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…ocess is dead ## What changes were proposed in this pull request? When the Python process is dead, the JVM StreamingContext is still running. Hence we will see a lot of Py4jException before the JVM process exits. It's better to stop the JVM StreamingContext to avoid those annoying logs. ## How was this patch tested? Jenkins Author: Shixiong Zhu <[email protected]> Closes #15201 from zsxwing/stop-jvm-ssc.
…a non-partial aggregate
## What changes were proposed in this pull request?
We currently cannot execute an aggregate that contains a single distinct aggregate function and an one or more non-partially plannable aggregate functions, for example:
```sql
select grp,
collect_list(col1),
count(distinct col2)
from tbl_a
group by 1
```
This is a regression from Spark 1.6. This is caused by the fact that the single distinct aggregation code path assumes that all aggregates can be planned in two phases (is partially aggregatable). This PR works around this issue by triggering the `RewriteDistinctAggregates` in such cases (this is similar to the approach taken in 1.6).
## How was this patch tested?
Created `RewriteDistinctAggregatesSuite` which checks if the aggregates with distinct aggregate functions get rewritten into two `Aggregates` and an `Expand`. Added a regression test to `DataFrameAggregateSuite`.
Author: Herman van Hovell <[email protected]>
Closes #15187 from hvanhovell/SPARK-17616.
## What changes were proposed in this pull request? Allow Spark 2.x to load instances of LDA, LocalLDAModel, and DistributedLDAModel saved from Spark 1.6. ## How was this patch tested? I tested this manually, saving the 3 types from 1.6 and loading them into master (2.x). In the future, we can add generic tests for testing backwards compatibility across all ML models in SPARK-15573. Author: Joseph K. Bradley <[email protected]> Closes #15034 from jkbradley/lda-backwards.
## What changes were proposed in this pull request? A [PR](a6aade0) was merged concurrently that made the unit test for PR #15122 not test anything anymore. This PR fixes the test. ## How was this patch tested? Changed line https://github.com/apache/spark/blob/0d634875026ccf1eaf984996e9460d7673561f80/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/FileStreamSource.scala#L137 from `false` to `true` and made sure the unit test failed. Author: Burak Yavuz <[email protected]> Closes #15203 from brkyvz/fix-test.
## What changes were proposed in this pull request? "agg_plan" are hardcoded in HashAggregateExec, which have potential issue, so removing them. ## How was this patch tested? existing tests. Author: Yucai Yu <[email protected]> Closes #15199 from yucai/agg_plan.
This was missing, preventing code that uses javax.crypto to properly compile in Spark. Author: Marcelo Vanzin <[email protected]> Closes #15204 from vanzin/SPARK-17639.
…ch iteration ## What changes were proposed in this pull request? RandomForest currently sends the entire forest to each worker on each iteration. This is because (a) the node queue is FIFO and (b) the closure references the entire array of trees (topNodes). (a) causes RFs to handle splits in many trees, especially early on in learning. (b) sends all trees explicitly. This PR: (a) Change the RF node queue to be FILO (a stack), so that RFs tend to focus on 1 or a few trees before focusing on others. (b) Change topNodes to pass only the trees required on that iteration. ## How was this patch tested? Unit tests: * Existing tests for correctness of tree learning * Manually modifying code and running tests to verify that a small number of trees are communicated on each iteration * This last item is hard to test via unit tests given the current APIs. Author: Joseph K. Bradley <[email protected]> Closes #14359 from jkbradley/rfs-fewer-trees.
…amSource.FileEntry ## What changes were proposed in this pull request? Avoid using -1 as the default batchId for FileStreamSource.FileEntry so that we can make sure not writing any FileEntry(..., batchId = -1) into the log. This also avoids people misusing it in future (#15203 is an example). ## How was this patch tested? Jenkins. Author: Shixiong Zhu <[email protected]> Closes #15206 from zsxwing/cleanup.
## What changes were proposed in this pull request? This PR proposes to close some stale PRs and ones suggested to be closed by committer(s) Closes #12415 Closes #14765 Closes #15118 Closes #15184 Closes #15183 Closes #9440 Closes #15023 Closes #14643 Closes #14827 ## How was this patch tested? N/A Author: hyukjinkwon <[email protected]> Closes #15198 from HyukjinKwon/stale-prs.
…of Accumulator V2 ## What changes were proposed in this pull request? Move the internals of the PySpark accumulator API from the old deprecated API on top of the new accumulator API. ## How was this patch tested? The existing PySpark accumulator tests (both unit tests and doc tests at the start of accumulator.py). Author: Holden Karau <[email protected]> Closes #14467 from holdenk/SPARK-16861-refactor-pyspark-accumulator-api.
…ark.mlp consistent with MultilayerPerceptronClassifier ## What changes were proposed in this pull request? update `MultilayerPerceptronClassifierWrapper.fit` paramter type: `layers: Array[Int]` `seed: String` update several default params in sparkR `spark.mlp`: `tol` --> 1e-6 `stepSize` --> 0.03 `seed` --> NULL ( when seed == NULL, the scala-side wrapper regard it as a `null` value and the seed will use the default one ) r-side `seed` only support 32bit integer. remove `layers` default value, and move it in front of those parameters with default value. add `layers` parameter validation check. ## How was this patch tested? tests added. Author: WeichenXu <[email protected]> Closes #15051 from WeichenXu123/update_py_mlp_default.
… running sparkr in RStudio
## What changes were proposed in this pull request?
Spark will add sparkr.zip to archive only when it is yarn mode (SparkSubmit.scala).
```
if (args.isR && clusterManager == YARN) {
val sparkRPackagePath = RUtils.localSparkRPackagePath
if (sparkRPackagePath.isEmpty) {
printErrorAndExit("SPARK_HOME does not exist for R application in YARN mode.")
}
val sparkRPackageFile = new File(sparkRPackagePath.get, SPARKR_PACKAGE_ARCHIVE)
if (!sparkRPackageFile.exists()) {
printErrorAndExit(s"$SPARKR_PACKAGE_ARCHIVE does not exist for R application in YARN mode.")
}
val sparkRPackageURI = Utils.resolveURI(sparkRPackageFile.getAbsolutePath).toString
// Distribute the SparkR package.
// Assigns a symbol link name "sparkr" to the shipped package.
args.archives = mergeFileLists(args.archives, sparkRPackageURI + "#sparkr")
// Distribute the R package archive containing all the built R packages.
if (!RUtils.rPackages.isEmpty) {
val rPackageFile =
RPackageUtils.zipRLibraries(new File(RUtils.rPackages.get), R_PACKAGE_ARCHIVE)
if (!rPackageFile.exists()) {
printErrorAndExit("Failed to zip all the built R packages.")
}
val rPackageURI = Utils.resolveURI(rPackageFile.getAbsolutePath).toString
// Assigns a symbol link name "rpkg" to the shipped package.
args.archives = mergeFileLists(args.archives, rPackageURI + "#rpkg")
}
}
```
So it is necessary to pass spark.master from R process to JVM. Otherwise sparkr.zip won't be distributed to executor. Besides that I also pass spark.yarn.keytab/spark.yarn.principal to spark side, because JVM process need them to access secured cluster.
## How was this patch tested?
Verify it manually in R Studio using the following code.
```
Sys.setenv(SPARK_HOME="/Users/jzhang/github/spark")
.libPaths(c(file.path(Sys.getenv(), "R", "lib"), .libPaths()))
library(SparkR)
sparkR.session(master="yarn-client", sparkConfig = list(spark.executor.instances="1"))
df <- as.DataFrame(mtcars)
head(df)
```
…
Author: Jeff Zhang <[email protected]>
Closes #14784 from zjffdu/SPARK-17210.
For some sources, it is difficult to provide a global ordering based only on the data in the offset. Since we don't use comparison for correctness, lets remove it. Author: Michael Armbrust <[email protected]> Closes #15207 from marmbrus/removeComparable.
Currently task metrics don't support executor CPU time, so there's no way to calculate how much CPU time a stage/task took from History Server metrics. This PR enables reporting CPU time. Author: jisookim <[email protected]> Closes #10212 from jisookim0513/add-cpu-time-metric.
## What changes were proposed in this pull request? This PR sets the R package version while tagging releases. Note that since R doesn't accept `-SNAPSHOT` in version number field, we remove that while setting the next version ## How was this patch tested? Tested manually by running locally Author: Shivaram Venkataraman <[email protected]> Closes #15223 from shivaram/sparkr-version-change.
…dition to existing null string array ## What changes were proposed in this pull request? To match Tokenizer and for compatibility with Word2Vec, output a nullable string array type in NGram ## How was this patch tested? Jenkins tests. Author: Sean Owen <[email protected]> Closes #15179 from srowen/SPARK-10835.
…ve at most one 0 ## What changes were proposed in this pull request? Match ProbabilisticClassifer.thresholds requirements to R randomForest cutoff, requiring all > 0 ## How was this patch tested? Jenkins tests plus new test cases Author: Sean Owen <[email protected]> Closes #15149 from srowen/SPARK-17057.
## What changes were proposed in this pull request? Add ```sparkr-vignettes.html``` to ```.gitignore```. ## How was this patch tested? No need test. Author: Yanbo Liang <[email protected]> Closes #15215 from yanboliang/ignore.
## What changes were proposed in this pull request? This pull request adds Scala/Java DataFrame API for null ordering (NULLS FIRST | LAST). Also did some minor clean up for related code (e.g. incorrect indentation), and renamed "orderby-nulls-ordering.sql" to be consistent with existing test files. ## How was this patch tested? Added a new test case in DataFrameSuite. Author: petermaxlee <[email protected]> Author: Xin Wu <[email protected]> Closes #15123 from petermaxlee/SPARK-17551.
## What changes were proposed in this pull request? When a malformed URL was sent to Executors through `sc.addJar` and `sc.addFile`, the executors become unusable, because they constantly throw `MalformedURLException`s and can never acknowledge that the file or jar is just bad input. This PR tries to fix that problem by making sure MalformedURLs can never be submitted through `sc.addJar` and `sc.addFile`. Another solution would be to blacklist bad files and jars on Executors. Maybe fail the first time, and then ignore the second time (but print a warning message). ## How was this patch tested? Unit tests in SparkContextSuite Author: Burak Yavuz <[email protected]> Closes #15224 from brkyvz/SPARK-17650.
…hon API. ## What changes were proposed in this pull request? #14597 modified ```ChiSqSelector``` to support ```fpr``` type selector, however, it left some issue need to be addressed: * We should allow users to set selector type explicitly rather than switching them by using different setting function, since the setting order will involves some unexpected issue. For example, if users both set ```numTopFeatures``` and ```percentile```, it will train ```kbest``` or ```percentile``` model based on the order of setting (the latter setting one will be trained). This make users confused, and we should allow users to set selector type explicitly. We handle similar issues at other place of ML code base such as ```GeneralizedLinearRegression``` and ```LogisticRegression```. * Meanwhile, if there are more than one parameter except ```alpha``` can be set for ```fpr``` model, we can not handle it elegantly in the existing framework. And similar issues for ```kbest``` and ```percentile``` model. Setting selector type explicitly can solve this issue also. * If setting selector type explicitly by users is allowed, we should handle param interaction such as if users set ```selectorType = percentile``` and ```alpha = 0.1```, we should notify users the parameter ```alpha``` will take no effect. We should handle complex parameter interaction checks at ```transformSchema```. (FYI #11620) * We should use lower case of the selector type names to follow MLlib convention. * Add ML Python API. ## How was this patch tested? Unit test. Author: Yanbo Liang <[email protected]> Closes #15214 from yanboliang/spark-17017.
## What changes were proposed in this pull request? This change modifies the implementation of DataFrameWriter.save such that it works with jdbc, and the call to jdbc merely delegates to save. ## How was this patch tested? This was tested via unit tests in the JDBCWriteSuite, of which I added one new test to cover this scenario. ## Additional details rxin This seems to have been most recently touched by you and was also commented on in the JIRA. This contribution is my original work and I license the work to the project under the project's open source license. Author: Justin Pihony <[email protected]> Author: Justin Pihony <[email protected]> Closes #12601 from JustinPihony/jdbc_reconciliation.
## What changes were proposed in this pull request? This was suggested in 101663f#commitcomment-17114968. This PR adds `testImplicits` to `MLlibTestSparkContext` so that some implicits such as `toDF()` can be sued across ml tests. This PR also changes all the usages of `spark.createDataFrame( ... )` to `toDF()` where applicable in ml tests in Scala. ## How was this patch tested? Existing tests should work. Author: hyukjinkwon <[email protected]> Closes #14035 from HyukjinKwon/minor-ml-test.
…enerBus ## What changes were proposed in this pull request? Log how many Spark events got dropped in LiveListenerBus so that the user can get insights on how to set a correct event queue size. ## How was this patch tested? Jenkins Author: Shixiong Zhu <[email protected]> Closes #15220 from zsxwing/SPARK-17649.
…in filestream without globbing
## 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 <[email protected]>
Author: Liang-Chi Hsieh <[email protected]>
Closes #14803 from viirya/filestreamsource-option.
## 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 <[email protected]> Closes #15225 from sameeragarwal/error-msg.
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 <[email protected]> Closes #15244 from ammills01/master.
… directory recursively ## 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 <[email protected]> Closes #15216 from yanboliang/spark-17577-2.
## 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.
…nsafeArrayData ## 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 <[email protected]> Closes #13680 from kiszk/SPARK-15962.
…mat("jdbc").load
## What changes were proposed in this pull request?
This PR proposes to make `DataFrameReader.jdbc` call `DataFrameReader.format("jdbc").load` consistently with other APIs in `DataFrameReader`/`DataFrameWriter` and avoid calling `sparkSession.baseRelationToDataFrame(..)` here and there.
The changes were mostly copied from `DataFrameWriter.jdbc()` which was recently updated.
```diff
- val params = extraOptions.toMap ++ connectionProperties.asScala.toMap
- val options = new JDBCOptions(url, table, params)
- val relation = JDBCRelation(parts, options)(sparkSession)
- sparkSession.baseRelationToDataFrame(relation)
+ this.extraOptions = this.extraOptions ++ connectionProperties.asScala
+ // explicit url and dbtable should override all
+ this.extraOptions += ("url" -> url, "dbtable" -> table)
+ format("jdbc").load()
```
## How was this patch tested?
Existing tests should cover this.
Author: hyukjinkwon <[email protected]>
Closes #15499 from HyukjinKwon/SPARK-17955.
This reverts commit bfe7885. The commit caused build failures on Hadoop 2.2 profile: ``` [error] /scratch/rxin/spark/core/src/main/scala/org/apache/spark/util/Utils.scala:1489: value read is not a member of object org.apache.commons.io.IOUtils [error] var numBytes = IOUtils.read(gzInputStream, buf) [error] ^ [error] /scratch/rxin/spark/core/src/main/scala/org/apache/spark/util/Utils.scala:1492: value read is not a member of object org.apache.commons.io.IOUtils [error] numBytes = IOUtils.read(gzInputStream, buf) [error] ^ ```
## What changes were proposed in this pull request? Actually drain commit queue rather than just iterating it. iterator() on a concurrent linked queue won't remove items from the queue, poll() will. ## How was this patch tested? Unit tests Author: cody koeninger <[email protected]> Closes #15407 from koeninger/SPARK-17841.
… in Highly Skewed Partition Sizes ## What changes were proposed in this pull request? This change is a followup for #15389 which calls `_to_java_object_rdd()` to solve this issue. Due to the concern of the possible expensive cost of the call, we can choose to decrease the batch size to solve this issue too. Simple benchmark: import time num_partitions = 20000 a = sc.parallelize(range(int(1e6)), 2) start = time.time() l = a.repartition(num_partitions).glom().map(len).collect() end = time.time() print(end - start) Before: 419.447577953 _to_java_object_rdd(): 421.916361094 decreasing the batch size: 423.712255955 ## How was this patch tested? Jenkins tests. Author: Liang-Chi Hsieh <[email protected]> Closes #15445 from viirya/repartition-batch-size.
…ener APIs ## What changes were proposed in this pull request? As per rxin request, here are further API changes - Changed `Stream(Started/Progress/Terminated)` events to `Stream*Event` - Changed the fields in `StreamingQueryListener.on***` from `query*` to `event` ## How was this patch tested? Existing unit tests. Author: Tathagata Das <[email protected]> Closes #15530 from tdas/SPARK-17731-1.
## What changes were proposed in this pull request? There was a bug introduced in #14690 which broke refreshByPath with converted hive tables (though, it turns out it was very difficult to refresh converted hive tables anyways, since you had to specify the exact path of one of the partitions). This changes refreshByPath to invalidate by prefix instead of exact match, and fixes the issue. cc sameeragarwal for refreshByPath changes mallman ## How was this patch tested? Extended unit test. Author: Eric Liang <[email protected]> Closes #15521 from ericl/fix-caching.
## What changes were proposed in this pull request? Fix hadoop2.2 compilation error. ## How was this patch tested? Existing tests. cc tdas zsxwing Author: Yu Peng <[email protected]> Closes #15537 from loneknightpy/fix-17711.
…y database in destination table name(but have to be same as source table) ## What changes were proposed in this pull request? Unlike Hive, in Spark SQL, ALTER TABLE RENAME TO cannot move a table from one database to another(e.g. `ALTER TABLE db1.tbl RENAME TO db2.tbl2`), and will report error if the database in source table and destination table is different. So in #14955 , we forbid users to specify database of destination table in ALTER TABLE RENAME TO, to be consistent with other database systems and also make it easier to rename tables in non-current database, e.g. users can write `ALTER TABLE db1.tbl RENAME TO tbl2`, instead of `ALTER TABLE db1.tbl RENAME TO db1.tbl2`. However, this is a breaking change. Users may already have queries that specify database of destination table in ALTER TABLE RENAME TO. This PR reverts most of #14955 , and simplify the usage of ALTER TABLE RENAME TO by making database of source table the default database of destination table, instead of current database, so that users can still write `ALTER TABLE db1.tbl RENAME TO tbl2`, which is consistent with other databases like MySQL, Postgres, etc. ## How was this patch tested? The added back tests and some new tests. Author: Wenchen Fan <[email protected]> Closes #15434 from cloud-fan/revert.
## What changes were proposed in this pull request? In http://spark.apache.org/docs/latest/sql-programming-guide.html, Section "Untyped Dataset Operations (aka DataFrame Operations)" Link to R DataFrame doesn't work that return The requested URL /docs/latest/api/R/DataFrame.html was not found on this server. Correct link is SparkDataFrame.html for spark 2.0 ## How was this patch tested? Manual checked. Author: Tommy YU <[email protected]> Closes #15543 from Wenpei/spark-18001.
## What changes were proposed in this pull request? `SerializationUtils.clone()` of commons-lang3 (<3.5) has a bug that breaks thread safety, which gets stack sometimes caused by race condition of initializing hash map. See https://issues.apache.org/jira/browse/LANG-1251. ## How was this patch tested? Existing tests. Author: Takuya UESHIN <[email protected]> Closes #15548 from ueshin/issues/SPARK-17985.
…eir own text when there aren't any complete apps ## What changes were proposed in this pull request? I've added a method to `ApplicationHistoryProvider` that returns the html paragraph to display when there are no applications. This allows providers other than `FsHistoryProvider` to determine what is printed. The current hard coded text is now moved into `FsHistoryProvider` since it assumed that's what was being used before. I chose to make the function return html rather than text because the current text block had inline html in it and it allows a new implementation of `ApplicationHistoryProvider` more versatility. I did not see any security issues with this since injecting html here requires implementing `ApplicationHistoryProvider` and can't be done outside of code. ## How was this patch tested? Manual testing and dev/run-tests No visible changes to the UI Author: Alex Bozarth <[email protected]> Closes #15490 from ajbozarth/spark10541.
…ather than throwing ClassCastException
## What changes were proposed in this pull request?
This PR proposes to check the second argument, `ascendingOrder` rather than throwing `ClassCastException` exception message.
```sql
select sort_array(array('b', 'd'), '1');
```
**Before**
```
16/10/19 13:16:08 ERROR SparkSQLDriver: Failed in [select sort_array(array('b', 'd'), '1')]
java.lang.ClassCastException: org.apache.spark.unsafe.types.UTF8String cannot be cast to java.lang.Boolean
at scala.runtime.BoxesRunTime.unboxToBoolean(BoxesRunTime.java:85)
at org.apache.spark.sql.catalyst.expressions.SortArray.nullSafeEval(collectionOperations.scala:185)
at org.apache.spark.sql.catalyst.expressions.BinaryExpression.eval(Expression.scala:416)
at org.apache.spark.sql.catalyst.optimizer.ConstantFolding$$anonfun$apply$1$$anonfun$applyOrElse$1.applyOrElse(expressions.scala:50)
at org.apache.spark.sql.catalyst.optimizer.ConstantFolding$$anonfun$apply$1$$anonfun$applyOrElse$1.applyOrElse(expressions.scala:43)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:292)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:292)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:74)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:291)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:297)
```
**After**
```
Error in query: cannot resolve 'sort_array(array('b', 'd'), '1')' due to data type mismatch: Sort order in second argument requires a boolean literal.; line 1 pos 7;
```
## How was this patch tested?
Unit test in `DataFrameFunctionsSuite`.
Author: hyukjinkwon <[email protected]>
Closes #15532 from HyukjinKwon/SPARK-17989.
## What changes were proposed in this pull request? This patch refactors WriterContainer to simplify the logic and make control flow more obvious.The previous code setup made it pretty difficult to track the actual dependencies on variables and setups because the driver side and the executor side were using the same set of variables. ## How was this patch tested? N/A - this should be covered by existing tests. Author: Reynold Xin <[email protected]> Closes #15551 from rxin/writercontainer-refactor.
…Id index value overflowing ## What changes were proposed in this pull request? - Fix bug of RDD `zipWithIndex` generating wrong result when one partition contains more than 2147483647 records. - Fix bug of RDD `zipWithUniqueId` generating wrong result when one partition contains more than 2147483647 records. ## How was this patch tested? test added. Author: WeichenXu <[email protected]> Closes #15550 from WeichenXu123/fix_rdd_zipWithIndex_overflow.
## What changes were proposed in this pull request? This should apply to non-converted metastore relations. WIP to see if this causes any test failures. ## How was this patch tested? Existing tests. Author: Eric Liang <[email protected]> Closes #15475 from ericl/try-enabling-pruning.
## What changes were proposed in this pull request? Add a SPARK_NO_DAEMONIZE environment variable flag to spark-daemon.sh that causes the process it would run to be run in the foreground. It looks like there has been some prior work in #3881, but there was some talk about these being refactored. I'm not sure if that happened or not, but that PR is almost 2 years old at this point so it was worth revisiting. ## How was this patch tested? ./dev/run-tests still seems to work. It doesn't look like these scripts have tests, but if I missed them just let me know. Author: Mike Ihbe <[email protected]> Closes #15338 from mikejihbe/SPARK-11653.
…TA LOCAL INPATH
## What changes were proposed in this pull request?
Currently, Spark 2.0 raises an `input path does not exist` AnalysisException if the file name contains '*'. It is misleading since it occurs when there exist some matched files. Also, it was a supported feature in Spark 1.6.2. This PR aims to support wildcard characters in filename for `LOAD DATA LOCAL INPATH` SQL command like Spark 1.6.2.
**Reported Error Scenario**
```scala
scala> sql("CREATE TABLE t(a string)")
res0: org.apache.spark.sql.DataFrame = []
scala> sql("LOAD DATA LOCAL INPATH '/tmp/x*' INTO TABLE t")
org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: /tmp/x*;
```
## How was this patch tested?
Pass the Jenkins test with a new test case.
Author: Dongjoon Hyun <[email protected]>
Closes #15376 from dongjoon-hyun/SPARK-17796.
…ct case sensitivity configuration ## What changes were proposed in this pull request? SHOW COLUMNS command validates the user supplied database name with database name from qualified table name name to make sure both of them are consistent. This comparison should respect case sensitivity. ## How was this patch tested? Added tests in DDLSuite and existing tests were moved to use new sql based test infrastructure. Author: Dilip Biswal <[email protected]> Closes #15423 from dilipbiswal/dkb_show_column_fix.
## What changes were proposed in this pull request? Jira : https://issues.apache.org/jira/browse/SPARK-17698 `ExtractEquiJoinKeys` is incorrectly using filter predicates as the join condition for joins. `canEvaluate` [0] tries to see if the an `Expression` can be evaluated using output of a given `Plan`. In case of filter predicates (eg. `a.id='1'`), the `Expression` passed for the right hand side (ie. '1' ) is a `Literal` which does not have any attribute references. Thus `expr.references` is an empty set which theoretically is a subset of any set. This leads to `canEvaluate` returning `true` and `a.id='1'` is treated as a join predicate. While this does not lead to incorrect results but in case of bucketed + sorted tables, we might miss out on avoiding un-necessary shuffle + sort. See example below: [0] : https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala#L91 eg. ``` val df = (1 until 10).toDF("id").coalesce(1) hc.sql("DROP TABLE IF EXISTS table1").collect df.write.bucketBy(8, "id").sortBy("id").saveAsTable("table1") hc.sql("DROP TABLE IF EXISTS table2").collect df.write.bucketBy(8, "id").sortBy("id").saveAsTable("table2") sqlContext.sql(""" SELECT a.id, b.id FROM table1 a FULL OUTER JOIN table2 b ON a.id = b.id AND a.id='1' AND b.id='1' """).explain(true) ``` BEFORE: This is doing shuffle + sort over table scan outputs which is not needed as both tables are bucketed and sorted on the same columns and have same number of buckets. This should be a single stage job. ``` SortMergeJoin [id#38, cast(id#38 as double), 1.0], [id#39, 1.0, cast(id#39 as double)], FullOuter :- *Sort [id#38 ASC NULLS FIRST, cast(id#38 as double) ASC NULLS FIRST, 1.0 ASC NULLS FIRST], false, 0 : +- Exchange hashpartitioning(id#38, cast(id#38 as double), 1.0, 200) : +- *FileScan parquet default.table1[id#38] Batched: true, Format: ParquetFormat, InputPaths: file:spark-warehouse/table1, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int> +- *Sort [id#39 ASC NULLS FIRST, 1.0 ASC NULLS FIRST, cast(id#39 as double) ASC NULLS FIRST], false, 0 +- Exchange hashpartitioning(id#39, 1.0, cast(id#39 as double), 200) +- *FileScan parquet default.table2[id#39] Batched: true, Format: ParquetFormat, InputPaths: file:spark-warehouse/table2, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int> ``` AFTER : ``` SortMergeJoin [id#32], [id#33], FullOuter, ((cast(id#32 as double) = 1.0) && (cast(id#33 as double) = 1.0)) :- *FileScan parquet default.table1[id#32] Batched: true, Format: ParquetFormat, InputPaths: file:spark-warehouse/table1, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int> +- *FileScan parquet default.table2[id#33] Batched: true, Format: ParquetFormat, InputPaths: file:spark-warehouse/table2, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int> ``` ## How was this patch tested? - Added a new test case for this scenario : `SPARK-17698 Join predicates should not contain filter clauses` - Ran all the tests in `BucketedReadSuite` Author: Tejas Patil <[email protected]> Closes #15272 from tejasapatil/SPARK-17698_join_predicate_filter_clause.
## What changes were proposed in this pull request? Add mapValues to KeyValueGroupedDataset ## How was this patch tested? New test in DatasetSuite for groupBy function, mapValues, flatMap Author: Koert Kuipers <[email protected]> Closes #13526 from koertkuipers/feat-keyvaluegroupeddataset-mapvalues.
## What changes were proposed in this pull request? The newly implemented Structured Streaming `KafkaSource` did calculate the preferred locations for each topic partition, but didn't offer this information through RDD's `getPreferredLocations` method. So here propose to add this method in `KafkaSourceRDD`. ## How was this patch tested? Manual verification. Author: jerryshao <[email protected]> Closes #15545 from jerryshao/SPARK-17999.
## What changes were proposed in this pull request? Currently each data source OutputWriter is responsible for specifying the entire file name for each file output. This, however, does not make any sense because we rely on file naming schemes for certain behaviors in Spark SQL, e.g. bucket id. The current approach allows individual data sources to break the implementation of bucketing. On the flip side, we also don't want to move file naming entirely out of data sources, because different data sources do want to specify different extensions. This patch divides file name specification into two parts: the first part is a prefix specified by the caller of OutputWriter (in WriteOutput), and the second part is the suffix that can be specified by the OutputWriter itself. Note that a side effect of this change is that now all file based data sources also support bucketing automatically. There are also some other minor cleanups: - Removed the UUID passed through generic Configuration string - Some minor rewrites for better clarity - Renamed "path" in multiple places to "stagingDir", to more accurately reflect its meaning ## How was this patch tested? This should be covered by existing data source tests. Author: Reynold Xin <[email protected]> Closes #15562 from rxin/SPARK-18021.
## What changes were proposed in this pull request? Update docs to not suggest to package Spark before running tests. ## How was this patch tested? Not creating a JIRA since this pretty small. We haven't had the need to run mvn package before mvn test since 1.6 at least, or so I am told. So, updating the docs to not be misguiding. Author: Mark Grover <[email protected]> Closes #15572 from markgrover/doc_update.
…StreamSourceSuite failure ## What changes were proposed in this pull request? My hunch is `mkdirs` fails. Just add more checks to collect more info. ## How was this patch tested? Jenkins Author: Shixiong Zhu <[email protected]> Closes #15577 from zsxwing/SPARK-18030-debug.
## What changes were proposed in this pull request?
Fix for a bunch of test warnings that were added recently.
We need to investigate why warnings are not turning into errors.
```
Warnings -----------------------------------------------------------------------
1. createDataFrame uses files for large objects (test_sparkSQL.R#215) - Use Sepal_Length instead of Sepal.Length as column name
2. createDataFrame uses files for large objects (test_sparkSQL.R#215) - Use Sepal_Width instead of Sepal.Width as column name
3. createDataFrame uses files for large objects (test_sparkSQL.R#215) - Use Petal_Length instead of Petal.Length as column name
4. createDataFrame uses files for large objects (test_sparkSQL.R#215) - Use Petal_Width instead of Petal.Width as column name
Consider adding
importFrom("utils", "object.size")
to your NAMESPACE file.
```
## How was this patch tested?
unit tests
Author: Felix Cheung <[email protected]>
Closes #15560 from felixcheung/rwarnings.
…tput of LogicalRelation ## What changes were proposed in this pull request? In `PruneFileSourcePartitions`, we will replace the `LogicalRelation` with a pruned one. However, this replacement may change the output of the `LogicalRelation` if it doesn't have `expectedOutputAttributes`. This PR fixes it. ## How was this patch tested? the new `PruneFileSourcePartitionsSuite` Author: Wenchen Fan <[email protected]> Closes #15569 from cloud-fan/partition-bug.
## What changes were proposed in this pull request? 1) Upgrade the Py4J version on the Java side 2) Update the py4j src zip file we bundle with Spark ## How was this patch tested? Existing doctests & unit tests pass Author: Jagadeesan <[email protected]> Closes #15514 from jagadeesanas2/SPARK-17960.
## What changes were proposed in this pull request?
`Array[T]()` -> `Array.empty[T]` to avoid allocating 0-length arrays.
Use regex `find . -name '*.scala' | xargs -i bash -c 'egrep "Array\[[A-Za-z]+\]\(\)" -n {} && echo {}'` to find modification candidates.
cc srowen
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <[email protected]>
Closes #15564 from zhengruifeng/avoid_0_length_array.
…line ## What changes were proposed in this pull request? Updated the Executors added/removed bubble in the time line so it's clearer where it starts. Now the bubble is left justified on the start time (still also denoted by the line) rather than center justified. ## How was this patch tested? Manually tested UI <img width="596" alt="screen shot 2016-10-17 at 6 04 36 pm" src="https://cloud.githubusercontent.com/assets/13952758/19496563/e6c9186e-953c-11e6-85e4-63309a553f65.png"> <img width="492" alt="screen shot 2016-10-17 at 5 54 09 pm" src="https://cloud.githubusercontent.com/assets/13952758/19496568/e9f06132-953c-11e6-8901-54405ebc7f5b.png"> Author: Alex Bozarth <[email protected]> Closes #15536 from ajbozarth/spark13275.
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