This patch bring the pull based progress API into Python, also a example in Python.
Author: Davies Liu <davies@databricks.com>
Closes#3027 from davies/progress_api and squashes the following commits:
b1ba984 [Davies Liu] fix style
d3b9253 [Davies Liu] add tests, mute the exception after stop
4297327 [Davies Liu] Merge branch 'master' of github.com:apache/spark into progress_api
969fa9d [Davies Liu] Merge branch 'master' of github.com:apache/spark into progress_api
25590c9 [Davies Liu] update with Java API
360de2d [Davies Liu] Merge branch 'master' of github.com:apache/spark into progress_api
c0f1021 [Davies Liu] Merge branch 'master' of github.com:apache/spark into progress_api
023afb3 [Davies Liu] add Python API and example for progress API
A jar file containing Python sources in it could be used as a Python package, just like zip file.
spark-submit already put the jar file into PYTHONPATH, this patch also put it in the sys.path, then it could be used in Python worker.
Author: Davies Liu <davies@databricks.com>
Closes#4652 from davies/jar and squashes the following commits:
17d3f76 [Davies Liu] support .jar as python package
1. added explain()
2. add isLocal()
3. do not call show() in __repl__
4. add foreach() and foreachPartition()
5. add distinct()
6. fix functions.col()/column()/lit()
7. fix unit tests in sql/functions.py
8. fix unicode in showString()
Author: Davies Liu <davies@databricks.com>
Closes#4645 from davies/df6 and squashes the following commits:
6b46a2c [Davies Liu] fix DataFrame Python API
There is chance of dead lock that the Python process is waiting for ending mark from JVM, but which is eaten by corrupted stream.
This PR checks the ending mark from Python in non-block way, so it will not blocked by Python process.
There is a small chance that the ending mark is sent by Python process but not available right now, then Python process will not be used.
cc JoshRosen pwendell
Author: Davies Liu <davies@databricks.com>
Closes#4601 from davies/freeze and squashes the following commits:
e15a8c3 [Davies Liu] update logging
890329c [Davies Liu] Merge branch 'freeze' of github.com:davies/spark into freeze
2bd2228 [Davies Liu] add more logging
656d544 [Davies Liu] Update PythonRDD.scala
05e1085 [Davies Liu] check ending mark in non-block way
Added a bunch of tags.
Also changed parquetFile to take varargs rather than a string followed by varargs.
Author: Reynold Xin <rxin@databricks.com>
Closes#4636 from rxin/df-doc and squashes the following commits:
651f80c [Reynold Xin] Fixed parquetFile in PySpark.
8dc3024 [Reynold Xin] [SQL] Various DataFrame doc changes.
I created a patch that disables the environment variables.
Thereby scala or python shell log a warning message to notify user about the deprecation
with the following message:
scala: "ADD_JARS environment variable is deprecated, use --jar spark submit argument instead"
python: "Warning: ADD_FILES environment variable is deprecated, use --py-files argument instead"
Is it what is expected or the code associated with the variables should be just completely removed?
Should it be somewhere documented?
Author: azagrebin <azagrebin@gmail.com>
Closes#4616 from azagrebin/master and squashes the following commits:
bab1aa9 [azagrebin] [SPARK-3340] Deprecate ADD_JARS and ADD_FILES: minor readability issue
0643895 [azagrebin] [SPARK-3340] Deprecate ADD_JARS and ADD_FILES: add warning messages
42f0107 [azagrebin] [SPARK-3340] Deprecate ADD_JARS and ADD_FILES
This patch changes PySpark so that the GatewayServer's port is communicated back to the Python process that launches it over a local socket instead of a pipe. The old pipe-based approach was brittle and could fail if `spark-submit` printed unexpected to stdout.
To accomplish this, I wrote a custom `PythonGatewayServer.main()` function to use in place of Py4J's `GatewayServer.main()`.
Closes#3424.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#4603 from JoshRosen/SPARK-2313 and squashes the following commits:
6a7740b [Josh Rosen] Remove EchoOutputThread since it's no longer needed
0db501f [Josh Rosen] Use select() so that we don't block if GatewayServer dies.
9bdb4b6 [Josh Rosen] Handle case where getListeningPort returns -1
3fb7ed1 [Josh Rosen] Remove stdout=PIPE
2458934 [Josh Rosen] Use underscore to mark env var. as private
d12c95d [Josh Rosen] Use Logging and Utils.tryOrExit()
e5f9730 [Josh Rosen] Wrap everything in a giant try-block
2f70689 [Josh Rosen] Use stdin PIPE to share fate with driver
8bf956e [Josh Rosen] Initial cut at passing Py4J gateway port back to driver via socket
This PR allow Python users to set params in constructors and in setParams, where we use decorator `keyword_only` to force keyword arguments. The trade-off is discussed in the design doc of SPARK-4586.
Generated doc:
CC: davies rxin
Author: Xiangrui Meng <meng@databricks.com>
Closes#4564 from mengxr/py-pipeline-kw and squashes the following commits:
fedf720 [Xiangrui Meng] use toDF
d565f2c [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into py-pipeline-kw
cbc15d3 [Xiangrui Meng] fix style
5032097 [Xiangrui Meng] update pipeline signature
950774e [Xiangrui Meng] simplify keyword_only and update constructor/setParams signatures
fdde5fc [Xiangrui Meng] fix style
c9384b8 [Xiangrui Meng] fix sphinx doc
8e59180 [Xiangrui Meng] add setParams and make constructors take params, where we force keyword args
Author: Yin Huai <yhuai@databricks.com>
Closes#4542 from yhuai/moveSaveMode and squashes the following commits:
65a4425 [Yin Huai] Move SaveMode to sql package.
1. DataFrame.renameColumn
2. DataFrame.show() and _repr_
3. Use simpleString() rather than jsonValue in DataFrame.dtypes
4. createDataFrame from local Python data, including pandas.DataFrame
Author: Davies Liu <davies@databricks.com>
Closes#4528 from davies/df3 and squashes the following commits:
014acea [Davies Liu] fix typo
6ba526e [Davies Liu] fix tests
46f5f95 [Davies Liu] address comments
6cbc154 [Davies Liu] dataframe.show() and improve dtypes
6f94f25 [Davies Liu] create DataFrame from local Python data
Deprecate inferSchema() and applySchema(), use createDataFrame() instead, which could take an optional `schema` to create an DataFrame from an RDD. The `schema` could be StructType or list of names of columns.
Author: Davies Liu <davies@databricks.com>
Closes#4498 from davies/create and squashes the following commits:
08469c1 [Davies Liu] remove Scala/Java API for now
c80a7a9 [Davies Liu] fix hive test
d1bd8f2 [Davies Liu] cleanup applySchema
9526e97 [Davies Liu] createDataFrame from RDD with columns
Author: Michael Armbrust <michael@databricks.com>
Closes#4436 from marmbrus/dfToString and squashes the following commits:
8a3c35f [Michael Armbrust] Merge remote-tracking branch 'origin/master' into dfToString
b72a81b [Michael Armbrust] add toString
```
pyspark.sql.DataFrame.to_pandas = to_pandas(self) unbound pyspark.sql.DataFrame method
Collect all the rows and return a `pandas.DataFrame`.
>>> df.to_pandas() # doctest: +SKIP
age name
0 2 Alice
1 5 Bob
pyspark.sql.Column.to_pandas = to_pandas(self) unbound pyspark.sql.Column method
Return a pandas.Series from the column
>>> df.age.to_pandas() # doctest: +SKIP
0 2
1 5
dtype: int64
```
Not tests by jenkins (they depends on pandas)
Author: Davies Liu <davies@databricks.com>
Closes#4476 from davies/to_pandas and squashes the following commits:
6276fb6 [Davies Liu] Convert DataFrame to pandas.DataFrame and Series
See https://issues.apache.org/jira/browse/SPARK-5633 for details
Author: Vladimir Vladimirov <vladimir.vladimirov@magnetic.com>
Closes#4403 from smartkiwi/master and squashes the following commits:
94c014e [Vladimir Vladimirov] SPARK-5633 pyspark saveAsTextFile support for compression codec
This PR adds three major improvements to Parquet data source:
1. Partition discovery
While reading Parquet files resides in Hive style partition directories, `ParquetRelation2` automatically discovers partitioning information and infers partition column types.
This is also a partial work for [SPARK-5182] [1], which aims to provide first class partitioning support for the data source API. Related code in this PR can be easily extracted to the data source API level in future versions.
1. Schema merging
When enabled, Parquet data source collects schema information from all Parquet part-files and tries to merge them. Exceptions are thrown when incompatible schemas are detected. This feature is controlled by data source option `parquet.mergeSchema`, and is enabled by default.
1. Metastore Parquet table conversion moved to analysis phase
This greatly simplifies the conversion logic. `ParquetConversion` strategy can be removed once the old Parquet implementation is removed in the future.
This version of Parquet data source aims to entirely replace the old Parquet implementation. However, the old version hasn't been removed yet. Users can fall back to the old version by turning off SQL configuration `spark.sql.parquet.useDataSourceApi`.
Other JIRA tickets fixed as side effects in this PR:
- [SPARK-5509] [3]: `EqualTo` now uses a proper `Ordering` to compare binary types.
- [SPARK-3575] [4]: Metastore schema is now preserved and passed to `ParquetRelation2` via data source option `parquet.metastoreSchema`.
TODO:
- [ ] More test cases for partition discovery
- [x] Fix write path after data source write support (#4294) is merged
It turned out to be non-trivial to fall back to old Parquet implementation on the write path when Parquet data source is enabled. Since we're planning to include data source write support in 1.3.0, I simply ignored two test cases involving Parquet insertion for now.
- [ ] Fix outdated comments and documentations
PS: This PR looks big, but more than a half of the changed lines in this PR are trivial changes to test cases. To test Parquet with and without the new data source, almost all Parquet test cases are moved into wrapper driver functions. This introduces hundreds of lines of changes.
[1]: https://issues.apache.org/jira/browse/SPARK-5182
[2]: https://issues.apache.org/jira/browse/SPARK-5528
[3]: https://issues.apache.org/jira/browse/SPARK-5509
[4]: https://issues.apache.org/jira/browse/SPARK-3575
<!-- Reviewable:start -->
[<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/4308)
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Author: Cheng Lian <lian@databricks.com>
Closes#4308 from liancheng/parquet-partition-discovery and squashes the following commits:
b6946e6 [Cheng Lian] Fixes MiMA issues, addresses comments
8232e17 [Cheng Lian] Write support for Parquet data source
a49bd28 [Cheng Lian] Fixes spelling typo in trait name "CreateableRelationProvider"
808380f [Cheng Lian] Fixes issues introduced while rebasing
50dd8d1 [Cheng Lian] Addresses @rxin's comment, fixes UDT schema merging
adf2aae [Cheng Lian] Fixes compilation error introduced while rebasing
4e0175f [Cheng Lian] Fixes Python Parquet API, we need Py4J array to call varargs method
0d8ec1d [Cheng Lian] Adds more test cases
b35c8c6 [Cheng Lian] Fixes some typos and outdated comments
dd704fd [Cheng Lian] Fixes Python Parquet API
596c312 [Cheng Lian] Uses switch to control whether use Parquet data source or not
7d0f7a2 [Cheng Lian] Fixes Metastore Parquet table conversion
a1896c7 [Cheng Lian] Fixes all existing Parquet test suites except for ParquetMetastoreSuite
5654c9d [Cheng Lian] Draft version of Parquet partition discovery and schema merging
1. Removed LocalHiveContext in Python.
2. Reduced DSL UDF support from 22 arguments to 10 arguments so JavaDoc/ScalaDoc look nicer.
Author: Reynold Xin <rxin@databricks.com>
Closes#4374 from rxin/df-style and squashes the following commits:
e493342 [Reynold Xin] [SQL][DataFrame] Minor cleanup.
Author: Reynold Xin <rxin@databricks.com>
Closes#4376 from rxin/SPARK-5605 and squashes the following commits:
c55f5fa [Reynold Xin] Added a Python test.
f4b8dbb [Reynold Xin] [SPARK-5605][SQL][DF] Allow using String to specify colum name in DSL aggregate functions.
Add a seed for tests.
Author: Davies Liu <davies@databricks.com>
Closes#4358 from davies/flaky_test and squashes the following commits:
02371c3 [Davies Liu] Merge branch 'master' of github.com:apache/spark into flaky_test
ced499b [Davies Liu] add seed for test
Added `awaitTerminationOrTimeout` to return if the waiting time elapsed:
* `true` if it's stopped.
* `false` if the waiting time elapsed before returning from the method.
* throw the reported error if it's thrown during the execution.
Also deprecated `awaitTermination(timeout: Long)`.
Author: zsxwing <zsxwing@gmail.com>
Closes#4171 from zsxwing/SPARK-5379 and squashes the following commits:
c9e660b [zsxwing] Add a unit test for awaitTerminationOrTimeout
8a89f92 [zsxwing] Add awaitTerminationOrTimeout to python
cdc820b [zsxwing] Add awaitTerminationOrTimeout
In Spark 1.2 we added a `binaryRecords` input method for loading flat binary data. This format is useful for numerical array data, e.g. in scientific computing applications. This PR adds support for the same format in Streaming applications, where it is similarly useful, especially for streaming time series or sensor data.
Summary of additions
- adding `binaryRecordsStream` to Spark Streaming
- exposing `binaryRecordsStream` in the new PySpark Streaming
- new unit tests in Scala and Python
This required adding an optional Hadoop configuration param to `fileStream` and `FileInputStream`, but was otherwise straightforward.
tdas davies
Author: freeman <the.freeman.lab@gmail.com>
Closes#3803 from freeman-lab/streaming-binary-records and squashes the following commits:
b676534 [freeman] Clarify note
5ff1b75 [freeman] Add note to java streaming context
eba925c [freeman] Simplify notes
c4237b8 [freeman] Add experimental tag
30eba67 [freeman] Add filter and newFilesOnly alongside conf
c2cfa6d [freeman] Expose new version of fileStream with conf in java
34d20ef [freeman] Add experimental tag
14bca9a [freeman] Add experimental tag
b85bffc [freeman] Formatting
47560f4 [freeman] Space formatting
9a3715a [freeman] Refactor to reflect changes to FileInputSuite
7373f73 [freeman] Add note and defensive assertion for byte length
3ceb684 [freeman] Merge remote-tracking branch 'upstream/master' into streaming-binary-records
317b6d1 [freeman] Make test inline
fcb915c [freeman] Formatting
becb344 [freeman] Formatting
d3e75b2 [freeman] Add tests in python
a4324a3 [freeman] Line length
029d49c [freeman] Formatting
1c739aa [freeman] Simpler default arg handling
94d90d0 [freeman] Spelling
2843e9d [freeman] Add params to docstring
8b70fbc [freeman] Reorganization
28bff9b [freeman] Fix missing arg
9398bcb [freeman] Expose optional hadoop configuration
23dd69f [freeman] Tests for binaryRecordsStream
36cb0fd [freeman] Add binaryRecordsStream to scala
fe4e803 [freeman] Add binaryRecordStream to Java API
ecef0eb [freeman] Add binaryRecordsStream to python
8550c26 [freeman] Expose additional argument combination
```scala
df.selectExpr("abs(colA)", "colB")
df.filter("age > 21")
```
Author: Reynold Xin <rxin@databricks.com>
Closes#4348 from rxin/SPARK-5579 and squashes the following commits:
2baeef2 [Reynold Xin] Fix Python.
b416372 [Reynold Xin] [SPARK-5579][SQL][DataFrame] Support for project/filter using SQL expressions.
The only issue is that `analyzeBlock` is removed, which was marked as a developer API. I didn't change other tests in the ALSSuite under `spark.mllib` to ensure that the implementation is correct.
CC: srowen coderxiang
Author: Xiangrui Meng <meng@databricks.com>
Closes#4321 from mengxr/SPARK-5536 and squashes the following commits:
5a3cee8 [Xiangrui Meng] update python tests that are too strict
e840acf [Xiangrui Meng] ignore scala style check for ALS.train
e9a721c [Xiangrui Meng] update mima excludes
9ee6a36 [Xiangrui Meng] merge master
9a8aeac [Xiangrui Meng] update tests
d8c3271 [Xiangrui Meng] remove analyzeBlocks
d68eee7 [Xiangrui Meng] add checkpoint to new ALS
22a56f8 [Xiangrui Meng] wrap old ALS
c387dff [Xiangrui Meng] support random seed
3bdf24b [Xiangrui Meng] make storage level configurable in the new ALS
This PR brings the Python API for Spark Streaming Kafka data source.
```
class KafkaUtils(__builtin__.object)
| Static methods defined here:
|
| createStream(ssc, zkQuorum, groupId, topics, storageLevel=StorageLevel(True, True, False, False,
2), keyDecoder=<function utf8_decoder>, valueDecoder=<function utf8_decoder>)
| Create an input stream that pulls messages from a Kafka Broker.
|
| :param ssc: StreamingContext object
| :param zkQuorum: Zookeeper quorum (hostname:port,hostname:port,..).
| :param groupId: The group id for this consumer.
| :param topics: Dict of (topic_name -> numPartitions) to consume.
| Each partition is consumed in its own thread.
| :param storageLevel: RDD storage level.
| :param keyDecoder: A function used to decode key
| :param valueDecoder: A function used to decode value
| :return: A DStream object
```
run the example:
```
bin/spark-submit --driver-class-path external/kafka-assembly/target/scala-*/spark-streaming-kafka-assembly-*.jar examples/src/main/python/streaming/kafka_wordcount.py localhost:2181 test
```
Author: Davies Liu <davies@databricks.com>
Author: Tathagata Das <tdas@databricks.com>
Closes#3715 from davies/kafka and squashes the following commits:
d93bfe0 [Davies Liu] Update make-distribution.sh
4280d04 [Davies Liu] address comments
e6d0427 [Davies Liu] Merge branch 'master' of github.com:apache/spark into kafka
f257071 [Davies Liu] add tests for null in RDD
23b039a [Davies Liu] address comments
9af51c4 [Davies Liu] Merge branch 'kafka' of github.com:davies/spark into kafka
a74da87 [Davies Liu] address comments
dc1eed0 [Davies Liu] Update kafka_wordcount.py
31e2317 [Davies Liu] Update kafka_wordcount.py
370ba61 [Davies Liu] Update kafka.py
97386b3 [Davies Liu] address comment
2c567a5 [Davies Liu] update logging and comment
33730d1 [Davies Liu] Merge branch 'master' of github.com:apache/spark into kafka
adeeb38 [Davies Liu] Merge pull request #3 from tdas/kafka-python-api
aea8953 [Tathagata Das] Kafka-assembly for Python API
eea16a7 [Davies Liu] refactor
f6ce899 [Davies Liu] add example and fix bugs
98c8d17 [Davies Liu] fix python style
5697a01 [Davies Liu] bypass decoder in scala
048dbe6 [Davies Liu] fix python style
75d485e [Davies Liu] add mqtt
07923c4 [Davies Liu] support kafka in Python
1. Throw UnsupportedOperationException if a Column is not computable.
2. Perform eager analysis on DataFrame so we can catch errors when they happen (not when an action is run).
Author: Reynold Xin <rxin@databricks.com>
Author: Davies Liu <davies@databricks.com>
Closes#4296 from rxin/col-computability and squashes the following commits:
6527b86 [Reynold Xin] Merge pull request #8 from davies/col-computability
fd92bc7 [Reynold Xin] Merge branch 'master' into col-computability
f79034c [Davies Liu] fix python tests
5afe1ff [Reynold Xin] Fix scala test.
17f6bae [Reynold Xin] Various fixes.
b932e86 [Reynold Xin] Added eager analysis for error reporting.
e6f00b8 [Reynold Xin] [SQL][API] ComputableColumn vs IncomputableColumn
Whenever a directory is created by the utility method, immediately restrict
its permissions so that only the owner has access to its contents.
Signed-off-by: Josh Rosen <joshrosen@databricks.com>
This PR is implementing the Gradient Boosted Trees for Python API.
Author: Kazuki Taniguchi <kazuki.t.1018@gmail.com>
Closes#3951 from kazk1018/gbt_for_py and squashes the following commits:
620d247 [Kazuki Taniguchi] [SPARK-5094][MLlib] Add Python API for Gradient Boosted Trees
This fixes an exception that prevented users from calling `help()` on Python DataFrame instances.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#4278 from JoshRosen/SPARK-5464-python-dataframe-help-command and squashes the following commits:
08f95f7 [Josh Rosen] Fix exception when calling help() on Python DataFrame instances
Turns out Scala does generate static methods for ones defined in a companion object. Finally no need to separate api.java.dsl and api.scala.dsl.
Author: Reynold Xin <rxin@databricks.com>
Closes#4276 from rxin/dsl and squashes the following commits:
30aa611 [Reynold Xin] Add all files.
1a9d215 [Reynold Xin] [SPARK-5445][SQL] Consolidate Java and Scala DSL static methods.
There is only a single `stat.py` file for the `mllib.stat` package. We recently added `MultivariateGaussian` under `mllib.stat.distribution` in Scala/Java. It would be nice to refactor `stat.py` and make it easy to expand. Note that `ChiSqTestResult` is moved from `mllib.stat` to `mllib.stat.test`. The latter is used in Scala/Java. It is only used in the return value of `Statistics.chiSqTest`, so this should be an okay change.
davies
Author: Xiangrui Meng <meng@databricks.com>
Closes#4266 from mengxr/py-stat-refactor and squashes the following commits:
1a5e1db [Xiangrui Meng] refactor stat.py
Also removed the literal implicit transformation since it is pretty scary for API design. Instead, created a new lit method for creating literals. This doesn't break anything from a compatibility perspective because Literal was added two days ago.
Author: Reynold Xin <rxin@databricks.com>
Closes#4241 from rxin/df-docupdate and squashes the following commits:
c0f4810 [Reynold Xin] Fix Python merge conflict.
094c7d7 [Reynold Xin] Minor style fix. Reset Python tests.
3c89f4a [Reynold Xin] Package.
dfe6962 [Reynold Xin] Updated Python aggregate.
5dd4265 [Reynold Xin] Made dsl Java callable.
14b3c27 [Reynold Xin] Fix literal expression for symbols.
68b31cb [Reynold Xin] Literal.
4cfeb78 [Reynold Xin] [SPARK-5097][SQL] Address DataFrame code review feedback.
We have seen many use cases of `treeAggregate`/`treeReduce` outside the ML domain. Maybe it is time to move them to Core. pwendell
Author: Xiangrui Meng <meng@databricks.com>
Closes#4228 from mengxr/SPARK-5430 and squashes the following commits:
20ad40d [Xiangrui Meng] exclude tree* from mima
e89a43e [Xiangrui Meng] fix compile and update java doc
3ae1a4b [Xiangrui Meng] add treeReduce/treeAggregate to Python
6f948c5 [Xiangrui Meng] add treeReduce/treeAggregate to JavaRDDLike
d600b6c [Xiangrui Meng] move treeReduce and treeAggregate to core
This PR adds Python API for ML pipeline and parameters. The design doc can be found on the JIRA page. It includes transformers and an estimator to demo the simple text classification example code.
TODO:
- [x] handle parameters in LRModel
- [x] unit tests
- [x] missing some docs
CC: davies jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Author: Davies Liu <davies@databricks.com>
Closes#4151 from mengxr/SPARK-4586 and squashes the following commits:
415268e [Xiangrui Meng] remove inherit_doc from __init__
edbd6fe [Xiangrui Meng] move Identifiable to ml.util
44c2405 [Xiangrui Meng] Merge pull request #2 from davies/ml
dd1256b [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-4586
14ae7e2 [Davies Liu] fix docs
54ca7df [Davies Liu] fix tests
78638df [Davies Liu] Merge branch 'SPARK-4586' of github.com:mengxr/spark into ml
fc59a02 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-4586
1dca16a [Davies Liu] refactor
090b3a3 [Davies Liu] Merge branch 'master' of github.com:apache/spark into ml
0882513 [Xiangrui Meng] update doc style
a4f4dbf [Xiangrui Meng] add unit test for LR
7521d1c [Xiangrui Meng] add unit tests to HashingTF and Tokenizer
ba0ba1e [Xiangrui Meng] add unit tests for pipeline
0586c7b [Xiangrui Meng] add more comments to the example
5153cff [Xiangrui Meng] simplify java models
036ca04 [Xiangrui Meng] gen numFeatures
46fa147 [Xiangrui Meng] update mllib/pom.xml to include python files in the assembly
1dcc17e [Xiangrui Meng] update code gen and make param appear in the doc
f66ba0c [Xiangrui Meng] make params a property
d5efd34 [Xiangrui Meng] update doc conf and move embedded param map to instance attribute
f4d0fe6 [Xiangrui Meng] use LabeledDocument and Document in example
05e3e40 [Xiangrui Meng] update example
d3e8dbe [Xiangrui Meng] more docs optimize pipeline.fit impl
56de571 [Xiangrui Meng] fix style
d0c5bb8 [Xiangrui Meng] a working copy
bce72f4 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-4586
17ecfb9 [Xiangrui Meng] code gen for shared params
d9ea77c [Xiangrui Meng] update doc
c18dca1 [Xiangrui Meng] make the example working
dadd84e [Xiangrui Meng] add base classes and docs
a3015cf [Xiangrui Meng] add Estimator and Transformer
46eea43 [Xiangrui Meng] a pipeline in python
33b68e0 [Xiangrui Meng] a working LR
This PR is based on #3255 , fix conflicts and code style.
Closes#3255.
Author: Yandu Oppacher <yandu.oppacher@jadedpixel.com>
Author: Davies Liu <davies@databricks.com>
Closes#3901 from davies/refactor-python-profile-code and squashes the following commits:
b4a9306 [Davies Liu] fix tests
4b79ce8 [Davies Liu] add docstring for profiler_cls
2700e47 [Davies Liu] use BasicProfiler as default
349e341 [Davies Liu] more refactor
6a5d4df [Davies Liu] refactor and fix tests
31bf6b6 [Davies Liu] fix code style
0864b5d [Yandu Oppacher] Remove unused method
76a6c37 [Yandu Oppacher] Added a profile collector to accumulate the profilers per stage
9eefc36 [Yandu Oppacher] Fix doc
9ace076 [Yandu Oppacher] Refactor of profiler, and moved tests around
8739aff [Yandu Oppacher] Code review fixes
9bda3ec [Yandu Oppacher] Refactor profiler code
Since Java and Scala both have access to iterate over partitions via the "toLocalIterator" function, python should also have that same ability.
Author: Michael Nazario <mnazario@palantir.com>
Closes#4237 from mnazario/feature/toLocalIterator and squashes the following commits:
1c58526 [Michael Nazario] Fix documentation off by one error
0cdc8f8 [Michael Nazario] Add toLocalIterator to PySpark
Author: Sandy Ryza <sandy@cloudera.com>
Closes#4251 from sryza/sandy-spark-5458 and squashes the following commits:
460827a [Sandy Ryza] Python too
d2dc160 [Sandy Ryza] SPARK-5458. Refer to aggregateByKey instead of combineByKey in docs
This is found through reading RDD from `sc.newAPIHadoopRDD` and writing it back using `rdd.saveAsNewAPIHadoopFile` in pyspark.
It turns out that whenever there are multiple RDD conversions from JavaRDD to PythonRDD then back to JavaRDD, the exception below happens:
```
15/01/16 10:28:31 ERROR Executor: Exception in task 0.0 in stage 3.0 (TID 7)
java.lang.ClassCastException: [Ljava.lang.Object; cannot be cast to java.util.ArrayList
at org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:157)
at org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:153)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308)
```
The test case code below reproduces it:
```
from pyspark.rdd import RDD
dl = [
(u'2', {u'director': u'David Lean'}),
(u'7', {u'director': u'Andrew Dominik'})
]
dl_rdd = sc.parallelize(dl)
tmp = dl_rdd._to_java_object_rdd()
tmp2 = sc._jvm.SerDe.javaToPython(tmp)
t = RDD(tmp2, sc)
t.count()
tmp = t._to_java_object_rdd()
tmp2 = sc._jvm.SerDe.javaToPython(tmp)
t = RDD(tmp2, sc)
t.count() # it blows up here during the 2nd time of conversion
```
Author: Winston Chen <wchen@quid.com>
Closes#4146 from wingchen/master and squashes the following commits:
903df7d [Winston Chen] SPARK-5361, update to toSeq based on the PR
5d90a83 [Winston Chen] SPARK-5361, make python pretty, so to pass PEP 8 checks
126be6b [Winston Chen] SPARK-5361, add in test case
4cf1187 [Winston Chen] SPARK-5361, add in test case
9f1a097 [Winston Chen] add in tuple handling while converting form python RDD back to JavaRDD
This patch adds more helpful error messages for invalid programs that define nested RDDs, broadcast RDDs, perform actions inside of transformations (e.g. calling `count()` from inside of `map()`), and call certain methods on stopped SparkContexts. Currently, these invalid programs lead to confusing NullPointerExceptions at runtime and have been a major source of questions on the mailing list and StackOverflow.
In a few cases, I chose to log warnings instead of throwing exceptions in order to avoid any chance that this patch breaks programs that worked "by accident" in earlier Spark releases (e.g. programs that define nested RDDs but never run any jobs with them).
In SparkContext, the new `assertNotStopped()` method is used to check whether methods are being invoked on a stopped SparkContext. In some cases, user programs will not crash in spite of calling methods on stopped SparkContexts, so I've only added `assertNotStopped()` calls to methods that always throw exceptions when called on stopped contexts (e.g. by dereferencing a null `dagScheduler` pointer).
Author: Josh Rosen <joshrosen@databricks.com>
Closes#3884 from JoshRosen/SPARK-5063 and squashes the following commits:
a38774b [Josh Rosen] Fix spelling typo
a943e00 [Josh Rosen] Convert two exceptions into warnings in order to avoid breaking user programs in some edge-cases.
2d0d7f7 [Josh Rosen] Fix test to reflect 1.2.1 compatibility
3f0ea0c [Josh Rosen] Revert two unintentional formatting changes
8e5da69 [Josh Rosen] Remove assertNotStopped() calls for methods that were sometimes safe to call on stopped SC's in Spark 1.2
8cff41a [Josh Rosen] IllegalStateException fix
6ef68d0 [Josh Rosen] Fix Python line length issues.
9f6a0b8 [Josh Rosen] Add improved error messages to PySpark.
13afd0f [Josh Rosen] SparkException -> IllegalStateException
8d404f3 [Josh Rosen] Merge remote-tracking branch 'origin/master' into SPARK-5063
b39e041 [Josh Rosen] Fix BroadcastSuite test which broadcasted an RDD
99cc09f [Josh Rosen] Guard against calling methods on stopped SparkContexts.
34833e8 [Josh Rosen] Add more descriptive error message.
57cc8a1 [Josh Rosen] Add error message when directly broadcasting RDD.
15b2e6b [Josh Rosen] [SPARK-5063] Useful error messages for nested RDDs and actions inside of transformations
This implements the functionality for SPARK-4749 and provides units tests in Scala and PySpark
Author: nate.crosswhite <nate.crosswhite@stresearch.com>
Author: nxwhite-str <nxwhite-str@users.noreply.github.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#3610 from nxwhite-str/master and squashes the following commits:
a2ebbd3 [nxwhite-str] Merge pull request #1 from mengxr/SPARK-4749-kmeans-seed
7668124 [Xiangrui Meng] minor updates
f8d5928 [nate.crosswhite] Addressing PR issues
277d367 [nate.crosswhite] Merge remote-tracking branch 'upstream/master'
9156a57 [nate.crosswhite] Merge remote-tracking branch 'upstream/master'
5d087b4 [nate.crosswhite] Adding KMeans train with seed and Scala unit test
616d111 [nate.crosswhite] Merge remote-tracking branch 'upstream/master'
35c1884 [nate.crosswhite] Add kmeans initial seed to pyspark API
Pretty minor, but submitted for consideration -- this would at least help people make this check in the most efficient way I know.
Author: Sean Owen <sowen@cloudera.com>
Closes#4074 from srowen/SPARK-5270 and squashes the following commits:
66885b8 [Sean Owen] Add note that JavaRDDLike should not be implemented by user code
2e9b490 [Sean Owen] More tests, and Mima-exclude the new isEmpty method in JavaRDDLike
28395ff [Sean Owen] Add isEmpty to Java, Python
7dd04b7 [Sean Owen] Add efficient RDD.isEmpty()
As part of SPARK-5193:
1. Removed UDFRegistration as a mixin in SQLContext and made it a field ("udf").
2. For Java UDFs, renamed dataType to returnType.
3. For Scala UDFs, added type tags.
4. Added all Java UDF registration methods to Scala's UDFRegistration.
5. Documentation
Author: Reynold Xin <rxin@databricks.com>
Closes#4056 from rxin/udf-registration and squashes the following commits:
ae9c556 [Reynold Xin] Updated example.
675a3c9 [Reynold Xin] Style fix
47c24ff [Reynold Xin] Python fix.
5f00c45 [Reynold Xin] Restore data type position in java udf and added typetags.
032f006 [Reynold Xin] [SPARK-5193][SQL] Reconcile Java and Scala UDFRegistration.
After the default batchSize changed to 0 (batched based on the size of object), but parallelize() still use BatchedSerializer with batchSize=1, this PR will use batchSize=1024 for parallelize by default.
Also, BatchedSerializer did not work well with list and numpy.ndarray, this improve BatchedSerializer by using __len__ and __getslice__.
Here is the benchmark for parallelize 1 millions int with list or ndarray:
| before | after | improvements
------- | ------------ | ------------- | -------
list | 11.7 s | 0.8 s | 14x
numpy.ndarray | 32 s | 0.7 s | 40x
Author: Davies Liu <davies@databricks.com>
Closes#4024 from davies/opt_numpy and squashes the following commits:
7618c7c [Davies Liu] improve performance of parallelize list/ndarray
Slightly different than the scala code which converts the sparsevector into a densevector and then checks the index.
I also hope I've added tests in the right place.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#4025 from MechCoder/spark-2909 and squashes the following commits:
07d0f26 [MechCoder] STY: Rename item to index
f02148b [MechCoder] [SPARK-2909] [Mlib] SparseVector in pyspark now supports indexing
It will introduce problems if the object in dict/list/tuple can not support by py4j, such as Vector.
Also, pickle may have better performance for larger object (less RPC).
In some cases that the object in dict/list can not be pickled (such as JavaObject), we should still use MapConvert/ListConvert.
This PR should be ported into branch-1.2
Author: Davies Liu <davies@databricks.com>
Closes#4023 from davies/listconvert and squashes the following commits:
55d4ab2 [Davies Liu] fix MapConverter and ListConverter in MLlib
When attempting to infer the schema of an RDD that contains namedtuples, pyspark fails to identify the records as namedtuples, resulting in it raising an error.
Example:
```python
from pyspark import SparkContext
from pyspark.sql import SQLContext
from collections import namedtuple
import os
sc = SparkContext()
rdd = sc.textFile(os.path.join(os.getenv('SPARK_HOME'), 'README.md'))
TextLine = namedtuple('TextLine', 'line length')
tuple_rdd = rdd.map(lambda l: TextLine(line=l, length=len(l)))
tuple_rdd.take(5) # This works
sqlc = SQLContext(sc)
# The following line raises an error
schema_rdd = sqlc.inferSchema(tuple_rdd)
```
The error raised is:
```
File "/opt/spark-1.2.0-bin-hadoop2.4/python/pyspark/worker.py", line 107, in main
process()
File "/opt/spark-1.2.0-bin-hadoop2.4/python/pyspark/worker.py", line 98, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/opt/spark-1.2.0-bin-hadoop2.4/python/pyspark/serializers.py", line 227, in dump_stream
vs = list(itertools.islice(iterator, batch))
File "/opt/spark-1.2.0-bin-hadoop2.4/python/pyspark/rdd.py", line 1107, in takeUpToNumLeft
yield next(iterator)
File "/opt/spark-1.2.0-bin-hadoop2.4/python/pyspark/sql.py", line 816, in convert_struct
raise ValueError("unexpected tuple: %s" % obj)
TypeError: not all arguments converted during string formatting
```
Author: Gabe Mulley <gabe@edx.org>
Closes#3978 from mulby/inferschema-namedtuple and squashes the following commits:
98c61cc [Gabe Mulley] Ensure exception message is populated correctly
375d96b [Gabe Mulley] Ensure schema can be inferred from a namedtuple
...ySpark MLlib
This is a follow up to PR3680 https://github.com/apache/spark/pull/3680 .
Author: RJ Nowling <rnowling@gmail.com>
Closes#3955 from rnowling/spark4891 and squashes the following commits:
1236a01 [RJ Nowling] Fix Python style issues
7a01a78 [RJ Nowling] Fix Python style issues
174beab [RJ Nowling] [SPARK-4891][PySpark][MLlib] Add gamma/log normal/exp dist sampling to PySpark MLlib
This is a small change addressing a potentially significant bug in how PySpark + MLlib handles non-float64 numpy arrays. The automatic conversion to `DenseVector` that occurs when passing RDDs to MLlib algorithms in PySpark should automatically upcast to float64s, but currently this wasn't actually happening. As a result, non-float64 would be silently parsed inappropriately during SerDe, yielding erroneous results when running, for example, KMeans.
The PR includes the fix, as well as a new test for the correct conversion behavior.
davies
Author: freeman <the.freeman.lab@gmail.com>
Closes#3902 from freeman-lab/fix-vector-convert and squashes the following commits:
764db47 [freeman] Add a test for proper conversion behavior
704f97e [freeman] Return array after changing type
This PR is a fixed version of the original PR #3237 by watermen and scwf.
This adds the ability to specify how many elements to print in `DStream.print`.
Author: Yadong Qi <qiyadong2010@gmail.com>
Author: q00251598 <qiyadong@huawei.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: wangfei <wangfei1@huawei.com>
Closes#3865 from tdas/print-num and squashes the following commits:
cd34e9e [Tathagata Das] Fix bug
7c09f16 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into HEAD
bb35d1a [Yadong Qi] Update MimaExcludes.scala
f8098ca [Yadong Qi] Update MimaExcludes.scala
f6ac3cb [Yadong Qi] Update MimaExcludes.scala
e4ed897 [Yadong Qi] Update MimaExcludes.scala
3b9d5cf [wangfei] fix conflicts
ec8a3af [q00251598] move to Spark 1.3
26a70c0 [q00251598] extend the Python DStream's print
b589a4b [q00251598] add another print function
Creates a top level directory script (as `build/mvn`) to automatically download zinc and the specific version of scala used to easily build spark. This will also download and install maven if the user doesn't already have it and all packages are hosted under the `build/` directory. Tested on both Linux and OSX OS's and both work. All commands pass through to the maven binary so it acts exactly as a traditional maven call would.
Author: Brennon York <brennon.york@capitalone.com>
Closes#3707 from brennonyork/SPARK-4501 and squashes the following commits:
0e5a0e4 [Brennon York] minor incorrect doc verbage (with -> this)
9b79e38 [Brennon York] fixed merge conflicts with dev/run-tests, properly quoted args in sbt/sbt, fixed bug where relative paths would fail if passed in from build/mvn
d2d41b6 [Brennon York] added blurb about leverging zinc with build/mvn
b979c58 [Brennon York] updated the merge conflict
c5634de [Brennon York] updated documentation to overview build/mvn, updated all points where sbt/sbt was referenced with build/sbt
b8437ba [Brennon York] set progress bars for curl and wget when not run on jenkins, no progress bar when run on jenkins, moved sbt script to build/sbt, wrote stub and warning under sbt/sbt which calls build/sbt, modified build/sbt to use the correct directory, fixed bug in build/sbt-launch-lib.bash to correctly pull the sbt version
be11317 [Brennon York] added switch to silence download progress only if AMPLAB_JENKINS is set
28d0a99 [Brennon York] updated to remove the python dependency, uses grep instead
7e785a6 [Brennon York] added silent and quiet flags to curl and wget respectively, added single echo output to denote start of a download if download is needed
14a5da0 [Brennon York] removed unnecessary zinc output on startup
1af4a94 [Brennon York] fixed bug with uppercase vs lowercase variable
3e8b9b3 [Brennon York] updated to properly only restart zinc if it was freshly installed
a680d12 [Brennon York] Added comments to functions and tested various mvn calls
bb8cc9d [Brennon York] removed package files
ef017e6 [Brennon York] removed OS complexities, setup generic install_app call, removed extra file complexities, removed help, removed forced install (defaults now), removed double-dash from cli
07bf018 [Brennon York] Updated to specifically handle pulling down the correct scala version
f914dea [Brennon York] Beginning final portions of localized scala home
69c4e44 [Brennon York] working linux and osx installers for purely local mvn build
4a1609c [Brennon York] finalizing working linux install for maven to local ./build/apache-maven folder
cbfcc68 [Brennon York] Changed the default sbt/sbt to build/sbt and added a build/mvn which will automatically download, install, and execute maven with zinc for easier build capability
This PR modifies the python `SchemaRDD` to use `sample()` and `takeSample()` from Scala instead of the slower python implementations from `rdd.py`. This is worthwhile because the `Row`'s are already serialized as Java objects.
In order to use the faster `takeSample()`, a `takeSampleToPython()` method was implemented in `SchemaRDD.scala` following the pattern of `collectToPython()`.
Author: jbencook <jbenjamincook@gmail.com>
Author: J. Benjamin Cook <jbenjamincook@gmail.com>
Closes#3764 from jbencook/master and squashes the following commits:
6fbc769 [J. Benjamin Cook] [SPARK-4860][pyspark][sql] fixing sloppy indentation for takeSampleToPython() arguments
5170da2 [J. Benjamin Cook] [SPARK-4860][pyspark][sql] fixing typo: from RDD to SchemaRDD
de22f70 [jbencook] [SPARK-4860][pyspark][sql] using sample() method from JavaSchemaRDD
b916442 [jbencook] [SPARK-4860][pyspark][sql] adding sample() to JavaSchemaRDD
020cbdf [jbencook] [SPARK-4860][pyspark][sql] using Scala implementations of `sample()` and `takeSample()`
Modify python annotations for sphinx. There is no change to build process from.
https://github.com/apache/spark/blob/master/docs/README.md
Author: lewuathe <lewuathe@me.com>
Closes#3685 from Lewuathe/sphinx-tag-for-pydoc and squashes the following commits:
88a0fd9 [lewuathe] [SPARK-4822] Fix DevelopApi and WARN tags
3d7a398 [lewuathe] [SPARK-4822] Use sphinx tags for Python doc annotations
+ small doc edit
+ include edit to make IntelliJ happy
CC: davies mengxr
Note to davies -- this does not fix the "WARNING: Literal block expected; none found." warnings since that seems to involve spacing which IntelliJ does not like. (Those warnings occur when generating the Python docs.)
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#3669 from jkbradley/python-warnings and squashes the following commits:
4587868 [Joseph K. Bradley] fixed warning
8cb073c [Joseph K. Bradley] Updated based on davies recommendation
c51eca4 [Joseph K. Bradley] Updated rst file for pyspark.mllib.rand doc. Small doc edit. Small include edit to make IntelliJ happy.
This PR brings support of using StructType(and other hashable types) as key in MapType.
Author: Davies Liu <davies@databricks.com>
Closes#3714 from davies/fix_struct_in_map and squashes the following commits:
68585d7 [Davies Liu] fix primitive types in MapType
9601534 [Davies Liu] support StructType as key in MapType
This PR tests the pyspark Chi-squared hypothesis test from this commit: c8abddc516 and moves some of the error messaging in to python.
It is a port of the Scala tests here: [HypothesisTestSuite.scala](https://github.com/apache/spark/blob/master/mllib/src/test/scala/org/apache/spark/mllib/stat/HypothesisTestSuite.scala)
Hopefully, SPARK-2980 can be closed.
Author: jbencook <jbenjamincook@gmail.com>
Closes#3679 from jbencook/master and squashes the following commits:
44078e0 [jbencook] checking that bad input throws the correct exceptions
f12ee10 [jbencook] removing checks for ValueError since input tests are on the Scala side
7536cf1 [jbencook] removing python checks for invalid input
a17ee84 [jbencook] [SPARK-2980][mllib] adding unit tests for the pyspark chi-squared test
3aeb0d9 [jbencook] [SPARK-2980][mllib] bringing Chi-squared error messages to the python side
UTF8Deserializer can not be used in BatchedSerializer, so always use PickleSerializer() when change batchSize in zip().
Also, if two RDD have the same batch size already, they did not need re-serialize any more.
Author: Davies Liu <davies@databricks.com>
Closes#3706 from davies/fix_4841 and squashes the following commits:
20ce3a3 [Davies Liu] fix bug in _reserialize()
e3ebf7c [Davies Liu] add comment
379d2c8 [Davies Liu] fix zip with textFile()
I improved `IDFModel.transform` to allow using a single vector.
[[SPARK-4494] IDFModel.transform() add support for single vector - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-4494)
Author: Yuu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes#3603 from yu-iskw/idf and squashes the following commits:
256ff3d [Yuu ISHIKAWA] Fix typo
a3bf566 [Yuu ISHIKAWA] - Fix typo - Optimize import order - Aggregate the assertion tests - Modify `IDFModel.transform` API for pyspark
d25e49b [Yuu ISHIKAWA] Add the implementation of `IDFModel.transform` for a term frequency vector
Major changes:
* Added programming guide sections for tree ensembles
* Added examples for tree ensembles
* Updated DecisionTree programming guide with more info on parameters
* **API change**: Standardized the tree parameter for the number of classes (for classification)
Minor changes:
* Updated decision tree documentation
* Updated existing tree and tree ensemble examples
* Use train/test split, and compute test error instead of training error.
* Fixed decision_tree_runner.py to actually use the number of classes it computes from data. (small bug fix)
Note: I know this is a lot of lines, but most is covered by:
* Programming guide sections for gradient boosting and random forests. (The changes are probably best viewed by generating the docs locally.)
* New examples (which were copied from the programming guide)
* The "numClasses" renaming
I have run all examples and relevant unit tests.
CC: mengxr manishamde codedeft
Author: Joseph K. Bradley <joseph@databricks.com>
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#3461 from jkbradley/ensemble-docs and squashes the following commits:
70a75f3 [Joseph K. Bradley] updated forest vs boosting comparison
d1de753 [Joseph K. Bradley] Added note about toString and toDebugString for DecisionTree to migration guide
8e87f8f [Joseph K. Bradley] Combined GBT and RandomForest guides into one ensembles guide
6fab846 [Joseph K. Bradley] small fixes based on review
b9f8576 [Joseph K. Bradley] updated decision tree doc
375204c [Joseph K. Bradley] fixed python style
2b60b6e [Joseph K. Bradley] merged Java RandomForest examples into 1 file. added header. Fixed small bug in same example in the programming guide.
706d332 [Joseph K. Bradley] updated python DT runner to print full model if it is small
c76c823 [Joseph K. Bradley] added migration guide for mllib
abe5ed7 [Joseph K. Bradley] added examples for random forest in Java and Python to examples folder
07fc11d [Joseph K. Bradley] Renamed numClassesForClassification to numClasses everywhere in trees and ensembles. This is a breaking API change, but it was necessary to correct an API inconsistency in Spark 1.1 (where Python DecisionTree used numClasses but Scala used numClassesForClassification).
cdfdfbc [Joseph K. Bradley] added examples for GBT
6372a2b [Joseph K. Bradley] updated decision tree examples to use random split. tested all of them.
ad3e695 [Joseph K. Bradley] added gbt and random forest to programming guide. still need to update their examples
Re-implement the Python broadcast using file:
1) serialize the python object using cPickle, write into disks.
2) Create a wrapper in JVM (for the dumped file), it read data from during serialization
3) Using TorrentBroadcast or HttpBroadcast to transfer the data (compressed) into executors
4) During deserialization, writing the data into disk.
5) Passing the path into Python worker, read data from disk and unpickle it into python object, until the first access.
It fixes the performance regression introduced in #2659, has similar performance as 1.1, but support object larger than 2G, also improve the memory efficiency (only one compressed copy in driver and executor).
Testing with a 500M broadcast and 4 tasks (excluding the benefit from reused worker in 1.2):
name | 1.1 | 1.2 with this patch | improvement
---------|--------|---------|--------
python-broadcast-w-bytes | 25.20 | 9.33 | 170.13% |
python-broadcast-w-set | 4.13 | 4.50 | -8.35% |
Testing with 100 tasks (16 CPUs):
name | 1.1 | 1.2 with this patch | improvement
---------|--------|---------|--------
python-broadcast-w-bytes | 38.16 | 8.40 | 353.98%
python-broadcast-w-set | 23.29 | 9.59 | 142.80%
Author: Davies Liu <davies@databricks.com>
Closes#3417 from davies/pybroadcast and squashes the following commits:
50a58e0 [Davies Liu] address comments
b98de1d [Davies Liu] disable gc while unpickle
e5ee6b9 [Davies Liu] support large string
09303b8 [Davies Liu] read all data into memory
dde02dd [Davies Liu] improve performance of python broadcast
The Row object is created on the fly once the field is accessed, so we should access them by getattr() in asDict(0
Author: Davies Liu <davies@databricks.com>
Closes#3434 from davies/fix_asDict and squashes the following commits:
b20f1e7 [Davies Liu] fix asDict() with nested Row()
This PR change the underline array of DenseVector to numpy.ndarray to avoid the conversion, because most of the users will using numpy.array.
It also improve the serialization of DenseVector.
Before this change:
trial | trainingTime | testTime
-------|--------|--------
0 | 5.126 | 1.786
1 |2.698 |1.693
After the change:
trial | trainingTime | testTime
-------|--------|--------
0 |4.692 |0.554
1 |2.307 |0.525
This could partially fix the performance regression during test.
Author: Davies Liu <davies@databricks.com>
Closes#3420 from davies/ser2 and squashes the following commits:
0e1e6f3 [Davies Liu] fix tests
426f5db [Davies Liu] impove toArray()
44707ec [Davies Liu] add name for ISO-8859-1
fa7d791 [Davies Liu] address comments
1cfb137 [Davies Liu] handle zero sparse vector
2548ee2 [Davies Liu] fix tests
9e6389d [Davies Liu] bugfix
470f702 [Davies Liu] speed up DenseMatrix
f0d3c40 [Davies Liu] speedup SparseVector
ef6ce70 [Davies Liu] speed up dense vector
The Pyrolite is pretty slow (comparing to the adhoc serializer in 1.1), it cause much performance regression in 1.2, because we cache the serialized Python object in JVM, deserialize them into Java object in each step.
This PR change to cache the deserialized JavaRDD instead of PythonRDD to avoid the deserialization of Pyrolite. It should have similar memory usage as before, but much faster.
Author: Davies Liu <davies@databricks.com>
Closes#3397 from davies/cache and squashes the following commits:
7f6e6ce [Davies Liu] Update -> Updater
4b52edd [Davies Liu] using named argument
63b984e [Davies Liu] fix
7da0332 [Davies Liu] add unpersist()
dff33e1 [Davies Liu] address comments
c2bdfc2 [Davies Liu] refactor
d572f00 [Davies Liu] Merge branch 'master' into cache
f1063e1 [Davies Liu] cache serialized java object
In RDDSampler, it try use numpy to gain better performance for possion(), but the number of call of random() is only (1+faction) * N in the pure python implementation of possion(), so there is no much performance gain from numpy.
numpy is not a dependent of pyspark, so it maybe introduce some problem, such as there is no numpy installed in slaves, but only installed master, as reported in SPARK-927.
It also complicate the code a lot, so we may should remove numpy from RDDSampler.
I also did some benchmark to verify that:
```
>>> from pyspark.mllib.random import RandomRDDs
>>> rdd = RandomRDDs.uniformRDD(sc, 1 << 20, 1).cache()
>>> rdd.count() # cache it
>>> rdd.sample(True, 0.9).count() # measure this line
```
the results:
|withReplacement | random | numpy.random |
------- | ------------ | -------
|True | 1.5 s| 1.4 s|
|False| 0.6 s | 0.8 s|
closes#2313
Note: this patch including some commits that not mirrored to github, it will be OK after it catches up.
Author: Davies Liu <davies@databricks.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#3351 from davies/numpy and squashes the following commits:
5c438d7 [Davies Liu] fix comment
c5b9252 [Davies Liu] Merge pull request #1 from mengxr/SPARK-4477
98eb31b [Xiangrui Meng] make poisson sampling slightly faster
ee17d78 [Davies Liu] remove = for float
13f7b05 [Davies Liu] Merge branch 'master' of http://git-wip-us.apache.org/repos/asf/spark into numpy
f583023 [Davies Liu] fix tests
51649f5 [Davies Liu] remove numpy in RDDSampler
78bf997 [Davies Liu] fix tests, do not use numpy in randomSplit, no performance gain
f5fdf63 [Davies Liu] fix bug with int in weights
4dfa2cd [Davies Liu] refactor
f866bcf [Davies Liu] remove unneeded change
c7a2007 [Davies Liu] switch to python implementation
95a48ac [Davies Liu] Merge branch 'master' of github.com:apache/spark into randomSplit
0d9b256 [Davies Liu] refactor
1715ee3 [Davies Liu] address comments
41fce54 [Davies Liu] randomSplit()
```
class RandomForestModel
| A model trained by RandomForest
|
| numTrees(self)
| Get number of trees in forest.
|
| predict(self, x)
| Predict values for a single data point or an RDD of points using the model trained.
|
| toDebugString(self)
| Full model
|
| totalNumNodes(self)
| Get total number of nodes, summed over all trees in the forest.
|
class RandomForest
| trainClassifier(cls, data, numClassesForClassification, categoricalFeaturesInfo, numTrees, featureSubsetStrategy='auto', impurity='gini', maxDepth=4, maxBins=32, seed=None):
| Method to train a decision tree model for binary or multiclass classification.
|
| :param data: Training dataset: RDD of LabeledPoint.
| Labels should take values {0, 1, ..., numClasses-1}.
| :param numClassesForClassification: number of classes for classification.
| :param categoricalFeaturesInfo: Map storing arity of categorical features.
| E.g., an entry (n -> k) indicates that feature n is categorical
| with k categories indexed from 0: {0, 1, ..., k-1}.
| :param numTrees: Number of trees in the random forest.
| :param featureSubsetStrategy: Number of features to consider for splits at each node.
| Supported: "auto" (default), "all", "sqrt", "log2", "onethird".
| If "auto" is set, this parameter is set based on numTrees:
| if numTrees == 1, set to "all";
| if numTrees > 1 (forest) set to "sqrt".
| :param impurity: Criterion used for information gain calculation.
| Supported values: "gini" (recommended) or "entropy".
| :param maxDepth: Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means
| 1 internal node + 2 leaf nodes. (default: 4)
| :param maxBins: maximum number of bins used for splitting features (default: 100)
| :param seed: Random seed for bootstrapping and choosing feature subsets.
| :return: RandomForestModel that can be used for prediction
|
| trainRegressor(cls, data, categoricalFeaturesInfo, numTrees, featureSubsetStrategy='auto', impurity='variance', maxDepth=4, maxBins=32, seed=None):
| Method to train a decision tree model for regression.
|
| :param data: Training dataset: RDD of LabeledPoint.
| Labels are real numbers.
| :param categoricalFeaturesInfo: Map storing arity of categorical features.
| E.g., an entry (n -> k) indicates that feature n is categorical
| with k categories indexed from 0: {0, 1, ..., k-1}.
| :param numTrees: Number of trees in the random forest.
| :param featureSubsetStrategy: Number of features to consider for splits at each node.
| Supported: "auto" (default), "all", "sqrt", "log2", "onethird".
| If "auto" is set, this parameter is set based on numTrees:
| if numTrees == 1, set to "all";
| if numTrees > 1 (forest) set to "onethird".
| :param impurity: Criterion used for information gain calculation.
| Supported values: "variance".
| :param maxDepth: Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means
| 1 internal node + 2 leaf nodes.(default: 4)
| :param maxBins: maximum number of bins used for splitting features (default: 100)
| :param seed: Random seed for bootstrapping and choosing feature subsets.
| :return: RandomForestModel that can be used for prediction
|
```
Author: Davies Liu <davies@databricks.com>
Closes#3320 from davies/forest and squashes the following commits:
8003dfc [Davies Liu] reorder
53cf510 [Davies Liu] fix docs
4ca593d [Davies Liu] fix docs
e0df852 [Davies Liu] fix docs
0431746 [Davies Liu] rebased
2b6f239 [Davies Liu] Merge branch 'master' of github.com:apache/spark into forest
885abee [Davies Liu] address comments
dae7fc0 [Davies Liu] address comments
89a000f [Davies Liu] fix docs
565d476 [Davies Liu] add python api for random forest
If there some big broadcasts (or other object) in Python worker, the free memory could be used for sorting will be too small, then it will keep spilling small files into disks, finally failed with too many open files.
This PR try to delay the spilling until the used memory goes over limit and start to increase since last spilling, it will increase the size of spilling files, improve the stability and performance in this cases. (We also do this in ExternalAggregator).
Author: Davies Liu <davies@databricks.com>
Closes#3252 from davies/sort and squashes the following commits:
711fb6c [Davies Liu] improve sort spilling
This commit should be merged for 1.2 release.
cc tdas
Author: Ken Takagiwa <ugw.gi.world@gmail.com>
Closes#3311 from giwa/patch-3 and squashes the following commits:
ab474a8 [Ken Takagiwa] [DOC][PySpark][Streaming] Fix docstring for sphinx
```
pyspark.RDD.randomSplit(self, weights, seed=None)
Randomly splits this RDD with the provided weights.
:param weights: weights for splits, will be normalized if they don't sum to 1
:param seed: random seed
:return: split RDDs in an list
>>> rdd = sc.parallelize(range(10), 1)
>>> rdd1, rdd2, rdd3 = rdd.randomSplit([0.4, 0.6, 1.0], 11)
>>> rdd1.collect()
[3, 6]
>>> rdd2.collect()
[0, 5, 7]
>>> rdd3.collect()
[1, 2, 4, 8, 9]
```
Author: Davies Liu <davies@databricks.com>
Closes#3193 from davies/randomSplit and squashes the following commits:
78bf997 [Davies Liu] fix tests, do not use numpy in randomSplit, no performance gain
f5fdf63 [Davies Liu] fix bug with int in weights
4dfa2cd [Davies Liu] refactor
f866bcf [Davies Liu] remove unneeded change
c7a2007 [Davies Liu] switch to python implementation
95a48ac [Davies Liu] Merge branch 'master' of github.com:apache/spark into randomSplit
0d9b256 [Davies Liu] refactor
1715ee3 [Davies Liu] address comments
41fce54 [Davies Liu] randomSplit()
This patch will bring support for broadcasting objects larger than 2G.
pickle, zlib, FrameSerializer and Array[Byte] all can not support objects larger than 2G, so this patch introduce LargeObjectSerializer to serialize broadcast objects, the object will be serialized and compressed into small chunks, it also change the type of Broadcast[Array[Byte]]] into Broadcast[Array[Array[Byte]]]].
Testing for support broadcast objects larger than 2G is slow and memory hungry, so this is tested manually, could be added into SparkPerf.
Author: Davies Liu <davies@databricks.com>
Author: Davies Liu <davies.liu@gmail.com>
Closes#2659 from davies/huge and squashes the following commits:
7b57a14 [Davies Liu] add more tests for broadcast
28acff9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
a2f6a02 [Davies Liu] bug fix
4820613 [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
5875c73 [Davies Liu] address comments
10a349b [Davies Liu] address comments
0c33016 [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
6182c8f [Davies Liu] Merge branch 'master' into huge
d94b68f [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
2514848 [Davies Liu] address comments
fda395b [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge
1c2d928 [Davies Liu] fix scala style
091b107 [Davies Liu] broadcast objects larger than 2G
```
class LogisticRegressionWithLBFGS
| train(cls, data, iterations=100, initialWeights=None, corrections=10, tolerance=0.0001, regParam=0.01, intercept=False)
| Train a logistic regression model on the given data.
|
| :param data: The training data, an RDD of LabeledPoint.
| :param iterations: The number of iterations (default: 100).
| :param initialWeights: The initial weights (default: None).
| :param regParam: The regularizer parameter (default: 0.01).
| :param regType: The type of regularizer used for training
| our model.
| :Allowed values:
| - "l1" for using L1 regularization
| - "l2" for using L2 regularization
| - None for no regularization
| (default: "l2")
| :param intercept: Boolean parameter which indicates the use
| or not of the augmented representation for
| training data (i.e. whether bias features
| are activated or not).
| :param corrections: The number of corrections used in the LBFGS update (default: 10).
| :param tolerance: The convergence tolerance of iterations for L-BFGS (default: 1e-4).
|
| >>> data = [
| ... LabeledPoint(0.0, [0.0, 1.0]),
| ... LabeledPoint(1.0, [1.0, 0.0]),
| ... ]
| >>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data))
| >>> lrm.predict([1.0, 0.0])
| 1
| >>> lrm.predict([0.0, 1.0])
| 0
| >>> lrm.predict(sc.parallelize([[1.0, 0.0], [0.0, 1.0]])).collect()
| [1, 0]
```
Author: Davies Liu <davies@databricks.com>
Closes#3307 from davies/lbfgs and squashes the following commits:
34bd986 [Davies Liu] Merge branch 'master' of http://git-wip-us.apache.org/repos/asf/spark into lbfgs
5a945a6 [Davies Liu] address comments
941061b [Davies Liu] Merge branch 'master' of github.com:apache/spark into lbfgs
03e5543 [Davies Liu] add it to docs
ed2f9a8 [Davies Liu] add regType
76cd1b6 [Davies Liu] reorder arguments
4429a74 [Davies Liu] Update classification.py
9252783 [Davies Liu] python api for LogisticRegressionWithLBFGS
In PySpark, ALS can take an RDD of (user, product, rating) tuples as input. However, model.predict outputs an RDD of Rating. So on the input side, users can use r[0], r[1], r[2], while on the output side, users have to use r.user, r.product, r.rating. We should allow lookup by index in Rating by making Rating a namedtuple.
davies
<!-- Reviewable:start -->
[<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/3261)
<!-- Reviewable:end -->
Author: Xiangrui Meng <meng@databricks.com>
Closes#3261 from mengxr/SPARK-4396 and squashes the following commits:
543aef0 [Xiangrui Meng] use named tuple to implement ALS
0b61bae [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-4396
d3bd7d4 [Xiangrui Meng] allow lookup by index in Python's Rating
This PR add setThrehold() and clearThreshold() for LogisticRegressionModel and SVMModel, also support RDD of vector in LogisticRegressionModel.predict(), SVNModel.predict() and NaiveBayes.predict()
Author: Davies Liu <davies@databricks.com>
Closes#3305 from davies/setThreshold and squashes the following commits:
d0b835f [Davies Liu] Merge branch 'master' of github.com:apache/spark into setThreshold
e4acd76 [Davies Liu] address comments
2231a5f [Davies Liu] bugfix
7bd9009 [Davies Liu] address comments
0b0a8a7 [Davies Liu] address comments
c1e5573 [Davies Liu] improve classification
When JVM is started in a Python process, it should exit once the stdin is closed.
test: add spark.driver.memory in conf/spark-defaults.conf
```
daviesdm:~/work/spark$ cat conf/spark-defaults.conf
spark.driver.memory 8g
daviesdm:~/work/spark$ bin/pyspark
>>> quit
daviesdm:~/work/spark$ jps
4931 Jps
286
daviesdm:~/work/spark$ python wc.py
943738
0.719928026199
daviesdm:~/work/spark$ jps
286
4990 Jps
```
Author: Davies Liu <davies@databricks.com>
Closes#3274 from davies/exit and squashes the following commits:
df0e524 [Davies Liu] address comments
ce8599c [Davies Liu] address comments
050651f [Davies Liu] JVM should exit after Python exit
`sc.parallelize(range(1 << 20), 1).count()` may take 15 seconds to finish and the rdd object stores the entire list, making task size very large. This PR adds a specialized version for xrange.
JoshRosen davies
Author: Xiangrui Meng <meng@databricks.com>
Closes#3264 from mengxr/SPARK-4398 and squashes the following commits:
8953c41 [Xiangrui Meng] follow davies' suggestion
cbd58e3 [Xiangrui Meng] specialize sc.parallelize(xrange)
The current default regParam is 1.0 and regType is claimed to be none in Python (but actually it is l2), while regParam = 0.0 and regType is L2 in Scala. We should make the default values consistent. This PR sets the default regType to L2 and regParam to 0.01. Note that the default regParam value in LIBLINEAR (and hence scikit-learn) is 1.0. However, we use average loss instead of total loss in our formulation. Hence regParam=1.0 is definitely too heavy.
In LinearRegression, we set regParam=0.0 and regType=None, because we have separate classes for Lasso and Ridge, both of which use regParam=0.01 as the default.
davies atalwalkar
Author: Xiangrui Meng <meng@databricks.com>
Closes#3232 from mengxr/SPARK-4372 and squashes the following commits:
9979837 [Xiangrui Meng] update Ridge/Lasso to use default regParam 0.01 cast input arguments
d3ba096 [Xiangrui Meng] change 'none' back to None
1909a6e [Xiangrui Meng] change default regParam to 0.01 and regType to L2 in LR and SVM
This PR rename random.py to rand.py to avoid the side affects of conflict with random module, but still keep the same interface as before.
```
>>> from pyspark.mllib.random import RandomRDDs
```
```
$ pydoc pyspark.mllib.random
Help on module random in pyspark.mllib:
NAME
random - Python package for random data generation.
FILE
/Users/davies/work/spark/python/pyspark/mllib/rand.py
CLASSES
__builtin__.object
pyspark.mllib.random.RandomRDDs
class RandomRDDs(__builtin__.object)
| Generator methods for creating RDDs comprised of i.i.d samples from
| some distribution.
|
| Static methods defined here:
|
| normalRDD(sc, size, numPartitions=None, seed=None)
```
cc mengxr
reference link: http://xion.org.pl/2012/05/06/hacking-python-imports/
Author: Davies Liu <davies@databricks.com>
Closes#3216 from davies/random and squashes the following commits:
7ac4e8b [Davies Liu] rename random.py to rand.py
Fix TreeModel.predict() with RDD, added tests for it.
(Also checked that other models don't have this issue)
Author: Davies Liu <davies@databricks.com>
Closes#3230 from davies/predict and squashes the following commits:
81172aa [Davies Liu] fix predict
This PR check all of the existing Python MLlib API to make sure that numpy.array is supported as Vector (also RDD of numpy.array).
It also improve some docstring and doctest.
cc mateiz mengxr
Author: Davies Liu <davies@databricks.com>
Closes#3189 from davies/numpy and squashes the following commits:
d5057c4 [Davies Liu] fix tests
6987611 [Davies Liu] support numpy.array for all MLlib API
SPARK-1553 added alternating nonnegative least squares to MLLib, however it's not possible to access it via the python API. This pull request resolves that.
Author: Michelangelo D'Agostino <mdagostino@civisanalytics.com>
Closes#3095 from mdagost/python_nmf and squashes the following commits:
a6743ad [Michelangelo D'Agostino] Use setters instead of static methods in PythonMLLibAPI. Remove the new static methods I added. Set seed in tests. Change ratings to ratingsRDD in both train and trainImplicit for consistency.
7cffd39 [Michelangelo D'Agostino] Swapped nonnegative and seed in a few more places.
3fdc851 [Michelangelo D'Agostino] Moved seed to the end of the python parameter list.
bdcc154 [Michelangelo D'Agostino] Change seed type to java.lang.Long so that it can handle null.
cedf043 [Michelangelo D'Agostino] Added in ability to set the seed from python and made that play nice with the nonnegative changes. Also made the python ALS tests more exact.
a72fdc9 [Michelangelo D'Agostino] Expose nonnegative ALS in the python API.
This PR fix sortBy()/sortByKey() on empty RDD.
This should be back ported into 1.1/1.2
Author: Davies Liu <davies@databricks.com>
Closes#3162 from davies/fix_sort and squashes the following commits:
84f64b7 [Davies Liu] add tests
52995b5 [Davies Liu] fix sortByKey() on empty RDD
add binaryFiles() and binaryRecords() in Python
```
binaryFiles(self, path, minPartitions=None):
:: Developer API ::
Read a directory of binary files from HDFS, a local file system
(available on all nodes), or any Hadoop-supported file system URI
as a byte array. Each file is read as a single record and returned
in a key-value pair, where the key is the path of each file, the
value is the content of each file.
Note: Small files are preferred, large file is also allowable, but
may cause bad performance.
binaryRecords(self, path, recordLength):
Load data from a flat binary file, assuming each record is a set of numbers
with the specified numerical format (see ByteBuffer), and the number of
bytes per record is constant.
:param path: Directory to the input data files
:param recordLength: The length at which to split the records
```
Author: Davies Liu <davies@databricks.com>
Closes#3078 from davies/binary and squashes the following commits:
cd0bdbd [Davies Liu] Merge branch 'master' of github.com:apache/spark into binary
3aa349b [Davies Liu] add experimental notes
24e84b6 [Davies Liu] Merge branch 'master' of github.com:apache/spark into binary
5ceaa8a [Davies Liu] Merge branch 'master' of github.com:apache/spark into binary
1900085 [Davies Liu] bugfix
bb22442 [Davies Liu] add binaryFiles and binaryRecords in Python
```
pyspark.mllib.stat.StatisticschiSqTest(observed, expected=None)
:: Experimental ::
If `observed` is Vector, conduct Pearson's chi-squared goodness
of fit test of the observed data against the expected distribution,
or againt the uniform distribution (by default), with each category
having an expected frequency of `1 / len(observed)`.
(Note: `observed` cannot contain negative values)
If `observed` is matrix, conduct Pearson's independence test on the
input contingency matrix, which cannot contain negative entries or
columns or rows that sum up to 0.
If `observed` is an RDD of LabeledPoint, conduct Pearson's independence
test for every feature against the label across the input RDD.
For each feature, the (feature, label) pairs are converted into a
contingency matrix for which the chi-squared statistic is computed.
All label and feature values must be categorical.
:param observed: it could be a vector containing the observed categorical
counts/relative frequencies, or the contingency matrix
(containing either counts or relative frequencies),
or an RDD of LabeledPoint containing the labeled dataset
with categorical features. Real-valued features will be
treated as categorical for each distinct value.
:param expected: Vector containing the expected categorical counts/relative
frequencies. `expected` is rescaled if the `expected` sum
differs from the `observed` sum.
:return: ChiSquaredTest object containing the test statistic, degrees
of freedom, p-value, the method used, and the null hypothesis.
```
Author: Davies Liu <davies@databricks.com>
Closes#3091 from davies/his and squashes the following commits:
145d16c [Davies Liu] address comments
0ab0764 [Davies Liu] fix float
5097d54 [Davies Liu] add Hypothesis test Python API
This PR simplify serializer, always use batched serializer (AutoBatchedSerializer as default), even batch size is 1.
Author: Davies Liu <davies@databricks.com>
This patch had conflicts when merged, resolved by
Committer: Josh Rosen <joshrosen@databricks.com>
Closes#2920 from davies/fix_autobatch and squashes the following commits:
e544ef9 [Davies Liu] revert unrelated change
6880b14 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
1d557fc [Davies Liu] fix tests
8180907 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
76abdce [Davies Liu] clean up
53fa60b [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
d7ac751 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
2cc2497 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
b4292ce [Davies Liu] fix bug in master
d79744c [Davies Liu] recover hive tests
be37ece [Davies Liu] refactor
eb3938d [Davies Liu] refactor serializer in scala
8d77ef2 [Davies Liu] simplify serializer, use AutoBatchedSerializer by default.
Register MLlib's Vector as a SQL user-defined type (UDT) in both Scala and Python. With this PR, we can easily map a RDD[LabeledPoint] to a SchemaRDD, and then select columns or save to a Parquet file. Examples in Scala/Python are attached. The Scala code was copied from jkbradley.
~~This PR contains the changes from #3068 . I will rebase after #3068 is merged.~~
marmbrus jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#3070 from mengxr/SPARK-3573 and squashes the following commits:
3a0b6e5 [Xiangrui Meng] organize imports
236f0a0 [Xiangrui Meng] register vector as UDT and provide dataset examples
Following #2919, this PR adds Python UDT (for internal use only) with tests under "pyspark.tests". Before `SQLContext.applySchema`, we check whether we need to convert user-type instances into SQL recognizable data. In the current implementation, a Python UDT must be paired with a Scala UDT for serialization on the JVM side. A following PR will add VectorUDT in MLlib for both Scala and Python.
marmbrus jkbradley davies
Author: Xiangrui Meng <meng@databricks.com>
Closes#3068 from mengxr/SPARK-4192-sql and squashes the following commits:
acff637 [Xiangrui Meng] merge master
dba5ea7 [Xiangrui Meng] only use pyClass for Python UDT output sqlType as well
2c9d7e4 [Xiangrui Meng] move import to global setup; update needsConversion
7c4a6a9 [Xiangrui Meng] address comments
75223db [Xiangrui Meng] minor update
f740379 [Xiangrui Meng] remove UDT from default imports
e98d9d0 [Xiangrui Meng] fix py style
4e84fce [Xiangrui Meng] remove local hive tests and add more tests
39f19e0 [Xiangrui Meng] add tests
b7f666d [Xiangrui Meng] add Python UDT
This patch will try to infer schema for RDD which has empty value (None, [], {}) in the first row. It will try first 100 rows and merge the types into schema, also merge fields of StructType together. If there is still NullType in schema, then it will show an warning, tell user to try with sampling.
If sampling is presented, it will infer schema from all the rows after sampling.
Also, add samplingRatio for jsonFile() and jsonRDD()
Author: Davies Liu <davies.liu@gmail.com>
Author: Davies Liu <davies@databricks.com>
Closes#2716 from davies/infer and squashes the following commits:
e678f6d [Davies Liu] Merge branch 'master' of github.com:apache/spark into infer
34b5c63 [Davies Liu] Merge branch 'master' of github.com:apache/spark into infer
567dc60 [Davies Liu] update docs
9767b27 [Davies Liu] Merge branch 'master' into infer
e48d7fb [Davies Liu] fix tests
29e94d5 [Davies Liu] let NullType inherit from PrimitiveType
ee5d524 [Davies Liu] Merge branch 'master' of github.com:apache/spark into infer
540d1d5 [Davies Liu] merge fields for StructType
f93fd84 [Davies Liu] add more tests
3603e00 [Davies Liu] take more rows to infer schema, or infer the schema by sampling the RDD
The current way of seed distribution makes the random sequences from partition i and i+1 offset by 1.
~~~
In [14]: import random
In [15]: r1 = random.Random(10)
In [16]: r1.randint(0, 1)
Out[16]: 1
In [17]: r1.random()
Out[17]: 0.4288890546751146
In [18]: r1.random()
Out[18]: 0.5780913011344704
In [19]: r2 = random.Random(10)
In [20]: r2.randint(0, 1)
Out[20]: 1
In [21]: r2.randint(0, 1)
Out[21]: 0
In [22]: r2.random()
Out[22]: 0.5780913011344704
~~~
Note: The new tests are not for this bug fix.
Author: Xiangrui Meng <meng@databricks.com>
Closes#3010 from mengxr/SPARK-4148 and squashes the following commits:
869ae4b [Xiangrui Meng] move tests tests.py
c1bacd9 [Xiangrui Meng] fix seed distribution and add some tests for rdd.sample
- Adds optional precision and scale to Spark SQL's decimal type, which behave similarly to those in Hive 13 (https://cwiki.apache.org/confluence/download/attachments/27362075/Hive_Decimal_Precision_Scale_Support.pdf)
- Replaces our internal representation of decimals with a Decimal class that can store small values in a mutable Long, saving memory in this situation and letting some operations happen directly on Longs
This is still marked WIP because there are a few TODOs, but I'll remove that tag when done.
Author: Matei Zaharia <matei@databricks.com>
Closes#2983 from mateiz/decimal-1 and squashes the following commits:
35e6b02 [Matei Zaharia] Fix issues after merge
227f24a [Matei Zaharia] Review comments
31f915e [Matei Zaharia] Implement Davies's suggestions in Python
eb84820 [Matei Zaharia] Support reading/writing decimals as fixed-length binary in Parquet
4dc6bae [Matei Zaharia] Fix decimal support in PySpark
d1d9d68 [Matei Zaharia] Fix compile error and test issues after rebase
b28933d [Matei Zaharia] Support decimal precision/scale in Hive metastore
2118c0d [Matei Zaharia] Some test and bug fixes
81db9cb [Matei Zaharia] Added mutable Decimal that will be more efficient for small precisions
7af0c3b [Matei Zaharia] Add optional precision and scale to DecimalType, but use Unlimited for now
ec0a947 [Matei Zaharia] Make the result of AVG on Decimals be Decimal, not Double
Add `metadata: Metadata` to `StructField` to store extra information of columns. `Metadata` is a simple wrapper over `Map[String, Any]` with value types restricted to Boolean, Long, Double, String, Metadata, and arrays of those types. SerDe is via JSON.
Metadata is preserved through simple operations like `SELECT`.
marmbrus liancheng
Author: Xiangrui Meng <meng@databricks.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#2701 from mengxr/structfield-metadata and squashes the following commits:
dedda56 [Xiangrui Meng] merge remote
5ef930a [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata
c35203f [Xiangrui Meng] Merge pull request #1 from marmbrus/pr/2701
886b85c [Michael Armbrust] Expose Metadata and MetadataBuilder through the public scala and java packages.
589f314 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata
1e2abcf [Xiangrui Meng] change default value of metadata to None in python
611d3c2 [Xiangrui Meng] move metadata from Expr to NamedExpr
ddfcfad [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata
a438440 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata
4266f4d [Xiangrui Meng] add StructField.toString back for backward compatibility
3f49aab [Xiangrui Meng] remove StructField.toString
24a9f80 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata
473a7c5 [Xiangrui Meng] merge master
c9d7301 [Xiangrui Meng] organize imports
1fcbf13 [Xiangrui Meng] change metadata type in StructField for Scala/Java
60cc131 [Xiangrui Meng] add doc and header
60614c7 [Xiangrui Meng] add metadata
e42c452 [Xiangrui Meng] merge master
93518fb [Xiangrui Meng] support metadata in python
905bb89 [Xiangrui Meng] java conversions
618e349 [Xiangrui Meng] make tests work in scala
61b8e0f [Xiangrui Meng] merge master
7e5a322 [Xiangrui Meng] do not output metadata in StructField.toString
c41a664 [Xiangrui Meng] merge master
d8af0ed [Xiangrui Meng] move tests to SQLQuerySuite
67fdebb [Xiangrui Meng] add test on join
d65072e [Xiangrui Meng] remove Map.empty
367d237 [Xiangrui Meng] add test
c194d5e [Xiangrui Meng] add metadata field to StructField and Attribute
Then we can do `rdd.setName('abc').cache().count()`.
Author: Xiangrui Meng <meng@databricks.com>
Closes#3011 from mengxr/rdd-setname and squashes the following commits:
10d0d60 [Xiangrui Meng] update test
4ac3bbd [Xiangrui Meng] return self in rdd.setName
In #2241 hive-thriftserver is not enabled. This patch enable hive-thriftserver to support hive-0.13.1 by using a shim layer refer to #2241.
1 A light shim layer(code in sql/hive-thriftserver/hive-version) for each different hive version to handle api compatibility
2 New pom profiles "hive-default" and "hive-versions"(copy from #2241) to activate different hive version
3 SBT cmd for different version as follows:
hive-0.12.0 --- sbt/sbt -Phive,hadoop-2.3 -Phive-0.12.0 assembly
hive-0.13.1 --- sbt/sbt -Phive,hadoop-2.3 -Phive-0.13.1 assembly
4 Since hive-thriftserver depend on hive subproject, this patch should be merged with #2241 to enable hive-0.13.1 for hive-thriftserver
Author: wangfei <wangfei1@huawei.com>
Author: scwf <wangfei1@huawei.com>
Closes#2685 from scwf/shim-thriftserver1 and squashes the following commits:
f26f3be [wangfei] remove clean to save time
f5cac74 [wangfei] remove local hivecontext test
578234d [wangfei] use new shaded hive
18fb1ff [wangfei] exclude kryo in hive pom
fa21d09 [wangfei] clean package assembly/assembly
8a4daf2 [wangfei] minor fix
0d7f6cf [wangfei] address comments
f7c93ae [wangfei] adding build with hive 0.13 before running tests
bcf943f [wangfei] Merge branch 'master' of https://github.com/apache/spark into shim-thriftserver1
c359822 [wangfei] reuse getCommandProcessor in hiveshim
52674a4 [scwf] sql/hive included since examples depend on it
3529e98 [scwf] move hive module to hive profile
f51ff4e [wangfei] update and fix conflicts
f48d3a5 [scwf] Merge branch 'master' of https://github.com/apache/spark into shim-thriftserver1
41f727b [scwf] revert pom changes
13afde0 [scwf] fix small bug
4b681f4 [scwf] enable thriftserver in profile hive-0.13.1
0bc53aa [scwf] fixed when result filed is null
dfd1c63 [scwf] update run-tests to run hive-0.12.0 default now
c6da3ce [scwf] Merge branch 'master' of https://github.com/apache/spark into shim-thriftserver
7c66b8e [scwf] update pom according spark-2706
ae47489 [scwf] update and fix conflicts
Create several helper functions to call MLlib Java API, convert the arguments to Java type and convert return value to Python object automatically, this simplify serialization in MLlib Python API very much.
After this, the MLlib Python API does not need to deal with serialization details anymore, it's easier to add new API.
cc mengxr
Author: Davies Liu <davies@databricks.com>
Closes#2995 from davies/cleanup and squashes the following commits:
8fa6ec6 [Davies Liu] address comments
16b85a0 [Davies Liu] Merge branch 'master' of github.com:apache/spark into cleanup
43743e5 [Davies Liu] bugfix
731331f [Davies Liu] simplify serialization in MLlib Python API
Call Python UDF on ArrayType/MapType/PrimitiveType, the returnType can also be ArrayType/MapType/PrimitiveType.
For StructType, it will act as tuple (without attributes). If returnType is StructType, it also should be tuple.
Author: Davies Liu <davies@databricks.com>
Closes#2973 from davies/udf_array and squashes the following commits:
306956e [Davies Liu] Merge branch 'master' of github.com:apache/spark into udf_array
2c00e43 [Davies Liu] fix merge
11395fa [Davies Liu] Merge branch 'master' of github.com:apache/spark into udf_array
9df50a2 [Davies Liu] address comments
79afb4e [Davies Liu] type conversionfor python udf
Add json and python api for date type.
By using Pickle, `java.sql.Date` was serialized as calendar, and recognized in python as `datetime.datetime`.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#2901 from adrian-wang/spark3988 and squashes the following commits:
c51a24d [Daoyuan Wang] convert datetime to date
5670626 [Daoyuan Wang] minor line combine
f760d8e [Daoyuan Wang] fix indent
444f100 [Daoyuan Wang] fix a typo
1d74448 [Daoyuan Wang] fix scala style
8d7dd22 [Daoyuan Wang] add json and python api for date type
This change replaces usages of colt with commons-math3 equivalents, and makes some minor necessary adjustments to related code and tests to match.
Author: Sean Owen <sowen@cloudera.com>
Closes#2928 from srowen/SPARK-4022 and squashes the following commits:
61a232f [Sean Owen] Fix failure due to different sampling in JavaAPISuite.sample()
16d66b8 [Sean Owen] Simplify seeding with call to reseedRandomGenerator
a1a78e0 [Sean Owen] Use Well19937c
31c7641 [Sean Owen] Fix Python Poisson test by choosing a different seed; about 88% of seeds should work but 1 didn't, it seems
5c9c67f [Sean Owen] Additional test fixes from review
d8f88e0 [Sean Owen] Replace colt with commons-math3. Some tests do not pass yet.
In case of take() or exception in Python, python worker may exit before JVM read() all the response, then the write thread may raise "Connection reset" exception.
Python should always wait JVM to close the socket first.
cc JoshRosen This is a warm fix, or the tests will be flaky, sorry for that.
Author: Davies Liu <davies@databricks.com>
Closes#2941 from davies/fix_exit and squashes the following commits:
9d4d21e [Davies Liu] fix race
Added a method to Row to turn row into dict:
```
>>> row = Row(a=1)
>>> row.asDict()
{'a': 1}
```
Author: Davies Liu <davies@databricks.com>
Closes#2896 from davies/dict and squashes the following commits:
8d97366 [Davies Liu] convert Row into dict
KyroSerializer can not serialize customized class without registered explicitly, use it as default serializer in PySpark will introduce some regression in MLlib.
cc mengxr
Author: Davies Liu <davies@databricks.com>
Closes#2916 from davies/revert and squashes the following commits:
43eb6d3 [Davies Liu] donot use KyroSerializer as default serializer
After take(), maybe there are some garbage left in the socket, then next task assigned to this worker will hang because of corrupted data.
We should make sure the socket is clean before reuse it, write END_OF_STREAM at the end, and check it after read out all result from python.
Author: Davies Liu <davies.liu@gmail.com>
Author: Davies Liu <davies@databricks.com>
Closes#2838 from davies/fix_reuse and squashes the following commits:
8872914 [Davies Liu] fix tests
660875b [Davies Liu] fix bug while reuse worker after take()
Change maximum value for default seed during RDD sampling so that it is strictly less than 2 ** 32. This prevents a bug in the most recent version of NumPy, which cannot accept random seeds above this bound.
Adds an extra test that uses the default seed (instead of setting it manually, as in the docstrings).
mengxr
Author: freeman <the.freeman.lab@gmail.com>
Closes#2889 from freeman-lab/pyspark-sampling and squashes the following commits:
dc385ef [freeman] Change maximum value for default seed
https://issues.apache.org/jira/browse/SPARK-3770
We need access to the underlying latent user features from python. However, the userFeatures RDD from the MatrixFactorizationModel isn't accessible from the python bindings. I've added a method to the underlying scala class to turn the RDD[(Int, Array[Double])] to an RDD[String]. This is then accessed from the python recommendation.py
Author: Michelangelo D'Agostino <mdagostino@civisanalytics.com>
Closes#2636 from mdagost/mf_user_features and squashes the following commits:
c98f9e2 [Michelangelo D'Agostino] Added unit tests for userFeatures and productFeatures and merged master.
d5eadf8 [Michelangelo D'Agostino] Merge branch 'master' into mf_user_features
2481a2a [Michelangelo D'Agostino] Merged master and resolved conflict.
a6ffb96 [Michelangelo D'Agostino] Eliminated a function from our first approach to this problem that is no longer needed now that we added the fromTuple2RDD function.
2aa1bf8 [Michelangelo D'Agostino] Implemented a function called fromTuple2RDD in PythonMLLibAPI and used it to expose the MF userFeatures and productFeatures in python.
34cb2a2 [Michelangelo D'Agostino] A couple of lint cleanups and a comment.
cdd98e3 [Michelangelo D'Agostino] It's working now.
e1fbe5e [Michelangelo D'Agostino] Added scala function to stringify userFeatures for access in python.
Author: Holden Karau <holden@pigscanfly.ca>
Closes#2861 from holdenk/SPARK-4015-Documentation-in-the-streaming-context-references-non-existent-function and squashes the following commits:
081db8a [Holden Karau] fix pyspark streaming doc too
0e03863 [Holden Karau] replace awaitTransformation with awaitTermination
Convert the input rdd to RDD of Vector.
cc mengxr
Author: Davies Liu <davies@databricks.com>
Closes#2870 from davies/fix4023 and squashes the following commits:
1eac767 [Davies Liu] address comments
0871576 [Davies Liu] convert rdd into RDD of Vector
DecisionTree splits on continuous features by choosing an array of values from a subsample of the data.
Currently, it does not check for identical values in the subsample, so it could end up having multiple copies of the same split. In this PR, we choose splits for a continuous feature in 3 steps:
1. Sort sample values for this feature
2. Get number of occurrence of each distinct value
3. Iterate the value count array computed in step 2 to choose splits.
After find splits, `numSplits` and `numBins` in metadata will be updated.
CC: mengxr manishamde jkbradley, please help me review this, thanks.
Author: Qiping Li <liqiping1991@gmail.com>
Author: chouqin <liqiping1991@gmail.com>
Author: liqi <liqiping1991@gmail.com>
Author: qiping.lqp <qiping.lqp@alibaba-inc.com>
Closes#2780 from chouqin/dt-findsplits and squashes the following commits:
18d0301 [Qiping Li] check explicitly findsplits return distinct splits
8dc28ab [chouqin] remove blank lines
ffc920f [chouqin] adjust code based on comments and add more test cases
9857039 [chouqin] Merge branch 'master' of https://github.com/apache/spark into dt-findsplits
d353596 [qiping.lqp] fix pyspark doc test
9e64699 [Qiping Li] fix random forest unit test
3c72913 [Qiping Li] fix random forest unit test
092efcb [Qiping Li] fix bug
f69f47f [Qiping Li] fix bug
ab303a4 [Qiping Li] fix bug
af6dc97 [Qiping Li] fix bug
2a8267a [Qiping Li] fix bug
c339a61 [Qiping Li] fix bug
369f812 [Qiping Li] fix style
8f46af6 [Qiping Li] add comments and unit test
9e7138e [Qiping Li] Merge branch 'dt-findsplits' of https://github.com/chouqin/spark into dt-findsplits
1b25a35 [Qiping Li] Merge branch 'master' of https://github.com/apache/spark into dt-findsplits
0cd744a [liqi] fix bug
3652823 [Qiping Li] fix bug
af7cb79 [Qiping Li] Choose splits for continuous features in DecisionTree more adaptively
Having Python examples in Streaming Programming Guide.
Also add RecoverableNetworkWordCount example.
Author: Davies Liu <davies.liu@gmail.com>
Author: Davies Liu <davies@databricks.com>
Closes#2808 from davies/pyguide and squashes the following commits:
8d4bec4 [Davies Liu] update readme
26a7e37 [Davies Liu] fix format
3821c4d [Davies Liu] address comments, add missing file
7e4bb8a [Davies Liu] add Python examples in Streaming Programming Guide
In the current implementation it was possible for the reference to change after analysis.
Author: Michael Armbrust <michael@databricks.com>
Closes#2717 from marmbrus/pythonUdfResults and squashes the following commits:
da14879 [Michael Armbrust] Fix test
6343bcb [Michael Armbrust] add test
9533286 [Michael Armbrust] Correctly preserve the result attribute of python UDFs though transformations
Customized pickler should be registered before unpickling, but in executor, there is no way to register the picklers before run the tasks.
So, we need to register the picklers in the tasks itself, duplicate the javaToPython() and pythonToJava() in MLlib, call SerDe.initialize() before pickling or unpickling.
Author: Davies Liu <davies.liu@gmail.com>
Closes#2830 from davies/fix_pickle and squashes the following commits:
0c85fb9 [Davies Liu] revert the privacy change
6b94e15 [Davies Liu] use JavaConverters instead of JavaConversions
0f02050 [Davies Liu] hotfix: Customized pickler does not work in cluster
In the comment (Line 1083), it says: "Otherwise, interpolate the number of partitions we need to try, but overestimate it by 50%."
`(1.5 * num * partsScanned / buf.size).toInt` is the guess of "num of total partitions needed". In every iteration, we should consider the increment `(1.5 * num * partsScanned / buf.size).toInt - partsScanned`
Existing implementation 'exponentially' grows `partsScanned ` ( roughly: `x_{n+1} >= (1.5 + 1) x_n`)
This could be a performance problem. (unless this is the intended behavior)
Author: yingjieMiao <yingjie@42go.com>
Closes#2648 from yingjieMiao/rdd_take and squashes the following commits:
d758218 [yingjieMiao] scala style fix
a8e74bb [yingjieMiao] python style fix
4b6e777 [yingjieMiao] infix operator style fix
4391d3b [yingjieMiao] typo fix.
692f4e6 [yingjieMiao] cap numPartsToTry
c4483dc [yingjieMiao] style fix
1d2c410 [yingjieMiao] also change in rdd.py and AsyncRDD
d31ff7e [yingjieMiao] handle the edge case after 1 iteration
a2aa36b [yingjieMiao] RDD take method: overestimate too much
Sphinx documents contains a corrupted ReST format and have some warnings.
The purpose of this issue is same as https://issues.apache.org/jira/browse/SPARK-3773.
commit: 0e8203f4fb
output
```
$ cd ./python/docs
$ make clean html
rm -rf _build/*
sphinx-build -b html -d _build/doctrees . _build/html
Making output directory...
Running Sphinx v1.2.3
loading pickled environment... not yet created
building [html]: targets for 4 source files that are out of date
updating environment: 4 added, 0 changed, 0 removed
reading sources... [100%] pyspark.sql
/Users/<user>/MyRepos/Scala/spark/python/pyspark/mllib/feature.py:docstring of pyspark.mllib.feature.Word2VecModel.findSynonyms:4: WARNING: Field list ends without a blank line; unexpected unindent.
/Users/<user>/MyRepos/Scala/spark/python/pyspark/mllib/feature.py:docstring of pyspark.mllib.feature.Word2VecModel.transform:3: WARNING: Field list ends without a blank line; unexpected unindent.
/Users/<user>/MyRepos/Scala/spark/python/pyspark/sql.py:docstring of pyspark.sql:4: WARNING: Bullet list ends without a blank line; unexpected unindent.
looking for now-outdated files... none found
pickling environment... done
checking consistency... done
preparing documents... done
writing output... [100%] pyspark.sql
writing additional files... (12 module code pages) _modules/index search
copying static files... WARNING: html_static_path entry u'/Users/<user>/MyRepos/Scala/spark/python/docs/_static' does not exist
done
copying extra files... done
dumping search index... done
dumping object inventory... done
build succeeded, 4 warnings.
Build finished. The HTML pages are in _build/html.
```
Author: cocoatomo <cocoatomo77@gmail.com>
Closes#2766 from cocoatomo/issues/3909-sphinx-build-warnings and squashes the following commits:
2c7faa8 [cocoatomo] [SPARK-3909][PySpark][Doc] A corrupted format in Sphinx documents and building warnings
./python/run-tests search a Python 2.6 executable on PATH and use it if available.
When using Python 2.6, it is going to import unittest2 module which is not a standard library in Python 2.6, so it fails with ImportError.
Author: cocoatomo <cocoatomo77@gmail.com>
Closes#2759 from cocoatomo/issues/3867-unittest2-import-error and squashes the following commits:
f068eb5 [cocoatomo] [SPARK-3867] ./python/run-tests failed when it run with Python 2.6 and unittest2 is not installed
Use AutoBatchedSerializer by default, which will choose the proper batch size based on size of serialized objects, let the size of serialized batch fall in into [64k - 640k].
In JVM, the serializer will also track the objects in batch to figure out duplicated objects, larger batch may cause OOM in JVM.
Author: Davies Liu <davies.liu@gmail.com>
Closes#2740 from davies/batchsize and squashes the following commits:
52cdb88 [Davies Liu] update docs
185f2b9 [Davies Liu] use AutoBatchedSerializer by default
This PR uses JSON instead of `toString` to serialize `DataType`s. The latter is not only hard to parse but also flaky in many cases.
Since we already write schema information to Parquet metadata in the old style, we have to reserve the old `DataType` parser and ensure downward compatibility. The old parser is now renamed to `CaseClassStringParser` and moved into `object DataType`.
JoshRosen davies Please help review PySpark related changes, thanks!
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#2563 from liancheng/datatype-to-json and squashes the following commits:
fc92eb3 [Cheng Lian] Reverts debugging code, simplifies primitive type JSON representation
438c75f [Cheng Lian] Refactors PySpark DataType JSON SerDe per comments
6b6387b [Cheng Lian] Removes debugging code
6a3ee3a [Cheng Lian] Addresses per review comments
dc158b5 [Cheng Lian] Addresses PEP8 issues
99ab4ee [Cheng Lian] Adds compatibility est case for Parquet type conversion
a983a6c [Cheng Lian] Adds PySpark support
f608c6e [Cheng Lian] De/serializes DataType objects from/to JSON
Retire Epydoc, use Sphinx to generate API docs.
Refine Sphinx docs, also convert some docstrings into Sphinx style.
It looks like:
Author: Davies Liu <davies.liu@gmail.com>
Closes#2689 from davies/docs and squashes the following commits:
bf4a0a5 [Davies Liu] fix links
3fb1572 [Davies Liu] fix _static in jekyll
65a287e [Davies Liu] fix scripts and logo
8524042 [Davies Liu] Merge branch 'master' of github.com:apache/spark into docs
d5b874a [Davies Liu] Merge branch 'master' of github.com:apache/spark into docs
4bc1c3c [Davies Liu] refactor
746d0b6 [Davies Liu] @param -> :param
240b393 [Davies Liu] replace epydoc with sphinx doc
mengxr
Added PySpark support for Word2Vec
Change list
(1) PySpark support for Word2Vec
(2) SerDe support of string sequence both on python side and JVM side
(3) Test for SerDe of string sequence on JVM side
Author: Liquan Pei <liquanpei@gmail.com>
Closes#2356 from Ishiihara/Word2Vec-python and squashes the following commits:
476ea34 [Liquan Pei] style fixes
b13a0b9 [Liquan Pei] resolve merge conflicts and minor fixes
8671eba [Liquan Pei] Merge remote-tracking branch 'upstream/master' into Word2Vec-python
daf88a6 [Liquan Pei] modification according to feedback
a73fa19 [Liquan Pei] clean up
3d8007b [Liquan Pei] fix findSynonyms for vector
1bdcd2e [Liquan Pei] minor fixes
cdef9f4 [Liquan Pei] add missing comments
b7447eb [Liquan Pei] modify according to feedback
b9a7383 [Liquan Pei] cache words RDD in fit
89490bf [Liquan Pei] add tests and Word2VecModelWrapper
78bbb53 [Liquan Pei] use pickle for seq string SerDe
a264b08 [Liquan Pei] Merge remote-tracking branch 'upstream/master' into Word2Vec-python
ca1e5ff [Liquan Pei] fix test
68e7276 [Liquan Pei] minor style fixes
48d5e72 [Liquan Pei] Functionality improvement
0ad3ac1 [Liquan Pei] minor fix
c867fdf [Liquan Pei] add Word2Vec to pyspark
When building Sphinx documents for PySpark, we have 12 warnings.
Their causes are almost docstrings in broken ReST format.
To reproduce this issue, we should run following commands on the commit: 6e27cb630d.
```bash
$ cd ./python/docs
$ make clean html
...
/Users/<user>/MyRepos/Scala/spark/python/pyspark/__init__.py:docstring of pyspark.SparkContext.sequenceFile:4: ERROR: Unexpected indentation.
/Users/<user>/MyRepos/Scala/spark/python/pyspark/__init__.py:docstring of pyspark.RDD.saveAsSequenceFile:4: ERROR: Unexpected indentation.
/Users/<user>/MyRepos/Scala/spark/python/pyspark/mllib/classification.py:docstring of pyspark.mllib.classification.LogisticRegressionWithSGD.train:14: ERROR: Unexpected indentation.
/Users/<user>/MyRepos/Scala/spark/python/pyspark/mllib/classification.py:docstring of pyspark.mllib.classification.LogisticRegressionWithSGD.train:16: WARNING: Definition list ends without a blank line; unexpected unindent.
/Users/<user>/MyRepos/Scala/spark/python/pyspark/mllib/classification.py:docstring of pyspark.mllib.classification.LogisticRegressionWithSGD.train:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/Users/<user>/MyRepos/Scala/spark/python/pyspark/mllib/classification.py:docstring of pyspark.mllib.classification.SVMWithSGD.train:14: ERROR: Unexpected indentation.
/Users/<user>/MyRepos/Scala/spark/python/pyspark/mllib/classification.py:docstring of pyspark.mllib.classification.SVMWithSGD.train:16: WARNING: Definition list ends without a blank line; unexpected unindent.
/Users/<user>/MyRepos/Scala/spark/python/pyspark/mllib/classification.py:docstring of pyspark.mllib.classification.SVMWithSGD.train:17: WARNING: Block quote ends without a blank line; unexpected unindent.
/Users/<user>/MyRepos/Scala/spark/python/docs/pyspark.mllib.rst:50: WARNING: missing attribute mentioned in :members: or __all__: module pyspark.mllib.regression, attribute RidgeRegressionModelLinearRegressionWithSGD
/Users/<user>/MyRepos/Scala/spark/python/pyspark/mllib/tree.py:docstring of pyspark.mllib.tree.DecisionTreeModel.predict:3: ERROR: Unexpected indentation.
...
checking consistency... /Users/<user>/MyRepos/Scala/spark/python/docs/modules.rst:: WARNING: document isn't included in any toctree
...
copying static files... WARNING: html_static_path entry u'/Users/<user>/MyRepos/Scala/spark/python/docs/_static' does not exist
...
build succeeded, 12 warnings.
```
Author: cocoatomo <cocoatomo77@gmail.com>
Closes#2653 from cocoatomo/issues/3773-sphinx-build-warnings and squashes the following commits:
6f65661 [cocoatomo] [SPARK-3773][PySpark][Doc] Sphinx build warning
This patch try to speed up tests of PySpark, re-use the SparkContext in tests.py and mllib/tests.py to reduce the overhead of create SparkContext, remove some test cases, which did not make sense. It also improve the performance of some cases, such as MergerTests and SortTests.
before this patch:
real 21m27.320s
user 4m42.967s
sys 0m17.343s
after this patch:
real 9m47.541s
user 2m12.947s
sys 0m14.543s
It almost cut the time by half.
Author: Davies Liu <davies.liu@gmail.com>
Closes#2646 from davies/tests and squashes the following commits:
c54de60 [Davies Liu] revert change about memory limit
6a2a4b0 [Davies Liu] refactor of tests, speedup 100%
Add a toString method to GeneralizedLinearModel, also change `__str__` to `__repr__` for some classes, to provide better message in repr.
This PR is based on #1388, thanks to sryza!
closes#1388
Author: Sandy Ryza <sandy@cloudera.com>
Author: Davies Liu <davies.liu@gmail.com>
Closes#2625 from davies/string and squashes the following commits:
3544aad [Davies Liu] fix LinearModel
0bcd642 [Davies Liu] Merge branch 'sandy-spark-2461' of github.com:sryza/spark
1ce5c2d [Sandy Ryza] __repr__ back to __str__ in a couple places
aa9e962 [Sandy Ryza] Switch __str__ to __repr__
a0c5041 [Sandy Ryza] Add labels back in
1aa17f5 [Sandy Ryza] Match existing conventions
fac1bc4 [Sandy Ryza] Fix PEP8 error
f7b58ed [Sandy Ryza] SPARK-2461. Add a toString method to GeneralizedLinearModel
1. broadcast is triggle unexpected
2. fd is leaked in JVM (also leak in parallelize())
3. broadcast is not unpersisted in JVM after RDD is not be used any more.
cc JoshRosen , sorry for these stupid bugs.
Author: Davies Liu <davies.liu@gmail.com>
Closes#2603 from davies/fix_broadcast and squashes the following commits:
080a743 [Davies Liu] fix bugs in broadcast large closure of RDD
DecisionTreeRunner functionality additions:
* Allow user to pass in a test dataset
* Do not print full model if the model is too large.
As part of this, modify DecisionTreeModel and RandomForestModel to allow printing less info. Proposed updates:
* toString: prints model summary
* toDebugString: prints full model (named after RDD.toDebugString)
Similar update to Python API:
* __repr__() now prints a model summary
* toDebugString() now prints the full model
CC: mengxr chouqin manishamde codedeft Small update (whomever can take a look). Thanks!
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#2604 from jkbradley/dtrunner-update and squashes the following commits:
b2b3c60 [Joseph K. Bradley] re-added python sql doc test, temporarily removed before
07b1fae [Joseph K. Bradley] repr() now prints a model summary toDebugString() now prints the full model
1d0d93d [Joseph K. Bradley] Updated DT and RF to print less when toString is called. Added toDebugString for verbose printing.
22eac8c [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
e007a95 [Joseph K. Bradley] Updated DecisionTreeRunner to accept a test dataset.
1. doc updates
2. simple checks on vector dimensions
3. use column major for matrices
davies jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#2548 from mengxr/mllib-py-clean and squashes the following commits:
6dce2df [Xiangrui Meng] address comments
116b5db [Xiangrui Meng] use np.dot instead of array.dot
75f2fcc [Xiangrui Meng] fix python style
fefce00 [Xiangrui Meng] better check of vector size with more tests
067ef71 [Xiangrui Meng] majored -> major
ef853f9 [Xiangrui Meng] update python linalg api and small fixes
Currently, the schema of object in ArrayType or MapType is attached lazily, it will have better performance but introduce issues while serialization or accessing nested objects.
This patch will apply schema to the objects of ArrayType or MapType immediately when accessing them, will be a little bit slower, but much robust.
Author: Davies Liu <davies.liu@gmail.com>
Closes#2526 from davies/nested and squashes the following commits:
2399ae5 [Davies Liu] fix serialization of List and Map in SchemaRDD
leftOuterJoin and rightOuterJoin are already implemented. This patch adds fullOuterJoin.
Author: Aaron Staple <aaron.staple@gmail.com>
Closes#1395 from staple/SPARK-546 and squashes the following commits:
1f5595c [Aaron Staple] Fix python style
7ac0aa9 [Aaron Staple] [SPARK-546] Add full outer join to RDD and DStream.
3b5d137 [Aaron Staple] In JavaPairDStream, make class tag specification in rightOuterJoin consistent with other functions.
31f2956 [Aaron Staple] Fix left outer join documentation comments.
function.func_code.co_names has all the names used in the function, including name of attributes. It will pickle some unnecessary globals if there is a global having the same name with attribute (in co_names).
There is a regression introduced by #2144, revert part of changes in that PR.
cc JoshRosen
Author: Davies Liu <davies.liu@gmail.com>
Closes#2522 from davies/globals and squashes the following commits:
dfbccf5 [Davies Liu] fix bug while pickle globals of function
Python modules added through addPyFile should take precedence over system modules.
This patch put the path for user added module in the front of sys.path (just after '').
Author: Davies Liu <davies.liu@gmail.com>
Closes#2492 from davies/path and squashes the following commits:
4a2af78 [Davies Liu] fix tests
f7ff4da [Davies Liu] ad license header
6b0002f [Davies Liu] add tests
c16c392 [Davies Liu] put addPyFile in front of sys.path
Author: Matthew Farrellee <matt@redhat.com>
Closes#2467 from mattf/master-pyspark-remove-numslices-from-tests and squashes the following commits:
c49a87b [Matthew Farrellee] [PySpark] remove unnecessary use of numSlices from pyspark tests
Fix the issue when applySchema() to an RDD of Row.
Also add type mapping for BinaryType.
Author: Davies Liu <davies.liu@gmail.com>
Closes#2448 from davies/row and squashes the following commits:
dd220cf [Davies Liu] fix test
3f3f188 [Davies Liu] add more test
f559746 [Davies Liu] add tests, fix serialization
9688fd2 [Davies Liu] support applySchema to RDD of Row
Currently, we serialize the data between JVM and Python case by case manually, this cannot scale to support so many APIs in MLlib.
This patch will try to address this problem by serialize the data using pickle protocol, using Pyrolite library to serialize/deserialize in JVM. Pickle protocol can be easily extended to support customized class.
All the modules are refactored to use this protocol.
Known issues: There will be some performance regression (both CPU and memory, the serialized data increased)
Author: Davies Liu <davies.liu@gmail.com>
Closes#2378 from davies/pickle_mllib and squashes the following commits:
dffbba2 [Davies Liu] Merge branch 'master' of github.com:apache/spark into pickle_mllib
810f97f [Davies Liu] fix equal of matrix
032cd62 [Davies Liu] add more type check and conversion for user_product
bd738ab [Davies Liu] address comments
e431377 [Davies Liu] fix cache of rdd, refactor
19d0967 [Davies Liu] refactor Picklers
2511e76 [Davies Liu] cleanup
1fccf1a [Davies Liu] address comments
a2cc855 [Davies Liu] fix tests
9ceff73 [Davies Liu] test size of serialized Rating
44e0551 [Davies Liu] fix cache
a379a81 [Davies Liu] fix pickle array in python2.7
df625c7 [Davies Liu] Merge commit '154d141' into pickle_mllib
154d141 [Davies Liu] fix autobatchedpickler
44736d7 [Davies Liu] speed up pickling array in Python 2.7
e1d1bfc [Davies Liu] refactor
708dc02 [Davies Liu] fix tests
9dcfb63 [Davies Liu] fix style
88034f0 [Davies Liu] rafactor, address comments
46a501e [Davies Liu] choose batch size automatically
df19464 [Davies Liu] memorize the module and class name during pickleing
f3506c5 [Davies Liu] Merge branch 'master' into pickle_mllib
722dd96 [Davies Liu] cleanup _common.py
0ee1525 [Davies Liu] remove outdated tests
b02e34f [Davies Liu] remove _common.py
84c721d [Davies Liu] Merge branch 'master' into pickle_mllib
4d7963e [Davies Liu] remove muanlly serialization
6d26b03 [Davies Liu] fix tests
c383544 [Davies Liu] classification
f2a0856 [Davies Liu] mllib/regression
d9f691f [Davies Liu] mllib/util
cccb8b1 [Davies Liu] mllib/tree
8fe166a [Davies Liu] Merge branch 'pickle' into pickle_mllib
aa2287e [Davies Liu] random
f1544c4 [Davies Liu] refactor clustering
52d1350 [Davies Liu] use new protocol in mllib/stat
b30ef35 [Davies Liu] use pickle to serialize data for mllib/recommendation
f44f771 [Davies Liu] enable tests about array
3908f5c [Davies Liu] Merge branch 'master' into pickle
c77c87b [Davies Liu] cleanup debugging code
60e4e2f [Davies Liu] support unpickle array.array for Python 2.6
Py4j can not handle large string efficiently, so we should use broadcast for large closure automatically. (Broadcast use local filesystem to pass through data).
Author: Davies Liu <davies.liu@gmail.com>
Closes#2417 from davies/command and squashes the following commits:
fbf4e97 [Davies Liu] bugfix
aefd508 [Davies Liu] use broadcast automatically for large closure
Using Sphinx to generate API docs for PySpark.
requirement: Sphinx
```
$ cd python/docs/
$ make html
```
The generated API docs will be located at python/docs/_build/html/index.html
It can co-exists with those generated by Epydoc.
This is the first working version, after merging in, then we can continue to improve it and replace the epydoc finally.
Author: Davies Liu <davies.liu@gmail.com>
Closes#2292 from davies/sphinx and squashes the following commits:
425a3b1 [Davies Liu] cleanup
1573298 [Davies Liu] move docs to python/docs/
5fe3903 [Davies Liu] Merge branch 'master' into sphinx
9468ab0 [Davies Liu] fix makefile
b408f38 [Davies Liu] address all comments
e2ccb1b [Davies Liu] update name and version
9081ead [Davies Liu] generate PySpark API docs using Sphinx
SchemaRDD overrides RDD functions, including collect, count, and take, with optimized versions making use of the query optimizer. The java and python interface classes wrapping SchemaRDD need to ensure the optimized versions are called as well. This patch overrides relevant calls in the python and java interfaces with optimized versions.
Adds a new Row serialization pathway between python and java, based on JList[Array[Byte]] versus the existing RDD[Array[Byte]]. I wasn’t overjoyed about doing this, but I noticed that some QueryPlans implement optimizations in executeCollect(), which outputs an Array[Row] rather than the typical RDD[Row] that can be shipped to python using the existing serialization code. To me it made sense to ship the Array[Row] over to python directly instead of converting it back to an RDD[Row] just for the purpose of sending the Rows to python using the existing serialization code.
Author: Aaron Staple <aaron.staple@gmail.com>
Closes#1592 from staple/SPARK-2314 and squashes the following commits:
89ff550 [Aaron Staple] Merge with master.
6bb7b6c [Aaron Staple] Fix typo.
b56d0ac [Aaron Staple] [SPARK-2314][SQL] Override count in JavaSchemaRDD, forwarding to SchemaRDD's count.
0fc9d40 [Aaron Staple] Fix comment typos.
f03cdfa [Aaron Staple] [SPARK-2314][SQL] Override collect and take in sql.py, forwarding to SchemaRDD's collect.
Added missing rdd.distinct(numPartitions) and associated tests
Author: Matthew Farrellee <matt@redhat.com>
Closes#2383 from mattf/SPARK-3519 and squashes the following commits:
30b837a [Matthew Farrellee] Combine test cases to save on JVM startups
6bc4a2c [Matthew Farrellee] [SPARK-3519] add distinct(n) to SchemaRDD in PySpark
7a17f2b [Matthew Farrellee] [SPARK-3519] add distinct(n) to PySpark
Also made some cosmetic cleanups.
Author: Aaron Staple <aaron.staple@gmail.com>
Closes#2385 from staple/SPARK-1087 and squashes the following commits:
7b3bb13 [Aaron Staple] Address review comments, cosmetic cleanups.
10ba6e1 [Aaron Staple] [SPARK-1087] Move python traceback utilities into new traceback_utils.py file.
Pyrolite can not unpickle array.array which pickled by Python 2.6, this patch fix it by extend Pyrolite.
There is a bug in Pyrolite when unpickle array of float/double, this patch workaround it by reverse the endianness for float/double. This workaround should be removed after Pyrolite have a new release to fix this issue.
I had send an PR to Pyrolite to fix it: https://github.com/irmen/Pyrolite/pull/11
Author: Davies Liu <davies.liu@gmail.com>
Closes#2365 from davies/pickle and squashes the following commits:
f44f771 [Davies Liu] enable tests about array
3908f5c [Davies Liu] Merge branch 'master' into pickle
c77c87b [Davies Liu] cleanup debugging code
60e4e2f [Davies Liu] support unpickle array.array for Python 2.6
Added minInstancesPerNode, minInfoGain params to:
* DecisionTreeRunner.scala example
* Python API (tree.py)
Also:
* Fixed typo in tree suite test "do not choose split that does not satisfy min instance per node requirements"
* small style fixes
CC: mengxr
Author: qiping.lqp <qiping.lqp@alibaba-inc.com>
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Author: chouqin <liqiping1991@gmail.com>
Closes#2349 from jkbradley/chouqin-dt-preprune and squashes the following commits:
61b2e72 [Joseph K. Bradley] Added max of 10GB for maxMemoryInMB in Strategy.
a95e7c8 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into chouqin-dt-preprune
95c479d [Joseph K. Bradley] * Fixed typo in tree suite test "do not choose split that does not satisfy min instance per node requirements" * small style fixes
e2628b6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into chouqin-dt-preprune
19b01af [Joseph K. Bradley] Merge remote-tracking branch 'chouqin/dt-preprune' into chouqin-dt-preprune
f1d11d1 [chouqin] fix typo
c7ebaf1 [chouqin] fix typo
39f9b60 [chouqin] change edge `minInstancesPerNode` to 2 and add one more test
c6e2dfc [Joseph K. Bradley] Added minInstancesPerNode and minInfoGain parameters to DecisionTreeRunner.scala and to Python API in tree.py
0278a11 [chouqin] remove `noSplit` and set `Predict` private to tree
d593ec7 [chouqin] fix docs and change minInstancesPerNode to 1
efcc736 [qiping.lqp] fix bug
10b8012 [qiping.lqp] fix style
6728fad [qiping.lqp] minor fix: remove empty lines
bb465ca [qiping.lqp] Merge branch 'master' of https://github.com/apache/spark into dt-preprune
cadd569 [qiping.lqp] add api docs
46b891f [qiping.lqp] fix bug
e72c7e4 [qiping.lqp] add comments
845c6fa [qiping.lqp] fix style
f195e83 [qiping.lqp] fix style
987cbf4 [qiping.lqp] fix bug
ff34845 [qiping.lqp] separate calculation of predict of node from calculation of info gain
ac42378 [qiping.lqp] add min info gain and min instances per node parameters in decision tree
Aggregate the number of bytes spilled into disks during aggregation or sorting, show them in Web UI.
This patch is blocked by SPARK-3465. (It includes a fix for that).
Author: Davies Liu <davies.liu@gmail.com>
Closes#2336 from davies/metrics and squashes the following commits:
e37df38 [Davies Liu] remove outdated comments
1245eb7 [Davies Liu] remove the temporary fix
ebd2f43 [Davies Liu] Merge branch 'master' into metrics
7e4ad04 [Davies Liu] Merge branch 'master' into metrics
fbe9029 [Davies Liu] show spilled bytes in Python in web ui
Reuse Python worker to avoid the overhead of fork() Python process for each tasks. It also tracks the broadcasts for each worker, avoid sending repeated broadcasts.
This can reduce the time for dummy task from 22ms to 13ms (-40%). It can help to reduce the latency for Spark Streaming.
For a job with broadcast (43M after compress):
```
b = sc.broadcast(set(range(30000000)))
print sc.parallelize(range(24000), 100).filter(lambda x: x in b.value).count()
```
It will finish in 281s without reused worker, and it will finish in 65s with reused worker(4 CPUs). After reusing the worker, it can save about 9 seconds for transfer and deserialize the broadcast for each tasks.
It's enabled by default, could be disabled by `spark.python.worker.reuse = false`.
Author: Davies Liu <davies.liu@gmail.com>
Closes#2259 from davies/reuse-worker and squashes the following commits:
f11f617 [Davies Liu] Merge branch 'master' into reuse-worker
3939f20 [Davies Liu] fix bug in serializer in mllib
cf1c55e [Davies Liu] address comments
3133a60 [Davies Liu] fix accumulator with reused worker
760ab1f [Davies Liu] do not reuse worker if there are any exceptions
7abb224 [Davies Liu] refactor: sychronized with itself
ac3206e [Davies Liu] renaming
8911f44 [Davies Liu] synchronized getWorkerBroadcasts()
6325fc1 [Davies Liu] bugfix: bid >= 0
e0131a2 [Davies Liu] fix name of config
583716e [Davies Liu] only reuse completed and not interrupted worker
ace2917 [Davies Liu] kill python worker after timeout
6123d0f [Davies Liu] track broadcasts for each worker
8d2f08c [Davies Liu] reuse python worker
Currently, SchemaRDD._jschema_rdd is SchemaRDD, the Scala API (coalesce(), repartition()) can not been called in Python easily, there is no way to specify the implicit parameter `ord`. The _jrdd is an JavaRDD, so _jschema_rdd should also be JavaSchemaRDD.
In this patch, change _schema_rdd to JavaSchemaRDD, also added an assert for it. If some methods are missing from JavaSchemaRDD, then it's called by _schema_rdd.baseSchemaRDD().xxx().
BTW, Do we need JavaSQLContext?
Author: Davies Liu <davies.liu@gmail.com>
Closes#2369 from davies/fix_schemardd and squashes the following commits:
abee159 [Davies Liu] use JavaSchemaRDD as SchemaRDD._jschema_rdd
After this patch, we can run PySpark in PyPy (testing with PyPy 2.3.1 in Mac 10.9), for example:
```
PYSPARK_PYTHON=pypy ./bin/spark-submit wordcount.py
```
The performance speed up will depend on work load (from 20% to 3000%). Here are some benchmarks:
Job | CPython 2.7 | PyPy 2.3.1 | Speed up
------- | ------------ | ------------- | -------
Word Count | 41s | 15s | 2.7x
Sort | 46s | 44s | 1.05x
Stats | 174s | 3.6s | 48x
Here is the code used for benchmark:
```python
rdd = sc.textFile("text")
def wordcount():
rdd.flatMap(lambda x:x.split('/'))\
.map(lambda x:(x,1)).reduceByKey(lambda x,y:x+y).collectAsMap()
def sort():
rdd.sortBy(lambda x:x, 1).count()
def stats():
sc.parallelize(range(1024), 20).flatMap(lambda x: xrange(5024)).stats()
```
Author: Davies Liu <davies.liu@gmail.com>
Closes#2144 from davies/pypy and squashes the following commits:
9aed6c5 [Davies Liu] use protocol 2 in CloudPickle
4bc1f04 [Davies Liu] refactor
b20ab3a [Davies Liu] pickle sys.stdout and stderr in portable way
3ca2351 [Davies Liu] Merge branch 'master' into pypy
fae8b19 [Davies Liu] improve attrgetter, add tests
591f830 [Davies Liu] try to run tests with PyPy in run-tests
c8d62ba [Davies Liu] cleanup
f651fd0 [Davies Liu] fix tests using array with PyPy
1b98fb3 [Davies Liu] serialize itemgetter/attrgetter in portable ways
3c1dbfe [Davies Liu] Merge branch 'master' into pypy
42fb5fa [Davies Liu] Merge branch 'master' into pypy
cb2d724 [Davies Liu] fix tests
9986692 [Davies Liu] Merge branch 'master' into pypy
25b4ca7 [Davies Liu] support PyPy
Author: RJ Nowling <rnowling@gmail.com>
Closes#2370 from rnowling/python_rdd_docstrings and squashes the following commits:
5230574 [RJ Nowling] Add blank line so that Python RDD.top() docstring renders correctly
str is much efficient than unicode (both CPU and memory), it'e better to use str in textFileRDD. In order to keep compatibility, use unicode by default. (Maybe change it in the future).
use_unicode=True:
daviesliudm:~/work/spark$ time python wc.py
(u'./universe/spark/sql/core/target/java/org/apache/spark/sql/execution/ExplainCommand$.java', 7776)
real 2m8.298s
user 0m0.185s
sys 0m0.064s
use_unicode=False
daviesliudm:~/work/spark$ time python wc.py
('./universe/spark/sql/core/target/java/org/apache/spark/sql/execution/ExplainCommand$.java', 7776)
real 1m26.402s
user 0m0.182s
sys 0m0.062s
We can see that it got 32% improvement!
Author: Davies Liu <davies.liu@gmail.com>
Closes#1951 from davies/unicode and squashes the following commits:
8352d57 [Davies Liu] update version number
a286f2f [Davies Liu] rollback loads()
85246e5 [Davies Liu] add docs for use_unicode
a0295e1 [Davies Liu] add an option to use str in textFile()
allow for best practice code,
```
try:
sc = SparkContext()
app(sc)
finally:
sc.stop()
```
to be written using a "with" statement,
```
with SparkContext() as sc:
app(sc)
```
Author: Matthew Farrellee <matt@redhat.com>
Closes#2335 from mattf/SPARK-3458 and squashes the following commits:
5b4e37c [Matthew Farrellee] [SPARK-3458] enable python "with" statements for SparkContext
Adjust the default values of decision tree, based on the memory requirement discussed in https://github.com/apache/spark/pull/2125 :
1. maxMemoryInMB: 128 -> 256
2. maxBins: 100 -> 32
3. maxDepth: 4 -> 5 (in some example code)
jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#2322 from mengxr/tree-defaults and squashes the following commits:
cda453a [Xiangrui Meng] fix tests
5900445 [Xiangrui Meng] update comments
8c81831 [Xiangrui Meng] update default values of tree:
Tiny PR making SQLContext a new-style class. This allows various type logic to work more effectively
```Python
In [1]: import pyspark
In [2]: pyspark.sql.SQLContext.mro()
Out[2]: [pyspark.sql.SQLContext, object]
```
Author: Matthew Rocklin <mrocklin@gmail.com>
Closes#2288 from mrocklin/sqlcontext-new-style-class and squashes the following commits:
4aadab6 [Matthew Rocklin] update other old-style classes
a2dc02f [Matthew Rocklin] pyspark.sql.SQLContext is new-style class
I didn't add this to the transformations list in the docs because it's kind of obscure, but would be happy to do so if others think it would be helpful.
Author: Sandy Ryza <sandy@cloudera.com>
Closes#2274 from sryza/sandy-spark-2978 and squashes the following commits:
4a5332a [Sandy Ryza] Fix Java test
c04b447 [Sandy Ryza] Fix Python doc and add back deleted code
433ad5b [Sandy Ryza] Add Java test
4c25a54 [Sandy Ryza] Add s at the end and a couple other fixes
9b0ba99 [Sandy Ryza] Fix compilation
36e0571 [Sandy Ryza] Fix import ordering
48c12c2 [Sandy Ryza] Add Java version and additional doc
e5381cd [Sandy Ryza] Fix python style warnings
f147634 [Sandy Ryza] SPARK-2978. Transformation with MR shuffle semantics
This code removes the SerializingAdapter code that was copied from PiCloud
Author: Ward Viaene <ward.viaene@bigdatapartnership.com>
Closes#2287 from wardviaene/feature/pythonsys and squashes the following commits:
5f0d426 [Ward Viaene] SPARK-3415: modified test class to do dump and load
5f5d559 [Ward Viaene] SPARK-3415: modified test class name and call cloudpickle.dumps instead using StringIO
afc4a9a [Ward Viaene] SPARK-3415: added newlines to pass lint
aaf10b7 [Ward Viaene] SPARK-3415: removed references to SerializingAdapter and rewrote test
65ffeff [Ward Viaene] removed duplicate test
a958866 [Ward Viaene] SPARK-3415: test script
e263bf5 [Ward Viaene] SPARK-3415: removes legacy SerializingAdapter code
The underline JavaRDD for PipelineRDD is created lazily, it's delayed until call _jrdd.
The id of JavaRDD is cached as `_id`, it saves a RPC call in py4j for later calls.
closes#1276
Author: Davies Liu <davies.liu@gmail.com>
Closes#2296 from davies/id and squashes the following commits:
e197958 [Davies Liu] fix style
9721716 [Davies Liu] fix id of PipelineRDD
Author: GuoQiang Li <witgo@qq.com>
Closes#2175 from witgo/SPARK-3273 and squashes the following commits:
cf9c65a [GuoQiang Li] We should read the version information from the same place
2a44e2f [GuoQiang Li] The spark version in the welcome message of pyspark is not correct
Author: Holden Karau <holden@pigscanfly.ca>
Closes#2280 from holdenk/SPARK-3406-Python-RDD-persist-api-does-not-have-default-storage-level and squashes the following commits:
33eaade [Holden Karau] As Josh pointed out, sql also override persist. Make persist behave the same as in the underlying RDD as well
e658227 [Holden Karau] Fix the test I added
e95a6c5 [Holden Karau] The Python persist function did not have a default storageLevel unlike the Scala API. Noticed this issue because we got a bug report back from the book where we had documented it as if it was the same as the Scala API
Instead of jumping straight from 1 partition to all partitions, do exponential
growth and double the number of partitions to attempt each time instead.
Fix proposed by Paul Nepywoda
Author: Andrew Ash <andrew@andrewash.com>
Closes#2117 from ash211/SPARK-3211 and squashes the following commits:
8b2299a [Andrew Ash] Quadruple instead of double for a minor speedup
e5f7e4d [Andrew Ash] Update comment to better reflect what we're doing
09a27f7 [Andrew Ash] Update PySpark to be less OOM-prone as well
3a156b8 [Andrew Ash] SPARK-3211 .take() is OOM-prone with empty partitions
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#2251 from sarutak/SPARK-3378 and squashes the following commits:
0bfe234 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3378
bb5938f [Kousuke Saruta] Replaced rest of "SparkSQL" with "Spark SQL"
6df66de [Kousuke Saruta] Replaced "SparkSQL" with "Spark SQL"
Author: Matthew Farrellee <matt@redhat.com>
Closes#2183 from mattf/SPARK-2435 and squashes the following commits:
ee0ee99 [Matthew Farrellee] [SPARK-2435] Add shutdown hook to pyspark
After this patch, broadcast can be used in Python UDF.
Author: Davies Liu <davies.liu@gmail.com>
Closes#2243 from davies/udf_broadcast and squashes the following commits:
7b88861 [Davies Liu] support broadcast in UDF
Put all public API in __all__, also put them all in pyspark.__init__.py, then we can got all the documents for public API by `pydoc pyspark`. It also can be used by other programs (such as Sphinx or Epydoc) to generate only documents for public APIs.
Author: Davies Liu <davies.liu@gmail.com>
Closes#2205 from davies/public and squashes the following commits:
c6c5567 [Davies Liu] fix message
f7b35be [Davies Liu] put SchemeRDD, Row in pyspark.sql module
7e3016a [Davies Liu] add __all__ in mllib
6281b48 [Davies Liu] fix doc for SchemaRDD
6caab21 [Davies Liu] add public interfaces into pyspark.__init__.py
RDD.countApproxDistinct(relativeSD=0.05):
:: Experimental ::
Return approximate number of distinct elements in the RDD.
The algorithm used is based on streamlib's implementation of
"HyperLogLog in Practice: Algorithmic Engineering of a State
of The Art Cardinality Estimation Algorithm", available
<a href="http://dx.doi.org/10.1145/2452376.2452456">here</a>.
This support all the types of objects, which is supported by
Pyrolite, nearly all builtin types.
param relativeSD Relative accuracy. Smaller values create
counters that require more space.
It must be greater than 0.000017.
>>> n = sc.parallelize(range(1000)).map(str).countApproxDistinct()
>>> 950 < n < 1050
True
>>> n = sc.parallelize([i % 20 for i in range(1000)]).countApproxDistinct()
>>> 18 < n < 22
True
Author: Davies Liu <davies.liu@gmail.com>
Closes#2142 from davies/countApproxDistinct and squashes the following commits:
e20da47 [Davies Liu] remove the correction in Python
c38c4e4 [Davies Liu] fix doc tests
2ab157c [Davies Liu] fix doc tests
9d2565f [Davies Liu] add commments and link for hash collision correction
d306492 [Davies Liu] change range of hash of tuple to [0, maxint]
ded624f [Davies Liu] calculate hash in Python
4cba98f [Davies Liu] add more tests
a85a8c6 [Davies Liu] Merge branch 'master' into countApproxDistinct
e97e342 [Davies Liu] add countApproxDistinct()
Rather than specifying the path to SparkFiles we need to use the filename.
Author: Holden Karau <holden@pigscanfly.ca>
Closes#2210 from holdenk/SPARK-3318-documentation-for-addfiles-should-say-to-use-file-not-path and squashes the following commits:
a25d27a [Holden Karau] Update the JavaSparkContext addFile method to be clear about using fileName with SparkFiles as well
0ebcb05 [Holden Karau] Documentation update in addFile on how to use SparkFiles.get to specify filename rather than path
remove invalid docs
Author: Davies Liu <davies.liu@gmail.com>
Closes#2202 from davies/keep and squashes the following commits:
aa3b44f [Davies Liu] remove invalid docs
RDD.lookup(key)
Return the list of values in the RDD for key `key`. This operation
is done efficiently if the RDD has a known partitioner by only
searching the partition that the key maps to.
>>> l = range(1000)
>>> rdd = sc.parallelize(zip(l, l), 10)
>>> rdd.lookup(42) # slow
[42]
>>> sorted = rdd.sortByKey()
>>> sorted.lookup(42) # fast
[42]
It also clean up the code in RDD.py, and fix several bugs (related to preservesPartitioning).
Author: Davies Liu <davies.liu@gmail.com>
Closes#2093 from davies/lookup and squashes the following commits:
1789cd4 [Davies Liu] `f` in foreach could be generator or not.
2871b80 [Davies Liu] Merge branch 'master' into lookup
c6390ea [Davies Liu] address all comments
0f1bce8 [Davies Liu] add test case for lookup()
be0e8ba [Davies Liu] fix preservesPartitioning
eb1305d [Davies Liu] add RDD.lookup(key)
This is an effort to bring the Windows scripts up to speed after recent splashing changes in #1845.
Author: Andrew Or <andrewor14@gmail.com>
Closes#2129 from andrewor14/windows-config and squashes the following commits:
881a8f0 [Andrew Or] Add reference to Windows taskkill
92e6047 [Andrew Or] Update a few comments (minor)
22b1acd [Andrew Or] Fix style again (minor)
afcffea [Andrew Or] Fix style (minor)
72004c2 [Andrew Or] Actually respect --driver-java-options
803218b [Andrew Or] Actually respect SPARK_*_CLASSPATH
eeb34a0 [Andrew Or] Update outdated comment (minor)
35caecc [Andrew Or] In Windows, actually kill Java processes on exit
f97daa2 [Andrew Or] Fix Windows spark shell stdin issue
83ebe60 [Andrew Or] Parse special driver configs in Windows (broken)
Using external sort to support sort large datasets in reduce stage.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1978 from davies/sort and squashes the following commits:
bbcd9ba [Davies Liu] check spilled bytes in tests
b125d2f [Davies Liu] add test for external sort in rdd
eae0176 [Davies Liu] choose different disks from different processes and instances
1f075ed [Davies Liu] Merge branch 'master' into sort
eb53ca6 [Davies Liu] Merge branch 'master' into sort
644abaf [Davies Liu] add license in LICENSE
19f7873 [Davies Liu] improve tests
55602ee [Davies Liu] use external sort in sortBy() and sortByKey()
Make `ScalaReflection` be able to handle like:
- `Seq[Int]` as `ArrayType(IntegerType, containsNull = false)`
- `Seq[java.lang.Integer]` as `ArrayType(IntegerType, containsNull = true)`
- `Map[Int, Long]` as `MapType(IntegerType, LongType, valueContainsNull = false)`
- `Map[Int, java.lang.Long]` as `MapType(IntegerType, LongType, valueContainsNull = true)`
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#1889 from ueshin/issues/SPARK-2969 and squashes the following commits:
24f1c5c [Takuya UESHIN] Change the default value of ArrayType.containsNull to true in Python API.
79f5b65 [Takuya UESHIN] Change the default value of ArrayType.containsNull to true in Java API.
7cd1a7a [Takuya UESHIN] Fix json test failures.
2cfb862 [Takuya UESHIN] Change the default value of ArrayType.containsNull to true.
2f38e61 [Takuya UESHIN] Revert the default value of MapTypes.valueContainsNull.
9fa02f5 [Takuya UESHIN] Fix a test failure.
1a9a96b [Takuya UESHIN] Modify ScalaReflection to handle ArrayType.containsNull and MapType.valueContainsNull.
RDD.histogram(buckets)
Compute a histogram using the provided buckets. The buckets
are all open to the right except for the last which is closed.
e.g. [1,10,20,50] means the buckets are [1,10) [10,20) [20,50],
which means 1<=x<10, 10<=x<20, 20<=x<=50. And on the input of 1
and 50 we would have a histogram of 1,0,1.
If your histogram is evenly spaced (e.g. [0, 10, 20, 30]),
this can be switched from an O(log n) inseration to O(1) per
element(where n = # buckets).
Buckets must be sorted and not contain any duplicates, must be
at least two elements.
If `buckets` is a number, it will generates buckets which is
evenly spaced between the minimum and maximum of the RDD. For
example, if the min value is 0 and the max is 100, given buckets
as 2, the resulting buckets will be [0,50) [50,100]. buckets must
be at least 1 If the RDD contains infinity, NaN throws an exception
If the elements in RDD do not vary (max == min) always returns
a single bucket.
It will return an tuple of buckets and histogram.
>>> rdd = sc.parallelize(range(51))
>>> rdd.histogram(2)
([0, 25, 50], [25, 26])
>>> rdd.histogram([0, 5, 25, 50])
([0, 5, 25, 50], [5, 20, 26])
>>> rdd.histogram([0, 15, 30, 45, 60], True)
([0, 15, 30, 45, 60], [15, 15, 15, 6])
>>> rdd = sc.parallelize(["ab", "ac", "b", "bd", "ef"])
>>> rdd.histogram(("a", "b", "c"))
(('a', 'b', 'c'), [2, 2])
closes#122, it's duplicated.
Author: Davies Liu <davies.liu@gmail.com>
Closes#2091 from davies/histgram and squashes the following commits:
a322f8a [Davies Liu] fix deprecation of e.message
84e85fa [Davies Liu] remove evenBuckets, add more tests (including str)
d9a0722 [Davies Liu] address comments
0e18a2d [Davies Liu] add histgram() API
RDD.zipWithIndex()
Zips this RDD with its element indices.
The ordering is first based on the partition index and then the
ordering of items within each partition. So the first item in
the first partition gets index 0, and the last item in the last
partition receives the largest index.
This method needs to trigger a spark job when this RDD contains
more than one partitions.
>>> sc.parallelize(range(4), 2).zipWithIndex().collect()
[(0, 0), (1, 1), (2, 2), (3, 3)]
RDD.zipWithUniqueId()
Zips this RDD with generated unique Long ids.
Items in the kth partition will get ids k, n+k, 2*n+k, ..., where
n is the number of partitions. So there may exist gaps, but this
method won't trigger a spark job, which is different from
L{zipWithIndex}
>>> sc.parallelize(range(4), 2).zipWithUniqueId().collect()
[(0, 0), (2, 1), (1, 2), (3, 3)]
Author: Davies Liu <davies.liu@gmail.com>
Closes#2092 from davies/zipWith and squashes the following commits:
cebe5bf [Davies Liu] improve test cases, reverse the order of index
0d2a128 [Davies Liu] add zipWithIndex() and zipWithUniqueId()
RDD.countApprox(self, timeout, confidence=0.95)
:: Experimental ::
Approximate version of count() that returns a potentially incomplete
result within a timeout, even if not all tasks have finished.
>>> rdd = sc.parallelize(range(1000), 10)
>>> rdd.countApprox(1000, 1.0)
1000
RDD.sumApprox(self, timeout, confidence=0.95)
Approximate operation to return the sum within a timeout
or meet the confidence.
>>> rdd = sc.parallelize(range(1000), 10)
>>> r = sum(xrange(1000))
>>> (rdd.sumApprox(1000) - r) / r < 0.05
RDD.meanApprox(self, timeout, confidence=0.95)
:: Experimental ::
Approximate operation to return the mean within a timeout
or meet the confidence.
>>> rdd = sc.parallelize(range(1000), 10)
>>> r = sum(xrange(1000)) / 1000.0
>>> (rdd.meanApprox(1000) - r) / r < 0.05
True
Author: Davies Liu <davies.liu@gmail.com>
Closes#2095 from davies/approx and squashes the following commits:
e8c252b [Davies Liu] add approx API for RDD
RDD.max(key=None)
param key: A function used to generate key for comparing
>>> rdd = sc.parallelize([1.0, 5.0, 43.0, 10.0])
>>> rdd.max()
43.0
>>> rdd.max(key=str)
5.0
RDD.min(key=None)
Find the minimum item in this RDD.
param key: A function used to generate key for comparing
>>> rdd = sc.parallelize([2.0, 5.0, 43.0, 10.0])
>>> rdd.min()
2.0
>>> rdd.min(key=str)
10.0
RDD.top(num, key=None)
Get the top N elements from a RDD.
Note: It returns the list sorted in descending order.
>>> sc.parallelize([10, 4, 2, 12, 3]).top(1)
[12]
>>> sc.parallelize([2, 3, 4, 5, 6], 2).top(2)
[6, 5]
>>> sc.parallelize([10, 4, 2, 12, 3]).top(3, key=str)
[4, 3, 2]
Author: Davies Liu <davies.liu@gmail.com>
Closes#2094 from davies/cmp and squashes the following commits:
ccbaf25 [Davies Liu] add `key` to top()
ad7e374 [Davies Liu] fix tests
2f63512 [Davies Liu] change `comp` to `key` in min/max
dd91e08 [Davies Liu] add `comp` argument for RDD.max() and RDD.min()
We read the py4j port from the stdout of the `bin/spark-submit` subprocess. If there is interference in stdout (e.g. a random echo in `spark-submit`), we throw an exception with a warning message. We do not, however, distinguish between this case from the case where no stdout is produced at all.
I wasted a non-trivial amount of time being baffled by this exception in search of places where I print random whitespace (in vain, of course). A clearer exception message that distinguishes between these cases will prevent similar headaches that I have gone through.
Author: Andrew Or <andrewor14@gmail.com>
Closes#2067 from andrewor14/python-exception and squashes the following commits:
742f823 [Andrew Or] Further clarify warning messages
e96a7a0 [Andrew Or] Distinguish between unexpected output and no output at all
Fix sortByKey() with take()
The function `f` used in mapPartitions should always return an iterator.
Author: Davies Liu <davies.liu@gmail.com>
Closes#2045 from davies/fix_sortbykey and squashes the following commits:
1160f59 [Davies Liu] fix sortByKey() with take()
This PR fixes two bugs related to `spark.local.dirs` and `SPARK_LOCAL_DIRS`, one where `Utils.getLocalDir()` might return an invalid directory (SPARK-2974) and another where the `SPARK_LOCAL_DIRS` override didn't affect the driver, which could cause problems when running tasks in local mode (SPARK-2975).
This patch fixes both issues: the new `Utils.getOrCreateLocalRootDirs(conf: SparkConf)` utility method manages the creation of local directories and handles the precedence among the different configuration options, so we should see the same behavior whether we're running in local mode or on a worker.
It's kind of a pain to mock out environment variables in tests (no easy way to mock System.getenv), so I added a `private[spark]` method to SparkConf for accessing environment variables (by default, it just delegates to System.getenv). By subclassing SparkConf and overriding this method, we can mock out SPARK_LOCAL_DIRS in tests.
I also fixed a typo in PySpark where we used `SPARK_LOCAL_DIR` instead of `SPARK_LOCAL_DIRS` (I think this was technically innocuous, but it seemed worth fixing).
Author: Josh Rosen <joshrosen@apache.org>
Closes#2002 from JoshRosen/local-dirs and squashes the following commits:
efad8c6 [Josh Rosen] Address review comments:
1dec709 [Josh Rosen] Minor updates to Javadocs.
7f36999 [Josh Rosen] Use env vars to detect if running in YARN container.
399ac25 [Josh Rosen] Update getLocalDir() documentation.
bb3ad89 [Josh Rosen] Remove duplicated YARN getLocalDirs() code.
3e92d44 [Josh Rosen] Move local dirs override logic into Utils; fix bugs:
b2c4736 [Josh Rosen] Add failing tests for SPARK-2974 and SPARK-2975.
007298b [Josh Rosen] Allow environment variables to be mocked in tests.
6d9259b [Josh Rosen] Fix typo in PySpark: SPARK_LOCAL_DIR should be SPARK_LOCAL_DIRS
Though we don't use default argument for methods in RandomRDDs, it is still not easy for Java users to use because the output type is either `RDD[Double]` or `RDD[Vector]`. Java users should expect `JavaDoubleRDD` and `JavaRDD[Vector]`, respectively. We should create dedicated methods for Java users, and allow default arguments in Scala methods in RandomRDDs, to make life easier for both Java and Scala users. This PR also contains documentation for random data generation. brkyvz
Author: Xiangrui Meng <meng@databricks.com>
Closes#2041 from mengxr/stat-doc and squashes the following commits:
fc5eedf [Xiangrui Meng] add missing comma
ffde810 [Xiangrui Meng] address comments
aef6d07 [Xiangrui Meng] add doc for random data generation
b99d94b [Xiangrui Meng] add java-friendly methods to RandomRDDs
If two RDDs have different batch size in serializers, then it will try to re-serialize the one with smaller batch size, then call RDD.zip() in Spark.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1894 from davies/zip and squashes the following commits:
c4652ea [Davies Liu] add more test cases
6d05fc8 [Davies Liu] Merge branch 'master' into zip
813b1e4 [Davies Liu] add more tests for failed cases
a4aafda [Davies Liu] fix zip with serializers which have different batch sizes.
This fixes SPARK-3114, an issue where we inadvertently broke Python UDFs in Spark SQL.
This PR modifiers the test runner script to always run the PySpark SQL tests, irrespective of whether SparkSQL itself has been modified. It also includes Davies' fix for the bug.
Closes#2026.
Author: Josh Rosen <joshrosen@apache.org>
Author: Davies Liu <davies.liu@gmail.com>
Closes#2027 from JoshRosen/pyspark-sql-fix and squashes the following commits:
9af2708 [Davies Liu] bugfix: disable compression of command
0d8d3a4 [Josh Rosen] Always run Python Spark SQL tests.
Added examples for statistical summarization:
* Scala: StatisticalSummary.scala
** Tests: correlation, MultivariateOnlineSummarizer
* python: statistical_summary.py
** Tests: correlation (since MultivariateOnlineSummarizer has no Python API)
Added examples for random and sampled RDDs:
* Scala: RandomAndSampledRDDs.scala
* python: random_and_sampled_rdds.py
* Both test:
** RandomRDDGenerators.normalRDD, normalVectorRDD
** RDD.sample, takeSample, sampleByKey
Added sc.stop() to all examples.
CorrelationSuite.scala
* Added 1 test for RDDs with only 1 value
RowMatrix.scala
* numCols(): Added check for numRows = 0, with error message.
* computeCovariance(): Added check for numRows <= 1, with error message.
Python SparseVector (pyspark/mllib/linalg.py)
* Added toDense() function
python/run-tests script
* Added stat.py (doc test)
CC: mengxr dorx Main changes were examples to show usage across APIs.
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#1878 from jkbradley/mllib-stats-api-check and squashes the following commits:
ea5c047 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
dafebe2 [Joseph K. Bradley] Bug fixes for examples SampledRDDs.scala and sampled_rdds.py: Check for division by 0 and for missing key in maps.
8d1e555 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
60c72d9 [Joseph K. Bradley] Fixed stat.py doc test to work for Python versions printing nan or NaN.
b20d90a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
4e5d15e [Joseph K. Bradley] Changed pyspark/mllib/stat.py doc tests to use NaN instead of nan.
32173b7 [Joseph K. Bradley] Stats examples update.
c8c20dc [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
cf70b07 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
0b7cec3 [Joseph K. Bradley] Small updates based on code review. Renamed statistical_summary.py to correlations.py
ab48f6e [Joseph K. Bradley] RowMatrix.scala * numCols(): Added check for numRows = 0, with error message. * computeCovariance(): Added check for numRows <= 1, with error message.
65e4ebc [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
8195c78 [Joseph K. Bradley] Added examples for random and sampled RDDs: * Scala: RandomAndSampledRDDs.scala * python: random_and_sampled_rdds.py * Both test: ** RandomRDDGenerators.normalRDD, normalVectorRDD ** RDD.sample, takeSample, sampleByKey
064985b [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
ee918e9 [Joseph K. Bradley] Added examples for statistical summarization: * Scala: StatisticalSummary.scala ** Tests: correlation, MultivariateOnlineSummarizer * python: statistical_summary.py ** Tests: correlation (since MultivariateOnlineSummarizer has no Python API)
Small DecisionTree updates:
* Changed main DecisionTree aggregate to treeAggregate.
* Fixed bug in python example decision_tree_runner.py with missing argument (since categoricalFeaturesInfo is no longer an optional argument for trainClassifier).
* Fixed same bug in python doc tests, and added tree.py to doc tests.
CC: mengxr
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#2015 from jkbradley/dt-opt2 and squashes the following commits:
b5114fa [Joseph K. Bradley] Fixed python tree.py doc test (extra newline)
8e4665d [Joseph K. Bradley] Added tree.py to python doc tests. Fixed bug from missing categoricalFeaturesInfo argument.
b7b2922 [Joseph K. Bradley] Fixed bug in python example decision_tree_runner.py with missing argument. Changed main DecisionTree aggregate to treeAggregate.
85bbc1f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt2
66d076f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt2
a0ed0da [Joseph K. Bradley] Renamed DTMetadata to DecisionTreeMetadata. Small doc updates.
3726d20 [Joseph K. Bradley] Small code improvements based on code review.
ac0b9f8 [Joseph K. Bradley] Small updates based on code review. Main change: Now using << instead of math.pow.
db0d773 [Joseph K. Bradley] scala style fix
6a38f48 [Joseph K. Bradley] Added DTMetadata class for cleaner code
931a3a7 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt2
797f68a [Joseph K. Bradley] Fixed DecisionTreeSuite bug for training second level. Needed to update treePointToNodeIndex with groupShift.
f40381c [Joseph K. Bradley] Merge branch 'dt-opt1' into dt-opt2
5f2dec2 [Joseph K. Bradley] Fixed scalastyle issue in TreePoint
6b5651e [Joseph K. Bradley] Updates based on code review. 1 major change: persisting to memory + disk, not just memory.
2d2aaaf [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt1
26d10dd [Joseph K. Bradley] Removed tree/model/Filter.scala since no longer used. Removed debugging println calls in DecisionTree.scala.
356daba [Joseph K. Bradley] Merge branch 'dt-opt1' into dt-opt2
430d782 [Joseph K. Bradley] Added more debug info on binning error. Added some docs.
d036089 [Joseph K. Bradley] Print timing info to logDebug.
e66f1b1 [Joseph K. Bradley] TreePoint * Updated doc * Made some methods private
8464a6e [Joseph K. Bradley] Moved TimeTracker to tree/impl/ in its own file, and cleaned it up. Removed debugging println calls from DecisionTree. Made TreePoint extend Serialiable
a87e08f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt1
c1565a5 [Joseph K. Bradley] Small DecisionTree updates: * Simplification: Updated calculateGainForSplit to take aggregates for a single (feature, split) pair. * Internal doc: findAggForOrderedFeatureClassification
b914f3b [Joseph K. Bradley] DecisionTree optimization: eliminated filters + small changes
b2ed1f3 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt
0f676e2 [Joseph K. Bradley] Optimizations + Bug fix for DecisionTree
3211f02 [Joseph K. Bradley] Optimizing DecisionTree * Added TreePoint representation to avoid calling findBin multiple times. * (not working yet, but debugging)
f61e9d2 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing
bcf874a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing
511ec85 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing
a95bc22 [Joseph K. Bradley] timing for DecisionTree internals
bugfix: It will raise an exception when it try to encode non-ASCII strings into unicode. It should only encode unicode as "utf-8".
Author: Davies Liu <davies.liu@gmail.com>
Closes#2018 from davies/fix_utf8 and squashes the following commits:
4db7967 [Davies Liu] fix saveAsTextFile() with utf-8
Passing large object by py4j is very slow (cost much memory), so pass broadcast objects via files (similar to parallelize()).
Add an option to keep object in driver (it's False by default) to save memory in driver.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1912 from davies/broadcast and squashes the following commits:
e06df4a [Davies Liu] load broadcast from disk in driver automatically
db3f232 [Davies Liu] fix serialization of accumulator
631a827 [Davies Liu] Merge branch 'master' into broadcast
c7baa8c [Davies Liu] compress serrialized broadcast and command
9a7161f [Davies Liu] fix doc tests
e93cf4b [Davies Liu] address comments: add test
6226189 [Davies Liu] improve large broadcast
https://issues.apache.org/jira/browse/SPARK-3035
fix for wrong document.
Author: iAmGhost <kdh7807@gmail.com>
Closes#1942 from iAmGhost/master and squashes the following commits:
487528a [iAmGhost] [SPARK-3035] Wrong example with SparkContext.addFile fix for wrong document.
`RandomRDDGenerators` means factory for `RandomRDDGenerator`. However, its methods return RDDs but not RDDGenerators. So a more proper (and shorter) name would be `RandomRDDs`.
dorx brkyvz
Author: Xiangrui Meng <meng@databricks.com>
Closes#1979 from mengxr/randomrdds and squashes the following commits:
b161a2d [Xiangrui Meng] rename RandomRDDGenerators to RandomRDDs
Test code in `sql.py` tries to compare two floating-point numbers directly, and cased [build failure(s)](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/18365/consoleFull).
[Doctest documentation](https://docs.python.org/3/library/doctest.html#warnings) recommends using numbers in the form of `I/2**J` to avoid the precision issue.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#1925 from liancheng/fix-pysql-fp-test and squashes the following commits:
0fbf584 [Cheng Lian] Removed unnecessary `...' from inferSchema doctest
e8059d4 [Cheng Lian] Using safe floating-point numbers in doctest
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
1. skip partitionBy() when numOfPartition is 1
2. use bisect_left (O(lg(N))) instread of loop (O(N)) in
rangePartitioner
Author: Davies Liu <davies.liu@gmail.com>
Closes#1898 from davies/sort and squashes the following commits:
0a9608b [Davies Liu] Merge branch 'master' into sort
1cf9565 [Davies Liu] improve performance of sortByKey()
because Pyrolite does not support array from Python 2.6
Author: Davies Liu <davies.liu@gmail.com>
Closes#1928 from davies/fix_array and squashes the following commits:
858e6c5 [Davies Liu] convert array into list
For both Scala and Python.
The ser/de util functions were moved out of `PythonMLLibAPI` and into their own object to avoid creating the `PythonMLLibAPI` object inside of `MultivariateStatisticalSummarySerialized`, which is then referenced inside of a method in `PythonMLLibAPI`.
`MultivariateStatisticalSummarySerialized` was created to serialize the `Vector` fields in `MultivariateStatisticalSummary`.
Author: Doris Xin <doris.s.xin@gmail.com>
Closes#1911 from dorx/colStats and squashes the following commits:
77b9924 [Doris Xin] developerAPI tag
de9cbbe [Doris Xin] reviewer comments and moved more ser/de
459faba [Doris Xin] colStats in Statistics for both Scala and Python
Python 2.6 does not handle float error well as 2.7+
Author: Davies Liu <davies.liu@gmail.com>
Closes#1910 from davies/fix_test and squashes the following commits:
7e51200 [Davies Liu] fix flaky tests
https://issues.apache.org/jira/browse/SPARK-2844
Author: Ahir Reddy <ahirreddy@gmail.com>
Closes#1768 from ahirreddy/python-hive-context-fix and squashes the following commits:
7972d3b [Ahir Reddy] Correctly set JVM HiveContext if it is passed into Python HiveContext constructor
- Modify python/run-tests to test with Python 2.6
- Use unittest2 when running on Python 2.6.
- Fix issue with namedtuple.
- Skip TestOutputFormat.test_newhadoop on Python 2.6 until SPARK-2951 is fixed.
- Fix MLlib _deserialize_double on Python 2.6.
Closes#1868. Closes#1042.
Author: Josh Rosen <joshrosen@apache.org>
Closes#1874 from JoshRosen/python2.6 and squashes the following commits:
983d259 [Josh Rosen] [SPARK-2954] Fix MLlib _deserialize_double on Python 2.6.
5d18fd7 [Josh Rosen] [SPARK-2948] [SPARK-2910] [SPARK-2101] Python 2.6 fixes
1. do not use signal handler for SIGCHILD, it's easy to cause deadlock
2. handle EINTR during accept()
3. pass errno into JVM
4. handle EAGAIN during fork()
Now, it can pass 50k tasks tests in 180 seconds.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1842 from davies/qa and squashes the following commits:
f0ea451 [Davies Liu] fix lint
03a2e8c [Davies Liu] cleanup dead children every seconds
32cb829 [Davies Liu] fix lint
0cd0817 [Davies Liu] fix bugs in deamon.py
As sryza reported, spark-shell doesn't accept any flags.
The root cause is wrong usage of spark-submit in spark-shell and it come to the surface by #1801
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#1715, Closes#1864, and Closes#1861Closes#1825 from sarutak/SPARK-2894 and squashes the following commits:
47f3510 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2894
2c899ed [Kousuke Saruta] Removed useless code from java_gateway.py
98287ed [Kousuke Saruta] Removed useless code from java_gateway.py
513ad2e [Kousuke Saruta] Modified util.sh to enable to use option including white spaces
28a374e [Kousuke Saruta] Modified java_gateway.py to recognize arguments
5afc584 [Cheng Lian] Filter out spark-submit options when starting Python gateway
e630d19 [Cheng Lian] Fixing pyspark and spark-shell CLI options
Added 6 static train methods to match Python API, but without default arguments (but with Python default args noted in docs).
Added factory classes for Algo and Impurity, but made private[mllib].
CC: mengxr dorx Please let me know if there are other changes which would help with API consistency---thanks!
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#1798 from jkbradley/dt-python-consistency and squashes the following commits:
6f7edf8 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-python-consistency
a0d7dbe [Joseph K. Bradley] DecisionTree: In Java-friendly train* methods, changed to use JavaRDD instead of RDD.
ee1d236 [Joseph K. Bradley] DecisionTree API updates: * Removed train() function in Python API (tree.py) ** Removed corresponding function in Scala/Java API (the ones taking basic types)
00f820e [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-python-consistency
fe6dbfa [Joseph K. Bradley] removed unnecessary imports
e358661 [Joseph K. Bradley] DecisionTree API change: * Added 6 static train methods to match Python API, but without default arguments (but with Python default args noted in docs).
c699850 [Joseph K. Bradley] a few doc comments
eaf84c0 [Joseph K. Bradley] Added DecisionTree static train() methods API to match Python, but without default parameters
Author: RJ Nowling <rnowling@gmail.com>
Closes#1808 from rnowling/pyspark_docs and squashes the following commits:
c06d774 [RJ Nowling] Add blanklines to Python docstrings so example code renders correctly
As described in [SPARK-2627](https://issues.apache.org/jira/browse/SPARK-2627), we'd like Python code to automatically be checked for PEP 8 compliance by Jenkins. This pull request aims to do that.
Notes:
* We may need to install [`pep8`](https://pypi.python.org/pypi/pep8) on the build server.
* I'm expecting tests to fail now that PEP 8 compliance is being checked as part of the build. I'm fine with cleaning up any remaining PEP 8 violations as part of this pull request.
* I did not understand why the RAT and scalastyle reports are saved to text files. I did the same for the PEP 8 check, but only so that the console output style can match those for the RAT and scalastyle checks. The PEP 8 report is removed right after the check is complete.
* Updates to the ["Contributing to Spark"](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark) guide will be submitted elsewhere, as I don't believe that text is part of the Spark repo.
Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Author: nchammas <nicholas.chammas@gmail.com>
Closes#1744 from nchammas/master and squashes the following commits:
274b238 [Nicholas Chammas] [SPARK-2627] [PySpark] minor indentation changes
983d963 [nchammas] Merge pull request #5 from apache/master
1db5314 [nchammas] Merge pull request #4 from apache/master
0e0245f [Nicholas Chammas] [SPARK-2627] undo erroneous whitespace fixes
bf30942 [Nicholas Chammas] [SPARK-2627] PEP8: comment spacing
6db9a44 [nchammas] Merge pull request #3 from apache/master
7b4750e [Nicholas Chammas] merge upstream changes
91b7584 [Nicholas Chammas] [SPARK-2627] undo unnecessary line breaks
44e3e56 [Nicholas Chammas] [SPARK-2627] use tox.ini to exclude files
b09fae2 [Nicholas Chammas] don't wrap comments unnecessarily
bfb9f9f [Nicholas Chammas] [SPARK-2627] keep up with the PEP 8 fixes
9da347f [nchammas] Merge pull request #2 from apache/master
aa5b4b5 [Nicholas Chammas] [SPARK-2627] follow Spark bash style for if blocks
d0a83b9 [Nicholas Chammas] [SPARK-2627] check that pep8 downloaded fine
dffb5dd [Nicholas Chammas] [SPARK-2627] download pep8 at runtime
a1ce7ae [Nicholas Chammas] [SPARK-2627] space out test report sections
21da538 [Nicholas Chammas] [SPARK-2627] it's PEP 8, not PEP8
6f4900b [Nicholas Chammas] [SPARK-2627] more misc PEP 8 fixes
fe57ed0 [Nicholas Chammas] removing merge conflict backups
9c01d4c [nchammas] Merge pull request #1 from apache/master
9a66cb0 [Nicholas Chammas] resolving merge conflicts
a31ccc4 [Nicholas Chammas] [SPARK-2627] miscellaneous PEP 8 fixes
beaa9ac [Nicholas Chammas] [SPARK-2627] fail check on non-zero status
723ed39 [Nicholas Chammas] always delete the report file
0541ebb [Nicholas Chammas] [SPARK-2627] call Python linter from run-tests
12440fa [Nicholas Chammas] [SPARK-2627] add Scala linter
61c07b9 [Nicholas Chammas] [SPARK-2627] add Python linter
75ad552 [Nicholas Chammas] make check output style consistent
Handle null in schemaRDD during converting them into Python.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1802 from davies/json and squashes the following commits:
88e6b1f [Davies Liu] handle null in schemaRDD()
This PR aims to finalize accepted data value types in Python RDDs provided to Python `applySchema`.
JIRA: https://issues.apache.org/jira/browse/SPARK-2854
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1793 from yhuai/SPARK-2854 and squashes the following commits:
32f0708 [Yin Huai] LongType only accepts long values.
c2b23dd [Yin Huai] Do data type conversions based on the specified Spark SQL data type.
serializer is imported multiple times during doctests, so it's better to make _hijack_namedtuple() safe to be called multiple times.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1771 from davies/fix and squashes the following commits:
1a9e336 [Davies Liu] fix unit tests
Add an hook to replace original namedtuple with an pickable one, then namedtuple could be used in RDDs.
PS: pyspark should be import BEFORE "from collections import namedtuple"
Author: Davies Liu <davies.liu@gmail.com>
Closes#1623 from davies/namedtuple and squashes the following commits:
045dad8 [Davies Liu] remove unrelated code changes
4132f32 [Davies Liu] address comment
55b1c1a [Davies Liu] fix tests
61f86eb [Davies Liu] replace all the reference of namedtuple to new hacked one
98df6c6 [Davies Liu] Merge branch 'master' of github.com:apache/spark into namedtuple
f7b1bde [Davies Liu] add hack for CloudPickleSerializer
0c5c849 [Davies Liu] Merge branch 'master' of github.com:apache/spark into namedtuple
21991e6 [Davies Liu] hack namedtuple in __main__ module, make it picklable.
93b03b8 [Davies Liu] pickable namedtuple
Kill only the python worker related to cancelled tasks.
The daemon will start a background thread to monitor all the opened sockets for all workers. If the socket is closed by JVM, this thread will kill the worker.
When an task is cancelled, the socket to worker will be closed, then the worker will be killed by deamon.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1643 from davies/kill and squashes the following commits:
8ffe9f3 [Davies Liu] kill worker by deamon, because runtime.exec() is too heavy
46ca150 [Davies Liu] address comment
acd751c [Davies Liu] kill the worker when task is canceled
Many users have reported being confused by the distinction between the `sql` and `hql` methods. Specifically, many users think that `sql(...)` cannot be used to read hive tables. In this PR I introduce a new configuration option `spark.sql.dialect` that picks which dialect with be used for parsing. For SQLContext this must be set to `sql`. In `HiveContext` it defaults to `hiveql` but can also be set to `sql`.
The `hql` and `hiveql` methods continue to act the same but are now marked as deprecated.
**This is a possibly breaking change for some users unless they set the dialect manually, though this is unlikely.**
For example: `hiveContex.sql("SELECT 1")` will now throw a parsing exception by default.
Author: Michael Armbrust <michael@databricks.com>
Closes#1746 from marmbrus/sqlLanguageConf and squashes the following commits:
ad375cc [Michael Armbrust] Merge remote-tracking branch 'apache/master' into sqlLanguageConf
20c43f8 [Michael Armbrust] override function instead of just setting the value
7e4ae93 [Michael Armbrust] Deprecate hql() method in favor of a config option, 'spark.sql.dialect'
There have been user complaints that the difference between `registerAsTable` and `saveAsTable` is too subtle. This PR addresses this by renaming `registerAsTable` to `registerTempTable`, which more clearly reflects what is happening. `registerAsTable` remains, but will cause a deprecation warning.
Author: Michael Armbrust <michael@databricks.com>
Closes#1743 from marmbrus/registerTempTable and squashes the following commits:
d031348 [Michael Armbrust] Merge remote-tracking branch 'apache/master' into registerTempTable
4dff086 [Michael Armbrust] Fix .java files too
89a2f12 [Michael Armbrust] Merge remote-tracking branch 'apache/master' into registerTempTable
0b7b71e [Michael Armbrust] Rename registerAsTable to registerTempTable
The cause is explained in https://issues.apache.org/jira/browse/SPARK-2797.
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1745 from yhuai/SPARK-2797 and squashes the following commits:
7b1627d [Yin Huai] The unpersist method of the Scala RDD cannot be called without the input parameter (blocking) from PySpark.
This patch adds the ability to register lambda functions written in Python, Java or Scala as UDFs for use in SQL or HiveQL.
Scala:
```scala
registerFunction("strLenScala", (_: String).length)
sql("SELECT strLenScala('test')")
```
Python:
```python
sqlCtx.registerFunction("strLenPython", lambda x: len(x), IntegerType())
sqlCtx.sql("SELECT strLenPython('test')")
```
Java:
```java
sqlContext.registerFunction("stringLengthJava", new UDF1<String, Integer>() {
Override
public Integer call(String str) throws Exception {
return str.length();
}
}, DataType.IntegerType);
sqlContext.sql("SELECT stringLengthJava('test')");
```
Author: Michael Armbrust <michael@databricks.com>
Closes#1063 from marmbrus/udfs and squashes the following commits:
9eda0fe [Michael Armbrust] newline
747c05e [Michael Armbrust] Add some scala UDF tests.
d92727d [Michael Armbrust] Merge remote-tracking branch 'apache/master' into udfs
005d684 [Michael Armbrust] Fix naming and formatting.
d14dac8 [Michael Armbrust] Fix last line of autogened java files.
8135c48 [Michael Armbrust] Move UDF unit tests to pyspark.
40b0ffd [Michael Armbrust] Merge remote-tracking branch 'apache/master' into udfs
6a36890 [Michael Armbrust] Switch logging so that SQLContext can be serializable.
7a83101 [Michael Armbrust] Drop toString
795fd15 [Michael Armbrust] Try to avoid capturing SQLContext.
e54fb45 [Michael Armbrust] Docs and tests.
437cbe3 [Michael Armbrust] Update use of dataTypes, fix some python tests, address review comments.
01517d6 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into udfs
8e6c932 [Michael Armbrust] WIP
3f96a52 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into udfs
6237c8d [Michael Armbrust] WIP
2766f0b [Michael Armbrust] Move udfs support to SQL from hive. Add support for Java UDFs.
0f7d50c [Michael Armbrust] Draft of native Spark SQL UDFs for Scala and Python.
Added experimental Python API for Decision Trees.
API:
* class DecisionTreeModel
** predict() for single examples and RDDs, taking both feature vectors and LabeledPoints
** numNodes()
** depth()
** __str__()
* class DecisionTree
** trainClassifier()
** trainRegressor()
** train()
Examples and testing:
* Added example testing classification and regression with batch prediction: examples/src/main/python/mllib/tree.py
* Have also tested example usage in doc of python/pyspark/mllib/tree.py which tests single-example prediction with dense and sparse vectors
Also: Small bug fix in python/pyspark/mllib/_common.py: In _linear_predictor_typecheck, changed check for RDD to use isinstance() instead of type() in order to catch RDD subclasses.
CC mengxr manishamde
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#1727 from jkbradley/decisiontree-python-new and squashes the following commits:
3744488 [Joseph K. Bradley] Renamed test tree.py to decision_tree_runner.py Small updates based on github review.
6b86a9d [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
affceb9 [Joseph K. Bradley] * Fixed bug in doc tests in pyspark/mllib/util.py caused by change in loadLibSVMFile behavior. (It used to threshold labels at 0 to make them 0/1, but it now leaves them as they are.) * Fixed small bug in loadLibSVMFile: If a data file had no features, then loadLibSVMFile would create a single all-zero feature.
67a29bc [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
cf46ad7 [Joseph K. Bradley] Python DecisionTreeModel * predict(empty RDD) returns an empty RDD instead of an error. * Removed support for calling predict() on LabeledPoint and RDD[LabeledPoint] * predict() does not cache serialized RDD any more.
aa29873 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
bf21be4 [Joseph K. Bradley] removed old run() func from DecisionTree
fa10ea7 [Joseph K. Bradley] Small style update
7968692 [Joseph K. Bradley] small braces typo fix
e34c263 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
4801b40 [Joseph K. Bradley] Small style update to DecisionTreeSuite
db0eab2 [Joseph K. Bradley] Merge branch 'decisiontree-bugfix2' into decisiontree-python-new
6873fa9 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
225822f [Joseph K. Bradley] Bug: In DecisionTree, the method sequentialBinSearchForOrderedCategoricalFeatureInClassification() indexed bins from 0 to (math.pow(2, featureCategories.toInt - 1) - 1). This upper bound is the bound for unordered categorical features, not ordered ones. The upper bound should be the arity (i.e., max value) of the feature.
93953f1 [Joseph K. Bradley] Likely done with Python API.
6df89a9 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
4562c08 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
665ba78 [Joseph K. Bradley] Small updates towards Python DecisionTree API
188cb0d [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new
6622247 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
b8fac57 [Joseph K. Bradley] Finished Python DecisionTree API and example but need to test a bit more.
2b20c61 [Joseph K. Bradley] Small doc and style updates
1b29c13 [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new
584449a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
dab0b67 [Joseph K. Bradley] Added documentation for DecisionTree internals
8bb8aa0 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix
978cfcf [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix
6eed482 [Joseph K. Bradley] In DecisionTree: Changed from using procedural syntax for functions returning Unit to explicitly writing Unit return type.
376dca2 [Joseph K. Bradley] Updated meaning of maxDepth by 1 to fit scikit-learn and rpart. * In code, replaced usages of maxDepth <-- maxDepth + 1 * In params, replace settings of maxDepth <-- maxDepth - 1
e06e423 [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new
bab3f19 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
59750f8 [Joseph K. Bradley] * Updated Strategy to check numClassesForClassification only if algo=Classification. * Updates based on comments: ** DecisionTreeRunner *** Made dataFormat arg default to libsvm ** Small cleanups ** tree.Node: Made recursive helper methods private, and renamed them.
52e17c5 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix
f5a036c [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new
da50db7 [Joseph K. Bradley] Added one more test to DecisionTreeSuite: stump with 2 continuous variables for binary classification. Caused problems in past, but fixed now.
8e227ea [Joseph K. Bradley] Changed Strategy so it only requires numClassesForClassification >= 2 for classification
cd1d933 [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new
8ea8750 [Joseph K. Bradley] Bug fix: Off-by-1 when finding thresholds for splits for continuous features.
8a758db [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new
5fe44ed [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
2283df8 [Joseph K. Bradley] 2 bug fixes.
73fbea2 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix
5f920a1 [Joseph K. Bradley] Demonstration of bug before submitting fix: Updated DecisionTreeSuite so that 3 tests fail. Will describe bug in next commit.
f825352 [Joseph K. Bradley] Wrote Python API and example for DecisionTree. Also added toString, depth, and numNodes methods to DecisionTreeModel.
When standalone Workers launch executors, they inherit the Spark home set by the driver. This means if the worker machines do not share the same directory structure as the driver node, the Workers will attempt to run scripts (e.g. bin/compute-classpath.sh) that do not exist locally and fail. This is a common scenario if the driver is launched from outside of the cluster.
The solution is to simply not pass the driver's Spark home to the Workers. This PR further makes an attempt to avoid overloading the usages of `spark.home`, which is now only used for setting executor Spark home on Mesos and in python.
This is based on top of #1392 and originally reported by YanTangZhai. Tested on standalone cluster.
Author: Andrew Or <andrewor14@gmail.com>
Closes#1734 from andrewor14/spark-home-reprise and squashes the following commits:
f71f391 [Andrew Or] Revert changes in python
1c2532c [Andrew Or] Merge branch 'master' of github.com:apache/spark into spark-home-reprise
188fc5d [Andrew Or] Avoid using spark.home where possible
09272b7 [Andrew Or] Always use Worker's working directory as spark home
These changes allow StatCounters to work properly on NumPy arrays, to fix the issue reported here (https://issues.apache.org/jira/browse/SPARK-2012).
If NumPy is installed, the NumPy functions ``maximum``, ``minimum``, and ``sqrt``, which work on arrays, are used to merge statistics. If not, we fall back on scalar operators, so it will work on arrays with NumPy, but will also work without NumPy.
New unit tests added, along with a check for NumPy in the tests.
Author: Jeremy Freeman <the.freeman.lab@gmail.com>
Closes#1725 from freeman-lab/numpy-max-statcounter and squashes the following commits:
fe973b1 [Jeremy Freeman] Avoid duplicate array import in tests
7f0e397 [Jeremy Freeman] Refactored check for numpy
8e764dd [Jeremy Freeman] Explicit numpy imports
875414c [Jeremy Freeman] Fixed indents
1c8a832 [Jeremy Freeman] Unit tests for StatCounter with NumPy arrays
176a127 [Jeremy Freeman] Use numpy arrays in StatCounter
Related to issue: [SPARK-2550](https://issues.apache.org/jira/browse/SPARK-2550?jql=project%20%3D%20SPARK%20AND%20resolution%20%3D%20Unresolved%20AND%20priority%20%3D%20Major%20ORDER%20BY%20key%20DESC).
Author: Michael Giannakopoulos <miccagiann@gmail.com>
Closes#1624 from miccagiann/new-branch and squashes the following commits:
c02e5f5 [Michael Giannakopoulos] Merge cleanly with upstream/master.
8dcb888 [Michael Giannakopoulos] Putting the if/else if statements in brackets.
fed8eaa [Michael Giannakopoulos] Adding a space in the message related to the IllegalArgumentException.
44e6ff0 [Michael Giannakopoulos] Adding a blank line before python class LinearRegressionWithSGD.
8eba9c5 [Michael Giannakopoulos] Change function signatures. Exception is thrown from the scala component and not from the python one.
638be47 [Michael Giannakopoulos] Modified code to comply with code standards.
ec50ee9 [Michael Giannakopoulos] Shorten the if-elif-else statement in regression.py file
b962744 [Michael Giannakopoulos] Replaced the enum classes, with strings-keywords for defining the values of 'regType' parameter.
78853ec [Michael Giannakopoulos] Providing intercept and regualizer functionallity for linear methods in only one function.
3ac8874 [Michael Giannakopoulos] Added support for regularizer and intercection parameters for linear regression method.
Curently, daemon.py forks a pool of numProcessors subprocesses, and those processes fork themselves again to create the actual Python worker processes that handle data.
I think that this extra layer of indirection is unnecessary and adds a lot of complexity. This commit attempts to remove this middle layer of subprocesses by launching the workers directly from daemon.py.
See https://github.com/mesos/spark/pull/563 for the original PR that added daemon.py, where I raise some issues with the current design.
Author: Josh Rosen <joshrosen@apache.org>
Closes#1680 from JoshRosen/pyspark-daemon and squashes the following commits:
5abbcb9 [Josh Rosen] Replace magic number: 4 -> EINTR
5495dff [Josh Rosen] Throw IllegalStateException if worker launch fails.
b79254d [Josh Rosen] Detect failed fork() calls; improve error logging.
282c2c4 [Josh Rosen] Remove daemon.py exit logging, since it caused problems:
8554536 [Josh Rosen] Fix daemon’s shutdown(); log shutdown reason.
4e0fab8 [Josh Rosen] Remove shared-memory exit_flag; don't die on worker death.
e9892b4 [Josh Rosen] [WIP] [SPARK-2764] Simplify daemon.py process structure.
Convert Row in JavaSchemaRDD into Array[Any] and unpickle them as tuple in Python, then convert them into namedtuple, so use can access fields just like attributes.
This will let nested structure can be accessed as object, also it will reduce the size of serialized data and better performance.
root
|-- field1: integer (nullable = true)
|-- field2: string (nullable = true)
|-- field3: struct (nullable = true)
| |-- field4: integer (nullable = true)
| |-- field5: array (nullable = true)
| | |-- element: integer (containsNull = false)
|-- field6: array (nullable = true)
| |-- element: struct (containsNull = false)
| | |-- field7: string (nullable = true)
Then we can access them by row.field3.field5[0] or row.field6[5].field7
It also will infer the schema in Python, convert Row/dict/namedtuple/objects into tuple before serialization, then call applySchema in JVM. During inferSchema(), the top level of dict in row will be StructType, but any nested dictionary will be MapType.
You can use pyspark.sql.Row to convert unnamed structure into Row object, make the RDD can be inferable. Such as:
ctx.inferSchema(rdd.map(lambda x: Row(a=x[0], b=x[1]))
Or you could use Row to create a class just like namedtuple, for example:
Person = Row("name", "age")
ctx.inferSchema(rdd.map(lambda x: Person(*x)))
Also, you can call applySchema to apply an schema to a RDD of tuple/list and turn it into a SchemaRDD. The `schema` should be StructType, see the API docs for details.
schema = StructType([StructField("name, StringType, True),
StructType("age", IntegerType, True)])
ctx.applySchema(rdd, schema)
PS: In order to use namedtuple to inferSchema, you should make namedtuple picklable.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1598 from davies/nested and squashes the following commits:
f1d15b6 [Davies Liu] verify schema with the first few rows
8852aaf [Davies Liu] check type of schema
abe9e6e [Davies Liu] address comments
61b2292 [Davies Liu] add @deprecated to pythonToJavaMap
1e5b801 [Davies Liu] improve cache of classes
51aa135 [Davies Liu] use Row to infer schema
e9c0d5c [Davies Liu] remove string typed schema
353a3f2 [Davies Liu] fix code style
63de8f8 [Davies Liu] fix typo
c79ca67 [Davies Liu] fix serialization of nested data
6b258b5 [Davies Liu] fix pep8
9d8447c [Davies Liu] apply schema provided by string of names
f5df97f [Davies Liu] refactor, address comments
9d9af55 [Davies Liu] use arrry to applySchema and infer schema in Python
84679b3 [Davies Liu] Merge branch 'master' of github.com:apache/spark into nested
0eaaf56 [Davies Liu] fix doc tests
b3559b4 [Davies Liu] use generated Row instead of namedtuple
c4ddc30 [Davies Liu] fix conflict between name of fields and variables
7f6f251 [Davies Liu] address all comments
d69d397 [Davies Liu] refactor
2cc2d45 [Davies Liu] refactor
182fb46 [Davies Liu] refactor
bc6e9e1 [Davies Liu] switch to new Schema API
547bf3e [Davies Liu] Merge branch 'master' into nested
a435b5a [Davies Liu] add docs and code refactor
2c8debc [Davies Liu] Merge branch 'master' into nested
644665a [Davies Liu] use tuple and namedtuple for schemardd
RandomRDDGenerators but without support for randomRDD and randomVectorRDD, which take in arbitrary DistributionGenerator.
`randomRDD.py` is named to avoid collision with the built-in Python `random` package.
Author: Doris Xin <doris.s.xin@gmail.com>
Closes#1628 from dorx/pythonRDD and squashes the following commits:
55c6de8 [Doris Xin] review comments. all python units passed.
f831d9b [Doris Xin] moved default args logic into PythonMLLibAPI
2d73917 [Doris Xin] fix for linalg.py
8663e6a [Doris Xin] reverting back to a single python file for random
f47c481 [Doris Xin] docs update
687aac0 [Doris Xin] add RandomRDDGenerators.py to run-tests
4338f40 [Doris Xin] renamed randomRDD to rand and import as random
29d205e [Doris Xin] created mllib.random package
bd2df13 [Doris Xin] typos
07ddff2 [Doris Xin] units passed.
23b2ecd [Doris Xin] WIP
Prior to this change, every PySpark task completion opened a new socket to the accumulator server, passed its updates through, and then quit. I'm not entirely sure why PySpark always sends accumulator updates, but regardless this causes a very rapid buildup of ephemeral TCP connections that remain in the TCP_WAIT state for around a minute before being cleaned up.
Rather than trying to allow these sockets to be cleaned up faster, this patch simply reuses the connection between tasks completions (since they're fed updates in a single-threaded manner by the DAGScheduler anyway).
The only tricky part here was making sure that the AccumulatorServer was able to shutdown in a timely manner (i.e., stop polling for new data), and this was accomplished via minor feats of magic.
I have confirmed that this patch eliminates the buildup of ephemeral sockets due to the accumulator updates. However, I did note that there were still significant sockets being created against the PySpark daemon port, but my machine was not able to create enough sockets fast enough to fail. This may not be the last time we've seen this issue, though.
Author: Aaron Davidson <aaron@databricks.com>
Closes#1503 from aarondav/accum and squashes the following commits:
b3e12f7 [Aaron Davidson] SPARK-2282: Reuse Socket for sending accumulator updates to Pyspark
LocalHiveContext is redundant with HiveContext. The only difference is it creates `./metastore` instead of `./metastore_db`.
Author: Michael Armbrust <michael@databricks.com>
Closes#1641 from marmbrus/localHiveContext and squashes the following commits:
e5ec497 [Michael Armbrust] Add deprecation version
626e056 [Michael Armbrust] Don't remove from imports yet
905cc5f [Michael Armbrust] Merge remote-tracking branch 'apache/master' into localHiveContext
1c2727e [Michael Armbrust] Deprecate LocalHiveContext
Per discussion at https://issues.apache.org/jira/browse/SPARK-2341 , this is a look at deprecating the multiclass parameter. Thoughts welcome of course.
Author: Sean Owen <srowen@gmail.com>
Closes#1663 from srowen/SPARK-2341 and squashes the following commits:
8a3abd7 [Sean Owen] Suppress MIMA error for removed package private classes
18a8c8e [Sean Owen] Updates from review
83d0092 [Sean Owen] Deprecated methods with multiclass, and instead always parse target as a double (ie. multiclass = true)
JIRA issue: https://issues.apache.org/jira/browse/SPARK-2024
This PR is a followup to #455 and adds capabilities for saving PySpark RDDs using SequenceFile or any Hadoop OutputFormats.
* Added RDD methods ```saveAsSequenceFile```, ```saveAsHadoopFile``` and ```saveAsHadoopDataset```, for both old and new MapReduce APIs.
* Default converter for converting common data types to Writables. Users may specify custom converters to convert to desired data types.
* No out-of-box support for reading/writing arrays, since ArrayWritable itself doesn't have a no-arg constructor for creating an empty instance upon reading. Users need to provide ArrayWritable subtypes. Custom converters for converting arrays to suitable ArrayWritable subtypes are also needed when writing. When reading, the default converter will convert any custom ArrayWritable subtypes to ```Object[]``` and they get pickled to Python tuples.
* Added HBase and Cassandra output examples to show how custom output formats and converters can be used.
cc MLnick mateiz ahirreddy pwendell
Author: Kan Zhang <kzhang@apache.org>
Closes#1338 from kanzhang/SPARK-2024 and squashes the following commits:
c01e3ef [Kan Zhang] [SPARK-2024] code formatting
6591e37 [Kan Zhang] [SPARK-2024] renaming pickled -> pickledRDD
d998ad6 [Kan Zhang] [SPARK-2024] refectoring to get method params below 10
57a7a5e [Kan Zhang] [SPARK-2024] correcting typo
75ca5bd [Kan Zhang] [SPARK-2024] Better type checking for batch serialized RDD
0bdec55 [Kan Zhang] [SPARK-2024] Refactoring newly added tests
9f39ff4 [Kan Zhang] [SPARK-2024] Adding 2 saveAsHadoopDataset tests
0c134f3 [Kan Zhang] [SPARK-2024] Test refactoring and adding couple unbatched cases
7a176df [Kan Zhang] [SPARK-2024] Add saveAsSequenceFile to PySpark
The current PR contains the following changes:
* Expose `DataType`s in the sql package (internal details are private to sql).
* Users can create Rows.
* Introduce `applySchema` to create a `SchemaRDD` by applying a `schema: StructType` to an `RDD[Row]`.
* Add a function `simpleString` to every `DataType`. Also, the schema represented by a `StructType` can be visualized by `printSchema`.
* `ScalaReflection.typeOfObject` provides a way to infer the Catalyst data type based on an object. Also, we can compose `typeOfObject` with some custom logics to form a new function to infer the data type (for different use cases).
* `JsonRDD` has been refactored to use changes introduced by this PR.
* Add a field `containsNull` to `ArrayType`. So, we can explicitly mark if an `ArrayType` can contain null values. The default value of `containsNull` is `false`.
New APIs are introduced in the sql package object and SQLContext. You can find the scaladoc at
[sql package object](http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.package) and [SQLContext](http://yhuai.github.io/site/api/scala/index.html#org.apache.spark.sql.SQLContext).
An example of using `applySchema` is shown below.
```scala
import org.apache.spark.sql._
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
val schema =
StructType(
StructField("name", StringType, false) ::
StructField("age", IntegerType, true) :: Nil)
val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Row(p(0), p(1).trim.toInt))
val peopleSchemaRDD = sqlContext. applySchema(people, schema)
peopleSchemaRDD.printSchema
// root
// |-- name: string (nullable = false)
// |-- age: integer (nullable = true)
peopleSchemaRDD.registerAsTable("people")
sqlContext.sql("select name from people").collect.foreach(println)
```
I will add new contents to the SQL programming guide later.
JIRA: https://issues.apache.org/jira/browse/SPARK-2179
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1346 from yhuai/dataTypeAndSchema and squashes the following commits:
1d45977 [Yin Huai] Clean up.
a6e08b4 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
c712fbf [Yin Huai] Converts types of values based on defined schema.
4ceeb66 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
e5f8df5 [Yin Huai] Scaladoc.
122d1e7 [Yin Huai] Address comments.
03bfd95 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
2476ed0 [Yin Huai] Minor updates.
ab71f21 [Yin Huai] Format.
fc2bed1 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
bd40a33 [Yin Huai] Address comments.
991f860 [Yin Huai] Move "asJavaDataType" and "asScalaDataType" to DataTypeConversions.scala.
1cb35fe [Yin Huai] Add "valueContainsNull" to MapType.
3edb3ae [Yin Huai] Python doc.
692c0b9 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
1d93395 [Yin Huai] Python APIs.
246da96 [Yin Huai] Add java data type APIs to javadoc index.
1db9531 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
d48fc7b [Yin Huai] Minor updates.
33c4fec [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
b9f3071 [Yin Huai] Java API for applySchema.
1c9f33c [Yin Huai] Java APIs for DataTypes and Row.
624765c [Yin Huai] Tests for applySchema.
aa92e84 [Yin Huai] Update data type tests.
8da1a17 [Yin Huai] Add Row.fromSeq.
9c99bc0 [Yin Huai] Several minor updates.
1d9c13a [Yin Huai] Update applySchema API.
85e9b51 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
e495e4e [Yin Huai] More comments.
42d47a3 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
c3f4a02 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
2e58dbd [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
b8b7db4 [Yin Huai] 1. Move sql package object and package-info to sql-core. 2. Minor updates on APIs. 3. Update scala doc.
68525a2 [Yin Huai] Update JSON unit test.
3209108 [Yin Huai] Add unit tests.
dcaf22f [Yin Huai] Add a field containsNull to ArrayType to indicate if an array can contain null values or not. If an ArrayType is constructed by "ArrayType(elementType)" (the existing constructor), the value of containsNull is false.
9168b83 [Yin Huai] Update comments.
fc649d7 [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
eca7d04 [Yin Huai] Add two apply methods which will be used to extract StructField(s) from a StructType.
949d6bb [Yin Huai] When creating a SchemaRDD for a JSON dataset, users can apply an existing schema.
7a6a7e5 [Yin Huai] Fix bug introduced by the change made on SQLContext.inferSchema.
43a45e1 [Yin Huai] Remove sql.util.package introduced in a previous commit.
0266761 [Yin Huai] Format
03eec4c [Yin Huai] Merge remote-tracking branch 'upstream/master' into dataTypeAndSchema
90460ac [Yin Huai] Infer the Catalyst data type from an object and cast a data value to the expected type.
3fa0df5 [Yin Huai] Provide easier ways to construct a StructType.
16be3e5 [Yin Huai] This commit contains three changes: * Expose `DataType`s in the sql package (internal details are private to sql). * Introduce `createSchemaRDD` to create a `SchemaRDD` from an `RDD` with a provided schema (represented by a `StructType`) and a provided function to construct `Row`, * Add a function `simpleString` to every `DataType`. Also, the schema represented by a `StructType` can be visualized by `printSchema`.
Datetime and time in Python will be converted into java.util.Calendar after serialization, it will be converted into java.sql.Timestamp during inferSchema().
In javaToPython(), Timestamp will be converted into Calendar, then be converted into datetime in Python after pickling.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1601 from davies/date and squashes the following commits:
f0599b0 [Davies Liu] remove tests for sets and tuple in sql, fix list of list
c9d607a [Davies Liu] convert datetype for runtime
709d40d [Davies Liu] remove brackets
96db384 [Davies Liu] support datetime type for SchemaRDD
fix the problem with pickle operator.itemgetter with multiple index.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1627 from davies/itemgetter and squashes the following commits:
aabd7fa [Davies Liu] fix pickle itemgetter with cloudpickle
During rdd.take(n), JVM will close the socket if it had got enough data, the Python worker should keep silent in this case.
In the same time, the worker should not print the trackback into stderr if it send the traceback to JVM successfully.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1625 from davies/error and squashes the following commits:
4fbcc6d [Davies Liu] disable log4j during testing when exception is expected.
cc14202 [Davies Liu] keep silent in worker if JVM close the socket
This addresses a PySpark issue where a failed attempt to construct SparkContext would prevent any future SparkContext creation.
Author: Josh Rosen <joshrosen@apache.org>
Closes#1606 from JoshRosen/SPARK-1550 and squashes the following commits:
ec7fadc [Josh Rosen] [SPARK-1550] [PySpark] Allow SparkContext creation after failed attempts
Added a set of serializer/deserializer for Double in _common.py and PythonMLLibAPI in MLLib.
Author: Doris Xin <doris.s.xin@gmail.com>
Closes#1581 from dorx/doubleSerDe and squashes the following commits:
86a85b3 [Doris Xin] Merge branch 'master' into doubleSerDe
2bfe7a4 [Doris Xin] Removed magic byte
ad4d0d9 [Doris Xin] removed a space in unit
a9020bc [Doris Xin] units passed
7dad9af [Doris Xin] WIP
Similar to SPARK-1034, the problem was that Py4J didn’t cope well with the fake ClassTags used in the Java API. It doesn’t look like there’s any reason why PythonRDD needs to take a ClassTag, since it just ignores the type of the previous RDD, so I removed the type parameter and we no longer pass ClassTags from Python.
Author: Josh Rosen <joshrosen@apache.org>
Closes#1605 from JoshRosen/spark-2601 and squashes the following commits:
b68e118 [Josh Rosen] Fix Py4J error when transforming pickleFiles [SPARK-2601]
Add several default configs for PySpark, related to serialization in JVM.
spark.serializer = org.apache.spark.serializer.KryoSerializer
spark.serializer.objectStreamReset = 100
spark.rdd.compress = True
This will help to reduce the memory usage during RDD.partitionBy()
Author: Davies Liu <davies.liu@gmail.com>
Closes#1568 from davies/conf and squashes the following commits:
cd316f1 [Davies Liu] remove duplicated line
f71a355 [Davies Liu] rebase to master, add spark.rdd.compress = True
8f63f45 [Davies Liu] Merge branch 'master' into conf
8bc9f08 [Davies Liu] fix unittest
c04a83d [Davies Liu] some default configs for PySpark
Author: Josh Rosen <joshrosen@apache.org>
Closes#1596 from JoshRosen/spark-1458 and squashes the following commits:
fdbb0bf [Josh Rosen] Add SparkContext.version to Python & Java [SPARK-1458]
exact sample size not supported for now.
Author: Doris Xin <doris.s.xin@gmail.com>
Closes#1554 from dorx/pystratified and squashes the following commits:
4ba927a [Doris Xin] use rel diff (+- 50%) instead of abs diff (+- 50)
bdc3f8b [Doris Xin] updated unit to check sample holistically
7713c7b [Doris Xin] Python version of stratified sampling
During aggregation in Python worker, if the memory usage is above spark.executor.memory, it will do disk spilling aggregation.
It will split the aggregation into multiple stage, in each stage, it will partition the aggregated data by hash and dump them into disks. After all the data are aggregated, it will merge all the stages together (partition by partition).
Author: Davies Liu <davies.liu@gmail.com>
Closes#1460 from davies/spill and squashes the following commits:
cad91bf [Davies Liu] call gc.collect() after data.clear() to release memory as much as possible.
37d71f7 [Davies Liu] balance the partitions
902f036 [Davies Liu] add shuffle.py into run-tests
dcf03a9 [Davies Liu] fix memory_info() of psutil
67e6eba [Davies Liu] comment for MAX_TOTAL_PARTITIONS
f6bd5d6 [Davies Liu] rollback next_limit() again, the performance difference is huge:
e74b785 [Davies Liu] fix code style and change next_limit to memory_limit
400be01 [Davies Liu] address all the comments
6178844 [Davies Liu] refactor and improve docs
fdd0a49 [Davies Liu] add long doc string for ExternalMerger
1a97ce4 [Davies Liu] limit used memory and size of objects in partitionBy()
e6cc7f9 [Davies Liu] Merge branch 'master' into spill
3652583 [Davies Liu] address comments
e78a0a0 [Davies Liu] fix style
24cec6a [Davies Liu] get local directory by SPARK_LOCAL_DIR
57ee7ef [Davies Liu] update docs
286aaff [Davies Liu] let spilled aggregation in Python configurable
e9a40f6 [Davies Liu] recursive merger
6edbd1f [Davies Liu] Hash based disk spilling aggregation
Author: Prashant Sharma <prashant.s@imaginea.com>
Closes#1051 from ScrapCodes/SPARK-2014/pyspark-cache and squashes the following commits:
f192df7 [Prashant Sharma] Code Review
2a2f43f [Prashant Sharma] [SPARK-2014] Make PySpark store RDDs in MEMORY_ONLY_SER with compression by default
In CPython, hash of None is different cross machines, it will cause wrong result during shuffle. This PR will fix this.
Author: Davies Liu <davies.liu@gmail.com>
Closes#1371 from davies/hash_of_none and squashes the following commits:
d01745f [Davies Liu] add comments, remove outdated unit tests
5467141 [Davies Liu] disable hijack of hash, use it only for partitionBy()
b7118aa [Davies Liu] use __builtin__ instead of __builtins__
839e417 [Davies Liu] hijack hash to make hash of None consistant cross machines
to avoid overflow in `exp(x)` if `x` is large.
Author: Xiangrui Meng <meng@databricks.com>
Closes#1493 from mengxr/py-logistic and squashes the following commits:
259e863 [Xiangrui Meng] stabilize logistic function in pyspark
http://legacy.python.org/dev/peps/pep-0008/
## Programming Recommendations
- Comparisons to singletons like None should always be done with is or is not, never the equality operators.
Author: Ken Takagiwa <ken@Kens-MacBook-Pro.local>
Closes#1422 from giwa/apache_master and squashes the following commits:
7b361f3 [Ken Takagiwa] follow pep8 None should be checked using is or is not
Author: Prashant Sharma <prashant.s@imaginea.com>
Closes#1354 from ScrapCodes/pep8-comp-1 and squashes the following commits:
9858ea8 [Prashant Sharma] Code Review
d8851b7 [Prashant Sharma] Found # noqa works even inside comment blocks. Not sure if it works with all versions of python.
10c0cef [Prashant Sharma] Made rdd.py pep8 complaint by using Autopep8 and a little manual tweaking.
Author: Andrew Or <andrewor14@gmail.com>
Closes#1365 from andrewor14/master-fs and squashes the following commits:
497f100 [Andrew Or] Sneak in a space and hope no one will notice
05ba6da [Andrew Or] Remove unused val
It should not be the responsibility of the worker subprocess, which
does not intentionally fork, to try and cleanup child processes. Doing
so is complex and interferes with operations such as
platform.system().
If it is desirable to have tighter control over subprocesses, then
namespaces should be used and it should be the manager's resposibility
to handle cleanup.
Author: Matthew Farrellee <matt@redhat.com>
Closes#1247 from mattf/SPARK-1394 and squashes the following commits:
c36f308 [Matthew Farrellee] [SPARK-1394] Remove SIGCHLD handler in worker subprocess
This reverts a change introduced in 3870248740, which redirected all stderr to the OS pipe instead of directly to the `bin/pyspark` shell output. This causes a simple job to hang in two ways:
1. If the cluster is not configured correctly or does not have enough resources, the job hangs without producing any output, because the relevant warning messages are masked.
2. If the stderr volume is large, this could lead to a deadlock if we redirect everything to the OS pipe. From the [python docs](https://docs.python.org/2/library/subprocess.html):
```
Note Do not use stdout=PIPE or stderr=PIPE with this function as that can deadlock
based on the child process output volume. Use Popen with the communicate() method
when you need pipes.
```
Note that we cannot remove `stdout=PIPE` in a similar way, because we currently use it to communicate the py4j port. However, it should be fine (as it has been for a long time) because we do not produce a ton of traffic through `stdout`.
That commit was not merged in branch-1.0, so this fix is for master only.
Author: Andrew Or <andrewor14@gmail.com>
Closes#1178 from andrewor14/fix-python and squashes the following commits:
e68e870 [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-python
20849a8 [Andrew Or] Tone down stdout interference message
a09805b [Andrew Or] Return more than 1 line of error message to user
6dfbd1e [Andrew Or] Don't swallow original exception
0d1861f [Andrew Or] Provide more helpful output if stdout is garbled
21c9d7c [Andrew Or] Do not mask stderr from output
The jira for the issue can be found at: https://issues.apache.org/jira/browse/SPARK-2061
Most of spark has used over to consistently using `partitions` instead of `splits`. We should do likewise and add a `partitions` method to JavaRDDLike and have `splits` just call that. We should also go through all cases where other API's (e.g. Python) call `splits` and we should change those to use the newer API.
Author: Anant <anant.asty@gmail.com>
Closes#1062 from anantasty/SPARK-2061 and squashes the following commits:
b83ce6b [Anant] Fixed syntax issue
21f9210 [Anant] Fixed version number in deprecation string
9315b76 [Anant] made related changes to use partitions in python api
8c62dd1 [Anant] Made splits deprecated in JavaRDDLike
