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* [SPARK-15214][SQL] Code-generation for GenerateHerman van Hovell2016-11-192-25/+101
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds code generation to `Generate`. It supports two code paths: - General `TraversableOnce` based iteration. This used for regular `Generator` (code generation supporting) expressions. This code path expects the expression to return a `TraversableOnce[InternalRow]` and it will iterate over the returned collection. This PR adds code generation for the `stack` generator. - Specialized `ArrayData/MapData` based iteration. This is used for the `explode`, `posexplode` & `inline` functions and operates directly on the `ArrayData`/`MapData` result that the child of the generator returns. ### Benchmarks I have added some benchmarks and it seems we can create a nice speedup for explode: #### Environment ``` Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.11.6 Intel(R) Core(TM) i7-4980HQ CPU 2.80GHz ``` #### Explode Array ##### Before ``` generate explode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate explode array wholestage off 7377 / 7607 2.3 439.7 1.0X generate explode array wholestage on 6055 / 6086 2.8 360.9 1.2X ``` ##### After ``` generate explode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate explode array wholestage off 7432 / 7696 2.3 443.0 1.0X generate explode array wholestage on 631 / 646 26.6 37.6 11.8X ``` #### Explode Map ##### Before ``` generate explode map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate explode map wholestage off 12792 / 12848 1.3 762.5 1.0X generate explode map wholestage on 11181 / 11237 1.5 666.5 1.1X ``` ##### After ``` generate explode map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate explode map wholestage off 10949 / 10972 1.5 652.6 1.0X generate explode map wholestage on 870 / 913 19.3 51.9 12.6X ``` #### Posexplode ##### Before ``` generate posexplode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate posexplode array wholestage off 7547 / 7580 2.2 449.8 1.0X generate posexplode array wholestage on 5786 / 5838 2.9 344.9 1.3X ``` ##### After ``` generate posexplode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate posexplode array wholestage off 7535 / 7548 2.2 449.1 1.0X generate posexplode array wholestage on 620 / 624 27.1 37.0 12.1X ``` #### Inline ##### Before ``` generate inline array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate inline array wholestage off 6935 / 6978 2.4 413.3 1.0X generate inline array wholestage on 6360 / 6400 2.6 379.1 1.1X ``` ##### After ``` generate inline array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate inline array wholestage off 6940 / 6966 2.4 413.6 1.0X generate inline array wholestage on 1002 / 1012 16.7 59.7 6.9X ``` #### Stack ##### Before ``` generate stack: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate stack wholestage off 12980 / 13104 1.3 773.7 1.0X generate stack wholestage on 11566 / 11580 1.5 689.4 1.1X ``` ##### After ``` generate stack: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate stack wholestage off 12875 / 12949 1.3 767.4 1.0X generate stack wholestage on 840 / 845 20.0 50.0 15.3X ``` ## How was this patch tested? Existing tests. Author: Herman van Hovell <hvanhovell@databricks.com> Author: Herman van Hovell <hvanhovell@questtec.nl> Closes #13065 from hvanhovell/SPARK-15214.
* [SQL] Fix documentation for Concat and ConcatWsReynold Xin2016-11-191-6/+4
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* [SPARK-18508][SQL] Fix documentation error for DateDiffReynold Xin2016-11-191-2/+5
| | | | | | | | | | | | ## What changes were proposed in this pull request? The previous documentation and example for DateDiff was wrong. ## How was this patch tested? Doc only change. Author: Reynold Xin <rxin@databricks.com> Closes #15937 from rxin/datediff-doc.
* [SPARK-18445][BUILD][DOCS] Fix the markdown for `Note:`/`NOTE:`/`Note ↵hyukjinkwon2016-11-192-6/+6
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | that`/`'''Note:'''` across Scala/Java API documentation ## What changes were proposed in this pull request? It seems in Scala/Java, - `Note:` - `NOTE:` - `Note that` - `'''Note:'''` - `note` This PR proposes to fix those to `note` to be consistent. **Before** - Scala ![2016-11-17 6 16 39](https://cloud.githubusercontent.com/assets/6477701/20383180/1a7aed8c-acf2-11e6-9611-5eaf6d52c2e0.png) - Java ![2016-11-17 6 14 41](https://cloud.githubusercontent.com/assets/6477701/20383096/c8ffc680-acf1-11e6-914a-33460bf1401d.png) **After** - Scala ![2016-11-17 6 16 44](https://cloud.githubusercontent.com/assets/6477701/20383167/09940490-acf2-11e6-937a-0d5e1dc2cadf.png) - Java ![2016-11-17 6 13 39](https://cloud.githubusercontent.com/assets/6477701/20383132/e7c2a57e-acf1-11e6-9c47-b849674d4d88.png) ## How was this patch tested? The notes were found via ```bash grep -r "NOTE: " . | \ # Note:|NOTE:|Note that|'''Note:''' grep -v "// NOTE: " | \ # starting with // does not appear in API documentation. grep -E '.scala|.java' | \ # java/scala files grep -v Suite | \ # exclude tests grep -v Test | \ # exclude tests grep -e 'org.apache.spark.api.java' \ # packages appear in API documenation -e 'org.apache.spark.api.java.function' \ # note that this is a regular expression. So actual matches were mostly `org/apache/spark/api/java/functions ...` -e 'org.apache.spark.api.r' \ ... ``` ```bash grep -r "Note that " . | \ # Note:|NOTE:|Note that|'''Note:''' grep -v "// Note that " | \ # starting with // does not appear in API documentation. grep -E '.scala|.java' | \ # java/scala files grep -v Suite | \ # exclude tests grep -v Test | \ # exclude tests grep -e 'org.apache.spark.api.java' \ # packages appear in API documenation -e 'org.apache.spark.api.java.function' \ -e 'org.apache.spark.api.r' \ ... ``` ```bash grep -r "Note: " . | \ # Note:|NOTE:|Note that|'''Note:''' grep -v "// Note: " | \ # starting with // does not appear in API documentation. grep -E '.scala|.java' | \ # java/scala files grep -v Suite | \ # exclude tests grep -v Test | \ # exclude tests grep -e 'org.apache.spark.api.java' \ # packages appear in API documenation -e 'org.apache.spark.api.java.function' \ -e 'org.apache.spark.api.r' \ ... ``` ```bash grep -r "'''Note:'''" . | \ # Note:|NOTE:|Note that|'''Note:''' grep -v "// '''Note:''' " | \ # starting with // does not appear in API documentation. grep -E '.scala|.java' | \ # java/scala files grep -v Suite | \ # exclude tests grep -v Test | \ # exclude tests grep -e 'org.apache.spark.api.java' \ # packages appear in API documenation -e 'org.apache.spark.api.java.function' \ -e 'org.apache.spark.api.r' \ ... ``` And then fixed one by one comparing with API documentation/access modifiers. After that, manually tested via `jekyll build`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #15889 from HyukjinKwon/SPARK-18437.
* [SPARK-18442][SQL] Fix nullability of WrapOption.Takuya UESHIN2016-11-171-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? The nullability of `WrapOption` should be `false`. ## How was this patch tested? Existing tests. Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #15887 from ueshin/issues/SPARK-18442.
* [SPARK-18415][SQL] Weird Plan Output when CTE used in RunnableCommandgatorsmile2016-11-161-0/+8
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? Currently, when CTE is used in RunnableCommand, the Analyzer does not replace the logical node `With`. The child plan of RunnableCommand is not resolved. Thus, the output of the `With` plan node looks very confusing. For example, ``` sql( """ |CREATE VIEW cte_view AS |WITH w AS (SELECT 1 AS n), cte1 (select 2), cte2 as (select 3) |SELECT n FROM w """.stripMargin).explain() ``` The output is like ``` ExecutedCommand +- CreateViewCommand `cte_view`, WITH w AS (SELECT 1 AS n), cte1 (select 2), cte2 as (select 3) SELECT n FROM w, false, false, PersistedView +- 'With [(w,SubqueryAlias w +- Project [1 AS n#16] +- OneRowRelation$ ), (cte1,'SubqueryAlias cte1 +- 'Project [unresolvedalias(2, None)] +- OneRowRelation$ ), (cte2,'SubqueryAlias cte2 +- 'Project [unresolvedalias(3, None)] +- OneRowRelation$ )] +- 'Project ['n] +- 'UnresolvedRelation `w` ``` After the fix, the output is as shown below. ``` ExecutedCommand +- CreateViewCommand `cte_view`, WITH w AS (SELECT 1 AS n), cte1 (select 2), cte2 as (select 3) SELECT n FROM w, false, false, PersistedView +- CTE [w, cte1, cte2] : :- SubqueryAlias w : : +- Project [1 AS n#16] : : +- OneRowRelation$ : :- 'SubqueryAlias cte1 : : +- 'Project [unresolvedalias(2, None)] : : +- OneRowRelation$ : +- 'SubqueryAlias cte2 : +- 'Project [unresolvedalias(3, None)] : +- OneRowRelation$ +- 'Project ['n] +- 'UnresolvedRelation `w` ``` BTW, this PR also fixes the output of the view type. ### How was this patch tested? Manual Author: gatorsmile <gatorsmile@gmail.com> Closes #15854 from gatorsmile/cteName.
* [SPARK-18420][BUILD] Fix the errors caused by lint check in JavaXianyang Liu2016-11-163-3/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Small fix, fix the errors caused by lint check in Java - Clear unused objects and `UnusedImports`. - Add comments around the method `finalize` of `NioBufferedFileInputStream`to turn off checkstyle. - Cut the line which is longer than 100 characters into two lines. ## How was this patch tested? Travis CI. ``` $ build/mvn -T 4 -q -DskipTests -Pyarn -Phadoop-2.3 -Pkinesis-asl -Phive -Phive-thriftserver install $ dev/lint-java ``` Before: ``` Checkstyle checks failed at following occurrences: [ERROR] src/main/java/org/apache/spark/network/util/TransportConf.java:[21,8] (imports) UnusedImports: Unused import - org.apache.commons.crypto.cipher.CryptoCipherFactory. [ERROR] src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java:[516,5] (modifier) RedundantModifier: Redundant 'public' modifier. [ERROR] src/main/java/org/apache/spark/io/NioBufferedFileInputStream.java:[133] (coding) NoFinalizer: Avoid using finalizer method. [ERROR] src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeMapData.java:[71] (sizes) LineLength: Line is longer than 100 characters (found 113). [ERROR] src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java:[112] (sizes) LineLength: Line is longer than 100 characters (found 110). [ERROR] src/test/java/org/apache/spark/sql/catalyst/expressions/HiveHasherSuite.java:[31,17] (modifier) ModifierOrder: 'static' modifier out of order with the JLS suggestions. [ERROR]src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java:[64] (sizes) LineLength: Line is longer than 100 characters (found 103). [ERROR] src/main/java/org/apache/spark/examples/ml/JavaInteractionExample.java:[22,8] (imports) UnusedImports: Unused import - org.apache.spark.ml.linalg.Vectors. [ERROR] src/main/java/org/apache/spark/examples/ml/JavaInteractionExample.java:[51] (regexp) RegexpSingleline: No trailing whitespace allowed. ``` After: ``` $ build/mvn -T 4 -q -DskipTests -Pyarn -Phadoop-2.3 -Pkinesis-asl -Phive -Phive-thriftserver install $ dev/lint-java Using `mvn` from path: /home/travis/build/ConeyLiu/spark/build/apache-maven-3.3.9/bin/mvn Checkstyle checks passed. ``` Author: Xianyang Liu <xyliu0530@icloud.com> Closes #15865 from ConeyLiu/master.
* [SPARK-18433][SQL] Improve DataSource option keys to be more case-insensitiveDongjoon Hyun2016-11-162-2/+40
| | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR aims to improve DataSource option keys to be more case-insensitive DataSource partially use CaseInsensitiveMap in code-path. For example, the following fails to find url. ```scala val df = spark.createDataFrame(sparkContext.parallelize(arr2x2), schema2) df.write.format("jdbc") .option("UrL", url1) .option("dbtable", "TEST.SAVETEST") .options(properties.asScala) .save() ``` This PR makes DataSource options to use CaseInsensitiveMap internally and also makes DataSource to use CaseInsensitiveMap generally except `InMemoryFileIndex` and `InsertIntoHadoopFsRelationCommand`. We can not pass them CaseInsensitiveMap because they creates new case-sensitive HadoopConfs by calling newHadoopConfWithOptions(options) inside. ## How was this patch tested? Pass the Jenkins test with newly added test cases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15884 from dongjoon-hyun/SPARK-18433.
* [SPARK-18377][SQL] warehouse path should be a static confWenchen Fan2016-11-151-8/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? it's weird that every session can set its own warehouse path at runtime, we should forbid it and make it a static conf. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #15825 from cloud-fan/warehouse.
* [SPARK-18300][SQL] Fix scala 2.10 build for FoldablePropagationHerman van Hovell2016-11-151-6/+27
| | | | | | | | | | | | ## What changes were proposed in this pull request? Commit https://github.com/apache/spark/commit/f14ae4900ad0ed66ba36108b7792d56cd6767a69 broke the scala 2.10 build. This PR fixes this by simplifying the used pattern match. ## How was this patch tested? Tested building manually. Ran `build/sbt -Dscala-2.10 -Pscala-2.10 package`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15891 from hvanhovell/SPARK-18300-scala-2.10.
* [SPARK-17732][SQL] ALTER TABLE DROP PARTITION should support comparatorsDongjoon Hyun2016-11-152-9/+27
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR aims to support `comparators`, e.g. '<', '<=', '>', '>=', again in Apache Spark 2.0 for backward compatibility. **Spark 1.6** ``` scala scala> sql("CREATE TABLE sales(id INT) PARTITIONED BY (country STRING, quarter STRING)") res0: org.apache.spark.sql.DataFrame = [result: string] scala> sql("ALTER TABLE sales DROP PARTITION (country < 'KR')") res1: org.apache.spark.sql.DataFrame = [result: string] ``` **Spark 2.0** ``` scala scala> sql("CREATE TABLE sales(id INT) PARTITIONED BY (country STRING, quarter STRING)") res0: org.apache.spark.sql.DataFrame = [] scala> sql("ALTER TABLE sales DROP PARTITION (country < 'KR')") org.apache.spark.sql.catalyst.parser.ParseException: mismatched input '<' expecting {')', ','}(line 1, pos 42) ``` After this PR, it's supported. ## How was this patch tested? Pass the Jenkins test with a newly added testcase. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15704 from dongjoon-hyun/SPARK-17732-2.
* [SPARK-18300][SQL] Do not apply foldable propagation with expand as a child.Herman van Hovell2016-11-152-30/+55
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The `FoldablePropagation` optimizer rule, pulls foldable values out from under an `Expand`. This breaks the `Expand` in two ways: - It rewrites the output attributes of the `Expand`. We explicitly define output attributes for `Expand`, these are (unfortunately) considered as part of the expressions of the `Expand` and can be rewritten. - Expand can actually change the column (it will typically re-use the attributes or the underlying plan). This means that we cannot safely propagate the expressions from under an `Expand`. This PR fixes this and (hopefully) other issues by explicitly whitelisting allowed operators. ## How was this patch tested? Added tests to `FoldablePropagationSuite` and to `SQLQueryTestSuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15857 from hvanhovell/SPARK-18300.
* [SPARK-18430][SQL] Fixed Exception Messages when Hitting an Invocation ↵gatorsmile2016-11-141-1/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Exception of Function Lookup ### What changes were proposed in this pull request? When the exception is an invocation exception during function lookup, we return a useless/confusing error message: For example, ```Scala df.selectExpr("concat_ws()") ``` Below is the error message we got: ``` null; line 1 pos 0 org.apache.spark.sql.AnalysisException: null; line 1 pos 0 ``` To get the meaningful error message, we need to get the cause. The fix is exactly the same as what we did in https://github.com/apache/spark/pull/12136. After the fix, the message we got is the exception issued in the constuctor of function implementation: ``` requirement failed: concat_ws requires at least one argument.; line 1 pos 0 org.apache.spark.sql.AnalysisException: requirement failed: concat_ws requires at least one argument.; line 1 pos 0 ``` ### How was this patch tested? Added test cases. Author: gatorsmile <gatorsmile@gmail.com> Closes #15878 from gatorsmile/functionNotFound.
* [SPARK-18124] Observed delay based Event Time WatermarksMichael Armbrust2016-11-147-7/+103
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR adds a new method `withWatermark` to the `Dataset` API, which can be used specify an _event time watermark_. An event time watermark allows the streaming engine to reason about the point in time after which we no longer expect to see late data. This PR also has augmented `StreamExecution` to use this watermark for several purposes: - To know when a given time window aggregation is finalized and thus results can be emitted when using output modes that do not allow updates (e.g. `Append` mode). - To minimize the amount of state that we need to keep for on-going aggregations, by evicting state for groups that are no longer expected to change. Although, we do still maintain all state if the query requires (i.e. if the event time is not present in the `groupBy` or when running in `Complete` mode). An example that emits windowed counts of records, waiting up to 5 minutes for late data to arrive. ```scala df.withWatermark("eventTime", "5 minutes") .groupBy(window($"eventTime", "1 minute") as 'window) .count() .writeStream .format("console") .mode("append") // In append mode, we only output finalized aggregations. .start() ``` ### Calculating the watermark. The current event time is computed by looking at the `MAX(eventTime)` seen this epoch across all of the partitions in the query minus some user defined _delayThreshold_. An additional constraint is that the watermark must increase monotonically. Note that since we must coordinate this value across partitions occasionally, the actual watermark used is only guaranteed to be at least `delay` behind the actual event time. In some cases we may still process records that arrive more than delay late. This mechanism was chosen for the initial implementation over processing time for two reasons: - it is robust to downtime that could affect processing delay - it does not require syncing of time or timezones between the producer and the processing engine. ### Other notable implementation details - A new trigger metric `eventTimeWatermark` outputs the current value of the watermark. - We mark the event time column in the `Attribute` metadata using the key `spark.watermarkDelay`. This allows downstream operations to know which column holds the event time. Operations like `window` propagate this metadata. - `explain()` marks the watermark with a suffix of `-T${delayMs}` to ease debugging of how this information is propagated. - Currently, we don't filter out late records, but instead rely on the state store to avoid emitting records that are both added and filtered in the same epoch. ### Remaining in this PR - [ ] The test for recovery is currently failing as we don't record the watermark used in the offset log. We will need to do so to ensure determinism, but this is deferred until #15626 is merged. ### Other follow-ups There are some natural additional features that we should consider for future work: - Ability to write records that arrive too late to some external store in case any out-of-band remediation is required. - `Update` mode so you can get partial results before a group is evicted. - Other mechanisms for calculating the watermark. In particular a watermark based on quantiles would be more robust to outliers. Author: Michael Armbrust <michael@databricks.com> Closes #15702 from marmbrus/watermarks.
* [SPARK-17348][SQL] Incorrect results from subquery transformationNattavut Sutyanyong2016-11-142-7/+44
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Return an Analysis exception when there is a correlated non-equality predicate in a subquery and the correlated column from the outer reference is not from the immediate parent operator of the subquery. This PR prevents incorrect results from subquery transformation in such case. Test cases, both positive and negative tests, are added. ## How was this patch tested? sql/test, catalyst/test, hive/test, and scenarios that will produce incorrect results without this PR and product correct results when subquery transformation does happen. Author: Nattavut Sutyanyong <nsy.can@gmail.com> Closes #15763 from nsyca/spark-17348.
* [SPARK-18387][SQL] Add serialization to checkEvaluation.Ryan Blue2016-11-114-41/+36
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This removes the serialization test from RegexpExpressionsSuite and replaces it by serializing all expressions in checkEvaluation. This also fixes math constant expressions by making LeafMathExpression Serializable and fixes NumberFormat values that are null or invalid after serialization. ## How was this patch tested? This patch is to tests. Author: Ryan Blue <blue@apache.org> Closes #15847 from rdblue/SPARK-18387-fix-serializable-expressions.
* [SPARK-18185] Fix all forms of INSERT / OVERWRITE TABLE for Datasource tablesEric Liang2016-11-103-14/+12
| | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? As of current 2.1, INSERT OVERWRITE with dynamic partitions against a Datasource table will overwrite the entire table instead of only the partitions matching the static keys, as in Hive. It also doesn't respect custom partition locations. This PR adds support for all these operations to Datasource tables managed by the Hive metastore. It is implemented as follows - During planning time, the full set of partitions affected by an INSERT or OVERWRITE command is read from the Hive metastore. - The planner identifies any partitions with custom locations and includes this in the write task metadata. - FileFormatWriter tasks refer to this custom locations map when determining where to write for dynamic partition output. - When the write job finishes, the set of written partitions is compared against the initial set of matched partitions, and the Hive metastore is updated to reflect the newly added / removed partitions. It was necessary to introduce a method for staging files with absolute output paths to `FileCommitProtocol`. These files are not handled by the Hadoop output committer but are moved to their final locations when the job commits. The overwrite behavior of legacy Datasource tables is also changed: no longer will the entire table be overwritten if a partial partition spec is present. cc cloud-fan yhuai ## How was this patch tested? Unit tests, existing tests. Author: Eric Liang <ekl@databricks.com> Author: Wenchen Fan <wenchen@databricks.com> Closes #15814 from ericl/sc-5027.
* [SPARK-17990][SPARK-18302][SQL] correct several partition related behaviours ↵Wenchen Fan2016-11-105-84/+314
| | | | | | | | | | | | | | | | | | | | | | | of ExternalCatalog ## What changes were proposed in this pull request? This PR corrects several partition related behaviors of `ExternalCatalog`: 1. default partition location should not always lower case the partition column names in path string(fix `HiveExternalCatalog`) 2. rename partition should not always lower case the partition column names in updated partition path string(fix `HiveExternalCatalog`) 3. rename partition should update the partition location only for managed table(fix `InMemoryCatalog`) 4. create partition with existing directory should be fine(fix `InMemoryCatalog`) 5. create partition with non-existing directory should create that directory(fix `InMemoryCatalog`) 6. drop partition from external table should not delete the directory(fix `InMemoryCatalog`) ## How was this patch tested? new tests in `ExternalCatalogSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #15797 from cloud-fan/partition.
* [SPARK-18368][SQL] Fix regexp replace when serializedRyan Blue2016-11-092-2/+16
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This makes the result value both transient and lazy, so that if the RegExpReplace object is initialized then serialized, `result: StringBuffer` will be correctly initialized. ## How was this patch tested? * Verified that this patch fixed the query that found the bug. * Added a test case that fails without the fix. Author: Ryan Blue <blue@apache.org> Closes #15834 from rdblue/SPARK-18368-fix-regexp-replace.
* Revert "[SPARK-18368] Fix regexp_replace with task serialization."Yin Huai2016-11-092-10/+7
| | | | This reverts commit b9192bb3ffc319ebee7dbd15c24656795e454749.
* [SPARK-18368] Fix regexp_replace with task serialization.Ryan Blue2016-11-082-7/+10
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This makes the result value both transient and lazy, so that if the RegExpReplace object is initialized then serialized, `result: StringBuffer` will be correctly initialized. ## How was this patch tested? * Verified that this patch fixed the query that found the bug. * Added a test case that fails without the fix. Author: Ryan Blue <blue@apache.org> Closes #15816 from rdblue/SPARK-18368-fix-regexp-replace.
* [SPARK-17868][SQL] Do not use bitmasks during parsing and analysis of ↵jiangxingbo2016-11-085-137/+474
| | | | | | | | | | | | | | | | | | | | CUBE/ROLLUP/GROUPING SETS ## What changes were proposed in this pull request? We generate bitmasks for grouping sets during the parsing process, and use these during analysis. These bitmasks are difficult to work with in practice and have lead to numerous bugs. This PR removes these and use actual sets instead, however we still need to generate these offsets for the grouping_id. This PR does the following works: 1. Replace bitmasks by actual grouping sets durning Parsing/Analysis stage of CUBE/ROLLUP/GROUPING SETS; 2. Add new testsuite `ResolveGroupingAnalyticsSuite` to test the `Analyzer.ResolveGroupingAnalytics` rule directly; 3. Fix a minor bug in `ResolveGroupingAnalytics`. ## How was this patch tested? By existing test cases, and add new testsuite `ResolveGroupingAnalyticsSuite` to test directly. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #15484 from jiangxb1987/group-set.
* [SPARK-18137][SQL] Fix RewriteDistinctAggregates UnresolvedException when a ↵root2016-11-081-9/+26
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | UDAF has a foldable TypeCheck ## What changes were proposed in this pull request? In RewriteDistinctAggregates rewrite funtion,after the UDAF's childs are mapped to AttributeRefference, If the UDAF(such as ApproximatePercentile) has a foldable TypeCheck for the input, It will failed because the AttributeRefference is not foldable,then the UDAF is not resolved, and then nullify on the unresolved object will throw a Exception. In this PR, only map Unfoldable child to AttributeRefference, this can avoid the UDAF's foldable TypeCheck. and then only Expand Unfoldable child, there is no need to Expand a static value(foldable value). **Before sql result** > select percentile_approxy(key,0.99999),count(distinct key),sume(distinc key) from src limit 1 > org.apache.spark.sql.catalyst.analysis.UnresolvedException: Invalid call to dataType on unresolved object, tree: 'percentile_approx(CAST(src.`key` AS DOUBLE), CAST(0.99999BD AS DOUBLE), 10000) > at org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute.dataType(unresolved.scala:92) > at org.apache.spark.sql.catalyst.optimizer.RewriteDistinctAggregates$.org$apache$spark$sql$catalyst$optimizer$RewriteDistinctAggregates$$nullify(RewriteDistinctAggregates.scala:261) **After sql result** > select percentile_approxy(key,0.99999),count(distinct key),sume(distinc key) from src limit 1 > [498.0,309,79136] ## How was this patch tested? Add a test case in HiveUDFSuit. Author: root <root@iZbp1gsnrlfzjxh82cz80vZ.(none)> Closes #15668 from windpiger/RewriteDistinctUDAFUnresolveExcep.
* [SPARK-18207][SQL] Fix a compilation error due to HashExpression.doGenCodeKazuaki Ishizaki2016-11-082-7/+33
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR avoids a compilation error due to more than 64KB Java byte code size. This error occur since generate java code for computing a hash value for a row is too big. This PR fixes this compilation error by splitting a big code chunk into multiple methods by calling `CodegenContext.splitExpression` at `HashExpression.doGenCode` The test case requires a calculation of hash code for a row that includes 1000 String fields. `HashExpression.doGenCode` generate a lot of Java code for this computation into one function. As a result, the size of the corresponding Java bytecode is more than 64 KB. Generated code without this PR ````java /* 027 */ public UnsafeRow apply(InternalRow i) { /* 028 */ boolean isNull = false; /* 029 */ /* 030 */ int value1 = 42; /* 031 */ /* 032 */ boolean isNull2 = i.isNullAt(0); /* 033 */ UTF8String value2 = isNull2 ? null : (i.getUTF8String(0)); /* 034 */ if (!isNull2) { /* 035 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value2.getBaseObject(), value2.getBaseOffset(), value2.numBytes(), value1); /* 036 */ } /* 037 */ /* 038 */ /* 039 */ boolean isNull3 = i.isNullAt(1); /* 040 */ UTF8String value3 = isNull3 ? null : (i.getUTF8String(1)); /* 041 */ if (!isNull3) { /* 042 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value3.getBaseObject(), value3.getBaseOffset(), value3.numBytes(), value1); /* 043 */ } /* 044 */ /* 045 */ ... /* 7024 */ /* 7025 */ boolean isNull1001 = i.isNullAt(999); /* 7026 */ UTF8String value1001 = isNull1001 ? null : (i.getUTF8String(999)); /* 7027 */ if (!isNull1001) { /* 7028 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value1001.getBaseObject(), value1001.getBaseOffset(), value1001.numBytes(), value1); /* 7029 */ } /* 7030 */ /* 7031 */ /* 7032 */ boolean isNull1002 = i.isNullAt(1000); /* 7033 */ UTF8String value1002 = isNull1002 ? null : (i.getUTF8String(1000)); /* 7034 */ if (!isNull1002) { /* 7035 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value1002.getBaseObject(), value1002.getBaseOffset(), value1002.numBytes(), value1); /* 7036 */ } ```` Generated code with this PR ````java /* 3807 */ private void apply_249(InternalRow i) { /* 3808 */ /* 3809 */ boolean isNull998 = i.isNullAt(996); /* 3810 */ UTF8String value998 = isNull998 ? null : (i.getUTF8String(996)); /* 3811 */ if (!isNull998) { /* 3812 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value998.getBaseObject(), value998.getBaseOffset(), value998.numBytes(), value1); /* 3813 */ } /* 3814 */ /* 3815 */ boolean isNull999 = i.isNullAt(997); /* 3816 */ UTF8String value999 = isNull999 ? null : (i.getUTF8String(997)); /* 3817 */ if (!isNull999) { /* 3818 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value999.getBaseObject(), value999.getBaseOffset(), value999.numBytes(), value1); /* 3819 */ } /* 3820 */ /* 3821 */ boolean isNull1000 = i.isNullAt(998); /* 3822 */ UTF8String value1000 = isNull1000 ? null : (i.getUTF8String(998)); /* 3823 */ if (!isNull1000) { /* 3824 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value1000.getBaseObject(), value1000.getBaseOffset(), value1000.numBytes(), value1); /* 3825 */ } /* 3826 */ /* 3827 */ boolean isNull1001 = i.isNullAt(999); /* 3828 */ UTF8String value1001 = isNull1001 ? null : (i.getUTF8String(999)); /* 3829 */ if (!isNull1001) { /* 3830 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value1001.getBaseObject(), value1001.getBaseOffset(), value1001.numBytes(), value1); /* 3831 */ } /* 3832 */ /* 3833 */ } /* 3834 */ ... /* 4532 */ private void apply_0(InternalRow i) { /* 4533 */ /* 4534 */ boolean isNull2 = i.isNullAt(0); /* 4535 */ UTF8String value2 = isNull2 ? null : (i.getUTF8String(0)); /* 4536 */ if (!isNull2) { /* 4537 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value2.getBaseObject(), value2.getBaseOffset(), value2.numBytes(), value1); /* 4538 */ } /* 4539 */ /* 4540 */ boolean isNull3 = i.isNullAt(1); /* 4541 */ UTF8String value3 = isNull3 ? null : (i.getUTF8String(1)); /* 4542 */ if (!isNull3) { /* 4543 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value3.getBaseObject(), value3.getBaseOffset(), value3.numBytes(), value1); /* 4544 */ } /* 4545 */ /* 4546 */ boolean isNull4 = i.isNullAt(2); /* 4547 */ UTF8String value4 = isNull4 ? null : (i.getUTF8String(2)); /* 4548 */ if (!isNull4) { /* 4549 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value4.getBaseObject(), value4.getBaseOffset(), value4.numBytes(), value1); /* 4550 */ } /* 4551 */ /* 4552 */ boolean isNull5 = i.isNullAt(3); /* 4553 */ UTF8String value5 = isNull5 ? null : (i.getUTF8String(3)); /* 4554 */ if (!isNull5) { /* 4555 */ value1 = org.apache.spark.unsafe.hash.Murmur3_x86_32.hashUnsafeBytes(value5.getBaseObject(), value5.getBaseOffset(), value5.numBytes(), value1); /* 4556 */ } /* 4557 */ /* 4558 */ } ... /* 7344 */ public UnsafeRow apply(InternalRow i) { /* 7345 */ boolean isNull = false; /* 7346 */ /* 7347 */ value1 = 42; /* 7348 */ apply_0(i); /* 7349 */ apply_1(i); ... /* 7596 */ apply_248(i); /* 7597 */ apply_249(i); /* 7598 */ apply_250(i); /* 7599 */ apply_251(i); ... ```` ## How was this patch tested? Add a new test in `DataFrameSuite` Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Closes #15745 from kiszk/SPARK-18207.
* [SPARK-18217][SQL] Disallow creating permanent views based on temporary ↵gatorsmile2016-11-072-0/+46
| | | | | | | | | | | | | | | | | | | | | | | | | | views or UDFs ### What changes were proposed in this pull request? Based on the discussion in [SPARK-18209](https://issues.apache.org/jira/browse/SPARK-18209). It doesn't really make sense to create permanent views based on temporary views or temporary UDFs. To disallow the supports and issue the exceptions, this PR needs to detect whether a temporary view/UDF is being used when defining a permanent view. Basically, this PR can be split to two sub-tasks: **Task 1:** detecting a temporary view from the query plan of view definition. When finding an unresolved temporary view, Analyzer replaces it by a `SubqueryAlias` with the corresponding logical plan, which is stored in an in-memory HashMap. After replacement, it is impossible to detect whether the `SubqueryAlias` is added/generated from a temporary view. Thus, to detect the usage of a temporary view in view definition, this PR traverses the unresolved logical plan and uses the name of an `UnresolvedRelation` to detect whether it is a (global) temporary view. **Task 2:** detecting a temporary UDF from the query plan of view definition. Detecting usage of a temporary UDF in view definition is not straightfoward. First, in the analyzed plan, we are having different forms to represent the functions. More importantly, some classes (e.g., `HiveGenericUDF`) are not accessible from `CreateViewCommand`, which is part of `sql/core`. Thus, we used the unanalyzed plan `child` of `CreateViewCommand` to detect the usage of a temporary UDF. Because the plan has already been successfully analyzed, we can assume the functions have been defined/registered. Second, in Spark, the functions have four forms: Spark built-in functions, built-in hash functions, permanent UDFs and temporary UDFs. We do not have any direct way to determine whether a function is temporary or not. Thus, we introduced a function `isTemporaryFunction` in `SessionCatalog`. This function contains the detailed logics to determine whether a function is temporary or not. ### How was this patch tested? Added test cases. Author: gatorsmile <gatorsmile@gmail.com> Closes #15764 from gatorsmile/blockTempFromPermViewCreation.
* [SPARK-18295][SQL] Make to_json function null safe (matching it to from_json)hyukjinkwon2016-11-072-11/+16
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR proposes to match up the behaviour of `to_json` to `from_json` function for null-safety. Currently, it throws `NullPointException` but this PR fixes this to produce `null` instead. with the data below: ```scala import spark.implicits._ val df = Seq(Some(Tuple1(Tuple1(1))), None).toDF("a") df.show() ``` ``` +----+ | a| +----+ | [1]| |null| +----+ ``` the codes below ```scala import org.apache.spark.sql.functions._ df.select(to_json($"a")).show() ``` produces.. **Before** throws `NullPointException` as below: ``` java.lang.NullPointerException at org.apache.spark.sql.catalyst.json.JacksonGenerator.org$apache$spark$sql$catalyst$json$JacksonGenerator$$writeFields(JacksonGenerator.scala:138) at org.apache.spark.sql.catalyst.json.JacksonGenerator$$anonfun$write$1.apply$mcV$sp(JacksonGenerator.scala:194) at org.apache.spark.sql.catalyst.json.JacksonGenerator.org$apache$spark$sql$catalyst$json$JacksonGenerator$$writeObject(JacksonGenerator.scala:131) at org.apache.spark.sql.catalyst.json.JacksonGenerator.write(JacksonGenerator.scala:193) at org.apache.spark.sql.catalyst.expressions.StructToJson.eval(jsonExpressions.scala:544) at org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:142) at org.apache.spark.sql.catalyst.expressions.InterpretedProjection.apply(Projection.scala:48) at org.apache.spark.sql.catalyst.expressions.InterpretedProjection.apply(Projection.scala:30) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) ``` **After** ``` +---------------+ |structtojson(a)| +---------------+ | {"_1":1}| | null| +---------------+ ``` ## How was this patch tested? Unit test in `JsonExpressionsSuite.scala` and `JsonFunctionsSuite.scala`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #15792 from HyukjinKwon/SPARK-18295.
* [SPARK-17490][SQL] Optimize SerializeFromObject() for a primitive arrayKazuaki Ishizaki2016-11-085-14/+103
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Waiting for merging #13680 This PR optimizes `SerializeFromObject()` for an primitive array. This is derived from #13758 to address one of problems by using a simple way in #13758. The current implementation always generates `GenericArrayData` from `SerializeFromObject()` for any type of an array in a logical plan. This involves a boxing at a constructor of `GenericArrayData` when `SerializedFromObject()` has an primitive array. This PR enables to generate `UnsafeArrayData` from `SerializeFromObject()` for a primitive array. It can avoid boxing to create an instance of `ArrayData` in the generated code by Catalyst. This PR also generate `UnsafeArrayData` in a case for `RowEncoder.serializeFor` or `CatalystTypeConverters.createToCatalystConverter`. Performance improvement of `SerializeFromObject()` is up to 2.0x ``` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.4.11-200.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without this PR Write an array in Dataset: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 556 / 608 15.1 66.3 1.0X Double 1668 / 1746 5.0 198.8 0.3X with this PR Write an array in Dataset: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 352 / 401 23.8 42.0 1.0X Double 821 / 885 10.2 97.9 0.4X ``` Here is an example program that will happen in mllib as described in [SPARK-16070](https://issues.apache.org/jira/browse/SPARK-16070). ``` sparkContext.parallelize(Seq(Array(1, 2)), 1).toDS.map(e => e).show ``` Generated code before applying this PR ``` java /* 039 */ protected void processNext() throws java.io.IOException { /* 040 */ while (inputadapter_input.hasNext()) { /* 041 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next(); /* 042 */ int[] inputadapter_value = (int[])inputadapter_row.get(0, null); /* 043 */ /* 044 */ Object mapelements_obj = ((Expression) references[0]).eval(null); /* 045 */ scala.Function1 mapelements_value1 = (scala.Function1) mapelements_obj; /* 046 */ /* 047 */ boolean mapelements_isNull = false || false; /* 048 */ int[] mapelements_value = null; /* 049 */ if (!mapelements_isNull) { /* 050 */ Object mapelements_funcResult = null; /* 051 */ mapelements_funcResult = mapelements_value1.apply(inputadapter_value); /* 052 */ if (mapelements_funcResult == null) { /* 053 */ mapelements_isNull = true; /* 054 */ } else { /* 055 */ mapelements_value = (int[]) mapelements_funcResult; /* 056 */ } /* 057 */ /* 058 */ } /* 059 */ mapelements_isNull = mapelements_value == null; /* 060 */ /* 061 */ serializefromobject_argIsNulls[0] = mapelements_isNull; /* 062 */ serializefromobject_argValue = mapelements_value; /* 063 */ /* 064 */ boolean serializefromobject_isNull = false; /* 065 */ for (int idx = 0; idx < 1; idx++) { /* 066 */ if (serializefromobject_argIsNulls[idx]) { serializefromobject_isNull = true; break; } /* 067 */ } /* 068 */ /* 069 */ final ArrayData serializefromobject_value = serializefromobject_isNull ? null : new org.apache.spark.sql.catalyst.util.GenericArrayData(serializefromobject_argValue); /* 070 */ serializefromobject_holder.reset(); /* 071 */ /* 072 */ serializefromobject_rowWriter.zeroOutNullBytes(); /* 073 */ /* 074 */ if (serializefromobject_isNull) { /* 075 */ serializefromobject_rowWriter.setNullAt(0); /* 076 */ } else { /* 077 */ // Remember the current cursor so that we can calculate how many bytes are /* 078 */ // written later. /* 079 */ final int serializefromobject_tmpCursor = serializefromobject_holder.cursor; /* 080 */ /* 081 */ if (serializefromobject_value instanceof UnsafeArrayData) { /* 082 */ final int serializefromobject_sizeInBytes = ((UnsafeArrayData) serializefromobject_value).getSizeInBytes(); /* 083 */ // grow the global buffer before writing data. /* 084 */ serializefromobject_holder.grow(serializefromobject_sizeInBytes); /* 085 */ ((UnsafeArrayData) serializefromobject_value).writeToMemory(serializefromobject_holder.buffer, serializefromobject_holder.cursor); /* 086 */ serializefromobject_holder.cursor += serializefromobject_sizeInBytes; /* 087 */ /* 088 */ } else { /* 089 */ final int serializefromobject_numElements = serializefromobject_value.numElements(); /* 090 */ serializefromobject_arrayWriter.initialize(serializefromobject_holder, serializefromobject_numElements, 4); /* 091 */ /* 092 */ for (int serializefromobject_index = 0; serializefromobject_index < serializefromobject_numElements; serializefromobject_index++) { /* 093 */ if (serializefromobject_value.isNullAt(serializefromobject_index)) { /* 094 */ serializefromobject_arrayWriter.setNullInt(serializefromobject_index); /* 095 */ } else { /* 096 */ final int serializefromobject_element = serializefromobject_value.getInt(serializefromobject_index); /* 097 */ serializefromobject_arrayWriter.write(serializefromobject_index, serializefromobject_element); /* 098 */ } /* 099 */ } /* 100 */ } /* 101 */ /* 102 */ serializefromobject_rowWriter.setOffsetAndSize(0, serializefromobject_tmpCursor, serializefromobject_holder.cursor - serializefromobject_tmpCursor); /* 103 */ } /* 104 */ serializefromobject_result.setTotalSize(serializefromobject_holder.totalSize()); /* 105 */ append(serializefromobject_result); /* 106 */ if (shouldStop()) return; /* 107 */ } /* 108 */ } /* 109 */ } ``` Generated code after applying this PR ``` java /* 035 */ protected void processNext() throws java.io.IOException { /* 036 */ while (inputadapter_input.hasNext()) { /* 037 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next(); /* 038 */ int[] inputadapter_value = (int[])inputadapter_row.get(0, null); /* 039 */ /* 040 */ Object mapelements_obj = ((Expression) references[0]).eval(null); /* 041 */ scala.Function1 mapelements_value1 = (scala.Function1) mapelements_obj; /* 042 */ /* 043 */ boolean mapelements_isNull = false || false; /* 044 */ int[] mapelements_value = null; /* 045 */ if (!mapelements_isNull) { /* 046 */ Object mapelements_funcResult = null; /* 047 */ mapelements_funcResult = mapelements_value1.apply(inputadapter_value); /* 048 */ if (mapelements_funcResult == null) { /* 049 */ mapelements_isNull = true; /* 050 */ } else { /* 051 */ mapelements_value = (int[]) mapelements_funcResult; /* 052 */ } /* 053 */ /* 054 */ } /* 055 */ mapelements_isNull = mapelements_value == null; /* 056 */ /* 057 */ boolean serializefromobject_isNull = mapelements_isNull; /* 058 */ final ArrayData serializefromobject_value = serializefromobject_isNull ? null : org.apache.spark.sql.catalyst.expressions.UnsafeArrayData.fromPrimitiveArray(mapelements_value); /* 059 */ serializefromobject_isNull = serializefromobject_value == null; /* 060 */ serializefromobject_holder.reset(); /* 061 */ /* 062 */ serializefromobject_rowWriter.zeroOutNullBytes(); /* 063 */ /* 064 */ if (serializefromobject_isNull) { /* 065 */ serializefromobject_rowWriter.setNullAt(0); /* 066 */ } else { /* 067 */ // Remember the current cursor so that we can calculate how many bytes are /* 068 */ // written later. /* 069 */ final int serializefromobject_tmpCursor = serializefromobject_holder.cursor; /* 070 */ /* 071 */ if (serializefromobject_value instanceof UnsafeArrayData) { /* 072 */ final int serializefromobject_sizeInBytes = ((UnsafeArrayData) serializefromobject_value).getSizeInBytes(); /* 073 */ // grow the global buffer before writing data. /* 074 */ serializefromobject_holder.grow(serializefromobject_sizeInBytes); /* 075 */ ((UnsafeArrayData) serializefromobject_value).writeToMemory(serializefromobject_holder.buffer, serializefromobject_holder.cursor); /* 076 */ serializefromobject_holder.cursor += serializefromobject_sizeInBytes; /* 077 */ /* 078 */ } else { /* 079 */ final int serializefromobject_numElements = serializefromobject_value.numElements(); /* 080 */ serializefromobject_arrayWriter.initialize(serializefromobject_holder, serializefromobject_numElements, 4); /* 081 */ /* 082 */ for (int serializefromobject_index = 0; serializefromobject_index < serializefromobject_numElements; serializefromobject_index++) { /* 083 */ if (serializefromobject_value.isNullAt(serializefromobject_index)) { /* 084 */ serializefromobject_arrayWriter.setNullInt(serializefromobject_index); /* 085 */ } else { /* 086 */ final int serializefromobject_element = serializefromobject_value.getInt(serializefromobject_index); /* 087 */ serializefromobject_arrayWriter.write(serializefromobject_index, serializefromobject_element); /* 088 */ } /* 089 */ } /* 090 */ } /* 091 */ /* 092 */ serializefromobject_rowWriter.setOffsetAndSize(0, serializefromobject_tmpCursor, serializefromobject_holder.cursor - serializefromobject_tmpCursor); /* 093 */ } /* 094 */ serializefromobject_result.setTotalSize(serializefromobject_holder.totalSize()); /* 095 */ append(serializefromobject_result); /* 096 */ if (shouldStop()) return; /* 097 */ } /* 098 */ } /* 099 */ } ``` ## How was this patch tested? Added a test in `DatasetSuite`, `RowEncoderSuite`, and `CatalystTypeConvertersSuite` Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Closes #15044 from kiszk/SPARK-17490.
* [SPARK-17108][SQL] Fix BIGINT and INT comparison failure in spark sqlWeiqing Yang2016-11-071-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add a function to check if two integers are compatible when invoking `acceptsType()` in `DataType`. ## How was this patch tested? Manually. E.g. ``` spark.sql("create table t3(a map<bigint, array<string>>)") spark.sql("select * from t3 where a[1] is not null") ``` Before: ``` cannot resolve 't.`a`[1]' due to data type mismatch: argument 2 requires bigint type, however, '1' is of int type.; line 1 pos 22 org.apache.spark.sql.AnalysisException: cannot resolve 't.`a`[1]' due to data type mismatch: argument 2 requires bigint type, however, '1' is of int type.; line 1 pos 22 at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42) at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:82) at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:307) ``` After: Run the sql queries above. No errors. Author: Weiqing Yang <yangweiqing001@gmail.com> Closes #15448 from weiqingy/SPARK_17108.
* [SPARK-18125][SQL] Fix a compilation error in codegen due to splitExpressionLiang-Chi Hsieh2016-11-071-6/+21
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? As reported in the jira, sometimes the generated java code in codegen will cause compilation error. Code snippet to test it: case class Route(src: String, dest: String, cost: Int) case class GroupedRoutes(src: String, dest: String, routes: Seq[Route]) val ds = sc.parallelize(Array( Route("a", "b", 1), Route("a", "b", 2), Route("a", "c", 2), Route("a", "d", 10), Route("b", "a", 1), Route("b", "a", 5), Route("b", "c", 6)) ).toDF.as[Route] val grped = ds.map(r => GroupedRoutes(r.src, r.dest, Seq(r))) .groupByKey(r => (r.src, r.dest)) .reduceGroups { (g1: GroupedRoutes, g2: GroupedRoutes) => GroupedRoutes(g1.src, g1.dest, g1.routes ++ g2.routes) }.map(_._2) The problem here is, in `ReferenceToExpressions` we evaluate the children vars to local variables. Then the result expression is evaluated to use those children variables. In the above case, the result expression code is too long and will be split by `CodegenContext.splitExpression`. So those local variables cannot be accessed and cause compilation error. ## How was this patch tested? Jenkins tests. Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #15693 from viirya/fix-codege-compilation-error.
* [SPARK-18296][SQL] Use consistent naming for expression test suitesReynold Xin2016-11-065-6/+5
| | | | | | | | | | | | ## What changes were proposed in this pull request? We have an undocumented naming convention to call expression unit tests ExpressionsSuite, and the end-to-end tests FunctionsSuite. It'd be great to make all test suites consistent with this naming convention. ## How was this patch tested? This is a test-only naming change. Author: Reynold Xin <rxin@databricks.com> Closes #15793 from rxin/SPARK-18296.
* [SPARK-18173][SQL] data source tables should support truncating partitionWenchen Fan2016-11-062-4/+30
| | | | | | | | | | | | | ## What changes were proposed in this pull request? Previously `TRUNCATE TABLE ... PARTITION` will always truncate the whole table for data source tables, this PR fixes it and improve `InMemoryCatalog` to make this command work with it. ## How was this patch tested? existing tests Author: Wenchen Fan <wenchen@databricks.com> Closes #15688 from cloud-fan/truncate.
* [SPARK-17854][SQL] rand/randn allows null/long as input seedhyukjinkwon2016-11-062-22/+34
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR proposes `rand`/`randn` accept `null` as input in Scala/SQL and `LongType` as input in SQL. In this case, it treats the values as `0`. So, this PR includes both changes below: - `null` support It seems MySQL also accepts this. ``` sql mysql> select rand(0); +---------------------+ | rand(0) | +---------------------+ | 0.15522042769493574 | +---------------------+ 1 row in set (0.00 sec) mysql> select rand(NULL); +---------------------+ | rand(NULL) | +---------------------+ | 0.15522042769493574 | +---------------------+ 1 row in set (0.00 sec) ``` and also Hive does according to [HIVE-14694](https://issues.apache.org/jira/browse/HIVE-14694) So the codes below: ``` scala spark.range(1).selectExpr("rand(null)").show() ``` prints.. **Before** ``` Input argument to rand must be an integer literal.;; line 1 pos 0 org.apache.spark.sql.AnalysisException: Input argument to rand must be an integer literal.;; line 1 pos 0 at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$5.apply(FunctionRegistry.scala:465) at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$5.apply(FunctionRegistry.scala:444) ``` **After** ``` +-----------------------+ |rand(CAST(NULL AS INT))| +-----------------------+ | 0.13385709732307427| +-----------------------+ ``` - `LongType` support in SQL. In addition, it make the function allows to take `LongType` consistently within Scala/SQL. In more details, the codes below: ``` scala spark.range(1).select(rand(1), rand(1L)).show() spark.range(1).selectExpr("rand(1)", "rand(1L)").show() ``` prints.. **Before** ``` +------------------+------------------+ | rand(1)| rand(1)| +------------------+------------------+ |0.2630967864682161|0.2630967864682161| +------------------+------------------+ Input argument to rand must be an integer literal.;; line 1 pos 0 org.apache.spark.sql.AnalysisException: Input argument to rand must be an integer literal.;; line 1 pos 0 at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$5.apply(FunctionRegistry.scala:465) at ``` **After** ``` +------------------+------------------+ | rand(1)| rand(1)| +------------------+------------------+ |0.2630967864682161|0.2630967864682161| +------------------+------------------+ +------------------+------------------+ | rand(1)| rand(1)| +------------------+------------------+ |0.2630967864682161|0.2630967864682161| +------------------+------------------+ ``` ## How was this patch tested? Unit tests in `DataFrameSuite.scala` and `RandomSuite.scala`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #15432 from HyukjinKwon/SPARK-17854.
* [SPARK-17849][SQL] Fix NPE problem when using grouping setswangyang2016-11-051-2/+7
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Prior this pr, the following code would cause an NPE: `case class point(a:String, b:String, c:String, d: Int)` `val data = Seq( point("1","2","3", 1), point("4","5","6", 1), point("7","8","9", 1) )` `sc.parallelize(data).toDF().registerTempTable("table")` `spark.sql("select a, b, c, count(d) from table group by a, b, c GROUPING SETS ((a)) ").show()` The reason is that when the grouping_id() behavior was changed in #10677, some code (which should be changed) was left out. Take the above code for example, prior #10677, the bit mask for set "(a)" was `001`, while after #10677 the bit mask was changed to `011`. However, the `nonNullBitmask` was not changed accordingly. This pr will fix this problem. ## How was this patch tested? add integration tests Author: wangyang <wangyang@haizhi.com> Closes #15416 from yangw1234/groupingid.
* [SPARK-18287][SQL] Move hash expressions from misc.scala into hash.scalaReynold Xin2016-11-054-880/+932
| | | | | | | | | | | | ## What changes were proposed in this pull request? As the title suggests, this patch moves hash expressions from misc.scala into hash.scala, to make it easier to find the hash functions. I wanted to do this a while ago but decided to wait for the branch-2.1 cut so the chance of conflicts will be smaller. ## How was this patch tested? Test cases were also moved out of MiscFunctionsSuite into HashExpressionsSuite. Author: Reynold Xin <rxin@databricks.com> Closes #15784 from rxin/SPARK-18287.
* [SPARK-17183][SPARK-17983][SPARK-18101][SQL] put hive serde table schema to ↵Wenchen Fan2016-11-054-7/+51
| | | | | | | | | | | | | | | | | table properties like data source table ## What changes were proposed in this pull request? For data source tables, we will put its table schema, partition columns, etc. to table properties, to work around some hive metastore issues, e.g. not case-preserving, bad decimal type support, etc. We should also do this for hive serde tables, to reduce the difference between hive serde tables and data source tables, e.g. column names should be case preserving. ## How was this patch tested? existing tests, and a new test in `HiveExternalCatalog` Author: Wenchen Fan <wenchen@databricks.com> Closes #14750 from cloud-fan/minor1.
* [SPARK-18260] Make from_json null safeBurak Yavuz2016-11-052-1/+11
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? `from_json` is currently not safe against `null` rows. This PR adds a fix and a regression test for it. ## How was this patch tested? Regression test Author: Burak Yavuz <brkyvz@gmail.com> Closes #15771 from brkyvz/json_fix.
* [SPARK-17337][SQL] Do not pushdown predicates through filters with ↵Herman van Hovell2016-11-041-1/+15
| | | | | | | | | | | | | | | | predicate subqueries ## What changes were proposed in this pull request? The `PushDownPredicate` rule can create a wrong result if we try to push a filter containing a predicate subquery through a project when the subquery and the project share attributes (have the same source). The current PR fixes this by making sure that we do not push down when there is a predicate subquery that outputs the same attributes as the filters new child plan. ## How was this patch tested? Added a test to `SubquerySuite`. nsyca has done previous work this. I have taken test from his initial PR. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15761 from hvanhovell/SPARK-17337.
* [SPARK-18244][SQL] Rename partitionProviderIsHive -> tracksPartitionsInCatalogReynold Xin2016-11-032-5/+6
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch renames partitionProviderIsHive to tracksPartitionsInCatalog, as the old name was too Hive specific. ## How was this patch tested? Should be covered by existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #15750 from rxin/SPARK-18244.
* [SQL] minor - internal doc improvement for InsertIntoTable.Reynold Xin2016-11-031-0/+16
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? I was reading this part of the code and was really confused by the "partition" parameter. This patch adds some documentation for it to reduce confusion in the future. I also looked around other logical plans but most of them are either already documented, or pretty self-evident to people that know Spark SQL. ## How was this patch tested? N/A - doc change only. Author: Reynold Xin <rxin@databricks.com> Closes #15749 from rxin/doc-improvement.
* [SPARK-17122][SQL] support drop current databaseDaoyuan Wang2016-11-032-2/+15
| | | | | | | | | | | | | ## What changes were proposed in this pull request? In Spark 1.6 and earlier, we can drop the database we are using. In Spark 2.0, native implementation prevent us from dropping current database, which may break some old queries. This PR would re-enable the feature. ## How was this patch tested? one new unit test in `SessionCatalogSuite`. Author: Daoyuan Wang <daoyuan.wang@intel.com> Closes #15011 from adrian-wang/dropcurrent.
* [SPARK-18175][SQL] Improve the test case coverage of implicit type castinggatorsmile2016-11-021-56/+199
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? So far, we have limited test case coverage about implicit type casting. We need to draw a matrix to find all the possible casting pairs. - Reorged the existing test cases - Added all the possible type casting pairs - Drawed a matrix to show the implicit type casting. The table is very wide. Maybe hard to review. Thus, you also can access the same table via the link to [a google sheet](https://docs.google.com/spreadsheets/d/19PS4ikrs-Yye_mfu-rmIKYGnNe-NmOTt5DDT1fOD3pI/edit?usp=sharing). SourceType\CastToType | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | BinaryType | BooleanType | StringType | DateType | TimestampType | ArrayType | MapType | StructType | NullType | CalendarIntervalType | DecimalType | NumericType | IntegralType ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ------------ | ----------- **ByteType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(3, 0) | ByteType | ByteType **ShortType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(5, 0) | ShortType | ShortType **IntegerType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(10, 0) | IntegerType | IntegerType **LongType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(20, 0) | LongType | LongType **DoubleType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(30, 15) | DoubleType | IntegerType **FloatType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(14, 7) | FloatType | IntegerType **Dec(10, 2)** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | X | X | StringType | X | X | X | X | X | X | X | DecimalType(10, 2) | Dec(10, 2) | IntegerType **BinaryType** | X | X | X | X | X | X | X | BinaryType | X | StringType | X | X | X | X | X | X | X | X | X | X **BooleanType** | X | X | X | X | X | X | X | X | BooleanType | StringType | X | X | X | X | X | X | X | X | X | X **StringType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | BinaryType | X | StringType | DateType | TimestampType | X | X | X | X | X | DecimalType(38, 18) | DoubleType | X **DateType** | X | X | X | X | X | X | X | X | X | StringType | DateType | TimestampType | X | X | X | X | X | X | X | X **TimestampType** | X | X | X | X | X | X | X | X | X | StringType | DateType | TimestampType | X | X | X | X | X | X | X | X **ArrayType** | X | X | X | X | X | X | X | X | X | X | X | X | ArrayType* | X | X | X | X | X | X | X **MapType** | X | X | X | X | X | X | X | X | X | X | X | X | X | MapType* | X | X | X | X | X | X **StructType** | X | X | X | X | X | X | X | X | X | X | X | X | X | X | StructType* | X | X | X | X | X **NullType** | ByteType | ShortType | IntegerType | LongType | DoubleType | FloatType | Dec(10, 2) | BinaryType | BooleanType | StringType | DateType | TimestampType | ArrayType | MapType | StructType | NullType | CalendarIntervalType | DecimalType(38, 18) | DoubleType | IntegerType **CalendarIntervalType** | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | CalendarIntervalType | X | X | X Note: ArrayType\*, MapType\*, StructType\* are castable only when the internal child types also match; otherwise, not castable ### How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #15691 from gatorsmile/implicitTypeCasting.
* [SPARK-17963][SQL][DOCUMENTATION] Add examples (extend) in each expression ↵hyukjinkwon2016-11-0236-421/+1211
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | and improve documentation ## What changes were proposed in this pull request? This PR proposes to change the documentation for functions. Please refer the discussion from https://github.com/apache/spark/pull/15513 The changes include - Re-indent the documentation - Add examples/arguments in `extended` where the arguments are multiple or specific format (e.g. xml/ json). For examples, the documentation was updated as below: ### Functions with single line usage **Before** - `pow` ``` sql Usage: pow(x1, x2) - Raise x1 to the power of x2. Extended Usage: > SELECT pow(2, 3); 8.0 ``` - `current_timestamp` ``` sql Usage: current_timestamp() - Returns the current timestamp at the start of query evaluation. Extended Usage: No example for current_timestamp. ``` **After** - `pow` ``` sql Usage: pow(expr1, expr2) - Raises `expr1` to the power of `expr2`. Extended Usage: Examples: > SELECT pow(2, 3); 8.0 ``` - `current_timestamp` ``` sql Usage: current_timestamp() - Returns the current timestamp at the start of query evaluation. Extended Usage: No example/argument for current_timestamp. ``` ### Functions with (already) multiple line usage **Before** - `approx_count_distinct` ``` sql Usage: approx_count_distinct(expr) - Returns the estimated cardinality by HyperLogLog++. approx_count_distinct(expr, relativeSD=0.05) - Returns the estimated cardinality by HyperLogLog++ with relativeSD, the maximum estimation error allowed. Extended Usage: No example for approx_count_distinct. ``` - `percentile_approx` ``` sql Usage: percentile_approx(col, percentage [, accuracy]) - Returns the approximate percentile value of numeric column `col` at the given percentage. The value of percentage must be between 0.0 and 1.0. The `accuracy` parameter (default: 10000) is a positive integer literal which controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields better accuracy, `1.0/accuracy` is the relative error of the approximation. percentile_approx(col, array(percentage1 [, percentage2]...) [, accuracy]) - Returns the approximate percentile array of column `col` at the given percentage array. Each value of the percentage array must be between 0.0 and 1.0. The `accuracy` parameter (default: 10000) is a positive integer literal which controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields better accuracy, `1.0/accuracy` is the relative error of the approximation. Extended Usage: No example for percentile_approx. ``` **After** - `approx_count_distinct` ``` sql Usage: approx_count_distinct(expr[, relativeSD]) - Returns the estimated cardinality by HyperLogLog++. `relativeSD` defines the maximum estimation error allowed. Extended Usage: No example/argument for approx_count_distinct. ``` - `percentile_approx` ``` sql Usage: percentile_approx(col, percentage [, accuracy]) - Returns the approximate percentile value of numeric column `col` at the given percentage. The value of percentage must be between 0.0 and 1.0. The `accuracy` parameter (default: 10000) is a positive numeric literal which controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields better accuracy, `1.0/accuracy` is the relative error of the approximation. When `percentage` is an array, each value of the percentage array must be between 0.0 and 1.0. In this case, returns the approximate percentile array of column `col` at the given percentage array. Extended Usage: Examples: > SELECT percentile_approx(10.0, array(0.5, 0.4, 0.1), 100); [10.0,10.0,10.0] > SELECT percentile_approx(10.0, 0.5, 100); 10.0 ``` ## How was this patch tested? Manually tested **When examples are multiple** ``` sql spark-sql> describe function extended reflect; Function: reflect Class: org.apache.spark.sql.catalyst.expressions.CallMethodViaReflection Usage: reflect(class, method[, arg1[, arg2 ..]]) - Calls a method with reflection. Extended Usage: Examples: > SELECT reflect('java.util.UUID', 'randomUUID'); c33fb387-8500-4bfa-81d2-6e0e3e930df2 > SELECT reflect('java.util.UUID', 'fromString', 'a5cf6c42-0c85-418f-af6c-3e4e5b1328f2'); a5cf6c42-0c85-418f-af6c-3e4e5b1328f2 ``` **When `Usage` is in single line** ``` sql spark-sql> describe function extended min; Function: min Class: org.apache.spark.sql.catalyst.expressions.aggregate.Min Usage: min(expr) - Returns the minimum value of `expr`. Extended Usage: No example/argument for min. ``` **When `Usage` is already in multiple lines** ``` sql spark-sql> describe function extended percentile_approx; Function: percentile_approx Class: org.apache.spark.sql.catalyst.expressions.aggregate.ApproximatePercentile Usage: percentile_approx(col, percentage [, accuracy]) - Returns the approximate percentile value of numeric column `col` at the given percentage. The value of percentage must be between 0.0 and 1.0. The `accuracy` parameter (default: 10000) is a positive numeric literal which controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields better accuracy, `1.0/accuracy` is the relative error of the approximation. When `percentage` is an array, each value of the percentage array must be between 0.0 and 1.0. In this case, returns the approximate percentile array of column `col` at the given percentage array. Extended Usage: Examples: > SELECT percentile_approx(10.0, array(0.5, 0.4, 0.1), 100); [10.0,10.0,10.0] > SELECT percentile_approx(10.0, 0.5, 100); 10.0 ``` **When example/argument is missing** ``` sql spark-sql> describe function extended rank; Function: rank Class: org.apache.spark.sql.catalyst.expressions.Rank Usage: rank() - Computes the rank of a value in a group of values. The result is one plus the number of rows preceding or equal to the current row in the ordering of the partition. The values will produce gaps in the sequence. Extended Usage: No example/argument for rank. ``` Author: hyukjinkwon <gurwls223@gmail.com> Closes #15677 from HyukjinKwon/SPARK-17963-1.
* [SPARK-17470][SQL] unify path for data source table and locationUri for hive ↵Wenchen Fan2016-11-021-9/+31
| | | | | | | | | | | | | | | | | | | | | | | | serde table ## What changes were proposed in this pull request? Due to a limitation of hive metastore(table location must be directory path, not file path), we always store `path` for data source table in storage properties, instead of the `locationUri` field. However, we should not expose this difference to `CatalogTable` level, but just treat it as a hack in `HiveExternalCatalog`, like we store table schema of data source table in table properties. This PR unifies `path` and `locationUri` outside of `HiveExternalCatalog`, both data source table and hive serde table should use the `locationUri` field. This PR also unifies the way we handle default table location for managed table. Previously, the default table location of hive serde managed table is set by external catalog, but the one of data source table is set by command. After this PR, we follow the hive way and the default table location is always set by external catalog. For managed non-file-based tables, we will assign a default table location and create an empty directory for it, the table location will be removed when the table is dropped. This is reasonable as metastore doesn't care about whether a table is file-based or not, and an empty table directory has no harm. For external non-file-based tables, ideally we can omit the table location, but due to a hive metastore issue, we will assign a random location to it, and remove it right after the table is created. See SPARK-15269 for more details. This is fine as it's well isolated in `HiveExternalCatalog`. To keep the existing behaviour of the `path` option, in this PR we always add the `locationUri` to storage properties using key `path`, before passing storage properties to `DataSource` as data source options. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #15024 from cloud-fan/path.
* [SPARK-18214][SQL] Simplify RuntimeReplaceable type coercionReynold Xin2016-11-026-81/+49
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? RuntimeReplaceable is used to create aliases for expressions, but the way it deals with type coercion is pretty weird (each expression is responsible for how to handle type coercion, which does not obey the normal implicit type cast rules). This patch simplifies its handling by allowing the analyzer to traverse into the actual expression of a RuntimeReplaceable. ## How was this patch tested? - Correctness should be guaranteed by existing unit tests already - Removed SQLCompatibilityFunctionSuite and moved it sql-compatibility-functions.sql - Added a new test case in sql-compatibility-functions.sql for verifying explain behavior. Author: Reynold Xin <rxin@databricks.com> Closes #15723 from rxin/SPARK-18214.
* [SPARK-14393][SQL] values generated by non-deterministic functions shouldn't ↵Xiangrui Meng2016-11-0217-51/+125
| | | | | | | | | | | | | | | | | | | | | | | | change after coalesce or union ## What changes were proposed in this pull request? When a user appended a column using a "nondeterministic" function to a DataFrame, e.g., `rand`, `randn`, and `monotonically_increasing_id`, the expected semantic is the following: - The value in each row should remain unchanged, as if we materialize the column immediately, regardless of later DataFrame operations. However, since we use `TaskContext.getPartitionId` to get the partition index from the current thread, the values from nondeterministic columns might change if we call `union` or `coalesce` after. `TaskContext.getPartitionId` returns the partition index of the current Spark task, which might not be the corresponding partition index of the DataFrame where we defined the column. See the unit tests below or JIRA for examples. This PR uses the partition index from `RDD.mapPartitionWithIndex` instead of `TaskContext` and fixes the partition initialization logic in whole-stage codegen, normal codegen, and codegen fallback. `initializeStatesForPartition(partitionIndex: Int)` was added to `Projection`, `Nondeterministic`, and `Predicate` (codegen) and initialized right after object creation in `mapPartitionWithIndex`. `newPredicate` now returns a `Predicate` instance rather than a function for proper initialization. ## How was this patch tested? Unit tests. (Actually I'm not very confident that this PR fixed all issues without introducing new ones ...) cc: rxin davies Author: Xiangrui Meng <meng@databricks.com> Closes #15567 from mengxr/SPARK-14393.
* [SPARK-17683][SQL] Support ArrayType in Literal.applyTakeshi YAMAMURO2016-11-022-2/+82
| | | | | | | | | | | | | ## What changes were proposed in this pull request? This pr is to add pattern-matching entries for array data in `Literal.apply`. ## How was this patch tested? Added tests in `LiteralExpressionSuite`. Author: Takeshi YAMAMURO <linguin.m.s@gmail.com> Closes #15257 from maropu/SPARK-17683.
* [SPARK-16839][SQL] Simplify Struct creation code patheyal farago2016-11-027-177/+135
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Simplify struct creation, especially the aspect of `CleanupAliases` which missed some aliases when handling trees created by `CreateStruct`. This PR includes: 1. A failing test (create struct with nested aliases, some of the aliases survive `CleanupAliases`). 2. A fix that transforms `CreateStruct` into a `CreateNamedStruct` constructor, effectively eliminating `CreateStruct` from all expression trees. 3. A `NamePlaceHolder` used by `CreateStruct` when column names cannot be extracted from unresolved `NamedExpression`. 4. A new Analyzer rule that resolves `NamePlaceHolder` into a string literal once the `NamedExpression` is resolved. 5. `CleanupAliases` code was simplified as it no longer has to deal with `CreateStruct`'s top level columns. ## How was this patch tested? Running all tests-suits in package org.apache.spark.sql, especially including the analysis suite, making sure added test initially fails, after applying suggested fix rerun the entire analysis package successfully. Modified few tests that expected `CreateStruct` which is now transformed into `CreateNamedStruct`. Author: eyal farago <eyal farago> Author: Herman van Hovell <hvanhovell@databricks.com> Author: eyal farago <eyal.farago@gmail.com> Author: Eyal Farago <eyal.farago@actimize.com> Author: Hyukjin Kwon <gurwls223@gmail.com> Author: eyalfa <eyal.farago@gmail.com> Closes #15718 from hvanhovell/SPARK-16839-2.
* [SPARK-18076][CORE][SQL] Fix default Locale used in DateFormat, NumberFormat ↵Sean Owen2016-11-026-29/+32
| | | | | | | | | | | | | | | to Locale.US ## What changes were proposed in this pull request? Fix `Locale.US` for all usages of `DateFormat`, `NumberFormat` ## How was this patch tested? Existing tests. Author: Sean Owen <sowen@cloudera.com> Closes #15610 from srowen/SPARK-18076.
* [SPARK-18183][SPARK-18184] Fix INSERT [INTO|OVERWRITE] TABLE ... PARTITION ↵Eric Liang2016-11-024-7/+38
| | | | | | | | | | | | | | | | | | | | | | | for Datasource tables ## What changes were proposed in this pull request? There are a couple issues with the current 2.1 behavior when inserting into Datasource tables with partitions managed by Hive. (1) OVERWRITE TABLE ... PARTITION will actually overwrite the entire table instead of just the specified partition. (2) INSERT|OVERWRITE does not work with partitions that have custom locations. This PR fixes both of these issues for Datasource tables managed by Hive. The behavior for legacy tables or when `manageFilesourcePartitions = false` is unchanged. There is one other issue in that INSERT OVERWRITE with dynamic partitions will overwrite the entire table instead of just the updated partitions, but this behavior is pretty complicated to implement for Datasource tables. We should address that in a future release. ## How was this patch tested? Unit tests. Author: Eric Liang <ekl@databricks.com> Closes #15705 from ericl/sc-4942.
* [SPARK-17764][SQL] Add `to_json` supporting to convert nested struct column ↵hyukjinkwon2016-11-014-2/+278
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | to JSON string ## What changes were proposed in this pull request? This PR proposes to add `to_json` function in contrast with `from_json` in Scala, Java and Python. It'd be useful if we can convert a same column from/to json. Also, some datasources do not support nested types. If we are forced to save a dataframe into those data sources, we might be able to work around by this function. The usage is as below: ``` scala val df = Seq(Tuple1(Tuple1(1))).toDF("a") df.select(to_json($"a").as("json")).show() ``` ``` bash +--------+ | json| +--------+ |{"_1":1}| +--------+ ``` ## How was this patch tested? Unit tests in `JsonFunctionsSuite` and `JsonExpressionsSuite`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #15354 from HyukjinKwon/SPARK-17764.
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