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* Revert "[SPARK-13668][SQL] Reorder filter/join predicates to short-circuit ↵Davies Liu2016-03-092-127/+13
| | | | | | isNotNull checks" This reverts commit e430614eae53c8864b31a1dc64db83e27100d1d9.
* [SPARK-13242] [SQL] codegen fallback in case-when if there many branchesDavies Liu2016-03-091-0/+1
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? If there are many branches in a CaseWhen expression, the generated code could go above the 64K limit for single java method, will fail to compile. This PR change it to fallback to interpret mode if there are more than 20 branches. This PR is based on #11243 and #11221, thanks to joehalliwell Closes #11243 Closes #11221 ## How was this patch tested? Add a test with 50 branches. Author: Davies Liu <davies@databricks.com> Closes #11592 from davies/fix_when.
* [SPARK-13702][CORE][SQL][MLLIB] Use diamond operator for generic instance ↵Dongjoon Hyun2016-03-093-3/+3
| | | | | | | | | | | | | | | | | | | | | | | | | creation in Java code. ## What changes were proposed in this pull request? In order to make `docs/examples` (and other related code) more simple/readable/user-friendly, this PR replaces existing codes like the followings by using `diamond` operator. ``` - final ArrayList<Product2<Object, Object>> dataToWrite = - new ArrayList<Product2<Object, Object>>(); + final ArrayList<Product2<Object, Object>> dataToWrite = new ArrayList<>(); ``` Java 7 or higher supports **diamond** operator which replaces the type arguments required to invoke the constructor of a generic class with an empty set of type parameters (<>). Currently, Spark Java code use mixed usage of this. ## How was this patch tested? Manual. Pass the existing tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11541 from dongjoon-hyun/SPARK-13702.
* [SPARK-13692][CORE][SQL] Fix trivial Coverity/Checkstyle defectsDongjoon Hyun2016-03-092-2/+7
| | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This issue fixes the following potential bugs and Java coding style detected by Coverity and Checkstyle. - Implement both null and type checking in equals functions. - Fix wrong type casting logic in SimpleJavaBean2.equals. - Add `implement Cloneable` to `UTF8String` and `SortedIterator`. - Remove dereferencing before null check in `AbstractBytesToBytesMapSuite`. - Fix coding style: Add '{}' to single `for` statement in mllib examples. - Remove unused imports in `ColumnarBatch` and `JavaKinesisStreamSuite`. - Remove unused fields in `ChunkFetchIntegrationSuite`. - Add `stop()` to prevent resource leak. Please note that the last two checkstyle errors exist on newly added commits after [SPARK-13583](https://issues.apache.org/jira/browse/SPARK-13583). ## How was this patch tested? manual via `./dev/lint-java` and Coverity site. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11530 from dongjoon-hyun/SPARK-13692.
* [SPARK-7286][SQL] Deprecate !== in favour of =!=Jakob Odersky2016-03-084-12/+30
| | | | | | | | | | | | | | | This PR replaces #9925 which had issues with CI. **Please see the original PR for any previous discussions.** ## What changes were proposed in this pull request? Deprecate the SparkSQL column operator !== and use =!= as an alternative. Fixes subtle issues related to operator precedence (basically, !== does not have the same priority as its logical negation, ===). ## How was this patch tested? All currently existing tests. Author: Jakob Odersky <jodersky@gmail.com> Closes #11588 from jodersky/SPARK-7286.
* [SPARK-13754] Keep old data source name for backwards compatibilityHossein2016-03-082-1/+12
| | | | | | | | | | | | | | | ## Motivation CSV data source was contributed by Databricks. It is the inlined version of https://github.com/databricks/spark-csv. The data source name was `com.databricks.spark.csv`. As a result there are many tables created on older versions of spark with that name as the source. For backwards compatibility we should keep the old name. ## Proposed changes `com.databricks.spark.csv` was added to list of `backwardCompatibilityMap` in `ResolvedDataSource.scala` ## Tests A unit test was added to `CSVSuite` to parse a csv file using the old name. Author: Hossein <hossein@databricks.com> Closes #11589 from falaki/SPARK-13754.
* [SPARK-13750][SQL] fix sizeInBytes of HadoopFsRelationDavies Liu2016-03-082-0/+44
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fix the sizeInBytes of HadoopFsRelation. ## How was this patch tested? Added regression test for that. Author: Davies Liu <davies@databricks.com> Closes #11590 from davies/fix_sizeInBytes.
* [SPARK-13755] Escape quotes in SQL plan visualization node labelsJosh Rosen2016-03-081-7/+7
| | | | | | | | When generating Graphviz DOT files in the SQL query visualization we need to escape double-quotes inside node labels. This is a followup to #11309, which fixed a similar graph in Spark Core's DAG visualization. Author: Josh Rosen <joshrosen@databricks.com> Closes #11587 from JoshRosen/graphviz-escaping.
* [SPARK-13668][SQL] Reorder filter/join predicates to short-circuit isNotNull ↵Sameer Agarwal2016-03-082-13/+127
| | | | | | | | | | | | | | | | | | checks ## What changes were proposed in this pull request? If a filter predicate or a join condition consists of `IsNotNull` checks, we should reorder these checks such that these non-nullability checks are evaluated before the rest of the predicates. For e.g., if a filter predicate is of the form `a > 5 && isNotNull(b)`, we should rewrite this as `isNotNull(b) && a > 5` during physical plan generation. ## How was this patch tested? new unit tests that verify the physical plan for both filters and joins in `ReorderedPredicateSuite` Author: Sameer Agarwal <sameer@databricks.com> Closes #11511 from sameeragarwal/reorder-isnotnull.
* [SPARK-13738][SQL] Cleanup Data Source resolutionMichael Armbrust2016-03-089-167/+167
| | | | | | | | | | | Follow-up to #11509, that simply refactors the interface that we use when resolving a pluggable `DataSource`. - Multiple functions share the same set of arguments so we make this a case class, called `DataSource`. Actual resolution is now done by calling a function on this class. - Instead of having multiple methods named `apply` (some of which do writing some of which do reading) we now explicitly have `resolveRelation()` and `write(mode, df)`. - Get rid of `Array[String]` since this is an internal API and was forcing us to awkwardly call `toArray` in a bunch of places. Author: Michael Armbrust <michael@databricks.com> Closes #11572 from marmbrus/dataSourceResolution.
* [SPARK-13404] [SQL] Create variables for input row when it's actually usedDavies Liu2016-03-077-152/+218
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR change the way how we generate the code for the output variables passing from a plan to it's parent. Right now, they are generated before call consume() of it's parent. It's not efficient, if the parent is a Filter or Join, which could filter out most the rows, the time to access some of the columns that are not used by the Filter or Join are wasted. This PR try to improve this by defering the access of columns until they are actually used by a plan. After this PR, a plan does not need to generate code to evaluate the variables for output, just passing the ExprCode to its parent by `consume()`. In `parent.consumeChild()`, it will check the output from child and `usedInputs`, generate the code for those columns that is part of `usedInputs` before calling `doConsume()`. This PR also change the `if` from ``` if (cond) { xxx } ``` to ``` if (!cond) continue; xxx ``` The new one could help to reduce the nested indents for multiple levels of Filter and BroadcastHashJoin. It also added some comments for operators. ## How was the this patch tested? Unit tests. Manually ran TPCDS Q55, this PR improve the performance about 30% (scale=10, from 2.56s to 1.96s) Author: Davies Liu <davies@databricks.com> Closes #11274 from davies/gen_defer.
* [SPARK-13689][SQL] Move helper things in CatalystQl to new utils objectAndrew Or2016-03-071-1/+3
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? When we add more DDL parsing logic in the future, SparkQl will become very big. To keep it smaller, we'll introduce helper "parser objects", e.g. one to parse alter table commands. However, these parser objects will need to access some helper methods that exist in CatalystQl. The proposal is to move those methods to an isolated ParserUtils object. This is based on viirya's changes in #11048. It prefaces the bigger fix for SPARK-13139 to make the diff of that patch smaller. ## How was this patch tested? No change in functionality, so just Jenkins. Author: Andrew Or <andrew@databricks.com> Closes #11529 from andrewor14/parser-utils.
* [SPARK-13665][SQL] Separate the concerns of HadoopFsRelationMichael Armbrust2016-03-0728-1544/+1102
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | `HadoopFsRelation` is used for reading most files into Spark SQL. However today this class mixes the concerns of file management, schema reconciliation, scan building, bucketing, partitioning, and writing data. As a result, many data sources are forced to reimplement the same functionality and the various layers have accumulated a fair bit of inefficiency. This PR is a first cut at separating this into several components / interfaces that are each described below. Additionally, all implementations inside of Spark (parquet, csv, json, text, orc, svmlib) have been ported to the new API `FileFormat`. External libraries, such as spark-avro will also need to be ported to work with Spark 2.0. ### HadoopFsRelation A simple `case class` that acts as a container for all of the metadata required to read from a datasource. All discovery, resolution and merging logic for schemas and partitions has been removed. This an internal representation that no longer needs to be exposed to developers. ```scala case class HadoopFsRelation( sqlContext: SQLContext, location: FileCatalog, partitionSchema: StructType, dataSchema: StructType, bucketSpec: Option[BucketSpec], fileFormat: FileFormat, options: Map[String, String]) extends BaseRelation ``` ### FileFormat The primary interface that will be implemented by each different format including external libraries. Implementors are responsible for reading a given format and converting it into `InternalRow` as well as writing out an `InternalRow`. A format can optionally return a schema that is inferred from a set of files. ```scala trait FileFormat { def inferSchema( sqlContext: SQLContext, options: Map[String, String], files: Seq[FileStatus]): Option[StructType] def prepareWrite( sqlContext: SQLContext, job: Job, options: Map[String, String], dataSchema: StructType): OutputWriterFactory def buildInternalScan( sqlContext: SQLContext, dataSchema: StructType, requiredColumns: Array[String], filters: Array[Filter], bucketSet: Option[BitSet], inputFiles: Array[FileStatus], broadcastedConf: Broadcast[SerializableConfiguration], options: Map[String, String]): RDD[InternalRow] } ``` The current interface is based on what was required to get all the tests passing again, but still mixes a couple of concerns (i.e. `bucketSet` is passed down to the scan instead of being resolved by the planner). Additionally, scans are still returning `RDD`s instead of iterators for single files. In a future PR, bucketing should be removed from this interface and the scan should be isolated to a single file. ### FileCatalog This interface is used to list the files that make up a given relation, as well as handle directory based partitioning. ```scala trait FileCatalog { def paths: Seq[Path] def partitionSpec(schema: Option[StructType]): PartitionSpec def allFiles(): Seq[FileStatus] def getStatus(path: Path): Array[FileStatus] def refresh(): Unit } ``` Currently there are two implementations: - `HDFSFileCatalog` - based on code from the old `HadoopFsRelation`. Infers partitioning by recursive listing and caches this data for performance - `HiveFileCatalog` - based on the above, but it uses the partition spec from the Hive Metastore. ### ResolvedDataSource Produces a logical plan given the following description of a Data Source (which can come from DataFrameReader or a metastore): - `paths: Seq[String] = Nil` - `userSpecifiedSchema: Option[StructType] = None` - `partitionColumns: Array[String] = Array.empty` - `bucketSpec: Option[BucketSpec] = None` - `provider: String` - `options: Map[String, String]` This class is responsible for deciding which of the Data Source APIs a given provider is using (including the non-file based ones). All reconciliation of partitions, buckets, schema from metastores or inference is done here. ### DataSourceAnalysis / DataSourceStrategy Responsible for analyzing and planning reading/writing of data using any of the Data Source APIs, including: - pruning the files from partitions that will be read based on filters. - appending partition columns* - applying additional filters when a data source can not evaluate them internally. - constructing an RDD that is bucketed correctly when required* - sanity checking schema match-up and other analysis when writing. *In the future we should do that following: - Break out file handling into its own Strategy as its sufficiently complex / isolated. - Push the appending of partition columns down in to `FileFormat` to avoid an extra copy / unvectorization. - Use a custom RDD for scans instead of `SQLNewNewHadoopRDD2` Author: Michael Armbrust <michael@databricks.com> Author: Wenchen Fan <wenchen@databricks.com> Closes #11509 from marmbrus/fileDataSource.
* [SPARK-13442][SQL] Make type inference recognize boolean typeshyukjinkwon2016-03-074-0/+38
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-13442 This PR adds the support for inferring `BooleanType` for schema. It supports to infer case-insensitive `true` / `false` as `BooleanType`. Unittests were added for `CSVInferSchemaSuite` and `CSVSuite` for end-to-end test. ## How was the this patch tested? This was tested with unittests and with `dev/run_tests` for coding style Author: hyukjinkwon <gurwls223@gmail.com> Closes #11315 from HyukjinKwon/SPARK-13442.
* [SPARK-13495][SQL] Add Null Filters in the query plan for Filters/Joins ↵Sameer Agarwal2016-03-072-5/+3
| | | | | | | | | | | | | | | | | | | | | | based on their data constraints ## What changes were proposed in this pull request? This PR adds an optimizer rule to eliminate reading (unnecessary) NULL values if they are not required for correctness by inserting `isNotNull` filters is the query plan. These filters are currently inserted beneath existing `Filter` and `Join` operators and are inferred based on their data constraints. Note: While this optimization is applicable to all types of join, it primarily benefits `Inner` and `LeftSemi` joins. ## How was this patch tested? 1. Added a new `NullFilteringSuite` that tests for `IsNotNull` filters in the query plan for joins and filters. Also, tests interaction with the `CombineFilters` optimizer rules. 2. Test generated ExpressionTrees via `OrcFilterSuite` 3. Test filter source pushdown logic via `SimpleTextHadoopFsRelationSuite` cc yhuai nongli Author: Sameer Agarwal <sameer@databricks.com> Closes #11372 from sameeragarwal/gen-isnotnull.
* [SPARK-13694][SQL] QueryPlan.expressions should always include all expressionsWenchen Fan2016-03-071-1/+1
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? It's weird that expressions don't always have all the expressions in it. This PR marks `QueryPlan.expressions` final to forbid sub classes overriding it to exclude some expressions. Currently only `Generate` override it, we can use `producedAttributes` to fix the unresolved attribute problem for it. Note that this PR doesn't fix the problem in #11497 ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #11532 from cloud-fan/generate.
* [SPARK-13255] [SQL] Update vectorized reader to directly return ↵Nong Li2016-03-049-45/+284
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ColumnarBatch instead of InternalRows. ## What changes were proposed in this pull request? (Please fill in changes proposed in this fix) Currently, the parquet reader returns rows one by one which is bad for performance. This patch updates the reader to directly return ColumnarBatches. This is only enabled with whole stage codegen, which is the only operator currently that is able to consume ColumnarBatches (instead of rows). The current implementation is a bit of a hack to get this to work and we should do more refactoring of these low level interfaces to make this work better. ## How was this patch tested? ``` Results: TPCDS: Best/Avg Time(ms) Rate(M/s) Per Row(ns) --------------------------------------------------------------------------------- q55 (before) 8897 / 9265 12.9 77.2 q55 5486 / 5753 21.0 47.6 ``` Author: Nong Li <nong@databricks.com> Closes #11435 from nongli/spark-13255.
* [SPARK-13633][SQL] Move things into catalyst.parser packageAndrew Or2016-03-046-6/+9
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch simply moves things to existing package `o.a.s.sql.catalyst.parser` in an effort to reduce the size of the diff in #11048. This is conceptually the same as a recently merged patch #11482. ## How was this patch tested? Jenkins. Author: Andrew Or <andrew@databricks.com> Closes #11506 from andrewor14/parser-package.
* [SPARK-12941][SQL][MASTER] Spark-SQL JDBC Oracle dialect fails to map string ↵thomastechs2016-03-031-0/+6
| | | | | | | | | | | | | | | | | | datatypes to Oracle VARCHAR datatype mapping ## What changes were proposed in this pull request? A test suite added for the bug fix -SPARK 12941; for the mapping of the StringType to corresponding in Oracle ## How was this patch tested? manual tests done (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) Author: thomastechs <thomas.sebastian@tcs.com> Author: THOMAS SEBASTIAN <thomas.sebastian@tcs.com> Closes #11489 from thomastechs/thomastechs-12941-master-new.
* [SPARK-13415][SQL] Visualize subquery in SQL web UIDavies Liu2016-03-037-124/+110
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR support visualization for subquery in SQL web UI, also improve the explain of subquery, especially when it's used together with whole stage codegen. For example: ```python >>> sqlContext.range(100).registerTempTable("range") >>> sqlContext.sql("select id / (select sum(id) from range) from range where id > (select id from range limit 1)").explain(True) == Parsed Logical Plan == 'Project [unresolvedalias(('id / subquery#9), None)] : +- 'SubqueryAlias subquery#9 : +- 'Project [unresolvedalias('sum('id), None)] : +- 'UnresolvedRelation `range`, None +- 'Filter ('id > subquery#8) : +- 'SubqueryAlias subquery#8 : +- 'GlobalLimit 1 : +- 'LocalLimit 1 : +- 'Project [unresolvedalias('id, None)] : +- 'UnresolvedRelation `range`, None +- 'UnresolvedRelation `range`, None == Analyzed Logical Plan == (id / scalarsubquery()): double Project [(cast(id#0L as double) / cast(subquery#9 as double)) AS (id / scalarsubquery())#11] : +- SubqueryAlias subquery#9 : +- Aggregate [(sum(id#0L),mode=Complete,isDistinct=false) AS sum(id)#10L] : +- SubqueryAlias range : +- Range 0, 100, 1, 4, [id#0L] +- Filter (id#0L > subquery#8) : +- SubqueryAlias subquery#8 : +- GlobalLimit 1 : +- LocalLimit 1 : +- Project [id#0L] : +- SubqueryAlias range : +- Range 0, 100, 1, 4, [id#0L] +- SubqueryAlias range +- Range 0, 100, 1, 4, [id#0L] == Optimized Logical Plan == Project [(cast(id#0L as double) / cast(subquery#9 as double)) AS (id / scalarsubquery())#11] : +- SubqueryAlias subquery#9 : +- Aggregate [(sum(id#0L),mode=Complete,isDistinct=false) AS sum(id)#10L] : +- Range 0, 100, 1, 4, [id#0L] +- Filter (id#0L > subquery#8) : +- SubqueryAlias subquery#8 : +- GlobalLimit 1 : +- LocalLimit 1 : +- Project [id#0L] : +- Range 0, 100, 1, 4, [id#0L] +- Range 0, 100, 1, 4, [id#0L] == Physical Plan == WholeStageCodegen : +- Project [(cast(id#0L as double) / cast(subquery#9 as double)) AS (id / scalarsubquery())#11] : : +- Subquery subquery#9 : : +- WholeStageCodegen : : : +- TungstenAggregate(key=[], functions=[(sum(id#0L),mode=Final,isDistinct=false)], output=[sum(id)#10L]) : : : +- INPUT : : +- Exchange SinglePartition, None : : +- WholeStageCodegen : : : +- TungstenAggregate(key=[], functions=[(sum(id#0L),mode=Partial,isDistinct=false)], output=[sum#14L]) : : : +- Range 0, 1, 4, 100, [id#0L] : +- Filter (id#0L > subquery#8) : : +- Subquery subquery#8 : : +- CollectLimit 1 : : +- WholeStageCodegen : : : +- Project [id#0L] : : : +- Range 0, 1, 4, 100, [id#0L] : +- Range 0, 1, 4, 100, [id#0L] ``` The web UI looks like: ![subquery](https://cloud.githubusercontent.com/assets/40902/13377963/932bcbae-dda7-11e5-82f7-03c9be85d77c.png) This PR also change the tree structure of WholeStageCodegen to make it consistent than others. Before this change, Both WholeStageCodegen and InputAdapter hold a references to the same plans, those could be updated without notify another, causing problems, this is discovered by #11403 . ## How was this patch tested? Existing tests, also manual tests with the example query, check the explain and web UI. Author: Davies Liu <davies@databricks.com> Closes #11417 from davies/viz_subquery.
* [SPARK-13584][SQL][TESTS] Make ContinuousQueryManagerSuite not output logs ↵Shixiong Zhu2016-03-034-9/+21
| | | | | | | | | | | | | | | | | | to the console ## What changes were proposed in this pull request? Make ContinuousQueryManagerSuite not output logs to the console. The logs will still output to `unit-tests.log`. I also updated `SQLListenerMemoryLeakSuite` to use `quietly` to avoid changing the log level which won't output logs to `unit-tests.log`. ## How was this patch tested? Just check Jenkins output. Author: Shixiong Zhu <shixiong@databricks.com> Closes #11439 from zsxwing/quietly-ContinuousQueryManagerSuite.
* [SPARK-13632][SQL] Move commands.scala to command packageAndrew Or2016-03-0310-9/+16
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch simply moves things to a new package in an effort to reduce the size of the diff in #11048. Currently the new package only has one file, but in the future we'll add many new commands in SPARK-13139. ## How was this patch tested? Jenkins. Author: Andrew Or <andrew@databricks.com> Closes #11482 from andrewor14/commands-package.
* [MINOR] Fix typos in comments and testcase name of codeDongjoon Hyun2016-03-032-2/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes typos in comments and testcase name of code. ## How was this patch tested? manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11481 from dongjoon-hyun/minor_fix_typos_in_code.
* [SPARK-13543][SQL] Support for specifying compression codec for Parquet/ORC ↵hyukjinkwon2016-03-036-20/+184
| | | | | | | | | | | | | | | | via option() ## What changes were proposed in this pull request? This PR adds the support to specify compression codecs for both ORC and Parquet. ## How was this patch tested? unittests within IDE and code style tests with `dev/run_tests`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #11464 from HyukjinKwon/SPARK-13543.
* [SPARK-13423][HOTFIX] Static analysis fixes for 2.x / fixed for Scala 2.10Sean Owen2016-03-031-1/+1
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fixes compile problem due to inadvertent use of `Option.contains`, only in Scala 2.11. The change should have been to replace `Option.exists(_ == x)` with `== Some(x)`. Replacing exists with contains only makes sense for collections. Replacing use of `Option.exists` still makes sense though as it's misleading. ## How was this patch tested? Jenkins tests / compilation (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Author: Sean Owen <sowen@cloudera.com> Closes #11493 from srowen/SPARK-13423.2.
* [SPARK-13583][CORE][STREAMING] Remove unused imports and add checkstyle ruleDongjoon Hyun2016-03-0327-35/+10
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? After SPARK-6990, `dev/lint-java` keeps Java code healthy and helps PR review by saving much time. This issue aims remove unused imports from Java/Scala code and add `UnusedImports` checkstyle rule to help developers. ## How was this patch tested? ``` ./dev/lint-java ./build/sbt compile ``` Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11438 from dongjoon-hyun/SPARK-13583.
* [SPARK-13423][WIP][CORE][SQL][STREAMING] Static analysis fixes for 2.xSean Owen2016-03-0313-45/+44
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Make some cross-cutting code improvements according to static analysis. These are individually up for discussion since they exist in separate commits that can be reverted. The changes are broadly: - Inner class should be static - Mismatched hashCode/equals - Overflow in compareTo - Unchecked warnings - Misuse of assert, vs junit.assert - get(a) + getOrElse(b) -> getOrElse(a,b) - Array/String .size -> .length (occasionally, -> .isEmpty / .nonEmpty) to avoid implicit conversions - Dead code - tailrec - exists(_ == ) -> contains find + nonEmpty -> exists filter + size -> count - reduce(_+_) -> sum map + flatten -> map The most controversial may be .size -> .length simply because of its size. It is intended to avoid implicits that might be expensive in some places. ## How was the this patch tested? Existing Jenkins unit tests. Author: Sean Owen <sowen@cloudera.com> Closes #11292 from srowen/SPARK-13423.
* [SPARK-13466] [SQL] Remove projects that become redundant after column ↵Liang-Chi Hsieh2016-03-031-4/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | pruning rule JIRA: https://issues.apache.org/jira/browse/SPARK-13466 ## What changes were proposed in this pull request? With column pruning rule in optimizer, some Project operators will become redundant. We should remove these redundant Projects. For an example query: val input = LocalRelation('key.int, 'value.string) val query = Project(Seq($"x.key", $"y.key"), Join( SubqueryAlias("x", input), BroadcastHint(SubqueryAlias("y", input)), Inner, None)) After the first run of column pruning, it would like: Project(Seq($"x.key", $"y.key"), Join( Project(Seq($"x.key"), SubqueryAlias("x", input)), Project(Seq($"y.key"), <-- inserted by the rule BroadcastHint(SubqueryAlias("y", input))), Inner, None)) Actually we don't need the outside Project now. This patch will remove it: Join( Project(Seq($"x.key"), SubqueryAlias("x", input)), Project(Seq($"y.key"), BroadcastHint(SubqueryAlias("y", input))), Inner, None) ## How was the this patch tested? Unit test is added into ColumnPruningSuite. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #11341 from viirya/remove-redundant-project.
* [SPARK-13528][SQL] Make the short names of compression codecs consistent in ↵Takeshi YAMAMURO2016-03-024-33/+37
| | | | | | | | | | | | | | ParquetRelation ## What changes were proposed in this pull request? This pr to make the short names of compression codecs in `ParquetRelation` consistent against other ones. This pr comes from #11324. ## How was this patch tested? Add more tests in `TextSuite`. Author: Takeshi YAMAMURO <linguin.m.s@gmail.com> Closes #11408 from maropu/SPARK-13528.
* [SPARK-13574] [SQL] Add benchmark to measure string dictionary decode.Nong Li2016-03-021-18/+52
| | | | | | | | | | ## What changes were proposed in this pull request? Also updated the other benchmarks when the default to use vectorized decode was flipped. Author: Nong Li <nong@databricks.com> Closes #11454 from nongli/benchmark.
* [SPARK-13601] call failure callbacks before writer.close()Davies Liu2016-03-021-46/+49
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? In order to tell OutputStream that the task has failed or not, we should call the failure callbacks BEFORE calling writer.close(). ## How was this patch tested? Added new unit tests. Author: Davies Liu <davies@databricks.com> Closes #11450 from davies/callback.
* [SPARK-13609] [SQL] Support Column Pruning for MapPartitionsgatorsmile2016-03-021-1/+10
| | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? This PR is to prune unnecessary columns when the operator is `MapPartitions`. The solution is to add an extra `Project` in the child node. For the other two operators `AppendColumns` and `MapGroups`, it sounds doable. More discussions are required. The major reason is the current implementation of the `inputPlan` of `groupBy` is based on the child of `AppendColumns`. It might be a bug? Thus, will submit a separate PR. #### How was this patch tested? Added a test case in ColumnPruningSuite to verify the rule. Added another test case in DatasetSuite.scala to verify the data. Author: gatorsmile <gatorsmile@gmail.com> Closes #11460 from gatorsmile/datasetPruningNew.
* [SPARK-13167][SQL] Include rows with null values for partition column when ↵sureshthalamati2016-03-012-1/+45
| | | | | | | | | | | | | | | reading from JDBC datasources. Rows with null values in partition column are not included in the results because none of the partition where clause specify is null predicate on the partition column. This fix adds is null predicate on the partition column to the first JDBC partition where clause. Example: JDBCPartition(THEID < 1 or THEID is null, 0),JDBCPartition(THEID >= 1 AND THEID < 2,1), JDBCPartition(THEID >= 2, 2) Author: sureshthalamati <suresh.thalamati@gmail.com> Closes #11063 from sureshthalamati/nullable_jdbc_part_col_spark-13167.
* [SPARK-13598] [SQL] remove LeftSemiJoinBNLDavies Liu2016-03-014-95/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Broadcast left semi join without joining keys is already supported in BroadcastNestedLoopJoin, it has the same implementation as LeftSemiJoinBNL, we should remove that. ## How was this patch tested? Updated unit tests. Author: Davies Liu <davies@databricks.com> Closes #11448 from davies/remove_bnl.
* [SPARK-13582] [SQL] defer dictionary decoding in parquet readerDavies Liu2016-03-0113-203/+207
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR defer the resolution from a id of dictionary to value until the column is actually accessed (inside getInt/getLong), this is very useful for those columns and rows that are filtered out. It's also useful for binary type, we will not need to copy all the byte arrays. This PR also change the underlying type for small decimal that could be fit within a Int, in order to use getInt() to lookup the value from IntDictionary. ## How was this patch tested? Manually test TPCDS Q7 with scale factor 10, saw about 30% improvements (after PR #11274). Author: Davies Liu <davies@databricks.com> Closes #11437 from davies/decode_dict.
* [SPARK-13511] [SQL] Add wholestage codegen for limitLiang-Chi Hsieh2016-03-012-2/+47
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-13511 ## What changes were proposed in this pull request? Current limit operator doesn't support wholestage codegen. This is open to add support for it. In the `doConsume` of `GlobalLimit` and `LocalLimit`, we use a count term to count the processed rows. Once the row numbers catches the limit number, we set the variable `stopEarly` of `BufferedRowIterator` newly added in this pr to `true` that indicates we want to stop processing remaining rows. Then when the wholestage codegen framework checks `shouldStop()`, it will stop the processing of the row iterator. Before this, the executed plan for a query `sqlContext.range(N).limit(100).groupBy().sum()` is: TungstenAggregate(key=[], functions=[(sum(id#5L),mode=Final,isDistinct=false)], output=[sum(id)#6L]) +- TungstenAggregate(key=[], functions=[(sum(id#5L),mode=Partial,isDistinct=false)], output=[sum#9L]) +- GlobalLimit 100 +- Exchange SinglePartition, None +- LocalLimit 100 +- Range 0, 1, 1, 524288000, [id#5L] After add wholestage codegen support: WholeStageCodegen : +- TungstenAggregate(key=[], functions=[(sum(id#40L),mode=Final,isDistinct=false)], output=[sum(id)#41L]) : +- TungstenAggregate(key=[], functions=[(sum(id#40L),mode=Partial,isDistinct=false)], output=[sum#44L]) : +- GlobalLimit 100 : +- INPUT +- Exchange SinglePartition, None +- WholeStageCodegen : +- LocalLimit 100 : +- Range 0, 1, 1, 524288000, [id#40L] ## How was this patch tested? A test is added into BenchmarkWholeStageCodegen. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #11391 from viirya/wholestage-limit.
* [SPARK-13123][SQL] Implement whole state codegen for sortSameer Agarwal2016-02-294-29/+119
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds support for implementing whole state codegen for sort. Builds heaving on nongli 's PR: https://github.com/apache/spark/pull/11008 (which actually implements the feature), and adds the following changes on top: - [x] Generated code updates peak execution memory metrics - [x] Unit tests in `WholeStageCodegenSuite` and `SQLMetricsSuite` ## How was this patch tested? New unit tests in `WholeStageCodegenSuite` and `SQLMetricsSuite`. Further, all existing sort tests should pass. Author: Sameer Agarwal <sameer@databricks.com> Author: Nong Li <nong@databricks.com> Closes #11359 from sameeragarwal/sort-codegen.
* [SPARK-13509][SPARK-13507][SQL] Support for writing CSV with a single ↵hyukjinkwon2016-02-294-10/+26
| | | | | | | | | | | | | | | | | | | | function call https://issues.apache.org/jira/browse/SPARK-13507 https://issues.apache.org/jira/browse/SPARK-13509 ## What changes were proposed in this pull request? This PR adds the support to write CSV data directly by a single call to the given path. Several unitests were added for each functionality. ## How was this patch tested? This was tested with unittests and with `dev/run_tests` for coding style Author: hyukjinkwon <gurwls223@gmail.com> Author: Hyukjin Kwon <gurwls223@gmail.com> Closes #11389 from HyukjinKwon/SPARK-13507-13509.
* [SPARK-13540][SQL] Supports using nested classes within Scala objects as ↵Cheng Lian2016-03-011-0/+10
| | | | | | | | | | | | | | | | | | Dataset element type ## What changes were proposed in this pull request? Nested classes defined within Scala objects are translated into Java static nested classes. Unlike inner classes, they don't need outer scopes. But the analyzer still thinks that an outer scope is required. This PR fixes this issue simply by checking whether a nested class is static before looking up its outer scope. ## How was this patch tested? A test case is added to `DatasetSuite`. It checks contents of a Dataset whose element type is a nested class declared in a Scala object. Author: Cheng Lian <lian@databricks.com> Closes #11421 from liancheng/spark-13540-object-as-outer-scope.
* [SPARK-13309][SQL] Fix type inference issue with CSV dataRahul Tanwani2016-02-284-10/+32
| | | | | | | | Fix type inference issue for sparse CSV data - https://issues.apache.org/jira/browse/SPARK-13309 Author: Rahul Tanwani <rahul@Rahuls-MacBook-Pro.local> Closes #11194 from tanwanirahul/master.
* [SPARK-13537][SQL] Fix readBytes in VectorizedPlainValuesReaderLiang-Chi Hsieh2016-02-281-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-13537 ## What changes were proposed in this pull request? In readBytes of VectorizedPlainValuesReader, we use buffer[offset] to access bytes in buffer. It is incorrect because offset is added with Platform.BYTE_ARRAY_OFFSET when initialization. We should fix it. ## How was this patch tested? `ParquetHadoopFsRelationSuite` sometimes (depending on the randomly generated data) will be [failed](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/52136/consoleFull) by this bug. After applying this, the test can be passed. I added a test to `ParquetHadoopFsRelationSuite` with the data which will fail without this patch. The error exception: [info] ParquetHadoopFsRelationSuite: [info] - test all data types - StringType (440 milliseconds) [info] - test all data types - BinaryType (434 milliseconds) [info] - test all data types - BooleanType (406 milliseconds) 20:59:38.618 ERROR org.apache.spark.executor.Executor: Exception in task 0.0 in stage 2597.0 (TID 67966) java.lang.ArrayIndexOutOfBoundsException: 46 at org.apache.spark.sql.execution.datasources.parquet.VectorizedPlainValuesReader.readBytes(VectorizedPlainValuesReader.java:88) Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #11418 from viirya/fix-readbytes.
* [SPARK-13526][SQL] Move SQLContext per-session states to new classAndrew Or2016-02-274-75/+151
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This creates a `SessionState`, which groups a few fields that existed in `SQLContext`. Because `HiveContext` extends `SQLContext` we also need to make changes there. This is mainly a cleanup task that will soon pave the way for merging the two contexts. ## How was this patch tested? Existing unit tests; this patch introduces no change in behavior. Author: Andrew Or <andrew@databricks.com> Closes #11405 from andrewor14/refactor-session.
* [SPARK-13533][SQL] Fix readBytes in VectorizedPlainValuesReaderNong Li2016-02-271-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix readBytes in VectorizedPlainValuesReader. This fixes a copy and paste issue. ## How was this patch tested? Ran ParquetHadoopFsRelationSuite which failed before this. Author: Nong Li <nong@databricks.com> Closes #11414 from nongli/spark-13533.
* [SPARK-13530][SQL] Add ShortType support to UnsafeRowParquetRecordReaderLiang-Chi Hsieh2016-02-272-1/+36
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-13530 ## What changes were proposed in this pull request? By enabling vectorized parquet scanner by default, the unit test `ParquetHadoopFsRelationSuite` based on `HadoopFsRelationTest` will be failed due to the lack of short type support in `UnsafeRowParquetRecordReader`. We should fix it. The error exception: [info] ParquetHadoopFsRelationSuite: [info] - test all data types - StringType (499 milliseconds) [info] - test all data types - BinaryType (447 milliseconds) [info] - test all data types - BooleanType (520 milliseconds) [info] - test all data types - ByteType (418 milliseconds) 00:22:58.920 ERROR org.apache.spark.executor.Executor: Exception in task 0.0 in stage 124.0 (TID 1949) org.apache.commons.lang.NotImplementedException: Unimplemented type: ShortType at org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader$ColumnReader.readIntBatch(UnsafeRowParquetRecordReader.java:769) at org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader$ColumnReader.readBatch(UnsafeRowParquetRecordReader.java:640) at org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader$ColumnReader.access$000(UnsafeRowParquetRecordReader.java:461) at org.apache.spark.sql.execution.datasources.parquet.UnsafeRowParquetRecordReader.nextBatch(UnsafeRowParquetRecordReader.java:224) ## How was this patch tested? The unit test `ParquetHadoopFsRelationSuite` based on `HadoopFsRelationTest` will be [failed](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/52110/consoleFull) due to the lack of short type support in UnsafeRowParquetRecordReader. By adding this support, the test can be passed. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #11412 from viirya/add-shorttype-support.
* [SPARK-13518][SQL] Enable vectorized parquet scanner by defaultNong Li2016-02-261-4/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Change the default of the flag to enable this feature now that the implementation is complete. ## How was this patch tested? The new parquet reader should be a drop in, so will be exercised by the existing tests. Author: Nong Li <nong@databricks.com> Closes #11397 from nongli/spark-13518.
* [SPARK-13499] [SQL] Performance improvements for parquet reader.Nong Li2016-02-263-54/+59
| | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch includes these performance fixes: - Remove unnecessary setNotNull() calls. The NULL bits are cleared already. - Speed up RLE group decoding - Speed up dictionary decoding by decoding NULLs directly into the result. ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) In addition to the updated benchmarks, on TPCDS, the result of these changes running Q55 (sf40) is: ``` TPCDS: Best/Avg Time(ms) Rate(M/s) Per Row(ns) --------------------------------------------------------------------------------- q55 (Before) 6398 / 6616 18.0 55.5 q55 (After) 4983 / 5189 23.1 43.3 ``` Author: Nong Li <nong@databricks.com> Closes #11375 from nongli/spark-13499.
* [SPARK-12313] [SQL] improve performance of BroadcastNestedLoopJoinDavies Liu2016-02-266-91/+294
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, BroadcastNestedLoopJoin is implemented for worst case, it's too slow, very easy to hang forever. This PR will create fast path for some joinType and buildSide, also improve the worst case (will use much less memory than before). Before this PR, one task requires O(N*K) + O(K) in worst cases, N is number of rows from one partition of streamed table, it could hang the job (because of GC). In order to workaround this for InnerJoin, we have to disable auto-broadcast, switch to CartesianProduct: This could be workaround for InnerJoin, see https://forums.databricks.com/questions/6747/how-do-i-get-a-cartesian-product-of-a-huge-dataset.html In this PR, we will have fast path for these joins : InnerJoin with BuildLeft or BuildRight LeftOuterJoin with BuildRight RightOuterJoin with BuildLeft LeftSemi with BuildRight These fast paths are all stream based (take one pass on streamed table), required O(1) memory. All other join types and build types will take two pass on streamed table, one pass to find the matched rows that includes streamed part, which require O(1) memory, another pass to find the rows from build table that does not have a matched row from streamed table, which required O(K) memory, K is the number rows from build side, one bit per row, should be much smaller than the memory for broadcast. The following join types work in this way: LeftOuterJoin with BuildLeft RightOuterJoin with BuildRight FullOuterJoin with BuildLeft or BuildRight LeftSemi with BuildLeft This PR also added tests for all the join types for BroadcastNestedLoopJoin. After this PR, for InnerJoin with one small table, BroadcastNestedLoopJoin should be faster than CartesianProduct, we don't need that workaround anymore. ## How was the this patch tested? Added unit tests. Author: Davies Liu <davies@databricks.com> Closes #11328 from davies/nested_loop.
* [SPARK-13457][SQL] Removes DataFrame RDD operationsCheng Lian2016-02-276-28/+5
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This is another try of PR #11323. This PR removes DataFrame RDD operations except for `foreach` and `foreachPartitions` (they are actions rather than transformations). Original calls are now replaced by calls to methods of `DataFrame.rdd`. PR #11323 was reverted because it introduced a regression: both `DataFrame.foreach` and `DataFrame.foreachPartitions` wrap underlying RDD operations with `withNewExecutionId` to track Spark jobs. But they are removed in #11323. ## How was the this patch tested? No extra tests are added. Existing tests should do the work. Author: Cheng Lian <lian@databricks.com> Closes #11388 from liancheng/remove-df-rdd-ops.
* [SPARK-13503][SQL] Support to specify the (writing) option for compression ↵hyukjinkwon2016-02-255-27/+57
| | | | | | | | | | | | | | | | | | codec for TEXT ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-13503 This PR makes the TEXT datasource can compress output by option instead of manually setting Hadoop configurations. For reflecting codec by names, it is similar with https://github.com/apache/spark/pull/10805 and https://github.com/apache/spark/pull/10858. ## How was this patch tested? This was tested with unittests and with `dev/run_tests` for coding style Author: hyukjinkwon <gurwls223@gmail.com> Closes #11384 from HyukjinKwon/SPARK-13503.
* [SPARK-13487][SQL] User-facing RuntimeConfig interfaceReynold Xin2016-02-254-1/+261
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch creates the public API for runtime configuration and an implementation for it. The public runtime configuration includes configs for existing SQL, as well as Hadoop Configuration. This new interface is currently dead code. It will be added to SQLContext and a session entry point to Spark when we add that. ## How was this patch tested? a new unit test suite Author: Reynold Xin <rxin@databricks.com> Closes #11378 from rxin/SPARK-13487.
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