We set `sqlContext = null` in `afterAll`. However, this doesn't change `SQLContext.activeContext` and then `SQLContext.getOrCreate` might use the `SparkContext` from previous test suite and hence causes the error. This PR calls `clearActive` in `beforeAll` and `afterAll` to avoid using an old context from other test suites.
cc: yhuai
Author: Xiangrui Meng <meng@databricks.com>
Closes#9677 from mengxr/SPARK-11672.2.
Bug: Timestamp is not updated if there is data but the corresponding state is not updated. This is wrong, and timeout is defined as "no data for a while", not "not state update for a while".
Fix: Update timestamp when timestamp when timeout is specified, otherwise no need.
Also refactored the code for better testability and added unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#9648 from tdas/SPARK-11681.
The support for closing WriteAheadLog files after writes was just merged in. Closing every file after a write is a very expensive operation as it creates many small files on S3. It's not necessary to enable it on HDFS anyway.
However, when you have many small files on S3, recovery takes very long. In addition, files start stacking up pretty quickly, and deletes may not be able to keep up, therefore deletes can also be parallelized.
This PR adds support for the two parallelization steps mentioned above, in addition to a couple more failures I encountered during recovery.
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#9373 from brkyvz/par-recovery.
TODO
- [x] Add Java API
- [x] Add API tests
- [x] Add a function test
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#9636 from zsxwing/java-track.
This PR adds a new method, `reduce`, to `GroupedDataset`, which allows similar operations to `reduceByKey` on a traditional `PairRDD`.
```scala
val ds = Seq("abc", "xyz", "hello").toDS()
ds.groupBy(_.length).reduce(_ + _).collect() // not actually commutative :P
res0: Array(3 -> "abcxyz", 5 -> "hello")
```
While implementing this method and its test cases several more deficiencies were found in our encoder handling. Specifically, in order to support positional resolution, named resolution and tuple composition, it is important to keep the unresolved encoder around and to use it when constructing new `Datasets` with the same object type but different output attributes. We now divide the encoder lifecycle into three phases (that mirror the lifecycle of standard expressions) and have checks at various boundaries:
- Unresoved Encoders: all users facing encoders (those constructed by implicits, static methods, or tuple composition) are unresolved, meaning they have only `UnresolvedAttributes` for named fields and `BoundReferences` for fields accessed by ordinal.
- Resolved Encoders: internal to a `[Grouped]Dataset` the encoder is resolved, meaning all input has been resolved to a specific `AttributeReference`. Any encoders that are placed into a logical plan for use in object construction should be resolved.
- BoundEncoder: Are constructed by physical plans, right before actual conversion from row -> object is performed.
It is left to future work to add explicit checks for resolution and provide good error messages when it fails. We might also consider enforcing the above constraints in the type system (i.e. `fromRow` only exists on a `ResolvedEncoder`), but we should probably wait before spending too much time on this.
Author: Michael Armbrust <michael@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9673 from marmbrus/pr/9628.
Per discussion in the initial Pipelines LDA PR [https://github.com/apache/spark/pull/9513], we should make LDAModel abstract and create a LocalLDAModel. This code simplification should be done before the 1.6 release to ensure API compatibility in future releases.
CC feynmanliang mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9678 from jkbradley/lda-pipelines-2.
This helps debug issues caused by multiple SparkContext instances. JoshRosen andrewor14
~~~
scala> sc.stop()
scala> sc.parallelize(0 until 10)
java.lang.IllegalStateException: Cannot call methods on a stopped SparkContext.
This stopped SparkContext was created at:
org.apache.spark.SparkContext.<init>(SparkContext.scala:82)
org.apache.spark.repl.SparkILoop.createSparkContext(SparkILoop.scala:1017)
$iwC$$iwC.<init>(<console>:9)
$iwC.<init>(<console>:18)
<init>(<console>:20)
.<init>(<console>:24)
.<clinit>(<console>)
.<init>(<console>:7)
.<clinit>(<console>)
$print(<console>)
sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
java.lang.reflect.Method.invoke(Method.java:606)
org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1340)
org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)
org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:857)
The active context was created at:
(No active SparkContext.)
~~~
Author: Xiangrui Meng <meng@databricks.com>
Closes#9675 from mengxr/SPARK-11709.
Should not create SparkContext in the constructor of `TrackStateRDDSuite`. This is a follow up PR for #9256 to fix the test for maven build.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#9668 from zsxwing/hotfix.
The stop() callback was trying to close the launcher connection in the
same thread that handles connection data, which ended up causing a
deadlock. So avoid that by dispatching the stop() request in its own
thread.
On top of that, add some exception safety to a few parts of the code,
and use "destroyForcibly" from Java 8 if it's available, to force
kill the child process. The flip side is that "kill()" may not actually
work if running Java 7.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#9633 from vanzin/SPARK-11655.
switched stddev support from DeclarativeAggregate to ImperativeAggregate.
Author: JihongMa <linlin200605@gmail.com>
Closes#9380 from JihongMA/SPARK-11420.
Parquet supports some unsigned datatypes. However, Since Spark does not support unsigned datatypes, it needs to emit an exception with a clear message rather then with the one saying illegal datatype.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#9646 from HyukjinKwon/SPARK-10113.
When looking up Hive temporary functions, we should always use the `SessionState` within the execution Hive client, since temporary functions are registered there.
Author: Cheng Lian <lian@databricks.com>
Closes#9664 from liancheng/spark-11191.fix-temp-function.
The code convertToCanonicalEdges is such that srcIds are smaller than dstIds but the scaladoc suggested otherwise. Have fixed the same.
Author: Gaurav Kumar <gauravkumar37@gmail.com>
Closes#9666 from gauravkumar37/patch-1.
Remove some old yarn related building codes, please review, thanks a lot.
Author: jerryshao <sshao@hortonworks.com>
Closes#9625 from jerryshao/remove-old-module.
This causes compile failure with Scala 2.11. See https://issues.scala-lang.org/browse/SI-8813. (Jenkins won't test Scala 2.11. I tested compile locally.) JoshRosen
Author: Xiangrui Meng <meng@databricks.com>
Closes#9644 from mengxr/SPARK-11674.
`to_unix_timestamp` is the deterministic version of `unix_timestamp`, as it accepts at least one parameters.
Since the behavior here is quite similar to `unix_timestamp`, I think the dataframe API is not necessary here.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#9347 from adrian-wang/to_unix_timestamp.
This adds a pivot method to the dataframe api.
Following the lead of cube and rollup this adds a Pivot operator that is translated into an Aggregate by the analyzer.
Currently the syntax is like:
~~courseSales.pivot(Seq($"year"), $"course", Seq("dotNET", "Java"), sum($"earnings"))~~
~~Would we be interested in the following syntax also/alternatively? and~~
courseSales.groupBy($"year").pivot($"course", "dotNET", "Java").agg(sum($"earnings"))
//or
courseSales.groupBy($"year").pivot($"course").agg(sum($"earnings"))
Later we can add it to `SQLParser`, but as Hive doesn't support it we cant add it there, right?
~~Also what would be the suggested Java friendly method signature for this?~~
Author: Andrew Ray <ray.andrew@gmail.com>
Closes#7841 from aray/sql-pivot.
This is a followup for #9317 to replace volatile fields with AtomicBoolean and AtomicReference.
Author: Reynold Xin <rxin@databricks.com>
Closes#9611 from rxin/SPARK-10827.
This patch aims to reduce the test time and flakiness of HiveSparkSubmitSuite, SparkSubmitSuite, and CliSuite.
Key changes:
- Disable IO synchronization calls for Derby writes, since durability doesn't matter for tests. This was done for HiveCompatibilitySuite in #6651 and resulted in huge test speedups.
- Add a few missing `--conf`s to disable various Spark UIs. The CliSuite, in particular, never disabled these UIs, leaving it prone to port-contention-related flakiness.
- Fix two instances where tests defined `beforeAll()` methods which were never called because the appropriate traits were not mixed in. I updated these tests suites to extend `BeforeAndAfterEach` so that they play nicely with our `ResetSystemProperties` trait.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9623 from JoshRosen/SPARK-11647.
tdas koeninger
This updates the Spark Streaming + Kafka Integration Guide doc with a working method to access the offsets of a `KafkaRDD` through Python.
Author: Nick Evans <me@nicolasevans.org>
Closes#9289 from manygrams/update_kafka_direct_python_docs.
Several elements could be drained if the main thread is not fast enough. zsxwing warned me about a similar problem, but missed it here :( Submitting the fix using a waiter.
cc tdas
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#9605 from brkyvz/fix-flaky-test.
This patch modifies Spark's closure cleaner (and a few other places) to use ASM 5, which is necessary in order to support cleaning of closures that were compiled by Java 8.
In order to avoid ASM dependency conflicts, Spark excludes ASM from all of its dependencies and uses a shaded version of ASM 4 that comes from `reflectasm` (see [SPARK-782](https://issues.apache.org/jira/browse/SPARK-782) and #232). This patch updates Spark to use a shaded version of ASM 5.0.4 that was published by the Apache XBean project; the POM used to create the shaded artifact can be found at https://github.com/apache/geronimo-xbean/blob/xbean-4.4/xbean-asm5-shaded/pom.xml.
http://movingfulcrum.tumblr.com/post/80826553604/asm-framework-50-the-missing-migration-guide was a useful resource while upgrading the code to use the new ASM5 opcodes.
I also added a new regression tests in the `java8-tests` subproject; the existing tests were insufficient to catch this bug, which only affected Scala 2.11 user code which was compiled targeting Java 8.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9512 from JoshRosen/SPARK-6152.
We need to support custom classes like java beans and combine them into tuple, and it's very hard to do it with the TypeTag-based approach.
We should keep only the compose-based way to create tuple encoder.
This PR also move `Encoder` to `org.apache.spark.sql`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9567 from cloud-fan/java.
If it returns Text, we can reuse this in Spark SQL to provide a WholeTextFile data source and directly convert the Text into UTF8String without extra string decoding and encoding.
Author: Reynold Xin <rxin@databricks.com>
Closes#9622 from rxin/SPARK-11646.
org.apache.spark.ml.feature.Word2Vec.transform() very slow. we should not read broadcast every sentence.
Author: Yuming Wang <q79969786@gmail.com>
Author: yuming.wang <q79969786@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#9592 from 979969786/master.
https://issues.apache.org/jira/browse/SPARK-11500
As filed in SPARK-11500, if merging schemas is enabled, the order of files to touch is a matter which might affect the ordering of the output columns.
This was mostly because of the use of `Set` and `Map` so I replaced them to `LinkedHashSet` and `LinkedHashMap` to keep the insertion order.
Also, I changed `reduceOption` to `reduceLeftOption`, and replaced the order of `filesToTouch` from `metadataStatuses ++ commonMetadataStatuses ++ needMerged` to `needMerged ++ metadataStatuses ++ commonMetadataStatuses` in order to touch the part-files first which always have the schema in footers whereas the others might not exist.
One nit is, If merging schemas is not enabled, but when multiple files are given, there is no guarantee of the output order, since there might not be a summary file for the first file, which ends up putting ahead the columns of the other files.
However, I thought this should be okay since disabling merging schemas means (assumes) all the files have the same schemas.
In addition, in the test code for this, I only checked the names of fields.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#9517 from HyukjinKwon/SPARK-11500.
Current updateStateByKey provides stateful processing in Spark Streaming. It allows the user to maintain per-key state and manage that state using an updateFunction. The updateFunction is called for each key, and it uses new data and existing state of the key, to generate an updated state. However, based on community feedback, we have learnt the following lessons.
* Need for more optimized state management that does not scan every key
* Need to make it easier to implement common use cases - (a) timeout of idle data, (b) returning items other than state
The high level idea that of this PR
* Introduce a new API trackStateByKey that, allows the user to update per-key state, and emit arbitrary records. The new API is necessary as this will have significantly different semantics than the existing updateStateByKey API. This API will have direct support for timeouts.
* Internally, the system will keep the state data as a map/list within the partitions of the state RDDs. The new data RDDs will be partitioned appropriately, and for all the key-value data, it will lookup the map/list in the state RDD partition and create a new list/map of updated state data. The new state RDD partition will be created based on the update data and if necessary, with old data.
Here is the detailed design doc. Please take a look and provide feedback as comments.
https://docs.google.com/document/d/1NoALLyd83zGs1hNGMm0Pc5YOVgiPpMHugGMk6COqxxE/edit#heading=h.ph3w0clkd4em
This is still WIP. Major things left to be done.
- [x] Implement basic functionality of state tracking, with initial RDD and timeouts
- [x] Unit tests for state tracking
- [x] Unit tests for initial RDD and timeout
- [ ] Unit tests for TrackStateRDD
- [x] state creating, updating, removing
- [ ] emitting
- [ ] checkpointing
- [x] Misc unit tests for State, TrackStateSpec, etc.
- [x] Update docs and experimental tags
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#9256 from tdas/trackStateByKey.
Only install signal in main thread, or it will fail to create context in not-main thread.
Author: Davies Liu <davies@databricks.com>
Closes#9574 from davies/python_signal.
Checked names, none of them should conflict with anything in base
shivaram davies rxin
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#9489 from felixcheung/rstddev.
This PR adds model save/load for spark.ml's LogisticRegressionModel. It also does minor refactoring of the default save/load classes to reuse code.
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#9606 from jkbradley/logreg-io2.
Currently, when a DStream sets the scope for RDD generated by it, that scope is not allowed to be overridden by the RDD operations. So in case of `DStream.foreachRDD`, all the RDDs generated inside the foreachRDD get the same scope - `foreachRDD <time>`, as set by the `ForeachDStream`. So it is hard to debug generated RDDs in the RDD DAG viz in the Spark UI.
This patch allows the RDD operations inside `DStream.transform` and `DStream.foreachRDD` to append their own scopes to the earlier DStream scope.
I have also slightly tweaked how callsites are set such that the short callsite reflects the RDD operation name and line number. This tweak is necessary as callsites are not managed through scopes (which support nesting and overriding) and I didnt want to add another local property to control nesting and overriding of callsites.
## Before:
## After:
The code that was used to generate this is:
```
val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_AND_DISK_SER)
val words = lines.flatMap(_.split(" "))
val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
wordCounts.foreachRDD { rdd =>
val temp = rdd.map { _ -> 1 }.reduceByKey( _ + _)
val temp2 = temp.map { _ -> 1}.reduceByKey(_ + _)
val count = temp2.count
println(count)
}
```
Note
- The inner scopes of the RDD operations map/reduceByKey inside foreachRDD is visible
- The short callsites of stages refers to the line number of the RDD ops rather than the same line number of foreachRDD in all three cases.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#9315 from tdas/SPARK-11361.
See http://search-hadoop.com/m/q3RTtjpe8r1iRbTj2 for discussion.
Summary: addition of VisibleForTesting annotation resulted in spark-shell malfunctioning.
Author: tedyu <yuzhihong@gmail.com>
Closes#9585 from tedyu/master.
