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* [SPARK-19650] Commands should not trigger a Spark jobHerman van Hovell2017-02-244-6/+6
| | | | | | | | | | | | Spark executes SQL commands eagerly. It does this by creating an RDD which contains the command's results. The downside to this is that any action on this RDD triggers a Spark job which is expensive and is unnecessary. This PR fixes this by avoiding the materialization of an `RDD` for `Command`s; it just materializes the result and puts them in a `LocalRelation`. Added a regression test to `SQLQuerySuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #17027 from hvanhovell/no-job-command.
* [SPARK-18699][SQL] Put malformed tokens into a new field when parsing CSV dataTakeshi Yamamuro2017-02-231-0/+2
| | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This pr added a logic to put malformed tokens into a new field when parsing CSV data in case of permissive modes. In the current master, if the CSV parser hits these malformed ones, it throws an exception below (and then a job fails); ``` Caused by: java.lang.IllegalArgumentException at java.sql.Date.valueOf(Date.java:143) at org.apache.spark.sql.catalyst.util.DateTimeUtils$.stringToTime(DateTimeUtils.scala:137) at org.apache.spark.sql.execution.datasources.csv.CSVTypeCast$$anonfun$castTo$6.apply$mcJ$sp(CSVInferSchema.scala:272) at org.apache.spark.sql.execution.datasources.csv.CSVTypeCast$$anonfun$castTo$6.apply(CSVInferSchema.scala:272) at org.apache.spark.sql.execution.datasources.csv.CSVTypeCast$$anonfun$castTo$6.apply(CSVInferSchema.scala:272) at scala.util.Try.getOrElse(Try.scala:79) at org.apache.spark.sql.execution.datasources.csv.CSVTypeCast$.castTo(CSVInferSchema.scala:269) at ``` In case that users load large CSV-formatted data, the job failure makes users get some confused. So, this fix set NULL for original columns and put malformed tokens in a new field. ## How was this patch tested? Added tests in `CSVSuite`. Author: Takeshi Yamamuro <yamamuro@apache.org> Closes #16928 from maropu/SPARK-18699-2.
* [SPARK-18871][SQL][TESTS] New test cases for IN/NOT IN subquery 3rd batchKevin Yu2017-02-166-0/+1297
| | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This is 3ird batch of test case for IN/NOT IN subquery. In this PR, it has these test files: `in-having.sql` `in-joins.sql` `in-multiple-columns.sql` These are the queries and results from running on DB2. [in-having DB2 version](https://github.com/apache/spark/files/772668/in-having.sql.db2.txt) [output of in-having](https://github.com/apache/spark/files/772670/in-having.sql.db2.out.txt) [in-joins DB2 version](https://github.com/apache/spark/files/772672/in-joins.sql.db2.txt) [output of in-joins](https://github.com/apache/spark/files/772673/in-joins.sql.db2.out.txt) [in-multiple-columns DB2 version](https://github.com/apache/spark/files/772678/in-multiple-columns.sql.db2.txt) [output of in-multiple-columns](https://github.com/apache/spark/files/772680/in-multiple-columns.sql.db2.out.txt) ## How was this patch tested? This pr is adding new test cases. We compare the result from spark with the result from another RDBMS(We used DB2 LUW). If the results are the same, we assume the result is correct. Author: Kevin Yu <qyu@us.ibm.com> Closes #16841 from kevinyu98/spark-18871-33.
* [SPARK-18871][SQL][TESTS] New test cases for IN/NOT IN subquery 4th batchKevin Yu2017-02-156-0/+2114
| | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This is 4th batch of test case for IN/NOT IN subquery. In this PR, it has these test files: `in-set-operations.sql` `in-with-cte.sql` `not-in-joins.sql` Here are the queries and results from running on DB2. [in-set-operations DB2 version](https://github.com/apache/spark/files/772846/in-set-operations.sql.db2.txt) [Output of in-set-operations](https://github.com/apache/spark/files/772848/in-set-operations.sql.db2.out.txt) [in-with-cte DB2 version](https://github.com/apache/spark/files/772849/in-with-cte.sql.db2.txt) [Output of in-with-cte](https://github.com/apache/spark/files/772856/in-with-cte.sql.db2.out.txt) [not-in-joins DB2 version](https://github.com/apache/spark/files/772851/not-in-joins.sql.db2.txt) [Output of not-in-joins](https://github.com/apache/spark/files/772852/not-in-joins.sql.db2.out.txt) ## How was this patch tested? This pr is adding new test cases. We compare the result from spark with the result from another RDBMS(We used DB2 LUW). If the results are the same, we assume the result is correct. Author: Kevin Yu <qyu@us.ibm.com> Closes #16915 from kevinyu98/spark-18871-44.
* [SPARK-18872][SQL][TESTS] New test cases for EXISTS subquery (Joins + CTE)Dilip Biswal2017-02-154-0/+933
| | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds the third and final set of tests for EXISTS subquery. File name | Brief description ------------------------| ----------------- exists-cte.sql |Tests Exist subqueries referencing CTE exists-joins-and-set-ops.sql|Tests Exists subquery used in Joins (Both when joins occurs in outer and suquery blocks) DB2 results are attached here as reference : [exists-cte-db2.txt](https://github.com/apache/spark/files/752091/exists-cte-db2.txt) [exists-joins-and-set-ops-db2.txt](https://github.com/apache/spark/files/753283/exists-joins-and-set-ops-db2.txt) (updated) ## How was this patch tested? The test result is compared with the result run from another SQL engine (in this case is IBM DB2). If the result are equivalent, we assume the result is correct. Author: Dilip Biswal <dbiswal@us.ibm.com> Closes #16802 from dilipbiswal/exists-pr3.
* [SPARK-18873][SQL][TEST] New test cases for scalar subquery (part 2 of 2) - ↵Nattavut Sutyanyong2017-02-154-66/+701
| | | | | | | | | | | | | | scalar subquery in predicate context ## What changes were proposed in this pull request? This PR adds new test cases for scalar subquery in predicate context ## How was this patch tested? The test result is compared with the result run from another SQL engine (in this case is IBM DB2). If the result are equivalent, we assume the result is correct. Author: Nattavut Sutyanyong <nsy.can@gmail.com> Closes #16798 from nsyca/18873-2.
* [SPARK-18871][SQL][TESTS] New test cases for IN/NOT IN subquery 2nd batchKevin Yu2017-02-158-114/+1023
| | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This is 2nd batch of test case for IN/NOT IN subquery. In this PR, it has these test cases: `in-limit.sql` `in-order-by.sql` `not-in-group-by.sql` These are the queries and results from running on DB2. [in-limit DB2 version](https://github.com/apache/spark/files/743267/in-limit.sql.db2.out.txt) [in-order-by DB2 version](https://github.com/apache/spark/files/743269/in-order-by.sql.db2.txt) [not-in-group-by DB2 version](https://github.com/apache/spark/files/743271/not-in-group-by.sql.db2.txt) [output of in-limit.sql DB2](https://github.com/apache/spark/files/743276/in-limit.sql.db2.out.txt) [output of in-order-by.sql DB2](https://github.com/apache/spark/files/743278/in-order-by.sql.db2.out.txt) [output of not-in-group-by.sql DB2](https://github.com/apache/spark/files/743279/not-in-group-by.sql.db2.out.txt) ## How was this patch tested? This pr is adding new test cases. Author: Kevin Yu <qyu@us.ibm.com> Closes #16759 from kevinyu98/spark-18871-2.
* [SPARK-18872][SQL][TESTS] New test cases for EXISTS subquery (Aggregate, ↵Dilip Biswal2017-02-096-0/+885
| | | | | | | | | | | | | | | | | | | | | | | | | | Having, Orderby, Limit) ## What changes were proposed in this pull request? This PR adds the second set of tests for EXISTS subquery. File name | Brief description ------------------------| ----------------- exists-aggregate.sql |Tests aggregate expressions in outer query and EXISTS subquery. exists-having.sql|Tests HAVING clause in subquery. exists-orderby-limit.sql|Tests EXISTS subquery support with ORDER BY and LIMIT clauses. DB2 results are attached here as reference : [exists-aggregate-db2.txt](https://github.com/apache/spark/files/743287/exists-aggregate-db2.txt) [exists-having-db2.txt](https://github.com/apache/spark/files/743286/exists-having-db2.txt) [exists-orderby-limit-db2.txt](https://github.com/apache/spark/files/743288/exists-orderby-limit-db2.txt) ## How the patch was tested. The test result is compared with the result run from another SQL engine (in this case is IBM DB2). If the result are equivalent, we assume the result is correct. Author: Dilip Biswal <dbiswal@us.ibm.com> Closes #16760 from dilipbiswal/exists-pr2.
* [SPARK-18873][SQL][TEST] New test cases for scalar subquery (part 1 of 2) - ↵Nattavut Sutyanyong2017-02-072-0/+328
| | | | | | | | | | | | | | scalar subquery in SELECT clause ## What changes were proposed in this pull request? This PR adds new test cases for scalar subquery in SELECT clause. ## How was this patch tested? The test result is compared with the result run from another SQL engine (in this case is IBM DB2). If the result are equivalent, we assume the result is correct. Author: Nattavut Sutyanyong <nsy.can@gmail.com> Closes #16712 from nsyca/18873.
* [SPARK-18609][SPARK-18841][SQL] Fix redundant Alias removal in the optimizerHerman van Hovell2017-02-074-4/+146
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The optimizer tries to remove redundant alias only projections from the query plan using the `RemoveAliasOnlyProject` rule. The current rule identifies removes such a project and rewrites the project's attributes in the **entire** tree. This causes problems when parts of the tree are duplicated (for instance a self join on a temporary view/CTE) and the duplicated part contains the alias only project, in this case the rewrite will break the tree. This PR fixes these problems by using a blacklist for attributes that are not to be moved, and by making sure that attribute remapping is only done for the parent tree, and not for unrelated parts of the query plan. The current tree transformation infrastructure works very well if the transformation at hand requires little or a global contextual information. In this case we need to know both the attributes that were not to be moved, and we also needed to know which child attributes were modified. This cannot be done easily using the current infrastructure, and solutions typically involves transversing the query plan multiple times (which is super slow). I have moved around some code in `TreeNode`, `QueryPlan` and `LogicalPlan`to make this much more straightforward; this basically allows you to manually traverse the tree. This PR subsumes the following PRs by windpiger: Closes https://github.com/apache/spark/pull/16267 Closes https://github.com/apache/spark/pull/16255 ## How was this patch tested? I have added unit tests to `RemoveRedundantAliasAndProjectSuite` and I have added integration tests to the `SQLQueryTestSuite.union` and `SQLQueryTestSuite.cte` test cases. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #16757 from hvanhovell/SPARK-18609.
* [SPARK-18872][SQL][TESTS] New test cases for EXISTS subqueryDilip Biswal2017-01-294-0/+589
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds the first set of tests for EXISTS subquery. File name | Brief description ------------------------| ----------------- exists-basic.sql |Tests EXISTS and NOT EXISTS subqueries with both correlated and local predicates. exists-within-and-or.sql|Tests EXISTS and NOT EXISTS subqueries embedded in AND or OR expression. DB2 results are attached here as reference : [exists-basic-db2.txt](https://github.com/apache/spark/files/733031/exists-basic-db2.txt) [exists-and-or-db2.txt](https://github.com/apache/spark/files/733030/exists-and-or-db2.txt) ## How was this patch tested? This patch is adding tests. Author: Dilip Biswal <dbiswal@us.ibm.com> Closes #16710 from dilipbiswal/exist-basic.
* [SPARK-18863][SQL] Output non-aggregate expressions without GROUP BY in a ↵Nattavut Sutyanyong2017-01-252-0/+108
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | subquery does not yield an error ## What changes were proposed in this pull request? This PR will report proper error messages when a subquery expression contain an invalid plan. This problem is fixed by calling CheckAnalysis for the plan inside a subquery. ## How was this patch tested? Existing tests and two new test cases on 2 forms of subquery, namely, scalar subquery and in/exists subquery. ```` -- TC 01.01 -- The column t2b in the SELECT of the subquery is invalid -- because it is neither an aggregate function nor a GROUP BY column. select t1a, t2b from t1, t2 where t1b = t2c and t2b = (select max(avg) from (select t2b, avg(t2b) avg from t2 where t2a = t1.t1b ) ) ; -- TC 01.02 -- Invalid due to the column t2b not part of the output from table t2. select * from t1 where t1a in (select min(t2a) from t2 group by t2c having t2c in (select max(t3c) from t3 group by t3b having t3b > t2b )) ; ```` Author: Nattavut Sutyanyong <nsy.can@gmail.com> Closes #16572 from nsyca/18863.
* [SPARK-19017][SQL] NOT IN subquery with more than one column may return ↵Nattavut Sutyanyong2017-01-242-0/+114
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | incorrect results ## What changes were proposed in this pull request? This PR fixes the code in Optimizer phase where the NULL-aware expression of a NOT IN query is expanded in Rule `RewritePredicateSubquery`. Example: The query select a1,b1 from t1 where (a1,b1) not in (select a2,b2 from t2); has the (a1, b1) = (a2, b2) rewritten from (before this fix): Join LeftAnti, ((isnull((_1#2 = a2#16)) || isnull((_2#3 = b2#17))) || ((_1#2 = a2#16) && (_2#3 = b2#17))) to (after this fix): Join LeftAnti, (((_1#2 = a2#16) || isnull((_1#2 = a2#16))) && ((_2#3 = b2#17) || isnull((_2#3 = b2#17)))) ## How was this patch tested? sql/test, catalyst/test and new test cases in SQLQueryTestSuite. Author: Nattavut Sutyanyong <nsy.can@gmail.com> Closes #16467 from nsyca/19017.
* [SPARK-19229][SQL] Disallow Creating Hive Source Tables when Hive Support is ↵gatorsmile2017-01-228-14/+14
| | | | | | | | | | | | | | Not Enabled ### What changes were proposed in this pull request? It is weird to create Hive source tables when using InMemoryCatalog. We are unable to operate it. This PR is to block users to create Hive source tables. ### How was this patch tested? Fixed the test cases Author: gatorsmile <gatorsmile@gmail.com> Closes #16587 from gatorsmile/blockHiveTable.
* [SPARK-19219][SQL] Fix Parquet log output defaultsNick Lavers2017-01-171-2/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Changing the default parquet logging levels to reflect the changes made in PR [#15538](https://github.com/apache/spark/pull/15538), in order to prevent the flood of log messages by default. ## How was this patch tested? Default log output when reading from parquet 1.6 files was compared with and without this change. The change eliminates the extraneous logging and makes the output readable. Author: Nick Lavers <nick.lavers@videoamp.com> Closes #16580 from nicklavers/spark-19219-set_default_parquet_log_level.
* [SPARK-19178][SQL] convert string of large numbers to int should return nullWenchen Fan2017-01-122-0/+221
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? When we convert a string to integral, we will convert that string to `decimal(20, 0)` first, so that we can turn a string with decimal format to truncated integral, e.g. `CAST('1.2' AS int)` will return `1`. However, this brings problems when we convert a string with large numbers to integral, e.g. `CAST('1234567890123' AS int)` will return `1912276171`, while Hive returns null as we expected. This is a long standing bug(seems it was there the first day Spark SQL was created), this PR fixes this bug by adding the native support to convert `UTF8String` to integral. ## How was this patch tested? new regression tests Author: Wenchen Fan <wenchen@databricks.com> Closes #16550 from cloud-fan/string-to-int.
* [SPARK-18969][SQL] Support grouping by nondeterministic expressionsWenchen Fan2017-01-121-3/+7
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently nondeterministic expressions are allowed in `Aggregate`(see the [comment](https://github.com/apache/spark/blob/v2.0.2/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala#L249-L251)), but the `PullOutNondeterministic` analyzer rule failed to handle `Aggregate`, this PR fixes it. close https://github.com/apache/spark/pull/16379 There is still one remaining issue: `SELECT a + rand() FROM t GROUP BY a + rand()` is not allowed, because the 2 `rand()` are different(we generate random seed as the default seed for `rand()`). https://issues.apache.org/jira/browse/SPARK-19035 is tracking this issue. ## How was this patch tested? a new test suite Author: Wenchen Fan <wenchen@databricks.com> Closes #16404 from cloud-fan/groupby.
* [SPARK-18871][SQL] New test cases for IN/NOT IN subqueryKevin Yu2017-01-054-0/+884
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR extends the existing IN/NOT IN subquery test cases coverage, adds more test cases to the IN subquery test suite. Based on the discussion, we will create `subquery/in-subquery` sub structure under `sql/core/src/test/resources/sql-tests/inputs` directory. This is the high level grouping for IN subquery: `subquery/in-subquery/` `subquery/in-subquery/simple-in.sql` `subquery/in-subquery/in-group-by.sql (in parent side, subquery, and both)` `subquery/in-subquery/not-in-group-by.sql` `subquery/in-subquery/in-order-by.sql` `subquery/in-subquery/in-limit.sql` `subquery/in-subquery/in-having.sql` `subquery/in-subquery/in-joins.sql` `subquery/in-subquery/not-in-joins.sql` `subquery/in-subquery/in-set-operations.sql` `subquery/in-subquery/in-with-cte.sql` `subquery/in-subquery/not-in-with-cte.sql` subquery/in-subquery/in-multiple-columns.sql` We will deliver it through multiple prs, this is the first pr for the IN subquery, it has `subquery/in-subquery/simple-in.sql` `subquery/in-subquery/in-group-by.sql (in parent side, subquery, and both)` These are the results from running on DB2. [Modified test file of in-group-by.sql used to run on DB2](https://github.com/apache/spark/files/683367/in-group-by.sql.db2.txt) [Output of the run result on DB2](https://github.com/apache/spark/files/683362/in-group-by.sql.db2.out.txt) [Modified test file of simple-in.sql used to run on DB2](https://github.com/apache/spark/files/683378/simple-in.sql.db2.txt) [Output of the run result on DB2](https://github.com/apache/spark/files/683379/simple-in.sql.db2.out.txt) ## How was this patch tested? This patch is adding tests. Author: Kevin Yu <qyu@us.ibm.com> Closes #16337 from kevinyu98/spark-18871.
* [SPARK-17910][SQL] Allow users to update the comment of a columnjiangxingbo2016-12-152-0/+361
| | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Right now, once a user set the comment of a column with create table command, he/she cannot update the comment. It will be useful to provide a public interface (e.g. SQL) to do that. This PR implements the following SQL statement: ``` ALTER TABLE table [PARTITION partition_spec] CHANGE [COLUMN] column_old_name column_new_name column_dataType [COMMENT column_comment] [FIRST | AFTER column_name]; ``` For further expansion, we could support alter `name`/`dataType`/`index` of a column too. ## How was this patch tested? Add new test cases in `ExternalCatalogSuite` and `SessionCatalogSuite`. Add sql file test for `ALTER TABLE CHANGE COLUMN` statement. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #15717 from jiangxb1987/change-column.
* [SPARK-18814][SQL] CheckAnalysis rejects TPCDS query 32Nattavut Sutyanyong2016-12-142-0/+66
| | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Move the checking of GROUP BY column in correlated scalar subquery from CheckAnalysis to Analysis to fix a regression caused by SPARK-18504. This problem can be reproduced with a simple script now. Seq((1,1)).toDF("pk","pv").createOrReplaceTempView("p") Seq((1,1)).toDF("ck","cv").createOrReplaceTempView("c") sql("select * from p,c where p.pk=c.ck and c.cv = (select avg(c1.cv) from c c1 where c1.ck = p.pk)").show The requirements are: 1. We need to reference the same table twice in both the parent and the subquery. Here is the table c. 2. We need to have a correlated predicate but to a different table. Here is from c (as c1) in the subquery to p in the parent. 3. We will then "deduplicate" c1.ck in the subquery to `ck#<n1>#<n2>` at `Project` above `Aggregate` of `avg`. Then when we compare `ck#<n1>#<n2>` and the original group by column `ck#<n1>` by their canonicalized form, which is #<n2> != #<n1>. That's how we trigger the exception added in SPARK-18504. ## How was this patch tested? SubquerySuite and a simplified version of TPCDS-Q32 Author: Nattavut Sutyanyong <nsy.can@gmail.com> Closes #16246 from nsyca/18814.
* [SPARK-17932][SQL][FOLLOWUP] Change statement `SHOW TABLES EXTENDED` to ↵jiangxingbo2016-12-132-10/+12
| | | | | | | | | | | | | | | | | | | | | `SHOW TABLE EXTENDED` ## What changes were proposed in this pull request? Change the statement `SHOW TABLES [EXTENDED] [(IN|FROM) database_name] [[LIKE] 'identifier_with_wildcards'] [PARTITION(partition_spec)]` to the following statements: - SHOW TABLES [(IN|FROM) database_name] [[LIKE] 'identifier_with_wildcards'] - SHOW TABLE EXTENDED [(IN|FROM) database_name] LIKE 'identifier_with_wildcards' [PARTITION(partition_spec)] After this change, the statements `SHOW TABLE/SHOW TABLES` have the same syntax with that HIVE has. ## How was this patch tested? Modified the test sql file `show-tables.sql`; Modified the test suite `DDLSuite`. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #16262 from jiangxb1987/show-table-extended.
* [SPARK-18776][SS] Make Offset for FileStreamSource corrected formatted in jsonTathagata Das2016-12-083-2/+3
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? - Changed FileStreamSource to use new FileStreamSourceOffset rather than LongOffset. The field is named as `logOffset` to make it more clear that this is a offset in the file stream log. - Fixed bug in FileStreamSourceLog, the field endId in the FileStreamSourceLog.get(startId, endId) was not being used at all. No test caught it earlier. Only my updated tests caught it. Other minor changes - Dont use batchId in the FileStreamSource, as calling it batch id is extremely miss leading. With multiple sources, it may happen that a new batch has no new data from a file source. So offset of FileStreamSource != batchId after that batch. ## How was this patch tested? Updated unit test. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #16205 from tdas/SPARK-18776.
* [SPARK-18671][SS][TEST-MAVEN] Follow up PR to fix test for MavenTathagata Das2016-12-061-1/+0
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Maven compilation seem to not allow resource is sql/test to be easily referred to in kafka-0-10-sql tests. So moved the kafka-source-offset-version-2.1.0 from sql test resources to kafka-0-10-sql test resources. ## How was this patch tested? Manually ran maven test Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #16183 from tdas/SPARK-18671-1.
* [SPARK-18671][SS][TEST] Added tests to ensure stability of that all ↵Tathagata Das2016-12-069-0/+28
| | | | | | | | | | | | | | | Structured Streaming log formats ## What changes were proposed in this pull request? To be able to restart StreamingQueries across Spark version, we have already made the logs (offset log, file source log, file sink log) use json. We should added tests with actual json files in the Spark such that any incompatible changes in reading the logs is immediately caught. This PR add tests for FileStreamSourceLog, FileStreamSinkLog, and OffsetSeqLog. ## How was this patch tested? new unit tests Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #16128 from tdas/SPARK-18671.
* [SPARK-18657][SPARK-18668] Make StreamingQuery.id persists across restart ↵Tathagata Das2016-12-051-0/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | and not auto-generate StreamingQuery.name ## What changes were proposed in this pull request? Here are the major changes in this PR. - Added the ability to recover `StreamingQuery.id` from checkpoint location, by writing the id to `checkpointLoc/metadata`. - Added `StreamingQuery.runId` which is unique for every query started and does not persist across restarts. This is to identify each restart of a query separately (same as earlier behavior of `id`). - Removed auto-generation of `StreamingQuery.name`. The purpose of name was to have the ability to define an identifier across restarts, but since id is precisely that, there is no need for a auto-generated name. This means name becomes purely cosmetic, and is null by default. - Added `runId` to `StreamingQueryListener` events and `StreamingQueryProgress`. Implementation details - Renamed existing `StreamExecutionMetadata` to `OffsetSeqMetadata`, and moved it to the file `OffsetSeq.scala`, because that is what this metadata is tied to. Also did some refactoring to make the code cleaner (got rid of a lot of `.json` and `.getOrElse("{}")`). - Added the `id` as the new `StreamMetadata`. - When a StreamingQuery is created it gets or writes the `StreamMetadata` from `checkpointLoc/metadata`. - All internal logging in `StreamExecution` uses `(name, id, runId)` instead of just `name` TODO - [x] Test handling of name=null in json generation of StreamingQueryProgress - [x] Test handling of name=null in json generation of StreamingQueryListener events - [x] Test python API of runId ## How was this patch tested? Updated unit tests and new unit tests Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #16113 from tdas/SPARK-18657.
* [SPARK-18655][SS] Ignore Structured Streaming 2.0.2 logs in history serverShixiong Zhu2016-11-301-0/+5
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? As `queryStatus` in StreamingQueryListener events was removed in #15954, parsing 2.0.2 structured streaming logs will throw the following errror: ``` [info] com.fasterxml.jackson.databind.exc.UnrecognizedPropertyException: Unrecognized field "queryStatus" (class org.apache.spark.sql.streaming.StreamingQueryListener$QueryTerminatedEvent), not marked as ignorable (2 known properties: "id", "exception"]) [info] at [Source: {"Event":"org.apache.spark.sql.streaming.StreamingQueryListener$QueryTerminatedEvent","queryStatus":{"name":"query-1","id":1,"timestamp":1480491532753,"inputRate":0.0,"processingRate":0.0,"latency":null,"sourceStatuses":[{"description":"FileStreamSource[file:/Users/zsx/stream]","offsetDesc":"#0","inputRate":0.0,"processingRate":0.0,"triggerDetails":{"latency.getOffset.source":"1","triggerId":"1"}}],"sinkStatus":{"description":"FileSink[/Users/zsx/stream2]","offsetDesc":"[#0]"},"triggerDetails":{}},"exception":null}; line: 1, column: 521] (through reference chain: org.apache.spark.sql.streaming.QueryTerminatedEvent["queryStatus"]) [info] at com.fasterxml.jackson.databind.exc.UnrecognizedPropertyException.from(UnrecognizedPropertyException.java:51) [info] at com.fasterxml.jackson.databind.DeserializationContext.reportUnknownProperty(DeserializationContext.java:839) [info] at com.fasterxml.jackson.databind.deser.std.StdDeserializer.handleUnknownProperty(StdDeserializer.java:1045) [info] at com.fasterxml.jackson.databind.deser.BeanDeserializerBase.handleUnknownProperty(BeanDeserializerBase.java:1352) [info] at com.fasterxml.jackson.databind.deser.BeanDeserializerBase.handleUnknownProperties(BeanDeserializerBase.java:1306) [info] at com.fasterxml.jackson.databind.deser.BeanDeserializer._deserializeUsingPropertyBased(BeanDeserializer.java:453) [info] at com.fasterxml.jackson.databind.deser.BeanDeserializerBase.deserializeFromObjectUsingNonDefault(BeanDeserializerBase.java:1099) ... ``` This PR just ignores such errors and adds a test to make sure we can read 2.0.2 logs. ## How was this patch tested? `query-event-logs-version-2.0.2.txt` has all types of events generated by Structured Streaming in Spark 2.0.2. `testQuietly("ReplayListenerBus should ignore broken event jsons generated in 2.0.2")` verified we can load them without any error. Author: Shixiong Zhu <shixiong@databricks.com> Closes #16085 from zsxwing/SPARK-18655.
* [SPARK-17932][SQL] Support SHOW TABLES EXTENDED LIKE ↵jiangxingbo2016-11-302-0/+218
| | | | | | | | | | | | | | | | | | | | 'identifier_with_wildcards' statement ## What changes were proposed in this pull request? Currently we haven't implemented `SHOW TABLE EXTENDED` in Spark 2.0. This PR is to implement the statement. Goals: 1. Support `SHOW TABLES EXTENDED LIKE 'identifier_with_wildcards'`; 2. Explicitly output an unsupported error message for `SHOW TABLES [EXTENDED] ... PARTITION` statement; 3. Improve test cases for `SHOW TABLES` statement. ## How was this patch tested? 1. Add new test cases in file `show-tables.sql`. 2. Modify tests for `SHOW TABLES` in `DDLSuite`. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #15958 from jiangxb1987/show-table-extended.
* [SPARK-18622][SQL] Fix the datatype of the Sum aggregate functionHerman van Hovell2016-11-302-0/+107
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The result of a `sum` aggregate function is typically a Decimal, Double or a Long. Currently the output dataType is based on input's dataType. The `FunctionArgumentConversion` rule will make sure that the input is promoted to the largest type, and that also ensures that the output uses a (hopefully) sufficiently large output dataType. The issue is that sum is in a resolved state when we cast the input type, this means that rules assuming that the dataType of the expression does not change anymore could have been applied in the mean time. This is what happens if we apply `WidenSetOperationTypes` before applying the casts, and this breaks analysis. The most straight forward and future proof solution is to make `sum` always output the widest dataType in its class (Long for IntegralTypes, Decimal for DecimalTypes & Double for FloatType and DoubleType). This PR implements that solution. We should move expression specific type casting rules into the given Expression at some point. ## How was this patch tested? Added (regression) tests to SQLQueryTestSuite's `union.sql`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #16063 from hvanhovell/SPARK-18622.
* [SPARK-18614][SQL] Incorrect predicate pushdown from ExistenceJoinNattavut Sutyanyong2016-11-292-2/+18
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? ExistenceJoin should be treated the same as LeftOuter and LeftAnti, not InnerLike and LeftSemi. This is not currently exposed because the rewrite of [NOT] EXISTS OR ... to ExistenceJoin happens in rule RewritePredicateSubquery, which is in a separate rule set and placed after the rule PushPredicateThroughJoin. During the transformation in the rule PushPredicateThroughJoin, an ExistenceJoin never exists. The semantics of ExistenceJoin says we need to preserve all the rows from the left table through the join operation as if it is a regular LeftOuter join. The ExistenceJoin augments the LeftOuter operation with a new column called exists, set to true when the join condition in the ON clause is true and false otherwise. The filter of any rows will happen in the Filter operation above the ExistenceJoin. Example: A(c1, c2): { (1, 1), (1, 2) } // B can be any value as it is irrelevant in this example B(c1): { (NULL) } select A.* from A where exists (select 1 from B where A.c1 = A.c2) or A.c2=2 In this example, the correct result is all the rows from A. If the pattern ExistenceJoin around line 935 in Optimizer.scala is indeed active, the code will push down the predicate A.c1 = A.c2 to be a Filter on relation A, which will incorrectly filter the row (1,2) from A. ## How was this patch tested? Since this is not an exposed case, no new test cases is added. The scenario is discovered via a code review of another PR and confirmed to be valid with peer. Author: Nattavut Sutyanyong <nsy.can@gmail.com> Closes #16044 from nsyca/spark-18614.
* [SPARK-18597][SQL] Do not push-down join conditions to the right side of a ↵Herman van Hovell2016-11-282-0/+36
| | | | | | | | | | | | | | | | LEFT ANTI join ## What changes were proposed in this pull request? We currently push down join conditions of a Left Anti join to both sides of the join. This is similar to Inner, Left Semi and Existence (a specialized left semi) join. The problem is that this changes the semantics of the join; a left anti join filters out rows that matches the join condition. This PR fixes this by only pushing down conditions to the left hand side of the join. This is similar to the behavior of left outer join. ## How was this patch tested? Added tests to `FilterPushdownSuite.scala` and created a SQLQueryTestSuite file for left anti joins with a regression test. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #16026 from hvanhovell/SPARK-18597.
* [SPARK-18300][SQL] Do not apply foldable propagation with expand as a child.Herman van Hovell2016-11-152-1/+12
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The `FoldablePropagation` optimizer rule, pulls foldable values out from under an `Expand`. This breaks the `Expand` in two ways: - It rewrites the output attributes of the `Expand`. We explicitly define output attributes for `Expand`, these are (unfortunately) considered as part of the expressions of the `Expand` and can be rewritten. - Expand can actually change the column (it will typically re-use the attributes or the underlying plan). This means that we cannot safely propagate the expressions from under an `Expand`. This PR fixes this and (hopefully) other issues by explicitly whitelisting allowed operators. ## How was this patch tested? Added tests to `FoldablePropagationSuite` and to `SQLQueryTestSuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15857 from hvanhovell/SPARK-18300.
* [SPARK-18430][SQL] Fixed Exception Messages when Hitting an Invocation ↵gatorsmile2016-11-142-0/+23
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Exception of Function Lookup ### What changes were proposed in this pull request? When the exception is an invocation exception during function lookup, we return a useless/confusing error message: For example, ```Scala df.selectExpr("concat_ws()") ``` Below is the error message we got: ``` null; line 1 pos 0 org.apache.spark.sql.AnalysisException: null; line 1 pos 0 ``` To get the meaningful error message, we need to get the cause. The fix is exactly the same as what we did in https://github.com/apache/spark/pull/12136. After the fix, the message we got is the exception issued in the constuctor of function implementation: ``` requirement failed: concat_ws requires at least one argument.; line 1 pos 0 org.apache.spark.sql.AnalysisException: requirement failed: concat_ws requires at least one argument.; line 1 pos 0 ``` ### How was this patch tested? Added test cases. Author: gatorsmile <gatorsmile@gmail.com> Closes #15878 from gatorsmile/functionNotFound.
* [SPARK-17993][SQL] Fix Parquet log output redirectionMichael Allman2016-11-101-2/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | (Link to Jira issue: https://issues.apache.org/jira/browse/SPARK-17993) ## What changes were proposed in this pull request? PR #14690 broke parquet log output redirection for converted partitioned Hive tables. For example, when querying parquet files written by Parquet-mr 1.6.0 Spark prints a torrent of (harmless) warning messages from the Parquet reader: ``` Oct 18, 2016 7:42:18 PM WARNING: org.apache.parquet.CorruptStatistics: Ignoring statistics because created_by could not be parsed (see PARQUET-251): parquet-mr version 1.6.0 org.apache.parquet.VersionParser$VersionParseException: Could not parse created_by: parquet-mr version 1.6.0 using format: (.+) version ((.*) )?\(build ?(.*)\) at org.apache.parquet.VersionParser.parse(VersionParser.java:112) at org.apache.parquet.CorruptStatistics.shouldIgnoreStatistics(CorruptStatistics.java:60) at org.apache.parquet.format.converter.ParquetMetadataConverter.fromParquetStatistics(ParquetMetadataConverter.java:263) at org.apache.parquet.hadoop.ParquetFileReader$Chunk.readAllPages(ParquetFileReader.java:583) at org.apache.parquet.hadoop.ParquetFileReader.readNextRowGroup(ParquetFileReader.java:513) at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.checkEndOfRowGroup(VectorizedParquetRecordReader.java:270) at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:225) at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextKeyValue(VectorizedParquetRecordReader.java:137) at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39) at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:102) at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:162) at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:102) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.scan_nextBatch$(Unknown Source) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:372) at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:231) at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:225) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319) at org.apache.spark.rdd.RDD.iterator(RDD.scala:283) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at org.apache.spark.scheduler.Task.run(Task.scala:99) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) ``` This only happens during execution, not planning, and it doesn't matter what log level the `SparkContext` is set to. That's because Parquet (versions < 1.9) doesn't use slf4j for logging. Note, you can tell that log redirection is not working here because the log message format does not conform to the default Spark log message format. This is a regression I noted as something we needed to fix as a follow up. It appears that the problem arose because we removed the call to `inferSchema` during Hive table conversion. That call is what triggered the output redirection. ## How was this patch tested? I tested this manually in four ways: 1. Executing `spark.sqlContext.range(10).selectExpr("id as a").write.mode("overwrite").parquet("test")`. 2. Executing `spark.read.format("parquet").load(legacyParquetFile).show` for a Parquet file `legacyParquetFile` written using Parquet-mr 1.6.0. 3. Executing `select * from legacy_parquet_table limit 1` for some unpartitioned Parquet-based Hive table written using Parquet-mr 1.6.0. 4. Executing `select * from legacy_partitioned_parquet_table where partcol=x limit 1` for some partitioned Parquet-based Hive table written using Parquet-mr 1.6.0. I ran each test with a new instance of `spark-shell` or `spark-sql`. Incidentally, I found that test case 3 was not a regression—redirection was not occurring in the master codebase prior to #14690. I spent some time working on a unit test, but based on my experience working on this ticket I feel that automated testing here is far from feasible. cc ericl dongjoon-hyun Author: Michael Allman <michael@videoamp.com> Closes #15538 from mallman/spark-17993-fix_parquet_log_redirection.
* [SPARK-17854][SQL] rand/randn allows null/long as input seedhyukjinkwon2016-11-062-0/+101
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR proposes `rand`/`randn` accept `null` as input in Scala/SQL and `LongType` as input in SQL. In this case, it treats the values as `0`. So, this PR includes both changes below: - `null` support It seems MySQL also accepts this. ``` sql mysql> select rand(0); +---------------------+ | rand(0) | +---------------------+ | 0.15522042769493574 | +---------------------+ 1 row in set (0.00 sec) mysql> select rand(NULL); +---------------------+ | rand(NULL) | +---------------------+ | 0.15522042769493574 | +---------------------+ 1 row in set (0.00 sec) ``` and also Hive does according to [HIVE-14694](https://issues.apache.org/jira/browse/HIVE-14694) So the codes below: ``` scala spark.range(1).selectExpr("rand(null)").show() ``` prints.. **Before** ``` Input argument to rand must be an integer literal.;; line 1 pos 0 org.apache.spark.sql.AnalysisException: Input argument to rand must be an integer literal.;; line 1 pos 0 at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$5.apply(FunctionRegistry.scala:465) at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$5.apply(FunctionRegistry.scala:444) ``` **After** ``` +-----------------------+ |rand(CAST(NULL AS INT))| +-----------------------+ | 0.13385709732307427| +-----------------------+ ``` - `LongType` support in SQL. In addition, it make the function allows to take `LongType` consistently within Scala/SQL. In more details, the codes below: ``` scala spark.range(1).select(rand(1), rand(1L)).show() spark.range(1).selectExpr("rand(1)", "rand(1L)").show() ``` prints.. **Before** ``` +------------------+------------------+ | rand(1)| rand(1)| +------------------+------------------+ |0.2630967864682161|0.2630967864682161| +------------------+------------------+ Input argument to rand must be an integer literal.;; line 1 pos 0 org.apache.spark.sql.AnalysisException: Input argument to rand must be an integer literal.;; line 1 pos 0 at org.apache.spark.sql.catalyst.analysis.FunctionRegistry$$anonfun$5.apply(FunctionRegistry.scala:465) at ``` **After** ``` +------------------+------------------+ | rand(1)| rand(1)| +------------------+------------------+ |0.2630967864682161|0.2630967864682161| +------------------+------------------+ +------------------+------------------+ | rand(1)| rand(1)| +------------------+------------------+ |0.2630967864682161|0.2630967864682161| +------------------+------------------+ ``` ## How was this patch tested? Unit tests in `DataFrameSuite.scala` and `RandomSuite.scala`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #15432 from HyukjinKwon/SPARK-17854.
* [SPARK-17849][SQL] Fix NPE problem when using grouping setswangyang2016-11-052-0/+59
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Prior this pr, the following code would cause an NPE: `case class point(a:String, b:String, c:String, d: Int)` `val data = Seq( point("1","2","3", 1), point("4","5","6", 1), point("7","8","9", 1) )` `sc.parallelize(data).toDF().registerTempTable("table")` `spark.sql("select a, b, c, count(d) from table group by a, b, c GROUPING SETS ((a)) ").show()` The reason is that when the grouping_id() behavior was changed in #10677, some code (which should be changed) was left out. Take the above code for example, prior #10677, the bit mask for set "(a)" was `001`, while after #10677 the bit mask was changed to `011`. However, the `nonNullBitmask` was not changed accordingly. This pr will fix this problem. ## How was this patch tested? add integration tests Author: wangyang <wangyang@haizhi.com> Closes #15416 from yangw1234/groupingid.
* [SPARK-18214][SQL] Simplify RuntimeReplaceable type coercionReynold Xin2016-11-023-1/+153
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? RuntimeReplaceable is used to create aliases for expressions, but the way it deals with type coercion is pretty weird (each expression is responsible for how to handle type coercion, which does not obey the normal implicit type cast rules). This patch simplifies its handling by allowing the analyzer to traverse into the actual expression of a RuntimeReplaceable. ## How was this patch tested? - Correctness should be guaranteed by existing unit tests already - Removed SQLCompatibilityFunctionSuite and moved it sql-compatibility-functions.sql - Added a new test case in sql-compatibility-functions.sql for verifying explain behavior. Author: Reynold Xin <rxin@databricks.com> Closes #15723 from rxin/SPARK-18214.
* [SPARK-16839][SQL] Simplify Struct creation code patheyal farago2016-11-021-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Simplify struct creation, especially the aspect of `CleanupAliases` which missed some aliases when handling trees created by `CreateStruct`. This PR includes: 1. A failing test (create struct with nested aliases, some of the aliases survive `CleanupAliases`). 2. A fix that transforms `CreateStruct` into a `CreateNamedStruct` constructor, effectively eliminating `CreateStruct` from all expression trees. 3. A `NamePlaceHolder` used by `CreateStruct` when column names cannot be extracted from unresolved `NamedExpression`. 4. A new Analyzer rule that resolves `NamePlaceHolder` into a string literal once the `NamedExpression` is resolved. 5. `CleanupAliases` code was simplified as it no longer has to deal with `CreateStruct`'s top level columns. ## How was this patch tested? Running all tests-suits in package org.apache.spark.sql, especially including the analysis suite, making sure added test initially fails, after applying suggested fix rerun the entire analysis package successfully. Modified few tests that expected `CreateStruct` which is now transformed into `CreateNamedStruct`. Author: eyal farago <eyal farago> Author: Herman van Hovell <hvanhovell@databricks.com> Author: eyal farago <eyal.farago@gmail.com> Author: Eyal Farago <eyal.farago@actimize.com> Author: Hyukjin Kwon <gurwls223@gmail.com> Author: eyalfa <eyal.farago@gmail.com> Closes #15718 from hvanhovell/SPARK-16839-2.
* [SPARK-18148][SQL] Misleading Error Message for Aggregation Without ↵jiangxingbo2016-11-012-29/+128
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Window/GroupBy ## What changes were proposed in this pull request? Aggregation Without Window/GroupBy expressions will fail in `checkAnalysis`, the error message is a bit misleading, we should generate a more specific error message for this case. For example, ``` spark.read.load("/some-data") .withColumn("date_dt", to_date($"date")) .withColumn("year", year($"date_dt")) .withColumn("week", weekofyear($"date_dt")) .withColumn("user_count", count($"userId")) .withColumn("daily_max_in_week", max($"user_count").over(weeklyWindow)) ) ``` creates the following output: ``` org.apache.spark.sql.AnalysisException: expression '`randomColumn`' is neither present in the group by, nor is it an aggregate function. Add to group by or wrap in first() (or first_value) if you don't care which value you get.; ``` In the error message above, `randomColumn` doesn't appear in the query(acturally it's added by function `withColumn`), so the message is not enough for the user to address the problem. ## How was this patch tested? Manually test Before: ``` scala> spark.sql("select col, count(col) from tbl") org.apache.spark.sql.AnalysisException: expression 'tbl.`col`' is neither present in the group by, nor is it an aggregate function. Add to group by or wrap in first() (or first_value) if you don't care which value you get.;; ``` After: ``` scala> spark.sql("select col, count(col) from tbl") org.apache.spark.sql.AnalysisException: grouping expressions sequence is empty, and 'tbl.`col`' is not an aggregate function. Wrap '(count(col#231L) AS count(col)#239L)' in windowing function(s) or wrap 'tbl.`col`' in first() (or first_value) if you don't care which value you get.;; ``` Also add new test sqls in `group-by.sql`. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #15672 from jiangxb1987/groupBy-empty.
* Revert "[SPARK-16839][SQL] redundant aliases after cleanupAliases"Herman van Hovell2016-11-012-3/+3
| | | | This reverts commit 5441a6269e00e3903ae6c1ea8deb4ddf3d2e9975.
* [SPARK-16839][SQL] redundant aliases after cleanupAliaseseyal farago2016-11-012-3/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Simplify struct creation, especially the aspect of `CleanupAliases` which missed some aliases when handling trees created by `CreateStruct`. This PR includes: 1. A failing test (create struct with nested aliases, some of the aliases survive `CleanupAliases`). 2. A fix that transforms `CreateStruct` into a `CreateNamedStruct` constructor, effectively eliminating `CreateStruct` from all expression trees. 3. A `NamePlaceHolder` used by `CreateStruct` when column names cannot be extracted from unresolved `NamedExpression`. 4. A new Analyzer rule that resolves `NamePlaceHolder` into a string literal once the `NamedExpression` is resolved. 5. `CleanupAliases` code was simplified as it no longer has to deal with `CreateStruct`'s top level columns. ## How was this patch tested? running all tests-suits in package org.apache.spark.sql, especially including the analysis suite, making sure added test initially fails, after applying suggested fix rerun the entire analysis package successfully. modified few tests that expected `CreateStruct` which is now transformed into `CreateNamedStruct`. Credit goes to hvanhovell for assisting with this PR. Author: eyal farago <eyal farago> Author: eyal farago <eyal.farago@gmail.com> Author: Herman van Hovell <hvanhovell@databricks.com> Author: Eyal Farago <eyal.farago@actimize.com> Author: Hyukjin Kwon <gurwls223@gmail.com> Author: eyalfa <eyal.farago@gmail.com> Closes #14444 from eyalfa/SPARK-16839_redundant_aliases_after_cleanupAliases.
* [SPARK-18143][SQL] Ignore Structured Streaming event logs to avoid breaking ↵Shixiong Zhu2016-10-312-0/+8
| | | | | | | | | | | | | | | | | history server ## What changes were proposed in this pull request? Because of the refactoring work in Structured Streaming, the event logs generated by Strucutred Streaming in Spark 2.0.0 and 2.0.1 cannot be parsed. This PR just ignores these logs in ReplayListenerBus because no places use them. ## How was this patch tested? - Generated events logs using Spark 2.0.0 and 2.0.1, and saved them as `structured-streaming-query-event-logs-2.0.0.txt` and `structured-streaming-query-event-logs-2.0.1.txt` - The new added test makes sure ReplayListenerBus will skip these bad jsons. Author: Shixiong Zhu <shixiong@databricks.com> Closes #15663 from zsxwing/fix-event-log.
* [SPARK-18094][SQL][TESTS] Move group analytics test cases from ↵jiangxingbo2016-10-262-3/+290
| | | | | | | | | | | | | | | | | | | | | | | | | | | | `SQLQuerySuite` into a query file test. ## What changes were proposed in this pull request? Currently we have several test cases for group analytics(ROLLUP/CUBE/GROUPING SETS) in `SQLQuerySuite`, should better move them into a query file test. The following test cases are moved to `group-analytics.sql`: ``` test("rollup") test("grouping sets when aggregate functions containing groupBy columns") test("cube") test("grouping sets") test("grouping and grouping_id") test("grouping and grouping_id in having") test("grouping and grouping_id in sort") ``` This is followup work of #15582 ## How was this patch tested? Modified query file `group-analytics.sql`, which will be tested by `SQLQueryTestSuite`. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #15624 from jiangxb1987/group-analytics-test.
* [SPARK-18045][SQL][TESTS] Move `HiveDataFrameAnalyticsSuite` to package `sql`jiangxingbo2016-10-232-0/+100
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The testsuite `HiveDataFrameAnalyticsSuite` has nothing to do with HIVE, we should move it to package `sql`. The original test cases in that suite are splited into two existing testsuites: `DataFrameAggregateSuite` tests for the functions and ~~`SQLQuerySuite`~~`SQLQueryTestSuite` tests for the SQL statements. ## How was this patch tested? ~~Modified `SQLQuerySuite` in package `sql`.~~ Add query file for `SQLQueryTestSuite`. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #15582 from jiangxb1987/group-analytics-test.
* [SPARK-17860][SQL] SHOW COLUMN's database conflict check should respect case ↵Dilip Biswal2016-10-202-0/+275
| | | | | | | | | | | | | | | | | sensitivity configuration ## What changes were proposed in this pull request? SHOW COLUMNS command validates the user supplied database name with database name from qualified table name name to make sure both of them are consistent. This comparison should respect case sensitivity. ## How was this patch tested? Added tests in DDLSuite and existing tests were moved to use new sql based test infrastructure. Author: Dilip Biswal <dbiswal@us.ibm.com> Closes #15423 from dilipbiswal/dkb_show_column_fix.
* [SPARK-17989][SQL] Check ascendingOrder type in sort_array function rather ↵hyukjinkwon2016-10-192-3/+24
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | than throwing ClassCastException ## What changes were proposed in this pull request? This PR proposes to check the second argument, `ascendingOrder` rather than throwing `ClassCastException` exception message. ```sql select sort_array(array('b', 'd'), '1'); ``` **Before** ``` 16/10/19 13:16:08 ERROR SparkSQLDriver: Failed in [select sort_array(array('b', 'd'), '1')] java.lang.ClassCastException: org.apache.spark.unsafe.types.UTF8String cannot be cast to java.lang.Boolean at scala.runtime.BoxesRunTime.unboxToBoolean(BoxesRunTime.java:85) at org.apache.spark.sql.catalyst.expressions.SortArray.nullSafeEval(collectionOperations.scala:185) at org.apache.spark.sql.catalyst.expressions.BinaryExpression.eval(Expression.scala:416) at org.apache.spark.sql.catalyst.optimizer.ConstantFolding$$anonfun$apply$1$$anonfun$applyOrElse$1.applyOrElse(expressions.scala:50) at org.apache.spark.sql.catalyst.optimizer.ConstantFolding$$anonfun$apply$1$$anonfun$applyOrElse$1.applyOrElse(expressions.scala:43) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:292) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:292) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:74) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:291) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:297) ``` **After** ``` Error in query: cannot resolve 'sort_array(array('b', 'd'), '1')' due to data type mismatch: Sort order in second argument requires a boolean literal.; line 1 pos 7; ``` ## How was this patch tested? Unit test in `DataFrameFunctionsSuite`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #15532 from HyukjinKwon/SPARK-17989.
* [SPARK-17328][SQL] Fix NPE with EXPLAIN DESCRIBE TABLEDongjoon Hyun2016-10-052-14/+48
| | | | | | | | | | | | | | | | | | | | | | | | ## 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 Hovell2016-10-043-12/+22
| | | | | | | | | | | | | | ## 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-17612][SQL] Support `DESCRIBE table PARTITION` SQL syntaxDongjoon Hyun2016-09-292-0/+117
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## 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-17551][SQL] Add DataFrame API for null orderingxin wu2016-09-252-0/+0
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This pull request adds Scala/Java DataFrame API for null ordering (NULLS FIRST | LAST). Also did some minor clean up for related code (e.g. incorrect indentation), and renamed "orderby-nulls-ordering.sql" to be consistent with existing test files. ## How was this patch tested? Added a new test case in DataFrameSuite. Author: petermaxlee <petermaxlee@gmail.com> Author: Xin Wu <xinwu@us.ibm.com> Closes #15123 from petermaxlee/SPARK-17551.
* [SPARK-17114][SQL] Fix aggregates grouped by literals with empty inputHerman van Hovell2016-09-152-0/+68
| | | | | | | | | | | | | | ## 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.
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