Guys, I fully agree that configured servers at Amazon is the best choice.
But when you need to check that your changes has no performance drop you're able to use your own PC or PCs to checks that. All you need is to benchmark already released version vs version with your fix at same environment. So, seems we should have couple of configuration recommendations - reasonable for standalone PC - reasonable for cluster On Fri, Sep 15, 2017 at 12:20 PM, Nikolay Izhikov <nizhikov....@gmail.com> wrote: > Hello, Dmitriy. > > I think experienced members of community have specific number for > benchmarking. > > Can we start from reference hardware configuration: Num of CPU, RAM and > HDD(SDD?) configuration, network configs, etc. > > Can someone share that kind of knowledge - Which hardware is best for > Ignite benchmarking? > > I found some numbers here - [1]. Is it well suited for Apache Ignite? > > [1] https://www.gridgain.com/resources/benchmarks/gridgain-vs- > hazelcast-benchmarks > > 14.09.2017 23:27, Dmitriy Setrakyan пишет: > > Alexey, I completely agree. However, for the benchmarks to be useful, then >> need to be run on the same hardware all the time. Apache Ignite does not >> have servers sitting around, available to run the benchmarks. >> >> Would be nice to see how other projects address it. Can Amazon donate >> servers for the Apache projects? >> >> D. >> >> On Thu, Sep 14, 2017 at 6:25 AM, Aleksei Zaitsev <ign...@alexzaitzev.pro> >> wrote: >> >> Hi, Igniters. >>> >>> Recently I’ve done some research in benchmarks for Ignite, and noticed >>> that we don’t have any rules for running benchmarks and collecting result >>> from them. Although sometimes we have tasks, which results need to be >>> measured. I propose to formalize such things as: >>> * set of benchmarks, >>> * parameters of launching them, >>> * way of result collection and interpretation, >>> * Ignite cluster configuration. >>> >>> I don’t think that we need to run benchmarks before every merge into >>> master, but in some cases it should be mandatory to compare new results >>> with reference values to be sure that changes do not lead to the >>> performance degradation. >>> >>> What do you think? >>> >>> >>
