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* [SPARK-17514] df.take(1) and df.limit(1).collect() should perform the same ↵Josh Rosen2016-09-141-4/+1
| | | | | | | | | | | | | | | | | | | | in Python ## What changes were proposed in this pull request? In PySpark, `df.take(1)` runs a single-stage job which computes only one partition of the DataFrame, while `df.limit(1).collect()` computes all partitions and runs a two-stage job. This difference in performance is confusing. The reason why `limit(1).collect()` is so much slower is that `collect()` internally maps to `df.rdd.<some-pyspark-conversions>.toLocalIterator`, which causes Spark SQL to build a query where a global limit appears in the middle of the plan; this, in turn, ends up being executed inefficiently because limits in the middle of plans are now implemented by repartitioning to a single task rather than by running a `take()` job on the driver (this was done in #7334, a patch which was a prerequisite to allowing partition-local limits to be pushed beneath unions, etc.). In order to fix this performance problem I think that we should generalize the fix from SPARK-10731 / #8876 so that `DataFrame.collect()` also delegates to the Scala implementation and shares the same performance properties. This patch modifies `DataFrame.collect()` to first collect all results to the driver and then pass them to Python, allowing this query to be planned using Spark's `CollectLimit` optimizations. ## How was this patch tested? Added a regression test in `sql/tests.py` which asserts that the expected number of jobs, stages, and tasks are run for both queries. Author: Josh Rosen <joshrosen@databricks.com> Closes #15068 from JoshRosen/pyspark-collect-limit.
* [SPARK-17298][SQL] Require explicit CROSS join for cartesian productsSrinath Shankar2016-09-031-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Require the use of CROSS join syntax in SQL (and a new crossJoin DataFrame API) to specify explicit cartesian products between relations. By cartesian product we mean a join between relations R and S where there is no join condition involving columns from both R and S. If a cartesian product is detected in the absence of an explicit CROSS join, an error must be thrown. Turning on the "spark.sql.crossJoin.enabled" configuration flag will disable this check and allow cartesian products without an explicit CROSS join. The new crossJoin DataFrame API must be used to specify explicit cross joins. The existing join(DataFrame) method will produce a INNER join that will require a subsequent join condition. That is df1.join(df2) is equivalent to select * from df1, df2. ## How was this patch tested? Added cross-join.sql to the SQLQueryTestSuite to test the check for cartesian products. Added a couple of tests to the DataFrameJoinSuite to test the crossJoin API. Modified various other test suites to explicitly specify a cross join where an INNER join or a comma-separated list was previously used. Author: Srinath Shankar <srinath@databricks.com> Closes #14866 from srinathshankar/crossjoin.
* [SPARK-16772] Correct API doc references to PySpark classes + formatting fixesNicholas Chammas2016-07-281-1/+1
| | | | | | | | | | | | | | | | | | ## What's Been Changed The PR corrects several broken or missing class references in the Python API docs. It also correct formatting problems. For example, you can see [here](http://spark.apache.org/docs/2.0.0/api/python/pyspark.sql.html#pyspark.sql.SQLContext.registerFunction) how Sphinx is not picking up the reference to `DataType`. That's because the reference is relative to the current module, whereas `DataType` is in a different module. You can also see [here](http://spark.apache.org/docs/2.0.0/api/python/pyspark.sql.html#pyspark.sql.SQLContext.createDataFrame) how the formatting for byte, tinyint, and so on is italic instead of monospace. That's because in ReST single backticks just make things italic, unlike in Markdown. ## Testing I tested this PR by [building the Python docs](https://github.com/apache/spark/tree/master/docs#generating-the-documentation-html) and reviewing the results locally in my browser. I confirmed that the broken or missing class references were resolved, and that the formatting was corrected. Author: Nicholas Chammas <nicholas.chammas@gmail.com> Closes #14393 from nchammas/python-docstring-fixes.
* [SPARK-16651][PYSPARK][DOC] Make `withColumnRenamed/drop` description more ↵Dongjoon Hyun2016-07-221-0/+2
| | | | | | | | | | | | | | | | consistent with Scala API ## What changes were proposed in this pull request? `withColumnRenamed` and `drop` is a no-op if the given column name does not exists. Python documentation also describe that, but this PR adds more explicit line consistently with Scala to reduce the ambiguity. ## How was this patch tested? It's about docs. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14288 from dongjoon-hyun/SPARK-16651.
* [DOC] improve python doc for rdd.histogram and dataframe.joinMortada Mehyar2016-07-181-5/+5
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? doc change only ## How was this patch tested? doc change only Author: Mortada Mehyar <mortada.mehyar@gmail.com> Closes #14253 from mortada/histogram_typos.
* [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-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-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 documentationhyukjinkwon2016-07-061-4/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## 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-16266][SQL][STREAING] Moved DataStreamReader/Writer from pyspark.sql ↵Tathagata Das2016-06-281-1/+2
| | | | | | | | | | | | | | | | | | 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.
* [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-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-15953][WIP][STREAMING] Renamed ContinuousQuery to StreamingQueryTathagata Das2016-06-151-1/+1
| | | | | | | | | | 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-15933][SQL][STREAMING] Refactored DF reader-writer to use readStream ↵Tathagata Das2016-06-141-2/+16
| | | | | | | | | | | | | | | | 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-15392][SQL] fix default value of size estimation of logical planDavies Liu2016-05-191-1/+1
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? We use autoBroadcastJoinThreshold + 1L as the default value of size estimation, that is not good in 2.0, because we will calculate the size based on size of schema, then the estimation could be less than autoBroadcastJoinThreshold if you have an SELECT on top of an DataFrame created from RDD. This PR change the default value to Long.MaxValue. ## How was this patch tested? Added regression tests. Author: Davies Liu <davies@databricks.com> Closes #13183 from davies/fix_default_size.
* [SPARK-14603][SQL][FOLLOWUP] Verification of Metadata Operations by Session ↵gatorsmile2016-05-191-2/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Catalog #### What changes were proposed in this pull request? This follow-up PR is to address the remaining comments in https://github.com/apache/spark/pull/12385 The major change in this PR is to issue better error messages in PySpark by using the mechanism that was proposed by davies in https://github.com/apache/spark/pull/7135 For example, in PySpark, if we input the following statement: ```python >>> l = [('Alice', 1)] >>> df = sqlContext.createDataFrame(l) >>> df.createTempView("people") >>> df.createTempView("people") ``` Before this PR, the exception we will get is like ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/dataframe.py", line 152, in createTempView self._jdf.createTempView(name) File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 933, in __call__ File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/utils.py", line 63, in deco return f(*a, **kw) File "/Users/xiaoli/IdeaProjects/sparkDelivery/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 o35.createTempView. : org.apache.spark.sql.catalyst.analysis.TempTableAlreadyExistsException: Temporary table 'people' already exists; at org.apache.spark.sql.catalyst.catalog.SessionCatalog.createTempView(SessionCatalog.scala:324) at org.apache.spark.sql.SparkSession.createTempView(SparkSession.scala:523) at org.apache.spark.sql.Dataset.createTempView(Dataset.scala:2328) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:606) 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) ``` After this PR, the exception we will get become cleaner: ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/dataframe.py", line 152, in createTempView self._jdf.createTempView(name) File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 933, in __call__ File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/utils.py", line 75, in deco raise AnalysisException(s.split(': ', 1)[1], stackTrace) pyspark.sql.utils.AnalysisException: u"Temporary table 'people' already exists;" ``` #### How was this patch tested? Fixed an existing PySpark test case Author: gatorsmile <gatorsmile@gmail.com> Closes #13126 from gatorsmile/followup-14684.
* [SPARK-15171][SQL] Deprecate registerTempTable and add dataset.createTempViewSean Zhong2016-05-121-3/+48
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Deprecates registerTempTable and add dataset.createTempView, dataset.createOrReplaceTempView. ## How was this patch tested? Unit tests. Author: Sean Zhong <seanzhong@databricks.com> Closes #12945 from clockfly/spark-15171.
* [SPARK-15278] [SQL] Remove experimental tag from Python DataFrameReynold Xin2016-05-111-2/+2
| | | | | | | | | | | | ## What changes were proposed in this pull request? Earlier we removed experimental tag for Scala/Java DataFrames, but haven't done so for Python. This patch removes the experimental flag for Python and declares them stable. ## How was this patch tested? N/A. Author: Reynold Xin <rxin@databricks.com> Closes #13062 from rxin/SPARK-15278.
* [MINOR] remove dead codeDavies Liu2016-05-041-9/+0
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* [SPARK-14555] First cut of Python API for Structured StreamingBurak Yavuz2016-04-201-0/+12
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch provides a first cut of python APIs for structured streaming. This PR provides the new classes: - ContinuousQuery - Trigger - ProcessingTime in pyspark under `pyspark.sql.streaming`. In addition, it contains the new methods added under: - `DataFrameWriter` a) `startStream` b) `trigger` c) `queryName` - `DataFrameReader` a) `stream` - `DataFrame` a) `isStreaming` This PR doesn't contain all methods exposed for `ContinuousQuery`, for example: - `exception` - `sourceStatuses` - `sinkStatus` They may be added in a follow up. This PR also contains some very minor doc fixes in the Scala side. ## How was this patch tested? Python doc tests TODO: - [ ] verify Python docs look good Author: Burak Yavuz <brkyvz@gmail.com> Author: Burak Yavuz <burak@databricks.com> Closes #12320 from brkyvz/stream-python.
* [SPARK-14717] [PYTHON] Scala, Python APIs for Dataset.unpersist differ in ↵felixcheung2016-04-191-1/+3
| | | | | | | | | | | | | | | | | | default blocking value ## What changes were proposed in this pull request? Change unpersist blocking parameter default value to match Scala ## How was this patch tested? unit tests, manual tests jkbradley davies Author: felixcheung <felixcheung_m@hotmail.com> Closes #12507 from felixcheung/pyunpersist.
* [SPARK-14573][PYSPARK][BUILD] Fix PyDoc Makefile & highlighting issuesHolden Karau2016-04-141-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? The PyDoc Makefile used "=" rather than "?=" for setting env variables so it overwrote the user values. This ignored the environment variables we set for linting allowing warnings through. This PR also fixes the warnings that had been introduced. ## How was this patch tested? manual local export & make Author: Holden Karau <holden@us.ibm.com> Closes #12336 from holdenk/SPARK-14573-fix-pydoc-makefile.
* [SPARK-14334] [SQL] add toLocalIterator for Dataset/DataFrameDavies Liu2016-04-041-0/+14
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? RDD.toLocalIterator() could be used to fetch one partition at a time to reduce the memory usage. Right now, for Dataset/Dataframe we have to use df.rdd.toLocalIterator, which is super slow also requires lots of memory (because of the Java serializer or even Kyro serializer). This PR introduce an optimized toLocalIterator for Dataset/DataFrame, which is much faster and requires much less memory. For a partition with 5 millions rows, `df.rdd.toIterator` took about 100 seconds, but df.toIterator took less than 7 seconds. For 10 millions row, rdd.toIterator will crash (not enough memory) with 4G heap, but df.toLocalIterator could finished in 12 seconds. The JDBC server has been updated to use DataFrame.toIterator. ## How was this patch tested? Existing tests. Author: Davies Liu <davies@databricks.com> Closes #12114 from davies/local_iterator.
* [SPARK-14142][SQL] Replace internal use of unionAll with unionReynold Xin2016-03-241-2/+2
| | | | | | | | | | | | ## What changes were proposed in this pull request? unionAll has been deprecated in SPARK-14088. ## How was this patch tested? Should be covered by all existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #11946 from rxin/SPARK-14142.
* [SPARK-14088][SQL] Some Dataset API touch-upReynold Xin2016-03-221-2/+12
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? 1. Deprecated unionAll. It is pretty confusing to have both "union" and "unionAll" when the two do the same thing in Spark but are different in SQL. 2. Rename reduce in KeyValueGroupedDataset to reduceGroups so it is more consistent with rest of the functions in KeyValueGroupedDataset. Also makes it more obvious what "reduce" and "reduceGroups" mean. Previously it was confusing because it could be reducing a Dataset, or just reducing groups. 3. Added a "name" function, which is more natural to name columns than "as" for non-SQL users. 4. Remove "subtract" function since it is just an alias for "except". ## How was this patch tested? All changes should be covered by existing tests. Also added couple test cases to cover "name". Author: Reynold Xin <rxin@databricks.com> Closes #11908 from rxin/SPARK-14088.
* [SPARK-10380][SQL] Fix confusing documentation examples for ↵Reynold Xin2016-03-141-5/+15
| | | | | | | | | | | | | | | | astype/drop_duplicates. ## What changes were proposed in this pull request? We have seen users getting confused by the documentation for astype and drop_duplicates, because the examples in them do not use these functions (but do uses their aliases). This patch simply removes all examples for these functions, and say that they are aliases. ## How was this patch tested? Existing PySpark unit tests. Closes #11543. Author: Reynold Xin <rxin@databricks.com> Closes #11698 from rxin/SPARK-10380.
* [SPARK-13671] [SPARK-13311] [SQL] Use different physical plans for RDD and ↵Davies Liu2016-03-121-2/+1
| | | | | | | | | | | | | | | | | | | | data sources ## What changes were proposed in this pull request? This PR split the PhysicalRDD into two classes, PhysicalRDD and PhysicalScan. PhysicalRDD is used for DataFrames that is created from existing RDD. PhysicalScan is used for DataFrame that is created from data sources. This enable use to apply different optimization on both of them. Also fix the problem for sameResult() on two DataSourceScan. Also fix the equality check to toString for `In`. It's better to use Seq there, but we can't break this public API (sad). ## How was this patch tested? Existing tests. Manually tested with TPCDS query Q59 and Q64, all those duplicated exchanges can be re-used now, also saw there are 40+% performance improvement (saving half of the scan). Author: Davies Liu <davies@databricks.com> Closes #11514 from davies/existing_rdd.
* [SPARK-13594][SQL] remove typed operations(e.g. map, flatMap) from python ↵Wenchen Fan2016-03-021-40/+2
| | | | | | | | | | | | | | | | DataFrame ## What changes were proposed in this pull request? Remove `map`, `flatMap`, `mapPartitions` from python DataFrame, to prepare for Dataset API in the future. ## How was this patch tested? existing tests Author: Wenchen Fan <wenchen@databricks.com> Closes #11445 from cloud-fan/python-clean.
* [SPARK-13479][SQL][PYTHON] Added Python API for approxQuantileJoseph K. Bradley2016-02-241-0/+54
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? * Scala DataFrameStatFunctions: Added version of approxQuantile taking a List instead of an Array, for Python compatbility * Python DataFrame and DataFrameStatFunctions: Added approxQuantile ## How was this patch tested? * unit test in sql/tests.py Documentation was copied from the existing approxQuantile exactly. Author: Joseph K. Bradley <joseph@databricks.com> Closes #11356 from jkbradley/approx-quantile-python.
* [SPARK-13250] [SQL] Update PhysicallRDD to convert to UnsafeRow if using the ↵Nong Li2016-02-241-1/+2
| | | | | | | | | | | | | | | | | vectorized scanner. Some parts of the engine rely on UnsafeRow which the vectorized parquet scanner does not want to produce. This add a conversion in Physical RDD. In the case where codegen is used (and the scan is the start of the pipeline), there is no requirement to use UnsafeRow. This patch adds update PhysicallRDD to support codegen, which eliminates the need for the UnsafeRow conversion in all cases. The result of these changes for TPCDS-Q19 at the 10gb sf reduces the query time from 9.5 seconds to 6.5 seconds. Author: Nong Li <nong@databricks.com> Closes #11141 from nongli/spark-13250.
* [SPARK-13329] [SQL] considering output for statistics of logical planDavies Liu2016-02-231-2/+2
| | | | | | | | | | | | | | | | | | | | | The current implementation of statistics of UnaryNode does not considering output (for example, Project may product much less columns than it's child), we should considering it to have a better guess. We usually only join with few columns from a parquet table, the size of projected plan could be much smaller than the original parquet files. Having a better guess of size help we choose between broadcast join or sort merge join. After this PR, I saw a few queries choose broadcast join other than sort merge join without turning spark.sql.autoBroadcastJoinThreshold for every query, ended up with about 6-8X improvements on end-to-end time. We use `defaultSize` of DataType to estimate the size of a column, currently For DecimalType/StringType/BinaryType and UDT, we are over-estimate too much (4096 Bytes), so this PR change them to some more reasonable values. Here are the new defaultSize for them: DecimalType: 8 or 16 bytes, based on the precision StringType: 20 bytes BinaryType: 100 bytes UDF: default size of SQL type These numbers are not perfect (hard to have a perfect number for them), but should be better than 4096. Author: Davies Liu <davies@databricks.com> Closes #11210 from davies/statics.
* [SPARK-13296][SQL] Move UserDefinedFunction into sql.expressions.Reynold Xin2016-02-131-1/+1
| | | | | | | | | | | | | | | | This pull request has the following changes: 1. Moved UserDefinedFunction into expressions package. This is more consistent with how we structure the packages for window functions and UDAFs. 2. Moved UserDefinedPythonFunction into execution.python package, so we don't have a random private class in the top level sql package. 3. Move everything in execution/python.scala into the newly created execution.python package. Most of the diffs are just straight copy-paste. Author: Reynold Xin <rxin@databricks.com> Closes #11181 from rxin/SPARK-13296.
* [SPARK-12706] [SQL] grouping() and grouping_id()Davies Liu2016-02-101-11/+11
| | | | | | | | | | | | Grouping() returns a column is aggregated or not, grouping_id() returns the aggregation levels. grouping()/grouping_id() could be used with window function, but does not work in having/sort clause, will be fixed by another PR. The GROUPING__ID/grouping_id() in Hive is wrong (according to docs), we also did it wrongly, this PR change that to match the behavior in most databases (also the docs of Hive). Author: Davies Liu <davies@databricks.com> Closes #10677 from davies/grouping.
* [SPARK-5865][API DOC] Add doc warnings for methods that return local data ↵Tommy YU2016-02-061-0/+6
| | | | | | | | | | | | | structures rxin srowen I work out note message for rdd.take function, please help to review. If it's fine, I can apply to all other function later. Author: Tommy YU <tummyyu@163.com> Closes #10874 from Wenpei/spark-5865-add-warning-for-localdatastructure.
* [SPARK-12756][SQL] use hash expression in ExchangeWenchen Fan2016-01-131-13/+13
| | | | | | | | | | This PR makes bucketing and exchange share one common hash algorithm, so that we can guarantee the data distribution is same between shuffle and bucketed data source, which enables us to only shuffle one side when join a bucketed table and a normal one. This PR also fixes the tests that are broken by the new hash behaviour in shuffle. Author: Wenchen Fan <wenchen@databricks.com> Closes #10703 from cloud-fan/use-hash-expr-in-shuffle.
* [SPARK-12600][SQL] Remove deprecated methods in Spark SQLReynold Xin2016-01-041-47/+1
| | | | | | Author: Reynold Xin <rxin@databricks.com> Closes #10559 from rxin/remove-deprecated-sql.
* [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.
* [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 levelsgatorsmile2015-12-181-3/+3
| | | | | | | | | | | | | | 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.
* [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-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-11720][SQL][ML] Handle edge cases when count = 0 or 1 for Stats functionJihongMa2015-11-181-1/+1
| | | | | | | | return Double.NaN for mean/average when count == 0 for all numeric types that is converted to Double, Decimal type continue to return null. Author: JihongMa <linlin200605@gmail.com> Closes #9705 from JihongMA/SPARK-11720.
* [SPARK-11420] Updating Stddev support via Imperative AggregateJihongMa2015-11-121-1/+1
| | | | | | | | switched stddev support from DeclarativeAggregate to ImperativeAggregate. Author: JihongMa <linlin200605@gmail.com> Closes #9380 from JihongMA/SPARK-11420.
* [SPARK-9830][SQL] Remove AggregateExpression1 and Aggregate Operator used to ↵Yin Huai2015-11-101-1/+1
| | | | | | | | | | | | | | | | | | | evaluate AggregateExpression1s https://issues.apache.org/jira/browse/SPARK-9830 This PR contains the following main changes. * Removing `AggregateExpression1`. * Removing `Aggregate` operator, which is used to evaluate `AggregateExpression1`. * Removing planner rule used to plan `Aggregate`. * Linking `MultipleDistinctRewriter` to analyzer. * Renaming `AggregateExpression2` to `AggregateExpression` and `AggregateFunction2` to `AggregateFunction`. * Updating places where we create aggregate expression. The way to create aggregate expressions is `AggregateExpression(aggregateFunction, mode, isDistinct)`. * Changing `val`s in `DeclarativeAggregate`s that touch children of this function to `lazy val`s (when we create aggregate expression in DataFrame API, children of an aggregate function can be unresolved). Author: Yin Huai <yhuai@databricks.com> Closes #9556 from yhuai/removeAgg1.
* [SPARK-11410] [PYSPARK] Add python bindings for repartition and sortW…Nong Li2015-11-061-16/+101
| | | | | | | | …ithinPartitions. Author: Nong Li <nong@databricks.com> Closes #9504 from nongli/spark-11410.
* [SPARK-10116][CORE] XORShiftRandom.hashSeed is random in high bitsImran Rashid2015-11-061-3/+3
| | | | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-10116 This is really trivial, just happened to notice it -- if `XORShiftRandom.hashSeed` is really supposed to have random bits throughout (as the comment implies), it needs to do something for the conversion to `long`. mengxr mkolod Author: Imran Rashid <irashid@cloudera.com> Closes #8314 from squito/SPARK-10116.
* [SPARK-11279][PYSPARK] Add DataFrame#toDF in PySparkJeff Zhang2015-10-261-0/+12
| | | | | | Author: Jeff Zhang <zjffdu@apache.org> Closes #9248 from zjffdu/SPARK-11279.
* [SPARK-11205][PYSPARK] Delegate to scala DataFrame API rather than p…Jeff Zhang2015-10-201-1/+2
| | | | | | | | | | …rint in python No test needed. Verify it manually in pyspark shell Author: Jeff Zhang <zjffdu@apache.org> Closes #9177 from zjffdu/SPARK-11205.
* [SPARK-10782] [PYTHON] Update dropDuplicates documentationasokadiggs2015-09-291-0/+2
| | | | | | | | Documentation for dropDuplicates() and drop_duplicates() is one and the same. Resolved the error in the example for drop_duplicates using the same approach used for groupby and groupBy, by indicating that dropDuplicates and drop_duplicates are aliases. Author: asokadiggs <asoka.diggs@intel.com> Closes #8930 from asokadiggs/jira-10782.
* [SPARK-10731] [SQL] Delegate to Scala's DataFrame.take implementation in ↵Reynold Xin2015-09-231-1/+4
| | | | | | | | | | | | Python DataFrame. Python DataFrame.head/take now requires scanning all the partitions. This pull request changes them to delegate the actual implementation to Scala DataFrame (by calling DataFrame.take). This is more of a hack for fixing this issue in 1.5.1. A more proper fix is to change executeCollect and executeTake to return InternalRow rather than Row, and thus eliminate the extra round-trip conversion. Author: Reynold Xin <rxin@databricks.com> Closes #8876 from rxin/SPARK-10731.
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