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* [GRAPHX][EXAMPLES] move graphx test data directory and update graphx documentWeichenXu2016-07-023-9/+9
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? There are two test data files used for graphx examples existing in directory "graphx/data" I move it into "data/" directory because the "graphx" directory is used for code files and other test data files (such as mllib, streaming test data) are all in there. I also update the graphx document where reference the data files which I move place. ## How was this patch tested? N/A Author: WeichenXu <WeichenXu123@outlook.com> Closes #14010 from WeichenXu123/move_graphx_data_dir.
* [SPARK-16095][YARN] Yarn cluster mode should report correct state to ↵peng.zhang2016-07-012-15/+31
| | | | | | | | | | | | | | SparkLauncher ## What changes were proposed in this pull request? Yarn cluster mode should return correct state for SparkLauncher ## How was this patch tested? unit test Author: peng.zhang <peng.zhang@xiaomi.com> Closes #13962 from renozhang/SPARK-16095-spark-launcher-wrong-state.
* [SPARK-16233][R][TEST] ORC test should be enabled only when HiveContext is ↵Dongjoon Hyun2016-07-011-0/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | available. ## What changes were proposed in this pull request? ORC test should be enabled only when HiveContext is available. ## How was this patch tested? Manual. ``` $ R/run-tests.sh ... 1. create DataFrame from RDD (test_sparkSQL.R#200) - Hive is not build with SparkSQL, skipped 2. test HiveContext (test_sparkSQL.R#1021) - Hive is not build with SparkSQL, skipped 3. read/write ORC files (test_sparkSQL.R#1728) - Hive is not build with SparkSQL, skipped 4. enableHiveSupport on SparkSession (test_sparkSQL.R#2448) - Hive is not build with SparkSQL, skipped 5. sparkJars tag in SparkContext (test_Windows.R#21) - This test is only for Windows, skipped DONE =========================================================================== Tests passed. ``` Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14019 from dongjoon-hyun/SPARK-16233.
* [SPARK-16335][SQL] Structured streaming should fail if source directory does ↵Reynold Xin2016-07-014-35/+36
| | | | | | | | | | | | | | 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-16299][SPARKR] Capture errors from R workers in daemon.R to avoid ↵Sun Rui2016-07-011-1/+1
| | | | | | | | | | | | | | deletion of R session temporary directory. ## What changes were proposed in this pull request? Capture errors from R workers in daemon.R to avoid deletion of R session temporary directory. See detailed description at https://issues.apache.org/jira/browse/SPARK-16299 ## How was this patch tested? SparkR unit tests. Author: Sun Rui <sunrui2016@gmail.com> Closes #13975 from sun-rui/SPARK-16299.
* [SPARK-16012][SPARKR] Implement gapplyCollect which will apply a R function ↵Narine Kokhlikyan2016-07-015-67/+177
| | | | | | | | | | | | | | | | on each group similar to gapply and collect the result back to R data.frame ## What changes were proposed in this pull request? gapplyCollect() does gapply() on a SparkDataFrame and collect the result back to R. Compared to gapply() + collect(), gapplyCollect() offers performance optimization as well as programming convenience, as no schema is needed to be provided. This is similar to dapplyCollect(). ## How was this patch tested? Added test cases for gapplyCollect similar to dapplyCollect Author: Narine Kokhlikyan <narine@slice.com> Closes #13760 from NarineK/gapplyCollect.
* [SPARK-16208][SQL] Add `PropagateEmptyRelation` optimizerDongjoon Hyun2016-07-013-1/+242
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds a new logical optimizer, `PropagateEmptyRelation`, to collapse a logical plans consisting of only empty LocalRelations. **Optimizer Targets** 1. Binary(or Higher)-node Logical Plans - Union with all empty children. - Join with one or two empty children (including Intersect/Except). 2. Unary-node Logical Plans - Project/Filter/Sample/Join/Limit/Repartition with all empty children. - Aggregate with all empty children and without AggregateFunction expressions, COUNT. - Generate with Explode because other UserDefinedGenerators like Hive UDTF returns results. **Sample Query** ```sql WITH t1 AS (SELECT a FROM VALUES 1 t(a)), t2 AS (SELECT b FROM VALUES 1 t(b) WHERE 1=2) SELECT a,b FROM t1, t2 WHERE a=b GROUP BY a,b HAVING a>1 ORDER BY a,b ``` **Before** ```scala scala> sql("with t1 as (select a from values 1 t(a)), t2 as (select b from values 1 t(b) where 1=2) select a,b from t1, t2 where a=b group by a,b having a>1 order by a,b").explain == Physical Plan == *Sort [a#0 ASC, b#1 ASC], true, 0 +- Exchange rangepartitioning(a#0 ASC, b#1 ASC, 200) +- *HashAggregate(keys=[a#0, b#1], functions=[]) +- Exchange hashpartitioning(a#0, b#1, 200) +- *HashAggregate(keys=[a#0, b#1], functions=[]) +- *BroadcastHashJoin [a#0], [b#1], Inner, BuildRight :- *Filter (isnotnull(a#0) && (a#0 > 1)) : +- LocalTableScan [a#0] +- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint))) +- *Filter (isnotnull(b#1) && (b#1 > 1)) +- LocalTableScan <empty>, [b#1] ``` **After** ```scala scala> sql("with t1 as (select a from values 1 t(a)), t2 as (select b from values 1 t(b) where 1=2) select a,b from t1, t2 where a=b group by a,b having a>1 order by a,b").explain == Physical Plan == LocalTableScan <empty>, [a#0, b#1] ``` ## How was this patch tested? Pass the Jenkins tests (including a new testsuite). Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13906 from dongjoon-hyun/SPARK-16208.
* [SPARK-16222][SQL] JDBC Sources - Handling illegal input values for ↵gatorsmile2016-07-017-45/+98
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | `fetchsize` and `batchsize` #### What changes were proposed in this pull request? For JDBC data sources, users can specify `batchsize` for multi-row inserts and `fetchsize` for multi-row fetch. A few issues exist: - The property keys are case sensitive. Thus, the existing test cases for `fetchsize` use incorrect names, `fetchSize`. Basically, the test cases are broken. - No test case exists for `batchsize`. - We do not detect the illegal input values for `fetchsize` and `batchsize`. For example, when `batchsize` is zero, we got the following exception: ``` Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost): java.lang.ArithmeticException: / by zero ``` when `fetchsize` is less than zero, we got the exception from the underlying JDBC driver: ``` Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost): org.h2.jdbc.JdbcSQLException: Invalid value "-1" for parameter "rows" [90008-183] ``` This PR fixes all the above issues, and issue the appropriate exceptions when detecting the illegal inputs for `fetchsize` and `batchsize`. Also update the function descriptions. #### How was this patch tested? Test cases are fixed and added. Author: gatorsmile <gatorsmile@gmail.com> Closes #13919 from gatorsmile/jdbcProperties.
* [SPARK-15761][MLLIB][PYSPARK] Load ipython when default python is Python3MechCoder2016-07-011-1/+3
| | | | | | | | | | | | | ## What changes were proposed in this pull request? I would like to use IPython with Python 3.5. It is annoying when it fails with IPython requires Python 2.7+; please install python2.7 or set PYSPARK_PYTHON when I have a version greater than 2.7 ## How was this patch tested It now works with IPython and Python3 Author: MechCoder <mks542@nyu.edu> Closes #13503 from MechCoder/spark-15761.
* [SPARK-16182][CORE] Utils.scala -- terminateProcess() should call ↵Sean Owen2016-07-012-31/+47
| | | | | | | | | | | | | | | | | | Process.destroyForcibly() if and only if Process.destroy() fails ## What changes were proposed in this pull request? Utils.terminateProcess should `destroy()` first and only fall back to `destroyForcibly()` if it fails. It's kind of bad that we're force-killing executors -- and only in Java 8. See JIRA for an example of the impact: no shutdown While here: `Utils.waitForProcess` should use the Java 8 method if available instead of a custom implementation. ## How was this patch tested? Existing tests, which cover the force-kill case, and Amplab tests, which will cover both Java 7 and Java 8 eventually. However I tested locally on Java 8 and the PR builder will try Java 7 here. Author: Sean Owen <sowen@cloudera.com> Closes #13973 from srowen/SPARK-16182.
* [SPARK-12177][STREAMING][KAFKA] limit api surface areacody koeninger2016-07-0113-193/+222
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This is an alternative to the refactoring proposed by https://github.com/apache/spark/pull/13996 ## How was this patch tested? unit tests also tested under scala 2.10 via mvn -Dscala-2.10 Author: cody koeninger <cody@koeninger.org> Closes #13998 from koeninger/kafka-0-10-refactor.
* [SPARK-16331][SQL] Reduce code generation timeHiroshi Inoue2016-06-301-39/+43
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? During the code generation, a `LocalRelation` often has a huge `Vector` object as `data`. In the simple example below, a `LocalRelation` has a Vector with 1000000 elements of `UnsafeRow`. ``` val numRows = 1000000 val ds = (1 to numRows).toDS().persist() benchmark.addCase("filter+reduce") { iter => ds.filter(a => (a & 1) == 0).reduce(_ + _) } ``` At `TreeNode.transformChildren`, all elements of the vector is unnecessarily iterated to check whether any children exist in the vector since `Vector` is Traversable. This part significantly increases code generation time. This patch avoids this overhead by checking the number of children before iterating all elements; `LocalRelation` does not have children since it extends `LeafNode`. The performance of the above example ``` without this patch Java HotSpot(TM) 64-Bit Server VM 1.8.0_91-b14 on Mac OS X 10.11.5 Intel(R) Core(TM) i5-5257U CPU 2.70GHz compilationTime: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ filter+reduce 4426 / 4533 0.2 4426.0 1.0X with this patch compilationTime: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ filter+reduce 3117 / 3391 0.3 3116.6 1.0X ``` ## How was this patch tested? using existing unit tests Author: Hiroshi Inoue <inouehrs@jp.ibm.com> Closes #14000 from inouehrs/compilation-time-reduction.
* [SPARK-14608][ML] transformSchema needs better documentationYuhao Yang2016-06-301-1/+4
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? jira: https://issues.apache.org/jira/browse/SPARK-14608 PipelineStage.transformSchema currently has minimal documentation. It should have more to explain it can: check schema check parameter interactions ## How was this patch tested? unit test Author: Yuhao Yang <hhbyyh@gmail.com> Author: Yuhao Yang <yuhao.yang@intel.com> Closes #12384 from hhbyyh/transformSchemaDoc.
* [SPARK-15954][SQL] Disable loading test tables in Python testsReynold Xin2016-06-302-161/+185
| | | | | | | | | | | | | | ## 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-15643][DOC][ML] Add breaking changes to ML migration guideNick Pentreath2016-06-301-3/+101
| | | | | | | | | | | | This PR adds the breaking changes from [SPARK-14810](https://issues.apache.org/jira/browse/SPARK-14810) to the migration guide. ## How was this patch tested? Built docs locally. Author: Nick Pentreath <nickp@za.ibm.com> Closes #13924 from MLnick/SPARK-15643-migration-guide.
* [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-16276][SQL] Implement elt SQL functionpetermaxlee2016-07-016-2/+82
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch implements the elt function, as it is implemented in Hive. ## How was this patch tested? Added expression unit test in StringExpressionsSuite and end-to-end test in StringFunctionsSuite. Author: petermaxlee <petermaxlee@gmail.com> Closes #13966 from petermaxlee/SPARK-16276.
* [SPARK-16313][SQL] Spark should not silently drop exceptions in file listingReynold Xin2016-06-306-13/+29
| | | | | | | | | | | | ## 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-16336][SQL] Suggest doing table refresh upon FileNotFoundExceptionpetermaxlee2016-06-302-1/+102
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch appends a message to suggest users running refresh table or reloading data frames when Spark sees a FileNotFoundException due to stale, cached metadata. ## How was this patch tested? Added a unit test for this in MetadataCacheSuite. Author: petermaxlee <petermaxlee@gmail.com> Closes #14003 from petermaxlee/SPARK-16336.
* [SPARK-16256][DOCS] Fix window operation diagramTathagata Das2016-06-304-1/+1
| | | | | | Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #14001 from tdas/SPARK-16256-2.
* [SPARK-16212][STREAMING][KAFKA] code cleanup from review feedbackcody koeninger2016-06-303-14/+12
| | | | | | | | | | | | ## What changes were proposed in this pull request? code cleanup in kafka-0-8 to match suggested changes for kafka-0-10 branch ## How was this patch tested? unit tests Author: cody koeninger <cody@koeninger.org> Closes #13908 from koeninger/kafka-0-8-cleanup.
* [SPARK-16289][SQL] Implement posexplode table generating functionDongjoon Hyun2016-06-3014-72/+276
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## 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-15865][CORE] Blacklist should not result in job hanging with less ↵Imran Rashid2016-06-309-15/+194
| | | | | | | | | | | | | | | | | | | | | than 4 executors ## What changes were proposed in this pull request? Before this change, when you turn on blacklisting with `spark.scheduler.executorTaskBlacklistTime`, but you have fewer than `spark.task.maxFailures` executors, you can end with a job "hung" after some task failures. Whenever a taskset is unable to schedule anything on resourceOfferSingleTaskSet, we check whether the last pending task can be scheduled on *any* known executor. If not, the taskset (and any corresponding jobs) are failed. * Worst case, this is O(maxTaskFailures + numTasks). But unless many executors are bad, this should be small * This does not fail as fast as possible -- when a task becomes unschedulable, we keep scheduling other tasks. This is to avoid an O(numPendingTasks * numExecutors) operation * Also, it is conceivable this fails too quickly. You may be 1 millisecond away from unblacklisting a place for a task to run, or acquiring a new executor. ## How was this patch tested? Added unit test which failed before the change, ran new test 5k times manually, ran all scheduler tests manually, and the full suite via jenkins. Author: Imran Rashid <irashid@cloudera.com> Closes #13603 from squito/progress_w_few_execs_and_blacklist.
* [SPARK-13850] Force the sorter to Spill when number of elements in th…Sital Kedia2016-06-3012-12/+60
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Force the sorter to Spill when number of elements in the pointer array reach a certain size. This is to workaround the issue of timSort failing on large buffer size. ## How was this patch tested? Tested by running a job which was failing without this change due to TimSort bug. Author: Sital Kedia <skedia@fb.com> Closes #13107 from sitalkedia/fix_TimSort.
* [SPARK-15820][PYSPARK][SQL] Add Catalog.refreshTable into python APIWeichenXu2016-06-302-1/+6
| | | | | | | | | | | | | | ## 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.
* [SPARK-16071][SQL] Checks size limit when doubling the array size in ↵Sean Zhong2016-06-302-2/+53
| | | | | | | | | | | | | | | | BufferHolder ## What changes were proposed in this pull request? This PR Checks the size limit when doubling the array size in BufferHolder to avoid integer overflow. ## How was this patch tested? Manual test. Author: Sean Zhong <seanzhong@databricks.com> Closes #13829 from clockfly/SPARK-16071_2.
* [SPARK-12177][TEST] Removed test to avoid compilation issue in scala 2.10Tathagata Das2016-06-301-4/+4
| | | | | | | | | | | | ## What changes were proposed in this pull request? The commented lines failed scala 2.10 build. This is because of change in behavior of case classes between 2.10 and 2.11. In scala 2.10, if companion object of a case class has explicitly defined apply(), then the implicit apply method is not generated. In scala 2.11 it is generated. Hence, the lines compile fine in 2.11 but not in 2.10. This simply comments the tests to fix broken build. Correct solution is pending. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #13992 from tdas/SPARK-12177.
* [SPARK-16241][ML] model loading backward compatibility for ml NaiveBayeszlpmichelle2016-06-301-4/+7
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? model loading backward compatibility for ml NaiveBayes ## How was this patch tested? existing ut and manual test for loading models saved by Spark 1.6. Author: zlpmichelle <zlpmichelle@gmail.com> Closes #13940 from zlpmichelle/naivebayes.
* [SPARK-16256][DOCS] Minor fixes on the Structured Streaming Programming GuideTathagata Das2016-06-291-21/+23
| | | | | | Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #13978 from tdas/SPARK-16256-1.
* [SPARK-12177][STREAMING][KAFKA] Update KafkaDStreams to new Kafka 0.10 ↵cody koeninger2016-06-2922-6/+3359
| | | | | | | | | | | | | | | | Consumer API ## What changes were proposed in this pull request? New Kafka consumer api for the released 0.10 version of Kafka ## How was this patch tested? Unit tests, manual tests Author: cody koeninger <cody@koeninger.org> Closes #11863 from koeninger/kafka-0.9.
* [SPARK-16294][SQL] Labelling support for the include_example Jekyll pluginCheng Lian2016-06-295-43/+43
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds labelling support for the `include_example` Jekyll plugin, so that we may split a single source file into multiple line blocks with different labels, and include them in multiple code snippets in the generated HTML page. ## How was this patch tested? Manually tested. <img width="923" alt="screenshot at jun 29 19-53-21" src="https://cloud.githubusercontent.com/assets/230655/16451099/66a76db2-3e33-11e6-84fb-63104c2f0688.png"> Author: Cheng Lian <lian@databricks.com> Closes #13972 from liancheng/include-example-with-labels.
* [SPARK-16274][SQL] Implement xpath_booleanpetermaxlee2016-06-305-1/+154
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch implements xpath_boolean expression for Spark SQL, a xpath function that returns true or false. The implementation is modelled after Hive's xpath_boolean, except that how the expression handles null inputs. Hive throws a NullPointerException at runtime if either of the input is null. This implementation returns null if either of the input is null. ## How was this patch tested? Created two new test suites. One for unit tests covering the expression, and the other for end-to-end test in SQL. Author: petermaxlee <petermaxlee@gmail.com> Closes #13964 from petermaxlee/SPARK-16274.
* [SPARK-16267][TEST] Replace deprecated `CREATE TEMPORARY TABLE ... USING` ↵Dongjoon Hyun2016-06-297-18/+18
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | from testsuites. ## What changes were proposed in this pull request? After SPARK-15674, `DDLStrategy` prints out the following deprecation messages in the testsuites. ``` 12:10:53.284 WARN org.apache.spark.sql.execution.SparkStrategies$DDLStrategy: CREATE TEMPORARY TABLE normal_orc_source USING... is deprecated, please use CREATE TEMPORARY VIEW viewName USING... instead ``` Total : 40 - JDBCWriteSuite: 14 - DDLSuite: 6 - TableScanSuite: 6 - ParquetSourceSuite: 5 - OrcSourceSuite: 2 - SQLQuerySuite: 2 - HiveCommandSuite: 2 - JsonSuite: 1 - PrunedScanSuite: 1 - FilteredScanSuite 1 This PR replaces `CREATE TEMPORARY TABLE` with `CREATE TEMPORARY VIEW` in order to remove the deprecation messages in the above testsuites except `DDLSuite`, `SQLQuerySuite`, `HiveCommandSuite`. The Jenkins results shows only remaining 10 messages. https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/61422/consoleFull ## How was this patch tested? This is a testsuite-only change. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13956 from dongjoon-hyun/SPARK-16267.
* [SPARK-16134][SQL] optimizer rules for typed filterWenchen Fan2016-06-308-91/+162
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds 3 optimizer rules for typed filter: 1. push typed filter down through `SerializeFromObject` and eliminate the deserialization in filter condition. 2. pull typed filter up through `SerializeFromObject` and eliminate the deserialization in filter condition. 3. combine adjacent typed filters and share the deserialized object among all the condition expressions. This PR also adds `TypedFilter` logical plan, to separate it from normal filter, so that the concept is more clear and it's easier to write optimizer rules. ## How was this patch tested? `TypedFilterOptimizationSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #13846 from cloud-fan/filter.
* [SPARK-16228][SQL] HiveSessionCatalog should return `double`-param functions ↵Dongjoon Hyun2016-06-292-1/+22
| | | | | | | | | | | | | | | | | | | | | | | | | for decimal param lookups ## What changes were proposed in this pull request? This PR supports a fallback lookup by casting `DecimalType` into `DoubleType` for the external functions with `double`-type parameter. **Reported Error Scenarios** ```scala scala> sql("select percentile(value, 0.5) from values 1,2,3 T(value)") org.apache.spark.sql.AnalysisException: ... No matching method for class org.apache.hadoop.hive.ql.udf.UDAFPercentile with (int, decimal(38,18)). Possible choices: _FUNC_(bigint, array<double>) _FUNC_(bigint, double) ; line 1 pos 7 scala> sql("select percentile_approx(value, 0.5) from values 1.0,2.0,3.0 T(value)") org.apache.spark.sql.AnalysisException: ... Only a float/double or float/double array argument is accepted as parameter 2, but decimal(38,18) was passed instead.; line 1 pos 7 ``` ## How was this patch tested? Pass the Jenkins tests (including a new testcase). Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13930 from dongjoon-hyun/SPARK-16228.
* [SPARK-16238] Metrics for generated method and class bytecode sizeEric Liang2016-06-293-1/+55
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This extends SPARK-15860 to include metrics for the actual bytecode size of janino-generated methods. They can be accessed in the same way as any other codahale metric, e.g. ``` scala> org.apache.spark.metrics.source.CodegenMetrics.METRIC_GENERATED_CLASS_BYTECODE_SIZE.getSnapshot().getValues() res7: Array[Long] = Array(532, 532, 532, 542, 1479, 2670, 3585, 3585) scala> org.apache.spark.metrics.source.CodegenMetrics.METRIC_GENERATED_METHOD_BYTECODE_SIZE.getSnapshot().getValues() res8: Array[Long] = Array(5, 5, 5, 5, 10, 10, 10, 10, 15, 15, 15, 38, 63, 79, 88, 94, 94, 94, 132, 132, 165, 165, 220, 220) ``` ## How was this patch tested? Small unit test, also verified manually that the performance impact is minimal (<10%). hvanhovell Author: Eric Liang <ekl@databricks.com> Closes #13934 from ericl/spark-16238.
* [SPARK-16006][SQL] Attemping to write empty DataFrame with no fields throws ↵Dongjoon Hyun2016-06-292-2/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | non-intuitive exception ## What changes were proposed in this pull request? This PR allows `emptyDataFrame.write` since the user didn't specify any partition columns. **Before** ```scala scala> spark.emptyDataFrame.write.parquet("/tmp/t1") org.apache.spark.sql.AnalysisException: Cannot use all columns for partition columns; scala> spark.emptyDataFrame.write.csv("/tmp/t1") org.apache.spark.sql.AnalysisException: Cannot use all columns for partition columns; ``` After this PR, there occurs no exceptions and the created directory has only one file, `_SUCCESS`, as expected. ## How was this patch tested? Pass the Jenkins tests including updated test cases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13730 from dongjoon-hyun/SPARK-16006.
* [SPARK-16301] [SQL] The analyzer rule for resolving using joins should ↵Yin Huai2016-06-292-7/+49
| | | | | | | | | | | | | | respect the case sensitivity setting. ## What changes were proposed in this pull request? The analyzer rule for resolving using joins should respect the case sensitivity setting. ## How was this patch tested? New tests in ResolveNaturalJoinSuite Author: Yin Huai <yhuai@databricks.com> Closes #13977 from yhuai/SPARK-16301.
* [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-16256][SQL][STREAMING] Added Structured Streaming Programming GuideTathagata Das2016-06-298-0/+1157
| | | | | | | | Title defines all. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #13945 from tdas/SPARK-16256.
* [SPARK-14480][SQL] Remove meaningless StringIteratorReader for CSV data source.hyukjinkwon2016-06-294-291/+33
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR removes meaningless `StringIteratorReader` for CSV data source. In `CSVParser.scala`, there is an `Reader` wrapping `Iterator` but there are two problems by this. Firstly, it was actually not faster than processing line by line with Iterator due to additional logics to wrap `Iterator` to `Reader`. Secondly, this brought a bit of complexity because it needs additional logics to allow every line to be read bytes by bytes. So, it was pretty difficult to figure out issues about parsing, (eg. SPARK-14103). A benchmark was performed manually and the results were below: - Original codes with Reader wrapping Iterator |End-to-end (ns) | Parse Time (ns) | |-----------------------|------------------------| |14116265034 |2008277960 | - New codes with Iterator |End-to-end (ns) | Parse Time (ns) | |-----------------------|------------------------| |13451699644 | 1549050564 | For the details for the environment, dataset and methods, please refer the JIRA ticket. ## How was this patch tested? Existing tests should cover this. Author: hyukjinkwon <gurwls223@gmail.com> Closes #13808 from HyukjinKwon/SPARK-14480-small.
* [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-16140][MLLIB][SPARKR][DOCS] Group k-means method in generated R docXin Ren2016-06-292-39/+35
| | | | | | | | | | | | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-16140 ## What changes were proposed in this pull request? Group the R doc of spark.kmeans, predict(KM), summary(KM), read/write.ml(KM) under Rd spark.kmeans. The example code was updated. ## How was this patch tested? Tested on my local machine And on my laptop `jekyll build` is failing to build API docs, so here I can only show you the html I manually generated from Rd files, with no CSS applied, but the doc content should be there. ![screenshotkmeans](https://cloud.githubusercontent.com/assets/3925641/16403203/c2c9ca1e-3ca7-11e6-9e29-f2164aee75fc.png) Author: Xin Ren <iamshrek@126.com> Closes #13921 from keypointt/SPARK-16140.
* [MINOR][SPARKR] Fix arguments of survreg in SparkRYanbo Liang2016-06-291-2/+2
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix wrong arguments description of ```survreg``` in SparkR. ## How was this patch tested? ```Arguments``` section of ```survreg``` doc before this PR (with wrong description for ```path``` and missing ```overwrite```): ![image](https://cloud.githubusercontent.com/assets/1962026/16447548/fe7a5ed4-3da1-11e6-8b96-b5bf2083b07e.png) After this PR: ![image](https://cloud.githubusercontent.com/assets/1962026/16447617/368e0b18-3da2-11e6-8277-45640fb11859.png) Author: Yanbo Liang <ybliang8@gmail.com> Closes #13970 from yanboliang/spark-16143-followup.
* [SPARK-15990][YARN] Add rolling log aggregation support for Spark on yarnjerryshao2016-06-293-0/+67
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Yarn supports rolling log aggregation since 2.6, previously log will only be aggregated to HDFS after application is finished, it is quite painful for long running applications like Spark Streaming, thriftserver. Also out of disk problem will be occurred when log file is too large. So here propose to add support of rolling log aggregation for Spark on yarn. One limitation for this is that log4j should be set to change to file appender, now in Spark itself uses console appender by default, in which file will not be created again once removed after aggregation. But I think lots of production users should have changed their log4j configuration instead of default on, so this is not a big problem. ## How was this patch tested? Manually verified with Hadoop 2.7.1. Author: jerryshao <sshao@hortonworks.com> Closes #13712 from jerryshao/SPARK-15990.
* [SPARK-15858][ML] Fix calculating error by tree stack over flow prob…Mahmoud Rawas2016-06-292-43/+34
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? What changes were proposed in this pull request? Improving evaluateEachIteration function in mllib as it fails when trying to calculate error by tree for a model that has more than 500 trees ## How was this patch tested? the batch tested on productions data set (2K rows x 2K features) training a gradient boosted model without validation with 1000 maxIteration settings, then trying to produce the error by tree, the new patch was able to perform the calculation within 30 seconds, while previously it was take hours then fail. **PS**: It would be better if this PR can be cherry picked into release branches 1.6.1 and 2.0 Author: Mahmoud Rawas <mhmoudr@gmail.com> Author: Mahmoud Rawas <Mahmoud.Rawas@quantium.com.au> Closes #13624 from mhmoudr/SPARK-15858.master.
* [SPARK-16261][EXAMPLES][ML] Fixed incorrect appNames in ML ExamplesBryan Cutler2016-06-294-4/+4
| | | | | | | | | | | | | ## What changes were proposed in this pull request? Some appNames in ML examples are incorrect, mostly in PySpark but one in Scala. This corrects the names. ## How was this patch tested? Style, local tests Author: Bryan Cutler <cutlerb@gmail.com> Closes #13949 from BryanCutler/pyspark-example-appNames-fix-SPARK-16261.
* [SPARK-16157][SQL] Add New Methods for comments in StructField and StructTypegatorsmile2016-06-2910-21/+125
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? Based on the previous discussion with cloud-fan hvanhovell in another related PR https://github.com/apache/spark/pull/13764#discussion_r67994276, it looks reasonable to add convenience methods for users to add `comment` when defining `StructField`. Currently, the column-related `comment` attribute is stored in `Metadata` of `StructField`. For example, users can add the `comment` attribute using the following way: ```Scala StructType( StructField( "cl1", IntegerType, nullable = false, new MetadataBuilder().putString("comment", "test").build()) :: Nil) ``` This PR is to add more user friendly methods for the `comment` attribute when defining a `StructField`. After the changes, users are provided three different ways to do it: ```Scala val struct = (new StructType) .add("a", "int", true, "test1") val struct = (new StructType) .add("c", StringType, true, "test3") val struct = (new StructType) .add(StructField("d", StringType).withComment("test4")) ``` #### How was this patch tested? Added test cases: - `DataTypeSuite` is for testing three types of API changes, - `DataFrameReaderWriterSuite` is for parquet, json and csv formats - using in-memory catalog - `OrcQuerySuite.scala` is for orc format using Hive-metastore Author: gatorsmile <gatorsmile@gmail.com> Closes #13860 from gatorsmile/newMethodForComment.
* [SPARK-16291][SQL] CheckAnalysis should capture nested aggregate functions ↵Cheng Lian2016-06-293-5/+12
| | | | | | | | | | | | | | | | | | | | that reference no input attributes ## What changes were proposed in this pull request? `MAX(COUNT(*))` is invalid since aggregate expression can't be nested within another aggregate expression. This case should be captured at analysis phase, but somehow sneaks off to runtime. The reason is that when checking aggregate expressions in `CheckAnalysis`, a checking branch treats all expressions that reference no input attributes as valid ones. However, `MAX(COUNT(*))` is translated into `MAX(COUNT(1))` at analysis phase and also references no input attribute. This PR fixes this issue by removing the aforementioned branch. ## How was this patch tested? New test case added in `AnalysisErrorSuite`. Author: Cheng Lian <lian@databricks.com> Closes #13968 from liancheng/spark-16291-nested-agg-functions.
* [TRIVIAL][DOCS][STREAMING][SQL] The return type mentioned in the Javadoc is ↵Holden Karau2016-06-291-2/+2
| | | | | | | | | | | | | | | | incorrect for toJavaRDD, … ## What changes were proposed in this pull request? Change the return type mentioned in the JavaDoc for `toJavaRDD` / `javaRDD` to match the actual return type & be consistent with the scala rdd return type. ## How was this patch tested? Docs only change. Author: Holden Karau <holden@us.ibm.com> Closes #13954 from holdenk/trivial-streaming-tojavardd-doc-fix.
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