Re: Comparison of storm and flink

[Niels Basjes]

Hi,

I don't have a perfect list available but these are some of the things to
keep in mind:
1) end2end Latency. Some systems (like spark) use microbatching which
introduces a latency of seconds
2) Do you get "exactly once guarantees"? Storm can give you that but then
the throughput goes really down.
3) Ease of programming. How 'nice' is the api you have to work with.
4) Resiliance of state. If you need some state over several events, Does
the framework support this and has built in recovery of this stat in case
of a faillure?
5) Tools. What kind of tools are " ready to run" available? I.e. kmeans,
and things like that.
6) Deployment. How do you run it? Do you need a separate infrastructure or
can you deploy it in an existing yarn/mesos/...
7) Security: Can it access kerberos secured resources (like hbase, hdfs or
any other service) in a long running situation.

As a final note: I've been hacking at Storm for over a year now and last
summer I found Flink. Today Storm is for me no longer an option and we are
taking down what we already had running.

Niels Basjes
On 23 Jan 2016 20:59, "Vinaya M S"  wrote:

> Hi Flink user group,
>
> I am working on a project for the Insight Data Engineering Program in New
> York to compare streaming tools. The program is designed for software
> engineers and those straight from the university to transition to a data
> engineering role.  After completing the project, we present demos of the
> project to several companies in NYC that we are interested in working for
> (including top companies like NY Times, Capital One, Bloomberg, etc).
>
> I have decided to work on a project to compare streaming tools, namely
> Flink and Storm.  I already have Twitter data stored and would like to
> design tests to benchmark the the two tools if possible.
>
> I wanted to be extra-careful in constructing a benchmark to work on and
> present at companies here in NY.  Do you have any recommendations to tests
> to run with the Twitter data that I have that would showcase when to and
> not use Flink compared to Storm?
>
> Thanks!
> Vinaya
>

Comparison of storm and flink

[Vinaya M S]

Hi Flink user group,

I am working on a project for the Insight Data Engineering Program in New
York to compare streaming tools. The program is designed for software
engineers and those straight from the university to transition to a data
engineering role.  After completing the project, we present demos of the
project to several companies in NYC that we are interested in working for
(including top companies like NY Times, Capital One, Bloomberg, etc).

I have decided to work on a project to compare streaming tools, namely
Flink and Storm.  I already have Twitter data stored and would like to
design tests to benchmark the the two tools if possible.

I wanted to be extra-careful in constructing a benchmark to work on and
present at companies here in NY.  Do you have any recommendations to tests
to run with the Twitter data that I have that would showcase when to and
not use Flink compared to Storm?

Thanks!
Vinaya

Re: Comparison of storm and flink

[Slim Baltagi]

Hi Vinaya

1. Comparing streaming tools ( in this case Storm and Flink) should not be
based on performance benchmarks only! For example, slides 16-36 list over 96
criteria, that we identified at Capital One, to compare two streaming tools   
http://www.slideshare.net/sbaltagi/flink-vs-spark/17

2. Now, if you are focusing on performance only, I'll suggest a few related
resources: 

- Benchmarking Streaming Computation Engines at Yahoo!  
http://yahooeng.tumblr.com/post/135321837876/benchmarking-streaming-computation-engines-at
 
December 16, 2015 Code at github:
https://github.com/yahoo/streaming-benchmarks

-  There is some work started by some Flink contributors to create some
performance scripts for Flink, Spark, and MapReduce here: There is Apache
Flink: Performance and Testing  https://github.com/project-flink/flink-perf

- Some first numbers on performance of streaming jobs with Apache Flink are
here:
http://data-artisans.com/high-throughput-low-latency-and-exactly-once-stream-processing-with-apache-flink/
 
under the section: 'Show me the numbers'. Code used is at:
https://github.com/dataArtisans/performance  

- Yangjun Wang is currently working on his Master thesis at Aalto university
in Helsinki, Finland. The topic of his thesis is about building a standard
benchmark system for streaming processing systems like Apache Storm, Spark
and Flink. Code at github
https://github.com/wangyangjun/StreamBench/tree/master/StreamBench

3. I am giving a talk in NYC on Tuesday February 2nd, 2016 on Apache Flink
and I will be touching a bit on benchmarks
http://www.meetup.com/New-York-City-NYC-Apache-Flink-Meetup/events/228113118/
You are welcome to attend. 

Thanks

Slim Baltagi 



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