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From "hsy541@gmail.com" <hsy...@gmail.com>
Subject Re: Help is processing huge data through Kafka-storm cluster
Date Tue, 17 Jun 2014 17:44:44 GMT
Hi Shaikh,

I heard some throughput bottleneck of storm. It cannot really scale up with
kafka.
I recommend you to try DataTorrent platform(https://www.datatorrent.com/)

The platform itself is not open-source but it has a open-source library (
https://github.com/DataTorrent/Malhar) which contains a kafka ingestion
functions.
The library is pretty cool, it can scale up dynamically with kafka
partitions and is fully HA.

And in your case you might be able to use the platform for free.(It's free
if your application doesn't require large amount of memory)

With datatorrent platform and the open-source library I can scale my
application up to 300k/s (10 nodes, 3 replica, 1kb msg, 0.8.0 client).
I heard the performance of kafka client has been improved for 0.8.1 release
:)

Best,
Siyuan


On Sat, Jun 14, 2014 at 8:14 AM, Shaikh Ahmed <rnsr.shaikh@gmail.com> wrote:

> Hi,
>
> Daily we are downloaded 28 Million of messages and Monthly it goes up to
> 800+ million.
>
> We want to process this amount of data through our kafka and storm cluster
> and would like to store in HBase cluster.
>
> We are targeting to process one month of data in one day. Is it possible?
>
> We have setup our cluster thinking that we can process million of messages
> in one sec as mentioned on web. Unfortunately, we have ended-up with
> processing only 1200-1700 message per second.  if we continue with this
> speed than it will take min 10 days to process 30 days of data, which is
> the relevant solution in our case.
>
> I suspect that we have to change some configuration to achieve this goal.
> Looking for help from experts to support me in achieving this task.
>
> *Kafka Cluster:*
> Kafka is running on two dedicated machines with 48 GB of RAM and 2TB of
> storage. We have total 11 nodes kafka cluster spread across these two
> servers.
>
> *Kafka Configuration:*
> producer.type=async
> compression.codec=none
> request.required.acks=-1
> serializer.class=kafka.serializer.StringEncoder
> queue.buffering.max.ms=100000
> batch.num.messages=10000
> queue.buffering.max.messages=100000
> default.replication.factor=3
> controlled.shutdown.enable=true
> auto.leader.rebalance.enable=true
> num.network.threads=2
> num.io.threads=8
> num.partitions=4
> log.retention.hours=12
> log.segment.bytes=536870912
> log.retention.check.interval.ms=60000
> log.cleaner.enable=false
>
> *Storm Cluster:*
> Storm is running with 5 supervisor and 1 nimbus on IBM servers with 48 GB
> of RAM and 8TB of storage. These servers are shared with hbase cluster.
>
> *Kafka spout configuration*
> kafkaConfig.bufferSizeBytes = 1024*1024*8;
> kafkaConfig.fetchSizeBytes = 1024*1024*4;
> kafkaConfig.forceFromStart = true;
>
> *Topology: StormTopology*
> Spout           - Partition: 4
> First Bolt     -  parallelism hint: 6 and Num tasks: 5
> Second Bolt -  parallelism hint: 5
> Third Bolt     -   parallelism hint: 3
> Fourth Bolt   -  parallelism hint: 3 and Num tasks: 4
> Fifth Bolt      -  parallelism hint: 3
> Sixth Bolt     -  parallelism hint: 3
>
> *Supervisor configuration:*
>
> storm.local.dir: "/app/storm"
> storm.zookeeper.port: 2181
> storm.cluster.mode: "distributed"
> storm.local.mode.zmq: false
> supervisor.slots.ports:
>     - 6700
>     - 6701
>     - 6702
>     - 6703
> supervisor.worker.start.timeout.secs: 180
> supervisor.worker.timeout.secs: 30
> supervisor.monitor.frequency.secs: 3
> supervisor.heartbeat.frequency.secs: 5
> supervisor.enable: true
>
> storm.messaging.netty.server_worker_threads: 2
> storm.messaging.netty.client_worker_threads: 2
> storm.messaging.netty.buffer_size: 52428800 #50MB buffer
> storm.messaging.netty.max_retries: 25
> storm.messaging.netty.max_wait_ms: 1000
> storm.messaging.netty.min_wait_ms: 100
>
>
> supervisor.childopts: "-Xmx1024m -Djava.net.preferIPv4Stack=true"
> worker.childopts: "-Xmx2048m -Djava.net.preferIPv4Stack=true"
>
>
> Please let me know if more information needed..
>
> Thanks in advance.
>
> Regards,
> Riyaz
>

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