Hi,
On Thu, Jan 1, 2015 at 6:21 PM, Soumya Simanta <soumya.sima...@gmail.com>
wrote:
> Endre,
>
> Thank you for responding.
> I was trying to do something like this. However, I'm not sure if what you
> have mentioned above will work because the Redis set API returns a
> Future[Boolean]. So in the map after my mapAsyncUnordered I've no
> reference to the operation that finished before that unless the Redis
> returns a reference in the response to each call.
>
You can always transform that future to return a Future[Id] easily.
> Please let me know if this is not correct.
>
> However, if I use mapAsync the order should be maintained correct ?
>
Yes it does.
> Do you have any idea of how much performance difference is between
> mapAsync and mapAsyncUnordered ?
>
It depends on the latency distribution of the actual async call. The
ordered one suffers more from stragglers (unexpectedly long calls) since
every later result must wait, while the unordered is largely unaffected of
this. If the timing distribution of the call has a low deviation then I
expect not much difference between the two.
> I can maintain two lists (before and after). I'm yet to try the other
> approach that Konard pointed.
>
> What I'm learning is - doing things asynchronously is fast and fun BUT
> requires a different mindset :-)
>
This recognition is the most important step to become a powerful Akka
programmer :)
-Endre
> Thanks.
> -Soumya
>
>
>
>
>
> On Thursday, January 1, 2015 3:48:54 AM UTC-5, Akka Team wrote:
>>
>> Hi,
>>
>> One pitfall in your simple "map" approach is that you might measure the
>> wrong values. Remember that mapAsyncUnordered is, well, unordered and
>> batching, so you cannot expect that the elements come out in the same order
>> as they entered. On approach would be to record the "start time" of
>> elements in a concurrent Map in a map stage in front of the redis write,
>> then record the "end time" of elements in a map stage after, reading from
>> the Map of start times.
>>
>> The drawback of the above approach is that on top of what you want to
>> measure, you get overhead from:
>>
>> - using a concurrent map
>> - adding two map stages
>>
>> The measurement pseudocode can look like this:
>>
>> .map{ id => putInMap(id, currentTime); id }
>> .mapAsync(...)
>> .map { id => currentTime - getFromMap(id) }
>> .fold(0.0)(_ + _)
>> .map(_ / NumberOfElements)
>> // Now you have a Future of the time average.
>>
>> Of course you can also just collect the measurements in a Seq in the fold
>> instead of averaging, and then you can do whatever analysis you want after.
>>
>> -Endre
>>
>> On Sun, Dec 28, 2014 at 2:50 AM, Soumya Simanta <soumya....@gmail.com>
>> wrote:
>>
>>> Konrad,
>>>
>>> Thank you for pointing me the correct direction. I'll give it a shot.
>>>
>>> -Soumya
>>>
>>>
>>> On Saturday, December 27, 2014 7:48:30 AM UTC-5, Konrad Malawski wrote:
>>>>
>>>> Hi Soumya,
>>>>
>>>> I don’t think what you’ll end up measuring this way will be very
>>>> useful. I mean, between the completion of the future and the triggering of
>>>> the map there are multiple asynchronous boundaries… So you won’t be
>>>> measuring how fast the set operation was, but how much time was between
>>>> these asynchronous boundaries - which could have been backpressured by the
>>>> way.
>>>>
>>>>
>>>> I suggest directly wrapping the set call with your measurement logic
>>>> instead - since that is what you want to measure it seems.
>>>>
>>>>
>>>> By the way, we do have a “timed” element, in our extras section:
>>>> https://github.com/akka/akka/blob/release-2.3-dev/akka-strea
>>>> m/src/main/scala/akka/stream/extra/Timed.scala You can `import
>>>> Timed._` and then use it as shown here: https://github.com/akka/akka/b
>>>> lob/release-2.3-dev/akka-stream-tests/src/test/scala/akka/stream/extra/
>>>> FlowTimedSpec.scala
>>>>
>>>> It’s a rather old element and I’m not sure if we’ll be keeping it, but
>>>> you can use it as a source of inspiration in case you end up needing that
>>>> kind of measurement.
>>>>
>>>>
>>>>
>>>>
>>>> On 26 December 2014 at 05:46:55, Soumya Simanta (soumya....@gmail.com)
>>>> wrote:
>>>>
>>>> This is related to this
>>>> <https://groups.google.com/forum/#!topic/akka-user/NrSkEwMrS3s> thread
>>>> but sufficiently different that I decided to create new thread. Hope that's
>>>> okay.
>>>>
>>>> I would like to create a histogram of latency of a large number of set
>>>> operations
>>>> ( set returns a Future[Boolean]) using LatencyUtils
>>>> <https://github.com/LatencyUtils/LatencyUtils>
>>>>
>>>> Basically I need to start recording the time before the set operation
>>>> (inside mapAsyncUnordered(k => redis.set(k + rnd, message))) and then
>>>> somehow record the end time in a map operation( .map( //record the end
>>>> time here) after this. I'm having a hard time trying to figure this
>>>> out.
>>>>
>>>> My understanding is that the even though the mapAsyncUnordered doesn't
>>>> maintain the order of operations the map following the mapAsynUnordered
>>>> will
>>>> maintain the order from the previous stage because of TCP maintaining the
>>>> order. Is this correct?
>>>>
>>>>
>>>> val redis = RedisClient("localhost")
>>>>
>>>> val random1 = UUID.randomUUID().toString
>>>>
>>>> def insertValues(rnd: String): Flow[Int, Boolean] = {
>>>> Flow[Int].mapAsyncUnordered(k => redis.set(k + rnd, message)).map(
>>>> //record the end time here)
>>>> }
>>>>
>>>> val blackhole = BlackholeSink
>>>>
>>>> val maxSeq = 5000000
>>>> val seqSource = Source( () => (1 to maxSeq).iterator )
>>>> *val streamWithSeqSource =
>>>> seqSource.via(insertValues(random1)).runWith(blackhole)*
>>>>
>>>>
>>>> --
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>>
>>
>>
>> --
>> Akka Team
>> Typesafe - The software stack for applications that scale
>> Blog: letitcrash.com
>> Twitter: @akkateam
>>
> --
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Blog: letitcrash.com
Twitter: @akkateam
--
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>>>>>>>>>> Check the FAQ:
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