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* [SPARK-13883][SQL] Parquet Implementation of FileFormat.buildReaderMichael Armbrust2016-03-2113-46/+366
| | | | | | | | | | | | This PR add implements the new `buildReader` interface for the Parquet `FileFormat`. An simple implementation of `FileScanRDD` is also included. This code should be tested by the many existing tests for parquet. Author: Michael Armbrust <michael@databricks.com> Author: Sameer Agarwal <sameer@databricks.com> Author: Nong Li <nong@databricks.com> Closes #11709 from marmbrus/parquetReader.
* [SPARK-14016][SQL] Support high-precision decimals in vectorized parquet readerSameer Agarwal2016-03-212-4/+13
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch adds support for reading `DecimalTypes` with high (> 18) precision in `VectorizedColumnReader` ## How was this patch tested? 1. `VectorizedColumnReader` initially had a gating condition on `primitiveType.getDecimalMetadata().getPrecision() > Decimal.MAX_LONG_DIGITS()` that made us fall back on parquet-mr for handling high-precision decimals. This condition is now removed. 2. In particular, the `ParquetHadoopFsRelationSuite` (that tests for all supported hive types -- including `DecimalType(25, 5)`) fails when the gating condition is removed (https://github.com/apache/spark/pull/11808) and should now pass with this change. Author: Sameer Agarwal <sameer@databricks.com> Closes #11869 from sameeragarwal/bigdecimal-parquet.
* Revert "[SPARK-13019][DOCS] Replace example code in mllib-statistics.md ↵Xiangrui Meng2016-03-2119-1076/+382
| | | | | | using include_example" This reverts commit 1af8de200c4d3357bcb09e7bbc6deece00e885f2.
* [SPARK-13320][SQL] Support Star in CreateStruct/CreateArray and Error ↵gatorsmile2016-03-224-77/+113
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Handling when DataFrame/DataSet Functions using Star This PR resolves two issues: First, expanding * inside aggregate functions of structs when using Dataframe/Dataset APIs. For example, ```scala structDf.groupBy($"a").agg(min(struct($"record.*"))) ``` Second, it improves the error messages when having invalid star usage when using Dataframe/Dataset APIs. For example, ```scala pagecounts4PartitionsDS .map(line => (line._1, line._3)) .toDF() .groupBy($"_1") .agg(sum("*") as "sumOccurances") ``` Before the fix, the invalid usage will issue a confusing error message, like: ``` org.apache.spark.sql.AnalysisException: cannot resolve '_1' given input columns _1, _2; ``` After the fix, the message is like: ``` org.apache.spark.sql.AnalysisException: Invalid usage of '*' in function 'sum' ``` cc: rxin nongli cloud-fan Author: gatorsmile <gatorsmile@gmail.com> Closes #11208 from gatorsmile/sumDataSetResolution.
* [SPARK-13990] Automatically pick serializer when caching RDDsJosh Rosen2016-03-2116-155/+226
| | | | | | | | | | | | Building on the `SerializerManager` introduced in SPARK-13926/ #11755, this patch Spark modifies Spark's BlockManager to use RDD's ClassTags in order to select the best serializer to use when caching RDD blocks. When storing a local block, the BlockManager `put()` methods use implicits to record ClassTags and stores those tags in the blocks' BlockInfo records. When reading a local block, the stored ClassTag is used to pick the appropriate serializer. When a block is stored with replication, the class tag is written into the block transfer metadata and will also be stored in the remote BlockManager. There are two or three places where we don't properly pass ClassTags, including TorrentBroadcast and BlockRDD. I think this happens to work because the missing ClassTag always happens to be `ClassTag.Any`, but it might be worth looking more carefully at those places to see whether we should be more explicit. Author: Josh Rosen <joshrosen@databricks.com> Closes #11801 from JoshRosen/pick-best-serializer-for-caching.
* [SPARK-13898][SQL] Merge DatasetHolder and DataFrameHolderReynold Xin2016-03-2114-137/+36
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch merges DatasetHolder and DataFrameHolder. This makes more sense because DataFrame/Dataset are now one class. In addition, fixed some minor issues with pull request #11732. ## How was this patch tested? Updated existing unit tests that test these implicits. Author: Reynold Xin <rxin@databricks.com> Closes #11737 from rxin/SPARK-13898.
* [SPARK-13916][SQL] Add a metric to WholeStageCodegen to measure duration.Nong Li2016-03-216-14/+97
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? WholeStageCodegen naturally breaks the execution into pipelines that are easier to measure duration. This is more granular than the task timings (a task can be multiple pipelines) and is integrated with the web ui. We currently report total time (across all tasks), min/mask/median to get a sense of how long each is taking. ## How was this patch tested? Manually tested looking at the web ui. Author: Nong Li <nong@databricks.com> Closes #11741 from nongli/spark-13916.
* [SPARK-13019][DOCS] Replace example code in mllib-statistics.md using ↵Xin Ren2016-03-2119-382/+1076
| | | | | | | | | | | | | | | | | | | | include_example https://issues.apache.org/jira/browse/SPARK-13019 The example code in the user guide is embedded in the markdown and hence it is not easy to test. It would be nice to automatically test them. This JIRA is to discuss options to automate example code testing and see what we can do in Spark 1.6. Goal is to move actual example code to spark/examples and test compilation in Jenkins builds. Then in the markdown, we can reference part of the code to show in the user guide. This requires adding a Jekyll tag that is similar to https://github.com/jekyll/jekyll/blob/master/lib/jekyll/tags/include.rb, e.g., called include_example. `{% include_example scala/org/apache/spark/examples/mllib/SummaryStatisticsExample.scala %}` Jekyll will find `examples/src/main/scala/org/apache/spark/examples/mllib/SummaryStatisticsExample.scala` and pick code blocks marked "example" and replace code block in `{% highlight %}` in the markdown. See more sub-tasks in parent ticket: https://issues.apache.org/jira/browse/SPARK-11337 Author: Xin Ren <iamshrek@126.com> Closes #11108 from keypointt/SPARK-13019.
* [SPARK-14004][FOLLOW-UP] Implementations of NonSQLExpression should not ↵Wenchen Fan2016-03-212-4/+2
| | | | | | | | | | | | | | | | override sql method ## What changes were proposed in this pull request? There is only one exception: `PythonUDF`. However, I don't think the `PythonUDF#` prefix is useful, as we can only create python udf under python context. This PR removes the `PythonUDF#` prefix from `PythonUDF.toString`, so that it doesn't need to overrde `sql`. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #11859 from cloud-fan/tmp.
* [SPARK-13805] [SQL] Generate code that get a value in each column from ↵Kazuaki Ishizaki2016-03-219-33/+77
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ColumnVector when ColumnarBatch is used ## What changes were proposed in this pull request? This PR generates code that get a value in each column from ```ColumnVector``` instead of creating ```InternalRow``` when ```ColumnarBatch``` is accessed. This PR improves benchmark program by up to 15%. This PR consists of two parts: 1. Get an ```ColumnVector ``` by using ```ColumnarBatch.column()``` method 2. Get a value of each column by using ```rdd_col${COLIDX}.getInt(ROWIDX)``` instead of ```rdd_row.getInt(COLIDX)``` This is a motivated example. ```` sqlContext.conf.setConfString(SQLConf.PARQUET_VECTORIZED_READER_ENABLED.key, "true") sqlContext.conf.setConfString(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key, "true") val values = 10 withTempPath { dir => withTempTable("t1", "tempTable") { sqlContext.range(values).registerTempTable("t1") sqlContext.sql("select id % 2 as p, cast(id as INT) as id from t1") .write.partitionBy("p").parquet(dir.getCanonicalPath) sqlContext.read.parquet(dir.getCanonicalPath).registerTempTable("tempTable") sqlContext.sql("select sum(p) from tempTable").collect } } ```` The original code ````java ... /* 072 */ while (!shouldStop() && rdd_batchIdx < numRows) { /* 073 */ InternalRow rdd_row = rdd_batch.getRow(rdd_batchIdx++); /* 074 */ /*** CONSUME: TungstenAggregate(key=[], functions=[(sum(cast(p#4 as bigint)),mode=Partial,isDistinct=false)], output=[sum#10L]) */ /* 075 */ /* input[0, int] */ /* 076 */ boolean rdd_isNull = rdd_row.isNullAt(0); /* 077 */ int rdd_value = rdd_isNull ? -1 : (rdd_row.getInt(0)); ... ```` The code generated by this PR ````java /* 072 */ while (!shouldStop() && rdd_batchIdx < numRows) { /* 073 */ org.apache.spark.sql.execution.vectorized.ColumnVector rdd_col0 = rdd_batch.column(0); /* 074 */ /*** CONSUME: TungstenAggregate(key=[], functions=[(sum(cast(p#4 as bigint)),mode=Partial,isDistinct=false)], output=[sum#10L]) */ /* 075 */ /* input[0, int] */ /* 076 */ boolean rdd_isNull = rdd_col0.getIsNull(rdd_batchIdx); /* 077 */ int rdd_value = rdd_isNull ? -1 : (rdd_col0.getInt(rdd_batchIdx)); ... /* 128 */ rdd_batchIdx++; /* 129 */ } /* 130 */ if (shouldStop()) return; ```` Performance Without this PR ```` model name : Intel(R) Xeon(R) CPU E5-2667 v2 3.30GHz Partitioned Table: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------- Read data column 434 / 488 36.3 27.6 1.0X Read partition column 302 / 346 52.1 19.2 1.4X Read both columns 588 / 643 26.8 37.4 0.7X ```` With this PR ```` model name : Intel(R) Xeon(R) CPU E5-2667 v2 3.30GHz Partitioned Table: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------- Read data column 392 / 516 40.1 24.9 1.0X Read partition column 256 / 318 61.4 16.3 1.5X Read both columns 523 / 539 30.1 33.3 0.7X ```` ## How was this patch tested? Tested by existing test suites and benchmark Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Closes #11636 from kiszk/SPARK-13805.
* [SPARK-14007] [SQL] Manage the memory used by hash map in shuffled hash joinDavies Liu2016-03-215-133/+52
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR try acquire the memory for hash map in shuffled hash join, fail the task if there is no enough memory (otherwise it could OOM the executor). It also removed unused HashedRelation. ## How was this patch tested? Existing unit tests. Manual tests with TPCDS Q78. Author: Davies Liu <davies@databricks.com> Closes #11826 from davies/cleanup_hash2.
* [SPARK-14004][SQL] NamedExpressions should have at most one qualifierCheng Lian2016-03-2112-63/+62
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This is a more aggressive version of PR #11820, which not only fixes the original problem, but also does the following updates to enforce the at-most-one-qualifier constraint: - Renames `NamedExpression.qualifiers` to `NamedExpression.qualifier` - Uses `Option[String]` rather than `Seq[String]` for `NamedExpression.qualifier` Quoted PR description of #11820 here: > Current implementations of `AttributeReference.sql` and `Alias.sql` joins all available qualifiers, which is logically wrong. But this implementation mistake doesn't cause any real SQL generation bugs though, since there is always at most one qualifier for any given `AttributeReference` or `Alias`. ## How was this patch tested? Existing tests should be enough. Author: Cheng Lian <lian@databricks.com> Closes #11822 from liancheng/spark-14004-aggressive.
* [SPARK-13456][SQL] fix creating encoders for case classes defined in Spark shellWenchen Fan2016-03-214-6/+115
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? case classes defined in REPL are wrapped by line classes, and we have a trick for scala 2.10 REPL to automatically register the wrapper classes to `OuterScope` so that we can use when create encoders. However, this trick doesn't work right after we upgrade to scala 2.11, and unfortunately the tests are only in scala 2.10, which makes this bug hidden until now. This PR moves the encoder tests to scala 2.11 `ReplSuite`, and fixes this bug by another approach(the previous trick can't port to scala 2.11 REPL): make `OuterScope` smarter that can detect classes defined in REPL and load the singleton of line wrapper classes automatically. ## How was this patch tested? the migrated encoder tests in `ReplSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #11410 from cloud-fan/repl.
* [SPARK-13826][SQL] Ad-hoc Dataset API ScalaDoc fixesCheng Lian2016-03-211-18/+21
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Ad-hoc Dataset API ScalaDoc fixes ## How was this patch tested? By building and checking ScalaDoc locally. Author: Cheng Lian <lian@databricks.com> Closes #11862 from liancheng/ds-doc-fixes.
* [SPARK-14039][SQL][MINOR] make SubqueryHolder an inner classWenchen Fan2016-03-211-9/+9
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? `SubqueryHolder` is only used when generate SQL string in `SQLBuilder`, it's more clear to make it an inner class in `SQLBuilder`. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #11861 from cloud-fan/gensql.
* [SPARK-13986][CORE][MLLIB] Remove `DeveloperApi`-annotations for non-publicsDongjoon Hyun2016-03-218-16/+0
| | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Spark uses `DeveloperApi` annotation, but sometimes it seems to conflict with visibility. This PR tries to fix those conflict by removing annotations for non-publics. The following is the example. **JobResult.scala** ```scala DeveloperApi sealed trait JobResult DeveloperApi case object JobSucceeded extends JobResult -DeveloperApi private[spark] case class JobFailed(exception: Exception) extends JobResult ``` ## How was this patch tested? Pass the existing Jenkins test. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11797 from dongjoon-hyun/SPARK-13986.
* [SPARK-14000][SQL] case class with a tuple field can't work in DatasetWenchen Fan2016-03-214-6/+29
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? When we validate an encoder, we may call `dataType` on unresolved expressions. This PR fix the validation so that we will resolve attributes first. ## How was this patch tested? a new test in `DatasetSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #11816 from cloud-fan/encoder.
* [SPARK-12789][SQL] Support Order By Ordinal in SQLgatorsmile2016-03-219-11/+117
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? This PR is to support order by position in SQL, e.g. ```SQL select c1, c2, c3 from tbl order by 1 desc, 3 ``` should be equivalent to ```SQL select c1, c2, c3 from tbl order by c1 desc, c3 asc ``` This is controlled by config option `spark.sql.orderByOrdinal`. - When true, the ordinal numbers are treated as the position in the select list. - When false, the ordinal number in order/sort By clause are ignored. - Only convert integer literals (not foldable expressions). If found foldable expressions, ignore them - This also works with select *. **Question**: Do we still need sort by columns that contain zero reference? In this case, it will have no impact on the sorting results. IMO, we should not allow users do it. rxin cloud-fan marmbrus yhuai hvanhovell -- Update: In these cases, they are ignored in this case. **Note**: This PR is taken from https://github.com/apache/spark/pull/10731. When merging this PR, please give the credit to zhichao-li Also cc all the people who are involved in the previous discussion: adrian-wang chenghao-intel tejasapatil #### How was this patch tested? Added a few test cases for both positive and negative test cases. Author: gatorsmile <gatorsmile@gmail.com> Closes #11815 from gatorsmile/orderByPosition.
* [SPARK-14028][STREAMING][KINESIS][TESTS] Remove deprecated methods; fix two ↵proflin2016-03-214-98/+13
| | | | | | | | | | | | | | | | | other warnings ## What changes were proposed in this pull request? - Removed two methods that has been deprecated since 1.4 - Fixed two other compilation warnings ## How was this patch tested? existing test suits Author: proflin <proflin.me@gmail.com> Closes #11850 from lw-lin/streaming-kinesis-deprecates-warnings.
* [MINOR][DOCS] Add proper periods and spaces for CLI help messages and ↵Dongjoon Hyun2016-03-213-16/+16
| | | | | | | | | | | | | | | | `config` doc. ## What changes were proposed in this pull request? This PR adds some proper periods and spaces to Spark CLI help messages and SQL/YARN conf docs for consistency. ## How was this patch tested? Manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11848 from dongjoon-hyun/add_proper_period_and_space.
* [SPARK-14011][CORE][SQL] Enable `LineLength` Java checkstyle ruleDongjoon Hyun2016-03-2174-505/+579
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? [Spark Coding Style Guide](https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide) has 100-character limit on lines, but it's disabled for Java since 11/09/15. This PR enables **LineLength** checkstyle again. To help that, this also introduces **RedundantImport** and **RedundantModifier**, too. The following is the diff on `checkstyle.xml`. ```xml - <!-- TODO: 11/09/15 disabled - the lengths are currently > 100 in many places --> - <!-- <module name="LineLength"> <property name="max" value="100"/> <property name="ignorePattern" value="^package.*|^import.*|a href|href|http://|https://|ftp://"/> </module> - --> <module name="NoLineWrap"/> <module name="EmptyBlock"> <property name="option" value="TEXT"/> -167,5 +164,7 </module> <module name="CommentsIndentation"/> <module name="UnusedImports"/> + <module name="RedundantImport"/> + <module name="RedundantModifier"/> ``` ## How was this patch tested? Currently, `lint-java` is disabled in Jenkins. It needs a manual test. After passing the Jenkins tests, `dev/lint-java` should passes locally. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11831 from dongjoon-hyun/SPARK-14011.
* [SPARK-13764][SQL] Parse modes in JSON data sourcehyukjinkwon2016-03-218-45/+164
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, there is no way to control the behaviour when fails to parse corrupt records in JSON data source . This PR adds the support for parse modes just like CSV data source. There are three modes below: - `PERMISSIVE` : When it fails to parse, this sets `null` to to field. This is a default mode when it has been this mode. - `DROPMALFORMED`: When it fails to parse, this drops the whole record. - `FAILFAST`: When it fails to parse, it just throws an exception. This PR also make JSON data source share the `ParseModes` in CSV data source. ## How was this patch tested? Unit tests were used and `./dev/run_tests` for code style tests. Author: hyukjinkwon <gurwls223@gmail.com> Closes #11756 from HyukjinKwon/SPARK-13764.
* [SPARK-14019][SQL] Remove noop SortOrder in Sortgatorsmile2016-03-212-0/+78
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? This PR is to add a new Optimizer rule for pruning Sort if its SortOrder is no-op. In the phase of **Optimizer**, if a specific `SortOrder` does not have any reference, it has no effect on the sorting results. If `Sort` is empty, remove the whole `Sort`. For example, in the following SQL query ```SQL SELECT * FROM t ORDER BY NULL + 5 ``` Before the fix, the plan is like ``` == Analyzed Logical Plan == a: int, b: int Sort [(cast(null as int) + 5) ASC], true +- Project [a#92,b#93] +- SubqueryAlias t +- Project [_1#89 AS a#92,_2#90 AS b#93] +- LocalRelation [_1#89,_2#90], [[1,2],[1,2]] == Optimized Logical Plan == Sort [null ASC], true +- LocalRelation [a#92,b#93], [[1,2],[1,2]] == Physical Plan == WholeStageCodegen : +- Sort [null ASC], true, 0 : +- INPUT +- Exchange rangepartitioning(null ASC, 5), None +- LocalTableScan [a#92,b#93], [[1,2],[1,2]] ``` After the fix, the plan is like ``` == Analyzed Logical Plan == a: int, b: int Sort [(cast(null as int) + 5) ASC], true +- Project [a#92,b#93] +- SubqueryAlias t +- Project [_1#89 AS a#92,_2#90 AS b#93] +- LocalRelation [_1#89,_2#90], [[1,2],[1,2]] == Optimized Logical Plan == LocalRelation [a#92,b#93], [[1,2],[1,2]] == Physical Plan == LocalTableScan [a#92,b#93], [[1,2],[1,2]] ``` cc rxin cloud-fan marmbrus Thanks! #### How was this patch tested? Added a test suite for covering this rule Author: gatorsmile <gatorsmile@gmail.com> Closes #11840 from gatorsmile/sortElimination.
* [SPARK-13993][PYSPARK] Add pyspark Rformula/RforumlaModel save/loadXusen Yin2016-03-201-3/+27
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-13993 ## How was this patch tested? doctest Author: Xusen Yin <yinxusen@gmail.com> Closes #11807 from yinxusen/SPARK-13993.
* [SPARK-12182][ML] Distributed binning for trees in spark.mlsethah2016-03-202-61/+60
| | | | | | | | This PR changes the `findSplits` method in spark.ml to perform split calculations on the workers. This PR is meant to copy [PR-8246](https://github.com/apache/spark/pull/8246) which added the same feature for MLlib. Author: sethah <seth.hendrickson16@gmail.com> Closes #10231 from sethah/SPARK-12182.
* [SPARK-10680][TESTS] Increase 'connectionTimeout' to make ↵Shixiong Zhu2016-03-191-5/+5
| | | | | | | | | | | | | | | | RequestTimeoutIntegrationSuite more stable ## What changes were proposed in this pull request? Increase 'connectionTimeout' to make RequestTimeoutIntegrationSuite more stable ## How was this patch tested? Existing unit tests Author: Shixiong Zhu <shixiong@databricks.com> Closes #11833 from zsxwing/SPARK-10680.
* [SPARK-13897][SQL] RelationalGroupedDataset and KeyValueGroupedDatasetReynold Xin2016-03-195-67/+70
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Previously, Dataset.groupBy returns a GroupedData, and Dataset.groupByKey returns a GroupedDataset. The naming is very similar, and unfortunately does not convey the real differences between the two. Assume we are grouping by some keys (K). groupByKey is a key-value style group by, in which the schema of the returned dataset is a tuple of just two fields: key and value. groupBy, on the other hand, is a relational style group by, in which the schema of the returned dataset is flattened and contain |K| + |V| fields. This pull request also removes the experimental tag from RelationalGroupedDataset. It has been with DataFrame since 1.3, and we have enough confidence now to stabilize it. ## How was this patch tested? This is a rename to improve API understandability. Should be covered by all existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #11841 from rxin/SPARK-13897.
* [MINOR][DOCS] Use `spark-submit` instead of `sparkR` to submit R script.Dongjoon Hyun2016-03-191-5/+5
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Since `sparkR` is not used for submitting R Scripts from Spark 2.0, a user faces the following error message if he follows the instruction on `R/README.md`. This PR updates `R/README.md`. ```bash $ ./bin/sparkR examples/src/main/r/dataframe.R Running R applications through 'sparkR' is not supported as of Spark 2.0. Use ./bin/spark-submit <R file> ``` ## How was this patch tested? Manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11842 from dongjoon-hyun/update_r_readme.
* [SPARK-14018][SQL] Use 64-bit num records in BenchmarkWholeStageCodegenReynold Xin2016-03-191-4/+4
| | | | | | | | | | | | ## What changes were proposed in this pull request? 500L << 20 is actually pretty close to 32-bit int limit. I was trying to increase this to 500L << 23 and got negative numbers instead. ## How was this patch tested? I'm only modifying test code. Author: Reynold Xin <rxin@databricks.com> Closes #11839 from rxin/SPARK-14018.
* [SPARK-14012][SQL] Extract VectorizedColumnReader from ↵Sameer Agarwal2016-03-182-450/+476
| | | | | | | | | | | | | | | | VectorizedParquetRecordReader ## What changes were proposed in this pull request? This is a minor followup on https://github.com/apache/spark/pull/11799 that extracts out the `VectorizedColumnReader` from `VectorizedParquetRecordReader` into its own file. ## How was this patch tested? N/A (refactoring only) Author: Sameer Agarwal <sameer@databricks.com> Closes #11834 from sameeragarwal/rename.
* [MINOR][DOCS] Update build descriptions and commandsDongjoon Hyun2016-03-184-10/+13
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR updates Scala and Hadoop versions in the build description and commands in `Building Spark` documents. ## How was this patch tested? N/A Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11838 from dongjoon-hyun/fix_doc_building_spark.
* [SPARK-13629][ML] Add binary toggle Param to CountVectorizerYuhao Yang2016-03-181-14/+9
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This is a continued work for https://github.com/apache/spark/pull/11536#issuecomment-198511013, containing some comment update and style adjustment. jkbradley ## How was this patch tested? unit tests. Author: Yuhao Yang <hhbyyh@gmail.com> Closes #11830 from hhbyyh/cvToggle.
* [SPARK-13989] [SQL] Remove non-vectorized/unsafe-row parquet record readerSameer Agarwal2016-03-188-364/+75
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR cleans up the new parquet record reader with the following changes: 1. Removes the non-vectorized parquet reader code from `UnsafeRowParquetRecordReader`. 2. Removes the non-vectorized column reader code from `ColumnReader`. 3. Renames `UnsafeRowParquetRecordReader` to `VectorizedParquetRecordReader` and `ColumnReader` to `VectorizedColumnReader` 4. Deprecate `PARQUET_UNSAFE_ROW_RECORD_READER_ENABLED` ## How was this patch tested? Refactoring only; Existing tests should reveal any problems. Author: Sameer Agarwal <sameer@databricks.com> Closes #11799 from sameeragarwal/vectorized-parquet.
* [SPARK-13972][SQL][FOLLOW-UP] When creating the query execution for a ↵Yin Huai2016-03-181-1/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | converted SQL query, we eagerly trigger analysis ## What changes were proposed in this pull request? As part of testing generating SQL query from a analyzed SQL plan, we run the generated SQL for tests in HiveComparisonTest. This PR makes the generated SQL get eagerly analyzed. So, when a generated SQL has any analysis error, we can see the error message created by ``` case NonFatal(e) => fail( s"""Failed to analyze the converted SQL string: | |# Original HiveQL query string: |$queryString | |# Resolved query plan: |${originalQuery.analyzed.treeString} | |# Converted SQL query string: |$convertedSQL """.stripMargin, e) ``` Right now, if we can parse a generated SQL but fail to analyze it, we will see error message generated by the following code (it only mentions that we cannot execute the original query, i.e. `queryString`). ``` case e: Throwable => val errorMessage = s""" |Failed to execute query using catalyst: |Error: ${e.getMessage} |${stackTraceToString(e)} |$queryString |$query |== HIVE - ${hive.size} row(s) == |${hive.mkString("\n")} """.stripMargin ``` ## How was this patch tested? Existing tests. Author: Yin Huai <yhuai@databricks.com> Closes #11825 from yhuai/SPARK-13972-follow-up.
* [SPARK-13958] Executor OOM due to unbounded growth of pointer array in…Sital Kedia2016-03-182-2/+24
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This change fixes the executor OOM which was recently introduced in PR apache/spark#11095 (Please fill in changes proposed in this fix) ## How was this patch tested? Tested by running a spark job on the cluster. (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) … Sorter Author: Sital Kedia <skedia@fb.com> Closes #11794 from sitalkedia/SPARK-13958.
* [SPARK-13885][YARN] Fix attempt id regression for Spark running on Yarnjerryshao2016-03-184-25/+8
| | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This regression is introduced in #9182, previously attempt id is simply as counter "1" or "2". With the change of #9182, it is changed to full name as "appattemtp-xxx-00001", this will affect all the parts which uses this attempt id, like event log file name, history server app url link. So here change it back to the counter to keep consistent with previous code. Also revert back this patch #11518, this patch fix the url link of history log according to the new way of attempt id, since here we change back to the previous way, so this patch is not necessary, here to revert it. Also clean "spark.yarn.app.id" and "spark.yarn.app.attemptId", since it is useless now. ## How was this patch tested? Test it with unit test and manually test different scenario: 1. application running in yarn-client mode. 2. application running in yarn-cluster mode. 3. application running in yarn-cluster mode with multiple attempts. Checked both the event log file name and url link. CC vanzin tgravescs , please help to review, thanks a lot. Author: jerryshao <sshao@hortonworks.com> Closes #11721 from jerryshao/SPARK-13885.
* [SPARK-13977] [SQL] Brings back Shuffled hash joinDavies Liu2016-03-1813-118/+277
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? ShuffledHashJoin (also outer join) is removed in 1.6, in favor of SortMergeJoin, which is more robust and also fast. ShuffledHashJoin is still useful in this case: 1) one table is much smaller than the other one, then cost to build a hash table on smaller table is smaller than sorting the larger table 2) any partition of the small table could fit in memory. This PR brings back ShuffledHashJoin, basically revert #9645, and fix the conflict. Also merging outer join and left-semi join into the same class. This PR does not implement full outer join, because it's not implemented efficiently (requiring build hash table on both side). A simple benchmark (one table is 5x smaller than other one) show that ShuffledHashJoin could be 2X faster than SortMergeJoin. ## How was this patch tested? Added new unit tests for ShuffledHashJoin. Author: Davies Liu <davies@databricks.com> Closes #11788 from davies/shuffle_join.
* [SPARK-14004][SQL][MINOR] AttributeReference and Alias should only use the ↵Cheng Lian2016-03-191-4/+2
| | | | | | | | | | | | | | | | | | | | first qualifier to generate SQL strings ## What changes were proposed in this pull request? Current implementations of `AttributeReference.sql` and `Alias.sql` joins all available qualifiers, which is logically wrong. But this implementation mistake doesn't cause any real SQL generation bugs though, since there is always at most one qualifier for any given `AttributeReference` or `Alias`. This PR fixes this issue by only picking the first qualifiers. ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) Existing tests should be enough. Author: Cheng Lian <lian@databricks.com> Closes #11820 from liancheng/spark-14004-single-qualifier.
* [SPARK-13972][SQ] hive tests should fail if SQL generation failedWenchen Fan2016-03-181-43/+28
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Now we should be able to convert all logical plans to SQL string, if they are parsed from hive query. This PR changes the error handling to throw exceptions instead of just log. We will send new PRs for spotted bugs, and merge this one after all bugs are fixed. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #11782 from cloud-fan/test.
* [MINOR][DOC] Fix nits in JavaStreamingTestExampleZheng RuiFeng2016-03-181-9/+11
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix some nits discussed in https://github.com/apache/spark/pull/11776#issuecomment-198207419 use !rdd.isEmpty instead of rdd.count > 0 use static instead of AtomicInteger remove unneeded "throws Exception" ## How was this patch tested? manual tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #11821 from zhengruifeng/je_fix.
* [SPARK-14001][SQL] support multi-children Union in SQLBuilderWenchen Fan2016-03-182-10/+11
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? The fix is simple, use the existing `CombineUnions` rule to combine adjacent Unions before build SQL string. ## How was this patch tested? The re-enabled test Author: Wenchen Fan <wenchen@databricks.com> Closes #11818 from cloud-fan/bug-fix.
* [MINOR][ML] When trainingSummary is None, it should throw RuntimeException.Yanbo Liang2016-03-184-20/+8
| | | | | | | | | | | | ## What changes were proposed in this pull request? When trainingSummary is None, it should throw ```RuntimeException```. cc mengxr ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #11784 from yanboliang/fix-summary.
* [SPARK-13826][SQL] Addendum: update documentation for DatasetsReynold Xin2016-03-184-31/+70
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch updates documentations for Datasets. I also updated some internal documentation for exchange/broadcast. ## How was this patch tested? Just documentation/api stability update. Author: Reynold Xin <rxin@databricks.com> Closes #11814 from rxin/dataset-docs.
* [SPARK-13930] [SQL] Apply fast serialization on collect limit operatorLiang-Chi Hsieh2016-03-172-28/+71
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? JIRA: https://issues.apache.org/jira/browse/SPARK-13930 Recently the fast serialization has been introduced to collecting DataFrame/Dataset (#11664). The same technology can be used on collect limit operator too. ## How was this patch tested? Add a benchmark for collect limit to `BenchmarkWholeStageCodegen`. Without this patch: model name : Westmere E56xx/L56xx/X56xx (Nehalem-C) collect limit: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------- collect limit 1 million 3413 / 3768 0.3 3255.0 1.0X collect limit 2 millions 9728 / 10440 0.1 9277.3 0.4X With this patch: model name : Westmere E56xx/L56xx/X56xx (Nehalem-C) collect limit: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------- collect limit 1 million 833 / 1284 1.3 794.4 1.0X collect limit 2 millions 3348 / 4005 0.3 3193.3 0.2X Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #11759 from viirya/execute-take.
* [SPARK-13826][SQL] Revises Dataset ScalaDocCheng Lian2016-03-171-319/+522
| | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR revises Dataset API ScalaDoc. All public methods are divided into the following groups * `groupname basic`: Basic Dataset functions * `groupname action`: Actions * `groupname untypedrel`: Untyped Language Integrated Relational Queries * `groupname typedrel`: Typed Language Integrated Relational Queries * `groupname func`: Functional Transformations * `groupname rdd`: RDD Operations * `groupname output`: Output Operations `since` tag and sample code are also updated. We may want to add more sample code for typed APIs. ## How was this patch tested? Documentation change. Checked by building unidoc locally. Author: Cheng Lian <lian@databricks.com> Closes #11769 from liancheng/spark-13826-ds-api-doc.
* [SPARK-12719][HOTFIX] Fix compilation against Scala 2.10tedyu2016-03-181-8/+8
| | | | | | | | | | | | PR #11696 introduced a complex pattern match that broke Scala 2.10 match unreachability check and caused build failure. This PR fixes this issue by expanding this pattern match into several simpler ones. Note that tuning or turning off `-Dscalac.patmat.analysisBudget` doesn't work for this case. Compilation against Scala 2.10 Author: tedyu <yuzhihong@gmail.com> Closes #11798 from yy2016/master.
* [SPARK-13921] Store serialized blocks as multiple chunks in MemoryStoreJosh Rosen2016-03-1718-138/+463
| | | | | | | | | | | | This patch modifies the BlockManager, MemoryStore, and several other storage components so that serialized cached blocks are stored as multiple small chunks rather than as a single contiguous ByteBuffer. This change will help to improve the efficiency of memory allocation and the accuracy of memory accounting when serializing blocks. Our current serialization code uses a ByteBufferOutputStream, which doubles and re-allocates its backing byte array; this increases the peak memory requirements during serialization (since we need to hold extra memory while expanding the array). In addition, we currently don't account for the extra wasted space at the end of the ByteBuffer's backing array, so a 129 megabyte serialized block may actually consume 256 megabytes of memory. After switching to storing blocks in multiple chunks, we'll be able to efficiently trim the backing buffers so that no space is wasted. This change is also a prerequisite to being able to cache blocks which are larger than 2GB (although full support for that depends on several other changes which have not bee implemented yet). Author: Josh Rosen <joshrosen@databricks.com> Closes #11748 from JoshRosen/chunked-block-serialization.
* [SPARK-13976][SQL] do not remove sub-queries added by user when generate SQLWenchen Fan2016-03-182-2/+14
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? We haven't figured out the corrected logical to add sub-queries yet, so we should not clear all sub-queries before generate SQL. This PR changed the logic to only remove sub-queries above table relation. an example for this bug, original SQL: `SELECT a FROM (SELECT a FROM tbl) t WHERE a = 1` before this PR, we will generate: ``` SELECT attr_1 AS a FROM SELECT attr_1 FROM ( SELECT a AS attr_1 FROM tbl ) AS sub_q0 WHERE attr_1 = 1 ``` We missed a sub-query and this SQL string is illegal. After this PR, we will generate: ``` SELECT attr_1 AS a FROM ( SELECT attr_1 FROM ( SELECT a AS attr_1 FROM tbl ) AS sub_q0 WHERE attr_1 = 1 ) AS t ``` TODO: for long term, we should find a way to add sub-queries correctly, so that arbitrary logical plans can be converted to SQL string. ## How was this patch tested? `LogicalPlanToSQLSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #11786 from cloud-fan/bug-fix.
* [SPARK-13974][SQL] sub-query names do not need to be globally unique while ↵Wenchen Fan2016-03-181-12/+9
| | | | | | | | | | | | | | | | | | generate SQL ## What changes were proposed in this pull request? We only need to make sub-query names unique every time we generate a SQL string, but not all the time. This PR moves the `newSubqueryName` method to `class SQLBuilder` and remove `object SQLBuilder`. also addressed 2 minor comments in https://github.com/apache/spark/pull/11696 ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #11783 from cloud-fan/tmp.
* [SPARK-10788][MLLIB][ML] Remove duplicate bins for decision treessethah2016-03-179-49/+54
| | | | | | | | | | | | | | | | | | Decision trees in spark.ml (RandomForest.scala) communicate twice as much data as needed for unordered categorical features. Here's an example. Say there are 3 categories A, B, C. We consider 3 splits: * A vs. B, C * A, B vs. C * A, C vs. B Currently, we collect statistics for each of the 6 subsets of categories (3 * 2 = 6). However, we could instead collect statistics for the 3 subsets on the left-hand side of the 3 possible splits: A and A,B and A,C. If we also have stats for the entire node, then we can compute the stats for the 3 subsets on the right-hand side of the splits. In pseudomath: stats(B,C) = stats(A,B,C) - stats(A). This patch adds a parent stats array to the `DTStatsAggregator` so that the right child stats do not need to be stored. The right child stats are computed by subtracting left child stats from the parent stats for unordered categorical features. Author: sethah <seth.hendrickson16@gmail.com> Closes #9474 from sethah/SPARK-10788.
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