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* more wipescapeJakob Odersky2016-10-203-7/+4
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* wipJakob Odersky2016-10-192-1/+10
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* Implement Hive's 'like' behaviourJakob Odersky2016-10-183-37/+36
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* fix style issuesJakob Odersky2016-10-131-2/+7
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* Add more documentationJakob Odersky2016-10-132-11/+63
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* Fix testsJakob Odersky2016-10-071-2/+2
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* Factor out foldLeft to a for loop and mutable stateJakob Odersky2016-10-071-4/+5
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* Add more tests for various combinations of backslashes and SQL pattern ↵Jakob Odersky2016-10-071-0/+14
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* Fix backslash escaping in 'LIKE' patterns.Jakob Odersky2016-10-072-19/+19
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* [SPARK-17798][SQL] Remove redundant Experimental annotations in sql.streamingReynold Xin2016-10-063-60/+1
| | | | | | | | | | | | ## What changes were proposed in this pull request? I was looking through API annotations to catch mislabeled APIs, and realized DataStreamReader and DataStreamWriter classes are already annotated as Experimental, and as a result there is no need to annotate each method within them. ## How was this patch tested? N/A Author: Reynold Xin <rxin@databricks.com> Closes #15373 from rxin/SPARK-17798.
* [SPARK-17750][SQL] Fix CREATE VIEW with INTERVAL arithmetic.Dongjoon Hyun2016-10-064-1/+43
| | | | | | | | | | | | | | | | | | | | | ## 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-17346][SQL][TEST-MAVEN] Generate the sql test jar to fix the maven buildShixiong Zhu2016-10-051-0/+27
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Generate the sql test jar to fix the maven build ## How was this patch tested? Jenkins Author: Shixiong Zhu <shixiong@databricks.com> Closes #15368 from zsxwing/sql-test-jar.
* [SPARK-17346][SQL] Add Kafka source for Structured StreamingShixiong Zhu2016-10-052-11/+37
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds a new project ` external/kafka-0-10-sql` for Structured Streaming Kafka source. It's based on the design doc: https://docs.google.com/document/d/19t2rWe51x7tq2e5AOfrsM9qb8_m7BRuv9fel9i0PqR8/edit?usp=sharing tdas did most of work and part of them was inspired by koeninger's work. ### Introduction The Kafka source is a structured streaming data source to poll data from Kafka. The schema of reading data is as follows: Column | Type ---- | ---- key | binary value | binary topic | string partition | int offset | long timestamp | long timestampType | int The source can deal with deleting topics. However, the user should make sure there is no Spark job processing the data when deleting a topic. ### Configuration The user can use `DataStreamReader.option` to set the following configurations. Kafka Source's options | value | default | meaning ------ | ------- | ------ | ----- startingOffset | ["earliest", "latest"] | "latest" | The start point when a query is started, either "earliest" which is from the earliest offset, or "latest" which is just from the latest offset. Note: This only applies when a new Streaming query is started, and that resuming will always pick up from where the query left off. failOnDataLost | [true, false] | true | Whether to fail the query when it's possible that data is lost (e.g., topics are deleted, or offsets are out of range). This may be a false alarm. You can disable it when it doesn't work as you expected. subscribe | A comma-separated list of topics | (none) | The topic list to subscribe. Only one of "subscribe" and "subscribeParttern" options can be specified for Kafka source. subscribePattern | Java regex string | (none) | The pattern used to subscribe the topic. Only one of "subscribe" and "subscribeParttern" options can be specified for Kafka source. kafka.consumer.poll.timeoutMs | long | 512 | The timeout in milliseconds to poll data from Kafka in executors fetchOffset.numRetries | int | 3 | Number of times to retry before giving up fatch Kafka latest offsets. fetchOffset.retryIntervalMs | long | 10 | milliseconds to wait before retrying to fetch Kafka offsets Kafka's own configurations can be set via `DataStreamReader.option` with `kafka.` prefix, e.g, `stream.option("kafka.bootstrap.servers", "host:port")` ### Usage * Subscribe to 1 topic ```Scala spark .readStream .format("kafka") .option("kafka.bootstrap.servers", "host:port") .option("subscribe", "topic1") .load() ``` * Subscribe to multiple topics ```Scala spark .readStream .format("kafka") .option("kafka.bootstrap.servers", "host:port") .option("subscribe", "topic1,topic2") .load() ``` * Subscribe to a pattern ```Scala spark .readStream .format("kafka") .option("kafka.bootstrap.servers", "host:port") .option("subscribePattern", "topic.*") .load() ``` ## How was this patch tested? The new unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Author: Tathagata Das <tathagata.das1565@gmail.com> Author: Shixiong Zhu <zsxwing@gmail.com> Author: cody koeninger <cody@koeninger.org> Closes #15102 from zsxwing/kafka-source.
* [SPARK-17758][SQL] Last returns wrong result in case of empty partitionHerman van Hovell2016-10-053-13/+184
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The result of the `Last` function can be wrong when the last partition processed is empty. It can return `null` instead of the expected value. For example, this can happen when we process partitions in the following order: ``` - Partition 1 [Row1, Row2] - Partition 2 [Row3] - Partition 3 [] ``` In this case the `Last` function will currently return a null, instead of the value of `Row3`. This PR fixes this by adding a `valueSet` flag to the `Last` function. ## How was this patch tested? We only used end to end tests for `DeclarativeAggregateFunction`s. I have added an evaluator for these functions so we can tests them in catalyst. I have added a `LastTestSuite` to test the `Last` aggregate function. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15348 from hvanhovell/SPARK-17758.
* [SPARK-17328][SQL] Fix NPE with EXPLAIN DESCRIBE TABLEDongjoon Hyun2016-10-055-18/+68
| | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes the following NPE scenario in two ways. **Reported Error Scenario** ```scala scala> sql("EXPLAIN DESCRIBE TABLE x").show(truncate = false) INFO SparkSqlParser: Parsing command: EXPLAIN DESCRIBE TABLE x java.lang.NullPointerException ``` - **DESCRIBE**: Extend `DESCRIBE` syntax to accept `TABLE`. - **EXPLAIN**: Prevent NPE in case of the parsing failure of target statement, e.g., `EXPLAIN DESCRIBE TABLES x`. ## How was this patch tested? Pass the Jenkins test with a new test case. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15357 from dongjoon-hyun/SPARK-17328.
* [SPARK-17258][SQL] Parse scientific decimal literals as decimalsHerman van Hovell2016-10-047-39/+38
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently Spark SQL parses regular decimal literals (e.g. `10.00`) as decimals and scientific decimal literals (e.g. `10.0e10`) as doubles. The difference between the two confuses most users. This PR unifies the parsing behavior and also parses scientific decimal literals as decimals. This implications in tests are limited to a single Hive compatibility test. ## How was this patch tested? Updated tests in `ExpressionParserSuite` and `SQLQueryTestSuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #14828 from hvanhovell/SPARK-17258.
* [SPARK-17495][SQL] Add Hash capability semantically equivalent to Hive'sTejas Patil2016-10-045-151/+582
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Jira : https://issues.apache.org/jira/browse/SPARK-17495 Spark internally uses Murmur3Hash for partitioning. This is different from the one used by Hive. For queries which use bucketing this leads to different results if one tries the same query on both engines. For us, we want users to have backward compatibility to that one can switch parts of applications across the engines without observing regressions. This PR includes `HiveHash`, `HiveHashFunction`, `HiveHasher` which mimics Hive's hashing at https://github.com/apache/hive/blob/master/serde/src/java/org/apache/hadoop/hive/serde2/objectinspector/ObjectInspectorUtils.java#L638 I am intentionally not introducing any usages of this hash function in rest of the code to keep this PR small. My eventual goal is to have Hive bucketing support in Spark. Once this PR gets in, I will make hash function pluggable in relevant areas (eg. `HashPartitioning`'s `partitionIdExpression` has Murmur3 hardcoded : https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala#L265) ## How was this patch tested? Added `HiveHashSuite` Author: Tejas Patil <tejasp@fb.com> Closes #15047 from tejasapatil/SPARK-17495_hive_hash.
* [SPARK-17549][SQL] Only collect table size stat in driver for cached relation.Marcelo Vanzin2016-10-042-18/+20
| | | | | | | | | | | | | | | | | | | | | | | | This reverts commit 9ac68dbc5720026ea92acc61d295ca64d0d3d132. Turns out the original fix was correct. Original change description: The existing code caches all stats for all columns for each partition in the driver; for a large relation, this causes extreme memory usage, which leads to gc hell and application failures. It seems that only the size in bytes of the data is actually used in the driver, so instead just colllect that. In executors, the full stats are still kept, but that's not a big problem; we expect the data to be distributed and thus not really incur in too much memory pressure in each individual executor. There are also potential improvements on the executor side, since the data being stored currently is very wasteful (e.g. storing boxed types vs. primitive types for stats). But that's a separate issue. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #15304 from vanzin/SPARK-17549.2.
* [SPARK-16962][CORE][SQL] Fix misaligned record accesses for SPARC architecturessumansomasundar2016-10-041-1/+10
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Made changes to record length offsets to make them uniform throughout various areas of Spark core and unsafe ## How was this patch tested? This change affects only SPARC architectures and was tested on X86 architectures as well for regression. Author: sumansomasundar <suman.somasundar@oracle.com> Closes #14762 from sumansomasundar/master.
* [SPARK-17773] Input/Output] Add VoidObjectInspectorErgin Seyfe2016-10-032-0/+3
| | | | | | | | | | | | | | | | | | | | | ## 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-17702][SQL] Code generation including too many mutable states exceeds ↵Takuya UESHIN2016-10-038-12/+48
| | | | | | | | | | | | | | | | | JVM size limit. ## What changes were proposed in this pull request? Code generation including too many mutable states exceeds JVM size limit to extract values from `references` into fields in the constructor. We should split the generated extractions in the constructor into smaller functions. ## How was this patch tested? I added some tests to check if the generated codes for the expressions exceed or not. Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #15275 from ueshin/issues/SPARK-17702.
* [SPARK-17112][SQL] "select null" via JDBC triggers IllegalArgumentException ↵Dongjoon Hyun2016-10-032-6/+46
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | in Thriftserver ## What changes were proposed in this pull request? Currently, Spark Thrift Server raises `IllegalArgumentException` for queries whose column types are `NullType`, e.g., `SELECT null` or `SELECT if(true,null,null)`. This PR fixes that by returning `void` like Hive 1.2. **Before** ```sql $ bin/beeline -u jdbc:hive2://localhost:10000 -e "select null" Connecting to jdbc:hive2://localhost:10000 Connected to: Spark SQL (version 2.1.0-SNAPSHOT) Driver: Hive JDBC (version 1.2.1.spark2) Transaction isolation: TRANSACTION_REPEATABLE_READ Error: java.lang.IllegalArgumentException: Unrecognized type name: null (state=,code=0) Closing: 0: jdbc:hive2://localhost:10000 $ bin/beeline -u jdbc:hive2://localhost:10000 -e "select if(true,null,null)" Connecting to jdbc:hive2://localhost:10000 Connected to: Spark SQL (version 2.1.0-SNAPSHOT) Driver: Hive JDBC (version 1.2.1.spark2) Transaction isolation: TRANSACTION_REPEATABLE_READ Error: java.lang.IllegalArgumentException: Unrecognized type name: null (state=,code=0) Closing: 0: jdbc:hive2://localhost:10000 ``` **After** ```sql $ bin/beeline -u jdbc:hive2://localhost:10000 -e "select null" Connecting to jdbc:hive2://localhost:10000 Connected to: Spark SQL (version 2.1.0-SNAPSHOT) Driver: Hive JDBC (version 1.2.1.spark2) Transaction isolation: TRANSACTION_REPEATABLE_READ +-------+--+ | NULL | +-------+--+ | NULL | +-------+--+ 1 row selected (3.242 seconds) Beeline version 1.2.1.spark2 by Apache Hive Closing: 0: jdbc:hive2://localhost:10000 $ bin/beeline -u jdbc:hive2://localhost:10000 -e "select if(true,null,null)" Connecting to jdbc:hive2://localhost:10000 Connected to: Spark SQL (version 2.1.0-SNAPSHOT) Driver: Hive JDBC (version 1.2.1.spark2) Transaction isolation: TRANSACTION_REPEATABLE_READ +-------------------------+--+ | (IF(true, NULL, NULL)) | +-------------------------+--+ | NULL | +-------------------------+--+ 1 row selected (0.201 seconds) Beeline version 1.2.1.spark2 by Apache Hive Closing: 0: jdbc:hive2://localhost:10000 ``` ## How was this patch tested? * Pass the Jenkins test with a new testsuite. * Also, Manually, after starting Spark Thrift Server, run the following command. ```sql $ bin/beeline -u jdbc:hive2://localhost:10000 -e "select null" $ bin/beeline -u jdbc:hive2://localhost:10000 -e "select if(true,null,null)" ``` **Hive 1.2** ```sql hive> create table null_table as select null; hive> desc null_table; OK _c0 void ``` Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15325 from dongjoon-hyun/SPARK-17112.
* [SPARK-17753][SQL] Allow a complex expression as the input a value based ↵Herman van Hovell2016-10-033-7/+11
| | | | | | | | | | | | | | case statement ## What changes were proposed in this pull request? We currently only allow relatively simple expressions as the input for a value based case statement. Expressions like `case (a > 1) or (b = 2) when true then 1 when false then 0 end` currently fail. This PR adds support for such expressions. ## How was this patch tested? Added a test to the ExpressionParserSuite. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15322 from hvanhovell/SPARK-17753.
* [SPARK-17073][SQL] generate column-level statisticsZhenhua Wang2016-10-0313-114/+906
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## 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-14914][CORE][SQL] Skip/fix some test cases on Windows due to ↵Tao LI2016-10-021-1/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | limitation of Windows ## What changes were proposed in this pull request? This PR proposes to fix/skip some tests failed on Windows. This PR takes over https://github.com/apache/spark/pull/12696. **Before** - **SparkSubmitSuite** ``` [info] - launch simple application with spark-submit *** FAILED *** (202 milliseconds) [info] java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specifie [info] - includes jars passed in through --jars *** FAILED *** (1 second, 625 milliseconds) [info] java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified ``` - **DiskStoreSuite** ``` [info] - reads of memory-mapped and non memory-mapped files are equivalent *** FAILED *** (1 second, 78 milliseconds) [info] diskStoreMapped.remove(blockId) was false (DiskStoreSuite.scala:41) ``` **After** - **SparkSubmitSuite** ``` [info] - launch simple application with spark-submit (578 milliseconds) [info] - includes jars passed in through --jars (1 second, 875 milliseconds) ``` - **DiskStoreSuite** ``` [info] DiskStoreSuite: [info] - reads of memory-mapped and non memory-mapped files are equivalent !!! CANCELED !!! (766 milliseconds ``` For `CreateTableAsSelectSuite` and `FsHistoryProviderSuite`, I could not reproduce as the Java version seems higher than the one that has the bugs about `setReadable(..)` and `setWritable(...)` but as they are bugs reported clearly, it'd be sensible to skip those. We should revert the changes for both back as soon as we drop the support of Java 7. ## How was this patch tested? Manually tested via AppVeyor. Closes #12696 Author: Tao LI <tl@microsoft.com> Author: U-FAREAST\tl <tl@microsoft.com> Author: hyukjinkwon <gurwls223@gmail.com> Closes #15320 from HyukjinKwon/SPARK-14914.
* [SPARK-17509][SQL] When wrapping catalyst datatype to Hive data type avoid…Sital Kedia2016-10-022-177/+145
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? When wrapping catalyst datatypes to Hive data type, wrap function was doing an expensive pattern matching which was consuming around 11% of cpu time. Avoid the pattern matching by returning the wrapper only once and reuse it. ## How was this patch tested? Tested by running the job on cluster and saw around 8% cpu improvements. Author: Sital Kedia <skedia@fb.com> Closes #15064 from sitalkedia/skedia/hive_wrapper.
* [SPARK-17717][SQL] Add Exist/find methods to Catalog [FOLLOW-UP]Herman van Hovell2016-10-013-82/+67
| | | | | | | | | | | | ## What changes were proposed in this pull request? We added find and exists methods for Databases, Tables and Functions to the user facing Catalog in PR https://github.com/apache/spark/pull/15301. However, it was brought up that the semantics of the `find` methods are more in line a `get` method (get an object or else fail). So we rename these in this PR. ## How was this patch tested? Existing tests. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15308 from hvanhovell/SPARK-17717-2.
* [SPARK-17740] Spark tests should mock / interpose HDFS to ensure that ↵Eric Liang2016-09-303-4/+19
| | | | | | | | | | | | | | | | | | | streams are closed ## What changes were proposed in this pull request? As a followup to SPARK-17666, ensure filesystem connections are not leaked at least in unit tests. This is done here by intercepting filesystem calls as suggested by JoshRosen . At the end of each test, we assert no filesystem streams are left open. This applies to all tests using SharedSQLContext or SharedSparkContext. ## How was this patch tested? I verified that tests in sql and core are indeed using the filesystem backend, and fixed the detected leaks. I also checked that reverting https://github.com/apache/spark/pull/15245 causes many actual test failures due to connection leaks. Author: Eric Liang <ekl@databricks.com> Author: Eric Liang <ekhliang@gmail.com> Closes #15306 from ericl/sc-4672.
* [SPARK-17739][SQL] Collapse adjacent similar Window operatorsDongjoon Hyun2016-09-302-0/+90
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, Spark does not collapse adjacent windows with the same partitioning and sorting. This PR implements `CollapseWindow` optimizer to do the followings. 1. If the partition specs and order specs are the same, collapse into the parent. 2. If the partition specs are the same and one order spec is a prefix of the other, collapse to the more specific one. For example: ```scala val df = spark.range(1000).select($"id" % 100 as "grp", $"id", rand() as "col1", rand() as "col2") // Add summary statistics for all columns import org.apache.spark.sql.expressions.Window val cols = Seq("id", "col1", "col2") val window = Window.partitionBy($"grp").orderBy($"id") val result = cols.foldLeft(df) { (base, name) => base.withColumn(s"${name}_avg", avg(col(name)).over(window)) .withColumn(s"${name}_stddev", stddev(col(name)).over(window)) .withColumn(s"${name}_min", min(col(name)).over(window)) .withColumn(s"${name}_max", max(col(name)).over(window)) } ``` **Before** ```scala scala> result.explain == Physical Plan == Window [max(col2#19) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_max#234], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [min(col2#19) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_min#216], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [stddev_samp(col2#19) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_stddev#191], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [avg(col2#19) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_avg#167], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [max(col1#18) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_max#152], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [min(col1#18) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_min#138], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [stddev_samp(col1#18) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_stddev#117], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [avg(col1#18) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_avg#97], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [max(id#14L) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_max#86L], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [min(id#14L) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_min#76L], [grp#17L], [id#14L ASC NULLS FIRST] +- *Project [grp#17L, id#14L, col1#18, col2#19, id_avg#26, id_stddev#42] +- Window [stddev_samp(_w0#59) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_stddev#42], [grp#17L], [id#14L ASC NULLS FIRST] +- *Project [grp#17L, id#14L, col1#18, col2#19, id_avg#26, cast(id#14L as double) AS _w0#59] +- Window [avg(id#14L) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_avg#26], [grp#17L], [id#14L ASC NULLS FIRST] +- *Sort [grp#17L ASC NULLS FIRST, id#14L ASC NULLS FIRST], false, 0 +- Exchange hashpartitioning(grp#17L, 200) +- *Project [(id#14L % 100) AS grp#17L, id#14L, rand(-6329949029880411066) AS col1#18, rand(-7251358484380073081) AS col2#19] +- *Range (0, 1000, step=1, splits=Some(8)) ``` **After** ```scala scala> result.explain == Physical Plan == Window [max(col2#5) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_max#220, min(col2#5) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_min#202, stddev_samp(col2#5) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_stddev#177, avg(col2#5) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_avg#153, max(col1#4) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_max#138, min(col1#4) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_min#124, stddev_samp(col1#4) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_stddev#103, avg(col1#4) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_avg#83, max(id#0L) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_max#72L, min(id#0L) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_min#62L], [grp#3L], [id#0L ASC NULLS FIRST] +- *Project [grp#3L, id#0L, col1#4, col2#5, id_avg#12, id_stddev#28] +- Window [stddev_samp(_w0#45) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_stddev#28], [grp#3L], [id#0L ASC NULLS FIRST] +- *Project [grp#3L, id#0L, col1#4, col2#5, id_avg#12, cast(id#0L as double) AS _w0#45] +- Window [avg(id#0L) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_avg#12], [grp#3L], [id#0L ASC NULLS FIRST] +- *Sort [grp#3L ASC NULLS FIRST, id#0L ASC NULLS FIRST], false, 0 +- Exchange hashpartitioning(grp#3L, 200) +- *Project [(id#0L % 100) AS grp#3L, id#0L, rand(6537478539664068821) AS col1#4, rand(-8961093871295252795) AS col2#5] +- *Range (0, 1000, step=1, splits=Some(8)) ``` ## How was this patch tested? Pass the Jenkins tests with a newly added testsuite. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15317 from dongjoon-hyun/SPARK-17739.
* [SPARK-17703][SQL] Add unnamed version of addReferenceObj for minor objects.Takuya UESHIN2016-09-303-4/+28
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? There are many minor objects in references, which are extracted to the generated class field, e.g. `errMsg` in `GetExternalRowField` or `ValidateExternalType`, but number of fields in class is limited so we should reduce the number. This pr adds unnamed version of `addReferenceObj` for these minor objects not to store the object into field but refer it from the `references` field at the time of use. ## How was this patch tested? Existing tests. Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #15276 from ueshin/issues/SPARK-17703.
* [SPARK-17738] [SQL] fix ARRAY/MAP in columnar cacheDavies Liu2016-09-302-7/+7
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? The actualSize() of array and map is different from the actual size, the header is Int, rather than Long. ## How was this patch tested? The flaky test should be fixed. Author: Davies Liu <davies@databricks.com> Closes #15305 from davies/fix_MAP.
* [SPARK-17717][SQL] Add exist/find methods to Catalog.Herman van Hovell2016-09-293-24/+329
| | | | | | | | | | | | ## What changes were proposed in this pull request? The current user facing catalog does not implement methods for checking object existence or finding objects. You could theoretically do this using the `list*` commands, but this is rather cumbersome and can actually be costly when there are many objects. This PR adds `exists*` and `find*` methods for Databases, Table and Functions. ## How was this patch tested? Added tests to `org.apache.spark.sql.internal.CatalogSuite` Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15301 from hvanhovell/SPARK-17717.
* [SPARK-17612][SQL] Support `DESCRIBE table PARTITION` SQL syntaxDongjoon Hyun2016-09-296-18/+287
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## 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-17653][SQL] Remove unnecessary distincts in multiple unionsLiang-Chi Hsieh2016-09-293-30/+89
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently for `Union [Distinct]`, a `Distinct` operator is necessary to be on the top of `Union`. Once there are adjacent `Union [Distinct]`, there will be multiple `Distinct` in the query plan. E.g., For a query like: select 1 a union select 2 b union select 3 c Before this patch, its physical plan looks like: *HashAggregate(keys=[a#13], functions=[]) +- Exchange hashpartitioning(a#13, 200) +- *HashAggregate(keys=[a#13], functions=[]) +- Union :- *HashAggregate(keys=[a#13], functions=[]) : +- Exchange hashpartitioning(a#13, 200) : +- *HashAggregate(keys=[a#13], functions=[]) : +- Union : :- *Project [1 AS a#13] : : +- Scan OneRowRelation[] : +- *Project [2 AS b#14] : +- Scan OneRowRelation[] +- *Project [3 AS c#15] +- Scan OneRowRelation[] Only the top distinct should be necessary. After this patch, the physical plan looks like: *HashAggregate(keys=[a#221], functions=[], output=[a#221]) +- Exchange hashpartitioning(a#221, 5) +- *HashAggregate(keys=[a#221], functions=[], output=[a#221]) +- Union :- *Project [1 AS a#221] : +- Scan OneRowRelation[] :- *Project [2 AS b#222] : +- Scan OneRowRelation[] +- *Project [3 AS c#223] +- Scan OneRowRelation[] ## How was this patch tested? Jenkins tests. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #15238 from viirya/remove-extra-distinct-union.
* [SPARK-17699] Support for parsing JSON string columnsMichael Armbrust2016-09-2918-23/+175
| | | | | | | | | | | | | | | | | | Spark SQL has great support for reading text files that contain JSON data. However, in many cases the JSON data is just one column amongst others. This is particularly true when reading from sources such as Kafka. This PR adds a new functions `from_json` that converts a string column into a nested `StructType` with a user specified schema. Example usage: ```scala val df = Seq("""{"a": 1}""").toDS() val schema = new StructType().add("a", IntegerType) df.select(from_json($"value", schema) as 'json) // => [json: <a: int>] ``` This PR adds support for java, scala and python. I leveraged our existing JSON parsing support by moving it into catalyst (so that we could define expressions using it). I left SQL out for now, because I'm not sure how users would specify a schema. Author: Michael Armbrust <michael@databricks.com> Closes #15274 from marmbrus/jsonParser.
* [SPARK-17614][SQL] sparkSession.read() .jdbc(***) use the sql syntax "where ↵Sean Owen2016-09-292-5/+16
| | | | | | | | | | | | | | | | 1=0" that Cassandra does not support ## What changes were proposed in this pull request? Use dialect's table-exists query rather than hard-coded WHERE 1=0 query ## How was this patch tested? Existing tests. Author: Sean Owen <sowen@cloudera.com> Closes #15196 from srowen/SPARK-17614.
* [SPARK-17712][SQL] Fix invalid pushdown of data-independent filters beneath ↵Josh Rosen2016-09-282-1/+18
| | | | | | | | | | | | | | | | | | | | aggregates ## What changes were proposed in this pull request? This patch fixes a minor correctness issue impacting the pushdown of filters beneath aggregates. Specifically, if a filter condition references no grouping or aggregate columns (e.g. `WHERE false`) then it would be incorrectly pushed beneath an aggregate. Intuitively, the only case where you can push a filter beneath an aggregate is when that filter is deterministic and is defined over the grouping columns / expressions, since in that case the filter is acting to exclude entire groups from the query (like a `HAVING` clause). The existing code would only push deterministic filters beneath aggregates when all of the filter's references were grouping columns, but this logic missed the case where a filter has no references. For example, `WHERE false` is deterministic but is independent of the actual data. This patch fixes this minor bug by adding a new check to ensure that we don't push filters beneath aggregates when those filters don't reference any columns. ## How was this patch tested? New regression test in FilterPushdownSuite. Author: Josh Rosen <joshrosen@databricks.com> Closes #15289 from JoshRosen/SPARK-17712.
* [SPARK-17641][SQL] Collect_list/Collect_set should not collect null values.Herman van Hovell2016-09-282-1/+18
| | | | | | | | | | | | ## What changes were proposed in this pull request? We added native versions of `collect_set` and `collect_list` in Spark 2.0. These currently also (try to) collect null values, this is different from the original Hive implementation. This PR fixes this by adding a null check to the `Collect.update` method. ## How was this patch tested? Added a regression test to `DataFrameAggregateSuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15208 from hvanhovell/SPARK-17641.
* [SPARK-17713][SQL] Move row-datasource related tests out of JDBCSuiteEric Liang2016-09-282-8/+72
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? As a followup for https://github.com/apache/spark/pull/15273 we should move non-JDBC specific tests out of that suite. ## How was this patch tested? Ran the test. Author: Eric Liang <ekl@databricks.com> Closes #15287 from ericl/spark-17713.
* [SPARK-17673][SQL] Incorrect exchange reuse with RowDataSourceScanEric Liang2016-09-282-0/+12
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? It seems the equality check for reuse of `RowDataSourceScanExec` nodes doesn't respect the output schema. This can cause self-joins or unions over the same underlying data source to return incorrect results if they select different fields. ## How was this patch tested? New unit test passes after the fix. Author: Eric Liang <ekl@databricks.com> Closes #15273 from ericl/spark-17673.
* [SPARK-17666] Ensure that RecordReaders are closed by data source file scansJosh Rosen2016-09-277-7/+41
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch addresses a potential cause of resource leaks in data source file scans. As reported in [SPARK-17666](https://issues.apache.org/jira/browse/SPARK-17666), tasks which do not fully-consume their input may cause file handles / network connections (e.g. S3 connections) to be leaked. Spark's `NewHadoopRDD` uses a TaskContext callback to [close its record readers](https://github.com/apache/spark/blame/master/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala#L208), but the new data source file scans will only close record readers once their iterators are fully-consumed. This patch modifies `RecordReaderIterator` and `HadoopFileLinesReader` to add `close()` methods and modifies all six implementations of `FileFormat.buildReader()` to register TaskContext task completion callbacks to guarantee that cleanup is eventually performed. ## How was this patch tested? Tested manually for now. Author: Josh Rosen <joshrosen@databricks.com> Closes #15245 from JoshRosen/SPARK-17666-close-recordreader.
* [SPARK-17618] Guard against invalid comparisons between UnsafeRow and other ↵Josh Rosen2016-09-271-1/+6
| | | | | | | | | | formats This patch ports changes from #15185 to Spark 2.x. In that patch, a correctness bug in Spark 1.6.x which was caused by an invalid `equals()` comparison between an `UnsafeRow` and another row of a different format. Spark 2.x is not affected by that specific correctness bug but it can still reap the error-prevention benefits of that patch's changes, which modify ``UnsafeRow.equals()` to throw an IllegalArgumentException if it is called with an object that is not an `UnsafeRow`. Author: Josh Rosen <joshrosen@databricks.com> Closes #15265 from JoshRosen/SPARK-17618-master.
* [SPARK-17677][SQL] Break WindowExec.scala into multiple filesReynold Xin2016-09-277-1015/+1112
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? As of Spark 2.0, all the window function execution code are in WindowExec.scala. This file is pretty large (over 1k loc) and has a lot of different abstractions in them. This patch creates a new package sql.execution.window, moves WindowExec.scala in it, and breaks WindowExec.scala into multiple, more maintainable pieces: - AggregateProcessor.scala - BoundOrdering.scala - RowBuffer.scala - WindowExec - WindowFunctionFrame.scala ## How was this patch tested? This patch mostly moves code around, and should not change any existing test coverage. Author: Reynold Xin <rxin@databricks.com> Closes #15252 from rxin/SPARK-17677.
* [SPARK-17660][SQL] DESC FORMATTED for VIEW Lacks View Definitiongatorsmile2016-09-272-0/+28
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### 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-17682][SQL] Mark children as final for unary, binary, leaf ↵Reynold Xin2016-09-275-14/+9
| | | | | | | | | | | | | | expressions and plan nodes ## What changes were proposed in this pull request? This patch marks the children method as final in unary, binary, and leaf expressions and plan nodes (both logical plan and physical plan), as brought up in http://apache-spark-developers-list.1001551.n3.nabble.com/Should-LeafExpression-have-children-final-override-like-Nondeterministic-td19104.html ## How was this patch tested? This is a simple modifier change and has no impact on test coverage. Author: Reynold Xin <rxin@databricks.com> Closes #15256 from rxin/SPARK-17682.
* [SPARK-16516][SQL] Support for pushing down filters for decimal and ↵hyukjinkwon2016-09-283-9/+89
| | | | | | | | | | | | | | | | | | | | | | 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-16777][SQL] Do not use deprecated listType API in ParquetSchemaConverterhyukjinkwon2016-09-281-9/+17
| | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR removes build waning as below. ```scala [WARNING] .../spark/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaConverter.scala:448: method listType in object ConversionPatterns is deprecated: see corresponding Javadoc for more information. [WARNING] ConversionPatterns.listType( [WARNING] ^ [WARNING] .../spark/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaConverter.scala:464: method listType in object ConversionPatterns is deprecated: see corresponding Javadoc for more information. [WARNING] ConversionPatterns.listType( [WARNING] ^ ``` This should not use `listOfElements` (recommended to be replaced from `listType`) instead because the new method checks if the name of elements in Parquet's `LIST` is `element` in Parquet schema and throws an exception if not. However, It seems Spark prior to 1.4.x writes `ArrayType` with Parquet's `LIST` but with `array` as its element name. Therefore, this PR avoids to use both `listOfElements` and `listType` but just use the existing schema builder to construct the same `GroupType`. ## How was this patch tested? Existing tests should cover this. Author: hyukjinkwon <gurwls223@gmail.com> Closes #14399 from HyukjinKwon/SPARK-16777.
* [SPARK-15962][SQL] Introduce implementation with a dense format for ↵Kazuaki Ishizaki2016-09-279-224/+740
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | UnsafeArrayData ## What changes were proposed in this pull request? This PR introduces more compact representation for ```UnsafeArrayData```. ```UnsafeArrayData``` needs to accept ```null``` value in each entry of an array. In the current version, it has three parts ``` [numElements] [offsets] [values] ``` `Offsets` has the number of `numElements`, and represents `null` if its value is negative. It may increase memory footprint, and introduces an indirection for accessing each of `values`. This PR uses bitvectors to represent nullability for each element like `UnsafeRow`, and eliminates an indirection for accessing each element. The new ```UnsafeArrayData``` has four parts. ``` [numElements][null bits][values or offset&length][variable length portion] ``` In the `null bits` region, we store 1 bit per element, represents whether an element is null. Its total size is ceil(numElements / 8) bytes, and it is aligned to 8-byte boundaries. In the `values or offset&length` region, we store the content of elements. For fields that hold fixed-length primitive types, such as long, double, or int, we store the value directly in the field. For fields with non-primitive or variable-length values, we store a relative offset (w.r.t. the base address of the array) that points to the beginning of the variable-length field and length (they are combined into a long). Each is word-aligned. For `variable length portion`, each is aligned to 8-byte boundaries. The new format can reduce memory footprint and improve performance of accessing each element. An example of memory foot comparison: 1024x1024 elements integer array Size of ```baseObject``` for ```UnsafeArrayData```: 8 + 1024x1024 + 1024x1024 = 2M bytes Size of ```baseObject``` for ```UnsafeArrayData```: 8 + 1024x1024/8 + 1024x1024 = 1.25M bytes In summary, we got 1.0-2.6x performance improvements over the code before applying this PR. Here are performance results of [benchmark programs](https://github.com/kiszk/spark/blob/04d2e4b6dbdc4eff43ce18b3c9b776e0129257c7/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/UnsafeArrayDataBenchmark.scala): **Read UnsafeArrayData**: 1.7x and 1.6x performance improvements over the code before applying this PR ```` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.4.11-200.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without SPARK-15962 Read UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 430 / 436 390.0 2.6 1.0X Double 456 / 485 367.8 2.7 0.9X With SPARK-15962 Read UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 252 / 260 666.1 1.5 1.0X Double 281 / 292 597.7 1.7 0.9X ```` **Write UnsafeArrayData**: 1.0x and 1.1x performance improvements over the code before applying this PR ```` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.0.4-301.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without SPARK-15962 Write UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 203 / 273 103.4 9.7 1.0X Double 239 / 356 87.9 11.4 0.8X With SPARK-15962 Write UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 196 / 249 107.0 9.3 1.0X Double 227 / 367 92.3 10.8 0.9X ```` **Get primitive array from UnsafeArrayData**: 2.6x and 1.6x performance improvements over the code before applying this PR ```` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.0.4-301.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without SPARK-15962 Get primitive array from UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 207 / 217 304.2 3.3 1.0X Double 257 / 363 245.2 4.1 0.8X With SPARK-15962 Get primitive array from UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 151 / 198 415.8 2.4 1.0X Double 214 / 394 293.6 3.4 0.7X ```` **Create UnsafeArrayData from primitive array**: 1.7x and 2.1x performance improvements over the code before applying this PR ```` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.0.4-301.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without SPARK-15962 Create UnsafeArrayData from primitive array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 340 / 385 185.1 5.4 1.0X Double 479 / 705 131.3 7.6 0.7X With SPARK-15962 Create UnsafeArrayData from primitive array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 206 / 211 306.0 3.3 1.0X Double 232 / 406 271.6 3.7 0.9X ```` 1.7x and 1.4x performance improvements in [```UDTSerializationBenchmark```](https://github.com/apache/spark/blob/master/mllib/src/test/scala/org/apache/spark/mllib/linalg/UDTSerializationBenchmark.scala) over the code before applying this PR ```` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.4.11-200.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without SPARK-15962 VectorUDT de/serialization: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ serialize 442 / 533 0.0 441927.1 1.0X deserialize 217 / 274 0.0 217087.6 2.0X With SPARK-15962 VectorUDT de/serialization: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ serialize 265 / 318 0.0 265138.5 1.0X deserialize 155 / 197 0.0 154611.4 1.7X ```` ## How was this patch tested? Added unit tests into ```UnsafeArraySuite``` Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Closes #13680 from kiszk/SPARK-15962.
* [SPARK-17652] Fix confusing exception message while reserving capacitySameer Agarwal2016-09-262-9/+9
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This minor patch fixes a confusing exception message while reserving additional capacity in the vectorized parquet reader. ## How was this patch tested? Exisiting Unit Tests Author: Sameer Agarwal <sameerag@cs.berkeley.edu> Closes #15225 from sameeragarwal/error-msg.
* [SPARK-17153][SQL] Should read partition data when reading new files in ↵Liang-Chi Hsieh2016-09-264-3/+104
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | filestream without globbing ## What changes were proposed in this pull request? When reading file stream with non-globbing path, the results return data with all `null`s for the partitioned columns. E.g., case class A(id: Int, value: Int) val data = spark.createDataset(Seq( A(1, 1), A(2, 2), A(2, 3)) ) val url = "/tmp/test" data.write.partitionBy("id").parquet(url) spark.read.parquet(url).show +-----+---+ |value| id| +-----+---+ | 2| 2| | 3| 2| | 1| 1| +-----+---+ val s = spark.readStream.schema(spark.read.load(url).schema).parquet(url) s.writeStream.queryName("test").format("memory").start() sql("SELECT * FROM test").show +-----+----+ |value| id| +-----+----+ | 2|null| | 3|null| | 1|null| +-----+----+ ## How was this patch tested? Jenkins tests. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #14803 from viirya/filestreamsource-option.
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