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* [SPARK-14582][SQL] increase parallelism for small tablesDavies Liu2016-04-222-1/+9
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR try to increase the parallelism for small table (a few of big files) to reduce the query time, by decrease the maxSplitBytes, the goal is to have at least one task per CPU in the cluster, if the total size of all files is bigger than openCostInBytes * 2 * nCPU. For example, a small/medium table could be used as dimension table in huge query, this will be useful to reduce the time waiting for broadcast. ## How was this patch tested? Existing tests. Author: Davies Liu <davies@databricks.com> Closes #12344 from davies/more_partition.
* [SPARK-14796][SQL] Add spark.sql.optimizer.inSetConversionThreshold config ↵Dongjoon Hyun2016-04-225-7/+40
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | option. ## What changes were proposed in this pull request? Currently, `OptimizeIn` optimizer replaces `In` expression into `InSet` expression if the size of set is greater than a constant, 10. This issue aims to make a configuration `spark.sql.optimizer.inSetConversionThreshold` for that. After this PR, `OptimizerIn` is configurable. ```scala scala> sql("select a in (1,2,3) from (select explode(array(1,2)) a) T").explain() == Physical Plan == WholeStageCodegen : +- Project [a#7 IN (1,2,3) AS (a IN (1, 2, 3))#8] : +- INPUT +- Generate explode([1,2]), false, false, [a#7] +- Scan OneRowRelation[] scala> sqlContext.setConf("spark.sql.optimizer.inSetConversionThreshold", "2") scala> sql("select a in (1,2,3) from (select explode(array(1,2)) a) T").explain() == Physical Plan == WholeStageCodegen : +- Project [a#16 INSET (1,2,3) AS (a IN (1, 2, 3))#17] : +- INPUT +- Generate explode([1,2]), false, false, [a#16] +- Scan OneRowRelation[] ``` ## How was this patch tested? Pass the Jenkins tests (with a new testcase) Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12562 from dongjoon-hyun/SPARK-14796.
* [SPARK-14669] [SQL] Fix some SQL metrics in codegen and added moreDavies Liu2016-04-229-31/+99
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? 1. Fix the "spill size" of TungstenAggregate and Sort 2. Rename "data size" to "peak memory" to match the actual meaning (also consistent with task metrics) 3. Added "data size" for ShuffleExchange and BroadcastExchange 4. Added some timing for Sort, Aggregate and BroadcastExchange (this requires another patch to work) ## How was this patch tested? Existing tests. ![metrics](https://cloud.githubusercontent.com/assets/40902/14573908/21ad2f00-030d-11e6-9e2c-c544f30039ea.png) Author: Davies Liu <davies@databricks.com> Closes #12425 from davies/fix_metrics.
* [SPARK-14791] [SQL] fix risk condition between broadcast and subqueryDavies Liu2016-04-224-15/+34
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? SparkPlan.prepare() could be called in different threads (BroadcastExchange will call it in a thread pool), it only make sure that doPrepare() will only be called once, the second call to prepare() may return earlier before all the children had finished prepare(). Then some operator may call doProduce() before prepareSubqueries(), `null` will be used as the result of subquery, which is wrong. This cause TPCDS Q23B returns wrong answer sometimes. This PR added synchronization for prepare(), make sure all the children had finished prepare() before return. Also call prepare() in produce() (similar to execute()). Added checking for ScalarSubquery to make sure that the subquery has finished before using the result. ## How was this patch tested? Manually tested with Q23B, no wrong answer anymore. Author: Davies Liu <davies@databricks.com> Closes #12600 from davies/fix_risk.
* [SPARK-14763][SQL] fix subquery resolutionDavies Liu2016-04-227-49/+173
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, a column could be resolved wrongly if there are columns from both outer table and subquery have the same name, we should only resolve the attributes that can't be resolved within subquery. They may have same exprId than other attributes in subquery, so we should create alias for them. Also, the column in IN subquery could have same exprId, we should create alias for them. ## How was this patch tested? Added regression tests. Manually tests TPCDS Q70 and Q95, work well after this patch. Author: Davies Liu <davies@databricks.com> Closes #12539 from davies/fix_subquery.
* [SPARK-14762] [SQL] TPCDS Q90 fails to parseHerman van Hovell2016-04-222-6/+51
| | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? TPCDS Q90 fails to parse because it uses a reserved keyword as an Identifier; `AT` was used as an alias for one of the subqueries. `AT` is not a reserved keyword and should have been registerd as a in the `nonReserved` rule. In order to prevent this from happening again I have added tests for all keywords that are non-reserved in Hive. See the `nonReserved`, `sql11ReservedKeywordsUsedAsCastFunctionName` & `sql11ReservedKeywordsUsedAsIdentifier` rules in https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/parse/IdentifiersParser.g. ### How was this patch tested? Added tests to for all Hive non reserved keywords to `TableIdentifierParserSuite`. cc davies Author: Herman van Hovell <hvanhovell@questtec.nl> Closes #12537 from hvanhovell/SPARK-14762.
* [SPARK-14841][SQL] Move SQLBuilder into sql/coreReynold Xin2016-04-228-19/+19
| | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch moves SQLBuilder into sql/core so we can in the future move view generation also into sql/core. ## How was this patch tested? Also moved unit tests. Author: Reynold Xin <rxin@databricks.com> Author: Wenchen Fan <wenchen@databricks.com> Closes #12602 from rxin/SPARK-14841.
* [SPARK-13266] [SQL] None read/writer options were not transalated to "null"Liang-Chi Hsieh2016-04-221-1/+5
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In Python, the `option` and `options` method of `DataFrameReader` and `DataFrameWriter` were sending the string "None" instead of `null` when passed `None`, therefore making it impossible to send an actual `null`. This fixes that problem. This is based on #11305 from mathieulongtin. ## How was this patch tested? Added test to readwriter.py. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Author: mathieu longtin <mathieu.longtin@nuance.com> Closes #12494 from viirya/py-df-none-option.
* [SPARK-14848][SQL] Compare as Set in DatasetSuite - Java encoderPete Robbins2016-04-221-2/+2
| | | | | | | | | | | | ## What changes were proposed in this pull request? Change test to compare sets rather than sequence ## How was this patch tested? Full test runs on little endian and big endian platforms Author: Pete Robbins <robbinspg@gmail.com> Closes #12610 from robbinspg/DatasetSuiteFix.
* [SPARK-6429] Implement hashCode and equals togetherJoan2016-04-2213-18/+57
| | | | | | | | | | | ## What changes were proposed in this pull request? Implement some `hashCode` and `equals` together in order to enable the scalastyle. This is a first batch, I will continue to implement them but I wanted to know your thoughts. Author: Joan <joan@goyeau.com> Closes #12157 from joan38/SPARK-6429-HashCode-Equals.
* [SPARK-14609][SQL] Native support for LOAD DATA DDL commandLiang-Chi Hsieh2016-04-2211-8/+427
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add the native support for LOAD DATA DDL command that loads data into Hive table/partition. ## How was this patch tested? `HiveDDLCommandSuite` and `HiveQuerySuite`. Besides, few Hive tests (`WindowQuerySuite`, `HiveTableScanSuite` and `HiveSerDeSuite`) also use `LOAD DATA` command. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #12412 from viirya/ddl-load-data.
* [SPARK-14826][SQL] Remove HiveQueryExecutionReynold Xin2016-04-2220-436/+420
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch removes HiveQueryExecution. As part of this, I consolidated all the describe commands into DescribeTableCommand. ## How was this patch tested? Should be covered by existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #12588 from rxin/SPARK-14826.
* [SPARK-14835][SQL] Remove MetastoreRelation dependency from SQLBuilderReynold Xin2016-04-212-8/+22
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch removes SQLBuilder's dependency on MetastoreRelation. We should be able to move SQLBuilder into the sql/core package after this change. ## How was this patch tested? N/A - covered by existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #12594 from rxin/SPARK-14835.
* [SPARK-14369] [SQL] Locality support for FileScanRDDCheng Lian2016-04-216-37/+291
| | | | | | | | | | | | | | | | | | | | | | | | | | (This PR is a rebased version of PR #12153.) ## What changes were proposed in this pull request? This PR adds preliminary locality support for `FileFormat` data sources by overriding `FileScanRDD.preferredLocations()`. The strategy can be divided into two parts: 1. Block location lookup Unlike `HadoopRDD` or `NewHadoopRDD`, `FileScanRDD` doesn't have access to the underlying `InputFormat` or `InputSplit`, and thus can't rely on `InputSplit.getLocations()` to gather locality information. Instead, this PR queries block locations using `FileSystem.getBlockLocations()` after listing all `FileStatus`es in `HDFSFileCatalog` and convert all `FileStatus`es into `LocatedFileStatus`es. Note that although S3/S3A/S3N file systems don't provide valid locality information, their `getLocatedStatus()` implementations don't actually issue remote calls either. So there's no need to special case these file systems. 2. Selecting preferred locations For each `FilePartition`, we pick up top 3 locations that containing the most data to be retrieved. This isn't necessarily the best algorithm out there. Further improvements may be brought up in follow-up PRs. ## How was this patch tested? Tested by overriding default `FileSystem` implementation for `file:///` with a mocked one, which returns mocked block locations. Author: Cheng Lian <lian@databricks.com> Closes #12527 from liancheng/spark-14369-locality-rebased.
* [SPARK-14680] [SQL] Support all datatypes to use VectorizedHashmap in ↵Sameer Agarwal2016-04-215-39/+322
| | | | | | | | | | | | | | | | TungstenAggregate ## What changes were proposed in this pull request? This PR adds support for all primitive datatypes, decimal types and stringtypes in the VectorizedHashmap during aggregation. ## How was this patch tested? Existing tests for group-by aggregates should already test for all these datatypes. Additionally, manually inspected the generated code for all supported datatypes (details below). Author: Sameer Agarwal <sameer@databricks.com> Closes #12440 from sameeragarwal/all-datatypes.
* [SPARK-14793] [SQL] Code generation for large complex type exceeds JVM size ↵Takuya UESHIN2016-04-213-53/+144
| | | | | | | | | | | | | | | | | | limit. ## What changes were proposed in this pull request? Code generation for complex type, `CreateArray`, `CreateMap`, `CreateStruct`, `CreateNamedStruct`, exceeds JVM size limit for large elements. We should split generated code into multiple `apply` functions if the complex types have large elements, like `UnsafeProjection` or others for large expressions. ## How was this patch tested? I added some tests to check if the generated codes for the expressions exceed or not. Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #12559 from ueshin/issues/SPARK-14793.
* [SPARK-14824][SQL] Rename HiveContext object to HiveUtilsAndrew Or2016-04-2120-55/+55
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Just a rename so we can get rid of `HiveContext.scala`. Note that this will conflict with #12585. ## How was this patch tested? No change in functionality. Author: Andrew Or <andrew@databricks.com> Closes #12586 from andrewor14/rename-hc-object.
* [SPARK-14821][SQL] Implement AnalyzeTable in sql/core and remove ↵Reynold Xin2016-04-2113-226/+199
| | | | | | | | | | | | | | | | HiveSqlAstBuilder ## What changes were proposed in this pull request? This patch moves analyze table parsing into SparkSqlAstBuilder and removes HiveSqlAstBuilder. In order to avoid extensive refactoring, I created a common trait for CatalogRelation and MetastoreRelation, and match on that. In the future we should probably just consolidate the two into a single thing so we don't need this common trait. ## How was this patch tested? Updated unit tests. Author: Reynold Xin <rxin@databricks.com> Closes #12584 from rxin/SPARK-14821.
* [SPARK-14724] Use radix sort for shuffles and sort operator when possibleEric Liang2016-04-218-10/+64
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Spark currently uses TimSort for all in-memory sorts, including sorts done for shuffle. One low-hanging fruit is to use radix sort when possible (e.g. sorting by integer keys). This PR adds a radix sort implementation to the unsafe sort package and switches shuffles and sorts to use it when possible. The current implementation does not have special support for null values, so we cannot radix-sort `LongType`. I will address this in a follow-up PR. ## How was this patch tested? Unit tests, enabling radix sort on existing tests. Microbenchmark results: ``` Running benchmark: radix sort 25000000 Java HotSpot(TM) 64-Bit Server VM 1.8.0_66-b17 on Linux 3.13.0-44-generic Intel(R) Core(TM) i7-4600U CPU 2.10GHz radix sort 25000000: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------- reference TimSort key prefix array 15546 / 15859 1.6 621.9 1.0X reference Arrays.sort 2416 / 2446 10.3 96.6 6.4X radix sort one byte 133 / 137 188.4 5.3 117.2X radix sort two bytes 255 / 258 98.2 10.2 61.1X radix sort eight bytes 991 / 997 25.2 39.6 15.7X radix sort key prefix array 1540 / 1563 16.2 61.6 10.1X ``` I also ran a mix of the supported TPCDS queries and compared TimSort vs RadixSort metrics. The overall benchmark ran ~10% faster with radix sort on. In the breakdown below, the radix-enabled sort phases averaged about 20x faster than TimSort, however sorting is only a small fraction of the overall runtime. About half of the TPCDS queries were able to take advantage of radix sort. ``` TPCDS on master: 2499s real time, 8185s executor - 1171s in TimSort, avg 267 MB/s (note the /s accounting is weird here since dataSize counts the record sizes too) TPCDS with radix enabled: 2294s real time, 7391s executor - 596s in TimSort, avg 254 MB/s - 26s in radix sort, avg 4.2 GB/s ``` cc davies rxin Author: Eric Liang <ekl@databricks.com> Closes #12490 from ericl/sort-benchmark.
* [SPARK-14774][SQL] Write unscaled values in ColumnVector.putDecimalSameer Agarwal2016-04-213-30/+37
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? We recently made `ColumnarBatch.row` mutable and added a new `ColumnVector.putDecimal` method to support putting `Decimal` values in the `ColumnarBatch`. This unfortunately introduced a bug wherein we were not updating the vector with the proper unscaled values. ## How was this patch tested? This codepath is hit only when the vectorized aggregate hashmap is enabled. https://github.com/apache/spark/pull/12440 makes sure that a number of regression tests/benchmarks test this bugfix. Author: Sameer Agarwal <sameer@databricks.com> Closes #12541 from sameeragarwal/fix-bigdecimal.
* [SPARK-14798][SQL] Move native command and script transformation parsing ↵Reynold Xin2016-04-2115-182/+192
| | | | | | | | | | | | | | into SparkSqlAstBuilder ## What changes were proposed in this pull request? This patch moves native command and script transformation into SparkSqlAstBuilder. This builds on #12561. See the last commit for diff. ## How was this patch tested? Updated test cases to reflect this. Author: Reynold Xin <rxin@databricks.com> Closes #12564 from rxin/SPARK-14798.
* [MINOR] Comment whitespace changes in #12553Andrew Or2016-04-211-9/+10
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* [SPARK-13643][SQL] Implement SparkSessionAndrew Or2016-04-215-197/+961
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? After removing most of `HiveContext` in 8fc267ab3322e46db81e725a5cb1adb5a71b2b4d we can now move existing functionality in `SQLContext` to `SparkSession`. As of this PR `SQLContext` becomes a simple wrapper that has a `SparkSession` and delegates all functionality to it. ## How was this patch tested? Jenkins. Author: Andrew Or <andrew@databricks.com> Closes #12553 from andrewor14/implement-spark-session.
* [SPARK-14801][SQL] Move MetastoreRelation to its own fileReynold Xin2016-04-212-205/+232
| | | | | | | | | | | | ## What changes were proposed in this pull request? This class is currently in HiveMetastoreCatalog.scala, which is a large file that makes refactoring and searching of usage difficult. Moving it out so I can then do SPARK-14799 and make the review of that simpler. ## How was this patch tested? N/A - this is a straightforward move and should be covered by existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #12567 from rxin/SPARK-14801.
* [SPARK-14795][SQL] Remove the use of Hive's variable substitutionReynold Xin2016-04-213-11/+8
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch builds on #12556 and completely removes the use of Hive's variable substitution. ## How was this patch tested? Covered by existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #12561 from rxin/SPARK-14795.
* [SPARK-14799][SQL] Remove MetastoreRelation dependency from AnalyzeTable - ↵Reynold Xin2016-04-211-26/+23
| | | | | | | | | | | | | | part 1 ## What changes were proposed in this pull request? This patch isolates AnalyzeTable's dependency on MetastoreRelation into a single line. After this we can work on converging MetastoreRelation and CatalogTable. ## How was this patch tested? Covered by existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #12566 from rxin/SPARK-14799.
* [SPARK-14783] Preserve full exception stacktrace in IsolatedClientLoaderJosh Rosen2016-04-211-1/+1
| | | | | | | | In IsolatedClientLoader, we have a`catch` block which throws an exception without wrapping the original exception, causing the full exception stacktrace and any nested exceptions to be lost. This patch fixes this, improving the usefulness of classloading error messages. Author: Josh Rosen <joshrosen@databricks.com> Closes #12548 from JoshRosen/improve-logging-for-hive-classloader-issues.
* [SPARK-14797][BUILD] Spark SQL POM should not hardcode spark-sketch_2.11 dep.Josh Rosen2016-04-211-1/+1
| | | | | | | | | | | | Spark SQL's POM hardcodes a dependency on `spark-sketch_2.11`, which causes Scala 2.10 builds to include the `_2.11` dependency. This is harmless since `spark-sketch` is a pure-Java module (see #12334 for a discussion of dropping the Scala version suffixes from these modules' artifactIds), but it's confusing to people looking at the published POMs. This patch fixes this by using `${scala.binary.version}` to substitute the correct suffix, and also adds a set of Maven Enforcer rules to ensure that `_2.11` artifacts are not used in 2.10 builds (and vice-versa). /cc ahirreddy, who spotted this issue. Author: Josh Rosen <joshrosen@databricks.com> Closes #12563 from JoshRosen/fix-sketch-scala-version.
* [HOTFIX] Remove wrong DDL testsLiang-Chi Hsieh2016-04-211-13/+0
| | | | | | | | | | | | | ## What changes were proposed in this pull request? As we moved most parsing rules to `SparkSqlParser`, some tests expected to throw exception are not correct anymore. ## How was this patch tested? `DDLCommandSuite` Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #12572 from viirya/hotfix-ddl.
* [SPARK-14753][CORE] remove internal flag in AccumulableWenchen Fan2016-04-211-3/+2
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? the `Accumulable.internal` flag is only used to avoid registering internal accumulators for 2 certain cases: 1. `TaskMetrics.createTempShuffleReadMetrics`: the accumulators in the temp shuffle read metrics should not be registered. 2. `TaskMetrics.fromAccumulatorUpdates`: the created task metrics is only used to post event, accumulators inside it should not be registered. For 1, we can create a `TempShuffleReadMetrics` that don't create accumulators, just keep the data and merge it at last. For 2, we can un-register these accumulators immediately. TODO: remove `internal` flag in `AccumulableInfo` with followup PR ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #12525 from cloud-fan/acc.
* [SPARK-14794][SQL] Don't pass analyze command into HiveReynold Xin2016-04-212-6/+8
| | | | | | | | | | | | ## What changes were proposed in this pull request? We shouldn't pass analyze command to Hive because some of those would require running MapReduce jobs. For now, let's just always run the no scan analyze. ## How was this patch tested? Updated test case to reflect this change. Author: Reynold Xin <rxin@databricks.com> Closes #12558 from rxin/parser-analyze.
* [HOTFIX] Disable flaky testsReynold Xin2016-04-211-2/+2
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* [SPARK-14792][SQL] Move as many parsing rules as possible into SQL parserReynold Xin2016-04-2114-489/+568
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch moves as many parsing rules as possible into SQL parser. There are only three more left after this patch: (1) run native command, (2) analyze, and (3) script IO. These 3 will be dealt with in a follow-up PR. ## How was this patch tested? No test change. This simply moves code around. Author: Reynold Xin <rxin@databricks.com> Closes #12556 from rxin/SPARK-14792.
* [SPARK-14786] Remove hive-cli dependency from hive subprojectJosh Rosen2016-04-203-7/+33
| | | | | | | | | | | | The `hive` subproject currently depends on `hive-cli` in order to perform a check to see whether a `SessionState` is an instance of `org.apache.hadoop.hive.cli.CliSessionState` (see #9589). The introduction of this `hive-cli` dependency has caused problems for users whose Hive metastore JAR classpaths don't include the `hive-cli` classes (such as in #11495). This patch removes this dependency on `hive-cli` and replaces the `isInstanceOf` check by reflection. I added a Maven Enforcer rule to ban `hive-cli` from the `hive` subproject in order to make sure that this dependency is not accidentally reintroduced. /cc rxin yhuai adrian-wang preecet Author: Josh Rosen <joshrosen@databricks.com> Closes #12551 from JoshRosen/remove-hive-cli-dep-from-hive-subproject.
* [SPARK-14782][SPARK-14778][SQL] Remove HiveConf dependency from ↵Reynold Xin2016-04-204-40/+27
| | | | | | | | | | | | | | | | HiveSqlAstBuilder ## What changes were proposed in this pull request? The patch removes HiveConf dependency from HiveSqlAstBuilder. This is required in order to merge HiveSqlParser and SparkSqlAstBuilder, which would require getting rid of the Hive specific dependencies in HiveSqlParser. This patch also accomplishes [SPARK-14778] Remove HiveSessionState.substitutor. ## How was this patch tested? This should be covered by existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #12550 from rxin/SPARK-14782.
* [SPARK-14775][SQL] Remove TestHiveSparkSession.rewritePathsReynold Xin2016-04-204-22/+17
| | | | | | | | | | | | ## What changes were proposed in this pull request? The path rewrite in TestHiveSparkSession is pretty hacky. I think we can remove those complexity and just do a string replacement when we read the query files in. This would remove the overloading of runNativeSql in TestHive, which will simplify the removal of Hive specific variable substitution. ## How was this patch tested? This is a small test refactoring to simplify test infrastructure. Author: Reynold Xin <rxin@databricks.com> Closes #12543 from rxin/SPARK-14775.
* [SPARK-14769][SQL] Create built-in functionality for variable substitutionReynold Xin2016-04-203-0/+215
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? In order to fully merge the Hive parser and the SQL parser, we'd need to support variable substitution in Spark. The implementation of the substitute algorithm is mostly copied from Hive, but I simplified the overall structure quite a bit and added more comprehensive test coverage. Note that this pull request does not yet use this functionality anywhere. ## How was this patch tested? Added VariableSubstitutionSuite for unit tests. Author: Reynold Xin <rxin@databricks.com> Closes #12538 from rxin/SPARK-14769.
* [SPARK-14770][SQL] Remove unused queries in hive module test resourcesReynold Xin2016-04-20690-5352/+0
| | | | | | | | | | | | ## What changes were proposed in this pull request? We currently have five folders in queries: clientcompare, clientnegative, clientpositive, negative, and positive. Only clientpositive is used. We can remove the rest. ## How was this patch tested? N/A - removing unused test resources. Author: Reynold Xin <rxin@databricks.com> Closes #12540 from rxin/SPARK-14770.
* [SPARK-14749][SQL, TESTS] PlannerSuite failed when it run individuallySubhobrata Dey2016-04-201-1/+4
| | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? 3 testcases namely, ``` "count is partially aggregated" "count distinct is partially aggregated" "mixed aggregates are partially aggregated" ``` were failing when running PlannerSuite individually. The PR provides a fix for this. ## How was this patch tested? unit tests (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Author: Subhobrata Dey <sbcd90@gmail.com> Closes #12532 from sbcd90/plannersuitetestsfix.
* [SPARK-14678][SQL] Add a file sink log to support versioning and compactionShixiong Zhu2016-04-206-27/+616
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds a special log for FileStreamSink for two purposes: - Versioning. A future Spark version should be able to read the metadata of an old FileStreamSink. - Compaction. As reading from many small files is usually pretty slow, we should compact small metadata files into big files. FileStreamSinkLog has a new log format instead of Java serialization format. It will write one log file for each batch. The first line of the log file is the version number, and there are multiple JSON lines following. Each JSON line is a JSON format of FileLog. FileStreamSinkLog will compact log files every "spark.sql.sink.file.log.compactLen" batches into a big file. When doing a compact, it will read all history logs and merge them with the new batch. During the compaction, it will also delete the files that are deleted (marked by FileLog.action). When the reader uses allLogs to list all files, this method only returns the visible files (drops the deleted files). ## How was this patch tested? FileStreamSinkLogSuite Author: Shixiong Zhu <shixiong@databricks.com> Closes #12435 from zsxwing/sink-log.
* [SPARK-14720][SPARK-13643] Move Hive-specific methods into HiveSessionState ↵Andrew Or2016-04-2042-547/+790
| | | | | | | | | | | | | | | | | | | and Create a SparkSession class ## What changes were proposed in this pull request? This PR has two main changes. 1. Move Hive-specific methods from HiveContext to HiveSessionState, which help the work of removing HiveContext. 2. Create a SparkSession Class, which will later be the entry point of Spark SQL users. ## How was this patch tested? Existing tests This PR is trying to fix test failures of https://github.com/apache/spark/pull/12485. Author: Andrew Or <andrew@databricks.com> Author: Yin Huai <yhuai@databricks.com> Closes #12522 from yhuai/spark-session.
* [SPARK-14741][SQL] Fixed error in reading json file stream inside a ↵Tathagata Das2016-04-202-1/+26
| | | | | | | | | | | | | | | | | | | | | | | partitioned directory ## What changes were proposed in this pull request? Consider the following directory structure dir/col=X/some-files If we create a text format streaming dataframe on `dir/col=X/` then it should not consider as partitioning in columns. Even though the streaming dataframe does not do so, the generated batch dataframes pick up col as a partitioning columns, causing mismatch streaming source schema and generated df schema. This leads to runtime failure: ``` 18:55:11.262 ERROR org.apache.spark.sql.execution.streaming.StreamExecution: Query query-0 terminated with error java.lang.AssertionError: assertion failed: Invalid batch: c#2 != c#7,type#8 ``` The reason is that the partition inferring code has no idea of a base path, above which it should not search of partitions. This PR makes sure that the batch DF is generated with the basePath set as the original path on which the file stream source is defined. ## How was this patch tested? New unit test Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #12517 from tdas/SPARK-14741.
* [SPARK-14555] First cut of Python API for Structured StreamingBurak Yavuz2016-04-203-19/+19
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch provides a first cut of python APIs for structured streaming. This PR provides the new classes: - ContinuousQuery - Trigger - ProcessingTime in pyspark under `pyspark.sql.streaming`. In addition, it contains the new methods added under: - `DataFrameWriter` a) `startStream` b) `trigger` c) `queryName` - `DataFrameReader` a) `stream` - `DataFrame` a) `isStreaming` This PR doesn't contain all methods exposed for `ContinuousQuery`, for example: - `exception` - `sourceStatuses` - `sinkStatus` They may be added in a follow up. This PR also contains some very minor doc fixes in the Scala side. ## How was this patch tested? Python doc tests TODO: - [ ] verify Python docs look good Author: Burak Yavuz <brkyvz@gmail.com> Author: Burak Yavuz <burak@databricks.com> Closes #12320 from brkyvz/stream-python.
* [SPARK-14687][CORE][SQL][MLLIB] Call path.getFileSystem(conf) instead of ↵Liwei Lin2016-04-202-2/+2
| | | | | | | | | | | | | | | | call FileSystem.get(conf) ## What changes were proposed in this pull request? - replaced `FileSystem.get(conf)` calls with `path.getFileSystem(conf)` ## How was this patch tested? N/A Author: Liwei Lin <lwlin7@gmail.com> Closes #12450 from lw-lin/fix-fs-get.
* [SPARK-9013][SQL] generate MutableProjection directly instead of return a ↵Wenchen Fan2016-04-2014-39/+35
| | | | | | | | | | | | function `MutableProjection` is not thread-safe and we won't use it in multiple threads. I think the reason that we return `() => MutableProjection` is not about thread safety, but to save the costs of generating code when we need same but individual mutable projections. However, I only found one place that use this [feature](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala#L122-L123), and comparing to the troubles it brings, I think we should generate `MutableProjection` directly instead of return a function. Author: Wenchen Fan <wenchen@databricks.com> Closes #7373 from cloud-fan/project.
* [MINOR] [SQL] Re-enable `explode()` and `json_tuple()` testcases in ↵Dongjoon Hyun2016-04-191-4/+2
| | | | | | | | | | | | | | | | ExpressionToSQLSuite ## What changes were proposed in this pull request? Since [SPARK-12719: SQL Generation supports for generators](https://issues.apache.org/jira/browse/SPARK-12719) was resolved, this PR enables the related testcases: `explode()` and `json_tuple()`. ## How was this patch tested? Pass the Jenkins tests (with re-enabled test cases). Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12329 from dongjoon-hyun/minor_enable_testcases.
* [SPARK-14600] [SQL] Push predicates through ExpandWenchen Fan2016-04-195-14/+34
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-14600 This PR makes `Expand.output` have different attributes from the grouping attributes produced by the underlying `Project`, as they have different meaning, so that we can safely push down filter through `Expand` ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #12496 from cloud-fan/expand.
* [SPARK-14704][CORE] create accumulators in TaskMetricsWenchen Fan2016-04-191-2/+2
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Before this PR, we create accumulators at driver side(and register them) and send them to executor side, then we create `TaskMetrics` with these accumulators at executor side. After this PR, we will create `TaskMetrics` at driver side and send it to executor side, so that we can create accumulators inside `TaskMetrics` directly, which is cleaner. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #12472 from cloud-fan/acc.
* [SPARK-13419] [SQL] Update SubquerySuite to use checkAnswer for validationLuciano Resende2016-04-191-28/+38
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Change SubquerySuite to validate test results utilizing checkAnswer helper method ## How was this patch tested? Existing tests Author: Luciano Resende <lresende@apache.org> Closes #12269 from lresende/SPARK-13419.
* [SPARK-13929] Use Scala reflection for UDTsJoan2016-04-197-114/+157
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Enable ScalaReflection and User Defined Types for plain Scala classes. This involves the move of `schemaFor` from `ScalaReflection` trait (which is Runtime and Compile time (macros) reflection) to the `ScalaReflection` object (runtime reflection only) as I believe this code wouldn't work at compile time anyway as it manipulates `Class`'s that are not compiled yet. ## How was this patch tested? Unit test Author: Joan <joan@goyeau.com> Closes #12149 from joan38/SPARK-13929-Scala-reflection.
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