This pull request simply fixes a few minor coding style issues in csv, as I was reviewing the change post-hoc.
Author: Reynold Xin <rxin@databricks.com>
Closes#10919 from rxin/csv-minor.
I saw several failures from recent PR builds, e.g., https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/50015/consoleFull. This PR marks the test as ignored and we will fix the flakyness in SPARK-10086.
gliptak Do you know why the test failure didn't show up in the Jenkins "Test Result"?
cc: jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#10909 from mengxr/SPARK-10086.
https://issues.apache.org/jira/browse/SPARK-12834
We use `SerDe.dumps()` to serialize `JavaArray` and `JavaList` in `PythonMLLibAPI`, then deserialize them with `PickleSerializer` in Python side. However, there is no need to transform them in such an inefficient way. Instead of it, we can use type conversion to convert them, e.g. `list(JavaArray)` or `list(JavaList)`. What's more, there is an issue to Ser/De Scala Array as I said in https://issues.apache.org/jira/browse/SPARK-12780
Author: Xusen Yin <yinxusen@gmail.com>
Closes#10772 from yinxusen/SPARK-12834.
Add Python API for ml.feature.QuantileDiscretizer.
One open question: Do we want to do this stuff to re-use the java model, create a new model, or use a different wrapper around the java model.
cc brkyvz & mengxr
Author: Holden Karau <holden@us.ibm.com>
Closes#10085 from holdenk/SPARK-11937-SPARK-11922-Python-API-for-ml.feature.QuantileDiscretizer.
This PR adds an initial implementation of bloom filter in the newly added sketch module. The implementation is based on the [`BloomFilter` class in guava](https://code.google.com/p/guava-libraries/source/browse/guava/src/com/google/common/hash/BloomFilter.java).
Some difference from the design doc:
* expose `bitSize` instead of `sizeInBytes` to user.
* always need the `expectedInsertions` parameter when create bloom filter.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10883 from cloud-fan/bloom-filter.
As we begin to use unsafe row writing framework(`BufferHolder` and `UnsafeRowWriter`) in more and more places(`UnsafeProjection`, `UnsafeRowParquetRecordReader`, `GenerateColumnAccessor`, etc.), we should add more doc to it and make it easier to use.
This PR abstract the technique used in `UnsafeRowParquetRecordReader`: avoid unnecessary operatition as more as possible. For example, do not always point the row to the buffer at the end, we only need to update the size of row. If all fields are of primitive type, we can even save the row size updating. Then we can apply this technique to more places easily.
a local benchmark shows `UnsafeProjection` is up to 1.7x faster after this PR:
**old version**
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
unsafe projection: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
single long 2616.04 102.61 1.00 X
single nullable long 3032.54 88.52 0.86 X
primitive types 9121.05 29.43 0.29 X
nullable primitive types 12410.60 21.63 0.21 X
```
**new version**
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
unsafe projection: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
single long 1533.34 175.07 1.00 X
single nullable long 2306.73 116.37 0.66 X
primitive types 8403.93 31.94 0.18 X
nullable primitive types 12448.39 21.56 0.12 X
```
For single non-nullable long(the best case), we can have about 1.7x speed up. Even it's nullable, we can still have 1.3x speed up. For other cases, it's not such a boost as the saved operations only take a little proportion of the whole process. The benchmark code is included in this PR.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10809 from cloud-fan/unsafe-projection.
This PR adds serialization support for `CountMinSketch`.
A version number is added to version the serialized binary format.
Author: Cheng Lian <lian@databricks.com>
Closes#10893 from liancheng/cms-serialization.
```PCAModel``` can output ```explainedVariance``` at Python side.
cc mengxr srowen
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10830 from yanboliang/spark-12905.
When users are using `partitionBy` and `bucketBy` at the same time, some bucketing columns might be part of partitioning columns. For example,
```
df.write
.format(source)
.partitionBy("i")
.bucketBy(8, "i", "k")
.saveAsTable("bucketed_table")
```
However, in the above case, adding column `i` into `bucketBy` is useless. It is just wasting extra CPU when reading or writing bucket tables. Thus, like Hive, we can issue an exception and let users do the change.
Also added a test case for checking if the information of `sortBy` and `bucketBy` columns are correctly saved in the metastore table.
Could you check if my understanding is correct? cloud-fan rxin marmbrus Thanks!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10891 from gatorsmile/commonKeysInPartitionByBucketBy.
Added color coding to the Executors page for Active Tasks, Failed Tasks, Completed Tasks and Task Time.
Active Tasks is shaded blue with it's range based on percentage of total cores used.
Failed Tasks is shaded red ranging over the first 10% of total tasks failed
Completed Tasks is shaded green ranging over 10% of total tasks including failed and active tasks, but only when there are active or failed tasks on that executor.
Task Time is shaded red when GC Time goes over 10% of total time with it's range directly corresponding to the percent of total time.
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#10154 from ajbozarth/spark12149.
Update user guide for RFormula feature interactions. Meanwhile we also update other new features such as supporting string label in Spark 1.6.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10222 from yanboliang/spark-11965.
[SPARK-12755][CORE] Stop the event logger before the DAG scheduler to avoid a race condition where the standalone master attempts to build the app's history UI before the event log is stopped.
This contribution is my original work, and I license this work to the Spark project under the project's open source license.
Author: Michael Allman <michael@videoamp.com>
Closes#10700 from mallman/stop_event_logger_first.
https://issues.apache.org/jira/browse/SPARK-12901
This PR refactors the options in JSON and CSV datasources.
In more details,
1. `JSONOptions` uses the same format as `CSVOptions`.
2. Not case classes.
3. `CSVRelation` that does not have to be serializable (it was `with Serializable` but I removed)
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#10895 from HyukjinKwon/SPARK-12901.
When actual row length doesn't conform to specified schema field length, we should give a better error message instead of throwing an unintuitive `ArrayOutOfBoundsException`.
Author: Cheng Lian <lian@databricks.com>
Closes#10886 from liancheng/spark-12624.
…ialize HiveContext in PySpark
davies Mind to review ?
This is the error message after this PR
```
15/12/03 16:59:53 WARN ObjectStore: Failed to get database default, returning NoSuchObjectException
/Users/jzhang/github/spark/python/pyspark/sql/context.py:689: UserWarning: You must build Spark with Hive. Export 'SPARK_HIVE=true' and run build/sbt assembly
warnings.warn("You must build Spark with Hive. "
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/jzhang/github/spark/python/pyspark/sql/context.py", line 663, in read
return DataFrameReader(self)
File "/Users/jzhang/github/spark/python/pyspark/sql/readwriter.py", line 56, in __init__
self._jreader = sqlContext._ssql_ctx.read()
File "/Users/jzhang/github/spark/python/pyspark/sql/context.py", line 692, in _ssql_ctx
raise e
py4j.protocol.Py4JJavaError: An error occurred while calling None.org.apache.spark.sql.hive.HiveContext.
: java.lang.RuntimeException: java.net.ConnectException: Call From jzhangMBPr.local/127.0.0.1 to 0.0.0.0:9000 failed on connection exception: java.net.ConnectException: Connection refused; For more details see: http://wiki.apache.org/hadoop/ConnectionRefused
at org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:522)
at org.apache.spark.sql.hive.client.ClientWrapper.<init>(ClientWrapper.scala:194)
at org.apache.spark.sql.hive.client.IsolatedClientLoader.createClient(IsolatedClientLoader.scala:238)
at org.apache.spark.sql.hive.HiveContext.executionHive$lzycompute(HiveContext.scala:218)
at org.apache.spark.sql.hive.HiveContext.executionHive(HiveContext.scala:208)
at org.apache.spark.sql.hive.HiveContext.functionRegistry$lzycompute(HiveContext.scala:462)
at org.apache.spark.sql.hive.HiveContext.functionRegistry(HiveContext.scala:461)
at org.apache.spark.sql.UDFRegistration.<init>(UDFRegistration.scala:40)
at org.apache.spark.sql.SQLContext.<init>(SQLContext.scala:330)
at org.apache.spark.sql.hive.HiveContext.<init>(HiveContext.scala:90)
at org.apache.spark.sql.hive.HiveContext.<init>(HiveContext.scala:101)
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 py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:234)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
at py4j.Gateway.invoke(Gateway.java:214)
at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:79)
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:68)
at py4j.GatewayConnection.run(GatewayConnection.java:209)
at java.lang.Thread.run(Thread.java:745)
```
Author: Jeff Zhang <zjffdu@apache.org>
Closes#10126 from zjffdu/SPARK-12120.
Minor since so few people use them, but it would probably be good to have a requirements file for our python release tools for easier setup (also version pinning).
cc JoshRosen who looked at the original JIRA.
Author: Holden Karau <holden@us.ibm.com>
Closes#10871 from holdenk/SPARK-10498-add-requirements-file-for-dev-python-tools.
ErrorPositionSuite and one of the HiveComparisonTest tests have been consistently failing on the Hadoop 2.3 SBT build (but on no other builds). I believe that this is due to test isolation issues (e.g. tests sharing state via the sets of temporary tables that are registered to TestHive).
This patch attempts to improve the isolation of these tests in order to address this issue.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10884 from JoshRosen/fix-failing-hadoop-2.3-hive-tests.
This pull request implements strength reduction for comparing integral expressions and decimal literals, which is more common now because we switch to parsing fractional literals as decimal types (rather than doubles). I added the rules to the existing DecimalPrecision rule with some refactoring to simplify the control flow. I also moved DecimalPrecision rule into its own file due to the growing size.
Author: Reynold Xin <rxin@databricks.com>
Closes#10882 from rxin/SPARK-12904-1.
Clarify that modifying a driver local variable won't have the desired effect in cluster modes, and may or may not work as intended in local mode
Author: Sean Owen <sowen@cloudera.com>
Closes#10866 from srowen/SPARK-12760.
…local vs cluster
srowen thanks for the PR at https://github.com/apache/spark/pull/10866! sorry it took me a while.
This is related to https://github.com/apache/spark/pull/10866, basically the assignment in the lambda expression in the python example is actually invalid
```
In [1]: data = [1, 2, 3, 4, 5]
In [2]: counter = 0
In [3]: rdd = sc.parallelize(data)
In [4]: rdd.foreach(lambda x: counter += x)
File "<ipython-input-4-fcb86c182bad>", line 1
rdd.foreach(lambda x: counter += x)
^
SyntaxError: invalid syntax
```
Author: Mortada Mehyar <mortada.mehyar@gmail.com>
Closes#10867 from mortada/doc_python_fix.
Added CSS style to force names of input streams with receivers to wrap
Author: Alex Bozarth <ajbozart@us.ibm.com>
Closes#10873 from ajbozarth/spark12859.
This PR adds an initial implementation of count min sketch, contained in a new module spark-sketch under `common/sketch`. The implementation is based on the [`CountMinSketch` class in stream-lib][1].
As required by the [design doc][2], spark-sketch should have no external dependency.
Two classes, `Murmur3_x86_32` and `Platform` are copied to spark-sketch from spark-unsafe for hashing facilities. They'll also be used in the upcoming bloom filter implementation.
The following features will be added in future follow-up PRs:
- Serialization support
- DataFrame API integration
[1]: aac6b4d23a/src/main/java/com/clearspring/analytics/stream/frequency/CountMinSketch.java
[2]: https://issues.apache.org/jira/secure/attachment/12782378/BloomFilterandCount-MinSketchinSpark2.0.pdf
Author: Cheng Lian <lian@databricks.com>
Closes#10851 from liancheng/count-min-sketch.
https://issues.apache.org/jira/browse/SPARK-12872
This PR makes the JSON datasource can compress output by option instead of manually setting Hadoop configurations.
For reflecting codec by names, it is similar with https://github.com/apache/spark/pull/10805.
As `CSVCompressionCodecs` can be shared with other datasources, it became a separate class to share as `CompressionCodecs`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#10858 from HyukjinKwon/SPARK-12872.
- Remove Akka dependency from core. Note: the streaming-akka project still uses Akka.
- Remove HttpFileServer
- Remove Akka configs from SparkConf and SSLOptions
- Rename `spark.akka.frameSize` to `spark.rpc.message.maxSize`. I think it's still worth to keep this config because using `DirectTaskResult` or `IndirectTaskResult` depends on it.
- Update comments and docs
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10854 from zsxwing/remove-akka.
I've tried to solve some of the issues mentioned in: https://issues.apache.org/jira/browse/SPARK-12629
Please, let me know what do you think.
Thanks!
Author: Narine Kokhlikyan <narine.kokhlikyan@gmail.com>
Closes#10580 from NarineK/sparkrSavaAsRable.
When users turn off bucketing in SQLConf, we should issue some messages to tell users these operations will be converted to normal way.
Also added a test case for this scenario and fixed the helper function.
Do you think this PR is helpful when using bucket tables? cloud-fan Thank you!
Author: gatorsmile <gatorsmile@gmail.com>
Closes#10870 from gatorsmile/bucketTableWritingTestcases.
Without importing the print_function, the lines later on like ```print("Usage: direct_kafka_wordcount.py <broker_list> <topic>", file=sys.stderr)``` fail when using python2.*. Import fixes that problem and doesn't break anything on python3 either.
Author: Mark Grover <mark@apache.org>
Closes#10872 from markgrover/python2_compat.
When all labels are the same, it's a dangerous ground for LogisticRegression without intercept to converge. GLMNET doesn't support this case, and will just exit. GLM can train, but will have a warning message saying the algorithm doesn't converge.
Author: DB Tsai <dbt@netflix.com>
Closes#10862 from dbtsai/add-tests.
Several Spark properties equivalent to Spark submit command line options are missing.
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#10491 from felixcheung/sparksubmitdoc.
Testing code:
```
$ ./install-dev.sh
USING R_HOME = /usr/bin
ERROR: this R is version 2.15.1, package 'SparkR' requires R >= 3.0
```
Using the new argument:
```
$ ./install-dev.sh /content/username/SOFTWARE/R-3.2.3
USING R_HOME = /content/username/SOFTWARE/R-3.2.3/bin
* installing *source* package ‘SparkR’ ...
** R
** inst
** preparing package for lazy loading
Creating a new generic function for ‘colnames’ in package ‘SparkR’
Creating a new generic function for ‘colnames<-’ in package ‘SparkR’
Creating a new generic function for ‘cov’ in package ‘SparkR’
Creating a new generic function for ‘na.omit’ in package ‘SparkR’
Creating a new generic function for ‘filter’ in package ‘SparkR’
Creating a new generic function for ‘intersect’ in package ‘SparkR’
Creating a new generic function for ‘sample’ in package ‘SparkR’
Creating a new generic function for ‘transform’ in package ‘SparkR’
Creating a new generic function for ‘subset’ in package ‘SparkR’
Creating a new generic function for ‘summary’ in package ‘SparkR’
Creating a new generic function for ‘lag’ in package ‘SparkR’
Creating a new generic function for ‘rank’ in package ‘SparkR’
Creating a new generic function for ‘sd’ in package ‘SparkR’
Creating a new generic function for ‘var’ in package ‘SparkR’
Creating a new generic function for ‘predict’ in package ‘SparkR’
Creating a new generic function for ‘rbind’ in package ‘SparkR’
Creating a generic function for ‘lapply’ from package ‘base’ in package ‘SparkR’
Creating a generic function for ‘Filter’ from package ‘base’ in package ‘SparkR’
Creating a generic function for ‘alias’ from package ‘stats’ in package ‘SparkR’
Creating a generic function for ‘substr’ from package ‘base’ in package ‘SparkR’
Creating a generic function for ‘%in%’ from package ‘base’ in package ‘SparkR’
Creating a generic function for ‘mean’ from package ‘base’ in package ‘SparkR’
Creating a generic function for ‘unique’ from package ‘base’ in package ‘SparkR’
Creating a generic function for ‘nrow’ from package ‘base’ in package ‘SparkR’
Creating a generic function for ‘ncol’ from package ‘base’ in package ‘SparkR’
Creating a generic function for ‘head’ from package ‘utils’ in package ‘SparkR’
Creating a generic function for ‘factorial’ from package ‘base’ in package ‘SparkR’
Creating a generic function for ‘atan2’ from package ‘base’ in package ‘SparkR’
Creating a generic function for ‘ifelse’ from package ‘base’ in package ‘SparkR’
** help
No man pages found in package ‘SparkR’
*** installing help indices
** building package indices
** testing if installed package can be loaded
* DONE (SparkR)
```
Author: Shubhanshu Mishra <smishra8@illinois.edu>
Closes#10836 from napsternxg/master.
The current parser turns a decimal literal, for example ```12.1```, into a Double. The problem with this approach is that we convert an exact literal into a non-exact ```Double```. The PR changes this behavior, a Decimal literal is now converted into an extact ```BigDecimal```.
The behavior for scientific decimals, for example ```12.1e01```, is unchanged. This will be converted into a Double.
This PR replaces the ```BigDecimal``` literal by a ```Double``` literal, because the ```BigDecimal``` is the default now. You can use the double literal by appending a 'D' to the value, for instance: ```3.141527D```
cc davies rxin
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#10796 from hvanhovell/SPARK-12848.
Benchmark it on 4 different schemas, the result:
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
Hash For simple: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
interpreted version 31.47 266.54 1.00 X
codegen version 64.52 130.01 0.49 X
```
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
Hash For normal: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
interpreted version 4068.11 0.26 1.00 X
codegen version 1175.92 0.89 3.46 X
```
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
Hash For array: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
interpreted version 9276.70 0.06 1.00 X
codegen version 14762.23 0.04 0.63 X
```
```
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
Hash For map: Avg Time(ms) Avg Rate(M/s) Relative Rate
-------------------------------------------------------------------------------
interpreted version 58869.79 0.01 1.00 X
codegen version 9285.36 0.06 6.34 X
```
Author: Wenchen Fan <wenchen@databricks.com>
Closes#10816 from cloud-fan/hash-benchmark.
The existing `Union` logical operator only supports two children. Thus, adding a new logical operator `Unions` which can have arbitrary number of children to replace the existing one.
`Union` logical plan is a binary node. However, a typical use case for union is to union a very large number of input sources (DataFrames, RDDs, or files). It is not uncommon to union hundreds of thousands of files. In this case, our optimizer can become very slow due to the large number of logical unions. We should change the Union logical plan to support an arbitrary number of children, and add a single rule in the optimizer to collapse all adjacent `Unions` into a single `Unions`. Note that this problem doesn't exist in physical plan, because the physical `Unions` already supports arbitrary number of children.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#10577 from gatorsmile/unionAllMultiChildren.
Include the following changes:
1. Add "streaming-akka" project and org.apache.spark.streaming.akka.AkkaUtils for creating an actorStream
2. Remove "StreamingContext.actorStream" and "JavaStreamingContext.actorStream"
3. Update the ActorWordCount example and add the JavaActorWordCount example
4. Make "streaming-zeromq" depend on "streaming-akka" and update the codes accordingly
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10744 from zsxwing/streaming-akka-2.
Including the following changes:
1. Add StreamingListenerForwardingBus to WrappedStreamingListenerEvent process events in `onOtherEvent` to StreamingListener
2. Remove StreamingListenerBus
3. Merge AsynchronousListenerBus and LiveListenerBus to the same class LiveListenerBus
4. Add `logEvent` method to SparkListenerEvent so that EventLoggingListener can use it to ignore WrappedStreamingListenerEvents
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#10779 from zsxwing/streaming-listener.
Add Since annotations to ml.param and ml.*
Author: Takahashi Hiroshi <takahashi.hiroshi@lab.ntt.co.jp>
Author: Hiroshi Takahashi <takahashi.hiroshi@lab.ntt.co.jp>
Closes#8935 from taishi-oss/issue10263.
