Re: Re:[EXT] [DISCUSS] Predictive Analytics for NiFi Metrics
Hi Rob, Thanks for the UI PR. Taking a look. -Andy ‐‐‐ Original Message ‐‐‐ On Tuesday, August 20, 2019 10:04 AM, Robert Fellows wrote: > Mark and Yolanda, > I submitted the PR I mentioned yesterday for the UI changes that surface > the exposed prediction data. Let me know what you think. > > https://github.com/apache/nifi/pull/3660 > > Thanks, > Rob > > On Mon, Aug 19, 2019 at 4:17 PM Yolanda Davis yolanda.m.da...@gmail.com > wrote: > > > Hi Mark and Rob > > Mark thanks so much for the info on your work and Rob thanks for jumping in > > on the UI! I just wanted to add, Mark, that looking at your branch I think > > we also may have some opportunities to exchange notes or collaborate on the > > backend as well. The work in the feature branch is still in progress (with > > some decoupling to ensure we can allow flexible configuration of models). > > Please feel free to review and leave comments under the parent JIRA. At > > the same time I'll take a deeper dive on your branch and perhaps we can > > exchange notes on potential areas for improvement/collaboration if it makes > > sense? > > Thanks Again, > > -yolanda > > On Mon, Aug 19, 2019 at 3:34 PM Robert Fellows rob.fell...@gmail.com > > wrote: > > > > > Hey Mark, > > > I've started working on some UI based on the initial commit for this > > > proposal. What you have done and what I am working on have a bit of > > > overlap, but not much. > > > I'm working on getting the predicted count and bytes into the existing > > > connection metric display that is already on the canvas. The only overlap > > > looks like it might be in the > > > Summary table. I plan on adding a PR for my additions hopefully tomorrow. > > > Maybe once it is up we can discuss how we bring the them together where > > > it > > > makes sense? > > > This is the main JIRA case: > > > https://issues.apache.org/jira/browse/NIFI-6510 > > > And this is the subtask that I am working toward: > > > https://issues.apache.org/jira/browse/NIFI-6568 > > > -- Rob Fellows > > > On Mon, Aug 19, 2019 at 2:26 PM Owens, Mark jmow...@evoforge.org > > > wrote: > > > > > > > The images from the preview email do not appear to be displaying. They > > > > can > > > > be viewed at: > > > > https://github.com/jmark99/nifi-images > > > > From: Owens, Mark jmow...@evoforge.org > > > > Sent: Monday, August 19, 2019 2:25 PM > > > > To: dev@nifi.apache.org > > > > Subject: RE: Re:[EXT] [DISCUSS] Predictive Analytics for NiFi Metrics > > > > Hi Yolanda, > > > > I've been working on a feature that appears to possibly overlap with > > > > the > > > > > > > work you are pursuing. Perhaps we should see if/should we try to > > > > coordinate > > > > our efforts. I've been updating NiFi to predict the time to queue > > > > overflow > > > > for both flowfiles and bytes and displaying that information in the > > > > GUI. > > > > > > > For the initial attempt, I’ve been using a simple model of straight > > > > line > > > > > > > prediction over a sliding window of 15 minutes to predict when flows > > > > will > > > > > > > fail. This estimate is then displayed on both the NiFi Summary page > > > > under > > > > > > > the connections tab and in the status history graphs. Below are > > > > examples > > > > > > > of what would be displayed to the user. > > > > [cid:image001.png@01D55696.E4CCD550] > > > > The Connection tab contains a new column on the right that displays the > > > > prediction for both flow files and data size. The user can select a > > > > maximum > > > > time at which specific times are no longer displayed. In this example, > > > > if > > > > > > > the prediction lies beyond 12 hours then the display simply indicates > > > > that > > > > the flow is greater than 12 hours away from failure at the moment. > > > > [cid:image002.png@01D55697.2C8AC500] > > > > This display graphs the prediction for byte overflow over time. Note > > > > that > > > > > > > if the estimate is greater than the user provided maximum value of > > > > interest > > > > the graph maxes out at that time, effectively indicating no overflow > > > > concerns. > > >
Re: Re:[EXT] [DISCUSS] Predictive Analytics for NiFi Metrics
Mark and Yolanda, I submitted the PR I mentioned yesterday for the UI changes that surface the exposed prediction data. Let me know what you think. https://github.com/apache/nifi/pull/3660 Thanks, Rob On Mon, Aug 19, 2019 at 4:17 PM Yolanda Davis wrote: > Hi Mark and Rob > > Mark thanks so much for the info on your work and Rob thanks for jumping in > on the UI! I just wanted to add, Mark, that looking at your branch I think > we also may have some opportunities to exchange notes or collaborate on the > backend as well. The work in the feature branch is still in progress (with > some decoupling to ensure we can allow flexible configuration of models). > Please feel free to review and leave comments under the parent JIRA. At > the same time I'll take a deeper dive on your branch and perhaps we can > exchange notes on potential areas for improvement/collaboration if it makes > sense? > > Thanks Again, > > -yolanda > > > On Mon, Aug 19, 2019 at 3:34 PM Robert Fellows > wrote: > > > Hey Mark, > > I've started working on some UI based on the initial commit for this > > proposal. What you have done and what I am working on have a bit of > > overlap, but not much. > > I'm working on getting the predicted count and bytes into the existing > > connection metric display that is already on the canvas. The only overlap > > looks like it might be in the > > Summary table. I plan on adding a PR for my additions hopefully tomorrow. > > Maybe once it is up we can discuss how we bring the them together where > it > > makes sense? > > > > This is the main JIRA case: > > https://issues.apache.org/jira/browse/NIFI-6510 > > And this is the subtask that I am working toward: > > https://issues.apache.org/jira/browse/NIFI-6568 > > > > > > -- Rob Fellows > > > > On Mon, Aug 19, 2019 at 2:26 PM Owens, Mark > wrote: > > > > > The images from the preview email do not appear to be displaying. They > > can > > > be viewed at: > > > https://github.com/jmark99/nifi-images > > > > > > From: Owens, Mark > > > Sent: Monday, August 19, 2019 2:25 PM > > > To: dev@nifi.apache.org > > > Subject: RE: Re:[EXT] [DISCUSS] Predictive Analytics for NiFi Metrics > > > > > > > > > Hi Yolanda, > > > > > > > > > > > > I've been working on a feature that appears to possibly overlap with > the > > > work you are pursuing. Perhaps we should see if/should we try to > > coordinate > > > our efforts. I've been updating NiFi to predict the time to queue > > overflow > > > for both flowfiles and bytes and displaying that information in the > GUI. > > > For the initial attempt, I’ve been using a simple model of straight > line > > > prediction over a sliding window of 15 minutes to predict when flows > will > > > fail. This estimate is then displayed on both the NiFi Summary page > under > > > the connections tab and in the status history graphs. Below are > examples > > > of what would be displayed to the user. > > > > > > > > > > > > [cid:image001.png@01D55696.E4CCD550] > > > > > > > > > > > > The Connection tab contains a new column on the right that displays the > > > prediction for both flow files and data size. The user can select a > > maximum > > > time at which specific times are no longer displayed. In this example, > if > > > the prediction lies beyond 12 hours then the display simply indicates > > that > > > the flow is greater than 12 hours away from failure at the moment. > > > > > > > > > > > > [cid:image002.png@01D55697.2C8AC500] > > > > > > > > > > > > This display graphs the prediction for byte overflow over time. Note > that > > > if the estimate is greater than the user provided maximum value of > > interest > > > the graph maxes out at that time, effectively indicating no overflow > > > concerns. > > > > > > > > > > > > [cid:image003.png@01D55697.965C27D0] > > > > > > > > > > > > A similar display for flowfile count is displayed as well. > > > > > > > > > > > > The current state of work can be found at > > > https://github.com/jmark99/nifi/tree/time-to-overflow > > > > > > > > > > > > I welcome your (or any others) feedback on this effort. > > > > > > > > > > > > Thanks, >
Re: Re:[EXT] [DISCUSS] Predictive Analytics for NiFi Metrics
Hi Mark and Rob Mark thanks so much for the info on your work and Rob thanks for jumping in on the UI! I just wanted to add, Mark, that looking at your branch I think we also may have some opportunities to exchange notes or collaborate on the backend as well. The work in the feature branch is still in progress (with some decoupling to ensure we can allow flexible configuration of models). Please feel free to review and leave comments under the parent JIRA. At the same time I'll take a deeper dive on your branch and perhaps we can exchange notes on potential areas for improvement/collaboration if it makes sense? Thanks Again, -yolanda On Mon, Aug 19, 2019 at 3:34 PM Robert Fellows wrote: > Hey Mark, > I've started working on some UI based on the initial commit for this > proposal. What you have done and what I am working on have a bit of > overlap, but not much. > I'm working on getting the predicted count and bytes into the existing > connection metric display that is already on the canvas. The only overlap > looks like it might be in the > Summary table. I plan on adding a PR for my additions hopefully tomorrow. > Maybe once it is up we can discuss how we bring the them together where it > makes sense? > > This is the main JIRA case: > https://issues.apache.org/jira/browse/NIFI-6510 > And this is the subtask that I am working toward: > https://issues.apache.org/jira/browse/NIFI-6568 > > > -- Rob Fellows > > On Mon, Aug 19, 2019 at 2:26 PM Owens, Mark wrote: > > > The images from the preview email do not appear to be displaying. They > can > > be viewed at: > > https://github.com/jmark99/nifi-images > > > > From: Owens, Mark > > Sent: Monday, August 19, 2019 2:25 PM > > To: dev@nifi.apache.org > > Subject: RE: Re:[EXT] [DISCUSS] Predictive Analytics for NiFi Metrics > > > > > > Hi Yolanda, > > > > > > > > I've been working on a feature that appears to possibly overlap with the > > work you are pursuing. Perhaps we should see if/should we try to > coordinate > > our efforts. I've been updating NiFi to predict the time to queue > overflow > > for both flowfiles and bytes and displaying that information in the GUI. > > For the initial attempt, I’ve been using a simple model of straight line > > prediction over a sliding window of 15 minutes to predict when flows will > > fail. This estimate is then displayed on both the NiFi Summary page under > > the connections tab and in the status history graphs. Below are examples > > of what would be displayed to the user. > > > > > > > > [cid:image001.png@01D55696.E4CCD550] > > > > > > > > The Connection tab contains a new column on the right that displays the > > prediction for both flow files and data size. The user can select a > maximum > > time at which specific times are no longer displayed. In this example, if > > the prediction lies beyond 12 hours then the display simply indicates > that > > the flow is greater than 12 hours away from failure at the moment. > > > > > > > > [cid:image002.png@01D55697.2C8AC500] > > > > > > > > This display graphs the prediction for byte overflow over time. Note that > > if the estimate is greater than the user provided maximum value of > interest > > the graph maxes out at that time, effectively indicating no overflow > > concerns. > > > > > > > > [cid:image003.png@01D55697.965C27D0] > > > > > > > > A similar display for flowfile count is displayed as well. > > > > > > > > The current state of work can be found at > > https://github.com/jmark99/nifi/tree/time-to-overflow > > > > > > > > I welcome your (or any others) feedback on this effort. > > > > > > > > Thanks, > > Mark > > > > > > > > P.S. If the images are not displaying, they can be viewed at > > https://github.com/jmark99/nifi-images > > > > > > > > > > > > > > > > -Original Message- > > From: Yolanda Davis > yolanda.m.da...@gmail.com>> > > Sent: Monday, August 19, 2019 11:29 AM > > To: dev@nifi.apache.org<mailto:dev@nifi.apache.org> > > Subject: Re:[EXT] [DISCUSS] Predictive Analytics for NiFi Metrics > > > > > > > > Hello All, > > > > > > > > I just wanted to follow up on the discussion we started a couple of weeks > > ago concerning an analytics framework for NiFi metrics. Working with > Andy > > Christianson and Matt Burgess we shaped our ideas and drafted a proposal > > for
Re: Re:[EXT] [DISCUSS] Predictive Analytics for NiFi Metrics
Hey Mark, I've started working on some UI based on the initial commit for this proposal. What you have done and what I am working on have a bit of overlap, but not much. I'm working on getting the predicted count and bytes into the existing connection metric display that is already on the canvas. The only overlap looks like it might be in the Summary table. I plan on adding a PR for my additions hopefully tomorrow. Maybe once it is up we can discuss how we bring the them together where it makes sense? This is the main JIRA case: https://issues.apache.org/jira/browse/NIFI-6510 And this is the subtask that I am working toward: https://issues.apache.org/jira/browse/NIFI-6568 -- Rob Fellows On Mon, Aug 19, 2019 at 2:26 PM Owens, Mark wrote: > The images from the preview email do not appear to be displaying. They can > be viewed at: > https://github.com/jmark99/nifi-images > > From: Owens, Mark > Sent: Monday, August 19, 2019 2:25 PM > To: dev@nifi.apache.org > Subject: RE: Re:[EXT] [DISCUSS] Predictive Analytics for NiFi Metrics > > > Hi Yolanda, > > > > I've been working on a feature that appears to possibly overlap with the > work you are pursuing. Perhaps we should see if/should we try to coordinate > our efforts. I've been updating NiFi to predict the time to queue overflow > for both flowfiles and bytes and displaying that information in the GUI. > For the initial attempt, I’ve been using a simple model of straight line > prediction over a sliding window of 15 minutes to predict when flows will > fail. This estimate is then displayed on both the NiFi Summary page under > the connections tab and in the status history graphs. Below are examples > of what would be displayed to the user. > > > > [cid:image001.png@01D55696.E4CCD550] > > > > The Connection tab contains a new column on the right that displays the > prediction for both flow files and data size. The user can select a maximum > time at which specific times are no longer displayed. In this example, if > the prediction lies beyond 12 hours then the display simply indicates that > the flow is greater than 12 hours away from failure at the moment. > > > > [cid:image002.png@01D55697.2C8AC500] > > > > This display graphs the prediction for byte overflow over time. Note that > if the estimate is greater than the user provided maximum value of interest > the graph maxes out at that time, effectively indicating no overflow > concerns. > > > > [cid:image003.png@01D55697.965C27D0] > > > > A similar display for flowfile count is displayed as well. > > > > The current state of work can be found at > https://github.com/jmark99/nifi/tree/time-to-overflow > > > > I welcome your (or any others) feedback on this effort. > > > > Thanks, > Mark > > > > P.S. If the images are not displaying, they can be viewed at > https://github.com/jmark99/nifi-images > > > > > > > > -Original Message- > From: Yolanda Davis yolanda.m.da...@gmail.com>> > Sent: Monday, August 19, 2019 11:29 AM > To: dev@nifi.apache.org<mailto:dev@nifi.apache.org> > Subject: Re:[EXT] [DISCUSS] Predictive Analytics for NiFi Metrics > > > > Hello All, > > > > I just wanted to follow up on the discussion we started a couple of weeks > ago concerning an analytics framework for NiFi metrics. Working with Andy > Christianson and Matt Burgess we shaped our ideas and drafted a proposal > for this feature on the Apache NiFi Wiki [1] . We've also begun > implementing some of these ideas in a feature branch (which is work in > > progress) [2]. We’d appreciate any questions or feedback you may have. > > > > Thanks, > > > > -yolanda > > > > [1] - > > > https://cwiki.apache.org/confluence/display/NIFI/Operational+Analytics+Framework+for+NiFi > > [2] - https://github.com/apache/nifi/commits/analytics-framework > > > > On Wed, Jul 31, 2019 at 9:58 AM Andy Christianson .invalid<mailto:aichr...@protonmail.com.invalid>> wrote: > > > > > As someone who operated a 24/7 mission-critical NiFi flow, this > > > feature would have been a life saver. If I'm heading home on a Friday, > > > it would be great to have some blinking red lights to let me know that > > > the system predicts that it is going to experience backpressure > > > sometime over the weekend, so that corrective action could be taken > before leaving. > > > > > > Since there is support in the community for this, I created a JIRA to > > > track the effort: > > > > > > https://issues.apache.org/jira/browse/NIFI-6510 > > > > > > I also created a JIRA to
RE: Re:[EXT] [DISCUSS] Predictive Analytics for NiFi Metrics
The images from the preview email do not appear to be displaying. They can be viewed at: https://github.com/jmark99/nifi-images From: Owens, Mark Sent: Monday, August 19, 2019 2:25 PM To: dev@nifi.apache.org Subject: RE: Re:[EXT] [DISCUSS] Predictive Analytics for NiFi Metrics Hi Yolanda, I've been working on a feature that appears to possibly overlap with the work you are pursuing. Perhaps we should see if/should we try to coordinate our efforts. I've been updating NiFi to predict the time to queue overflow for both flowfiles and bytes and displaying that information in the GUI. For the initial attempt, I’ve been using a simple model of straight line prediction over a sliding window of 15 minutes to predict when flows will fail. This estimate is then displayed on both the NiFi Summary page under the connections tab and in the status history graphs. Below are examples of what would be displayed to the user. [cid:image001.png@01D55696.E4CCD550] The Connection tab contains a new column on the right that displays the prediction for both flow files and data size. The user can select a maximum time at which specific times are no longer displayed. In this example, if the prediction lies beyond 12 hours then the display simply indicates that the flow is greater than 12 hours away from failure at the moment. [cid:image002.png@01D55697.2C8AC500] This display graphs the prediction for byte overflow over time. Note that if the estimate is greater than the user provided maximum value of interest the graph maxes out at that time, effectively indicating no overflow concerns. [cid:image003.png@01D55697.965C27D0] A similar display for flowfile count is displayed as well. The current state of work can be found at https://github.com/jmark99/nifi/tree/time-to-overflow I welcome your (or any others) feedback on this effort. Thanks, Mark P.S. If the images are not displaying, they can be viewed at https://github.com/jmark99/nifi-images -Original Message- From: Yolanda Davis mailto:yolanda.m.da...@gmail.com>> Sent: Monday, August 19, 2019 11:29 AM To: dev@nifi.apache.org<mailto:dev@nifi.apache.org> Subject: Re:[EXT] [DISCUSS] Predictive Analytics for NiFi Metrics Hello All, I just wanted to follow up on the discussion we started a couple of weeks ago concerning an analytics framework for NiFi metrics. Working with Andy Christianson and Matt Burgess we shaped our ideas and drafted a proposal for this feature on the Apache NiFi Wiki [1] . We've also begun implementing some of these ideas in a feature branch (which is work in progress) [2]. We’d appreciate any questions or feedback you may have. Thanks, -yolanda [1] - https://cwiki.apache.org/confluence/display/NIFI/Operational+Analytics+Framework+for+NiFi [2] - https://github.com/apache/nifi/commits/analytics-framework On Wed, Jul 31, 2019 at 9:58 AM Andy Christianson mailto:aichr...@protonmail.com.invalid>> wrote: > As someone who operated a 24/7 mission-critical NiFi flow, this > feature would have been a life saver. If I'm heading home on a Friday, > it would be great to have some blinking red lights to let me know that > the system predicts that it is going to experience backpressure > sometime over the weekend, so that corrective action could be taken before > leaving. > > Since there is support in the community for this, I created a JIRA to > track the effort: > > https://issues.apache.org/jira/browse/NIFI-6510 > > I also created a JIRA to track the remote protocol: > > https://issues.apache.org/jira/browse/NIFI-6511 > > > Regards, > > Andy > > > Sent from ProtonMail, Swiss-based encrypted email. > > ‐‐‐ Original Message ‐‐‐ > On Wednesday, July 31, 2019 6:57 AM, Arpad Boda > mailto:ab...@apache.org>> wrote: > > > If you could share a bit more details about your OPC and Modbus > > usage, > that > > would be highly appreciated! > > > > On Wed, Jul 31, 2019 at 12:01 PM Craig Knell > > craig.kn...@gmail.com<mailto:craig.kn...@gmail.com> > wrote: > > > > > Sounds. Great > > > Let me know if you need some help > > > Best regards > > > Craig > > > > > > > On 31 Jul 2019, at 17:31, Arpad Boda > > > > ab...@cloudera.com.invalid<mailto:ab...@cloudera.com.invalid> > wrote: > > > > Craig, > > > > OPC ( https://issues.apache.org/jira/browse/MINIFICPP-819 ) and > Modbus ( > > > > https://issues.apache.org/jira/browse/MINIFICPP-897 ) are on the > way for > > > > MiNiFi c++, hopefully both will be part of next release (0.7.0). > > > > It's gonna be legen... wait for it! :) Regards, Arpad > > > > > > &
RE: Re:[EXT] [DISCUSS] Predictive Analytics for NiFi Metrics
Hi Yolanda, I've been working on a feature that appears to possibly overlap with the work you are pursuing. Perhaps we should see if/should we try to coordinate our efforts. I've been updating NiFi to predict the time to queue overflow for both flowfiles and bytes and displaying that information in the GUI. For the initial attempt, I’ve been using a simple model of straight line prediction over a sliding window of 15 minutes to predict when flows will fail. This estimate is then displayed on both the NiFi Summary page under the connections tab and in the status history graphs. Below are examples of what would be displayed to the user. [cid:image001.png@01D55696.E4CCD550] The Connection tab contains a new column on the right that displays the prediction for both flow files and data size. The user can select a maximum time at which specific times are no longer displayed. In this example, if the prediction lies beyond 12 hours then the display simply indicates that the flow is greater than 12 hours away from failure at the moment. [cid:image002.png@01D55697.2C8AC500] This display graphs the prediction for byte overflow over time. Note that if the estimate is greater than the user provided maximum value of interest the graph maxes out at that time, effectively indicating no overflow concerns. [cid:image003.png@01D55697.965C27D0] A similar display for flowfile count is displayed as well. The current state of work can be found at https://github.com/jmark99/nifi/tree/time-to-overflow I welcome your (or any others) feedback on this effort. Thanks, Mark P.S. If the images are not displaying, they can be viewed at https://github.com/jmark99/nifi-images -Original Message- From: Yolanda Davis Sent: Monday, August 19, 2019 11:29 AM To: dev@nifi.apache.org Subject: Re:[EXT] [DISCUSS] Predictive Analytics for NiFi Metrics Hello All, I just wanted to follow up on the discussion we started a couple of weeks ago concerning an analytics framework for NiFi metrics. Working with Andy Christianson and Matt Burgess we shaped our ideas and drafted a proposal for this feature on the Apache NiFi Wiki [1] . We've also begun implementing some of these ideas in a feature branch (which is work in progress) [2]. We’d appreciate any questions or feedback you may have. Thanks, -yolanda [1] - https://cwiki.apache.org/confluence/display/NIFI/Operational+Analytics+Framework+for+NiFi [2] - https://github.com/apache/nifi/commits/analytics-framework On Wed, Jul 31, 2019 at 9:58 AM Andy Christianson mailto:aichr...@protonmail.com.invalid>> wrote: > As someone who operated a 24/7 mission-critical NiFi flow, this > feature would have been a life saver. If I'm heading home on a Friday, > it would be great to have some blinking red lights to let me know that > the system predicts that it is going to experience backpressure > sometime over the weekend, so that corrective action could be taken before > leaving. > > Since there is support in the community for this, I created a JIRA to > track the effort: > > https://issues.apache.org/jira/browse/NIFI-6510 > > I also created a JIRA to track the remote protocol: > > https://issues.apache.org/jira/browse/NIFI-6511 > > > Regards, > > Andy > > > Sent from ProtonMail, Swiss-based encrypted email. > > ‐‐‐ Original Message ‐‐‐ > On Wednesday, July 31, 2019 6:57 AM, Arpad Boda > mailto:ab...@apache.org>> wrote: > > > If you could share a bit more details about your OPC and Modbus > > usage, > that > > would be highly appreciated! > > > > On Wed, Jul 31, 2019 at 12:01 PM Craig Knell > > craig.kn...@gmail.com<mailto:craig.kn...@gmail.com> > wrote: > > > > > Sounds. Great > > > Let me know if you need some help > > > Best regards > > > Craig > > > > > > > On 31 Jul 2019, at 17:31, Arpad Boda > > > > ab...@cloudera.com.invalid<mailto:ab...@cloudera.com.invalid> > wrote: > > > > Craig, > > > > OPC ( https://issues.apache.org/jira/browse/MINIFICPP-819 ) and > Modbus ( > > > > https://issues.apache.org/jira/browse/MINIFICPP-897 ) are on the > way for > > > > MiNiFi c++, hopefully both will be part of next release (0.7.0). > > > > It's gonna be legen... wait for it! :) Regards, Arpad > > > > > > > > > On Wed, Jul 31, 2019 at 2:30 AM Craig Knell > > > > > craig.kn...@gmail.com<mailto:craig.kn...@gmail.com> > > > > > wrote: > > > > > > > > > Hi Folks > > > > > That's our use case now. All our Models are run in python. > > > > > Currently we send
Re:[EXT] [DISCUSS] Predictive Analytics for NiFi Metrics
Hello All, I just wanted to follow up on the discussion we started a couple of weeks ago concerning an analytics framework for NiFi metrics. Working with Andy Christianson and Matt Burgess we shaped our ideas and drafted a proposal for this feature on the Apache NiFi Wiki [1] . We've also begun implementing some of these ideas in a feature branch (which is work in progress) [2]. We’d appreciate any questions or feedback you may have. Thanks, -yolanda [1] - https://cwiki.apache.org/confluence/display/NIFI/Operational+Analytics+Framework+for+NiFi [2] - https://github.com/apache/nifi/commits/analytics-framework On Wed, Jul 31, 2019 at 9:58 AM Andy Christianson wrote: > As someone who operated a 24/7 mission-critical NiFi flow, this feature > would have been a life saver. If I'm heading home on a Friday, it would be > great to have some blinking red lights to let me know that the system > predicts that it is going to experience backpressure sometime over the > weekend, so that corrective action could be taken before leaving. > > Since there is support in the community for this, I created a JIRA to > track the effort: > > https://issues.apache.org/jira/browse/NIFI-6510 > > I also created a JIRA to track the remote protocol: > > https://issues.apache.org/jira/browse/NIFI-6511 > > > Regards, > > Andy > > > Sent from ProtonMail, Swiss-based encrypted email. > > ‐‐‐ Original Message ‐‐‐ > On Wednesday, July 31, 2019 6:57 AM, Arpad Boda wrote: > > > If you could share a bit more details about your OPC and Modbus usage, > that > > would be highly appreciated! > > > > On Wed, Jul 31, 2019 at 12:01 PM Craig Knell craig.kn...@gmail.com > wrote: > > > > > Sounds. Great > > > Let me know if you need some help > > > Best regards > > > Craig > > > > > > > On 31 Jul 2019, at 17:31, Arpad Boda ab...@cloudera.com.invalid > wrote: > > > > Craig, > > > > OPC ( https://issues.apache.org/jira/browse/MINIFICPP-819 ) and > Modbus ( > > > > https://issues.apache.org/jira/browse/MINIFICPP-897 ) are on the > way for > > > > MiNiFi c++, hopefully both will be part of next release (0.7.0). > > > > It's gonna be legen... wait for it! :) > > > > Regards, > > > > Arpad > > > > > > > > > On Wed, Jul 31, 2019 at 2:30 AM Craig Knell craig.kn...@gmail.com > > > > > wrote: > > > > > > > > > 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.w...@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 > alopre...@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 > > > > > > > alopre...@apache.org > > > > > > > alopresto.apa...@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.da...@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
Re: [DISCUSS] Predictive Analytics for NiFi Metrics
Hi Craig, Thanks for your feedback and insight on your use cases. What version of MiNiFi are you running? Concerning performing edge ML this may be possible for you with MiNiFi C++ version 0.6.0. That release supports the creation of python processors which can be added to your flow to execute models. Andy Christianson sent me info from a blog written by Marc Parisi on this topic here: https://www.parisi.io/index.php/2019/03/27/hey-bro/ In creating an analytics framework for models we may look to simplify things further where instead of creating a processor for models you could perhaps just implement a simple interface and rely on the engine to execute things as needed. But for now perhaps the python processsor could help fill the gap for you? -yolanda On Wed, Jul 31, 2019 at 6:01 AM Craig Knell wrote: > Sounds. Great > > Let me know if you need some help > > Best regards > > Craig > > > > > On 31 Jul 2019, at 17:31, Arpad Boda wrote: > > > > Craig, > > > > OPC ( https://issues.apache.org/jira/browse/MINIFICPP-819 ) and Modbus ( > > https://issues.apache.org/jira/browse/MINIFICPP-897 ) are on the way for > > MiNiFi c++, hopefully both will be part of next release (0.7.0). > > It's gonna be legen... wait for it! :) > > > > Regards, > > Arpad > > > >> On Wed, Jul 31, 2019 at 2:30 AM Craig Knell > wrote: > >> > >> 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 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 > >> 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 > alopre...@apache.org > alopresto.apa...@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.da...@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
Re: [DISCUSS] Predictive Analytics for NiFi Metrics
As someone who operated a 24/7 mission-critical NiFi flow, this feature would have been a life saver. If I'm heading home on a Friday, it would be great to have some blinking red lights to let me know that the system predicts that it is going to experience backpressure sometime over the weekend, so that corrective action could be taken before leaving. Since there is support in the community for this, I created a JIRA to track the effort: https://issues.apache.org/jira/browse/NIFI-6510 I also created a JIRA to track the remote protocol: https://issues.apache.org/jira/browse/NIFI-6511 Regards, Andy Sent from ProtonMail, Swiss-based encrypted email. ‐‐‐ Original Message ‐‐‐ On Wednesday, July 31, 2019 6:57 AM, Arpad Boda wrote: > If you could share a bit more details about your OPC and Modbus usage, that > would be highly appreciated! > > On Wed, Jul 31, 2019 at 12:01 PM Craig Knell craig.kn...@gmail.com wrote: > > > Sounds. Great > > Let me know if you need some help > > Best regards > > Craig > > > > > On 31 Jul 2019, at 17:31, Arpad Boda ab...@cloudera.com.invalid wrote: > > > Craig, > > > OPC ( https://issues.apache.org/jira/browse/MINIFICPP-819 ) and Modbus ( > > > https://issues.apache.org/jira/browse/MINIFICPP-897 ) are on the way for > > > MiNiFi c++, hopefully both will be part of next release (0.7.0). > > > It's gonna be legen... wait for it! :) > > > Regards, > > > Arpad > > > > > > > On Wed, Jul 31, 2019 at 2:30 AM Craig Knell craig.kn...@gmail.com > > > > wrote: > > > > > > > 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.w...@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 alopre...@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 > > > > > > alopre...@apache.org > > > > > > alopresto.apa...@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.da...@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 > > > > >
Re: [DISCUSS] Predictive Analytics for NiFi Metrics
If you could share a bit more details about your OPC and Modbus usage, that would be highly appreciated! On Wed, Jul 31, 2019 at 12:01 PM Craig Knell wrote: > Sounds. Great > > Let me know if you need some help > > Best regards > > Craig > > > > > On 31 Jul 2019, at 17:31, Arpad Boda wrote: > > > > Craig, > > > > OPC ( https://issues.apache.org/jira/browse/MINIFICPP-819 ) and Modbus ( > > https://issues.apache.org/jira/browse/MINIFICPP-897 ) are on the way for > > MiNiFi c++, hopefully both will be part of next release (0.7.0). > > It's gonna be legen... wait for it! :) > > > > Regards, > > Arpad > > > >> On Wed, Jul 31, 2019 at 2:30 AM Craig Knell > wrote: > >> > >> 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 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 > >> 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 > alopre...@apache.org > alopresto.apa...@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.da...@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
Re: [DISCUSS] Predictive Analytics for NiFi Metrics
Sounds. Great Let me know if you need some help Best regards Craig > On 31 Jul 2019, at 17:31, Arpad Boda wrote: > > Craig, > > OPC ( https://issues.apache.org/jira/browse/MINIFICPP-819 ) and Modbus ( > https://issues.apache.org/jira/browse/MINIFICPP-897 ) are on the way for > MiNiFi c++, hopefully both will be part of next release (0.7.0). > It's gonna be legen... wait for it! :) > > Regards, > Arpad > >> On Wed, Jul 31, 2019 at 2:30 AM Craig Knell wrote: >> >> 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 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 >> 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 alopre...@apache.org alopresto.apa...@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.da...@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.da...@gmail.com > @YolandaMDavis >> >> >> >> -- >> Regards >> >> Craig Knell >> Mobile: +61 402 128 615 >> Skype:
Re: [DISCUSS] Predictive Analytics for NiFi Metrics
Craig, OPC ( https://issues.apache.org/jira/browse/MINIFICPP-819 ) and Modbus ( https://issues.apache.org/jira/browse/MINIFICPP-897 ) are on the way for MiNiFi c++, hopefully both will be part of next release (0.7.0). It's gonna be legen... wait for it! :) Regards, Arpad On Wed, Jul 31, 2019 at 2:30 AM Craig Knell wrote: > 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 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 > 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 > > > alopre...@apache.org > > > alopresto.apa...@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.da...@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.da...@gmail.com > > > > @YolandaMDavis > > > > > > > > > > -- > Regards > > Craig Knell > Mobile: +61 402 128 615 > Skype: craigknell >
Re: [DISCUSS] Predictive Analytics for NiFi Metrics
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 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 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 > > alopre...@apache.org > > alopresto.apa...@gmail.com > > PGP Fingerprint: 70EC B3E5 98A6 5A3F D3C4 BACE 3C6E F65B 2F7D EF69 > > > > > On Jul 30, 2019, at 5:55 AM, Yolanda Davis > > 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.da...@gmail.com > > > @YolandaMDavis > > > > -- Regards Craig Knell Mobile: +61 402 128 615 Skype: craigknell
Re: [DISCUSS] Predictive Analytics for NiFi Metrics
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 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 > alopre...@apache.org > alopresto.apa...@gmail.com > PGP Fingerprint: 70EC B3E5 98A6 5A3F D3C4 BACE 3C6E F65B 2F7D EF69 > > > On Jul 30, 2019, at 5:55 AM, Yolanda Davis > 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.da...@gmail.com > > @YolandaMDavis > >
Re: [DISCUSS] Predictive Analytics for NiFi Metrics
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 alopre...@apache.org alopresto.apa...@gmail.com PGP Fingerprint: 70EC B3E5 98A6 5A3F D3C4 BACE 3C6E F65B 2F7D EF69 > On Jul 30, 2019, at 5:55 AM, Yolanda Davis 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.da...@gmail.com > @YolandaMDavis
[DISCUSS] Predictive Analytics for NiFi Metrics
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.da...@gmail.com @YolandaMDavis
