Hi Endre,
That's a very valid suggestion. I'm quite new to Akka (finished about
35% of its docs). I'm still trying to understand how to properly
parallelize tasks. You and Viktor mentioned back-pressure. Can you go a bit
deeper in that. For example, what is back-pressure and how to build it into
my actor solutions ? (Info links would be all I need). I asked a similar
question like this on StackOverflow but no one could point me to the right
direction.
Thank you for linking Akka-stream's docs.
Allen
On Saturday, January 10, 2015 at 5:38:42 AM UTC-5, drewhk wrote:
>
> Hi,
>
> On Fri, Jan 9, 2015 at 10:53 PM, Allen Nie <aimi...@gmail.com
> <javascript:>> wrote:
>
>> Hey Viktor,
>>
>> I'm trying to use Akka to parallelize this process. There shouldn't
>> be any bottleneck, and I don't understand why I got memory overflow with my
>> first version (actor version). The main task is to read in a line, break it
>> up, and turn each segments (strings) into an integer, then prints it out to
>> a CSV file (vectorization process).
>>
>> def processLine(line: String): Unit = {
>>
>> val vector: ListBuffer[String] = ListBuffer()
>> val segs = line.split(",")
>>
>> println(segs(0))
>>
>> (1 to segs.length - 1).map {i =>
>> val factorArray = dictionaries(i-1)
>> vector += factorArray._2.indexOf(segs(i)).toString //get the factor
>> level of string
>> }
>>
>> timer ! OneDone
>>
>> printer ! Print(vector.toList)}
>>
>>
>> When I'm doing this in pure Akka (with actors), since I created 40
>> million objects: Row(line: String), I get memory overflow issue.
>>
>
> No surprise there, you just slurp up all rows faster than the actors can
> keep up processing them, so most of them are in a mailbox. In fact if your
> actors do something trivially simple, the whole overhead of asynchronously
> passing elements to the actors might be larger than what you gain. In these
> cases it is recommended to pass batches of Rows instead of one-by-one.
> Remember, parallelisation only gains when the overhead of it is smaller
> than the task it parallelizes.
>
>
>
>> If I use Akka-stream, there is no memory overflow issue, but the
>> performance is too similar to the non-parallelized version (even slower).
>>
>
> No surprise there either, you did nothing to parallelize or pipeline any
> computation in the stream, so you get the overhead of asynchronous
> processing and none of the benefits of it (but at least you get
> backpressure).
>
> You have a few approaches to get the benefints of multi-core processing
> with streams:
> - if you have multiple processing steps for a row you can pipeline them,
> see the intro part of this doc page:
> http://doc.akka.io/docs/akka-stream-and-http-experimental/1.0-M2/scala/stream-rate.html
> - you can use mapAsync to have similar effects but with one computation
> step, see here:
> http://doc.akka.io/docs/akka-stream-and-http-experimental/1.0-M2/scala/stream-integrations.html#Illustrating_ordering_and_parallelism
> - you can explicitly add fan-out elements to parallelise among multiple
> explicit workers, see here:
> http://doc.akka.io/docs/akka-stream-and-http-experimental/1.0-M2/scala/stream-cookbook.html#Balancing_jobs_to_a_fixed_pool_of_workers
>
> Overall, for this kind of tasks I recommend using Streams, but you need to
> read the documentation first to understand how it works.
>
> -Endre
>
>
>>
>> It's my first time using Akka-stream. So I'm unfamiliar with the
>> optimization you were talking about.
>>
>> Sincerely,
>> Allen
>>
>> On Friday, January 9, 2015 at 4:03:13 PM UTC-5, √ wrote:
>>>
>>> Hi Allen,
>>>
>>> What's the bottleneck?
>>> Have you tried enabling the experimental optimizations?
>>>
>>> On Fri, Jan 9, 2015 at 9:52 PM, Allen Nie <aimi...@gmail.com> wrote:
>>>
>>>> Thank you Soumya,
>>>>
>>>> I think Akka-streams is the way to go. However, I would also
>>>> appreciate some performance boost as well - still have 40 million lines to
>>>> go through! But thanks anyway!
>>>>
>>>>
>>>> On Friday, January 9, 2015 at 12:43:49 PM UTC-5, Soumya Simanta wrote:
>>>>>
>>>>> I would recommend using the Akka-streams API for this.
>>>>> Here is sample. I was able to process a 1G file with around 1.5
>>>>> million records in *20MB* of memory. The file read and the writing on
>>>>> the console rates are different but the streams API handles that. This
>>>>> is
>>>>> not the fastest but you at least won't run out of memory.
>>>>>
>>>>>
>>>>>
>>>>> <https://lh6.googleusercontent.com/-zdX0n1pvueE/VLATDja3K4I/AAAAAAAAv18/BH7V1RAuxT8/s1600/1gb_file_processing.png>
>>>>>
>>>>> import java.io.FileInputStream
>>>>> import java.util.Scanner
>>>>>
>>>>> import akka.actor.ActorSystem
>>>>> import akka.stream.{FlowMaterializer, MaterializerSettings}
>>>>> import akka.stream.scaladsl.Source
>>>>>
>>>>> import scala.util.Try
>>>>>
>>>>>
>>>>> object StreamingFileReader extends App {
>>>>>
>>>>>
>>>>> val inputStream = new FileInputStream("/path/to/file")
>>>>> val sc = new Scanner(inputStream, "UTF-8")
>>>>>
>>>>> implicit val system = ActorSystem("Sys")
>>>>> val settings = MaterializerSettings(system)
>>>>> implicit val materializer =
>>>>> FlowMaterializer(settings.copy(maxInputBufferSize
>>>>> = 256, initialInputBufferSize = 256))
>>>>>
>>>>> val fileSource = Source(() => Iterator.continually(sc.nextLine()))
>>>>>
>>>>> import system.dispatcher
>>>>>
>>>>> fileSource.map { line =>
>>>>> line //do nothing
>>>>> //in the for each print the line.
>>>>> }.foreach(println).onComplete { _ =>
>>>>> Try {
>>>>> sc.close()
>>>>> inputStream.close()
>>>>> }
>>>>> system.shutdown()
>>>>> }
>>>>> }
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> On Friday, January 9, 2015 at 10:53:33 AM UTC-5, Allen Nie wrote:
>>>>>>
>>>>>> Hi,
>>>>>>
>>>>>> I am trying to process a csv file with 40 million lines of data
>>>>>> in there. It's a 5GB size file. I'm trying to use Akka to parallelize
>>>>>> the
>>>>>> task. However, it seems like I can't stop the quick memory growth. It
>>>>>> expanded from 1GB to almost 15GB (the limit I set) under 5 minutes. This
>>>>>> is
>>>>>> the code in my main() method:
>>>>>>
>>>>>> val inputStream = new
>>>>>> FileInputStream("E:\\Allen\\DataScience\\train\\train.csv")val sc = new
>>>>>> Scanner(inputStream, "UTF-8")
>>>>>> var counter = 0
>>>>>> while (sc.hasNextLine) {
>>>>>>
>>>>>> rowActors(counter % 20) ! Row(sc.nextLine())
>>>>>>
>>>>>> counter += 1}
>>>>>>
>>>>>> sc.close()
>>>>>> inputStream.close()
>>>>>>
>>>>>> Someone pointed out that I was essentially creating 40 million
>>>>>> Row objects, which naturally will take up a lot of space. My row actor
>>>>>> is
>>>>>> not doing much. Just simply transforming each line into an array of
>>>>>> integers (if you are familiar with the concept of vectorizing, that's
>>>>>> what
>>>>>> I'm doing). Then the transformed array gets printed out. Done. I
>>>>>> originally
>>>>>> thought there was a memory leak but maybe I'm not managing memory right.
>>>>>> Can I get any wise suggestions from the Akka experts here??
>>>>>>
>>>>>>
>>>>>>
>>>>>> <http://i.stack.imgur.com/yQ4xx.png>
>>>>>>
>>>>>> --
>>>> >>>>>>>>>> Read the docs: http://akka.io/docs/
>>>> >>>>>>>>>> Check the FAQ: http://doc.akka.io/docs/akka/
>>>> current/additional/faq.html
>>>> >>>>>>>>>> Search the archives: https://groups.google.com/
>>>> group/akka-user
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>>>>
>>>
>>>
>>>
>>> --
>>> Cheers,
>>> √
>>>
>> --
>> >>>>>>>>>> Read the docs: http://akka.io/docs/
>> >>>>>>>>>> Check the FAQ:
>> http://doc.akka.io/docs/akka/current/additional/faq.html
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>
--
>>>>>>>>>> Read the docs: http://akka.io/docs/
>>>>>>>>>> Check the FAQ:
>>>>>>>>>> http://doc.akka.io/docs/akka/current/additional/faq.html
>>>>>>>>>> Search the archives: https://groups.google.com/group/akka-user
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