aboutsummaryrefslogtreecommitdiff
path: root/python
Commit message (Collapse)AuthorAgeFilesLines
* [SPARK-18352][SQL] Support parsing multiline json filesNathan Howell2017-02-164-10/+37
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? If a new option `wholeFile` is set to `true` the JSON reader will parse each file (instead of a single line) as a value. This is done with Jackson streaming and it should be capable of parsing very large documents, assuming the row will fit in memory. Because the file is not buffered in memory the corrupt record handling is also slightly different when `wholeFile` is enabled: the corrupt column will contain the filename instead of the literal JSON if there is a parsing failure. It would be easy to extend this to add the parser location (line, column and byte offsets) to the output if desired. These changes have allowed types other than `String` to be parsed. Support for `UTF8String` and `Text` have been added (alongside `String` and `InputFormat`) and no longer require a conversion to `String` just for parsing. I've also included a few other changes that generate slightly better bytecode and (imo) make it more obvious when and where boxing is occurring in the parser. These are included as separate commits, let me know if they should be flattened into this PR or moved to a new one. ## How was this patch tested? New and existing unit tests. No performance or load tests have been run. Author: Nathan Howell <nhowell@godaddy.com> Closes #16386 from NathanHowell/SPARK-18352.
* [SPARK-18080][ML][PYTHON] Python API & Examples for Locality Sensitive HashingYun Ni2017-02-151-0/+291
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This pull request includes python API and examples for LSH. The API changes was based on yanboliang 's PR #15768 and resolved conflicts and API changes on the Scala API. The examples are consistent with Scala examples of MinHashLSH and BucketedRandomProjectionLSH. ## How was this patch tested? API and examples are tested using spark-submit: `bin/spark-submit examples/src/main/python/ml/min_hash_lsh.py` `bin/spark-submit examples/src/main/python/ml/bucketed_random_projection_lsh.py` User guide changes are generated and manually inspected: `SKIP_API=1 jekyll build` Author: Yun Ni <yunn@uber.com> Author: Yanbo Liang <ybliang8@gmail.com> Author: Yunni <Euler57721@gmail.com> Closes #16715 from Yunni/spark-18080.
* [SPARK-19604][TESTS] Log the start of every Python testYin Huai2017-02-151-1/+1
| | | | | | | | | | | | ## What changes were proposed in this pull request? Right now, we only have info level log after we finish the tests of a Python test file. We should also log the start of a test. So, if a test is hanging, we can tell which test file is running. ## How was this patch tested? This is a change for python tests. Author: Yin Huai <yhuai@databricks.com> Closes #16935 from yhuai/SPARK-19604.
* [SPARK-18937][SQL] Timezone support in CSV/JSON parsingTakuya UESHIN2017-02-152-24/+39
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This is a follow-up pr of #16308. This pr enables timezone support in CSV/JSON parsing. We should introduce `timeZone` option for CSV/JSON datasources (the default value of the option is session local timezone). The datasources should use the `timeZone` option to format/parse to write/read timestamp values. Notice that while reading, if the timestampFormat has the timezone info, the timezone will not be used because we should respect the timezone in the values. For example, if you have timestamp `"2016-01-01 00:00:00"` in `GMT`, the values written with the default timezone option, which is `"GMT"` because session local timezone is `"GMT"` here, are: ```scala scala> spark.conf.set("spark.sql.session.timeZone", "GMT") scala> val df = Seq(new java.sql.Timestamp(1451606400000L)).toDF("ts") df: org.apache.spark.sql.DataFrame = [ts: timestamp] scala> df.show() +-------------------+ |ts | +-------------------+ |2016-01-01 00:00:00| +-------------------+ scala> df.write.json("/path/to/gmtjson") ``` ```sh $ cat /path/to/gmtjson/part-* {"ts":"2016-01-01T00:00:00.000Z"} ``` whereas setting the option to `"PST"`, they are: ```scala scala> df.write.option("timeZone", "PST").json("/path/to/pstjson") ``` ```sh $ cat /path/to/pstjson/part-* {"ts":"2015-12-31T16:00:00.000-08:00"} ``` We can properly read these files even if the timezone option is wrong because the timestamp values have timezone info: ```scala scala> val schema = new StructType().add("ts", TimestampType) schema: org.apache.spark.sql.types.StructType = StructType(StructField(ts,TimestampType,true)) scala> spark.read.schema(schema).json("/path/to/gmtjson").show() +-------------------+ |ts | +-------------------+ |2016-01-01 00:00:00| +-------------------+ scala> spark.read.schema(schema).option("timeZone", "PST").json("/path/to/gmtjson").show() +-------------------+ |ts | +-------------------+ |2016-01-01 00:00:00| +-------------------+ ``` And even if `timezoneFormat` doesn't contain timezone info, we can properly read the values with setting correct timezone option: ```scala scala> df.write.option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss").option("timeZone", "JST").json("/path/to/jstjson") ``` ```sh $ cat /path/to/jstjson/part-* {"ts":"2016-01-01T09:00:00"} ``` ```scala // wrong result scala> spark.read.schema(schema).option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss").json("/path/to/jstjson").show() +-------------------+ |ts | +-------------------+ |2016-01-01 09:00:00| +-------------------+ // correct result scala> spark.read.schema(schema).option("timestampFormat", "yyyy-MM-dd'T'HH:mm:ss").option("timeZone", "JST").json("/path/to/jstjson").show() +-------------------+ |ts | +-------------------+ |2016-01-01 00:00:00| +-------------------+ ``` This pr also makes `JsonToStruct` and `StructToJson` `TimeZoneAwareExpression` to be able to evaluate values with timezone option. ## How was this patch tested? Existing tests and added some tests. Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #16750 from ueshin/issues/SPARK-18937.
* [SPARK-19399][SPARKR] Add R coalesce API for DataFrame and ColumnFelix Cheung2017-02-151-1/+9
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add coalesce on DataFrame for down partitioning without shuffle and coalesce on Column ## How was this patch tested? manual, unit tests Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16739 from felixcheung/rcoalesce.
* [SPARK-19160][PYTHON][SQL] Add udf decoratorzero3232017-02-152-7/+91
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds `udf` decorator syntax as proposed in [SPARK-19160](https://issues.apache.org/jira/browse/SPARK-19160). This allows users to define UDF using simplified syntax: ```python from pyspark.sql.decorators import udf udf(IntegerType()) def add_one(x): """Adds one""" if x is not None: return x + 1 ``` without need to define a separate function and udf. ## How was this patch tested? Existing unit tests to ensure backward compatibility and additional unit tests covering new functionality. Author: zero323 <zero323@users.noreply.github.com> Closes #16533 from zero323/SPARK-19160.
* [SPARK-19590][PYSPARK][ML] Update the document for QuantileDiscretizer in ↵VinceShieh2017-02-151-1/+11
| | | | | | | | | | | | | | | | | pyspark ## What changes were proposed in this pull request? This PR is to document the changes on QuantileDiscretizer in pyspark for PR: https://github.com/apache/spark/pull/15428 ## How was this patch tested? No test needed Signed-off-by: VinceShieh <vincent.xieintel.com> Author: VinceShieh <vincent.xie@intel.com> Closes #16922 from VinceShieh/spark-19590.
* [SPARK-18541][PYTHON] Add metadata parameter to pyspark.sql.Column.alias()Sheamus K. Parkes2017-02-142-3/+33
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add a `metadata` keyword parameter to `pyspark.sql.Column.alias()` to allow users to mix-in metadata while manipulating `DataFrame`s in `pyspark`. Without this, I believe it was necessary to pass back through `SparkSession.createDataFrame` each time a user wanted to manipulate `StructField.metadata` in `pyspark`. This pull request also improves consistency between the Scala and Python APIs (i.e. I did not add any functionality that was not already in the Scala API). Discussed ahead of time on JIRA with marmbrus ## How was this patch tested? Added unit tests (and doc tests). Ran the pertinent tests manually. Author: Sheamus K. Parkes <shea.parkes@milliman.com> Closes #16094 from shea-parkes/pyspark-column-alias-metadata.
* [SPARK-19162][PYTHON][SQL] UserDefinedFunction should validate that func is ↵zero3232017-02-142-0/+12
| | | | | | | | | | | | | | | | callable ## What changes were proposed in this pull request? UDF constructor checks if `func` argument is callable and if it is not, fails fast instead of waiting for an action. ## How was this patch tested? Unit tests. Author: zero323 <zero323@users.noreply.github.com> Closes #16535 from zero323/SPARK-19162.
* [SPARK-19453][PYTHON][SQL][DOC] Correct and extend DataFrame.replace docstringzero3232017-02-141-6/+12
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? - Provides correct description of the semantics of a `dict` argument passed as `to_replace`. - Describes type requirements for collection arguments. - Describes behavior with `to_replace: List[T]` and `value: T` ## How was this patch tested? Manual testing, documentation build. Author: zero323 <zero323@users.noreply.github.com> Closes #16792 from zero323/SPARK-19453.
* [SPARK-19429][PYTHON][SQL] Support slice arguments in Column.__getitem__zero3232017-02-132-3/+16
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? - Add support for `slice` arguments in `Column.__getitem__`. - Remove obsolete `__getslice__` bindings. ## How was this patch tested? Existing unit tests, additional tests covering `[]` with `slice`. Author: zero323 <zero323@users.noreply.github.com> Closes #16771 from zero323/SPARK-19429.
* [SPARK-19427][PYTHON][SQL] Support data type string as a returnType argument ↵zero3232017-02-132-3/+20
| | | | | | | | | | | | | | | | | | | | | | | of UDF ## What changes were proposed in this pull request? Add support for data type string as a return type argument of `UserDefinedFunction`: ```python f = udf(lambda x: x, "integer") f.returnType ## IntegerType ``` ## How was this patch tested? Existing unit tests, additional unit tests covering new feature. Author: zero323 <zero323@users.noreply.github.com> Closes #16769 from zero323/SPARK-19427.
* [SPARK-19506][ML][PYTHON] Import warnings in pyspark.ml.utilzero3232017-02-131-0/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add missing `warnings` import. ## How was this patch tested? Manual tests. Author: zero323 <zero323@users.noreply.github.com> Closes #16846 from zero323/SPARK-19506.
* [SPARK-16609] Add to_date/to_timestamp with format functionsanabranch2017-02-072-6/+55
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This pull request adds two new user facing functions: - `to_date` which accepts an expression and a format and returns a date. - `to_timestamp` which accepts an expression and a format and returns a timestamp. For example, Given a date in format: `2016-21-05`. (YYYY-dd-MM) ### Date Function *Previously* ``` to_date(unix_timestamp(lit("2016-21-05"), "yyyy-dd-MM").cast("timestamp")) ``` *Current* ``` to_date(lit("2016-21-05"), "yyyy-dd-MM") ``` ### Timestamp Function *Previously* ``` unix_timestamp(lit("2016-21-05"), "yyyy-dd-MM").cast("timestamp") ``` *Current* ``` to_timestamp(lit("2016-21-05"), "yyyy-dd-MM") ``` ### Tasks - [X] Add `to_date` to Scala Functions - [x] Add `to_date` to Python Functions - [x] Add `to_date` to SQL Functions - [X] Add `to_timestamp` to Scala Functions - [x] Add `to_timestamp` to Python Functions - [x] Add `to_timestamp` to SQL Functions - [x] Add function to R ## How was this patch tested? - [x] Add Functions to `DateFunctionsSuite` - Test new `ParseToTimestamp` Expression (*not necessary*) - Test new `ParseToDate` Expression (*not necessary*) - [x] Add test for R - [x] Add test for Python in test.py Please review http://spark.apache.org/contributing.html before opening a pull request. Author: anabranch <wac.chambers@gmail.com> Author: Bill Chambers <bill@databricks.com> Author: anabranch <bill@databricks.com> Closes #16138 from anabranch/SPARK-16609.
* [SPARK-19467][ML][PYTHON] Remove cyclic imports from pyspark.ml.pipelinezero3232017-02-061-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Remove cyclic imports between `pyspark.ml.pipeline` and `pyspark.ml`. ## How was this patch tested? Existing unit tests. Author: zero323 <zero323@users.noreply.github.com> Closes #16814 from zero323/SPARK-19467.
* [SPARK-19421][ML][PYSPARK] Remove numClasses and numFeatures methods in ↵Zheng RuiFeng2017-02-051-16/+0
| | | | | | | | | | | | | | | LinearSVC ## What changes were proposed in this pull request? Methods `numClasses` and `numFeatures` in LinearSVCModel are already usable by inheriting `JavaClassificationModel` we should not explicitly add them. ## How was this patch tested? existing tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #16727 from zhengruifeng/nits_in_linearSVC.
* [SPARK-19389][ML][PYTHON][DOC] Minor doc fixes for ML Python Params and ↵Joseph K. Bradley2017-02-022-17/+5
| | | | | | | | | | | | | | | | | | LinearSVC ## What changes were proposed in this pull request? * Removed Since tags in Python Params since they are inherited by other classes * Fixed doc links for LinearSVC ## How was this patch tested? * doc tests * generating docs locally and checking manually Author: Joseph K. Bradley <joseph@databricks.com> Closes #16723 from jkbradley/pyparam-fix-doc.
* [SPARK-14352][SQL] approxQuantile should support multi columnsZheng RuiFeng2017-02-012-8/+52
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? 1, add the multi-cols support based on current private api 2, add the multi-cols support to pyspark ## How was this patch tested? unit tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Author: Ruifeng Zheng <ruifengz@foxmail.com> Closes #12135 from zhengruifeng/quantile4multicols.
* [SPARK-19163][PYTHON][SQL] Delay _judf initialization to the __call__zero3232017-01-312-11/+68
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Defer `UserDefinedFunction._judf` initialization to the first call. This prevents unintended `SparkSession` initialization. This allows users to define and import UDF without creating a context / session as a side effect. [SPARK-19163](https://issues.apache.org/jira/browse/SPARK-19163) ## How was this patch tested? Unit tests. Author: zero323 <zero323@users.noreply.github.com> Closes #16536 from zero323/SPARK-19163.
* [SPARK-17161][PYSPARK][ML] Add PySpark-ML JavaWrapper convenience function ↵Bryan Cutler2017-01-313-3/+77
| | | | | | | | | | | | | | | | to create Py4J JavaArrays ## What changes were proposed in this pull request? Adding convenience function to Python `JavaWrapper` so that it is easy to create a Py4J JavaArray that is compatible with current class constructors that have a Scala `Array` as input so that it is not necessary to have a Java/Python friendly constructor. The function takes a Java class as input that is used by Py4J to create the Java array of the given class. As an example, `OneVsRest` has been updated to use this and the alternate constructor is removed. ## How was this patch tested? Added unit tests for the new convenience function and updated `OneVsRest` doctests which use this to persist the model. Author: Bryan Cutler <cutlerb@gmail.com> Closes #14725 from BryanCutler/pyspark-new_java_array-CountVectorizer-SPARK-17161.
* [SPARK-19403][PYTHON][SQL] Correct pyspark.sql.column.__all__ list.zero3232017-01-301-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This removes from the `__all__` list class names that are not defined (visible) in the `pyspark.sql.column`. ## How was this patch tested? Existing unit tests. Author: zero323 <zero323@users.noreply.github.com> Closes #16742 from zero323/SPARK-19403.
* [SPARK-19336][ML][PYSPARK] LinearSVC Python APIwm624@hotmail.com2017-01-273-1/+156
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add Python API for the newly added LinearSVC algorithm. ## How was this patch tested? Add new doc string test. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #16694 from wangmiao1981/ser.
* [SPARK-18020][STREAMING][KINESIS] Checkpoint SHARD_END to finish reading ↵Takeshi YAMAMURO2017-01-251-1/+1
| | | | | | | | | | | | | | closed shards ## What changes were proposed in this pull request? This pr is to fix an issue occurred when resharding Kinesis streams; the resharding makes the KCL throw an exception because Spark does not checkpoint `SHARD_END` when finishing reading closed shards in `KinesisRecordProcessor#shutdown`. This bug finally leads to stopping subscribing new split (or merged) shards. ## How was this patch tested? Added a test in `KinesisStreamSuite` to check if it works well when splitting/merging shards. Author: Takeshi YAMAMURO <linguin.m.s@gmail.com> Closes #16213 from maropu/SPARK-18020.
* [SPARK-19064][PYSPARK] Fix pip installing of sub componentsHolden Karau2017-01-251-0/+5
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix instalation of mllib and ml sub components, and more eagerly cleanup cache files during test script & make-distribution. ## How was this patch tested? Updated sanity test script to import mllib and ml sub-components. Author: Holden Karau <holden@us.ibm.com> Closes #16465 from holdenk/SPARK-19064-fix-pip-install-sub-components.
* [SPARK-19307][PYSPARK] Make sure user conf is propagated to SparkContext.Marcelo Vanzin2017-01-252-0/+23
| | | | | | | | | | | | The code was failing to propagate the user conf in the case where the JVM was already initialized, which happens when a user submits a python script via spark-submit. Tested with new unit test and by running a python script in a real cluster. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #16682 from vanzin/SPARK-19307.
* [SPARK-19229][SQL] Disallow Creating Hive Source Tables when Hive Support is ↵gatorsmile2017-01-221-4/+4
| | | | | | | | | | | | | | Not Enabled ### What changes were proposed in this pull request? It is weird to create Hive source tables when using InMemoryCatalog. We are unable to operate it. This PR is to block users to create Hive source tables. ### How was this patch tested? Fixed the test cases Author: gatorsmile <gatorsmile@gmail.com> Closes #16587 from gatorsmile/blockHiveTable.
* [SPARK-18589][SQL] Fix Python UDF accessing attributes from both side of joinDavies Liu2017-01-201-0/+9
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? PythonUDF is unevaluable, which can not be used inside a join condition, currently the optimizer will push a PythonUDF which accessing both side of join into the join condition, then the query will fail to plan. This PR fix this issue by checking the expression is evaluable or not before pushing it into Join. ## How was this patch tested? Add a regression test. Author: Davies Liu <davies@databricks.com> Closes #16581 from davies/pyudf_join.
* [SPARK-14272][ML] Add Loglikelihood in GaussianMixtureSummaryZheng RuiFeng2017-01-191-0/+10
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? add loglikelihood in GMM.summary ## How was this patch tested? added tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Author: Ruifeng Zheng <ruifengz@foxmail.com> Closes #12064 from zhengruifeng/gmm_metric.
* [SPARK-19223][SQL][PYSPARK] Fix InputFileBlockHolder for datasources which ↵Liang-Chi Hsieh2017-01-181-0/+24
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | are based on HadoopRDD or NewHadoopRDD ## What changes were proposed in this pull request? For some datasources which are based on HadoopRDD or NewHadoopRDD, such as spark-xml, InputFileBlockHolder doesn't work with Python UDF. The method to reproduce it is, running the following codes with `bin/pyspark --packages com.databricks:spark-xml_2.11:0.4.1`: from pyspark.sql.functions import udf,input_file_name from pyspark.sql.types import StringType from pyspark.sql import SparkSession def filename(path): return path session = SparkSession.builder.appName('APP').getOrCreate() session.udf.register('sameText', filename) sameText = udf(filename, StringType()) df = session.read.format('xml').load('a.xml', rowTag='root').select('*', input_file_name().alias('file')) df.select('file').show() # works df.select(sameText(df['file'])).show() # returns empty content The issue is because in `HadoopRDD` and `NewHadoopRDD` we set the file block's info in `InputFileBlockHolder` before the returned iterator begins consuming. `InputFileBlockHolder` will record this info into thread local variable. When running Python UDF in batch, we set up another thread to consume the iterator from child plan's output rdd, so we can't read the info back in another thread. To fix this, we have to set the info in `InputFileBlockHolder` after the iterator begins consuming. So the info can be read in correct thread. ## How was this patch tested? Manual test with above example codes for spark-xml package on pyspark: `bin/pyspark --packages com.databricks:spark-xml_2.11:0.4.1`. Added pyspark test. Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #16585 from viirya/fix-inputfileblock-hadooprdd.
* [SPARK-19239][PYSPARK] Check parameters whether equals None when specify the ↵DjvuLee2017-01-171-3/+6
| | | | | | | | | | | | | | | | | | | | column in jdbc API ## What changes were proposed in this pull request? The `jdbc` API do not check the `lowerBound` and `upperBound` when we specified the ``column``, and just throw the following exception: >```int() argument must be a string or a number, not 'NoneType'``` If we check the parameter, we can give a more friendly suggestion. ## How was this patch tested? Test using the pyspark shell, without the lowerBound and upperBound parameters. Author: DjvuLee <lihu@bytedance.com> Closes #16599 from djvulee/pysparkFix.
* [SPARK-19019] [PYTHON] Fix hijacked `collections.namedtuple` and port ↵hyukjinkwon2017-01-172-31/+87
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | cloudpickle changes for PySpark to work with Python 3.6.0 ## What changes were proposed in this pull request? Currently, PySpark does not work with Python 3.6.0. Running `./bin/pyspark` simply throws the error as below and PySpark does not work at all: ``` Traceback (most recent call last): File ".../spark/python/pyspark/shell.py", line 30, in <module> import pyspark File ".../spark/python/pyspark/__init__.py", line 46, in <module> from pyspark.context import SparkContext File ".../spark/python/pyspark/context.py", line 36, in <module> from pyspark.java_gateway import launch_gateway File ".../spark/python/pyspark/java_gateway.py", line 31, in <module> from py4j.java_gateway import java_import, JavaGateway, GatewayClient File "<frozen importlib._bootstrap>", line 961, in _find_and_load File "<frozen importlib._bootstrap>", line 950, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 646, in _load_unlocked File "<frozen importlib._bootstrap>", line 616, in _load_backward_compatible File ".../spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 18, in <module> File "/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/pydoc.py", line 62, in <module> import pkgutil File "/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/pkgutil.py", line 22, in <module> ModuleInfo = namedtuple('ModuleInfo', 'module_finder name ispkg') File ".../spark/python/pyspark/serializers.py", line 394, in namedtuple cls = _old_namedtuple(*args, **kwargs) TypeError: namedtuple() missing 3 required keyword-only arguments: 'verbose', 'rename', and 'module' ``` The root cause seems because some arguments of `namedtuple` are now completely keyword-only arguments from Python 3.6.0 (See https://bugs.python.org/issue25628). We currently copy this function via `types.FunctionType` which does not set the default values of keyword-only arguments (meaning `namedtuple.__kwdefaults__`) and this seems causing internally missing values in the function (non-bound arguments). This PR proposes to work around this by manually setting it via `kwargs` as `types.FunctionType` seems not supporting to set this. Also, this PR ports the changes in cloudpickle for compatibility for Python 3.6.0. ## How was this patch tested? Manually tested with Python 2.7.6 and Python 3.6.0. ``` ./bin/pyspsark ``` , manual creation of `namedtuple` both in local and rdd with Python 3.6.0, and Jenkins tests for other Python versions. Also, ``` ./run-tests --python-executables=python3.6 ``` ``` Will test against the following Python executables: ['python3.6'] Will test the following Python modules: ['pyspark-core', 'pyspark-ml', 'pyspark-mllib', 'pyspark-sql', 'pyspark-streaming'] Finished test(python3.6): pyspark.sql.tests (192s) Finished test(python3.6): pyspark.accumulators (3s) Finished test(python3.6): pyspark.mllib.tests (198s) Finished test(python3.6): pyspark.broadcast (3s) Finished test(python3.6): pyspark.conf (2s) Finished test(python3.6): pyspark.context (14s) Finished test(python3.6): pyspark.ml.classification (21s) Finished test(python3.6): pyspark.ml.evaluation (11s) Finished test(python3.6): pyspark.ml.clustering (20s) Finished test(python3.6): pyspark.ml.linalg.__init__ (0s) Finished test(python3.6): pyspark.streaming.tests (240s) Finished test(python3.6): pyspark.tests (240s) Finished test(python3.6): pyspark.ml.recommendation (19s) Finished test(python3.6): pyspark.ml.feature (36s) Finished test(python3.6): pyspark.ml.regression (37s) Finished test(python3.6): pyspark.ml.tuning (28s) Finished test(python3.6): pyspark.mllib.classification (26s) Finished test(python3.6): pyspark.mllib.evaluation (18s) Finished test(python3.6): pyspark.mllib.clustering (44s) Finished test(python3.6): pyspark.mllib.linalg.__init__ (0s) Finished test(python3.6): pyspark.mllib.feature (26s) Finished test(python3.6): pyspark.mllib.fpm (23s) Finished test(python3.6): pyspark.mllib.random (8s) Finished test(python3.6): pyspark.ml.tests (92s) Finished test(python3.6): pyspark.mllib.stat.KernelDensity (0s) Finished test(python3.6): pyspark.mllib.linalg.distributed (25s) Finished test(python3.6): pyspark.mllib.stat._statistics (15s) Finished test(python3.6): pyspark.mllib.recommendation (24s) Finished test(python3.6): pyspark.mllib.regression (26s) Finished test(python3.6): pyspark.profiler (9s) Finished test(python3.6): pyspark.mllib.tree (16s) Finished test(python3.6): pyspark.shuffle (1s) Finished test(python3.6): pyspark.mllib.util (18s) Finished test(python3.6): pyspark.serializers (11s) Finished test(python3.6): pyspark.rdd (20s) Finished test(python3.6): pyspark.sql.conf (8s) Finished test(python3.6): pyspark.sql.catalog (17s) Finished test(python3.6): pyspark.sql.column (18s) Finished test(python3.6): pyspark.sql.context (18s) Finished test(python3.6): pyspark.sql.group (27s) Finished test(python3.6): pyspark.sql.dataframe (33s) Finished test(python3.6): pyspark.sql.functions (35s) Finished test(python3.6): pyspark.sql.types (6s) Finished test(python3.6): pyspark.sql.streaming (13s) Finished test(python3.6): pyspark.streaming.util (0s) Finished test(python3.6): pyspark.sql.session (16s) Finished test(python3.6): pyspark.sql.window (4s) Finished test(python3.6): pyspark.sql.readwriter (35s) Tests passed in 433 seconds ``` Author: hyukjinkwon <gurwls223@gmail.com> Closes #16429 from HyukjinKwon/SPARK-19019.
* [SPARK-19148][SQL] do not expose the external table concept in CatalogWenchen Fan2017-01-171-3/+24
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In https://github.com/apache/spark/pull/16296 , we reached a consensus that we should hide the external/managed table concept to users and only expose custom table path. This PR renames `Catalog.createExternalTable` to `createTable`(still keep the old versions for backward compatibility), and only set the table type to EXTERNAL if `path` is specified in options. ## How was this patch tested? new tests in `CatalogSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #16528 from cloud-fan/create-table.
* [SPARK-18687][PYSPARK][SQL] Backward compatibility - creating a Dataframe on ↵Vinayak2017-01-132-2/+7
| | | | | | | | | | | | | | | | | a new SQLContext object fails with a Derby error Change is for SQLContext to reuse the active SparkSession during construction if the sparkContext supplied is the same as the currently active SparkContext. Without this change, a new SparkSession is instantiated that results in a Derby error when attempting to create a dataframe using a new SQLContext object even though the SparkContext supplied to the new SQLContext is same as the currently active one. Refer https://issues.apache.org/jira/browse/SPARK-18687 for details on the error and a repro. Existing unit tests and a new unit test added to pyspark-sql: /python/run-tests --python-executables=python --modules=pyspark-sql Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Vinayak <vijoshi5@in.ibm.com> Author: Vinayak Joshi <vijoshi@users.noreply.github.com> Closes #16119 from vijoshi/SPARK-18687_master.
* [SPARK-19055][SQL][PYSPARK] Fix SparkSession initialization when ↵Liang-Chi Hsieh2017-01-122-6/+33
| | | | | | | | | | | | | | | | | | | | | | SparkContext is stopped ## What changes were proposed in this pull request? In SparkSession initialization, we store created the instance of SparkSession into a class variable _instantiatedContext. Next time we can use SparkSession.builder.getOrCreate() to retrieve the existing SparkSession instance. However, when the active SparkContext is stopped and we create another new SparkContext to use, the existing SparkSession is still associated with the stopped SparkContext. So the operations with this existing SparkSession will be failed. We need to detect such case in SparkSession and renew the class variable _instantiatedContext if needed. ## How was this patch tested? New test added in PySpark. Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #16454 from viirya/fix-pyspark-sparksession.
* [SPARK-19164][PYTHON][SQL] Remove unused UserDefinedFunction._broadcastzero3232017-01-121-6/+0
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Removes `UserDefinedFunction._broadcast` and `UserDefinedFunction.__del__` method. ## How was this patch tested? Existing unit tests. Author: zero323 <zero323@users.noreply.github.com> Closes #16538 from zero323/SPARK-19164.
* [SPARK-19140][SS] Allow update mode for non-aggregation streaming queriesShixiong Zhu2017-01-101-8/+19
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR allow update mode for non-aggregation streaming queries. It will be same as the append mode if a query has no aggregations. ## How was this patch tested? Jenkins Author: Shixiong Zhu <shixiong@databricks.com> Closes #16520 from zsxwing/update-without-agg.
* [SPARK-17645][MLLIB][ML][FOLLOW-UP] document minor changePeng, Meng2017-01-102-7/+8
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add FDR test case in ml/feature/ChiSqSelectorSuite. Improve some comments in the code. This is a follow-up pr for #15212. ## How was this patch tested? ut Author: Peng, Meng <peng.meng@intel.com> Closes #16434 from mpjlu/fdr_fwe_update.
* [SPARK-17847][ML] Reduce shuffled data size of GaussianMixture & copy the ↵Yanbo Liang2017-01-091-18/+8
| | | | | | | | | | | | | | | | | | | | | | | | | | | implementation from mllib to ml ## What changes were proposed in this pull request? Copy `GaussianMixture` implementation from mllib to ml, then we can add new features to it. I left mllib `GaussianMixture` untouched, unlike some other algorithms to wrap the ml implementation. For the following reasons: - mllib `GaussianMixture` allows k == 1, but ml does not. - mllib `GaussianMixture` supports setting initial model, but ml does not support currently. (We will definitely add this feature for ml in the future) We can get around these issues to make mllib as a wrapper calling into ml, but I'd prefer to leave mllib untouched which can make ml clean. Meanwhile, There is a big performance improvement for `GaussianMixture` in this PR. Since the covariance matrix of multivariate gaussian distribution is symmetric, we can only store the upper triangular part of the matrix and it will greatly reduce the shuffled data size. In my test, this change will reduce shuffled data size by about 50% and accelerate the job execution. Before this PR: ![image](https://cloud.githubusercontent.com/assets/1962026/19641622/4bb017ac-9996-11e6-8ece-83db184b620a.png) After this PR: ![image](https://cloud.githubusercontent.com/assets/1962026/19641635/629c21fe-9996-11e6-91e9-83ab74ae0126.png) ## How was this patch tested? Existing tests and added new tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15413 from yanboliang/spark-17847.
* [SPARK-19126][DOCS] Update Join Documentation Across Languagesanabranch2017-01-081-2/+3
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? - [X] Make sure all join types are clearly mentioned - [X] Make join labeling/style consistent - [X] Make join label ordering docs the same - [X] Improve join documentation according to above for Scala - [X] Improve join documentation according to above for Python - [X] Improve join documentation according to above for R ## How was this patch tested? No tests b/c docs. Please review http://spark.apache.org/contributing.html before opening a pull request. Author: anabranch <wac.chambers@gmail.com> Closes #16504 from anabranch/SPARK-19126.
* [SPARK-19127][DOCS] Update Rank Function Documentationanabranch2017-01-081-6/+10
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? - [X] Fix inconsistencies in function reference for dense rank and dense - [X] Make all languages equivalent in their reference to `dense_rank` and `rank`. ## How was this patch tested? N/A for docs. Please review http://spark.apache.org/contributing.html before opening a pull request. Author: anabranch <wac.chambers@gmail.com> Closes #16505 from anabranch/SPARK-19127.
* [SPARK-13748][PYSPARK][DOC] Add the description for explictly setting None ↵hyukjinkwon2017-01-071-1/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | for a named argument for a Row ## What changes were proposed in this pull request? It seems allowed to not set a key and value for a dict to represent the value is `None` or missing as below: ``` python spark.createDataFrame([{"x": 1}, {"y": 2}]).show() ``` ``` +----+----+ | x| y| +----+----+ | 1|null| |null| 2| +----+----+ ``` However, it seems it is not for `Row` as below: ``` python spark.createDataFrame([Row(x=1), Row(y=2)]).show() ``` ``` scala 16/06/19 16:25:56 ERROR Executor: Exception in task 6.0 in stage 66.0 (TID 316) java.lang.IllegalStateException: Input row doesn't have expected number of values required by the schema. 2 fields are required while 1 values are provided. at org.apache.spark.sql.execution.python.EvaluatePython$.fromJava(EvaluatePython.scala:147) at org.apache.spark.sql.SparkSession$$anonfun$7.apply(SparkSession.scala:656) at org.apache.spark.sql.SparkSession$$anonfun$7.apply(SparkSession.scala:656) at scala.collection.Iterator$$anon$11.next(Iterator.scala:409) at scala.collection.Iterator$$anon$11.next(Iterator.scala:409) at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:247) at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:780) ``` The behaviour seems right but it seems it might confuse users just like this JIRA was reported. This PR adds the explanation for `Row` class. ## How was this patch tested? N/A Author: hyukjinkwon <gurwls223@gmail.com> Closes #13771 from HyukjinKwon/SPARK-13748.
* [MINOR][DOCS] Remove consecutive duplicated words/typo in Spark RepoNiranjan Padmanabhan2017-01-043-4/+4
| | | | | | | | | | | | ## What changes were proposed in this pull request? There are many locations in the Spark repo where the same word occurs consecutively. Sometimes they are appropriately placed, but many times they are not. This PR removes the inappropriately duplicated words. ## How was this patch tested? N/A since only docs or comments were updated. Author: Niranjan Padmanabhan <niranjan.padmanabhan@gmail.com> Closes #16455 from neurons/np.structure_streaming_doc.
* [SPARK-19002][BUILD][PYTHON] Check pep8 against all Python scriptshyukjinkwon2017-01-021-0/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR proposes to check pep8 against all other Python scripts and fix the errors as below: ```bash ./dev/create-release/generate-contributors.py ./dev/create-release/releaseutils.py ./dev/create-release/translate-contributors.py ./dev/lint-python ./python/docs/epytext.py ./examples/src/main/python/mllib/decision_tree_classification_example.py ./examples/src/main/python/mllib/decision_tree_regression_example.py ./examples/src/main/python/mllib/gradient_boosting_classification_example.py ./examples/src/main/python/mllib/gradient_boosting_regression_example.py ./examples/src/main/python/mllib/linear_regression_with_sgd_example.py ./examples/src/main/python/mllib/logistic_regression_with_lbfgs_example.py ./examples/src/main/python/mllib/naive_bayes_example.py ./examples/src/main/python/mllib/random_forest_classification_example.py ./examples/src/main/python/mllib/random_forest_regression_example.py ./examples/src/main/python/mllib/svm_with_sgd_example.py ./examples/src/main/python/streaming/network_wordjoinsentiments.py ./sql/hive/src/test/resources/data/scripts/cat.py ./sql/hive/src/test/resources/data/scripts/cat_error.py ./sql/hive/src/test/resources/data/scripts/doubleescapedtab.py ./sql/hive/src/test/resources/data/scripts/dumpdata_script.py ./sql/hive/src/test/resources/data/scripts/escapedcarriagereturn.py ./sql/hive/src/test/resources/data/scripts/escapednewline.py ./sql/hive/src/test/resources/data/scripts/escapedtab.py ./sql/hive/src/test/resources/data/scripts/input20_script.py ./sql/hive/src/test/resources/data/scripts/newline.py ``` ## How was this patch tested? - `./python/docs/epytext.py` ```bash cd ./python/docs $$ make html ``` - pep8 check (Python 2.7 / Python 3.3.6) ``` ./dev/lint-python ``` - `./dev/merge_spark_pr.py` (Python 2.7 only / Python 3.3.6 not working) ```bash python -m doctest -v ./dev/merge_spark_pr.py ``` - `./dev/create-release/releaseutils.py` `./dev/create-release/generate-contributors.py` `./dev/create-release/translate-contributors.py` (Python 2.7 only / Python 3.3.6 not working) ```bash python generate-contributors.py python translate-contributors.py ``` - Examples (Python 2.7 / Python 3.3.6) ```bash ./bin/spark-submit examples/src/main/python/mllib/decision_tree_classification_example.py ./bin/spark-submit examples/src/main/python/mllib/decision_tree_regression_example.py ./bin/spark-submit examples/src/main/python/mllib/gradient_boosting_classification_example.py ./bin/spark-submit examples/src/main/python/mllib/gradient_boosting_regression_example.p ./bin/spark-submit examples/src/main/python/mllib/random_forest_classification_example.py ./bin/spark-submit examples/src/main/python/mllib/random_forest_regression_example.py ``` - Examples (Python 2.7 only / Python 3.3.6 not working) ``` ./bin/spark-submit examples/src/main/python/mllib/linear_regression_with_sgd_example.py ./bin/spark-submit examples/src/main/python/mllib/logistic_regression_with_lbfgs_example.py ./bin/spark-submit examples/src/main/python/mllib/naive_bayes_example.py ./bin/spark-submit examples/src/main/python/mllib/svm_with_sgd_example.py ``` - `sql/hive/src/test/resources/data/scripts/*.py` (Python 2.7 / Python 3.3.6 within suggested changes) Manually tested only changed ones. - `./dev/github_jira_sync.py` (Python 2.7 only / Python 3.3.6 not working) Manually tested this after disabling actually adding comments and links. And also via Jenkins tests. Author: hyukjinkwon <gurwls223@gmail.com> Closes #16405 from HyukjinKwon/minor-pep8.
* [SPARK-17645][MLLIB][ML] add feature selector method based on: False ↵Peng2016-12-282-15/+109
| | | | | | | | | | | | | | | | | | | | | | | | | | | Discovery Rate (FDR) and Family wise error rate (FWE) ## What changes were proposed in this pull request? Univariate feature selection works by selecting the best features based on univariate statistical tests. FDR and FWE are a popular univariate statistical test for feature selection. In 2005, the Benjamini and Hochberg paper on FDR was identified as one of the 25 most-cited statistical papers. The FDR uses the Benjamini-Hochberg procedure in this PR. https://en.wikipedia.org/wiki/False_discovery_rate. In statistics, FWE is the probability of making one or more false discoveries, or type I errors, among all the hypotheses when performing multiple hypotheses tests. https://en.wikipedia.org/wiki/Family-wise_error_rate We add FDR and FWE methods for ChiSqSelector in this PR, like it is implemented in scikit-learn. http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection ## How was this patch tested? ut will be added soon (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Author: Peng <peng.meng@intel.com> Author: Peng, Meng <peng.meng@intel.com> Closes #15212 from mpjlu/fdr_fwe.
* [SPARK-18949][SQL] Add recoverPartitions API to Cataloggatorsmile2016-12-201-0/+5
| | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? Currently, we only have a SQL interface for recovering all the partitions in the directory of a table and update the catalog. `MSCK REPAIR TABLE` or `ALTER TABLE table RECOVER PARTITIONS`. (Actually, very hard for me to remember `MSCK` and have no clue what it means) After the new "Scalable Partition Handling", the table repair becomes much more important for making visible the data in the created data source partitioned table. Thus, this PR is to add it into the Catalog interface. After this PR, users can repair the table by ```Scala spark.catalog.recoverPartitions("testTable") ``` ### How was this patch tested? Modified the existing test cases. Author: gatorsmile <gatorsmile@gmail.com> Closes #16356 from gatorsmile/repairTable.
* [SPARK-18576][PYTHON] Add basic TaskContext information to PySparkHolden Karau2016-12-204-1/+165
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Adds basic TaskContext information to PySpark. ## How was this patch tested? New unit tests to `tests.py` & existing unit tests. Author: Holden Karau <holden@us.ibm.com> Closes #16211 from holdenk/SPARK-18576-pyspark-taskcontext.
* [SPARK-18281] [SQL] [PYSPARK] Remove timeout for reading data through socket ↵Liang-Chi Hsieh2016-12-202-6/+17
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | for local iterator ## What changes were proposed in this pull request? There is a timeout failure when using `rdd.toLocalIterator()` or `df.toLocalIterator()` for a PySpark RDD and DataFrame: df = spark.createDataFrame([[1],[2],[3]]) it = df.toLocalIterator() row = next(it) df2 = df.repartition(1000) # create many empty partitions which increase materialization time so causing timeout it2 = df2.toLocalIterator() row = next(it2) The cause of this issue is, we open a socket to serve the data from JVM side. We set timeout for connection and reading through the socket in Python side. In Python we use a generator to read the data, so we only begin to connect the socket once we start to ask data from it. If we don't consume it immediately, there is connection timeout. In the other side, the materialization time for RDD partitions is unpredictable. So we can't set a timeout for reading data through the socket. Otherwise, it is very possibly to fail. ## How was this patch tested? Added tests into PySpark. Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #16263 from viirya/fix-pyspark-localiterator.
* [SPARK-18888] partitionBy in DataStreamWriter in Python throws _to_seq not ↵Burak Yavuz2016-12-152-3/+5
| | | | | | | | | | | | | | | | defined ## What changes were proposed in this pull request? `_to_seq` wasn't imported. ## How was this patch tested? Added partitionBy to existing write path unit test Author: Burak Yavuz <brkyvz@gmail.com> Closes #16297 from brkyvz/SPARK-18888.
* [SPARK-18852][SS] StreamingQuery.lastProgress should be null when ↵Shixiong Zhu2016-12-142-3/+24
| | | | | | | | | | | | | | | | | | recentProgress is empty ## What changes were proposed in this pull request? Right now `StreamingQuery.lastProgress` throws NoSuchElementException and it's hard to be used in Python since Python user will just see Py4jError. This PR just makes it return null instead. ## How was this patch tested? `test("lastProgress should be null when recentProgress is empty")` Author: Shixiong Zhu <shixiong@databricks.com> Closes #16273 from zsxwing/SPARK-18852.
* [SPARK-18628][ML] Update Scala param and Python param to have quoteskrishnakalyan32016-12-111-2/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Updated Scala param and Python param to have quotes around the options making it easier for users to read. ## How was this patch tested? Manually checked the docstrings Author: krishnakalyan3 <krishnakalyan3@gmail.com> Closes #16242 from krishnakalyan3/doc-string.
generated by cgit v1.2.3 (git 2.39.1) at 2024-07-30 23:18:26 +0000