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* [SPARK-13579][BUILD] Stop building the main Spark assembly.Marcelo Vanzin2016-04-0426-319/+231
| | | | | | | | | | | | | | | | | | | | This change modifies the "assembly/" module to just copy needed dependencies to its build directory, and modifies the packaging script to pick those up (and remove duplicate jars packages in the examples module). I also made some minor adjustments to dependencies to remove some test jars from the final packaging, and remove jars that conflict with each other when packaged separately (e.g. servlet api). Also note that this change restores guava in applications' classpaths, even though it's still shaded inside Spark. This is now needed for the Hadoop libraries that are packaged with Spark, which now are not processed by the shade plugin. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #11796 from vanzin/SPARK-13579.
* [SPARK-14259] [SQL] Merging small files together based on the cost of openingDavies Liu2016-04-043-19/+21
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR basically re-do the things in #12068 but with a different model, which should work better in case of small files with different sizes. ## How was this patch tested? Updated existing tests. Ran a query on thousands of partitioned small files locally, with all default settings (the cost to open a file should be over estimated), the durations of tasks become smaller and smaller, which is good (the last few tasks will be shortest). Author: Davies Liu <davies@databricks.com> Closes #12095 from davies/file_cost.
* [SPARK-14334] [SQL] add toLocalIterator for Dataset/DataFrameDavies Liu2016-04-048-16/+83
| | | | | | | | | | | | | | | | | | ## 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-14358] Change SparkListener from a trait to an abstract classReynold Xin2016-04-047-279/+276
| | | | | | | | | | | | ## What changes were proposed in this pull request? Scala traits are difficult to maintain binary compatibility on, and as a result we had to introduce JavaSparkListener. In Spark 2.0 we can change SparkListener from a trait to an abstract class and then remove JavaSparkListener. ## How was this patch tested? Updated related unit tests. Author: Reynold Xin <rxin@databricks.com> Closes #12142 from rxin/SPARK-14358.
* [SPARK-14364][SPARK] HeartbeatReceiver object should be privateReynold Xin2016-04-041-1/+2
| | | | | | | | | | | | ## What changes were proposed in this pull request? It's a mistake that HeartbeatReceiver object was made public in Spark 1.x. ## How was this patch tested? N/A Author: Reynold Xin <rxin@databricks.com> Closes #12148 from rxin/SPARK-14364.
* [SPARK-12981] [SQL] extract Pyhton UDF in physical planDavies Liu2016-04-048-70/+64
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently we extract Python UDFs into a special logical plan EvaluatePython in analyzer, But EvaluatePython is not part of catalyst, many rules have no knowledge of it , which will break many things (for example, filter push down or column pruning). We should treat Python UDFs as normal expressions, until we want to evaluate in physical plan, we could extract them in end of optimizer, or physical plan. This PR extract Python UDFs in physical plan. Closes #10935 ## How was this patch tested? Added regression tests. Author: Davies Liu <davies@databricks.com> Closes #12127 from davies/py_udf.
* [SPARK-14176][SQL] Add DataFrameWriter.trigger to set the stream batch periodShixiong Zhu2016-04-049-13/+413
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add a processing time trigger to control the batch processing speed ## How was this patch tested? Unit tests Author: Shixiong Zhu <shixiong@databricks.com> Closes #11976 from zsxwing/trigger.
* [SPARK-13784][ML] Persistence for RandomForestClassifier, RandomForestRegressorJoseph K. Bradley2016-04-049-103/+424
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? **Main change**: Added save/load for RandomForestClassifier, RandomForestRegressor (implementation details below) Modified numTrees method (*deprecation*) * Goal: Use default implementations of unit tests which assume Estimators and Models share the same set of Params. * What this PR does: Moves method numTrees outside of trait TreeEnsembleModel. Adds it to GBT and RF Models. Deprecates it in RF Models in favor of new method getNumTrees. In Spark 2.1, we can have RF Models include Param numTrees. Minor items * Fixes bugs in GBTClassificationModel, GBTRegressionModel fromOld methods where they assign the wrong old UID. **Implementation details** * Split DecisionTreeModelReadWrite.loadTreeNodes into 2 methods in order to reuse some code for ensembles. * Added EnsembleModelReadWrite object with save/load implementations usable for RFs and GBTs * These store all trees' nodes in a single DataFrame, and all trees' metadata in a second DataFrame. * Split trait RandomForestParams into parts in order to add more Estimator Params to RF models * Split DefaultParamsWriter.saveMetadata into two methods to allow ensembles to store sub-models' metadata in a single DataFrame. Same for DefaultParamsReader.loadMetadata ## How was this patch tested? Adds standard unit tests for RF save/load Author: Joseph K. Bradley <joseph@databricks.com> Author: GayathriMurali <gayathri.m.softie@gmail.com> Closes #12118 from jkbradley/GayathriMurali-SPARK-13784.
* [SPARK-14137] [SQL] Cleanup hash joinDavies Liu2016-04-046-401/+268
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR did a few cleanup on HashedRelation and HashJoin: 1) Merge HashedRelation and UniqueHashedRelation together 2) Return an iterator from HashedRelation, so we donot need a create many UnsafeRow objects. 3) Return a copy of HashedRelation for thread-safety in BroadcastJoin, so we can re-use the UnafeRow objects. 4) Cleanup HashJoin, share most of the code between BroadcastHashJoin and ShuffleHashJoin 5) Removed UniqueLongHashedRelation, which will be replaced by LongUnsafeMap (another PR). 6) Update benchmark, before this patch, the selectivity of joins are too high. ## How was this patch tested? Existing tests. Author: Davies Liu <davies@databricks.com> Closes #12102 from davies/cleanup_hash.
* [SPARK-14360][SQL] QueryExecution.debug.codegen() to dump codegenReynold Xin2016-04-041-0/+16
| | | | | | | | | | | | ## What changes were proposed in this pull request? We recently added the ability to dump the generated code for a given query. However, the method is only available through an implicit after an import. It'd slightly simplify things if it can be called directly in queryExecution. ## How was this patch tested? Manually tested in spark-shell. Author: Reynold Xin <rxin@databricks.com> Closes #12144 from rxin/SPARK-14360.
* [SPARK-14356] Update spark.sql.execution.debug to work on DatasetsMatei Zaharia2016-04-032-2/+8
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Update DebugQuery to work on Datasets of any type, not just DataFrames. ## How was this patch tested? Added unit tests, checked in spark-shell. Author: Matei Zaharia <matei@databricks.com> Closes #12140 from mateiz/debug-dataset.
* [SPARK-14355][BUILD] Fix typos in Exception/Testcase/Comments and static ↵Dongjoon Hyun2016-04-0359-93/+94
| | | | | | | | | | | | | | | | | | | | | analysis results ## What changes were proposed in this pull request? This PR contains the following 5 types of maintenance fix over 59 files (+94 lines, -93 lines). - Fix typos(exception/log strings, testcase name, comments) in 44 lines. - Fix lint-java errors (MaxLineLength) in 6 lines. (New codes after SPARK-14011) - Use diamond operators in 40 lines. (New codes after SPARK-13702) - Fix redundant semicolon in 5 lines. - Rename class `InferSchemaSuite` to `CSVInferSchemaSuite` in CSVInferSchemaSuite.scala. ## How was this patch tested? Manual and pass the Jenkins tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12139 from dongjoon-hyun/SPARK-14355.
* [SPARK-14163][CORE] SumEvaluator and countApprox cannot reliably handle RDDs ↵Marcin Tustin2016-04-033-13/+148
| | | | | | | | | | | | | | | | | | | | | of size 1 ## What changes were proposed in this pull request? This special cases 0 and 1 counts to avoid passing 0 degrees of freedom. ## How was this patch tested? Tests run successfully. New test added. ## Note: This recreates #11982 which was closed to due to non-updated diff. rxin srowen Commented there. This also adds tests, reworks the code to perform the special casing (based on srowen's comments), and adds equality machinery for BoundedDouble, as well as changing how it is transformed to string. Author: Marcin Tustin <mtustin@handybook.com> Author: Marcin Tustin <mtustin@handy.com> Closes #12016 from mtustin-handy/SPARK-14163.
* [SPARK-14341][SQL] Throw exception on unsupported create / drop macro ddlbomeng2016-04-033-2/+14
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? We throw an AnalysisException that looks like this: ``` scala> sqlContext.sql("CREATE TEMPORARY MACRO SIGMOID (x DOUBLE) 1.0 / (1.0 + EXP(-x))") org.apache.spark.sql.catalyst.parser.ParseException: Unsupported SQL statement == SQL == CREATE TEMPORARY MACRO SIGMOID (x DOUBLE) 1.0 / (1.0 + EXP(-x)) at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.nativeCommand(ParseDriver.scala:66) at org.apache.spark.sql.catalyst.parser.AbstractSqlParser$$anonfun$parsePlan$1.apply(ParseDriver.scala:56) at org.apache.spark.sql.catalyst.parser.AbstractSqlParser$$anonfun$parsePlan$1.apply(ParseDriver.scala:53) at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:86) at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:53) at org.apache.spark.sql.SQLContext.parseSql(SQLContext.scala:198) at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:749) ... 48 elided ``` ## How was this patch tested? Add test cases in HiveQuerySuite.scala Author: bomeng <bmeng@us.ibm.com> Closes #12125 from bomeng/SPARK-14341.
* [SPARK-14350][SQL] EXPLAIN output should be in a single cellDongjoon Hyun2016-04-032-2/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? EXPLAIN output should be in a single cell. **Before** ``` scala> sql("explain select 1").collect() res0: Array[org.apache.spark.sql.Row] = Array([== Physical Plan ==], [WholeStageCodegen], [: +- Project [1 AS 1#1]], [: +- INPUT], [+- Scan OneRowRelation[]]) ``` **After** ``` scala> sql("explain select 1").collect() res1: Array[org.apache.spark.sql.Row] = Array([== Physical Plan == WholeStageCodegen : +- Project [1 AS 1#4] : +- INPUT +- Scan OneRowRelation[]]) ``` Or, ``` scala> sql("explain select 1").head res1: org.apache.spark.sql.Row = [== Physical Plan == WholeStageCodegen : +- Project [1 AS 1#5] : +- INPUT +- Scan OneRowRelation[]] ``` Please note that `Spark-shell(Scala-shell)` trims long string output. So, you may need to use `println` to get full strings. ``` scala> println(sql("explain codegen select 'a' as a group by 1").head) [Found 2 WholeStageCodegen subtrees. == Subtree 1 / 2 == WholeStageCodegen ... /* 059 */ } /* 060 */ } ] ``` ## How was this patch tested? Pass the Jenkins tests. (Testcases are updated.) Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12137 from dongjoon-hyun/SPARK-14350.
* [SPARK-14231] [SQL] JSON data source infers floating-point values as a ↵hyukjinkwon2016-04-026-14/+71
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | double when they do not fit in a decimal ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-14231 Currently, JSON data source supports to infer `DecimalType` for big numbers and `floatAsBigDecimal` option which reads floating-point values as `DecimalType`. But there are few restrictions in Spark `DecimalType` below: 1. The precision cannot be bigger than 38. 2. scale cannot be bigger than precision. Currently, both restrictions are not being handled. This PR handles the cases by inferring them as `DoubleType`. Also, the option name was changed from `floatAsBigDecimal` to `prefersDecimal` as suggested [here](https://issues.apache.org/jira/browse/SPARK-14231?focusedCommentId=15215579&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15215579). So, the codes below: ```scala def doubleRecords: RDD[String] = sqlContext.sparkContext.parallelize( s"""{"a": 1${"0" * 38}, "b": 0.01}""" :: s"""{"a": 2${"0" * 38}, "b": 0.02}""" :: Nil) val jsonDF = sqlContext.read .option("prefersDecimal", "true") .json(doubleRecords) jsonDF.printSchema() ``` produces below: - **Before** ```scala org.apache.spark.sql.AnalysisException: Decimal scale (2) cannot be greater than precision (1).; at org.apache.spark.sql.types.DecimalType.<init>(DecimalType.scala:44) at org.apache.spark.sql.execution.datasources.json.InferSchema$.org$apache$spark$sql$execution$datasources$json$InferSchema$$inferField(InferSchema.scala:144) at org.apache.spark.sql.execution.datasources.json.InferSchema$.org$apache$spark$sql$execution$datasources$json$InferSchema$$inferField(InferSchema.scala:108) at ... ``` - **After** ```scala root |-- a: double (nullable = true) |-- b: double (nullable = true) ``` ## How was this patch tested? Unit tests were used and `./dev/run_tests` for coding style tests. Author: hyukjinkwon <gurwls223@gmail.com> Closes #12030 from HyukjinKwon/SPARK-14231.
* [HOTFIX] Fix Scala 2.10 compilationReynold Xin2016-04-021-2/+2
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* [SPARK-13996] [SQL] Add more not null attributes for Filter codegenLiang-Chi Hsieh2016-04-021-4/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? JIRA: https://issues.apache.org/jira/browse/SPARK-13996 Filter codegen finds the attributes not null by checking IsNotNull(a) expression with a condition if child.output.contains(a). However, the current approach to checking it is not comprehensive. We can improve it. E.g., for this plan: val rdd = sqlContext.sparkContext.makeRDD(Seq(Row(1, "1"), Row(null, "1"), Row(2, "2"))) val schema = new StructType().add("k", IntegerType).add("v", StringType) val smallDF = sqlContext.createDataFrame(rdd, schema) val df = smallDF.filter("isnotnull(k + 1)") The code snippet generated without this patch: /* 031 */ protected void processNext() throws java.io.IOException { /* 032 */ /*** PRODUCE: Filter isnotnull((k#0 + 1)) */ /* 033 */ /* 034 */ /*** PRODUCE: INPUT */ /* 035 */ /* 036 */ while (!shouldStop() && inputadapter_input.hasNext()) { /* 037 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next(); /* 038 */ /*** CONSUME: Filter isnotnull((k#0 + 1)) */ /* 039 */ /* input[0, int] */ /* 040 */ boolean filter_isNull = inputadapter_row.isNullAt(0); /* 041 */ int filter_value = filter_isNull ? -1 : (inputadapter_row.getInt(0)); /* 042 */ /* 043 */ /* isnotnull((input[0, int] + 1)) */ /* 044 */ /* (input[0, int] + 1) */ /* 045 */ boolean filter_isNull3 = true; /* 046 */ int filter_value3 = -1; /* 047 */ /* 048 */ if (!filter_isNull) { /* 049 */ filter_isNull3 = false; // resultCode could change nullability. /* 050 */ filter_value3 = filter_value + 1; /* 051 */ /* 052 */ } /* 053 */ if (!(!(filter_isNull3))) continue; /* 054 */ /* 055 */ filter_metricValue.add(1); With this patch: /* 031 */ protected void processNext() throws java.io.IOException { /* 032 */ /*** PRODUCE: Filter isnotnull((k#0 + 1)) */ /* 033 */ /* 034 */ /*** PRODUCE: INPUT */ /* 035 */ /* 036 */ while (!shouldStop() && inputadapter_input.hasNext()) { /* 037 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next(); /* 038 */ /*** CONSUME: Filter isnotnull((k#0 + 1)) */ /* 039 */ /* input[0, int] */ /* 040 */ boolean filter_isNull = inputadapter_row.isNullAt(0); /* 041 */ int filter_value = filter_isNull ? -1 : (inputadapter_row.getInt(0)); /* 042 */ /* 043 */ if (filter_isNull) continue; /* 044 */ /* 045 */ filter_metricValue.add(1); ## How was this patch tested? Existing tests. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #11810 from viirya/add-more-not-null-attrs.
* [SPARK-14056] Appends s3 specific configurations and spark.hadoop con…Sital Kedia2016-04-022-8/+15
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Appends s3 specific configurations and spark.hadoop configurations to hive configuration. ## How was this patch tested? Tested by running a job on cluster. …figurations to hive configuration. Author: Sital Kedia <skedia@fb.com> Closes #11876 from sitalkedia/hiveConf.
* [SPARK-14342][CORE][DOCS][TESTS] Remove straggler references to TachyonLiwei Lin2016-04-026-24/+24
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Straggler references to Tachyon were removed: - for docs, `tachyon` has been generalized as `off-heap memory`; - for Mesos test suits, the key-value `tachyon:true`/`tachyon:false` has been changed to `os:centos`/`os:ubuntu`, since `os` is an example constrain used by the [Mesos official docs](http://mesos.apache.org/documentation/attributes-resources/). ## How was this patch tested? Existing test suites. Author: Liwei Lin <lwlin7@gmail.com> Closes #12129 from lw-lin/tachyon-cleanup.
* [MINOR][DOCS] Use multi-line JavaDoc comments in Scala code.Dongjoon Hyun2016-04-0277-747/+786
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR aims to fix all Scala-Style multiline comments into Java-Style multiline comments in Scala codes. (All comment-only changes over 77 files: +786 lines, −747 lines) ## How was this patch tested? Manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12130 from dongjoon-hyun/use_multiine_javadoc_comments.
* [SPARK-14338][SQL] Improve `SimplifyConditionals` rule to handle `null` in ↵Dongjoon Hyun2016-04-022-8/+21
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | IF/CASEWHEN ## What changes were proposed in this pull request? Currently, `SimplifyConditionals` handles `true` and `false` to optimize branches. This PR improves `SimplifyConditionals` to take advantage of `null` conditions for `if` and `CaseWhen` expressions, too. **Before** ``` scala> sql("SELECT IF(null, 1, 0)").explain() == Physical Plan == WholeStageCodegen : +- Project [if (null) 1 else 0 AS (IF(CAST(NULL AS BOOLEAN), 1, 0))#4] : +- INPUT +- Scan OneRowRelation[] scala> sql("select case when cast(null as boolean) then 1 else 2 end").explain() == Physical Plan == WholeStageCodegen : +- Project [CASE WHEN null THEN 1 ELSE 2 END AS CASE WHEN CAST(NULL AS BOOLEAN) THEN 1 ELSE 2 END#14] : +- INPUT +- Scan OneRowRelation[] ``` **After** ``` scala> sql("SELECT IF(null, 1, 0)").explain() == Physical Plan == WholeStageCodegen : +- Project [0 AS (IF(CAST(NULL AS BOOLEAN), 1, 0))#4] : +- INPUT +- Scan OneRowRelation[] scala> sql("select case when cast(null as boolean) then 1 else 2 end").explain() == Physical Plan == WholeStageCodegen : +- Project [2 AS CASE WHEN CAST(NULL AS BOOLEAN) THEN 1 ELSE 2 END#4] : +- INPUT +- Scan OneRowRelation[] ``` **Hive** ``` hive> select if(null,1,2); OK 2 hive> select case when cast(null as boolean) then 1 else 2 end; OK 2 ``` ## How was this patch tested? Pass the Jenkins tests (including new extended test cases). Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12122 from dongjoon-hyun/SPARK-14338.
* [HOTFIX] Disable StateStoreSuite.maintenanceReynold Xin2016-04-021-1/+1
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* [MINOR] Typo fixesJacek Laskowski2016-04-0216-49/+52
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Typo fixes. No functional changes. ## How was this patch tested? Built the sources and ran with samples. Author: Jacek Laskowski <jacek@japila.pl> Closes #11802 from jaceklaskowski/typo-fixes.
* [HOTFIX] Fix compilation break.Reynold Xin2016-04-022-5/+4
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* [MINOR][SQL] Fix comments styl and correct several styles and nits in CSV ↵hyukjinkwon2016-04-014-49/+48
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | data source ## What changes were proposed in this pull request? While trying to create a PR (which was not an issue at the end), I just corrected some style nits. So, I removed the changes except for some coding style corrections. - According to the [scala-style-guide#documentation-style](https://github.com/databricks/scala-style-guide#documentation-style), Scala style comments are discouraged. >```scala >/** This is a correct one-liner, short description. */ > >/** > * This is correct multi-line JavaDoc comment. And > * this is my second line, and if I keep typing, this would be > * my third line. > */ > >/** In Spark, we don't use the ScalaDoc style so this > * is not correct. > */ >``` - Double newlines between consecutive methods was removed. According to [scala-style-guide#blank-lines-vertical-whitespace](https://github.com/databricks/scala-style-guide#blank-lines-vertical-whitespace), single newline appears when >Between consecutive members (or initializers) of a class: fields, constructors, methods, nested classes, static initializers, instance initializers. - Remove uesless parentheses in tests - Use `mapPartitions` instead of `mapPartitionsWithIndex()`. ## How was this patch tested? Unit tests were used and `dev/run_tests` for style tests. Author: hyukjinkwon <gurwls223@gmail.com> Closes #12109 from HyukjinKwon/SPARK-14271.
* [SPARK-14285][SQL] Implement common type-safe aggregate functionsReynold Xin2016-04-019-111/+342
| | | | | | | | | | | | ## What changes were proposed in this pull request? In the Dataset API, it is fairly difficult for users to perform simple aggregations in a type-safe way at the moment because there are no aggregators that have been implemented. This pull request adds a few common aggregate functions in expressions.scala.typed package, and also creates the expressions.java.typed package without implementation. The java implementation should probably come as a separate pull request. One challenge there is to resolve the type difference between Scala primitive types and Java boxed types. ## How was this patch tested? Added unit tests for them. Author: Reynold Xin <rxin@databricks.com> Closes #12077 from rxin/SPARK-14285.
* [SPARK-14251][SQL] Add SQL command for printing out generated code for debuggingDongjoon Hyun2016-04-017-31/+67
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR implements `EXPLAIN CODEGEN` SQL command which returns generated codes like `debugCodegen`. In `spark-shell`, we don't need to `import debug` module. In `spark-sql`, we can use this SQL command now. **Before** ``` scala> import org.apache.spark.sql.execution.debug._ scala> sql("select 'a' as a group by 1").debugCodegen() Found 2 WholeStageCodegen subtrees. == Subtree 1 / 2 == ... Generated code: ... == Subtree 2 / 2 == ... Generated code: ... ``` **After** ``` scala> sql("explain extended codegen select 'a' as a group by 1").collect().foreach(println) [Found 2 WholeStageCodegen subtrees.] [== Subtree 1 / 2 ==] ... [] [Generated code:] ... [] [== Subtree 2 / 2 ==] ... [] [Generated code:] ... ``` ## How was this patch tested? Pass the Jenkins tests (including new testcases) Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12099 from dongjoon-hyun/SPARK-14251.
* [SPARK-14138] [SQL] [MASTER] Fix generated SpecificColumnarIterator code can ↵Kazuaki Ishizaki2016-04-012-5/+51
| | | | | | | | | | | | | | | | exceed JVM size limit for cached DataFrames ## What changes were proposed in this pull request? This PR reduces Java byte code size of method in ```SpecificColumnarIterator``` by using a approach to make a group for lot of ```ColumnAccessor``` instantiations or method calls (more than 200) into a method ## How was this patch tested? Added a new unit test, which includes large instantiations and method calls, to ```InMemoryColumnarQuerySuite``` Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Closes #12108 from kiszk/SPARK-14138-master.
* [SPARK-14244][SQL] Don't use SizeBasedWindowFunction.n created on executor ↵Cheng Lian2016-04-014-9/+67
| | | | | | | | | | | | | | | | side when evaluating window functions ## What changes were proposed in this pull request? `SizeBasedWindowFunction.n` is a global singleton attribute created for evaluating size based aggregate window functions like `CUME_DIST`. However, this attribute gets different expression IDs when created on both driver side and executor side. This PR adds `withPartitionSize` method to `SizeBasedWindowFunction` so that we can easily rewrite `SizeBasedWindowFunction.n` on executor side. ## How was this patch tested? A test case is added in `HiveSparkSubmitSuite`, which supports launching multi-process clusters. Author: Cheng Lian <lian@databricks.com> Closes #12040 from liancheng/spark-14244-fix-sized-window-function.
* [SPARK-14308][ML][MLLIB] Remove unused mllib tree classes and move private ↵sethah2016-04-0118-409/+15
| | | | | | | | | | | | | | | | | | | | | | classes to ML ## What changes were proposed in this pull request? Decision tree helper classes will be migrated to ML. This patch moves those internal classes that are not part of the public API and removes ones that are no longer used, after [SPARK-12183](https://github.com/apache/spark/pull/11855). No functional changes are made. Details: * Bin.scala is removed as the ML implementation does not require bins * mllib NodeIdCache is removed. It was only used by the mllib implementation previously, which no longer exists * mllib TreePoint is removed. It was only used by the mllib implementation previously, which no longer exists * BaggedPoint, DTStatsAggregator, DecisionTreeMetadata, BaggedPointSuite and TimeTracker are all moved to ML. ## How was this patch tested? No functional changes are made. Existing unit tests ensure behavior is unchanged. Author: sethah <seth.hendrickson16@gmail.com> Closes #12097 from sethah/cleanup_mllib_tree.
* [SPARK-7425][ML] spark.ml Predictor should support other numeric types for labelBenFradet2016-04-0124-49/+294
| | | | | | | | Currently, the Predictor abstraction expects the input labelCol type to be DoubleType, but we should support other numeric types. This will involve updating the PredictorParams.validateAndTransformSchema method. Author: BenFradet <benjamin.fradet@gmail.com> Closes #10355 from BenFradet/SPARK-7425.
* [SPARK-13241][WEB UI] Added long values for dates in ApplicationAttemptInfo APIAlex Bozarth2016-04-019-2/+92
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Adding long values for each Date in the ApplicationAttemptInfo API for easier use in code ## How was the this patch tested? Tested with dev/run-tests Author: Alex Bozarth <ajbozart@us.ibm.com> Closes #11326 from ajbozarth/spark13241.
* [SPARK-12857][STREAMING] Standardize "records" and "events" on "records"Liwei Lin2016-04-014-40/+41
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently the Streaming tab in web UI uses records and events interchangeably; this PR tries to standardize them on "records". "records" is chosen over "events" because: - "records" is used extensively throughout the streaming documents, codes, and comments - "events" is used only in Streaming UI related codes and comments ## How was this patch tested? - existing test suites - manually checking on the Streaming UI tab Author: Liwei Lin <lwlin7@gmail.com> Closes #12032 from lw-lin/streaming-events-to-records.
* [SPARK-13825][CORE] Upgrade to Scala 2.11.8Jacek Laskowski2016-04-016-23/+23
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Upgrade to 2.11.8 (from the current 2.11.7) ## How was this patch tested? A manual build Author: Jacek Laskowski <jacek@japila.pl> Closes #11681 from jaceklaskowski/SPARK-13825-scala-2_11_8.
* [SPARK-14255][SQL] Streaming AggregationMichael Armbrust2016-04-0133-305/+827
| | | | | | | | | | | | | | | | | | | | | | This PR adds the ability to perform aggregations inside of a `ContinuousQuery`. In order to implement this feature, the planning of aggregation has augmented with a new `StatefulAggregationStrategy`. Unlike batch aggregation, stateful-aggregation uses the `StateStore` (introduced in #11645) to persist the results of partial aggregation across different invocations. The resulting physical plan performs the aggregation using the following progression: - Partial Aggregation - Shuffle - Partial Merge (now there is at most 1 tuple per group) - StateStoreRestore (now there is 1 tuple from this batch + optionally one from the previous) - Partial Merge (now there is at most 1 tuple per group) - StateStoreSave (saves the tuple for the next batch) - Complete (output the current result of the aggregation) The following refactoring was also performed to allow us to plug into existing code: - The get/put implementation is taken from #12013 - The logic for breaking down and de-duping the physical execution of aggregation has been move into a new pattern `PhysicalAggregation` - The `AttributeReference` used to identify the result of an `AggregateFunction` as been moved into the `AggregateExpression` container. This change moves the reference into the same object as the other intermediate references used in aggregation and eliminates the need to pass around a `Map[(AggregateFunction, Boolean), Attribute]`. Further clean up (using a different aggregation container for logical/physical plans) is deferred to a followup. - Some planning logic is moved from the `SessionState` into the `QueryExecution` to make it easier to override in the streaming case. - The ability to write a `StreamTest` that checks only the output of the last batch has been added to simulate the future addition of output modes. Author: Michael Armbrust <michael@databricks.com> Closes #12048 from marmbrus/statefulAgg.
* [SPARK-14316][SQL] StateStoreCoordinator should extend ThreadSafeRpcEndpointShixiong Zhu2016-04-012-7/+5
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? RpcEndpoint is not thread safe and allows multiple messages to be processed at the same time. StateStoreCoordinator should use ThreadSafeRpcEndpoint. ## How was this patch tested? Existing unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes #12100 from zsxwing/fix-StateStoreCoordinator.
* [SPARK-13992] Add support for off-heap cachingJosh Rosen2016-04-0120-176/+345
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This patch adds support for caching blocks in the executor processes using direct / off-heap memory. ## User-facing changes **Updated semantics of `OFF_HEAP` storage level**: In Spark 1.x, the `OFF_HEAP` storage level indicated that an RDD should be cached in Tachyon. Spark 2.x removed the external block store API that Tachyon caching was based on (see #10752 / SPARK-12667), so `OFF_HEAP` became an alias for `MEMORY_ONLY_SER`. As of this patch, `OFF_HEAP` means "serialized and cached in off-heap memory or on disk". Via the `StorageLevel` constructor, `useOffHeap` can be set if `serialized == true` and can be used to construct custom storage levels which support replication. **Storage UI reporting**: the storage UI will now report whether in-memory blocks are stored on- or off-heap. **Only supported by UnifiedMemoryManager**: for simplicity, this feature is only supported when the default UnifiedMemoryManager is used; applications which use the legacy memory manager (`spark.memory.useLegacyMode=true`) are not currently able to allocate off-heap storage memory, so using off-heap caching will fail with an error when legacy memory management is enabled. Given that we plan to eventually remove the legacy memory manager, this is not a significant restriction. **Memory management policies:** the policies for dividing available memory between execution and storage are the same for both on- and off-heap memory. For off-heap memory, the total amount of memory available for use by Spark is controlled by `spark.memory.offHeap.size`, which is an absolute size. Off-heap storage memory obeys `spark.memory.storageFraction` in order to control the amount of unevictable storage memory. For example, if `spark.memory.offHeap.size` is 1 gigabyte and Spark uses the default `storageFraction` of 0.5, then up to 500 megabytes of off-heap cached blocks will be protected from eviction due to execution memory pressure. If necessary, we can split `spark.memory.storageFraction` into separate on- and off-heap configurations, but this doesn't seem necessary now and can be done later without any breaking changes. **Use of off-heap memory does not imply use of off-heap execution (or vice-versa)**: for now, the settings controlling the use of off-heap execution memory (`spark.memory.offHeap.enabled`) and off-heap caching are completely independent, so Spark SQL can be configured to use off-heap memory for execution while continuing to cache blocks on-heap. If desired, we can change this in a followup patch so that `spark.memory.offHeap.enabled` affect the default storage level for cached SQL tables. ## Internal changes - Rename `ByteArrayChunkOutputStream` to `ChunkedByteBufferOutputStream` - It now returns a `ChunkedByteBuffer` instead of an array of byte arrays. - Its constructor now accept an `allocator` function which is called to allocate `ByteBuffer`s. This allows us to control whether it allocates regular ByteBuffers or off-heap DirectByteBuffers. - Because block serialization is now performed during the unroll process, a `ChunkedByteBufferOutputStream` which is configured with a `DirectByteBuffer` allocator will use off-heap memory for both unroll and storage memory. - The `MemoryStore`'s MemoryEntries now tracks whether blocks are stored on- or off-heap. - `evictBlocksToFreeSpace()` now accepts a `MemoryMode` parameter so that we don't try to evict off-heap blocks in response to on-heap memory pressure (or vice-versa). - Make sure that off-heap buffers are properly de-allocated during MemoryStore eviction. - The JVM limits the total size of allocated direct byte buffers using the `-XX:MaxDirectMemorySize` flag and the default tends to be fairly low (< 512 megabytes in some JVMs). To work around this limitation, this patch adds a custom DirectByteBuffer allocator which ignores this memory limit. Author: Josh Rosen <joshrosen@databricks.com> Closes #11805 from JoshRosen/off-heap-caching.
* [SPARK-12864][YARN] initialize executorIdCounter after ApplicationMaster ↵zhonghaihua2016-04-014-2/+29
| | | | | | | | | | | | | killed for max n… Currently, when max number of executor failures reached the `maxNumExecutorFailures`, `ApplicationMaster` will be killed and re-register another one.This time, `YarnAllocator` will be created a new instance. But, the value of property `executorIdCounter` in `YarnAllocator` will reset to `0`. Then the Id of new executor will starting from `1`. This will confuse with the executor has already created before, which will cause FetchFailedException. This situation is just in yarn client mode, so this is an issue in yarn client mode. For more details, [link to jira issues SPARK-12864](https://issues.apache.org/jira/browse/SPARK-12864) This PR introduce a mechanism to initialize `executorIdCounter` after `ApplicationMaster` killed. Author: zhonghaihua <793507405@qq.com> Closes #10794 from zhonghaihua/initExecutorIdCounterAfterAMKilled.
* [SPARK-13674] [SQL] Add wholestage codegen support to SampleLiang-Chi Hsieh2016-04-016-15/+104
| | | | | | | | | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-13674 ## What changes were proposed in this pull request? Sample operator doesn't support wholestage codegen now. This pr is to add support to it. ## How was this patch tested? A test is added into `BenchmarkWholeStageCodegen`. Besides, all tests should be passed. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #11517 from viirya/add-wholestage-sample.
* [SPARK-14160] Time Windowing functions for DatasetsBurak Yavuz2016-04-017-0/+735
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds the function `window` as a column expression. `window` can be used to bucket rows into time windows given a time column. With this expression, performing time series analysis on batch data, as well as streaming data should become much more simpler. ### Usage Assume the following schema: `sensor_id, measurement, timestamp` To average 5 minute data every 1 minute (window length of 5 minutes, slide duration of 1 minute), we will use: ```scala df.groupBy(window("timestamp", “5 minutes”, “1 minute”), "sensor_id") .agg(mean("measurement").as("avg_meas")) ``` This will generate windows such as: ``` 09:00:00-09:05:00 09:01:00-09:06:00 09:02:00-09:07:00 ... ``` Intervals will start at every `slideDuration` starting at the unix epoch (1970-01-01 00:00:00 UTC). To start intervals at a different point of time, e.g. 30 seconds after a minute, the `startTime` parameter can be used. ```scala df.groupBy(window("timestamp", “5 minutes”, “1 minute”, "30 second"), "sensor_id") .agg(mean("measurement").as("avg_meas")) ``` This will generate windows such as: ``` 09:00:30-09:05:30 09:01:30-09:06:30 09:02:30-09:07:30 ... ``` Support for Python will be made in a follow up PR after this. ## How was this patch tested? This patch has some basic unit tests for the `TimeWindow` expression testing that the parameters pass validation, and it also has some unit/integration tests testing the correctness of the windowing and usability in complex operations (multi-column grouping, multi-column projections, joins). Author: Burak Yavuz <brkyvz@gmail.com> Author: Michael Armbrust <michael@databricks.com> Closes #12008 from brkyvz/df-time-window.
* [SPARK-14070][SQL] Use ORC data source for SQL queries on ORC tablesTejas Patil2016-04-016-75/+220
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch enables use of OrcRelation for SQL queries which read data from Hive tables. Changes in this patch: - Added a new rule `OrcConversions` which would alter the plan to use `OrcRelation`. In this diff, the conversion is done only for reads. - Added a new config `spark.sql.hive.convertMetastoreOrc` to control the conversion BEFORE ``` scala> hqlContext.sql("SELECT * FROM orc_table").explain(true) == Parsed Logical Plan == 'Project [unresolvedalias(*, None)] +- 'UnresolvedRelation `orc_table`, None == Analyzed Logical Plan == key: string, value: string Project [key#171,value#172] +- MetastoreRelation default, orc_table, None == Optimized Logical Plan == MetastoreRelation default, orc_table, None == Physical Plan == HiveTableScan [key#171,value#172], MetastoreRelation default, orc_table, None ``` AFTER ``` scala> hqlContext.sql("SELECT * FROM orc_table").explain(true) == Parsed Logical Plan == 'Project [unresolvedalias(*, None)] +- 'UnresolvedRelation `orc_table`, None == Analyzed Logical Plan == key: string, value: string Project [key#76,value#77] +- SubqueryAlias orc_table +- Relation[key#76,value#77] ORC part: struct<>, data: struct<key:string,value:string> == Optimized Logical Plan == Relation[key#76,value#77] ORC part: struct<>, data: struct<key:string,value:string> == Physical Plan == WholeStageCodegen : +- Scan ORC part: struct<>, data: struct<key:string,value:string>[key#76,value#77] InputPaths: file:/user/hive/warehouse/orc_table ``` ## How was this patch tested? - Added a new unit test. Ran existing unit tests - Ran with production like data ## Performance gains Ran on a production table in Facebook (note that the data was in DWRF file format which is similar to ORC) Best case : when there was no matching rows for the predicate in the query (everything is filtered out) ``` CPU time Wall time Total wall time across all tasks ================================================================ Without the change 541_515 sec 25.0 mins 165.8 hours With change 407 sec 1.5 mins 15 mins ``` Average case: A subset of rows in the data match the query predicate ``` CPU time Wall time Total wall time across all tasks ================================================================ Without the change 624_630 sec 31.0 mins 199.0 h With change 14_769 sec 5.3 mins 7.7 h ``` Author: Tejas Patil <tejasp@fb.com> Closes #11891 from tejasapatil/orc_ppd.
* [SPARK-14191][SQL] Remove invalid Expand operator constraintsLiang-Chi Hsieh2016-04-012-1/+31
| | | | | | | | | | | | | | | | | | | | | | | | | `Expand` operator now uses its child plan's constraints as its valid constraints (i.e., the base of constraints). This is not correct because `Expand` will set its group by attributes to null values. So the nullability of these attributes should be true. E.g., for an `Expand` operator like: val input = LocalRelation('a.int, 'b.int, 'c.int).where('c.attr > 10 && 'a.attr < 5 && 'b.attr > 2) Expand( Seq( Seq('c, Literal.create(null, StringType), 1), Seq('c, 'a, 2)), Seq('c, 'a, 'gid.int), Project(Seq('a, 'c), input)) The `Project` operator has the constraints `IsNotNull('a)`, `IsNotNull('b)` and `IsNotNull('c)`. But the `Expand` should not have `IsNotNull('a)` in its constraints. This PR is the first step for this issue and remove invalid constraints of `Expand` operator. A test is added to `ConstraintPropagationSuite`. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Author: Michael Armbrust <michael@databricks.com> Closes #11995 from viirya/fix-expand-constraints.
* [SPARK-13995][SQL] Extract correct IsNotNull constraints for ExpressionLiang-Chi Hsieh2016-04-017-37/+134
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? JIRA: https://issues.apache.org/jira/browse/SPARK-13995 We infer relative `IsNotNull` constraints from logical plan's expressions in `constructIsNotNullConstraints` now. However, we don't consider the case of (nested) `Cast`. For example: val tr = LocalRelation('a.int, 'b.long) val plan = tr.where('a.attr === 'b.attr).analyze Then, the plan's constraints will have `IsNotNull(Cast(resolveColumn(tr, "a"), LongType))`, instead of `IsNotNull(resolveColumn(tr, "a"))`. This PR fixes it. Besides, as `IsNotNull` constraints are most useful for `Attribute`, we should do recursing through any `Expression` that is null intolerant and construct `IsNotNull` constraints for all `Attribute`s under these Expressions. For example, consider the following constraints: val df = Seq((1,2,3)).toDF("a", "b", "c") df.where("a + b = c").queryExecution.analyzed.constraints The inferred isnotnull constraints should be isnotnull(a), isnotnull(b), isnotnull(c), instead of isnotnull(a + c) and isnotnull(c). ## How was this patch tested? Test is added into `ConstraintPropagationSuite`. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #11809 from viirya/constraint-cast.
* [SPARK-14305][ML][PYSPARK] PySpark ml.clustering BisectingKMeans support ↵Yanbo Liang2016-04-011-2/+15
| | | | | | | | | | | | | | | export/import ## What changes were proposed in this pull request? PySpark ml.clustering BisectingKMeans support export/import ## How was this patch tested? doc test. cc jkbradley Author: Yanbo Liang <ybliang8@gmail.com> Closes #12112 from yanboliang/spark-14305.
* [SPARK-12343][YARN] Simplify Yarn client and client argumentjerryshao2016-04-0116-342/+186
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently in Spark on YARN, configurations can be passed through SparkConf, env and command arguments, some parts are duplicated, like client argument and SparkConf. So here propose to simplify the command arguments. ## How was this patch tested? This patch is tested manually with unit test. CC vanzin tgravescs , please help to suggest this proposal. The original purpose of this JIRA is to remove `ClientArguments`, through refactoring some arguments like `--class`, `--arg` are not so easy to replace, so here I remove the most part of command line arguments, only keep the minimal set. Author: jerryshao <sshao@hortonworks.com> Closes #11603 from jerryshao/SPARK-12343.
* [MINOR] [SQL] Update usage of `debug` by removing `typeCheck` and adding ↵Dongjoon Hyun2016-04-011-2/+2
| | | | | | | | | | | | | | | | | | `debugCodegen` ## What changes were proposed in this pull request? This PR updates the usage comments of `debug` according to the following commits. - [SPARK-9754](https://issues.apache.org/jira/browse/SPARK-9754) removed `typeCheck`. - [SPARK-14227](https://issues.apache.org/jira/browse/SPARK-14227) added `debugCodegen`. ## How was this patch tested? Manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12094 from dongjoon-hyun/minor_fix_debug_usage.
* [SPARK-14133][SQL] Throws exception for unsupported create/drop/alter index ↵sureshthalamati2016-04-013-6/+31
| | | | | | | | | | | | | | | | | | | | | | , and lock/unlock operations. ## What changes were proposed in this pull request? This PR throws Unsupported Operation exception for create index, drop index, alter index , lock table , lock database, unlock table, and unlock database operations that are not supported in Spark SQL. Currently these operations are executed executed by Hive. Error: spark-sql> drop index my_index on my_table; Error in query: Unsupported operation: drop index(line 1, pos 0) ## How was this patch tested? Added test cases to HiveQuerySuite yhuai hvanhovell andrewor14 Author: sureshthalamati <suresh.thalamati@gmail.com> Closes #12069 from sureshthalamati/unsupported_ddl_spark-14133.
* [SPARK-14184][SQL] Support native execution of SHOW DATABASE command and fix ↵Dilip Biswal2016-04-018-18/+163
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | SHOW TABLE to use table identifier pattern ## What changes were proposed in this pull request? This PR addresses the following 1. Supports native execution of SHOW DATABASES command 2. Fixes SHOW TABLES to apply the identifier_with_wildcards pattern if supplied. SHOW TABLE syntax ``` SHOW TABLES [IN database_name] ['identifier_with_wildcards']; ``` SHOW DATABASES syntax ``` SHOW (DATABASES|SCHEMAS) [LIKE 'identifier_with_wildcards']; ``` ## How was this patch tested? Tests added in SQLQuerySuite (both hive and sql contexts) and DDLCommandSuite Note: Since the table name pattern was not working , tests are added in both SQLQuerySuite to verify the application of the table pattern. Author: Dilip Biswal <dbiswal@us.ibm.com> Closes #11991 from dilipbiswal/dkb_show_database.
* [SPARK-14295][MLLIB][HOTFIX] Fixes Scala 2.10 compilation failureCheng Lian2016-04-011-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fixes a compilation failure introduced in PR #12088 under Scala 2.10. ## How was this patch tested? Compilation. Author: Cheng Lian <lian@databricks.com> Closes #12107 from liancheng/spark-14295-hotfix.
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