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* [SPARK-13823][SPARK-13397][SPARK-13395][CORE] More warnings, StandardCharset ↵Sean Owen2016-03-1613-21/+33
| | | | | | | | | | | | | | | | | | | | follow up ## What changes were proposed in this pull request? Follow up to https://github.com/apache/spark/pull/11657 - Also update `String.getBytes("UTF-8")` to use `StandardCharsets.UTF_8` - And fix one last new Coverity warning that turned up (use of unguarded `wait()` replaced by simpler/more robust `java.util.concurrent` classes in tests) - And while we're here cleaning up Coverity warnings, just fix about 15 more build warnings ## How was this patch tested? Jenkins tests Author: Sean Owen <sowen@cloudera.com> Closes #11725 from srowen/SPARK-13823.2.
* [SPARK-12653][SQL] Re-enable test "SPARK-8489: MissingRequirementError ↵Dongjoon Hyun2016-03-164-2/+7
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | during reflection" ## What changes were proposed in this pull request? The purpose of [SPARK-12653](https://issues.apache.org/jira/browse/SPARK-12653) is re-enabling a regression test. Historically, the target regression test is added by [SPARK-8498](https://github.com/apache/spark/commit/093c34838d1db7a9375f36a9a2ab5d96a23ae683), but is temporarily disabled by [SPARK-12615](https://github.com/apache/spark/commit/8ce645d4eeda203cf5e100c4bdba2d71edd44e6a) due to binary compatibility error. The following is the current error message at the submitting spark job with the pre-built `test.jar` file in the target regression test. ``` Exception in thread "main" java.lang.NoSuchMethodError: org.apache.spark.SparkContext$.$lessinit$greater$default$6()Lscala/collection/Map; ``` Simple rebuilding `test.jar` can not recover the purpose of testcase since we need to support both Scala 2.10 and 2.11 for a while. For example, we will face the following Scala 2.11 error if we use `test.jar` built by Scala 2.10. ``` Exception in thread "main" java.lang.NoSuchMethodError: scala.reflect.api.JavaUniverse.runtimeMirror(Ljava/lang/ClassLoader;)Lscala/reflect/api/JavaMirrors$JavaMirror; ``` This PR replace the existing `test.jar` with `test-2.10.jar` and `test-2.11.jar` and improve the regression test to use the suitable jar file. ## How was this patch tested? Pass the existing Jenkins test. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11744 from dongjoon-hyun/SPARK-12653.
* [SPARK-13899][SQL] Produce InternalRow instead of external Row at CSV data ↵hyukjinkwon2016-03-154-22/+42
| | | | | | | | | | | | | | | | | | | | source ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-13899 This PR makes CSV data source produce `InternalRow` instead of `Row`. Basically, this resembles JSON data source. It uses the same codes for casting. ## How was this patch tested? Unit tests were used within IDE and code style was checked by `./dev/run_tests`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #11717 from HyukjinKwon/SPARK-13899.
* [SPARK-13917] [SQL] generate broadcast semi joinDavies Liu2016-03-1511-139/+124
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR brings codegen support for broadcast left-semi join. ## How was this patch tested? Existing tests. Added benchmark, the result show 7X speedup. Author: Davies Liu <davies@databricks.com> Closes #11742 from davies/gen_semi.
* [MINOR][TEST][SQL] Remove wrong "expected" parameter in checkNaNWithoutCodegenYucai Yu2016-03-151-1/+0
| | | | | | | | | | | ## What changes were proposed in this pull request? Remove the wrong "expected" parameter in MathFunctionsSuite.scala's checkNaNWithoutCodegen. This function is to check NaN value, so the "expected" parameter is useless. The Callers do not pass "expected" value and the similar function like checkNaNWithGeneratedProjection and checkNaNWithOptimization do not use it also. Author: Yucai Yu <yucai.yu@intel.com> Closes #11718 from yucai/unused_expected.
* [SPARK-13918][SQL] Merge SortMergeJoin and SortMergerOuterJoinDavies Liu2016-03-159-535/+467
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR just move some code from SortMergeOuterJoin into SortMergeJoin. This is for support codegen for outer join. ## How was this patch tested? existing tests. Author: Davies Liu <davies@databricks.com> Closes #11743 from davies/gen_smjouter.
* [SPARK-13895][SQL] DataFrameReader.text should return Dataset[String]Reynold Xin2016-03-153-12/+16
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch changes DataFrameReader.text()'s return type from DataFrame to Dataset[String]. Closes #11731. ## How was this patch tested? Updated existing integration tests to reflect the change. Author: Reynold Xin <rxin@databricks.com> Closes #11739 from rxin/SPARK-13895.
* [SPARK-13896][SQL][STRING] Dataset.toJSON should return DatasetStavros Kontopoulos2016-03-153-8/+10
| | | | | | | | | | | ## What changes were proposed in this pull request? Change the return type of toJson in Dataset class ## How was this patch tested? No additional unit test required. Author: Stavros Kontopoulos <stavros.kontopoulos@typesafe.com> Closes #11732 from skonto/fix_toJson.
* [SPARK-13893][SQL] Remove SQLContext.catalog/analyzer (internal method)Reynold Xin2016-03-1527-99/+105
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Our internal code can go through SessionState.catalog and SessionState.analyzer. This brings two small benefits: 1. Reduces internal dependency on SQLContext. 2. Removes 2 public methods in Java (Java does not obey package private visibility). More importantly, according to the design in SPARK-13485, we'd need to claim this catalog function for the user-facing public functions, rather than having an internal field. ## How was this patch tested? Existing unit/integration test code. Author: Reynold Xin <rxin@databricks.com> Closes #11716 from rxin/SPARK-13893.
* [SPARK-13660][SQL][TESTS] ContinuousQuerySuite floods the logs with garbageXin Ren2016-03-151-2/+2
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Use method 'testQuietly' to avoid ContinuousQuerySuite flooding the console logs with garbage Make ContinuousQuerySuite not output logs to the console. The logs will still output to unit-tests.log. ## How was this patch tested? Just check Jenkins output. Author: Xin Ren <iamshrek@126.com> Closes #11703 from keypointt/SPARK-13660.
* [SPARK-13840][SQL] Split Optimizer Rule ColumnPruning to ColumnPruning and ↵gatorsmile2016-03-154-14/+26
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | EliminateOperator #### What changes were proposed in this pull request? Before this PR, two Optimizer rules `ColumnPruning` and `PushPredicateThroughProject` reverse each other's effects. Optimizer always reaches the max iteration when optimizing some queries. Extra `Project` are found in the plan. For example, below is the optimized plan after reaching 100 iterations: ``` Join Inner, Some((cast(id1#16 as bigint) = id1#18L)) :- Project [id1#16] : +- Filter isnotnull(cast(id1#16 as bigint)) : +- Project [id1#16] : +- Relation[id1#16,newCol#17] JSON part: struct<>, data: struct<id1:int,newCol:int> +- Filter isnotnull(id1#18L) +- Relation[id1#18L] JSON part: struct<>, data: struct<id1:bigint> ``` This PR splits the optimizer rule `ColumnPruning` to `ColumnPruning` and `EliminateOperators` The issue becomes worse when having another rule `NullFiltering`, which could add extra Filters for `IsNotNull`. We have to be careful when introducing extra `Filter` if the benefit is not large enough. Another PR will be submitted by sameeragarwal to handle this issue. cc sameeragarwal marmbrus In addition, `ColumnPruning` should not push `Project` through non-deterministic `Filter`. This could cause wrong results. This will be put in a separate PR. cc davies cloud-fan yhuai #### How was this patch tested? Modified the existing test cases. Author: gatorsmile <gatorsmile@gmail.com> Closes #11682 from gatorsmile/viewDuplicateNames.
* [SPARK-13890][SQL] Remove some internal classes' dependency on SQLContextReynold Xin2016-03-1428-95/+95
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In general it is better for internal classes to not depend on the external class (in this case SQLContext) to reduce coupling between user-facing APIs and the internal implementations. This patch removes SQLContext dependency from some internal classes such as SparkPlanner, SparkOptimizer. As part of this patch, I also removed the following internal methods from SQLContext: ``` protected[sql] def functionRegistry: FunctionRegistry protected[sql] def optimizer: Optimizer protected[sql] def sqlParser: ParserInterface protected[sql] def planner: SparkPlanner protected[sql] def continuousQueryManager protected[sql] def prepareForExecution: RuleExecutor[SparkPlan] ``` ## How was this patch tested? Existing unit/integration tests. Author: Reynold Xin <rxin@databricks.com> Closes #11712 from rxin/sqlContext-planner.
* [SPARK-13870][SQL] Add scalastyle escaping correctly in CVSSuite.scalaDongjoon Hyun2016-03-141-1/+3
| | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? When initial creating `CVSSuite.scala` in SPARK-12833, there was a typo on `scalastyle:on`: `scalstyle:on`. So, it turns off ScalaStyle checking for the rest of the file mistakenly. So, it can not find a violation on the code of `SPARK-12668` added recently. This issue fixes the existing escaping correctly and adds a new escaping for `SPARK-12668` code like the following. ```scala test("test aliases sep and encoding for delimiter and charset") { + // scalastyle:off val cars = sqlContext ... .load(testFile(carsFile8859)) + // scalastyle:on ``` This will prevent future potential problems, too. ## How was this patch tested? Pass the Jenkins test. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11700 from dongjoon-hyun/SPARK-13870.
* [SPARK-13884][SQL] Remove DescribeCommand's dependency on LogicalPlanReynold Xin2016-03-146-46/+49
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch removes DescribeCommand's dependency on LogicalPlan. After this patch, DescribeCommand simply accepts a TableIdentifier. It minimizes the dependency, and blocks my next patch (removes SQLContext dependency from SparkPlanner). ## How was this patch tested? Should be covered by existing unit tests and Hive compatibility tests that run describe table. Author: Reynold Xin <rxin@databricks.com> Closes #11710 from rxin/SPARK-13884.
* [SPARK-13353][SQL] fast serialization for collecting DataFrame/DatasetDavies Liu2016-03-144-6/+74
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? When we call DataFrame/Dataset.collect(), Java serializer (or Kryo Serializer) will be used to serialize the UnsafeRows in executor, then deserialize them into UnsafeRows in driver. Java serializer (and Kyro serializer) are slow on millions rows, because they try to find out the same rows, but usually there is no same rows. This PR will serialize the UnsafeRows as byte array by packing them together, then Java serializer (or Kyro serializer) serialize the bytes very fast (there are fewer blocks and byte array are not compared by content). The UnsafeRow format is highly compressible, the serialized bytes are also compressed (configurable by spark.io.compression.codec). ## How was this patch tested? Existing unit tests. Add a benchmark for collect, before this patch: ``` Intel(R) Core(TM) i7-4558U CPU 2.80GHz collect: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------- collect 1 million 3991 / 4311 0.3 3805.7 1.0X collect 2 millions 10083 / 10637 0.1 9616.0 0.4X collect 4 millions 29551 / 30072 0.0 28182.3 0.1X ``` ``` Intel(R) Core(TM) i7-4558U CPU 2.80GHz collect: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------- collect 1 million 775 / 1170 1.4 738.9 1.0X collect 2 millions 1153 / 1758 0.9 1099.3 0.7X collect 4 millions 4451 / 5124 0.2 4244.9 0.2X ``` We can see about 5-7X speedup. Author: Davies Liu <davies@databricks.com> Closes #11664 from davies/serialize_row.
* [SPARK-13661][SQL] avoid the copy in HashedRelationDavies Liu2016-03-142-4/+9
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Avoid the copy in HashedRelation, since most of the HashedRelation are built with Array[Row], added the copy() for LeftSemiJoinHash. This could help to reduce the memory consumption for Broadcast join. ## How was this patch tested? Existing tests. Author: Davies Liu <davies@databricks.com> Closes #11666 from davies/remove_copy.
* [SPARK-13880][SPARK-13881][SQL] Rename DataFrame.scala Dataset.scala, and ↵Reynold Xin2016-03-152-21/+2
| | | | | | | | | | | | | | | remove LegacyFunctions ## What changes were proposed in this pull request? 1. Rename DataFrame.scala Dataset.scala, since the class is now named Dataset. 2. Remove LegacyFunctions. It was introduced in Spark 1.6 for backward compatibility, and can be removed in Spark 2.0. ## How was this patch tested? Should be covered by existing unit/integration tests. Author: Reynold Xin <rxin@databricks.com> Closes #11704 from rxin/SPARK-13880.
* [SPARK-13791][SQL] Add MetadataLog and HDFSMetadataLogShixiong Zhu2016-03-145-173/+357
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? - Add a MetadataLog interface for metadata reliably storage. - Add HDFSMetadataLog as a MetadataLog implementation based on HDFS. - Update FileStreamSource to use HDFSMetadataLog instead of managing metadata by itself. ## How was this patch tested? unit tests Author: Shixiong Zhu <shixiong@databricks.com> Closes #11625 from zsxwing/metadata-log.
* [SPARK-13882][SQL] Remove org.apache.spark.sql.execution.localReynold Xin2016-03-1430-2060/+0
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? We introduced some local operators in org.apache.spark.sql.execution.local package but never fully wired the engine to actually use these. We still plan to implement a full local mode, but it's probably going to be fairly different from what the current iterator-based local mode would look like. Based on what we know right now, we might want a push-based columnar version of these operators. Let's just remove them for now, and we can always re-introduced them in the future by looking at branch-1.6. ## How was this patch tested? This is simply dead code removal. Author: Reynold Xin <rxin@databricks.com> Closes #11705 from rxin/SPARK-13882.
* [SPARK-13664][SQL] Add a strategy for planning partitioned and bucketed ↵Michael Armbrust2016-03-1421-85/+804
| | | | | | | | | | | | | | | | | | | | | | | | | | | scans of files This PR adds a new strategy, `FileSourceStrategy`, that can be used for planning scans of collections of files that might be partitioned or bucketed. Compared with the existing planning logic in `DataSourceStrategy` this version has the following desirable properties: - It removes the need to have `RDD`, `broadcastedHadoopConf` and other distributed concerns in the public API of `org.apache.spark.sql.sources.FileFormat` - Partition column appending is delegated to the format to avoid an extra copy / devectorization when appending partition columns - It minimizes the amount of data that is shipped to each executor (i.e. it does not send the whole list of files to every worker in the form of a hadoop conf) - it natively supports bucketing files into partitions, and thus does not require coalescing / creating a `UnionRDD` with the correct partitioning. - Small files are automatically coalesced into fewer tasks using an approximate bin-packing algorithm. Currently only a testing source is planned / tested using this strategy. In follow-up PRs we will port the existing formats to this API. A stub for `FileScanRDD` is also added, but most methods remain unimplemented. Other minor cleanups: - partition pruning is pushed into `FileCatalog` so both the new and old code paths can use this logic. This will also allow future implementations to use indexes or other tricks (i.e. a MySQL metastore) - The partitions from the `FileCatalog` now propagate information about file sizes all the way up to the planner so we can intelligently spread files out. - `Array` -> `Seq` in some internal APIs to avoid unnecessary `toArray` calls - Rename `Partition` to `PartitionDirectory` to differentiate partitions used earlier in pruning from those where we have already enumerated the files and their sizes. Author: Michael Armbrust <michael@databricks.com> Closes #11646 from marmbrus/fileStrategy.
* [SPARK-13626][CORE] Avoid duplicate config deprecation warnings.Marcelo Vanzin2016-03-145-8/+21
| | | | | | | | | | | | | | | | | | | | | | | | Three different things were needed to get rid of spurious warnings: - silence deprecation warnings when cloning configuration - change the way SparkHadoopUtil instantiates SparkConf to silence warnings - avoid creating new SparkConf instances where it's not needed. On top of that, I changed the way that Logging.scala detects the repl; now it uses a method that is overridden in the repl's Main class, and the hack in Utils.scala is not needed anymore. This makes the 2.11 repl behave like the 2.10 one and set the default log level to WARN, which is a lot better. Previously, this wasn't working because the 2.11 repl triggers log initialization earlier than the 2.10 one. I also removed and simplified some other code in the 2.11 repl's Main to avoid replicating logic that already exists elsewhere in Spark. Tested the 2.11 repl in local and yarn modes. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #11510 from vanzin/SPARK-13626.
* [SPARK-13658][SQL] BooleanSimplification rule is slow with large boolean ↵Liang-Chi Hsieh2016-03-142-29/+27
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | expressions JIRA: https://issues.apache.org/jira/browse/SPARK-13658 ## What changes were proposed in this pull request? Quoted from JIRA description: When run TPCDS Q3 [1] with lots predicates to filter out the partitions, the optimizer rule BooleanSimplification take about 2 seconds (it use lots of sematicsEqual, which require copy the whole tree). It will great if we could speedup it. [1] https://github.com/cloudera/impala-tpcds-kit/blob/master/queries/q3.sql How to speed up it: When we ask the canonicalized expression in `Expression`, it calls `Canonicalize.execute` on itself. `Canonicalize.execute` basically transforms up all expressions included in this expression. However, we don't keep the canonicalized versions for these children expressions. So in next time we ask the canonicalized expressions for the children expressions (e.g., `BooleanSimplification`), we will rerun `Canonicalize.execute` on each of them. It wastes much time. By forcing the children expressions to get and keep their canonicalized versions first, we can avoid re-canonicalize these expressions. I simply benchmark it with an expression which is part of the where clause in TPCDS Q3: val testRelation = LocalRelation('ss_sold_date_sk.int, 'd_moy.int, 'i_manufact_id.int, 'ss_item_sk.string, 'i_item_sk.string, 'd_date_sk.int) val input = ('d_date_sk === 'ss_sold_date_sk) && ('ss_item_sk === 'i_item_sk) && ('i_manufact_id === 436) && ('d_moy === 12) && (('ss_sold_date_sk > 2415355 && 'ss_sold_date_sk < 2415385) || ('ss_sold_date_sk > 2415720 && 'ss_sold_date_sk < 2415750) || ('ss_sold_date_sk > 2416085 && 'ss_sold_date_sk < 2416115) || ('ss_sold_date_sk > 2416450 && 'ss_sold_date_sk < 2416480) || ('ss_sold_date_sk > 2416816 && 'ss_sold_date_sk < 2416846) || ('ss_sold_date_sk > 2417181 && 'ss_sold_date_sk < 2417211) || ('ss_sold_date_sk > 2417546 && 'ss_sold_date_sk < 2417576) || ('ss_sold_date_sk > 2417911 && 'ss_sold_date_sk < 2417941) || ('ss_sold_date_sk > 2418277 && 'ss_sold_date_sk < 2418307) || ('ss_sold_date_sk > 2418642 && 'ss_sold_date_sk < 2418672) || ('ss_sold_date_sk > 2419007 && 'ss_sold_date_sk < 2419037) || ('ss_sold_date_sk > 2419372 && 'ss_sold_date_sk < 2419402) || ('ss_sold_date_sk > 2419738 && 'ss_sold_date_sk < 2419768) || ('ss_sold_date_sk > 2420103 && 'ss_sold_date_sk < 2420133) || ('ss_sold_date_sk > 2420468 && 'ss_sold_date_sk < 2420498) || ('ss_sold_date_sk > 2420833 && 'ss_sold_date_sk < 2420863) || ('ss_sold_date_sk > 2421199 && 'ss_sold_date_sk < 2421229) || ('ss_sold_date_sk > 2421564 && 'ss_sold_date_sk < 2421594) || ('ss_sold_date_sk > 2421929 && 'ss_sold_date_sk < 2421959) || ('ss_sold_date_sk > 2422294 && 'ss_sold_date_sk < 2422324) || ('ss_sold_date_sk > 2422660 && 'ss_sold_date_sk < 2422690) || ('ss_sold_date_sk > 2423025 && 'ss_sold_date_sk < 2423055) || ('ss_sold_date_sk > 2423390 && 'ss_sold_date_sk < 2423420) || ('ss_sold_date_sk > 2423755 && 'ss_sold_date_sk < 2423785) || ('ss_sold_date_sk > 2424121 && 'ss_sold_date_sk < 2424151) || ('ss_sold_date_sk > 2424486 && 'ss_sold_date_sk < 2424516) || ('ss_sold_date_sk > 2424851 && 'ss_sold_date_sk < 2424881) || ('ss_sold_date_sk > 2425216 && 'ss_sold_date_sk < 2425246) || ('ss_sold_date_sk > 2425582 && 'ss_sold_date_sk < 2425612) || ('ss_sold_date_sk > 2425947 && 'ss_sold_date_sk < 2425977) || ('ss_sold_date_sk > 2426312 && 'ss_sold_date_sk < 2426342) || ('ss_sold_date_sk > 2426677 && 'ss_sold_date_sk < 2426707) || ('ss_sold_date_sk > 2427043 && 'ss_sold_date_sk < 2427073) || ('ss_sold_date_sk > 2427408 && 'ss_sold_date_sk < 2427438) || ('ss_sold_date_sk > 2427773 && 'ss_sold_date_sk < 2427803) || ('ss_sold_date_sk > 2428138 && 'ss_sold_date_sk < 2428168) || ('ss_sold_date_sk > 2428504 && 'ss_sold_date_sk < 2428534) || ('ss_sold_date_sk > 2428869 && 'ss_sold_date_sk < 2428899) || ('ss_sold_date_sk > 2429234 && 'ss_sold_date_sk < 2429264) || ('ss_sold_date_sk > 2429599 && 'ss_sold_date_sk < 2429629) || ('ss_sold_date_sk > 2429965 && 'ss_sold_date_sk < 2429995) || ('ss_sold_date_sk > 2430330 && 'ss_sold_date_sk < 2430360) || ('ss_sold_date_sk > 2430695 && 'ss_sold_date_sk < 2430725) || ('ss_sold_date_sk > 2431060 && 'ss_sold_date_sk < 2431090) || ('ss_sold_date_sk > 2431426 && 'ss_sold_date_sk < 2431456) || ('ss_sold_date_sk > 2431791 && 'ss_sold_date_sk < 2431821) || ('ss_sold_date_sk > 2432156 && 'ss_sold_date_sk < 2432186) || ('ss_sold_date_sk > 2432521 && 'ss_sold_date_sk < 2432551) || ('ss_sold_date_sk > 2432887 && 'ss_sold_date_sk < 2432917) || ('ss_sold_date_sk > 2433252 && 'ss_sold_date_sk < 2433282) || ('ss_sold_date_sk > 2433617 && 'ss_sold_date_sk < 2433647) || ('ss_sold_date_sk > 2433982 && 'ss_sold_date_sk < 2434012) || ('ss_sold_date_sk > 2434348 && 'ss_sold_date_sk < 2434378) || ('ss_sold_date_sk > 2434713 && 'ss_sold_date_sk < 2434743))) val plan = testRelation.where(input).analyze val actual = Optimize.execute(plan) With this patch: 352 milliseconds 346 milliseconds 340 milliseconds Without this patch: 585 milliseconds 880 milliseconds 677 milliseconds ## How was this patch tested? Existing tests should pass. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #11647 from viirya/improve-expr-canonicalize.
* [SPARK-13139][SQL] Follow-ups to #11573Andrew Or2016-03-144-68/+94
| | | | | | | | | | Addressing outstanding comments in #11573. Jenkins, new test case in `DDLCommandSuite` Author: Andrew Or <andrew@databricks.com> Closes #11667 from andrewor14/ddl-parser-followups.
* [SPARK-13207][SQL] Make partitioning discovery ignore _SUCCESS files.Yin Huai2016-03-142-9/+44
| | | | | | | | | | | | | | If a _SUCCESS appears in the inner partitioning dir, partition discovery will treat that _SUCCESS file as a data file. Then, partition discovery will fail because it finds that the dir structure is not valid. We should ignore those `_SUCCESS` files. In future, it is better to ignore all files/dirs starting with `_` or `.`. This PR does not make this change. I am thinking about making this change simple, so we can consider of getting it in branch 1.6. To ignore all files/dirs starting with `_` or `, the main change is to let ParquetRelation have another way to get metadata files. Right now, it relies on FileStatusCache's cachedLeafStatuses, which returns file statuses of both metadata files (e.g. metadata files used by parquet) and data files, which requires more changes. https://issues.apache.org/jira/browse/SPARK-13207 Author: Yin Huai <yhuai@databricks.com> Closes #11088 from yhuai/SPARK-13207.
* [MINOR][DOCS] Fix more typos in comments/strings.Dongjoon Hyun2016-03-1439-44/+44
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes 135 typos over 107 files: * 121 typos in comments * 11 typos in testcase name * 3 typos in log messages ## How was this patch tested? Manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11689 from dongjoon-hyun/fix_more_typos.
* [SPARK-13823][CORE][STREAMING][SQL] Always specify Charset in String <-> ↵Sean Owen2016-03-1329-61/+102
| | | | | | | | | | | | | | | | | | | | byte[] conversions (and remaining Coverity items) ## What changes were proposed in this pull request? - Fixes calls to `new String(byte[])` or `String.getBytes()` that rely on platform default encoding, to use UTF-8 - Same for `InputStreamReader` and `OutputStreamWriter` constructors - Standardizes on UTF-8 everywhere - Standardizes specifying the encoding with `StandardCharsets.UTF-8`, not the Guava constant or "UTF-8" (which means handling `UnuspportedEncodingException`) - (also addresses the other remaining Coverity scan issues, which are pretty trivial; these are separated into commit https://github.com/srowen/spark/commit/1deecd8d9ca986d8adb1a42d315890ce5349d29c ) ## How was this patch tested? Jenkins tests Author: Sean Owen <sowen@cloudera.com> Closes #11657 from srowen/SPARK-13823.
* [SQL] fix typo in DataSourceRegisterJacky Li2016-03-131-1/+1
| | | | | | | | | | | | | ## What changes were proposed in this pull request? fix typo in DataSourceRegister ## How was this patch tested? found when going through latest code Author: Jacky Li <jacky.likun@huawei.com> Closes #11686 from jackylk/patch-12.
* [SPARK-13841][SQL] Removes Dataset.collectRows()/takeRows()Cheng Lian2016-03-133-39/+22
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR removes two methods, `collectRows()` and `takeRows()`, from `Dataset[T]`. These methods were added in PR #11443, and were later considered not useful. ## How was this patch tested? Existing tests should do the work. Author: Cheng Lian <lian@databricks.com> Closes #11678 from liancheng/remove-collect-rows-and-take-rows.
* [SPARK-13828][SQL] Bring back stack trace of AnalysisException thrown from ↵Cheng Lian2016-03-122-2/+13
| | | | | | | | | | | | QueryExecution.assertAnalyzed PR #11443 added an extra `plan: Option[LogicalPlan]` argument to `AnalysisException` and attached partially analyzed plan to thrown `AnalysisException` in `QueryExecution.assertAnalyzed()`. However, the original stack trace wasn't properly inherited. This PR fixes this issue by inheriting the stack trace. A test case is added to verify that the first entry of `AnalysisException` stack trace isn't from `QueryExecution`. Author: Cheng Lian <lian@databricks.com> Closes #11677 from liancheng/analysis-exception-stacktrace.
* [SPARK-13671] [SPARK-13311] [SQL] Use different physical plans for RDD and ↵Davies Liu2016-03-1211-62/+109
| | | | | | | | | | | | | | | | | | | | data sources ## What changes were proposed in this pull request? This PR split the PhysicalRDD into two classes, PhysicalRDD and PhysicalScan. PhysicalRDD is used for DataFrames that is created from existing RDD. PhysicalScan is used for DataFrame that is created from data sources. This enable use to apply different optimization on both of them. Also fix the problem for sameResult() on two DataSourceScan. Also fix the equality check to toString for `In`. It's better to use Seq there, but we can't break this public API (sad). ## How was this patch tested? Existing tests. Manually tested with TPCDS query Q59 and Q64, all those duplicated exchanges can be re-used now, also saw there are 40+% performance improvement (saving half of the scan). Author: Davies Liu <davies@databricks.com> Closes #11514 from davies/existing_rdd.
* [SPARK-13139][SQL] Parse Hive DDL commands ourselvesAndrew Or2016-03-118-36/+1318
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch is ported over from viirya's changes in #11048. Currently for most DDLs we just pass the query text directly to Hive. Instead, we should parse these commands ourselves and in the future (not part of this patch) use the `HiveCatalog` to process these DDLs. This is a pretext to merging `SQLContext` and `HiveContext`. Note: As of this patch we still pass the query text to Hive. The difference is that we now parse the commands ourselves so in the future we can just use our own catalog. ## How was this patch tested? Jenkins, new `DDLCommandSuite`, which comprises of about 40% of the changes here. Author: Andrew Or <andrew@databricks.com> Closes #11573 from andrewor14/parser-plus-plus.
* [SPARK-13780][SQL] Add missing dependency to build.Marcelo Vanzin2016-03-111-0/+4
| | | | | | | | | | This is needed to avoid odd compiler errors when building just the sql package with maven, because of odd interactions between scalac and shaded classes. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #11640 from vanzin/SPARK-13780.
* [SPARK-13817][BUILD][SQL] Re-enable MiMA and removes object DataFrameCheng Lian2016-03-1121-57/+55
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? PR #11443 temporarily disabled MiMA check, this PR re-enables it. One extra change is that `object DataFrame` is also removed. The only purpose of introducing `object DataFrame` was to use it as an internal factory for creating `Dataset[Row]`. By replacing this internal factory with `Dataset.newDataFrame`, both `DataFrame` and `DataFrame$` are entirely removed from the API, so that we can simply put a `MissingClassProblem` filter in `MimaExcludes.scala` for most DataFrame API changes. ## How was this patch tested? Tested by MiMA check triggered by Jenkins. Author: Cheng Lian <lian@databricks.com> Closes #11656 from liancheng/re-enable-mima.
* [HOT-FIX] fix compileWenchen Fan2016-03-111-2/+2
| | | | | | | | Fix the compilation failure introduced by https://github.com/apache/spark/pull/11555 because of a merge conflict. Author: Wenchen Fan <wenchen@databricks.com> Closes #11648 from cloud-fan/hotbug.
* [SPARK-12718][SPARK-13720][SQL] SQL generation support for window functionsWenchen Fan2016-03-116-39/+236
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add SQL generation support for window functions. The idea is simple, just treat `Window` operator like `Project`, i.e. add subquery to its child when necessary, generate a `SELECT ... FROM ...` SQL string, implement `sql` method for window related expressions, e.g. `WindowSpecDefinition`, `WindowFrame`, etc. This PR also fixed SPARK-13720 by improving the process of adding extra `SubqueryAlias`(the `RecoverScopingInfo` rule). Before this PR, we update the qualifiers in project list while adding the subquery. However, this is incomplete as we need to update qualifiers in all ancestors that refer attributes here. In this PR, we split `RecoverScopingInfo` into 2 rules: `AddSubQuery` and `UpdateQualifier`. `AddSubQuery` only add subquery if necessary, and `UpdateQualifier` will re-propagate and update qualifiers bottom up. Ideally we should put the bug fix part in an individual PR, but this bug also blocks the window stuff, so I put them together here. Many thanks to gatorsmile for the initial discussion and test cases! ## How was this patch tested? new tests in `LogicalPlanToSQLSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #11555 from cloud-fan/window.
* [SPARK-13732][SPARK-13797][SQL] Remove projectList from Window and Eliminate ↵gatorsmile2016-03-117-27/+94
| | | | | | | | | | | | | | | | | | | | | | | | | useless Window #### What changes were proposed in this pull request? `projectList` is useless. Its value is always the same as the child.output. Remove it from the class `Window`. Removal can simplify the codes in Analyzer and Optimizer. This PR is based on the discussion started by cloud-fan in a separate PR: https://github.com/apache/spark/pull/5604#discussion_r55140466 This PR also eliminates useless `Window`. cloud-fan yhuai #### How was this patch tested? Existing test cases cover it. Author: gatorsmile <gatorsmile@gmail.com> Author: xiaoli <lixiao1983@gmail.com> Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local> Closes #11565 from gatorsmile/removeProjListWindow.
* [SPARK-13789] Infer additional constraints from attribute equalitySameer Agarwal2016-03-102-0/+35
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds support for inferring an additional set of data constraints based on attribute equality. For e.g., if an operator has constraints of the form (`a = 5`, `a = b`), we can now automatically infer an additional constraint of the form `b = 5` ## How was this patch tested? Tested that new constraints are properly inferred for filters (by adding a new test) and equi-joins (by modifying an existing test) Author: Sameer Agarwal <sameer@databricks.com> Closes #11618 from sameeragarwal/infer-isequal-constraints.
* [SPARK-13244][SQL] Migrates DataFrame to DatasetCheng Lian2016-03-1033-1127/+693
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR unifies DataFrame and Dataset by migrating existing DataFrame operations to Dataset and make `DataFrame` a type alias of `Dataset[Row]`. Most Scala code changes are source compatible, but Java API is broken as Java knows nothing about Scala type alias (mostly replacing `DataFrame` with `Dataset<Row>`). There are several noticeable API changes related to those returning arrays: 1. `collect`/`take` - Old APIs in class `DataFrame`: ```scala def collect(): Array[Row] def take(n: Int): Array[Row] ``` - New APIs in class `Dataset[T]`: ```scala def collect(): Array[T] def take(n: Int): Array[T] def collectRows(): Array[Row] def takeRows(n: Int): Array[Row] ``` Two specialized methods `collectRows` and `takeRows` are added because Java doesn't support returning generic arrays. Thus, for example, `DataFrame.collect(): Array[T]` actually returns `Object` instead of `Array<T>` from Java side. Normally, Java users may fall back to `collectAsList` and `takeAsList`. The two new specialized versions are added to avoid performance regression in ML related code (but maybe I'm wrong and they are not necessary here). 1. `randomSplit` - Old APIs in class `DataFrame`: ```scala def randomSplit(weights: Array[Double], seed: Long): Array[DataFrame] def randomSplit(weights: Array[Double]): Array[DataFrame] ``` - New APIs in class `Dataset[T]`: ```scala def randomSplit(weights: Array[Double], seed: Long): Array[Dataset[T]] def randomSplit(weights: Array[Double]): Array[Dataset[T]] ``` Similar problem as above, but hasn't been addressed for Java API yet. We can probably add `randomSplitAsList` to fix this one. 1. `groupBy` Some original `DataFrame.groupBy` methods have conflicting signature with original `Dataset.groupBy` methods. To distinguish these two, typed `Dataset.groupBy` methods are renamed to `groupByKey`. Other noticeable changes: 1. Dataset always do eager analysis now We used to support disabling DataFrame eager analysis to help reporting partially analyzed malformed logical plan on analysis failure. However, Dataset encoders requires eager analysi during Dataset construction. To preserve the error reporting feature, `AnalysisException` now takes an extra `Option[LogicalPlan]` argument to hold the partially analyzed plan, so that we can check the plan tree when reporting test failures. This plan is passed by `QueryExecution.assertAnalyzed`. ## How was this patch tested? Existing tests do the work. ## TODO - [ ] Fix all tests - [ ] Re-enable MiMA check - [ ] Update ScalaDoc (`since`, `group`, and example code) Author: Cheng Lian <lian@databricks.com> Author: Yin Huai <yhuai@databricks.com> Author: Wenchen Fan <wenchen@databricks.com> Author: Cheng Lian <liancheng@users.noreply.github.com> Closes #11443 from liancheng/ds-to-df.
* [SPARK-13751] [SQL] generate better code for FilterDavies Liu2016-03-103-14/+57
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR improve the codegen of Filter by: 1. filter out the rows early if it have null value in it that will cause the condition result in null or false. After this, we could simplify the condition, because the input are not nullable anymore. 2. Split the condition as conjunctive predicates, then check them one by one. Here is a piece of generated code for Filter in TPCDS Q55: ```java /* 109 */ /*** CONSUME: Filter ((((isnotnull(d_moy#149) && isnotnull(d_year#147)) && (d_moy#149 = 11)) && (d_year#147 = 1999)) && isnotnull(d_date_sk#141)) */ /* 110 */ /* input[0, int] */ /* 111 */ boolean project_isNull2 = rdd_row.isNullAt(0); /* 112 */ int project_value2 = project_isNull2 ? -1 : (rdd_row.getInt(0)); /* 113 */ /* input[1, int] */ /* 114 */ boolean project_isNull3 = rdd_row.isNullAt(1); /* 115 */ int project_value3 = project_isNull3 ? -1 : (rdd_row.getInt(1)); /* 116 */ /* input[2, int] */ /* 117 */ boolean project_isNull4 = rdd_row.isNullAt(2); /* 118 */ int project_value4 = project_isNull4 ? -1 : (rdd_row.getInt(2)); /* 119 */ /* 120 */ if (project_isNull3) continue; /* 121 */ if (project_isNull4) continue; /* 122 */ if (project_isNull2) continue; /* 123 */ /* 124 */ /* (input[1, int] = 11) */ /* 125 */ boolean filter_value6 = false; /* 126 */ filter_value6 = project_value3 == 11; /* 127 */ if (!filter_value6) continue; /* 128 */ /* 129 */ /* (input[2, int] = 1999) */ /* 130 */ boolean filter_value9 = false; /* 131 */ filter_value9 = project_value4 == 1999; /* 132 */ if (!filter_value9) continue; /* 133 */ /* 134 */ filter_metricValue1.add(1); /* 135 */ /* 136 */ /*** CONSUME: Project [d_date_sk#141] */ /* 137 */ /* 138 */ project_rowWriter1.write(0, project_value2); /* 139 */ append(project_result1.copy()); ``` ## How was this patch tested? Existing tests. Author: Davies Liu <davies@databricks.com> Closes #11585 from davies/gen_filter.
* [SPARK-3854][BUILD] Scala style: require spaces before `{`.Dongjoon Hyun2016-03-1011-14/+14
| | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Since the opening curly brace, '{', has many usages as discussed in [SPARK-3854](https://issues.apache.org/jira/browse/SPARK-3854), this PR adds a ScalaStyle rule to prevent '){' pattern for the following majority pattern and fixes the code accordingly. If we enforce this in ScalaStyle from now, it will improve the Scala code quality and reduce review time. ``` // Correct: if (true) { println("Wow!") } // Incorrect: if (true){ println("Wow!") } ``` IntelliJ also shows new warnings based on this. ## How was this patch tested? Pass the Jenkins ScalaStyle test. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11637 from dongjoon-hyun/SPARK-3854.
* [SQL][TEST] Increased timeouts to reduce flakiness in ↵Tathagata Das2016-03-101-15/+15
| | | | | | | | | | | | | | | | ContinuousQueryManagerSuite ## What changes were proposed in this pull request? ContinuousQueryManager is sometimes flaky on Jenkins. I could not reproduce it on my machine, so I guess it about the waiting times which causes problems if Jenkins is loaded. I have increased the wait time in the hope that it will be less flaky. ## How was this patch tested? I reran the unit test many times on a loop in my machine. I am going to run it a few time in Jenkins, that's the real test. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #11638 from tdas/cqm-flaky-test.
* [SPARK-13790] Speed up ColumnVector's getDecimalNong Li2016-03-103-3/+14
| | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? We should reuse an object similar to the other non-primitive type getters. For a query that computes averages over decimal columns, this shows a 10% speedup on overall query times. ## How was this patch tested? Existing tests and this benchmark ``` TPCDS Snappy: Best/Avg Time(ms) Rate(M/s) Per Row(ns) -------------------------------------------------------------------------------- q27-agg (master) 10627 / 11057 10.8 92.3 q27-agg (this patch) 9722 / 9832 11.8 84.4 ``` Author: Nong Li <nong@databricks.com> Closes #11624 from nongli/spark-13790.
* [SPARK-13759][SQL] Add IsNotNull constraints for expressions with an inequalitySameer Agarwal2016-03-104-6/+26
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds support for inferring `IsNotNull` constraints from expressions with an `!==`. More specifically, if an operator has a condition on `a !== b`, we know that both `a` and `b` in the operator output can no longer be null. ## How was this patch tested? 1. Modified a test in `ConstraintPropagationSuite` to test for expressions with an inequality. 2. Added a test in `NullFilteringSuite` for making sure an Inner join with a "non-equal" condition appropriately filters out null from their input. cc nongli Author: Sameer Agarwal <sameer@databricks.com> Closes #11594 from sameeragarwal/isnotequal-constraints.
* [SPARK-13636] [SQL] Directly consume UnsafeRow in wholestage codegen plansLiang-Chi Hsieh2016-03-108-23/+43
| | | | | | | | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-13636 ## What changes were proposed in this pull request? As shown in the wholestage codegen verion of Sort operator, when Sort is top of Exchange (or other operator that produce UnsafeRow), we will create variables from UnsafeRow, than create another UnsafeRow using these variables. We should avoid the unnecessary unpack and pack variables from UnsafeRows. ## How was this patch tested? All existing wholestage codegen tests should be passed. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #11484 from viirya/direct-consume-unsaferow.
* [MINOR][SQL] Replace DataFrameWriter.stream() with startStream() in comments.Dongjoon Hyun2016-03-091-3/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? According to #11627 , this PR replace `DataFrameWriter.stream()` with `startStream()` in comments of `ContinuousQueryListener.java`. ## How was this patch tested? Manual. (It changes on comments.) Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11629 from dongjoon-hyun/minor_rename.
* [SPARK-13794][SQL] Rename DataFrameWriter.stream() DataFrameWriter.startStream()Reynold Xin2016-03-092-15/+15
| | | | | | | | | | | | ## What changes were proposed in this pull request? The new name makes it more obvious with the verb "start" that we are actually starting some execution. ## How was this patch tested? This is just a rename. Existing unit tests should cover it. Author: Reynold Xin <rxin@databricks.com> Closes #11627 from rxin/SPARK-13794.
* [SPARK-13766][SQL] Consistent file extensions for files written by internal ↵hyukjinkwon2016-03-099-11/+29
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | data sources ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-13766 This PR makes the file extensions (written by internal datasource) consistent. **Before** - TEXT, CSV and JSON ``` [.COMPRESSION_CODEC_NAME] ``` - Parquet ``` [.COMPRESSION_CODEC_NAME].parquet ``` - ORC ``` .orc ``` **After** - TEXT, CSV and JSON ``` .txt[.COMPRESSION_CODEC_NAME] .csv[.COMPRESSION_CODEC_NAME] .json[.COMPRESSION_CODEC_NAME] ``` - Parquet ``` [.COMPRESSION_CODEC_NAME].parquet ``` - ORC ``` [.COMPRESSION_CODEC_NAME].orc ``` When the compression codec is set, - For Parquet and ORC, each still stays in Parquet and ORC format but just have compressed data internally. So, I think it is okay to name `.parquet` and `.orc` at the end. - For Text, CSV and JSON, each does not stays in each format but it has different data format according to compression codec. So, each has the names `.json`, `.csv` and `.txt` before the compression extension. ## How was this patch tested? Unit tests are used and `./dev/run_tests` for coding style tests. Author: hyukjinkwon <gurwls223@gmail.com> Closes #11604 from HyukjinKwon/SPARK-13766.
* Revert "[SPARK-13760][SQL] Fix BigDecimal constructor for FloatType"Yin Huai2016-03-091-1/+1
| | | | This reverts commit 926e9c45a21c5b71ef0832d63b8dae7d4f3d8826.
* [SPARK-13760][SQL] Fix BigDecimal constructor for FloatTypeSameer Agarwal2016-03-091-1/+1
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? A very minor change for using `BigDecimal.decimal(f: Float)` instead of `BigDecimal(f: float)`. The latter is deprecated and can result in inconsistencies due to an implicit conversion to `Double`. ## How was this patch tested? N/A cc yhuai Author: Sameer Agarwal <sameer@databricks.com> Closes #11597 from sameeragarwal/bigdecimal.
* [SPARK-13747][SQL] Fix concurrent query with fork-join poolAndrew Or2016-03-091-0/+14
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix this use case, which was already fixed in SPARK-10548 in 1.6 but was broken in master due to #9264: ``` (1 to 100).par.foreach { _ => sc.parallelize(1 to 5).map { i => (i, i) }.toDF("a", "b").count() } ``` This threw `IllegalArgumentException` consistently before this patch. For more detail, see the JIRA. ## How was this patch tested? New test in `SQLExecutionSuite`. Author: Andrew Or <andrew@databricks.com> Closes #11586 from andrewor14/fix-concurrent-sql.
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