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 <hemant9...@gmail.com>
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
>
