xin] Code review feedback
ca9eeee [Reynold Xin] Minor update.
7f1434b [Reynold Xin] [SPARK-2936] Migrate Netty network module from Java to Scala

(cherry picked from commit ba28a8fcbc3ba432e7ea4d6f0b535450a6ec96c6)
Signed-off-by: Reynold Xin <rxin@apache.org>
[SPARK-2468] Netty based block server / client module

This is a rewrite of the original Netty module that was added about 1.5 years ago. The old code was turned off by default and didn't really work because it lacked a frame decoder (only worked with very very small blocks).

For this pull request, I tried to make the changes non-instrusive to the rest of Spark. I only added an init and shutdown to BlockManager/DiskBlockManager, and a bunch of comments to help me understand the existing code base.

Compared with the old Netty module, this one features:
- It appears to work :)
- SPARK-2941: option to specicy nio vs oio vs epoll for channel/transport. By default nio is used. (Not using Epoll yet because I have found some bugs with its implementation)
- SPARK-2943: options to specify send buf and receive buf for users who want to do hyper tuning
- SPARK-2942: io errors are reported from server to client (the protocol uses negative length to indicate error)
- SPARK-2940: fetching multiple blocks in a single request to reduce syscalls
- SPARK-2959: clients share a single thread pool
- SPARK-2990: use PooledByteBufAllocator to reduce GC (basically a Netty managed pool of buffers with jmalloc)
- SPARK-2625: added fetchWaitTime metric and fixed thread-safety issue in metrics update.
- SPARK-2367: bump Netty version to 4.0.21.Final to address an Epoll bug (https://groups.google.com/forum/#!topic/netty/O7m-HxCJpCA)

Compared with the existing communication manager, this one features:
- IMO it is substantially easier to understand
- zero-copy send for the server for on-disk blocks
- one-copy receive (due to a frame decoder)
- don't quote me on this, but I think a lot less sys calls
- SPARK-2990: use PooledByteBufAllocator to reduce GC (basically a Netty managed pool of buffers with jmalloc)
- SPARK-2941: option to specicy nio vs oio vs epoll for channel/transport. By default nio is used. (Not using Epoll yet because I have found some bugs with its implementation)
- SPARK-2943: options to specify send buf and receive buf for users who want to do hyper tuning

TODOs before it can fully replace the existing ConnectionManager, if that ever happens (most of them should probably be done in separate PRs since this needs to be turned on explicitly)
- [x] Basic test cases
- [ ] More unit/integration tests for failures
- [ ] Performance analysis
- [ ] Support client connection reuse so we don't need to keep opening new connections (not sure how useful this would be)
- [ ] Support putting blocks in addition to fetching blocks (i.e. two way transfer)
- [x] Support serving non-disk blocks
- [ ] Support SASL authentication

For a more comprehensive list, see https://issues.apache.org/jira/browse/SPARK-2468

Thanks to @coderplay for peer coding with me on a Sunday.

Author: Reynold Xin <rxin@apache.org>

Closes #1907 from rxin/netty and squashes the following commits:

f921421 [Reynold Xin] Upgrade Netty to 4.0.22.Final to fix another Epoll bug.
4b174ca [Reynold Xin] Shivaram's code review comment.
4a3dfe7 [Reynold Xin] Switched to nio for default (instead of epoll on Linux).
56bfb9d [Reynold Xin] Bump Netty version to 4.0.21.Final for some bug fixes.
b443a4b [Reynold Xin] Added debug message to help debug Jenkins failures.
57fc4d7 [Reynold Xin] Added test cases for BlockHeaderEncoder and BlockFetchingClientHandlerSuite.
22623e9 [Reynold Xin] Added exception handling and test case for BlockServerHandler and BlockFetchingClientHandler.
6550dd7 [Reynold Xin] Fixed block mgr init bug.
60c2edf [Reynold Xin] Beefed up server/client integration tests.
38d88d5 [Reynold Xin] Added missing test files.
6ce3f3c [Reynold Xin] Added some basic test cases.
47f7ce0 [Reynold Xin] Created server and client packages and moved files there.
b16f412 [Reynold Xin] Added commit count.
f13022d [Reynold Xin] Remove unused clone() in BlockFetcherIterator.
c57d68c [Reynold Xin] Added back missing files.
842dfa7 [Reynold Xin] Made everything work with proper reference counting.
3fae001 [Reynold Xin] Connected the new netty network module with rest of Spark.
1a8f6d4 [Reynold Xin] Completed protocol documentation.
2951478 [Reynold Xin] New Netty implementation.
cc7843d [Reynold Xin] Basic skeleton.

(cherry picked from commit 3a8b68b7353fea50245686903b308fa9eb52cb51)
Signed-off-by: Reynold Xin <rxin@apache.org>
Add DistanceMeasure
Add tests for calculating Manhattan distance