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* [SPARK-17750][SQL] Fix CREATE VIEW with INTERVAL arithmetic.Dongjoon Hyun2016-10-063-1/+41
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, Spark raises `RuntimeException` when creating a view with timestamp with INTERVAL arithmetic like the following. The root cause is the arithmetic expression, `TimeAdd`, was transformed into `timeadd` function as a VIEW definition. This PR fixes the SQL definition of `TimeAdd` and `TimeSub` expressions. ```scala scala> sql("CREATE TABLE dates (ts TIMESTAMP)") scala> sql("CREATE VIEW view1 AS SELECT ts + INTERVAL 1 DAY FROM dates") java.lang.RuntimeException: Failed to analyze the canonicalized SQL: ... ``` ## How was this patch tested? Pass Jenkins with a new testcase. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15318 from dongjoon-hyun/SPARK-17750.
* [SPARK-17773] Input/Output] Add VoidObjectInspectorErgin Seyfe2016-10-031-0/+1
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Added VoidObjectInspector to the list of PrimitiveObjectInspectors ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) Executing following query was failing. select SOME_UDAF*(a.arr) from ( select Array(null) as arr from dim_one_row ) a After the fix, I am getting the correct output: res0: Array[org.apache.spark.sql.Row] = Array([null]) Author: Ergin Seyfe <eseyfe@fb.com> Closes #15337 from seyfe/add_void_object_inspector.
* [SPARK-17073][SQL] generate column-level statisticsZhenhua Wang2016-10-032-26/+94
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Generate basic column statistics for all the atomic types: - numeric types: max, min, num of nulls, ndv (number of distinct values) - date/timestamp types: they are also represented as numbers internally, so they have the same stats as above. - string: avg length, max length, num of nulls, ndv - binary: avg length, max length, num of nulls - boolean: num of nulls, num of trues, num of falsies Also support storing and loading these statistics. One thing to notice: We support analyzing columns independently, e.g.: sql1: `ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS key;` sql2: `ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS value;` when running sql2 to collect column stats for `value`, we don’t remove stats of columns `key` which are analyzed in sql1 and not in sql2. As a result, **users need to guarantee consistency** between sql1 and sql2. If the table has been changed before sql2, users should re-analyze column `key` when they want to analyze column `value`: `ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS key, value;` ## How was this patch tested? add unit tests Author: Zhenhua Wang <wzh_zju@163.com> Closes #15090 from wzhfy/colStats.
* [SPARK-17612][SQL] Support `DESCRIBE table PARTITION` SQL syntaxDongjoon Hyun2016-09-291-1/+76
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR implements `DESCRIBE table PARTITION` SQL Syntax again. It was supported until Spark 1.6.2, but was dropped since 2.0.0. **Spark 1.6.2** ```scala scala> sql("CREATE TABLE partitioned_table (a STRING, b INT) PARTITIONED BY (c STRING, d STRING)") res1: org.apache.spark.sql.DataFrame = [result: string] scala> sql("ALTER TABLE partitioned_table ADD PARTITION (c='Us', d=1)") res2: org.apache.spark.sql.DataFrame = [result: string] scala> sql("DESC partitioned_table PARTITION (c='Us', d=1)").show(false) +----------------------------------------------------------------+ |result | +----------------------------------------------------------------+ |a string | |b int | |c string | |d string | | | |# Partition Information | |# col_name data_type comment | | | |c string | |d string | +----------------------------------------------------------------+ ``` **Spark 2.0** - **Before** ```scala scala> sql("CREATE TABLE partitioned_table (a STRING, b INT) PARTITIONED BY (c STRING, d STRING)") res0: org.apache.spark.sql.DataFrame = [] scala> sql("ALTER TABLE partitioned_table ADD PARTITION (c='Us', d=1)") res1: org.apache.spark.sql.DataFrame = [] scala> sql("DESC partitioned_table PARTITION (c='Us', d=1)").show(false) org.apache.spark.sql.catalyst.parser.ParseException: Unsupported SQL statement ``` - **After** ```scala scala> sql("CREATE TABLE partitioned_table (a STRING, b INT) PARTITIONED BY (c STRING, d STRING)") res0: org.apache.spark.sql.DataFrame = [] scala> sql("ALTER TABLE partitioned_table ADD PARTITION (c='Us', d=1)") res1: org.apache.spark.sql.DataFrame = [] scala> sql("DESC partitioned_table PARTITION (c='Us', d=1)").show(false) +-----------------------+---------+-------+ |col_name |data_type|comment| +-----------------------+---------+-------+ |a |string |null | |b |int |null | |c |string |null | |d |string |null | |# Partition Information| | | |# col_name |data_type|comment| |c |string |null | |d |string |null | +-----------------------+---------+-------+ scala> sql("DESC EXTENDED partitioned_table PARTITION (c='Us', d=1)").show(100,false) +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------+ |col_name |data_type|comment| +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------+ |a |string |null | |b |int |null | |c |string |null | |d |string |null | |# Partition Information | | | |# col_name |data_type|comment| |c |string |null | |d |string |null | | | | | |Detailed Partition Information CatalogPartition( Partition Values: [Us, 1] Storage(Location: file:/Users/dhyun/SPARK-17612-DESC-PARTITION/spark-warehouse/partitioned_table/c=Us/d=1, InputFormat: org.apache.hadoop.mapred.TextInputFormat, OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat, Serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, Properties: [serialization.format=1]) Partition Parameters:{transient_lastDdlTime=1475001066})| | | +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------+ scala> sql("DESC FORMATTED partitioned_table PARTITION (c='Us', d=1)").show(100,false) +--------------------------------+---------------------------------------------------------------------------------------+-------+ |col_name |data_type |comment| +--------------------------------+---------------------------------------------------------------------------------------+-------+ |a |string |null | |b |int |null | |c |string |null | |d |string |null | |# Partition Information | | | |# col_name |data_type |comment| |c |string |null | |d |string |null | | | | | |# Detailed Partition Information| | | |Partition Value: |[Us, 1] | | |Database: |default | | |Table: |partitioned_table | | |Location: |file:/Users/dhyun/SPARK-17612-DESC-PARTITION/spark-warehouse/partitioned_table/c=Us/d=1| | |Partition Parameters: | | | | transient_lastDdlTime |1475001066 | | | | | | |# Storage Information | | | |SerDe Library: |org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe | | |InputFormat: |org.apache.hadoop.mapred.TextInputFormat | | |OutputFormat: |org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat | | |Compressed: |No | | |Storage Desc Parameters: | | | | serialization.format |1 | | +--------------------------------+---------------------------------------------------------------------------------------+-------+ ``` ## How was this patch tested? Pass the Jenkins tests with a new testcase. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15168 from dongjoon-hyun/SPARK-17612.
* [SPARK-17660][SQL] DESC FORMATTED for VIEW Lacks View Definitiongatorsmile2016-09-271-0/+19
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? Before this PR, `DESC FORMATTED` does not have a section for the view definition. We should add it for permanent views, like what Hive does. ``` +----------------------------+-------------------------------------------------------------------------------------------------------------------------------------+-------+ |col_name |data_type |comment| +----------------------------+-------------------------------------------------------------------------------------------------------------------------------------+-------+ |a |int |null | | | | | |# Detailed Table Information| | | |Database: |default | | |Owner: |xiaoli | | |Create Time: |Sat Sep 24 21:46:19 PDT 2016 | | |Last Access Time: |Wed Dec 31 16:00:00 PST 1969 | | |Location: | | | |Table Type: |VIEW | | |Table Parameters: | | | | transient_lastDdlTime |1474778779 | | | | | | |# Storage Information | | | |SerDe Library: |org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe | | |InputFormat: |org.apache.hadoop.mapred.SequenceFileInputFormat | | |OutputFormat: |org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat | | |Compressed: |No | | |Storage Desc Parameters: | | | | serialization.format |1 | | | | | | |# View Information | | | |View Original Text: |SELECT * FROM tbl | | |View Expanded Text: |SELECT `gen_attr_0` AS `a` FROM (SELECT `gen_attr_0` FROM (SELECT `a` AS `gen_attr_0` FROM `default`.`tbl`) AS gen_subquery_0) AS tbl| | +----------------------------+-------------------------------------------------------------------------------------------------------------------------------------+-------+ ``` ### How was this patch tested? Added a test case Author: gatorsmile <gatorsmile@gmail.com> Closes #15234 from gatorsmile/descFormattedView.
* [SPARK-16516][SQL] Support for pushing down filters for decimal and ↵hyukjinkwon2016-09-282-9/+88
| | | | | | | | | | | | | | | | | | | | | | timestamp types in ORC ## What changes were proposed in this pull request? It seems ORC supports all the types in ([`PredicateLeaf.Type`](https://github.com/apache/hive/blob/e085b7e9bd059d91aaf013df0db4d71dca90ec6f/storage-api/src/java/org/apache/hadoop/hive/ql/io/sarg/PredicateLeaf.java#L50-L56)) which includes timestamp type and decimal type. In more details, the types listed in [`SearchArgumentImpl.boxLiteral()`](https://github.com/apache/hive/blob/branch-1.2/ql/src/java/org/apache/hadoop/hive/ql/io/sarg/SearchArgumentImpl.java#L1068-L1093) can be used as a filter value. FYI, inital `case` caluse for supported types was introduced in https://github.com/apache/spark/commit/65d71bd9fbfe6fe1b741c80fed72d6ae3d22b028 and this was not changed overtime. At that time, Hive version was, 0.13 which supports only some types for filter-push down (See [SearchArgumentImpl.java#L945-L965](https://github.com/apache/hive/blob/branch-0.13/ql/src/java/org/apache/hadoop/hive/ql/io/sarg/SearchArgumentImpl.java#L945-L965) at 0.13). However, the version was upgraded into 1.2.x and now it supports more types (See [SearchArgumentImpl.java#L1068-L1093](https://github.com/apache/hive/blob/branch-1.2/ql/src/java/org/apache/hadoop/hive/ql/io/sarg/SearchArgumentImpl.java#L1068-L1093) at 1.2.0) ## How was this patch tested? Unit tests in `OrcFilterSuite` and `OrcQuerySuite` Author: hyukjinkwon <gurwls223@gmail.com> Closes #14172 from HyukjinKwon/SPARK-16516.
* [SPARK-17609][SQL] SessionCatalog.tableExists should not check temp viewWenchen Fan2016-09-222-3/+3
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? After #15054 , there is no place in Spark SQL that need `SessionCatalog.tableExists` to check temp views, so this PR makes `SessionCatalog.tableExists` only check permanent table/view and removes some hacks. This PR also improves the `getTempViewOrPermanentTableMetadata` that is introduced in #15054 , to make the code simpler. ## How was this patch tested? existing tests Author: Wenchen Fan <wenchen@databricks.com> Closes #15160 from cloud-fan/exists.
* [SPARK-17051][SQL] we should use hadoopConf in InsertIntoHiveTableWenchen Fan2016-09-201-4/+28
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Hive confs in hive-site.xml will be loaded in `hadoopConf`, so we should use `hadoopConf` in `InsertIntoHiveTable` instead of `SessionState.conf` ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #14634 from cloud-fan/bug.
* [SPARK-17502][SQL] Fix Multiple Bugs in DDL Statements on Temporary Viewsgatorsmile2016-09-203-32/+54
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? - When the permanent tables/views do not exist but the temporary view exists, the expected error should be `NoSuchTableException` for partition-related ALTER TABLE commands. However, it always reports a confusing error message. For example, ``` Partition spec is invalid. The spec (a, b) must match the partition spec () defined in table '`testview`'; ``` - When the permanent tables/views do not exist but the temporary view exists, the expected error should be `NoSuchTableException` for `ALTER TABLE ... UNSET TBLPROPERTIES`. However, it reports a missing table property. For example, ``` Attempted to unset non-existent property 'p' in table '`testView`'; ``` - When `ANALYZE TABLE` is called on a view or a temporary view, we should issue an error message. However, it reports a strange error: ``` ANALYZE TABLE is not supported for Project ``` - When inserting into a temporary view that is generated from `Range`, we will get the following error message: ``` assertion failed: No plan for 'InsertIntoTable Range (0, 10, step=1, splits=Some(1)), false, false +- Project [1 AS 1#20] +- OneRowRelation$ ``` This PR is to fix the above four issues. ### How was this patch tested? Added multiple test cases Author: gatorsmile <gatorsmile@gmail.com> Closes #15054 from gatorsmile/tempViewDDL.
* [SPARK-17541][SQL] fix some DDL bugs about table management when same-name ↵Wenchen Fan2016-09-182-11/+12
| | | | | | | | | | | | | | | | | | | | | | temp view exists ## What changes were proposed in this pull request? In `SessionCatalog`, we have several operations(`tableExists`, `dropTable`, `loopupRelation`, etc) that handle both temp views and metastore tables/views. This brings some bugs to DDL commands that want to handle temp view only or metastore table/view only. These bugs are: 1. `CREATE TABLE USING` will fail if a same-name temp view exists 2. `Catalog.dropTempView`will un-cache and drop metastore table if a same-name table exists 3. `saveAsTable` will fail or have unexpected behaviour if a same-name temp view exists. These bug fixes are pulled out from https://github.com/apache/spark/pull/14962 and targets both master and 2.0 branch ## How was this patch tested? new regression tests Author: Wenchen Fan <wenchen@databricks.com> Closes #15099 from cloud-fan/fix-view.
* [SPARK-17518][SQL] Block Users to Specify the Internal Data Source Provider Hivegatorsmile2016-09-181-0/+51
| | | | | | | | | | | | ### What changes were proposed in this pull request? In Spark 2.1, we introduced a new internal provider `hive` for telling Hive serde tables from data source tables. This PR is to block users to specify this in `DataFrameWriter` and SQL APIs. ### How was this patch tested? Added a test case Author: gatorsmile <gatorsmile@gmail.com> Closes #15073 from gatorsmile/formatHive.
* [SPARK-17440][SPARK-17441] Fixed Multiple Bugs in ALTER TABLEgatorsmile2016-09-151-30/+37
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? For the following `ALTER TABLE` DDL, we should issue an exception when the target table is a `VIEW`: ```SQL ALTER TABLE viewName SET LOCATION '/path/to/your/lovely/heart' ALTER TABLE viewName SET SERDE 'whatever' ALTER TABLE viewName SET SERDEPROPERTIES ('x' = 'y') ALTER TABLE viewName PARTITION (a=1, b=2) SET SERDEPROPERTIES ('x' = 'y') ALTER TABLE viewName ADD IF NOT EXISTS PARTITION (a='4', b='8') ALTER TABLE viewName DROP IF EXISTS PARTITION (a='2') ALTER TABLE viewName RECOVER PARTITIONS ALTER TABLE viewName PARTITION (a='1', b='q') RENAME TO PARTITION (a='100', b='p') ``` In addition, `ALTER TABLE RENAME PARTITION` is unable to handle data source tables, just like the other `ALTER PARTITION` commands. We should issue an exception instead. ### How was this patch tested? Added a few test cases. Author: gatorsmile <gatorsmile@gmail.com> Closes #15004 from gatorsmile/altertable.
* [SPARK-10747][SQL] Support NULLS FIRST|LAST clause in ORDER BYXin Wu2016-09-1430-27/+69
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, ORDER BY clause returns nulls value according to sorting order (ASC|DESC), considering null value is always smaller than non-null values. However, SQL2003 standard support NULLS FIRST or NULLS LAST to allow users to specify whether null values should be returned first or last, regardless of sorting order (ASC|DESC). This PR is to support this new feature. ## How was this patch tested? New test cases are added to test NULLS FIRST|LAST for regular select queries and windowing queries. (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Author: Xin Wu <xinwu@us.ibm.com> Closes #14842 from xwu0226/SPARK-10747.
* [SPARK-17409][SQL] Do Not Optimize Query in CTAS More Than Oncegatorsmile2016-09-141-3/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? As explained in https://github.com/apache/spark/pull/14797: >Some analyzer rules have assumptions on logical plans, optimizer may break these assumption, we should not pass an optimized query plan into QueryExecution (will be analyzed again), otherwise we may some weird bugs. For example, we have a rule for decimal calculation to promote the precision before binary operations, use PromotePrecision as placeholder to indicate that this rule should not apply twice. But a Optimizer rule will remove this placeholder, that break the assumption, then the rule applied twice, cause wrong result. We should not optimize the query in CTAS more than once. For example, ```Scala spark.range(99, 101).createOrReplaceTempView("tab1") val sqlStmt = "SELECT id, cast(id as long) * cast('1.0' as decimal(38, 18)) as num FROM tab1" sql(s"CREATE TABLE tab2 USING PARQUET AS $sqlStmt") checkAnswer(spark.table("tab2"), sql(sqlStmt)) ``` Before this PR, the results do not match ``` == Results == !== Correct Answer - 2 == == Spark Answer - 2 == ![100,100.000000000000000000] [100,null] [99,99.000000000000000000] [99,99.000000000000000000] ``` After this PR, the results match. ``` +---+----------------------+ |id |num | +---+----------------------+ |99 |99.000000000000000000 | |100|100.000000000000000000| +---+----------------------+ ``` In this PR, we do not treat the `query` in CTAS as a child. Thus, the `query` will not be optimized when optimizing CTAS statement. However, we still need to analyze it for normalizing and verifying the CTAS in the Analyzer. Thus, we do it in the analyzer rule `PreprocessDDL`, because so far only this rule needs the analyzed plan of the `query`. ### How was this patch tested? Added a test Author: gatorsmile <gatorsmile@gmail.com> Closes #15048 from gatorsmile/ctasOptimized.
* [SPARK-17531] Don't initialize Hive Listeners for the Execution ClientBurak Yavuz2016-09-131-0/+36
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? If a user provides listeners inside the Hive Conf, the configuration for these listeners are passed to the Hive Execution Client as well. This may cause issues for two reasons: 1. The Execution Client will actually generate garbage 2. The listener class needs to be both in the Spark Classpath and Hive Classpath This PR empties the listener configurations in `HiveUtils.newTemporaryConfiguration` so that the execution client will not contain the listener confs, but the metadata client will. ## How was this patch tested? Unit tests Author: Burak Yavuz <brkyvz@gmail.com> Closes #15086 from brkyvz/null-listeners.
* [SPARK-17330][SPARK UT] Clean up spark-warehouse in UTtone-zhang2016-09-111-1/+5
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Check the database warehouse used in Spark UT, and remove the existing database file before run the UT (SPARK-8368). ## How was this patch tested? Run Spark UT with the command for several times: ./build/sbt -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver "test-only *HiveSparkSubmitSuit*" Without the patch, the test case can be passed only at the first time, and always failed from the second time. With the patch the test case always can be passed correctly. Author: tone-zhang <tone.zhang@linaro.org> Closes #14894 from tone-zhang/issue1.
* [SPARK-15453][SQL] FileSourceScanExec to extract `outputOrdering` informationTejas Patil2016-09-101-2/+61
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Jira : https://issues.apache.org/jira/browse/SPARK-15453 Extracting sort ordering information in `FileSourceScanExec` so that planner can make use of it. My motivation to make this change was to get Sort Merge join in par with Hive's Sort-Merge-Bucket join when the source tables are bucketed + sorted. Query: ``` val df = (0 until 16).map(i => (i % 8, i * 2, i.toString)).toDF("i", "j", "k").coalesce(1) df.write.bucketBy(8, "j", "k").sortBy("j", "k").saveAsTable("table8") df.write.bucketBy(8, "j", "k").sortBy("j", "k").saveAsTable("table9") context.sql("SELECT * FROM table8 a JOIN table9 b ON a.j=b.j AND a.k=b.k").explain(true) ``` Before: ``` == Physical Plan == *SortMergeJoin [j#120, k#121], [j#123, k#124], Inner :- *Sort [j#120 ASC, k#121 ASC], false, 0 : +- *Project [i#119, j#120, k#121] : +- *Filter (isnotnull(k#121) && isnotnull(j#120)) : +- *FileScan orc default.table8[i#119,j#120,k#121] Batched: false, Format: ORC, InputPaths: file:/Users/tejasp/Desktop/dev/tp-spark/spark-warehouse/table8, PartitionFilters: [], PushedFilters: [IsNotNull(k), IsNotNull(j)], ReadSchema: struct<i:int,j:int,k:string> +- *Sort [j#123 ASC, k#124 ASC], false, 0 +- *Project [i#122, j#123, k#124] +- *Filter (isnotnull(k#124) && isnotnull(j#123)) +- *FileScan orc default.table9[i#122,j#123,k#124] Batched: false, Format: ORC, InputPaths: file:/Users/tejasp/Desktop/dev/tp-spark/spark-warehouse/table9, PartitionFilters: [], PushedFilters: [IsNotNull(k), IsNotNull(j)], ReadSchema: struct<i:int,j:int,k:string> ``` After: (note that the `Sort` step is no longer there) ``` == Physical Plan == *SortMergeJoin [j#49, k#50], [j#52, k#53], Inner :- *Project [i#48, j#49, k#50] : +- *Filter (isnotnull(k#50) && isnotnull(j#49)) : +- *FileScan orc default.table8[i#48,j#49,k#50] Batched: false, Format: ORC, InputPaths: file:/Users/tejasp/Desktop/dev/tp-spark/spark-warehouse/table8, PartitionFilters: [], PushedFilters: [IsNotNull(k), IsNotNull(j)], ReadSchema: struct<i:int,j:int,k:string> +- *Project [i#51, j#52, k#53] +- *Filter (isnotnull(j#52) && isnotnull(k#53)) +- *FileScan orc default.table9[i#51,j#52,k#53] Batched: false, Format: ORC, InputPaths: file:/Users/tejasp/Desktop/dev/tp-spark/spark-warehouse/table9, PartitionFilters: [], PushedFilters: [IsNotNull(j), IsNotNull(k)], ReadSchema: struct<i:int,j:int,k:string> ``` ## How was this patch tested? Added a test case in `JoinSuite`. Ran all other tests in `JoinSuite` Author: Tejas Patil <tejasp@fb.com> Closes #14864 from tejasapatil/SPARK-15453_smb_optimization.
* [SPARK-17354] [SQL] Partitioning by dates/timestamps should work with ↵hyukjinkwon2016-09-091-0/+21
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Parquet vectorized reader ## What changes were proposed in this pull request? This PR fixes `ColumnVectorUtils.populate` so that Parquet vectorized reader can read partitioned table with dates/timestamps. This works fine with Parquet normal reader. This is being only called within [VectorizedParquetRecordReader.java#L185](https://github.com/apache/spark/blob/master/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedParquetRecordReader.java#L185). When partition column types are explicitly given to `DateType` or `TimestampType` (rather than inferring the type of partition column), this fails with the exception below: ``` 16/09/01 10:30:07 ERROR Executor: Exception in task 0.0 in stage 5.0 (TID 6) java.lang.ClassCastException: java.lang.Integer cannot be cast to java.sql.Date at org.apache.spark.sql.execution.vectorized.ColumnVectorUtils.populate(ColumnVectorUtils.java:89) at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.initBatch(VectorizedParquetRecordReader.java:185) at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.initBatch(VectorizedParquetRecordReader.java:204) at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anonfun$buildReader$1.apply(ParquetFileFormat.scala:362) ... ``` ## How was this patch tested? Unit tests in `SQLQuerySuite`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #14919 from HyukjinKwon/SPARK-17354.
* [SPARK-17408][TEST] Flaky test: org.apache.spark.sql.hive.StatisticsSuitegatorsmile2016-09-071-61/+80
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/64956/testReport/junit/org.apache.spark.sql.hive/StatisticsSuite/test_statistics_of_LogicalRelation_converted_from_MetastoreRelation/ ``` org.apache.spark.sql.hive.StatisticsSuite.test statistics of LogicalRelation converted from MetastoreRelation Failing for the past 1 build (Since Failed#64956 ) Took 1.4 sec. Error Message org.scalatest.exceptions.TestFailedException: 6871 did not equal 4236 Stacktrace sbt.ForkMain$ForkError: org.scalatest.exceptions.TestFailedException: 6871 did not equal 4236 at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:500) ``` This fix does not check the exact value of `sizeInBytes`. Instead, we compare whether it is larger than zero and compare the values between different values. In addition, we also combine `checkMetastoreRelationStats` and `checkLogicalRelationStats` into the same checking function. ### How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #14978 from gatorsmile/spark17408.
* [SPARK-17369][SQL] MetastoreRelation toJSON throws AssertException due to ↵Sean Zhong2016-09-061-0/+39
| | | | | | | | | | | | | | | | | | missing otherCopyArgs ## What changes were proposed in this pull request? `TreeNode.toJSON` requires a subclass to explicitly override otherCopyArgs to include currying construction arguments, otherwise it reports AssertException telling that the construction argument values' count doesn't match the construction argument names' count. For class `MetastoreRelation`, it has a currying construction parameter `client: HiveClient`, but Spark forgets to add it to the list of otherCopyArgs. ## How was this patch tested? Unit tests. Author: Sean Zhong <seanzhong@databricks.com> Closes #14928 from clockfly/metastore_relation_toJSON.
* [SPARK-17072][SQL] support table-level statistics generation and storing ↵wangzhenhua2016-09-052-13/+167
| | | | | | | | | | | | | | | | | | | | | | | into/loading from metastore ## What changes were proposed in this pull request? 1. Support generation table-level statistics for - hive tables in HiveExternalCatalog - data source tables in HiveExternalCatalog - data source tables in InMemoryCatalog. 2. Add a property "catalogStats" in CatalogTable to hold statistics in Spark side. 3. Put logics of statistics transformation between Spark and Hive in HiveClientImpl. 4. Extend Statistics class by adding rowCount (will add estimatedSize when we have column stats). ## How was this patch tested? add unit tests Author: wangzhenhua <wangzhenhua@huawei.com> Author: Zhenhua Wang <wangzhenhua@huawei.com> Closes #14712 from wzhfy/tableStats.
* [SPARK-17393][SQL] Error Handling when CTAS Against the Same Data Source ↵gatorsmile2016-09-051-0/+52
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Table Using Overwrite Mode ### What changes were proposed in this pull request? When we trying to read a table and then write to the same table using the `Overwrite` save mode, we got a very confusing error message: For example, ```Scala Seq((1, 2)).toDF("i", "j").write.saveAsTable("tab1") table("tab1").write.mode(SaveMode.Overwrite).saveAsTable("tab1") ``` ``` Job aborted. org.apache.spark.SparkException: Job aborted. at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand$$anonfun$run$1.apply$mcV$sp ... Caused by: org.apache.spark.SparkException: Task failed while writing rows at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:266) at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(InsertIntoHadoopFsRelationCommand.scala:143) at org.apache.spark.sql.execution.datasources ``` After the PR, we will issue an `AnalysisException`: ``` Cannot overwrite table `tab1` that is also being read from ``` ### How was this patch tested? Added test cases. Author: gatorsmile <gatorsmile@gmail.com> Closes #14954 from gatorsmile/ctasQueryAnalyze.
* [SPARK-17324][SQL] Remove Direct Usage of HiveClient in InsertIntoHiveTablegatorsmile2016-09-042-6/+44
| | | | | | | | | | | | ### What changes were proposed in this pull request? This is another step to get rid of HiveClient from `HiveSessionState`. All the metastore interactions should be through `ExternalCatalog` interface. However, the existing implementation of `InsertIntoHiveTable ` still requires Hive clients. This PR is to remove HiveClient by moving the metastore interactions into `ExternalCatalog`. ### How was this patch tested? Existing test cases Author: gatorsmile <gatorsmile@gmail.com> Closes #14888 from gatorsmile/removeClientFromInsertIntoHiveTable.
* [MINOR][SQL] Not dropping all necessary tablesSandeep Singh2016-09-031-7/+6
| | | | | | | | | | | | ## What changes were proposed in this pull request? was not dropping table `parquet_t3` ## How was this patch tested? tested `LogicalPlanToSQLSuite` locally Author: Sandeep Singh <sandeep@techaddict.me> Closes #13767 from techaddict/minor-8.
* [SPARK-17298][SQL] Require explicit CROSS join for cartesian productsSrinath Shankar2016-09-034-10/+6
| | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Require the use of CROSS join syntax in SQL (and a new crossJoin DataFrame API) to specify explicit cartesian products between relations. By cartesian product we mean a join between relations R and S where there is no join condition involving columns from both R and S. If a cartesian product is detected in the absence of an explicit CROSS join, an error must be thrown. Turning on the "spark.sql.crossJoin.enabled" configuration flag will disable this check and allow cartesian products without an explicit CROSS join. The new crossJoin DataFrame API must be used to specify explicit cross joins. The existing join(DataFrame) method will produce a INNER join that will require a subsequent join condition. That is df1.join(df2) is equivalent to select * from df1, df2. ## How was this patch tested? Added cross-join.sql to the SQLQueryTestSuite to test the check for cartesian products. Added a couple of tests to the DataFrameJoinSuite to test the crossJoin API. Modified various other test suites to explicitly specify a cross join where an INNER join or a comma-separated list was previously used. Author: Srinath Shankar <srinath@databricks.com> Closes #14866 from srinathshankar/crossjoin.
* [SPARK-16926][SQL] Add unit test to compare table and partition column metadata.Brian Cho2016-09-021-0/+35
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add unit test for changes made in PR #14515. It makes sure that a newly created table has the same number of columns in table and partition metadata. This test fails before the changes introduced in #14515. ## How was this patch tested? Run new unit test. Author: Brian Cho <bcho@fb.com> Closes #14930 from dafrista/partition-metadata-unit-test.
* [SPARK-16525] [SQL] Enable Row Based HashMap in HashAggregateExecQifan Pu2016-09-011-3/+3
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR is the second 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 `RowBatch`. We then automatically pick between the two implementations based on certain knobs. In this second-step PR, we enable `RowBasedHashMapGenerator` in `HashAggregateExec`. ## How was this patch tested? Added tests: `RowBasedAggregateHashMapSuite` and ` VectorizedAggregateHashMapSuite` Additional micro-benchmarks tests and TPCDS results will be added in a separate PR in the series. Author: Qifan Pu <qifan.pu@gmail.com> Author: ooq <qifan.pu@gmail.com> Closes #14176 from ooq/rowbasedfastaggmap-pr2.
* [SPARK-17263][SQL] Add hexadecimal literal parsingHerman van Hovell2016-09-011-0/+1
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds the ability to parse SQL (hexadecimal) binary literals (AKA bit strings). It follows the following syntax `X'[Hexadecimal Characters]+'`, for example: `X'01AB'` would create a binary the following binary array `0x01AB`. If an uneven number of hexadecimal characters is passed, then the upper 4 bits of the initial byte are kept empty, and the lower 4 bits are filled using the first character. For example `X'1C7'` would create the following binary array `0x01C7`. Binary data (Array[Byte]) does not have a proper `hashCode` and `equals` functions. This meant that comparing `Literal`s containing binary data was a pain. I have updated Literal.hashCode and Literal.equals to deal properly with binary data. ## How was this patch tested? Added tests to the `ExpressionParserSuite`, `SQLQueryTestSuite` and `ExpressionSQLBuilderSuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #14832 from hvanhovell/SPARK-17263.
* [SPARK-17257][SQL] the physical plan of CREATE TABLE or CTAS should take ↵Wenchen Fan2016-09-011-3/+3
| | | | | | | | | | | | | | | | CatalogTable ## What changes were proposed in this pull request? This is kind of a follow-up of https://github.com/apache/spark/pull/14482 . As we put `CatalogTable` in the logical plan directly, it makes sense to let physical plans take `CatalogTable` directly, instead of extracting some fields of `CatalogTable` in planner and then construct a new `CatalogTable` in physical plan. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #14823 from cloud-fan/create-table.
* [SPARK-17353][SPARK-16943][SPARK-16942][SQL] Fix multiple bugs in CREATE ↵gatorsmile2016-09-011-1/+225
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | TABLE LIKE command ### What changes were proposed in this pull request? The existing `CREATE TABLE LIKE` command has multiple issues: - The generated table is non-empty when the source table is a data source table. The major reason is the data source table is using the table property `path` to store the location of table contents. Currently, we keep it unchanged. Thus, we still create the same table with the same location. - The table type of the generated table is `EXTERNAL` when the source table is an external Hive Serde table. Currently, we explicitly set it to `MANAGED`, but Hive is checking the table property `EXTERNAL` to decide whether the table is `EXTERNAL` or not. (See https://github.com/apache/hive/blob/master/metastore/src/java/org/apache/hadoop/hive/metastore/ObjectStore.java#L1407-L1408) Thus, the created table is still `EXTERNAL`. - When the source table is a `VIEW`, the metadata of the generated table contains the original view text and view original text. So far, this does not break anything, but it could cause something wrong in Hive. (For example, https://github.com/apache/hive/blob/master/metastore/src/java/org/apache/hadoop/hive/metastore/ObjectStore.java#L1405-L1406) - The issue regarding the table `comment`. To follow what Hive does, the table comment should be cleaned, but the column comments should be still kept. - The `INDEX` table is not supported. Thus, we should throw an exception in this case. - `owner` should not be retained. `ToHiveTable` set it [here](https://github.com/apache/spark/blob/e679bc3c1cd418ef0025d2ecbc547c9660cac433/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala#L793) no matter which value we set in `CatalogTable`. We set it to an empty string for avoiding the confusing output in Explain. - Add a support for temp tables - Like Hive, we should not copy the table properties from the source table to the created table, especially for the statistics-related properties, which could be wrong in the created table. - `unsupportedFeatures` should not be copied from the source table. The created table does not have these unsupported features. - When the type of source table is a view, the target table is using the default format of data source tables: `spark.sql.sources.default`. This PR is to fix the above issues. ### How was this patch tested? Improve the test coverage by adding more test cases Author: gatorsmile <gatorsmile@gmail.com> Closes #14531 from gatorsmile/createTableLike.
* [SPARK-16283][SQL] Implements percentile_approx aggregation function which ↵Sean Zhong2016-09-011-0/+5
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | supports partial aggregation. ## What changes were proposed in this pull request? This PR implements aggregation function `percentile_approx`. Function `percentile_approx` returns the approximate percentile(s) of a column at the given percentage(s). A percentile is a watermark value below which a given percentage of the column values fall. For example, the percentile of column `col` at percentage 50% is the median value of column `col`. ### Syntax: ``` # Returns percentile at a given percentage value. The approximation error can be reduced by increasing parameter accuracy, at the cost of memory. percentile_approx(col, percentage [, accuracy]) # Returns percentile value array at given percentage value array percentile_approx(col, array(percentage1 [, percentage2]...) [, accuracy]) ``` ### Features: 1. This function supports partial aggregation. 2. The memory consumption is bounded. The larger `accuracy` parameter we choose, we smaller error we get. The default accuracy value is 10000, to match with Hive default setting. Choose a smaller value for smaller memory footprint. 3. This function supports window function aggregation. ### Example usages: ``` ## Returns the 25th percentile value, with default accuracy SELECT percentile_approx(col, 0.25) FROM table ## Returns an array of percentile value (25th, 50th, 75th), with default accuracy SELECT percentile_approx(col, array(0.25, 0.5, 0.75)) FROM table ## Returns 25th percentile value, with custom accuracy value 100, larger accuracy parameter yields smaller approximation error SELECT percentile_approx(col, 0.25, 100) FROM table ## Returns the 25th, and 50th percentile values, with custom accuracy value 100 SELECT percentile_approx(col, array(0.25, 0.5), 100) FROM table ``` ### NOTE: 1. The `percentile_approx` implementation is different from Hive, so the result returned on same query maybe slightly different with Hive. This implementation uses `QuantileSummaries` as the underlying probabilistic data structure, and mainly follows paper `Space-efficient Online Computation of Quantile Summaries` by Greenwald, Michael and Khanna, Sanjeev. (http://dx.doi.org/10.1145/375663.375670)` 2. The current implementation of `QuantileSummaries` doesn't support automatic compression. This PR has a rule to do compression automatically at the caller side, but it may not be optimal. ## How was this patch tested? Unit test, and Sql query test. ## Acknowledgement 1. This PR's work in based on lw-lin's PR https://github.com/apache/spark/pull/14298, with improvements like supporting partial aggregation, fixing out of memory issue. Author: Sean Zhong <seanzhong@databricks.com> Closes #14868 from clockfly/appro_percentile_try_2.
* [SPARK-17180][SPARK-17309][SPARK-17323][SQL] create AlterViewAsCommand to ↵Wenchen Fan2016-08-311-3/+74
| | | | | | | | | | | | | | | | | | | | | | handle ALTER VIEW AS ## What changes were proposed in this pull request? Currently we use `CreateViewCommand` to implement ALTER VIEW AS, which has 3 bugs: 1. SPARK-17180: ALTER VIEW AS should alter temp view if view name has no database part and temp view exists 2. SPARK-17309: ALTER VIEW AS should issue exception if view does not exist. 3. SPARK-17323: ALTER VIEW AS should keep the previous table properties, comment, create_time, etc. The root cause is, ALTER VIEW AS is quite different from CREATE VIEW, we need different code path to handle them. However, in `CreateViewCommand`, there is no way to distinguish ALTER VIEW AS and CREATE VIEW, we have to introduce extra flag. But instead of doing this, I think a more natural way is to separate the ALTER VIEW AS logic into a new command. ## How was this patch tested? new tests in SQLViewSuite Author: Wenchen Fan <wenchen@databricks.com> Closes #14874 from cloud-fan/minor4.
* [SPARK-17276][CORE][TEST] Stop env params output on Jenkins job pageXin Ren2016-08-301-2/+2
| | | | | | | | | | | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-17276 ## What changes were proposed in this pull request? When trying to find error msg in a failed Jenkins build job, I'm annoyed by the huge env output. The env parameter output should be muted. ![screen shot 2016-08-26 at 10 52 07 pm](https://cloud.githubusercontent.com/assets/3925641/18025581/b8d567ba-6be2-11e6-9eeb-6aec223f1730.png) ## How was this patch tested? Tested manually on local laptop. Author: Xin Ren <iamshrek@126.com> Closes #14848 from keypointt/SPARK-17276.
* [SPARK-17234][SQL] Table Existence Checking when Index Table with the Same ↵gatorsmile2016-08-302-0/+39
| | | | | | | | | | | | | | | | | | | Name Exists ### What changes were proposed in this pull request? Hive Index tables are not supported by Spark SQL. Thus, we issue an exception when users try to access Hive Index tables. When the internal function `tableExists` tries to access Hive Index tables, it always gets the same error message: ```Hive index table is not supported```. This message could be confusing to users, since their SQL operations could be completely unrelated to Hive Index tables. For example, when users try to alter a table to a new name and there exists an index table with the same name, the expected exception should be a `TableAlreadyExistsException`. This PR made the following changes: - Introduced a new `AnalysisException` type: `SQLFeatureNotSupportedException`. When users try to access an `Index Table`, we will issue a `SQLFeatureNotSupportedException`. - `tableExists` returns `true` when hitting a `SQLFeatureNotSupportedException` and the feature is `Hive index table`. - Add a checking `requireTableNotExists` for `SessionCatalog`'s `createTable` API; otherwise, the current implementation relies on the Hive's internal checking. ### How was this patch tested? Added a test case Author: gatorsmile <gatorsmile@gmail.com> Closes #14801 from gatorsmile/tableExists.
* [SPARK-17063] [SQL] Improve performance of MSCK REPAIR TABLE with Hive metastoreDavies Liu2016-08-291-0/+38
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR split the the single `createPartitions()` call into smaller batches, which could prevent Hive metastore from OOM (caused by millions of partitions). It will also try to gather all the fast stats (number of files and total size of all files) in parallel to avoid the bottle neck of listing the files in metastore sequential, which is controlled by spark.sql.gatherFastStats (enabled by default). ## How was this patch tested? Tested locally with 10000 partitions and 100 files with embedded metastore, without gathering fast stats in parallel, adding partitions took 153 seconds, after enable that, gathering the fast stats took about 34 seconds, adding these partitions took 25 seconds (most of the time spent in object store), 59 seconds in total, 2.5X faster (with larger cluster, gathering will much faster). Author: Davies Liu <davies@databricks.com> Closes #14607 from davies/repair_batch.
* [SPARK-17246][SQL] Add BigDecimal literalHerman van Hovell2016-08-261-0/+1
| | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds parser support for `BigDecimal` literals. If you append the suffix `BD` to a valid number then this will be interpreted as a `BigDecimal`, for example `12.0E10BD` will interpreted into a BigDecimal with scale -9 and precision 3. This is useful in situations where you need exact values. ## How was this patch tested? Added tests to `ExpressionParserSuite`, `ExpressionSQLBuilderSuite` and `SQLQueryTestSuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #14819 from hvanhovell/SPARK-17246.
* [SPARK-16216][SQL][FOLLOWUP] Enable timestamp type tests for JSON and verify ↵hyukjinkwon2016-08-261-4/+0
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | all unsupported types in CSV ## What changes were proposed in this pull request? This PR enables the tests for `TimestampType` for JSON and unifies the logics for verifying schema when writing in CSV. In more details, this PR, - Enables the tests for `TimestampType` for JSON and This was disabled due to an issue in `DatatypeConverter.parseDateTime` which parses dates incorrectly, for example as below: ```scala val d = javax.xml.bind.DatatypeConverter.parseDateTime("0900-01-01T00:00:00.000").getTime println(d.toString) ``` ``` Fri Dec 28 00:00:00 KST 899 ``` However, since we use `FastDateFormat`, it seems we are safe now. ```scala val d = FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSS").parse("0900-01-01T00:00:00.000") println(d) ``` ``` Tue Jan 01 00:00:00 PST 900 ``` - Verifies all unsupported types in CSV There is a separate logics to verify the schemas in `CSVFileFormat`. This is actually not quite correct enough because we don't support `NullType` and `CalanderIntervalType` as well `StructType`, `ArrayType`, `MapType`. So, this PR adds both types. ## How was this patch tested? Tests in `JsonHadoopFsRelation` and `CSVSuite` Author: hyukjinkwon <gurwls223@gmail.com> Closes #14829 from HyukjinKwon/SPARK-16216-followup.
* [SPARK-17205] Literal.sql should handle Infinity and NaNJosh Rosen2016-08-261-0/+6
| | | | | | | | This patch updates `Literal.sql` to properly generate SQL for `NaN` and `Infinity` float and double literals: these special values need to be handled differently from regular values, since simply appending a suffix to the value's `toString()` representation will not work for these values. Author: Josh Rosen <joshrosen@databricks.com> Closes #14777 from JoshRosen/SPARK-17205.
* [SPARK-17190][SQL] Removal of HiveSharedStategatorsmile2016-08-255-14/+14
| | | | | | | | | | | | | | ### What changes were proposed in this pull request? Since `HiveClient` is used to interact with the Hive metastore, it should be hidden in `HiveExternalCatalog`. After moving `HiveClient` into `HiveExternalCatalog`, `HiveSharedState` becomes a wrapper of `HiveExternalCatalog`. Thus, removal of `HiveSharedState` becomes straightforward. After removal of `HiveSharedState`, the reflection logic is directly applied on the choice of `ExternalCatalog` types, based on the configuration of `CATALOG_IMPLEMENTATION`. ~~`HiveClient` is also used/invoked by the other entities besides HiveExternalCatalog, we defines the following two APIs: getClient and getNewClient~~ ### How was this patch tested? The existing test cases Author: gatorsmile <gatorsmile@gmail.com> Closes #14757 from gatorsmile/removeHiveClient.
* [SPARK-16216][SQL] Read/write timestamps and dates in ISO 8601 and ↵hyukjinkwon2016-08-241-0/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | dateFormat/timestampFormat option for CSV and JSON ## What changes were proposed in this pull request? ### Default - ISO 8601 Currently, CSV datasource is writing `Timestamp` and `Date` as numeric form and JSON datasource is writing both as below: - CSV ``` // TimestampType 1414459800000000 // DateType 16673 ``` - Json ``` // TimestampType 1970-01-01 11:46:40.0 // DateType 1970-01-01 ``` So, for CSV we can't read back what we write and for JSON it becomes ambiguous because the timezone is being missed. So, this PR make both **write** `Timestamp` and `Date` in ISO 8601 formatted string (please refer the [ISO 8601 specification](https://www.w3.org/TR/NOTE-datetime)). - For `Timestamp` it becomes as below: (`yyyy-MM-dd'T'HH:mm:ss.SSSZZ`) ``` 1970-01-01T02:00:01.000-01:00 ``` - For `Date` it becomes as below (`yyyy-MM-dd`) ``` 1970-01-01 ``` ### Custom date format option - `dateFormat` This PR also adds the support to write and read dates and timestamps in a formatted string as below: - **DateType** - With `dateFormat` option (e.g. `yyyy/MM/dd`) ``` +----------+ | date| +----------+ |2015/08/26| |2014/10/27| |2016/01/28| +----------+ ``` ### Custom date format option - `timestampFormat` - **TimestampType** - With `dateFormat` option (e.g. `dd/MM/yyyy HH:mm`) ``` +----------------+ | date| +----------------+ |2015/08/26 18:00| |2014/10/27 18:30| |2016/01/28 20:00| +----------------+ ``` ## How was this patch tested? Unit tests were added in `CSVSuite` and `JsonSuite`. For JSON, existing tests cover the default cases. Author: hyukjinkwon <gurwls223@gmail.com> Closes #14279 from HyukjinKwon/SPARK-16216-json-csv.
* [SPARK-17186][SQL] remove catalog table type INDEXWenchen Fan2016-08-231-1/+1
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Actually Spark SQL doesn't support index, the catalog table type `INDEX` is from Hive. However, most operations in Spark SQL can't handle index table, e.g. create table, alter table, etc. Logically index table should be invisible to end users, and Hive also generates special table name for index table to avoid users accessing it directly. Hive has special SQL syntax to create/show/drop index tables. At Spark SQL side, although we can describe index table directly, but the result is unreadable, we should use the dedicated SQL syntax to do it(e.g. `SHOW INDEX ON tbl`). Spark SQL can also read index table directly, but the result is always empty.(Can hive read index table directly?) This PR remove the table type `INDEX`, to make it clear that Spark SQL doesn't support index currently. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #14752 from cloud-fan/minor2.
* [SPARK-17194] Use single quotes when generating SQL for string literalsJosh Rosen2016-08-2319-20/+21
| | | | | | | | When Spark emits SQL for a string literal, it should wrap the string in single quotes, not double quotes. Databases which adhere more strictly to the ANSI SQL standards, such as Postgres, allow only single-quotes to be used for denoting string literals (see http://stackoverflow.com/a/1992331/590203). Author: Josh Rosen <joshrosen@databricks.com> Closes #14763 from JoshRosen/SPARK-17194.
* [SPARK-17162] Range does not support SQL generationEric Liang2016-08-223-1/+21
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The range operator previously didn't support SQL generation, which made it not possible to use in views. ## How was this patch tested? Unit tests. cc hvanhovell Author: Eric Liang <ekl@databricks.com> Closes #14724 from ericl/spark-17162.
* [SPARK-16498][SQL] move hive hack for data source table into HiveExternalCatalogWenchen Fan2016-08-215-64/+116
| | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Spark SQL doesn't have its own meta store yet, and use hive's currently. However, hive's meta store has some limitations(e.g. columns can't be too many, not case-preserving, bad decimal type support, etc.), so we have some hacks to successfully store data source table metadata into hive meta store, i.e. put all the information in table properties. This PR moves these hacks to `HiveExternalCatalog`, tries to isolate hive specific logic in one place. changes overview: 1. **before this PR**: we need to put metadata(schema, partition columns, etc.) of data source tables to table properties before saving it to external catalog, even the external catalog doesn't use hive metastore(e.g. `InMemoryCatalog`) **after this PR**: the table properties tricks are only in `HiveExternalCatalog`, the caller side doesn't need to take care of it anymore. 2. **before this PR**: because the table properties tricks are done outside of external catalog, so we also need to revert these tricks when we read the table metadata from external catalog and use it. e.g. in `DescribeTableCommand` we will read schema and partition columns from table properties. **after this PR**: The table metadata read from external catalog is exactly the same with what we saved to it. bonus: now we can create data source table using `SessionCatalog`, if schema is specified. breaks: `schemaStringLengthThreshold` is not configurable anymore. `hive.default.rcfile.serde` is not configurable anymore. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #14155 from cloud-fan/catalog-table.
* [SPARK-17104][SQL] LogicalRelation.newInstance should follow the semantics ↵Liang-Chi Hsieh2016-08-201-0/+7
| | | | | | | | | | | | | | | | of MultiInstanceRelation ## What changes were proposed in this pull request? Currently `LogicalRelation.newInstance()` simply creates another `LogicalRelation` object with the same parameters. However, the `newInstance()` method inherited from `MultiInstanceRelation` should return a copy of object with unique expression ids. Current `LogicalRelation.newInstance()` can cause failure when doing self-join. ## How was this patch tested? Jenkins tests. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #14682 from viirya/fix-localrelation.
* [SPARK-17150][SQL] Support SQL generation for inline tablespetermaxlee2016-08-202-0/+12
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch adds support for SQL generation for inline tables. With this, it would be possible to create a view that depends on inline tables. ## How was this patch tested? Added a test case in LogicalPlanToSQLSuite. Author: petermaxlee <petermaxlee@gmail.com> Closes #14709 from petermaxlee/SPARK-17150.
* [SPARK-17149][SQL] array.sql for testing array related functionspetermaxlee2016-08-191-9/+0
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch creates array.sql in SQLQueryTestSuite for testing array related functions, including: - indexing - array creation - size - array_contains - sort_array ## How was this patch tested? The patch itself is about adding tests. Author: petermaxlee <petermaxlee@gmail.com> Closes #14708 from petermaxlee/SPARK-17149.
* [SPARK-16736][CORE][SQL] purge superfluous fs callsSteve Loughran2016-08-172-4/+2
| | | | | | | | | | | | | | | A review of the code, working back from Hadoop's `FileSystem.exists()` and `FileSystem.isDirectory()` code, then removing uses of the calls when superfluous. 1. delete is harmless if called on a nonexistent path, so don't do any checks before deletes 1. any `FileSystem.exists()` check before `getFileStatus()` or `open()` is superfluous as the operation itself does the check. Instead the `FileNotFoundException` is caught and triggers the downgraded path. When a `FileNotFoundException` was thrown before, the code still creates a new FNFE with the error messages. Though now the inner exceptions are nested, for easier diagnostics. Initially, relying on Jenkins test runs. One troublespot here is that some of the codepaths are clearly error situations; it's not clear that they have coverage anyway. Trying to create the failure conditions in tests would be ideal, but it will also be hard. Author: Steve Loughran <stevel@apache.org> Closes #14371 from steveloughran/cloud/SPARK-16736-superfluous-fs-calls.
* [SPARK-17068][SQL] Make view-usage visible during analysisHerman van Hovell2016-08-161-1/+13
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds a field to subquery alias in order to make the usage of views in a resolved `LogicalPlan` more visible (and more understandable). For example, the following view and query: ```sql create view constants as select 1 as id union all select 1 union all select 42 select * from constants; ``` ...now yields the following analyzed plan: ``` Project [id#39] +- SubqueryAlias c, `default`.`constants` +- Project [gen_attr_0#36 AS id#39] +- SubqueryAlias gen_subquery_0 +- Union :- Union : :- Project [1 AS gen_attr_0#36] : : +- OneRowRelation$ : +- Project [1 AS gen_attr_1#37] : +- OneRowRelation$ +- Project [42 AS gen_attr_2#38] +- OneRowRelation$ ``` ## How was this patch tested? Added tests for the two code paths in `SessionCatalogSuite` (sql/core) and `HiveMetastoreCatalogSuite` (sql/hive) Author: Herman van Hovell <hvanhovell@databricks.com> Closes #14657 from hvanhovell/SPARK-17068.
* [SPARK-17013][SQL] Parse negative numeric literalspetermaxlee2016-08-111-2/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch updates the SQL parser to parse negative numeric literals as numeric literals, instead of unary minus of positive literals. This allows the parser to parse the minimal value for each data type, e.g. "-32768S". ## How was this patch tested? Updated test cases. Author: petermaxlee <petermaxlee@gmail.com> Closes #14608 from petermaxlee/SPARK-17013.
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