| Commit message (Collapse) | Author | Age | Files | Lines |
| |
|
| |
|
|
|
|
| |
build/dep-resolution atleast a bit faster.
|
| |
|
|\ |
|
| |\ |
|
| | | |
|
| | | |
|
| | | |
|
|/ / |
|
|/ |
|
|\ |
|
| |\
| | |
| | |
| | |
| | |
| | |
| | |
| | | |
add http timeout for httpbroadcast
While pulling task bytecode from HttpBroadcast server, there's no timeout value set. This may cause spark executor code hang and other task in the same executor process wait for the lock. I have encountered the issue in my cluster. Here's the stacktrace I captured : https://gist.github.com/haitaoyao/7655830
So add a time out value to ensure the task fail fast.
|
| | | |
|
| | | |
|
| | | |
|
|\| |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | | |
Conflicts:
core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala
core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithContextRDD.scala
core/src/main/scala/org/apache/spark/rdd/RDD.scala
python/pyspark/rdd.py
|
| |\ \
| | | |
| | | |
| | | |
| | | |
| | | |
| | | |
| | | |
| | | |
| | | |
| | | |
| | | |
| | | |
| | | | |
Custom Serializers for PySpark
This pull request adds support for custom serializers to PySpark. For now, all Python-transformed (or parallelize()d RDDs) are serialized with the same serializer that's specified when creating SparkContext.
For now, PySpark includes `PickleSerDe` and `MarshalSerDe` classes for using Python's `pickle` and `marshal` serializers. It's pretty easy to add support for other serializers, although I still need to add instructions on this.
A few notable changes:
- The Scala `PythonRDD` class no longer manipulates Pickled objects; data from `textFile` is written to Python as MUTF-8 strings. The Python code performs the appropriate bookkeeping to track which deserializer should be used when reading an underlying JavaRDD. This mechanism could also be used to support other data exchange formats, such as MsgPack.
- Several magic numbers were refactored into constants.
- Batching is implemented by wrapping / decorating an unbatched SerDe.
|
| | | | |
|
| | | | |
|
| | | | |
|
| | | |
| | | |
| | | |
| | | |
| | | |
| | | |
| | | |
| | | |
| | | | |
For now, this only adds MarshalSerializer, but it lays the groundwork
for other supporting custom serializers. Many of these mechanisms
can also be used to support deserialization of different data formats
sent by Java, such as data encoded by MsgPack.
This also fixes a bug in SparkContext.union().
|
| | | |
| | | |
| | | |
| | | |
| | | |
| | | | |
If we support custom serializers, the Python
worker will know what type of input to expect,
so we won't need to wrap Tuple2 and Strings into
pickled tuples and strings.
|
| | | |
| | | |
| | | |
| | | |
| | | |
| | | | |
Write the length of the accumulators section up-front rather
than terminating it with a negative length. I find this
easier to read.
|
| |\ \ \
| | | | |
| | | | |
| | | | |
| | | | |
| | | | | |
Log a warning if a task's serialized size is very big
As per Reynold's instructions, we now create a warning level log entry if a task's serialized size is too big. "Too big" is currently defined as 100kb. This warning message is generated at most once for each stage.
|
| | | | | |
|
| |\ \ \ \
| | | | | |
| | | | | |
| | | | | | |
[SPARK-963] Fixed races in JobLoggerSuite
|
| |/ / / /
| | | | |
| | | | |
| | | | | |
jobLogger state
|
| |\ \ \ \
| | |_|_|/
| |/| | |
| | | | | |
Improve docs for shuffle instrumentation
|
| | | | | |
|
| |\ \ \ \
| | |_|/ /
| |/| | |
| | | | |
| | | | |
| | | | | |
Add histogram functionality to DoubleRDDFunctions
This pull request add histogram functionality to the DoubleRDDFunctions.
|
| | | | | |
|
| | | | | |
|
| | | | | |
|
| | | | | |
|
| | | | | |
|
| | | | | |
|
| | | | | |
|
| | | | | |
|
| |\ \ \ \
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | | |
OpenHashSet fixes
Incorporated ideas from pull request #200.
- Use Murmur Hash 3 finalization step to scramble the bits of HashCode
instead of the simpler version in java.util.HashMap; the latter one
had trouble with ranges of consecutive integers. Murmur Hash 3 is used
by fastutil.
- Don't check keys for equality when re-inserting due to growing the
table; the keys will already be unique.
- Remember the grow threshold instead of recomputing it on each insert
Also added unit tests for size estimation for specialized hash sets and maps.
|
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | | |
- Use Murmur Hash 3 finalization step to scramble the bits of HashCode
instead of the simpler version in java.util.HashMap; the latter one
had trouble with ranges of consecutive integers. Murmur Hash 3 is used
by fastutil.
- Don't check keys for equality when re-inserting due to growing the
table; the keys will already be unique
- Remember the grow threshold instead of recomputing it on each insert
|
| | | |/ /
| | |/| | |
|
| |\ \ \ \
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | | |
Update tuning.md
Clarify when serializer is used based on recent user@ mailing list discussion.
|
| | | | | |
| | | | | |
| | | | | | |
Clarify when serializer is used based on recent user@ mailing list discussion.
|
| |\ \ \ \ \
| | |/ / / /
| |/| | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | | |
Use the proper partition index in mapPartitionsWIthIndex
mapPartitionsWithIndex uses TaskContext.partitionId as the partition index. TaskContext.partitionId used to be identical to the partition index in a RDD. However, pull request #186 introduced a scenario (with partition pruning) that the two can be different. This pull request uses the right partition index in all mapPartitionsWithIndex related calls.
Also removed the extra MapPartitionsWIthContextRDD and put all the mapPartitions related functionality in MapPartitionsRDD.
|
| | | | | |
| | | | | |
| | | | | |
| | | | | | |
Also changed the semantics of the index parameter in mapPartitionsWithIndex from the partition index of the output partition to the partition index in the current RDD.
|
| |\ \ \ \ \
| | |_|/ / /
| |/| | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | | |
For SPARK-527, Support spark-shell when running on YARN
sync to trunk and resubmit here
In current YARN mode approaching, the application is run in the Application Master as a user program thus the whole spark context is on remote.
This approaching won't support application that involve local interaction and need to be run on where it is launched.
So In this pull request I have a YarnClientClusterScheduler and backend added.
With this scheduler, the user application is launched locally,While the executor will be launched by YARN on remote nodes with a thin AM which only launch the executor and monitor the Driver Actor status, so that when client app is done, it can finish the YARN Application as well.
This enables spark-shell to run upon YARN.
This also enable other Spark applications to have the spark context to run locally with a master-url "yarn-client". Thus e.g. SparkPi could have the result output locally on console instead of output in the log of the remote machine where AM is running on.
Docs also updated to show how to use this yarn-client mode.
|
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | | |
With this scheduler, the user application is launched locally,
While the executor will be launched by YARN on remote nodes.
This enables spark-shell to run upon YARN.
|
| | | | | | |
|
| | | | | | |
|