flink-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Greg Hogan <c...@greghogan.com>
Subject Re: spark vs flink batch performance
Date Fri, 18 Nov 2016 14:30:07 GMT
"For csv reading, i deliberately did not use csv reader since i want to run
same code across spark and flink."

If your objective deviates from writing and running the fastest Spark and
fastest Flink programs, then your comparison is worthless.


On Fri, Nov 18, 2016 at 5:37 AM, CPC <achalil@gmail.com> wrote:

> Hi Gabor,
>
> Thank you for your kind response. I forget to mention that i have actually
> three workers. This is why i set default paralelism to 6.
>
> For csv reading, i deliberately did not use csv reader since i want to run
> same code across spark and flink. Collect is returning 40k records which is
> not so big.
>
> I will try same test with spark 1.5 and 1.6 as well to understand whether
> spark 2.x series has some performance improvements because in those kind of
> tests, spark and flink was either on par or flink 10-15% faster than spark
> in the past. Aside from that are any configuration parameters you may
> propose to fine tune flink?
>
> Best,
> Anıl
>
> On Nov 18, 2016 12:25, "Gábor Gévay" <ggab90@gmail.com> wrote:
>
>> Hello,
>>
>> Your program looks mostly fine, but there are a few minor things that
>> might help a bit:
>>
>> Parallelism: In your attached flink-conf.yaml, you have 2 task slots
>> per task manager, and if you have 1 task manager, then your total
>> number of task slots is also 2. However, your default parallelism is
>> 6. In Flink, the recommended default parallelism is exactly the total
>> number of task slots [1]. (This is in contrast to Spark, where the
>> recommended setting is 2-3 per CPU core [2].)
>>
>> CSV reading: If your input is a CSV file, then you should use
>> readCsvFile (instead of readTextFile and then parsing it manually).
>>
>> Collect call: How large is the DataSet that you are using collect on?
>> If it is large, then we might try to figure out a way to get the top
>> 10 elements without first collecting the DataSet.
>>
>> Best,
>> Gábor
>>
>> [1] https://flink.apache.org/faq.html#what-is-the-parallelism-ho
>> w-do-i-set-it
>> [2] https://spark.apache.org/docs/latest/tuning.html#level-of-parallelism
>>
>>
>>
>>
>>
>> 2016-11-16 22:38 GMT+01:00 CPC <achalil@gmail.com>:
>> > Hi all,
>> >
>> > I am trying to compare spark and flink batch performance. In my test i
>> am
>> > using ratings.csv in
>> > http://files.grouplens.org/datasets/movielens/ml-latest.zip dataset. I
>> also
>> > concatenated ratings.csv 16 times to increase dataset size(total of
>> > 390465536 records almost 10gb).I am reading from google storage with
>> > gcs-connector and  file schema is : userId,movieId,rating,timestamp.
>> > Basically i am calculating average rating per movie
>> >
>> > Code for flink(i tested CombineHint.HASH and CombineHint.SORT)
>> >>
>> >> case class Rating(userID: String, movieID: String, rating: Double,
>> date:
>> >> Timestamp)
>> >
>> >
>> >>
>> >> def parseRating(line: String): Rating = {
>> >>   val arr = line.split(",")
>> >>   Rating(arr(0), arr(1), arr(2).toDouble, new Timestamp((arr(3).toLong
>> *
>> >> 1000)))
>> >> }
>> >
>> >
>> >>
>> >> val ratings: DataSet[Rating] =
>> >> env.readTextFile("gs://cpcflink/wikistream/ratingsheadless16x.csv").map(a
>> =>
>> >> parseRating(a))
>> >> ratings
>> >>   .map(i => (i.movieID, 1, i.rating))
>> >>   .groupBy(0).reduce((l, r) => (l._1, l._2 + r._2, l._3 + r._3),
>> >> CombineHint.HASH)
>> >>   .map(i => (i._1, i._3 /
>> >> i._2)).collect().sortBy(_._1).sortBy(_._2)(Ordering.Double.r
>> everse).take(10)
>> >
>> >
>> > with CombineHint.HASH 3m49s and with CombineHint.SORT 5m9s
>> >
>> > Code for Spark(i tested reduceByKey and reduceByKeyLocaly)
>> >>
>> >> case class Rating(userID: String, movieID: String, rating: Double,
>> date:
>> >> Timestamp)
>> >> def parseRating(line: String): Rating = {
>> >>   val arr = line.split(",")
>> >>   Rating(arr(0), arr(1), arr(2).toDouble, new Timestamp((arr(3).toLong
>> *
>> >> 1000)))
>> >> }
>> >> val conf = new SparkConf().setAppName("Simple Application")
>> >> val sc = new SparkContext(conf)
>> >> val keyed: RDD[(String, (Int, Double))] =
>> >> sc.textFile("gs://cpcflink/wikistream/ratingsheadless16x.csv
>> ").map(parseRating).map(r
>> >> => (r.movieID, (1, r.rating)))
>> >> keyed.reduceByKey((l, r) => (l._1 + r._1, l._2 + r._2)).mapValues(i =>
>> >> i._2 /
>> >> i._1).collect.sortBy(_._1).sortBy(a=>a._2)(Ordering.Double.r
>> everse).take(10).foreach(println)
>> >
>> >
>> > with reduceByKeyLocaly 2.9 minute(almost 2m54s) and reduceByKey 3.1
>> > minute(almost 3m6s)
>> >
>> > Machine config on google cloud:
>> > taskmanager/sparkmaster: n1-standard-1 (1 vCPU, 3.75 GB memory)
>> > jobmanager/sparkworkers: n1-standard-2 (2 vCPUs, 7.5 GB memory)
>> > java version:jdk jdk-8u102
>> > flink:1.1.3
>> > spark:2.0.2
>> >
>> > I also attached flink-conf.yaml. Although it is not such a big
>> difference
>> > there is a 40% performance difference between spark and flink. Is there
>> > something i am doing wrong? If there is not how can i fine tune flink
>> or is
>> > it normal spark has better performance with batch data?
>> >
>> > Thank you in advance...
>>
>

Mime
View raw message