spark decision tree #16191
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spark decision tree #16191
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This PR adds a new method `withWatermark` to the `Dataset` API, which can be used specify an _event time watermark_. An event time watermark allows the streaming engine to reason about the point in time after which we no longer expect to see late data. This PR also has augmented `StreamExecution` to use this watermark for several purposes:
- To know when a given time window aggregation is finalized and thus results can be emitted when using output modes that do not allow updates (e.g. `Append` mode).
- To minimize the amount of state that we need to keep for on-going aggregations, by evicting state for groups that are no longer expected to change. Although, we do still maintain all state if the query requires (i.e. if the event time is not present in the `groupBy` or when running in `Complete` mode).
An example that emits windowed counts of records, waiting up to 5 minutes for late data to arrive.
```scala
df.withWatermark("eventTime", "5 minutes")
.groupBy(window($"eventTime", "1 minute") as 'window)
.count()
.writeStream
.format("console")
.mode("append") // In append mode, we only output finalized aggregations.
.start()
```
### Calculating the watermark.
The current event time is computed by looking at the `MAX(eventTime)` seen this epoch across all of the partitions in the query minus some user defined _delayThreshold_. An additional constraint is that the watermark must increase monotonically.
Note that since we must coordinate this value across partitions occasionally, the actual watermark used is only guaranteed to be at least `delay` behind the actual event time. In some cases we may still process records that arrive more than delay late.
This mechanism was chosen for the initial implementation over processing time for two reasons:
- it is robust to downtime that could affect processing delay
- it does not require syncing of time or timezones between the producer and the processing engine.
### Other notable implementation details
- A new trigger metric `eventTimeWatermark` outputs the current value of the watermark.
- We mark the event time column in the `Attribute` metadata using the key `spark.watermarkDelay`. This allows downstream operations to know which column holds the event time. Operations like `window` propagate this metadata.
- `explain()` marks the watermark with a suffix of `-T${delayMs}` to ease debugging of how this information is propagated.
- Currently, we don't filter out late records, but instead rely on the state store to avoid emitting records that are both added and filtered in the same epoch.
### Remaining in this PR
- [ ] The test for recovery is currently failing as we don't record the watermark used in the offset log. We will need to do so to ensure determinism, but this is deferred until #15626 is merged.
### Other follow-ups
There are some natural additional features that we should consider for future work:
- Ability to write records that arrive too late to some external store in case any out-of-band remediation is required.
- `Update` mode so you can get partial results before a group is evicted.
- Other mechanisms for calculating the watermark. In particular a watermark based on quantiles would be more robust to outliers.
Author: Michael Armbrust <michael@databricks.com>
Closes #15702 from marmbrus/watermarks.
## What changes were proposed in this pull request? 1, Add link of `VertexRDD` and `EdgeRDD` 2, Notify in `Vertex and Edge RDDs` that not all methods are listed 3, `VertexID` -> `VertexId` ## How was this patch tested? No tests, only docs is modified Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15875 from zhengruifeng/update_graphop_doc.
…n Exception of Function Lookup
### What changes were proposed in this pull request?
When the exception is an invocation exception during function lookup, we return a useless/confusing error message:
For example,
```Scala
df.selectExpr("concat_ws()")
```
Below is the error message we got:
```
null; line 1 pos 0
org.apache.spark.sql.AnalysisException: null; line 1 pos 0
```
To get the meaningful error message, we need to get the cause. The fix is exactly the same as what we did in #12136. After the fix, the message we got is the exception issued in the constuctor of function implementation:
```
requirement failed: concat_ws requires at least one argument.; line 1 pos 0
org.apache.spark.sql.AnalysisException: requirement failed: concat_ws requires at least one argument.; line 1 pos 0
```
### How was this patch tested?
Added test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes #15878 from gatorsmile/functionNotFound.
## What changes were proposed in this pull request? Adds support for CNI-isolated containers ## How was this patch tested? I launched SparkPi both with and without `spark.mesos.network.name`, and verified the job completed successfully. Author: Michael Gummelt <mgummelt@mesosphere.io> Closes #15740 from mgummelt/spark-342-cni.
## What changes were proposed in this pull request? 1,Remove `runs` from docs of mllib.KMeans 2,Add notes for `k` according to comments in sources ## How was this patch tested? existing tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15873 from zhengruifeng/update_doc_mllib_kmeans.
… child. ## What changes were proposed in this pull request? The `FoldablePropagation` optimizer rule, pulls foldable values out from under an `Expand`. This breaks the `Expand` in two ways: - It rewrites the output attributes of the `Expand`. We explicitly define output attributes for `Expand`, these are (unfortunately) considered as part of the expressions of the `Expand` and can be rewritten. - Expand can actually change the column (it will typically re-use the attributes or the underlying plan). This means that we cannot safely propagate the expressions from under an `Expand`. This PR fixes this and (hopefully) other issues by explicitly whitelisting allowed operators. ## How was this patch tested? Added tests to `FoldablePropagationSuite` and to `SQLQueryTestSuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15857 from hvanhovell/SPARK-18300.
… configurable. ## What changes were proposed in this pull request? The largest parallelism in PartitioningAwareFileIndex #listLeafFilesInParallel() is 10000 in hard code. We may need to make this number configurable. And in PR, I reduce it to 100. ## How was this patch tested? Existing ut. Author: genmao.ygm <genmao.ygm@genmaoygmdeMacBook-Air.local> Author: dylon <hustyugm@gmail.com> Closes #15829 from uncleGen/SPARK-18379.
…teByKey Added RDD batch time as an input parameter to the update function in updateStateByKey. Author: Aaditya Ramesh <aramesh@conviva.com> Closes #11122 from aramesh117/SPARK-13027.
…om checkpoints ## What changes were proposed in this pull request? It would be nice if memory sinks can also recover from checkpoints. For correctness reasons, the only time we should support it is in `Complete` OutputMode. We can support this in CompleteMode, because the output of the StateStore is already persisted in the checkpoint directory. ## How was this patch tested? Unit test Author: Burak Yavuz <brkyvz@gmail.com> Closes #15801 from brkyvz/mem-stream.
## What changes were proposed in this pull request? This PR is to define 'spark.yarn.am.port' in yarn config.scala just like other Yarn configurations. That makes code easier to maintain. ## How was this patch tested? Build passed & tested some Yarn unit tests. Author: Weiqing Yang <yangweiqing001@gmail.com> Closes #15858 from weiqingy/yarn.
…ileFormatWriter ## What changes were proposed in this pull request? SPARK-18012 refactored the file write path in FileStreamSink using FileFormatWriter which always uses the default non-streaming QueryExecution to perform the writes. This is wrong for FileStreamSink, because the streaming QueryExecution (i.e. IncrementalExecution) should be used for correctly incrementalizing aggregation. The addition of watermarks in SPARK-18124, file stream sink should logically supports aggregation + watermark + append mode. But actually it fails with ``` 16:23:07.389 ERROR org.apache.spark.sql.execution.streaming.StreamExecution: Query query-0 terminated with error java.lang.AssertionError: assertion failed: No plan for EventTimeWatermark timestamp#7: timestamp, interval 10 seconds +- LocalRelation [timestamp#7] at scala.Predef$.assert(Predef.scala:170) at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:92) at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:77) at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:74) at scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157) at scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157) at scala.collection.Iterator$class.foreach(Iterator.scala:893) at scala.collection.AbstractIterator.foreach(Iterator.scala:1336) at scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:157) at scala.collection.AbstractIterator.foldLeft(Iterator.scala:1336) at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2.apply(QueryPlanner.scala:74) at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2.apply(QueryPlanner.scala:66) at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434) at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440) at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:92) at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:77) at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$2$$anonfun$apply$2.apply(QueryPlanner.scala:74) ``` This PR fixes it by passing the correct query execution. ## How was this patch tested? New unit test Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #15885 from tdas/SPARK-18440.
…when stopped even if it was not started
## What changes were proposed in this pull request?
Several tests are being failed on Windows due to the failure of removing the checkpoint dir between each tests.
This is caused by not closed file in `ReceiverTracker`. When it is not started, it does not close it even if `stop()` is called.
```
Test org.apache.spark.streaming.JavaAPISuite.testCheckpointMasterRecovery started
Test org.apache.spark.streaming.JavaAPISuite.testCheckpointMasterRecovery failed: java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\1478983663710-0, took 3.828 sec
at org.apache.spark.util.Utils$.deleteRecursively(Utils.scala:1010)
at org.apache.spark.util.Utils.deleteRecursively(Utils.scala)
at org.apache.spark.streaming.JavaAPISuite.testCheckpointMasterRecovery(JavaAPISuite.java:1809)
...
```
```
- mapWithState - basic operations with simple API (7 seconds, 640 milliseconds)
Exception encountered when attempting to run a suite with class name: org.apache.spark.streaming.MapWithStateSuite *** ABORTED *** (12 seconds, 688 milliseconds)
java.io.IOException: Failed to delete: C:\projects\spark\streaming\checkpoint\spark-b8486e2b-6468-4e6f-bb24-88277d2c033c
...
```
## How was this patch tested?
Tests in `JavaAPISuite` and `MapWithStateSuite`.
Manually tested via AppVeyor:
**Before**
- `org.apache.spark.streaming.JavaAPISuite`
Build: https://ci.appveyor.com/project/spark-test/spark/build/71-MapWithStateSuite-1
Diff: master...spark-test:188c828e682ec45b75d15c3dfc782bcdc8ce024c
- `org.apache.spark.streaming.MapWithStateSuite`
Build: https://ci.appveyor.com/project/spark-test/spark/build/72-MapWithStateSuite-1
Diff: master...spark-test:8f6945d0ccde022a23d3848f6b7fe6da1e7c902e
**After**
- `org.apache.spark.streaming.JavaAPISuite`
Build started: [Streaming] `org.apache.spark.streaming.JavaAPISuite` [](https://ci.appveyor.com/project/spark-test/spark/branch/3D74F2D5-B0D5-4E1D-874C-685AE694FD37)
Diff: master...spark-test:3D74F2D5-B0D5-4E1D-874C-685AE694FD37
- `org.apache.spark.streaming.MapWithStateSuite`
Build started: [Streaming] `org.apache.spark.streaming.MapWithStateSuite` [](https://ci.appveyor.com/project/spark-test/spark/branch/C8E88B64-49F0-4157-9AFA-FC3ACC442351)
Diff: master...spark-test:C8E88B64-49F0-4157-9AFA-FC3ACC442351
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #15867 from HyukjinKwon/SPARK-18423.
## What changes were proposed in this pull request?
This PR aims to support `comparators`, e.g. '<', '<=', '>', '>=', again in Apache Spark 2.0 for backward compatibility.
**Spark 1.6**
``` scala
scala> sql("CREATE TABLE sales(id INT) PARTITIONED BY (country STRING, quarter STRING)")
res0: org.apache.spark.sql.DataFrame = [result: string]
scala> sql("ALTER TABLE sales DROP PARTITION (country < 'KR')")
res1: org.apache.spark.sql.DataFrame = [result: string]
```
**Spark 2.0**
``` scala
scala> sql("CREATE TABLE sales(id INT) PARTITIONED BY (country STRING, quarter STRING)")
res0: org.apache.spark.sql.DataFrame = []
scala> sql("ALTER TABLE sales DROP PARTITION (country < 'KR')")
org.apache.spark.sql.catalyst.parser.ParseException:
mismatched input '<' expecting {')', ','}(line 1, pos 42)
```
After this PR, it's supported.
## How was this patch tested?
Pass the Jenkins test with a newly added testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #15704 from dongjoon-hyun/SPARK-17732-2.
## What changes were proposed in this pull request? Commit f14ae49 broke the scala 2.10 build. This PR fixes this by simplifying the used pattern match. ## How was this patch tested? Tested building manually. Ran `build/sbt -Dscala-2.10 -Pscala-2.10 package`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15891 from hvanhovell/SPARK-18300-scala-2.10.
## What changes were proposed in this pull request? it's weird that every session can set its own warehouse path at runtime, we should forbid it and make it a static conf. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #15825 from cloud-fan/warehouse.
## What changes were proposed in this pull request? ```spark.mlp``` should support ```RFormula``` like other ML algorithm wrappers. BTW, I did some cleanup and improvement for ```spark.mlp```. ## How was this patch tested? Unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #15883 from yanboliang/spark-18438.
…ensitive
## What changes were proposed in this pull request?
This PR aims to improve DataSource option keys to be more case-insensitive
DataSource partially use CaseInsensitiveMap in code-path. For example, the following fails to find url.
```scala
val df = spark.createDataFrame(sparkContext.parallelize(arr2x2), schema2)
df.write.format("jdbc")
.option("UrL", url1)
.option("dbtable", "TEST.SAVETEST")
.options(properties.asScala)
.save()
```
This PR makes DataSource options to use CaseInsensitiveMap internally and also makes DataSource to use CaseInsensitiveMap generally except `InMemoryFileIndex` and `InsertIntoHadoopFsRelationCommand`. We can not pass them CaseInsensitiveMap because they creates new case-sensitive HadoopConfs by calling newHadoopConfWithOptions(options) inside.
## How was this patch tested?
Pass the Jenkins test with newly added test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #15884 from dongjoon-hyun/SPARK-18433.
## Before  ## After  Author: Liwei Lin <lwlin7@gmail.com> Closes #15903 from lw-lin/kafka-doc-lines.
## What changes were proposed in this pull request? Avoid NPE in KinesisRecordProcessor when shutdown happens without successful init ## How was this patch tested? Existing tests Author: Sean Owen <sowen@cloudera.com> Closes #15882 from srowen/SPARK-18400.
## What changes were proposed in this pull request? This PR provides structured kafka wordcount examples ## How was this patch tested? Author: uncleGen <hustyugm@gmail.com> Closes #15849 from uncleGen/SPARK-18410.
…programming-guide' documentation ## What changes were proposed in this pull request? Fix typos in the 'configuration', 'monitoring' and 'sql-programming-guide' documentation. ## How was this patch tested? Manually. Author: Weiqing Yang <yangweiqing001@gmail.com> Closes #15886 from weiqingy/fixTypo.
## What changes were proposed in this pull request? Add missing ParamValidations for ML algos ## How was this patch tested? existing tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15881 from zhengruifeng/arg_checking.
## What changes were proposed in this pull request? Add links to API docs for ML algos ## How was this patch tested? Manual checking for the API links Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15890 from zhengruifeng/algo_link.
## What changes were proposed in this pull request? Small fix, fix the errors caused by lint check in Java - Clear unused objects and `UnusedImports`. - Add comments around the method `finalize` of `NioBufferedFileInputStream`to turn off checkstyle. - Cut the line which is longer than 100 characters into two lines. ## How was this patch tested? Travis CI. ``` $ build/mvn -T 4 -q -DskipTests -Pyarn -Phadoop-2.3 -Pkinesis-asl -Phive -Phive-thriftserver install $ dev/lint-java ``` Before: ``` Checkstyle checks failed at following occurrences: [ERROR] src/main/java/org/apache/spark/network/util/TransportConf.java:[21,8] (imports) UnusedImports: Unused import - org.apache.commons.crypto.cipher.CryptoCipherFactory. [ERROR] src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java:[516,5] (modifier) RedundantModifier: Redundant 'public' modifier. [ERROR] src/main/java/org/apache/spark/io/NioBufferedFileInputStream.java:[133] (coding) NoFinalizer: Avoid using finalizer method. [ERROR] src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeMapData.java:[71] (sizes) LineLength: Line is longer than 100 characters (found 113). [ERROR] src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java:[112] (sizes) LineLength: Line is longer than 100 characters (found 110). [ERROR] src/test/java/org/apache/spark/sql/catalyst/expressions/HiveHasherSuite.java:[31,17] (modifier) ModifierOrder: 'static' modifier out of order with the JLS suggestions. [ERROR]src/main/java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java:[64] (sizes) LineLength: Line is longer than 100 characters (found 103). [ERROR] src/main/java/org/apache/spark/examples/ml/JavaInteractionExample.java:[22,8] (imports) UnusedImports: Unused import - org.apache.spark.ml.linalg.Vectors. [ERROR] src/main/java/org/apache/spark/examples/ml/JavaInteractionExample.java:[51] (regexp) RegexpSingleline: No trailing whitespace allowed. ``` After: ``` $ build/mvn -T 4 -q -DskipTests -Pyarn -Phadoop-2.3 -Pkinesis-asl -Phive -Phive-thriftserver install $ dev/lint-java Using `mvn` from path: /home/travis/build/ConeyLiu/spark/build/apache-maven-3.3.9/bin/mvn Checkstyle checks passed. ``` Author: Xianyang Liu <xyliu0530@icloud.com> Closes #15865 from ConeyLiu/master.
### What changes were proposed in this pull request?
Currently, when CTE is used in RunnableCommand, the Analyzer does not replace the logical node `With`. The child plan of RunnableCommand is not resolved. Thus, the output of the `With` plan node looks very confusing.
For example,
```
sql(
"""
|CREATE VIEW cte_view AS
|WITH w AS (SELECT 1 AS n), cte1 (select 2), cte2 as (select 3)
|SELECT n FROM w
""".stripMargin).explain()
```
The output is like
```
ExecutedCommand
+- CreateViewCommand `cte_view`, WITH w AS (SELECT 1 AS n), cte1 (select 2), cte2 as (select 3)
SELECT n FROM w, false, false, PersistedView
+- 'With [(w,SubqueryAlias w
+- Project [1 AS n#16]
+- OneRowRelation$
), (cte1,'SubqueryAlias cte1
+- 'Project [unresolvedalias(2, None)]
+- OneRowRelation$
), (cte2,'SubqueryAlias cte2
+- 'Project [unresolvedalias(3, None)]
+- OneRowRelation$
)]
+- 'Project ['n]
+- 'UnresolvedRelation `w`
```
After the fix, the output is as shown below.
```
ExecutedCommand
+- CreateViewCommand `cte_view`, WITH w AS (SELECT 1 AS n), cte1 (select 2), cte2 as (select 3)
SELECT n FROM w, false, false, PersistedView
+- CTE [w, cte1, cte2]
: :- SubqueryAlias w
: : +- Project [1 AS n#16]
: : +- OneRowRelation$
: :- 'SubqueryAlias cte1
: : +- 'Project [unresolvedalias(2, None)]
: : +- OneRowRelation$
: +- 'SubqueryAlias cte2
: +- 'Project [unresolvedalias(3, None)]
: +- OneRowRelation$
+- 'Project ['n]
+- 'UnresolvedRelation `w`
```
BTW, this PR also fixes the output of the view type.
### How was this patch tested?
Manual
Author: gatorsmile <gatorsmile@gmail.com>
Closes #15854 from gatorsmile/cteName.
…atchId and add triggerDetails to json in StreamingQueryStatus ## What changes were proposed in this pull request? SPARK-18459: triggerId seems like a number that should be increasing with each trigger, whether or not there is data in it. However, actually, triggerId increases only where there is a batch of data in a trigger. So its better to rename it to batchId. SPARK-18460: triggerDetails was missing from json representation. Fixed it. ## How was this patch tested? Updated existing unit tests. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #15895 from tdas/SPARK-18459.
… monitoring streaming queries ## What changes were proposed in this pull request? <img width="941" alt="screen shot 2016-11-15 at 6 27 32 pm" src="https://cloud.githubusercontent.com/assets/663212/20332521/4190b858-ab61-11e6-93a6-4bdc05105ed9.png"> <img width="940" alt="screen shot 2016-11-15 at 6 27 45 pm" src="https://cloud.githubusercontent.com/assets/663212/20332525/44a0d01e-ab61-11e6-8668-47f925490d4f.png"> Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #15897 from tdas/SPARK-18461.
## What changes were proposed in this pull request? This PR aims to provide a pip installable PySpark package. This does a bunch of work to copy the jars over and package them with the Python code (to prevent challenges from trying to use different versions of the Python code with different versions of the JAR). It does not currently publish to PyPI but that is the natural follow up (SPARK-18129). Done: - pip installable on conda [manual tested] - setup.py installed on a non-pip managed system (RHEL) with YARN [manual tested] - Automated testing of this (virtualenv) - packaging and signing with release-build* Possible follow up work: - release-build update to publish to PyPI (SPARK-18128) - figure out who owns the pyspark package name on prod PyPI (is it someone with in the project or should we ask PyPI or should we choose a different name to publish with like ApachePySpark?) - Windows support and or testing ( SPARK-18136 ) - investigate details of wheel caching and see if we can avoid cleaning the wheel cache during our test - consider how we want to number our dev/snapshot versions Explicitly out of scope: - Using pip installed PySpark to start a standalone cluster - Using pip installed PySpark for non-Python Spark programs *I've done some work to test release-build locally but as a non-committer I've just done local testing. ## How was this patch tested? Automated testing with virtualenv, manual testing with conda, a system wide install, and YARN integration. release-build changes tested locally as a non-committer (no testing of upload artifacts to Apache staging websites) Author: Holden Karau <holden@us.ibm.com> Author: Juliet Hougland <juliet@cloudera.com> Author: Juliet Hougland <not@myemail.com> Closes #15659 from holdenk/SPARK-1267-pip-install-pyspark.
…r partial aggregation support
## What changes were proposed in this pull request?
While being evaluated in Spark SQL, Hive UDAFs don't support partial aggregation. This PR migrates `HiveUDAFFunction`s to `TypedImperativeAggregate`, which already provides partial aggregation support for aggregate functions that may use arbitrary Java objects as aggregation states.
The following snippet shows the effect of this PR:
```scala
import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax
sql(s"CREATE FUNCTION hive_max AS '${classOf[GenericUDAFMax].getName}'")
spark.range(100).createOrReplaceTempView("t")
// A query using both Spark SQL native `max` and Hive `max`
sql(s"SELECT max(id), hive_max(id) FROM t").explain()
```
Before this PR:
```
== Physical Plan ==
SortAggregate(key=[], functions=[max(id#1L), default.hive_max(default.hive_max, HiveFunctionWrapper(org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax,org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax7475f57e), id#1L, false, 0, 0)])
+- Exchange SinglePartition
+- *Range (0, 100, step=1, splits=Some(1))
```
After this PR:
```
== Physical Plan ==
SortAggregate(key=[], functions=[max(id#1L), default.hive_max(default.hive_max, HiveFunctionWrapper(org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax,org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax5e18a6a7), id#1L, false, 0, 0)])
+- Exchange SinglePartition
+- SortAggregate(key=[], functions=[partial_max(id#1L), partial_default.hive_max(default.hive_max, HiveFunctionWrapper(org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax,org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax5e18a6a7), id#1L, false, 0, 0)])
+- *Range (0, 100, step=1, splits=Some(1))
```
The tricky part of the PR is mostly about updating and passing around aggregation states of `HiveUDAFFunction`s since the aggregation state of a Hive UDAF may appear in three different forms. Let's take a look at the testing `MockUDAF` added in this PR as an example. This UDAF computes the count of non-null values together with the count of nulls of a given column. Its aggregation state may appear as the following forms at different time:
1. A `MockUDAFBuffer`, which is a concrete subclass of `GenericUDAFEvaluator.AggregationBuffer`
The form used by Hive UDAF API. This form is required by the following scenarios:
- Calling `GenericUDAFEvaluator.iterate()` to update an existing aggregation state with new input values.
- Calling `GenericUDAFEvaluator.terminate()` to get the final aggregated value from an existing aggregation state.
- Calling `GenericUDAFEvaluator.merge()` to merge other aggregation states into an existing aggregation state.
The existing aggregation state to be updated must be in this form.
Conversions:
- To form 2:
`GenericUDAFEvaluator.terminatePartial()`
- To form 3:
Convert to form 2 first, and then to 3.
2. An `Object[]` array containing two `java.lang.Long` values.
The form used to interact with Hive's `ObjectInspector`s. This form is required by the following scenarios:
- Calling `GenericUDAFEvaluator.terminatePartial()` to convert an existing aggregation state in form 1 to form 2.
- Calling `GenericUDAFEvaluator.merge()` to merge other aggregation states into an existing aggregation state.
The input aggregation state must be in this form.
Conversions:
- To form 1:
No direct method. Have to create an empty `AggregationBuffer` and merge it into the empty buffer.
- To form 3:
`unwrapperFor()`/`unwrap()` method of `HiveInspectors`
3. The byte array that holds data of an `UnsafeRow` with two `LongType` fields.
The form used by Spark SQL to shuffle partial aggregation results. This form is required because `TypedImperativeAggregate` always asks its subclasses to serialize their aggregation states into a byte array.
Conversions:
- To form 1:
Convert to form 2 first, and then to 1.
- To form 2:
`wrapperFor()`/`wrap()` method of `HiveInspectors`
Here're some micro-benchmark results produced by the most recent master and this PR branch.
Master:
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.5
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
hive udaf vs spark af: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
w/o groupBy 339 / 372 3.1 323.2 1.0X
w/ groupBy 503 / 529 2.1 479.7 0.7X
```
This PR:
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.5
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
hive udaf vs spark af: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
w/o groupBy 116 / 126 9.0 110.8 1.0X
w/ groupBy 151 / 159 6.9 144.0 0.8X
```
Benchmark code snippet:
```scala
test("Hive UDAF benchmark") {
val N = 1 << 20
sparkSession.sql(s"CREATE TEMPORARY FUNCTION hive_max AS '${classOf[GenericUDAFMax].getName}'")
val benchmark = new Benchmark(
name = "hive udaf vs spark af",
valuesPerIteration = N,
minNumIters = 5,
warmupTime = 5.seconds,
minTime = 5.seconds,
outputPerIteration = true
)
benchmark.addCase("w/o groupBy") { _ =>
sparkSession.range(N).agg("id" -> "hive_max").collect()
}
benchmark.addCase("w/ groupBy") { _ =>
sparkSession.range(N).groupBy($"id" % 10).agg("id" -> "hive_max").collect()
}
benchmark.run()
sparkSession.sql(s"DROP TEMPORARY FUNCTION IF EXISTS hive_max")
}
```
## How was this patch tested?
New test suite `HiveUDAFSuite` is added.
Author: Cheng Lian <lian@databricks.com>
Closes #15703 from liancheng/partial-agg-hive-udaf.
…Service ## What changes were proposed in this pull request? Suggest users to increase `NodeManager's` heap size if `External Shuffle Service` is enabled as `NM` can spend a lot of time doing GC resulting in shuffle operations being a bottleneck due to `Shuffle Read blocked time` bumped up. Also because of GC `NodeManager` can use an enormous amount of CPU and cluster performance will suffer. I have seen NodeManager using 5-13G RAM and up to 2700% CPU with `spark_shuffle` service on. ## How was this patch tested? #### Added step 5:  Author: Artur Sukhenko <artur.sukhenko@gmail.com> Closes #15906 from Devian-ua/nmHeapSize.
Poisson GLM fails for many standard data sets (see example in test or JIRA). The issue is incorrect initialization leading to almost zero probability and weights. Specifically, the mean is initialized as the response, which could be zero. Applying the log link results in very negative numbers (protected against -Inf), which again leads to close to zero probability and weights in the weighted least squares. Fix and test are included in the commits. ## What changes were proposed in this pull request? Update initialization in Poisson GLM ## How was this patch tested? Add test in GeneralizedLinearRegressionSuite srowen sethah yanboliang HyukjinKwon mengxr Author: actuaryzhang <actuaryzhang10@gmail.com> Closes #16131 from actuaryzhang/master.
## What changes were proposed in this pull request? Fix reservoir sampling bias for small k. An off-by-one error meant that the probability of replacement was slightly too high -- k/(l-1) after l element instead of k/l, which matters for small k. ## How was this patch tested? Existing test plus new test case. Author: Sean Owen <sowen@cloudera.com> Closes #16129 from srowen/SPARK-18678.
Member
|
Could you pleade close this @zhuangxue ? |
…esToBytesMap ## What changes were proposed in this pull request? BytesToBytesMap currently does not release the in-memory storage (the longArray variable) after it spills to disk. This is typically not a problem during aggregation because the longArray should be much smaller than the pages, and because we grow the longArray at a conservative rate. However this can lead to an OOM when an already running task is allocated more than its fair share, this can happen because of a scheduling delay. In this case the longArray can grow beyond the fair share of memory for the task. This becomes problematic when the task spills and the long array is not freed, that causes subsequent memory allocation requests to be denied by the memory manager resulting in an OOM. This PR fixes this issuing by freeing the longArray when the BytesToBytesMap spills. ## How was this patch tested? Existing tests and tested on realworld workloads. Author: Jie Xiong <jiexiong@fb.com> Author: jiexiong <jiexiong@gmail.com> Closes #15722 from jiexiong/jie_oom_fix.
…y column is not attribute ## What changes were proposed in this pull request? Fixes AnalysisException for pivot queries that have group by columns that are expressions and not attributes by substituting the expressions output attribute in the second aggregation and final projection. ## How was this patch tested? existing and additional unit tests Author: Andrew Ray <ray.andrew@gmail.com> Closes #16177 from aray/SPARK-17760.
## What changes were proposed in this pull request? It's better to add a warning log when skipping a corrupted file. It will be helpful when we want to finish the job first, then find them in the log and fix these files. ## How was this patch tested? Jenkins Author: Shixiong Zhu <shixiong@databricks.com> Closes #16192 from zsxwing/SPARK-18764.
## What changes were proposed in this pull request? When SSL is enabled, the Spark shell shows: ``` Spark context Web UI available at https://192.168.99.1:4040 ``` This is wrong because 4040 is http, not https. It redirects to the https port. More importantly, this introduces several broken links in the UI. For example, in the master UI, the worker link is https:8081 instead of http:8081 or https:8481. CC: mengxr liancheng I manually tested accessing by accessing MasterPage, WorkerPage and HistoryServer with SSL enabled. Author: sarutak <sarutak@oss.nttdata.co.jp> Closes #16190 from sarutak/SPARK-18761.
…taLossSuite ## What changes were proposed in this pull request? Fixed the following failures: ``` org.scalatest.exceptions.TestFailedDueToTimeoutException: The code passed to eventually never returned normally. Attempted 3745 times over 1.0000790851666665 minutes. Last failure message: assertion failed: failOnDataLoss-0 not deleted after timeout. ``` ``` sbt.ForkMain$ForkError: org.apache.spark.sql.streaming.StreamingQueryException: Query query-66 terminated with exception: null at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches(StreamExecution.scala:252) at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:146) Caused by: sbt.ForkMain$ForkError: java.lang.NullPointerException: null at java.util.ArrayList.addAll(ArrayList.java:577) at org.apache.kafka.clients.Metadata.getClusterForCurrentTopics(Metadata.java:257) at org.apache.kafka.clients.Metadata.update(Metadata.java:177) at org.apache.kafka.clients.NetworkClient$DefaultMetadataUpdater.handleResponse(NetworkClient.java:605) at org.apache.kafka.clients.NetworkClient$DefaultMetadataUpdater.maybeHandleCompletedReceive(NetworkClient.java:582) at org.apache.kafka.clients.NetworkClient.handleCompletedReceives(NetworkClient.java:450) at org.apache.kafka.clients.NetworkClient.poll(NetworkClient.java:269) at org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.clientPoll(ConsumerNetworkClient.java:360) at org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.poll(ConsumerNetworkClient.java:224) at org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.poll(ConsumerNetworkClient.java:192) at org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.awaitPendingRequests(ConsumerNetworkClient.java:260) at org.apache.kafka.clients.consumer.internals.AbstractCoordinator.ensureActiveGroup(AbstractCoordinator.java:222) at org.apache.kafka.clients.consumer.internals.ConsumerCoordinator.ensurePartitionAssignment(ConsumerCoordinator.java:366) at org.apache.kafka.clients.consumer.KafkaConsumer.pollOnce(KafkaConsumer.java:978) at org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:938) at ... ``` ## How was this patch tested? Tested in #16048 by running many times. Author: Shixiong Zhu <shixiong@databricks.com> Closes #16109 from zsxwing/fix-kafka-flaky-test.
Based on an informal survey, users find this option easier to understand / remember. Author: Michael Armbrust <michael@databricks.com> Closes #16182 from marmbrus/renameRecentProgress.
## What changes were proposed in this pull request? `makeRootConverter` is only called with a `StructType` value. By making this method less general we can remove pattern matches, which are never actually hit outside of the test suite. ## How was this patch tested? The existing tests. Author: Nathan Howell <nhowell@godaddy.com> Closes #16084 from NathanHowell/SPARK-18654.
… python example and document ## What changes were proposed in this pull request? Logistic Regression summary is added in Python API. We need to add example and document for summary. The newly added example is consistent with Scala and Java examples. ## How was this patch tested? Manually tests: Run the example with spark-submit; copy & paste code into pyspark; build document and check the document. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #16064 from wangmiao1981/py.
… should be sent only to the listeners in the same session as the query ## What changes were proposed in this pull request? Listeners added with `sparkSession.streams.addListener(l)` are added to a SparkSession. So events only from queries in the same session as a listener should be posted to the listener. Currently, all the events gets rerouted through the Spark's main listener bus, that is, - StreamingQuery posts event to StreamingQueryListenerBus. Only the queries associated with the same session as the bus posts events to it. - StreamingQueryListenerBus posts event to Spark's main LiveListenerBus as a SparkEvent. - StreamingQueryListenerBus also subscribes to LiveListenerBus events thus getting back the posted event in a different thread. - The received is posted to the registered listeners. The problem is that *all StreamingQueryListenerBuses in all sessions* gets the events and posts them to their listeners. This is wrong. In this PR, I solve it by making StreamingQueryListenerBus track active queries (by their runIds) when a query posts the QueryStarted event to the bus. This allows the rerouted events to be filtered using the tracked queries. Note that this list needs to be maintained separately from the `StreamingQueryManager.activeQueries` because a terminated query is cleared from `StreamingQueryManager.activeQueries` as soon as it is stopped, but the this ListenerBus must clear a query only after the termination event of that query has been posted lazily, much after the query has been terminated. Credit goes to zsxwing for coming up with the initial idea. ## How was this patch tested? Updated test harness code to use the correct session, and added new unit test. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #16186 from tdas/SPARK-18758.
…or L1 and elastic-net ## What changes were proposed in this pull request? WeightedLeastSquares now supports L1 and elastic net penalties and has an additional solver option: QuasiNewton. The docs are updated to reflect this change. ## How was this patch tested? Docs only. Generated documentation to make sure Latex looks ok. Author: sethah <seth.hendrickson16@gmail.com> Closes #16139 from sethah/SPARK-18705.
## What changes were proposed in this pull request? Reviewing SparkR ML wrappers API for 2.1 release, mainly two issues: * Remove ```probabilityCol``` from the argument list of ```spark.logit``` and ```spark.randomForest```. Since it was used when making prediction and should be an argument of ```predict```, and we will work on this at [SPARK-18618](https://issues.apache.org/jira/browse/SPARK-18618) in the next release cycle. * Fix ```spark.als``` params to make it consistent with MLlib. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #16169 from yanboliang/spark-18326.
…Files is enabled ## What changes were proposed in this pull request? When `ignoreCorruptFiles` is enabled, it's better to also ignore non-existing files. ## How was this patch tested? Jenkins Author: Shixiong Zhu <shixiong@databricks.com> Closes #16203 from zsxwing/ignore-file-not-found.
## What changes were proposed in this pull request? * Add all R examples for ML wrappers which were added during 2.1 release cycle. * Split the whole ```ml.R``` example file into individual example for each algorithm, which will be convenient for users to rerun them. * Add corresponding examples to ML user guide. * Update ML section of SparkR user guide. Note: MLlib Scala/Java/Python examples will be consistent, however, SparkR examples may different from them, since R users may use the algorithms in a different way, for example, using R ```formula``` to specify ```featuresCol``` and ```labelCol```. ## How was this patch tested? Run all examples manually. Author: Yanbo Liang <ybliang8@gmail.com> Closes #16148 from yanboliang/spark-18325.
…ation and fix tests to pass on Windows
## What changes were proposed in this pull request?
There are some tests failed on Windows due to the wrong format of path and the limitation of path length as below:
This PR proposes both to fix the failed tests by fixing the path for the tests below:
- `InsertSuite`
```
Exception encountered when attempting to run a suite with class name: org.apache.spark.sql.sources.InsertSuite *** ABORTED *** (12 seconds, 547 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-177945ef-9128-42b4-8c07-de31f78bbbd6;
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:382)
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$14.apply(DataSource.scala:370)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
```
- `PathOptionSuite`
```
- path option also exist for write path *** FAILED *** (1 second, 93 milliseconds)
"C:[projectsspark arget mp]spark-5ab34a58-df8d-..." did not equal "C:[\projects\spark\target\tmp\]spark-5ab34a58-df8d-..." (PathOptionSuite.scala:93)
org.scalatest.exceptions.TestFailedException:
at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:500)
at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555)
...
```
- `UDFSuite`
```
- SPARK-8005 input_file_name *** FAILED *** (2 seconds, 234 milliseconds)
"file:///C:/projects/spark/target/tmp/spark-e4e5720a-2006-48f9-8b11-797bf59794bf/part-00001-26fb05e4-603d-471d-ae9d-b9549e0c7765.snappy.parquet" did not contain "C:\projects\spark\target\tmp\spark-e4e5720a-2006-48f9-8b11-797bf59794bf" (UDFSuite.scala:67)
org.scalatest.exceptions.TestFailedException:
at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:500)
at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555)
...
```
and to skip the tests belows which are being failed on Windows due to path length limitation.
- `SparkLauncherSuite`
```
Test org.apache.spark.launcher.SparkLauncherSuite.testChildProcLauncher failed: java.lang.AssertionError: expected:<0> but was:<1>, took 0.062 sec
at org.apache.spark.launcher.SparkLauncherSuite.testChildProcLauncher(SparkLauncherSuite.java:177)
...
```
The stderr from the process is `The filename or extension is too long` which is equivalent to the one below.
- `BroadcastJoinSuite`
```
04:09:40.882 ERROR org.apache.spark.deploy.worker.ExecutorRunner: Error running executor
java.io.IOException: Cannot run program "C:\Progra~1\Java\jdk1.8.0\bin\java" (in directory "C:\projects\spark\work\app-20161205040542-0000\51658"): CreateProcess error=206, The filename or extension is too long
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1048)
at org.apache.spark.deploy.worker.ExecutorRunner.org$apache$spark$deploy$worker$ExecutorRunner$$fetchAndRunExecutor(ExecutorRunner.scala:167)
at org.apache.spark.deploy.worker.ExecutorRunner$$anon$1.run(ExecutorRunner.scala:73)
Caused by: java.io.IOException: CreateProcess error=206, The filename or extension is too long
at java.lang.ProcessImpl.create(Native Method)
at java.lang.ProcessImpl.<init>(ProcessImpl.java:386)
at java.lang.ProcessImpl.start(ProcessImpl.java:137)
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1029)
... 2 more
04:09:40.929 ERROR org.apache.spark.deploy.worker.ExecutorRunner: Error running executor
(appearently infinite same error messages)
...
```
## How was this patch tested?
Manually tested via AppVeyor.
**Before**
`InsertSuite`: https://ci.appveyor.com/project/spark-test/spark/build/148-InsertSuite-pr
`PathOptionSuite`: https://ci.appveyor.com/project/spark-test/spark/build/139-PathOptionSuite-pr
`UDFSuite`: https://ci.appveyor.com/project/spark-test/spark/build/143-UDFSuite-pr
`SparkLauncherSuite`: https://ci.appveyor.com/project/spark-test/spark/build/141-SparkLauncherSuite-pr
`BroadcastJoinSuite`: https://ci.appveyor.com/project/spark-test/spark/build/145-BroadcastJoinSuite-pr
**After**
`PathOptionSuite`: https://ci.appveyor.com/project/spark-test/spark/build/140-PathOptionSuite-pr
`SparkLauncherSuite`: https://ci.appveyor.com/project/spark-test/spark/build/142-SparkLauncherSuite-pr
`UDFSuite`: https://ci.appveyor.com/project/spark-test/spark/build/144-UDFSuite-pr
`InsertSuite`: https://ci.appveyor.com/project/spark-test/spark/build/147-InsertSuite-pr
`BroadcastJoinSuite`: https://ci.appveyor.com/project/spark-test/spark/build/149-BroadcastJoinSuite-pr
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #16147 from HyukjinKwon/fix-tests.
…ythonExec so input_file_name function can work with UDF in pyspark
## What changes were proposed in this pull request?
`input_file_name` doesn't return filename when working with UDF in PySpark. An example shows the problem:
from pyspark.sql.functions import *
from pyspark.sql.types import *
def filename(path):
return path
sourceFile = udf(filename, StringType())
spark.read.json("tmp.json").select(sourceFile(input_file_name())).show()
+---------------------------+
|filename(input_file_name())|
+---------------------------+
| |
+---------------------------+
The cause of this issue is, we group rows in `BatchEvalPythonExec` for batching processing of PythonUDF. Currently we group rows first and then evaluate expressions on the rows. If the data is less than the required number of rows for a group, the iterator will be consumed to the end before the evaluation. However, once the iterator reaches the end, we will unset input filename. So the input_file_name expression can't return correct filename.
This patch fixes the approach to group the batch of rows. We evaluate the expression first and then group evaluated results to batch.
## How was this patch tested?
Added unit test to PySpark.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes #16115 from viirya/fix-py-udf-input-filename.
…auncher. These directories are added to the classpath of applications when testing or using SPARK_PREPEND_CLASSES, otherwise updated classes are not seen. Also, add the mesos directory which was missing. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #16202 from vanzin/SPARK-18662.
…leanup ## What changes were proposed in this pull request? - Removed the`attempt.completed ` filter so cleaner would include the orphan inprogress files. - Use loading time for inprogress files as lastUpdated. Keep using the modTime for completed files. First one will prevent deletion of inprogress job files. Second one will ensure that lastUpdated time won't change for completed jobs in an event of HistoryServer reboot. ## How was this patch tested? Added new unittests and via existing tests. Author: Ergin Seyfe <eseyfe@fb.com> Closes #16165 from seyfe/clear_old_inprogress_files.
… records ## What changes were proposed in this pull request? Fixes a bug in the python implementation of rdd cartesian product related to batching that showed up in repeated cartesian products with seemingly random results. The root cause being multiple iterators pulling from the same stream in the wrong order because of logic that ignored batching. `CartesianDeserializer` and `PairDeserializer` were changed to implement `_load_stream_without_unbatching` and borrow the one line implementation of `load_stream` from `BatchedSerializer`. The default implementation of `_load_stream_without_unbatching` was changed to give consistent results (always an iterable) so that it could be used without additional checks. `PairDeserializer` no longer extends `CartesianDeserializer` as it was not really proper. If wanted a new common super class could be added. Both `CartesianDeserializer` and `PairDeserializer` now only extend `Serializer` (which has no `dump_stream` implementation) since they are only meant for *de*serialization. ## How was this patch tested? Additional unit tests (sourced from #14248) plus one for testing a cartesian with zip. Author: Andrew Ray <ray.andrew@gmail.com> Closes #16121 from aray/fix-cartesian.
## What changes were proposed in this pull request? This PR has 2 key changes. One, we are building source package (aka bundle package) for SparkR which could be released on CRAN. Two, we should include in the official Spark binary distributions SparkR installed from this source package instead (which would have help/vignettes rds needed for those to work when the SparkR package is loaded in R, whereas earlier approach with devtools does not) But, because of various differences in how R performs different tasks, this PR is a fair bit more complicated. More details below. This PR also includes a few minor fixes. ### more details These are the additional steps in make-distribution; please see [here](https://github.com/apache/spark/blob/master/R/CRAN_RELEASE.md) on what's going to a CRAN release, which is now run during make-distribution.sh. 1. package needs to be installed because the first code block in vignettes is `library(SparkR)` without lib path 2. `R CMD build` will build vignettes (this process runs Spark/SparkR code and captures outputs into pdf documentation) 3. `R CMD check` on the source package will install package and build vignettes again (this time from source packaged) - this is a key step required to release R package on CRAN (will skip tests here but tests will need to pass for CRAN release process to success - ideally, during release signoff we should install from the R source package and run tests) 4. `R CMD Install` on the source package (this is the only way to generate doc/vignettes rds files correctly, not in step # 1) (the output of this step is what we package into Spark dist and sparkr.zip) Alternatively, R CMD build should already be installing the package in a temp directory though it might just be finding this location and set it to lib.loc parameter; another approach is perhaps we could try calling `R CMD INSTALL --build pkg` instead. But in any case, despite installing the package multiple times this is relatively fast. Building vignettes takes a while though. ## How was this patch tested? Manually, CI. Author: Felix Cheung <felixcheung_m@hotmail.com> Closes #16014 from felixcheung/rdist.
…Utils.tryOrStopSparkContext ## What changes were proposed in this pull request? When `SparkContext.stop` is called in `Utils.tryOrStopSparkContext` (the following three places), it will cause deadlock because the `stop` method needs to wait for the thread running `stop` to exit. - ContextCleaner.keepCleaning - LiveListenerBus.listenerThread.run - TaskSchedulerImpl.start This PR adds `SparkContext.stopInNewThread` and uses it to eliminate the potential deadlock. I also removed my changes in #15775 since they are not necessary now. ## How was this patch tested? Jenkins Author: Shixiong Zhu <shixiong@databricks.com> Closes #16178 from zsxwing/fix-stop-deadlock.
vanzin
pushed a commit
to vanzin/spark
that referenced
this pull request
Dec 8, 2016
Closes apache#16191 Closes apache#16198 Closes apache#14561 Closes apache#14223 Closes apache#7739 Closes apache#13026
Closed
## What changes were proposed in this pull request?
This patch fixes the format specification in explain for file sources (Parquet and Text formats are the only two that are different from the rest):
Before:
```
scala> spark.read.text("test.text").explain()
== Physical Plan ==
*FileScan text [value#15] Batched: false, Format: org.apache.spark.sql.execution.datasources.text.TextFileFormatxyz, Location: InMemoryFileIndex[file:/scratch/rxin/spark/test.text], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<value:string>
```
After:
```
scala> spark.read.text("test.text").explain()
== Physical Plan ==
*FileScan text [value#15] Batched: false, Format: Text, Location: InMemoryFileIndex[file:/scratch/rxin/spark/test.text], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<value:string>
```
Also closes #14680.
## How was this patch tested?
Verified in spark-shell.
Author: Reynold Xin <rxin@databricks.com>
Closes #16187 from rxin/SPARK-18760.
zifeif2
pushed a commit
to zifeif2/spark
that referenced
this pull request
Nov 22, 2025
Closes apache#16191 Closes apache#16198 Closes apache#14561 Closes apache#14223 Closes apache#7739 Closes apache#13026 Closes apache#16217
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What algorithm is used in spark decision tree (is ID3, C4.5 or CART)?