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https://issues.apache.org/jira/browse/NIFI-6510?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16925819#comment-16925819
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ASF subversion and git services commented on NIFI-6510:
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Commit 8a8b9c1d086ac41b10647f09bdd7d8174a921de5 in nifi's branch
refs/heads/master from Andy I. Christianson
[ https://gitbox.apache.org/repos/asf?p=nifi.git;h=8a8b9c1 ]
NIFI-6510 - Analytics framework (#3681)
* NIFI-6510 Implement initial analytic engine
* NIFI-6510 Implemented basic linear regression model for queue counts
* NIFI-6510 Initial analytics REST endpoint and supporting objects
* NIFI-6510 Connect the dots for StatusAnalytics -> API
* NIFI-6510 Added poc engine with prediction model caching
(cherry picked from commit e013b91)
DFA-9 - updated logging and corrected logic for checking if not in backpressure
(cherry picked from commit a1f8e70)
* NIFI-6510 Updated objects and interfaces to reflect 4 prediction metrics
(cherry picked from commit 050e0fc)
(cherry picked from commit 9fd365f)
* NIFI-6510 adjustments for interface updates, added call to
StandardEventAccess, updated interface to use connection id
(cherry picked from commit 14854ff)
DFA-9 - reduced snapshot interval to 1 minute
(cherry picked from commit 36abb0a)
* NIFI-6510 Split StatusAnalytics interface into Engine and per-Connection
versions
* NIFI-6510 Remove redundant connection prediction interfaces as we can just
use ConnectionStatusAnalytics directly
* NIFI-6510 Revert "DFA-9 Remove redundant connection prediction interfaces as
we can just use ConnectionStatusAnalytics directly"
This reverts commit 5b9fead1471059098c0e98343fb337070f1c75c1.
* NIFI-6510 Added prediction fields for use by UI, still need to be populated
* NIFI-6510 Analytics Framework Introduction (#10)
* DFA-9 - Initial refactor for Status Analytics - created additional interfaces
for models, refactored callers to use StatusAnalytics objects with connection
context. Implemented SimpleRegression model.
DFA-9 - added logging
* DFA-9 - relocated query window to CSA from model, adding the prediction
percentages and time interval
* DFA-9 - checkstyle fixes
* NIFI-6510 Add prediction percent values and predicted interval seconds
(cherry picked from commit e60015d)
* NIFI-6510 Changes to inject flowManager instead of flow controller, also
changes to properly reflect when predictions can be made vs not.
(cherry picked from commit 6fae058)
* NIFI-6510 Added tests for engine
(cherry picked from commit 6d7a13b)
* NIFI-6150 Added tests for connection status analytics class, corrected
variable names
(cherry picked from commit 58c7c81)
* NIFI-6150 Make checkstyle happy
(cherry picked from commit b6e35ac)
* NIFI-6150 Fixed NaN check and refactored time prediction. Switched to use non
caching engine for testing
* NIFI-6510 Fixed checkstyle issue in TestConnectionStatusAnalytics
* NIFI-6510 Adjusted interval and incorporated R-squared check
Updates to support multiple variables for features, clearing cached regression
model based on r-squared values
Added ordinary least squares model, which truly uses multivariable regression.
Refactor of interfaces to include more general interface for variate models
(that include scoring support).
Ratcheck fixes
Added test for SimpleRegression. Minor fix for OLS model
fixed test errors
fixed checkstyle errors
(cherry picked from commit fab411b)
* NIFI-6510 Added property to nifi.properties - Prediction Interval for
connection status analytics (#11)
* NIFI-6566 - Refactor to decouple model instance from status analytics object.
Also allow configurable model from nifi.properties
NIFI-6566 - changes to allow scoring configurations for model in nifi.properties
NIFI-6566 - added default implementation value to NiFiProperties
NIFI-6566 - correction to default variable name in NiFiProperties, removed
unnecessary init method from ConnectionStatusAnalytics
Signed-off-by: Matthew Burgess <mattyb...@apache.org>
This closes #3663
* NIFI-6585 - Refactored tests to use mocked models and extract functions.
Added check in ConnectionStatusAnalytics to confirm expected model by type
* NIFI-6586 - documentation and comments
This closes NIFI-6586
Signed-off-by: Andrew I. Christianson <a...@andyic.org>
* NIFI-6568 - Surface time-to-back-pressure and initial predictions in the UI
* Add multi-line tooltips with detail for connection queue back pressure
graphics.
* Add estimated time to back pressure to connections summary table.
* Add back pressure prediction ticks.
* add moment.js to format predicted time to back pressure
* tweak summary table headings to match data displayed. re-order connection
summary columns
* NIFI-6568 - Properly sort the min estimated time to back pressure in the
connection summary table. Also added a js doc comment.
* NIFI-6510 - add an enable/disable property for analytics
* NIFI-6510 - documentation updates for enable/disable property
* NIFI-6510 - UI: handle the scenario where backpressure predictions are
disabled (#3685)
* NIFI-6510 - admin guide updates to further describe model functionality
* NIFI-6510 - code quality fixes (if statement and constructor)
* NIFI-6510 - log warnings when properties could not be retrieved. fixed
incorrect property retrieval for score threshold
* NIFI-6510 Extract out predictions into their own DTO
* NIFI-6510 Optimize imports
* NIFI-6510 Fix formatting
* NIFI-6510 Optimize imports
* NIFI-6510 Optimize imports
* NIFI-6510 - Notice updates for Commons math and Caffeine
* NIFI-6510 - UI updates to account for minor API changes for back pressure
predictions (#3697)
* NIFI-6510 - Fix issue displaying estimated time to back pressure in
connection summary table when only one of the predictions is known.
Signed-off-by: Matthew Burgess <mattyb...@apache.org>
This closes #3705
* NIFI-6510 Rip out useless members
* NIFI-6510 - dto updates to check for -1 value
* NIFI-6510 - checkstyle fix
* NIFI-6510 - rolled back last change and applied minNonNegative method
* NIFI-6510 Rip out useless members
> Predictive Analytics for NiFi Metrics
> -------------------------------------
>
> Key: NIFI-6510
> URL: https://issues.apache.org/jira/browse/NIFI-6510
> Project: Apache NiFi
> Issue Type: Improvement
> Reporter: Andrew Christianson
> Assignee: Yolanda M. Davis
> Priority: Major
> Fix For: 1.10.0
>
> Time Spent: 6h 10m
> Remaining Estimate: 0h
>
> From Yolanda's email to the list:
>
> {noformat}
> 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.{noformat}
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