aboutsummaryrefslogtreecommitdiff
Commit message (Collapse)AuthorAgeFilesLines
* [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.
* [SPARK-17710][HOTFIX] Fix ClassCircularityError in ReplSuite tests in Maven ↵Weiqing Yang2016-09-281-2/+4
| | | | | | | | | | | | | | | | | | | | | | | | | build: use 'Class.forName' instead of 'Utils.classForName' ## What changes were proposed in this pull request? Fix ClassCircularityError in ReplSuite tests when Spark is built by Maven build. ## How was this patch tested? (1) ``` build/mvn -DskipTests -Phadoop-2.3 -Pyarn -Phive -Phive-thriftserver -Pkinesis-asl -Pmesos clean package ``` Then test: ``` build/mvn -Dtest=none -DwildcardSuites=org.apache.spark.repl.ReplSuite test ``` ReplSuite tests passed (2) Manual Tests against some Spark applications in Yarn client mode and Yarn cluster mode. Need to check if spark caller contexts are written into HDFS hdfs-audit.log and Yarn RM audit log successfully. Author: Weiqing Yang <yangweiqing001@gmail.com> Closes #15286 from Sherry302/SPARK-16757.
* [SPARK-17641][SQL] Collect_list/Collect_set should not collect null values.Herman van Hovell2016-09-282-1/+18
| | | | | | | | | | | | ## What changes were proposed in this pull request? We added native versions of `collect_set` and `collect_list` in Spark 2.0. These currently also (try to) collect null values, this is different from the original Hive implementation. This PR fixes this by adding a null check to the `Collect.update` method. ## How was this patch tested? Added a regression test to `DataFrameAggregateSuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15208 from hvanhovell/SPARK-17641.
* [SPARK-17713][SQL] Move row-datasource related tests out of JDBCSuiteEric Liang2016-09-282-8/+72
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? As a followup for https://github.com/apache/spark/pull/15273 we should move non-JDBC specific tests out of that suite. ## How was this patch tested? Ran the test. Author: Eric Liang <ekl@databricks.com> Closes #15287 from ericl/spark-17713.
* [SPARK-17673][SQL] Incorrect exchange reuse with RowDataSourceScanEric Liang2016-09-282-0/+12
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? It seems the equality check for reuse of `RowDataSourceScanExec` nodes doesn't respect the output schema. This can cause self-joins or unions over the same underlying data source to return incorrect results if they select different fields. ## How was this patch tested? New unit test passes after the fix. Author: Eric Liang <ekl@databricks.com> Closes #15273 from ericl/spark-17673.
* [SPARK-17644][CORE] Do not add failedStages when abortStage for fetch failurew002289702016-09-282-12/+70
| | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? | Time |Thread 1 , Job1 | Thread 2 , Job2 | |:-------------:|:-------------:|:-----:| | 1 | abort stage due to FetchFailed | | | 2 | failedStages += failedStage | | | 3 | | task failed due to FetchFailed | | 4 | | can not post ResubmitFailedStages because failedStages is not empty | Then job2 of thread2 never resubmit the failed stage and hang. We should not add the failedStages when abortStage for fetch failure ## How was this patch tested? added unit test Author: w00228970 <wangfei1@huawei.com> Author: wangfei <wangfei_hello@126.com> Closes #15213 from scwf/dag-resubmit.
* [MINOR][PYSPARK][DOCS] Fix examples in PySpark documentationhyukjinkwon2016-09-284-10/+10
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR proposes to fix wrongly indented examples in PySpark documentation ``` - >>> json_sdf = spark.readStream.format("json")\ - .schema(sdf_schema)\ - .load(tempfile.mkdtemp()) + >>> json_sdf = spark.readStream.format("json") \\ + ... .schema(sdf_schema) \\ + ... .load(tempfile.mkdtemp()) ``` ``` - people.filter(people.age > 30).join(department, people.deptId == department.id)\ + people.filter(people.age > 30).join(department, people.deptId == department.id) \\ ``` ``` - >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])), \ - LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))] + >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])), + ... LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))] ``` ``` - >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, -1.23), (2, 4.56e-7)])), \ - LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))] + >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, -1.23), (2, 4.56e-7)])), + ... LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))] ``` ``` - ... for x in iterator: - ... print(x) + ... for x in iterator: + ... print(x) ``` ## How was this patch tested? Manually tested. **Before** ![2016-09-26 8 36 02](https://cloud.githubusercontent.com/assets/6477701/18834471/05c7a478-8431-11e6-94bb-09aa37b12ddb.png) ![2016-09-26 9 22 16](https://cloud.githubusercontent.com/assets/6477701/18834472/06c8735c-8431-11e6-8775-78631eab0411.png) <img width="601" alt="2016-09-27 2 29 27" src="https://cloud.githubusercontent.com/assets/6477701/18861294/29c0d5b4-84bf-11e6-99c5-3c9d913c125d.png"> <img width="1056" alt="2016-09-27 2 29 58" src="https://cloud.githubusercontent.com/assets/6477701/18861298/31694cd8-84bf-11e6-9e61-9888cb8c2089.png"> <img width="1079" alt="2016-09-27 2 30 05" src="https://cloud.githubusercontent.com/assets/6477701/18861301/359722da-84bf-11e6-97f9-5f5365582d14.png"> **After** ![2016-09-26 9 29 47](https://cloud.githubusercontent.com/assets/6477701/18834467/0367f9da-8431-11e6-86d9-a490d3297339.png) ![2016-09-26 9 30 24](https://cloud.githubusercontent.com/assets/6477701/18834463/f870fae0-8430-11e6-9482-01fc47898492.png) <img width="515" alt="2016-09-27 2 28 19" src="https://cloud.githubusercontent.com/assets/6477701/18861305/3ff88b88-84bf-11e6-902c-9f725e8a8b10.png"> <img width="652" alt="2016-09-27 3 50 59" src="https://cloud.githubusercontent.com/assets/6477701/18863053/592fbc74-84ca-11e6-8dbf-99cf57947de8.png"> <img width="709" alt="2016-09-27 3 51 03" src="https://cloud.githubusercontent.com/assets/6477701/18863060/601607be-84ca-11e6-80aa-a401df41c321.png"> Author: hyukjinkwon <gurwls223@gmail.com> Closes #15242 from HyukjinKwon/minor-example-pyspark.
* [SPARK-17017][ML][MLLIB][ML][DOC] Updated the ml/mllib feature selection ↵Shuai Lin2016-09-282-8/+20
| | | | | | | | | | | | | | | | | | | docs for ChiSqSelector ## What changes were proposed in this pull request? A follow up for #14597 to update feature selection docs about ChiSqSelector. ## How was this patch tested? Generated html docs. It can be previewed at: * ml: http://sparkdocs.lins05.pw/spark-17017/ml-features.html#chisqselector * mllib: http://sparkdocs.lins05.pw/spark-17017/mllib-feature-extraction.html#chisqselector Author: Shuai Lin <linshuai2012@gmail.com> Closes #15236 from lins05/spark-17017-update-docs-for-chisq-selector-fpr.
* [SPARK-17499][SPARKR][FOLLOWUP] Check null first for layers in spark.mlp to ↵hyukjinkwon2016-09-271-0/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | avoid warnings in test results ## What changes were proposed in this pull request? Some tests in `test_mllib.r` are as below: ```r expect_error(spark.mlp(df, layers = NULL), "layers must be a integer vector with length > 1.") expect_error(spark.mlp(df, layers = c()), "layers must be a integer vector with length > 1.") ``` The problem is, `is.na` is internally called via `na.omit` in `spark.mlp` which causes warnings as below: ``` Warnings ----------------------------------------------------------------------- 1. spark.mlp (test_mllib.R#400) - is.na() applied to non-(list or vector) of type 'NULL' 2. spark.mlp (test_mllib.R#401) - is.na() applied to non-(list or vector) of type 'NULL' ``` ## How was this patch tested? Manually tested. Also, Jenkins tests and AppVeyor. Author: hyukjinkwon <gurwls223@gmail.com> Closes #15232 from HyukjinKwon/remove-warnnings.
* [SPARK-17666] Ensure that RecordReaders are closed by data source file scansJosh Rosen2016-09-278-9/+46
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch addresses a potential cause of resource leaks in data source file scans. As reported in [SPARK-17666](https://issues.apache.org/jira/browse/SPARK-17666), tasks which do not fully-consume their input may cause file handles / network connections (e.g. S3 connections) to be leaked. Spark's `NewHadoopRDD` uses a TaskContext callback to [close its record readers](https://github.com/apache/spark/blame/master/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala#L208), but the new data source file scans will only close record readers once their iterators are fully-consumed. This patch modifies `RecordReaderIterator` and `HadoopFileLinesReader` to add `close()` methods and modifies all six implementations of `FileFormat.buildReader()` to register TaskContext task completion callbacks to guarantee that cleanup is eventually performed. ## How was this patch tested? Tested manually for now. Author: Josh Rosen <joshrosen@databricks.com> Closes #15245 from JoshRosen/SPARK-17666-close-recordreader.
* [SPARK-17056][CORE] Fix a wrong assert regarding unroll memory in MemoryStoreLiang-Chi Hsieh2016-09-271-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? There is an assert in MemoryStore's putIteratorAsValues method which is used to check if unroll memory is not released too much. This assert looks wrong. ## How was this patch tested? Jenkins tests. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #14642 from viirya/fix-unroll-memory.
* [SPARK-17618] Guard against invalid comparisons between UnsafeRow and other ↵Josh Rosen2016-09-271-1/+6
| | | | | | | | | | formats This patch ports changes from #15185 to Spark 2.x. In that patch, a correctness bug in Spark 1.6.x which was caused by an invalid `equals()` comparison between an `UnsafeRow` and another row of a different format. Spark 2.x is not affected by that specific correctness bug but it can still reap the error-prevention benefits of that patch's changes, which modify ``UnsafeRow.equals()` to throw an IllegalArgumentException if it is called with an object that is not an `UnsafeRow`. Author: Josh Rosen <joshrosen@databricks.com> Closes #15265 from JoshRosen/SPARK-17618-master.
* [SPARK-17677][SQL] Break WindowExec.scala into multiple filesReynold Xin2016-09-277-1015/+1112
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? As of Spark 2.0, all the window function execution code are in WindowExec.scala. This file is pretty large (over 1k loc) and has a lot of different abstractions in them. This patch creates a new package sql.execution.window, moves WindowExec.scala in it, and breaks WindowExec.scala into multiple, more maintainable pieces: - AggregateProcessor.scala - BoundOrdering.scala - RowBuffer.scala - WindowExec - WindowFunctionFrame.scala ## How was this patch tested? This patch mostly moves code around, and should not change any existing test coverage. Author: Reynold Xin <rxin@databricks.com> Closes #15252 from rxin/SPARK-17677.
* [SPARK-17660][SQL] DESC FORMATTED for VIEW Lacks View Definitiongatorsmile2016-09-272-0/+28
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? Before this PR, `DESC FORMATTED` does not have a section for the view definition. We should add it for permanent views, like what Hive does. ``` +----------------------------+-------------------------------------------------------------------------------------------------------------------------------------+-------+ |col_name |data_type |comment| +----------------------------+-------------------------------------------------------------------------------------------------------------------------------------+-------+ |a |int |null | | | | | |# Detailed Table Information| | | |Database: |default | | |Owner: |xiaoli | | |Create Time: |Sat Sep 24 21:46:19 PDT 2016 | | |Last Access Time: |Wed Dec 31 16:00:00 PST 1969 | | |Location: | | | |Table Type: |VIEW | | |Table Parameters: | | | | transient_lastDdlTime |1474778779 | | | | | | |# Storage Information | | | |SerDe Library: |org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe | | |InputFormat: |org.apache.hadoop.mapred.SequenceFileInputFormat | | |OutputFormat: |org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat | | |Compressed: |No | | |Storage Desc Parameters: | | | | serialization.format |1 | | | | | | |# View Information | | | |View Original Text: |SELECT * FROM tbl | | |View Expanded Text: |SELECT `gen_attr_0` AS `a` FROM (SELECT `gen_attr_0` FROM (SELECT `a` AS `gen_attr_0` FROM `default`.`tbl`) AS gen_subquery_0) AS tbl| | +----------------------------+-------------------------------------------------------------------------------------------------------------------------------------+-------+ ``` ### How was this patch tested? Added a test case Author: gatorsmile <gatorsmile@gmail.com> Closes #15234 from gatorsmile/descFormattedView.
* [SPARK-17682][SQL] Mark children as final for unary, binary, leaf ↵Reynold Xin2016-09-275-14/+9
| | | | | | | | | | | | | | expressions and plan nodes ## What changes were proposed in this pull request? This patch marks the children method as final in unary, binary, and leaf expressions and plan nodes (both logical plan and physical plan), as brought up in http://apache-spark-developers-list.1001551.n3.nabble.com/Should-LeafExpression-have-children-final-override-like-Nondeterministic-td19104.html ## How was this patch tested? This is a simple modifier change and has no impact on test coverage. Author: Reynold Xin <rxin@databricks.com> Closes #15256 from rxin/SPARK-17682.
* [SPARK-16516][SQL] Support for pushing down filters for decimal and ↵hyukjinkwon2016-09-283-9/+89
| | | | | | | | | | | | | | | | | | | | | | timestamp types in ORC ## What changes were proposed in this pull request? It seems ORC supports all the types in ([`PredicateLeaf.Type`](https://github.com/apache/hive/blob/e085b7e9bd059d91aaf013df0db4d71dca90ec6f/storage-api/src/java/org/apache/hadoop/hive/ql/io/sarg/PredicateLeaf.java#L50-L56)) which includes timestamp type and decimal type. In more details, the types listed in [`SearchArgumentImpl.boxLiteral()`](https://github.com/apache/hive/blob/branch-1.2/ql/src/java/org/apache/hadoop/hive/ql/io/sarg/SearchArgumentImpl.java#L1068-L1093) can be used as a filter value. FYI, inital `case` caluse for supported types was introduced in https://github.com/apache/spark/commit/65d71bd9fbfe6fe1b741c80fed72d6ae3d22b028 and this was not changed overtime. At that time, Hive version was, 0.13 which supports only some types for filter-push down (See [SearchArgumentImpl.java#L945-L965](https://github.com/apache/hive/blob/branch-0.13/ql/src/java/org/apache/hadoop/hive/ql/io/sarg/SearchArgumentImpl.java#L945-L965) at 0.13). However, the version was upgraded into 1.2.x and now it supports more types (See [SearchArgumentImpl.java#L1068-L1093](https://github.com/apache/hive/blob/branch-1.2/ql/src/java/org/apache/hadoop/hive/ql/io/sarg/SearchArgumentImpl.java#L1068-L1093) at 1.2.0) ## How was this patch tested? Unit tests in `OrcFilterSuite` and `OrcQuerySuite` Author: hyukjinkwon <gurwls223@gmail.com> Closes #14172 from HyukjinKwon/SPARK-16516.
* [SPARK-16777][SQL] Do not use deprecated listType API in ParquetSchemaConverterhyukjinkwon2016-09-281-9/+17
| | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR removes build waning as below. ```scala [WARNING] .../spark/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaConverter.scala:448: method listType in object ConversionPatterns is deprecated: see corresponding Javadoc for more information. [WARNING] ConversionPatterns.listType( [WARNING] ^ [WARNING] .../spark/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaConverter.scala:464: method listType in object ConversionPatterns is deprecated: see corresponding Javadoc for more information. [WARNING] ConversionPatterns.listType( [WARNING] ^ ``` This should not use `listOfElements` (recommended to be replaced from `listType`) instead because the new method checks if the name of elements in Parquet's `LIST` is `element` in Parquet schema and throws an exception if not. However, It seems Spark prior to 1.4.x writes `ArrayType` with Parquet's `LIST` but with `array` as its element name. Therefore, this PR avoids to use both `listOfElements` and `listType` but just use the existing schema builder to construct the same `GroupType`. ## How was this patch tested? Existing tests should cover this. Author: hyukjinkwon <gurwls223@gmail.com> Closes #14399 from HyukjinKwon/SPARK-16777.
* [SPARK-16757] Set up Spark caller context to HDFS and YARNWeiqing Yang2016-09-278-10/+126
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? 1. Pass `jobId` to Task. 2. Invoke Hadoop APIs. * A new function `setCallerContext` is added in `Utils`. `setCallerContext` function invokes APIs of `org.apache.hadoop.ipc.CallerContext` to set up spark caller contexts, which will be written into `hdfs-audit.log` and Yarn RM audit log. * For HDFS: Spark sets up its caller context by invoking`org.apache.hadoop.ipc.CallerContext` in `Task` and Yarn `Client` and `ApplicationMaster`. * For Yarn: Spark sets up its caller context by invoking `org.apache.hadoop.ipc.CallerContext` in Yarn `Client`. ## How was this patch tested? Manual Tests against some Spark applications in Yarn client mode and Yarn cluster mode. Need to check if spark caller contexts are written into HDFS hdfs-audit.log and Yarn RM audit log successfully. For example, run SparkKmeans in Yarn client mode: ``` ./bin/spark-submit --verbose --executor-cores 3 --num-executors 1 --master yarn --deploy-mode client --class org.apache.spark.examples.SparkKMeans examples/target/original-spark-examples_2.11-2.1.0-SNAPSHOT.jar hdfs://localhost:9000/lr_big.txt 2 5 ``` **Before**: There will be no Spark caller context in records of `hdfs-audit.log` and Yarn RM audit log. **After**: Spark caller contexts will be written in records of `hdfs-audit.log` and Yarn RM audit log. These are records in `hdfs-audit.log`: ``` 2016-09-20 11:54:24,116 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=open src=/lr_big.txt dst=null perm=null proto=rpc callerContext=SPARK_CLIENT_AppId_application_1474394339641_0005 2016-09-20 11:54:28,164 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=open src=/lr_big.txt dst=null perm=null proto=rpc callerContext=SPARK_TASK_AppId_application_1474394339641_0005_JobId_0_StageId_0_AttemptId_0_TaskId_2_AttemptNum_0 2016-09-20 11:54:28,164 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=open src=/lr_big.txt dst=null perm=null proto=rpc callerContext=SPARK_TASK_AppId_application_1474394339641_0005_JobId_0_StageId_0_AttemptId_0_TaskId_1_AttemptNum_0 2016-09-20 11:54:28,164 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=open src=/lr_big.txt dst=null perm=null proto=rpc callerContext=SPARK_TASK_AppId_application_1474394339641_0005_JobId_0_StageId_0_AttemptId_0_TaskId_0_AttemptNum_0 ``` ``` 2016-09-20 11:59:33,868 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=mkdirs src=/private/tmp/hadoop-wyang/nm-local-dir/usercache/wyang/appcache/application_1474394339641_0006/container_1474394339641_0006_01_000001/spark-warehouse dst=null perm=wyang:supergroup:rwxr-xr-x proto=rpc callerContext=SPARK_APPLICATION_MASTER_AppId_application_1474394339641_0006_AttemptId_1 2016-09-20 11:59:37,214 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=open src=/lr_big.txt dst=null perm=null proto=rpc callerContext=SPARK_TASK_AppId_application_1474394339641_0006_AttemptId_1_JobId_0_StageId_0_AttemptId_0_TaskId_1_AttemptNum_0 2016-09-20 11:59:37,215 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=open src=/lr_big.txt dst=null perm=null proto=rpc callerContext=SPARK_TASK_AppId_application_1474394339641_0006_AttemptId_1_JobId_0_StageId_0_AttemptId_0_TaskId_2_AttemptNum_0 2016-09-20 11:59:37,215 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=open src=/lr_big.txt dst=null perm=null proto=rpc callerContext=SPARK_TASK_AppId_application_1474394339641_0006_AttemptId_1_JobId_0_StageId_0_AttemptId_0_TaskId_0_AttemptNum_0 2016-09-20 11:59:42,391 INFO FSNamesystem.audit: allowed=true ugi=wyang (auth:SIMPLE) ip=/127.0.0.1 cmd=open src=/lr_big.txt dst=null perm=null proto=rpc callerContext=SPARK_TASK_AppId_application_1474394339641_0006_AttemptId_1_JobId_0_StageId_0_AttemptId_0_TaskId_3_AttemptNum_0 ``` This is a record in Yarn RM log: ``` 2016-09-20 11:59:24,050 INFO org.apache.hadoop.yarn.server.resourcemanager.RMAuditLogger: USER=wyang IP=127.0.0.1 OPERATION=Submit Application Request TARGET=ClientRMService RESULT=SUCCESS APPID=application_1474394339641_0006 CALLERCONTEXT=SPARK_CLIENT_AppId_application_1474394339641_0006 ``` Author: Weiqing Yang <yangweiqing001@gmail.com> Closes #14659 from Sherry302/callercontextSubmit.
* [SPARK-17138][ML][MLIB] Add Python API for multinomial logistic regressionWeichenXu2016-09-271-20/+70
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add Python API for multinomial logistic regression. - add `family` param in python api. - expose `coefficientMatrix` and `interceptVector` for `LogisticRegressionModel` - add python-side testcase for multinomial logistic regression - update python doc. ## How was this patch tested? existing and added doc tests. Author: WeichenXu <WeichenXu123@outlook.com> Closes #14852 from WeichenXu123/add_MLOR_python.
generated by cgit v1.2.3 (git 2.39.1) at 2024-07-23 16:21:16 +0000