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https://issues.apache.org/jira/browse/NIFI-5922?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16844982#comment-16844982
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ASF subversion and git services commented on NIFI-5922:
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Commit 60b8fcac1ba4dff64c28139ddf282d915f4c221b in nifi's branch
refs/heads/master from Mark Payne
[ https://gitbox.apache.org/repos/asf?p=nifi.git;h=60b8fca ]
NIFI-5922: Bug fixes; initialize, setup, and enable controller services; code
cleanup
> Stateless NiFi, an alternative runtime for NiFi flows
> -----------------------------------------------------
>
> Key: NIFI-5922
> URL: https://issues.apache.org/jira/browse/NIFI-5922
> Project: Apache NiFi
> Issue Type: Improvement
> Reporter: Sam Hjelmfelt
> Priority: Major
> Time Spent: 3h 50m
> Remaining Estimate: 0h
>
> NiFi-Fn is a library for running NiFi flows as stateless functions. It
> provides similar delivery guarantees as NiFi without the need for on-disk
> repositories by waiting to confirm receipt ofincoming data until it has been
> written to the destination. This is similar to Storm’s acking mechanism and
> Spark’s interface for committing Kafka offsets, except that in NiFi-Fn, this
> is completely handled by the framework while still supporting all NiFi
> processors and controller services natively without change.This results in
> the ability to run NiFi flows as ephemeral, stateless functions and should be
> able to rival MirrorMaker, Distcp, and Scoop for performance,efficiency, and
> scalability while leveraging the vast library of NiFi processors and the NiFi
> UI for building custom flows.
> By leveraging container engines (e.g.YARN, Kubernetes), long-running NiFi-Fn
> flows can be deployed that take full advantage of the platform’s scale and
> multi-tenancy features. By leveraging Function as a Service engines (FaaS)
> (e.g. AWS Lambda, Apache OpenWhisk), NiFi-Fn flows can be attached to event
> sources (or just cron) for event-driven data movement where flows only run
> when triggered and pricing is measured at the 100ms granularity. By combining
> the two, large-scale batch processing could also be performed.
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