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From "Jeremy Hanna (JIRA)" <>
Subject [jira] [Updated] (CASSANDRA-9870) Improve cassandra-stress graphing
Date Mon, 26 Oct 2015 16:08:28 GMT


Jeremy Hanna updated CASSANDRA-9870:
    Labels: stress  (was: )

> Improve cassandra-stress graphing
> ---------------------------------
>                 Key: CASSANDRA-9870
>                 URL:
>             Project: Cassandra
>          Issue Type: Improvement
>          Components: Tools
>            Reporter: Benedict
>            Assignee: Ryan McGuire
>              Labels: stress
>         Attachments: reads.svg
> CASSANDRA-7918 introduces graph output from a stress run, but these graphs are a little
limited. Attached to the ticket is an example of some improved graphs which can serve as the
*basis* for some improvements, which I will briefly describe. They should not be taken as
the exact end goal, but we should aim for at least their functionality. Preferably with some
Javascript advantages thrown in, such as the hiding of datasets/graphs for clarity. Any ideas
for improvements are *definitely* encouraged.
> Some overarching design principles:
> * Display _on *one* screen_ all of the information necessary to get a good idea of how
two or more branches compare to each other. Ideally we will reintroduce this, painting multiple
graphs onto one screen, stretched to fit.
> * Axes must be truncated to only the interesting dimensions, to ensure there is no wasted
> * Each graph displaying multiple kinds of data should use colour _and shape_ to help
easily distinguish the different datasets.
> * Each graph should be tailored to the data it is representing, and we should have multiple
views of each data.
> The data can roughly be partitioned into three kinds:
> * throughput
> * latency
> * gc
> These can each be viewed in different ways:
> * as a continuous plot of:
> ** raw data
> ** scaled/compared to a "base" branch, or other metric
> ** cumulatively
> * as box plots
> ** ideally, these will plot median, outer quartiles, outer deciles and absolute limits
of the distribution, so the shape of the data can be best understood
> Each compresses the information differently, losing different information, so that collectively
they help to understand the data.
> Some basic rules for presentation that work well:
> * Latency information should be plotted to a logarithmic scale, to avoid high latencies
drowning out low ones
> * GC information should be plotted cumulatively, to avoid differing throughputs giving
the impression of worse GC. It should also have a line that is rescaled by the amount of work
(number of operations) completed
> * Throughput should be plotted as the actual numbers
> To walk the graphs top-left to bottom-right, we have:
> * Spot throughput comparison of branches to the baseline branch, as an improvement ratio
(which can of course be negative, but is not in this example)
> * Raw throughput of all branches (no baseline)
> * Raw throughput as a box plot
> * Latency percentiles, compared to baseline. The percentage improvement at any point
in time vs baseline is calculated, and then multiplied by the overall median for the entire
run. This simply permits the non-baseline branches to scatter their wins/loss around a relatively
clustered line for each percentile. It's probably the most "dishonest" graph but comparing
something like latency where each data point can have very high variance is difficult, and
this gives you an idea of clustering of improvements/losses.
> * Latency percentiles, raw, each with a different shape; lowest percentiles plotted as
a solid line as they vary least, with higher percentiles each getting their own subtly different
shape to scatter.
> * Latency box plots
> * GC time, plotted cumulatively and also scaled by work done
> * GC Mb, plotted cumulatively and also scaled by work done
> * GC time, raw
> * GC time as a box plot
> These do mostly introduce the concept of a "baseline" branch. It may be that, ideally,
this baseline be selected by a dropdown so the javascript can transform the output dynamically.
This would permit more interesting comparisons to be made on the fly.
> There are also some complexities, such as deciding which datapoints to compare against
baseline when times get out-of-whack (due to GC, etc, causing a lack of output for a period).
The version I uploaded does a merge of the times, permitting a small degree of variance, and
ignoring those datapoints we cannot pair. One option here might be to change stress' behaviour
to always print to a strict schedule, instead of trying to get absolutely accurate apportionment
of timings. If this makes things much simpler, it can be done.
> As previously stated, but may be lost in the wall-of-text, these should be taken as a
starting point / sign post, rather than a golden rule for the end goal. But ideally they will
be the lower bound of what we can deliver.

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