This helps in case the exception happened while serializing a record to
be sent to Java, leaving the stream to Java in an inconsistent state
where PythonRDD won't be able to read the error.
We've used camel case in other Spark methods so it felt reasonable to
keep using it here and make the code match Scala/Java as much as
possible. Note that parameter names matter in Python because it allows
passing optional parameters by name.
- Added a Python wrapper for Naive Bayes
- Updated the Scala Naive Bayes to match the style of our other
algorithms better and in particular make it easier to call from Java
(added builder pattern, removed default value in train method)
- Updated Python MLlib functions to not require a SparkContext; we can
get that from the RDD the user gives
- Added a toString method in LabeledPoint
- Made the Python MLlib tests run as part of run-tests as well (before
they could only be run individually through each file)
Revert PR 381
This PR missed a bunch of test cases that require "spark.cleaner.ttl". I think it is what is causing test failures on Jenkins right now (though it's a bit hard to tell because the DNS for cs.berkeley.edu is down).
I'm submitting this to see if it fixes jeknins. I did try just patching various tests but it was taking a really long time because there are a bunch of them, so for now I'm just seeing if a revert works.
We clone hadoop key and values by default and reuse objects if asked to.
We try to clone for most common types of writables and we call WritableUtils.clone otherwise intention is to optimize, for example for NullWritable there is no need and for Long, int and String creating a new object with value set would be faster than doing copy on object hopefully.
There is another way to do this PR where we ask for both key and values whether to clone them or not, but could not think of a use case for it except either of them is actually a NullWritable for which I have already worked around. So thought that would be unnecessary.
Change clientId to random clientId
The client identifier should be unique across all clients connecting to the same server. A convenience method is provided to generate a random client id that should satisfy this criteria - generateClientId(). Returns a randomly generated client identifier based on the current user's login name and the system time. As the client identifier is used by the server to identify a client when it reconnects, the client must use the same identifier between connections if durable subscriptions are to be used.
API for automatic driver recovery for streaming programs and other bug fixes
1. Added Scala and Java API for automatically loading checkpoint if it exists in the provided checkpoint directory.
Scala API: `StreamingContext.getOrCreate(<checkpoint dir>, <function to create new StreamingContext>)` returns a StreamingContext
Java API: `JavaStreamingContext.getOrCreate(<checkpoint dir>, <factory obj of type JavaStreamingContextFactory>)`, return a JavaStreamingContext
See the RecoverableNetworkWordCount below as an example of how to use it.
2. Refactored streaming.Checkpoint*** code to fix bugs and make the DStream metadata checkpoint writing and reading more robust. Specifically, it fixes and improves the logic behind backing up and writing metadata checkpoint files. Also, it ensure that spark.driver.* and spark.hostPort is cleared from SparkConf before being written to checkpoint.
3. Fixed bug in cleaning up of checkpointed RDDs created by DStream. Specifically, this fix ensures that checkpointed RDD's files are not prematurely cleaned up, thus ensuring reliable recovery.
4. TimeStampedHashMap is upgraded to optionally update the timestamp on map.get(key). This allows clearing of data based on access time (i.e., clear records were last accessed before a threshold timestamp).
5. Added caching for file modification time in FileInputDStream using the updated TimeStampedHashMap. Without the caching, enumerating the mod times to find new files can take seconds if there are 1000s of files. This cache is automatically cleared.
This PR is not entirely final as I may make some minor additions - a Java examples, and adding StreamingContext.getOrCreate to unit test.
Edit: Java example to be added later, unit test added.
External Sorting for Aggregator and CoGroupedRDDs (Revisited)
(This pull request is re-opened from https://github.com/apache/incubator-spark/pull/303, which was closed because Jenkins / github was misbehaving)
The target issue for this patch is the out-of-memory exceptions triggered by aggregate operations such as reduce, groupBy, join, and cogroup. The existing AppendOnlyMap used by these operations resides purely in memory, and grows with the size of the input data until the amount of allocated memory is exceeded. Under large workloads, this problem is aggravated by the fact that OOM frequently occurs only after a very long (> 1 hour) map phase, in which case the entire job must be restarted.
The solution is to spill the contents of this map to disk once a certain memory threshold is exceeded. This functionality is provided by ExternalAppendOnlyMap, which additionally sorts this buffer before writing it out to disk, and later merges these buffers back in sorted order.
Under normal circumstances in which OOM is not triggered, ExternalAppendOnlyMap is simply a wrapper around AppendOnlyMap and incurs little overhead. Only when the memory usage is expected to exceed the given threshold does ExternalAppendOnlyMap spill to disk.
Aside from trivial formatting changes, use nulls instead of Options for
DiskMapIterator, and add documentation for spark.shuffle.externalSorting
and spark.shuffle.memoryFraction.
Also, set spark.shuffle.memoryFraction to 0.3, and spark.storage.memoryFraction = 0.6.
Yarn client addjar and misc fixes
Fix the addJar functionality in yarn-client mode, add support for the other options supported in yarn-standalone mode, set the application type on yarn in hadoop 2.X, add documentation, change heartbeat interval to be same code as the yarn-standalone so it doesn't take so long to get containers and exit.
Make DEBUG-level logs consummable.
Removes two things that caused issues with the debug logs:
(a) Internal polling in the DAGScheduler was polluting the logs.
(b) The Scala REPL logs were really noisy.
Removes two things that caused issues with the debug logs:
(a) Internal polling in the DAGScheduler was polluting the logs.
(b) The Scala REPL logs were really noisy.
Fix bug added when we changed AppDescription.maxCores to an Option
The Scala compiler warned about this -- we were comparing an Option against an integer now.
Set default logging to WARN for Spark streaming examples.
This programatically sets the log level to WARN by default for streaming
tests. If the user has already specified a log4j.properties file,
the user's file will take precedence over this default.