Hey Gerard,
This is a very good question!
*TL;DR: *The performance should be same, except in case of shuffle-based
operations where the number of reducers is not explicitly specified.
Let me answer in more detail by dividing the set of DStream operations into
three categories.
*1. Map-like operations (map, flatmap, filter, etc.) that does not involve
any shuffling of data:* Performance should virtually be the same in both
cases. Either ways, in each batch, the operations on the batch's RDD are
first set on the driver, and then the actions like on the RDD are executed.
There are very very minor differences in the two cases of early foreachRDD
and late foreachRDD (e.x, cleaning up for function closures, etc.) but
those should make almost not difference in the performance.
*2. Operations involving shuffle: *Here is there is a subtle difference in
both cases if the number of partitions is not specified. The default number
of partitions used when using dstream.reduceByKey() and than when using
dstream.foreachRDD(_.reduceByKey()) are different, and one needs to play
around with the number of reducers to see what performs better. But if the
number of reducers is explicitly specified and is the same both cases, then
the performance should be similar. Note that this difference in the default
numbers are not guaranteed to be like this, it could change in future
implementations.
*3. Aggregation-like operations (count, reduce): *Here there is another
subtle execution difference between
- dstream.count() which produces a DStream of single-element RDDs, the
element being the count, and
- dstream.foreachRDD(_.count()) which returns the count directly.
In the first case, some random worker node is chosen for the reduce, in
another the driver is chosen for the reduce. There should not be a
significant performance difference.
*4. Other operations* including window ops and stateful ops
(updateStateByKey), are obviously not part of the discussion as they cannot
be (easily) done through early foreachRDD.
Hope this helps!
TD
PS: Sorry for not noticing this question earlier.
On Wed, Oct 22, 2014 at 5:37 AM, Gerard Maas <gerard.m...@gmail.com> wrote:
> PS: Just to clarify my statement:
>
> >>Unlike the feared RDD operations on the driver, it's my understanding
> that these Dstream ops on the driver are merely creating an execution plan
> for each RDD.
>
> With "feared RDD operations on the driver" I meant to contrast an rdd
> action like rdd.collect that would pull all rdd data to the driver, with
> dstream.foreachRDD(rdd => rdd.op) for which documentation says 'it runs on
> the driver' yet, all that it looks to be running on the driver is the
> scheduling of 'op' on that rdd, just like it happens for all rdd other
> operations
> (thanks to Sean for the clarification)
>
> So, not to move focus away from the original question:
>
> In Spark Streaming, would it be better to do foreachRDD early in a
> pipeline or instead do as much Dstream transformations before going into
> the foreachRDD call?
>
> Between these two pieces of code, from a performance perspective, what
> would be preferred and why:
>
> - Early foreachRDD:
>
> dstream.foreachRDD(rdd =>
> val records = rdd.map(elem => record(elem))
> targets.foreach(target => records.filter{record =>
> isTarget(target,record)}.writeToCassandra(target,table))
> )
>
> - As most dstream transformations as possible before foreachRDD:
>
> val recordStream = dstream.map(elem => record(elem))
> targets.foreach{target => recordStream.filter(record =>
> isTarget(target,record)).foreachRDD(_.writeToCassandra(target,table))}
>
> ?
>
> kr, Gerard.
>
>
>
> On Wed, Oct 22, 2014 at 2:12 PM, Gerard Maas <gerard.m...@gmail.com>
> wrote:
>
>> Thanks Matt,
>>
>> Unlike the feared RDD operations on the driver, it's my understanding
>> that these Dstream ops on the driver are merely creating an execution plan
>> for each RDD.
>> My question still remains: Is it better to foreachRDD early in the
>> process or do as much Dstream transformations before going into the
>> foreachRDD call?
>>
>> Maybe this will require some empirical testing specific to each
>> implementation?
>>
>> -kr, Gerard.
>>
>>
>> On Mon, Oct 20, 2014 at 5:07 PM, Matt Narrell <matt.narr...@gmail.com>
>> wrote:
>>
>>> http://spark.apache.org/docs/latest/streaming-programming-guide.html
>>>
>>> foreachRDD is executed on the driver….
>>>
>>> mn
>>>
>>> On Oct 20, 2014, at 3:07 AM, Gerard Maas <gerard.m...@gmail.com> wrote:
>>>
>>> Pinging TD -- I'm sure you know :-)
>>>
>>> -kr, Gerard.
>>>
>>> On Fri, Oct 17, 2014 at 11:20 PM, Gerard Maas <gerard.m...@gmail.com>
>>> wrote:
>>>
>>>> Hi,
>>>>
>>>> We have been implementing several Spark Streaming jobs that are
>>>> basically processing data and inserting it into Cassandra, sorting it among
>>>> different keyspaces.
>>>>
>>>> We've been following the pattern:
>>>>
>>>> dstream.foreachRDD(rdd =>
>>>> val records = rdd.map(elem => record(elem))
>>>> targets.foreach(target => records.filter{record =>
>>>> isTarget(target,record)}.writeToCassandra(target,table))
>>>> )
>>>>
>>>> I've been wondering whether there would be a performance difference in
>>>> transforming the dstream instead of transforming the RDD within the dstream
>>>> with regards to how the transformations get scheduled.
>>>>
>>>> Instead of the RDD-centric computation, I could transform the dstream
>>>> until the last step, where I need an rdd to store.
>>>> For example, the previous transformation could be written as:
>>>>
>>>> val recordStream = dstream.map(elem => record(elem))
>>>> targets.foreach{target => recordStream.filter(record =>
>>>> isTarget(target,record)).foreachRDD(_.writeToCassandra(target,table))}
>>>>
>>>> Would be a difference in execution and/or performance? What would be
>>>> the preferred way to do this?
>>>>
>>>> Bonus question: Is there a better (more performant) way to sort the
>>>> data in different "buckets" instead of filtering the data collection times
>>>> the #buckets?
>>>>
>>>> thanks, Gerard.
>>>>
>>>>
>>>
>>>
>>
>
