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Usage Engine Private Edition now supports horizontal scaling of batch workflows, increasing or decreasing 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,’ enabling them to complete tasks in parallel rather than sequentially. Usage Engine’s solution consists of two new agents, a Scalable Inter Workflow Forwarding agent and a Scalable Inter Workflow Collection agent (Scalable InterWF). A Two new profile has profiles have also been created - the Partition Profile and the Scalable Inter Workflow Profile. The feature uses the existing agents, Data Aggregator and DeduplicationDuplicate UDR, which have been updated to support a Kafka storage type. Kafka must be configured for all storage within your scalable batch solution.
How it works
Scalable workflows operate by splitting batch data into partitions so that multiple workflows can cooperate to process a batch. Each scaled workflow is assigned one or more partitions and will process all the data assigned to them. When workflows are started or stopped, a rebalance is performed where partitions are reassigned to the new set of workflows.
This example shows a batch processing setup where you collect files and perform duplication checks and aggregation. We want to make this solution scalable to improve the processing times of our data during periods of high usage. We have set up two workflows in our batch scaling solution.
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In the File collection workflow the Scalable InterWF Forwarding agent in the File collection workflow manages the sends data to the partitions. It uses one or more unique ID Fields (e.g. customer ID) to determine which partition a UDR belongs to.
The maximum number of partitions created is determined by the Max Scale Factor parameter in the Partition Profile.
Note!
The number of partitions will be the same across all 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.
The Duplication Check workflow 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. Any duplicate keys are saved in a separate topic.
The Aggregation workflow will collect data from an inter-workflow topic and use a separate aggregation session storage topic.
Prerequisites for Kafka/batch scaling?
Your workflow must be designed in a way that can process batch workflows, for example, there has to be at least one common denominator in the data that links individual records such as, an id field. This field will then be used as the customer 'key' and will determine which partitions the data is assigned to.
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Processing workflow isthe workflow that scales, that is, you can run from one up to the Max Scale Factor of WFs that will cooperate to do the processing. In this example, records go through a duplication check and are aggregated. Persistent storage for Duplicate UDR check and aggregation is also partitioned.
Subsections
This section contains the following subsections:
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