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* [SPARK-17239][ML][DOC] Update user guide for multiclass logistic regressionsethah2016-10-057-7/+255
| | | | | | | | | | | | ## What changes were proposed in this pull request? Updates user guide to reflect that LogisticRegression now supports multiclass. Also adds new examples to show multiclass training. ## How was this patch tested? Ran locally using spark-submit, run-example, and copy/paste from user guide into shells. Generated docs and verified correct output. Author: sethah <seth.hendrickson16@gmail.com> Closes #15349 from sethah/SPARK-17239.
* [SPARK-17328][SQL] Fix NPE with EXPLAIN DESCRIBE TABLEDongjoon Hyun2016-10-055-18/+68
| | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes the following NPE scenario in two ways. **Reported Error Scenario** ```scala scala> sql("EXPLAIN DESCRIBE TABLE x").show(truncate = false) INFO SparkSqlParser: Parsing command: EXPLAIN DESCRIBE TABLE x java.lang.NullPointerException ``` - **DESCRIBE**: Extend `DESCRIBE` syntax to accept `TABLE`. - **EXPLAIN**: Prevent NPE in case of the parsing failure of target statement, e.g., `EXPLAIN DESCRIBE TABLES x`. ## How was this patch tested? Pass the Jenkins test with a new test case. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15357 from dongjoon-hyun/SPARK-17328.
* [SPARK-17258][SQL] Parse scientific decimal literals as decimalsHerman van Hovell2016-10-047-39/+38
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently Spark SQL parses regular decimal literals (e.g. `10.00`) as decimals and scientific decimal literals (e.g. `10.0e10`) as doubles. The difference between the two confuses most users. This PR unifies the parsing behavior and also parses scientific decimal literals as decimals. This implications in tests are limited to a single Hive compatibility test. ## How was this patch tested? Updated tests in `ExpressionParserSuite` and `SQLQueryTestSuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #14828 from hvanhovell/SPARK-17258.
* [SPARK-17658][SPARKR] read.df/write.df API taking path optionally in SparkRhyukjinkwon2016-10-046-13/+127
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? `write.df`/`read.df` API require path which is not actually always necessary in Spark. Currently, it only affects the datasources implementing `CreatableRelationProvider`. Currently, Spark currently does not have internal data sources implementing this but it'd affect other external datasources. In addition we'd be able to use this way in Spark's JDBC datasource after https://github.com/apache/spark/pull/12601 is merged. **Before** - `read.df` ```r > read.df(source = "json") Error in dispatchFunc("read.df(path = NULL, source = NULL, schema = NULL, ...)", : argument "x" is missing with no default ``` ```r > read.df(path = c(1, 2)) Error in dispatchFunc("read.df(path = NULL, source = NULL, schema = NULL, ...)", : argument "x" is missing with no default ``` ```r > read.df(c(1, 2)) Error in invokeJava(isStatic = TRUE, className, methodName, ...) : java.lang.ClassCastException: java.lang.Double cannot be cast to java.lang.String at org.apache.spark.sql.execution.datasources.DataSource.hasMetadata(DataSource.scala:300) at ... In if (is.na(object)) { : ... ``` - `write.df` ```r > write.df(df, source = "json") Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘write.df’ for signature ‘"function", "missing"’ ``` ```r > write.df(df, source = c(1, 2)) Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘write.df’ for signature ‘"SparkDataFrame", "missing"’ ``` ```r > write.df(df, mode = TRUE) Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘write.df’ for signature ‘"SparkDataFrame", "missing"’ ``` **After** - `read.df` ```r > read.df(source = "json") Error in loadDF : analysis error - Unable to infer schema for JSON at . It must be specified manually; ``` ```r > read.df(path = c(1, 2)) Error in f(x, ...) : path should be charactor, null or omitted. ``` ```r > read.df(c(1, 2)) Error in f(x, ...) : path should be charactor, null or omitted. ``` - `write.df` ```r > write.df(df, source = "json") Error in save : illegal argument - 'path' is not specified ``` ```r > write.df(df, source = c(1, 2)) Error in .local(df, path, ...) : source should be charactor, null or omitted. It is 'parquet' by default. ``` ```r > write.df(df, mode = TRUE) Error in .local(df, path, ...) : mode should be charactor or omitted. It is 'error' by default. ``` ## How was this patch tested? Unit tests in `test_sparkSQL.R` Author: hyukjinkwon <gurwls223@gmail.com> Closes #15231 from HyukjinKwon/write-default-r.
* [SPARK-17495][SQL] Add Hash capability semantically equivalent to Hive'sTejas Patil2016-10-046-151/+631
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Jira : https://issues.apache.org/jira/browse/SPARK-17495 Spark internally uses Murmur3Hash for partitioning. This is different from the one used by Hive. For queries which use bucketing this leads to different results if one tries the same query on both engines. For us, we want users to have backward compatibility to that one can switch parts of applications across the engines without observing regressions. This PR includes `HiveHash`, `HiveHashFunction`, `HiveHasher` which mimics Hive's hashing at https://github.com/apache/hive/blob/master/serde/src/java/org/apache/hadoop/hive/serde2/objectinspector/ObjectInspectorUtils.java#L638 I am intentionally not introducing any usages of this hash function in rest of the code to keep this PR small. My eventual goal is to have Hive bucketing support in Spark. Once this PR gets in, I will make hash function pluggable in relevant areas (eg. `HashPartitioning`'s `partitionIdExpression` has Murmur3 hardcoded : https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala#L265) ## How was this patch tested? Added `HiveHashSuite` Author: Tejas Patil <tejasp@fb.com> Closes #15047 from tejasapatil/SPARK-17495_hive_hash.
* [SPARK-17549][SQL] Only collect table size stat in driver for cached relation.Marcelo Vanzin2016-10-042-18/+20
| | | | | | | | | | | | | | | | | | | | | | | | This reverts commit 9ac68dbc5720026ea92acc61d295ca64d0d3d132. Turns out the original fix was correct. Original change description: The existing code caches all stats for all columns for each partition in the driver; for a large relation, this causes extreme memory usage, which leads to gc hell and application failures. It seems that only the size in bytes of the data is actually used in the driver, so instead just colllect that. In executors, the full stats are still kept, but that's not a big problem; we expect the data to be distributed and thus not really incur in too much memory pressure in each individual executor. There are also potential improvements on the executor side, since the data being stored currently is very wasteful (e.g. storing boxed types vs. primitive types for stats). But that's a separate issue. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #15304 from vanzin/SPARK-17549.2.
* [SPARKR][DOC] minor formatting and output cleanup for R vignettesFelix Cheung2016-10-041-11/+20
| | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Clean up output, format table, truncate long example output, hide warnings (new - Left; existing - Right) ![image](https://cloud.githubusercontent.com/assets/8969467/19064018/5dcde4d0-89bc-11e6-857b-052df3f52a4e.png) ![image](https://cloud.githubusercontent.com/assets/8969467/19064034/6db09956-89bc-11e6-8e43-232d5c3fe5e6.png) ![image](https://cloud.githubusercontent.com/assets/8969467/19064058/88f09590-89bc-11e6-9993-61639e29dfdd.png) ![image](https://cloud.githubusercontent.com/assets/8969467/19064066/95ccbf64-89bc-11e6-877f-45af03ddcadc.png) ![image](https://cloud.githubusercontent.com/assets/8969467/19064082/a8445404-89bc-11e6-8532-26d8bc9b206f.png) ## How was this patch tested? Run create-doc.sh manually Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #15340 from felixcheung/vignettes.
* [SPARK-17744][ML] Parity check between the ml and mllib test suites for NBZheng RuiFeng2016-10-047-12/+70
| | | | | | | | | | | | | ## What changes were proposed in this pull request? 1,parity check and add missing test suites for ml's NB 2,remove some unused imports ## How was this patch tested? manual tests in spark-shell Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15312 from zhengruifeng/nb_test_parity.
* [SPARK-16962][CORE][SQL] Fix misaligned record accesses for SPARC architecturessumansomasundar2016-10-046-46/+144
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Made changes to record length offsets to make them uniform throughout various areas of Spark core and unsafe ## How was this patch tested? This change affects only SPARC architectures and was tested on X86 architectures as well for regression. Author: sumansomasundar <suman.somasundar@oracle.com> Closes #14762 from sumansomasundar/master.
* [SPARK-17671][WEBUI] Spark 2.0 history server summary page is slow even set ↵Sean Owen2016-10-047-35/+22
| | | | | | | | | | | | | | | | | | spark.history.ui.maxApplications ## What changes were proposed in this pull request? Return Iterator of applications internally in history server, for consistency and performance. See https://github.com/apache/spark/pull/15248 for some back-story. The code called by and calling HistoryServer.getApplicationList wants an Iterator, but this method materializes an Iterable, which potentially causes a performance problem. It's simpler too to make this internal method also pass through an Iterator. ## How was this patch tested? Existing tests. Author: Sean Owen <sowen@cloudera.com> Closes #15321 from srowen/SPARK-17671.
* [SPARK-17559][MLLIB] persist edges if their storage level is non in ↵ding2016-10-041-1/+4
| | | | | | | | | | | | | | PeriodicGraphCheckpointer ## What changes were proposed in this pull request? When use PeriodicGraphCheckpointer to persist graph, sometimes the edges isn't persisted. As currently only when vertices's storage level is none, graph is persisted. However there is a chance vertices's storage level is not none while edges's is none. Eg. graph created by a outerJoinVertices operation, vertices is automatically cached while edges is not. In this way, edges will not be persisted if we use PeriodicGraphCheckpointer do persist. We need separately check edges's storage level and persisted it if it's none. ## How was this patch tested? manual tests Author: ding <ding@localhost.localdomain> Closes #15124 from dding3/spark-persisitEdge.
* [SPARK-17773] Input/Output] Add VoidObjectInspectorErgin Seyfe2016-10-032-0/+3
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Added VoidObjectInspector to the list of PrimitiveObjectInspectors ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) Executing following query was failing. select SOME_UDAF*(a.arr) from ( select Array(null) as arr from dim_one_row ) a After the fix, I am getting the correct output: res0: Array[org.apache.spark.sql.Row] = Array([null]) Author: Ergin Seyfe <eseyfe@fb.com> Closes #15337 from seyfe/add_void_object_inspector.
* [SPARK-17702][SQL] Code generation including too many mutable states exceeds ↵Takuya UESHIN2016-10-038-12/+48
| | | | | | | | | | | | | | | | | JVM size limit. ## What changes were proposed in this pull request? Code generation including too many mutable states exceeds JVM size limit to extract values from `references` into fields in the constructor. We should split the generated extractions in the constructor into smaller functions. ## How was this patch tested? I added some tests to check if the generated codes for the expressions exceed or not. Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #15275 from ueshin/issues/SPARK-17702.
* [SPARK-17112][SQL] "select null" via JDBC triggers IllegalArgumentException ↵Dongjoon Hyun2016-10-032-6/+46
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | in Thriftserver ## What changes were proposed in this pull request? Currently, Spark Thrift Server raises `IllegalArgumentException` for queries whose column types are `NullType`, e.g., `SELECT null` or `SELECT if(true,null,null)`. This PR fixes that by returning `void` like Hive 1.2. **Before** ```sql $ bin/beeline -u jdbc:hive2://localhost:10000 -e "select null" Connecting to jdbc:hive2://localhost:10000 Connected to: Spark SQL (version 2.1.0-SNAPSHOT) Driver: Hive JDBC (version 1.2.1.spark2) Transaction isolation: TRANSACTION_REPEATABLE_READ Error: java.lang.IllegalArgumentException: Unrecognized type name: null (state=,code=0) Closing: 0: jdbc:hive2://localhost:10000 $ bin/beeline -u jdbc:hive2://localhost:10000 -e "select if(true,null,null)" Connecting to jdbc:hive2://localhost:10000 Connected to: Spark SQL (version 2.1.0-SNAPSHOT) Driver: Hive JDBC (version 1.2.1.spark2) Transaction isolation: TRANSACTION_REPEATABLE_READ Error: java.lang.IllegalArgumentException: Unrecognized type name: null (state=,code=0) Closing: 0: jdbc:hive2://localhost:10000 ``` **After** ```sql $ bin/beeline -u jdbc:hive2://localhost:10000 -e "select null" Connecting to jdbc:hive2://localhost:10000 Connected to: Spark SQL (version 2.1.0-SNAPSHOT) Driver: Hive JDBC (version 1.2.1.spark2) Transaction isolation: TRANSACTION_REPEATABLE_READ +-------+--+ | NULL | +-------+--+ | NULL | +-------+--+ 1 row selected (3.242 seconds) Beeline version 1.2.1.spark2 by Apache Hive Closing: 0: jdbc:hive2://localhost:10000 $ bin/beeline -u jdbc:hive2://localhost:10000 -e "select if(true,null,null)" Connecting to jdbc:hive2://localhost:10000 Connected to: Spark SQL (version 2.1.0-SNAPSHOT) Driver: Hive JDBC (version 1.2.1.spark2) Transaction isolation: TRANSACTION_REPEATABLE_READ +-------------------------+--+ | (IF(true, NULL, NULL)) | +-------------------------+--+ | NULL | +-------------------------+--+ 1 row selected (0.201 seconds) Beeline version 1.2.1.spark2 by Apache Hive Closing: 0: jdbc:hive2://localhost:10000 ``` ## How was this patch tested? * Pass the Jenkins test with a new testsuite. * Also, Manually, after starting Spark Thrift Server, run the following command. ```sql $ bin/beeline -u jdbc:hive2://localhost:10000 -e "select null" $ bin/beeline -u jdbc:hive2://localhost:10000 -e "select if(true,null,null)" ``` **Hive 1.2** ```sql hive> create table null_table as select null; hive> desc null_table; OK _c0 void ``` Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15325 from dongjoon-hyun/SPARK-17112.
* [SPARK-17753][SQL] Allow a complex expression as the input a value based ↵Herman van Hovell2016-10-033-7/+11
| | | | | | | | | | | | | | case statement ## What changes were proposed in this pull request? We currently only allow relatively simple expressions as the input for a value based case statement. Expressions like `case (a > 1) or (b = 2) when true then 1 when false then 0 end` currently fail. This PR adds support for such expressions. ## How was this patch tested? Added a test to the ExpressionParserSuite. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15322 from hvanhovell/SPARK-17753.
* [SPARK-17587][PYTHON][MLLIB] SparseVector __getitem__ should follow ↵zero3232016-10-034-16/+36
| | | | | | | | | | | | | | | | | | __getitem__ contract ## What changes were proposed in this pull request? Replaces` ValueError` with `IndexError` when index passed to `ml` / `mllib` `SparseVector.__getitem__` is out of range. This ensures correct iteration behavior. Replaces `ValueError` with `IndexError` for `DenseMatrix` and `SparkMatrix` in `ml` / `mllib`. ## How was this patch tested? PySpark `ml` / `mllib` unit tests. Additional unit tests to prove that the problem has been resolved. Author: zero323 <zero323@users.noreply.github.com> Closes #15144 from zero323/SPARK-17587.
* [SPARK-17679] [PYSPARK] remove unnecessary Py4J ListConverter patchJason White2016-10-034-24/+6
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR removes a patch on ListConverter from https://github.com/apache/spark/pull/5570, as it is no longer necessary. The underlying issue in Py4J https://github.com/bartdag/py4j/issues/160 was patched in https://github.com/bartdag/py4j/commit/224b94b6665e56a93a064073886e1d803a4969d2 and is present in 0.10.3, the version currently in use in Spark. ## How was this patch tested? The original test added in https://github.com/apache/spark/pull/5570 remains. Author: Jason White <jason.white@shopify.com> Closes #15254 from JasonMWhite/remove_listconverter_patch.
* [SPARK-17718][DOCS][MLLIB] Make loss function formulation label note clearer ↵Sean Owen2016-10-031-4/+5
| | | | | | | | | | | | | | | | in MLlib docs ## What changes were proposed in this pull request? Move note about labels being +1/-1 in formulation only to be just under the table of formulations. ## How was this patch tested? Doc build Author: Sean Owen <sowen@cloudera.com> Closes #15330 from srowen/SPARK-17718.
* [SPARK-17073][SQL] generate column-level statisticsZhenhua Wang2016-10-0313-114/+906
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Generate basic column statistics for all the atomic types: - numeric types: max, min, num of nulls, ndv (number of distinct values) - date/timestamp types: they are also represented as numbers internally, so they have the same stats as above. - string: avg length, max length, num of nulls, ndv - binary: avg length, max length, num of nulls - boolean: num of nulls, num of trues, num of falsies Also support storing and loading these statistics. One thing to notice: We support analyzing columns independently, e.g.: sql1: `ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS key;` sql2: `ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS value;` when running sql2 to collect column stats for `value`, we don’t remove stats of columns `key` which are analyzed in sql1 and not in sql2. As a result, **users need to guarantee consistency** between sql1 and sql2. If the table has been changed before sql2, users should re-analyze column `key` when they want to analyze column `value`: `ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS key, value;` ## How was this patch tested? add unit tests Author: Zhenhua Wang <wzh_zju@163.com> Closes #15090 from wzhfy/colStats.
* [SPARK-17736][DOCUMENTATION][SPARKR] Update R README for rmarkdown,…Jagadeesan2016-10-031-2/+2
| | | | | | | | | | | | ## What changes were proposed in this pull request? To build R docs (which are built when R tests are run), users need to install pandoc and rmarkdown. This was done for Jenkins in ~~[SPARK-17420](https://issues.apache.org/jira/browse/SPARK-17420)~~ … pandoc] Author: Jagadeesan <as2@us.ibm.com> Closes #15309 from jagadeesanas2/SPARK-17736.
* [SPARK-17598][SQL][WEB UI] User-friendly name for Spark Thrift Server in web UIAlex Bozarth2016-10-031-1/+1
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The name of Spark Thrift JDBC/ODBC Server in web UI reflects the name of the class, i.e. org.apache.spark.sql.hive.thrift.HiveThriftServer2. I changed it to Thrift JDBC/ODBC Server (like Spark shell for spark-shell) as recommended by jaceklaskowski. Note the user can still change the name adding `--name "App Name"` parameter to the start script as before ## How was this patch tested? By running the script with various parameters and checking the web ui ![screen shot 2016-09-27 at 12 19 12 pm](https://cloud.githubusercontent.com/assets/13952758/18888329/aebca47c-84ac-11e6-93d0-6e98684977c5.png) Author: Alex Bozarth <ajbozart@us.ibm.com> Closes #15268 from ajbozarth/spark17598.
* [SPARK-14914][CORE][SQL] Skip/fix some test cases on Windows due to ↵Tao LI2016-10-025-12/+16
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | limitation of Windows ## What changes were proposed in this pull request? This PR proposes to fix/skip some tests failed on Windows. This PR takes over https://github.com/apache/spark/pull/12696. **Before** - **SparkSubmitSuite** ``` [info] - launch simple application with spark-submit *** FAILED *** (202 milliseconds) [info] java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specifie [info] - includes jars passed in through --jars *** FAILED *** (1 second, 625 milliseconds) [info] java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "C:\projects\spark"): CreateProcess error=2, The system cannot find the file specified ``` - **DiskStoreSuite** ``` [info] - reads of memory-mapped and non memory-mapped files are equivalent *** FAILED *** (1 second, 78 milliseconds) [info] diskStoreMapped.remove(blockId) was false (DiskStoreSuite.scala:41) ``` **After** - **SparkSubmitSuite** ``` [info] - launch simple application with spark-submit (578 milliseconds) [info] - includes jars passed in through --jars (1 second, 875 milliseconds) ``` - **DiskStoreSuite** ``` [info] DiskStoreSuite: [info] - reads of memory-mapped and non memory-mapped files are equivalent !!! CANCELED !!! (766 milliseconds ``` For `CreateTableAsSelectSuite` and `FsHistoryProviderSuite`, I could not reproduce as the Java version seems higher than the one that has the bugs about `setReadable(..)` and `setWritable(...)` but as they are bugs reported clearly, it'd be sensible to skip those. We should revert the changes for both back as soon as we drop the support of Java 7. ## How was this patch tested? Manually tested via AppVeyor. Closes #12696 Author: Tao LI <tl@microsoft.com> Author: U-FAREAST\tl <tl@microsoft.com> Author: hyukjinkwon <gurwls223@gmail.com> Closes #15320 from HyukjinKwon/SPARK-14914.
* [SPARK-17509][SQL] When wrapping catalyst datatype to Hive data type avoid…Sital Kedia2016-10-022-177/+145
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? When wrapping catalyst datatypes to Hive data type, wrap function was doing an expensive pattern matching which was consuming around 11% of cpu time. Avoid the pattern matching by returning the wrapper only once and reuse it. ## How was this patch tested? Tested by running the job on cluster and saw around 8% cpu improvements. Author: Sital Kedia <skedia@fb.com> Closes #15064 from sitalkedia/skedia/hive_wrapper.
* [SPARK-17704][ML][MLLIB] ChiSqSelector performance improvement.Sean Owen2016-10-013-13/+13
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Partial revert of #15277 to instead sort and store input to model rather than require sorted input ## How was this patch tested? Existing tests. Author: Sean Owen <sowen@cloudera.com> Closes #15299 from srowen/SPARK-17704.2.
* [SPARK-17717][SQL] Add Exist/find methods to Catalog [FOLLOW-UP]Herman van Hovell2016-10-014-88/+71
| | | | | | | | | | | | ## What changes were proposed in this pull request? We added find and exists methods for Databases, Tables and Functions to the user facing Catalog in PR https://github.com/apache/spark/pull/15301. However, it was brought up that the semantics of the `find` methods are more in line a `get` method (get an object or else fail). So we rename these in this PR. ## How was this patch tested? Existing tests. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15308 from hvanhovell/SPARK-17717-2.
* [SPARK-17740] Spark tests should mock / interpose HDFS to ensure that ↵Eric Liang2016-09-305-7/+147
| | | | | | | | | | | | | | | | | | | streams are closed ## What changes were proposed in this pull request? As a followup to SPARK-17666, ensure filesystem connections are not leaked at least in unit tests. This is done here by intercepting filesystem calls as suggested by JoshRosen . At the end of each test, we assert no filesystem streams are left open. This applies to all tests using SharedSQLContext or SharedSparkContext. ## How was this patch tested? I verified that tests in sql and core are indeed using the filesystem backend, and fixed the detected leaks. I also checked that reverting https://github.com/apache/spark/pull/15245 causes many actual test failures due to connection leaks. Author: Eric Liang <ekl@databricks.com> Author: Eric Liang <ekhliang@gmail.com> Closes #15306 from ericl/sc-4672.
* [MINOR][DOC] Add an up-to-date description for default serialization during ↵Dongjoon Hyun2016-09-301-0/+1
| | | | | | | | | | | | | | | | shuffling ## What changes were proposed in this pull request? This PR aims to make the doc up-to-date. The documentation is generally correct, but after https://issues.apache.org/jira/browse/SPARK-13926, Spark starts to choose Kyro as a default serialization library during shuffling of simple types, arrays of simple types, or string type. ## How was this patch tested? This is a documentation update. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15315 from dongjoon-hyun/SPARK-DOC-SERIALIZER.
* [SPARK-17739][SQL] Collapse adjacent similar Window operatorsDongjoon Hyun2016-09-302-0/+90
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, Spark does not collapse adjacent windows with the same partitioning and sorting. This PR implements `CollapseWindow` optimizer to do the followings. 1. If the partition specs and order specs are the same, collapse into the parent. 2. If the partition specs are the same and one order spec is a prefix of the other, collapse to the more specific one. For example: ```scala val df = spark.range(1000).select($"id" % 100 as "grp", $"id", rand() as "col1", rand() as "col2") // Add summary statistics for all columns import org.apache.spark.sql.expressions.Window val cols = Seq("id", "col1", "col2") val window = Window.partitionBy($"grp").orderBy($"id") val result = cols.foldLeft(df) { (base, name) => base.withColumn(s"${name}_avg", avg(col(name)).over(window)) .withColumn(s"${name}_stddev", stddev(col(name)).over(window)) .withColumn(s"${name}_min", min(col(name)).over(window)) .withColumn(s"${name}_max", max(col(name)).over(window)) } ``` **Before** ```scala scala> result.explain == Physical Plan == Window [max(col2#19) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_max#234], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [min(col2#19) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_min#216], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [stddev_samp(col2#19) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_stddev#191], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [avg(col2#19) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_avg#167], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [max(col1#18) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_max#152], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [min(col1#18) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_min#138], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [stddev_samp(col1#18) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_stddev#117], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [avg(col1#18) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_avg#97], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [max(id#14L) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_max#86L], [grp#17L], [id#14L ASC NULLS FIRST] +- Window [min(id#14L) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_min#76L], [grp#17L], [id#14L ASC NULLS FIRST] +- *Project [grp#17L, id#14L, col1#18, col2#19, id_avg#26, id_stddev#42] +- Window [stddev_samp(_w0#59) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_stddev#42], [grp#17L], [id#14L ASC NULLS FIRST] +- *Project [grp#17L, id#14L, col1#18, col2#19, id_avg#26, cast(id#14L as double) AS _w0#59] +- Window [avg(id#14L) windowspecdefinition(grp#17L, id#14L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_avg#26], [grp#17L], [id#14L ASC NULLS FIRST] +- *Sort [grp#17L ASC NULLS FIRST, id#14L ASC NULLS FIRST], false, 0 +- Exchange hashpartitioning(grp#17L, 200) +- *Project [(id#14L % 100) AS grp#17L, id#14L, rand(-6329949029880411066) AS col1#18, rand(-7251358484380073081) AS col2#19] +- *Range (0, 1000, step=1, splits=Some(8)) ``` **After** ```scala scala> result.explain == Physical Plan == Window [max(col2#5) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_max#220, min(col2#5) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_min#202, stddev_samp(col2#5) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_stddev#177, avg(col2#5) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col2_avg#153, max(col1#4) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_max#138, min(col1#4) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_min#124, stddev_samp(col1#4) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_stddev#103, avg(col1#4) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS col1_avg#83, max(id#0L) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_max#72L, min(id#0L) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_min#62L], [grp#3L], [id#0L ASC NULLS FIRST] +- *Project [grp#3L, id#0L, col1#4, col2#5, id_avg#12, id_stddev#28] +- Window [stddev_samp(_w0#45) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_stddev#28], [grp#3L], [id#0L ASC NULLS FIRST] +- *Project [grp#3L, id#0L, col1#4, col2#5, id_avg#12, cast(id#0L as double) AS _w0#45] +- Window [avg(id#0L) windowspecdefinition(grp#3L, id#0L ASC NULLS FIRST, RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS id_avg#12], [grp#3L], [id#0L ASC NULLS FIRST] +- *Sort [grp#3L ASC NULLS FIRST, id#0L ASC NULLS FIRST], false, 0 +- Exchange hashpartitioning(grp#3L, 200) +- *Project [(id#0L % 100) AS grp#3L, id#0L, rand(6537478539664068821) AS col1#4, rand(-8961093871295252795) AS col2#5] +- *Range (0, 1000, step=1, splits=Some(8)) ``` ## How was this patch tested? Pass the Jenkins tests with a newly added testsuite. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15317 from dongjoon-hyun/SPARK-17739.
* [SPARK-15353][CORE] Making peer selection for block replication pluggableShubham Chopra2016-09-309-99/+492
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR makes block replication strategies pluggable. It provides two trait that can be implemented, one that maps a host to its topology and is used in the master, and the second that helps prioritize a list of peers for block replication and would run in the executors. This patch contains default implementations of these traits that make sure current Spark behavior is unchanged. ## How was this patch tested? This patch should not change Spark behavior in any way, and was tested with unit tests for storage. Author: Shubham Chopra <schopra31@bloomberg.net> Closes #13152 from shubhamchopra/RackAwareBlockReplication.
* [SPARK-17703][SQL] Add unnamed version of addReferenceObj for minor objects.Takuya UESHIN2016-09-303-4/+28
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? There are many minor objects in references, which are extracted to the generated class field, e.g. `errMsg` in `GetExternalRowField` or `ValidateExternalType`, but number of fields in class is limited so we should reduce the number. This pr adds unnamed version of `addReferenceObj` for these minor objects not to store the object into field but refer it from the `references` field at the time of use. ## How was this patch tested? Existing tests. Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #15276 from ueshin/issues/SPARK-17703.
* [SPARK-17738] [SQL] fix ARRAY/MAP in columnar cacheDavies Liu2016-09-302-7/+7
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? The actualSize() of array and map is different from the actual size, the header is Int, rather than Long. ## How was this patch tested? The flaky test should be fixed. Author: Davies Liu <davies@databricks.com> Closes #15305 from davies/fix_MAP.
* [SPARK-14077][ML][FOLLOW-UP] Revert change for NB Model's Load to maintain ↵Zheng RuiFeng2016-09-301-4/+7
| | | | | | | | | | | | | | compatibility with the model stored before 2.0 ## What changes were proposed in this pull request? Revert change for NB Model's Load to maintain compatibility with the model stored before 2.0 ## How was this patch tested? local build Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15313 from zhengruifeng/revert_save_load.
* [SPARK-14077][ML] Refactor NaiveBayes to support weighted instancesZheng RuiFeng2016-09-293-112/+191
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? 1,support weighted data 2,use dataset/dataframe instead of rdd 3,make mllib as a wrapper to call ml ## How was this patch tested? local manual tests in spark-shell unit tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #12819 from zhengruifeng/weighted_nb.
* [SPARK-17717][SQL] Add exist/find methods to Catalog.Herman van Hovell2016-09-294-25/+339
| | | | | | | | | | | | ## What changes were proposed in this pull request? The current user facing catalog does not implement methods for checking object existence or finding objects. You could theoretically do this using the `list*` commands, but this is rather cumbersome and can actually be costly when there are many objects. This PR adds `exists*` and `find*` methods for Databases, Table and Functions. ## How was this patch tested? Added tests to `org.apache.spark.sql.internal.CatalogSuite` Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15301 from hvanhovell/SPARK-17717.
* [SPARK-17697][ML] Fixed bug in summary calculations that pattern match ↵Bryan Cutler2016-09-294-7/+49
| | | | | | | | | | | | | | against label without casting ## What changes were proposed in this pull request? In calling LogisticRegression.evaluate and GeneralizedLinearRegression.evaluate using a Dataset where the Label is not of a double type, calculations pattern match against a double and throw a MatchError. This fix casts the Label column to a DoubleType to ensure there is no MatchError. ## How was this patch tested? Added unit tests to call evaluate with a dataset that has Label as other numeric types. Author: Bryan Cutler <cutlerb@gmail.com> Closes #15288 from BryanCutler/binaryLOR-numericCheck-SPARK-17697.
* [SPARK-17412][DOC] All test should not be run by `root` or any admin userDongjoon Hyun2016-09-291-0/+1
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? `FsHistoryProviderSuite` fails if `root` user runs it. The test case **SPARK-3697: ignore directories that cannot be read** depends on `setReadable(false, false)` to make test data files and expects the number of accessible files is 1. But, `root` can access all files, so it returns 2. This PR adds the assumption explicitly on doc. `building-spark.md`. ## How was this patch tested? This is a documentation change. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15291 from dongjoon-hyun/SPARK-17412.
* [SPARK-17676][CORE] FsHistoryProvider should ignore hidden filesImran Rashid2016-09-292-3/+40
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? FsHistoryProvider was writing a hidden file (to check the fs's clock). Even though it deleted the file immediately, sometimes another thread would try to scan the files on the fs in-between, and then there would be an error msg logged which was very misleading for the end-user. (The logged error was harmless, though.) ## How was this patch tested? I added one unit test, but to be clear, that test was passing before. The actual change in behavior in that test is just logging (after the change, there is no more logged error), which I just manually verified. Author: Imran Rashid <irashid@cloudera.com> Closes #15250 from squito/SPARK-17676.
* [SPARK-17721][MLLIB][ML] Fix for multiplying transposed SparseMatrix with ↵Bjarne Fruergaard2016-09-294-4/+46
| | | | | | | | | | | | | | | | | | | | | | | | SparseVector ## What changes were proposed in this pull request? * changes the implementation of gemv with transposed SparseMatrix and SparseVector both in mllib-local and mllib (identical) * adds a test that was failing before this change, but succeeds with these changes. The problem in the previous implementation was that it only increments `i`, that is enumerating the columns of a row in the SparseMatrix, when the row-index of the vector matches the column-index of the SparseMatrix. In cases where a particular row of the SparseMatrix has non-zero values at column-indices lower than corresponding non-zero row-indices of the SparseVector, the non-zero values of the SparseVector are enumerated without ever matching the column-index at index `i` and the remaining column-indices i+1,...,indEnd-1 are never attempted. The test cases in this PR illustrate this issue. ## How was this patch tested? I have run the specific `gemv` tests in both mllib-local and mllib. I am currently still running `./dev/run-tests`. ## ___ As per instructions, I hereby state that this is my original work and that I license the work to the project (Apache Spark) under the project's open source license. Mentioning dbtsai, viirya and brkyvz whom I can see have worked/authored on these parts before. Author: Bjarne Fruergaard <bwahlgreen@gmail.com> Closes #15296 from bwahlgreen/bugfix-spark-17721.
* [SPARK-17612][SQL] Support `DESCRIBE table PARTITION` SQL syntaxDongjoon Hyun2016-09-296-18/+287
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR implements `DESCRIBE table PARTITION` SQL Syntax again. It was supported until Spark 1.6.2, but was dropped since 2.0.0. **Spark 1.6.2** ```scala scala> sql("CREATE TABLE partitioned_table (a STRING, b INT) PARTITIONED BY (c STRING, d STRING)") res1: org.apache.spark.sql.DataFrame = [result: string] scala> sql("ALTER TABLE partitioned_table ADD PARTITION (c='Us', d=1)") res2: org.apache.spark.sql.DataFrame = [result: string] scala> sql("DESC partitioned_table PARTITION (c='Us', d=1)").show(false) +----------------------------------------------------------------+ |result | +----------------------------------------------------------------+ |a string | |b int | |c string | |d string | | | |# Partition Information | |# col_name data_type comment | | | |c string | |d string | +----------------------------------------------------------------+ ``` **Spark 2.0** - **Before** ```scala scala> sql("CREATE TABLE partitioned_table (a STRING, b INT) PARTITIONED BY (c STRING, d STRING)") res0: org.apache.spark.sql.DataFrame = [] scala> sql("ALTER TABLE partitioned_table ADD PARTITION (c='Us', d=1)") res1: org.apache.spark.sql.DataFrame = [] scala> sql("DESC partitioned_table PARTITION (c='Us', d=1)").show(false) org.apache.spark.sql.catalyst.parser.ParseException: Unsupported SQL statement ``` - **After** ```scala scala> sql("CREATE TABLE partitioned_table (a STRING, b INT) PARTITIONED BY (c STRING, d STRING)") res0: org.apache.spark.sql.DataFrame = [] scala> sql("ALTER TABLE partitioned_table ADD PARTITION (c='Us', d=1)") res1: org.apache.spark.sql.DataFrame = [] scala> sql("DESC partitioned_table PARTITION (c='Us', d=1)").show(false) +-----------------------+---------+-------+ |col_name |data_type|comment| +-----------------------+---------+-------+ |a |string |null | |b |int |null | |c |string |null | |d |string |null | |# Partition Information| | | |# col_name |data_type|comment| |c |string |null | |d |string |null | +-----------------------+---------+-------+ scala> sql("DESC EXTENDED partitioned_table PARTITION (c='Us', d=1)").show(100,false) +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------+ |col_name |data_type|comment| +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------+ |a |string |null | |b |int |null | |c |string |null | |d |string |null | |# Partition Information | | | |# col_name |data_type|comment| |c |string |null | |d |string |null | | | | | |Detailed Partition Information CatalogPartition( Partition Values: [Us, 1] Storage(Location: file:/Users/dhyun/SPARK-17612-DESC-PARTITION/spark-warehouse/partitioned_table/c=Us/d=1, InputFormat: org.apache.hadoop.mapred.TextInputFormat, OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat, Serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, Properties: [serialization.format=1]) Partition Parameters:{transient_lastDdlTime=1475001066})| | | +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------+ scala> sql("DESC FORMATTED partitioned_table PARTITION (c='Us', d=1)").show(100,false) +--------------------------------+---------------------------------------------------------------------------------------+-------+ |col_name |data_type |comment| +--------------------------------+---------------------------------------------------------------------------------------+-------+ |a |string |null | |b |int |null | |c |string |null | |d |string |null | |# Partition Information | | | |# col_name |data_type |comment| |c |string |null | |d |string |null | | | | | |# Detailed Partition Information| | | |Partition Value: |[Us, 1] | | |Database: |default | | |Table: |partitioned_table | | |Location: |file:/Users/dhyun/SPARK-17612-DESC-PARTITION/spark-warehouse/partitioned_table/c=Us/d=1| | |Partition Parameters: | | | | transient_lastDdlTime |1475001066 | | | | | | |# Storage Information | | | |SerDe Library: |org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe | | |InputFormat: |org.apache.hadoop.mapred.TextInputFormat | | |OutputFormat: |org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat | | |Compressed: |No | | |Storage Desc Parameters: | | | | serialization.format |1 | | +--------------------------------+---------------------------------------------------------------------------------------+-------+ ``` ## How was this patch tested? Pass the Jenkins tests with a new testcase. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15168 from dongjoon-hyun/SPARK-17612.
* [SPARK-17653][SQL] Remove unnecessary distincts in multiple unionsLiang-Chi Hsieh2016-09-293-30/+89
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently for `Union [Distinct]`, a `Distinct` operator is necessary to be on the top of `Union`. Once there are adjacent `Union [Distinct]`, there will be multiple `Distinct` in the query plan. E.g., For a query like: select 1 a union select 2 b union select 3 c Before this patch, its physical plan looks like: *HashAggregate(keys=[a#13], functions=[]) +- Exchange hashpartitioning(a#13, 200) +- *HashAggregate(keys=[a#13], functions=[]) +- Union :- *HashAggregate(keys=[a#13], functions=[]) : +- Exchange hashpartitioning(a#13, 200) : +- *HashAggregate(keys=[a#13], functions=[]) : +- Union : :- *Project [1 AS a#13] : : +- Scan OneRowRelation[] : +- *Project [2 AS b#14] : +- Scan OneRowRelation[] +- *Project [3 AS c#15] +- Scan OneRowRelation[] Only the top distinct should be necessary. After this patch, the physical plan looks like: *HashAggregate(keys=[a#221], functions=[], output=[a#221]) +- Exchange hashpartitioning(a#221, 5) +- *HashAggregate(keys=[a#221], functions=[], output=[a#221]) +- Union :- *Project [1 AS a#221] : +- Scan OneRowRelation[] :- *Project [2 AS b#222] : +- Scan OneRowRelation[] +- *Project [3 AS c#223] +- Scan OneRowRelation[] ## How was this patch tested? Jenkins tests. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #15238 from viirya/remove-extra-distinct-union.
* [SPARK-17699] Support for parsing JSON string columnsMichael Armbrust2016-09-2919-23/+198
| | | | | | | | | | | | | | | | | | Spark SQL has great support for reading text files that contain JSON data. However, in many cases the JSON data is just one column amongst others. This is particularly true when reading from sources such as Kafka. This PR adds a new functions `from_json` that converts a string column into a nested `StructType` with a user specified schema. Example usage: ```scala val df = Seq("""{"a": 1}""").toDS() val schema = new StructType().add("a", IntegerType) df.select(from_json($"value", schema) as 'json) // => [json: <a: int>] ``` This PR adds support for java, scala and python. I leveraged our existing JSON parsing support by moving it into catalyst (so that we could define expressions using it). I left SQL out for now, because I'm not sure how users would specify a schema. Author: Michael Armbrust <michael@databricks.com> Closes #15274 from marmbrus/jsonParser.
* [SPARK-17715][SCHEDULER] Make task launch logs DEBUGBrian Cho2016-09-291-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Ramp down the task launch logs from INFO to DEBUG. Task launches can happen orders of magnitude more than executor registration so it makes the logs easier to handle if they are different log levels. For larger jobs, there can be 100,000s of task launches which makes the driver log huge. ## How was this patch tested? No tests, as this is a trivial change. Author: Brian Cho <bcho@fb.com> Closes #15290 from dafrista/ramp-down-task-logging.
* [SPARK-17672] Spark 2.0 history server web Ui takes too long for a single ↵Gang Wu2016-09-296-1/+19
| | | | | | | | | | | | | | application Added a new API getApplicationInfo(appId: String) in class ApplicationHistoryProvider and class SparkUI to get app info. In this change, FsHistoryProvider can directly fetch one app info in O(1) time complexity compared to O(n) before the change which used an Iterator.find() interface. Both ApplicationCache and OneApplicationResource classes adopt this new api. manual tests Author: Gang Wu <wgtmac@uber.com> Closes #15247 from wgtmac/SPARK-17671.
* [SPARK-17648][CORE] TaskScheduler really needs offers to be an IndexedSeqImran Rashid2016-09-297-27/+26
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The Seq[WorkerOffer] is accessed by index, so it really should be an IndexedSeq, otherwise an O(n) operation becomes O(n^2). In practice this hasn't been an issue b/c where these offers are generated, the call to `.toSeq` just happens to create an IndexedSeq anyway.I got bitten by this in performance tests I was doing, and its better for the types to be more precise so eg. a change in Scala doesn't destroy performance. ## How was this patch tested? Unit tests via jenkins. Author: Imran Rashid <irashid@cloudera.com> Closes #15221 from squito/SPARK-17648.
* [DOCS] Reorganize explanation of Accumulators and Broadcast VariablesJosé Hiram Soltren2016-09-291-164/+164
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The discussion of the interaction of Accumulators and Broadcast Variables should logically follow the discussion on Checkpointing. As currently written, this section discusses Checkpointing before it is formally introduced. To remedy this: - Rename this section to "Accumulators, Broadcast Variables, and Checkpoints", and - Move this section after "Checkpointing". ## How was this patch tested? Testing: ran $ SKIP_API=1 jekyll build , and verified changes in a Web browser pointed at docs/_site/index.html. Author: José Hiram Soltren <jose@cloudera.com> Closes #15281 from jsoltren/doc-changes.
* [MINOR][DOCS] Fix th doc. of spark-streaming with kinesisTakeshi YAMAMURO2016-09-291-6/+3
| | | | | | | | | | | | ## What changes were proposed in this pull request? This pr is just to fix the document of `spark-kinesis-integration`. Since `SPARK-17418` prevented all the kinesis stuffs (including kinesis example code) from publishing, `bin/run-example streaming.KinesisWordCountASL` and `bin/run-example streaming.JavaKinesisWordCountASL` does not work. Instead, it fetches the kinesis jar from the Spark Package. Author: Takeshi YAMAMURO <linguin.m.s@gmail.com> Closes #15260 from maropu/DocFixKinesis.
* [SPARK-17614][SQL] sparkSession.read() .jdbc(***) use the sql syntax "where ↵Sean Owen2016-09-292-5/+16
| | | | | | | | | | | | | | | | 1=0" that Cassandra does not support ## What changes were proposed in this pull request? Use dialect's table-exists query rather than hard-coded WHERE 1=0 query ## How was this patch tested? Existing tests. Author: Sean Owen <sowen@cloudera.com> Closes #15196 from srowen/SPARK-17614.
* [SPARK-17704][ML][MLLIB] ChiSqSelector performance improvement.Yanbo Liang2016-09-292-18/+30
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Several performance improvement for ```ChiSqSelector```: 1, Keep ```selectedFeatures``` ordered ascendent. ```ChiSqSelectorModel.transform``` need ```selectedFeatures``` ordered to make prediction. We should sort it when training model rather than making prediction, since users usually train model once and use the model to do prediction multiple times. 2, When training ```fpr``` type ```ChiSqSelectorModel```, it's not necessary to sort the ChiSq test result by statistic. ## How was this patch tested? Existing unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15277 from yanboliang/spark-17704.
* [SPARK-16356][FOLLOW-UP][ML] Enforce ML test of exception for ↵Yanbo Liang2016-09-291-4/+9
| | | | | | | | | | | | | | | local/distributed Dataset. ## What changes were proposed in this pull request? #14035 added ```testImplicits``` to ML unit tests and promoted ```toDF()```, but left one minor issue at ```VectorIndexerSuite```. If we create the DataFrame by ```Seq(...).toDF()```, it will throw different error/exception compared with ```sc.parallelize(Seq(...)).toDF()``` for one of the test cases. After in-depth study, I found it was caused by different behavior of local and distributed Dataset if the UDF failed at ```assert```. If the data is local Dataset, it throws ```AssertionError``` directly; If the data is distributed Dataset, it throws ```SparkException``` which is the wrapper of ```AssertionError```. I think we should enforce this test to cover both case. ## How was this patch tested? Unit test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15261 from yanboliang/spark-16356.
* [SPARK-17712][SQL] Fix invalid pushdown of data-independent filters beneath ↵Josh Rosen2016-09-282-1/+18
| | | | | | | | | | | | | | | | | | | | aggregates ## What changes were proposed in this pull request? This patch fixes a minor correctness issue impacting the pushdown of filters beneath aggregates. Specifically, if a filter condition references no grouping or aggregate columns (e.g. `WHERE false`) then it would be incorrectly pushed beneath an aggregate. Intuitively, the only case where you can push a filter beneath an aggregate is when that filter is deterministic and is defined over the grouping columns / expressions, since in that case the filter is acting to exclude entire groups from the query (like a `HAVING` clause). The existing code would only push deterministic filters beneath aggregates when all of the filter's references were grouping columns, but this logic missed the case where a filter has no references. For example, `WHERE false` is deterministic but is independent of the actual data. This patch fixes this minor bug by adding a new check to ensure that we don't push filters beneath aggregates when those filters don't reference any columns. ## How was this patch tested? New regression test in FilterPushdownSuite. Author: Josh Rosen <joshrosen@databricks.com> Closes #15289 from JoshRosen/SPARK-17712.
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