- Sep 29, 2016
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Bryan Cutler authored
[SPARK-17697][ML] Fixed bug in summary calculations that pattern match 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.
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Dongjoon Hyun authored
## 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.
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Imran Rashid authored
## 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.
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Bjarne Fruergaard authored
## 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.
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Dongjoon Hyun authored
## 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. -
Liang-Chi Hsieh authored
## 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. -
Michael Armbrust authored
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. -
Brian Cho authored
## 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.
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Gang Wu authored
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.
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Imran Rashid authored
## 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.
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José Hiram Soltren authored
## 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.
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Takeshi YAMAMURO authored
## 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.
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Sean Owen authored
[SPARK-17614][SQL] sparkSession.read() .jdbc(***) use the sql syntax "where 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.
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Yanbo Liang authored
## 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.
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Yanbo Liang authored
## 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.
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- Sep 28, 2016
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Josh Rosen authored
## 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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Weiqing Yang authored
[SPARK-17710][HOTFIX] Fix ClassCircularityError in ReplSuite tests in Maven 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.
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Herman van Hovell authored
## 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.
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Eric Liang authored
## 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.
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Eric Liang authored
## 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.
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w00228970 authored
## 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.
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hyukjinkwon authored
## 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**   <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**   <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. -
Shuai Lin authored
## 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.
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- Sep 27, 2016
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hyukjinkwon authored
[SPARK-17499][SPARKR][FOLLOWUP] Check null first for layers in spark.mlp to 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.
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Josh Rosen authored
## 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.
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Liang-Chi Hsieh authored
## 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.
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Josh Rosen authored
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.
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Reynold Xin authored
## 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.
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gatorsmile authored
### 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.
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Reynold Xin authored
## 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.
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hyukjinkwon authored
## 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.
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hyukjinkwon authored
## 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.
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Weiqing Yang authored
## 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. -
WeichenXu authored
## 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.
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Kazuaki Ishizaki authored
## What changes were proposed in this pull request? This PR introduces more compact representation for ```UnsafeArrayData```. ```UnsafeArrayData``` needs to accept ```null``` value in each entry of an array. In the current version, it has three parts ``` [numElements] [offsets] [values] ``` `Offsets` has the number of `numElements`, and represents `null` if its value is negative. It may increase memory footprint, and introduces an indirection for accessing each of `values`. This PR uses bitvectors to represent nullability for each element like `UnsafeRow`, and eliminates an indirection for accessing each element. The new ```UnsafeArrayData``` has four parts. ``` [numElements][null bits][values or offset&length][variable length portion] ``` In the `null bits` region, we store 1 bit per element, represents whether an element is null. Its total size is ceil(numElements / 8) bytes, and it is aligned to 8-byte boundaries. In the `values or offset&length` region, we store the content of elements. For fields that hold fixed-length primitive types, such as long, double, or int, we store the value directly in the field. For fields with non-primitive or variable-length values, we store a relative offset (w.r.t. the base address of the array) that points to the beginning of the variable-length field and length (they are combined into a long). Each is word-aligned. For `variable length portion`, each is aligned to 8-byte boundaries. The new format can reduce memory footprint and improve performance of accessing each element. An example of memory foot comparison: 1024x1024 elements integer array Size of ```baseObject``` for ```UnsafeArrayData```: 8 + 1024x1024 + 1024x1024 = 2M bytes Size of ```baseObject``` for ```UnsafeArrayData```: 8 + 1024x1024/8 + 1024x1024 = 1.25M bytes In summary, we got 1.0-2.6x performance improvements over the code before applying this PR. Here are performance results of [benchmark programs](https://github.com/kiszk/spark/blob/04d2e4b6dbdc4eff43ce18b3c9b776e0129257c7/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/UnsafeArrayDataBenchmark.scala): **Read UnsafeArrayData**: 1.7x and 1.6x performance improvements over the code before applying this PR ```` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.4.11-200.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without SPARK-15962 Read UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 430 / 436 390.0 2.6 1.0X Double 456 / 485 367.8 2.7 0.9X With SPARK-15962 Read UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 252 / 260 666.1 1.5 1.0X Double 281 / 292 597.7 1.7 0.9X ```` **Write UnsafeArrayData**: 1.0x and 1.1x performance improvements over the code before applying this PR ```` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.0.4-301.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without SPARK-15962 Write UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 203 / 273 103.4 9.7 1.0X Double 239 / 356 87.9 11.4 0.8X With SPARK-15962 Write UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 196 / 249 107.0 9.3 1.0X Double 227 / 367 92.3 10.8 0.9X ```` **Get primitive array from UnsafeArrayData**: 2.6x and 1.6x performance improvements over the code before applying this PR ```` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.0.4-301.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without SPARK-15962 Get primitive array from UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 207 / 217 304.2 3.3 1.0X Double 257 / 363 245.2 4.1 0.8X With SPARK-15962 Get primitive array from UnsafeArrayData: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 151 / 198 415.8 2.4 1.0X Double 214 / 394 293.6 3.4 0.7X ```` **Create UnsafeArrayData from primitive array**: 1.7x and 2.1x performance improvements over the code before applying this PR ```` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.0.4-301.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without SPARK-15962 Create UnsafeArrayData from primitive array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 340 / 385 185.1 5.4 1.0X Double 479 / 705 131.3 7.6 0.7X With SPARK-15962 Create UnsafeArrayData from primitive array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ Int 206 / 211 306.0 3.3 1.0X Double 232 / 406 271.6 3.7 0.9X ```` 1.7x and 1.4x performance improvements in [```UDTSerializationBenchmark```](https://github.com/apache/spark/blob/master/mllib/src/test/scala/org/apache/spark/mllib/linalg/UDTSerializationBenchmark.scala) over the code before applying this PR ```` OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.4.11-200.fc22.x86_64 Intel Xeon E3-12xx v2 (Ivy Bridge) Without SPARK-15962 VectorUDT de/serialization: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ serialize 442 / 533 0.0 441927.1 1.0X deserialize 217 / 274 0.0 217087.6 2.0X With SPARK-15962 VectorUDT de/serialization: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ serialize 265 / 318 0.0 265138.5 1.0X deserialize 155 / 197 0.0 154611.4 1.7X ```` ## How was this patch tested? Added unit tests into ```UnsafeArraySuite``` Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com> Closes #13680 from kiszk/SPARK-15962.
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Ding Fei authored
## What changes were proposed in this pull request? Fix two comments since Actor is not used anymore. Author: Ding Fei <danis@danix> Closes #15251 from danix800/comment-fixing.
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- Sep 26, 2016
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Yanbo Liang authored
## What changes were proposed in this pull request? #15140 exposed ```JavaSparkContext.addFile(path: String, recursive: Boolean)``` to Python/R, then we can update SparkR ```spark.addFile``` to support adding directory recursively. ## How was this patch tested? Added unit test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15216 from yanboliang/spark-17577-2.
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Andrew Mills authored
Corrected a link to the configuration.html page, it was pointing to a page that does not exist (configurations.html). Documentation change, verified in preview. Author: Andrew Mills <ammills01@users.noreply.github.com> Closes #15244 from ammills01/master.
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Sameer Agarwal authored
## What changes were proposed in this pull request? This minor patch fixes a confusing exception message while reserving additional capacity in the vectorized parquet reader. ## How was this patch tested? Exisiting Unit Tests Author: Sameer Agarwal <sameerag@cs.berkeley.edu> Closes #15225 from sameeragarwal/error-msg.
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Liang-Chi Hsieh authored
## What changes were proposed in this pull request? When reading file stream with non-globbing path, the results return data with all `null`s for the partitioned columns. E.g., case class A(id: Int, value: Int) val data = spark.createDataset(Seq( A(1, 1), A(2, 2), A(2, 3)) ) val url = "/tmp/test" data.write.partitionBy("id").parquet(url) spark.read.parquet(url).show +-----+---+ |value| id| +-----+---+ | 2| 2| | 3| 2| | 1| 1| +-----+---+ val s = spark.readStream.schema(spark.read.load(url).schema).parquet(url) s.writeStream.queryName("test").format("memory").start() sql("SELECT * FROM test").show +-----+----+ |value| id| +-----+----+ | 2|null| | 3|null| | 1|null| +-----+----+ ## How was this patch tested? Jenkins tests. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #14803 from viirya/filestreamsource-option.
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