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https://issues.apache.org/jira/browse/FLINK-1476?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14310626#comment-14310626
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Fabian Hueske commented on FLINK-1476:
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For a fair comparison, all system should get the same amount of resources (CPU,
memory, number of machines).
Since different systems use different mechanisms to allocate and distribute
resources, it is not straightforward to get comparable system configurations.
> Flink VS Spark on loop test
> ---------------------------
>
> Key: FLINK-1476
> URL: https://issues.apache.org/jira/browse/FLINK-1476
> Project: Flink
> Issue Type: Test
> Affects Versions: 0.7.0-incubating, 0.8
> Environment: 3 machines, every machines has 24 CPU cores and allocate
> 16 CPU cores for the tests. The memory situation is: 3 * 32G
> Reporter: xuhong
> Priority: Critical
>
> In the last days, i did some test on flink and spark. The test results
> shows that flink can do better on many operations, such as GroupBy, Join and
> some complex jobs. But when I do the KMeans, LinearRegression and other loop
> tests, i found that flink is no more excellent than spark. I want to konw,
> whether flink is more comfortable to do the loop jobs with spark.
> I add code: env.setDegreeOfParallelism(16) in each test to allocate same
> CPU cores as in Spark tests.
> My english is not good, i wish you guys can understand me!
> the following is some config of my Flnk:
> jobmanager.rpc.port: 6123
> jobmanager.heap.mb: 2048
> taskmanager.heap.mb: 2048
> taskmanager.numberOfTaskSlots: 24
> parallelization.degree.default: 72
> jobmanager.web.port: 8081
> webclient.port: 8085
> fs.overwrite-files: true
> taskmanager.memory.fraction: 0.8
> taskmanager.network.numberofBuffers: 70000
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