@pavan srikar - The design you have in place looks to be correct solution based on your described use case here. Every node in your cluster runs the exact same flow.xml.gz - You would typically configure your "PutDistributedMapCache" and "FetchDistributedMapCache" processors to use a "Distributed Cache Service" that every node has access to. - This allows you run a single "primary node" only flow that retrieves the token based on a one hour cron and writes it to the distributed Map cache and then have a second flow that every node runs that pulls that stored token value from the distributed map cache and uses it for your downstream calls. - Using the "RedisDistributedMapCacheClientService" controller service for example allows you to set a TTL on the values you store in the cache. This allows you to expire the stored token before it is no longer valid. For example token is good for 1 hour, so you could set TTL to 50 - 55 minutes. - Thank you, Matt - If you found this answer addressed your question, please take a moment to login in and click the "ACCEPT" link. ... View more

@Cooper Max NiFi has two processes as you see above that are running. The NiFi bootstrap process is what is kicked off when starting NiFi and it then spans off the main NiFi process. The bootstrap process then monitors for the pid of that main process and if it disappears, the output you see above is thrown and the bootstrap then attempts tp restart the main process. - If your nifi-app.log is not exhibiting any signs of issues going on in your dataflow leading up to this event, the killing of this NiFi process is being triggered external to NiFi. - Most commonly you may find that the server itself has killed the process. I would suggest looking at your server logs for the execution of "OOM killer". When memory resources on a server reach usage a level where the OS feels it could result in the server become unresponsive or crash, oom killer is launched which evaluates the running process and elects a process to be killed to free memory to protect the OS. Considering the memory footprint of a typical main NiFi JVM process, it is commonly selected by the oom killer. - To resolve this issue, you would need to reduce the amount of memory that is being consumed by running process on this same server. - Do not run NIFi on server where other service are co-located - Reduce the configured JVM setting for the NiFi process in the nifi-bootsrap.conf file. ----- Above may require you to re-evaluate your dataflow design(s) in NiFi to reduce heap memory usage. - Thank you, Matt - If you found this answer addressed your question, please take a moment to login in and click the "ACCEPT" link. ... View more

@Thomas Lebrun Provenance events are dated. While the provenance repository can be moved from one NiFi to another without issue, simply backing up a portion of it or all of it and trying to merge it with an existing provenance repository later is not possible. - Even trying to take an entire backed up provenance repository and placing it in a clean NiFi later would have its challenges. You would need to make sure the provenance retention settings in whatever NiFi you placed this backed up Provenance repository extended beyond the age of the oldest event in that backed up provenance repository or NiFi would simply purge all the events on startup. - A better option might be to consider building a dataflow on each of your NiFi instances/clusters that uses the SiteToSiteProvenanceReportingTask to send provenance events to another NiFi where it would have a dataflow build to wrote out those events to your choice of long term storage or auditing endpoint of your choice. The provenance events output by this reporting task are just JSON. - https://nifi.apache.org/docs/nifi-docs/components/org.apache.nifi/nifi-site-to-site-reporting-nar/1.7.1/org.apache.nifi.reporting.SiteToSiteProvenanceReportingTask/index.html - Thank you, Matt ... View more

@yazeed salem Your NiFi expression Language statement looks good. I even tested base on your example and it routed my flowfiles correctly Make sure that each of the FlowFiles being processed have required FlowFile Attributes set on them. You can stop your RouteOnAttribute processor and allow a few files to queue in the connection feeding it. Then right click on connection and select "List queue". You can then click on "details" icon to far left of any FlowFIle to verify that it does have correct attributes set on it. - - Thank you, Matt - If you found this answer addressed your question, please take a moment to login in and click the "ACCEPT" link. ... View more

@sri chaturvedi - You are only going to benefit from setting run duration to 50ms if the processing of each incoming FlowFile to the putSQL processor is taking fractions of the 50ms duration. Details on "Run duration" and how it works can be found here: https://community.hortonworks.com/articles/221807/understanding-nifi-processors-run-duration-functio.html --------- When you set a run duration on a lot of processors, when those threads are executed, they will consume that CPU thread for possibly longer then needed. This means that other processors may end up waiting longer for a thread. - Consider this example: Your putSQL happens to be taking 10 ms to execute the put of a FlowFile. That means that with a 50 ms run duration it would put 5 FlowFiles within the single thread execution. What happens if incoming connection queue only has 1 FlowFile at time of execution? The processor holds that thread for 40 ms longer then needed. That is 40 ms of cpu time not available to another processor. - Since there is some time overhead in staring and stopping threads, run duration is very useful when you have a high sustained dataflow. It can actually decrease performance when used in dataflow where there is not high volume of FlowFiles (High volume is relative to the processors designed task.) - ---------- When it comes to concurrent tasks, this dictates parallel processor execution. - Since your canvas has 2000 processors, you need to understand that all these processor cannot execute at the exact same time. There is only so much CPU available and NiFi has a configureable thread pool size. This means that many processors may be just waiting in line for their chance to to get time on the CPU. - Details on processor Concurrent task setting recommendations can be found here: https://community.hortonworks.com/articles/221808/understanding-nifi-max-thread-pools-and-processor.html - ----------- - You also mentioned NiFi UI slowness. It may be related to nothing above: https://community.hortonworks.com/articles/184786/hdfnifi-improving-the-performance-of-your-ui.html - Thank you, Matt - If you found this answer addressed your question, please take a moment to login in and click the "ACCEPT" link. ... View more