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* [SPARK-2189][SQL] Adds dropTempTable APICheng Lian2014-11-023-0/+46
| | | | | | | | | | | | | This PR adds an API for unregistering temporary tables. If a temporary table has been cached before, it's unpersisted as well. Author: Cheng Lian <lian.cs.zju@gmail.com> Closes #3039 from liancheng/unregister-temp-table and squashes the following commits: 54ae99f [Cheng Lian] Fixes Scala styling issue 1948c14 [Cheng Lian] Removes the unpersist argument aca41d3 [Cheng Lian] Ensures thread safety 7d4fb2b [Cheng Lian] Adds unregisterTempTable API
* [SPARK-4185][SQL] JSON schema inference failed when dealing with type ↵Yin Huai2014-11-023-9/+20
| | | | | | | | | | | | | | conflicts in arrays JIRA: https://issues.apache.org/jira/browse/SPARK-4185. This PR also has the fix of #3052. Author: Yin Huai <huai@cse.ohio-state.edu> Closes #3056 from yhuai/SPARK-4185 and squashes the following commits: ed3a5a8 [Yin Huai] Correctly handle type conflicts between structs and primitive types in an array.
* [SPARK-3791][SQL] Provides Spark version and Hive version in HiveThriftServer2Cheng Lian2014-11-021-42/+27
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR overrides the `GetInfo` Hive Thrift API to provide correct version information. Another property `spark.sql.hive.version` is added to reveal the underlying Hive version. These are generally useful for Spark SQL ODBC driver providers. The Spark version information is extracted from the jar manifest. Also took the chance to remove the `SET -v` hack, which was a workaround for Simba ODBC driver connectivity. TODO - [x] Find a general way to figure out Hive (or even any dependency) version. This [blog post](http://blog.soebes.de/blog/2014/01/02/version-information-into-your-appas-with-maven/) suggests several methods to inspect application version. In the case of Spark, this can be tricky because the chosen method: 1. must applies to both Maven build and SBT build For Maven builds, we can retrieve the version information from the META-INF/maven directory within the assembly jar. But this doesn't work for SBT builds. 2. must not rely on the original jars of dependencies to extract specific dependency version, because Spark uses assembly jar. This implies we can't read Hive version from Hive jar files since standard Spark distribution doesn't include them. 3. should play well with `SPARK_PREPEND_CLASSES` to ease local testing during development. `SPARK_PREPEND_CLASSES` prevents classes to be loaded from the assembly jar, thus we can't locate the jar file and read its manifest. Given these, maybe the only reliable method is to generate a source file containing version information at build time. pwendell Do you have any suggestions from the perspective of the build process? **Update** Hive version is now retrieved from the newly introduced `HiveShim` object. Author: Cheng Lian <lian.cs.zju@gmail.com> Author: Cheng Lian <lian@databricks.com> Closes #2843 from liancheng/get-info and squashes the following commits: a873d0f [Cheng Lian] Updates test case 53f43cd [Cheng Lian] Retrieves underlying Hive verson via HiveShim 1d282b8 [Cheng Lian] Removes the Simba ODBC "SET -v" hack f857fce [Cheng Lian] Overrides Hive GetInfo Thrift API and adds Hive version property
* [SPARK-4182][SQL] Fixes ColumnStats classes for boolean, binary and complex ↵Cheng Lian2014-11-026-19/+82
| | | | | | | | | | | | | | | data types `NoopColumnStats` was once used for binary, boolean and complex data types. This `ColumnStats` doesn't return properly shaped column statistics and causes caching failure if a table contains columns of the aforementioned types. This PR adds `BooleanColumnStats`, `BinaryColumnStats` and `GenericColumnStats`, used for boolean, binary and all complex data types respectively. In addition, `NoopColumnStats` returns properly shaped column statistics containing null count and row count, but this class is now used for testing purpose only. Author: Cheng Lian <lian@databricks.com> Closes #3059 from liancheng/spark-4182 and squashes the following commits: b398cfd [Cheng Lian] Fixes failed test case fb3ee85 [Cheng Lian] Fixes SPARK-4182
* [SPARK-3247][SQL] An API for adding data sources to Spark SQLMichael Armbrust2014-11-0220-27/+1053
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR introduces a new set of APIs to Spark SQL to allow other developers to add support for reading data from new sources in `org.apache.spark.sql.sources`. New sources must implement the interface `BaseRelation`, which is responsible for describing the schema of the data. BaseRelations have three `Scan` subclasses, which are responsible for producing an RDD containing row objects. The [various Scan interfaces](https://github.com/marmbrus/spark/blob/foreign/sql/core/src/main/scala/org/apache/spark/sql/sources/package.scala#L50) allow for optimizations such as column pruning and filter push down, when the underlying data source can handle these operations. By implementing a class that inherits from RelationProvider these data sources can be accessed using using pure SQL. I've used the functionality to update the JSON support so it can now be used in this way as follows: ```sql CREATE TEMPORARY TABLE jsonTableSQL USING org.apache.spark.sql.json OPTIONS ( path '/home/michael/data.json' ) ``` Further example usage can be found in the test cases: https://github.com/marmbrus/spark/tree/foreign/sql/core/src/test/scala/org/apache/spark/sql/sources There is also a library that uses this new API to read avro data available here: https://github.com/marmbrus/sql-avro Author: Michael Armbrust <michael@databricks.com> Closes #2475 from marmbrus/foreign and squashes the following commits: 1ed6010 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into foreign ab2c31f [Michael Armbrust] fix test 1d41bb5 [Michael Armbrust] unify argument names 5b47901 [Michael Armbrust] Remove sealed, more filter types fab154a [Michael Armbrust] Merge remote-tracking branch 'origin/master' into foreign e3e690e [Michael Armbrust] Add hook for extraStrategies a70d602 [Michael Armbrust] Fix style, more tests, FilteredSuite => PrunedFilteredSuite 70da6d9 [Michael Armbrust] Modify API to ease binary compatibility and interop with Java 7d948ae [Michael Armbrust] Fix equality of AttributeReference. 5545491 [Michael Armbrust] Address comments 5031ac3 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into foreign 22963ef [Michael Armbrust] package objects compile wierdly... b069146 [Michael Armbrust] traits => abstract classes 34f836a [Michael Armbrust] Make @DeveloperApi 0d74bcf [Michael Armbrust] Add documention on object life cycle 3e06776 [Michael Armbrust] remove line wraps de3b68c [Michael Armbrust] Remove empty file 360cb30 [Michael Armbrust] style and java api 2957875 [Michael Armbrust] add override 0fd3a07 [Michael Armbrust] Draft of data sources API
* [SPARK-3930] [SPARK-3933] Support fixed-precision decimal in SQL, and some ↵Matei Zaharia2014-11-0125-87/+350
| | | | | | | | | | | | | | | | | | | | | | | | | optimizations - Adds optional precision and scale to Spark SQL's decimal type, which behave similarly to those in Hive 13 (https://cwiki.apache.org/confluence/download/attachments/27362075/Hive_Decimal_Precision_Scale_Support.pdf) - Replaces our internal representation of decimals with a Decimal class that can store small values in a mutable Long, saving memory in this situation and letting some operations happen directly on Longs This is still marked WIP because there are a few TODOs, but I'll remove that tag when done. Author: Matei Zaharia <matei@databricks.com> Closes #2983 from mateiz/decimal-1 and squashes the following commits: 35e6b02 [Matei Zaharia] Fix issues after merge 227f24a [Matei Zaharia] Review comments 31f915e [Matei Zaharia] Implement Davies's suggestions in Python eb84820 [Matei Zaharia] Support reading/writing decimals as fixed-length binary in Parquet 4dc6bae [Matei Zaharia] Fix decimal support in PySpark d1d9d68 [Matei Zaharia] Fix compile error and test issues after rebase b28933d [Matei Zaharia] Support decimal precision/scale in Hive metastore 2118c0d [Matei Zaharia] Some test and bug fixes 81db9cb [Matei Zaharia] Added mutable Decimal that will be more efficient for small precisions 7af0c3b [Matei Zaharia] Add optional precision and scale to DecimalType, but use Unlimited for now ec0a947 [Matei Zaharia] Make the result of AVG on Decimals be Decimal, not Double
* [SPARK-3569][SQL] Add metadata field to StructFieldXiangrui Meng2014-11-0112-36/+180
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Add `metadata: Metadata` to `StructField` to store extra information of columns. `Metadata` is a simple wrapper over `Map[String, Any]` with value types restricted to Boolean, Long, Double, String, Metadata, and arrays of those types. SerDe is via JSON. Metadata is preserved through simple operations like `SELECT`. marmbrus liancheng Author: Xiangrui Meng <meng@databricks.com> Author: Michael Armbrust <michael@databricks.com> Closes #2701 from mengxr/structfield-metadata and squashes the following commits: dedda56 [Xiangrui Meng] merge remote 5ef930a [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata c35203f [Xiangrui Meng] Merge pull request #1 from marmbrus/pr/2701 886b85c [Michael Armbrust] Expose Metadata and MetadataBuilder through the public scala and java packages. 589f314 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata 1e2abcf [Xiangrui Meng] change default value of metadata to None in python 611d3c2 [Xiangrui Meng] move metadata from Expr to NamedExpr ddfcfad [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata a438440 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata 4266f4d [Xiangrui Meng] add StructField.toString back for backward compatibility 3f49aab [Xiangrui Meng] remove StructField.toString 24a9f80 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata 473a7c5 [Xiangrui Meng] merge master c9d7301 [Xiangrui Meng] organize imports 1fcbf13 [Xiangrui Meng] change metadata type in StructField for Scala/Java 60cc131 [Xiangrui Meng] add doc and header 60614c7 [Xiangrui Meng] add metadata e42c452 [Xiangrui Meng] merge master 93518fb [Xiangrui Meng] support metadata in python 905bb89 [Xiangrui Meng] java conversions 618e349 [Xiangrui Meng] make tests work in scala 61b8e0f [Xiangrui Meng] merge master 7e5a322 [Xiangrui Meng] do not output metadata in StructField.toString c41a664 [Xiangrui Meng] merge master d8af0ed [Xiangrui Meng] move tests to SQLQuerySuite 67fdebb [Xiangrui Meng] add test on join d65072e [Xiangrui Meng] remove Map.empty 367d237 [Xiangrui Meng] add test c194d5e [Xiangrui Meng] add metadata field to StructField and Attribute
* [SPARK-4154][SQL] Query does not work if it has "not between " in Spark SQL ↵ravipesala2014-10-311-0/+5
| | | | | | | | | | | | | | and HQL if the query contains "not between" does not work like. SELECT * FROM src where key not between 10 and 20' Author: ravipesala <ravindra.pesala@huawei.com> Closes #3017 from ravipesala/SPARK-4154 and squashes the following commits: 65fc89e [ravipesala] Handled admin comments 32e6d42 [ravipesala] 'not between' is not working
* Revert "SPARK-1209 [CORE] SparkHadoop{MapRed,MapReduce}Util should not use ↵Andrew Or2014-10-301-1/+0
| | | | | | package org.apache.hadoop" This reverts commit 68cb69daf3022e973422e496ccf827ca3806ff30.
* [SPARK-3968][SQL] Use parquet-mr filter2 apiYash Datta2014-10-304-178/+307
| | | | | | | | | | | | | | | | | | | | | The parquet-mr project has introduced a new filter api (https://github.com/apache/incubator-parquet-mr/pull/4), along with several fixes . It can also eliminate entire RowGroups depending on certain statistics like min/max We can leverage that to further improve performance of queries with filters. Also filter2 api introduces ability to create custom filters. We can create a custom filter for the optimized In clause (InSet) , so that elimination happens in the ParquetRecordReader itself Author: Yash Datta <Yash.Datta@guavus.com> Closes #2841 from saucam/master and squashes the following commits: 8282ba0 [Yash Datta] SPARK-3968: fix scala code style and add some more tests for filtering on optional columns 515df1c [Yash Datta] SPARK-3968: Add a test case for filter pushdown on optional column 5f4530e [Yash Datta] SPARK-3968: Fix scala code style f304667 [Yash Datta] SPARK-3968: Using task metadata strategy for row group filtering ec53e92 [Yash Datta] SPARK-3968: No push down should result in case we are unable to create a record filter 48163c3 [Yash Datta] SPARK-3968: Code cleanup cc7b596 [Yash Datta] SPARK-3968: 1. Fix RowGroupFiltering not working 2. Use the serialization/deserialization from Parquet library for filter pushdown caed851 [Yash Datta] Revert "SPARK-3968: Not pushing the filters in case of OPTIONAL columns" since filtering on optional columns is now supported in filter2 api 49703c9 [Yash Datta] SPARK-3968: Not pushing the filters in case of OPTIONAL columns 9d09741 [Yash Datta] SPARK-3968: Change parquet filter pushdown to use filter2 api of parquet-mr
* [SPARK-4120][SQL] Join of multiple tables with syntax like SELECT .. FROM ↵ravipesala2014-10-301-0/+10
| | | | | | | | | | | | | | | | T1,T2,T3.. does not work in SparkSQL Right now it works for only 2 tables like below query. sql("SELECT * FROM records1 as a,records2 as b where a.key=b.key ") But it does not work for more than 2 tables like below query sql("SELECT * FROM records1 as a,records2 as b,records3 as c where a.key=b.key and a.key=c.key"). Author: ravipesala <ravindra.pesala@huawei.com> Closes #2987 from ravipesala/multijoin and squashes the following commits: 429b005 [ravipesala] Support multiple joins
* SPARK-1209 [CORE] SparkHadoop{MapRed,MapReduce}Util should not use package ↵Sean Owen2014-10-301-0/+1
| | | | | | | | | | | | | org.apache.hadoop (This is just a look at what completely moving the classes would look like. I know Patrick flagged that as maybe not OK, although, it's private?) Author: Sean Owen <sowen@cloudera.com> Closes #2814 from srowen/SPARK-1209 and squashes the following commits: ead1115 [Sean Owen] Disable MIMA warnings resulting from moving the class -- this was also part of the PairRDDFunctions type hierarchy though? 2d42c1d [Sean Owen] Move SparkHadoopMapRedUtil / SparkHadoopMapReduceUtil from org.apache.hadoop to org.apache.spark
* [SPARK-4003] [SQL] add 3 types for java SQL contextDaoyuan Wang2014-10-293-5/+59
| | | | | | | | | | | | In JavaSqlContext, we need to let java program use big decimal, timestamp, date types. Author: Daoyuan Wang <daoyuan.wang@intel.com> Closes #2850 from adrian-wang/javacontext and squashes the following commits: 4c4292c [Daoyuan Wang] change underlying type of JavaSchemaRDD as scala bb0508f [Daoyuan Wang] add test cases 3c58b0d [Daoyuan Wang] add 3 types for java SQL context
* [SPARK-4133] [SQL] [PySpark] type conversionfor python udfDavies Liu2014-10-283-87/+89
| | | | | | | | | | | | | | | | Call Python UDF on ArrayType/MapType/PrimitiveType, the returnType can also be ArrayType/MapType/PrimitiveType. For StructType, it will act as tuple (without attributes). If returnType is StructType, it also should be tuple. Author: Davies Liu <davies@databricks.com> Closes #2973 from davies/udf_array and squashes the following commits: 306956e [Davies Liu] Merge branch 'master' of github.com:apache/spark into udf_array 2c00e43 [Davies Liu] fix merge 11395fa [Davies Liu] Merge branch 'master' of github.com:apache/spark into udf_array 9df50a2 [Davies Liu] address comments 79afb4e [Davies Liu] type conversionfor python udf
* [SPARK-3343] [SQL] Add serde support for CTASCheng Hao2014-10-282-2/+21
| | | | | | | | | | | | Currently, `CTAS` (Create Table As Select) doesn't support specifying the `SerDe` in HQL. This PR will pass down the `ASTNode` into the physical operator `execution.CreateTableAsSelect`, which will extract the `CreateTableDesc` object via Hive `SemanticAnalyzer`. In the meantime, I also update the `HiveMetastoreCatalog.createTable` to optionally support the `CreateTableDesc` for table creation. Author: Cheng Hao <hao.cheng@intel.com> Closes #2570 from chenghao-intel/ctas_serde and squashes the following commits: e011ef5 [Cheng Hao] shim for both 0.12 & 0.13.1 cfb3662 [Cheng Hao] revert to hive 0.12 c8a547d [Cheng Hao] Support SerDe properties within CTAS
* [SPARK-3988][SQL] add public API for date typeDaoyuan Wang2014-10-286-15/+37
| | | | | | | | | | | | | | | | Add json and python api for date type. By using Pickle, `java.sql.Date` was serialized as calendar, and recognized in python as `datetime.datetime`. Author: Daoyuan Wang <daoyuan.wang@intel.com> Closes #2901 from adrian-wang/spark3988 and squashes the following commits: c51a24d [Daoyuan Wang] convert datetime to date 5670626 [Daoyuan Wang] minor line combine f760d8e [Daoyuan Wang] fix indent 444f100 [Daoyuan Wang] fix a typo 1d74448 [Daoyuan Wang] fix scala style 8d7dd22 [Daoyuan Wang] add json and python api for date type
* [SPARK-3814][SQL] Support for Bitwise AND(&), OR(|) ,XOR(^), NOT(~) in Spark ↵ravipesala2014-10-281-0/+16
| | | | | | | | | | | | | HQL and SQL Currently there is no support of Bitwise & , | in Spark HiveQl and Spark SQL as well. So this PR support the same. I am closing https://github.com/apache/spark/pull/2926 as it has conflicts to merge. And also added support for Bitwise AND(&), OR(|) ,XOR(^), NOT(~) And I handled all review comments in that PR Author: ravipesala <ravindra.pesala@huawei.com> Closes #2961 from ravipesala/SPARK-3814-NEW4 and squashes the following commits: a391c7a [ravipesala] Rebase with master
* [SQL] Correct a variable name in JavaApplySchemaSuite.applySchemaToJSONYin Huai2014-10-271-1/+1
| | | | | | | | | | `schemaRDD2` is not tested because `schemaRDD1` is registered again. Author: Yin Huai <huai@cse.ohio-state.edu> Closes #2869 from yhuai/JavaApplySchemaSuite and squashes the following commits: 95fe894 [Yin Huai] Correct variable name.
* [SQL] Fixes caching related JoinSuite failureCheng Lian2014-10-272-57/+64
| | | | | | | | | | | | PR #2860 refines in-memory table statistics and enables broader broadcasted hash join optimization for in-memory tables. This makes `JoinSuite` fail when some test suite caches test table `testData` and gets executed before `JoinSuite`. Because expected `ShuffledHashJoin`s are optimized to `BroadcastedHashJoin` according to collected in-memory table statistics. This PR fixes this issue by clearing the cache before testing join operator selection. A separate test case is also added to test broadcasted hash join operator selection. Author: Cheng Lian <lian@databricks.com> Closes #2960 from liancheng/fix-join-suite and squashes the following commits: 715b2de [Cheng Lian] Fixes caching related JoinSuite failure
* [SPARK-3959][SPARK-3960][SQL] SqlParser fails to parse literal ↵Kousuke Saruta2014-10-262-7/+135
| | | | | | | | | | | | | | | | | | | | | -9223372036854775808 (Long.MinValue). / We can apply unary minus only to literal. SqlParser fails to parse -9223372036854775808 (Long.MinValue) so we cannot write queries such like as follows. SELECT value FROM someTable WHERE value > -9223372036854775808 Additionally, because of the wrong syntax definition, we cannot apply unary minus only to literal. So, we cannot write such expressions. -(value1 + value2) // Parenthesized expressions -column // Columns -MAX(column) // Functions Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #2816 from sarutak/spark-sql-dsl-improvement2 and squashes the following commits: 32a5005 [Kousuke Saruta] Remove test setting for thriftserver c2bab5e [Kousuke Saruta] Fixed SPARK-3959 and SPARK-3960
* [SPARK-3483][SQL] Special chars in column namesravipesala2014-10-261-0/+6
| | | | | | | | | | Supporting special chars in column names by using back ticks. Closed https://github.com/apache/spark/pull/2804 and created this PR as it has merge conflicts Author: ravipesala <ravindra.pesala@huawei.com> Closes #2927 from ravipesala/SPARK-3483-NEW and squashes the following commits: f6329f3 [ravipesala] Rebased with master
* [SPARK-4068][SQL] NPE in jsonRDD schema inferenceYin Huai2014-10-263-20/+58
| | | | | | | | | | | | Please refer to added tests for cases that can trigger the bug. JIRA: https://issues.apache.org/jira/browse/SPARK-4068 Author: Yin Huai <huai@cse.ohio-state.edu> Closes #2918 from yhuai/SPARK-4068 and squashes the following commits: d360eae [Yin Huai] Handle nulls when building key paths from elements of an array.
* [SPARK-4052][SQL] Use scala.collection.Map for pattern matching instead of ↵Yin Huai2014-10-261-1/+1
| | | | | | | | | | | | | using Predef.Map (it is scala.collection.immutable.Map) Please check https://issues.apache.org/jira/browse/SPARK-4052 for cases triggering this bug. Author: Yin Huai <huai@cse.ohio-state.edu> Closes #2899 from yhuai/SPARK-4052 and squashes the following commits: 1188f70 [Yin Huai] Address liancheng's comments. b6712be [Yin Huai] Use scala.collection.Map instead of Predef.Map (scala.collection.immutable.Map).
* [SPARK-3537][SPARK-3914][SQL] Refines in-memory columnar table statisticsCheng Lian2014-10-269-145/+221
| | | | | | | | | | | | | | | | | | | | | | This PR refines in-memory columnar table statistics: 1. adds 2 more statistics for in-memory table columns: `count` and `sizeInBytes` 1. adds filter pushdown support for `IS NULL` and `IS NOT NULL`. 1. caches and propagates statistics in `InMemoryRelation` once the underlying cached RDD is materialized. Statistics are collected to driver side with an accumulator. This PR also fixes SPARK-3914 by properly propagating in-memory statistics. Author: Cheng Lian <lian@databricks.com> Closes #2860 from liancheng/propagates-in-mem-stats and squashes the following commits: 0cc5271 [Cheng Lian] Restricts visibility of o.a.s.s.c.p.l.Statistics c5ff904 [Cheng Lian] Fixes test table name conflict a8c818d [Cheng Lian] Refines tests 1d01074 [Cheng Lian] Bug fix: shouldn't call STRING.actualSize on null string value 7dc6a34 [Cheng Lian] Adds more in-memory table statistics and propagates them properly
* SPARK-3359 [DOCS] sbt/sbt unidoc doesn't work with Java 8Sean Owen2014-10-251-1/+1
| | | | | | | | | | This follows https://github.com/apache/spark/pull/2893 , but does not completely fix SPARK-3359 either. This fixes minor scaladoc/javadoc issues that Javadoc 8 will treat as errors. Author: Sean Owen <sowen@cloudera.com> Closes #2909 from srowen/SPARK-3359 and squashes the following commits: f62c347 [Sean Owen] Fix some javadoc issues that javadoc 8 considers errors. This is not all of the errors turned up when javadoc 8 runs on output of genjavadoc.
* [SQL] Update Hive test harness for Hive 12 and 13Michael Armbrust2014-10-241-1/+0
| | | | | | | | | | | | | As part of the upgrade I also copy the newest version of the query tests, and whitelist a bunch of new ones that are now passing. Author: Michael Armbrust <michael@databricks.com> Closes #2936 from marmbrus/fix13tests and squashes the following commits: d9cbdab [Michael Armbrust] Remove user specific tests 65801cd [Michael Armbrust] style and rat 8f6b09a [Michael Armbrust] Update test harness to work with both Hive 12 and 13. f044843 [Michael Armbrust] Update Hive query tests and golden files to 0.13
* [SPARK-4050][SQL] Fix caching of temporary tables with projections.Michael Armbrust2014-10-243-9/+41
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Previously cached data was found by `sameResult` plan matching on optimized plans. This technique however fails to locate the cached data when a temporary table with a projection is queried with a further reduced projection. The failure is due to the fact that optimization will collapse the projections, producing a plan that no longer produces the sameResult as the cached data (though the cached data still subsumes the desired data). For example consider the following previously failing test case. ```scala sql("CACHE TABLE tempTable AS SELECT key FROM testData") assertCached(sql("SELECT COUNT(*) FROM tempTable")) ``` In this PR I change the matching to occur after analysis instead of optimization, so that in the case of temporary tables, the plans will always match. I think this should work generally, however, this error does raise questions about the need to do more thorough subsumption checking when locating cached data. Another question is what sort of semantics we want to provide when uncaching data from temporary tables. For example consider the following sequence of commands: ```scala testData.select('key).registerTempTable("tempTable1") testData.select('key).registerTempTable("tempTable2") cacheTable("tempTable1") // This obviously works. assertCached(sql("SELECT COUNT(*) FROM tempTable1")) // It seems good that this works ... assertCached(sql("SELECT COUNT(*) FROM tempTable2")) // ... but is this valid? uncacheTable("tempTable2") // Should this still be cached? assertCached(sql("SELECT COUNT(*) FROM tempTable1"), 0) ``` Author: Michael Armbrust <michael@databricks.com> Closes #2912 from marmbrus/cachingBug and squashes the following commits: 9c822d4 [Michael Armbrust] remove commented out code 5c72fb7 [Michael Armbrust] Add a test case / question about uncaching semantics. 63a23e4 [Michael Armbrust] Perform caching on analyzed instead of optimized plan. 03f1cfe [Michael Armbrust] Clean-up / add tests to SameResult suite.
* [SPARK-3969][SQL] Optimizer should have a super class as an interface.Takuya UESHIN2014-10-201-2/+2
| | | | | | | | | | | | Some developers want to replace `Optimizer` to fit their projects but can't do so because currently `Optimizer` is an `object`. Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #2825 from ueshin/issues/SPARK-3969 and squashes the following commits: abbc53c [Takuya UESHIN] Re-rename Optimizer object. 4d2e1bc [Takuya UESHIN] Rename Optimizer object. 9547a23 [Takuya UESHIN] Extract abstract class from Optimizer for developers to be able to replace Optimizer.
* [SPARK-3800][SQL] Clean aliases from grouping expressionsMichael Armbrust2014-10-201-0/+17
| | | | | | | | | | Author: Michael Armbrust <michael@databricks.com> Closes #2658 from marmbrus/nestedAggs and squashes the following commits: 862b763 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into nestedAggs 3234521 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into nestedAggs 8b06fdc [Michael Armbrust] possible fix for grouping on nested fields
* [SPARK-3906][SQL] Adds multiple join support for SQLContextCheng Lian2014-10-201-0/+11
| | | | | | | | Author: Cheng Lian <lian.cs.zju@gmail.com> Closes #2767 from liancheng/multi-join and squashes the following commits: 9dc0d18 [Cheng Lian] Adds multiple join support for SQLContext
* SPARK-3926 [CORE] Result of JavaRDD.collectAsMap() is not SerializableSean Owen2014-10-181-1/+2
| | | | | | | | | | | | | Make JavaPairRDD.collectAsMap result Serializable since Java Maps generally are Author: Sean Owen <sowen@cloudera.com> Closes #2805 from srowen/SPARK-3926 and squashes the following commits: ecb78ee [Sean Owen] Fix conflict between java.io.Serializable and use of Scala's Serializable f4717f9 [Sean Owen] Oops, fix compile problem ae1b36f [Sean Owen] Expand to cover Maps returned from other Java API methods as well 51c26c2 [Sean Owen] Make JavaPairRDD.collectAsMap result Serializable since Java Maps generally are
* [SPARK-3855][SQL] Preserve the result attribute of python UDFs though ↵Michael Armbrust2014-10-172-3/+11
| | | | | | | | | | | | | | transformations In the current implementation it was possible for the reference to change after analysis. Author: Michael Armbrust <michael@databricks.com> Closes #2717 from marmbrus/pythonUdfResults and squashes the following commits: da14879 [Michael Armbrust] Fix test 6343bcb [Michael Armbrust] add test 9533286 [Michael Armbrust] Correctly preserve the result attribute of python UDFs though transformations
* SPARK-3874: Provide stable TaskContext APIPrashant Sharma2014-10-161-2/+2
| | | | | | | | | | | | | | | | | | This is a small number of clean-up changes on top of #2782. Closes #2782. Author: Prashant Sharma <prashant.s@imaginea.com> Author: Patrick Wendell <pwendell@gmail.com> Closes #2803 from pwendell/pr-2782 and squashes the following commits: 56d5b7a [Patrick Wendell] Minor clean-up 44089ec [Patrick Wendell] Clean-up the TaskContext API. ed551ce [Prashant Sharma] Fixed a typo df261d0 [Prashant Sharma] Josh's suggestion facf3b1 [Prashant Sharma] Fixed the mima issue. 7ecc2fe [Prashant Sharma] CR, Moved implementations to TaskContextImpl bbd9e05 [Prashant Sharma] adding missed out files to git. ef633f5 [Prashant Sharma] SPARK-3874, Provide stable TaskContext API
* [SQL] Add type checking debugging functionsMichael Armbrust2014-10-132-0/+118
| | | | | | | | | | | | | Adds some functions that were very useful when trying to track down the bug from #2656. This change also changes the tree output for query plans to include the `'` prefix to unresolved nodes and `!` prefix to nodes that refer to non-existent attributes. Author: Michael Armbrust <michael@databricks.com> Closes #2657 from marmbrus/debugging and squashes the following commits: 654b926 [Michael Armbrust] Clean-up, add tests 763af15 [Michael Armbrust] Add typeChecking debugging functions 8c69303 [Michael Armbrust] Add inputSet, references to QueryPlan. Improve tree string with a prefix to denote invalid or unresolved nodes. fbeab54 [Michael Armbrust] Better toString, factories for AttributeSet.
* [SPARK-3771][SQL] AppendingParquetOutputFormat should use reflection to ↵Takuya UESHIN2014-10-131-1/+9
| | | | | | | | | | | | | | | | prevent from breaking binary-compatibility. Original problem is [SPARK-3764](https://issues.apache.org/jira/browse/SPARK-3764). `AppendingParquetOutputFormat` uses a binary-incompatible method `context.getTaskAttemptID`. This causes binary-incompatible of Spark itself, i.e. if Spark itself is built against hadoop-1, the artifact is for only hadoop-1, and vice versa. Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #2638 from ueshin/issues/SPARK-3771 and squashes the following commits: efd3784 [Takuya UESHIN] Add a comment to explain the reason to use reflection. ec213c1 [Takuya UESHIN] Use reflection to prevent breaking binary-compatibility.
* [SPARK-2066][SQL] Adds checks for non-aggregate attributes with aggregationCheng Lian2014-10-131-0/+26
| | | | | | | | | | | This PR adds a new rule `CheckAggregation` to the analyzer to provide better error message for non-aggregate attributes with aggregation. Author: Cheng Lian <lian.cs.zju@gmail.com> Closes #2774 from liancheng/non-aggregate-attr and squashes the following commits: 5246004 [Cheng Lian] Passes test suites bf1878d [Cheng Lian] Adds checks for non-aggregate attributes with aggregation
* [SPARK-3407][SQL]Add Date type supportDaoyuan Wang2014-10-1314-13/+111
| | | | | | | | | | | | | | | | | | | | | | | Author: Daoyuan Wang <daoyuan.wang@intel.com> Closes #2344 from adrian-wang/date and squashes the following commits: f15074a [Daoyuan Wang] remove outdated lines 2038085 [Daoyuan Wang] update return type 00fe81f [Daoyuan Wang] address lian cheng's comments 0df6ea1 [Daoyuan Wang] rebase and remove simple string bb1b1ef [Daoyuan Wang] remove failing test aa96735 [Daoyuan Wang] not cast for same type compare 30bf48b [Daoyuan Wang] resolve rebase conflict 617d1a8 [Daoyuan Wang] add date_udf case to white list c37e848 [Daoyuan Wang] comment update 5429212 [Daoyuan Wang] change to long f8f219f [Daoyuan Wang] revise according to Cheng Hao 0e0a4f5 [Daoyuan Wang] minor format 4ddcb92 [Daoyuan Wang] add java api for date 0e3110e [Daoyuan Wang] try to fix timezone issue 17fda35 [Daoyuan Wang] set test list 2dfbb5b [Daoyuan Wang] support date type
* [SPARK-3861][SQL] Avoid rebuilding hash tables for broadcast joins on each ↵Reynold Xin2014-10-135-32/+187
| | | | | | | | | | | | | | | | | | | | partition Author: Reynold Xin <rxin@apache.org> Closes #2727 from rxin/SPARK-3861-broadcast-hash-2 and squashes the following commits: 9c7b1a2 [Reynold Xin] Revert "Reuse CompactBuffer in UniqueKeyHashedRelation." 97626a1 [Reynold Xin] Reuse CompactBuffer in UniqueKeyHashedRelation. 7fcffb5 [Reynold Xin] Make UniqueKeyHashedRelation private[joins]. 18eb214 [Reynold Xin] Merge branch 'SPARK-3861-broadcast-hash' into SPARK-3861-broadcast-hash-1 4b9d0c9 [Reynold Xin] UniqueKeyHashedRelation.get should return null if the value is null. e0ebdd1 [Reynold Xin] Added a test case. 90b58c0 [Reynold Xin] [SPARK-3861] Avoid rebuilding hash tables on each partition 0c0082b [Reynold Xin] Fix line length. cbc664c [Reynold Xin] Rename join -> joins package. a070d44 [Reynold Xin] Fix line length in HashJoin a39be8c [Reynold Xin] [SPARK-3857] Create a join package for various join operators.
* [SPARK-3824][SQL] Sets in-memory table default storage level to MEMORY_AND_DISKCheng Lian2014-10-093-12/+17
| | | | | | | | | | | | | Using `MEMORY_AND_DISK` as default storage level for in-memory table caching. Due to the in-memory columnar representation, recomputing an in-memory cached table partitions can be very expensive. Author: Cheng Lian <lian.cs.zju@gmail.com> Closes #2686 from liancheng/spark-3824 and squashes the following commits: 35d2ed0 [Cheng Lian] Removes extra space 1ab7967 [Cheng Lian] Reduces test data size to fit DiskStore.getBytes() ba565f0 [Cheng Lian] Maks CachedBatch serializable 07f0204 [Cheng Lian] Sets in-memory table default storage level to MEMORY_AND_DISK
* [SPARK-3654][SQL] Unifies SQL and HiveQL parsersCheng Lian2014-10-095-27/+26
| | | | | | | | | | | | | | | | | | | | | | This PR is a follow up of #2590, and tries to introduce a top level SQL parser entry point for all SQL dialects supported by Spark SQL. A top level parser `SparkSQLParser` is introduced to handle the syntaxes that all SQL dialects should recognize (e.g. `CACHE TABLE`, `UNCACHE TABLE` and `SET`, etc.). For all the syntaxes this parser doesn't recognize directly, it fallbacks to a specified function that tries to parse arbitrary input to a `LogicalPlan`. This function is typically another parser combinator like `SqlParser`. DDL syntaxes introduced in #2475 can be moved to here. The `ExtendedHiveQlParser` now only handle Hive specific extensions. Also took the chance to refactor/reformat `SqlParser` for better readability. Author: Cheng Lian <lian.cs.zju@gmail.com> Closes #2698 from liancheng/gen-sql-parser and squashes the following commits: ceada76 [Cheng Lian] Minor styling fixes 9738934 [Cheng Lian] Minor refactoring, removes optional trailing ";" in the parser bb2ab12 [Cheng Lian] SET property value can be empty string ce8860b [Cheng Lian] Passes test suites e86968e [Cheng Lian] Removes debugging code 8bcace5 [Cheng Lian] Replaces digit.+ to rep1(digit) (Scala style checking doesn't like it) d15d54f [Cheng Lian] Unifies SQL and HiveQL parsers
* [SPARK-3798][SQL] Store the output of a generator in a valMichael Armbrust2014-10-091-2/+3
| | | | | | | | | | This prevents it from changing during serialization, leading to corrupted results. Author: Michael Armbrust <michael@databricks.com> Closes #2656 from marmbrus/generateBug and squashes the following commits: efa32eb [Michael Armbrust] Store the output of a generator in a val. This prevents it from changing during serialization.
* [SPARK-3813][SQL] Support "case when" conditional functions in Spark SQL.ravipesala2014-10-091-2/+13
| | | | | | | | | | | | | | "case when" conditional function is already supported in Spark SQL but there is no support in SqlParser. So added parser support to it. Author : ravipesala ravindra.pesalahuawei.com Author: ravipesala <ravindra.pesala@huawei.com> Closes #2678 from ravipesala/SPARK-3813 and squashes the following commits: 70c75a7 [ravipesala] Fixed styles 713ea84 [ravipesala] Updated as per admin comments 709684f [ravipesala] Changed parser to support case when function.
* [SPARK-3858][SQL] Pass the generator alias into logical plan nodeNathan Howell2014-10-092-1/+9
| | | | | | | | | | The alias parameter is being ignored, which makes it more difficult to specify a qualifier for Generator expressions. Author: Nathan Howell <nhowell@godaddy.com> Closes #2721 from NathanHowell/SPARK-3858 and squashes the following commits: 8aa0f43 [Nathan Howell] [SPARK-3858][SQL] Pass the generator alias into logical plan node
* [SPARK-3339][SQL] Support for skipping json lines that fail to parseYin Huai2014-10-096-19/+116
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR aims to provide a way to skip/query corrupt JSON records. To do so, we introduce an internal column to hold corrupt records (the default name is `_corrupt_record`. This name can be changed by setting the value of `spark.sql.columnNameOfCorruptRecord`). When there is a parsing error, we will put the corrupt record in its unparsed format to the internal column. Users can skip/query this column through SQL. * To query those corrupt records ``` -- For Hive parser SELECT `_corrupt_record` FROM jsonTable WHERE `_corrupt_record` IS NOT NULL -- For our SQL parser SELECT _corrupt_record FROM jsonTable WHERE _corrupt_record IS NOT NULL ``` * To skip corrupt records and query regular records ``` -- For Hive parser SELECT field1, field2 FROM jsonTable WHERE `_corrupt_record` IS NULL -- For our SQL parser SELECT field1, field2 FROM jsonTable WHERE _corrupt_record IS NULL ``` Generally, it is not recommended to change the name of the internal column. If the name has to be changed to avoid possible name conflicts, you can use `sqlContext.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, <new column name>)` or `sqlContext.sql(SET spark.sql.columnNameOfCorruptRecord=<new column name>)`. Author: Yin Huai <huai@cse.ohio-state.edu> Closes #2680 from yhuai/corruptJsonRecord and squashes the following commits: 4c9828e [Yin Huai] Merge remote-tracking branch 'upstream/master' into corruptJsonRecord 309616a [Yin Huai] Change the default name of corrupt record to "_corrupt_record". b4a3632 [Yin Huai] Merge remote-tracking branch 'upstream/master' into corruptJsonRecord 9375ae9 [Yin Huai] Set the column name of corrupt json record back to the default one after the unit test. ee584c0 [Yin Huai] Provide a way to query corrupt json records as unparsed strings.
* [SPARK-3853][SQL] JSON Schema support for Timestamp fieldsMike Timper2014-10-092-0/+18
| | | | | | | | | | In JSONRDD.scala, add 'case TimestampType' in the enforceCorrectType function and a toTimestamp function. Author: Mike Timper <mike@aurorafeint.com> Closes #2720 from mtimper/master and squashes the following commits: 9386ab8 [Mike Timper] Fix and tests for SPARK-3853
* [SPARK-3857] Create joins package for various join operators.Reynold Xin2014-10-0814-645/+843
| | | | | | | | | | | Author: Reynold Xin <rxin@apache.org> Closes #2719 from rxin/sql-join-break and squashes the following commits: 0c0082b [Reynold Xin] Fix line length. cbc664c [Reynold Xin] Rename join -> joins package. a070d44 [Reynold Xin] Fix line length in HashJoin a39be8c [Reynold Xin] [SPARK-3857] Create a join package for various join operators.
* [SPARK-3713][SQL] Uses JSON to serialize DataType objectsCheng Lian2014-10-084-8/+51
| | | | | | | | | | | | | | | | | | | | | This PR uses JSON instead of `toString` to serialize `DataType`s. The latter is not only hard to parse but also flaky in many cases. Since we already write schema information to Parquet metadata in the old style, we have to reserve the old `DataType` parser and ensure downward compatibility. The old parser is now renamed to `CaseClassStringParser` and moved into `object DataType`. JoshRosen davies Please help review PySpark related changes, thanks! Author: Cheng Lian <lian.cs.zju@gmail.com> Closes #2563 from liancheng/datatype-to-json and squashes the following commits: fc92eb3 [Cheng Lian] Reverts debugging code, simplifies primitive type JSON representation 438c75f [Cheng Lian] Refactors PySpark DataType JSON SerDe per comments 6b6387b [Cheng Lian] Removes debugging code 6a3ee3a [Cheng Lian] Addresses per review comments dc158b5 [Cheng Lian] Addresses PEP8 issues 99ab4ee [Cheng Lian] Adds compatibility est case for Parquet type conversion a983a6c [Cheng Lian] Adds PySpark support f608c6e [Cheng Lian] De/serializes DataType objects from/to JSON
* [SPARK-3831] [SQL] Filter rule Improvement and bool expression optimization.Kousuke Saruta2014-10-082-2/+4
| | | | | | | | | | | | | | | | | | | | | | | If we write the filter which is always FALSE like SELECT * from person WHERE FALSE; 200 tasks will run. I think, 1 task is enough. And current optimizer cannot optimize the case NOT is duplicated like SELECT * from person WHERE NOT ( NOT (age > 30)); The filter rule above should be simplified Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #2692 from sarutak/SPARK-3831 and squashes the following commits: 25f3e20 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3831 23c750c [Kousuke Saruta] Improved unsupported predicate test case a11b9f3 [Kousuke Saruta] Modified NOT predicate test case in PartitionBatchPruningSuite 8ea872b [Kousuke Saruta] Fixed the number of tasks when the data of LocalRelation is empty.
* [SPARK-3645][SQL] Makes table caching eager by default and adds syntax for ↵Cheng Lian2014-10-055-74/+137
| | | | | | | | | | | | | | | | lazy caching Although lazy caching for in-memory table seems consistent with the `RDD.cache()` API, it's relatively confusing for users who mainly work with SQL and not familiar with Spark internals. The `CACHE TABLE t; SELECT COUNT(*) FROM t;` pattern is also commonly seen just to ensure predictable performance. This PR makes both the `CACHE TABLE t [AS SELECT ...]` statement and the `SQLContext.cacheTable()` API eager by default, and adds a new `CACHE LAZY TABLE t [AS SELECT ...]` syntax to provide lazy in-memory table caching. Also, took the chance to make some refactoring: `CacheCommand` and `CacheTableAsSelectCommand` are now merged and renamed to `CacheTableCommand` since the former is strictly a special case of the latter. A new `UncacheTableCommand` is added for the `UNCACHE TABLE t` statement. Author: Cheng Lian <lian.cs.zju@gmail.com> Closes #2513 from liancheng/eager-caching and squashes the following commits: fe92287 [Cheng Lian] Makes table caching eager by default and adds syntax for lazy caching
* [SPARK-3212][SQL] Use logical plan matching instead of temporary tables for ↵Michael Armbrust2014-10-0312-184/+382
| | | | | | | | | | | | | | | | | | | | | | | | | | | table caching _Also addresses: SPARK-1671, SPARK-1379 and SPARK-3641_ This PR introduces a new trait, `CacheManger`, which replaces the previous temporary table based caching system. Instead of creating a temporary table that shadows an existing table with and equivalent cached representation, the cached manager maintains a separate list of logical plans and their cached data. After optimization, this list is searched for any matching plan fragments. When a matching plan fragment is found it is replaced with the cached data. There are several advantages to this approach: - Calling .cache() on a SchemaRDD now works as you would expect, and uses the more efficient columnar representation. - Its now possible to provide a list of temporary tables, without having to decide if a given table is actually just a cached persistent table. (To be done in a follow-up PR) - In some cases it is possible that cached data will be used, even if a cached table was not explicitly requested. This is because we now look at the logical structure instead of the table name. - We now correctly invalidate when data is inserted into a hive table. Author: Michael Armbrust <michael@databricks.com> Closes #2501 from marmbrus/caching and squashes the following commits: 63fbc2c [Michael Armbrust] Merge remote-tracking branch 'origin/master' into caching. 0ea889e [Michael Armbrust] Address comments. 1e23287 [Michael Armbrust] Add support for cache invalidation for hive inserts. 65ed04a [Michael Armbrust] fix tests. bdf9a3f [Michael Armbrust] Merge remote-tracking branch 'origin/master' into caching b4b77f2 [Michael Armbrust] Address comments 6923c9d [Michael Armbrust] More comments / tests 80f26ac [Michael Armbrust] First draft of improved semantics for Spark SQL caching.
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