All containers are destroyed by default on termination so to analyze profiling data for portable runners, either disable container cleanup (using --retainDockerContainers=true) or use remote distributed file system path.On Mon, Nov 5, 2018 at 1:05 AM Robert Bradshaw <robertwb@xxxxxxxxxx> wrote:Any portable runner should pick it up automatically.
On Tue, Oct 30, 2018 at 3:32 AM Manu Zhang <owenzhang1990@xxxxxxxxx> wrote:
> Cool ! Can we document it somewhere such that other Runners could pick it up later ?
> Manu Zhang
> On Oct 29, 2018, 5:54 PM +0800, Maximilian Michels <mxm@xxxxxxxxxx>, wrote:
> This looks very helpful for debugging performance of portable pipelines.
> Great work!
> Enabling local directories for Flink or other portable Runners would be
> useful for debugging, e.g. per
> On 26.10.18 18:08, Robert Bradshaw wrote:
> Now that we've (mostly) moved from features to performance for
> BeamPython-on-Flink, I've been doing some profiling of Python code,
> and thought it may be useful for others as well (both those working on
> the SDK, and users who want to understand their own code), so I've
> tried to wrap this up into something useful.
> Python already had some existing profile options that we used with
> Dataflow, specifically --profile_cpu and --profile_location. I've
> hooked these up to both the DirectRunner and the SDK Harness Worker.
> One can now run commands like
> python -m apache_beam.examples.wordcount
> --output=counts.txt--profile_cpu --profile_location=path/to/directory
> and get nice graphs like the one attached. (Here the bulk of the time
> is spent reading from the default input in gcs. Another hint for
> reading the graph is that due to fusion the call graph is cyclic,
> passing through operations:86:receive for every output.)
> The raw python profile stats  are produced in that directory, along
> with a dot graph and an svg if both dot and gprof2dot are installed.
> There is also an important option --direct_runner_bundle_repeat which
> can be set to gain more accurate profiles on smaller data sets by
> re-playing the bundle without the (non-trivial) one-time setup costs.
> These flags also work on portability runners such as Flink, where the
> directory must be set to a distributed filesystem. Each bundle
> produces its own profile in that directory, and they can be
> concatenated and manually fed into tools like below. In that case
> there is a --profile_sample_rate which can be set to avoid profiling
> every single bundle (e.g. for a production job).
> The PR is up at https://github.com/apache/beam/pull/6847 Hope it's useful.
> - Robert
>  https://docs.python.org/2/library/profile.html
>  https://github.com/jrfonseca/gprof2dot