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* [SPARK-14817][ML][MLLIB][DOC] Made DataFrame-based API primary in MLlib guideJoseph K. Bradley2016-07-153-4/+7
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Made DataFrame-based API primary * Spark doc menu bar and other places now link to ml-guide.html, not mllib-guide.html * mllib-guide.html keeps RDD-specific list of features, with a link at the top redirecting people to ml-guide.html * ml-guide.html includes a "maintenance mode" announcement about the RDD-based API * **Reviewers: please check this carefully** * (minor) Titles for DF API no longer include "- spark.ml" suffix. Titles for RDD API have "- RDD-based API" suffix * Moved migration guide to ml-guide from mllib-guide * Also moved past guides from mllib-migration-guides to ml-migration-guides, with a redirect link on mllib-migration-guides * **Reviewers**: I did not change any of the content of the migration guides. Reorganized DataFrame-based guide: * ml-guide.html mimics the old mllib-guide.html page in terms of content: overview, migration guide, etc. * Moved Pipeline description into ml-pipeline.html and moved tuning into ml-tuning.html * **Reviewers**: I did not change the content of these guides, except some intro text. * Sidebar remains the same, but with pipeline and tuning sections added Other: * ml-classification-regression.html: Moved text about linear methods to new section in page ## How was this patch tested? Generated docs locally Author: Joseph K. Bradley <joseph@databricks.com> Closes #14213 from jkbradley/ml-guide-2.0.
* [SPARK-16546][SQL][PYSPARK] update python dataframe.dropWeichenXu2016-07-141-8/+19
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Make `dataframe.drop` API in python support multi-columns parameters, so that it is the same with scala API. ## How was this patch tested? The doc test. Author: WeichenXu <WeichenXu123@outlook.com> Closes #14203 from WeichenXu123/drop_python_api.
* [SPARK-16503] SparkSession should provide Spark versionLiwei Lin2016-07-131-0/+6
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch enables SparkSession to provide spark version. ## How was this patch tested? Manual test: ``` scala> sc.version res0: String = 2.1.0-SNAPSHOT scala> spark.version res1: String = 2.1.0-SNAPSHOT ``` ``` >>> sc.version u'2.1.0-SNAPSHOT' >>> spark.version u'2.1.0-SNAPSHOT' ``` Author: Liwei Lin <lwlin7@gmail.com> Closes #14165 from lw-lin/add-version.
* [SPARK-16536][SQL][PYSPARK][MINOR] Expose `sql` in PySpark ShellDongjoon Hyun2016-07-131-0/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR exposes `sql` in PySpark Shell like Scala/R Shells for consistency. **Background** * Scala ```scala scala> sql("select 1 a") res0: org.apache.spark.sql.DataFrame = [a: int] ``` * R ```r > sql("select 1") SparkDataFrame[1:int] ``` **Before** * Python ```python >>> sql("select 1 a") Traceback (most recent call last): File "<stdin>", line 1, in <module> NameError: name 'sql' is not defined ``` **After** * Python ```python >>> sql("select 1 a") DataFrame[a: int] ``` ## How was this patch tested? Manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14190 from dongjoon-hyun/SPARK-16536.
* [SPARK-14812][ML][MLLIB][PYTHON] Experimental, DeveloperApi annotation audit ↵Joseph K. Bradley2016-07-1313-197/+11
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | for ML ## What changes were proposed in this pull request? General decisions to follow, except where noted: * spark.mllib, pyspark.mllib: Remove all Experimental annotations. Leave DeveloperApi annotations alone. * spark.ml, pyspark.ml ** Annotate Estimator-Model pairs of classes and companion objects the same way. ** For all algorithms marked Experimental with Since tag <= 1.6, remove Experimental annotation. ** For all algorithms marked Experimental with Since tag = 2.0, leave Experimental annotation. * DeveloperApi annotations are left alone, except where noted. * No changes to which types are sealed. Exceptions where I am leaving items Experimental in spark.ml, pyspark.ml, mainly because the items are new: * Model Summary classes * MLWriter, MLReader, MLWritable, MLReadable * Evaluator and subclasses: There is discussion of changes around evaluating multiple metrics at once for efficiency. * RFormula: Its behavior may need to change slightly to match R in edge cases. * AFTSurvivalRegression * MultilayerPerceptronClassifier DeveloperApi changes: * ml.tree.Node, ml.tree.Split, and subclasses should no longer be DeveloperApi ## How was this patch tested? N/A Note to reviewers: * spark.ml.clustering.LDA underwent significant changes (additional methods), so let me know if you want me to leave it Experimental. * Be careful to check for cases where a class should no longer be Experimental but has an Experimental method, val, or other feature. I did not find such cases, but please verify. Author: Joseph K. Bradley <joseph@databricks.com> Closes #14147 from jkbradley/experimental-audit.
* [SPARK-16429][SQL] Include `StringType` columns in `describe()`Dongjoon Hyun2016-07-081-4/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, Spark `describe` supports `StringType`. However, `describe()` returns a dataset for only all numeric columns. This PR aims to include `StringType` columns in `describe()`, `describe` without argument. **Background** ```scala scala> spark.read.json("examples/src/main/resources/people.json").describe("age", "name").show() +-------+------------------+-------+ |summary| age| name| +-------+------------------+-------+ | count| 2| 3| | mean| 24.5| null| | stddev|7.7781745930520225| null| | min| 19| Andy| | max| 30|Michael| +-------+------------------+-------+ ``` **Before** ```scala scala> spark.read.json("examples/src/main/resources/people.json").describe().show() +-------+------------------+ |summary| age| +-------+------------------+ | count| 2| | mean| 24.5| | stddev|7.7781745930520225| | min| 19| | max| 30| +-------+------------------+ ``` **After** ```scala scala> spark.read.json("examples/src/main/resources/people.json").describe().show() +-------+------------------+-------+ |summary| age| name| +-------+------------------+-------+ | count| 2| 3| | mean| 24.5| null| | stddev|7.7781745930520225| null| | min| 19| Andy| | max| 30|Michael| +-------+------------------+-------+ ``` ## How was this patch tested? Pass the Jenkins with a update testcase. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14095 from dongjoon-hyun/SPARK-16429.
* [SPARK-13638][SQL] Add quoteAll option to CSV DataFrameWriterJurriaan Pruis2016-07-081-2/+5
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Adds an quoteAll option for writing CSV which will quote all fields. See https://issues.apache.org/jira/browse/SPARK-13638 ## How was this patch tested? Added a test to verify the output columns are quoted for all fields in the Dataframe Author: Jurriaan Pruis <email@jurriaanpruis.nl> Closes #13374 from jurriaan/csv-quote-all.
* [SPARK-16052][SQL] Improve `CollapseRepartition` optimizer for ↵Dongjoon Hyun2016-07-081-2/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Repartition/RepartitionBy ## What changes were proposed in this pull request? This PR improves `CollapseRepartition` to optimize the adjacent combinations of **Repartition** and **RepartitionBy**. Also, this PR adds a testsuite for this optimizer. **Target Scenario** ```scala scala> val dsView1 = spark.range(8).repartition(8, $"id") scala> dsView1.createOrReplaceTempView("dsView1") scala> sql("select id from dsView1 distribute by id").explain(true) ``` **Before** ```scala scala> sql("select id from dsView1 distribute by id").explain(true) == Parsed Logical Plan == 'RepartitionByExpression ['id] +- 'Project ['id] +- 'UnresolvedRelation `dsView1` == Analyzed Logical Plan == id: bigint RepartitionByExpression [id#0L] +- Project [id#0L] +- SubqueryAlias dsview1 +- RepartitionByExpression [id#0L], 8 +- Range (0, 8, splits=8) == Optimized Logical Plan == RepartitionByExpression [id#0L] +- RepartitionByExpression [id#0L], 8 +- Range (0, 8, splits=8) == Physical Plan == Exchange hashpartitioning(id#0L, 200) +- Exchange hashpartitioning(id#0L, 8) +- *Range (0, 8, splits=8) ``` **After** ```scala scala> sql("select id from dsView1 distribute by id").explain(true) == Parsed Logical Plan == 'RepartitionByExpression ['id] +- 'Project ['id] +- 'UnresolvedRelation `dsView1` == Analyzed Logical Plan == id: bigint RepartitionByExpression [id#0L] +- Project [id#0L] +- SubqueryAlias dsview1 +- RepartitionByExpression [id#0L], 8 +- Range (0, 8, splits=8) == Optimized Logical Plan == RepartitionByExpression [id#0L] +- Range (0, 8, splits=8) == Physical Plan == Exchange hashpartitioning(id#0L, 200) +- *Range (0, 8, splits=8) ``` ## How was this patch tested? Pass the Jenkins tests (including a new testsuite). Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13765 from dongjoon-hyun/SPARK-16052.
* [MINOR][PYSPARK][DOC] Fix wrongly formatted examples in PySpark documentationhyukjinkwon2016-07-066-23/+26
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes wrongly formatted examples in PySpark documentation as below: - **`SparkSession`** - **Before** ![2016-07-06 11 34 41](https://cloud.githubusercontent.com/assets/6477701/16605847/ae939526-436d-11e6-8ab8-6ad578362425.png) - **After** ![2016-07-06 11 33 56](https://cloud.githubusercontent.com/assets/6477701/16605845/ace9ee78-436d-11e6-8923-b76d4fc3e7c3.png) - **`Builder`** - **Before** ![2016-07-06 11 34 44](https://cloud.githubusercontent.com/assets/6477701/16605844/aba60dbc-436d-11e6-990a-c87bc0281c6b.png) - **After** ![2016-07-06 1 26 37](https://cloud.githubusercontent.com/assets/6477701/16607562/586704c0-437d-11e6-9483-e0af93d8f74e.png) This PR also fixes several similar instances across the documentation in `sql` PySpark module. ## How was this patch tested? N/A Author: hyukjinkwon <gurwls223@gmail.com> Closes #14063 from HyukjinKwon/minor-pyspark-builder.
* [SPARK-16348][ML][MLLIB][PYTHON] Use full classpaths for pyspark ML JVM callsJoseph K. Bradley2016-07-058-26/+28
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Issue: Omitting the full classpath can cause problems when calling JVM methods or classes from pyspark. This PR: Changed all uses of jvm.X in pyspark.ml and pyspark.mllib to use full classpath for X ## How was this patch tested? Existing unit tests. Manual testing in an environment where this was an issue. Author: Joseph K. Bradley <joseph@databricks.com> Closes #14023 from jkbradley/SPARK-16348.
* [SPARK-16335][SQL] Structured streaming should fail if source directory does ↵Reynold Xin2016-07-011-7/+4
| | | | | | | | | | | | | | not exist ## What changes were proposed in this pull request? In structured streaming, Spark does not report errors when the specified directory does not exist. This is a behavior different from the batch mode. This patch changes the behavior to fail if the directory does not exist (when the path is not a glob pattern). ## How was this patch tested? Updated unit tests to reflect the new behavior. Author: Reynold Xin <rxin@databricks.com> Closes #14002 from rxin/SPARK-16335.
* [SPARK-15954][SQL] Disable loading test tables in Python testsReynold Xin2016-06-301-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch introduces a flag to disable loading test tables in TestHiveSparkSession and disables that in Python. This fixes an issue in which python/run-tests would fail due to failure to load test tables. Note that these test tables are not used outside of HiveCompatibilitySuite. In the long run we should probably decouple the loading of test tables from the test Hive setup. ## How was this patch tested? This is a test only change. Author: Reynold Xin <rxin@databricks.com> Closes #14005 from rxin/SPARK-15954.
* [SPARK-16328][ML][MLLIB][PYSPARK] Add 'asML' and 'fromML' conversion methods ↵Nick Pentreath2016-06-302-0/+168
| | | | | | | | | | | | | | to PySpark linalg The move to `ml.linalg` created `asML`/`fromML` utility methods in Scala/Java for converting between representations. These are missing in Python, this PR adds them. ## How was this patch tested? New doctests. Author: Nick Pentreath <nickp@za.ibm.com> Closes #13997 from MLnick/SPARK-16328-python-linalg-convert.
* [SPARK-16313][SQL] Spark should not silently drop exceptions in file listingReynold Xin2016-06-302-2/+2
| | | | | | | | | | | | ## What changes were proposed in this pull request? Spark silently drops exceptions during file listing. This is a very bad behavior because it can mask legitimate errors and the resulting plan will silently have 0 rows. This patch changes it to not silently drop the errors. ## How was this patch tested? Manually verified. Author: Reynold Xin <rxin@databricks.com> Closes #13987 from rxin/SPARK-16313.
* [SPARK-16289][SQL] Implement posexplode table generating functionDongjoon Hyun2016-06-301-0/+21
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR implements `posexplode` table generating function. Currently, master branch raises the following exception for `map` argument. It's different from Hive. **Before** ```scala scala> sql("select posexplode(map('a', 1, 'b', 2))").show org.apache.spark.sql.AnalysisException: No handler for Hive UDF ... posexplode() takes an array as a parameter; line 1 pos 7 ``` **After** ```scala scala> sql("select posexplode(map('a', 1, 'b', 2))").show +---+---+-----+ |pos|key|value| +---+---+-----+ | 0| a| 1| | 1| b| 2| +---+---+-----+ ``` For `array` argument, `after` is the same with `before`. ``` scala> sql("select posexplode(array(1, 2, 3))").show +---+---+ |pos|col| +---+---+ | 0| 1| | 1| 2| | 2| 3| +---+---+ ``` ## How was this patch tested? Pass the Jenkins tests with newly added testcases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13971 from dongjoon-hyun/SPARK-16289.
* [SPARK-15820][PYSPARK][SQL] Add Catalog.refreshTable into python APIWeichenXu2016-06-301-0/+5
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add Catalog.refreshTable API into python interface for Spark-SQL. ## How was this patch tested? Existing test. Author: WeichenXu <WeichenXu123@outlook.com> Closes #13558 from WeichenXu123/update_python_sql_interface_refreshTable.
* [TRIVIAL] [PYSPARK] Clean up orc compression option as wellhyukjinkwon2016-06-291-2/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR corrects ORC compression option for PySpark as well. I think this was missed mistakenly in https://github.com/apache/spark/pull/13948. ## How was this patch tested? N/A Author: hyukjinkwon <gurwls223@gmail.com> Closes #13963 from HyukjinKwon/minor-orc-compress.
* [SPARK-16236][SQL][FOLLOWUP] Add Path Option back to Load API in DataFrameReadergatorsmile2016-06-291-1/+3
| | | | | | | | | | | | | | #### What changes were proposed in this pull request? In Python API, we have the same issue. Thanks for identifying this issue, zsxwing ! Below is an example: ```Python spark.read.format('json').load('python/test_support/sql/people.json') ``` #### How was this patch tested? Existing test cases cover the changes by this PR Author: gatorsmile <gatorsmile@gmail.com> Closes #13965 from gatorsmile/optionPaths.
* [SPARK-16266][SQL][STREAING] Moved DataStreamReader/Writer from pyspark.sql ↵Tathagata Das2016-06-286-499/+511
| | | | | | | | | | | | | | | | | | to pyspark.sql.streaming ## What changes were proposed in this pull request? - Moved DataStreamReader/Writer from pyspark.sql to pyspark.sql.streaming to make them consistent with scala packaging - Exposed the necessary classes in sql.streaming package so that they appear in the docs - Added pyspark.sql.streaming module to the docs ## How was this patch tested? - updated unit tests. - generated docs for testing visibility of pyspark.sql.streaming classes. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #13955 from tdas/SPARK-16266.
* [SPARK-16268][PYSPARK] SQLContext should import DataStreamReaderShixiong Zhu2016-06-281-2/+9
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fixed the following error: ``` >>> sqlContext.readStream Traceback (most recent call last): File "<stdin>", line 1, in <module> File "...", line 442, in readStream return DataStreamReader(self._wrapped) NameError: global name 'DataStreamReader' is not defined ``` ## How was this patch tested? The added test. Author: Shixiong Zhu <shixiong@databricks.com> Closes #13958 from zsxwing/fix-import.
* [MINOR][DOCS][STRUCTURED STREAMING] Minor doc fixes around `DataFrameWriter` ↵Burak Yavuz2016-06-281-2/+2
| | | | | | | | | | | | and `DataStreamWriter` ## What changes were proposed in this pull request? Fixes a couple old references to `DataFrameWriter.startStream` to `DataStreamWriter.start Author: Burak Yavuz <brkyvz@gmail.com> Closes #13952 from brkyvz/minor-doc-fix.
* [SPARK-16175] [PYSPARK] handle None for UDTDavies Liu2016-06-282-2/+16
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Scala UDT will bypass all the null and will not pass them into serialize() and deserialize() of UDT, this PR update the Python UDT to do this as well. ## How was this patch tested? Added tests. Author: Davies Liu <davies@databricks.com> Closes #13878 from davies/udt_null.
* [SPARK-16259][PYSPARK] cleanup options in DataFrame read/write APIDavies Liu2016-06-281-99/+20
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? There are some duplicated code for options in DataFrame reader/writer API, this PR clean them up, it also fix a bug for `escapeQuotes` of csv(). ## How was this patch tested? Existing tests. Author: Davies Liu <davies@databricks.com> Closes #13948 from davies/csv_options.
* [SPARK-16224] [SQL] [PYSPARK] SparkSession builder's configs need to be set ↵Yin Huai2016-06-284-1/+59
| | | | | | | | | | | | | | to the existing Scala SparkContext's SparkConf ## What changes were proposed in this pull request? When we create a SparkSession at the Python side, it is possible that a SparkContext has been created. For this case, we need to set configs of the SparkSession builder to the Scala SparkContext's SparkConf (we need to do so because conf changes on a active Python SparkContext will not be propagated to the JVM side). Otherwise, we may create a wrong SparkSession (e.g. Hive support is not enabled even if enableHiveSupport is called). ## How was this patch tested? New tests and manual tests. Author: Yin Huai <yhuai@databricks.com> Closes #13931 from yhuai/SPARK-16224.
* [SPARK-16242][MLLIB][PYSPARK] Conversion between old/new matrix columns in a ↵Yanbo Liang2016-06-281-0/+80
| | | | | | | | | | | | | | DataFrame (Python) ## What changes were proposed in this pull request? This PR implements python wrappers for #13888 to convert old/new matrix columns in a DataFrame. ## How was this patch tested? Doctest in python. Author: Yanbo Liang <ybliang8@gmail.com> Closes #13935 from yanboliang/spark-16242.
* [SPARK-16128][SQL] Allow setting length of characters to be truncated to, in ↵Prashant Sharma2016-06-281-3/+15
| | | | | | | | | | | | | | | | | | Dataset.show function. ## What changes were proposed in this pull request? Allowing truncate to a specific number of character is convenient at times, especially while operating from the REPL. Sometimes those last few characters make all the difference, and showing everything brings in whole lot of noise. ## How was this patch tested? Existing tests. + 1 new test in DataFrameSuite. For SparkR and pyspark, existing tests and manual testing. Author: Prashant Sharma <prashsh1@in.ibm.com> Author: Prashant Sharma <prashant@apache.org> Closes #13839 from ScrapCodes/add_truncateTo_DF.show.
* [SPARK-16220][SQL] Revert Change to Bring Back SHOW FUNCTIONS FunctionalityBill Chambers2016-06-271-1/+11
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? - Fix tests regarding show functions functionality - Revert `catalog.ListFunctions` and `SHOW FUNCTIONS` to return to `Spark 1.X` functionality. Cherry picked changes from this PR: https://github.com/apache/spark/pull/13413/files ## How was this patch tested? Unit tests. Author: Bill Chambers <bill@databricks.com> Author: Bill Chambers <wchambers@ischool.berkeley.edu> Closes #13916 from anabranch/master.
* [SPARK-16179][PYSPARK] fix bugs for Python udf in generateDavies Liu2016-06-241-0/+7
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fix the bug when Python UDF is used in explode (generator), GenerateExec requires that all the attributes in expressions should be resolvable from children when creating, we should replace the children first, then replace it's expressions. ``` >>> df.select(explode(f(*df))).show() Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/vlad/dev/spark/python/pyspark/sql/dataframe.py", line 286, in show print(self._jdf.showString(n, truncate)) File "/home/vlad/dev/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 933, in __call__ File "/home/vlad/dev/spark/python/pyspark/sql/utils.py", line 63, in deco return f(*a, **kw) File "/home/vlad/dev/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line 312, in get_return_value py4j.protocol.Py4JJavaError: An error occurred while calling o52.showString. : org.apache.spark.sql.catalyst.errors.package$TreeNodeException: makeCopy, tree: Generate explode(<lambda>(_1#0L)), false, false, [col#15L] +- Scan ExistingRDD[_1#0L] at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:50) at org.apache.spark.sql.catalyst.trees.TreeNode.makeCopy(TreeNode.scala:387) at org.apache.spark.sql.execution.SparkPlan.makeCopy(SparkPlan.scala:69) at org.apache.spark.sql.execution.SparkPlan.makeCopy(SparkPlan.scala:45) at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsDown(QueryPlan.scala:177) at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressions(QueryPlan.scala:144) at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract(ExtractPythonUDFs.scala:153) at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:114) at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:113) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69) at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:300) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:321) at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179) at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:319) at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:298) at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.apply(ExtractPythonUDFs.scala:113) at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.apply(ExtractPythonUDFs.scala:93) at org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:95) at org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:95) at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124) at scala.collection.immutable.List.foldLeft(List.scala:84) at org.apache.spark.sql.execution.QueryExecution.prepareForExecution(QueryExecution.scala:95) at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:85) at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:85) at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2557) at org.apache.spark.sql.Dataset.head(Dataset.scala:1923) at org.apache.spark.sql.Dataset.take(Dataset.scala:2138) at org.apache.spark.sql.Dataset.showString(Dataset.scala:239) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:280) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:211) at java.lang.Thread.run(Thread.java:745) Caused by: java.lang.reflect.InvocationTargetException at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method) at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) at java.lang.reflect.Constructor.newInstance(Constructor.java:423) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$makeCopy$1$$anonfun$apply$13.apply(TreeNode.scala:413) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$makeCopy$1$$anonfun$apply$13.apply(TreeNode.scala:413) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$makeCopy$1.apply(TreeNode.scala:412) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$makeCopy$1.apply(TreeNode.scala:387) at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49) ... 42 more Caused by: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: pythonUDF0#20 at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:50) at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:88) at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:87) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:279) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:279) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:278) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:284) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:284) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:321) at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179) at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:319) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:284) at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:268) at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:87) at org.apache.spark.sql.execution.GenerateExec.<init>(GenerateExec.scala:63) ... 52 more Caused by: java.lang.RuntimeException: Couldn't find pythonUDF0#20 in [_1#0L] at scala.sys.package$.error(package.scala:27) at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:94) at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:88) at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49) ... 67 more ``` ## How was this patch tested? Added regression tests. Author: Davies Liu <davies@databricks.com> Closes #13883 from davies/udf_in_generate.
* [SPARK-16077] [PYSPARK] catch the exception from pickle.whichmodule()Davies Liu2016-06-241-2/+12
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In the case that we don't know which module a object came from, will call pickle.whichmodule() to go throught all the loaded modules to find the object, which could fail because some modules, for example, six, see https://bitbucket.org/gutworth/six/issues/63/importing-six-breaks-pickling We should ignore the exception here, use `__main__` as the module name (it means we can't find the module). ## How was this patch tested? Manual tested. Can't have a unit test for this. Author: Davies Liu <davies@databricks.com> Closes #13788 from davies/whichmodule.
* [SPARK-16125][YARN] Fix not test yarn cluster mode correctly in YarnClusterSuitepeng.zhang2016-06-241-4/+0
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Since SPARK-13220(Deprecate "yarn-client" and "yarn-cluster"), YarnClusterSuite doesn't test "yarn cluster" mode correctly. This pull request fixes it. ## How was this patch tested? Unit test (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Author: peng.zhang <peng.zhang@xiaomi.com> Closes #13836 from renozhang/SPARK-16125-test-yarn-cluster-mode.
* [SPARK-16127][ML][PYPSARK] Audit @Since annotations related to ml.linalgNick Pentreath2016-06-222-6/+10
| | | | | | | | | | | | [SPARK-14615](https://issues.apache.org/jira/browse/SPARK-14615) and #12627 changed `spark.ml` pipelines to use the new `ml.linalg` classes for `Vector`/`Matrix`. Some `Since` annotations for public methods/vals have not been updated accordingly to be `2.0.0`. This PR updates them. ## How was this patch tested? Existing unit tests. Author: Nick Pentreath <nickp@za.ibm.com> Closes #13840 from MLnick/SPARK-16127-ml-linalg-since.
* [SPARK-15162][SPARK-15164][PYSPARK][DOCS][ML] update some pydocsHolden Karau2016-06-222-2/+37
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Mark ml.classification algorithms as experimental to match Scala algorithms, update PyDoc for for thresholds on `LogisticRegression` to have same level of info as Scala, and enable mathjax for PyDoc. ## How was this patch tested? Built docs locally & PySpark SQL tests Author: Holden Karau <holden@us.ibm.com> Closes #12938 from holdenk/SPARK-15162-SPARK-15164-update-some-pydocs.
* [SPARK-15741][PYSPARK][ML] Pyspark cleanup of set default seed to NoneBryan Cutler2016-06-214-7/+7
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Several places set the seed Param default value to None which will translate to a zero value on the Scala side. This is unnecessary because a default fixed value already exists and if a test depends on a zero valued seed, then it should explicitly set it to zero instead of relying on this translation. These cases can be safely removed except for the ALS doc test, which has been changed to set the seed value to zero. ## How was this patch tested? Ran PySpark tests locally Author: Bryan Cutler <cutlerb@gmail.com> Closes #13672 from BryanCutler/pyspark-cleanup-setDefault-seed-SPARK-15741.
* [SPARK-16086] [SQL] [PYSPARK] create Row without any fieldsDavies Liu2016-06-212-6/+12
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR allows us to create a Row without any fields. ## How was this patch tested? Added a test for empty row and udf without arguments. Author: Davies Liu <davies@databricks.com> Closes #13812 from davies/no_argus.
* [SPARK-13792][SQL] Addendum: Fix Python APIReynold Xin2016-06-211-21/+33
| | | | | | | | | | | | ## What changes were proposed in this pull request? This is a follow-up to https://github.com/apache/spark/pull/13795 to properly set CSV options in Python API. As part of this, I also make the Python option setting for both CSV and JSON more robust against positional errors. ## How was this patch tested? N/A Author: Reynold Xin <rxin@databricks.com> Closes #13800 from rxin/SPARK-13792-2.
* [SPARK-10258][DOC][ML] Add @Since annotations to ml.featureNick Pentreath2016-06-211-5/+5
| | | | | | | | | | | | | | This PR adds missing `Since` annotations to `ml.feature` package. Closes #8505. ## How was this patch tested? Existing tests. Author: Nick Pentreath <nickp@za.ibm.com> Closes #13641 from MLnick/add-since-annotations.
* Revert "[SPARK-16086] [SQL] fix Python UDF without arguments (for 1.6)"Xiangrui Meng2016-06-212-8/+6
| | | | This reverts commit a46553cbacf0e4012df89fe55385dec5beaa680a.
* [SPARK-13792][SQL] Limit logging of bad records in CSV data sourceReynold Xin2016-06-201-0/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This pull request adds a new option (maxMalformedLogPerPartition) in CSV reader to limit the maximum of logging message Spark generates per partition for malformed records. The error log looks something like ``` 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: More than 10 malformed records have been found on this partition. Malformed records from now on will not be logged. ``` Closes #12173 ## How was this patch tested? Manually tested. Author: Reynold Xin <rxin@databricks.com> Closes #13795 from rxin/SPARK-13792.
* [SPARK-16086] [SQL] fix Python UDF without arguments (for 1.6)Davies Liu2016-06-202-6/+8
| | | | | | | | | | | | | Fix the bug for Python UDF that does not have any arguments. Added regression tests. Author: Davies Liu <davies.liu@gmail.com> Closes #13793 from davies/fix_no_arguments. (cherry picked from commit abe36c53d126bb580e408a45245fd8e81806869c) Signed-off-by: Davies Liu <davies.liu@gmail.com>
* [SPARK-16079][PYSPARK][ML] Added missing import for ↵Bryan Cutler2016-06-202-2/+6
| | | | | | | | | | | | | | | | DecisionTreeRegressionModel used in GBTClassificationModel ## What changes were proposed in this pull request? Fixed missing import for DecisionTreeRegressionModel used in GBTClassificationModel trees method. ## How was this patch tested? Local tests Author: Bryan Cutler <cutlerb@gmail.com> Closes #13787 from BryanCutler/pyspark-GBTClassificationModel-import-SPARK-16079.
* [SPARK-15973][PYSPARK] Fix GroupedData DocumentationJosh Howes2016-06-171-11/+11
| | | | | | | | | | | | | | | | | *This contribution is my original work and that I license the work to the project under the project's open source license.* ## What changes were proposed in this pull request? Documentation updates to PySpark's GroupedData ## How was this patch tested? Manual Tests Author: Josh Howes <josh.howes@gmail.com> Author: Josh Howes <josh.howes@maxpoint.com> Closes #13724 from josh-howes/bugfix/SPARK-15973.
* [SPARK-15803] [PYSPARK] Support with statement syntax for SparkSessionJeff Zhang2016-06-171-0/+16
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Support with statement syntax for SparkSession in pyspark ## How was this patch tested? Manually verify it. Although I can add unit test for it, it would affect other unit test because the SparkContext is stopped after the with statement. Author: Jeff Zhang <zjffdu@apache.org> Closes #13541 from zjffdu/SPARK-15803.
* [SPARK-16035][PYSPARK] Fix SparseVector parser assertion for end parenthesisandreapasqua2016-06-171-1/+1
| | | | | | | | | | | ## What changes were proposed in this pull request? The check on the end parenthesis of the expression to parse was using the wrong variable. I corrected that. ## How was this patch tested? Manual test Author: andreapasqua <andrea@radius.com> Closes #13750 from andreapasqua/sparse-vector-parser-assertion-fix.
* [SPARK-15946][MLLIB] Conversion between old/new vector columns in a ↵Xiangrui Meng2016-06-171-0/+82
| | | | | | | | | | | | | | | | | | DataFrame (Python) ## What changes were proposed in this pull request? This PR implements python wrappers for #13662 to convert old/new vector columns in a DataFrame. ## How was this patch tested? doctest in Python cc: yanboliang Author: Xiangrui Meng <meng@databricks.com> Closes #13731 from mengxr/SPARK-15946.
* [SPARK-15981][SQL][STREAMING] Fixed bug and added tests in DataStreamReader ↵Tathagata Das2016-06-161-122/+136
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Python API ## What changes were proposed in this pull request? - Fixed bug in Python API of DataStreamReader. Because a single path was being converted to a array before calling Java DataStreamReader method (which takes a string only), it gave the following error. ``` File "/Users/tdas/Projects/Spark/spark/python/pyspark/sql/readwriter.py", line 947, in pyspark.sql.readwriter.DataStreamReader.json Failed example: json_sdf = spark.readStream.json(os.path.join(tempfile.mkdtemp(), 'data'), schema = sdf_schema) Exception raised: Traceback (most recent call last): File "/System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/doctest.py", line 1253, in __run compileflags, 1) in test.globs File "<doctest pyspark.sql.readwriter.DataStreamReader.json[0]>", line 1, in <module> json_sdf = spark.readStream.json(os.path.join(tempfile.mkdtemp(), 'data'), schema = sdf_schema) File "/Users/tdas/Projects/Spark/spark/python/pyspark/sql/readwriter.py", line 963, in json return self._df(self._jreader.json(path)) File "/Users/tdas/Projects/Spark/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 933, in __call__ answer, self.gateway_client, self.target_id, self.name) File "/Users/tdas/Projects/Spark/spark/python/pyspark/sql/utils.py", line 63, in deco return f(*a, **kw) File "/Users/tdas/Projects/Spark/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line 316, in get_return_value format(target_id, ".", name, value)) Py4JError: An error occurred while calling o121.json. Trace: py4j.Py4JException: Method json([class java.util.ArrayList]) does not exist at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:318) at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:326) at py4j.Gateway.invoke(Gateway.java:272) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:211) at java.lang.Thread.run(Thread.java:744) ``` - Reduced code duplication between DataStreamReader and DataFrameWriter - Added missing Python doctests ## How was this patch tested? New tests Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #13703 from tdas/SPARK-15981.
* [SPARK-15888] [SQL] fix Python UDF with aggregateDavies Liu2016-06-151-1/+9
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? After we move the ExtractPythonUDF rule into physical plan, Python UDF can't work on top of aggregate anymore, because they can't be evaluated before aggregate, should be evaluated after aggregate. This PR add another rule to extract these kind of Python UDF from logical aggregate, create a Project on top of Aggregate. ## How was this patch tested? Added regression tests. The plan of added test query looks like this: ``` == Parsed Logical Plan == 'Project [<lambda>('k, 's) AS t#26] +- Aggregate [<lambda>(key#5L)], [<lambda>(key#5L) AS k#17, sum(cast(<lambda>(value#6) as bigint)) AS s#22L] +- LogicalRDD [key#5L, value#6] == Analyzed Logical Plan == t: int Project [<lambda>(k#17, s#22L) AS t#26] +- Aggregate [<lambda>(key#5L)], [<lambda>(key#5L) AS k#17, sum(cast(<lambda>(value#6) as bigint)) AS s#22L] +- LogicalRDD [key#5L, value#6] == Optimized Logical Plan == Project [<lambda>(agg#29, agg#30L) AS t#26] +- Aggregate [<lambda>(key#5L)], [<lambda>(key#5L) AS agg#29, sum(cast(<lambda>(value#6) as bigint)) AS agg#30L] +- LogicalRDD [key#5L, value#6] == Physical Plan == *Project [pythonUDF0#37 AS t#26] +- BatchEvalPython [<lambda>(agg#29, agg#30L)], [agg#29, agg#30L, pythonUDF0#37] +- *HashAggregate(key=[<lambda>(key#5L)#31], functions=[sum(cast(<lambda>(value#6) as bigint))], output=[agg#29,agg#30L]) +- Exchange hashpartitioning(<lambda>(key#5L)#31, 200) +- *HashAggregate(key=[pythonUDF0#34 AS <lambda>(key#5L)#31], functions=[partial_sum(cast(pythonUDF1#35 as bigint))], output=[<lambda>(key#5L)#31,sum#33L]) +- BatchEvalPython [<lambda>(key#5L), <lambda>(value#6)], [key#5L, value#6, pythonUDF0#34, pythonUDF1#35] +- Scan ExistingRDD[key#5L,value#6] ``` Author: Davies Liu <davies@databricks.com> Closes #13682 from davies/fix_py_udf.
* [SPARK-15953][WIP][STREAMING] Renamed ContinuousQuery to StreamingQueryTathagata Das2016-06-157-100/+99
| | | | | | | | | | Renamed for simplicity, so that its obvious that its related to streaming. Existing unit tests. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #13673 from tdas/SPARK-15953.
* [SPARK-15935][PYSPARK] Fix a wrong format tag in the error messageShixiong Zhu2016-06-141-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? A follow up PR for #13655 to fix a wrong format tag. ## How was this patch tested? Jenkins unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes #13665 from zsxwing/fix.
* [SPARK-15933][SQL][STREAMING] Refactored DF reader-writer to use readStream ↵Tathagata Das2016-06-146-168/+570
| | | | | | | | | | | | | | | | and writeStream for streaming DFs ## What changes were proposed in this pull request? Currently, the DataFrameReader/Writer has method that are needed for streaming and non-streaming DFs. This is quite awkward because each method in them through runtime exception for one case or the other. So rather having half the methods throw runtime exceptions, its just better to have a different reader/writer API for streams. - [x] Python API!! ## How was this patch tested? Existing unit tests + two sets of unit tests for DataFrameReader/Writer and DataStreamReader/Writer. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #13653 from tdas/SPARK-15933.
* [SPARK-15935][PYSPARK] Enable test for sql/streaming.py and fix these testsShixiong Zhu2016-06-143-20/+54
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR just enables tests for sql/streaming.py and also fixes the failures. ## How was this patch tested? Existing unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes #13655 from zsxwing/python-streaming-test.
generated by cgit v1.2.3 (git 2.39.1) at 2024-07-06 06:03:57 +0000