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From "Alexander Alexandrov (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1195) Improvement of benchmarking infrastructure
Date Fri, 31 Oct 2014 14:56:33 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1195?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14191888#comment-14191888
] 

Alexander Alexandrov commented on FLINK-1195:
---------------------------------------------

Indeed from what you have described Peel seems to be a good fit and is also going to be further
developed in 2015.
Can you point me to the jobs that are running as part of the current benchmark?

> Improvement of benchmarking infrastructure
> ------------------------------------------
>
>                 Key: FLINK-1195
>                 URL: https://issues.apache.org/jira/browse/FLINK-1195
>             Project: Flink
>          Issue Type: Wish
>            Reporter: Till Rohrmann
>
> I noticed while running my ALS benchmarks that we still have some potential to improve
our benchmarking infrastructure. The current state is that we execute the benchmark jobs by
writing a script with a single set of parameters. The runtime is then manually retrieved from
the web interface of Flink and Spark, respectively.
> I think we need the following extensions:
> * Automatic runtime retrieval and storage in a file
> * Repeated execution of jobs to gather some "advanced" statistics such as mean and standard
deviation of the runtimes
> * Support for value sets for the individual parameters
> The automatic runtime retrieval would allow us to execute several benchmarks consecutively
without having to lookup the runtimes in the logs or in the web interface, which btw only
stores the runtimes of the last 5 jobs.
> What I mean with value sets is that would be nice to specify a set of parameter values
for which the benchmark is run without having to write for every single parameter combination
a benchmark script. I believe that this feature would become very handy when we want to look
at the runtime behaviour of Flink for different input sizes or degrees of parallelism, for
example. To illustrate what I mean:
> {code}
> INPUTSIZE = 1000, 2000, 4000, 8000
> DOP = 1, 2, 4, 8
> OUTPUT=benchmarkResults
> repetitions=10
> command=benchmark.jar -p $DOP $INPUTSIZE 
> {code} 
> Something like that would execute the benchmark job with (DOP=1, INPUTSIZE=1000), (DOP=2,
INPUTSIZE=2000),.... 10 times each, calculate for each parameter combination runtime statistics
and store the results in the file benchmarkResults.
> I believe that spending some effort now will pay off in the long run because we will
benchmark Flink continuously. What do you guys think?



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