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* [SPARK-16879][SQL] unify logical plans for CREATE TABLE and CTASWenchen Fan2016-08-0517-372/+417
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? we have various logical plans for CREATE TABLE and CTAS: `CreateTableUsing`, `CreateTableUsingAsSelect`, `CreateHiveTableAsSelectLogicalPlan`. This PR unifies them to reduce the complexity and centralize the error handling. ## How was this patch tested? existing tests Author: Wenchen Fan <wenchen@databricks.com> Closes #14482 from cloud-fan/table.
* [SPARK-15726][SQL] Make DatasetBenchmark fairer among Dataset, DataFrame and RDDHiroshi Inoue2016-08-051-25/+25
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? DatasetBenchmark compares the performances of RDD, DataFrame and Dataset while running the same operations. However, there are two problems that make the comparisons unfair. 1) In backToBackMap test case, only DataFrame implementation executes less work compared to RDD or Dataset implementations. This test case processes Long+String pairs, but the output from the DataFrame implementation does not include String part while RDD or Dataset generates Long+String pairs as output. This difference significantly changes the performance characteristics due to the String manipulation and creation overheads. 2) In back-to-back map and back-to-back filter test cases, `map` or `filter` operation is executed only once regardless of `numChains` parameter for RDD. Hence the execution times for RDD have been largely underestimated. Of course, these issues do not affect Spark users, but it may confuse Spark developers. ## How was this patch tested? By executing the DatasetBenchmark Author: Hiroshi Inoue <inouehrs@jp.ibm.com> Closes #13459 from inouehrs/fix_benchmark_fairness.
* [SPARK-16907][SQL] Fix performance regression for parquet table when ↵Sean Zhong2016-08-051-1/+7
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | vectorized parquet record reader is not being used ## What changes were proposed in this pull request? For non-partitioned parquet table, if the vectorized parquet record reader is not being used, Spark 2.0 adds an extra unnecessary memory copy to append partition values for each row. There are several typical cases that vectorized parquet record reader is not being used: 1. When the table schema is not flat, like containing nested fields. 2. When `spark.sql.parquet.enableVectorizedReader = false` By fixing this bug, we get about 20% - 30% performance gain in test case like this: ``` // Generates parquet table with nested columns spark.range(100000000).select(struct($"id").as("nc")).write.parquet("/tmp/data4") def time[R](block: => R): Long = { val t0 = System.nanoTime() val result = block // call-by-name val t1 = System.nanoTime() println("Elapsed time: " + (t1 - t0)/1000000 + "ms") (t1 - t0)/1000000 } val x = ((0 until 20).toList.map(x => time(spark.read.parquet("/tmp/data4").filter($"nc.id" < 100).collect()))).sum/20 ``` ## How was this patch tested? After a few times warm up, we get 26% performance improvement Before fix: ``` Average: 4584ms, raw data (10 tries): 4726ms 4509ms 4454ms 4879ms 4586ms 4733ms 4500ms 4361ms 4456ms 4640ms ``` After fix: ``` Average: 3614ms, raw data(10 tries): 3554ms 3740ms 4019ms 3439ms 3460ms 3664ms 3557ms 3584ms 3612ms 3531ms ``` Test env: Intel(R) Core(TM) i7-6700 CPU 3.40GHz, Intel SSD SC2KW24 Author: Sean Zhong <seanzhong@databricks.com> Closes #14445 from clockfly/fix_parquet_regression_2.
* [SPARK-16875][SQL] Add args checking for DataSet randomSplit and sampleZheng RuiFeng2016-08-041-2/+12
| | | | | | | | | | | | | ## What changes were proposed in this pull request? Add the missing args-checking for randomSplit and sample ## How was this patch tested? unit tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #14478 from zhengruifeng/fix_randomSplit.
* [SPARK-16884] Move DataSourceScanExec out of ExistingRDD.scala fileEric Liang2016-08-043-504/+525
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This moves DataSourceScanExec out so it's more discoverable, and now that it doesn't necessarily depend on an existing RDD. cc davies ## How was this patch tested? Existing tests. Author: Eric Liang <ekl@databricks.com> Closes #14487 from ericl/split-scan.
* [SPARK-16802] [SQL] fix overflow in LongToUnsafeRowMapDavies Liu2016-08-042-6/+55
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch fix the overflow in LongToUnsafeRowMap when the range of key is very wide (the key is much much smaller then minKey, for example, key is Long.MinValue, minKey is > 0). ## How was this patch tested? Added regression test (also for SPARK-16740) Author: Davies Liu <davies@databricks.com> Closes #14464 from davies/fix_overflow.
* [SPARK-16853][SQL] fixes encoder error in DataSet typed selectSean Zhong2016-08-043-9/+26
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? For DataSet typed select: ``` def select[U1: Encoder](c1: TypedColumn[T, U1]): Dataset[U1] ``` If type T is a case class or a tuple class that is not atomic, the resulting logical plan's schema will mismatch with `Dataset[T]` encoder's schema, which will cause encoder error and throw AnalysisException. ### Before change: ``` scala> case class A(a: Int, b: Int) scala> Seq((0, A(1,2))).toDS.select($"_2".as[A]) org.apache.spark.sql.AnalysisException: cannot resolve '`a`' given input columns: [_2]; .. ``` ### After change: ``` scala> case class A(a: Int, b: Int) scala> Seq((0, A(1,2))).toDS.select($"_2".as[A]).show +---+---+ | a| b| +---+---+ | 1| 2| +---+---+ ``` ## How was this patch tested? Unit test. Author: Sean Zhong <seanzhong@databricks.com> Closes #14474 from clockfly/SPARK-16853.
* [SPARK-16867][SQL] createTable and alterTable in ExternalCatalog should not ↵Wenchen Fan2016-08-046-31/+28
| | | | | | | | | | | | | | | | take db ## What changes were proposed in this pull request? These 2 methods take `CatalogTable` as parameter, which already have the database information. ## How was this patch tested? existing test Author: Wenchen Fan <wenchen@databricks.com> Closes #14476 from cloud-fan/minor5.
* [SPARK-16888][SQL] Implements eval method for expression AssertNotNullSean Zhong2016-08-042-7/+21
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Implements `eval()` method for expression `AssertNotNull` so that we can convert local projection on LocalRelation to another LocalRelation. ### Before change: ``` scala> import org.apache.spark.sql.catalyst.dsl.expressions._ scala> import org.apache.spark.sql.catalyst.expressions.objects.AssertNotNull scala> import org.apache.spark.sql.Column scala> case class A(a: Int) scala> Seq((A(1),2)).toDS().select(new Column(AssertNotNull("_1".attr, Nil))).explain java.lang.UnsupportedOperationException: Only code-generated evaluation is supported. at org.apache.spark.sql.catalyst.expressions.objects.AssertNotNull.eval(objects.scala:850) ... ``` ### After the change: ``` scala> Seq((A(1),2)).toDS().select(new Column(AssertNotNull("_1".attr, Nil))).explain(true) == Parsed Logical Plan == 'Project [assertnotnull('_1) AS assertnotnull(_1)#5] +- LocalRelation [_1#2, _2#3] == Analyzed Logical Plan == assertnotnull(_1): struct<a:int> Project [assertnotnull(_1#2) AS assertnotnull(_1)#5] +- LocalRelation [_1#2, _2#3] == Optimized Logical Plan == LocalRelation [assertnotnull(_1)#5] == Physical Plan == LocalTableScan [assertnotnull(_1)#5] ``` ## How was this patch tested? Unit test. Author: Sean Zhong <seanzhong@databricks.com> Closes #14486 from clockfly/assertnotnull_eval.
* [MINOR][SQL] Fix minor formatting issue of SortAggregateExec.toStringCheng Lian2016-08-041-3/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes a minor formatting issue (missing space after comma) of `SorgAggregateExec.toString`. Before: ``` SortAggregate(key=[a#76,b#77], functions=[max(c#78),min(c#78)], output=[a#76,b#77,max(c)#89,min(c)#90]) +- *Sort [a#76 ASC, b#77 ASC], false, 0 +- Exchange hashpartitioning(a#76, b#77, 200) +- SortAggregate(key=[a#76,b#77], functions=[partial_max(c#78),partial_min(c#78)], output=[a#76,b#77,max#99,min#100]) +- *Sort [a#76 ASC, b#77 ASC], false, 0 +- LocalTableScan <empty>, [a#76, b#77, c#78] ``` After: ``` SortAggregate(key=[a#76, b#77], functions=[max(c#78), min(c#78)], output=[a#76, b#77, max(c)#89, min(c)#90]) +- *Sort [a#76 ASC, b#77 ASC], false, 0 +- Exchange hashpartitioning(a#76, b#77, 200) +- SortAggregate(key=[a#76, b#77], functions=[partial_max(c#78), partial_min(c#78)], output=[a#76, b#77, max#99, min#100]) +- *Sort [a#76 ASC, b#77 ASC], false, 0 +- LocalTableScan <empty>, [a#76, b#77, c#78] ``` ## How was this patch tested? Manually tested. Author: Cheng Lian <lian@databricks.com> Closes #14480 from liancheng/fix-sort-based-agg-string-format.
* [SPARK-16814][SQL] Fix deprecated parquet constructor usageHolden Karau2016-08-033-6/+29
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Replace deprecated ParquetWriter with the new builders ## How was this patch tested? Existing tests Author: Holden Karau <holden@us.ibm.com> Closes #14419 from holdenk/SPARK-16814-fix-deprecated-parquet-constructor-usage.
* [SPARK-14204][SQL] register driverClass rather than user-specified classKevin McHale2016-08-031-1/+1
| | | | | | | | | | | | | | This is a pull request that was originally merged against branch-1.6 as #12000, now being merged into master as well. srowen zzcclp JoshRosen This pull request fixes an issue in which cluster-mode executors fail to properly register a JDBC driver when the driver is provided in a jar by the user, but the driver class name is derived from a JDBC URL (rather than specified by the user). The consequence of this is that all JDBC accesses under the described circumstances fail with an IllegalStateException. I reported the issue here: https://issues.apache.org/jira/browse/SPARK-14204 My proposed solution is to have the executors register the JDBC driver class under all circumstances, not only when the driver is specified by the user. This patch was tested manually. I built an assembly jar, deployed it to a cluster, and confirmed that the problem was fixed. Author: Kevin McHale <kevin@premise.com> Closes #14420 from mchalek/mchalek-jdbc_driver_registration.
* [SPARK-16596] [SQL] Refactor DataSourceScanExec to do partition discovery at ↵Eric Liang2016-08-039-291/+356
| | | | | | | | | | | | | | | | | | | | execution instead of planning time ## What changes were proposed in this pull request? Partition discovery is rather expensive, so we should do it at execution time instead of during physical planning. Right now there is not much benefit since ListingFileCatalog will read scan for all partitions at planning time anyways, but this can be optimized in the future. Also, there might be more information for partition pruning not available at planning time. This PR moves a lot of the file scan logic from planning to execution time. All file scan operations are handled by `FileSourceScanExec`, which handles both batched and non-batched file scans. This requires some duplication with `RowDataSourceScanExec`, but is probably worth it so that `FileSourceScanExec` does not need to depend on an input RDD. TODO: In another pr, move DataSourceScanExec to it's own file. ## How was this patch tested? Existing tests (it might be worth adding a test that catalog.listFiles() is delayed until execution, but this can be delayed until there is an actual benefit to doing so). Author: Eric Liang <ekl@databricks.com> Closes #14241 from ericl/refactor.
* [SPARK-16714][SPARK-16735][SPARK-16646] array, map, greatest, least's type ↵Wenchen Fan2016-08-033-18/+97
| | | | | | | | | | | | | | | | | | | | | | | | | | | coercion should handle decimal type ## What changes were proposed in this pull request? Here is a table about the behaviours of `array`/`map` and `greatest`/`least` in Hive, MySQL and Postgres: | |Hive|MySQL|Postgres| |---|---|---|---|---| |`array`/`map`|can find a wider type with decimal type arguments, and will truncate the wider decimal type if necessary|can find a wider type with decimal type arguments, no truncation problem|can find a wider type with decimal type arguments, no truncation problem| |`greatest`/`least`|can find a wider type with decimal type arguments, and truncate if necessary, but can't do string promotion|can find a wider type with decimal type arguments, no truncation problem, but can't do string promotion|can find a wider type with decimal type arguments, no truncation problem, but can't do string promotion| I think these behaviours makes sense and Spark SQL should follow them. This PR fixes `array` and `map` by using `findWiderCommonType` to get the wider type. This PR fixes `greatest` and `least` by add a `findWiderTypeWithoutStringPromotion`, which provides similar semantic of `findWiderCommonType`, but without string promotion. ## How was this patch tested? new tests in `TypeCoersionSuite` Author: Wenchen Fan <wenchen@databricks.com> Author: Yin Huai <yhuai@databricks.com> Closes #14439 from cloud-fan/bug.
* [SQL][MINOR] use stricter type parameter to make it clear that parquet ↵Wenchen Fan2016-08-033-10/+10
| | | | | | | | | | | | | | | | reader returns UnsafeRow ## What changes were proposed in this pull request? a small code style change, it's better to make the type parameter more accurate. ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #14458 from cloud-fan/parquet.
* [SPARK-16858][SQL][TEST] Removal of TestHiveSharedStategatorsmile2016-08-022-60/+20
| | | | | | | | | | | | | | ### What changes were proposed in this pull request? This PR is to remove `TestHiveSharedState`. Also, this is also associated with the Hive refractoring for removing `HiveSharedState`. ### How was this patch tested? The existing test cases Author: gatorsmile <gatorsmile@gmail.com> Closes #14463 from gatorsmile/removeTestHiveSharedState.
* [SPARK-16855][SQL] move Greatest and Least from conditionalExpressions.scala ↵Wenchen Fan2016-08-024-229/+228
| | | | | | | | | | | | | | | | to arithmetic.scala ## What changes were proposed in this pull request? `Greatest` and `Least` are not conditional expressions, but arithmetic expressions. ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #14460 from cloud-fan/move.
* [SPARK-16836][SQL] Add support for CURRENT_DATE/CURRENT_TIMESTAMP literalsHerman van Hovell2016-08-024-2/+32
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In Spark 1.6 (with Hive support) we could use `CURRENT_DATE` and `CURRENT_TIMESTAMP` functions as literals (without adding braces), for example: ```SQL select /* Spark 1.6: */ current_date, /* Spark 1.6 & Spark 2.0: */ current_date() ``` This was accidentally dropped in Spark 2.0. This PR reinstates this functionality. ## How was this patch tested? Added a case to ExpressionParserSuite. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #14442 from hvanhovell/SPARK-16836.
* [SPARK-16062] [SPARK-15989] [SQL] Fix two bugs of Python-only UDTsLiang-Chi Hsieh2016-08-022-3/+23
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? There are two related bugs of Python-only UDTs. Because the test case of second one needs the first fix too. I put them into one PR. If it is not appropriate, please let me know. ### First bug: When MapObjects works on Python-only UDTs `RowEncoder` will use `PythonUserDefinedType.sqlType` for its deserializer expression. If the sql type is `ArrayType`, we will have `MapObjects` working on it. But `MapObjects` doesn't consider `PythonUserDefinedType` as its input data type. It causes error like: import pyspark.sql.group from pyspark.sql.tests import PythonOnlyPoint, PythonOnlyUDT from pyspark.sql.types import * schema = StructType().add("key", LongType()).add("val", PythonOnlyUDT()) df = spark.createDataFrame([(i % 3, PythonOnlyPoint(float(i), float(i))) for i in range(10)], schema=schema) df.show() File "/home/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line 312, in get_return_value py4j.protocol.Py4JJavaError: An error occurred while calling o36.showString. : java.lang.RuntimeException: Error while decoding: scala.MatchError: org.apache.spark.sql.types.PythonUserDefinedTypef4ceede8 (of class org.apache.spark.sql.types.PythonUserDefinedType) ... ### Second bug: When Python-only UDTs is the element type of ArrayType import pyspark.sql.group from pyspark.sql.tests import PythonOnlyPoint, PythonOnlyUDT from pyspark.sql.types import * schema = StructType().add("key", LongType()).add("val", ArrayType(PythonOnlyUDT())) df = spark.createDataFrame([(i % 3, [PythonOnlyPoint(float(i), float(i))]) for i in range(10)], schema=schema) df.show() ## How was this patch tested? PySpark's sql tests. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #13778 from viirya/fix-pyudt.
* [SPARK-16837][SQL] TimeWindow incorrectly drops slideDuration in constructorsTom Magrino2016-08-022-2/+14
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix of incorrect arguments (dropping slideDuration and using windowDuration) in constructors for TimeWindow. The JIRA this addresses is here: https://issues.apache.org/jira/browse/SPARK-16837 ## How was this patch tested? Added a test to TimeWindowSuite to check that the results of TimeWindow object apply and TimeWindow class constructor are equivalent. Author: Tom Magrino <tmagrino@fb.com> Closes #14441 from tmagrino/windowing-fix.
* [SPARK-16850][SQL] Improve type checking error message for greatest/leastpetermaxlee2016-08-022-2/+15
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Greatest/least function does not have the most friendly error message for data types. This patch improves the error message to not show the Seq type, and use more human readable data types. Before: ``` org.apache.spark.sql.AnalysisException: cannot resolve 'greatest(CAST(1.0 AS DECIMAL(2,1)), "1.0")' due to data type mismatch: The expressions should all have the same type, got GREATEST (ArrayBuffer(DecimalType(2,1), StringType)).; line 1 pos 7 ``` After: ``` org.apache.spark.sql.AnalysisException: cannot resolve 'greatest(CAST(1.0 AS DECIMAL(2,1)), "1.0")' due to data type mismatch: The expressions should all have the same type, got GREATEST(decimal(2,1), string).; line 1 pos 7 ``` ## How was this patch tested? Manually verified the output and also added unit tests to ConditionalExpressionSuite. Author: petermaxlee <petermaxlee@gmail.com> Closes #14453 from petermaxlee/SPARK-16850.
* [SPARK-16793][SQL] Set the temporary warehouse path to sc'conf in TestHive.jiangxingbo2016-08-013-25/+21
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? With SPARK-15034, we could use the value of spark.sql.warehouse.dir to set the warehouse location. In TestHive, we can now simply set the temporary warehouse path in sc's conf, and thus, param "warehousePath" could be removed. ## How was this patch tested? exsiting testsuites. Author: jiangxingbo <jiangxingbo@meituan.com> Closes #14401 from jiangxb1987/warehousePath.
* [SPARK-16828][SQL] remove MaxOf and MinOfWenchen Fan2016-08-014-175/+0
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? These 2 expressions are not needed anymore after we have `Greatest` and `Least`. This PR removes them and related tests. ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #14434 from cloud-fan/minor1.
* [SPARK-16774][SQL] Fix use of deprecated timestamp constructor & improve ↵Holden Karau2016-08-012-7/+10
| | | | | | | | | | | | | | | | | | | | timezone handling ## What changes were proposed in this pull request? Removes the deprecated timestamp constructor and incidentally fixes the use which was using system timezone rather than the one specified when working near DST. This change also causes the roundtrip tests to fail since it now actually uses all the timezones near DST boundaries where it didn't before. Note: this is only a partial the solution, longer term we should follow up with https://issues.apache.org/jira/browse/SPARK-16788 to avoid this problem & simplify our timezone handling code. ## How was this patch tested? New tests for two timezones added so even if user timezone happens to coincided with one, the other tests should still fail. Important note: this (temporarily) disables the round trip tests until we can fix the issue more thoroughly. Author: Holden Karau <holden@us.ibm.com> Closes #14398 from holdenk/SPARK-16774-fix-use-of-deprecated-timestamp-constructor.
* [SPARK-16791][SQL] cast struct with timestamp field failseyal farago2016-08-012-1/+11
| | | | | | | | | | | | | ## What changes were proposed in this pull request? a failing test case + fix to SPARK-16791 (https://issues.apache.org/jira/browse/SPARK-16791) ## How was this patch tested? added a failing test case to CastSuit, then fixed the Cast code and rerun the entire CastSuit Author: eyal farago <eyal farago> Author: Eyal Farago <eyal.farago@actimize.com> Closes #14400 from eyalfa/SPARK-16791_cast_struct_with_timestamp_field_fails.
* [SPARK-16778][SQL][TRIVIAL] Fix deprecation warning with SQLContextHolden Karau2016-08-011-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Change to non-deprecated constructor for SQLContext. ## How was this patch tested? Existing tests Author: Holden Karau <holden@us.ibm.com> Closes #14406 from holdenk/SPARK-16778-fix-use-of-deprecated-SQLContext-constructor.
* [SPARK-16726][SQL] Improve `Union/Intersect/Except` error messages on ↵Dongjoon Hyun2016-08-012-13/+46
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | incompatible types ## What changes were proposed in this pull request? Currently, `UNION` queries on incompatible types show misleading error messages, i.e., `unresolved operator Union`. We had better show a more correct message. This will help users in the situation of [SPARK-16704](https://issues.apache.org/jira/browse/SPARK-16704). **Before** ```scala scala> sql("select 1,2,3 union (select 1,array(2),3)") org.apache.spark.sql.AnalysisException: unresolved operator 'Union; scala> sql("select 1,2,3 intersect (select 1,array(2),3)") org.apache.spark.sql.AnalysisException: unresolved operator 'Intersect; scala> sql("select 1,2,3 except (select 1,array(2),3)") org.apache.spark.sql.AnalysisException: unresolved operator 'Except; ``` **After** ```scala scala> sql("select 1,2,3 union (select 1,array(2),3)") org.apache.spark.sql.AnalysisException: Union can only be performed on tables with the compatible column types. ArrayType(IntegerType,false) <> IntegerType at the second column of the second table; scala> sql("select 1,2,3 intersect (select 1,array(2),3)") org.apache.spark.sql.AnalysisException: Intersect can only be performed on tables with the compatible column types. ArrayType(IntegerType,false) <> IntegerType at the second column of the second table; scala> sql("select 1,2,3 except (select array(1),array(2),3)") org.apache.spark.sql.AnalysisException: Except can only be performed on tables with the compatible column types. ArrayType(IntegerType,false) <> IntegerType at the first column of the second table; ``` ## How was this patch tested? Pass the Jenkins test with a new test case. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14355 from dongjoon-hyun/SPARK-16726.
* [SPARK-16805][SQL] Log timezone when query result does not matchReynold Xin2016-07-311-0/+3
| | | | | | | | | | | | ## What changes were proposed in this pull request? It is useful to log the timezone when query result does not match, especially on build machines that have different timezone from AMPLab Jenkins. ## How was this patch tested? This is a test-only change. Author: Reynold Xin <rxin@databricks.com> Closes #14413 from rxin/SPARK-16805.
* [SPARK-16731][SQL] use StructType in CatalogTable and remove CatalogColumnWenchen Fan2016-07-3117-175/+120
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? `StructField` has very similar semantic with `CatalogColumn`, except that `CatalogColumn` use string to express data type. I think it's reasonable to use `StructType` as the `CatalogTable.schema` and remove `CatalogColumn`. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #14363 from cloud-fan/column.
* [SPARK-16813][SQL] Remove private[sql] and private[spark] from catalyst packageReynold Xin2016-07-3118-32/+32
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? The catalyst package is meant to be internal, and as a result it does not make sense to mark things as private[sql] or private[spark]. It simply makes debugging harder when Spark developers need to inspect the plans at runtime. This patch removes all private[sql] and private[spark] visibility modifiers in org.apache.spark.sql.catalyst. ## How was this patch tested? N/A - just visibility changes. Author: Reynold Xin <rxin@databricks.com> Closes #14418 from rxin/SPARK-16813.
* [SPARK-16818] Exchange reuse incorrectly reuses scans over different sets of ↵Eric Liang2016-07-302-1/+36
| | | | | | | | | | | | | | | | | | | | partitions ## What changes were proposed in this pull request? This fixes a bug wherethe file scan operator does not take into account partition pruning in its implementation of `sameResult()`. As a result, executions may be incorrect on self-joins over the same base file relation. The patch here is minimal, but we should reconsider relying on `metadata` for implementing sameResult() in the future, as string representations may not be uniquely identifying. cc rxin ## How was this patch tested? Unit tests. Author: Eric Liang <ekl@databricks.com> Closes #14425 from ericl/spark-16818.
* [SPARK-16694][CORE] Use for/foreach rather than map for Unit expressions ↵Sean Owen2016-07-304-7/+7
| | | | | | | | | | | | | | | | whose side effects are required ## What changes were proposed in this pull request? Use foreach/for instead of map where operation requires execution of body, not actually defining a transformation ## How was this patch tested? Jenkins Author: Sean Owen <sowen@cloudera.com> Closes #14332 from srowen/SPARK-16694.
* [SPARK-16748][SQL] SparkExceptions during planning should not wrapped in ↵Tathagata Das2016-07-292-2/+16
| | | | | | | | | | | | | | TreeNodeException ## What changes were proposed in this pull request? We do not want SparkExceptions from job failures in the planning phase to create TreeNodeException. Hence do not wrap SparkException in TreeNodeException. ## How was this patch tested? New unit test Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #14395 from tdas/SPARK-16748.
* [SPARK-16664][SQL] Fix persist call on Data frames with more than 200…Wesley Tang2016-07-293-3/+12
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? f12f11e578169b47e3f8b18b299948c0670ba585 introduced this bug, missed foreach as map ## How was this patch tested? Test added Author: Wesley Tang <tangmingjun@mininglamp.com> Closes #14324 from breakdawn/master.
* [SPARK-16764][SQL] Recommend disabling vectorized parquet reader on ↵Sameer Agarwal2016-07-281-5/+19
| | | | | | | | | | | | | | | | OutOfMemoryError ## What changes were proposed in this pull request? We currently don't bound or manage the data array size used by column vectors in the vectorized reader (they're just bound by INT.MAX) which may lead to OOMs while reading data. As a short term fix, this patch intercepts the OutOfMemoryError exception and suggest the user to disable the vectorized parquet reader. ## How was this patch tested? Existing Tests Author: Sameer Agarwal <sameerag@cs.berkeley.edu> Closes #14387 from sameeragarwal/oom.
* [SPARK-16740][SQL] Fix Long overflow in LongToUnsafeRowMapSylvain Zimmer2016-07-281-1/+2
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Avoid overflow of Long type causing a NegativeArraySizeException a few lines later. ## How was this patch tested? Unit tests for HashedRelationSuite still pass. I can confirm the python script I included in https://issues.apache.org/jira/browse/SPARK-16740 works fine with this patch. Unfortunately I don't have the knowledge/time to write a Scala test case for HashedRelationSuite right now. As the patch is pretty obvious I hope it can be included without this. Thanks! Author: Sylvain Zimmer <sylvain@sylvainzimmer.com> Closes #14373 from sylvinus/master.
* [SPARK-16639][SQL] The query with having condition that contains grouping by ↵Liang-Chi Hsieh2016-07-282-6/+31
| | | | | | | | | | | | | | | | | | | | | column should work ## What changes were proposed in this pull request? The query with having condition that contains grouping by column will be failed during analysis. E.g., create table tbl(a int, b string); select count(b) from tbl group by a + 1 having a + 1 = 2; Having condition should be able to use grouping by column. ## How was this patch tested? Jenkins tests. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #14296 from viirya/having-contains-grouping-column.
* [SPARK-16552][SQL] Store the Inferred Schemas into External Catalog Tables ↵gatorsmile2016-07-287-79/+291
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | when Creating Tables #### What changes were proposed in this pull request? Currently, in Spark SQL, the initial creation of schema can be classified into two groups. It is applicable to both Hive tables and Data Source tables: **Group A. Users specify the schema.** _Case 1 CREATE TABLE AS SELECT_: the schema is determined by the result schema of the SELECT clause. For example, ```SQL CREATE TABLE tab STORED AS TEXTFILE AS SELECT * from input ``` _Case 2 CREATE TABLE_: users explicitly specify the schema. For example, ```SQL CREATE TABLE jsonTable (_1 string, _2 string) USING org.apache.spark.sql.json ``` **Group B. Spark SQL infers the schema at runtime.** _Case 3 CREATE TABLE_. Users do not specify the schema but the path to the file location. For example, ```SQL CREATE TABLE jsonTable USING org.apache.spark.sql.json OPTIONS (path '${tempDir.getCanonicalPath}') ``` Before this PR, Spark SQL does not store the inferred schema in the external catalog for the cases in Group B. When users refreshing the metadata cache, accessing the table at the first time after (re-)starting Spark, Spark SQL will infer the schema and store the info in the metadata cache for improving the performance of subsequent metadata requests. However, the runtime schema inference could cause undesirable schema changes after each reboot of Spark. This PR is to store the inferred schema in the external catalog when creating the table. When users intend to refresh the schema after possible changes on external files (table location), they issue `REFRESH TABLE`. Spark SQL will infer the schema again based on the previously specified table location and update/refresh the schema in the external catalog and metadata cache. In this PR, we do not use the inferred schema to replace the user specified schema for avoiding external behavior changes . Based on the design, user-specified schemas (as described in Group A) can be changed by ALTER TABLE commands, although we do not support them now. #### How was this patch tested? TODO: add more cases to cover the changes. Author: gatorsmile <gatorsmile@gmail.com> Closes #14207 from gatorsmile/userSpecifiedSchema.
* [SPARK-15232][SQL] Add subquery SQL building tests to LogicalPlanToSQLSuiteDongjoon Hyun2016-07-2715-0/+282
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? We currently test subquery SQL building using the `HiveCompatibilitySuite`. The is not desired since SQL building is actually a part of `sql/core` and because we are slowly reducing our dependency on Hive. This PR adds the same tests from the whitelist of `HiveCompatibilitySuite` into `LogicalPlanToSQLSuite`. ## How was this patch tested? This adds more testcases. Pass the Jenkins tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14383 from dongjoon-hyun/SPARK-15232.
* [SPARK-16730][SQL] Implement function aliases for type castspetermaxlee2016-07-283-7/+73
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Spark 1.x supports using the Hive type name as function names for doing casts, e.g. ```sql SELECT int(1.0); SELECT string(2.0); ``` The above query would work in Spark 1.x because Spark 1.x fail back to Hive for unimplemented functions, and break in Spark 2.0 because the fall back was removed. This patch implements function aliases using an analyzer rule for the following cast functions: - boolean - tinyint - smallint - int - bigint - float - double - decimal - date - timestamp - binary - string ## How was this patch tested? Added end-to-end tests in SQLCompatibilityFunctionSuite. Author: petermaxlee <petermaxlee@gmail.com> Closes #14364 from petermaxlee/SPARK-16730-2.
* [MINOR][DOC][SQL] Fix two documents regarding size in bytesLiang-Chi Hsieh2016-07-271-5/+7
| | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix two places in SQLConf documents regarding size in bytes and statistics. ## How was this patch tested? No. Just change document. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #14341 from viirya/fix-doc-size-in-bytes.
* [SPARK-16729][SQL] Throw analysis exception for invalid date castspetermaxlee2016-07-272-9/+16
| | | | | | | | | | | | ## What changes were proposed in this pull request? Spark currently throws exceptions for invalid casts for all other data types except date type. Somehow date type returns null. It should be consistent and throws analysis exception as well. ## How was this patch tested? Added a unit test case in CastSuite. Author: petermaxlee <petermaxlee@gmail.com> Closes #14358 from petermaxlee/SPARK-16729.
* [SPARK-16621][SQL] Generate stable SQLs in SQLBuilderDongjoon Hyun2016-07-27105-114/+124
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, the generated SQLs have not-stable IDs for generated attributes. The stable generated SQL will give more benefit for understanding or testing the queries. This PR provides stable SQL generation by the followings. - Provide unique ids for generated subqueries, `gen_subquery_xxx`. - Provide unique and stable ids for generated attributes, `gen_attr_xxx`. **Before** ```scala scala> new org.apache.spark.sql.catalyst.SQLBuilder(sql("select 1")).toSQL res0: String = SELECT `gen_attr_0` AS `1` FROM (SELECT 1 AS `gen_attr_0`) AS gen_subquery_0 scala> new org.apache.spark.sql.catalyst.SQLBuilder(sql("select 1")).toSQL res1: String = SELECT `gen_attr_4` AS `1` FROM (SELECT 1 AS `gen_attr_4`) AS gen_subquery_0 ``` **After** ```scala scala> new org.apache.spark.sql.catalyst.SQLBuilder(sql("select 1")).toSQL res1: String = SELECT `gen_attr_0` AS `1` FROM (SELECT 1 AS `gen_attr_0`) AS gen_subquery_0 scala> new org.apache.spark.sql.catalyst.SQLBuilder(sql("select 1")).toSQL res2: String = SELECT `gen_attr_0` AS `1` FROM (SELECT 1 AS `gen_attr_0`) AS gen_subquery_0 ``` ## How was this patch tested? Pass the existing Jenkins tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14257 from dongjoon-hyun/SPARK-16621.
* [SPARK-16524][SQL] Add RowBatch and RowBasedHashMapGeneratorQifan Pu2016-07-267-126/+1356
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR is the first step for the following feature: For hash aggregation in Spark SQL, we use a fast aggregation hashmap to act as a "cache" in order to boost aggregation performance. Previously, the hashmap is backed by a `ColumnarBatch`. This has performance issues when we have wide schema for the aggregation table (large number of key fields or value fields). In this JIRA, we support another implementation of fast hashmap, which is backed by a `RowBasedKeyValueBatch`. We then automatically pick between the two implementations based on certain knobs. In this first-step PR, implementations for `RowBasedKeyValueBatch` and `RowBasedHashMapGenerator` are added. ## How was this patch tested? Unit tests: `RowBasedKeyValueBatchSuite` Author: Qifan Pu <qifan.pu@gmail.com> Closes #14349 from ooq/SPARK-16524.
* [SPARK-16663][SQL] desc table should be consistent between data source and ↵Wenchen Fan2016-07-265-31/+47
| | | | | | | | | | | | | | | | | | | hive serde tables ## What changes were proposed in this pull request? Currently there are 2 inconsistence: 1. for data source table, we only print partition names, for hive table, we also print partition schema. After this PR, we will always print schema 2. if column doesn't have comment, data source table will print empty string, hive table will print null. After this PR, we will always print null ## How was this patch tested? new test in `HiveDDLSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #14302 from cloud-fan/minor3.
* [SPARK-16675][SQL] Avoid per-record type dispatch in JDBC when writinghyukjinkwon2016-07-262-36/+88
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, `JdbcUtils.savePartition` is doing type-based dispatch for each row to write appropriate values. So, appropriate setters for `PreparedStatement` can be created first according to the schema, and then apply them to each row. This approach is similar with `CatalystWriteSupport`. This PR simply make the setters to avoid this. ## How was this patch tested? Existing tests should cover this. Author: hyukjinkwon <gurwls223@gmail.com> Closes #14323 from HyukjinKwon/SPARK-16675.
* [SPARK-16706][SQL] support java map in encoderWenchen Fan2016-07-265-32/+236
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? finish the TODO, create a new expression `ExternalMapToCatalyst` to iterate the map directly. ## How was this patch tested? new test in `JavaDatasetSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #14344 from cloud-fan/java-map.
* [SPARK-16686][SQL] Remove PushProjectThroughSample since it is handled by ↵Liang-Chi Hsieh2016-07-264-29/+40
| | | | | | | | | | | | | | | | | | ColumnPruning ## What changes were proposed in this pull request? We push down `Project` through `Sample` in `Optimizer` by the rule `PushProjectThroughSample`. However, if the projected columns produce new output, they will encounter whole data instead of sampled data. It will bring some inconsistency between original plan (Sample then Project) and optimized plan (Project then Sample). In the extreme case such as attached in the JIRA, if the projected column is an UDF which is supposed to not see the sampled out data, the result of UDF will be incorrect. Since the rule `ColumnPruning` already handles general `Project` pushdown. We don't need `PushProjectThroughSample` anymore. The rule `ColumnPruning` also avoids the described issue. ## How was this patch tested? Jenkins tests. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #14327 from viirya/fix-sample-pushdown.
* [SPARK-16633][SPARK-16642][SPARK-16721][SQL] Fixes three issues related to ↵Yin Huai2016-07-254-40/+108
| | | | | | | | | | | | | | | | | | | | | | | lead and lag functions ## What changes were proposed in this pull request? This PR contains three changes. First, this PR changes the behavior of lead/lag back to Spark 1.6's behavior, which is described as below: 1. lead/lag respect null input values, which means that if the offset row exists and the input value is null, the result will be null instead of the default value. 2. If the offset row does not exist, the default value will be used. 3. OffsetWindowFunction's nullable setting also considers the nullability of its input (because of the first change). Second, this PR fixes the evaluation of lead/lag when the input expression is a literal. This fix is a result of the first change. In current master, if a literal is used as the input expression of a lead or lag function, the result will be this literal even if the offset row does not exist. Third, this PR makes ResolveWindowFrame not fire if a window function is not resolved. ## How was this patch tested? New tests in SQLWindowFunctionSuite Author: Yin Huai <yhuai@databricks.com> Closes #14284 from yhuai/lead-lag.
* [SPARK-16724] Expose DefinedByConstructorParamsMichael Armbrust2016-07-251-1/+1
| | | | | | | | We don't generally make things in catalyst/execution private. Instead they are just undocumented due to their lack of stability guarantees. Author: Michael Armbrust <michael@databricks.com> Closes #14356 from marmbrus/patch-1.
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