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* [SPARK-16371][SQL] Do not push down filters incorrectly when inner name and ↵hyukjinkwon2016-07-061-0/+14
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | outer name are the same in Parquet ## What changes were proposed in this pull request? Currently, if there is a schema as below: ``` root |-- _1: struct (nullable = true) | |-- _1: integer (nullable = true) ``` and if we execute the codes below: ```scala df.filter("_1 IS NOT NULL").count() ``` This pushes down a filter although this filter is being applied to `StructType`.(If my understanding is correct, Spark does not pushes down filters for those). The reason is, `ParquetFilters.getFieldMap` produces results below: ``` (_1,StructType(StructField(_1,IntegerType,true))) (_1,IntegerType) ``` and then it becomes a `Map` ``` (_1,IntegerType) ``` Now, because of ` ....lift(dataTypeOf(name)).map(_(name, value))`, this pushes down filters for `_1` which Parquet thinks is `IntegerType`. However, it is actually `StructType`. So, Parquet filter2 produces incorrect results, for example, the codes below: ``` df.filter("_1 IS NOT NULL").count() ``` produces always 0. This PR prevents this by not finding nested fields. ## How was this patch tested? Unit test in `ParquetFilterSuite`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #14067 from HyukjinKwon/SPARK-16371.
* [SPARK-16388][SQL] Remove spark.sql.nativeView and ↵Reynold Xin2016-07-061-8/+8
| | | | | | | | | | | | | | spark.sql.nativeView.canonical config ## What changes were proposed in this pull request? These two configs should always be true after Spark 2.0. This patch removes them from the config list. Note that ideally this should've gone into branch-2.0, but due to the timing of the release we should only merge this in master for Spark 2.1. ## How was this patch tested? Updated test cases. Author: Reynold Xin <rxin@databricks.com> Closes #14061 from rxin/SPARK-16388.
* [SPARK-16340][SQL] Support column arguments for `regexp_replace` Dataset ↵Dongjoon Hyun2016-07-051-1/+3
| | | | | | | | | | | | | | | | operation ## What changes were proposed in this pull request? Currently, `regexp_replace` function supports `Column` arguments in a query. This PR supports that in a `Dataset` operation, too. ## How was this patch tested? Pass the Jenkins tests with a updated testcase. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14060 from dongjoon-hyun/SPARK-16340.
* [SPARK-16286][SQL] Implement stack table generating functionDongjoon Hyun2016-07-061-0/+53
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR implements `stack` table generating function. ## How was this patch tested? Pass the Jenkins tests including new testcases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14033 from dongjoon-hyun/SPARK-16286.
* [SPARK-16311][SQL] Metadata refresh should work on temporary viewsReynold Xin2016-07-051-4/+4
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch fixes the bug that the refresh command does not work on temporary views. This patch is based on https://github.com/apache/spark/pull/13989, but removes the public Dataset.refresh() API as well as improved test coverage. Note that I actually think the public refresh() API is very useful. We can in the future implement it by also invalidating the lazy vals in QueryExecution (or alternatively just create a new QueryExecution). ## How was this patch tested? Re-enabled a previously ignored test, and added a new test suite for Hive testing behavior of temporary views against MetastoreRelation. Author: Reynold Xin <rxin@databricks.com> Author: petermaxlee <petermaxlee@gmail.com> Closes #14009 from rxin/SPARK-16311.
* [SPARK-9876][SQL][FOLLOWUP] Enable string and binary tests for Parquet ↵hyukjinkwon2016-07-051-4/+2
| | | | | | | | | | | | | | | | | | predicate pushdown and replace deprecated fromByteArray. ## What changes were proposed in this pull request? It seems Parquet has been upgraded to 1.8.1 by https://github.com/apache/spark/pull/13280. So, this PR enables string and binary predicate push down which was disabled due to [SPARK-11153](https://issues.apache.org/jira/browse/SPARK-11153) and [PARQUET-251](https://issues.apache.org/jira/browse/PARQUET-251) and cleans up some comments unremoved (I think by mistake). This PR also replace the API, `fromByteArray()` deprecated in [PARQUET-251](https://issues.apache.org/jira/browse/PARQUET-251). ## How was this patch tested? Unit tests in `ParquetFilters` Author: hyukjinkwon <gurwls223@gmail.com> Closes #13389 from HyukjinKwon/parquet-1.8-followup.
* [SPARK-15204][SQL] improve nullability inference for AggregatorKoert Kuipers2016-07-041-0/+9
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? TypedAggregateExpression sets nullable based on the schema of the outputEncoder ## How was this patch tested? Add test in DatasetAggregatorSuite Author: Koert Kuipers <koert@tresata.com> Closes #13532 from koertkuipers/feat-aggregator-nullable.
* [SPARK-16288][SQL] Implement inline table generating functionDongjoon Hyun2016-07-041-0/+60
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR implements `inline` table generating function. ## How was this patch tested? Pass the Jenkins tests with new testcase. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13976 from dongjoon-hyun/SPARK-16288.
* [SPARK-16278][SPARK-16279][SQL] Implement map_keys/map_values SQL functionsDongjoon Hyun2016-07-031-0/+16
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds `map_keys` and `map_values` SQL functions in order to remove Hive fallback. ## How was this patch tested? Pass the Jenkins tests including new testcases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13967 from dongjoon-hyun/SPARK-16278.
* [SPARK-16329][SQL] Star Expansion over Table Containing No Columngatorsmile2016-07-031-0/+31
| | | | | | | | | | | | | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? Star expansion over a table containing zero column does not work since 1.6. However, it works in Spark 1.5.1. This PR is to fix the issue in the master branch. For example, ```scala val rddNoCols = sqlContext.sparkContext.parallelize(1 to 10).map(_ => Row.empty) val dfNoCols = sqlContext.createDataFrame(rddNoCols, StructType(Seq.empty)) dfNoCols.registerTempTable("temp_table_no_cols") sqlContext.sql("select * from temp_table_no_cols").show ``` Without the fix, users will get the following the exception: ``` java.lang.IllegalArgumentException: requirement failed at scala.Predef$.require(Predef.scala:221) at org.apache.spark.sql.catalyst.analysis.UnresolvedStar.expand(unresolved.scala:199) ``` #### How was this patch tested? Tests are added Author: gatorsmile <gatorsmile@gmail.com> Closes #14007 from gatorsmile/starExpansionTableWithZeroColumn.
* [SPARK-16335][SQL] Structured streaming should fail if source directory does ↵Reynold Xin2016-07-011-23/+15
| | | | | | | | | | | | | | not exist ## What changes were proposed in this pull request? In structured streaming, Spark does not report errors when the specified directory does not exist. This is a behavior different from the batch mode. This patch changes the behavior to fail if the directory does not exist (when the path is not a glob pattern). ## How was this patch tested? Updated unit tests to reflect the new behavior. Author: Reynold Xin <rxin@databricks.com> Closes #14002 from rxin/SPARK-16335.
* [SPARK-16222][SQL] JDBC Sources - Handling illegal input values for ↵gatorsmile2016-07-012-39/+77
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | `fetchsize` and `batchsize` #### What changes were proposed in this pull request? For JDBC data sources, users can specify `batchsize` for multi-row inserts and `fetchsize` for multi-row fetch. A few issues exist: - The property keys are case sensitive. Thus, the existing test cases for `fetchsize` use incorrect names, `fetchSize`. Basically, the test cases are broken. - No test case exists for `batchsize`. - We do not detect the illegal input values for `fetchsize` and `batchsize`. For example, when `batchsize` is zero, we got the following exception: ``` Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost): java.lang.ArithmeticException: / by zero ``` when `fetchsize` is less than zero, we got the exception from the underlying JDBC driver: ``` Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost): org.h2.jdbc.JdbcSQLException: Invalid value "-1" for parameter "rows" [90008-183] ``` This PR fixes all the above issues, and issue the appropriate exceptions when detecting the illegal inputs for `fetchsize` and `batchsize`. Also update the function descriptions. #### How was this patch tested? Test cases are fixed and added. Author: gatorsmile <gatorsmile@gmail.com> Closes #13919 from gatorsmile/jdbcProperties.
* [SPARK-16276][SQL] Implement elt SQL functionpetermaxlee2016-07-011-0/+14
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch implements the elt function, as it is implemented in Hive. ## How was this patch tested? Added expression unit test in StringExpressionsSuite and end-to-end test in StringFunctionsSuite. Author: petermaxlee <petermaxlee@gmail.com> Closes #13966 from petermaxlee/SPARK-16276.
* [SPARK-16313][SQL] Spark should not silently drop exceptions in file listingReynold Xin2016-06-301-1/+1
| | | | | | | | | | | | ## What changes were proposed in this pull request? Spark silently drops exceptions during file listing. This is a very bad behavior because it can mask legitimate errors and the resulting plan will silently have 0 rows. This patch changes it to not silently drop the errors. ## How was this patch tested? Manually verified. Author: Reynold Xin <rxin@databricks.com> Closes #13987 from rxin/SPARK-16313.
* [SPARK-16336][SQL] Suggest doing table refresh upon FileNotFoundExceptionpetermaxlee2016-06-301-0/+88
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch appends a message to suggest users running refresh table or reloading data frames when Spark sees a FileNotFoundException due to stale, cached metadata. ## How was this patch tested? Added a unit test for this in MetadataCacheSuite. Author: petermaxlee <petermaxlee@gmail.com> Closes #14003 from petermaxlee/SPARK-16336.
* [SPARK-16289][SQL] Implement posexplode table generating functionDongjoon Hyun2016-06-302-60/+92
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR implements `posexplode` table generating function. Currently, master branch raises the following exception for `map` argument. It's different from Hive. **Before** ```scala scala> sql("select posexplode(map('a', 1, 'b', 2))").show org.apache.spark.sql.AnalysisException: No handler for Hive UDF ... posexplode() takes an array as a parameter; line 1 pos 7 ``` **After** ```scala scala> sql("select posexplode(map('a', 1, 'b', 2))").show +---+---+-----+ |pos|key|value| +---+---+-----+ | 0| a| 1| | 1| b| 2| +---+---+-----+ ``` For `array` argument, `after` is the same with `before`. ``` scala> sql("select posexplode(array(1, 2, 3))").show +---+---+ |pos|col| +---+---+ | 0| 1| | 1| 2| | 2| 3| +---+---+ ``` ## How was this patch tested? Pass the Jenkins tests with newly added testcases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13971 from dongjoon-hyun/SPARK-16289.
* [SPARK-13850] Force the sorter to Spill when number of elements in th…Sital Kedia2016-06-301-1/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Force the sorter to Spill when number of elements in the pointer array reach a certain size. This is to workaround the issue of timSort failing on large buffer size. ## How was this patch tested? Tested by running a job which was failing without this change due to TimSort bug. Author: Sital Kedia <skedia@fb.com> Closes #13107 from sitalkedia/fix_TimSort.
* [SPARK-16274][SQL] Implement xpath_booleanpetermaxlee2016-06-301-0/+32
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch implements xpath_boolean expression for Spark SQL, a xpath function that returns true or false. The implementation is modelled after Hive's xpath_boolean, except that how the expression handles null inputs. Hive throws a NullPointerException at runtime if either of the input is null. This implementation returns null if either of the input is null. ## How was this patch tested? Created two new test suites. One for unit tests covering the expression, and the other for end-to-end test in SQL. Author: petermaxlee <petermaxlee@gmail.com> Closes #13964 from petermaxlee/SPARK-16274.
* [SPARK-16267][TEST] Replace deprecated `CREATE TEMPORARY TABLE ... USING` ↵Dongjoon Hyun2016-06-295-11/+11
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | from testsuites. ## What changes were proposed in this pull request? After SPARK-15674, `DDLStrategy` prints out the following deprecation messages in the testsuites. ``` 12:10:53.284 WARN org.apache.spark.sql.execution.SparkStrategies$DDLStrategy: CREATE TEMPORARY TABLE normal_orc_source USING... is deprecated, please use CREATE TEMPORARY VIEW viewName USING... instead ``` Total : 40 - JDBCWriteSuite: 14 - DDLSuite: 6 - TableScanSuite: 6 - ParquetSourceSuite: 5 - OrcSourceSuite: 2 - SQLQuerySuite: 2 - HiveCommandSuite: 2 - JsonSuite: 1 - PrunedScanSuite: 1 - FilteredScanSuite 1 This PR replaces `CREATE TEMPORARY TABLE` with `CREATE TEMPORARY VIEW` in order to remove the deprecation messages in the above testsuites except `DDLSuite`, `SQLQuerySuite`, `HiveCommandSuite`. The Jenkins results shows only remaining 10 messages. https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/61422/consoleFull ## How was this patch tested? This is a testsuite-only change. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13956 from dongjoon-hyun/SPARK-16267.
* [SPARK-16134][SQL] optimizer rules for typed filterWenchen Fan2016-06-301-0/+1
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds 3 optimizer rules for typed filter: 1. push typed filter down through `SerializeFromObject` and eliminate the deserialization in filter condition. 2. pull typed filter up through `SerializeFromObject` and eliminate the deserialization in filter condition. 3. combine adjacent typed filters and share the deserialized object among all the condition expressions. This PR also adds `TypedFilter` logical plan, to separate it from normal filter, so that the concept is more clear and it's easier to write optimizer rules. ## How was this patch tested? `TypedFilterOptimizationSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #13846 from cloud-fan/filter.
* [SPARK-16006][SQL] Attemping to write empty DataFrame with no fields throws ↵Dongjoon Hyun2016-06-291-1/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | non-intuitive exception ## What changes were proposed in this pull request? This PR allows `emptyDataFrame.write` since the user didn't specify any partition columns. **Before** ```scala scala> spark.emptyDataFrame.write.parquet("/tmp/t1") org.apache.spark.sql.AnalysisException: Cannot use all columns for partition columns; scala> spark.emptyDataFrame.write.csv("/tmp/t1") org.apache.spark.sql.AnalysisException: Cannot use all columns for partition columns; ``` After this PR, there occurs no exceptions and the created directory has only one file, `_SUCCESS`, as expected. ## How was this patch tested? Pass the Jenkins tests including updated test cases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13730 from dongjoon-hyun/SPARK-16006.
* [SPARK-14480][SQL] Remove meaningless StringIteratorReader for CSV data source.hyukjinkwon2016-06-291-125/+0
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR removes meaningless `StringIteratorReader` for CSV data source. In `CSVParser.scala`, there is an `Reader` wrapping `Iterator` but there are two problems by this. Firstly, it was actually not faster than processing line by line with Iterator due to additional logics to wrap `Iterator` to `Reader`. Secondly, this brought a bit of complexity because it needs additional logics to allow every line to be read bytes by bytes. So, it was pretty difficult to figure out issues about parsing, (eg. SPARK-14103). A benchmark was performed manually and the results were below: - Original codes with Reader wrapping Iterator |End-to-end (ns) | Parse Time (ns) | |-----------------------|------------------------| |14116265034 |2008277960 | - New codes with Iterator |End-to-end (ns) | Parse Time (ns) | |-----------------------|------------------------| |13451699644 | 1549050564 | For the details for the environment, dataset and methods, please refer the JIRA ticket. ## How was this patch tested? Existing tests should cover this. Author: hyukjinkwon <gurwls223@gmail.com> Closes #13808 from HyukjinKwon/SPARK-14480-small.
* [SPARK-16157][SQL] Add New Methods for comments in StructField and StructTypegatorsmile2016-06-294-11/+30
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? Based on the previous discussion with cloud-fan hvanhovell in another related PR https://github.com/apache/spark/pull/13764#discussion_r67994276, it looks reasonable to add convenience methods for users to add `comment` when defining `StructField`. Currently, the column-related `comment` attribute is stored in `Metadata` of `StructField`. For example, users can add the `comment` attribute using the following way: ```Scala StructType( StructField( "cl1", IntegerType, nullable = false, new MetadataBuilder().putString("comment", "test").build()) :: Nil) ``` This PR is to add more user friendly methods for the `comment` attribute when defining a `StructField`. After the changes, users are provided three different ways to do it: ```Scala val struct = (new StructType) .add("a", "int", true, "test1") val struct = (new StructType) .add("c", StringType, true, "test3") val struct = (new StructType) .add(StructField("d", StringType).withComment("test4")) ``` #### How was this patch tested? Added test cases: - `DataTypeSuite` is for testing three types of API changes, - `DataFrameReaderWriterSuite` is for parquet, json and csv formats - using in-memory catalog - `OrcQuerySuite.scala` is for orc format using Hive-metastore Author: gatorsmile <gatorsmile@gmail.com> Closes #13860 from gatorsmile/newMethodForComment.
* [SPARK-16291][SQL] CheckAnalysis should capture nested aggregate functions ↵Cheng Lian2016-06-291-3/+1
| | | | | | | | | | | | | | | | | | | | that reference no input attributes ## What changes were proposed in this pull request? `MAX(COUNT(*))` is invalid since aggregate expression can't be nested within another aggregate expression. This case should be captured at analysis phase, but somehow sneaks off to runtime. The reason is that when checking aggregate expressions in `CheckAnalysis`, a checking branch treats all expressions that reference no input attributes as valid ones. However, `MAX(COUNT(*))` is translated into `MAX(COUNT(1))` at analysis phase and also references no input attribute. This PR fixes this issue by removing the aforementioned branch. ## How was this patch tested? New test case added in `AnalysisErrorSuite`. Author: Cheng Lian <lian@databricks.com> Closes #13968 from liancheng/spark-16291-nested-agg-functions.
* [MINOR][DOCS][STRUCTURED STREAMING] Minor doc fixes around `DataFrameWriter` ↵Burak Yavuz2016-06-281-2/+2
| | | | | | | | | | | | and `DataStreamWriter` ## What changes were proposed in this pull request? Fixes a couple old references to `DataFrameWriter.startStream` to `DataStreamWriter.start Author: Burak Yavuz <brkyvz@gmail.com> Closes #13952 from brkyvz/minor-doc-fix.
* [SPARK-16100][SQL] fix bug when use Map as the buffer type of AggregatorWenchen Fan2016-06-291-0/+15
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The root cause is in `MapObjects`. Its parameter `loopVar` is not declared as child, but sometimes can be same with `lambdaFunction`(e.g. the function that takes `loopVar` and produces `lambdaFunction` may be `identity`), which is a child. This brings trouble when call `withNewChildren`, it may mistakenly treat `loopVar` as a child and cause `IndexOutOfBoundsException: 0` later. This PR fixes this bug by simply pulling out the paremters from `LambdaVariable` and pass them to `MapObjects` directly. ## How was this patch tested? new test in `DatasetAggregatorSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #13835 from cloud-fan/map-objects.
* [SPARK-16236][SQL] Add Path Option back to Load API in DataFrameReadergatorsmile2016-06-281-0/+28
| | | | | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? koertkuipers identified the PR https://github.com/apache/spark/pull/13727/ changed the behavior of `load` API. After the change, the `load` API does not add the value of `path` into the `options`. Thank you! This PR is to add the option `path` back to `load()` API in `DataFrameReader`, if and only if users specify one and only one `path` in the `load` API. For example, users can see the `path` option after the following API call, ```Scala spark.read .format("parquet") .load("/test") ``` #### How was this patch tested? Added test cases. Author: gatorsmile <gatorsmile@gmail.com> Closes #13933 from gatorsmile/optionPath.
* [SPARK-16181][SQL] outer join with isNull filter may return wrong resultWenchen Fan2016-06-281-0/+9
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The root cause is: the output attributes of outer join are derived from its children, while they are actually different attributes(outer join can return null). We have already added some special logic to handle it, e.g. `PushPredicateThroughJoin` won't push down predicates through outer join side, `FixNullability`. This PR adds one more special logic in `FoldablePropagation`. ## How was this patch tested? new test in `DataFrameSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #13884 from cloud-fan/bug.
* [SPARK-16128][SQL] Allow setting length of characters to be truncated to, in ↵Prashant Sharma2016-06-282-4/+25
| | | | | | | | | | | | | | | | | | Dataset.show function. ## What changes were proposed in this pull request? Allowing truncate to a specific number of character is convenient at times, especially while operating from the REPL. Sometimes those last few characters make all the difference, and showing everything brings in whole lot of noise. ## How was this patch tested? Existing tests. + 1 new test in DataFrameSuite. For SparkR and pyspark, existing tests and manual testing. Author: Prashant Sharma <prashsh1@in.ibm.com> Author: Prashant Sharma <prashant@apache.org> Closes #13839 from ScrapCodes/add_truncateTo_DF.show.
* [SPARK-16220][SQL] Add scope to show functionsHerman van Hovell2016-06-273-6/+28
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Spark currently shows all functions when issue a `SHOW FUNCTIONS` command. This PR refines the `SHOW FUNCTIONS` command by allowing users to select all functions, user defined function or system functions. The following syntax can be used: **ALL** (default) ```SHOW FUNCTIONS``` ```SHOW ALL FUNCTIONS``` **SYSTEM** ```SHOW SYSTEM FUNCTIONS``` **USER** ```SHOW USER FUNCTIONS``` ## How was this patch tested? Updated tests and added tests to the DDLSuite Author: Herman van Hovell <hvanhovell@databricks.com> Closes #13929 from hvanhovell/SPARK-16220.
* [SPARK-16220][SQL] Revert Change to Bring Back SHOW FUNCTIONS FunctionalityBill Chambers2016-06-272-6/+5
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? - Fix tests regarding show functions functionality - Revert `catalog.ListFunctions` and `SHOW FUNCTIONS` to return to `Spark 1.X` functionality. Cherry picked changes from this PR: https://github.com/apache/spark/pull/13413/files ## How was this patch tested? Unit tests. Author: Bill Chambers <bill@databricks.com> Author: Bill Chambers <wchambers@ischool.berkeley.edu> Closes #13916 from anabranch/master.
* [SPARK-10591][SQL][TEST] Add a testcase to ensure if `checkAnswer` handles ↵Dongjoon Hyun2016-06-271-0/+7
| | | | | | | | | | | | | | | | map correctly ## What changes were proposed in this pull request? This PR adds a testcase to ensure if `checkAnswer` handles Map type correctly. ## How was this patch tested? Pass the jenkins tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13913 from dongjoon-hyun/SPARK-10591.
* [SPARK-16186] [SQL] Support partition batch pruning with `IN` predicate in ↵Dongjoon Hyun2016-06-241-1/+22
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | InMemoryTableScanExec ## What changes were proposed in this pull request? One of the most frequent usage patterns for Spark SQL is using **cached tables**. This PR improves `InMemoryTableScanExec` to handle `IN` predicate efficiently by pruning partition batches. Of course, the performance improvement varies over the queries and the datasets. But, for the following simple query, the query duration in Spark UI goes from 9 seconds to 50~90ms. It's about over 100 times faster. **Before** ```scala $ bin/spark-shell --driver-memory 6G scala> val df = spark.range(2000000000) scala> df.createOrReplaceTempView("t") scala> spark.catalog.cacheTable("t") scala> sql("select id from t where id = 1").collect() // About 2 mins scala> sql("select id from t where id = 1").collect() // less than 90ms scala> sql("select id from t where id in (1,2,3)").collect() // 9 seconds ``` **After** ```scala scala> sql("select id from t where id in (1,2,3)").collect() // less than 90ms ``` This PR has impacts over 35 queries of TPC-DS if the tables are cached. Note that this optimization is applied for `IN`. To apply `IN` predicate having more than 10 items, `spark.sql.optimizer.inSetConversionThreshold` option should be increased. ## How was this patch tested? Pass the Jenkins tests (including new testcases). Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13887 from dongjoon-hyun/SPARK-16186.
* [SPARK-16192][SQL] Add type checks in CollectSetTakeshi YAMAMURO2016-06-241-0/+10
| | | | | | | | | | | | | ## What changes were proposed in this pull request? `CollectSet` cannot have map-typed data because MapTypeData does not implement `equals`. So, this pr is to add type checks in `CheckAnalysis`. ## How was this patch tested? Added tests to check failures when we found map-typed data in `CollectSet`. Author: Takeshi YAMAMURO <linguin.m.s@gmail.com> Closes #13892 from maropu/SPARK-16192.
* [SPARK-16195][SQL] Allow users to specify empty over clause in window ↵Dilip Biswal2016-06-241-0/+12
| | | | | | | | | | | | | | | | | | | | | | | | | | expressions through dataset API ## What changes were proposed in this pull request? Allow to specify empty over clause in window expressions through dataset API In SQL, its allowed to specify an empty OVER clause in the window expression. ```SQL select area, sum(product) over () as c from windowData where product > 3 group by area, product having avg(month) > 0 order by avg(month), product ``` In this case the analytic function sum is presented based on all the rows of the result set Currently its not allowed through dataset API and is handled in this PR. ## How was this patch tested? Added a new test in DataframeWindowSuite Author: Dilip Biswal <dbiswal@us.ibm.com> Closes #13897 from dilipbiswal/spark-empty-over.
* Revert "[SPARK-16186] [SQL] Support partition batch pruning with `IN` ↵Davies Liu2016-06-241-22/+1
| | | | | | predicate in InMemoryTableScanExec" This reverts commit a65bcbc27dcd9b3053cb13c5d67251c8d48f4397.
* [SPARK-16186] [SQL] Support partition batch pruning with `IN` predicate in ↵Dongjoon Hyun2016-06-241-1/+22
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | InMemoryTableScanExec ## What changes were proposed in this pull request? One of the most frequent usage patterns for Spark SQL is using **cached tables**. This PR improves `InMemoryTableScanExec` to handle `IN` predicate efficiently by pruning partition batches. Of course, the performance improvement varies over the queries and the datasets. But, for the following simple query, the query duration in Spark UI goes from 9 seconds to 50~90ms. It's about over 100 times faster. **Before** ```scala $ bin/spark-shell --driver-memory 6G scala> val df = spark.range(2000000000) scala> df.createOrReplaceTempView("t") scala> spark.catalog.cacheTable("t") scala> sql("select id from t where id = 1").collect() // About 2 mins scala> sql("select id from t where id = 1").collect() // less than 90ms scala> sql("select id from t where id in (1,2,3)").collect() // 9 seconds ``` **After** ```scala scala> sql("select id from t where id in (1,2,3)").collect() // less than 90ms ``` This PR has impacts over 35 queries of TPC-DS if the tables are cached. Note that this optimization is applied for `IN`. To apply `IN` predicate having more than 10 items, `spark.sql.optimizer.inSetConversionThreshold` option should be increased. ## How was this patch tested? Pass the Jenkins tests (including new testcases). Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13887 from dongjoon-hyun/SPARK-16186.
* [SPARK-16123] Avoid NegativeArraySizeException while reserving additional ↵Sameer Agarwal2016-06-231-0/+19
| | | | | | | | | | | | | | | | capacity in VectorizedColumnReader ## What changes were proposed in this pull request? This patch fixes an overflow bug in vectorized parquet reader where both off-heap and on-heap variants of `ColumnVector.reserve()` can unfortunately overflow while reserving additional capacity during reads. ## How was this patch tested? Manual Tests Author: Sameer Agarwal <sameer@databricks.com> Closes #13832 from sameeragarwal/negative-array.
* [SPARK-16165][SQL] Fix the update logic for InMemoryTableScanExec.readBatchesDongjoon Hyun2016-06-241-0/+15
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, `readBatches` accumulator of `InMemoryTableScanExec` is updated only when `spark.sql.inMemoryColumnarStorage.partitionPruning` is true. Although this metric is used for only testing purpose, we had better have correct metric without considering SQL options. ## How was this patch tested? Pass the Jenkins tests (including a new testcase). Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13870 from dongjoon-hyun/SPARK-16165.
* [SPARK-15443][SQL] Fix 'explain' for streaming DatasetShixiong Zhu2016-06-232-0/+65
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? - Fix the `explain` command for streaming Dataset/DataFrame. E.g., ``` == Parsed Logical Plan == 'SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7] +- 'MapElements <function1>, obj#6: java.lang.String +- 'DeserializeToObject unresolveddeserializer(createexternalrow(getcolumnbyordinal(0, StringType).toString, StructField(value,StringType,true))), obj#5: org.apache.spark.sql.Row +- Filter <function1>.apply +- StreamingRelation FileSource[/Users/zsx/stream], [value#0] == Analyzed Logical Plan == value: string SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7] +- MapElements <function1>, obj#6: java.lang.String +- DeserializeToObject createexternalrow(value#0.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row +- Filter <function1>.apply +- StreamingRelation FileSource[/Users/zsx/stream], [value#0] == Optimized Logical Plan == SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7] +- MapElements <function1>, obj#6: java.lang.String +- DeserializeToObject createexternalrow(value#0.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row +- Filter <function1>.apply +- StreamingRelation FileSource[/Users/zsx/stream], [value#0] == Physical Plan == *SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7] +- *MapElements <function1>, obj#6: java.lang.String +- *DeserializeToObject createexternalrow(value#0.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row +- *Filter <function1>.apply +- StreamingRelation FileSource[/Users/zsx/stream], [value#0] ``` - Add `StreamingQuery.explain` to display the last execution plan. E.g., ``` == Parsed Logical Plan == SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7] +- MapElements <function1>, obj#6: java.lang.String +- DeserializeToObject createexternalrow(value#12.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row +- Filter <function1>.apply +- Relation[value#12] text == Analyzed Logical Plan == value: string SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7] +- MapElements <function1>, obj#6: java.lang.String +- DeserializeToObject createexternalrow(value#12.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row +- Filter <function1>.apply +- Relation[value#12] text == Optimized Logical Plan == SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7] +- MapElements <function1>, obj#6: java.lang.String +- DeserializeToObject createexternalrow(value#12.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row +- Filter <function1>.apply +- Relation[value#12] text == Physical Plan == *SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7] +- *MapElements <function1>, obj#6: java.lang.String +- *DeserializeToObject createexternalrow(value#12.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row +- *Filter <function1>.apply +- *Scan text [value#12] Format: org.apache.spark.sql.execution.datasources.text.TextFileFormat1836ab91, InputPaths: file:/Users/zsx/stream/a.txt, file:/Users/zsx/stream/b.txt, file:/Users/zsx/stream/c.txt, PushedFilters: [], ReadSchema: struct<value:string> ``` ## How was this patch tested? The added unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes #13815 from zsxwing/sdf-explain.
* [SPARK-16163] [SQL] Cache the statistics for logical plansDavies Liu2016-06-231-2/+1
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This calculation of statistics is not trivial anymore, it could be very slow on large query (for example, TPC-DS Q64 took several minutes to plan). During the planning of a query, the statistics of any logical plan should not change (even InMemoryRelation), so we should use `lazy val` to cache the statistics. For InMemoryRelation, the statistics could be updated after materialization, it's only useful when used in another query (before planning), because once we finished the planning, the statistics will not be used anymore. ## How was this patch tested? Testsed with TPC-DS Q64, it could be planned in a second after the patch. Author: Davies Liu <davies@databricks.com> Closes #13871 from davies/fix_statistics.
* [SPARK-16116][SQL] ConsoleSink should not require checkpointLocationShixiong Zhu2016-06-231-0/+10
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? When the user uses `ConsoleSink`, we should use a temp location if `checkpointLocation` is not specified. ## How was this patch tested? The added unit test. Author: Shixiong Zhu <shixiong@databricks.com> Closes #13817 from zsxwing/console-checkpoint.
* [SPARK-15230][SQL] distinct() does not handle column name with dot properlybomeng2016-06-231-0/+5
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? When table is created with column name containing dot, distinct() will fail to run. For example, ```scala val rowRDD = sparkContext.parallelize(Seq(Row(1), Row(1), Row(2))) val schema = StructType(Array(StructField("column.with.dot", IntegerType, nullable = false))) val df = spark.createDataFrame(rowRDD, schema) ``` running the following will have no problem: ```scala df.select(new Column("`column.with.dot`")) ``` but running the query with additional distinct() will cause exception: ```scala df.select(new Column("`column.with.dot`")).distinct() ``` The issue is that distinct() will try to resolve the column name, but the column name in the schema does not have backtick with it. So the solution is to add the backtick before passing the column name to resolve(). ## How was this patch tested? Added a new test case. Author: bomeng <bmeng@us.ibm.com> Closes #13140 from bomeng/SPARK-15230.
* [SPARK-16024][SQL][TEST] Verify Column Comment for Data Source Tablesgatorsmile2016-06-232-3/+20
| | | | | | | | | | | | | | #### What changes were proposed in this pull request? This PR is to improve test coverage. It verifies whether `Comment` of `Column` can be appropriate handled. The test cases verify the related parts in Parser, both SQL and DataFrameWriter interface, and both Hive Metastore catalog and In-memory catalog. #### How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #13764 from gatorsmile/dataSourceComment.
* [SPARK-16097][SQL] Encoders.tuple should handle null object correctlyWenchen Fan2016-06-221-0/+7
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Although the top level input object can not be null, but when we use `Encoders.tuple` to combine 2 encoders, their input objects are not top level anymore and can be null. We should handle this case. ## How was this patch tested? new test in DatasetSuite Author: Wenchen Fan <wenchen@databricks.com> Closes #13807 from cloud-fan/bug.
* [SPARK-16121] ListingFileCatalog does not list in parallel anymoreYin Huai2016-06-221-1/+44
| | | | | | | | | | | | ## What changes were proposed in this pull request? Seems the fix of SPARK-14959 breaks the parallel partitioning discovery. This PR fixes the problem ## How was this patch tested? Tested manually. (This PR also adds a proper test for SPARK-14959) Author: Yin Huai <yhuai@databricks.com> Closes #13830 from yhuai/SPARK-16121.
* [SPARK-16002][SQL] Sleep when no new data arrives to avoid 100% CPU usageShixiong Zhu2016-06-211-0/+5
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add a configuration to allow people to set a minimum polling delay when no new data arrives (default is 10ms). This PR also cleans up some INFO logs. ## How was this patch tested? Existing unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes #13718 from zsxwing/SPARK-16002.
* [SPARK-16044][SQL] input_file_name() returns empty strings in data sources ↵hyukjinkwon2016-06-201-2/+32
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | based on NewHadoopRDD ## What changes were proposed in this pull request? This PR makes `input_file_name()` function return the file paths not empty strings for external data sources based on `NewHadoopRDD`, such as [spark-redshift](https://github.com/databricks/spark-redshift/blob/cba5eee1ab79ae8f0fa9e668373a54d2b5babf6b/src/main/scala/com/databricks/spark/redshift/RedshiftRelation.scala#L149) and [spark-xml](https://github.com/databricks/spark-xml/blob/master/src/main/scala/com/databricks/spark/xml/util/XmlFile.scala#L39-L47). The codes with the external data sources below: ```scala df.select(input_file_name).show() ``` will produce - **Before** ``` +-----------------+ |input_file_name()| +-----------------+ | | +-----------------+ ``` - **After** ``` +--------------------+ | input_file_name()| +--------------------+ |file:/private/var...| +--------------------+ ``` ## How was this patch tested? Unit tests in `ColumnExpressionSuite`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #13759 from HyukjinKwon/SPARK-16044.
* [SPARK-16056][SPARK-16057][SPARK-16058][SQL] Fix Multiple Bugs in Column ↵gatorsmile2016-06-201-0/+65
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Partitioning in JDBC Source #### What changes were proposed in this pull request? This PR is to fix the following bugs: **Issue 1: Wrong Results when lowerBound is larger than upperBound in Column Partitioning** ```scala spark.read.jdbc( url = urlWithUserAndPass, table = "TEST.seq", columnName = "id", lowerBound = 4, upperBound = 0, numPartitions = 3, connectionProperties = new Properties) ``` **Before code changes:** The returned results are wrong and the generated partitions are wrong: ``` Part 0 id < 3 or id is null Part 1 id >= 3 AND id < 2 Part 2 id >= 2 ``` **After code changes:** Issue an `IllegalArgumentException` exception: ``` Operation not allowed: the lower bound of partitioning column is larger than the upper bound. lowerBound: 5; higherBound: 1 ``` **Issue 2: numPartitions is more than the number of key values between upper and lower bounds** ```scala spark.read.jdbc( url = urlWithUserAndPass, table = "TEST.seq", columnName = "id", lowerBound = 1, upperBound = 5, numPartitions = 10, connectionProperties = new Properties) ``` **Before code changes:** Returned correct results but the generated partitions are very inefficient, like: ``` Partition 0: id < 1 or id is null Partition 1: id >= 1 AND id < 1 Partition 2: id >= 1 AND id < 1 Partition 3: id >= 1 AND id < 1 Partition 4: id >= 1 AND id < 1 Partition 5: id >= 1 AND id < 1 Partition 6: id >= 1 AND id < 1 Partition 7: id >= 1 AND id < 1 Partition 8: id >= 1 AND id < 1 Partition 9: id >= 1 ``` **After code changes:** Adjust `numPartitions` and can return the correct answers: ``` Partition 0: id < 2 or id is null Partition 1: id >= 2 AND id < 3 Partition 2: id >= 3 AND id < 4 Partition 3: id >= 4 ``` **Issue 3: java.lang.ArithmeticException when numPartitions is zero** ```Scala spark.read.jdbc( url = urlWithUserAndPass, table = "TEST.seq", columnName = "id", lowerBound = 0, upperBound = 4, numPartitions = 0, connectionProperties = new Properties) ``` **Before code changes:** Got the following exception: ``` java.lang.ArithmeticException: / by zero ``` **After code changes:** Able to return a correct answer by disabling column partitioning when numPartitions is equal to or less than zero #### How was this patch tested? Added test cases to verify the results Author: gatorsmile <gatorsmile@gmail.com> Closes #13773 from gatorsmile/jdbcPartitioning.
* [SPARK-15982][SPARK-16009][SPARK-16007][SQL] Harmonize the behavior of ↵Tathagata Das2016-06-202-22/+322
| | | | | | | | | | | | | | | | | | | | | | | | | DataFrameReader.text/csv/json/parquet/orc ## What changes were proposed in this pull request? Issues with current reader behavior. - `text()` without args returns an empty DF with no columns -> inconsistent, its expected that text will always return a DF with `value` string field, - `textFile()` without args fails with exception because of the above reason, it expected the DF returned by `text()` to have a `value` field. - `orc()` does not have var args, inconsistent with others - `json(single-arg)` was removed, but that caused source compatibility issues - [SPARK-16009](https://issues.apache.org/jira/browse/SPARK-16009) - user specified schema was not respected when `text/csv/...` were used with no args - [SPARK-16007](https://issues.apache.org/jira/browse/SPARK-16007) The solution I am implementing is to do the following. - For each format, there will be a single argument method, and a vararg method. For json, parquet, csv, text, this means adding json(string), etc.. For orc, this means adding orc(varargs). - Remove the special handling of text(), csv(), etc. that returns empty dataframe with no fields. Rather pass on the empty sequence of paths to the datasource, and let each datasource handle it right. For e.g, text data source, should return empty DF with schema (value: string) - Deduped docs and fixed their formatting. ## How was this patch tested? Added new unit tests for Scala and Java tests Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #13727 from tdas/SPARK-15982.
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