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* [SQL][MINOR] ParserUtils.operationNotAllowed should throw exception directlyWenchen Fan2016-06-232-33/+33
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? It's weird that `ParserUtils.operationNotAllowed` returns an exception and the caller throw it. ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #13874 from cloud-fan/style.
* [SPARK-16123] Avoid NegativeArraySizeException while reserving additional ↵Sameer Agarwal2016-06-234-14/+47
| | | | | | | | | | | | | | | | capacity in VectorizedColumnReader ## What changes were proposed in this pull request? This patch fixes an overflow bug in vectorized parquet reader where both off-heap and on-heap variants of `ColumnVector.reserve()` can unfortunately overflow while reserving additional capacity during reads. ## How was this patch tested? Manual Tests Author: Sameer Agarwal <sameer@databricks.com> Closes #13832 from sameeragarwal/negative-array.
* [SPARK-16165][SQL] Fix the update logic for InMemoryTableScanExec.readBatchesDongjoon Hyun2016-06-242-3/+18
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, `readBatches` accumulator of `InMemoryTableScanExec` is updated only when `spark.sql.inMemoryColumnarStorage.partitionPruning` is true. Although this metric is used for only testing purpose, we had better have correct metric without considering SQL options. ## How was this patch tested? Pass the Jenkins tests (including a new testcase). Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13870 from dongjoon-hyun/SPARK-16165.
* [SPARK-15443][SQL] Fix 'explain' for streaming DatasetShixiong Zhu2016-06-238-2/+142
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? - Fix the `explain` command for streaming Dataset/DataFrame. E.g., ``` == Parsed Logical Plan == 'SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7] +- 'MapElements <function1>, obj#6: java.lang.String +- 'DeserializeToObject unresolveddeserializer(createexternalrow(getcolumnbyordinal(0, StringType).toString, StructField(value,StringType,true))), obj#5: org.apache.spark.sql.Row +- Filter <function1>.apply +- StreamingRelation FileSource[/Users/zsx/stream], [value#0] == Analyzed Logical Plan == value: string SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7] +- MapElements <function1>, obj#6: java.lang.String +- DeserializeToObject createexternalrow(value#0.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row +- Filter <function1>.apply +- StreamingRelation FileSource[/Users/zsx/stream], [value#0] == Optimized Logical Plan == SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7] +- MapElements <function1>, obj#6: java.lang.String +- DeserializeToObject createexternalrow(value#0.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row +- Filter <function1>.apply +- StreamingRelation FileSource[/Users/zsx/stream], [value#0] == Physical Plan == *SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7] +- *MapElements <function1>, obj#6: java.lang.String +- *DeserializeToObject createexternalrow(value#0.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row +- *Filter <function1>.apply +- StreamingRelation FileSource[/Users/zsx/stream], [value#0] ``` - Add `StreamingQuery.explain` to display the last execution plan. E.g., ``` == Parsed Logical Plan == SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7] +- MapElements <function1>, obj#6: java.lang.String +- DeserializeToObject createexternalrow(value#12.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row +- Filter <function1>.apply +- Relation[value#12] text == Analyzed Logical Plan == value: string SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7] +- MapElements <function1>, obj#6: java.lang.String +- DeserializeToObject createexternalrow(value#12.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row +- Filter <function1>.apply +- Relation[value#12] text == Optimized Logical Plan == SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7] +- MapElements <function1>, obj#6: java.lang.String +- DeserializeToObject createexternalrow(value#12.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row +- Filter <function1>.apply +- Relation[value#12] text == Physical Plan == *SerializeFromObject [staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, input[0, java.lang.String, true], true) AS value#7] +- *MapElements <function1>, obj#6: java.lang.String +- *DeserializeToObject createexternalrow(value#12.toString, StructField(value,StringType,true)), obj#5: org.apache.spark.sql.Row +- *Filter <function1>.apply +- *Scan text [value#12] Format: org.apache.spark.sql.execution.datasources.text.TextFileFormat1836ab91, InputPaths: file:/Users/zsx/stream/a.txt, file:/Users/zsx/stream/b.txt, file:/Users/zsx/stream/c.txt, PushedFilters: [], ReadSchema: struct<value:string> ``` ## How was this patch tested? The added unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes #13815 from zsxwing/sdf-explain.
* [SPARK-16164][SQL] Update `CombineFilters` to try to construct predicates ↵Dongjoon Hyun2016-06-232-1/+19
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | with child predicate first ## What changes were proposed in this pull request? This PR changes `CombineFilters` to compose the final predicate condition by using (`child predicate` AND `parent predicate`) instead of (`parent predicate` AND `child predicate`). This is a best effort approach. Some other optimization rules may destroy this order by reorganizing conjunctive predicates. **Reported Error Scenario** Chris McCubbin reported a bug when he used StringIndexer in an ML pipeline with additional filters. It seems that during filter pushdown, we changed the ordering in the logical plan. ```scala import org.apache.spark.ml.feature._ val df1 = (0 until 3).map(_.toString).toDF val indexer = new StringIndexer() .setInputCol("value") .setOutputCol("idx") .setHandleInvalid("skip") .fit(df1) val df2 = (0 until 5).map(_.toString).toDF val predictions = indexer.transform(df2) predictions.show() // this is okay predictions.where('idx > 2).show() // this will throw an exception ``` Please see the notebook at https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/1233855/2159162931615821/588180/latest.html for error messages. ## How was this patch tested? Pass the Jenkins tests (including a new testcase). Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13872 from dongjoon-hyun/SPARK-16164.
* [SPARK-16163] [SQL] Cache the statistics for logical plansDavies Liu2016-06-233-58/+30
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This calculation of statistics is not trivial anymore, it could be very slow on large query (for example, TPC-DS Q64 took several minutes to plan). During the planning of a query, the statistics of any logical plan should not change (even InMemoryRelation), so we should use `lazy val` to cache the statistics. For InMemoryRelation, the statistics could be updated after materialization, it's only useful when used in another query (before planning), because once we finished the planning, the statistics will not be used anymore. ## How was this patch tested? Testsed with TPC-DS Q64, it could be planned in a second after the patch. Author: Davies Liu <davies@databricks.com> Closes #13871 from davies/fix_statistics.
* [SPARK-16116][SQL] ConsoleSink should not require checkpointLocationShixiong Zhu2016-06-232-0/+18
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? When the user uses `ConsoleSink`, we should use a temp location if `checkpointLocation` is not specified. ## How was this patch tested? The added unit test. Author: Shixiong Zhu <shixiong@databricks.com> Closes #13817 from zsxwing/console-checkpoint.
* [SPARK-16162] Remove dead code OrcTableScan.Brian Cho2016-06-221-66/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? SPARK-14535 removed all calls to class OrcTableScan. This removes the dead code. ## How was this patch tested? Existing unit tests. Author: Brian Cho <bcho@fb.com> Closes #13869 from dafrista/clean-up-orctablescan.
* [SQL][MINOR] Fix minor formatting issues in SHOW CREATE TABLE outputCheng Lian2016-06-221-2/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes two minor formatting issues appearing in `SHOW CREATE TABLE` output. Before: ``` CREATE EXTERNAL TABLE ... ... WITH SERDEPROPERTIES ('serialization.format' = '1' ) ... TBLPROPERTIES ('avro.schema.url' = '/tmp/avro/test.avsc', 'transient_lastDdlTime' = '1466638180') ``` After: ``` CREATE EXTERNAL TABLE ... ... WITH SERDEPROPERTIES ( 'serialization.format' = '1' ) ... TBLPROPERTIES ( 'avro.schema.url' = '/tmp/avro/test.avsc', 'transient_lastDdlTime' = '1466638180' ) ``` ## How was this patch tested? Manually tested. Author: Cheng Lian <lian@databricks.com> Closes #13864 from liancheng/show-create-table-format-fix.
* [SPARK-15230][SQL] distinct() does not handle column name with dot properlybomeng2016-06-232-1/+12
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? When table is created with column name containing dot, distinct() will fail to run. For example, ```scala val rowRDD = sparkContext.parallelize(Seq(Row(1), Row(1), Row(2))) val schema = StructType(Array(StructField("column.with.dot", IntegerType, nullable = false))) val df = spark.createDataFrame(rowRDD, schema) ``` running the following will have no problem: ```scala df.select(new Column("`column.with.dot`")) ``` but running the query with additional distinct() will cause exception: ```scala df.select(new Column("`column.with.dot`")).distinct() ``` The issue is that distinct() will try to resolve the column name, but the column name in the schema does not have backtick with it. So the solution is to add the backtick before passing the column name to resolve(). ## How was this patch tested? Added a new test case. Author: bomeng <bmeng@us.ibm.com> Closes #13140 from bomeng/SPARK-15230.
* [SPARK-16159][SQL] Move RDD creation logic from FileSourceStrategy.applyReynold Xin2016-06-222-112/+154
| | | | | | | | | | | | ## What changes were proposed in this pull request? We embed partitioning logic in FileSourceStrategy.apply, making the function very long. This is a small refactoring to move it into its own functions. Eventually we would be able to move the partitioning functions into a physical operator, rather than doing it in physical planning. ## How was this patch tested? This is a simple code move. Author: Reynold Xin <rxin@databricks.com> Closes #13862 from rxin/SPARK-16159.
* [SPARK-16024][SQL][TEST] Verify Column Comment for Data Source Tablesgatorsmile2016-06-233-3/+34
| | | | | | | | | | | | | | #### What changes were proposed in this pull request? This PR is to improve test coverage. It verifies whether `Comment` of `Column` can be appropriate handled. The test cases verify the related parts in Parser, both SQL and DataFrameWriter interface, and both Hive Metastore catalog and In-memory catalog. #### How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #13764 from gatorsmile/dataSourceComment.
* [SPARK-15956][SQL] When unwrapping ORC avoid pattern matching at runtimeBrian Cho2016-06-225-150/+314
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Extend the returning of unwrapper functions from primitive types to all types. This PR is based on https://github.com/apache/spark/pull/13676. It only fixes a bug with scala-2.10 compilation. All credit should go to dafrista. ## How was this patch tested? The patch should pass all unit tests. Reading ORC files with non-primitive types with this change reduced the read time by ~15%. Author: Brian Cho <bcho@fb.com> Author: Herman van Hovell <hvanhovell@databricks.com> Closes #13854 from hvanhovell/SPARK-15956-scala210.
* [SPARK-16078][SQL] from_utc_timestamp/to_utc_timestamp should not depends on ↵Davies Liu2016-06-223-36/+73
| | | | | | | | | | | | | | | | | | | | | | local timezone ## What changes were proposed in this pull request? Currently, we use local timezone to parse or format a timestamp (TimestampType), then use Long as the microseconds since epoch UTC. In from_utc_timestamp() and to_utc_timestamp(), we did not consider the local timezone, they could return different results with different local timezone. This PR will do the conversion based on human time (in local timezone), it should return same result in whatever timezone. But because the mapping from absolute timestamp to human time is not exactly one-to-one mapping, it will still return wrong result in some timezone (also in the begging or ending of DST). This PR is kind of the best effort fix. In long term, we should make the TimestampType be timezone aware to fix this totally. ## How was this patch tested? Tested these function in all timezone. Author: Davies Liu <davies@databricks.com> Closes #13784 from davies/convert_tz.
* [SPARK-15956][SQL] Revert "[] When unwrapping ORC avoid pattern matching…Herman van Hovell2016-06-225-314/+150
| | | | | | | | This reverts commit 0a9c02759515c41de37db6381750bc3a316c860c. It breaks the 2.10 build, I'll fix this in a different PR. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #13853 from hvanhovell/SPARK-15956-revert.
* [SPARK-15956][SQL] When unwrapping ORC avoid pattern matching at runtimeBrian Cho2016-06-225-150/+314
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Extend the returning of unwrapper functions from primitive types to all types. ## How was this patch tested? The patch should pass all unit tests. Reading ORC files with non-primitive types with this change reduced the read time by ~15%. === The github diff is very noisy. Attaching the screenshots below for improved readability: ![screen shot 2016-06-14 at 5 33 16 pm](https://cloud.githubusercontent.com/assets/1514239/16064580/4d6f7a98-3257-11e6-9172-65e4baff948b.png) ![screen shot 2016-06-14 at 5 33 28 pm](https://cloud.githubusercontent.com/assets/1514239/16064587/5ae6c244-3257-11e6-8460-69eee70de219.png) Author: Brian Cho <bcho@fb.com> Closes #13676 from dafrista/improve-orc-master.
* [SPARK-16097][SQL] Encoders.tuple should handle null object correctlyWenchen Fan2016-06-222-13/+42
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Although the top level input object can not be null, but when we use `Encoders.tuple` to combine 2 encoders, their input objects are not top level anymore and can be null. We should handle this case. ## How was this patch tested? new test in DatasetSuite Author: Wenchen Fan <wenchen@databricks.com> Closes #13807 from cloud-fan/bug.
* [SPARK-16121] ListingFileCatalog does not list in parallel anymoreYin Huai2016-06-223-9/+101
| | | | | | | | | | | | ## What changes were proposed in this pull request? Seems the fix of SPARK-14959 breaks the parallel partitioning discovery. This PR fixes the problem ## How was this patch tested? Tested manually. (This PR also adds a proper test for SPARK-14959) Author: Yin Huai <yhuai@databricks.com> Closes #13830 from yhuai/SPARK-16121.
* [SPARK-15644][MLLIB][SQL] Replace SQLContext with SparkSession in MLlibgatorsmile2016-06-211-1/+1
| | | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? This PR is to use the latest `SparkSession` to replace the existing `SQLContext` in `MLlib`. `SQLContext` is removed from `MLlib`. Also fix a test case issue in `BroadcastJoinSuite`. BTW, `SQLContext` is not being used in the `MLlib` test suites. #### How was this patch tested? Existing test cases. Author: gatorsmile <gatorsmile@gmail.com> Author: xiaoli <lixiao1983@gmail.com> Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local> Closes #13380 from gatorsmile/sqlContextML.
* [SPARK-16104] [SQL] Do not creaate CSV writer object for every flush when ↵hyukjinkwon2016-06-212-11/+10
| | | | | | | | | | | | | | | | | | | | writing ## What changes were proposed in this pull request? This PR let `CsvWriter` object is not created for each time but able to be reused. This way was taken after from JSON data source. Original `CsvWriter` was being created for each row but it was enhanced in https://github.com/apache/spark/pull/13229. However, it still creates `CsvWriter` object for each `flush()` in `LineCsvWriter`. It seems it does not have to close the object and re-create this for every flush. It follows the original logic as it is but `CsvWriter` is reused by reseting `CharArrayWriter`. ## How was this patch tested? Existing tests should cover this. Author: hyukjinkwon <gurwls223@gmail.com> Closes #13809 from HyukjinKwon/write-perf.
* [SPARK-16002][SQL] Sleep when no new data arrives to avoid 100% CPU usageShixiong Zhu2016-06-216-4/+27
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add a configuration to allow people to set a minimum polling delay when no new data arrives (default is 10ms). This PR also cleans up some INFO logs. ## How was this patch tested? Existing unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes #13718 from zsxwing/SPARK-16002.
* [SPARK-16037][SQL] Follow-up: add DataFrameWriter.insertInto() test cases ↵Cheng Lian2016-06-211-0/+48
| | | | | | | | | | | | | | | | | | for by position resolution ## What changes were proposed in this pull request? This PR migrates some test cases introduced in #12313 as a follow-up of #13754 and #13766. These test cases cover `DataFrameWriter.insertInto()`, while the former two only cover SQL `INSERT` statements. Note that the `testPartitionedTable` utility method tests both Hive SerDe tables and data source tables. ## How was this patch tested? N/A Author: Cheng Lian <lian@databricks.com> Closes #13810 from liancheng/spark-16037-follow-up-tests.
* [SPARK-16084][SQL] Minor comments update for "DESCRIBE" tablebomeng2016-06-211-3/+3
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? 1. FORMATTED is actually supported, but partition is not supported; 2. Remove parenthesis as it is not necessary just like anywhere else. ## How was this patch tested? Minor issue. I do not think it needs a test case! Author: bomeng <bmeng@us.ibm.com> Closes #13791 from bomeng/SPARK-16084.
* [SPARK-16044][SQL] input_file_name() returns empty strings in data sources ↵hyukjinkwon2016-06-201-2/+32
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | based on NewHadoopRDD ## What changes were proposed in this pull request? This PR makes `input_file_name()` function return the file paths not empty strings for external data sources based on `NewHadoopRDD`, such as [spark-redshift](https://github.com/databricks/spark-redshift/blob/cba5eee1ab79ae8f0fa9e668373a54d2b5babf6b/src/main/scala/com/databricks/spark/redshift/RedshiftRelation.scala#L149) and [spark-xml](https://github.com/databricks/spark-xml/blob/master/src/main/scala/com/databricks/spark/xml/util/XmlFile.scala#L39-L47). The codes with the external data sources below: ```scala df.select(input_file_name).show() ``` will produce - **Before** ``` +-----------------+ |input_file_name()| +-----------------+ | | +-----------------+ ``` - **After** ``` +--------------------+ | input_file_name()| +--------------------+ |file:/private/var...| +--------------------+ ``` ## How was this patch tested? Unit tests in `ColumnExpressionSuite`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #13759 from HyukjinKwon/SPARK-16044.
* [SPARK-16056][SPARK-16057][SPARK-16058][SQL] Fix Multiple Bugs in Column ↵gatorsmile2016-06-202-15/+98
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Partitioning in JDBC Source #### What changes were proposed in this pull request? This PR is to fix the following bugs: **Issue 1: Wrong Results when lowerBound is larger than upperBound in Column Partitioning** ```scala spark.read.jdbc( url = urlWithUserAndPass, table = "TEST.seq", columnName = "id", lowerBound = 4, upperBound = 0, numPartitions = 3, connectionProperties = new Properties) ``` **Before code changes:** The returned results are wrong and the generated partitions are wrong: ``` Part 0 id < 3 or id is null Part 1 id >= 3 AND id < 2 Part 2 id >= 2 ``` **After code changes:** Issue an `IllegalArgumentException` exception: ``` Operation not allowed: the lower bound of partitioning column is larger than the upper bound. lowerBound: 5; higherBound: 1 ``` **Issue 2: numPartitions is more than the number of key values between upper and lower bounds** ```scala spark.read.jdbc( url = urlWithUserAndPass, table = "TEST.seq", columnName = "id", lowerBound = 1, upperBound = 5, numPartitions = 10, connectionProperties = new Properties) ``` **Before code changes:** Returned correct results but the generated partitions are very inefficient, like: ``` Partition 0: id < 1 or id is null Partition 1: id >= 1 AND id < 1 Partition 2: id >= 1 AND id < 1 Partition 3: id >= 1 AND id < 1 Partition 4: id >= 1 AND id < 1 Partition 5: id >= 1 AND id < 1 Partition 6: id >= 1 AND id < 1 Partition 7: id >= 1 AND id < 1 Partition 8: id >= 1 AND id < 1 Partition 9: id >= 1 ``` **After code changes:** Adjust `numPartitions` and can return the correct answers: ``` Partition 0: id < 2 or id is null Partition 1: id >= 2 AND id < 3 Partition 2: id >= 3 AND id < 4 Partition 3: id >= 4 ``` **Issue 3: java.lang.ArithmeticException when numPartitions is zero** ```Scala spark.read.jdbc( url = urlWithUserAndPass, table = "TEST.seq", columnName = "id", lowerBound = 0, upperBound = 4, numPartitions = 0, connectionProperties = new Properties) ``` **Before code changes:** Got the following exception: ``` java.lang.ArithmeticException: / by zero ``` **After code changes:** Able to return a correct answer by disabling column partitioning when numPartitions is equal to or less than zero #### How was this patch tested? Added test cases to verify the results Author: gatorsmile <gatorsmile@gmail.com> Closes #13773 from gatorsmile/jdbcPartitioning.
* [SPARK-13792][SQL] Limit logging of bad records in CSV data sourceReynold Xin2016-06-204-15/+40
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This pull request adds a new option (maxMalformedLogPerPartition) in CSV reader to limit the maximum of logging message Spark generates per partition for malformed records. The error log looks something like ``` 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4 16/06/20 18:50:14 WARN CSVRelation: More than 10 malformed records have been found on this partition. Malformed records from now on will not be logged. ``` Closes #12173 ## How was this patch tested? Manually tested. Author: Reynold Xin <rxin@databricks.com> Closes #13795 from rxin/SPARK-13792.
* [SPARK-16061][SQL][MINOR] The property ↵Kousuke Saruta2016-06-201-1/+1
| | | | | | | | | | | | | | "spark.streaming.stateStore.maintenanceInterval" should be renamed to "spark.sql.streaming.stateStore.maintenanceInterval" ## What changes were proposed in this pull request? The property spark.streaming.stateStore.maintenanceInterval should be renamed and harmonized with other properties related to Structured Streaming like spark.sql.streaming.stateStore.minDeltasForSnapshot. ## How was this patch tested? Existing unit tests. Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #13777 from sarutak/SPARK-16061.
* [SPARK-15982][SPARK-16009][SPARK-16007][SQL] Harmonize the behavior of ↵Tathagata Das2016-06-203-56/+420
| | | | | | | | | | | | | | | | | | | | | | | | | DataFrameReader.text/csv/json/parquet/orc ## What changes were proposed in this pull request? Issues with current reader behavior. - `text()` without args returns an empty DF with no columns -> inconsistent, its expected that text will always return a DF with `value` string field, - `textFile()` without args fails with exception because of the above reason, it expected the DF returned by `text()` to have a `value` field. - `orc()` does not have var args, inconsistent with others - `json(single-arg)` was removed, but that caused source compatibility issues - [SPARK-16009](https://issues.apache.org/jira/browse/SPARK-16009) - user specified schema was not respected when `text/csv/...` were used with no args - [SPARK-16007](https://issues.apache.org/jira/browse/SPARK-16007) The solution I am implementing is to do the following. - For each format, there will be a single argument method, and a vararg method. For json, parquet, csv, text, this means adding json(string), etc.. For orc, this means adding orc(varargs). - Remove the special handling of text(), csv(), etc. that returns empty dataframe with no fields. Rather pass on the empty sequence of paths to the datasource, and let each datasource handle it right. For e.g, text data source, should return empty DF with schema (value: string) - Deduped docs and fixed their formatting. ## How was this patch tested? Added new unit tests for Scala and Java tests Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #13727 from tdas/SPARK-15982.
* [SPARK-16050][TESTS] Remove the flaky test: ConsoleSinkSuiteShixiong Zhu2016-06-201-99/+0
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? ConsoleSinkSuite just collects content from stdout and compare them with the expected string. However, because Spark may not stop some background threads at once, there is a race condition that other threads are outputting logs to **stdout** while ConsoleSinkSuite is running. Then it will make ConsoleSinkSuite fail. Therefore, I just deleted `ConsoleSinkSuite`. If we want to test ConsoleSinkSuite in future, we should refactoring ConsoleSink to make it testable instead of depending on stdout. Therefore, this test is useless and I just delete it. ## How was this patch tested? Just removed a flaky test. Author: Shixiong Zhu <shixiong@databricks.com> Closes #13776 from zsxwing/SPARK-16050.
* [SPARK-16030][SQL] Allow specifying static partitions when inserting to data ↵Yin Huai2016-06-209-25/+436
| | | | | | | | | | | | | | | | source tables ## What changes were proposed in this pull request? This PR adds the static partition support to INSERT statement when the target table is a data source table. ## How was this patch tested? New tests in InsertIntoHiveTableSuite and DataSourceAnalysisSuite. **Note: This PR is based on https://github.com/apache/spark/pull/13766. The last commit is the actual change.** Author: Yin Huai <yhuai@databricks.com> Closes #13769 from yhuai/SPARK-16030-1.
* [SPARK-16036][SPARK-16037][SPARK-16034][SQL] Follow up code clean up and ↵Yin Huai2016-06-1910-61/+98
| | | | | | | | | | | | | | improvement ## What changes were proposed in this pull request? This PR is the follow-up PR for https://github.com/apache/spark/pull/13754/files and https://github.com/apache/spark/pull/13749. I will comment inline to explain my changes. ## How was this patch tested? Existing tests. Author: Yin Huai <yhuai@databricks.com> Closes #13766 from yhuai/caseSensitivity.
* [SPARK-16031] Add debug-only socket source in Structured StreamingMatei Zaharia2016-06-199-0/+293
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch adds a text-based socket source similar to the one in Spark Streaming for debugging and tutorials. The source is clearly marked as debug-only so that users don't try to run it in production applications, because this type of source cannot provide HA without storing a lot of state in Spark. ## How was this patch tested? Unit tests and manual tests in spark-shell. Author: Matei Zaharia <matei@databricks.com> Closes #13748 from mateiz/socket-source.
* [SPARK-15613] [SQL] Fix incorrect days to millis conversion due to Daylight ↵Davies Liu2016-06-194-4/+129
| | | | | | | | | | | | | | | | | | Saving Time ## What changes were proposed in this pull request? Internally, we use Int to represent a date (the days since 1970-01-01), when we convert that into unix timestamp (milli-seconds since epoch in UTC), we get the offset of a timezone using local millis (the milli-seconds since 1970-01-01 in a timezone), but TimeZone.getOffset() expect unix timestamp, the result could be off by one hour (in Daylight Saving Time (DST) or not). This PR change to use best effort approximate of posix timestamp to lookup the offset. In the event of changing of DST, Some time is not defined (for example, 2016-03-13 02:00:00 PST), or could lead to multiple valid result in UTC (for example, 2016-11-06 01:00:00), this best effort approximate should be enough in practice. ## How was this patch tested? Added regression tests. Author: Davies Liu <davies@databricks.com> Closes #13652 from davies/fix_timezone.
* [SPARK-16034][SQL] Checks the partition columns when calling ↵Sean Zhong2016-06-183-22/+50
| | | | | | | | | | | | | | | | dataFrame.write.mode("append").saveAsTable ## What changes were proposed in this pull request? `DataFrameWriter` can be used to append data to existing data source tables. It becomes tricky when partition columns used in `DataFrameWriter.partitionBy(columns)` don't match the actual partition columns of the underlying table. This pull request enforces the check so that the partition columns of these two always match. ## How was this patch tested? Unit test. Author: Sean Zhong <seanzhong@databricks.com> Closes #13749 from clockfly/SPARK-16034.
* [SPARK-16036][SPARK-16037][SQL] fix various table insertion problemsWenchen Fan2016-06-1812-185/+104
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The current table insertion has some weird behaviours: 1. inserting into a partitioned table with mismatch columns has confusing error message for hive table, and wrong result for datasource table 2. inserting into a partitioned table without partition list has wrong result for hive table. This PR fixes these 2 problems. ## How was this patch tested? new test in hive `SQLQuerySuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #13754 from cloud-fan/insert2.
* [SPARK-16023][SQL] Move InMemoryRelation to its own fileAndrew Or2016-06-172-185/+211
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Improve readability of `InMemoryTableScanExec.scala`, which has too much stuff in it. ## How was this patch tested? Jenkins Author: Andrew Or <andrew@databricks.com> Closes #13742 from andrewor14/move-inmemory-relation.
* [SPARK-16020][SQL] Fix complete mode aggregation with console sinkShixiong Zhu2016-06-173-1/+105
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? We cannot use `limit` on DataFrame in ConsoleSink because it will use a wrong planner. This PR just collects `DataFrame` and calls `show` on a batch DataFrame based on the result. This is fine since ConsoleSink is only for debugging. ## How was this patch tested? Manually confirmed ConsoleSink now works with complete mode aggregation. Author: Shixiong Zhu <shixiong@databricks.com> Closes #13740 from zsxwing/complete-console.
* [SPARK-15159][SPARKR] SparkR SparkSession APIFelix Cheung2016-06-171-12/+64
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR introduces the new SparkSession API for SparkR. `sparkR.session.getOrCreate()` and `sparkR.session.stop()` "getOrCreate" is a bit unusual in R but it's important to name this clearly. SparkR implementation should - SparkSession is the main entrypoint (vs SparkContext; due to limited functionality supported with SparkContext in SparkR) - SparkSession replaces SQLContext and HiveContext (both a wrapper around SparkSession, and because of API changes, supporting all 3 would be a lot more work) - Changes to SparkSession is mostly transparent to users due to SPARK-10903 - Full backward compatibility is expected - users should be able to initialize everything just in Spark 1.6.1 (`sparkR.init()`), but with deprecation warning - Mostly cosmetic changes to parameter list - users should be able to move to `sparkR.session.getOrCreate()` easily - An advanced syntax with named parameters (aka varargs aka "...") is supported; that should be closer to the Builder syntax that is in Scala/Python (which unfortunately does not work in R because it will look like this: `enableHiveSupport(config(config(master(appName(builder(), "foo"), "local"), "first", "value"), "next, "value"))` - Updating config on an existing SparkSession is supported, the behavior is the same as Python, in which config is applied to both SparkContext and SparkSession - Some SparkSession changes are not matched in SparkR, mostly because it would be breaking API change: `catalog` object, `createOrReplaceTempView` - Other SQLContext workarounds are replicated in SparkR, eg. `tables`, `tableNames` - `sparkR` shell is updated to use the SparkSession entrypoint (`sqlContext` is removed, just like with Scale/Python) - All tests are updated to use the SparkSession entrypoint - A bug in `read.jdbc` is fixed TODO - [x] Add more tests - [ ] Separate PR - update all roxygen2 doc coding example - [ ] Separate PR - update SparkR programming guide ## How was this patch tested? unit tests, manual tests shivaram sun-rui rxin Author: Felix Cheung <felixcheung_m@hotmail.com> Author: felixcheung <felixcheung_m@hotmail.com> Closes #13635 from felixcheung/rsparksession.
* [SPARK-16033][SQL] insertInto() can't be used together with partitionBy()Cheng Lian2016-06-172-3/+46
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? When inserting into an existing partitioned table, partitioning columns should always be determined by catalog metadata of the existing table to be inserted. Extra `partitionBy()` calls don't make sense, and mess up existing data because newly inserted data may have wrong partitioning directory layout. ## How was this patch tested? New test case added in `InsertIntoHiveTableSuite`. Author: Cheng Lian <lian@databricks.com> Closes #13747 from liancheng/spark-16033-insert-into-without-partition-by.
* [SPARK-15916][SQL] JDBC filter push down should respect operator precedencehyukjinkwon2016-06-172-2/+28
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes the problem that the precedence order is messed when pushing where-clause expression to JDBC layer. **Case 1:** For sql `select * from table where (a or b) and c`, the where-clause is wrongly converted to JDBC where-clause `a or (b and c)` after filter push down. The consequence is that JDBC may returns less or more rows than expected. **Case 2:** For sql `select * from table where always_false_condition`, the result table may not be empty if the JDBC RDD is partitioned using where-clause: ``` spark.read.jdbc(url, table, predicates = Array("partition 1 where clause", "partition 2 where clause"...) ``` ## How was this patch tested? Unit test. This PR also close #13640 Author: hyukjinkwon <gurwls223@gmail.com> Author: Sean Zhong <seanzhong@databricks.com> Closes #13743 from clockfly/SPARK-15916.
* [SPARK-16014][SQL] Rename optimizer rules to be more consistentReynold Xin2016-06-176-22/+19
| | | | | | | | | | | | ## What changes were proposed in this pull request? This small patch renames a few optimizer rules to make the naming more consistent, e.g. class name start with a verb. The main important "fix" is probably SamplePushDown -> PushProjectThroughSample. SamplePushDown is actually the wrong name, since the rule is not about pushing Sample down. ## How was this patch tested? Updated test cases. Author: Reynold Xin <rxin@databricks.com> Closes #13732 from rxin/SPARK-16014.
* Remove non-obvious conf settings from TPCDS benchmarkSameer Agarwal2016-06-171-2/+0
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? My fault -- these 2 conf entries are mysteriously hidden inside the benchmark code and makes it non-obvious to disable whole stage codegen and/or the vectorized parquet reader. PS: Didn't attach a JIRA as this change should otherwise be a no-op (both these conf are enabled by default in Spark) ## How was this patch tested? N/A Author: Sameer Agarwal <sameer@databricks.com> Closes #13726 from sameeragarwal/tpcds-conf.
* [SPARK-15811][SQL] fix the Python UDF in Scala 2.10Davies Liu2016-06-171-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Iterator can't be serialized in Scala 2.10, we should force it into a array to make sure that . ## How was this patch tested? Build with Scala 2.10 and ran all the Python unit tests manually (will be covered by a jenkins build). Author: Davies Liu <davies@databricks.com> Closes #13717 from davies/fix_udf_210.
* [SPARK-15706][SQL] Fix Wrong Answer when using IF NOT EXISTS in INSERT ↵gatorsmile2016-06-165-5/+85
| | | | | | | | | | | | | | | | OVERWRITE for DYNAMIC PARTITION #### What changes were proposed in this pull request? `IF NOT EXISTS` in `INSERT OVERWRITE` should not support dynamic partitions. If we specify `IF NOT EXISTS`, the inserted statement is not shown in the table. This PR is to issue an exception in this case, just like what Hive does. Also issue an exception if users specify `IF NOT EXISTS` if users do not specify any `PARTITION` specification. #### How was this patch tested? Added test cases into `PlanParserSuite` and `InsertIntoHiveTableSuite` Author: gatorsmile <gatorsmile@gmail.com> Closes #13447 from gatorsmile/insertIfNotExist.
* [SPARK-15822] [SQL] Prevent byte array backed classes from referencing freed ↵Pete Robbins2016-06-162-7/+17
| | | | | | | | | | | | | | | | | | | | | memory ## What changes were proposed in this pull request? `UTF8String` and all `Unsafe*` classes are backed by either on-heap or off-heap byte arrays. The code generated version `SortMergeJoin` buffers the left hand side join keys during iteration. This was actually problematic in off-heap mode when one of the keys is a `UTF8String` (or any other 'Unsafe*` object) and the left hand side iterator was exhausted (and released its memory); the buffered keys would reference freed memory. This causes Seg-faults and all kinds of other undefined behavior when we would use one these buffered keys. This PR fixes this problem by creating copies of the buffered variables. I have added a general method to the `CodeGenerator` for this. I have checked all places in which this could happen, and only `SortMergeJoin` had this problem. This PR is largely based on the work of robbinspg and he should be credited for this. closes https://github.com/apache/spark/pull/13707 ## How was this patch tested? Manually tested on problematic workloads. Author: Pete Robbins <robbinspg@gmail.com> Author: Herman van Hovell <hvanhovell@databricks.com> Closes #13723 from hvanhovell/SPARK-15822-2.
* [SPARK-15991] SparkContext.hadoopConfiguration should be always the base of ↵Yin Huai2016-06-165-17/+28
| | | | | | | | | | | | | | | | hadoop conf created by SessionState ## What changes were proposed in this pull request? Before this patch, after a SparkSession has been created, hadoop conf set directly to SparkContext.hadoopConfiguration will not affect the hadoop conf created by SessionState. This patch makes the change to always use SparkContext.hadoopConfiguration as the base. This patch also changes the behavior of hive-site.xml support added in https://github.com/apache/spark/pull/12689/. With this patch, we will load hive-site.xml to SparkContext.hadoopConfiguration. ## How was this patch tested? New test in SparkSessionBuilderSuite. Author: Yin Huai <yhuai@databricks.com> Closes #13711 from yhuai/SPARK-15991.
* [SPARK-15749][SQL] make the error message more meaningfulHuaxin Gao2016-06-162-3/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? For table test1 (C1 varchar (10), C2 varchar (10)), when I insert a row using ``` sqlContext.sql("insert into test1 values ('abc', 'def', 1)") ``` I got error message ``` Exception in thread "main" java.lang.RuntimeException: RelationC1#0,C2#1 JDBCRelation(test1) requires that the query in the SELECT clause of the INSERT INTO/OVERWRITE statement generates the same number of columns as its schema. ``` The error message is a little confusing. In my simple insert statement, it doesn't have a SELECT clause. I will change the error message to a more general one ``` Exception in thread "main" java.lang.RuntimeException: RelationC1#0,C2#1 JDBCRelation(test1) requires that the data to be inserted have the same number of columns as the target table. ``` ## How was this patch tested? I tested the patch using my simple unit test, but it's a very trivial change and I don't think I need to check in any test. Author: Huaxin Gao <huaxing@us.ibm.com> Closes #13492 from huaxingao/spark-15749.
* [MINOR][DOCS][SQL] Fix some comments about types(TypeCoercion,Partition) and ↵Dongjoon Hyun2016-06-164-5/+5
| | | | | | | | | | | | | | | | | | | exceptions. ## What changes were proposed in this pull request? This PR contains a few changes on code comments. - `HiveTypeCoercion` is renamed into `TypeCoercion`. - `NoSuchDatabaseException` is only used for the absence of database. - For partition type inference, only `DoubleType` is considered. ## How was this patch tested? N/A Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13674 from dongjoon-hyun/minor_doc_types.
* [SPARK-15998][SQL] Verification of SQLConf HIVE_METASTORE_PARTITION_PRUNINGgatorsmile2016-06-161-3/+57
| | | | | | | | | | | | | | #### What changes were proposed in this pull request? `HIVE_METASTORE_PARTITION_PRUNING` is a public `SQLConf`. When `true`, some predicates will be pushed down into the Hive metastore so that unmatching partitions can be eliminated earlier. The current default value is `false`. For performance improvement, users might turn this parameter on. So far, the code base does not have such a test case to verify whether this `SQLConf` properly works. This PR is to improve the test case coverage for avoiding future regression. #### How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #13716 from gatorsmile/addTestMetastorePartitionPruning.
* [SQL] Minor HashAggregateExec string output fixesCheng Lian2016-06-161-5/+5
| | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes some minor `.toString` format issues for `HashAggregateExec`. Before: ``` *HashAggregate(key=[a#234L,b#235L], functions=[count(1),max(c#236L)], output=[a#234L,b#235L,count(c)#247L,max(c)#248L]) ``` After: ``` *HashAggregate(keys=[a#234L, b#235L], functions=[count(1), max(c#236L)], output=[a#234L, b#235L, count(c)#247L, max(c)#248L]) ``` ## How was this patch tested? Manually tested. Author: Cheng Lian <lian@databricks.com> Closes #13710 from liancheng/minor-agg-string-fix.
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