I plan to utilize all 32 partitions in Azure event hubs. Requirement: "Ordered" processing per partition is critical.. Question: If I increase the TU's (Throughput Units) to max available of 20 across all 32 partitions, I get 40 MB of egress. Let's say I calculated that I need 500 parallel client threads processing in parallel (EventProcessorClient) to achieve my throughput needs. How do I achieve this level of parallelism with EventProcessorClient while honoring my "Ordering" requirement? Btw, In Kafka, I can create 500 partitions in a topic and Kafka allows only 1 thread per partition guaranteeing event order.
In-order processing in Azure event hubs with Partitions and multiple "event processor" clients
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In short, you really can't do what you're looking to do in the way that you're describing.
The
EventProcessorClientis bound to a given Event Hub and consumer group combination and will collaborate with other processors using the same Event Hub/consumer group to evenly distribute the load. Adding more processors than the number of partitions would result in them being idle. You could work around this by using additional consumer groups, but theEventProcessorClientinstances will only coordinate with others in the same consumer group; the processors for each consumer group would act independently and you'd end up processing the same events multiple times.There are also quotas on the service side that you may not be taking into account. Assuming that you're using the Standard tier, the maximum number of concurrent reads that you could have for one Event Hub, across all partitions, with the standard tier is 100. For a given Event Hub, you can create a maximum of 20 consumer groups. Each consumer group may have a maximum of 5 active readers at a time. The Event Hubs Quotas page discusses these limits. That said, a dedicated instance allows higher limits but you would still have a gap with the strict ordering that you're looking to achieve.
Without knowing more about your specific application scenarios, how long it takes for an event to be processed, the relative size of the event body, and what your throughput target is, its difficult to offer alternative suggestions that may better fit your needs.