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* [SPARK-14743][YARN] Add a configurable credential manager for Spark running ↵jerryshao2016-08-1024-573/+985
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | on YARN ## What changes were proposed in this pull request? Add a configurable token manager for Spark on running on yarn. ### Current Problems ### 1. Supported token provider is hard-coded, currently only hdfs, hbase and hive are supported and it is impossible for user to add new token provider without code changes. 2. Also this problem exits in timely token renewer and updater. ### Changes In This Proposal ### In this proposal, to address the problems mentioned above and make the current code more cleaner and easier to understand, mainly has 3 changes: 1. Abstract a `ServiceTokenProvider` as well as `ServiceTokenRenewable` interface for token provider. Each service wants to communicate with Spark through token way needs to implement this interface. 2. Provide a `ConfigurableTokenManager` to manage all the register token providers, also token renewer and updater. Also this class offers the API for other modules to obtain tokens, get renewal interval and so on. 3. Implement 3 built-in token providers `HDFSTokenProvider`, `HiveTokenProvider` and `HBaseTokenProvider` to keep the same semantics as supported today. Whether to load in these built-in token providers is controlled by configuration "spark.yarn.security.tokens.${service}.enabled", by default for all the built-in token providers are loaded. ### Behavior Changes ### For the end user there's no behavior change, we still use the same configuration `spark.yarn.security.tokens.${service}.enabled` to decide which token provider is enabled (hbase or hive). For user implemented token provider (assume the name of token provider is "test") needs to add into this class should have two configurations: 1. `spark.yarn.security.tokens.test.enabled` to true 2. `spark.yarn.security.tokens.test.class` to the full qualified class name. So we still keep the same semantics as current code while add one new configuration. ### Current Status ### - [x] token provider interface and management framework. - [x] implement built-in token providers (hdfs, hbase, hive). - [x] Coverage of unit test. - [x] Integrated test with security cluster. ## How was this patch tested? Unit test and integrated test. Please suggest and review, any comment is greatly appreciated. Author: jerryshao <sshao@hortonworks.com> Closes #14065 from jerryshao/SPARK-16342.
* [SPARK-12920][CORE] Honor "spark.ui.retainedStages" to reduce mem-pressureRajesh Balamohan2016-08-102-18/+36
| | | | | | | | | | When large number of jobs are run concurrently with Spark thrift server, thrift server starts running at high CPU due to GC pressure. Job UI retention causes memory pressure with large jobs. https://issues.apache.org/jira/secure/attachment/12783302/SPARK-12920.profiler_job_progress_listner.png has the profiler snapshot. This PR honors `spark.ui.retainedStages` strictly to reduce memory pressure. Manual and unit tests Author: Rajesh Balamohan <rbalamohan@apache.org> Closes #10846 from rajeshbalamohan/SPARK-12920.
* [SPARK-16928] [SQL] Recursive call of ColumnVector::getInt() breaks JIT inliningQifan Pu2016-08-104-25/+54
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In both `OnHeapColumnVector` and `OffHeapColumnVector`, we implemented `getInt()` with the following code pattern: ``` public int getInt(int rowId) { if (dictionary == null) { return intData[rowId]; } else { return dictionary.decodeToInt(dictionaryIds.getInt(rowId)); } } ``` As `dictionaryIds` is also a `ColumnVector`, this results in a recursive call of `getInt()` and breaks JIT inlining. As a result, `getInt()` will not get inlined. We fix this by adding a separate method `getDictId()` specific for `dictionaryIds` to use. ## How was this patch tested? We tested the difference with the following aggregate query on a TPCDS dataset (with scale factor = 5): ``` select max(ss_sold_date_sk) as max_ss_sold_date_sk, from store_sales ``` The query runtime is improved, from 202ms (before) to 159ms (after). Author: Qifan Pu <qifan.pu@gmail.com> Closes #14513 from ooq/SPARK-16928.
* [SPARK-16579][SPARKR] add install.spark functionJunyang Qian2016-08-107-4/+267
| | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add an install_spark function to the SparkR package. User can run `install_spark()` to install Spark to a local directory within R. Updates: Several changes have been made: - `install.spark()` - check existence of tar file in the cache folder, and download only if not found - trial priority of mirror_url look-up: user-provided -> preferred mirror site from apache website -> hardcoded backup option - use 2.0.0 - `sparkR.session()` - can install spark when not found in `SPARK_HOME` ## How was this patch tested? Manual tests, running the check-cran.sh script added in #14173. Author: Junyang Qian <junyangq@databricks.com> Closes #14258 from junyangq/SPARK-16579.
* [SPARK-16710][SPARKR][ML] spark.glm should support weightColYanbo Liang2016-08-103-5/+36
| | | | | | | | | | | | ## What changes were proposed in this pull request? Training GLMs on weighted dataset is very important use cases, but it is not supported by SparkR currently. Users can pass argument ```weights``` to specify the weights vector in native R. For ```spark.glm```, we can pass in the ```weightCol``` which is consistent with MLlib. ## How was this patch tested? Unit test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #14346 from yanboliang/spark-16710.
* [SPARK-15639] [SPARK-16321] [SQL] Push down filter at RowGroups level for ↵Liang-Chi Hsieh2016-08-105-147/+143
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | parquet reader ## What changes were proposed in this pull request? The base class `SpecificParquetRecordReaderBase` used for vectorized parquet reader will try to get pushed-down filters from the given configuration. This pushed-down filters are used for RowGroups-level filtering. However, we don't set up the filters to push down into the configuration. In other words, the filters are not actually pushed down to do RowGroups-level filtering. This patch is to fix this and tries to set up the filters for pushing down to configuration for the reader. The benchmark that excludes the time of writing Parquet file: test("Benchmark for Parquet") { val N = 500 << 12 withParquetTable((0 until N).map(i => (101, i)), "t") { val benchmark = new Benchmark("Parquet reader", N) benchmark.addCase("reading Parquet file", 10) { iter => sql("SELECT _1 FROM t where t._1 < 100").collect() } benchmark.run() } } `withParquetTable` in default will run tests for vectorized reader non-vectorized readers. I only let it run vectorized reader. When we set the block size of parquet as 1024 to have multiple row groups. The benchmark is: Before this patch: The retrieved row groups: 8063 Java HotSpot(TM) 64-Bit Server VM 1.8.0_71-b15 on Linux 3.19.0-25-generic Intel(R) Core(TM) i7-5557U CPU 3.10GHz Parquet reader: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ reading Parquet file 825 / 1233 2.5 402.6 1.0X After this patch: The retrieved row groups: 0 Java HotSpot(TM) 64-Bit Server VM 1.8.0_71-b15 on Linux 3.19.0-25-generic Intel(R) Core(TM) i7-5557U CPU 3.10GHz Parquet reader: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ reading Parquet file 306 / 503 6.7 149.6 1.0X Next, I run the benchmark for non-pushdown case using the same benchmark code but with disabled pushdown configuration. This time the parquet block size is default value. Before this patch: Java HotSpot(TM) 64-Bit Server VM 1.8.0_71-b15 on Linux 3.19.0-25-generic Intel(R) Core(TM) i7-5557U CPU 3.10GHz Parquet reader: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ reading Parquet file 136 / 238 15.0 66.5 1.0X After this patch: Java HotSpot(TM) 64-Bit Server VM 1.8.0_71-b15 on Linux 3.19.0-25-generic Intel(R) Core(TM) i7-5557U CPU 3.10GHz Parquet reader: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ reading Parquet file 124 / 193 16.5 60.7 1.0X For non-pushdown case, from the results, I think this patch doesn't affect normal code path. I've manually output the `totalRowCount` in `SpecificParquetRecordReaderBase` to see if this patch actually filter the row-groups. When running the above benchmark: After this patch: `totalRowCount = 0` Before this patch: `totalRowCount = 1024000` ## How was this patch tested? Existing tests should be passed. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #13701 from viirya/vectorized-reader-push-down-filter2.
* [SPARK-15899][SQL] Fix the construction of the file path with hadoop Pathavulanov2016-08-103-27/+35
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix the construction of the file path. Previous way of construction caused the creation of incorrect path on Windows. ## How was this patch tested? Run SQL unit tests on Windows Author: avulanov <nashb@yandex.ru> Closes #13868 from avulanov/SPARK-15899-file.
* [SPARK-16866][SQL] Infrastructure for file-based SQL end-to-end testspetermaxlee2016-08-106-38/+267
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch introduces SQLQueryTestSuite, a basic framework for end-to-end SQL test cases defined in spark/sql/core/src/test/resources/sql-tests. This is a more standard way to test SQL queries end-to-end in different open source database systems, because it is more manageable to work with files. This is inspired by HiveCompatibilitySuite, but simplified for general Spark SQL tests. Once this is merged, I can work towards porting SQLQuerySuite over, and eventually also move the existing HiveCompatibilitySuite to use this framework. Unlike HiveCompatibilitySuite, SQLQueryTestSuite compares both the output schema and the output data (in string form). When there is a mismatch, the error message looks like the following: ``` [info] - blacklist.sql !!! IGNORED !!! [info] - number-format.sql *** FAILED *** (2 seconds, 405 milliseconds) [info] Expected "...147483648 -214748364[8]", but got "...147483648 -214748364[9]" Result should match for query #1 (SQLQueryTestSuite.scala:171) [info] org.scalatest.exceptions.TestFailedException: [info] at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:495) [info] at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555) [info] at org.scalatest.Assertions$class.assertResult(Assertions.scala:1171) ``` ## How was this patch tested? This is a test infrastructure change. Author: petermaxlee <petermaxlee@gmail.com> Closes #14472 from petermaxlee/SPARK-16866.
* [SPARK-16324][SQL] regexp_extract should doc that it returns empty string ↵Sean Owen2016-08-103-2/+11
| | | | | | | | | | | | | | | | when match fails ## What changes were proposed in this pull request? Doc that regexp_extract returns empty string when regex or group does not match ## How was this patch tested? Jenkins test, with a few new test cases Author: Sean Owen <sowen@cloudera.com> Closes #14525 from srowen/SPARK-16324.
* [SPARK-16927][SPARK-16923] Override task properties at dispatcher.Timothy Chen2016-08-102-22/+33
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? - enable setting default properties for all jobs submitted through the dispatcher [SPARK-16927] - remove duplication of conf vars on cluster submitted jobs [SPARK-16923] (this is a small fix, so I'm including in the same PR) ## How was this patch tested? mesos/spark integration test suite manual testing Author: Timothy Chen <tnachen@gmail.com> Closes #14511 from mgummelt/override-props.
* Typo: Fow -> ForAndrew Ash2016-08-101-1/+1
| | | | | | Author: Andrew Ash <andrew@andrewash.com> Closes #14563 from ash211/patch-8.
* [SPARK-16185][SQL] Better Error Messages When Creating Table As Select ↵gatorsmile2016-08-103-1/+45
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Without Enabling Hive Support #### What changes were proposed in this pull request? When we do not turn on the Hive Support, the following query generates a confusing error message by Planner: ```Scala sql("CREATE TABLE t2 SELECT a, b from t1") ``` ``` assertion failed: No plan for CreateTable CatalogTable( Table: `t2` Created: Tue Aug 09 23:45:32 PDT 2016 Last Access: Wed Dec 31 15:59:59 PST 1969 Type: MANAGED Provider: hive Storage(InputFormat: org.apache.hadoop.mapred.TextInputFormat, OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat)), ErrorIfExists +- Relation[a#19L,b#20L] parquet java.lang.AssertionError: assertion failed: No plan for CreateTable CatalogTable( Table: `t2` Created: Tue Aug 09 23:45:32 PDT 2016 Last Access: Wed Dec 31 15:59:59 PST 1969 Type: MANAGED Provider: hive Storage(InputFormat: org.apache.hadoop.mapred.TextInputFormat, OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat)), ErrorIfExists +- Relation[a#19L,b#20L] parquet ``` This PR is to issue a better error message: ``` Hive support is required to use CREATE Hive TABLE AS SELECT ``` #### How was this patch tested? Added test cases in `DDLSuite.scala` Author: gatorsmile <gatorsmile@gmail.com> Closes #13886 from gatorsmile/createCatalogedTableAsSelect.
* [SPARK-10601][SQL] Support `MINUS` set operatorDongjoon Hyun2016-08-104-2/+21
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds `MINUS` set operator which is equivalent `EXCEPT DISTINCT`. This will slightly improve the compatibility with Oracle. ## How was this patch tested? Pass the Jenkins with newly added testcases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14570 from dongjoon-hyun/SPARK-10601.
* [SPARK-16959][SQL] Rebuild Table Comment when Retrieving Metadata from Hive ↵gatorsmile2016-08-102-3/+7
| | | | | | | | | | | | | | | | Metastore ### What changes were proposed in this pull request? The `comment` in `CatalogTable` returned from Hive is always empty. We store it in the table property when creating a table. However, when we try to retrieve the table metadata from Hive metastore, we do not rebuild it. The `comment` is always empty. This PR is to fix the issue. ### How was this patch tested? Fixed the test case to verify the change. Author: gatorsmile <gatorsmile@gmail.com> Closes #14550 from gatorsmile/tableComment.
* [MINOR][SPARKR] R API documentation for "coltypes" is confusingXin Ren2016-08-101-5/+4
| | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? R API documentation for "coltypes" is confusing, found when working on another ticket. Current version http://spark.apache.org/docs/2.0.0/api/R/coltypes.html, where parameters have 2 "x" which is a duplicate, and also the example is not very clear ![current](https://cloud.githubusercontent.com/assets/3925641/17386808/effb98ce-59a2-11e6-9657-d477d258a80c.png) ![screen shot 2016-08-03 at 5 56 00 pm](https://cloud.githubusercontent.com/assets/3925641/17386884/91831096-59a3-11e6-84af-39890b3d45d8.png) ## How was this patch tested? Tested manually on local machine. And the screenshots are like below: ![screen shot 2016-08-07 at 11 29 20 pm](https://cloud.githubusercontent.com/assets/3925641/17471144/df36633c-5cf6-11e6-8238-4e32ead0e529.png) ![screen shot 2016-08-03 at 5 56 22 pm](https://cloud.githubusercontent.com/assets/3925641/17386896/9d36cb26-59a3-11e6-9619-6dae29f7ab17.png) Author: Xin Ren <iamshrek@126.com> Closes #14489 from keypointt/rExample.
* Fixed typoMichał Kiełbowicz2016-08-091-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fixed small typo - "value ... ~~in~~ is null" ## How was this patch tested? Still compiles! Author: Michał Kiełbowicz <jupblb@users.noreply.github.com> Closes #14569 from jupblb/typo-fix.
* Make logDir easily copy/paste-ableAndrew Ash2016-08-091-1/+1
| | | | | | | | | | | In many terminals double-clicking and dragging also includes the trailing period. Simply remove this to make the value more easily copy/pasteable. Example value: `hdfs://mybox-123.net.example.com:8020/spark-events.` Author: Andrew Ash <andrew@andrewash.com> Closes #14566 from ash211/patch-9.
* [SPARK-16956] Make ApplicationState.MAX_NUM_RETRY configurableJosh Rosen2016-08-093-3/+21
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch introduces a new configuration, `spark.deploy.maxExecutorRetries`, to let users configure an obscure behavior in the standalone master where the master will kill Spark applications which have experienced too many back-to-back executor failures. The current setting is a hardcoded constant (10); this patch replaces that with a new cluster-wide configuration. **Background:** This application-killing was added in 6b5980da796e0204a7735a31fb454f312bc9daac (from September 2012) and I believe that it was designed to prevent a faulty application whose executors could never launch from DOS'ing the Spark cluster via an infinite series of executor launch attempts. In a subsequent patch (#1360), this feature was refined to prevent applications which have running executors from being killed by this code path. **Motivation for making this configurable:** Previously, if a Spark Standalone application experienced more than `ApplicationState.MAX_NUM_RETRY` executor failures and was left with no executors running then the Spark master would kill that application, but this behavior is problematic in environments where the Spark executors run on unstable infrastructure and can all simultaneously die. For instance, if your Spark driver runs on an on-demand EC2 instance while all workers run on ephemeral spot instances then it's possible for all executors to die at the same time while the driver stays alive. In this case, it may be desirable to keep the Spark application alive so that it can recover once new workers and executors are available. In order to accommodate this use-case, this patch modifies the Master to never kill faulty applications if `spark.deploy.maxExecutorRetries` is negative. I'd like to merge this patch into master, branch-2.0, and branch-1.6. ## How was this patch tested? I tested this manually using `spark-shell` and `local-cluster` mode. This is a tricky feature to unit test and historically this code has not changed very often, so I'd prefer to skip the additional effort of adding a testing framework and would rather rely on manual tests and review for now. Author: Josh Rosen <joshrosen@databricks.com> Closes #14544 from JoshRosen/add-setting-for-max-executor-failures.
* [SPARK-16905] SQL DDL: MSCK REPAIR TABLEDavies Liu2016-08-097-9/+211
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? MSCK REPAIR TABLE could be used to recover the partitions in external catalog based on partitions in file system. Another syntax is: ALTER TABLE table RECOVER PARTITIONS The implementation in this PR will only list partitions (not the files with a partition) in driver (in parallel if needed). ## How was this patch tested? Added unit tests for it and Hive compatibility test suite. Author: Davies Liu <davies@databricks.com> Closes #14500 from davies/repair_table.
* [SPARK-16950] [PYSPARK] fromOffsets parameter support in ↵Mariusz Strzelecki2016-08-093-13/+10
| | | | | | | | | | | | | | | | | | KafkaUtils.createDirectStream for python3 ## What changes were proposed in this pull request? Ability to use KafkaUtils.createDirectStream with starting offsets in python 3 by using java.lang.Number instead of Long during param mapping in scala helper. This allows py4j to pass Integer or Long to the map and resolves ClassCastException problems. ## How was this patch tested? unit tests jerryshao - could you please look at this PR? Author: Mariusz Strzelecki <mariusz.strzelecki@allegrogroup.com> Closes #14540 from szczeles/kafka_pyspark.
* [SPARK-16933][ML] Fix AFTAggregator in AFTSurvivalRegression serializes ↵Yanbo Liang2016-08-091-18/+29
| | | | | | | | | | | | | | | | | | | | unnecessary data. ## What changes were proposed in this pull request? Similar to ```LeastSquaresAggregator``` in #14109, ```AFTAggregator``` used for ```AFTSurvivalRegression``` ends up serializing the ```parameters``` and ```featuresStd```, which is not necessary and can cause performance issues for high dimensional data. This patch removes this serialization. This PR is highly inspired by #14109. ## How was this patch tested? I tested this locally and verified the serialization reduction. Before patch ![image](https://cloud.githubusercontent.com/assets/1962026/17512035/abb93f04-5dda-11e6-97d3-8ae6b61a0dfd.png) After patch ![image](https://cloud.githubusercontent.com/assets/1962026/17512024/9e0dc44c-5dda-11e6-93d0-6e130ba0d6aa.png) Author: Yanbo Liang <ybliang8@gmail.com> Closes #14519 from yanboliang/spark-16933.
* [SPARK-16964][SQL] Remove private[sql] and private[spark] from sql.execution ↵Reynold Xin2016-08-0963-177/+170
| | | | | | | | | | | | | | | | package ## What changes were proposed in this pull request? This package is meant to be internal, and as a result it does not make sense to mark things as private[sql] or private[spark]. It simply makes debugging harder when Spark developers need to inspect the plans at runtime. This patch removes all private[sql] and private[spark] visibility modifiers in org.apache.spark.sql.execution. ## How was this patch tested? N/A - just visibility changes. Author: Reynold Xin <rxin@databricks.com> Closes #14554 from rxin/remote-private.
* [SPARK-16809] enable history server links in dispatcher UIMichael Gummelt2016-08-097-10/+75
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Links the Spark Mesos Dispatcher UI to the history server UI - adds spark.mesos.dispatcher.historyServer.url - explicitly generates frameworkIDs for the launched drivers, so the dispatcher knows how to correlate drivers and frameworkIDs ## How was this patch tested? manual testing Author: Michael Gummelt <mgummelt@mesosphere.io> Author: Sergiusz Urbaniak <sur@mesosphere.io> Closes #14414 from mgummelt/history-server.
* [SPARK-16940][SQL] `checkAnswer` should raise `TestFailedException` for ↵Dongjoon Hyun2016-08-091-1/+9
| | | | | | | | | | | | | | | | | | | | | wrong results ## What changes were proposed in this pull request? This PR fixes the following to make `checkAnswer` raise `TestFailedException` again instead of `java.util.NoSuchElementException: key not found: TZ` in the environments without `TZ` variable. Also, this PR adds `QueryTestSuite` class for testing `QueryTest` itself. ```scala - |Timezone Env: ${sys.env("TZ")} + |Timezone Env: ${sys.env.getOrElse("TZ", "")} ``` ## How was this patch tested? Pass the Jenkins tests with a new test suite. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14528 from dongjoon-hyun/SPARK-16940.
* [SPARK-16522][MESOS] Spark application throws exception on exit.Sun Rui2016-08-092-1/+39
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Spark applications running on Mesos throw exception upon exit. For details, refer to https://issues.apache.org/jira/browse/SPARK-16522. I am not sure if there is any better fix, so wait for review comments. ## How was this patch tested? Manual test. Observed that the exception is gone upon application exit. Author: Sun Rui <sunrui2016@gmail.com> Closes #14175 from sun-rui/SPARK-16522.
* [SPARK-16606][CORE] Misleading warning for SparkContext.getOrCreate "WARN ↵Sean Owen2016-08-091-4/+10
| | | | | | | | | | | | | | | | | | | SparkContext: Use an existing SparkContext, some configuration may not take effect." ## What changes were proposed in this pull request? SparkContext.getOrCreate shouldn't warn about ignored config if - it wasn't ignored because a new context is created with it or - no config was actually provided ## How was this patch tested? Jenkins + existing tests. Author: Sean Owen <sowen@cloudera.com> Closes #14533 from srowen/SPARK-16606.
* [SPARK-16610][SQL] Add `orc.compress` as an alias for `compression` option.hyukjinkwon2016-08-092-3/+32
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? For ORC source, Spark SQL has a writer option `compression`, which is used to set the codec and its value will be also set to `orc.compress` (the orc conf used for codec). However, if a user only set `orc.compress` in the writer option, we should not use the default value of `compression` (snappy) as the codec. Instead, we should respect the value of `orc.compress`. This PR makes ORC data source not ignoring `orc.compress` when `comperssion` is unset. So, here is the behaviour, 1. Check `compression` and use this if it is set. 2. If `compression` is not set, check `orc.compress` and use it. 3. If `compression` and `orc.compress` are not set, then use the default snappy. ## How was this patch tested? Unit test in `OrcQuerySuite`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #14518 from HyukjinKwon/SPARK-16610.
* [SPARK-16563][SQL] fix spark sql thrift server FetchResults bugAlice2016-08-082-0/+60
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add a constant iterator which point to head of result. The header will be used to reset iterator when fetch result from first row repeatedly. JIRA ticket https://issues.apache.org/jira/browse/SPARK-16563 ## How was this patch tested? This bug was found when using Cloudera HUE connecting to spark sql thrift server, currently SQL statement result can be only fetched for once. The fix was tested manually with Cloudera HUE, With this fix, HUE can fetch spark SQL results repeatedly through thrift server. Author: Alice <alice.gugu@gmail.com> Author: Alice <guhq@garena.com> Closes #14218 from alicegugu/SparkSQLFetchResultsBug.
* [SPARK-16898][SQL] Adds argument type information for typed logical plan ↵Sean Zhong2016-08-094-11/+31
| | | | | | | | | | | | | | | | like MapElements, TypedFilter, and AppendColumn ## What changes were proposed in this pull request? This PR adds argument type information for typed logical plan like MapElements, TypedFilter, and AppendColumn, so that we can use these info in customized optimizer rule. ## How was this patch tested? Existing test. Author: Sean Zhong <seanzhong@databricks.com> Closes #14494 from clockfly/add_more_info_for_typed_operator.
* [SPARK-16749][SQL] Simplify processing logic in LEAD/LAG processing.Herman van Hovell2016-08-081-35/+18
| | | | | | | | | | | | ## What changes were proposed in this pull request? The logic for LEAD/LAG processing is more complex that it needs to be. This PR fixes that. ## How was this patch tested? Existing tests. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #14376 from hvanhovell/SPARK-16749.
* Update docs to include SASL support for RPCMichael Gummelt2016-08-082-4/+6
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Update docs to include SASL support for RPC Evidence: https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/rpc/netty/NettyRpcEnv.scala#L63 ## How was this patch tested? Docs change only Author: Michael Gummelt <mgummelt@mesosphere.io> Closes #14549 from mgummelt/sasl.
* [SPARK-16779][TRIVIAL] Avoid using postfix operators where they do not add ↵Holden Karau2016-08-0821-33/+6
| | | | | | | | | | | | | | | | much and remove whitelisting ## What changes were proposed in this pull request? Avoid using postfix operation for command execution in SQLQuerySuite where it wasn't whitelisted and audit existing whitelistings removing postfix operators from most places. Some notable places where postfix operation remains is in the XML parsing & time units (seconds, millis, etc.) where it arguably can improve readability. ## How was this patch tested? Existing tests. Author: Holden Karau <holden@us.ibm.com> Closes #14407 from holdenk/SPARK-16779.
* [SPARK-16953] Make requestTotalExecutors public Developer API to be ↵Tathagata Das2016-08-081-1/+2
| | | | | | | | | | | | consistent with requestExecutors/killExecutors ## What changes were proposed in this pull request? RequestExecutors and killExecutor are public developer APIs for managing the number of executors allocated to the SparkContext. For consistency, requestTotalExecutors should also be a public Developer API, as it provides similar functionality. In fact, using requestTotalExecutors is more convenient that requestExecutors as the former is idempotent and the latter is not. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #14541 from tdas/SPARK-16953.
* [SPARK-16586][CORE] Handle JVM errors printed to stdout.Marcelo Vanzin2016-08-081-0/+9
| | | | | | | | | | | | | | Some very rare JVM errors are printed to stdout, and that confuses the code in spark-class. So add a check so that those cases are detected and the proper error message is shown to the user. Tested by running spark-submit after setting "ulimit -v 32000". Closes #14231 Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #14508 from vanzin/SPARK-16586.
* [SPARK-16936][SQL] Case Sensitivity Support for Refresh Temp Tablegatorsmile2016-08-083-3/+28
| | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? Currently, the `refreshTable` API is always case sensitive. When users use the view name without the exact case match, the API silently ignores the call. Users might expect the command has been successfully completed. However, when users run the subsequent SQL commands, they might still get the exception, like ``` Job aborted due to stage failure: Task 1 in stage 4.0 failed 1 times, most recent failure: Lost task 1.0 in stage 4.0 (TID 7, localhost): java.io.FileNotFoundException: File file:/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-bd4b9ea6-9aec-49c5-8f05-01cff426211e/part-r-00000-0c84b915-c032-4f2e-abf5-1d48fdbddf38.snappy.parquet does not exist ``` This PR is to fix the issue. ### How was this patch tested? Added a test case. Author: gatorsmile <gatorsmile@gmail.com> Closes #14523 from gatorsmile/refreshTempTable.
* [SPARK-16457][SQL] Fix Wrong Messages when CTAS with a Partition By Clausegatorsmile2016-08-082-16/+32
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? When doing a CTAS with a Partition By clause, we got a wrong error message. For example, ```SQL CREATE TABLE gen__tmp PARTITIONED BY (key string) AS SELECT key, value FROM mytable1 ``` The error message we get now is like ``` Operation not allowed: Schema may not be specified in a Create Table As Select (CTAS) statement(line 2, pos 0) ``` However, based on the code, the message we should get is like ``` Operation not allowed: A Create Table As Select (CTAS) statement is not allowed to create a partitioned table using Hive's file formats. Please use the syntax of "CREATE TABLE tableName USING dataSource OPTIONS (...) PARTITIONED BY ...\" to create a partitioned table through a CTAS statement.(line 2, pos 0) ``` Currently, partitioning columns is part of the schema. This PR fixes the bug by changing the detection orders. #### How was this patch tested? Added test cases. Author: gatorsmile <gatorsmile@gmail.com> Closes #14113 from gatorsmile/ctas.
* [SPARK-16906][SQL] Adds auxiliary info like input class and input schema in ↵Sean Zhong2016-08-085-7/+14
| | | | | | | | | | | | | | | | TypedAggregateExpression ## What changes were proposed in this pull request? This PR adds auxiliary info like input class and input schema in TypedAggregateExpression ## How was this patch tested? Manual test. Author: Sean Zhong <seanzhong@databricks.com> Closes #14501 from clockfly/typed_aggregation.
* [SPARK-16804][SQL] Correlated subqueries containing non-deterministic ↵Nattavut Sutyanyong2016-08-083-0/+59
| | | | | | | | | | | | | | | | | operations return incorrect results ## What changes were proposed in this pull request? This patch fixes the incorrect results in the rule ResolveSubquery in Catalyst's Analysis phase by returning an error message when the LIMIT is found in the path from the parent table to the correlated predicate in the subquery. ## How was this patch tested? ./dev/run-tests a new unit test on the problematic pattern. Author: Nattavut Sutyanyong <nsy.can@gmail.com> Closes #14411 from nsyca/master.
* [SPARK-16945] Fix Java Lint errorsWeiqing Yang2016-08-084-10/+11
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR is to fix the minor Java linter errors as following: [ERROR] src/main/java/org/apache/spark/sql/catalyst/expressions/VariableLengthRowBasedKeyValueBatch.java:[42,10] (modifier) RedundantModifier: Redundant 'final' modifier. [ERROR] src/main/java/org/apache/spark/sql/catalyst/expressions/VariableLengthRowBasedKeyValueBatch.java:[97,10] (modifier) RedundantModifier: Redundant 'final' modifier. ## How was this patch tested? Manual test. dev/lint-java Using `mvn` from path: /usr/local/bin/mvn Checkstyle checks passed. Author: Weiqing Yang <yangweiqing001@gmail.com> Closes #14532 from Sherry302/master.
* [SPARK-16404][ML] LeastSquaresAggregators serializes unnecessary datasethah2016-08-081-20/+45
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Similar to `LogisticAggregator`, `LeastSquaresAggregator` used for linear regression ends up serializing the coefficients and the features standard deviations, which is not necessary and can cause performance issues for high dimensional data. This patch removes this serialization. In https://github.com/apache/spark/pull/13729 the approach was to pass these values directly to the add method. The approach used here, initially, is to mark these fields as transient instead which gives the benefit of keeping the signature of the add method simple and interpretable. The downside is that it requires the use of `transient lazy val`s which are difficult to reason about if one is not quite familiar with serialization in Scala/Spark. ## How was this patch tested? **MLlib** ![image](https://cloud.githubusercontent.com/assets/7275795/16703660/436f79fa-4524-11e6-9022-ef00058ec718.png) **ML without patch** ![image](https://cloud.githubusercontent.com/assets/7275795/16703831/c4d50b9e-4525-11e6-80cb-9b58c850cd41.png) **ML with patch** ![image](https://cloud.githubusercontent.com/assets/7275795/16703675/63e0cf40-4524-11e6-9120-1f512a70e083.png) Author: sethah <seth.hendrickson16@gmail.com> Closes #14109 from sethah/LIR_serialize.
* [SPARK-16919] Configurable update interval for console progress barTejas Patil2016-08-081-11/+12
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently the update interval for the console progress bar is hardcoded. This PR makes it configurable for users. ## How was this patch tested? Ran a long running job and with a high value of update interval, the updates were shown less frequently. Author: Tejas Patil <tejasp@fb.com> Closes #14507 from tejasapatil/SPARK-16919.
* [SPARK-16939][SQL] Fix build error by using `Tuple1` explicitly in ↵Dongjoon Hyun2016-08-071-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | StringFunctionsSuite ## What changes were proposed in this pull request? This PR aims to fix a build error on branch 1.6 at https://github.com/apache/spark/commit/8d8725208771a8815a60160a5a30dc6ea87a7e6a, but I think we had better have this consistently in master branch, too. It's because there exist other ongoing PR (https://github.com/apache/spark/pull/14525) about this. https://amplab.cs.berkeley.edu/jenkins/job/spark-branch-1.6-compile-maven-with-yarn-2.3/286/console ```scala [error] /home/jenkins/workspace/spark-branch-1.6-compile-maven-with-yarn-2.3/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala:82: value toDF is not a member of Seq[String] [error] val df = Seq("aaaac").toDF("s") [error] ^ ``` ## How was this patch tested? After passing Jenkins, run compilation test on branch 1.6. ``` build/mvn -DskipTests -Pyarn -Phadoop-2.3 -Pkinesis-asl -Phive -Phive-thriftserver install ``` Author: Dongjoon Hyun <dongjoon@apache.org> Closes #14526 from dongjoon-hyun/SPARK-16939.
* [SPARK-16409][SQL] regexp_extract with optional groups causes NPESean Owen2016-08-073-2/+22
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? regexp_extract actually returns null when it shouldn't when a regex matches but the requested optional group did not. This makes it return an empty string, as apparently designed. ## How was this patch tested? Additional unit test Author: Sean Owen <sowen@cloudera.com> Closes #14504 from srowen/SPARK-16409.
* [SPARK-16909][SPARK CORE] Streaming for postgreSQL JDBC driverPrince J Wesley2016-08-071-5/+11
| | | | | | | | | | As per the postgreSQL JDBC driver [implementation](https://github.com/pgjdbc/pgjdbc/blob/ab2a6d89081fc2c1fdb2a8600f413db33669022c/pgjdbc/src/main/java/org/postgresql/PGProperty.java#L99), the default record fetch size is 0(which means, it caches all record) This fix enforces default record fetch size as 10 to enable streaming of data. Author: Prince J Wesley <princejohnwesley@gmail.com> Closes #14502 from princejwesley/spark-postgres.
* [SPARK-16911] Fix the links in the programming guideShivansh2016-08-073-106/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix the broken links in the programming guide of the Graphx Migration and understanding closures ## How was this patch tested? By running the test cases and checking the links. Author: Shivansh <shiv4nsh@gmail.com> Closes #14503 from shiv4nsh/SPARK-16911.
* [SPARK-16870][DOCS] Summary:add "spark.sql.broadcastTimeout" into ↵keliang2016-08-071-0/+9
| | | | | | | | | | | | | | | | | | | | | | | docs/sql-programming-gu… ## What changes were proposed in this pull request? default value for spark.sql.broadcastTimeout is 300s. and this property do not show in any docs of spark. so add "spark.sql.broadcastTimeout" into docs/sql-programming-guide.md to help people to how to fix this timeout error when it happenned ## How was this patch tested? not need (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) …ide.md JIRA_ID:SPARK-16870 Description:default value for spark.sql.broadcastTimeout is 300s. and this property do not show in any docs of spark. so add "spark.sql.broadcastTimeout" into docs/sql-programming-guide.md to help people to how to fix this timeout error when it happenned Test:done Author: keliang <keliang@cmss.chinamobile.com> Closes #14477 from biglobster/keliang.
* [SPARK-16932][DOCS] Changed programming guide to not reference old ↵Bryan Cutler2016-08-071-14/+27
| | | | | | | | | | | | | | | accumulator API in Scala ## What changes were proposed in this pull request? In the programming guide, the accumulator section mixes up both the old and new APIs causing it to be confusing. This is not necessary for Scala, so all references to the old API are removed. For Java, it is somewhat fixed up except for the example of a custom accumulator because I don't think an API exists yet. Python has not currently implemented the new API. ## How was this patch tested? built doc locally Author: Bryan Cutler <cutlerb@gmail.com> Closes #14516 from BryanCutler/fixup-accumulator-programming-guide-SPARK-15702.
* document that Mesos cluster mode supports pythonMichael Gummelt2016-08-071-1/+2
| | | | | | | | update docs to be consistent with SPARK-14645 https://issues.apache.org/jira/browse/SPARK-14645 Author: Michael Gummelt <mgummelt@mesosphere.io> Closes #14514 from mgummelt/fix-docs.
* [SPARK-16925] Master should call schedule() after all executor exit events, ↵Josh Rosen2016-08-062-10/+22
| | | | | | | | | | | | | | | | | | | | | | | | | | not only failures ## What changes were proposed in this pull request? This patch fixes a bug in Spark's standalone Master which could cause applications to hang if tasks cause executors to exit with zero exit codes. As an example of the bug, run ``` sc.parallelize(1 to 1, 1).foreachPartition { _ => System.exit(0) } ``` on a standalone cluster which has a single Spark application. This will cause all executors to die but those executors won't be replaced unless another Spark application or worker joins or leaves the cluster (or if an executor exits with a non-zero exit code). This behavior is caused by a bug in how the Master handles the `ExecutorStateChanged` event: the current implementation calls `schedule()` only if the executor exited with a non-zero exit code, so a task which causes a JVM to unexpectedly exit "cleanly" will skip the `schedule()` call. This patch addresses this by modifying the `ExecutorStateChanged` to always unconditionally call `schedule()`. This should be safe because it should always be safe to call `schedule()`; adding extra `schedule()` calls can only affect performance and should not introduce correctness bugs. ## How was this patch tested? I added a regression test in `DistributedSuite`. Author: Josh Rosen <joshrosen@databricks.com> Closes #14510 from JoshRosen/SPARK-16925.
* [SPARK-16772][PYTHON][DOCS] Fix API doc references to UDFRegistration + ↵Nicholas Chammas2016-08-063-9/+5
| | | | | | | | | | | | | | | | | | | | | Update "important classes" ## Proposed Changes * Update the list of "important classes" in `pyspark.sql` to match 2.0. * Fix references to `UDFRegistration` so that the class shows up in the docs. It currently [doesn't](http://spark.apache.org/docs/latest/api/python/pyspark.sql.html). * Remove some unnecessary whitespace in the Python RST doc files. I reused the [existing JIRA](https://issues.apache.org/jira/browse/SPARK-16772) I created last week for similar API doc fixes. ## How was this patch tested? * I ran `lint-python` successfully. * I ran `make clean build` on the Python docs and confirmed the results are as expected locally in my browser. Author: Nicholas Chammas <nicholas.chammas@gmail.com> Closes #14496 from nchammas/SPARK-16772-UDFRegistration.
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