This was changed, but in fact, it's used for things other than tests.
So I've changed it back.
Author: Patrick Wendell <pwendell@gmail.com>
Closes#747 from pwendell/executor-env and squashes the following commits:
36a60a5 [Patrick Wendell] Rename testExecutorEnvs --> executorEnvs.
This initial commit resolves the conflicts in the Hive profiles as noted in https://issues.apache.org/jira/browse/SPARK-1802 .
Most of the fix was to note that Hive drags in Avro, and so if the hive module depends on Spark's version of the `avro-*` dependencies, it will pull in our exclusions as needed too. But I found we need to copy some exclusions between the two Avro dependencies to get this right. And then had to squash some commons-logging intrusions.
This turned up another annoying find, that `hive-exec` is basically an "assembly" artifact that _also_ packages all of its transitive dependencies. This means the final assembly shows lots of collisions between itself and its dependencies, and even other project dependencies. I have a TODO to examine whether that is going to be a deal-breaker or not.
In the meantime I'm going to tack on a second commit to this PR that will also fix some similar, last collisions in the YARN profile.
Author: Sean Owen <sowen@cloudera.com>
Closes#744 from srowen/SPARK-1802 and squashes the following commits:
a856604 [Sean Owen] Resolve JAR version conflicts specific to Hive profile
Three issues related to temp files that tests generate – these should be touched up for hygiene but are not urgent.
Modules have a log4j.properties which directs the unit-test.log output file to a directory like `[module]/target/unit-test.log`. But this ends up creating `[module]/[module]/target/unit-test.log` instead of former.
The `work/` directory is not deleted by "mvn clean", in the parent and in modules. Neither is the `checkpoint/` directory created under the various external modules.
Many tests create a temp directory, which is not usually deleted. This can be largely resolved by calling `deleteOnExit()` at creation and trying to call `Utils.deleteRecursively` consistently to clean up, sometimes in an `@After` method.
_If anyone seconds the motion, I can create a more significant change that introduces a new test trait along the lines of `LocalSparkContext`, which provides management of temp directories for subclasses to take advantage of._
Author: Sean Owen <sowen@cloudera.com>
Closes#732 from srowen/SPARK-1798 and squashes the following commits:
5af578e [Sean Owen] Try to consistently delete test temp dirs and files, and set deleteOnExit() for each
b21b356 [Sean Owen] Remove work/ and checkpoint/ dirs with mvn clean
bdd0f41 [Sean Owen] Remove duplicate module dir in log4j.properties output path for tests
Addressing issue in MimaBuild.scala.
Author: Ankur Dave <ankurdave@gmail.com>
Author: Joseph E. Gonzalez <joseph.e.gonzalez@gmail.com>
Closes#742 from jegonzal/edge_partition_serialization and squashes the following commits:
8ba6e0d [Ankur Dave] Add concatenation operators to MimaBuild.scala
cb2ed3a [Joseph E. Gonzalez] addressing missing exclusion in MimaBuild.scala
5d27824 [Ankur Dave] Disable reference tracking to fix serialization test
c0a9ae5 [Ankur Dave] Add failing test for EdgePartition Kryo serialization
a4a3faa [Joseph E. Gonzalez] Making EdgePartition serializable.
Enabled Mesos (0.18.1) dependency with shaded protobuf
Why is this needed?
Avoids any protobuf version collision between Mesos and any other
dependency in Spark e.g. Hadoop HDFS 2.2+ or 1.0.4.
Ticket: https://issues.apache.org/jira/browse/SPARK-1806
* Should close https://issues.apache.org/jira/browse/SPARK-1433
Author berngp
Author: Bernardo Gomez Palacio <bernardo.gomezpalacio@gmail.com>
Closes#741 from berngp/feature/SPARK-1806 and squashes the following commits:
5d70646 [Bernardo Gomez Palacio] SPARK-1806: Upgrade Mesos dependency to 0.18.1
The main issue this patch fixes is [SPARK-1772](https://issues.apache.org/jira/browse/SPARK-1772), in which Executors may not die when fatal exceptions (e.g., OOM) are thrown. This patch causes Executors to delegate to the ExecutorUncaughtExceptionHandler when a fatal exception is thrown.
This patch also continues the fight in the neverending war against `case t: Throwable =>`, by only catching Exceptions in many places, and adding a wrapper for Threads and Runnables to make sure any uncaught exceptions are at least printed to the logs.
It also turns out that it is unlikely that the IndestructibleActorSystem actually works, given testing ([here](https://gist.github.com/aarondav/ca1f0cdcd50727f89c0d)). The uncaughtExceptionHandler is not called from the places that we expected it would be.
[SPARK-1620](https://issues.apache.org/jira/browse/SPARK-1620) deals with part of this issue, but refactoring our Actor Systems to ensure that exceptions are dealt with properly is a much bigger change, outside the scope of this PR.
Author: Aaron Davidson <aaron@databricks.com>
Closes#715 from aarondav/throwable and squashes the following commits:
f9b9bfe [Aaron Davidson] Remove other redundant 'throw e'
e937a0a [Aaron Davidson] Address Prashant and Matei's comments
1867867 [Aaron Davidson] [RFC] SPARK-1772 Stop catching Throwable, let Executors die
This appears to address the issue with edge partition serialization. The solution appears to be just registering the `PrimitiveKeyOpenHashMap`. However I noticed that we appear to have forked that code in GraphX but retained the same name (which is confusing). I also renamed our local copy to `GraphXPrimitiveKeyOpenHashMap`. We should consider dropping that and using the one in Spark if possible.
Author: Ankur Dave <ankurdave@gmail.com>
Author: Joseph E. Gonzalez <joseph.e.gonzalez@gmail.com>
Closes#724 from jegonzal/edge_partition_serialization and squashes the following commits:
b0a525a [Ankur Dave] Disable reference tracking to fix serialization test
bb7f548 [Ankur Dave] Add failing test for EdgePartition Kryo serialization
67dac22 [Joseph E. Gonzalez] Making EdgePartition serializable.
Their was a minor bug in which negative partition ids could be generated when constructing a 2D partitioning of a graph. This could lead to an inefficient 2D partition for large vertex id values.
Author: Joseph E. Gonzalez <joseph.e.gonzalez@gmail.com>
Closes#709 from jegonzal/fix_2d_partitioning and squashes the following commits:
937c562 [Joseph E. Gonzalez] fixing bug in 2d partitioning algorithm where negative partition ids could be generated.
The previous check didn't account for the fact that the default
deploy mode is "client" unless otherwise specified. Also, this
sets the more narrowly defined SPARK_DRIVER_MEMORY instead of setting
SPARK_MEM.
Author: Patrick Wendell <pwendell@gmail.com>
Closes#730 from pwendell/spark-submit and squashes the following commits:
430b98f [Patrick Wendell] Feedback from Aaron
e788edf [Patrick Wendell] Changes based on Aaron's feedback
f508146 [Patrick Wendell] SPARK-1652: Set driver memory correctly in spark-submit.
This patch adds better balancing when performing a repartition of an
RDD. Previously the elements in the RDD were hash partitioned, meaning
if the RDD was skewed certain partitions would end up being very large.
This commit adds load balancing of elements across the repartitioned
RDD splits. The load balancing is not perfect: a given output partition
can have up to N more elements than the average if there are N input
partitions. However, some randomization is used to minimize the
probabiliy that this happens.
Author: Patrick Wendell <pwendell@gmail.com>
Closes#727 from pwendell/load-balance and squashes the following commits:
f9da752 [Patrick Wendell] Response to Matei's feedback
acfa46a [Patrick Wendell] SPARK-1770: Load balance elements when repartitioning.
Author: witgo <witgo@qq.com>
Closes#728 from witgo/scala_home and squashes the following commits:
cdfd8be [witgo] Merge branch 'master' of https://github.com/apache/spark into scala_home
fac094a [witgo] remove outdated runtime Information scala home
More info at. https://github.com/sbt/sbt/issues/1010
Author: Prashant Sharma <prashant.s@imaginea.com>
Closes#525 from ScrapCodes/sbt-inc-opt and squashes the following commits:
ba8fa42 [Prashant Sharma] Enabled incremental build that comes with sbt 0.13.2
SparkSubmit ignores `--jars` for YARN client. This is a bug.
This PR also automatically adds the application jar to `spark.jar`. Previously, when running as yarn-client, you must specify the jar additionally through `--files` (because `--jars` didn't work). Now you don't have to explicitly specify it through either.
Tested on a YARN cluster.
Author: Andrew Or <andrewor14@gmail.com>
Closes#710 from andrewor14/yarn-jars and squashes the following commits:
35d1928 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-jars
c27bf6c [Andrew Or] For yarn-cluster and python, do not add primaryResource to spark.jar
c92c5bf [Andrew Or] Minor cleanups
269f9f3 [Andrew Or] Fix format
013d840 [Andrew Or] Fix tests
1407474 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-jars
3bb75e8 [Andrew Or] Allow SparkSubmit --jars to take effect in yarn-client mode
TL;DR is there is a bit of JAR hell trouble with Netty, that can be mostly resolved and will resolve a test failure.
I hit the error described at http://apache-spark-user-list.1001560.n3.nabble.com/SparkContext-startup-time-out-td1753.html while running FlumeStreamingSuite, and have for a short while (is it just me?)
velvia notes:
"I have found a workaround. If you add akka 2.2.4 to your dependencies, then everything works, probably because akka 2.2.4 brings in newer version of Jetty."
There are at least 3 versions of Netty in play in the build:
- the new Flume 1.4.0 dependency brings in io.netty:netty:3.4.0.Final, and that is the immediate problem
- the custom version of akka 2.2.3 depends on io.netty:netty:3.6.6.
- but, Spark Core directly uses io.netty:netty-all:4.0.17.Final
The POMs try to exclude other versions of netty, but are excluding org.jboss.netty:netty, when in fact older versions of io.netty:netty (not netty-all) are also an issue.
The org.jboss.netty:netty excludes are largely unnecessary. I replaced many of them with io.netty:netty exclusions until everything agreed on io.netty:netty-all:4.0.17.Final.
But this didn't work, since Akka 2.2.3 doesn't work with Netty 4.x. Down-grading to 3.6.6.Final across the board made some Spark code not compile.
If the build *keeps* io.netty:netty:3.6.6.Final as well, everything seems to work. Part of the reason seems to be that Netty 3.x used the old `org.jboss.netty` packages. This is less than ideal, but is no worse than the current situation.
So this PR resolves the issue and improves the JAR hell, even if it leaves the existing theoretical Netty 3-vs-4 conflict:
- Remove org.jboss.netty excludes where possible, for clarity; they're not needed except with Hadoop artifacts
- Add io.netty:netty excludes where needed -- except, let akka keep its io.netty:netty
- Change a bit of test code that actually depended on Netty 3.x, to use 4.x equivalent
- Update SBT build accordingly
A better change would be to update Akka far enough such that it agrees on Netty 4.x, but I don't know if that's feasible.
Author: Sean Owen <sowen@cloudera.com>
Closes#723 from srowen/SPARK-1789 and squashes the following commits:
43661b7 [Sean Owen] Update and add Netty excludes to prevent some JAR conflicts that cause test issues
This PR makes the following changes, primarily in e4fbd329aef85fe2c38b0167255d2a712893d683:
1. *Unify RDDs to avoid zipPartitions.* A graph used to be four RDDs: vertices, edges, routing table, and triplet view. This commit merges them down to two: vertices (with routing table), and edges (with replicated vertices).
2. *Avoid duplicate shuffle in graph building.* We used to do two shuffles when building a graph: one to extract routing information from the edges and move it to the vertices, and another to find nonexistent vertices referred to by edges. With this commit, the latter is done as a side effect of the former.
3. *Avoid no-op shuffle when joins are fully eliminated.* This is a side effect of unifying the edges and the triplet view.
4. *Join elimination for mapTriplets.*
5. *Ship only the needed vertex attributes when upgrading the triplet view.* If the triplet view already contains source attributes, and we now need both attributes, only ship destination attributes rather than re-shipping both. This is done in `ReplicatedVertexView#upgrade`.
Author: Ankur Dave <ankurdave@gmail.com>
Closes#497 from ankurdave/unify-rdds and squashes the following commits:
332ab43 [Ankur Dave] Merge remote-tracking branch 'apache-spark/master' into unify-rdds
4933e2e [Ankur Dave] Exclude RoutingTable from binary compatibility check
5ba8789 [Ankur Dave] Add GraphX upgrade guide from Spark 0.9.1
13ac845 [Ankur Dave] Merge remote-tracking branch 'apache-spark/master' into unify-rdds
a04765c [Ankur Dave] Remove unnecessary toOps call
57202e8 [Ankur Dave] Replace case with pair parameter
75af062 [Ankur Dave] Add explicit return types
04d3ae5 [Ankur Dave] Convert implicit parameter to context bound
c88b269 [Ankur Dave] Revert upgradeIterator to if-in-a-loop
0d3584c [Ankur Dave] EdgePartition.size should be val
2a928b2 [Ankur Dave] Set locality wait
10b3596 [Ankur Dave] Clean up public API
ae36110 [Ankur Dave] Fix style errors
e4fbd32 [Ankur Dave] Unify GraphImpl RDDs + other graph load optimizations
d6d60e2 [Ankur Dave] In GraphLoader, coalesce to minEdgePartitions
62c7b78 [Ankur Dave] In Analytics, take PageRank numIter
d64e8d4 [Ankur Dave] Log current Pregel iteration
Tolerate empty strings in PythonRDD
Author: Kan Zhang <kzhang@apache.org>
Closes#644 from kanzhang/SPARK-1690 and squashes the following commits:
c62ad33 [Kan Zhang] Adding Python doctest
473ec4b [Kan Zhang] [SPARK-1690] Tolerating empty elements when saving Python RDD to text files
This fixes https://issues.apache.org/jira/browse/SPARK-1731 by adding the Python includes to the PYTHONPATH before depickling the broadcast values
@airhorns
Author: Bouke van der Bijl <boukevanderbijl@gmail.com>
Closes#656 from bouk/python-includes-before-broadcast and squashes the following commits:
7b0dfe4 [Bouke van der Bijl] Add Python includes to path before depickling broadcast values
Author: Andy Konwinski <andykonwinski@gmail.com>
Closes#650 from andyk/python-docs-link-fix and squashes the following commits:
a1f9d51 [Andy Konwinski] fix broken in link in python docs
This pull request contains a rebased patch from @heathermiller (https://github.com/heathermiller/spark/pull/1) to add ClassTags on Serializer and types that depend on it (Broadcast and AccumulableCollection). Putting these in the public API signatures now will allow us to use Scala Pickling for serialization down the line without breaking binary compatibility.
One question remaining is whether we also want them on Accumulator -- Accumulator is passed as part of a bigger Task or TaskResult object via the closure serializer so it doesn't seem super useful to add the ClassTag there. Broadcast and AccumulableCollection in contrast were being serialized directly.
CC @rxin, @pwendell, @heathermiller
Author: Matei Zaharia <matei@databricks.com>
Closes#700 from mateiz/spark-1708 and squashes the following commits:
1a3d8b0 [Matei Zaharia] Use fake ClassTag in Java
3b449ed [Matei Zaharia] test fix
2209a27 [Matei Zaharia] Code style fixes
9d48830 [Matei Zaharia] Add a ClassTag on Serializer and things that depend on it
Add `limit` transformation to `SchemaRDD`.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#711 from ueshin/issues/SPARK-1778 and squashes the following commits:
33169df [Takuya UESHIN] Add 'limit' transformation to SchemaRDD.
I think we are hitting this issue in some perf tests: 6aed5288fd
Credit to @aarondav !
Author: Michael Armbrust <michael@databricks.com>
Closes#684 from marmbrus/upgradeParquet and squashes the following commits:
e10a619 [Michael Armbrust] Upgrade parquet library.
Author: witgo <witgo@qq.com>
Closes#688 from witgo/SPARK-1644 and squashes the following commits:
56ad6ac [witgo] review commit
87c03e4 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1644
6ffa7e4 [witgo] review commit
a597414 [witgo] The org.datanucleus:* should not be packaged into spark-assembly-*.jar
https://issues.apache.org/jira/browse/SPARK-1686
moved from original JIRA (by @markhamstra):
In deploy.master.Master, the completeRecovery method is the last thing to be called when a standalone Master is recovering from failure. It is responsible for resetting some state, relaunching drivers, and eventually resuming its scheduling duties.
There are currently four places in Master.scala where completeRecovery is called. Three of them are from within the actor's receive method, and aren't problems. The last starts from within receive when the ElectedLeader message is received, but the actual completeRecovery() call is made from the Akka scheduler. That means that it will execute on a different scheduler thread, and Master itself will end up running (i.e., schedule() ) from that Akka scheduler thread.
In this PR, I added a new master message TriggerSchedule to trigger the "local" call of schedule() in the scheduler thread
Author: CodingCat <zhunansjtu@gmail.com>
Closes#639 from CodingCat/SPARK-1686 and squashes the following commits:
81bb4ca [CodingCat] rename variable
69e0a2a [CodingCat] style fix
36a2ac0 [CodingCat] address Aaron's comments
ec9b7bb [CodingCat] address the comments
02b37ca [CodingCat] keep schedule() calling in the main thread
Looks like this change was accidentally committed here: 06b15baab2
but the change does not show up in the PR itself (#704).
Other than not intending to go in with that PR, this also broke the test JavaAPISuite.repartition.
Author: Aaron Davidson <aaron@databricks.com>
Closes#716 from aarondav/shufflerand and squashes the following commits:
b1cf70b [Aaron Davidson] SPARK-1770: Revert accidental(?) fix
Author: witgo <witgo@qq.com>
Closes#712 from witgo/building-with-maven and squashes the following commits:
215523b [witgo] fix building spark with maven documentation
Removing a block through the blockmanager gave a scary warning messages in the driver.
```
2014-05-08 20:16:19,172 WARN BlockManagerMasterActor: Got unknown message: true
2014-05-08 20:16:19,172 WARN BlockManagerMasterActor: Got unknown message: true
2014-05-08 20:16:19,172 WARN BlockManagerMasterActor: Got unknown message: true
```
This is because the [BlockManagerSlaveActor](https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala#L44) would send back an acknowledgement ("true"). But the BlockManagerMasterActor would have sent the RemoveBlock message as a send, not as ask(), so would reject the receiver "true" as a unknown message.
@pwendell
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#708 from tdas/bm-fix and squashes the following commits:
ed4ef15 [Tathagata Das] Converted bang to ask to avoid scary warning when a block is removed.
This was used in the past to have a cache of deserialized ShuffleMapTasks, but that's been removed, so there's no need for a lock. It slows down Spark when task descriptions are large, e.g. due to large lineage graphs or local variables.
Author: Sandeep <sandeep@techaddict.me>
Closes#707 from techaddict/SPARK-1775 and squashes the following commits:
18d8ebf [Sandeep] SPARK-1775: Unneeded lock in ShuffleMapTask.deserializeInfo This was used in the past to have a cache of deserialized ShuffleMapTasks, but that's been removed, so there's no need for a lock. It slows down Spark when task descriptions are large, e.g. due to large lineage graphs or local variables.
Gives a nicely formatted message to the user when `run-example` is run to
tell them to use `spark-submit`.
Author: Patrick Wendell <pwendell@gmail.com>
Closes#704 from pwendell/examples and squashes the following commits:
1996ee8 [Patrick Wendell] Feedback form Andrew
3eb7803 [Patrick Wendell] Suggestions from TD
2474668 [Patrick Wendell] SPARK-1565 (Addendum): Replace `run-example` with `spark-submit`.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#539 from vanzin/yarn-app-name and squashes the following commits:
7d1ca4f [Marcelo Vanzin] [SPARK-1631] Correctly set the Yarn app name when launching the AM.
Right now, SparkSubmit ignores the `--name` flag for both yarn-client and yarn-cluster. This is a bug.
In client mode, SparkSubmit treats `--name` as a [cluster config](https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala#L170) and does not propagate this to SparkContext.
In cluster mode, SparkSubmit passes this flag to `org.apache.spark.deploy.yarn.Client`, which only uses it for the [YARN ResourceManager](https://github.com/apache/spark/blob/master/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala#L80), but does not propagate this to SparkContext.
This PR ensures that `spark.app.name` is always set if SparkSubmit receives the `--name` flag, which is what the usage promises. This makes it possible for applications to start a SparkContext with an empty conf `val sc = new SparkContext(new SparkConf)`, and inherit the app name from SparkSubmit.
Tested both modes on a YARN cluster.
Author: Andrew Or <andrewor14@gmail.com>
Closes#699 from andrewor14/yarn-app-name and squashes the following commits:
98f6a79 [Andrew Or] Fix tests
dea932f [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-app-name
c86d9ca [Andrew Or] Respect SparkSubmit --name on YARN
This is needed because broadcast values are broken on pyspark on Mesos, it tries to import pyspark but can't, as the PYTHONPATH is not set due to changes in ff5be9a4https://issues.apache.org/jira/browse/SPARK-1725
Author: Bouke van der Bijl <boukevanderbijl@gmail.com>
Closes#651 from bouk/include-spark-config-in-mesos-executor and squashes the following commits:
b2f1295 [Bouke van der Bijl] Inline PYTHONPATH in spark-executor
eedbbcc [Bouke van der Bijl] Include the sbin/spark-config.sh in spark-executor
Fixed a small bug caused by the inconsistency of index/data array size and vector length.
Author: Funes <tianshaocun@gmail.com>
Author: funes <tianshaocun@gmail.com>
Closes#661 from funes/bugfix and squashes the following commits:
edb2b9d [funes] remove unused import
75dced3 [Funes] update test case
d129a66 [Funes] Add test for sparse breeze by vector builder
64e7198 [Funes] Copy data only when necessary
b85806c [Funes] Bug fix of sparse vector conversion
Getting the lossHistory from Breeze's API which already excludes the rejection steps in line search. Also, remove the miniBatch in LBFGS since those quasi-Newton methods approximate the inverse of Hessian. It doesn't make sense if the gradients are computed from a varying objective.
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#582 from dbtsai/dbtsai-lbfgs-bug and squashes the following commits:
9cc6cf9 [DB Tsai] Removed the miniBatch in LBFGS.
1ba6a33 [DB Tsai] Formatting the code.
d72c679 [DB Tsai] Using Breeze's states to get the loss.
Fixed the documentation for that `loadLibSVMData` is changed to `loadLibSVMFile`.
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#703 from dbtsai/dbtsai-docfix and squashes the following commits:
71dd508 [DB Tsai] loadLibSVMData is changed to loadLibSVMFile
XtY should be Xty.
Author: Evan Sparks <evan.sparks@gmail.com>
Closes#696 from etrain/patch-2 and squashes the following commits:
634cb8d [Evan Sparks] Fixing typo in als.py
It makes little sense to start a TaskContext that is interrupted. Indeed, I searched for all use cases of it and didn't find a single instance in which `interrupted` is true on construction.
This was inspired by reviewing #640, which adds an additional `@volatile var completed` that is similar. These are not the most urgent changes, but I wanted to push them out before I forget.
Author: Andrew Or <andrewor14@gmail.com>
Closes#675 from andrewor14/task-context and squashes the following commits:
9575e02 [Andrew Or] Add space
69455d1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into task-context
c471490 [Andrew Or] Oops, removed one flag too many. Adding it back.
85311f8 [Andrew Or] Move interrupted flag from TaskContext constructor
Commit for initial feedback, basically I am curious if we should prompt user for providing args esp. when its mandatory. And can we skip if they are not ?
Also few other things that did not work like
`bin/spark-submit examples/target/scala-2.10/spark-examples-1.0.0-SNAPSHOT-hadoop1.0.4.jar --class org.apache.spark.examples.SparkALS --arg 100 500 10 5 2`
Not all the args get passed properly, may be I have messed up something will try to sort it out hopefully.
Author: Prashant Sharma <prashant.s@imaginea.com>
Closes#552 from ScrapCodes/SPARK-1565/update-examples and squashes the following commits:
669dd23 [Prashant Sharma] Review comments
2727e70 [Prashant Sharma] SPARK-1565, update examples to be used with spark-submit script.
* Add native min/max (was using hive before).
* Handle nulls correctly in Avg and Sum.
Author: Michael Armbrust <michael@databricks.com>
Closes#683 from marmbrus/aggFixes and squashes the following commits:
64fe30b [Michael Armbrust] Improve SparkSQL Aggregates * Add native min/max (was using hive before). * Handle nulls correctly in Avg and Sum.
Using matrix multiply to compute XtX and XtY yields a 5-20x speedup depending on problem size.
For example - the following takes 19s locally after this change vs. 5m21s before the change. (16x speedup).
bin/pyspark examples/src/main/python/als.py local[8] 1000 1000 50 10 10
Author: Evan Sparks <evan.sparks@gmail.com>
Closes#687 from etrain/patch-1 and squashes the following commits:
e094dbc [Evan Sparks] Touching only diaganols on update.
d1ab9b6 [Evan Sparks] Use numpy directly for matrix multiply.
Add --implicitPrefs as an command-line option to the example app MovieLensALS under examples/
Author: Sandeep <sandeep@techaddict.me>
Closes#597 from techaddict/SPARK-1668 and squashes the following commits:
8b371dc [Sandeep] Second Pass on reviews by mengxr
eca9d37 [Sandeep] based on mengxr's suggestions
937e54c [Sandeep] Changes
5149d40 [Sandeep] Changes based on review
1dd7657 [Sandeep] use mean()
42444d7 [Sandeep] Based on Suggestions by mengxr
e3082fa [Sandeep] SPARK-1668: Add implicit preference as an option to examples/MovieLensALS Add --implicitPrefs as an command-line option to the example app MovieLensALS under examples/
@etrain and I came with a PR for arbitrarily deep decision trees at the cost of multiple passes over the data at deep tree levels.
To summarize:
1) We take a parameter that indicates the amount of memory users want to reserve for computation on each worker (and 2x that at the driver).
2) Using that information, we calculate two things - the maximum depth to which we train as usual (which is, implicitly, the maximum number of nodes we want to train in parallel), and the size of the groups we should use in the case where we exceed this depth.
cc: @atalwalkar, @hirakendu, @mengxr
Author: Manish Amde <manish9ue@gmail.com>
Author: manishamde <manish9ue@gmail.com>
Author: Evan Sparks <sparks@cs.berkeley.edu>
Closes#475 from manishamde/deep_tree and squashes the following commits:
968ca9d [Manish Amde] merged master
7fc9545 [Manish Amde] added docs
ce004a1 [Manish Amde] minor formatting
b27ad2c [Manish Amde] formatting
426bb28 [Manish Amde] programming guide blurb
8053fed [Manish Amde] more formatting
5eca9e4 [Manish Amde] grammar
4731cda [Manish Amde] formatting
5e82202 [Manish Amde] added documentation, fixed off by 1 error in max level calculation
cbd9f14 [Manish Amde] modified scala.math to math
dad9652 [Manish Amde] removed unused imports
e0426ee [Manish Amde] renamed parameter
718506b [Manish Amde] added unit test
1517155 [Manish Amde] updated documentation
9dbdabe [Manish Amde] merge from master
719d009 [Manish Amde] updating user documentation
fecf89a [manishamde] Merge pull request #6 from etrain/deep_tree
0287772 [Evan Sparks] Fixing scalastyle issue.
2f1e093 [Manish Amde] minor: added doc for maxMemory parameter
2f6072c [manishamde] Merge pull request #5 from etrain/deep_tree
abc5a23 [Evan Sparks] Parameterizing max memory.
50b143a [Manish Amde] adding support for very deep trees
use more faster way to construct an array
Author: baishuo(白硕) <vc_java@hotmail.com>
Closes#588 from baishuo/master and squashes the following commits:
45b95fb [baishuo(白硕)] Update GradientDescentSuite.scala
c03b61c [baishuo(白硕)] Update GradientDescentSuite.scala
b666d27 [baishuo(白硕)] Update GradientDescentSuite.scala
Make loading/saving labeled data easier for pyspark users.
Also changed type check in `SparseVector` to allow numpy integers.
Author: Xiangrui Meng <meng@databricks.com>
Closes#672 from mengxr/pyspark-mllib-util and squashes the following commits:
2943fa7 [Xiangrui Meng] format docs
d61668d [Xiangrui Meng] add loadLibSVMFile and saveAsLibSVMFile to pyspark
Pass the configs as java options since the executor needs to know before it registers whether to create the connection using authentication or not. We could see about passing only the authentication configs but for now I just had it pass them all.
I also updating it to use a list to construct the command to make it the same as ClientBase and avoid any issues with spaces.
Author: Thomas Graves <tgraves@apache.org>
Closes#649 from tgravescs/SPARK-1569 and squashes the following commits:
0178ab8 [Thomas Graves] add akka settings
22a8735 [Thomas Graves] Change to only path spark.auth* configs
8ccc1d4 [Thomas Graves] SPARK-1569 Spark on Yarn, authentication broken
When at least one of the following conditions is true, PySpark cannot be loaded:
1. PYTHONPATH is not set
2. PYTHONPATH does not contain the python directory (or jar, in the case of YARN)
3. The jar does not contain pyspark files (YARN)
4. The jar does not contain py4j files (YARN)
However, we currently throw the same random `java.io.EOFException` for all of the above cases, when trying to read from the python daemon's output. This message is super unhelpful.
This PR includes the python stderr and the PYTHONPATH in the exception propagated to the driver. Now, the exception message looks something like:
```
Error from python worker:
: No module named pyspark
PYTHONPATH was:
/path/to/spark/python:/path/to/some/jar
java.io.EOFException
<stack trace>
```
whereas before it was just
```
java.io.EOFException
<stack trace>
```
Author: Andrew Or <andrewor14@gmail.com>
Closes#603 from andrewor14/pyspark-exception and squashes the following commits:
10d65d3 [Andrew Or] Throwable -> Exception, worker -> daemon
862d1d7 [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-exception
a5ed798 [Andrew Or] Use block string and interpolation instead of var (minor)
cc09c45 [Andrew Or] Account for the fact that the python daemon may not have terminated yet
444f019 [Andrew Or] Use the new RedirectThread + include system PYTHONPATH
aab00ae [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-exception
0cc2402 [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-exception
783efe2 [Andrew Or] Make python daemon stderr indentation consistent
9524172 [Andrew Or] Avoid potential NPE / error stream contention + Move things around
29f9688 [Andrew Or] Add back original exception type
e92d36b [Andrew Or] Include python worker stderr in the exception propagated to the driver
7c69360 [Andrew Or] Merge branch 'master' of github.com:apache/spark into pyspark-exception
cdbc185 [Andrew Or] Fix python attribute not found exception when PYTHONPATH is not set
dcc0353 [Andrew Or] Check both python and system environment variables for PYTHONPATH
6c09c21 [Andrew Or] Validate PYTHONPATH and PySpark modules before starting python workers
Author: Andrew Ash <andrew@andrewash.com>
Closes#680 from ash211/patch-3 and squashes the following commits:
9ce3746 [Andrew Ash] Typo fix: fetchting -> fetching
