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* [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.
* [MINOR][DOCS] Add proper periods and spaces for CLI help messages and ↵Dongjoon Hyun2016-03-211-12/+12
| | | | | | | | | | | | | | | | `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 Hyun2016-03-2110-128/+130
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## 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 sourcehyukjinkwon2016-03-217-45/+156
| | | | | | | | | | | | | | | | | | | | | | ## 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-13897][SQL] RelationalGroupedDataset and KeyValueGroupedDatasetReynold Xin2016-03-194-67/+69
| | | | | | | | | | | | | | | | ## 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.
* [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.
* [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-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.
* [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.
* [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-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.
* Revert "[SPARK-12719][HOTFIX] Fix compilation against Scala 2.10"Yin Huai2016-03-171-8/+8
| | | | This reverts commit 3ee7996187bbef008c10681bc4e048c6383f5187.
* [SPARK-12719][HOTFIX] Fix compilation against Scala 2.10tedyu2016-03-171-8/+8
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Compilation against Scala 2.10 fails with: ``` [error] [warn] /home/jenkins/workspace/spark-master-compile-sbt-scala-2.10/sql/hive/src/main/scala/org/apache/spark/sql/hive/SQLBuilder.scala:483: Cannot check match for unreachability. [error] (The analysis required more space than allowed. Please try with scalac -Dscalac.patmat.analysisBudget=512 or -Dscalac.patmat.analysisBudget=off.) [error] [warn] private def addSubqueryIfNeeded(plan: LogicalPlan): LogicalPlan = plan match { ``` ## How was this patch tested? Compilation against Scala 2.10 Author: tedyu <yuzhihong@gmail.com> Closes #11787 from yy2016/master.
* [SPARK-13838] [SQL] Clear variable code to prevent it to be re-evaluated in ↵Liang-Chi Hsieh2016-03-171-1/+3
| | | | | | | | | | | | | | | | | BoundAttribute JIRA: https://issues.apache.org/jira/browse/SPARK-13838 ## What changes were proposed in this pull request? We should also clear the variable code in `BoundReference.genCode` to prevent it to be evaluated twice, as we did in `evaluateVariables`. ## How was this patch tested? Existing tests. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #11674 from viirya/avoid-reevaluate.
* [SPARK-13427][SQL] Support USING clause in JOIN.Dilip Biswal2016-03-1712-113/+259
| | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Support queries that JOIN tables with USING clause. SELECT * from table1 JOIN table2 USING <column_list> USING clause can be used as a means to simplify the join condition when : 1) Equijoin semantics is desired and 2) The column names in the equijoin have the same name. We already have the support for Natural Join in Spark. This PR makes use of the already existing infrastructure for natural join to form the join condition and also the projection list. ## How was the this patch tested? Have added unit tests in SQLQuerySuite, CatalystQlSuite, ResolveNaturalJoinSuite Author: Dilip Biswal <dbiswal@us.ibm.com> Closes #11297 from dilipbiswal/spark-13427.
* [SPARK-12719][SQL] SQL generation support for GenerateWenchen Fan2016-03-172-7/+170
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds SQL generation support for `Generate` operator. It always converts `Generate` operator into `LATERAL VIEW` format as there are many limitations to put UDTF in project list. This PR is based on https://github.com/apache/spark/pull/11658, please see the last commit to review the real changes. Thanks dilipbiswal for his initial work! Takes over https://github.com/apache/spark/pull/11596 ## How was this patch tested? new tests in `LogicalPlanToSQLSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #11696 from cloud-fan/generate.
* [SPARK-13928] Move org.apache.spark.Logging into ↵Wenchen Fan2016-03-1787-83/+111
| | | | | | | | | | | | | | | | org.apache.spark.internal.Logging ## What changes were proposed in this pull request? Logging was made private in Spark 2.0. If we move it, then users would be able to create a Logging trait themselves to avoid changing their own code. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #11764 from cloud-fan/logger.
* Revert "[SPARK-13840][SQL] Split Optimizer Rule ColumnPruning to ↵Davies Liu2016-03-164-26/+14
| | | | | | ColumnPruning and EliminateOperator" This reverts commit 99bd2f0e94657687834c5c59c4270c1484c9f595.
* [SPARK-13403][SQL] Pass hadoopConfiguration to HiveConf constructors.Ryan Blue2016-03-165-5/+34
| | | | | | | | | | This commit updates the HiveContext so that sc.hadoopConfiguration is used to instantiate its internal instances of HiveConf. I tested this by overriding the S3 FileSystem implementation from spark-defaults.conf as "spark.hadoop.fs.s3.impl" (to avoid [HADOOP-12810](https://issues.apache.org/jira/browse/HADOOP-12810)). Author: Ryan Blue <blue@apache.org> Closes #11273 from rdblue/SPARK-13403-new-hive-conf-from-hadoop-conf.
* [SPARK-13926] Automatically use Kryo serializer when shuffling RDDs with ↵Josh Rosen2016-03-162-3/+3
| | | | | | | | | | | | | | simple types Because ClassTags are available when constructing ShuffledRDD we can use them to automatically use Kryo for shuffle serialization when the RDD's types are known to be compatible with Kryo. This patch introduces `SerializerManager`, a component which picks the "best" serializer for a shuffle given the elements' ClassTags. It will automatically pick a Kryo serializer for ShuffledRDDs whose key, value, and/or combiner types are primitives, arrays of primitives, or strings. In the future we can use this class as a narrow extension point to integrate specialized serializers for other types, such as ByteBuffers. In a planned followup patch, I will extend the BlockManager APIs so that we're able to use similar automatic serializer selection when caching RDDs (this is a little trickier because the ClassTags need to be threaded through many more places). Author: Josh Rosen <joshrosen@databricks.com> Closes #11755 from JoshRosen/automatically-pick-best-serializer.
* [SPARK-12855][MINOR][SQL][DOC][TEST] remove spark.sql.dialect from doc and testDaoyuan Wang2016-03-162-7/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Since developer API of plug-able parser has been removed in #10801 , docs should be updated accordingly. ## How was this patch tested? This patch will not affect the real code path. Author: Daoyuan Wang <daoyuan.wang@intel.com> Closes #11758 from adrian-wang/spark12855.
* [MINOR][SQL][BUILD] Remove duplicated linesDongjoon Hyun2016-03-161-1/+0
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR removes three minor duplicated lines. First one is making the following unreachable code warning. ``` JoinSuite.scala:52: unreachable code [warn] case j: BroadcastHashJoin => j ``` The other two are just consecutive repetitions in `Seq` of MiMa filters. ## How was this patch tested? Pass the existing Jenkins test. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11773 from dongjoon-hyun/remove_duplicated_line.
* [SPARK-13118][SQL] Expression encoding for optional synthetic classesJakob Odersky2016-03-163-3/+37
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix expression generation for optional types. Standard Java reflection causes issues when dealing with synthetic Scala objects (things that do not map to Java and thus contain a dollar sign in their name). This patch introduces Scala reflection in such cases. This patch also adds a regression test for Dataset's handling of classes defined in package objects (which was the initial purpose of this PR). ## How was this patch tested? A new test in ExpressionEncoderSuite that tests optional inner classes and a regression test for Dataset's handling of package objects. Author: Jakob Odersky <jakob@odersky.com> Closes #11708 from jodersky/SPARK-13118-package-objects.
* [SPARK-13873] [SQL] Avoid copy of UnsafeRow when there is no join in whole ↵Davies Liu2016-03-169-8/+35
| | | | | | | | | | | | | | | | | | stage codegen ## What changes were proposed in this pull request? We need to copy the UnsafeRow since a Join could produce multiple rows from single input rows. We could avoid that if there is no join (or the join will not produce multiple rows) inside WholeStageCodegen. Updated the benchmark for `collect`, we could see 20-30% speedup. ## How was this patch tested? existing unit tests. Author: Davies Liu <davies@databricks.com> Closes #11740 from davies/avoid_copy2.
* [SPARK-13719][SQL] Parse JSON rows having an array type and a struct type in ↵hyukjinkwon2016-03-164-14/+48
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | the same fieild ## What changes were proposed in this pull request? This https://github.com/apache/spark/pull/2400 added the support to parse JSON rows wrapped with an array. However, this throws an exception when the given data contains array data and struct data in the same field as below: ```json {"a": {"b": 1}} {"a": []} ``` and the schema is given as below: ```scala val schema = StructType( StructField("a", StructType( StructField("b", StringType) :: Nil )) :: Nil) ``` - **Before** ```scala sqlContext.read.schema(schema).json(path).show() ``` ```scala Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 7 in stage 0.0 failed 4 times, most recent failure: Lost task 7.3 in stage 0.0 (TID 10, 192.168.1.170): java.lang.ClassCastException: org.apache.spark.sql.types.GenericArrayData cannot be cast to org.apache.spark.sql.catalyst.InternalRow at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow$class.getStruct(rows.scala:50) at org.apache.spark.sql.catalyst.expressions.GenericMutableRow.getStruct(rows.scala:247) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificPredicate.eval(Unknown Source) ... ``` - **After** ```scala sqlContext.read.schema(schema).json(path).show() ``` ```bash +----+ | a| +----+ | [1]| |null| +----+ ``` For other data types, in this case it converts the given values are `null` but only this case emits an exception. This PR makes the support for wrapped rows applied only at the top level. ## How was this patch tested? Unit tests were used and `./dev/run_tests` for code style tests. Author: hyukjinkwon <gurwls223@gmail.com> Closes #11752 from HyukjinKwon/SPARK-3308-follow-up.
* [SPARK-13923][SQL] Implement SessionCatalogAndrew Or2016-03-1619-202/+1604
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? As part of the effort to merge `SQLContext` and `HiveContext`, this patch implements an internal catalog called `SessionCatalog` that handles temporary functions and tables and delegates metastore operations to `ExternalCatalog`. Currently, this is still dead code, but in the future it will be part of `SessionState` and will replace `o.a.s.sql.catalyst.analysis.Catalog`. A recent patch #11573 parses Hive commands ourselves in Spark, but still passes the entire query text to Hive. In a future patch, we will use `SessionCatalog` to implement the parsed commands. ## How was this patch tested? 800+ lines of tests in `SessionCatalogSuite`. Author: Andrew Or <andrew@databricks.com> Closes #11750 from andrewor14/temp-catalog.
* [SPARK-11011][SQL] Narrow type of UDT serializationJakob Odersky2016-03-167-50/+28
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Narrow down the parameter type of `UserDefinedType#serialize()`. Currently, the parameter type is `Any`, however it would logically make more sense to narrow it down to the type of the actual user defined type. ## How was this patch tested? Existing tests were successfully run on local machine. Author: Jakob Odersky <jakob@odersky.com> Closes #11379 from jodersky/SPARK-11011-udt-types.
* [SPARK-13869][SQL] Remove redundant conditions while combining filtersSameer Agarwal2016-03-162-4/+26
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? **[I'll link it to the JIRA once ASF JIRA is back online]** This PR modifies the existing `CombineFilters` rule to remove redundant conditions while combining individual filter predicates. For instance, queries of the form `table.where('a === 1 && 'b === 1).where('a === 1 && 'c === 1)` will now be optimized to ` table.where('a === 1 && 'b === 1 && 'c === 1)` (instead of ` table.where('a === 1 && 'a === 1 && 'b === 1 && 'c === 1)`) ## How was this patch tested? Unit test in `FilterPushdownSuite` Author: Sameer Agarwal <sameer@databricks.com> Closes #11670 from sameeragarwal/combine-filters.
* [SPARK-13871][SQL] Support for inferring filters from data constraintsSameer Agarwal2016-03-162-62/+63
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR generalizes the `NullFiltering` optimizer rule in catalyst to `InferFiltersFromConstraints` that can automatically infer all relevant filters based on an operator's constraints while making sure of 2 things: (a) no redundant filters are generated, and (b) filters that do not contribute to any further optimizations are not generated. ## How was this patch tested? Extended all tests in `InferFiltersFromConstraintsSuite` (that were initially based on `NullFilteringSuite` to test filter inference in `Filter` and `Join` operators. In particular the 2 tests ( `single inner join with pre-existing filters: filter out values on either side` and `multiple inner joins: filter out values on all sides on equi-join keys` attempts to highlight/test the real potential of this rule for join optimization. Author: Sameer Agarwal <sameer@databricks.com> Closes #11665 from sameeragarwal/infer-filters.
* [SPARK-13922][SQL] Filter rows with null attributes in vectorized parquet readerSameer Agarwal2016-03-163-5/+146
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | # What changes were proposed in this pull request? It's common for many SQL operators to not care about reading `null` values for correctness. Currently, this is achieved by performing `isNotNull` checks (for all relevant columns) on a per-row basis. Pushing these null filters in the vectorized parquet reader should bring considerable benefits (especially for cases when the underlying data doesn't contain any nulls or contains all nulls). ## How was this patch tested? Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz String with Nulls Scan (0%): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------- SQL Parquet Vectorized 1229 / 1648 8.5 117.2 1.0X PR Vectorized 833 / 846 12.6 79.4 1.5X PR Vectorized (Null Filtering) 732 / 782 14.3 69.8 1.7X Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz String with Nulls Scan (50%): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------- SQL Parquet Vectorized 995 / 1053 10.5 94.9 1.0X PR Vectorized 732 / 772 14.3 69.8 1.4X PR Vectorized (Null Filtering) 725 / 790 14.5 69.1 1.4X Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz String with Nulls Scan (95%): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------- SQL Parquet Vectorized 326 / 333 32.2 31.1 1.0X PR Vectorized 190 / 200 55.1 18.2 1.7X PR Vectorized (Null Filtering) 168 / 172 62.2 16.1 1.9X Author: Sameer Agarwal <sameer@databricks.com> Closes #11749 from sameeragarwal/perf-testing.
* [SPARK-12721][SQL] SQL Generation for Script Transformationgatorsmile2016-03-163-0/+134
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? This PR is to convert to SQL from analyzed logical plans containing operator `ScriptTransformation`. For example, below is the SQL containing `Transform` ``` SELECT TRANSFORM (a, b, c, d) USING 'cat' FROM parquet_t2 ``` Its logical plan is like ``` ScriptTransformation [a#210L,b#211L,c#212L,d#213L], cat, [key#208,value#209], HiveScriptIOSchema(List(),List(),Some(org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe),Some(org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe),List((field.delim, )),List((field.delim, )),Some(org.apache.hadoop.hive.ql.exec.TextRecordReader),Some(org.apache.hadoop.hive.ql.exec.TextRecordWriter),true) +- SubqueryAlias parquet_t2 +- Relation[a#210L,b#211L,c#212L,d#213L] ParquetRelation ``` The generated SQL will be like ``` SELECT TRANSFORM (`parquet_t2`.`a`, `parquet_t2`.`b`, `parquet_t2`.`c`, `parquet_t2`.`d`) USING 'cat' AS (`key` string, `value` string) FROM `default`.`parquet_t2` ``` #### How was this patch tested? Seven test cases are added to `LogicalPlanToSQLSuite`. Author: gatorsmile <gatorsmile@gmail.com> Author: xiaoli <lixiao1983@gmail.com> Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local> Closes #11503 from gatorsmile/transformToSQL.
* [SPARK-13827][SQL] Can't add subquery to an operator with same-name outputs ↵Wenchen Fan2016-03-163-103/+147
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | while generate SQL string ## What changes were proposed in this pull request? This PR tries to solve a fundamental issue in the `SQLBuilder`. When we want to turn a logical plan into SQL string and put it after FROM clause, we need to wrap it with a sub-query. However, a logical plan is allowed to have same-name outputs with different qualifiers(e.g. the `Join` operator), and this kind of plan can't be put under a subquery as we will erase and assign a new qualifier to all outputs and make it impossible to distinguish same-name outputs. To solve this problem, this PR renames all attributes with globally unique names(using exprId), so that we don't need qualifiers to resolve ambiguity anymore. For example, `SELECT x.key, MAX(y.key) OVER () FROM t x JOIN t y`, we will parse this SQL to a Window operator and a Project operator, and add a sub-query between them. The generated SQL looks like: ``` SELECT sq_1.key, sq_1.max FROM ( SELECT sq_0.key, sq_0.key, MAX(sq_0.key) OVER () AS max FROM ( SELECT x.key, y.key FROM t1 AS x JOIN t2 AS y ) AS sq_0 ) AS sq_1 ``` You can see, the `key` columns become ambiguous after `sq_0`. After this PR, it will generate something like: ``` SELECT attr_30 AS key, attr_37 AS max FROM ( SELECT attr_30, attr_37 FROM ( SELECT attr_30, attr_35, MAX(attr_35) AS attr_37 FROM ( SELECT attr_30, attr_35 FROM (SELECT key AS attr_30 FROM t1) AS sq_0 INNER JOIN (SELECT key AS attr_35 FROM t1) AS sq_1 ) AS sq_2 ) AS sq_3 ) AS sq_4 ``` The outermost SELECT is used to turn the generated named to real names back, and the innermost SELECT is used to alias real columns to our generated names. Between them, there is no name ambiguity anymore. ## How was this patch tested? existing tests and new tests in LogicalPlanToSQLSuite. Author: Wenchen Fan <wenchen@databricks.com> Closes #11658 from cloud-fan/gensql.
* [SPARK-13894][SQL] SqlContext.range return type from DataFrame to DataSetCheng Hao2016-03-1611-36/+37
| | | | | | | | | | | | | ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-13894 Change the return type of the `SQLContext.range` API from `DataFrame` to `Dataset`. ## How was this patch tested? No additional unit test required. Author: Cheng Hao <hao.cheng@intel.com> Closes #11730 from chenghao-intel/range.
* [SPARK-13924][SQL] officially support multi-insertWenchen Fan2016-03-163-20/+58
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? There is a feature of hive SQL called multi-insert. For example: ``` FROM src INSERT OVERWRITE TABLE dest1 SELECT key + 1 INSERT OVERWRITE TABLE dest2 SELECT key WHERE key > 2 INSERT OVERWRITE TABLE dest3 SELECT col EXPLODE(arr) exp AS col ... ``` We partially support it currently, with some limitations: 1) WHERE can't reference columns produced by LATERAL VIEW. 2) It's not executed eagerly, i.e. `sql("...multi-insert clause...")` won't take place right away like other commands, e.g. CREATE TABLE. This PR removes these limitations and make us fully support multi-insert. ## How was this patch tested? new tests in `SQLQuerySuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #11754 from cloud-fan/lateral-view.
* [SPARK-13823][SPARK-13397][SPARK-13395][CORE] More warnings, StandardCharset ↵Sean Owen2016-03-1613-21/+33
| | | | | | | | | | | | | | | | | | | | follow up ## What changes were proposed in this pull request? Follow up to https://github.com/apache/spark/pull/11657 - Also update `String.getBytes("UTF-8")` to use `StandardCharsets.UTF_8` - And fix one last new Coverity warning that turned up (use of unguarded `wait()` replaced by simpler/more robust `java.util.concurrent` classes in tests) - And while we're here cleaning up Coverity warnings, just fix about 15 more build warnings ## How was this patch tested? Jenkins tests Author: Sean Owen <sowen@cloudera.com> Closes #11725 from srowen/SPARK-13823.2.
* [SPARK-12653][SQL] Re-enable test "SPARK-8489: MissingRequirementError ↵Dongjoon Hyun2016-03-164-2/+7
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | during reflection" ## What changes were proposed in this pull request? The purpose of [SPARK-12653](https://issues.apache.org/jira/browse/SPARK-12653) is re-enabling a regression test. Historically, the target regression test is added by [SPARK-8498](https://github.com/apache/spark/commit/093c34838d1db7a9375f36a9a2ab5d96a23ae683), but is temporarily disabled by [SPARK-12615](https://github.com/apache/spark/commit/8ce645d4eeda203cf5e100c4bdba2d71edd44e6a) due to binary compatibility error. The following is the current error message at the submitting spark job with the pre-built `test.jar` file in the target regression test. ``` Exception in thread "main" java.lang.NoSuchMethodError: org.apache.spark.SparkContext$.$lessinit$greater$default$6()Lscala/collection/Map; ``` Simple rebuilding `test.jar` can not recover the purpose of testcase since we need to support both Scala 2.10 and 2.11 for a while. For example, we will face the following Scala 2.11 error if we use `test.jar` built by Scala 2.10. ``` Exception in thread "main" java.lang.NoSuchMethodError: scala.reflect.api.JavaUniverse.runtimeMirror(Ljava/lang/ClassLoader;)Lscala/reflect/api/JavaMirrors$JavaMirror; ``` This PR replace the existing `test.jar` with `test-2.10.jar` and `test-2.11.jar` and improve the regression test to use the suitable jar file. ## How was this patch tested? Pass the existing Jenkins test. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11744 from dongjoon-hyun/SPARK-12653.
* [SPARK-13899][SQL] Produce InternalRow instead of external Row at CSV data ↵hyukjinkwon2016-03-154-22/+42
| | | | | | | | | | | | | | | | | | | | source ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-13899 This PR makes CSV data source produce `InternalRow` instead of `Row`. Basically, this resembles JSON data source. It uses the same codes for casting. ## How was this patch tested? Unit tests were used within IDE and code style was checked by `./dev/run_tests`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #11717 from HyukjinKwon/SPARK-13899.
* [SPARK-13917] [SQL] generate broadcast semi joinDavies Liu2016-03-1511-139/+124
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR brings codegen support for broadcast left-semi join. ## How was this patch tested? Existing tests. Added benchmark, the result show 7X speedup. Author: Davies Liu <davies@databricks.com> Closes #11742 from davies/gen_semi.
* [MINOR][TEST][SQL] Remove wrong "expected" parameter in checkNaNWithoutCodegenYucai Yu2016-03-151-1/+0
| | | | | | | | | | | ## What changes were proposed in this pull request? Remove the wrong "expected" parameter in MathFunctionsSuite.scala's checkNaNWithoutCodegen. This function is to check NaN value, so the "expected" parameter is useless. The Callers do not pass "expected" value and the similar function like checkNaNWithGeneratedProjection and checkNaNWithOptimization do not use it also. Author: Yucai Yu <yucai.yu@intel.com> Closes #11718 from yucai/unused_expected.
* [SPARK-13918][SQL] Merge SortMergeJoin and SortMergerOuterJoinDavies Liu2016-03-159-535/+467
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR just move some code from SortMergeOuterJoin into SortMergeJoin. This is for support codegen for outer join. ## How was this patch tested? existing tests. Author: Davies Liu <davies@databricks.com> Closes #11743 from davies/gen_smjouter.
* [SPARK-13895][SQL] DataFrameReader.text should return Dataset[String]Reynold Xin2016-03-153-12/+16
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch changes DataFrameReader.text()'s return type from DataFrame to Dataset[String]. Closes #11731. ## How was this patch tested? Updated existing integration tests to reflect the change. Author: Reynold Xin <rxin@databricks.com> Closes #11739 from rxin/SPARK-13895.
generated by cgit v1.2.3 (git 2.39.1) at 2024-09-15 17:40:41 +0000