Hi, Repartition would create shuffle over network which I should avoid to reduce processing time because the size of messages at most in a batch will be 5G. Partitioning topic and parallelize receiving in Direct Stream might do the trick.
Sent from Samsung Mobile. <div>-------- Original message --------</div><div>From: Martin Eden <martineden...@gmail.com> </div><div>Date:16/07/2016 14:01 (GMT+05:30) </div><div>To: Diwakar Dhanuskodi <diwakar.dhanusk...@gmail.com> </div><div>Cc: user <user@spark.apache.org> </div><div>Subject: Re: Spark streaming takes longer time to read json into dataframes </div><div> </div>Hi, I would just do a repartition on the initial direct DStream since otherwise each RDD in the stream has exactly as many partitions as you have partitions in the Kafka topic (in your case 1). Like that receiving is still done in only 1 thread but at least the processing further down is done in parallel. If you want to parallelize your receiving as well I would partition my Kafka topic and then the RDDs in the initial DStream will have as many partitions as you set in Kafka. Have you seen this? http://spark.apache.org/docs/latest/streaming-kafka-integration.html M On Sat, Jul 16, 2016 at 5:26 AM, Diwakar Dhanuskodi <diwakar.dhanusk...@gmail.com> wrote: ---------- Forwarded message ---------- From: Diwakar Dhanuskodi <diwakar.dhanusk...@gmail.com> Date: Sat, Jul 16, 2016 at 9:30 AM Subject: Re: Spark streaming takes longer time to read json into dataframes To: Jean Georges Perrin <j...@jgp.net> Hello, I need it on memory. Increased executor memory to 25G and executor cores to 3. Got same result. There is always one task running under executor for rdd.read.json() because rdd partition size is 1 . Doing hash partitioning inside foreachRDD is a good approach? Regards, Diwakar. On Sat, Jul 16, 2016 at 9:20 AM, Jean Georges Perrin <j...@jgp.net> wrote: Do you need it on disk or just push it to memory? Can you try to increase memory or # of cores (I know it sounds basic) > On Jul 15, 2016, at 11:43 PM, Diwakar Dhanuskodi > <diwakar.dhanusk...@gmail.com> wrote: > > Hello, > > I have 400K json messages pulled from Kafka into spark streaming using > DirectStream approach. Size of 400K messages is around 5G. Kafka topic is > single partitioned. I am using rdd.read.json(_._2) inside foreachRDD to > convert rdd into dataframe. It takes almost 2.3 minutes to convert into > dataframe. > > I am running in Yarn client mode with executor memory as 15G and executor > cores as 2. > > Caching rdd before converting into dataframe doesn't change processing time. > Whether introducing hash partitions inside foreachRDD will help? (or) Will > partitioning topic and have more than one DirectStream help?. How can I > approach this situation to reduce time in converting to dataframe.. > > Regards, > Diwakar.
