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From Craig Knell <craig.kn...@gmail.com>
Subject Re: [DISCUSS] Predictive Analytics for NiFi Metrics
Date Wed, 31 Jul 2019 00:30:23 GMT
Hi Folks

That's our use case now.  All our Models are run in python.
Currently we send events to the ML via http, although this is not optimal

Our use case is edge ML where we want a light weight wrapper for
Python code base.
Jython however does not work with the code base
I'm think of changing the interface to some thing like REDIS for pub/sub
Id also like this to be a push deployment via minifi

Also support for sensors via protocols via Modbus and OPC would be great

Craig

On Wed, Jul 31, 2019 at 1:43 AM Joe Witt <joe.witt@gmail.com> wrote:
>
> Definitely something that I think would really help the community.  It
> might make sense to frame/structure these APIs such that an internal option
> could be available to reduce dependencies and get up and running but that
> also just as easily a remote implementation where the engine lives and is
> managed externally could also be supported.
>
> Thanks
>
>
> On Tue, Jul 30, 2019 at 1:40 PM Andy LoPresto <alopresto@apache.org> wrote:
>
> > Yolanda,
> >
> > I think this sounds like a great idea and will be very useful to
> > admins/users, as well as enabling some interesting next-level functionality
> > and insight generation. Thanks for putting this out there.
> >
> > Andy LoPresto
> > alopresto@apache.org
> > alopresto.apache@gmail.com
> > PGP Fingerprint: 70EC B3E5 98A6 5A3F D3C4  BACE 3C6E F65B 2F7D EF69
> >
> > > On Jul 30, 2019, at 5:55 AM, Yolanda Davis <yolanda.m.davis@gmail.com>
> > wrote:
> > >
> > > Hello Everyone,
> > >
> > > I wanted to reach out to the community to discuss potentially enhancing
> > > NiFi to include predictive analytics that can help users assess and
> > predict
> > > NiFi behavior and performance. Currently NiFi has lots of metrics
> > available
> > > for areas including jvm and flow component usage (via component status)
> > as
> > > well as provenance data which NiFi makes available either through the UI
> > or
> > > reporting tasks (for consumption by other systems). Past discussions in
> > the
> > > community cite users shipping this data to applications such as
> > Prometheus,
> > > ELK stacks, or Ambari metrics for further analysis in order to
> > > capture/review performance issues, detect anomalies, and send alerts or
> > > notifications.  These systems are efficient in capturing and helping to
> > > analyze these metrics however it requires customization work and
> > knowledge
> > > of NiFi operations to provide meaningful analytics within a flow context.
> > >
> > > In speaking with Matt Burgess and Andy Christianson on this topic we feel
> > > that there is an opportunity to introduce an analytics framework that
> > could
> > > provide users reasonable predictions on key performance indicators for
> > > flows, such as back pressure and flow rate, to help administrators
> > improve
> > > operational management of NiFi clusters.  This framework could offer
> > > several key features:
> > >
> > >   - Provide a flexible internal analytics engine and model api which
> > >   supports the addition of or enhancement to onboard models
> > >   - Support integration of remote or cloud based ML models
> > >   - Support both traditional and online (incremental) learning methods
> > >   - Provide support for model caching  (perhaps later inclusion into a
> > >   model repository or registry)
> > >   - UI enhancements to display prediction information either in existing
> > >   summary data, new data visualizations, or directly within the
> > flow/canvas
> > >   (where applicable)
> > >
> > > For an initial target we thought that back pressure prediction would be a
> > > good starting point for this initiative, given that back pressure
> > detection
> > > is a key indicator of flow performance and many of the metrics currently
> > > available would provide enough data points to create a reasonable
> > > performing model.  We have some ideas on how this could be achieved
> > however
> > > we wanted to discuss this more with the community to get thoughts about
> > > tackling this work, especially if there are specific use cases or other
> > > factors that should be considered.
> > >
> > > Looking forward to everyone's thoughts and input.
> > >
> > > Thanks,
> > >
> > > -yolanda
> > >
> > > --
> > > yolanda.m.davis@gmail.com
> > > @YolandaMDavis
> >
> >



-- 
Regards

Craig Knell
Mobile: +61 402 128 615
Skype: craigknell

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