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* [SPARK-11815][ML][PYSPARK] PySpark DecisionTreeClassifier & ↵Yanbo Liang2016-01-062-10/+17
| | | | | | | | | | DecisionTreeRegressor should support setSeed PySpark ```DecisionTreeClassifier``` & ```DecisionTreeRegressor``` should support ```setSeed``` like what we do at Scala side. Author: Yanbo Liang <ybliang8@gmail.com> Closes #9807 from yanboliang/spark-11815.
* [SPARK-11945][ML][PYSPARK] Add computeCost to KMeansModel for PySpark spark.mlYanbo Liang2016-01-061-0/+10
| | | | | | | | Add ```computeCost``` to ```KMeansModel``` as evaluator for PySpark spark.ml. Author: Yanbo Liang <ybliang8@gmail.com> Closes #9931 from yanboliang/SPARK-11945.
* [SPARK-11531][ML] SparseVector error MsgJoshi2016-01-061-1/+3
| | | | | | | | | PySpark SparseVector should have "Found duplicate indices" error message Author: Joshi <rekhajoshm@gmail.com> Author: Rekha Joshi <rekhajoshm@gmail.com> Closes #9525 from rekhajoshm/SPARK-11531.
* [SPARK-7675][ML][PYSPARK] sparkml params type conversionHolden Karau2016-01-064-103/+148
| | | | | | | | | | | | | From JIRA: Currently, PySpark wrappers for spark.ml Scala classes are brittle when accepting Param types. E.g., Normalizer's "p" param cannot be set to "2" (an integer); it must be set to "2.0" (a float). Fixing this is not trivial since there does not appear to be a natural place to insert the conversion before Python wrappers call Java's Params setter method. A possible fix will be to include a method "_checkType" to PySpark's Param class which checks the type, prints an error if needed, and converts types when relevant (e.g., int to float, or scipy matrix to array). The Java wrapper method which copies params to Scala can call this method when available. This fix instead checks the types at set time since I think failing sooner is better, but I can switch it around to check at copy time if that would be better. So far this only converts int to float and other conversions (like scipymatrix to array) are left for the future. Author: Holden Karau <holden@us.ibm.com> Closes #9581 from holdenk/SPARK-7675-PySpark-sparkml-Params-type-conversion.
* [SPARK-12041][ML][PYSPARK] Add columnSimilarities to IndexedRowMatrixKai Jiang2016-01-051-0/+14
| | | | | | | | Add `columnSimilarities` to IndexedRowMatrix for PySpark spark.mllib.linalg. Author: Kai Jiang <jiangkai@gmail.com> Closes #10158 from vectorijk/spark-12041.
* [SPARK-12511] [PYSPARK] [STREAMING] Make sure ↵Shixiong Zhu2016-01-052-10/+26
| | | | | | | | | | | | PythonDStream.registerSerializer is called only once There is an issue that Py4J's PythonProxyHandler.finalize blocks forever. (https://github.com/bartdag/py4j/pull/184) Py4j will create a PythonProxyHandler in Java for "transformer_serializer" when calling "registerSerializer". If we call "registerSerializer" twice, the second PythonProxyHandler will override the first one, then the first one will be GCed and trigger "PythonProxyHandler.finalize". To avoid that, we should not call"registerSerializer" more than once, so that "PythonProxyHandler" in Java side won't be GCed. Author: Shixiong Zhu <shixiong@databricks.com> Closes #10514 from zsxwing/SPARK-12511.
* [SPARK-12617] [PYSPARK] Clean up the leak sockets of Py4JShixiong Zhu2016-01-051-0/+61
| | | | | | | | This patch added Py4jCallbackConnectionCleaner to clean the leak sockets of Py4J every 30 seconds. This is a workaround before Py4J fixes the leak issue https://github.com/bartdag/py4j/issues/187 Author: Shixiong Zhu <shixiong@databricks.com> Closes #10579 from zsxwing/SPARK-12617.
* [SPARK-12480][FOLLOW-UP] use a single column vararg for hashWenchen Fan2016-01-051-0/+12
| | | | | | | | | | address comments in #10435 This makes the API easier to use if user programmatically generate the call to hash, and they will get analysis exception if the arguments of hash is empty. Author: Wenchen Fan <wenchen@databricks.com> Closes #10588 from cloud-fan/hash.
* [SPARK-12600][SQL] Remove deprecated methods in Spark SQLReynold Xin2016-01-048-225/+34
| | | | | | Author: Reynold Xin <rxin@databricks.com> Closes #10559 from rxin/remove-deprecated-sql.
* [SPARK-12611][SQL][PYSPARK][TESTS] Fix test_infer_schema_to_localHolden Karau2016-01-031-1/+1
| | | | | | | | Previously (when the PR was first created) not specifying b= explicitly was fine (and treated as default null) - instead be explicit about b being None in the test. Author: Holden Karau <holden@us.ibm.com> Closes #10564 from holdenk/SPARK-12611-fix-test-infer-schema-local.
* [SPARK-12537][SQL] Add option to accept quoting of all character backslash ↵Cazen2016-01-031-0/+2
| | | | | | | | | | | | | quoting mechanism We can provides the option to choose JSON parser can be enabled to accept quoting of all character or not. Author: Cazen <Cazen@korea.com> Author: Cazen Lee <cazen.lee@samsung.com> Author: Cazen Lee <Cazen@korea.com> Author: cazen.lee <cazen.lee@samsung.com> Closes #10497 from Cazen/master.
* [SPARK-12300] [SQL] [PYSPARK] fix schema inferance on local collectionsHolden Karau2015-12-302-7/+14
| | | | | | | | Current schema inference for local python collections halts as soon as there are no NullTypes. This is different than when we specify a sampling ratio of 1.0 on a distributed collection. This could result in incomplete schema information. Author: Holden Karau <holden@us.ibm.com> Closes #10275 from holdenk/SPARK-12300-fix-schmea-inferance-on-local-collections.
* [SPARK-12353][STREAMING][PYSPARK] Fix countByValue inconsistent output in ↵jerryshao2015-12-282-5/+16
| | | | | | | | | | Python API The semantics of Python countByValue is different from Scala API, it is more like countDistinctValue, so here change to make it consistent with Scala/Java API. Author: jerryshao <sshao@hortonworks.com> Closes #10350 from jerryshao/SPARK-12353.
* [SPARK-12520] [PYSPARK] Correct Descriptions and Add Use Cases in Equi-Joingatorsmile2015-12-271-1/+4
| | | | | | | | | | | | | | | After reading the JIRA https://issues.apache.org/jira/browse/SPARK-12520, I double checked the code. For example, users can do the Equi-Join like ```df.join(df2, 'name', 'outer').select('name', 'height').collect()``` - There exists a bug in 1.5 and 1.4. The code just ignores the third parameter (join type) users pass. However, the join type we called is `Inner`, even if the user-specified type is the other type (e.g., `Outer`). - After a PR: https://github.com/apache/spark/pull/8600, the 1.6 does not have such an issue, but the description has not been updated. Plan to submit another PR to fix 1.5 and issue an error message if users specify a non-inner join type when using Equi-Join. Author: gatorsmile <gatorsmile@gmail.com> Closes #10477 from gatorsmile/pyOuterJoin.
* [SPARK-12296][PYSPARK][MLLIB] Feature parity for pyspark mllib standard ↵Holden Karau2015-12-221-0/+40
| | | | | | | | | | scaler model Some methods are missing, such as ways to access the std, mean, etc. This PR is for feature parity for pyspark.mllib.feature.StandardScaler & StandardScalerModel. Author: Holden Karau <holden@us.ibm.com> Closes #10298 from holdenk/SPARK-12296-feature-parity-pyspark-mllib-StandardScalerModel.
* Doc typo: ltrim = trim from left end, not rightpshearer2015-12-211-1/+1
| | | | | | Author: pshearer <pshearer@massmutual.com> Closes #10414 from pshearer/patch-1.
* [PYSPARK] Pyspark typo & Add missing abstractmethod annotationJeff Zhang2015-12-212-2/+3
| | | | | | | | | | No jira is created since this is a trivial change. davies Please help review it Author: Jeff Zhang <zjffdu@apache.org> Closes #10143 from zjffdu/pyspark_typo.
* [SPARK-10158][PYSPARK][MLLIB] ALS better error message when using Long IDsBryan Cutler2015-12-201-0/+17
| | | | | | | | Added catch for casting Long to Int exception when PySpark ALS Ratings are serialized. It is easy to accidentally use Long IDs for user/product and before, it would fail with a somewhat cryptic "ClassCastException: java.lang.Long cannot be cast to java.lang.Integer." Now if this is done, a more descriptive error is shown, e.g. "PickleException: Ratings id 1205640308657491975 exceeds max integer value of 2147483647." Author: Bryan Cutler <bjcutler@us.ibm.com> Closes #9361 from BryanCutler/als-pyspark-long-id-error-SPARK-10158.
* [SQL] Fix mistake doc of join type for dataframe.joinYanbo Liang2015-12-191-1/+1
| | | | | | | | Fix mistake doc of join type for ```dataframe.join```. Author: Yanbo Liang <ybliang8@gmail.com> Closes #10378 from yanboliang/leftsemi.
* [SPARK-12091] [PYSPARK] Deprecate the JAVA-specific deserialized storage levelsgatorsmile2015-12-188-24/+35
| | | | | | | | | | | | | | The current default storage level of Python persist API is MEMORY_ONLY_SER. This is different from the default level MEMORY_ONLY in the official document and RDD APIs. davies Is this inconsistency intentional? Thanks! Updates: Since the data is always serialized on the Python side, the storage levels of JAVA-specific deserialization are not removed, such as MEMORY_ONLY. Updates: Based on the reviewers' feedback. In Python, stored objects will always be serialized with the [Pickle](https://docs.python.org/2/library/pickle.html) library, so it does not matter whether you choose a serialized level. The available storage levels in Python include `MEMORY_ONLY`, `MEMORY_ONLY_2`, `MEMORY_AND_DISK`, `MEMORY_AND_DISK_2`, `DISK_ONLY`, `DISK_ONLY_2` and `OFF_HEAP`. Author: gatorsmile <gatorsmile@gmail.com> Closes #10092 from gatorsmile/persistStorageLevel.
* [SQL] Update SQLContext.read.text docYanbo Liang2015-12-171-1/+1
| | | | | | | | Since we rename the column name from ```text``` to ```value``` for DataFrame load by ```SQLContext.read.text```, we need to update doc. Author: Yanbo Liang <ybliang8@gmail.com> Closes #10349 from yanboliang/text-value.
* [SPARK-11904][PYSPARK] reduceByKeyAndWindow does not require checkpointing ↵David Tolpin2015-12-161-22/+23
| | | | | | | | | | | | | | | | when invFunc is None when invFunc is None, `reduceByKeyAndWindow(func, None, winsize, slidesize)` is equivalent to reduceByKey(func).window(winsize, slidesize).reduceByKey(winsize, slidesize) and no checkpoint is necessary. The corresponding Scala code does exactly that, but Python code always creates a windowed stream with obligatory checkpointing. The patch fixes this. I do not know how to unit-test this. Author: David Tolpin <david.tolpin@gmail.com> Closes #9888 from dtolpin/master.
* [SPARK-12380] [PYSPARK] use SQLContext.getOrCreate in mllibDavies Liu2015-12-163-11/+9
| | | | | | | | MLlib should use SQLContext.getOrCreate() instead of creating new SQLContext. Author: Davies Liu <davies@databricks.com> Closes #10338 from davies/create_context.
* [SPARK-9690][ML][PYTHON] pyspark CrossValidator random seedMartin Menestret2015-12-161-7/+13
| | | | | | | | | | | | | Extend CrossValidator with HasSeed in PySpark. This PR replaces [https://github.com/apache/spark/pull/7997] CC: yanboliang thunterdb mmenestret Would one of you mind taking a look? Thanks! Author: Joseph K. Bradley <joseph@databricks.com> Author: Martin MENESTRET <mmenestret@ippon.fr> Closes #10268 from jkbradley/pyspark-cv-seed.
* [SPARK-12361][PYSPARK][TESTS] Should set PYSPARK_DRIVER_PYTHON before Python ↵Jeff Zhang2015-12-161-1/+2
| | | | | | | | | | tests Although this patch still doesn't solve the issue why the return code is 0 (see JIRA description), it resolves the issue of python version mismatch. Author: Jeff Zhang <zjffdu@apache.org> Closes #10322 from zjffdu/SPARK-12361.
* [SPARK-12016] [MLLIB] [PYSPARK] Wrap Word2VecModel when loading it in pysparkLiang-Chi Hsieh2015-12-141-1/+5
| | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-12016 We should not directly use Word2VecModel in pyspark. We need to wrap it in a Word2VecModelWrapper when loading it in pyspark. Author: Liang-Chi Hsieh <viirya@appier.com> Closes #10100 from viirya/fix-load-py-wordvecmodel.
* [SPARK-11713] [PYSPARK] [STREAMING] Initial RDD updateStateByKey for PySparkBryan Cutler2015-12-102-2/+31
| | | | | | | | Adding ability to define an initial state RDD for use with updateStateByKey PySpark. Added unit test and changed stateful_network_wordcount example to use initial RDD. Author: Bryan Cutler <bjcutler@us.ibm.com> Closes #10082 from BryanCutler/initial-rdd-updateStateByKey-SPARK-11713.
* [SPARK-12012][SQL] Show more comprehensive PhysicalRDD metadata when ↵Cheng Lian2015-12-091-1/+1
| | | | | | | | | | | | | | | | visualizing SQL query plan This PR adds a `private[sql]` method `metadata` to `SparkPlan`, which can be used to describe detail information about a physical plan during visualization. Specifically, this PR uses this method to provide details of `PhysicalRDD`s translated from a data source relation. For example, a `ParquetRelation` converted from Hive metastore table `default.psrc` is now shown as the following screenshot: ![image](https://cloud.githubusercontent.com/assets/230655/11526657/e10cb7e6-9916-11e5-9afa-f108932ec890.png) And here is the screenshot for a regular `ParquetRelation` (not converted from Hive metastore table) loaded from a really long path: ![output](https://cloud.githubusercontent.com/assets/230655/11680582/37c66460-9e94-11e5-8f50-842db5309d5a.png) Author: Cheng Lian <lian@databricks.com> Closes #10004 from liancheng/spark-12012.physical-rdd-metadata.
* [SPARK-12184][PYTHON] Make python api doc for pivot consistant with scala docAndrew Ray2015-12-071-5/+9
| | | | | | | | In SPARK-11946 the API for pivot was changed a bit and got updated doc, the doc changes were not made for the python api though. This PR updates the python doc to be consistent. Author: Andrew Ray <ray.andrew@gmail.com> Closes #10176 from aray/sql-pivot-python-doc.
* [SPARK-12132] [PYSPARK] raise KeyboardInterrupt inside SIGINT handlerDavies Liu2015-12-071-0/+1
| | | | | | | | | | | | | | | | | | | Currently, the current line is not cleared by Cltr-C After this patch ``` >>> asdfasdf^C Traceback (most recent call last): File "~/spark/python/pyspark/context.py", line 225, in signal_handler raise KeyboardInterrupt() KeyboardInterrupt ``` It's still worse than 1.5 (and before). Author: Davies Liu <davies@databricks.com> Closes #10134 from davies/fix_cltrc.
* [SPARK-12058][STREAMING][KINESIS][TESTS] fix Kinesis python testsBurak Yavuz2015-12-041-1/+0
| | | | | | | | | | | | Python tests require access to the `KinesisTestUtils` file. When this file exists under src/test, python can't access it, since it is not available in the assembly jar. However, if we move KinesisTestUtils to src/main, we need to add the KinesisProducerLibrary as a dependency. In order to avoid this, I moved KinesisTestUtils to src/main, and extended it with ExtendedKinesisTestUtils which is under src/test that adds support for the KPL. cc zsxwing tdas Author: Burak Yavuz <brkyvz@gmail.com> Closes #10050 from brkyvz/kinesis-py.
* [MINOR][ML] Use coefficients replace weightsYanbo Liang2015-12-032-2/+2
| | | | | | | | | Use ```coefficients``` replace ```weights```, I wish they are the last two. mengxr Author: Yanbo Liang <ybliang8@gmail.com> Closes #10065 from yanboliang/coefficients.
* [SPARK-12090] [PYSPARK] consider shuffle in coalesce()Davies Liu2015-12-011-1/+1
| | | | | | Author: Davies Liu <davies@databricks.com> Closes #10090 from davies/fix_coalesce.
* [SPARK-12002][STREAMING][PYSPARK] Fix python direct stream checkpoint ↵jerryshao2015-12-012-6/+56
| | | | | | | | | | | | | recovery issue Fixed a minor race condition in #10017 Closes #10017 Author: jerryshao <sshao@hortonworks.com> Author: Shixiong Zhu <shixiong@databricks.com> Closes #10074 from zsxwing/review-pr10017.
* [SPARK-12058][HOTFIX] Disable KinesisStreamTestsShixiong Zhu2015-11-301-0/+1
| | | | | | | | | | | | KinesisStreamTests in test.py is broken because of #9403. See https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/46896/testReport/(root)/KinesisStreamTests/test_kinesis_stream/ Because Streaming Python didn’t work when merging https://github.com/apache/spark/pull/9403, the PR build didn’t report the Python test failure actually. This PR just disabled the test to unblock #10039 Author: Shixiong Zhu <shixiong@databricks.com> Closes #10047 from zsxwing/disable-python-kinesis-test.
* [SPARK-11917][PYSPARK] Add SQLContext#dropTempTable to PySparkJeff Zhang2015-11-261-0/+9
| | | | | | Author: Jeff Zhang <zjffdu@apache.org> Closes #9903 from zjffdu/SPARK-11917.
* [SPARK-11980][SPARK-10621][SQL] Fix json_tuple and add test cases forgatorsmile2015-11-251-10/+34
| | | | | | | | | | | | | | Added Python test cases for the function `isnan`, `isnull`, `nanvl` and `json_tuple`. Fixed a bug in the function `json_tuple` rxin , could you help me review my changes? Please let me know anything is missing. Thank you! Have a good Thanksgiving day! Author: gatorsmile <gatorsmile@gmail.com> Closes #9977 from gatorsmile/json_tuple.
* [SPARK-11935][PYSPARK] Send the Python exceptions in TransformFunction and ↵Shixiong Zhu2015-11-252-10/+101
| | | | | | | | | | | | TransformFunctionSerializer to Java The Python exception track in TransformFunction and TransformFunctionSerializer is not sent back to Java. Py4j just throws a very general exception, which is hard to debug. This PRs adds `getFailure` method to get the failure message in Java side. Author: Shixiong Zhu <shixiong@databricks.com> Closes #9922 from zsxwing/SPARK-11935.
* [SPARK-11969] [SQL] [PYSPARK] visualization of SQL query for pysparkDavies Liu2015-11-251-1/+1
| | | | | | | | | | Currently, we does not have visualization for SQL query from Python, this PR fix that. cc zsxwing Author: Davies Liu <davies@databricks.com> Closes #9949 from davies/pyspark_sql_ui.
* [SPARK-11984][SQL][PYTHON] Fix typos in doc for pivot for scala and pythonfelixcheung2015-11-251-3/+3
| | | | | | Author: felixcheung <felixcheung_m@hotmail.com> Closes #9967 from felixcheung/pypivotdoc.
* [SPARK-11860][PYSAPRK][DOCUMENTATION] Invalid argument specification …Jeff Zhang2015-11-251-2/+3
| | | | | | | | | | …for registerFunction [Python] Straightforward change on the python doc Author: Jeff Zhang <zjffdu@apache.org> Closes #9901 from zjffdu/SPARK-11860.
* [SPARK-10621][SQL] Consistent naming for functions in SQL, Python, ScalaReynold Xin2015-11-241-17/+94
| | | | | | Author: Reynold Xin <rxin@databricks.com> Closes #9948 from rxin/SPARK-10621.
* [SPARK-11967][SQL] Consistent use of varargs for multiple paths in ↵Reynold Xin2015-11-241-7/+12
| | | | | | | | | | | | DataFrameReader This patch makes it consistent to use varargs in all DataFrameReader methods, including Parquet, JSON, text, and the generic load function. Also added a few more API tests for the Java API. Author: Reynold Xin <rxin@databricks.com> Closes #9945 from rxin/SPARK-11967.
* [SPARK-11946][SQL] Audit pivot API for 1.6.Reynold Xin2015-11-241-4/+8
| | | | | | | | | | | | | | | | | | | | Currently pivot's signature looks like ```scala scala.annotation.varargs def pivot(pivotColumn: Column, values: Column*): GroupedData scala.annotation.varargs def pivot(pivotColumn: String, values: Any*): GroupedData ``` I think we can remove the one that takes "Column" types, since callers should always be passing in literals. It'd also be more clear if the values are not varargs, but rather Seq or java.util.List. I also made similar changes for Python. Author: Reynold Xin <rxin@databricks.com> Closes #9929 from rxin/SPARK-11946.
* [SPARK-10560][PYSPARK][MLLIB][DOCS] Make StreamingLogisticRegressionWithSGD ↵Bryan Cutler2015-11-232-23/+46
| | | | | | | | | | | | | | Python API equal to Scala one This is to bring the API documentation of StreamingLogisticReressionWithSGD and StreamingLinearRegressionWithSGC in line with the Scala versions. -Fixed the algorithm descriptions -Added default values to parameter descriptions -Changed StreamingLogisticRegressionWithSGD regParam to default to 0, as in the Scala version Author: Bryan Cutler <bjcutler@us.ibm.com> Closes #9141 from BryanCutler/StreamingLogisticRegressionWithSGD-python-api-sync.
* [SPARK-11836][SQL] udf/cast should not create new SQLContextDavies Liu2015-11-232-6/+8
| | | | | | | | They should use the existing SQLContext. Author: Davies Liu <davies@databricks.com> Closes #9914 from davies/create_udf.
* [SPARK-11870][STREAMING][PYSPARK] Rethrow the exceptions in ↵Shixiong Zhu2015-11-202-0/+19
| | | | | | | | | | TransformFunction and TransformFunctionSerializer TransformFunction and TransformFunctionSerializer don't rethrow the exception, so when any exception happens, it just return None. This will cause some weird NPE and confuse people. Author: Shixiong Zhu <shixiong@databricks.com> Closes #9847 from zsxwing/pyspark-streaming-exception.
* [SPARK-11875][ML][PYSPARK] Update doc for PySpark HasCheckpointIntervalYanbo Liang2015-11-192-9/+11
| | | | | | | | | * Update doc for PySpark ```HasCheckpointInterval``` that users can understand how to disable checkpoint. * Update doc for PySpark ```cacheNodeIds``` of ```DecisionTreeParams``` to notify the relationship between ```cacheNodeIds``` and ```checkpointInterval```. Author: Yanbo Liang <ybliang8@gmail.com> Closes #9856 from yanboliang/spark-11875.
* [SPARK-11812][PYSPARK] invFunc=None works properly with python's ↵David Tolpin2015-11-192-3/+14
| | | | | | | | | | | | | reduceByKeyAndWindow invFunc is optional and can be None. Instead of invFunc (the parameter) invReduceFunc (a local function) was checked for trueness (that is, not None, in this context). A local function is never None, thus the case of invFunc=None (a common one when inverse reduction is not defined) was treated incorrectly, resulting in loss of data. In addition, the docstring used wrong parameter names, also fixed. Author: David Tolpin <david.tolpin@gmail.com> Closes #9775 from dtolpin/master.
* [SPARK-11820][ML][PYSPARK] PySpark LiR & LoR should support weightColYanbo Liang2015-11-182-16/+17
| | | | | | | | [SPARK-7685](https://issues.apache.org/jira/browse/SPARK-7685) and [SPARK-9642](https://issues.apache.org/jira/browse/SPARK-9642) have already supported setting weight column for ```LogisticRegression``` and ```LinearRegression```. It's a very important feature, PySpark should also support. mengxr Author: Yanbo Liang <ybliang8@gmail.com> Closes #9811 from yanboliang/spark-11820.
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