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Usage Engine Private Edition now supports batch scaling, making it possible to increase or decrease processing capacity as needed without manual intervention. As a general concept, batch scaling is a way to speed up processes by splitting the workload between multiple “workers” or resources, enabling them to complete tasks in parallel rather than sequentially. Usage Engine’s solution consists of two new agents, the Scalable InterWF Forwarder and Collector, and a new profile, the Partition Profile. It also uses the existing agents, Data Aggregator and Deduplication, which have been updated to include a Kafka storage profile. Add something here about recommended use cases as per the note above.
Prerequisites for Kafka?
Are there any prerequisites required to be able to configure automatic batch scaling?…
How it works
Assume that you have a batch use case where you collect files, and have to do duplication checks and aggregation. You want to be able to scale. You need 2 or 3 WFs. In the picture below we use 3 WFs.
You collect a large number of files and you want to process the data in them more efficiently. This can be achieved by creating… ,
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make your solution scalable to improve the processing times of your data during periods of high usage. You will need to create 2-3 workflows in your new batch scaling solution. In this example, we use three.
The File collection workflow(s) manage the InterWF (Inter workflow) partitions and they will use an ID Field (e.g. customer ID) to determine which shard/partition a UDR belongs to. - they manage the InterWF partitions
Add image example of the ‘File collection workflow’
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The number of partitions created is determined by the Max Scale Factor parameter
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. This
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is configured in ….
Note!
The number of partitions will be the same across all storage buckets/caches/topics. The points of storage will occur, for example,
With the passing of UDRs between workflows.
When duplicate UDR keys are detected.
For aggregated sessions.
Then, the The Duplication Check workflow(s) will check for duplicates across all partitions. Checked UDRs are placed in an additional topic with the same partitions as the corresponding Collection workflow topic. (The Duplication Duplicate keys are saved in a separate topic with the same number of partitions having the same ID fields.)
Finally, the The Aggregation workflow(s) will collect data from an inter-workflow topic , and use a separate aggregation session storage topic.
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Prerequisites for Kafka?
Are there any prerequisites required to be able to configure batch scaling using Kafka storage?
Subsections
This section contains the following subsections:
you use the new agent InterWF Collector, to pick up the files from the external system/ IF storage (InterWF partition). You also need to have Duplication checks after which you will use the InterWF Forwarder to take the non-duplicated files and feed them to the Aggregation partitions on the data (pretty common processes in any workflow group) You will use the current agents Deduplicate and Data Aggregator, however, they will have a new storage profile option for Kafka, which you need to configure. Finally you would use the other new agent
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From Chat GPT re: Topics - For draft purposes only: In comparison, a cache is a direct storage solution intended for fast access to data. Topics, on the other hand, are about managing and distributing data streams efficiently across systems. |
Subsections
This section contains the following subsections:
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