`breeze 0.8.1` dependent on `scala-logging-slf4j 2.1.1` The relevant code on #1369
Author: witgo <witgo@qq.com>
Closes#940 from witgo/breeze-8.0.1 and squashes the following commits:
65cc65e [witgo] update breeze to version 0.8.1
Right now, `MatrixFactorizationModel` can only predict a score for one or more `(user,product)` tuples. As a comment in the file notes, it would be more useful to expose a recommend method, that computes top N scoring products for a user (or vice versa – users for a product).
(This also corrects some long lines in the Java ALS test suite.)
As you can see, it's a little messy to access the class from Java. Should there be a Java-friendly wrapper for it? with a pointer about where that should go, I could add that.
Author: Sean Owen <srowen@gmail.com>
Closes#1687 from srowen/SPARK-2768 and squashes the following commits:
b349675 [Sean Owen] Additional review changes
c9edb04 [Sean Owen] Updates from code review
7bc35f9 [Sean Owen] Add recommend methods to MatrixFactorizationModel
This PR updates previous Manifest for KafkaInputDStream's Decoder to ClassTag, also fix the problem addressed in [SPARK-2103](https://issues.apache.org/jira/browse/SPARK-2103).
Previous Java interface cannot actually get the type of Decoder, so when using this Manifest to reconstruct the decode object will meet reflection exception.
Also for other two Java interfaces, ClassTag[String] is useless because calling Scala API will get the right implicit ClassTag.
Current Kafka unit test cannot actually verify the interface. I've tested these interfaces in my local and distribute settings.
Author: jerryshao <saisai.shao@intel.com>
Closes#1508 from jerryshao/SPARK-2103 and squashes the following commits:
e90c37b [jerryshao] Add Mima excludes
7529810 [jerryshao] Change Manifest to ClassTag for KafkaInputDStream's Decoder and fix Decoder construct issue when using Java API
[SPARK-2557](https://issues.apache.org/jira/browse/SPARK-2557)
Author: Ye Xianjin <advancedxy@gmail.com>
Closes#1464 from advancedxy/SPARK-2557 and squashes the following commits:
d844d67 [Ye Xianjin] add local-*-n-failures, bad-local-n, bad-local-n-failures test case
3bbc668 [Ye Xianjin] fix LOCAL_N_REGEX regular expression and make local_n_failures accept * as all cores on the computer
Author: Rahul Singhal <rahul.singhal@guavus.com>
Closes#1076 from rahulsinghaliitd/SPARK-2134 and squashes the following commits:
15f18b6 [Rahul Singhal] SPARK-2134: Report metrics before application finishes
This patch simply uses the ExternalSorter class from sort-based shuffle.
Closes#931 and Closes#1090
Author: Matei Zaharia <matei@databricks.com>
Closes#1677 from mateiz/spark-983 and squashes the following commits:
96b3fda [Matei Zaharia] SPARK-983. Support external sorting in sortByKey()
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#1578 from sarutak/SPARK-2670 and squashes the following commits:
85c8938 [Kousuke Saruta] Removed useless results.put for fail fast
e8713cc [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2670
d353984 [Kousuke Saruta] Refined assertion messages in BlockFetcherIteratorSuite.scala
03bcb02 [Kousuke Saruta] Merge branch 'SPARK-2670' of github.com:sarutak/spark into SPARK-2670
5d05855 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2670
4fca130 [Kousuke Saruta] Added test cases for BasicBlockFetcherIterator
b7b8250 [Kousuke Saruta] Modified BasicBlockFetchIterator to fail fast when local fetch error has been occurred
a3a9be1 [Kousuke Saruta] Modified BlockFetcherIterator for SPARK-2670
460dc01 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2670
e310c0b [Kousuke Saruta] Modified BlockFetcherIterator to handle local fetch failure as fatch fail
Author: Sandy Ryza <sandy@cloudera.com>
Closes#1642 from sryza/sandy-spark-2738 and squashes the following commits:
a923e4e [Sandy Ryza] SPARK-2738. Remove redundant imports in BlockManagerSuite
Without this patch, it imports everything available in the scope.
```scala
scala> val a = 10l
val a = 10l
a: Long = 10
scala> import a._
import a._
import a._
scala> case class A(a: Int) // show
case class A(a: Int) // show
class $read extends Serializable {
def <init>() = {
super.<init>;
()
};
class $iwC extends Serializable {
def <init>() = {
super.<init>;
()
};
class $iwC extends Serializable {
def <init>() = {
super.<init>;
()
};
import org.apache.spark.SparkContext._;
class $iwC extends Serializable {
def <init>() = {
super.<init>;
()
};
val $VAL5 = $line5.$read.INSTANCE;
import $VAL5.$iw.$iw.$iw.$iw.a;
class $iwC extends Serializable {
def <init>() = {
super.<init>;
()
};
import a._;
class $iwC extends Serializable {
def <init>() = {
super.<init>;
()
};
class $iwC extends Serializable {
def <init>() = {
super.<init>;
()
};
case class A extends scala.Product with scala.Serializable {
<caseaccessor> <paramaccessor> val a: Int = _;
def <init>(a: Int) = {
super.<init>;
()
}
}
};
val $iw = new $iwC.<init>
};
val $iw = new $iwC.<init>
};
val $iw = new $iwC.<init>
};
val $iw = new $iwC.<init>
};
val $iw = new $iwC.<init>
};
val $iw = new $iwC.<init>
}
object $read extends scala.AnyRef {
def <init>() = {
super.<init>;
()
};
val INSTANCE = new $read.<init>
}
defined class A
```
With this patch, it just imports only the necessary.
```scala
scala> val a = 10l
val a = 10l
a: Long = 10
scala> import a._
import a._
import a._
scala> case class A(a: Int) // show
case class A(a: Int) // show
class $read extends Serializable {
def <init>() = {
super.<init>;
()
};
class $iwC extends Serializable {
def <init>() = {
super.<init>;
()
};
class $iwC extends Serializable {
def <init>() = {
super.<init>;
()
};
case class A extends scala.Product with scala.Serializable {
<caseaccessor> <paramaccessor> val a: Int = _;
def <init>(a: Int) = {
super.<init>;
()
}
}
};
val $iw = new $iwC.<init>
};
val $iw = new $iwC.<init>
}
object $read extends scala.AnyRef {
def <init>() = {
super.<init>;
()
};
val INSTANCE = new $read.<init>
}
defined class A
scala>
```
This patch also adds a `:fallback` mode on being enabled it will restore the spark-shell's 1.0.0 behaviour.
Author: Prashant Sharma <scrapcodes@gmail.com>
Author: Yin Huai <huai@cse.ohio-state.edu>
Author: Prashant Sharma <prashant.s@imaginea.com>
Closes#1635 from ScrapCodes/repl-fix-necessary-imports and squashes the following commits:
b1968d2 [Prashant Sharma] Added toschemaRDD to test case.
0b712bb [Yin Huai] Add a REPL test to test importing a method.
02ad8ff [Yin Huai] Add a REPL test for importing SQLContext.createSchemaRDD.
ed6d0c7 [Prashant Sharma] Added a fallback mode, incase users run into issues while using repl.
b63d3b2 [Prashant Sharma] SPARK-2632, SPARK-2576. Fixed by only importing what is necessary during class definition.
Author: Haoyuan Li <haoyuan@cs.berkeley.edu>
Closes#1651 from haoyuan/upgrade-tachyon and squashes the following commits:
6f3f98f [Haoyuan Li] upgrade tachyon to 0.5.0
getRanks computes the wrong rank when numPartition >= size in the input RDDs before this patch. added units to address this bug.
Author: Doris Xin <doris.s.xin@gmail.com>
Closes#1710 from dorx/correlationBug and squashes the following commits:
733def4 [Doris Xin] bugs and reviewer comments.
31db920 [Doris Xin] revert unnecessary change
043ff83 [Doris Xin] bug fix for spearman corner case
Now the factors are persisted in memory only. If they get kicked off by later jobs, we might have to start the computation from very beginning. A better solution is changing the storage level to `MEMORY_AND_DISK`.
srowen
Author: Xiangrui Meng <meng@databricks.com>
Closes#1700 from mengxr/als-level and squashes the following commits:
c103d76 [Xiangrui Meng] change ALS factors storage level to MEMORY_AND_DISK
Author: GuoQiang Li <witgo@qq.com>
Closes#1683 from witgo/SPARK-2766 and squashes the following commits:
d0db00c [GuoQiang Li] ScalaReflectionSuite throw an llegalArgumentException in JDK 6
Since we let users create Rows. It makes sense to accept mutable Maps as values of MapType columns.
JIRA: https://issues.apache.org/jira/browse/SPARK-2779
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1705 from yhuai/SPARK-2779 and squashes the following commits:
00d72fd [Yin Huai] Use scala.collection.Map.
(1) Inconsistent aggregate (agg) indexing for unordered features.
(2) Fixed gain calculations for edge cases.
(3) One-off error in choosing thresholds for continuous features for small datasets.
(4) (not a bug) Changed meaning of tree depth by 1 to fit scikit-learn and rpart. (Depth 1 used to mean 1 leaf node; depth 0 now means 1 leaf node.)
Other updates, to help with tests:
* Updated DecisionTreeRunner to print more info.
* Added utility functions to DecisionTreeModel: toString, depth, numNodes
* Improved internal DecisionTree documentation
Bug fix details:
(1) Indexing was inconsistent for aggregate calculations for unordered features (in multiclass classification with categorical features, where the features had few enough values such that they could be considered unordered, i.e., isSpaceSufficientForAllCategoricalSplits=true).
* updateBinForUnorderedFeature indexed agg as (node, feature, featureValue, binIndex), where
** featureValue was from arr (so it was a feature value)
** binIndex was in [0,…, 2^(maxFeatureValue-1)-1)
* The rest of the code indexed agg as (node, feature, binIndex, label).
* Corrected this bug by changing updateBinForUnorderedFeature to use the second indexing pattern.
Unit tests in DecisionTreeSuite
* Updated a few tests to train a model and test its training accuracy, which catches the indexing bug from updateBinForUnorderedFeature() discussed above.
* Added new test (“stump with categorical variables for multiclass classification, with just enough bins”) to test bin extremes.
(2) Bug fix: calculateGainForSplit (for classification):
* It used to return dummy prediction values when either the right or left children had 0 weight. These were incorrect for multiclass classification. It has been corrected.
Updated impurities to allow for count = 0. This was related to the above bug fix for calculateGainForSplit (for classification).
Small updates to documentation and coding style.
(3) Bug fix: Off-by-1 when finding thresholds for splits for continuous features.
* Exhibited bug in new test in DecisionTreeSuite: “stump with 1 continuous variable for binary classification, to check off-by-1 error”
* Description: When finding thresholds for possible splits for continuous features in DecisionTree.findSplitsBins, the thresholds were set according to individual training examples’ feature values.
* Fix: The threshold is set to be the average of 2 consecutive (sorted) examples’ feature values. E.g.: If the old code set the threshold using example i, the new code sets the threshold using exam
* Note: In 4 DecisionTreeSuite tests with all labels identical, removed check of threshold since it is somewhat arbitrary.
CC: mengxr manishamde Please let me know if I missed something!
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#1673 from jkbradley/decisiontree-bugfix and squashes the following commits:
2b20c61 [Joseph K. Bradley] Small doc and style updates
dab0b67 [Joseph K. Bradley] Added documentation for DecisionTree internals
8bb8aa0 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix
978cfcf [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix
6eed482 [Joseph K. Bradley] In DecisionTree: Changed from using procedural syntax for functions returning Unit to explicitly writing Unit return type.
376dca2 [Joseph K. Bradley] Updated meaning of maxDepth by 1 to fit scikit-learn and rpart. * In code, replaced usages of maxDepth <-- maxDepth + 1 * In params, replace settings of maxDepth <-- maxDepth - 1
59750f8 [Joseph K. Bradley] * Updated Strategy to check numClassesForClassification only if algo=Classification. * Updates based on comments: ** DecisionTreeRunner *** Made dataFormat arg default to libsvm ** Small cleanups ** tree.Node: Made recursive helper methods private, and renamed them.
52e17c5 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix
da50db7 [Joseph K. Bradley] Added one more test to DecisionTreeSuite: stump with 2 continuous variables for binary classification. Caused problems in past, but fixed now.
8ea8750 [Joseph K. Bradley] Bug fix: Off-by-1 when finding thresholds for splits for continuous features.
2283df8 [Joseph K. Bradley] 2 bug fixes.
73fbea2 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix
5f920a1 [Joseph K. Bradley] Demonstration of bug before submitting fix: Updated DecisionTreeSuite so that 3 tests fail. Will describe bug in next commit.
RandomRDDGenerators but without support for randomRDD and randomVectorRDD, which take in arbitrary DistributionGenerator.
`randomRDD.py` is named to avoid collision with the built-in Python `random` package.
Author: Doris Xin <doris.s.xin@gmail.com>
Closes#1628 from dorx/pythonRDD and squashes the following commits:
55c6de8 [Doris Xin] review comments. all python units passed.
f831d9b [Doris Xin] moved default args logic into PythonMLLibAPI
2d73917 [Doris Xin] fix for linalg.py
8663e6a [Doris Xin] reverting back to a single python file for random
f47c481 [Doris Xin] docs update
687aac0 [Doris Xin] add RandomRDDGenerators.py to run-tests
4338f40 [Doris Xin] renamed randomRDD to rand and import as random
29d205e [Doris Xin] created mllib.random package
bd2df13 [Doris Xin] typos
07ddff2 [Doris Xin] units passed.
23b2ecd [Doris Xin] WIP
This PR resolves the following two tickets:
- [SPARK-2531](https://issues.apache.org/jira/browse/SPARK-2531): BNLJ currently assumes the build side is the right relation. This patch refactors some of its logic to take into account a BuildSide properly.
- [SPARK-2436](https://issues.apache.org/jira/browse/SPARK-2436): building on top of the above, we simply use the physical size statistics (if available) of both relations, and make the smaller relation the build side in the planner.
Author: Zongheng Yang <zongheng.y@gmail.com>
Closes#1448 from concretevitamin/bnlj-buildSide and squashes the following commits:
1780351 [Zongheng Yang] Use size estimation to decide optimal build side of BNLJ.
68e6c5b [Zongheng Yang] Consolidate two adjacent pattern matchings.
96d312a [Zongheng Yang] Use a while loop instead of collection methods chaining.
4bc525e [Zongheng Yang] Make BroadcastNestedLoopJoin take a BuildSide.
Prior to this change, every PySpark task completion opened a new socket to the accumulator server, passed its updates through, and then quit. I'm not entirely sure why PySpark always sends accumulator updates, but regardless this causes a very rapid buildup of ephemeral TCP connections that remain in the TCP_WAIT state for around a minute before being cleaned up.
Rather than trying to allow these sockets to be cleaned up faster, this patch simply reuses the connection between tasks completions (since they're fed updates in a single-threaded manner by the DAGScheduler anyway).
The only tricky part here was making sure that the AccumulatorServer was able to shutdown in a timely manner (i.e., stop polling for new data), and this was accomplished via minor feats of magic.
I have confirmed that this patch eliminates the buildup of ephemeral sockets due to the accumulator updates. However, I did note that there were still significant sockets being created against the PySpark daemon port, but my machine was not able to create enough sockets fast enough to fail. This may not be the last time we've seen this issue, though.
Author: Aaron Davidson <aaron@databricks.com>
Closes#1503 from aarondav/accum and squashes the following commits:
b3e12f7 [Aaron Davidson] SPARK-2282: Reuse Socket for sending accumulator updates to Pyspark
It should be more convenient if user can specify ascending and numPartitions when calling sortByKey.
Author: Rui Li <rui.li@intel.com>
Closes#1645 from lirui-intel/spark-2740 and squashes the following commits:
fb5d52e [Rui Li] SPARK-2740: allow user to specify ascending and numPartitions for sortByKey
Fix several awkward wordings and grammatical issues in the following
documents:
* docs/monitoring.md
* docs/streaming-programming-guide.md
Author: kballou <kballou@devnulllabs.io>
Closes#1662 from kennyballou/grammar_fixes and squashes the following commits:
e1b8ad6 [kballou] Docs: monitoring, streaming programming guide
This commit fixes a couple of issues in the merge_spark_pr.py developer script:
- Allow recovery from failed cherry-picks.
- Fix detection of pull requests that have already been merged.
Both of these fixes are useful when backporting changes.
Author: Josh Rosen <joshrosen@apache.org>
Closes#1668 from JoshRosen/pr-script-improvements and squashes the following commits:
ff4f33a [Josh Rosen] Default SPARK_HOME to cwd(); detect missing JIRA credentials.
ed5bc57 [Josh Rosen] Improvements for backporting using merge_spark_pr:
This PR tries to resolve the broken Jenkins maven test issue introduced by #1439. Now, we create a single query test to run both the setup work and the test query.
Author: Yin Huai <huai@cse.ohio-state.edu>
Closes#1669 from yhuai/SPARK-2523-fixTest and squashes the following commits:
358af1a [Yin Huai] Make partition_based_table_scan_with_different_serde run atomically.
This is roughly the TF-IDF implementation used in the Databricks Cloud Demo: http://databricks.com/cloud/ .
Both `HashingTF` and `IDF` are implemented as transformers, similar to scikit-learn.
Author: Xiangrui Meng <meng@databricks.com>
Closes#1671 from mengxr/tfidf and squashes the following commits:
7d65888 [Xiangrui Meng] use JavaConverters._
5fe9ec4 [Xiangrui Meng] fix unit test
6e214ec [Xiangrui Meng] add apache header
cfd9aed [Xiangrui Meng] add Java-friendly methods move classes to mllib.feature
3814440 [Xiangrui Meng] add HashingTF and IDF
The logging code that handles log4j initialization leads to an stack overflow error when used with log4j 2.x, which has just been released. This occurs even a downstream project has correctly adjusted SLF4J bindings, and that is the right thing to do for log4j 2.x, since it is effectively a separate project from 1.x.
Here is the relevant bit of Logging.scala:
```
private def initializeLogging() {
// If Log4j is being used, but is not initialized, load a default properties file
val binder = StaticLoggerBinder.getSingleton
val usingLog4j = binder.getLoggerFactoryClassStr.endsWith("Log4jLoggerFactory")
val log4jInitialized = LogManager.getRootLogger.getAllAppenders.hasMoreElements
if (!log4jInitialized && usingLog4j) {
val defaultLogProps = "org/apache/spark/log4j-defaults.properties"
Option(Utils.getSparkClassLoader.getResource(defaultLogProps)) match {
case Some(url) =>
PropertyConfigurator.configure(url)
log.info(s"Using Spark's default log4j profile: $defaultLogProps")
case None =>
System.err.println(s"Spark was unable to load $defaultLogProps")
}
}
Logging.initialized = true
// Force a call into slf4j to initialize it. Avoids this happening from mutliple threads
// and triggering this: http://mailman.qos.ch/pipermail/slf4j-dev/2010-April/002956.html
log
}
```
The first minor issue is that there is a call to a logger inside this method, which is initializing logging. In this situation, it ends up causing the initialization to be called recursively until the stack overflow. It would be slightly tidier to log this only after Logging.initialized = true. Or not at all. But it's not the root problem, or else, it would not work at all now.
The calls to log4j classes here always reference log4j 1.2 no matter what. For example, there is not getAllAppenders in log4j 2.x. That's fine. Really, "usingLog4j" means "using log4j 1.2" and "log4jInitialized" means "log4j 1.2 is initialized".
usingLog4j should be false for log4j 2.x, because the initialization only matters for log4j 1.2. But, it's true, and that's the real issue. And log4jInitialized is always false, since calls to the log4j 1.2 API are stubs and no-ops in this setup, where the caller has swapped in log4j 2.x. Hence the loop.
This is fixed, I believe, if "usingLog4j" can be false for log4j 2.x. The SLF4J static binding class has the same name for both versions, unfortunately, which causes the issue. However they're in different packages. For example, if the test included "... and begins with org.slf4j", it should work, as the SLF4J binding for log4j 2.x is provided by log4j 2.x at the moment, and is in package org.apache.logging.slf4j.
Of course, I assume that SLF4J will eventually offer its own binding. I hope to goodness they at least name the binding class differently, or else this will again not work. But then some other check can probably be made.
Author: Sean Owen <srowen@gmail.com>
Closes#1547 from srowen/SPARK-2646 and squashes the following commits:
92a9898 [Sean Owen] System.out -> System.err
94be4c7 [Sean Owen] Add back log message as System.out, with informational comment
a7f8876 [Sean Owen] Updates from review
6f3c1d3 [Sean Owen] Remove log statement in logging initialization, and distinguish log4j 1.2 from 2.0, to avoid stack overflow in initialization
...ags
Author: Sandy Ryza <sandy@cloudera.com>
Closes#1665 from sryza/sandy-spark-2664 and squashes the following commits:
0518c63 [Sandy Ryza] SPARK-2664. Deal with `--conf` options in spark-submit that relate to flags
This allows users to gain access to the InputSplit which backs each partition.
An alternative solution would have been to have a .withInputSplit() method which returns a new RDD[(InputSplit, (K, V))], but this is confusing because you could not cache this RDD or shuffle it, as InputSplit is not inherently serializable.
Author: Aaron Davidson <aaron@databricks.com>
Closes#973 from aarondav/hadoop and squashes the following commits:
9c9112b [Aaron Davidson] Add JavaAPISuite test
9942cd7 [Aaron Davidson] Add Java API
1284a3a [Aaron Davidson] SPARK-2028: Expose mapPartitionsWithInputSplit in HadoopRDD
LocalHiveContext is redundant with HiveContext. The only difference is it creates `./metastore` instead of `./metastore_db`.
Author: Michael Armbrust <michael@databricks.com>
Closes#1641 from marmbrus/localHiveContext and squashes the following commits:
e5ec497 [Michael Armbrust] Add deprecation version
626e056 [Michael Armbrust] Don't remove from imports yet
905cc5f [Michael Armbrust] Merge remote-tracking branch 'apache/master' into localHiveContext
1c2727e [Michael Armbrust] Deprecate LocalHiveContext
Author: Michael Armbrust <michael@databricks.com>
Closes#1647 from marmbrus/parquetCase and squashes the following commits:
a1799b7 [Michael Armbrust] move comment
2a2a68b [Michael Armbrust] Merge remote-tracking branch 'apache/master' into parquetCase
bb35d5b [Michael Armbrust] Fix test case that produced an invalid plan.
e6870bf [Michael Armbrust] Better error message.
539a2e1 [Michael Armbrust] Resolve original attributes in ParquetTableScan
This pull request is a small refactor so that a partial function (hence a closure) is not created. Instead, a regular function is used. The behavior of the code is not changed.
Author: Timothy Hunter <timhunter@databricks.com>
Closes#1674 from thunterdb/closure_issue and squashes the following commits:
e1e664d [Timothy Hunter] simplify closure
automatically set master according to `spark.master` in `spark-defaults.conf`
Author: CrazyJvm <crazyjvm@gmail.com>
Closes#1644 from CrazyJvm/standalone-guide and squashes the following commits:
bb12b95 [CrazyJvm] automatically set master according to `spark.master` in `spark-defaults.conf`
Author: Prashant Sharma <prashant.s@imaginea.com>
Closes#1463 from ScrapCodes/SPARK-2497/mima-exclude-all and squashes the following commits:
72077b1 [Prashant Sharma] Check separately for module symbols.
cd96192 [Prashant Sharma] SPARK-2497 Produce "member excludes" irrespective of the fact that class itself is excluded or not.
The Java API's use of fake ClassTags doesn't seem to cause any problems for Java users, but it can lead to issues when passing JavaRDDs' underlying RDDs to Scala code (e.g. in the MLlib Java API wrapper code). If we call collect() on a Scala RDD with an incorrect ClassTag, this causes ClassCastExceptions when we try to allocate an array of the wrong type (for example, see SPARK-2197).
There are a few possible fixes here. An API-breaking fix would be to completely remove the fake ClassTags and require Java API users to pass java.lang.Class instances to all parallelize() calls and add returnClass fields to all Function implementations. This would be extremely verbose.
Instead, this patch adds internal APIs to "repair" a Scala RDD with an incorrect ClassTag by wrapping it and overriding its ClassTag. This should be okay for cases where the Scala code that calls collect() knows what type of array should be allocated, which is the case in the MLlib wrappers.
Author: Josh Rosen <joshrosen@apache.org>
Closes#1639 from JoshRosen/SPARK-2737 and squashes the following commits:
572b4c8 [Josh Rosen] Replace newRDD[T] with mapPartitions().
469d941 [Josh Rosen] Preserve partitioner in retag().
af78816 [Josh Rosen] Allow retag() to get classTag implicitly.
d1d54e6 [Josh Rosen] [SPARK-2737] Add retag() method for changing RDDs' ClassTags.
We resolve relative paths to the local `file:/` system for `--jars` and `--files` in spark submit (#853). We should do the same for the history server.
Author: Andrew Or <andrewor14@gmail.com>
Closes#1280 from andrewor14/hist-serv-fix and squashes the following commits:
13ff406 [Andrew Or] Merge branch 'master' of github.com:apache/spark into hist-serv-fix
b393e17 [Andrew Or] Strip trailing "/" from logging directory
622a471 [Andrew Or] Fix test in EventLoggingListenerSuite
0e20f71 [Andrew Or] Shift responsibility of resolving paths up one level
b037c0c [Andrew Or] Use resolved paths for everything in history server
c7e36ee [Andrew Or] Resolve paths for event logging too
40e3933 [Andrew Or] Resolve history server file paths
"ERROR yarn.Client: Required AM memory (1024) is above the max threshold (1048) of this cluster" appears if this code is not changed. obviously, 1024 is less than 1048, so change this
Author: derek ma <maji3@asiainfo-linkage.com>
Closes#1494 from maji2014/master and squashes the following commits:
b0f6640 [derek ma] Required AM memory is "amMem", not "args.amMemory"
Author: Reynold Xin <rxin@apache.org>
Closes#1675 from rxin/unionrdd and squashes the following commits:
941d316 [Reynold Xin] Clear RDDs for checkpointing.
c9f05f2 [Reynold Xin] [SPARK-2758] UnionRDD's UnionPartition should not reference parent RDDs
This adds a new ShuffleManager based on sorting, as described in https://issues.apache.org/jira/browse/SPARK-2045. The bulk of the code is in an ExternalSorter class that is similar to ExternalAppendOnlyMap, but sorts key-value pairs by partition ID and can be used to create a single sorted file with a map task's output. (Longer-term I think this can take on the remaining functionality in ExternalAppendOnlyMap and replace it so we don't have code duplication.)
The main TODOs still left are:
- [x] enabling ExternalSorter to merge across spilled files
- [x] with an Ordering
- [x] without an Ordering, using the keys' hash codes
- [x] adding more tests (e.g. a version of our shuffle suite that runs on this)
- [x] rebasing on top of the size-tracking refactoring in #1165 when that is merged
- [x] disabling spilling if spark.shuffle.spill is set to false
Despite this though, this seems to work pretty well (running successfully in cases where the hash shuffle would OOM, such as 1000 reduce tasks on executors with only 1G memory), and it seems to be comparable in speed or faster than hash-based shuffle (it will create much fewer files for the OS to keep track of). So I'm posting it to get some early feedback.
After these TODOs are done, I'd also like to enable ExternalSorter to sort data within each partition by a key as well, which will allow us to use it to implement external spilling in reduce tasks in `sortByKey`.
Author: Matei Zaharia <matei@databricks.com>
Closes#1499 from mateiz/sort-based-shuffle and squashes the following commits:
bd841f9 [Matei Zaharia] Various review comments
d1c137fd [Matei Zaharia] Various review comments
a611159 [Matei Zaharia] Compile fixes due to rebase
62c56c8 [Matei Zaharia] Fix ShuffledRDD sometimes not returning Tuple2s.
f617432 [Matei Zaharia] Fix a failing test (seems to be due to change in SizeTracker logic)
9464d5f [Matei Zaharia] Simplify code and fix conflicts after latest rebase
0174149 [Matei Zaharia] Add cleanup behavior and cleanup tests for sort-based shuffle
eb4ee0d [Matei Zaharia] Remove customizable element type in ShuffledRDD
fa2e8db [Matei Zaharia] Allow nextBatchStream to be called after we're done looking at all streams
a34b352 [Matei Zaharia] Fix tracking of indices within a partition in SpillReader, and add test
03e1006 [Matei Zaharia] Add a SortShuffleSuite that runs ShuffleSuite with sort-based shuffle
3c7ff1f [Matei Zaharia] Obey the spark.shuffle.spill setting in ExternalSorter
ad65fbd [Matei Zaharia] Rebase on top of Aaron's Sorter change, and use Sorter in our buffer
44d2a93 [Matei Zaharia] Use estimateSize instead of atGrowThreshold to test collection sizes
5686f71 [Matei Zaharia] Optimize merging phase for in-memory only data:
5461cbb [Matei Zaharia] Review comments and more tests (e.g. tests with 1 element per partition)
e9ad356 [Matei Zaharia] Update ContextCleanerSuite to make sure shuffle cleanup tests use hash shuffle (since they were written for it)
c72362a [Matei Zaharia] Added bug fix and test for when iterators are empty
de1fb40 [Matei Zaharia] Make trait SizeTrackingCollection private[spark]
4988d16 [Matei Zaharia] tweak
c1b7572 [Matei Zaharia] Small optimization
ba7db7f [Matei Zaharia] Handle null keys in hash-based comparator, and add tests for collisions
ef4e397 [Matei Zaharia] Support for partial aggregation even without an Ordering
4b7a5ce [Matei Zaharia] More tests, and ability to sort data if a total ordering is given
e1f84be [Matei Zaharia] Fix disk block manager test
5a40a1c [Matei Zaharia] More tests
614f1b4 [Matei Zaharia] Add spill metrics to map tasks
cc52caf [Matei Zaharia] Add more error handling and tests for error cases
bbf359d [Matei Zaharia] More work
3a56341 [Matei Zaharia] More partial work towards sort-based shuffle
7a0895d [Matei Zaharia] Some more partial work towards sort-based shuffle
b615476 [Matei Zaharia] Scaffolding for sort-based shuffle
Author: strat0sphere <stratos.dimopoulos@gmail.com>
Closes#1676 from strat0sphere/patch-1 and squashes the following commits:
044d2fa [strat0sphere] Update DecisionTreeRunner.scala
Per discussion at https://issues.apache.org/jira/browse/SPARK-2341 , this is a look at deprecating the multiclass parameter. Thoughts welcome of course.
Author: Sean Owen <srowen@gmail.com>
Closes#1663 from srowen/SPARK-2341 and squashes the following commits:
8a3abd7 [Sean Owen] Suppress MIMA error for removed package private classes
18a8c8e [Sean Owen] Updates from review
83d0092 [Sean Owen] Deprecated methods with multiclass, and instead always parse target as a double (ie. multiclass = true)
Author: Michael Armbrust <michael@databricks.com>
Closes#1650 from marmbrus/dropCached and squashes the following commits:
e6ab80b [Michael Armbrust] Support if exists.
83426c6 [Michael Armbrust] Remove tables from cache when DROP TABLE is run.
Provide a version of the Spark tarball which does not package Hive. This is meant for HIve + Spark users.
Author: Brock Noland <brock@apache.org>
Closes#1667 from brockn/master and squashes the following commits:
5beafb2 [Brock Noland] SPARK-2741 - Publish version of spark assembly which does not contain Hive
The Maven-based builds in the build matrix have been failing for a few days:
https://amplab.cs.berkeley.edu/jenkins/view/Spark/
On inspection, it looks like the Spark SQL Java tests don't compile:
https://amplab.cs.berkeley.edu/jenkins/view/Spark/job/Spark-Master-Maven-pre-YARN/hadoop.version=1.0.4,label=centos/244/consoleFull
I confirmed it by repeating the command vs master:
`mvn -Dhadoop.version=1.0.4 -Dlabel=centos -DskipTests clean package`
The problem is that this module doesn't depend on JUnit. In fact, none of the modules do, but `com.novocode:junit-interface` (the SBT-JUnit bridge) pulls it in, in most places. However this module doesn't depend on `com.novocode:junit-interface`
Adding the `junit:junit` dependency fixes the compile problem. In fact, the other modules with Java tests should probably depend on it explicitly instead of happening to get it via `com.novocode:junit-interface`, since that is a bit SBT/Scala-specific (and I am not even sure it's needed).
Author: Sean Owen <srowen@gmail.com>
Closes#1660 from srowen/SPARK-2749 and squashes the following commits:
858ff7c [Sean Owen] Add explicit junit dep to other modules with Java tests for robustness
9636794 [Sean Owen] Add junit dep so that Spark SQL Java tests compile
JIRA issue: https://issues.apache.org/jira/browse/SPARK-2024
This PR is a followup to #455 and adds capabilities for saving PySpark RDDs using SequenceFile or any Hadoop OutputFormats.
* Added RDD methods ```saveAsSequenceFile```, ```saveAsHadoopFile``` and ```saveAsHadoopDataset```, for both old and new MapReduce APIs.
* Default converter for converting common data types to Writables. Users may specify custom converters to convert to desired data types.
* No out-of-box support for reading/writing arrays, since ArrayWritable itself doesn't have a no-arg constructor for creating an empty instance upon reading. Users need to provide ArrayWritable subtypes. Custom converters for converting arrays to suitable ArrayWritable subtypes are also needed when writing. When reading, the default converter will convert any custom ArrayWritable subtypes to ```Object[]``` and they get pickled to Python tuples.
* Added HBase and Cassandra output examples to show how custom output formats and converters can be used.
cc MLnick mateiz ahirreddy pwendell
Author: Kan Zhang <kzhang@apache.org>
Closes#1338 from kanzhang/SPARK-2024 and squashes the following commits:
c01e3ef [Kan Zhang] [SPARK-2024] code formatting
6591e37 [Kan Zhang] [SPARK-2024] renaming pickled -> pickledRDD
d998ad6 [Kan Zhang] [SPARK-2024] refectoring to get method params below 10
57a7a5e [Kan Zhang] [SPARK-2024] correcting typo
75ca5bd [Kan Zhang] [SPARK-2024] Better type checking for batch serialized RDD
0bdec55 [Kan Zhang] [SPARK-2024] Refactoring newly added tests
9f39ff4 [Kan Zhang] [SPARK-2024] Adding 2 saveAsHadoopDataset tests
0c134f3 [Kan Zhang] [SPARK-2024] Test refactoring and adding couple unbatched cases
7a176df [Kan Zhang] [SPARK-2024] Add saveAsSequenceFile to PySpark
Author: Michael Armbrust <michael@databricks.com>
Closes#1653 from marmbrus/fixDocs and squashes the following commits:
0aa1feb [Michael Armbrust] Fix compiling of catalyst docs.
