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https://issues.apache.org/jira/browse/SPARK-7848?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14557756#comment-14557756
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jay vyas commented on SPARK-7848:
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COPIED from the ASF Mailing list thread for convenience.....
{noformat}
Blocks are replicated immediately, before the driver launches any jobs using
them.
On Thu, May 21, 2015 at 2:05 AM, Hemant Bhanawat <[email protected]> wrote:
Honestly, given the length of my email, I didn't expect a reply. :-) Thanks for
reading and replying. However, I have a follow-up question:
I don't think if I understand the block replication completely. Are the blocks
replicated immediately after they are received by the receiver? Or are they
kept on the receiver node only and are moved only on shuffle? Has the
replication something to do with locality.wait?
Thanks,
Hemant
On Thu, May 21, 2015 at 2:21 AM, Tathagata Das <[email protected]> wrote:
Correcting the ones that are incorrect or incomplete. BUT this is good list for
things to remember about Spark Streaming.
On Wed, May 20, 2015 at 3:40 AM, Hemant Bhanawat <[email protected]> wrote:
Hi,
I have compiled a list (from online sources) of knobs/design considerations
that need to be taken care of by applications running on spark streaming. Is my
understanding correct? Any other important design consideration that I should
take care of?
A DStream is associated with a single receiver. For attaining read parallelism
multiple receivers i.e. multiple DStreams need to be created.
A receiver is run within an executor. It occupies one core. Ensure that there
are enough cores for processing after receiver slots are booked i.e.
spark.cores.max should take the receiver slots into account.
The receivers are allocated to executors in a round robin fashion.
When data is received from a stream source, receiver creates blocks of data. A
new block of data is generated every blockInterval milliseconds. N blocks of
data are created during the batchInterval where N = batchInterval/blockInterval.
These blocks are distributed by the BlockManager of the current executor to the
block managers of other executors. After that, the Network Input Tracker
running on the driver is informed about the block locations for further
processing.
A RDD is created on the driver for the blocks created during the batchInterval.
The blocks generated during the batchInterval are partitions of the RDD. Each
partition is a task in spark. blockInterval== batchinterval would mean that a
single partition is created and probably it is processed locally.
The map tasks on the blocks are processed in the executors (one that received
the block, and another where the block was replicated) that has the blocks
irrespective of block interval, unless non-local scheduling kicks in (as you
observed next).
Having bigger blockinterval means bigger blocks. A high value of
spark.locality.wait increases the chance of processing a block on the local
node. A balance needs to be found out between these two parameters to ensure
that the bigger blocks are processed locally.
Instead of relying on batchInterval and blockInterval, you can define the
number of partitions by calling dstream.repartition(n). This reshuffles the
data in RDD randomly to create n number of partitions.
Yes, for greater parallelism. Though comes at the cost of a shuffle.
An RDD's processing is scheduled by driver's jobscheduler as a job. At a given
point of time only one job is active. So, if one job is executing the other
jobs are queued.
If you have two dstreams there will be two RDDs formed and there will be two
jobs created which will be scheduled one after the another.
To avoid this, you can union two dstreams. This will ensure that a single
unionRDD is formed for the two RDDs of the dstreams. This unionRDD is then
considered as a single job. However the partitioning of the RDDs is not
impacted.
To further clarify, the jobs depend on the number of output operations (print,
foreachRDD, saveAsXFiles) and the number of RDD actions in those output
operations.
dstream1.union(dstream2).foreachRDD { rdd => rdd.count() } // one Spark job
per batch
dstream1.union(dstream2).foreachRDD { rdd => { rdd.count() ; rdd.count() } }
// TWO Spark jobs per batch
dstream1.foreachRDD { rdd => rdd.count } ; dstream2.foreachRDD { rdd =>
rdd.count } // TWO Spark jobs per batch
If the batch processing time is more than batchinterval then obviously the
receiver's memory will start filling up and will end up in throwing exceptions
(most probably BlockNotFoundException). Currently there is no way to pause the
receiver.
You can limit the rate of receiver using SparkConf config
spark.streaming.receiver.maxRate
For being fully fault tolerant, spark streaming needs to enable checkpointing.
Checkpointing increases the batch processing time.
Incomplete. There are two types of checkpointing - data and metadata. Only data
checkpointing, needed by only some operations, increase batch processing time.
Read -
http://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing
Furthemore, with checkpoint you can recover computation, but you may loose some
data (that was received but not processed before driver failed) for some
sources. Enabling write ahead logs and reliable source + receiver, allow zero
data loss. Read - WAL in
http://spark.apache.org/docs/latest/streaming-programming-guide.html#fault-tolerance-semantics
The frequency of metadata checkpoint cleaning can be controlled using
spark.cleaner.ttl. But, data checkpoint cleaning happens automatically when the
RDDs in the checkpoint are no more required.
Incorrect. metadata checkpointing or (DStream checkpointing) is self cleaning.
What are you are probably talking about is cleaning of shuffle and other data
in the executors. That can be cleaned using spark.cleaner.ttl, but it is a
brute force hammer and can clean more stuff than you intend. Its not
recommended to use that. Rather Spark has GC-triggered cleaning of all that,
when RDD objects are GCed, their shuffle data, cached data, etc are also
cleaned in the executors. You can trigger GC based cleaning by called
System.gc() in the driver periodically.
Thanks,
Hemant
{noformat}
> Update SparkStreaming docs to include "knobs"
> ----------------------------------------------
>
> Key: SPARK-7848
> URL: https://issues.apache.org/jira/browse/SPARK-7848
> Project: Spark
> Issue Type: Documentation
> Components: Streaming
> Reporter: jay vyas
>
> A recent email on the maligning list detailed a bunch of great "knobs" to
> remember for spark streaming.
> Lets integrate this into the docs where appropriate.
> I'll paste the raw text in a comment field below....
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