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* [SPARK-15095][SQL] drop binary mode in ThriftServerDavies Liu2016-05-033-47/+14
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR drop the support for binary mode in ThriftServer, only HTTP mode is supported now, to reduce the maintain burden. The code to support binary mode is still kept, just in case if we want it in future. ## How was this patch tested? Updated tests to use HTTP mode. Author: Davies Liu <davies@databricks.com> Closes #12876 from davies/hide_binary.
* [SPARK-15073][SQL] Hide SparkSession constructor from the publicAndrew Or2016-05-031-2/+12
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Users should use the builder pattern instead. ## How was this patch tested? Jenks. Author: Andrew Or <andrew@databricks.com> Closes #12873 from andrewor14/spark-session-constructor.
* [SPARK-14521] [SQL] StackOverflowError in Kryo when executing TPC-DSyzhou20012016-05-032-42/+129
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Observed stackOverflowError in Kryo when executing TPC-DS Query27. Spark thrift server disables kryo reference tracking (if not specified in conf). When "spark.kryo.referenceTracking" is set to true explicitly in spark-defaults.conf, query executes successfully. The root cause is that the TaskMemoryManager inside MemoryConsumer and LongToUnsafeRowMap were not transient and thus were serialized and broadcast around from within LongHashedRelation, which could potentially cause circular reference inside Kryo. But the TaskMemoryManager is per task and should not be passed around at the first place. This fix makes it transient. ## How was this patch tested? core/test, hive/test, sql/test, catalyst/test, dev/lint-scala, org.apache.spark.sql.hive.execution.HiveCompatibilitySuite, dev/scalastyle, manual test of TBC-DS Query 27 with 1GB data but without the "limit 100" which would cause a NPE due to SPARK-14752. Author: yzhou2001 <yzhou_1999@yahoo.com> Closes #12598 from yzhou2001/master.
* [SPARK-15087][CORE][SQL] Remove AccumulatorV2.localValue and keep only valueSandeep Singh2016-05-034-5/+5
| | | | | | | | | | | | ## What changes were proposed in this pull request? Remove AccumulatorV2.localValue and keep only value ## How was this patch tested? existing tests Author: Sandeep Singh <sandeep@techaddict.me> Closes #12865 from techaddict/SPARK-15087.
* [SPARK-14860][TESTS] Create a new Waiter in reset to bypass an issue of ↵Shixiong Zhu2016-05-031-9/+3
| | | | | | | | | | | | | | | | ScalaTest's Waiter.wait ## What changes were proposed in this pull request? This PR updates `QueryStatusCollector.reset` to create Waiter instead of calling `await(1 milliseconds)` to bypass an ScalaTest's issue that Waiter.await may block forever. ## How was this patch tested? I created a local stress test to call codes in `test("event ordering")` 100 times. It cannot pass without this patch. Author: Shixiong Zhu <shixiong@databricks.com> Closes #12623 from zsxwing/flaky-test.
* [SPARK-14716][SQL] Added support for partitioning in FileStreamSinkTathagata Das2016-05-038-52/+603
| | | | | | | | | | | | | | | | | # What changes were proposed in this pull request? Support partitioning in the file stream sink. This is implemented using a new, but simpler code path for writing parquet files - both unpartitioned and partitioned. This new code path does not use Output Committers, as we will eventually write the file names to the metadata log for "committing" them. This patch duplicates < 100 LOC from the WriterContainer. But its far simpler that WriterContainer as it does not involve output committing. In addition, it introduces the new APIs in FileFormat and OutputWriterFactory in an attempt to simplify the APIs (not have Job in the `FileFormat` API, not have bucket and other stuff in the `OutputWriterFactory.newInstance()` ). # Tests - New unit tests to test the FileStreamSinkWriter for partitioned and unpartitioned files - New unit test to partially test the FileStreamSink for partitioned files (does not test recovery of partition column data, as that requires change in the StreamFileCatalog, future PR). - Updated FileStressSuite to test number of records read from partitioned output files. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #12409 from tdas/streaming-partitioned-parquet.
* [SPARK-14884][SQL][STREAMING][WEBUI] Fix call site for continuous queriesLiwei Lin2016-05-032-4/+15
| | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Since we've been processing continuous queries in separate threads, the call sites are then `run at <unknown>:0`. It's not wrong but provides very little information; in addition, we can not distinguish two queries only from their call sites. This patch fixes this. ### Before [Jobs Tab] ![s1a](https://cloud.githubusercontent.com/assets/15843379/14766101/a47246b2-0a30-11e6-8d81-06a9a600113b.png) [SQL Tab] ![s1b](https://cloud.githubusercontent.com/assets/15843379/14766102/a4750226-0a30-11e6-9ada-773d977d902b.png) ### After [Jobs Tab] ![s2a](https://cloud.githubusercontent.com/assets/15843379/14766104/a89705b6-0a30-11e6-9830-0d40ec68527b.png) [SQL Tab] ![s2b](https://cloud.githubusercontent.com/assets/15843379/14766103/a8966728-0a30-11e6-8e4d-c2e326400478.png) ## How was this patch tested? Manually checks - see screenshots above. Author: Liwei Lin <lwlin7@gmail.com> Closes #12650 from lw-lin/fix-call-site.
* [SPARK-15088] [SQL] Remove SparkSqlSerializerReynold Xin2016-05-032-118/+0
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch removes SparkSqlSerializer. I believe this is now dead code. ## How was this patch tested? Removed a test case related to it. Author: Reynold Xin <rxin@databricks.com> Closes #12864 from rxin/SPARK-15088.
* [SPARK-15081] Move AccumulatorV2 and subclasses into util packageReynold Xin2016-05-034-4/+8
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch moves AccumulatorV2 and subclasses into util package. ## How was this patch tested? Updated relevant tests. Author: Reynold Xin <rxin@databricks.com> Closes #12863 from rxin/SPARK-15081.
* [SPARK-15053][BUILD] Fix Java Lint errors on Hive-Thriftserver moduleDongjoon Hyun2016-05-0329-71/+57
| | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This issue fixes or hides 181 Java linter errors introduced by SPARK-14987 which copied hive service code from Hive. We had better clean up these errors before releasing Spark 2.0. - Fix UnusedImports (15 lines), RedundantModifier (14 lines), SeparatorWrap (9 lines), MethodParamPad (6 lines), FileTabCharacter (5 lines), ArrayTypeStyle (3 lines), ModifierOrder (3 lines), RedundantImport (1 line), CommentsIndentation (1 line), UpperEll (1 line), FallThrough (1 line), OneStatementPerLine (1 line), NewlineAtEndOfFile (1 line) errors. - Ignore `LineLength` errors under `hive/service/*` (118 lines). - Ignore `MethodName` error in `PasswdAuthenticationProvider.java` (1 line). - Ignore `NoFinalizer` error in `ThreadWithGarbageCleanup.java` (1 line). ## How was this patch tested? After passing Jenkins building, run `dev/lint-java` manually. ```bash $ dev/lint-java Checkstyle checks passed. ``` Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12831 from dongjoon-hyun/SPARK-15053.
* [SPARK-13749][SQL][FOLLOW-UP] Faster pivot implementation for many distinct ↵Andrew Ray2016-05-022-3/+20
| | | | | | | | | | | | | | | | values with two phase aggregation ## What changes were proposed in this pull request? This is a follow up PR for #11583. It makes 3 lazy vals into just vals and adds unit test coverage. ## How was this patch tested? Existing unit tests and additional unit tests. Author: Andrew Ray <ray.andrew@gmail.com> Closes #12861 from aray/fast-pivot-follow-up.
* [SPARK-15077][SQL] Use a fair lock to avoid thread starvation in StreamExecutionShixiong Zhu2016-05-021-27/+52
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Right now `StreamExecution.awaitBatchLock` uses an unfair lock. `StreamExecution.awaitOffset` may run too long and fail some test because `StreamExecution.constructNextBatch` keeps getting the lock. See: https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.4/865/testReport/junit/org.apache.spark.sql.streaming/FileStreamSourceStressTestSuite/file_source_stress_test/ This PR uses a fair ReentrantLock to resolve the thread starvation issue. ## How was this patch tested? Modified `FileStreamSourceStressTestSuite.test("file source stress test")` to run the test codes 100 times locally. It always fails because of timeout without this patch. Author: Shixiong Zhu <shixiong@databricks.com> Closes #12852 from zsxwing/SPARK-15077.
* [SPARK-15062][SQL] fix list type infer serializer issuebomeng2016-05-022-5/+21
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Make serializer correctly inferred if the input type is `List[_]`, since `List[_]` is type of `Seq[_]`, before it was matched to different case (`case t if definedByConstructorParams(t)`). ## How was this patch tested? New test case was added. Author: bomeng <bmeng@us.ibm.com> Closes #12849 from bomeng/SPARK-15062.
* [SPARK-15047][SQL] Cleanup SQL ParserHerman van Hovell2016-05-026-77/+31
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR addresses a few minor issues in SQL parser: - Removes some unused rules and keywords in the grammar. - Removes code path for fallback SQL parsing (was needed for Hive native parsing). - Use `UnresolvedGenerator` instead of hard-coding `Explode` & `JsonTuple`. - Adds a more generic way of creating error messages for unsupported Hive features. - Use `visitFunctionName` as much as possible. - Interpret a `CatalogColumn`'s `DataType` directly instead of parsing it again. ## How was this patch tested? Existing tests. Author: Herman van Hovell <hvanhovell@questtec.nl> Closes #12826 from hvanhovell/SPARK-15047.
* [SPARK-14747][SQL] Add assertStreaming/assertNoneStreaming checks in ↵Liwei Lin2016-05-022-5/+210
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | DataFrameWriter ## Problem If an end user happens to write code mixed with continuous-query-oriented methods and non-continuous-query-oriented methods: ```scala ctx.read .format("text") .stream("...") // continuous query .write .text("...") // non-continuous query; should be startStream() here ``` He/she would get this somehow confusing exception: > Exception in thread "main" java.lang.AssertionError: assertion failed: No plan for FileSource[./continuous_query_test_input] at scala.Predef$.assert(Predef.scala:170) at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:59) at org.apache.spark.sql.catalyst.planning.QueryPlanner.planLater(QueryPlanner.scala:54) at ... ## What changes were proposed in this pull request? This PR adds checks for continuous-query-oriented methods and non-continuous-query-oriented methods in `DataFrameWriter`: <table> <tr> <td align="center"></td> <td align="center"><strong>can be called on continuous query?</strong></td> <td align="center"><strong>can be called on non-continuous query?</strong></td> </tr> <tr> <td align="center">mode</td> <td align="center"></td> <td align="center">yes</td> </tr> <tr> <td align="center">trigger</td> <td align="center">yes</td> <td align="center"></td> </tr> <tr> <td align="center">format</td> <td align="center">yes</td> <td align="center">yes</td> </tr> <tr> <td align="center">option/options</td> <td align="center">yes</td> <td align="center">yes</td> </tr> <tr> <td align="center">partitionBy</td> <td align="center">yes</td> <td align="center">yes</td> </tr> <tr> <td align="center">bucketBy</td> <td align="center"></td> <td align="center">yes</td> </tr> <tr> <td align="center">sortBy</td> <td align="center"></td> <td align="center">yes</td> </tr> <tr> <td align="center">save</td> <td align="center"></td> <td align="center">yes</td> </tr> <tr> <td align="center">queryName</td> <td align="center">yes</td> <td align="center"></td> </tr> <tr> <td align="center">startStream</td> <td align="center">yes</td> <td align="center"></td> </tr> <tr> <td align="center">insertInto</td> <td align="center"></td> <td align="center">yes</td> </tr> <tr> <td align="center">saveAsTable</td> <td align="center"></td> <td align="center">yes</td> </tr> <tr> <td align="center">jdbc</td> <td align="center"></td> <td align="center">yes</td> </tr> <tr> <td align="center">json</td> <td align="center"></td> <td align="center">yes</td> </tr> <tr> <td align="center">parquet</td> <td align="center"></td> <td align="center">yes</td> </tr> <tr> <td align="center">orc</td> <td align="center"></td> <td align="center">yes</td> </tr> <tr> <td align="center">text</td> <td align="center"></td> <td align="center">yes</td> </tr> <tr> <td align="center">csv</td> <td align="center"></td> <td align="center">yes</td> </tr> </table> After this PR's change, the friendly exception would be: > Exception in thread "main" org.apache.spark.sql.AnalysisException: text() can only be called on non-continuous queries; at org.apache.spark.sql.DataFrameWriter.assertNotStreaming(DataFrameWriter.scala:678) at org.apache.spark.sql.DataFrameWriter.text(DataFrameWriter.scala:629) at ss.SSDemo$.main(SSDemo.scala:47) ## How was this patch tested? dedicated unit tests were added Author: Liwei Lin <lwlin7@gmail.com> Closes #12521 from lw-lin/dataframe-writer-check.
* [SPARK-14785] [SQL] Support correlated scalar subqueriesHerman van Hovell2016-05-027-39/+195
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In this PR we add support for correlated scalar subqueries. An example of such a query is: ```SQL select * from tbl1 a where a.value > (select max(value) from tbl2 b where b.key = a.key) ``` The implementation adds the `RewriteCorrelatedScalarSubquery` rule to the Optimizer. This rule plans these subqueries using `LEFT OUTER` joins. It currently supports rewrites for `Project`, `Aggregate` & `Filter` logical plans. I could not find a well defined semantics for the use of scalar subqueries in an `Aggregate`. The current implementation currently evaluates the scalar subquery *before* aggregation. This means that you either have to make scalar subquery part of the grouping expression, or that you have to aggregate it further on. I am open to suggestions on this. The implementation currently forces the uniqueness of a scalar subquery by enforcing that it is aggregated and that the resulting column is wrapped in an `AggregateExpression`. ## How was this patch tested? Added tests to `SubquerySuite`. Author: Herman van Hovell <hvanhovell@questtec.nl> Closes #12822 from hvanhovell/SPARK-14785.
* [SPARK-12928][SQL] Oracle FLOAT datatype is not properly handled when ↵poolis2016-05-022-0/+15
| | | | | | | | | | | reading via JDBC The contribution is my original work and that I license the work to the project under the project's open source license. Author: poolis <gmichalopoulos@gmail.com> Author: Greg Michalopoulos <gmichalopoulos@gmail.com> Closes #10899 from poolis/spark-12928.
* [SPARK-15052][SQL] Use builder pattern to create SparkSessionReynold Xin2016-05-021-4/+127
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch creates a builder pattern for creating SparkSession. The new code is unused and mostly deadcode. I'm putting it up here for feedback. There are a few TODOs that can be done as follow-up pull requests: - [ ] Update tests to use this - [ ] Update examples to use this - [ ] Clean up SQLContext code w.r.t. this one (i.e. SparkSession shouldn't call into SQLContext.getOrCreate; it should be the other way around) - [ ] Remove SparkSession.withHiveSupport - [ ] Disable the old constructor (by making it private) so the only way to start a SparkSession is through this builder pattern ## How was this patch tested? Part of the future pull request is to clean this up and switch existing tests to use this. Author: Reynold Xin <rxin@databricks.com> Closes #12830 from rxin/sparksession-builder.
* [SPARK-13745] [SQL] Support columnar in memory representation on Big Endian ↵Pete Robbins2016-05-024-22/+110
| | | | | | | | | | | | | | | | platforms ## What changes were proposed in this pull request? parquet datasource and ColumnarBatch tests fail on big-endian platforms This patch adds support for the little-endian byte arrays being correctly interpreted on a big-endian platform ## How was this patch tested? Spark test builds ran on big endian z/Linux and regression build on little endian amd64 Author: Pete Robbins <robbinspg@gmail.com> Closes #12397 from robbinspg/master.
* [SPARK-14781] [SQL] support nested predicate subqueryDavies Liu2016-05-0214-61/+345
| | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In order to support nested predicate subquery, this PR introduce an internal join type ExistenceJoin, which will emit all the rows from left, plus an additional column, which presents there are any rows matched from right or not (it's not null-aware right now). This additional column could be used to replace the subquery in Filter. In theory, all the predicate subquery could use this join type, but it's slower than LeftSemi and LeftAnti, so it's only used for nested subquery (subquery inside OR). For example, the following SQL: ```sql SELECT a FROM t WHERE EXISTS (select 0) OR EXISTS (select 1) ``` This PR also fix a bug in predicate subquery push down through join (they should not). Nested null-aware subquery is still not supported. For example, `a > 3 OR b NOT IN (select bb from t)` After this, we could run TPCDS query Q10, Q35, Q45 ## How was this patch tested? Added unit tests. Author: Davies Liu <davies@databricks.com> Closes #12820 from davies/or_exists.
* [SPARK-14830][SQL] Add RemoveRepetitionFromGroupExpressions optimizer.Dongjoon Hyun2016-05-022-2/+33
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR aims to optimize GroupExpressions by removing repeating expressions. `RemoveRepetitionFromGroupExpressions` is added. **Before** ```scala scala> sql("select a+1 from values 1,2 T(a) group by a+1, 1+a, A+1, 1+A").explain() == Physical Plan == WholeStageCodegen : +- TungstenAggregate(key=[(a#0 + 1)#6,(1 + a#0)#7,(A#0 + 1)#8,(1 + A#0)#9], functions=[], output=[(a + 1)#5]) : +- INPUT +- Exchange hashpartitioning((a#0 + 1)#6, (1 + a#0)#7, (A#0 + 1)#8, (1 + A#0)#9, 200), None +- WholeStageCodegen : +- TungstenAggregate(key=[(a#0 + 1) AS (a#0 + 1)#6,(1 + a#0) AS (1 + a#0)#7,(A#0 + 1) AS (A#0 + 1)#8,(1 + A#0) AS (1 + A#0)#9], functions=[], output=[(a#0 + 1)#6,(1 + a#0)#7,(A#0 + 1)#8,(1 + A#0)#9]) : +- INPUT +- LocalTableScan [a#0], [[1],[2]] ``` **After** ```scala scala> sql("select a+1 from values 1,2 T(a) group by a+1, 1+a, A+1, 1+A").explain() == Physical Plan == WholeStageCodegen : +- TungstenAggregate(key=[(a#0 + 1)#6], functions=[], output=[(a + 1)#5]) : +- INPUT +- Exchange hashpartitioning((a#0 + 1)#6, 200), None +- WholeStageCodegen : +- TungstenAggregate(key=[(a#0 + 1) AS (a#0 + 1)#6], functions=[], output=[(a#0 + 1)#6]) : +- INPUT +- LocalTableScan [a#0], [[1],[2]] ``` ## How was this patch tested? Pass the Jenkins tests (with a new testcase) Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12590 from dongjoon-hyun/SPARK-14830.
* [SPARK-14579][SQL] Fix the race condition in ↵Shixiong Zhu2016-05-022-5/+14
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | StreamExecution.processAllAvailable again ## What changes were proposed in this pull request? #12339 didn't fix the race condition. MemorySinkSuite is still flaky: https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-maven-hadoop-2.2/814/testReport/junit/org.apache.spark.sql.streaming/MemorySinkSuite/registering_as_a_table/ Here is an execution order to reproduce it. | Time |Thread 1 | MicroBatchThread | |:-------------:|:-------------:|:-----:| | 1 | | `MemorySink.getOffset` | | 2 | | availableOffsets ++= newData (availableOffsets is not changed here) | | 3 | addData(newData) | | | 4 | Set `noNewData` to `false` in processAllAvailable | | | 5 | | `dataAvailable` returns `false` | | 6 | | noNewData = true | | 7 | `noNewData` is true so just return | | | 8 | assert results and fail | | | 9 | | `dataAvailable` returns true so process the new batch | This PR expands the scope of `awaitBatchLock.synchronized` to eliminate the above race. ## How was this patch tested? test("stress test"). It always failed before this patch. And it will pass after applying this patch. Ignore this test in the PR as it takes several minutes to finish. Author: Shixiong Zhu <shixiong@databricks.com> Closes #12582 from zsxwing/SPARK-14579-2.
* [SPARK-13749][SQL] Faster pivot implementation for many distinct values with ↵Andrew Ray2016-05-023-33/+296
| | | | | | | | | | | | | | | | | | two phase aggregation ## What changes were proposed in this pull request? The existing implementation of pivot translates into a single aggregation with one aggregate per distinct pivot value. When the number of distinct pivot values is large (say 1000+) this can get extremely slow since each input value gets evaluated on every aggregate even though it only affects the value of one of them. I'm proposing an alternate strategy for when there are 10+ (somewhat arbitrary threshold) distinct pivot values. We do two phases of aggregation. In the first we group by the grouping columns plus the pivot column and perform the specified aggregations (one or sometimes more). In the second aggregation we group by the grouping columns and use the new (non public) PivotFirst aggregate that rearranges the outputs of the first aggregation into an array indexed by the pivot value. Finally we do a project to extract the array entries into the appropriate output column. ## How was this patch tested? Additional unit tests in DataFramePivotSuite and manual larger scale testing. Author: Andrew Ray <ray.andrew@gmail.com> Closes #11583 from aray/fast-pivot.
* [SPARK-14637][SQL] object expressions cleanupWenchen Fan2016-05-021-118/+100
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Simplify and clean up some object expressions: 1. simplify the logic to handle `propagateNull` 2. add `propagateNull` parameter to `Invoke` 3. simplify the unbox logic in `Invoke` 4. other minor cleanup TODO: simplify `MapObjects` ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #12399 from cloud-fan/object.
* [SPARK-15049] Rename NewAccumulator to AccumulatorV2Reynold Xin2016-05-011-3/+3
| | | | | | | | | | | | ## What changes were proposed in this pull request? NewAccumulator isn't the best name if we ever come up with v3 of the API. ## How was this patch tested? Updated tests to reflect the change. Author: Reynold Xin <rxin@databricks.com> Closes #12827 from rxin/SPARK-15049.
* [SPARK-13425][SQL] Documentation for CSV datasource optionshyukjinkwon2016-05-012-4/+51
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds the explanation and documentation for CSV options for reading and writing. ## How was this patch tested? Style tests with `./dev/run_tests` for documentation style. Author: hyukjinkwon <gurwls223@gmail.com> Author: Hyukjin Kwon <gurwls223@gmail.com> Closes #12817 from HyukjinKwon/SPARK-13425.
* [SPARK-15033][SQL] fix a flaky test in CachedTableSuiteWenchen Fan2016-04-302-7/+14
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This is caused by https://github.com/apache/spark/pull/12776, which removes the `synchronized` from all methods in `AccumulatorContext`. However, a test in `CachedTableSuite` synchronize on `AccumulatorContext` and expecting no one else can change it, which is not true anymore. This PR update that test to not require to lock on `AccumulatorContext`. ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #12811 from cloud-fan/flaky.
* [SPARK-14143] Options for parsing NaNs, Infinity and nulls for numeric typesHossein2016-04-306-42/+174
| | | | | | | | | | | | 1. Adds the following options for parsing NaNs: nanValue 2. Adds the following options for parsing infinity: positiveInf, negativeInf. `TypeCast.castTo` is unit tested and an end-to-end test is added to `CSVSuite` Author: Hossein <hossein@databricks.com> Closes #11947 from falaki/SPARK-14143.
* [SPARK-15034][SPARK-15035][SPARK-15036][SQL] Use spark.sql.warehouse.dir as ↵Yin Huai2016-04-3010-22/+236
| | | | | | | | | | | | | | | the warehouse location This PR contains three changes: 1. We will use spark.sql.warehouse.dir set warehouse location. We will not use hive.metastore.warehouse.dir. 2. SessionCatalog needs to set the location to default db. Otherwise, when creating a table in SparkSession without hive support, the default db's path will be an empty string. 3. When we create a database, we need to make the path qualified. Existing tests and new tests Author: Yin Huai <yhuai@databricks.com> Closes #12812 from yhuai/warehouse.
* [SPARK-15028][SQL] Remove HiveSessionState.setDefaultOverrideConfsReynold Xin2016-04-306-55/+8
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch removes some code that are no longer relevant -- mainly HiveSessionState.setDefaultOverrideConfs. ## How was this patch tested? N/A Author: Reynold Xin <rxin@databricks.com> Closes #12806 from rxin/SPARK-15028.
* [SPARK-14850][ML] convert primitive array from/to unsafe array directly in ↵Wenchen Fan2016-04-293-3/+107
| | | | | | | | | | | | | | | | VectorUDT/MatrixUDT ## What changes were proposed in this pull request? This PR adds `fromPrimitiveArray` and `toPrimitiveArray` in `UnsafeArrayData`, so that we can do the conversion much faster in VectorUDT/MatrixUDT. ## How was this patch tested? existing tests and new test suite `UnsafeArraySuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #12640 from cloud-fan/ml.
* [SPARK-13667][SQL] Support for specifying custom date format for date and ↵hyukjinkwon2016-04-298-66/+173
| | | | | | | | | | | | | | | | | | | | | | | | | timestamp types at CSV datasource. ## What changes were proposed in this pull request? This PR adds the support to specify custom date format for `DateType` and `TimestampType`. For `TimestampType`, this uses the given format to infer schema and also to convert the values For `DateType`, this uses the given format to convert the values. If the `dateFormat` is not given, then it works with `DateTimeUtils.stringToTime()` for backwords compatibility. When it's given, then it uses `SimpleDateFormat` for parsing data. In addition, `IntegerType`, `DoubleType` and `LongType` have a higher priority than `TimestampType` in type inference. This means even if the given format is `yyyy` or `yyyy.MM`, it will be inferred as `IntegerType` or `DoubleType`. Since it is type inference, I think it is okay to give such precedences. In addition, I renamed `csv.CSVInferSchema` to `csv.InferSchema` as JSON datasource has `json.InferSchema`. Although they have the same names, I did this because I thought the parent package name can still differentiate each. Accordingly, the suite name was also changed from `CSVInferSchemaSuite` to `InferSchemaSuite`. ## How was this patch tested? unit tests are used and `./dev/run_tests` for coding style tests. Author: hyukjinkwon <gurwls223@gmail.com> Closes #11550 from HyukjinKwon/SPARK-13667.
* [SPARK-14591][SQL] Remove DataTypeParser and add more keywords to the ↵Yin Huai2016-04-299-232/+26
| | | | | | | | | | | | | | nonReserved list. ## What changes were proposed in this pull request? CatalystSqlParser can parse data types. So, we do not need to have an individual DataTypeParser. ## How was this patch tested? Existing tests Author: Yin Huai <yhuai@databricks.com> Closes #12796 from yhuai/removeDataTypeParser.
* [SPARK-14757] [SQL] Fix nullability bug in EqualNullSafe codegenReynold Xin2016-04-292-2/+3
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch fixes a null handling bug in EqualNullSafe's code generation. ## How was this patch tested? Updated unit test so they would fail without the fix. Closes #12628. Author: Reynold Xin <rxin@databricks.com> Author: Arash Nabili <arash@levyx.com> Closes #12799 from rxin/equalnullsafe.
* [SPARK-14917][SQL] Enable some ORC compressions tests for writinghyukjinkwon2016-04-291-29/+33
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-14917 As it is described in the JIRA, it seems Hive 1.2.1 which Spark uses now supports snappy and none. So, this PR enables some tests for writing ORC files with compression codes, `SNAPPY` and `NONE`. ## How was this patch tested? Unittests in `OrcQuerySuite` and `sbt scalastyle`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #12699 from HyukjinKwon/SPARK-14917.
* [SPARK-15012][SQL] Simplify configuration API furtherAndrew Or2016-04-2915-154/+104
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? 1. Remove all the `spark.setConf` etc. Just expose `spark.conf` 2. Make `spark.conf` take in things set in the core `SparkConf` as well, otherwise users may get confused This was done for both the Python and Scala APIs. ## How was this patch tested? `SQLConfSuite`, python tests. This one fixes the failed tests in #12787 Closes #12787 Author: Andrew Or <andrew@databricks.com> Author: Yin Huai <yhuai@databricks.com> Closes #12798 from yhuai/conf-api.
* [SPARK-15019][SQL] Propagate all Spark Confs to HiveConf created in ↵Yin Huai2016-04-297-40/+52
| | | | | | | | | | | | | | | | HiveClientImpl ## What changes were proposed in this pull request? This PR makes two changes: 1. We will propagate Spark Confs to HiveConf created in HiveClientImpl. So, users can also use spark conf to set warehouse location and metastore url. 2. In sql/hive, HiveClientImpl will be the only place where we create a new HiveConf. ## How was this patch tested? Existing tests. Author: Yin Huai <yhuai@databricks.com> Closes #12791 from yhuai/onlyUseHiveConfInHiveClientImpl.
* [SPARK-14858] [SQL] Enable subquery pushdownHerman van Hovell2016-04-2913-318/+390
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The previous subquery PRs did not include support for pushing subqueries used in filters (`WHERE`/`HAVING`) down. This PR adds this support. For example : ```scala range(0, 10).registerTempTable("a") range(5, 15).registerTempTable("b") range(7, 25).registerTempTable("c") range(3, 12).registerTempTable("d") val plan = sql("select * from a join b on a.id = b.id left join c on c.id = b.id where a.id in (select id from d)") plan.explain(true) ``` Leads to the following Analyzed & Optimized plans: ``` == Parsed Logical Plan == ... == Analyzed Logical Plan == id: bigint, id: bigint, id: bigint Project [id#0L,id#4L,id#8L] +- Filter predicate-subquery#16 [(id#0L = id#12L)] : +- SubqueryAlias predicate-subquery#16 [(id#0L = id#12L)] : +- Project [id#12L] : +- SubqueryAlias d : +- Range 3, 12, 1, 8, [id#12L] +- Join LeftOuter, Some((id#8L = id#4L)) :- Join Inner, Some((id#0L = id#4L)) : :- SubqueryAlias a : : +- Range 0, 10, 1, 8, [id#0L] : +- SubqueryAlias b : +- Range 5, 15, 1, 8, [id#4L] +- SubqueryAlias c +- Range 7, 25, 1, 8, [id#8L] == Optimized Logical Plan == Join LeftOuter, Some((id#8L = id#4L)) :- Join Inner, Some((id#0L = id#4L)) : :- Join LeftSemi, Some((id#0L = id#12L)) : : :- Range 0, 10, 1, 8, [id#0L] : : +- Range 3, 12, 1, 8, [id#12L] : +- Range 5, 15, 1, 8, [id#4L] +- Range 7, 25, 1, 8, [id#8L] == Physical Plan == ... ``` I have also taken the opportunity to move quite a bit of code around: - Rewriting subqueris and pulling out correlated predicated from subqueries has been moved into the analyzer. The analyzer transforms `Exists` and `InSubQuery` into `PredicateSubquery` expressions. A PredicateSubquery exposes the 'join' expressions and the proper references. This makes things like type coercion, optimization and planning easier to do. - I have added support for `Aggregate` plans in subqueries. Any correlated expressions will be added to the grouping expressions. I have removed support for `Union` plans, since pulling in an outer reference from beneath a Union has no value (a filtered value could easily be part of another Union child). - Resolution of subqueries is now done using `OuterReference`s. These are used to wrap any outer reference; this makes the identification of these references easier, and also makes dealing with duplicate attributes in the outer and inner plans easier. The resolution of subqueries initially used a resolution loop which would alternate between calling the analyzer and trying to resolve the outer references. We now use a dedicated analyzer which uses a special rule for outer reference resolution. These changes are a stepping stone for enabling correlated scalar subqueries, enabling all Hive tests & allowing us to use predicate subqueries anywhere. Current tests and added test cases in FilterPushdownSuite. Author: Herman van Hovell <hvanhovell@questtec.nl> Closes #12720 from hvanhovell/SPARK-14858.
* [SPARK-14988][PYTHON] SparkSession API follow-upsAndrew Or2016-04-296-4/+28
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Addresses comments in #12765. ## How was this patch tested? Python tests. Author: Andrew Or <andrew@databricks.com> Closes #12784 from andrewor14/python-followup.
* [SPARK-12919][SPARKR] Implement dapply() on DataFrame in SparkR.Sun Rui2016-04-296-9/+179
| | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? dapply() applies an R function on each partition of a DataFrame and returns a new DataFrame. The function signature is: dapply(df, function(localDF) {}, schema = NULL) R function input: local data.frame from the partition on local node R function output: local data.frame Schema specifies the Row format of the resulting DataFrame. It must match the R function's output. If schema is not specified, each partition of the result DataFrame will be serialized in R into a single byte array. Such resulting DataFrame can be processed by successive calls to dapply(). ## How was this patch tested? SparkR unit tests. Author: Sun Rui <rui.sun@intel.com> Author: Sun Rui <sunrui2016@gmail.com> Closes #12493 from sun-rui/SPARK-12919.
* [SPARK-15013][SQL] Remove hiveConf from HiveSessionStateYin Huai2016-04-292-30/+1
| | | | | | | | | | | | ## What changes were proposed in this pull request? The hiveConf in HiveSessionState is not actually used anymore. Let's remove it. ## How was this patch tested? Existing tests Author: Yin Huai <yhuai@databricks.com> Closes #12786 from yhuai/removeHiveConf.
* [SPARK-14981][SQL] Throws exception if DESC is specified for sorting columnsCheng Lian2016-04-293-15/+41
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently Spark SQL doesn't support sorting columns in descending order. However, the parser accepts the syntax and silently drops sorting directions. This PR fixes this by throwing an exception if `DESC` is specified as sorting direction of a sorting column. ## How was this patch tested? A test case is added to test the invalid sorting order by checking exception message. Author: Cheng Lian <lian@databricks.com> Closes #12759 from liancheng/spark-14981.
* [SPARK-15004][SQL] Remove zookeeper service discovery code in thrift-serverReynold Xin2016-04-295-506/+6
| | | | | | | | | | | | ## What changes were proposed in this pull request? We recently inlined Hive's thrift server code in SPARK-15004. This patch removes the code related to zookeeper service discovery, Tez, and Hive on Spark, since they are irrelevant. ## How was this patch tested? N/A - removing dead code Author: Reynold Xin <rxin@databricks.com> Closes #12780 from rxin/SPARK-15004.
* [SPARK-15011][SQL][TEST] Ignore ↵Yin Huai2016-04-291-1/+1
| | | | | | | | | | org.apache.spark.sql.hive.StatisticsSuite.analyze MetastoreRelation This test always fail with sbt's hadoop 2.3 and 2.4 tests. Let'e disable it for now and investigate the problem. Author: Yin Huai <yhuai@databricks.com> Closes #12783 from yhuai/SPARK-15011-ignore.
* [SPARK-14988][PYTHON] SparkSession catalog and conf APIAndrew Or2016-04-292-2/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? The `catalog` and `conf` APIs were exposed in `SparkSession` in #12713 and #12669. This patch adds those to the python API. ## How was this patch tested? Python tests. Author: Andrew Or <andrew@databricks.com> Closes #12765 from andrewor14/python-spark-session-more.
* [SPARK-14987][SQL] inline hive-service (cli) into sql/hive-thriftserverDavies Liu2016-04-29175-4/+69969
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR copy the thrift-server from hive-service-1.2 (including TCLIService.thrift and generated Java source code) into sql/hive-thriftserver, so we can do further cleanup and improvements. ## How was this patch tested? Existing tests. Author: Davies Liu <davies@databricks.com> Closes #12764 from davies/thrift_server.
* [SPARK-14994][SQL] Remove execution hive from HiveSessionStateReynold Xin2016-04-2920-309/+327
| | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch removes executionHive from HiveSessionState and HiveSharedState. ## How was this patch tested? Updated test cases. Author: Reynold Xin <rxin@databricks.com> Author: Yin Huai <yhuai@databricks.com> Closes #12770 from rxin/SPARK-14994.
* [SPARK-14996][SQL] Add TPCDS Benchmark Queries for SparkSQLSameer Agarwal2016-04-291-0/+1225
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds support for easily running and benchmarking a set of common TPCDS queries locally in SparkSQL. ## How was this patch tested? N/A Author: Sameer Agarwal <sameer@databricks.com> Closes #12771 from sameeragarwal/tpcds-2.
* [SPARK-12660][SPARK-14967][SQL] Implement Except Distinct by Left Anti Joingatorsmile2016-04-2912-111/+132
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? Replaces a logical `Except` operator with a `Left-anti Join` operator. This way, we can take advantage of all the benefits of join implementations (e.g. managed memory, code generation, broadcast joins). ```SQL SELECT a1, a2 FROM Tab1 EXCEPT SELECT b1, b2 FROM Tab2 ==> SELECT DISTINCT a1, a2 FROM Tab1 LEFT ANTI JOIN Tab2 ON a1<=>b1 AND a2<=>b2 ``` Note: 1. This rule is only applicable to EXCEPT DISTINCT. Do not use it for EXCEPT ALL. 2. This rule has to be done after de-duplicating the attributes; otherwise, the enerated join conditions will be incorrect. This PR also corrects the existing behavior in Spark. Before this PR, the behavior is like ```SQL test("except") { val df_left = Seq(1, 2, 2, 3, 3, 4).toDF("id") val df_right = Seq(1, 3).toDF("id") checkAnswer( df_left.except(df_right), Row(2) :: Row(2) :: Row(4) :: Nil ) } ``` After this PR, the result is corrected. We strictly follow the SQL compliance of `Except Distinct`. #### How was this patch tested? Modified and added a few test cases to verify the optimization rule and the results of operators. Author: gatorsmile <gatorsmile@gmail.com> Closes #12736 from gatorsmile/exceptByAntiJoin.
* [HOTFIX] Disable flaky test StatisticsSuite.analyze MetastoreRelationsReynold Xin2016-04-291-1/+2
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