Member since
02-01-2022
274
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97
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60
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My Accepted Solutions
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2177 | 12-05-2023 06:22 AM |
04-30-2024
08:28 AM
Excellent work here Oleksandr!!
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04-09-2024
01:48 PM
1 Kudo
What headers do you set inside of Postman?
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04-02-2024
11:31 AM
2 Kudos
If you meant Storing Apache NiFi Versioned Flows in a Git Repository hosted on Azure DevOps, then the steps should be similar to the steps you followed to connect to Github. The appropriate flowPersistenceProvider is GitFlowPersistenceProvider. You can follow the steps as mentioned here: Storing Apache NiFi Versioned Flows in a Git Repos... - Cloudera Community - 248713 Or the video: https://youtu.be/kK7eVppg9Aw Only changes you would have to do is to generate PAT in Azure DevOps with permissions on code read, write, modify. P.S. The solution is only available for Git under Azure DevOps, and not for TFS.
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03-19-2024
02:24 AM
2 Kudos
Congratulations
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03-14-2024
04:03 PM
@ehehtenandayo Has the reply helped resolve your issue? If so, please mark the appropriate reply as the solution, as it will make it easier for others to find the answer in the future. Thanks.
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02-26-2024
08:27 AM
3 Kudos
@krishna123 @jameswookyz @rafy NiFi processor are configured with a Run Schedule, by default processors are configured with a Run Schedule of 0 secs. This tells NiFi core to schedule this processor to execute as often as possible. The Scheduling part of the processor handles checking if any of the inbound connections to the processor with queued data or last execution resulted in data. If there is no inbound queued FlowFiles, the NiFi controller will yield the processor scheduling. This yielding is designed to prevent the processor from just constantly trying to schedule when there is no work to do. If there is work to do, the processor will get scheduled to execute. The scheduling typically consumes microseconds of CPU time. And the built-in yielding prevents excessive cpu usage when no work exists to execute upon. Adjusting the run schedule does not change behavior of yielding, but when flow is constant for periods of time, changing the run schedule alters the throughput performance. Hope this clarifies things. If you found any of the suggestions/solutions provided helped you with your issue, please take a moment to login and click "Accept as Solution" on one or more of them that helped. Thank you, Matt
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01-31-2024
02:21 AM
1 Kudo
Upon examination, implementing the queue as a First In First Out prioritizer and configuring the load balancing strategy to Partition by attribute with the kafka.partition attribute has proven effective in maintaining the order.
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01-26-2024
11:27 AM
@ZorteK Has the reply helped resolve your issue? If so, please mark the appropriate reply as the solution, as it will make it easier for others to find the answer in the future. Thanks.
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01-18-2024
05:39 AM
@Alfies Thanks for posting. I cant see anything that jumps as far as why this would not work in 1.21. I would wonder if it works in 1.24. If it does, then for sure that compatiblity was added since 1.21. If you are required to use 1.21, i would recommend opening a Jira to report the bug you are describing.
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