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* [SPARK-15698][SQL][STREAMING][FOLLW-UP] Fix FileStream source and sink log ↵jerryshao2016-09-203-11/+13
| | | | | | | | | | | | | | | | | | get configuration issue ## What changes were proposed in this pull request? This issue was introduced in the previous commit of SPARK-15698. Mistakenly change the way to get configuration back to original one, so here with the follow up PR to revert them up. ## How was this patch tested? N/A Ping zsxwing , please review again, sorry to bring the inconvenience. Thanks a lot. Author: jerryshao <sshao@hortonworks.com> Closes #15173 from jerryshao/SPARK-15698-follow.
* [SPARK-17513][SQL] Make StreamExecution garbage-collect its metadatapetermaxlee2016-09-203-0/+32
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR modifies StreamExecution such that it discards metadata for batches that have already been fully processed. I used the purge method that was added as part of SPARK-17235. This is a resubmission of 15126, which was based on work by frreiss in #15067, but fixed the test case along with some typos. ## How was this patch tested? A new test case in StreamingQuerySuite. The test case would fail without the changes in this pull request. Author: petermaxlee <petermaxlee@gmail.com> Closes #15166 from petermaxlee/SPARK-17513-2.
* [SPARK-17549][SQL] Revert "[] Only collect table size stat in driver for ↵Yin Huai2016-09-202-20/+18
| | | | | | | | | | cached relation." This reverts commit 39e2bad6a866d27c3ca594d15e574a1da3ee84cc because of the problem mentioned at https://issues.apache.org/jira/browse/SPARK-17549?focusedCommentId=15505060&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15505060 Author: Yin Huai <yhuai@databricks.com> Closes #15157 from yhuai/revert-SPARK-17549.
* [SPARK-15698][SQL][STREAMING] Add the ability to remove the old MetadataLog ↵jerryshao2016-09-209-222/+550
| | | | | | | | | | | | | | | | in FileStreamSource ## What changes were proposed in this pull request? Current `metadataLog` in `FileStreamSource` will add a checkpoint file in each batch but do not have the ability to remove/compact, which will lead to large number of small files when running for a long time. So here propose to compact the old logs into one file. This method is quite similar to `FileStreamSinkLog` but simpler. ## How was this patch tested? Unit test added. Author: jerryshao <sshao@hortonworks.com> Closes #13513 from jerryshao/SPARK-15698.
* [SPARK-17051][SQL] we should use hadoopConf in InsertIntoHiveTableWenchen Fan2016-09-202-10/+31
| | | | | | | | | | | | | | ## 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 Viewsgatorsmile2016-09-2011-156/+164
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### 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.
* Revert "[SPARK-17513][SQL] Make StreamExecution garbage-collect its metadata"Wenchen Fan2016-09-203-32/+0
| | | | This reverts commit be9d57fc9d8b10e4234c01c06ed43fd7dd12c07b.
* [SPARK-17513][SQL] Make StreamExecution garbage-collect its metadatapetermaxlee2016-09-193-0/+32
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR modifies StreamExecution such that it discards metadata for batches that have already been fully processed. I used the purge method that was added as part of SPARK-17235. This is based on work by frreiss in #15067, but fixed the test case along with some typos. ## How was this patch tested? A new test case in StreamingQuerySuite. The test case would fail without the changes in this pull request. Author: petermaxlee <petermaxlee@gmail.com> Author: frreiss <frreiss@us.ibm.com> Closes #15126 from petermaxlee/SPARK-17513.
* [SPARK-17160] Properly escape field names in code-generated error messagesJosh Rosen2016-09-193-8/+29
| | | | | | | | | | This patch addresses a corner-case escaping bug where field names which contain special characters were unsafely interpolated into error message string literals in generated Java code, leading to compilation errors. This patch addresses these issues by using `addReferenceObj` to store the error messages as string fields rather than inline string constants. Author: Josh Rosen <joshrosen@databricks.com> Closes #15156 from JoshRosen/SPARK-17160.
* [SPARK-17100] [SQL] fix Python udf in filter on top of outer joinDavies Liu2016-09-191-1/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? In optimizer, we try to evaluate the condition to see whether it's nullable or not, but some expressions are not evaluable, we should check that before evaluate it. ## How was this patch tested? Added regression tests. Author: Davies Liu <davies@databricks.com> Closes #15103 from davies/udf_join.
* [SPARK-16439] [SQL] bring back the separator in SQL UIDavies Liu2016-09-191-2/+2
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, the SQL metrics looks like `number of rows: 111111111111`, it's very hard to read how large the number is. So a separator was added by #12425, but removed by #14142, because the separator is weird in some locales (for example, pl_PL), this PR will add that back, but always use "," as the separator, since the SQL UI are all in English. ## How was this patch tested? Existing tests. ![metrics](https://cloud.githubusercontent.com/assets/40902/14573908/21ad2f00-030d-11e6-9e2c-c544f30039ea.png) Author: Davies Liu <davies@databricks.com> Closes #15106 from davies/metric_sep.
* [SPARK-17297][DOCS] Clarify window/slide duration as absolute time, not ↵Sean Owen2016-09-191-4/+11
| | | | | | | | | | | | | | | | relative to a calendar ## What changes were proposed in this pull request? Clarify that slide and window duration are absolute, and not relative to a calendar. ## How was this patch tested? Doc build (no functional change) Author: Sean Owen <sowen@cloudera.com> Closes #15142 from srowen/SPARK-17297.
* [SPARK-17571][SQL] AssertOnQuery.condition should always return Boolean valuepetermaxlee2016-09-183-4/+10
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? AssertOnQuery has two apply constructor: one that accepts a closure that returns boolean, and another that accepts a closure that returns Unit. This is actually very confusing because developers could mistakenly think that AssertOnQuery always require a boolean return type and verifies the return result, when indeed the value of the last statement is ignored in one of the constructors. This pull request makes the two constructor consistent and always require boolean value. It will overall make the test suites more robust against developer errors. As an evidence for the confusing behavior, this change also identified a bug with an existing test case due to file system time granularity. This pull request fixes that test case as well. ## How was this patch tested? This is a test only change. Author: petermaxlee <petermaxlee@gmail.com> Closes #15127 from petermaxlee/SPARK-17571.
* [SPARK-16462][SPARK-16460][SPARK-15144][SQL] Make CSV cast null values properlyLiwei Lin2016-09-185-81/+89
| | | | | | | | | | | | | | | | | | | | ## Problem CSV in Spark 2.0.0: - does not read null values back correctly for certain data types such as `Boolean`, `TimestampType`, `DateType` -- this is a regression comparing to 1.6; - does not read empty values (specified by `options.nullValue`) as `null`s for `StringType` -- this is compatible with 1.6 but leads to problems like SPARK-16903. ## What changes were proposed in this pull request? This patch makes changes to read all empty values back as `null`s. ## How was this patch tested? New test cases. Author: Liwei Lin <lwlin7@gmail.com> Closes #14118 from lw-lin/csv-cast-null.
* [SPARK-17506][SQL] Improve the check double values equality rule.jiangxingbo2016-09-182-7/+30
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In `ExpressionEvalHelper`, we check the equality between two double values by comparing whether the expected value is within the range [target - tolerance, target + tolerance], but this can cause a negative false when the compared numerics are very large. Before： ``` val1 = 1.6358558070241E306 val2 = 1.6358558070240974E306 ExpressionEvalHelper.compareResults(val1, val2) false ``` In fact, `val1` and `val2` are but with different precisions, we should tolerant this case by comparing with percentage range, eg.,expected is within range [target - target * tolerance_percentage, target + target * tolerance_percentage]. After: ``` val1 = 1.6358558070241E306 val2 = 1.6358558070240974E306 ExpressionEvalHelper.compareResults(val1, val2) true ``` ## How was this patch tested? Exsiting testcases. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #15059 from jiangxb1987/deq.
* [SPARK-17541][SQL] fix some DDL bugs about table management when same-name ↵Wenchen Fan2016-09-1810-46/+170
| | | | | | | | | | | | | | | | | | | | | | 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 Hivegatorsmile2016-09-186-3/+67
| | | | | | | | | | | | ### 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-17480][SQL][FOLLOWUP] Fix more instances which calls List.length/size ↵hyukjinkwon2016-09-179-33/+31
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | which is O(n) ## What changes were proposed in this pull request? This PR fixes all the instances which was fixed in the previous PR. To make sure, I manually debugged and also checked the Scala source. `length` in [LinearSeqOptimized.scala#L49-L57](https://github.com/scala/scala/blob/2.11.x/src/library/scala/collection/LinearSeqOptimized.scala#L49-L57) is O(n). Also, `size` calls `length` via [SeqLike.scala#L106](https://github.com/scala/scala/blob/2.11.x/src/library/scala/collection/SeqLike.scala#L106). For debugging, I have created these as below: ```scala ArrayBuffer(1, 2, 3) Array(1, 2, 3) List(1, 2, 3) Seq(1, 2, 3) ``` and then called `size` and `length` for each to debug. ## How was this patch tested? I ran the bash as below on Mac ```bash find . -name *.scala -type f -exec grep -il "while (.*\\.length)" {} \; | grep "src/main" find . -name *.scala -type f -exec grep -il "while (.*\\.size)" {} \; | grep "src/main" ``` and then checked each. Author: hyukjinkwon <gurwls223@gmail.com> Closes #15093 from HyukjinKwon/SPARK-17480-followup.
* [SPARK-17529][CORE] Implement BitSet.clearUntil and use it during merge joinsDavid Navas2016-09-171-2/+2
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add a clearUntil() method on BitSet (adapted from the pre-existing setUntil() method). Use this method to clear the subset of the BitSet which needs to be used during merge joins. ## How was this patch tested? dev/run-tests, as well as performance tests on skewed data as described in jira. I expect there to be a small local performance hit using BitSet.clearUntil rather than BitSet.clear for normally shaped (unskewed) joins (additional read on the last long). This is expected to be de-minimis and was not specifically tested. Author: David Navas <davidn@clearstorydata.com> Closes #15084 from davidnavas/bitSet.
* Correct fetchsize property name in docsDaniel Darabos2016-09-171-3/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Replace `fetchSize` with `fetchsize` in the docs. ## How was this patch tested? I manually tested `fetchSize` and `fetchsize`. The latter has an effect. See also [`JdbcUtils.scala#L38`](https://github.com/apache/spark/blob/v2.0.0/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala#L38) for the definition of the property. Author: Daniel Darabos <darabos.daniel@gmail.com> Closes #14975 from darabos/patch-3.
* [SPARK-17549][SQL] Only collect table size stat in driver for cached relation.Marcelo Vanzin2016-09-163-24/+32
| | | | | | | | | | | | | | | | | | | | | | | | | | | 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. On a mildly related change, I'm also adding code to catch exceptions in the code generator since Janino was breaking with the test data I tried this patch on. Tested with unit tests and by doing a count a very wide table (20k columns) with many partitions. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #15112 from vanzin/SPARK-17549.
* [SPARK-17561][DOCS] DataFrameWriter documentation formatting problemsSean Owen2016-09-163-29/+53
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix `<ul> / <li>` problems in SQL scaladoc. ## How was this patch tested? Scaladoc build and manual verification of generated HTML. Author: Sean Owen <sowen@cloudera.com> Closes #15117 from srowen/SPARK-17561.
* [SPARK-17426][SQL] Refactor `TreeNode.toJSON` to avoid OOM when converting ↵Sean Zhong2016-09-163-315/+333
| | | | | | | | | | | | | | | | | | | | | | | unknown fields to JSON ## What changes were proposed in this pull request? This PR is a follow up of SPARK-17356. Current implementation of `TreeNode.toJSON` recursively converts all fields of TreeNode to JSON, even if the field is of type `Seq` or type Map. This may trigger out of memory exception in cases like: 1. the Seq or Map can be very big. Converting them to JSON may take huge memory, which may trigger out of memory error. 2. Some user space input may also be propagated to the Plan. The user space input can be of arbitrary type, and may also be self-referencing. Trying to print user space input to JSON may trigger out of memory error or stack overflow error. For a code example, please check the Jira description of SPARK-17426. In this PR, we refactor the `TreeNode.toJSON` so that we only convert a field to JSON string if the field is a safe type. ## How was this patch tested? Unit test. Author: Sean Zhong <seanzhong@databricks.com> Closes #14990 from clockfly/json_oom2.
* [SPARK-17458][SQL] Alias specified for aggregates in a pivot are not honoredAndrew Ray2016-09-152-1/+20
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This change preserves aliases that are given for pivot aggregations ## How was this patch tested? New unit test Author: Andrew Ray <ray.andrew@gmail.com> Closes #15111 from aray/SPARK-17458.
* [SPARK-17364][SQL] Antlr lexer wrongly treats full qualified identifier as a ↵Sean Zhong2016-09-153-9/+63
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | decimal number token when parsing SQL string ## What changes were proposed in this pull request? The Antlr lexer we use to tokenize a SQL string may wrongly tokenize a fully qualified identifier as a decimal number token. For example, table identifier `default.123_table` is wrongly tokenized as ``` default // Matches lexer rule IDENTIFIER .123 // Matches lexer rule DECIMAL_VALUE _TABLE // Matches lexer rule IDENTIFIER ``` The correct tokenization for `default.123_table` should be: ``` default // Matches lexer rule IDENTIFIER, . // Matches a single dot 123_TABLE // Matches lexer rule IDENTIFIER ``` This PR fix the Antlr grammar so that it can tokenize fully qualified identifier correctly: 1. Fully qualified table name can be parsed correctly. For example, `select * from database.123_suffix`. 2. Fully qualified column name can be parsed correctly, for example `select a.123_suffix from a`. ### Before change #### Case 1: Failed to parse fully qualified column name ``` scala> spark.sql("select a.123_column from a").show org.apache.spark.sql.catalyst.parser.ParseException: extraneous input '.123' expecting {<EOF>, ... , IDENTIFIER, BACKQUOTED_IDENTIFIER}(line 1, pos 8) == SQL == select a.123_column from a --------^^^ ``` #### Case 2: Failed to parse fully qualified table name ``` scala> spark.sql("select * from default.123_table") org.apache.spark.sql.catalyst.parser.ParseException: extraneous input '.123' expecting {<EOF>, ... IDENTIFIER, BACKQUOTED_IDENTIFIER}(line 1, pos 21) == SQL == select * from default.123_table ---------------------^^^ ``` ### After Change #### Case 1: fully qualified column name, no ParseException thrown ``` scala> spark.sql("select a.123_column from a").show ``` #### Case 2: fully qualified table name, no ParseException thrown ``` scala> spark.sql("select * from default.123_table") ``` ## How was this patch tested? Unit test. Author: Sean Zhong <seanzhong@databricks.com> Closes #15006 from clockfly/SPARK-17364.
* [SPARK-17429][SQL] use ImplicitCastInputTypes with function Length岑玉海2016-09-152-5/+7
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? select length(11); select length(2.0); these sql will return errors, but hive is ok. this PR will support casting input types implicitly for function length the correct result is: select length(11) return 2 select length(2.0) return 3 Author: 岑玉海 <261810726@qq.com> Author: cenyuhai <cenyuhai@didichuxing.com> Closes #15014 from cenyuhai/SPARK-17429.
* [SPARK-17114][SQL] Fix aggregates grouped by literals with empty inputHerman van Hovell2016-09-154-3/+86
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes an issue with aggregates that have an empty input, and use a literals as their grouping keys. These aggregates are currently interpreted as aggregates **without** grouping keys, this triggers the ungrouped code path (which aways returns a single row). This PR fixes the `RemoveLiteralFromGroupExpressions` optimizer rule, which changes the semantics of the Aggregate by eliminating all literal grouping keys. ## How was this patch tested? Added tests to `SQLQueryTestSuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15101 from hvanhovell/SPARK-17114-3.
* [SPARK-17536][SQL] Minor performance improvement to JDBC batch insertsJohn Muller2016-09-151-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Optimize a while loop during batch inserts ## How was this patch tested? Unit tests were done, specifically "mvn test" for sql Author: John Muller <jmuller@us.imshealth.com> Closes #15098 from blue666man/SPARK-17536.
* [SPARK-17524][TESTS] Use specified spark.buffer.pageSizeAdam Roberts2016-09-151-2/+4
| | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR has the appendRowUntilExceedingPageSize test in RowBasedKeyValueBatchSuite use whatever spark.buffer.pageSize value a user has specified to prevent a test failure for anyone testing Apache Spark on a box with a reduced page size. The test is currently hardcoded to use the default page size which is 64 MB so this minor PR is a test improvement ## How was this patch tested? Existing unit tests with 1 MB page size and with 64 MB (the default) page size Author: Adam Roberts <aroberts@uk.ibm.com> Closes #15079 from a-roberts/patch-5.
* [SPARK-17440][SPARK-17441] Fixed Multiple Bugs in ALTER TABLEgatorsmile2016-09-154-59/+120
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### 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-17463][CORE] Make CollectionAccumulator and SetAccumulator's value ↵Shixiong Zhu2016-09-141-9/+15
| | | | | | | | | | | | | | | | can be read thread-safely ## What changes were proposed in this pull request? Make CollectionAccumulator and SetAccumulator's value can be read thread-safely to fix the ConcurrentModificationException reported in [JIRA](https://issues.apache.org/jira/browse/SPARK-17463). ## How was this patch tested? Existing tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes #15063 from zsxwing/SPARK-17463.
* [SPARK-10747][SQL] Support NULLS FIRST|LAST clause in ORDER BYXin Wu2016-09-1443-65/+558
| | | | | | | | | | | | | | | | | ## 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.
* [MINOR][SQL] Add missing functions for some options in SQLConf and use them ↵hyukjinkwon2016-09-1512-31/+49
| | | | | | | | | | | | | | | | | | | | where applicable ## What changes were proposed in this pull request? I first thought they are missing because they are kind of hidden options but it seems they are just missing. For example, `spark.sql.parquet.mergeSchema` is documented in [sql-programming-guide.md](https://github.com/apache/spark/blob/master/docs/sql-programming-guide.md) but this function is missing whereas many options such as `spark.sql.join.preferSortMergeJoin` are not documented but have its own function individually. So, this PR suggests making them consistent by adding the missing functions for some options in `SQLConf` and use them where applicable, in order to make them more readable. ## How was this patch tested? Existing tests should cover this. Author: hyukjinkwon <gurwls223@gmail.com> Closes #14678 from HyukjinKwon/sqlconf-cleanup.
* [SPARK-17514] df.take(1) and df.limit(1).collect() should perform the same ↵Josh Rosen2016-09-142-14/+7
| | | | | | | | | | | | | | | | | | | | in Python ## What changes were proposed in this pull request? In PySpark, `df.take(1)` runs a single-stage job which computes only one partition of the DataFrame, while `df.limit(1).collect()` computes all partitions and runs a two-stage job. This difference in performance is confusing. The reason why `limit(1).collect()` is so much slower is that `collect()` internally maps to `df.rdd.<some-pyspark-conversions>.toLocalIterator`, which causes Spark SQL to build a query where a global limit appears in the middle of the plan; this, in turn, ends up being executed inefficiently because limits in the middle of plans are now implemented by repartitioning to a single task rather than by running a `take()` job on the driver (this was done in #7334, a patch which was a prerequisite to allowing partition-local limits to be pushed beneath unions, etc.). In order to fix this performance problem I think that we should generalize the fix from SPARK-10731 / #8876 so that `DataFrame.collect()` also delegates to the Scala implementation and shares the same performance properties. This patch modifies `DataFrame.collect()` to first collect all results to the driver and then pass them to Python, allowing this query to be planned using Spark's `CollectLimit` optimizations. ## How was this patch tested? Added a regression test in `sql/tests.py` which asserts that the expected number of jobs, stages, and tasks are run for both queries. Author: Josh Rosen <joshrosen@databricks.com> Closes #15068 from JoshRosen/pyspark-collect-limit.
* [SPARK-17409][SQL] Do Not Optimize Query in CTAS More Than Oncegatorsmile2016-09-1413-42/+49
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### 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-17445][DOCS] Reference an ASF page as the main place to find ↵Sean Owen2016-09-143-12/+10
| | | | | | | | | | | | | | | | | | third-party packages ## What changes were proposed in this pull request? Point references to spark-packages.org to https://cwiki.apache.org/confluence/display/SPARK/Third+Party+Projects This will be accompanied by a parallel change to the spark-website repo, and additional changes to this wiki. ## How was this patch tested? Jenkins tests. Author: Sean Owen <sowen@cloudera.com> Closes #15075 from srowen/SPARK-17445.
* [SPARK-17480][SQL] Improve performance by removing or caching List.length ↵Ergin Seyfe2016-09-143-10/+9
| | | | | | | | | | | | | | | | | | | | which is O(n) ## What changes were proposed in this pull request? Scala's List.length method is O(N) and it makes the gatherCompressibilityStats function O(N^2). Eliminate the List.length calls by writing it in Scala way. https://github.com/scala/scala/blob/2.10.x/src/library/scala/collection/LinearSeqOptimized.scala#L36 As suggested. Extended the fix to HiveInspectors and AggregationIterator classes as well. ## How was this patch tested? Profiled a Spark job and found that CompressibleColumnBuilder is using 39% of the CPU. Out of this 39% CompressibleColumnBuilder->gatherCompressibilityStats is using 23% of it. 6.24% of the CPU is spend on List.length which is called inside gatherCompressibilityStats. After this change we started to save 6.24% of the CPU. Author: Ergin Seyfe <eseyfe@fb.com> Closes #15032 from seyfe/gatherCompressibilityStats.
* [SPARK-17530][SQL] Add Statistics into DESCRIBE FORMATTEDgatorsmile2016-09-143-8/+10
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? Statistics is missing in the output of `DESCRIBE FORMATTED`. This PR is to add it. After the PR, the output will be like: ``` +----------------------------+----------------------------------------------------------------------------------------------------------------------+-------+ |col_name |data_type |comment| +----------------------------+----------------------------------------------------------------------------------------------------------------------+-------+ |key |string |null | |value |string |null | | | | | |# Detailed Table Information| | | |Database: |default | | |Owner: |xiaoli | | |Create Time: |Tue Sep 13 14:36:57 PDT 2016 | | |Last Access Time: |Wed Dec 31 16:00:00 PST 1969 | | |Location: |file:/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/warehouse-9982e1db-df17-4376-a140-dbbee0203d83/texttable| | |Table Type: |MANAGED | | |Statistics: |sizeInBytes=5812, rowCount=500, isBroadcastable=false | | |Table Parameters: | | | | rawDataSize |-1 | | | numFiles |1 | | | transient_lastDdlTime |1473802620 | | | totalSize |5812 | | | COLUMN_STATS_ACCURATE |false | | | numRows |-1 | | | | | | |# 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 | | +----------------------------+----------------------------------------------------------------------------------------------------------------------+-------+ ``` Also improve the output of statistics in `DESCRIBE EXTENDED` by removing duplicate `Statistics`. Below is the example after the PR: ``` +----------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ |col_name |data_type |comment| +----------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ |key |string |null | |value |string |null | | | | | |# Detailed Table Information|CatalogTable( Table: `default`.`texttable` Owner: xiaoli Created: Tue Sep 13 14:38:43 PDT 2016 Last Access: Wed Dec 31 16:00:00 PST 1969 Type: MANAGED Schema: [StructField(key,StringType,true), StructField(value,StringType,true)] Provider: hive Properties: [rawDataSize=-1, numFiles=1, transient_lastDdlTime=1473802726, totalSize=5812, COLUMN_STATS_ACCURATE=false, numRows=-1] Statistics: sizeInBytes=5812, rowCount=500, isBroadcastable=false Storage(Location: file:/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/warehouse-8ea5c5a0-5680-4778-91cb-c6334cf8a708/texttable, 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]))| | +----------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ ``` ### How was this patch tested? Manually tested. Author: gatorsmile <gatorsmile@gmail.com> Closes #15083 from gatorsmile/descFormattedStats.
* [SPARK-17531] Don't initialize Hive Listeners for the Execution ClientBurak Yavuz2016-09-132-0/+43
| | | | | | | | | | | | | | | | | | ## 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-17142][SQL] Complex query triggers binding error in HashAggregateExecjiangxingbo2016-09-132-8/+39
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In `ReorderAssociativeOperator` rule, we extract foldable expressions with Add/Multiply arithmetics, and replace with eval literal. For example, `(a + 1) + (b + 2)` is optimized to `(a + b + 3)` by this rule. For aggregate operator, output expressions should be derived from groupingExpressions, current implemenation of `ReorderAssociativeOperator` rule may break this promise. A instance could be: ``` SELECT ((t1.a + 1) + (t2.a + 2)) AS out_col FROM testdata2 AS t1 INNER JOIN testdata2 AS t2 ON (t1.a = t2.a) GROUP BY (t1.a + 1), (t2.a + 2) ``` `((t1.a + 1) + (t2.a + 2))` is optimized to `(t1.a + t2.a + 3)`, which could not be derived from `ExpressionSet((t1.a +1), (t2.a + 2))`. Maybe we should improve the rule of `ReorderAssociativeOperator` by adding a GroupingExpressionSet to keep Aggregate.groupingExpressions, and respect these expressions during the optimize stage. ## How was this patch tested? Add new test case in `ReorderAssociativeOperatorSuite`. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #14917 from jiangxb1987/rao.
* [SPARK-17515] CollectLimit.execute() should perform per-partition limitsJosh Rosen2016-09-132-1/+11
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? CollectLimit.execute() incorrectly omits per-partition limits, leading to performance regressions in case this case is hit (which should not happen in normal operation, but can occur in some cases (see #15068 for one example). ## How was this patch tested? Regression test in SQLQuerySuite that asserts the number of records scanned from the input RDD. Author: Josh Rosen <joshrosen@databricks.com> Closes #15070 from JoshRosen/SPARK-17515.
* [SPARK-17474] [SQL] fix python udf in TakeOrderedAndProjectExecDavies Liu2016-09-123-12/+12
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? When there is any Python UDF in the Project between Sort and Limit, it will be collected into TakeOrderedAndProjectExec, ExtractPythonUDFs failed to pull the Python UDFs out because QueryPlan.expressions does not include the expression inside Option[Seq[Expression]]. Ideally, we should fix the `QueryPlan.expressions`, but tried with no luck (it always run into infinite loop). In PR, I changed the TakeOrderedAndProjectExec to no use Option[Seq[Expression]] to workaround it. cc JoshRosen ## How was this patch tested? Added regression test. Author: Davies Liu <davies@databricks.com> Closes #15030 from davies/all_expr.
* [SPARK-17415][SQL] Better error message for driver-side broadcast join OOMsSameer Agarwal2016-09-111-31/+42
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This is a trivial patch that catches all `OutOfMemoryError` while building the broadcast hash relation and rethrows it by wrapping it in a nice error message. ## How was this patch tested? Existing Tests Author: Sameer Agarwal <sameerag@cs.berkeley.edu> Closes #14979 from sameeragarwal/broadcast-join-error.
* [SPARK-17330][SPARK UT] Clean up spark-warehouse in UTtone-zhang2016-09-112-1/+7
| | | | | | | | | | | | | | | | | ## 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-17439][SQL] Fixing compression issues with approximate quantiles and ↵Timothy Hunter2016-09-112-6/+39
| | | | | | | | | | | | | | | | | adding more tests ## What changes were proposed in this pull request? This PR build on #14976 and fixes a correctness bug that would cause the wrong quantile to be returned for small target errors. ## How was this patch tested? This PR adds 8 unit tests that were failing without the fix. Author: Timothy Hunter <timhunter@databricks.com> Author: Sean Owen <sowen@cloudera.com> Closes #15002 from thunterdb/ml-1783.
* [SPARK-15453][SQL] FileSourceScanExec to extract `outputOrdering` informationTejas Patil2016-09-102-19/+123
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## 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 ↵hyukjinkwon2016-09-094-3/+78
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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-17405] RowBasedKeyValueBatch should use default page size to prevent OOMsEric Liang2016-09-081-8/+7
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Before this change, we would always allocate 64MB per aggregation task for the first-level hash map storage, even when running in low-memory situations such as local mode. This changes it to use the memory manager default page size, which is automatically reduced from 64MB in these situations. cc ooq JoshRosen ## How was this patch tested? Tested manually with `bin/spark-shell --master=local[32]` and verifying that `(1 to math.pow(10, 3).toInt).toDF("n").withColumn("m", 'n % 2).groupBy('m).agg(sum('n)).show` does not crash. Author: Eric Liang <ekl@databricks.com> Closes #15016 from ericl/sc-4483.
* [SPARK-17432][SQL] PreprocessDDL should respect case sensitivity when ↵Wenchen Fan2016-09-082-1/+13
| | | | | | | | | | | | | | | | | | checking duplicated columns ## What changes were proposed in this pull request? In `PreprocessDDL` we will check if table columns are duplicated. However, this checking ignores case sensitivity config(it's always case-sensitive) and lead to different result between `HiveExternalCatalog` and `InMemoryCatalog`. `HiveExternalCatalog` will throw exception because hive metastore is always case-nonsensitive, and `InMemoryCatalog` is fine. This PR fixes it. ## How was this patch tested? a new test in DDLSuite Author: Wenchen Fan <wenchen@databricks.com> Closes #14994 from cloud-fan/check-dup.
* [SPARK-17052][SQL] Remove Duplicate Test Cases auto_join from ↵gatorsmile2016-09-071-24/+26
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | HiveCompatibilitySuite.scala ### What changes were proposed in this pull request? The original [JIRA Hive-1642](https://issues.apache.org/jira/browse/HIVE-1642) delivered the test cases `auto_joinXYZ` for verifying the results when the joins are automatically converted to map-join. Basically, most of them are just copied from the corresponding `joinXYZ`. After comparison between `auto_joinXYZ` and `joinXYZ`, below is a list of duplicate cases: ``` "auto_join0", "auto_join1", "auto_join10", "auto_join11", "auto_join12", "auto_join13", "auto_join14", "auto_join14_hadoop20", "auto_join15", "auto_join17", "auto_join18", "auto_join2", "auto_join20", "auto_join21", "auto_join23", "auto_join24", "auto_join3", "auto_join4", "auto_join5", "auto_join6", "auto_join7", "auto_join8", "auto_join9" ``` We can remove all of them without affecting the test coverage. ### How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #14635 from gatorsmile/removeAuto.
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