aboutsummaryrefslogtreecommitdiff
Commit message (Collapse)AuthorAgeFilesLines
* [SPARK-13148][YARN] document zero-keytab Oozie application launch; add ↵Steve Loughran2016-05-263-2/+148
| | | | | | | | | | | diagnostics This patch provides detail on what to do for keytabless Oozie launches of spark apps, and adds some debug-level diagnostics of what credentials have been submitted Author: Steve Loughran <stevel@hortonworks.com> Author: Steve Loughran <stevel@apache.org> Closes #11033 from steveloughran/stevel/feature/SPARK-13148-oozie.
* [SPARK-10903][SPARKR] R - Simplify SQLContext method signatures and use a ↵felixcheung2016-05-266-297/+450
| | | | | | | | | | | | | | singleton Eliminate the need to pass sqlContext to method since it is a singleton - and we don't want to support multiple contexts in a R session. Changes are done in a back compat way with deprecation warning added. Method signature for S3 methods are added in a concise, clean approach such that in the next release the deprecated signature can be taken out easily/cleanly (just delete a few lines per method). Custom method dispatch is implemented to allow for multiple JVM reference types that are all 'jobj' in R and to avoid having to add 30 new exports. Author: felixcheung <felixcheung_m@hotmail.com> Closes #9192 from felixcheung/rsqlcontext.
* [SPARK-15523][ML][MLLIB] Update JPMML to 1.2.15Villu Ruusmann2016-05-266-22/+13
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? See https://issues.apache.org/jira/browse/SPARK-15523 This PR replaces PR #13293. It's isolated to a new branch, and contains some more squashed changes. ## How was this patch tested? 1. Executed `mvn clean package` in `mllib` directory 2. Executed `dev/test-dependencies.sh --replace-manifest` in the root directory. Author: Villu Ruusmann <villu.ruusmann@gmail.com> Closes #13297 from vruusmann/update-jpmml.
* [SPARK-15492][ML][DOC] Binarization scala example copy & paste to ↵wm624@hotmail.com2016-05-262-4/+3
| | | | | | | | | | | | | | | | | | | spark-shell error ## What changes were proposed in this pull request? (Please fill in changes proposed in this fix) The Binarization scala example val dataFrame : Dataframe = spark.createDataFrame(data).toDF("label", "feature"), which can't be pasted in the spark-shell as Dataframe is not imported. Compared with other examples, this explicit type is not required. So I removed Dataframe in the code. ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) Manually tested Author: wm624@hotmail.com <wm624@hotmail.com> Closes #13266 from wangmiao1981/unit.
* [SPARK-15537][SQL] fix dir delete issueBo Meng2016-05-262-21/+3
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? For some of the test cases, e.g. `OrcSourceSuite`, it will create temp folders and temp files inside them. But after tests finish, the folders are not removed. This will cause lots of temp files created and space occupied, if we keep running the test cases. The reason is dir.delete() won't work if dir is not empty. We need to recursively delete the content before deleting the folder. ## How was this patch tested? Manually checked the temp folder to make sure the temp files were deleted. Author: Bo Meng <mengbo@hotmail.com> Closes #13304 from bomeng/SPARK-15537.
* [SPARK-15543][SQL] Rename DefaultSources to make them more self-describingReynold Xin2016-05-2520-69/+99
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch renames various DefaultSources to make their names more self-describing. The choice of "DefaultSource" was from the days when we did not have a good way to specify short names. They are now named: - LibSVMFileFormat - CSVFileFormat - JdbcRelationProvider - JsonFileFormat - ParquetFileFormat - TextFileFormat Backward compatibility is maintained through aliasing. ## How was this patch tested? Updated relevant test cases too. Author: Reynold Xin <rxin@databricks.com> Closes #13311 from rxin/SPARK-15543.
* [SPARK-10372] [CORE] basic test framework for entire spark schedulerImran Rashid2016-05-267-13/+728
| | | | | | | | This is a basic framework for testing the entire scheduler. The tests this adds aren't very interesting -- the point of this PR is just to setup the framework, to keep the initial change small, but it can be built upon to test more features (eg., speculation, killing tasks, blacklisting, etc.). Author: Imran Rashid <irashid@cloudera.com> Closes #8559 from squito/SPARK-10372-scheduler-integs.
* [SPARK-15439][SPARKR] Failed to run unit test in SparkRwm624@hotmail.com2016-05-252-2/+10
| | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? (Please fill in changes proposed in this fix) There are some failures when running SparkR unit tests. In this PR, I fixed two of these failures in test_context.R and test_sparkSQL.R The first one is due to different masked name. I added missed names in the expected arrays. The second one is because one PR removed the logic of a previous fix of missing subset method. The file privilege issue is still there. I am debugging it. SparkR shell can run the test case successfully. test_that("pipeRDD() on RDDs", { actual <- collect(pipeRDD(rdd, "more")) When using run-test script, it complains no such directories as below: cannot open file '/tmp/Rtmp4FQbah/filee2273f9d47f7': No such file or directory ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) Manually test it Author: wm624@hotmail.com <wm624@hotmail.com> Closes #13284 from wangmiao1981/R.
* [SPARK-15533][SQL] Deprecate Dataset.explodeSameer Agarwal2016-05-251-11/+22
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch deprecates `Dataset.explode` and documents appropriate workarounds to use `flatMap()` or `functions.explode()` instead. ## How was this patch tested? N/A Author: Sameer Agarwal <sameer@databricks.com> Closes #13312 from sameeragarwal/deprecate.
* [SPARK-15525][SQL][BUILD] Upgrade ANTLR4 SBT pluginHerman van Hovell2016-05-257-15/+7
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? The ANTLR4 SBT plugin has been moved from its own repo to one on bintray. The version was also changed from `0.7.10` to `0.7.11`. The latter actually broke our build (ihji has fixed this by also adding `0.7.10` and others to the bin-tray repo). This PR upgrades the SBT-ANTLR4 plugin and ANTLR4 to their most recent versions (`0.7.11`/`4.5.3`). I have also removed a few obsolete build configurations. ## How was this patch tested? Manually running SBT/Maven builds. Author: Herman van Hovell <hvanhovell@questtec.nl> Closes #13299 from hvanhovell/SPARK-15525.
* [SPARK-15534][SPARK-15535][SQL] Truncate table fixesAndrew Or2016-05-254-23/+6
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Two changes: - When things fail, `TRUNCATE TABLE` just returns nothing. Instead, we should throw exceptions. - Remove `TRUNCATE TABLE ... COLUMN`, which was never supported by either Spark or Hive. ## How was this patch tested? Jenkins. Author: Andrew Or <andrew@databricks.com> Closes #13302 from andrewor14/truncate-table.
* Log warnings for numIterations * miniBatchFraction < 1.0Gio Borje2016-05-251-0/+5
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add a warning log for the case that `numIterations * miniBatchFraction <1.0` during gradient descent. If the product of those two numbers is less than `1.0`, then not all training examples will be used during optimization. To put this concretely, suppose that `numExamples = 100`, `miniBatchFraction = 0.2` and `numIterations = 3`. Then, 3 iterations will occur each sampling approximately 6 examples each. In the best case, each of the 6 examples are unique; hence 18/100 examples are used. This may be counter-intuitive to most users and led to the issue during the development of another Spark ML model: https://github.com/zhengruifeng/spark-libFM/issues/11. If a user actually does not require the training data set, it would be easier and more intuitive to use `RDD.sample`. ## How was this patch tested? `build/mvn -DskipTests clean package` build succeeds Author: Gio Borje <gborje@linkedin.com> Closes #13265 from Hydrotoast/master.
* [MINOR] [PYSPARK] [EXAMPLES] Changed examples to use ↵Bryan Cutler2016-05-255-5/+5
| | | | | | | | | | | | | | | SparkSession.sparkContext instead of _sc ## What changes were proposed in this pull request? Some PySpark examples need a SparkContext and get it by accessing _sc directly from the session. These examples should use the provided property `sparkContext` in `SparkSession` instead. ## How was this patch tested? Ran modified examples Author: Bryan Cutler <cutlerb@gmail.com> Closes #13303 from BryanCutler/pyspark-session-sparkContext-MINOR.
* [SPARK-14269][SCHEDULER] Eliminate unnecessary submitStage() call.Takuya UESHIN2016-05-252-25/+17
| | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently a method `submitStage()` for waiting stages is called on every iteration of the event loop in `DAGScheduler` to submit all waiting stages, but most of them are not necessary because they are not related to Stage status. The case we should try to submit waiting stages is only when their parent stages are successfully completed. This elimination can improve `DAGScheduler` performance. ## How was this patch tested? Added some checks and other existing tests, and our projects. We have a project bottle-necked by `DAGScheduler`, having about 2000 stages. Before this patch the almost all execution time in `Driver` process was spent to process `submitStage()` of `dag-scheduler-event-loop` thread but after this patch the performance was improved as follows: | | total execution time | `dag-scheduler-event-loop` thread time | `submitStage()` | |--------|---------------------:|---------------------------------------:|----------------:| | Before | 760 sec | 710 sec | 667 sec | | After | 440 sec | 14 sec | 10 sec | Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #12060 from ueshin/issues/SPARK-14269.
* [SPARK-15493][SQL] default QuoteEscapingEnabled flag to true when writing CSVJurriaan Pruis2016-05-2511-7/+69
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Default QuoteEscapingEnabled flag to true when writing CSV and add an escapeQuotes option to be able to change this. See https://github.com/uniVocity/univocity-parsers/blob/f3eb2af26374940e60d91d1703bde54619f50c51/src/main/java/com/univocity/parsers/csv/CsvWriterSettings.java#L231-L247 This change is needed to be able to write RFC 4180 compatible CSV files (https://tools.ietf.org/html/rfc4180#section-2) https://issues.apache.org/jira/browse/SPARK-15493 ## How was this patch tested? Added a test that verifies the output is quoted correctly. Author: Jurriaan Pruis <email@jurriaanpruis.nl> Closes #13267 from jurriaan/quote-escaping.
* [SPARK-15483][SQL] IncrementalExecution should use extra strategies.Takuya UESHIN2016-05-252-1/+17
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Extra strategies does not work for streams because `IncrementalExecution` uses modified planner with stateful operations but it does not include extra strategies. This pr fixes `IncrementalExecution` to include extra strategies to use them. ## How was this patch tested? I added a test to check if extra strategies work for streams. Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #13261 from ueshin/issues/SPARK-15483.
* [SPARK-15500][DOC][ML][PYSPARK] Remove default value in Param doc field in ALSNick Pentreath2016-05-252-6/+4
| | | | | | | | | | | | Remove "Default: MEMORY_AND_DISK" from `Param` doc field in ALS storage level params. This fixes up the output of `explainParam(s)` so that default values are not displayed twice. We can revisit in the case that [SPARK-15130](https://issues.apache.org/jira/browse/SPARK-15130) moves ahead with adding defaults in some way to PySpark param doc fields. Tests N/A. Author: Nick Pentreath <nickp@za.ibm.com> Closes #13277 from MLnick/SPARK-15500-als-remove-default-storage-param.
* [MINOR][MLLIB][STREAMING][SQL] Fix typoslfzCarlosC2016-05-2511-12/+12
| | | | | | | | | | fixed typos for source code for components [mllib] [streaming] and [SQL] None and obvious. Author: lfzCarlosC <lfz.carlos@gmail.com> Closes #13298 from lfzCarlosC/master.
* [MINOR][CORE] Fix a HadoopRDD log message and remove unused imports in rdd ↵Dongjoon Hyun2016-05-255-7/+4
| | | | | | | | | | | | | | | | | | | | | | | files. ## What changes were proposed in this pull request? This PR fixes the following typos in log message and comments of `HadoopRDD.scala`. Also, this removes unused imports. ```scala - logWarning("Caching NewHadoopRDDs as deserialized objects usually leads to undesired" + + logWarning("Caching HadoopRDDs as deserialized objects usually leads to undesired" + ... - // since its not removed yet + // since it's not removed yet ``` ## How was this patch tested? Manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13294 from dongjoon-hyun/minor_rdd_fix_log_message.
* [SPARK-15520][SQL] SparkSession builder in python should also allow ↵Eric Liang2016-05-251-11/+24
| | | | | | | | | | | | | | | | | | overriding confs of existing sessions ## What changes were proposed in this pull request? This fixes the python SparkSession builder to allow setting confs correctly. This was a leftover TODO from https://github.com/apache/spark/pull/13200. ## How was this patch tested? Python doc tests. cc andrewor14 Author: Eric Liang <ekl@databricks.com> Closes #13289 from ericl/spark-15520.
* [SPARK-15345][SQL][PYSPARK] SparkSession's conf doesn't take effect when ↵Jeff Zhang2016-05-253-3/+28
| | | | | | | | | | | | | | | | | | this already an existing SparkContext ## What changes were proposed in this pull request? Override the existing SparkContext is the provided SparkConf is different. PySpark part hasn't been fixed yet, will do that after the first round of review to ensure this is the correct approach. ## How was this patch tested? Manually verify it in spark-shell. rxin Please help review it, I think this is a very critical issue for spark 2.0 Author: Jeff Zhang <zjffdu@apache.org> Closes #13160 from zjffdu/SPARK-15345.
* [SPARK-9044] Fix "Storage" tab in UI so that it reflects RDD name change.Lukasz2016-05-253-2/+19
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? 1. Making 'name' field of RDDInfo mutable. 2. In StorageListener: catching the fact that RDD's name was changed and updating it in RDDInfo. ## How was this patch tested? 1. Manual verification - the 'Storage' tab now behaves as expected. 2. The commit also contains a new unit test which verifies this. Author: Lukasz <lgieron@gmail.com> Closes #13264 from lgieron/SPARK-9044.
* [SPARK-15436][SQL] Remove DescribeFunction and ShowFunctionsReynold Xin2016-05-2510-121/+145
| | | | | | | | | | | | ## What changes were proposed in this pull request? This patch removes the last two commands defined in the catalyst module: DescribeFunction and ShowFunctions. They were unnecessary since the parser could just generate DescribeFunctionCommand and ShowFunctionsCommand directly. ## How was this patch tested? Created a new SparkSqlParserSuite. Author: Reynold Xin <rxin@databricks.com> Closes #13292 from rxin/SPARK-15436.
* [SPARK-12071][DOC] Document the behaviour of NA in RKrishna Kalyan2016-05-241-0/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Under Upgrading From SparkR 1.5.x to 1.6.x section added the information, SparkSQL converts `NA` in R to `null`. ## How was this patch tested? Document update, no tests. Author: Krishna Kalyan <krishnakalyan3@gmail.com> Closes #13268 from krishnakalyan3/spark-12071-1.
* [SPARK-15412][PYSPARK][SPARKR][DOCS] Improve linear isotonic regression ↵Holden Karau2016-05-242-14/+20
| | | | | | | | | | | | | | | | | | pydoc & doc build insturctions ## What changes were proposed in this pull request? PySpark: Add links to the predictors from the models in regression.py, improve linear and isotonic pydoc in minor ways. User guide / R: Switch the installed package list to be enough to build the R docs on a "fresh" install on ubuntu and add sudo to match the rest of the commands. User Guide: Add a note about using gem2.0 for systems with both 1.9 and 2.0 (e.g. some ubuntu but maybe more). ## How was this patch tested? built pydocs locally, tested new user build instructions Author: Holden Karau <holden@us.ibm.com> Closes #13199 from holdenk/SPARK-15412-improve-linear-isotonic-regression-pydoc.
* [SPARK-15508][STREAMING][TESTS] Fix flaky test: ↵Shixiong Zhu2016-05-241-6/+15
| | | | | | | | | | | | | | | | | | | | JavaKafkaStreamSuite.testKafkaStream ## What changes were proposed in this pull request? `JavaKafkaStreamSuite.testKafkaStream` assumes when `sent.size == result.size`, the contents of `sent` and `result` should be same. However, that's not true. The content of `result` may not be the final content. This PR modified the test to always retry the assertions even if the contents of `sent` and `result` are not same. Here is the failure in Jenkins: http://spark-tests.appspot.com/tests/org.apache.spark.streaming.kafka.JavaKafkaStreamSuite/testKafkaStream ## How was this patch tested? Jenkins unit tests. Author: Shixiong Zhu <shixiong@databricks.com> Closes #13281 from zsxwing/flaky-kafka-test.
* [SPARK-15498][TESTS] fix slow testsWenchen Fan2016-05-245-79/+128
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes 3 slow tests: 1. `ParquetQuerySuite.read/write wide table`: This is not a good unit test as it runs more than 5 minutes. This PR removes it and add a new regression test in `CodeGenerationSuite`, which is more "unit". 2. `ParquetQuerySuite.returning batch for wide table`: reduce the threshold and use smaller data size. 3. `DatasetSuite.SPARK-14554: Dataset.map may generate wrong java code for wide table`: Improve `CodeFormatter.format`(introduced at https://github.com/apache/spark/pull/12979) can dramatically speed this it up. ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #13273 from cloud-fan/test.
* [SPARK-15365][SQL] When table size statistics are not available from ↵Parth Brahmbhatt2016-05-243-9/+83
| | | | | | | | | | | | | | metastore, we should fallback to HDFS ## What changes were proposed in this pull request? Currently if a table is used in join operation we rely on Metastore returned size to calculate if we can convert the operation to Broadcast join. This optimization only kicks in for table's that have the statistics available in metastore. Hive generally rolls over to HDFS if the statistics are not available directly from metastore and this seems like a reasonable choice to adopt given the optimization benefit of using broadcast joins. ## How was this patch tested? I have executed queries locally to test. Author: Parth Brahmbhatt <pbrahmbhatt@netflix.com> Closes #13150 from Parth-Brahmbhatt/SPARK-15365.
* [SPARK-15518] Rename various scheduler backend for consistencyReynold Xin2016-05-2414-107/+123
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch renames various scheduler backends to make them consistent: - LocalScheduler -> LocalSchedulerBackend - AppClient -> StandaloneAppClient - AppClientListener -> StandaloneAppClientListener - SparkDeploySchedulerBackend -> StandaloneSchedulerBackend - CoarseMesosSchedulerBackend -> MesosCoarseGrainedSchedulerBackend - MesosSchedulerBackend -> MesosFineGrainedSchedulerBackend ## How was this patch tested? Updated test cases to reflect the name change. Author: Reynold Xin <rxin@databricks.com> Closes #13288 from rxin/SPARK-15518.
* [SPARK-15512][CORE] repartition(0) should raise IllegalArgumentExceptionDongjoon Hyun2016-05-246-1/+27
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Previously, SPARK-8893 added the constraints on positive number of partitions for repartition/coalesce operations in general. This PR adds one missing part for that and adds explicit two testcases. **Before** ```scala scala> sc.parallelize(1 to 5).coalesce(0) java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive. ... scala> sc.parallelize(1 to 5).repartition(0).collect() res1: Array[Int] = Array() // empty scala> spark.sql("select 1").coalesce(0) res2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [1: int] scala> spark.sql("select 1").coalesce(0).collect() java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive. scala> spark.sql("select 1").repartition(0) res3: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [1: int] scala> spark.sql("select 1").repartition(0).collect() res4: Array[org.apache.spark.sql.Row] = Array() // empty ``` **After** ```scala scala> sc.parallelize(1 to 5).coalesce(0) java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive. ... scala> sc.parallelize(1 to 5).repartition(0) java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive. ... scala> spark.sql("select 1").coalesce(0) java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive. ... scala> spark.sql("select 1").repartition(0) java.lang.IllegalArgumentException: requirement failed: Number of partitions (0) must be positive. ... ``` ## How was this patch tested? Pass the Jenkins tests with new testcases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13282 from dongjoon-hyun/SPARK-15512.
* [SPARK-15458][SQL][STREAMING] Disable schema inference for streaming ↵Tathagata Das2016-05-243-91/+166
| | | | | | | | | | | | | | | | | | | | | | datasets on file streams ## What changes were proposed in this pull request? If the user relies on the schema to be inferred in file streams can break easily for multiple reasons - accidentally running on a directory which has no data - schema changing underneath - on restart, the query will infer schema again, and may unexpectedly infer incorrect schema, as the file in the directory may be different at the time of the restart. To avoid these complicated scenarios, for Spark 2.0, we are going to disable schema inferencing by default with a config, so that user is forced to consider explicitly what is the schema it wants, rather than the system trying to infer it and run into weird corner cases. In this PR, I introduce a SQLConf that determines whether schema inference for file streams is allowed or not. It is disabled by default. ## How was this patch tested? Updated unit tests that test error behavior with and without schema inference enabled. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #13238 from tdas/SPARK-15458.
* [SPARK-15502][DOC][ML][PYSPARK] add guide note that ALS only supports ↵Nick Pentreath2016-05-241-1/+5
| | | | | | | | | | | | | | | | | integer ids This PR adds a note to clarify that the ML API for ALS only supports integers for user/item ids, and that other types for these columns can be used but the ids must fall within integer range. (Refer [SPARK-14891](https://issues.apache.org/jira/browse/SPARK-14891)). Also cleaned up a reference to `mllib` in the ML doc. ## How was this patch tested? Built and viewed User Guide doc locally. Author: Nick Pentreath <nickp@za.ibm.com> Closes #13278 from MLnick/SPARK-15502-als-int-id-doc-note.
* [MINOR][CORE][TEST] Update obsolete `takeSample` test case.Dongjoon Hyun2016-05-241-8/+7
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes some obsolete comments and assertion in `takeSample` testcase of `RDDSuite.scala`. ## How was this patch tested? This fixes the testcase only. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13260 from dongjoon-hyun/SPARK-15481.
* [SPARK-15388][SQL] Fix spark sql CREATE FUNCTION with hive 1.2.1wangyang2016-05-242-2/+13
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? spark.sql("CREATE FUNCTION myfunc AS 'com.haizhi.bdp.udf.UDFGetGeoCode'") throws "org.apache.hadoop.hive.ql.metadata.HiveException:MetaException(message:NoSuchObjectException(message:Function default.myfunc does not exist))" with hive 1.2.1. I think it is introduced by pr #12853. Fixing it by catching Exception (not NoSuchObjectException) and string matching. ## How was this patch tested? added a unit test and also tested it manually Author: wangyang <wangyang@haizhi.com> Closes #13177 from wangyang1992/fixCreateFunc2.
* [SPARK-15405][YARN] Remove unnecessary upload of config archive.Marcelo Vanzin2016-05-242-8/+13
| | | | | | | | | | | | | We only need one copy of it. The client code that was uploading the second copy just needs to be modified to update the metadata in the cache, so that the AM knows where to find the configuration. Tested by running app on YARN and verifying in the logs only one archive is uploaded. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #13232 from vanzin/SPARK-15405.
* [SPARK-15433] [PYSPARK] PySpark core test should not use SerDe from ↵Liang-Chi Hsieh2016-05-242-3/+3
| | | | | | | | | | | | | | | PythonMLLibAPI ## What changes were proposed in this pull request? Currently PySpark core test uses the `SerDe` from `PythonMLLibAPI` which includes many MLlib things. It should use `SerDeUtil` instead. ## How was this patch tested? Existing tests. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #13214 from viirya/pycore-use-serdeutil.
* [SPARK-13135] [SQL] Don't print expressions recursively in generated codeDongjoon Hyun2016-05-2410-12/+82
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR is an up-to-date and a little bit improved version of #11019 of rxin for - (1) preventing recursive printing of expressions in generated code. Since the major function of this PR is indeed the above, he should be credited for the work he did. In addition to #11019, this PR improves the followings in code generation. - (2) Improve multiline comment indentation. - (3) Reduce the number of empty lines (mainly consecutive empty lines). - (4) Remove all space characters on empty lines. **Example** ```scala spark.range(1, 1000).select('id+1+2+3, 'id+4+5+6) ``` **Before** ``` Generated code: /* 001 */ public Object generate(Object[] references) { ... /* 005 */ /** /* 006 */ * Codegend pipeline for /* 007 */ * Project [(((id#0L + 1) + 2) + 3) AS (((id + 1) + 2) + 3)#3L,(((id#0L + 4) + 5) + 6) AS (((id + 4) + 5) + 6)#4L] /* 008 */ * +- Range 1, 1, 8, 999, [id#0L] /* 009 */ */ ... /* 075 */ // PRODUCE: Project [(((id#0L + 1) + 2) + 3) AS (((id + 1) + 2) + 3)#3L,(((id#0L + 4) + 5) + 6) AS (((id + 4) + 5) + 6)#4L] /* 076 */ /* 077 */ // PRODUCE: Range 1, 1, 8, 999, [id#0L] /* 078 */ /* 079 */ // initialize Range ... /* 092 */ // CONSUME: Project [(((id#0L + 1) + 2) + 3) AS (((id + 1) + 2) + 3)#3L,(((id#0L + 4) + 5) + 6) AS (((id + 4) + 5) + 6)#4L] /* 093 */ /* 094 */ // CONSUME: WholeStageCodegen /* 095 */ /* 096 */ // (((input[0, bigint, false] + 1) + 2) + 3) /* 097 */ // ((input[0, bigint, false] + 1) + 2) /* 098 */ // (input[0, bigint, false] + 1) ... /* 107 */ // (((input[0, bigint, false] + 4) + 5) + 6) /* 108 */ // ((input[0, bigint, false] + 4) + 5) /* 109 */ // (input[0, bigint, false] + 4) ... /* 126 */ } ``` **After** ``` Generated code: /* 001 */ public Object generate(Object[] references) { ... /* 005 */ /** /* 006 */ * Codegend pipeline for /* 007 */ * Project [(((id#0L + 1) + 2) + 3) AS (((id + 1) + 2) + 3)#3L,(((id#0L + 4) + 5) + 6) AS (((id + 4) + 5) + 6)#4L] /* 008 */ * +- Range 1, 1, 8, 999, [id#0L] /* 009 */ */ ... /* 075 */ // PRODUCE: Project [(((id#0L + 1) + 2) + 3) AS (((id + 1) + 2) + 3)#3L,(((id#0L + 4) + 5) + 6) AS (((id + 4) + 5) + 6)#4L] /* 076 */ // PRODUCE: Range 1, 1, 8, 999, [id#0L] /* 077 */ // initialize Range ... /* 090 */ // CONSUME: Project [(((id#0L + 1) + 2) + 3) AS (((id + 1) + 2) + 3)#3L,(((id#0L + 4) + 5) + 6) AS (((id + 4) + 5) + 6)#4L] /* 091 */ // CONSUME: WholeStageCodegen /* 092 */ // (((input[0, bigint, false] + 1) + 2) + 3) ... /* 101 */ // (((input[0, bigint, false] + 4) + 5) + 6) ... /* 118 */ } ``` ## How was this patch tested? Pass the Jenkins tests and see the result of the following command manually. ```scala scala> spark.range(1, 1000).select('id+1+2+3, 'id+4+5+6).queryExecution.debug.codegen() ``` Author: Dongjoon Hyun <dongjoonapache.org> Author: Reynold Xin <rxindatabricks.com> Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13192 from dongjoon-hyun/SPARK-13135.
* [SPARK-11753][SQL][TEST-HADOOP2.2] Make allowNonNumericNumbers option workLiang-Chi Hsieh2016-05-2410-53/+102
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Jackson suppprts `allowNonNumericNumbers` option to parse non-standard non-numeric numbers such as "NaN", "Infinity", "INF". Currently used Jackson version (2.5.3) doesn't support it all. This patch upgrades the library and make the two ignored tests in `JsonParsingOptionsSuite` passed. ## How was this patch tested? `JsonParsingOptionsSuite`. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Author: Liang-Chi Hsieh <viirya@appier.com> Closes #9759 from viirya/fix-json-nonnumric.
* [SPARK-15442][ML][PYSPARK] Add 'relativeError' param to PySpark ↵Nick Pentreath2016-05-242-20/+44
| | | | | | | | | | | | | | | | | | QuantileDiscretizer This PR adds the `relativeError` param to PySpark's `QuantileDiscretizer` to match Scala. Also cleaned up a duplication of `numBuckets` where the param is both a class and instance attribute (I removed the instance attr to match the style of params throughout `ml`). Finally, cleaned up the docs for `QuantileDiscretizer` to reflect that it now uses `approxQuantile`. ## How was this patch tested? A little doctest and built API docs locally to check HTML doc generation. Author: Nick Pentreath <nickp@za.ibm.com> Closes #13228 from MLnick/SPARK-15442-py-relerror-param.
* [SPARK-15397][SQL] fix string udf locate as hiveDaoyuan Wang2016-05-236-21/+30
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? in hive, `locate("aa", "aaa", 0)` would yield 0, `locate("aa", "aaa", 1)` would yield 1 and `locate("aa", "aaa", 2)` would yield 2, while in Spark, `locate("aa", "aaa", 0)` would yield 1, `locate("aa", "aaa", 1)` would yield 2 and `locate("aa", "aaa", 2)` would yield 0. This results from the different understanding of the third parameter in udf `locate`. It means the starting index and starts from 1, so when we use 0, the return would always be 0. ## How was this patch tested? tested with modified `StringExpressionsSuite` and `StringFunctionsSuite` Author: Daoyuan Wang <daoyuan.wang@intel.com> Closes #13186 from adrian-wang/locate.
* Revert "[SPARK-15285][SQL] Generated SpecificSafeProjection.apply method ↵Andrew Or2016-05-232-61/+6
| | | | | | grows beyond 64 KB" This reverts commit fa244e5a90690d6a31be50f2aa203ae1a2e9a1cf.
* [SPARK-15285][SQL] Generated SpecificSafeProjection.apply method grows ↵Kazuaki Ishizaki2016-05-232-6/+61
| | | | | | | | | | | | | | | | beyond 64 KB ## What changes were proposed in this pull request? This PR splits the generated code for ```SafeProjection.apply``` by using ```ctx.splitExpressions()```. This is because the large code body for ```NewInstance``` may grow beyond 64KB bytecode size for ```apply()``` method. ## How was this patch tested? Added new tests Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Closes #13243 from kiszk/SPARK-15285.
* [SPARK-15485][SQL][DOCS] Spark SQL Configurationgatorsmile2016-05-231-0/+42
| | | | | | | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? So far, the page Configuration in the official documentation does not have a section for Spark SQL. http://spark.apache.org/docs/latest/configuration.html For Spark users, the information and default values of these public configuration parameters are very useful. This PR is to add this missing section to the configuration.html. rxin yhuai marmbrus #### How was this patch tested? Below is the generated webpage. <img width="924" alt="screenshot 2016-05-23 11 35 57" src="https://cloud.githubusercontent.com/assets/11567269/15480492/b08fefc4-20da-11e6-9fa2-7cd5b699ed35.png"> <img width="914" alt="screenshot 2016-05-23 11 37 38" src="https://cloud.githubusercontent.com/assets/11567269/15480499/c5f9482e-20da-11e6-95ff-10821add1af4.png"> <img width="923" alt="screenshot 2016-05-23 11 36 11" src="https://cloud.githubusercontent.com/assets/11567269/15480506/cbd81644-20da-11e6-9d27-effb716b2fac.png"> <img width="920" alt="screenshot 2016-05-23 11 36 18" src="https://cloud.githubusercontent.com/assets/11567269/15480511/d013e332-20da-11e6-854a-cf8813c46f36.png"> Author: gatorsmile <gatorsmile@gmail.com> Closes #13263 from gatorsmile/configurationSQL.
* [SPARK-15464][ML][MLLIB][SQL][TESTS] Replace SQLContext and SparkContext ↵WeichenXu2016-05-2322-232/+298
| | | | | | | | | | | | | | | | with SparkSession using builder pattern in python test code ## What changes were proposed in this pull request? Replace SQLContext and SparkContext with SparkSession using builder pattern in python test code. ## How was this patch tested? Existing test. Author: WeichenXu <WeichenXu123@outlook.com> Closes #13242 from WeichenXu123/python_doctest_update_sparksession.
* [SPARK-15311][SQL] Disallow DML on Regular Tables when Using In-Memory Cataloggatorsmile2016-05-233-1/+65
| | | | | | | | | | | | | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? So far, when using In-Memory Catalog, we allow DDL operations for the tables. However, the corresponding DML operations are not supported for the tables that are neither temporary nor data source tables. For example, ```SQL CREATE TABLE tabName(i INT, j STRING) SELECT * FROM tabName INSERT OVERWRITE TABLE tabName SELECT 1, 'a' ``` In the above example, before this PR fix, we will get very confusing exception messages for either `SELECT` or `INSERT` ``` org.apache.spark.sql.AnalysisException: unresolved operator 'SimpleCatalogRelation default, CatalogTable(`default`.`tbl`,CatalogTableType(MANAGED),CatalogStorageFormat(None,Some(org.apache.hadoop.mapred.TextInputFormat),Some(org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat),None,false,Map()),List(CatalogColumn(i,int,true,None), CatalogColumn(j,string,true,None)),List(),List(),List(),-1,,1463928681802,-1,Map(),None,None,None,List()), None; ``` This PR is to issue appropriate exceptions in this case. The message will be like ``` org.apache.spark.sql.AnalysisException: Please enable Hive support when operating non-temporary tables: `tbl`; ``` #### How was this patch tested? Added a test case in `DDLSuite`. Author: gatorsmile <gatorsmile@gmail.com> Author: xiaoli <lixiao1983@gmail.com> Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local> Closes #13093 from gatorsmile/selectAfterCreate.
* [SPARK-15431][SQL] Support LIST FILE(s)|JAR(s) command nativelyXin Wu2016-05-2310-17/+149
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently command `ADD FILE|JAR <filepath | jarpath>` is supported natively in SparkSQL. However, when this command is run, the file/jar is added to the resources that can not be looked up by `LIST FILE(s)|JAR(s)` command because the `LIST` command is passed to Hive command processor in Spark-SQL or simply not supported in Spark-shell. There is no way users can find out what files/jars are added to the spark context. Refer to [Hive commands](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Cli) This PR is to support following commands: `LIST (FILE[s] [filepath ...] | JAR[s] [jarfile ...])` ### For example: ##### LIST FILE(s) ``` scala> spark.sql("add file hdfs://bdavm009.svl.ibm.com:8020/tmp/test.txt") res1: org.apache.spark.sql.DataFrame = [] scala> spark.sql("add file hdfs://bdavm009.svl.ibm.com:8020/tmp/test1.txt") res2: org.apache.spark.sql.DataFrame = [] scala> spark.sql("list file hdfs://bdavm009.svl.ibm.com:8020/tmp/test1.txt").show(false) +----------------------------------------------+ |result | +----------------------------------------------+ |hdfs://bdavm009.svl.ibm.com:8020/tmp/test1.txt| +----------------------------------------------+ scala> spark.sql("list files").show(false) +----------------------------------------------+ |result | +----------------------------------------------+ |hdfs://bdavm009.svl.ibm.com:8020/tmp/test1.txt| |hdfs://bdavm009.svl.ibm.com:8020/tmp/test.txt | +----------------------------------------------+ ``` ##### LIST JAR(s) ``` scala> spark.sql("add jar /Users/xinwu/spark/core/src/test/resources/TestUDTF.jar") res9: org.apache.spark.sql.DataFrame = [result: int] scala> spark.sql("list jar TestUDTF.jar").show(false) +---------------------------------------------+ |result | +---------------------------------------------+ |spark://192.168.1.234:50131/jars/TestUDTF.jar| +---------------------------------------------+ scala> spark.sql("list jars").show(false) +---------------------------------------------+ |result | +---------------------------------------------+ |spark://192.168.1.234:50131/jars/TestUDTF.jar| +---------------------------------------------+ ``` ## How was this patch tested? New test cases are added for Spark-SQL, Spark-Shell and SparkContext API code path. Author: Xin Wu <xinwu@us.ibm.com> Author: xin Wu <xinwu@us.ibm.com> Closes #13212 from xwu0226/list_command.
* [MINOR][SPARKR][DOC] Add a description for running unit tests in Windowshyukjinkwon2016-05-232-1/+27
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds the description for running unit tests in Windows. ## How was this patch tested? On a bare machine (Window 7, 32bits), this was manually built and tested. Author: hyukjinkwon <gurwls223@gmail.com> Closes #13217 from HyukjinKwon/minor-r-doc.
* [SPARK-15315][SQL] Adding error check to the CSV datasource writer for ↵sureshthalamati2016-05-232-1/+35
| | | | | | | | | | | | | | | | unsupported complex data types. ## What changes were proposed in this pull request? Adds error handling to the CSV writer for unsupported complex data types. Currently garbage gets written to the output csv files if the data frame schema has complex data types. ## How was this patch tested? Added new unit test case. Author: sureshthalamati <suresh.thalamati@gmail.com> Closes #13105 from sureshthalamati/csv_complex_types_SPARK-15315.
* [MINOR][SQL][DOCS] Add notes of the deterministic assumption on UDF functionsDongjoon Hyun2016-05-237-0/+15
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Spark assumes that UDF functions are deterministic. This PR adds explicit notes about that. ## How was this patch tested? It's only about docs. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13087 from dongjoon-hyun/SPARK-15282.
* [SPARK-15279][SQL] Catch conflicting SerDe when creating tableAndrew Or2016-05-234-33/+129
| | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The user may do something like: ``` CREATE TABLE my_tab ROW FORMAT SERDE 'anything' STORED AS PARQUET CREATE TABLE my_tab ROW FORMAT SERDE 'anything' STORED AS ... SERDE 'myserde' CREATE TABLE my_tab ROW FORMAT DELIMITED ... STORED AS ORC CREATE TABLE my_tab ROW FORMAT DELIMITED ... STORED AS ... SERDE 'myserde' ``` None of these should be allowed because the SerDe's conflict. As of this patch: - `ROW FORMAT DELIMITED` is only compatible with `TEXTFILE` - `ROW FORMAT SERDE` is only compatible with `TEXTFILE`, `RCFILE` and `SEQUENCEFILE` ## How was this patch tested? New tests in `DDLCommandSuite`. Author: Andrew Or <andrew@databricks.com> Closes #13068 from andrewor14/row-format-conflict.
generated by cgit v1.2.3 (git 2.39.1) at 2024-09-12 13:09:36 +0000