The Fifth Elephant 2015

A conference on data, machine learning, and distributed and parallel computing

Instrumenting your kafka & storm pipeline

Submitted by Bhasker Kode (@bhaskerkode) on Monday, 11 May 2015

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Technical level

Intermediate

Section

Full Talk

Status

Confirmed & Scheduled

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Total votes:  +12

Objective

tips to design your stream processing setup.
what all can go wrong, how to instrument it.

Description

introduction to a production setup that handles billions of events per week through our home-grown apache kafka client, process the stream with storm - and then aggregate into postgres. will also share pro’s and con’s of building our own kafka client as opposed to re-using one.

Speaker bio

Bosky (@bhaskerkode) leads a product engg team at Helpshift & works on erlang, clojure and golang.

building distributed systems since ‘06 across edtech, adtech & mobile in erlang, clojure & go.
=> http://in.linkedin.com/in/bhaskerkode & http://slideshare.net/bosky101

Built a kafka producer/micro-service used in production at helpshift, layer, and several other companies.
=> http://github.com/helpshift/ekaf ( Recommended by Apache kafka https://cwiki.apache.org/confluence/display/KAFKA/Clients )

Using storm in production for sentiment analysis, topic extraction, naive bayes classification, etc.
Eager to learn more about best practices in storm deployment/management. Incidentally another part that feeds into this system is in golang, uses the Shopify kafka producer.

Links

Slides

https://www.dropbox.com/s/c4rwq1tiu7nxvpd/fifthel-15-kafka-storm-at-helpshift-bhaskerkode.png?dl=0

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