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Posted to user@spark.apache.org by Brandon White <bw...@gmail.com> on 2015/07/29 00:06:07 UTC
Has anybody ever tried running Spark Streaming on 500 text streams?
val ssc = new StreamingContext(sc, Minutes(10))
//500 textFile streams watching S3 directories
val streams = streamPaths.par.map { path =>
ssc.textFileStream(path)
}
streams.par.foreach { stream =>
stream.foreachRDD { rdd =>
//do something
}
}
ssc.start()
Would something like this scale? What would be the limiting factor to
performance? What is the best way to parallelize this? Any other ideas on
design?
Re: Has anybody ever tried running Spark Streaming on 500 text streams?
Posted by Ashwin Giridharan <as...@gmail.com>.
@Das, Is there anyway to identify a kafka topic when we have unified
stream? As of now, for each topic I create dedicated DStream and use
foreachRDD on each of these Streams. If I have say 100 kafka topics, then
how can I use unified stream and still take topic specific actions inside
foreachRDD ?
On Tue, Jul 28, 2015 at 6:42 PM, Tathagata Das <td...@databricks.com> wrote:
> I dont think any one has really run 500 text streams.
> And parSequences do nothing out there, you are only parallelizing the
> setup code which does not really compute anything. Also it setsup 500
> foreachRDD operations that will get executed in each batch sequentially, so
> does not make sense. The write way to parallelize this is union all the
> streams.
>
> val streams = streamPaths.map { path =>
> ssc.textFileStream(path)
> }
> val unionedStream = streamingContext.union(streams)
> unionedStream.foreachRDD { rdd =>
> // do something
> }
>
> Then there is only one foreachRDD executed in every batch that will
> process in parallel all the new files in each batch interval.
> TD
>
>
> On Tue, Jul 28, 2015 at 3:06 PM, Brandon White <bw...@gmail.com>
> wrote:
>
>> val ssc = new StreamingContext(sc, Minutes(10))
>>
>> //500 textFile streams watching S3 directories
>> val streams = streamPaths.par.map { path =>
>> ssc.textFileStream(path)
>> }
>>
>> streams.par.foreach { stream =>
>> stream.foreachRDD { rdd =>
>> //do something
>> }
>> }
>>
>> ssc.start()
>>
>> Would something like this scale? What would be the limiting factor to
>> performance? What is the best way to parallelize this? Any other ideas on
>> design?
>>
>
>
--
Thanks & Regards,
Ashwin Giridharan
Re: Has anybody ever tried running Spark Streaming on 500 text streams?
Posted by Tathagata Das <td...@databricks.com>.
@Brandon, the file streams do not use receivers, so the bottleneck is not
about executors per se. But there could be couple of bottlenecks
1. Every batch interval, the 500 dstreams are going to get directory
listing from 500 directories, SEQUENTIALLY. So preparing the batch's RDDs
and jobs can take a time. So your batch interval cant be small, may have to
be 10s of seconds. Which is probably fine for your application, otherwise
you would not be using files in the first place.
2. Processing new files from 500 directories may take significant
computation power. Just make sure you get large enough cluster.
On Fri, Jul 31, 2015 at 2:40 PM, Ashwin Giridharan <as...@gmail.com>
wrote:
> Thanks a lot @Das @Cody. I moved from receiver based to direct stream and
> I can get the topics from the offset!!
>
> On Fri, Jul 31, 2015 at 4:41 PM, Brandon White <bw...@gmail.com>
> wrote:
>
>> Tathagata,
>>
>> Could the bottleneck possibility be the number of executor nodes in our
>> cluster? Since we are creating 500 Dstreams based off 500 textfile
>> directories, do we need at least 500 executors / nodes to be receivers for
>> each one of the streams?
>>
>> On Tue, Jul 28, 2015 at 6:09 PM, Tathagata Das <td...@databricks.com>
>> wrote:
>>
>>> @Ashwin: You could append the topic in the data.
>>>
>>> val kafkaStreams = topics.map { topic =>
>>> KafkaUtils.createDirectStream(....topic...).map { x => (x, topic) }
>>> }
>>> val unionedStream = context.union(kafkaStreams)
>>>
>>>
>>> @Brandon:
>>> I dont recommend it, but you could do something crazy like use the
>>> foreachRDD to farm out the jobs to a threadpool, but the final foreachRDD
>>> waits for all the jobs to complete.
>>>
>>> manyDStreams.foreach { dstream =>
>>> dstream1.foreachRDD { rdd =>
>>> // Add runnable that runs the job on RDD to threadpool
>>> // This does not wait for the job to finish
>>> }
>>> }
>>>
>>> anyOfTheManyDStreams.foreachRDD { _ =>
>>> // wait for all the current batch's jobs in the threadpool to
>>> complete.
>>>
>>> }
>>>
>>>
>>> This would run all the Spark jobs in the batch in parallel in thread
>>> pool, but it would also make sure all the jobs finish before the batch is
>>> marked as completed.
>>>
>>> On Tue, Jul 28, 2015 at 4:05 PM, Brandon White <bw...@gmail.com>
>>> wrote:
>>>
>>>> Thank you Tathagata. My main use case for the 500 streams is to append
>>>> new elements into their corresponding Spark SQL tables. Every stream is
>>>> mapped to a table so I'd like to use the streams to appended the new rdds
>>>> to the table. If I union all the streams, appending new elements becomes a
>>>> nightmare. So there is no other way to parallelize something like the
>>>> following? Will this still run sequence or timeout?
>>>>
>>>> //500 streams
>>>> streams.foreach { stream =>
>>>> stream.foreachRDD { rdd =>
>>>> val df = sqlContext.jsonRDD(rdd)
>>>> df.saveAsTable(streamTuple._1, SaveMode.Append)
>>>>
>>>> }
>>>> }
>>>>
>>>> On Tue, Jul 28, 2015 at 3:42 PM, Tathagata Das <td...@databricks.com>
>>>> wrote:
>>>>
>>>>> I dont think any one has really run 500 text streams.
>>>>> And parSequences do nothing out there, you are only parallelizing the
>>>>> setup code which does not really compute anything. Also it setsup 500
>>>>> foreachRDD operations that will get executed in each batch sequentially, so
>>>>> does not make sense. The write way to parallelize this is union all the
>>>>> streams.
>>>>>
>>>>> val streams = streamPaths.map { path =>
>>>>> ssc.textFileStream(path)
>>>>> }
>>>>> val unionedStream = streamingContext.union(streams)
>>>>> unionedStream.foreachRDD { rdd =>
>>>>> // do something
>>>>> }
>>>>>
>>>>> Then there is only one foreachRDD executed in every batch that will
>>>>> process in parallel all the new files in each batch interval.
>>>>> TD
>>>>>
>>>>>
>>>>> On Tue, Jul 28, 2015 at 3:06 PM, Brandon White <
>>>>> bwwinthehouse@gmail.com> wrote:
>>>>>
>>>>>> val ssc = new StreamingContext(sc, Minutes(10))
>>>>>>
>>>>>> //500 textFile streams watching S3 directories
>>>>>> val streams = streamPaths.par.map { path =>
>>>>>> ssc.textFileStream(path)
>>>>>> }
>>>>>>
>>>>>> streams.par.foreach { stream =>
>>>>>> stream.foreachRDD { rdd =>
>>>>>> //do something
>>>>>> }
>>>>>> }
>>>>>>
>>>>>> ssc.start()
>>>>>>
>>>>>> Would something like this scale? What would be the limiting factor to
>>>>>> performance? What is the best way to parallelize this? Any other ideas on
>>>>>> design?
>>>>>>
>>>>>
>>>>>
>>>>
>>>
>>
>
>
> --
> Thanks & Regards,
> Ashwin Giridharan
>
Re: Has anybody ever tried running Spark Streaming on 500 text streams?
Posted by Ashwin Giridharan <as...@gmail.com>.
Thanks a lot @Das @Cody. I moved from receiver based to direct stream and I
can get the topics from the offset!!
On Fri, Jul 31, 2015 at 4:41 PM, Brandon White <bw...@gmail.com>
wrote:
> Tathagata,
>
> Could the bottleneck possibility be the number of executor nodes in our
> cluster? Since we are creating 500 Dstreams based off 500 textfile
> directories, do we need at least 500 executors / nodes to be receivers for
> each one of the streams?
>
> On Tue, Jul 28, 2015 at 6:09 PM, Tathagata Das <td...@databricks.com>
> wrote:
>
>> @Ashwin: You could append the topic in the data.
>>
>> val kafkaStreams = topics.map { topic =>
>> KafkaUtils.createDirectStream(....topic...).map { x => (x, topic) }
>> }
>> val unionedStream = context.union(kafkaStreams)
>>
>>
>> @Brandon:
>> I dont recommend it, but you could do something crazy like use the
>> foreachRDD to farm out the jobs to a threadpool, but the final foreachRDD
>> waits for all the jobs to complete.
>>
>> manyDStreams.foreach { dstream =>
>> dstream1.foreachRDD { rdd =>
>> // Add runnable that runs the job on RDD to threadpool
>> // This does not wait for the job to finish
>> }
>> }
>>
>> anyOfTheManyDStreams.foreachRDD { _ =>
>> // wait for all the current batch's jobs in the threadpool to
>> complete.
>>
>> }
>>
>>
>> This would run all the Spark jobs in the batch in parallel in thread
>> pool, but it would also make sure all the jobs finish before the batch is
>> marked as completed.
>>
>> On Tue, Jul 28, 2015 at 4:05 PM, Brandon White <bw...@gmail.com>
>> wrote:
>>
>>> Thank you Tathagata. My main use case for the 500 streams is to append
>>> new elements into their corresponding Spark SQL tables. Every stream is
>>> mapped to a table so I'd like to use the streams to appended the new rdds
>>> to the table. If I union all the streams, appending new elements becomes a
>>> nightmare. So there is no other way to parallelize something like the
>>> following? Will this still run sequence or timeout?
>>>
>>> //500 streams
>>> streams.foreach { stream =>
>>> stream.foreachRDD { rdd =>
>>> val df = sqlContext.jsonRDD(rdd)
>>> df.saveAsTable(streamTuple._1, SaveMode.Append)
>>>
>>> }
>>> }
>>>
>>> On Tue, Jul 28, 2015 at 3:42 PM, Tathagata Das <td...@databricks.com>
>>> wrote:
>>>
>>>> I dont think any one has really run 500 text streams.
>>>> And parSequences do nothing out there, you are only parallelizing the
>>>> setup code which does not really compute anything. Also it setsup 500
>>>> foreachRDD operations that will get executed in each batch sequentially, so
>>>> does not make sense. The write way to parallelize this is union all the
>>>> streams.
>>>>
>>>> val streams = streamPaths.map { path =>
>>>> ssc.textFileStream(path)
>>>> }
>>>> val unionedStream = streamingContext.union(streams)
>>>> unionedStream.foreachRDD { rdd =>
>>>> // do something
>>>> }
>>>>
>>>> Then there is only one foreachRDD executed in every batch that will
>>>> process in parallel all the new files in each batch interval.
>>>> TD
>>>>
>>>>
>>>> On Tue, Jul 28, 2015 at 3:06 PM, Brandon White <bwwinthehouse@gmail.com
>>>> > wrote:
>>>>
>>>>> val ssc = new StreamingContext(sc, Minutes(10))
>>>>>
>>>>> //500 textFile streams watching S3 directories
>>>>> val streams = streamPaths.par.map { path =>
>>>>> ssc.textFileStream(path)
>>>>> }
>>>>>
>>>>> streams.par.foreach { stream =>
>>>>> stream.foreachRDD { rdd =>
>>>>> //do something
>>>>> }
>>>>> }
>>>>>
>>>>> ssc.start()
>>>>>
>>>>> Would something like this scale? What would be the limiting factor to
>>>>> performance? What is the best way to parallelize this? Any other ideas on
>>>>> design?
>>>>>
>>>>
>>>>
>>>
>>
>
--
Thanks & Regards,
Ashwin Giridharan
Re: Has anybody ever tried running Spark Streaming on 500 text streams?
Posted by Brandon White <bw...@gmail.com>.
Tathagata,
Could the bottleneck possibility be the number of executor nodes in our
cluster? Since we are creating 500 Dstreams based off 500 textfile
directories, do we need at least 500 executors / nodes to be receivers for
each one of the streams?
On Tue, Jul 28, 2015 at 6:09 PM, Tathagata Das <td...@databricks.com> wrote:
> @Ashwin: You could append the topic in the data.
>
> val kafkaStreams = topics.map { topic =>
> KafkaUtils.createDirectStream(....topic...).map { x => (x, topic) }
> }
> val unionedStream = context.union(kafkaStreams)
>
>
> @Brandon:
> I dont recommend it, but you could do something crazy like use the
> foreachRDD to farm out the jobs to a threadpool, but the final foreachRDD
> waits for all the jobs to complete.
>
> manyDStreams.foreach { dstream =>
> dstream1.foreachRDD { rdd =>
> // Add runnable that runs the job on RDD to threadpool
> // This does not wait for the job to finish
> }
> }
>
> anyOfTheManyDStreams.foreachRDD { _ =>
> // wait for all the current batch's jobs in the threadpool to
> complete.
>
> }
>
>
> This would run all the Spark jobs in the batch in parallel in thread pool,
> but it would also make sure all the jobs finish before the batch is marked
> as completed.
>
> On Tue, Jul 28, 2015 at 4:05 PM, Brandon White <bw...@gmail.com>
> wrote:
>
>> Thank you Tathagata. My main use case for the 500 streams is to append
>> new elements into their corresponding Spark SQL tables. Every stream is
>> mapped to a table so I'd like to use the streams to appended the new rdds
>> to the table. If I union all the streams, appending new elements becomes a
>> nightmare. So there is no other way to parallelize something like the
>> following? Will this still run sequence or timeout?
>>
>> //500 streams
>> streams.foreach { stream =>
>> stream.foreachRDD { rdd =>
>> val df = sqlContext.jsonRDD(rdd)
>> df.saveAsTable(streamTuple._1, SaveMode.Append)
>>
>> }
>> }
>>
>> On Tue, Jul 28, 2015 at 3:42 PM, Tathagata Das <td...@databricks.com>
>> wrote:
>>
>>> I dont think any one has really run 500 text streams.
>>> And parSequences do nothing out there, you are only parallelizing the
>>> setup code which does not really compute anything. Also it setsup 500
>>> foreachRDD operations that will get executed in each batch sequentially, so
>>> does not make sense. The write way to parallelize this is union all the
>>> streams.
>>>
>>> val streams = streamPaths.map { path =>
>>> ssc.textFileStream(path)
>>> }
>>> val unionedStream = streamingContext.union(streams)
>>> unionedStream.foreachRDD { rdd =>
>>> // do something
>>> }
>>>
>>> Then there is only one foreachRDD executed in every batch that will
>>> process in parallel all the new files in each batch interval.
>>> TD
>>>
>>>
>>> On Tue, Jul 28, 2015 at 3:06 PM, Brandon White <bw...@gmail.com>
>>> wrote:
>>>
>>>> val ssc = new StreamingContext(sc, Minutes(10))
>>>>
>>>> //500 textFile streams watching S3 directories
>>>> val streams = streamPaths.par.map { path =>
>>>> ssc.textFileStream(path)
>>>> }
>>>>
>>>> streams.par.foreach { stream =>
>>>> stream.foreachRDD { rdd =>
>>>> //do something
>>>> }
>>>> }
>>>>
>>>> ssc.start()
>>>>
>>>> Would something like this scale? What would be the limiting factor to
>>>> performance? What is the best way to parallelize this? Any other ideas on
>>>> design?
>>>>
>>>
>>>
>>
>
Re: Has anybody ever tried running Spark Streaming on 500 text streams?
Posted by Cody Koeninger <co...@koeninger.org>.
@Ashwin you don't need to append the topic to your data if you're using the
direct stream. You can get the topic from the offset range, see
http://spark.apache.org/docs/latest/streaming-kafka-integration.html
(search for "offsetRange")
If you're using the receiver based stream, you'll need to follow TD's
suggestion
On Tue, Jul 28, 2015 at 8:09 PM, Tathagata Das <td...@databricks.com> wrote:
> @Ashwin: You could append the topic in the data.
>
> val kafkaStreams = topics.map { topic =>
> KafkaUtils.createDirectStream(....topic...).map { x => (x, topic) }
> }
> val unionedStream = context.union(kafkaStreams)
>
>
> @Brandon:
> I dont recommend it, but you could do something crazy like use the
> foreachRDD to farm out the jobs to a threadpool, but the final foreachRDD
> waits for all the jobs to complete.
>
> manyDStreams.foreach { dstream =>
> dstream1.foreachRDD { rdd =>
> // Add runnable that runs the job on RDD to threadpool
> // This does not wait for the job to finish
> }
> }
>
> anyOfTheManyDStreams.foreachRDD { _ =>
> // wait for all the current batch's jobs in the threadpool to
> complete.
>
> }
>
>
> This would run all the Spark jobs in the batch in parallel in thread pool,
> but it would also make sure all the jobs finish before the batch is marked
> as completed.
>
> On Tue, Jul 28, 2015 at 4:05 PM, Brandon White <bw...@gmail.com>
> wrote:
>
>> Thank you Tathagata. My main use case for the 500 streams is to append
>> new elements into their corresponding Spark SQL tables. Every stream is
>> mapped to a table so I'd like to use the streams to appended the new rdds
>> to the table. If I union all the streams, appending new elements becomes a
>> nightmare. So there is no other way to parallelize something like the
>> following? Will this still run sequence or timeout?
>>
>> //500 streams
>> streams.foreach { stream =>
>> stream.foreachRDD { rdd =>
>> val df = sqlContext.jsonRDD(rdd)
>> df.saveAsTable(streamTuple._1, SaveMode.Append)
>>
>> }
>> }
>>
>> On Tue, Jul 28, 2015 at 3:42 PM, Tathagata Das <td...@databricks.com>
>> wrote:
>>
>>> I dont think any one has really run 500 text streams.
>>> And parSequences do nothing out there, you are only parallelizing the
>>> setup code which does not really compute anything. Also it setsup 500
>>> foreachRDD operations that will get executed in each batch sequentially, so
>>> does not make sense. The write way to parallelize this is union all the
>>> streams.
>>>
>>> val streams = streamPaths.map { path =>
>>> ssc.textFileStream(path)
>>> }
>>> val unionedStream = streamingContext.union(streams)
>>> unionedStream.foreachRDD { rdd =>
>>> // do something
>>> }
>>>
>>> Then there is only one foreachRDD executed in every batch that will
>>> process in parallel all the new files in each batch interval.
>>> TD
>>>
>>>
>>> On Tue, Jul 28, 2015 at 3:06 PM, Brandon White <bw...@gmail.com>
>>> wrote:
>>>
>>>> val ssc = new StreamingContext(sc, Minutes(10))
>>>>
>>>> //500 textFile streams watching S3 directories
>>>> val streams = streamPaths.par.map { path =>
>>>> ssc.textFileStream(path)
>>>> }
>>>>
>>>> streams.par.foreach { stream =>
>>>> stream.foreachRDD { rdd =>
>>>> //do something
>>>> }
>>>> }
>>>>
>>>> ssc.start()
>>>>
>>>> Would something like this scale? What would be the limiting factor to
>>>> performance? What is the best way to parallelize this? Any other ideas on
>>>> design?
>>>>
>>>
>>>
>>
>
Re: Has anybody ever tried running Spark Streaming on 500 text streams?
Posted by Tathagata Das <td...@databricks.com>.
@Ashwin: You could append the topic in the data.
val kafkaStreams = topics.map { topic =>
KafkaUtils.createDirectStream(....topic...).map { x => (x, topic) }
}
val unionedStream = context.union(kafkaStreams)
@Brandon:
I dont recommend it, but you could do something crazy like use the
foreachRDD to farm out the jobs to a threadpool, but the final foreachRDD
waits for all the jobs to complete.
manyDStreams.foreach { dstream =>
dstream1.foreachRDD { rdd =>
// Add runnable that runs the job on RDD to threadpool
// This does not wait for the job to finish
}
}
anyOfTheManyDStreams.foreachRDD { _ =>
// wait for all the current batch's jobs in the threadpool to complete.
}
This would run all the Spark jobs in the batch in parallel in thread pool,
but it would also make sure all the jobs finish before the batch is marked
as completed.
On Tue, Jul 28, 2015 at 4:05 PM, Brandon White <bw...@gmail.com>
wrote:
> Thank you Tathagata. My main use case for the 500 streams is to append new
> elements into their corresponding Spark SQL tables. Every stream is mapped
> to a table so I'd like to use the streams to appended the new rdds to the
> table. If I union all the streams, appending new elements becomes a
> nightmare. So there is no other way to parallelize something like the
> following? Will this still run sequence or timeout?
>
> //500 streams
> streams.foreach { stream =>
> stream.foreachRDD { rdd =>
> val df = sqlContext.jsonRDD(rdd)
> df.saveAsTable(streamTuple._1, SaveMode.Append)
>
> }
> }
>
> On Tue, Jul 28, 2015 at 3:42 PM, Tathagata Das <td...@databricks.com>
> wrote:
>
>> I dont think any one has really run 500 text streams.
>> And parSequences do nothing out there, you are only parallelizing the
>> setup code which does not really compute anything. Also it setsup 500
>> foreachRDD operations that will get executed in each batch sequentially, so
>> does not make sense. The write way to parallelize this is union all the
>> streams.
>>
>> val streams = streamPaths.map { path =>
>> ssc.textFileStream(path)
>> }
>> val unionedStream = streamingContext.union(streams)
>> unionedStream.foreachRDD { rdd =>
>> // do something
>> }
>>
>> Then there is only one foreachRDD executed in every batch that will
>> process in parallel all the new files in each batch interval.
>> TD
>>
>>
>> On Tue, Jul 28, 2015 at 3:06 PM, Brandon White <bw...@gmail.com>
>> wrote:
>>
>>> val ssc = new StreamingContext(sc, Minutes(10))
>>>
>>> //500 textFile streams watching S3 directories
>>> val streams = streamPaths.par.map { path =>
>>> ssc.textFileStream(path)
>>> }
>>>
>>> streams.par.foreach { stream =>
>>> stream.foreachRDD { rdd =>
>>> //do something
>>> }
>>> }
>>>
>>> ssc.start()
>>>
>>> Would something like this scale? What would be the limiting factor to
>>> performance? What is the best way to parallelize this? Any other ideas on
>>> design?
>>>
>>
>>
>
Re: Has anybody ever tried running Spark Streaming on 500 text streams?
Posted by Brandon White <bw...@gmail.com>.
Thank you Tathagata. My main use case for the 500 streams is to append new
elements into their corresponding Spark SQL tables. Every stream is mapped
to a table so I'd like to use the streams to appended the new rdds to the
table. If I union all the streams, appending new elements becomes a
nightmare. So there is no other way to parallelize something like the
following? Will this still run sequence or timeout?
//500 streams
streams.foreach { stream =>
stream.foreachRDD { rdd =>
val df = sqlContext.jsonRDD(rdd)
df.saveAsTable(streamTuple._1, SaveMode.Append)
}
}
On Tue, Jul 28, 2015 at 3:42 PM, Tathagata Das <td...@databricks.com> wrote:
> I dont think any one has really run 500 text streams.
> And parSequences do nothing out there, you are only parallelizing the
> setup code which does not really compute anything. Also it setsup 500
> foreachRDD operations that will get executed in each batch sequentially, so
> does not make sense. The write way to parallelize this is union all the
> streams.
>
> val streams = streamPaths.map { path =>
> ssc.textFileStream(path)
> }
> val unionedStream = streamingContext.union(streams)
> unionedStream.foreachRDD { rdd =>
> // do something
> }
>
> Then there is only one foreachRDD executed in every batch that will
> process in parallel all the new files in each batch interval.
> TD
>
>
> On Tue, Jul 28, 2015 at 3:06 PM, Brandon White <bw...@gmail.com>
> wrote:
>
>> val ssc = new StreamingContext(sc, Minutes(10))
>>
>> //500 textFile streams watching S3 directories
>> val streams = streamPaths.par.map { path =>
>> ssc.textFileStream(path)
>> }
>>
>> streams.par.foreach { stream =>
>> stream.foreachRDD { rdd =>
>> //do something
>> }
>> }
>>
>> ssc.start()
>>
>> Would something like this scale? What would be the limiting factor to
>> performance? What is the best way to parallelize this? Any other ideas on
>> design?
>>
>
>
Re: Has anybody ever tried running Spark Streaming on 500 text streams?
Posted by Tathagata Das <td...@databricks.com>.
I dont think any one has really run 500 text streams.
And parSequences do nothing out there, you are only parallelizing the setup
code which does not really compute anything. Also it setsup 500 foreachRDD
operations that will get executed in each batch sequentially, so does not
make sense. The write way to parallelize this is union all the streams.
val streams = streamPaths.map { path =>
ssc.textFileStream(path)
}
val unionedStream = streamingContext.union(streams)
unionedStream.foreachRDD { rdd =>
// do something
}
Then there is only one foreachRDD executed in every batch that will process
in parallel all the new files in each batch interval.
TD
On Tue, Jul 28, 2015 at 3:06 PM, Brandon White <bw...@gmail.com>
wrote:
> val ssc = new StreamingContext(sc, Minutes(10))
>
> //500 textFile streams watching S3 directories
> val streams = streamPaths.par.map { path =>
> ssc.textFileStream(path)
> }
>
> streams.par.foreach { stream =>
> stream.foreachRDD { rdd =>
> //do something
> }
> }
>
> ssc.start()
>
> Would something like this scale? What would be the limiting factor to
> performance? What is the best way to parallelize this? Any other ideas on
> design?
>