Actually i was wrong on the UDF point. By variables i meant the information that is encoded in the scope, like the subtask index, task name, taskmanager ID etc., however all these can be accessed from the MetricGroup that is returned by RuntimeContext#getMetricGroup(), which you can of course use in your UDF.

On 22.09.2016 05:47, Eswar Reddy wrote:
Thank you Chesnay. Good to know there are few wrappers available to get best of both worlds. I may mostly go without piggybacking though to have more control and learning for now, but I will keep an eye for new benefits I will get in future via piggybacking. The UDF point looks like a deal breaker, I will spend some more time understanding it( we can get Flink's runtime using this <> inside the UDF so by 'variables' you must have meant the metrics object that gets passed around)

@Sumit, Adding backend writers(reporter) is as much simple in DropWizard as well. Thanks for bringing it up though.


On Tue, Sep 20, 2016 at 11:33 PM, Chawla,Sumit < <>> wrote:

    In addition, It supports enabling multiple Reporters.  You can
    have same data pushed to multiple systems.  Plus its very easy to
    write new reporter for doing any customization.

    Sumit Chawla

    On Tue, Sep 20, 2016 at 2:10 AM, Chesnay Schepler
    < <>> wrote:

        Hello Eswar,

        as far as I'm aware the general structure of the Flink's
        metric system is rather similar to DropWizard. You can use
        DropWizard metrics by creating a simple wrapper, we even ship
        one for Histograms. Furthermore, you can also use DropWizard
        reporters, you only have to extend the DropWizardReporter
        class, essentially providing a factory method for your reporter.

        Using Flinks infrastructure provides the following benefits:
        * better resource usage, as only a single reporter instance
        per taskmanager exists
        * access to system metrics
        * namespace stuff; you cannot access all variables yourselves
        from a UDF without modifying the source of Flink; whether this
        is an advantage is of course dependent on what you are
        interested in


        On 20.09.2016 08:29, Eswar Reddy wrote:

        I see Flink support's built-in metrics to monitor various
        components of Flink. In addition, one can register
        application specific(custom) metrics to Flink's built-in
        metrics infra. The problem with this is user has to develop
        his custom metrics using Flink's metrics framework/API rather
        than a generic framework such as dropwizard. Alternatively,
        user can follow this
        approach where his   dropwizard metrics push code is
        co-located with actual app code within each Task and metrics
        are directly pushed to a backend writer(say, Graphite) from
        each Task.

        In this alternative, I am aware of having to handle mapping
        spatial granularity of Flink's run-time with metrics
        namespace, but doing it myself should not a big effort.
        Fault-tolerance comes automatically since app code and
        metrics push code are co-located in the Task. Is there
        anything else Flink's metrics infra handles automatically?
        Based on this I'd weigh using good old dropwizard vs Flink
        specific metrics framework.

        Finally, I guess feasibility an automatic
        dropwizard-to-flinkmetrics translation utility can be checked
        out, but I would like to first understand additional benefits
        of using flink's infra for custom metrics.


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