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* [SPARK-17388] [SQL] Support for inferring type date/timestamp/decimal for ↵hyukjinkwon2016-10-182-4/+59
| | | | | | | | | | | | | | | | | | | | | | partition column ## What changes were proposed in this pull request? Currently, Spark only supports to infer `IntegerType`, `LongType`, `DoubleType` and `StringType`. `DecimalType` is being tried but it seems it never infers type as `DecimalType` as `DoubleType` is being tried first. Also, it seems `DateType` and `TimestampType` could be inferred. As far as I know, it is pretty common to use both for a partition column. This PR fixes the incorrect `DecimalType` try and also adds the support for both `DateType` and `TimestampType` for inferring partition column type. ## How was this patch tested? Unit tests in `ParquetPartitionDiscoverySuite`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #14947 from HyukjinKwon/SPARK-17388.
* [SPARK-17899][SQL][FOLLOW-UP] debug mode should work for corrupted tableWenchen Fan2016-10-182-10/+17
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Debug mode should work for corrupted table, so that we can really debug ## How was this patch tested? new test in `MetastoreDataSourcesSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #15528 from cloud-fan/debug.
* [SQL][STREAMING][TEST] Follow up to remove Option.contains for Scala 2.10 ↵Tathagata Das2016-10-181-1/+3
| | | | | | | | | | | | | | | compatibility ## What changes were proposed in this pull request? Scala 2.10 does not have Option.contains, which broke Scala 2.10 build. ## How was this patch tested? Locally compiled and ran sql/core unit tests in 2.10 Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #15531 from tdas/metrics-flaky-test-fix-1.
* [SQL][STREAMING][TEST] Fix flaky tests in StreamingQueryListenerSuiteLiwei Lin2016-10-184-27/+41
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This work has largely been done by lw-lin in his PR #15497. This is a slight refactoring of it. ## What changes were proposed in this pull request? There were two sources of flakiness in StreamingQueryListener test. - When testing with manual clock, consecutive attempts to advance the clock can occur without the stream execution thread being unblocked and doing some work between the two attempts. Hence the following can happen with the current ManualClock. ``` +-----------------------------------+--------------------------------+ | StreamExecution thread | testing thread | +-----------------------------------+--------------------------------+ | ManualClock.waitTillTime(100) { | | | _isWaiting = true | | | wait(10) | | | still in wait(10) | if (_isWaiting) advance(100) | | still in wait(10) | if (_isWaiting) advance(200) | <- this should be disallowed ! | still in wait(10) | if (_isWaiting) advance(300) | <- this should be disallowed ! | wake up from wait(10) | | | current time is 600 | | | _isWaiting = false | | | } | | +-----------------------------------+--------------------------------+ ``` - Second source of flakiness is that the adding data to memory stream may get processing in any trigger, not just the first trigger. My fix is to make the manual clock wait for the other stream execution thread to start waiting for the clock at the right wait start time. That is, `advance(200)` (see above) will wait for stream execution thread to complete the wait that started at time 0, and start a new wait at time 200 (i.e. time stamp after the previous `advance(100)`). In addition, since this is a feature that is solely used by StreamExecution, I removed all the non-generic code from ManualClock and put them in StreamManualClock inside StreamTest. ## How was this patch tested? Ran existing unit test MANY TIME in Jenkins Author: Tathagata Das <tathagata.das1565@gmail.com> Author: Liwei Lin <lwlin7@gmail.com> Closes #15519 from tdas/metrics-flaky-test-fix.
* Revert "[SPARK-17974] Refactor FileCatalog classes to simplify the ↵Reynold Xin2016-10-1713-303/+354
| | | | | | inheritance tree" This reverts commit 8daa1a29b65a9b5337518458e9ece1619e8a01e3.
* [SPARK-17974] Refactor FileCatalog classes to simplify the inheritance treeEric Liang2016-10-1713-354/+303
| | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This renames `BasicFileCatalog => FileCatalog`, combines `SessionFileCatalog` with `PartitioningAwareFileCatalog`, and removes the old `FileCatalog` trait. In summary, ``` MetadataLogFileCatalog extends PartitioningAwareFileCatalog ListingFileCatalog extends PartitioningAwareFileCatalog PartitioningAwareFileCatalog extends FileCatalog TableFileCatalog extends FileCatalog ``` cc cloud-fan mallman ## How was this patch tested? Existing tests Author: Eric Liang <ekl@databricks.com> Closes #15518 from ericl/refactor-session-file-catalog.
* [SPARK-17620][SQL] Determine Serde by hive.default.fileformat when Creating ↵Dilip Biswal2016-10-173-9/+60
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Hive Serde Tables ## What changes were proposed in this pull request? Reopens the closed PR https://github.com/apache/spark/pull/15190 (Please refer to the above link for review comments on the PR) Make sure the hive.default.fileformat is used to when creating the storage format metadata. Output ``` SQL scala> spark.sql("SET hive.default.fileformat=orc") res1: org.apache.spark.sql.DataFrame = [key: string, value: string] scala> spark.sql("CREATE TABLE tmp_default(id INT)") res2: org.apache.spark.sql.DataFrame = [] ``` Before ```SQL scala> spark.sql("DESC FORMATTED tmp_default").collect.foreach(println) .. [# Storage Information,,] [SerDe Library:,org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe,] [InputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcInputFormat,] [OutputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat,] [Compressed:,No,] [Storage Desc Parameters:,,] [ serialization.format,1,] ``` After ```SQL scala> spark.sql("DESC FORMATTED tmp_default").collect.foreach(println) .. [# Storage Information,,] [SerDe Library:,org.apache.hadoop.hive.ql.io.orc.OrcSerde,] [InputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcInputFormat,] [OutputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat,] [Compressed:,No,] [Storage Desc Parameters:,,] [ serialization.format,1,] ``` ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) Added new tests to HiveDDLCommandSuite, SQLQuerySuite Author: Dilip Biswal <dbiswal@us.ibm.com> Closes #15495 from dilipbiswal/orc2.
* [SPARK-17751][SQL] Remove spark.sql.eagerAnalysis and Output the Plan if ↵gatorsmile2016-10-174-19/+10
| | | | | | | | | | | | | | | | | | | | | | | | | | | | Existed in AnalysisException ### What changes were proposed in this pull request? Dataset always does eager analysis now. Thus, `spark.sql.eagerAnalysis` is not used any more. Thus, we need to remove it. This PR also outputs the plan. Without the fix, the analysis error is like ``` cannot resolve '`k1`' given input columns: [k, v]; line 1 pos 12 ``` After the fix, the analysis error becomes: ``` org.apache.spark.sql.AnalysisException: cannot resolve '`k1`' given input columns: [k, v]; line 1 pos 12; 'Project [unresolvedalias(CASE WHEN ('k1 = 2) THEN 22 WHEN ('k1 = 4) THEN 44 ELSE 0 END, None), v#6] +- SubqueryAlias t +- Project [_1#2 AS k#5, _2#3 AS v#6] +- LocalRelation [_1#2, _2#3] ``` ### How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #15316 from gatorsmile/eagerAnalysis.
* [SPARK-17839][CORE] Use Nio's directbuffer instead of BufferedInputStream in ↵Sital Kedia2016-10-175-4/+279
| | | | | | | | | | | | | | | order to avoid additional copy from os buffer cache to user buffer ## What changes were proposed in this pull request? Currently we use BufferedInputStream to read the shuffle file which copies the file content from os buffer cache to the user buffer. This adds additional latency in reading the spill files. We made a change to use java nio's direct buffer to read the spill files and for certain pipelines spilling significant amount of data, we see up to 7% speedup for the entire pipeline. ## How was this patch tested? Tested by running the job in the cluster and observed up to 7% speedup. Author: Sital Kedia <skedia@fb.com> Closes #15408 from sitalkedia/skedia/nio_spill_read.
* Fix example of tf_idf with minDocFreqMaxime Rihouey2016-10-171-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The python example for tf_idf with the parameter "minDocFreq" is not properly set up because the same variable is used to transform the document for both with and without the "minDocFreq" parameter. The IDF(minDocFreq=2) is stored in the variable "idfIgnore" but then it is the original variable "idf" used to transform the "tf" instead of the "idfIgnore". ## How was this patch tested? Before the results for "tfidf" and "tfidfIgnore" were the same: tfidf: (1048576,[1046921],[3.75828890549]) (1048576,[1046920],[3.75828890549]) (1048576,[1046923],[3.75828890549]) (1048576,[892732],[3.75828890549]) (1048576,[892733],[3.75828890549]) (1048576,[892734],[3.75828890549]) tfidfIgnore: (1048576,[1046921],[3.75828890549]) (1048576,[1046920],[3.75828890549]) (1048576,[1046923],[3.75828890549]) (1048576,[892732],[3.75828890549]) (1048576,[892733],[3.75828890549]) (1048576,[892734],[3.75828890549]) After the fix those are how they should be: tfidf: (1048576,[1046921],[3.75828890549]) (1048576,[1046920],[3.75828890549]) (1048576,[1046923],[3.75828890549]) (1048576,[892732],[3.75828890549]) (1048576,[892733],[3.75828890549]) (1048576,[892734],[3.75828890549]) tfidfIgnore: (1048576,[1046921],[0.0]) (1048576,[1046920],[0.0]) (1048576,[1046923],[0.0]) (1048576,[892732],[0.0]) (1048576,[892733],[0.0]) (1048576,[892734],[0.0]) Author: Maxime Rihouey <maxime.rihouey@gmail.com> Closes #15503 from maximerihouey/patch-1.
* [MINOR][SQL] Add prettyName for current_database functionWeiqing Yang2016-10-161-0/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Added a `prettyname` for current_database function. ## How was this patch tested? Manually. Before: ``` scala> sql("select current_database()").show +-----------------+ |currentdatabase()| +-----------------+ | default| +-----------------+ ``` After: ``` scala> sql("select current_database()").show +------------------+ |current_database()| +------------------+ | default| +------------------+ ``` Author: Weiqing Yang <yangweiqing001@gmail.com> Closes #15506 from weiqingy/prettyName.
* [SPARK-17947][SQL] Add Doc and Comment about spark.sql.debuggatorsmile2016-10-171-0/+3
| | | | | | | | | | | | | | ### What changes were proposed in this pull request? Just document the impact of `spark.sql.debug`: When enabling the debug, Spark SQL internal table properties are not filtered out; however, some related DDL commands (e.g., Analyze Table and CREATE TABLE LIKE) might not work properly. ### How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #15494 from gatorsmile/addDocForSQLDebug.
* [SPARK-17819][SQL] Support default database in connection URIs for Spark ↵Dongjoon Hyun2016-10-162-0/+73
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Thrift Server ## What changes were proposed in this pull request? Currently, Spark Thrift Server ignores the default database in URI. This PR supports that like the following. ```sql $ bin/beeline -u jdbc:hive2://localhost:10000 -e "create database testdb" $ bin/beeline -u jdbc:hive2://localhost:10000/testdb -e "create table t(a int)" $ bin/beeline -u jdbc:hive2://localhost:10000/testdb -e "show tables" ... +------------+--------------+--+ | tableName | isTemporary | +------------+--------------+--+ | t | false | +------------+--------------+--+ 1 row selected (0.347 seconds) $ bin/beeline -u jdbc:hive2://localhost:10000 -e "show tables" ... +------------+--------------+--+ | tableName | isTemporary | +------------+--------------+--+ +------------+--------------+--+ No rows selected (0.098 seconds) ``` ## How was this patch tested? Manual. Note: I tried to add a test case for this, but I cannot found a suitable testsuite for this. I'll add the testcase if some advice is given. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15399 from dongjoon-hyun/SPARK-17819.
* Revert "[SPARK-17637][SCHEDULER] Packed scheduling for Spark tasks across ↵Reynold Xin2016-10-154-266/+19
| | | | | | | | executors" This reverts commit ed1463341455830b8867b721a1b34f291139baf3. The patch merged had obvious quality and documentation issue. The idea is useful, and we should work towards improving its quality and merging it in again.
* [SPARK-17637][SCHEDULER] Packed scheduling for Spark tasks across executorsZhan Zhang2016-10-154-19/+266
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Restructure the code and implement two new task assigner. PackedAssigner: try to allocate tasks to the executors with least available cores, so that spark can release reserved executors when dynamic allocation is enabled. BalancedAssigner: try to allocate tasks to the executors with more available cores in order to balance the workload across all executors. By default, the original round robin assigner is used. We test a pipeline, and new PackedAssigner save around 45% regarding the reserved cpu and memory with dynamic allocation enabled. ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) Both unit test in TaskSchedulerImplSuite and manual tests in production pipeline. Author: Zhan Zhang <zhanzhang@fb.com> Closes #15218 from zhzhan/packed-scheduler.
* [SPARK-17953][DOCUMENTATION] Fix typo in SparkSession scaladocJun Kim2016-10-151-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? ### Before: ```scala SparkSession.builder() .master("local") .appName("Word Count") .config("spark.some.config.option", "some-value"). .getOrCreate() ``` ### After: ```scala SparkSession.builder() .master("local") .appName("Word Count") .config("spark.some.config.option", "some-value") .getOrCreate() ``` There was one unexpected dot! Author: Jun Kim <i2r.jun@gmail.com> Closes #15498 from tae-jun/SPARK-17953.
* [SPARK-16980][SQL] Load only catalog table partition metadata required to ↵Michael Allman2016-10-1437-368/+914
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | answer a query (This PR addresses https://issues.apache.org/jira/browse/SPARK-16980.) ## What changes were proposed in this pull request? In a new Spark session, when a partitioned Hive table is converted to use Spark's `HadoopFsRelation` in `HiveMetastoreCatalog`, metadata for every partition of that table are retrieved from the metastore and loaded into driver memory. In addition, every partition's metadata files are read from the filesystem to perform schema inference. If a user queries such a table with predicates which prune that table's partitions, we would like to be able to answer that query without consulting partition metadata which are not involved in the query. When querying a table with a large number of partitions for some data from a small number of partitions (maybe even a single partition), the current conversion strategy is highly inefficient. I suspect this scenario is not uncommon in the wild. In addition to being inefficient in running time, the current strategy is inefficient in its use of driver memory. When the sum of the number of partitions of all tables loaded in a driver reaches a certain level (somewhere in the tens of thousands), their cached data exhaust all driver heap memory in the default configuration. I suspect this scenario is less common (in that not too many deployments work with tables with tens of thousands of partitions), however this does illustrate how large the memory footprint of this metadata can be. With tables with hundreds or thousands of partitions, I would expect the `HiveMetastoreCatalog` table cache to represent a significant portion of the driver's heap space. This PR proposes an alternative approach. Basically, it makes four changes: 1. It adds a new method, `listPartitionsByFilter` to the Catalyst `ExternalCatalog` trait which returns the partition metadata for a given sequence of partition pruning predicates. 1. It refactors the `FileCatalog` type hierarchy to include a new `TableFileCatalog` to efficiently return files only for partitions matching a sequence of partition pruning predicates. 1. It removes partition loading and caching from `HiveMetastoreCatalog`. 1. It adds a new Catalyst optimizer rule, `PruneFileSourcePartitions`, which applies a plan's partition-pruning predicates to prune out unnecessary partition files from a `HadoopFsRelation`'s underlying file catalog. The net effect is that when a query over a partitioned Hive table is planned, the analyzer retrieves the table metadata from `HiveMetastoreCatalog`. As part of this operation, the `HiveMetastoreCatalog` builds a `HadoopFsRelation` with a `TableFileCatalog`. It does not load any partition metadata or scan any files. The optimizer prunes-away unnecessary table partitions by sending the partition-pruning predicates to the relation's `TableFileCatalog `. The `TableFileCatalog` in turn calls the `listPartitionsByFilter` method on its external catalog. This queries the Hive metastore, passing along those filters. As a bonus, performing partition pruning during optimization leads to a more accurate relation size estimate. This, along with c481bdf, can lead to automatic, safe application of the broadcast optimization in a join where it might previously have been omitted. ## Open Issues 1. This PR omits partition metadata caching. I can add this once the overall strategy for the cold path is established, perhaps in a future PR. 1. This PR removes and omits partitioned Hive table schema reconciliation. As a result, it fails to find Parquet schema columns with upper case letters because of the Hive metastore's case-insensitivity. This issue may be fixed by #14750, but that PR appears to have stalled. ericl has contributed to this PR a workaround for Parquet wherein schema reconciliation occurs at query execution time instead of planning. Whether ORC requires a similar patch is an open issue. 1. This PR omits an implementation of `listPartitionsByFilter` for the `InMemoryCatalog`. 1. This PR breaks parquet log output redirection during query execution. I can work around this by running `Class.forName("org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$")` first thing in a Spark shell session, but I haven't figured out how to fix this properly. ## How was this patch tested? The current Spark unit tests were run, and some ad-hoc tests were performed to validate that only the necessary partition metadata is loaded. Author: Michael Allman <michael@videoamp.com> Author: Eric Liang <ekl@databricks.com> Author: Eric Liang <ekhliang@gmail.com> Closes #14690 from mallman/spark-16980-lazy_partition_fetching.
* [SPARK-17946][PYSPARK] Python crossJoin API similar to ScalaSrinath Shankar2016-10-143-7/+36
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add a crossJoin function to the DataFrame API similar to that in Scala. Joins with no condition (cartesian products) must be specified with the crossJoin API ## How was this patch tested? Added python tests to ensure that an AnalysisException if a cartesian product is specified without crossJoin(), and that cartesian products can execute if specified via crossJoin() (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request. Author: Srinath Shankar <srinath@databricks.com> Closes #15493 from srinathshankar/crosspython.
* [SPARK-17900][SQL] Graduate a list of Spark SQL APIs to stableReynold Xin2016-10-149-62/+51
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch graduates a list of Spark SQL APIs and mark them stable. The following are marked stable: Dataset/DataFrame - functions, since 1.3 - ColumnName, since 1.3 - DataFrameNaFunctions, since 1.3.1 - DataFrameStatFunctions, since 1.4 - UserDefinedFunction, since 1.3 - UserDefinedAggregateFunction, since 1.5 - Window and WindowSpec, since 1.4 Data sources: - DataSourceRegister, since 1.5 - RelationProvider, since 1.3 - SchemaRelationProvider, since 1.3 - CreatableRelationProvider, since 1.3 - BaseRelation, since 1.3 - TableScan, since 1.3 - PrunedScan, since 1.3 - PrunedFilteredScan, since 1.3 - InsertableRelation, since 1.3 The following are kept experimental / evolving: Data sources: - CatalystScan (tied to internal logical plans so it is not stable by definition) Structured streaming: - all classes (introduced new in 2.0 and will likely change) Dataset typed operations (introduced in 1.6 and 2.0 and might change, although probability is low) - all typed methods on Dataset - KeyValueGroupedDataset - o.a.s.sql.expressions.javalang.typed - o.a.s.sql.expressions.scalalang.typed - methods that return typed Dataset in SparkSession We should discuss more whether we want to mark Dataset typed operations stable in 2.1. ## How was this patch tested? N/A - just annotation changes. Author: Reynold Xin <rxin@databricks.com> Closes #15469 from rxin/SPARK-17900.
* [SPARK-11775][PYSPARK][SQL] Allow PySpark to register Java UDFJeff Zhang2016-10-145-4/+152
| | | | | | | | | | Currently pyspark can only call the builtin java UDF, but can not call custom java UDF. It would be better to allow that. 2 benefits: * Leverage the power of rich third party java library * Improve the performance. Because if we use python UDF, python daemons will be started on worker which will affect the performance. Author: Jeff Zhang <zjffdu@apache.org> Closes #9766 from zjffdu/SPARK-11775.
* [SPARK-16063][SQL] Add storageLevel to DatasetNick Pentreath2016-10-143-16/+68
| | | | | | | | | | | | [SPARK-11905](https://issues.apache.org/jira/browse/SPARK-11905) added support for `persist`/`cache` for `Dataset`. However, there is no user-facing API to check if a `Dataset` is cached and if so what the storage level is. This PR adds `getStorageLevel` to `Dataset`, analogous to `RDD.getStorageLevel`. Updated `DatasetCacheSuite`. Author: Nick Pentreath <nickp@za.ibm.com> Closes #13780 from MLnick/ds-storagelevel. Signed-off-by: Michael Armbrust <michael@databricks.com>
* [SPARK-17863][SQL] should not add column into DistinctDavies Liu2016-10-142-0/+26
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? We are trying to resolve the attribute in sort by pulling up some column for grandchild into child, but that's wrong when the child is Distinct, because the added column will change the behavior of Distinct, we should not do that. ## How was this patch tested? Added regression test. Author: Davies Liu <davies@databricks.com> Closes #15489 from davies/order_distinct.
* Revert "[SPARK-17620][SQL] Determine Serde by hive.default.fileformat when ↵Yin Huai2016-10-143-60/+9
| | | | | | Creating Hive Serde Tables" This reverts commit 7ab86244e30ca81eb4fa40ea77b4c2b8881cbab2.
* [SPARK-17620][SQL] Determine Serde by hive.default.fileformat when Creating ↵Dilip Biswal2016-10-143-9/+60
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Hive Serde Tables ## What changes were proposed in this pull request? Make sure the hive.default.fileformat is used to when creating the storage format metadata. Output ``` SQL scala> spark.sql("SET hive.default.fileformat=orc") res1: org.apache.spark.sql.DataFrame = [key: string, value: string] scala> spark.sql("CREATE TABLE tmp_default(id INT)") res2: org.apache.spark.sql.DataFrame = [] ``` Before ```SQL scala> spark.sql("DESC FORMATTED tmp_default").collect.foreach(println) .. [# Storage Information,,] [SerDe Library:,org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe,] [InputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcInputFormat,] [OutputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat,] [Compressed:,No,] [Storage Desc Parameters:,,] [ serialization.format,1,] ``` After ```SQL scala> spark.sql("DESC FORMATTED tmp_default").collect.foreach(println) .. [# Storage Information,,] [SerDe Library:,org.apache.hadoop.hive.ql.io.orc.OrcSerde,] [InputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcInputFormat,] [OutputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat,] [Compressed:,No,] [Storage Desc Parameters:,,] [ serialization.format,1,] ``` ## How was this patch tested? Added new tests to HiveDDLCommandSuite Author: Dilip Biswal <dbiswal@us.ibm.com> Closes #15190 from dilipbiswal/orc.
* [SPARK-17941][ML][TEST] Logistic regression tests should use sample weights.sethah2016-10-141-745/+748
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The sample weight testing for logistic regressions is not robust. Logistic regression suite already has many test cases comparing results to R glmnet. Since both libraries support sample weights, we should use sample weights in the test to increase coverage for sample weighting. This patch doesn't really add any code and makes the testing more complete. Also fixed some errors with the R code that was referenced in the test suit. Changed `standardization=T` to `standardize=T` since the former is invalid. ## How was this patch tested? Existing unit tests are modified. No non-test code is touched. Author: sethah <seth.hendrickson16@gmail.com> Closes #15488 from sethah/logreg_weight_tests.
* [TEST] Ignore flaky test in StreamingQueryListenerSuiteTathagata Das2016-10-141-1/+1
| | | | | | | | | | | | ## What changes were proposed in this pull request? Ignoring the flaky test introduced in #15307 https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7/1736/testReport/junit/org.apache.spark.sql.streaming/StreamingQueryListenerSuite/single_listener__check_trigger_statuses/ Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #15491 from tdas/metrics-flaky-test.
* Typo: form -> fromAndrew Ash2016-10-141-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Minor typo fix ## How was this patch tested? Existing unit tests on Jenkins Author: Andrew Ash <andrew@andrewash.com> Closes #15486 from ash211/patch-8.
* [DOC] Fix typo in sql hive docDhruve Ashar2016-10-141-1/+1
| | | | | | | | Change is too trivial to file a JIRA. Author: Dhruve Ashar <dhruveashar@gmail.com> Closes #15485 from dhruve/master.
* [SPARK-17073][SQL][FOLLOWUP] generate column-level statisticswangzhenhua2016-10-143-57/+197
| | | | | | | | | | | | ## What changes were proposed in this pull request? This pr adds some test cases for statistics: case sensitive column names, non ascii column names, refresh table, and also improves some documentation. ## How was this patch tested? add test cases Author: wangzhenhua <wangzhenhua@huawei.com> Closes #15360 from wzhfy/colStats2.
* [SPARK-17855][CORE] Remove query string from jar urlinvkrh2016-10-141-15/+9
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Spark-submit support jar url with http protocol. However, if the url contains any query strings, `worker.DriverRunner.downloadUserJar()` method will throw "Did not see expected jar" exception. This is because this method checks the existance of a downloaded jar whose name contains query strings. This is a problem when your jar is located on some web service which requires some additional information to retrieve the file. This pr just removes query strings before checking jar existance on worker. ## How was this patch tested? For now, you can only test this patch by manual test. * Deploy a spark cluster locally * Make sure apache httpd service is on * Save an uber jar, e.g spark-job.jar under `/var/www/html/` * Use http://localhost/spark-job.jar?param=1 as jar url when running `spark-submit` * Job should be launched Author: invkrh <invkrh@gmail.com> Closes #15420 from invkrh/spark-17855.
* [SPARK-17870][MLLIB][ML] Change statistic to pValue for SelectKBest and ↵Peng2016-10-145-15/+15
| | | | | | | | | | | | | | | | | SelectPercentile because of DoF difference ## What changes were proposed in this pull request? For feature selection method ChiSquareSelector, it is based on the ChiSquareTestResult.statistic (ChiSqure value) to select the features. It select the features with the largest ChiSqure value. But the Degree of Freedom (df) of ChiSqure value is different in Statistics.chiSqTest(RDD), and for different df, you cannot base on ChiSqure value to select features. So we change statistic to pValue for SelectKBest and SelectPercentile ## How was this patch tested? change existing test Author: Peng <peng.meng@intel.com> Closes #15444 from mpjlu/chisqure-bug.
* [SPARK-14634][ML] Add BisectingKMeansSummaryZheng RuiFeng2016-10-144-5/+91
| | | | | | | | | | | | ## What changes were proposed in this pull request? Add BisectingKMeansSummary ## How was this patch tested? unit test Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #12394 from zhengruifeng/biKMSummary.
* [SPARK-15402][ML][PYSPARK] PySpark ml.evaluation should support save/loadYanbo Liang2016-10-141-9/+36
| | | | | | | | | | | | ## What changes were proposed in this pull request? Since ```ml.evaluation``` has supported save/load at Scala side, supporting it at Python side is very straightforward and easy. ## How was this patch tested? Add python doctest. Author: Yanbo Liang <ybliang8@gmail.com> Closes #13194 from yanboliang/spark-15402.
* [SPARK-17903][SQL] MetastoreRelation should talk to external catalog instead ↵Wenchen Fan2016-10-1411-32/+66
| | | | | | | | | | | | | | | | of hive client ## What changes were proposed in this pull request? `HiveExternalCatalog` should be the only interface to talk to the hive metastore. In `MetastoreRelation` we can just use `ExternalCatalog` instead of `HiveClient` to interact with hive metastore, and add missing API in `ExternalCatalog`. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #15460 from cloud-fan/relation.
* [SPARK-17925][SQL] Break fileSourceInterfaces.scala into multiple piecesReynold Xin2016-10-145-284/+359
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch does a few changes to the file structure of data sources: - Break fileSourceInterfaces.scala into multiple pieces (HadoopFsRelation, FileFormat, OutputWriter) - Move ParquetOutputWriter into its own file I created this as a separate patch so it'd be easier to review my future PRs that focus on refactoring this internal logic. This patch only moves code around, and has no logic changes. ## How was this patch tested? N/A - should be covered by existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #15473 from rxin/SPARK-17925.
* [SPARK-17927][SQL] Remove dead code in WriterContainer.Reynold Xin2016-10-141-11/+0
| | | | | | | | | | | | ## What changes were proposed in this pull request? speculationEnabled and DATASOURCE_OUTPUTPATH seem like just dead code. ## How was this patch tested? Tests should fail if they are not dead code. Author: Reynold Xin <rxin@databricks.com> Closes #15477 from rxin/SPARK-17927.
* [SPARK-15957][FOLLOW-UP][ML][PYSPARK] Add Python API for RFormula ↵Yanbo Liang2016-10-132-4/+43
| | | | | | | | | | | | | | forceIndexLabel. ## What changes were proposed in this pull request? Follow-up work of #13675, add Python API for ```RFormula forceIndexLabel```. ## How was this patch tested? Unit test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15430 from yanboliang/spark-15957-python.
* [SPARK-17368][SQL] Add support for value class serialization and deserializationJakob Odersky2016-10-132-3/+13
| | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Value classes were unsupported because catalyst data types were obtained through reflection on erased types, which would resolve to a value class' wrapped type and hence lead to unavailable methods during code generation. E.g. the following class ```scala case class Foo(x: Int) extends AnyVal ``` would be seen as an `int` in catalyst and will cause instance cast failures when generated java code tries to treat it as a `Foo`. This patch simply removes the erasure step when getting data types for catalyst. ## How was this patch tested? Additional tests in `ExpressionEncoderSuite`. Author: Jakob Odersky <jakob@odersky.com> Closes #15284 from jodersky/value-classes.
* [SPARK-17661][SQL] Consolidate various listLeafFiles implementationspetermaxlee2016-10-134-224/+206
| | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? There are 4 listLeafFiles-related functions in Spark: - ListingFileCatalog.listLeafFiles (which calls HadoopFsRelation.listLeafFilesInParallel if the number of paths passed in is greater than a threshold; if it is lower, then it has its own serial version implemented) - HadoopFsRelation.listLeafFiles (called only by HadoopFsRelation.listLeafFilesInParallel) - HadoopFsRelation.listLeafFilesInParallel (called only by ListingFileCatalog.listLeafFiles) It is actually very confusing and error prone because there are effectively two distinct implementations for the serial version of listing leaf files. As an example, SPARK-17599 updated only one of the code path and ignored the other one. This code can be improved by: - Move all file listing code into ListingFileCatalog, since it is the only class that needs this. - Keep only one function for listing files in serial. ## How was this patch tested? This change should be covered by existing unit and integration tests. I also moved a test case for HadoopFsRelation.shouldFilterOut from HadoopFsRelationSuite to ListingFileCatalogSuite. Author: petermaxlee <petermaxlee@gmail.com> Closes #15235 from petermaxlee/SPARK-17661.
* [SPARK-17731][SQL][STREAMING] Metrics for structured streamingTathagata Das2016-10-1327-273/+1758
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Metrics are needed for monitoring structured streaming apps. Here is the design doc for implementing the necessary metrics. https://docs.google.com/document/d/1NIdcGuR1B3WIe8t7VxLrt58TJB4DtipWEbj5I_mzJys/edit?usp=sharing Specifically, this PR adds the following public APIs changes. ### New APIs - `StreamingQuery.status` returns a `StreamingQueryStatus` object (renamed from `StreamingQueryInfo`, see later) - `StreamingQueryStatus` has the following important fields - inputRate - Current rate (rows/sec) at which data is being generated by all the sources - processingRate - Current rate (rows/sec) at which the query is processing data from all the sources - ~~outputRate~~ - *Does not work with wholestage codegen* - latency - Current average latency between the data being available in source and the sink writing the corresponding output - sourceStatuses: Array[SourceStatus] - Current statuses of the sources - sinkStatus: SinkStatus - Current status of the sink - triggerStatus - Low-level detailed status of the last completed/currently active trigger - latencies - getOffset, getBatch, full trigger, wal writes - timestamps - trigger start, finish, after getOffset, after getBatch - numRows - input, output, state total/updated rows for aggregations - `SourceStatus` has the following important fields - inputRate - Current rate (rows/sec) at which data is being generated by the source - processingRate - Current rate (rows/sec) at which the query is processing data from the source - triggerStatus - Low-level detailed status of the last completed/currently active trigger - Python API for `StreamingQuery.status()` ### Breaking changes to existing APIs **Existing direct public facing APIs** - Deprecated direct public-facing APIs `StreamingQuery.sourceStatuses` and `StreamingQuery.sinkStatus` in favour of `StreamingQuery.status.sourceStatuses/sinkStatus`. - Branch 2.0 should have it deprecated, master should have it removed. **Existing advanced listener APIs** - `StreamingQueryInfo` renamed to `StreamingQueryStatus` for consistency with `SourceStatus`, `SinkStatus` - Earlier StreamingQueryInfo was used only in the advanced listener API, but now it is used in direct public-facing API (StreamingQuery.status) - Field `queryInfo` in listener events `QueryStarted`, `QueryProgress`, `QueryTerminated` changed have name `queryStatus` and return type `StreamingQueryStatus`. - Field `offsetDesc` in `SourceStatus` was Option[String], converted it to `String`. - For `SourceStatus` and `SinkStatus` made constructor private instead of private[sql] to make them more java-safe. Instead added `private[sql] object SourceStatus/SinkStatus.apply()` which are harder to accidentally use in Java. ## How was this patch tested? Old and new unit tests. - Rate calculation and other internal logic of StreamMetrics tested by StreamMetricsSuite. - New info in statuses returned through StreamingQueryListener is tested in StreamingQueryListenerSuite. - New and old info returned through StreamingQuery.status is tested in StreamingQuerySuite. - Source-specific tests for making sure input rows are counted are is source-specific test suites. - Additional tests to test minor additions in LocalTableScanExec, StateStore, etc. Metrics also manually tested using Ganglia sink Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #15307 from tdas/SPARK-17731.
* [SPARK-17834][SQL] Fetch the earliest offsets manually in KafkaSource ↵Shixiong Zhu2016-10-132-26/+48
| | | | | | | | | | | | | | | | instead of counting on KafkaConsumer ## What changes were proposed in this pull request? Because `KafkaConsumer.poll(0)` may update the partition offsets, this PR just calls `seekToBeginning` to manually set the earliest offsets for the KafkaSource initial offsets. ## How was this patch tested? Existing tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes #15397 from zsxwing/SPARK-17834.
* [SPARK-17827][SQL] maxColLength type should be Int for String and BinaryPete Robbins2016-10-133-7/+7
| | | | | | | | | | | | ## What changes were proposed in this pull request? correct the expected type from Length function to be Int ## How was this patch tested? Test runs on little endian and big endian platforms Author: Pete Robbins <robbinspg@gmail.com> Closes #15464 from robbinspg/SPARK-17827.
* [SPARK-17830][SQL] Annotate remaining SQL APIs with InterfaceStabilityReynold Xin2016-10-1335-122/+150
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch annotates all the remaining APIs in SQL (excluding streaming) with InterfaceStability. ## How was this patch tested? N/A - just annotation change. Author: Reynold Xin <rxin@databricks.com> Closes #15457 from rxin/SPARK-17830-2.
* [SPARK-17657][SQL] Disallow Users to Change Table Typegatorsmile2016-10-132-0/+37
| | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? Hive allows users to change the table type from `Managed` to `External` or from `External` to `Managed` by altering table's property `EXTERNAL`. See the JIRA: https://issues.apache.org/jira/browse/HIVE-1329 So far, Spark SQL does not correctly support it, although users can do it. Many assumptions are broken in the implementation. Thus, this PR is to disallow users to change it. In addition, we also do not allow users to set the property `EXTERNAL` when creating a table. ### How was this patch tested? Added test cases Author: gatorsmile <gatorsmile@gmail.com> Closes #15230 from gatorsmile/alterTableSetExternal.
* [SPARK-17686][CORE] Support printing out scala and java version with ↵jerryshao2016-10-131-1/+3
| | | | | | | | | | | | | | | | | | spark-submit --version command ## What changes were proposed in this pull request? In our universal gateway service we need to specify different jars to Spark according to scala version. For now only after launching Spark application can we know which version of Scala it depends on. It makes hard for us to support different Scala + Spark versions to pick the right jars. So here propose to print out Scala version according to Spark version in "spark-submit --version", so that user could leverage this output to make the choice without needing to launching application. ## How was this patch tested? Manually verified in local environment. Author: jerryshao <sshao@hortonworks.com> Closes #15456 from jerryshao/SPARK-17686.
* [SPARK-17899][SQL] add a debug mode to keep raw table properties in ↵Wenchen Fan2016-10-134-13/+42
| | | | | | | | | | | | | | | | | | | | HiveExternalCatalog ## What changes were proposed in this pull request? Currently `HiveExternalCatalog` will filter out the Spark SQL internal table properties, e.g. `spark.sql.sources.provider`, `spark.sql.sources.schema`, etc. This is reasonable for external users as they don't want to see these internal properties in `DESC TABLE`. However, as a Spark developer, sometimes we do wanna see the raw table properties. This PR adds a new internal SQL conf, `spark.sql.debug`, to enable debug mode and keep these raw table properties. This config can also be used in similar places where we wanna retain debug information in the future. ## How was this patch tested? new test in MetastoreDataSourcesSuite Author: Wenchen Fan <wenchen@databricks.com> Closes #15458 from cloud-fan/debug.
* [SPARK-11272][WEB UI] Add support for downloading event logs from ↵Alex Bozarth2016-10-132-1/+7
| | | | | | | | | | | | | | | | | | | | HistoryServer UI ## What changes were proposed in this pull request? This is a reworked PR based on feedback in #9238 after it was closed and not reopened. As suggested in that PR I've only added the download feature. This functionality already exists in the api and this allows easier access to download event logs to share with others. I've attached a screenshot of the committed version, but I will also include alternate options with screen shots in the comments below. I'm personally not sure which option is best. ## How was this patch tested? Manual testing ![screen shot 2016-10-07 at 6 11 12 pm](https://cloud.githubusercontent.com/assets/13952758/19209213/832fe48e-8cba-11e6-9840-749b1be4d399.png) Author: Alex Bozarth <ajbozart@us.ibm.com> Closes #15400 from ajbozarth/spark11272.
* minor doc fix for Row.scalabuzhihuojie2016-10-121-1/+1
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? minor doc fix for "getAnyValAs" in class Row ## How was this patch tested? None. (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Author: buzhihuojie <ren.weiluo@gmail.com> Closes #15452 from david-weiluo-ren/minorDocFixForRow.
* [SPARK-17866][SPARK-17867][SQL] Fix Dataset.dropduplicatesLiang-Chi Hsieh2016-10-132-4/+29
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Two issues regarding Dataset.dropduplicates: 1. Dataset.dropDuplicates should consider the columns with same column name We find and get the first resolved attribute from output with the given column name in `Dataset.dropDuplicates`. When we have the more than one columns with the same name. Other columns are put into aggregation columns, instead of grouping columns. 2. Dataset.dropDuplicates should not change the output of child plan We create new `Alias` with new exprId in `Dataset.dropDuplicates` now. However it causes problem when we want to select the columns as follows: val ds = Seq(("a", 1), ("a", 2), ("b", 1), ("a", 1)).toDS() // ds("_2") will cause analysis exception ds.dropDuplicates("_1").select(ds("_1").as[String], ds("_2").as[Int]) Because the two issues are both related to `Dataset.dropduplicates` and the code changes are not big, so submitting them together as one PR. ## How was this patch tested? Jenkins tests. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #15427 from viirya/fix-dropduplicates.
* [SPARK-17876] Write StructuredStreaming WAL to a stream instead of ↵Burak Yavuz2016-10-123-27/+38
| | | | | | | | | | | | | | | | | | | | | | | | | materializing all at once ## What changes were proposed in this pull request? The CompactibleFileStreamLog materializes the whole metadata log in memory as a String. This can cause issues when there are lots of files that are being committed, especially during a compaction batch. You may come across stacktraces that look like: ``` java.lang.OutOfMemoryError: Requested array size exceeds VM limit at java.lang.StringCoding.encode(StringCoding.java:350) at java.lang.String.getBytes(String.java:941) at org.apache.spark.sql.execution.streaming.FileStreamSinkLog.serialize(FileStreamSinkLog.scala:127) ``` The safer way is to write to an output stream so that we don't have to materialize a huge string. ## How was this patch tested? Existing unit tests Author: Burak Yavuz <brkyvz@gmail.com> Closes #15437 from brkyvz/ser-to-stream.
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