How can you achieve parallelism and concurrency within an Akka Stream application? Discuss the different options available and when they would be applicable.
In Akka Streams, parallelism and concurrency can be achieved through various methods such as asynchronous boundaries, mapAsync, and substreams.
1. Asynchronous Boundaries : Introduce async boundaries using .async() to allow different stages of the stream to run concurrently on separate threads. Applicable when independent stages have significant processing time.
2. MapAsync : Use mapAsync or mapAsyncUnordered for concurrent execution of a function with multiple input elements. Suitable when applying an expensive operation to each element in the stream.
3. Substreams : Partition the main stream into smaller streams (substreams) using groupBy or splitWhen/After, process them concurrently, and merge back using mergeSubstreams or concatSubstreams. Ideal for scenarios where data can be processed independently within partitions.
4. Balancing workload : Utilize balance and merge components to distribute work evenly across multiple workers and combine their results. Useful when dealing with varying processing times for different elements.
5. Throttling : Control the rate of processing by limiting the number of elements processed per unit of time using throttle. Helps prevent overwhelming downstream systems.
6. Buffering : Manage backpressure by buffering elements between stages using buffer or conflate. Can improve performance when there’s a mismatch in processing speeds between stages.
