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* [SPARK-14654][CORE] New accumulator APIWenchen Fan2016-04-2830-255/+154
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR introduces a new accumulator API which is much simpler than before: 1. the type hierarchy is simplified, now we only have an `Accumulator` class 2. Combine `initialValue` and `zeroValue` concepts into just one concept: `zeroValue` 3. there in only one `register` method, the accumulator registration and cleanup registration are combined. 4. the `id`,`name` and `countFailedValues` are combined into an `AccumulatorMetadata`, and is provided during registration. `SQLMetric` is a good example to show the simplicity of this new API. What we break: 1. no `setValue` anymore. In the new API, the intermedia type can be different from the result type, it's very hard to implement a general `setValue` 2. accumulator can't be serialized before registered. Problems need to be addressed in follow-ups: 1. with this new API, `AccumulatorInfo` doesn't make a lot of sense, the partial output is not partial updates, we need to expose the intermediate value. 2. `ExceptionFailure` should not carry the accumulator updates. Why do users care about accumulator updates for failed cases? It looks like we only use this feature to update the internal metrics, how about we sending a heartbeat to update internal metrics after the failure event? 3. the public event `SparkListenerTaskEnd` carries a `TaskMetrics`. Ideally this `TaskMetrics` don't need to carry external accumulators, as the only method of `TaskMetrics` that can access external accumulators is `private[spark]`. However, `SQLListener` use it to retrieve sql metrics. ## How was this patch tested? existing tests Author: Wenchen Fan <wenchen@databricks.com> Closes #12612 from cloud-fan/acc.
* [SPARK-14961] Build HashedRelation larger than 1GDavies Liu2016-04-272-57/+107
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, LongToUnsafeRowMap use byte array as the underlying page, which can't be larger 1G. This PR improves LongToUnsafeRowMap to scale up to 8G bytes by using array of Long instead of array of byte. ## How was this patch tested? Manually ran a test to confirm that both UnsafeHashedRelation and LongHashedRelation could build a map that larger than 2G. Author: Davies Liu <davies@databricks.com> Closes #12740 from davies/larger_broadcast.
* [SPARK-12143][SQL] Binary type support for Hive thrift serverhyukjinkwon2016-04-272-2/+23
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-12143 This PR adds the support for conversion between `SparkRow` in Spark and `RowSet` in Hive for `BinaryType` as `Array[Byte]` (JDBC) ## How was this patch tested? Unittests in `HiveThriftBinaryServerSuite` (regression test) Closes #10139 Author: hyukjinkwon <gurwls223@gmail.com> Closes #12733 from HyukjinKwon/SPARK-12143.
* [SPARK-14940][SQL] Move ExternalCatalog to own fileAndrew Or2016-04-2712-188/+210
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? `interfaces.scala` was getting big. This just moves the biggest class in there to a new file for cleanliness. ## How was this patch tested? Just moving things around. Author: Andrew Or <andrew@databricks.com> Closes #12721 from andrewor14/move-external-catalog.
* [SPARK-14954] [SQL] Add PARTITION BY and BUCKET BY clause for data source ↵Cheng Lian2016-04-273-3/+106
| | | | | | | | | | | | | | | | | | | | | CTAS syntax Currently, we can only create persisted partitioned and/or bucketed data source tables using the Dataset API but not using SQL DDL. This PR implements the following syntax to add partitioning and bucketing support to the SQL DDL: ``` CREATE TABLE <table-name> USING <provider> [OPTIONS (<key1> <value1>, <key2> <value2>, ...)] [PARTITIONED BY (col1, col2, ...)] [CLUSTERED BY (col1, col2, ...) [SORTED BY (col1, col2, ...)] INTO <n> BUCKETS] AS SELECT ... ``` Test cases are added in `MetastoreDataSourcesSuite` to check the newly added syntax. Author: Cheng Lian <lian@databricks.com> Author: Yin Huai <yhuai@databricks.com> Closes #12734 from liancheng/spark-14954.
* [SPARK-14664][SQL] Implement DecimalAggregates optimization for Window queriesDongjoon Hyun2016-04-273-12/+161
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR aims to implement decimal aggregation optimization for window queries by improving existing `DecimalAggregates`. Historically, `DecimalAggregates` optimizer is designed to transform general `sum/avg(decimal)`, but it breaks recently added windows queries like the followings. The following queries work well without the current `DecimalAggregates` optimizer. **Sum** ```scala scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").head java.lang.RuntimeException: Unsupported window function: MakeDecimal((sum(UnscaledValue(a#31)),mode=Complete,isDistinct=false),12,1) scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").explain() == Physical Plan == WholeStageCodegen : +- Project [sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#23] : +- INPUT +- Window [MakeDecimal((sum(UnscaledValue(a#21)),mode=Complete,isDistinct=false),12,1) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#23] +- Exchange SinglePartition, None +- Generate explode([1.0,2.0]), false, false, [a#21] +- Scan OneRowRelation[] ``` **Average** ```scala scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").head java.lang.RuntimeException: Unsupported window function: cast(((avg(UnscaledValue(a#40)),mode=Complete,isDistinct=false) / 10.0) as decimal(6,5)) scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").explain() == Physical Plan == WholeStageCodegen : +- Project [avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#44] : +- INPUT +- Window [cast(((avg(UnscaledValue(a#42)),mode=Complete,isDistinct=false) / 10.0) as decimal(6,5)) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#44] +- Exchange SinglePartition, None +- Generate explode([1.0,2.0]), false, false, [a#42] +- Scan OneRowRelation[] ``` After this PR, those queries work fine and new optimized physical plans look like the followings. **Sum** ```scala scala> sql("select sum(a) over () from (select explode(array(1.0,2.0)) a) t").explain() == Physical Plan == WholeStageCodegen : +- Project [sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#35] : +- INPUT +- Window [MakeDecimal((sum(UnscaledValue(a#33)),mode=Complete,isDistinct=false) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING),12,1) AS sum(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#35] +- Exchange SinglePartition, None +- Generate explode([1.0,2.0]), false, false, [a#33] +- Scan OneRowRelation[] ``` **Average** ```scala scala> sql("select avg(a) over () from (select explode(array(1.0,2.0)) a) t").explain() == Physical Plan == WholeStageCodegen : +- Project [avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#47] : +- INPUT +- Window [cast(((avg(UnscaledValue(a#45)),mode=Complete,isDistinct=false) windowspecdefinition(ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) / 10.0) as decimal(6,5)) AS avg(a) OVER ( ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)#47] +- Exchange SinglePartition, None +- Generate explode([1.0,2.0]), false, false, [a#45] +- Scan OneRowRelation[] ``` In this PR, *SUM over window* pattern matching is based on the code of hvanhovell ; he should be credited for the work he did. ## How was this patch tested? Pass the Jenkins tests (with newly added testcases) Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12421 from dongjoon-hyun/SPARK-14664.
* [SPARK-14874][SQL][STREAMING] Remove the obsolete Batch representationLiwei Lin2016-04-275-30/+4
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The `Batch` class, which had been used to indicate progress in a stream, was abandoned by [[SPARK-13985][SQL] Deterministic batches with ids](https://github.com/apache/spark/commit/caea15214571d9b12dcf1553e5c1cc8b83a8ba5b) and then became useless. This patch: - removes the `Batch` class - ~~does some related renaming~~ (update: this has been reverted) - fixes some related comments ## How was this patch tested? N/A Author: Liwei Lin <lwlin7@gmail.com> Closes #12638 from lw-lin/remove-batch.
* [SPARK-14950][SQL] Fix BroadcastHashJoin's unique key Anti-JoinsHerman van Hovell2016-04-272-14/+67
| | | | | | | | | | | | | | ### What changes were proposed in this pull request? Anti-Joins using BroadcastHashJoin's unique key code path are broken; it currently returns Semi Join results . This PR fixes this bug. ### How was this patch tested? Added tests cases to `ExistenceJoinSuite`. cc davies gatorsmile Author: Herman van Hovell <hvanhovell@questtec.nl> Closes #12730 from hvanhovell/SPARK-14950.
* [SPARK-14949][SQL] Remove HiveConf dependency from InsertIntoHiveTableReynold Xin2016-04-271-16/+14
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch removes the use of HiveConf from InsertIntoHiveTable. I think this is the last major use of HiveConf and after this we can try to remove the execution HiveConf. ## How was this patch tested? Internal refactoring and should be covered by existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #12728 from rxin/SPARK-14949.
* [SPARK-14130][SQL] Throw exceptions for ALTER TABLE ADD/REPLACE/CHANGE ↵Yin Huai2016-04-27184-819/+194
| | | | | | | | | | | | | | COLUMN, ALTER TABLE SET FILEFORMAT, DFS, and transaction related commands ## What changes were proposed in this pull request? This PR will make Spark SQL not allow ALTER TABLE ADD/REPLACE/CHANGE COLUMN, ALTER TABLE SET FILEFORMAT, DFS, and transaction related commands. ## How was this patch tested? Existing tests. For those tests that I put in the blacklist, I am adding the useful parts back to SQLQuerySuite. Author: Yin Huai <yhuai@databricks.com> Closes #12714 from yhuai/banNativeCommand.
* [SPARK-14944][SPARK-14943][SQL] Remove HiveConf from HiveTableScanExec, ↵Reynold Xin2016-04-266-46/+37
| | | | | | | | | | | | | | HiveTableReader, and ScriptTransformation ## What changes were proposed in this pull request? This patch removes HiveConf from HiveTableScanExec and HiveTableReader and instead just uses our own configuration system. I'm splitting the large change of removing HiveConf into multiple independent pull requests because it is very difficult to debug test failures when they are all combined in one giant one. ## How was this patch tested? Should be covered by existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #12727 from rxin/SPARK-14944.
* [SPARK-14913][SQL] Simplify configuration APIReynold Xin2016-04-2642-671/+368
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? We currently expose both Hadoop configuration and Spark SQL configuration in RuntimeConfig. I think we can remove the Hadoop configuration part, and simply generate Hadoop Configuration on the fly by passing all the SQL configurations into it. This way, there is a single interface (in Java/Scala/Python/SQL) for end-users. As part of this patch, I also removed some config options deprecated in Spark 1.x. ## How was this patch tested? Updated relevant tests. Author: Reynold Xin <rxin@databricks.com> Closes #12689 from rxin/SPARK-14913.
* [SPARK-13477][SQL] Expose new user-facing Catalog interfaceAndrew Or2016-04-2631-325/+1090
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? #12625 exposed a new user-facing conf interface in `SparkSession`. This patch adds a catalog interface. ## How was this patch tested? See `CatalogSuite`. Author: Andrew Or <andrew@databricks.com> Closes #12713 from andrewor14/user-facing-catalog.
* [SPARK-14445][SQL] Support native execution of SHOW COLUMNS and SHOW PARTITIONSDilip Biswal2016-04-2716-31/+401
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds Native execution of SHOW COLUMNS and SHOW PARTITION commands. Command Syntax: ``` SQL SHOW COLUMNS (FROM | IN) table_identifier [(FROM | IN) database] ``` ``` SQL SHOW PARTITIONS [db_name.]table_name [PARTITION(partition_spec)] ``` ## How was this patch tested? Added test cases in HiveCommandSuite to verify execution and DDLCommandSuite to verify plans. Author: Dilip Biswal <dbiswal@us.ibm.com> Closes #12222 from dilipbiswal/dkb_show_columns.
* [SPARK-14929] [SQL] Disable vectorized map for wide schemas & high-precision ↵Sameer Agarwal2016-04-264-31/+55
| | | | | | | | | | | | | | | | decimals ## What changes were proposed in this pull request? While the vectorized hash map in `TungstenAggregate` is currently supported for all primitive data types during partial aggregation, this patch only enables the hash map for a subset of cases that've been verified to show performance improvements on our benchmarks subject to an internal conf that sets an upper limit on the maximum length of the aggregate key/value schema. This list of supported use-cases should be expanded over time. ## How was this patch tested? This is no new change in functionality so existing tests should suffice. Performance tests were done on TPCDS benchmarks. Author: Sameer Agarwal <sameer@databricks.com> Closes #12710 from sameeragarwal/vectorized-enable.
* [SPARK-14853] [SQL] Support LeftSemi/LeftAnti in SortMergeJoinExecDavies Liu2016-04-2610-175/+194
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR update SortMergeJoinExec to support LeftSemi/LeftAnti, so it could support all the join types, same as other three join implementations: BroadcastHashJoinExec, ShuffledHashJoinExec,and BroadcastNestedLoopJoinExec. This PR also simplify the join selection in SparkStrategy. ## How was this patch tested? Added new tests. Author: Davies Liu <davies@databricks.com> Closes #12668 from davies/smj_semi.
* [MINOR] Follow-up to #12625Andrew Or2016-04-262-2/+2
| | | | | | | | | | ## What changes were proposed in this pull request? That patch mistakenly widened the visibility from `private[x]` to `protected[x]`. This patch reverts those changes. Author: Andrew Or <andrew@databricks.com> Closes #12686 from andrewor14/visibility.
* [SPARK-14912][SQL] Propagate data source options to Hadoop configurationReynold Xin2016-04-2611-52/+94
| | | | | | | | | | | | ## What changes were proposed in this pull request? We currently have no way for users to propagate options to the underlying library that rely in Hadoop configurations to work. For example, there are various options in parquet-mr that users might want to set, but the data source API does not expose a per-job way to set it. This patch propagates the user-specified options also into Hadoop Configuration. ## How was this patch tested? Used a mock data source implementation to test both the read path and the write path. Author: Reynold Xin <rxin@databricks.com> Closes #12688 from rxin/SPARK-14912.
* [SPARK-14910][SQL] Native DDL Command Support for Describe Function in ↵gatorsmile2016-04-264-3/+79
| | | | | | | | | | | | | | | | | | | | | | | | Non-identifier Format #### What changes were proposed in this pull request? The existing `Describe Function` only support the function name in `identifier`. This is different from what Hive behaves. That is why many test cases `udf_abc` in `HiveCompatibilitySuite` are not using our native DDL support. For example, - udf_not.q - udf_bitwise_not.q This PR is to resolve the issues. Now, we can support the command of `Describe Function` whose function names are in the following format: - `qualifiedName` (e.g., `db.func1`) - `STRING` (e.g., `'func1'`) - `comparisonOperator` (e.g,. `<`) - `arithmeticOperator` (e.g., `+`) - `predicateOperator` (e.g., `or`) Note, before this PR, we only have a native command support when the function name is in the format of `qualifiedName`. #### How was this patch tested? Added test cases in `DDLSuite.scala`. Also manually verified all the related test cases in `HiveCompatibilitySuite` passed. Author: gatorsmile <gatorsmile@gmail.com> Closes #12679 from gatorsmile/descFunction.
* [MINOR][DOCS] Minor typo fixesJacek Laskowski2016-04-268-12/+11
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Minor typo fixes (too minor to deserve separate a JIRA) ## How was this patch tested? local build Author: Jacek Laskowski <jacek@japila.pl> Closes #12469 from jaceklaskowski/minor-typo-fixes.
* [SPARK-14756][CORE] Use parseLong instead of valueOfAzeem Jiva2016-04-261-3/+3
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Use Long.parseLong which returns a primative. Use a series of appends() reduces the creation of an extra StringBuilder type ## How was this patch tested? Unit tests Author: Azeem Jiva <azeemj@gmail.com> Closes #12520 from javawithjiva/minor.
* [HOTFIX] Fix the problem for real this time.Reynold Xin2016-04-251-5/+5
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* [HOTFIX] Fix compilationReynold Xin2016-04-251-4/+4
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* [SPARK-14861][SQL] Replace internal usages of SQLContext with SparkSessionAndrew Or2016-04-2586-664/+719
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In Spark 2.0, `SparkSession` is the new thing. Internally we should stop using `SQLContext` everywhere since that's supposed to be not the main user-facing API anymore. In this patch I took care to not break any public APIs. The one place that's suspect is `o.a.s.ml.source.libsvm.DefaultSource`, but according to mengxr it's not supposed to be public so it's OK to change the underlying `FileFormat` trait. **Reviewers**: This is a big patch that may be difficult to review but the changes are actually really straightforward. If you prefer I can break it up into a few smaller patches, but it will delay the progress of this issue a little. ## How was this patch tested? No change in functionality intended. Author: Andrew Or <andrew@databricks.com> Closes #12625 from andrewor14/spark-session-refactor.
* [SPARK-14904][SQL] Put removed HiveContext in compatibility moduleAndrew Or2016-04-253-0/+170
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This is for users who can't upgrade and need to continue to use HiveContext. ## How was this patch tested? Added some basic tests for sanity check. This is based on #12672 and closes #12672. Author: Andrew Or <andrew@databricks.com> Author: Reynold Xin <rxin@databricks.com> Closes #12682 from rxin/add-back-hive-context.
* [SPARK-14870][SQL][FOLLOW-UP] Move decimalDataWithNulls in ↵Sameer Agarwal2016-04-252-15/+9
| | | | | | | | | | | | | | | | DataFrameAggregateSuite ## What changes were proposed in this pull request? Minor followup to https://github.com/apache/spark/pull/12651 ## How was this patch tested? Test-only change Author: Sameer Agarwal <sameer@databricks.com> Closes #12674 from sameeragarwal/tpcds-fix-2.
* [SPARK-14902][SQL] Expose RuntimeConfig in SparkSessionAndrew Or2016-04-2538-81/+131
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? `RuntimeConfig` is the new user-facing API in 2.0 added in #11378. Until now, however, it's been dead code. This patch uses `RuntimeConfig` in `SessionState` and exposes that through the `SparkSession`. ## How was this patch tested? New test in `SQLContextSuite`. Author: Andrew Or <andrew@databricks.com> Closes #12669 from andrewor14/use-runtime-conf.
* [SPARK-14888][SQL] UnresolvedFunction should use FunctionIdentifierReynold Xin2016-04-2513-92/+117
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch changes UnresolvedFunction and UnresolvedGenerator to use a FunctionIdentifier rather than just a String for function name. Also changed SessionCatalog to accept FunctionIdentifier in lookupFunction. ## How was this patch tested? Updated related unit tests. Author: Reynold Xin <rxin@databricks.com> Closes #12659 from rxin/SPARK-14888.
* [SPARK-14828][SQL] Start SparkSession in REPL instead of SQLContextAndrew Or2016-04-251-5/+29
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? ``` Spark context available as 'sc' (master = local[*], app id = local-1461283768192). Spark session available as 'spark'. Welcome to ____ __ / __/__ ___ _____/ /__ _\ \/ _ \/ _ `/ __/ '_/ /___/ .__/\_,_/_/ /_/\_\ version 2.0.0-SNAPSHOT /_/ Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_51) Type in expressions to have them evaluated. Type :help for more information. scala> sql("SHOW TABLES").collect() 16/04/21 17:09:39 WARN ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 1.2.0 16/04/21 17:09:39 WARN ObjectStore: Failed to get database default, returning NoSuchObjectException res0: Array[org.apache.spark.sql.Row] = Array([src,false]) scala> sql("SHOW TABLES").collect() res1: Array[org.apache.spark.sql.Row] = Array([src,false]) scala> spark.createDataFrame(Seq((1, 1), (2, 2), (3, 3))) res2: org.apache.spark.sql.DataFrame = [_1: int, _2: int] ``` Hive things are loaded lazily. ## How was this patch tested? Manual. Author: Andrew Or <andrew@databricks.com> Closes #12589 from andrewor14/spark-session-repl.
* [SPARK-13739][SQL] Push Predicate Through Windowgatorsmile2016-04-255-33/+294
| | | | | | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? For performance, predicates can be pushed through Window if and only if the following conditions are satisfied: 1. All the expressions are part of window partitioning key. The expressions can be compound. 2. Deterministic #### How was this patch tested? TODO: - [X] DSL needs to be modified for window - [X] more tests will be added. Author: gatorsmile <gatorsmile@gmail.com> Author: xiaoli <lixiao1983@gmail.com> Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local> Closes #11635 from gatorsmile/pushPredicateThroughWindow.
* [SPARK-14721][SQL] Remove HiveContext (part 2)Andrew Or2016-04-2518-100/+78
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This removes the class `HiveContext` itself along with all code usages associated with it. The bulk of the work was already done in #12485. This is mainly just code cleanup and actually removing the class. Note: A couple of things will break after this patch. These will be fixed separately. - the python HiveContext - all the documentation / comments referencing HiveContext - there will be no more HiveContext in the REPL (fixed by #12589) ## How was this patch tested? No change in functionality. Author: Andrew Or <andrew@databricks.com> Closes #12585 from andrewor14/delete-hive-context.
* [SPARK-14892][SQL][TEST] Disable the HiveCompatibilitySuite test case for ↵gatorsmile2016-04-251-2/+4
| | | | | | | | | | | | | | INPUTDRIVER and OUTPUTDRIVER. #### What changes were proposed in this pull request? Disable the test case involving INPUTDRIVER and OUTPUTDRIVER, which are not supported #### How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #12662 from gatorsmile/disableInOutDriver.
* [SPARK-14875][SQL] Makes OutputWriterFactory.newInstance publicCheng Lian2016-04-252-2/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This method was accidentally made `private[sql]` in Spark 2.0. This PR makes it public again, since 3rd party data sources like spark-avro depend on it. ## How was this patch tested? N/A Author: Cheng Lian <lian@databricks.com> Closes #12652 from liancheng/spark-14875.
* [SPARK-14870] [SQL] Fix NPE in TPCDS q14aSameer Agarwal2016-04-244-3/+37
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes a bug in `TungstenAggregate` that manifests while aggregating by keys over nullable `BigDecimal` columns. This causes a null pointer exception while executing TPCDS q14a. ## How was this patch tested? 1. Added regression test in `DataFrameAggregateSuite`. 2. Verified that TPCDS q14a works Author: Sameer Agarwal <sameer@databricks.com> Closes #12651 from sameeragarwal/tpcds-fix.
* [SPARK-14885][SQL] When creating a CatalogColumn, we should use the ↵Yin Huai2016-04-242-3/+3
| | | | | | | | | | | | | | | catalogString of a DataType object. ## What changes were proposed in this pull request? Right now, the data type field of a CatalogColumn is using the string representation. When we create this string from a DataType object, there are places where we use simpleString instead of catalogString. Although catalogString is the same as simpleString right now, it is still good to use catalogString. So, we will not silently introduce issues when we change the semantic of simpleString or the implementation of catalogString. ## How was this patch tested? Existing tests. Author: Yin Huai <yhuai@databricks.com> Closes #12654 from yhuai/useCatalogString.
* [SPARK-14868][BUILD] Enable NewLineAtEofChecker in checkstyle and fix ↵Dongjoon Hyun2016-04-241-1/+0
| | | | | | | | | | | | | | | | | | | | | | | | lint-java errors ## What changes were proposed in this pull request? Spark uses `NewLineAtEofChecker` rule in Scala by ScalaStyle. And, most Java code also comply with the rule. This PR aims to enforce the same rule `NewlineAtEndOfFile` by CheckStyle explicitly. Also, this fixes lint-java errors since SPARK-14465. The followings are the items. - Adds a new line at the end of the files (19 files) - Fixes 25 lint-java errors (12 RedundantModifier, 6 **ArrayTypeStyle**, 2 LineLength, 2 UnusedImports, 2 RegexpSingleline, 1 ModifierOrder) ## How was this patch tested? After the Jenkins test succeeds, `dev/lint-java` should pass. (Currently, Jenkins dose not run lint-java.) ```bash $ dev/lint-java Using `mvn` from path: /usr/local/bin/mvn Checkstyle checks passed. ``` Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12632 from dongjoon-hyun/SPARK-14868.
* [SPARK-14876][SQL] SparkSession should be case insensitive by defaultReynold Xin2016-04-245-190/+208
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch changes SparkSession to be case insensitive by default, in order to match other database systems. ## How was this patch tested? N/A - I'm sure some tests will fail and I will need to fix those. Author: Reynold Xin <rxin@databricks.com> Closes #12643 from rxin/SPARK-14876.
* Disable flaky script transformation testReynold Xin2016-04-241-2/+2
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* [SPARK-14548][SQL] Support not greater than and not less than operator in ↵jliwork2016-04-245-3/+15
| | | | | | | | | | | | | | | | | Spark SQL !< means not less than which is equivalent to >= !> means not greater than which is equivalent to <= I'd to create a PR to support these two operators. I've added new test cases in: DataFrameSuite, ExpressionParserSuite, JDBCSuite, PlanParserSuite, SQLQuerySuite dilipbiswal viirya gatorsmile Author: jliwork <jiali@us.ibm.com> Closes #12316 from jliwork/SPARK-14548.
* [SPARK-14691][SQL] Simplify and Unify Error Generation for Unsupported Alter ↵gatorsmile2016-04-245-138/+40
| | | | | | | | | | | | | | | | | | Table DDL #### What changes were proposed in this pull request? So far, we are capturing each unsupported Alter Table in separate visit functions. They should be unified and issue the same ParseException instead. This PR is to refactor the existing implementation and make error message consistent for Alter Table DDL. #### How was this patch tested? Updated the existing test cases and also added new test cases to ensure all the unsupported statements are covered. Author: gatorsmile <gatorsmile@gmail.com> Author: xiaoli <lixiao1983@gmail.com> Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local> Closes #12459 from gatorsmile/cleanAlterTable.
* [SPARK-14879][SQL] Move CreateMetastoreDataSource and ↵Yin Huai2016-04-239-451/+497
| | | | | | | | | | | | | | | CreateMetastoreDataSourceAsSelect to sql/core ## What changes were proposed in this pull request? CreateMetastoreDataSource and CreateMetastoreDataSourceAsSelect are not Hive-specific. So, this PR moves them from sql/hive to sql/core. Also, I am adding `Command` suffix to these two classes. ## How was this patch tested? Existing tests. Author: Yin Huai <yhuai@databricks.com> Closes #12645 from yhuai/moveCreateDataSource.
* [SPARK-14833][SQL][STREAMING][TEST] Refactor StreamTests to test for source ↵Tathagata Das2016-04-232-197/+233
| | | | | | | | | | | | | | | | | | | | | | | | | fault-tolerance correctly. ## What changes were proposed in this pull request? Current StreamTest allows testing of a streaming Dataset generated explicitly wraps a source. This is different from the actual production code path where the source object is dynamically created through a DataSource object every time a query is started. So all the fault-tolerance testing in FileSourceSuite and FileSourceStressSuite is not really testing the actual code path as they are just reusing the FileStreamSource object. This PR fixes StreamTest and the FileSource***Suite to test this correctly. Instead of maintaining a mapping of source --> expected offset in StreamTest (which requires reuse of source object), it now maintains a mapping of source index --> offset, so that it is independent of the source object. Summary of changes - StreamTest refactored to keep track of offset by source index instead of source - AddData, AddTextData and AddParquetData updated to find the FileStreamSource object from an active query, so that it can work with sources generated when query is started. - Refactored unit tests in FileSource***Suite to test using DataFrame/Dataset generated with public, rather than reusing the same FileStreamSource. This correctly tests fault tolerance. The refactoring changed a lot of indents in FileSourceSuite, so its recommended to hide whitespace changes with this - https://github.com/apache/spark/pull/12592/files?w=1 ## How was this patch tested? Refactored unit tests. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #12592 from tdas/SPARK-14833.
* [SPARK-14838] [SQL] Set default size for ObjecType to avoid failure when ↵Liang-Chi Hsieh2016-04-232-2/+24
| | | | | | | | | | | | | | | | estimating sizeInBytes in ObjectProducer ## What changes were proposed in this pull request? We have logical plans that produce domain objects which are `ObjectType`. As we can't estimate the size of `ObjectType`, we throw an `UnsupportedOperationException` if trying to do that. We should set a default size for `ObjectType` to avoid this failure. ## How was this patch tested? `DatasetSuite`. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #12599 from viirya/skip-broadcast-objectproducer.
* [SPARK-14856] Correct message in assertion for 'returning batch for wide table'tedyu2016-04-231-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? There was a typo in the message for second assertion in "returning batch for wide table" test ## How was this patch tested? Existing tests. Author: tedyu <yuzhihong@gmail.com> Closes #12639 from tedyu/master.
* [MINOR] [SQL] Fix error message string in nullSafeEvel of TernaryExpressionDongjoon Hyun2016-04-231-1/+2
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? TernaryExpressions should thows proper error message for itself. ```scala protected def nullSafeEval(input1: Any, input2: Any, input3: Any): Any = - sys.error(s"BinaryExpressions must override either eval or nullSafeEval") + sys.error(s"TernaryExpressions must override either eval or nullSafeEval") ``` ## How was this patch tested? Manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12642 from dongjoon-hyun/minor_fix_error_msg_in_ternaryexpression.
* [SPARK-14877][SQL] Remove HiveMetastoreTypes classReynold Xin2016-04-238-60/+23
| | | | | | | | | | | | ## What changes were proposed in this pull request? It is unnecessary as DataType.catalogString largely replaces the need for this class. ## How was this patch tested? Mostly removing dead code and should be covered by existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #12644 from rxin/SPARK-14877.
* [SPARK-14865][SQL] Better error handling for view creation.Reynold Xin2016-04-233-71/+100
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch improves error handling in view creation. CreateViewCommand itself will analyze the view SQL query first, and if it cannot successfully analyze it, throw an AnalysisException. In addition, I also added the following two conservative guards for easier identification of Spark bugs: 1. If there is a bug and the generated view SQL cannot be analyzed, throw an exception at runtime. Note that this is not an AnalysisException because it is not caused by the user and more likely indicate a bug in Spark. 2. SQLBuilder when it gets an unresolved plan, it will also show the plan in the error message. I also took the chance to simplify the internal implementation of CreateViewCommand, and *removed* a fallback path that would've masked an exception from before. ## How was this patch tested? 1. Added a unit test for the user facing error handling. 2. Manually introduced some bugs in Spark to test the internal defensive error handling. 3. Also added a test case to test nested views (not super relevant). Author: Reynold Xin <rxin@databricks.com> Closes #12633 from rxin/SPARK-14865.
* [SPARK-14869][SQL] Don't mask exceptions in ResolveRelationsReynold Xin2016-04-239-17/+25
| | | | | | | | | | | | ## What changes were proposed in this pull request? In order to support running SQL directly on files, we added some code in ResolveRelations to catch the exception thrown by catalog.lookupRelation and ignore it. This unfortunately masks all the exceptions. This patch changes the logic to simply test the table's existence. ## How was this patch tested? I manually hacked some bugs into Spark and made sure the exceptions were being propagated up. Author: Reynold Xin <rxin@databricks.com> Closes #12634 from rxin/SPARK-14869.
* [SPARK-14872][SQL] Restructure command packageReynold Xin2016-04-237-257/+317
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch restructures sql.execution.command package to break the commands into multiple files, in some logical organization: databases, tables, views, functions. I also renamed basicOperators.scala to basicLogicalOperators.scala and basicPhysicalOperators.scala. ## How was this patch tested? N/A - all I did was moving code around. Author: Reynold Xin <rxin@databricks.com> Closes #12636 from rxin/SPARK-14872.
* [SPARK-14871][SQL] Disable StatsReportListener to declutter outputReynold Xin2016-04-231-2/+0
| | | | | | | | | | | | ## What changes were proposed in this pull request? Spark SQL inherited from Shark to use the StatsReportListener. Unfortunately this clutters the spark-sql CLI output and makes it very difficult to read the actual query results. ## How was this patch tested? Built and tested in spark-sql CLI. Author: Reynold Xin <rxin@databricks.com> Closes #12635 from rxin/SPARK-14871.
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