final case class BatchedExecution(batchSize: Int) extends ExecutionModel with Product with Serializable
ExecutionModel specifying a mixed execution mode under which tasks are executed synchronously in batches up to a maximum size.
After a batch of tasks of the recommended size is executed, the next execution should be asynchronous, forked on a different logical thread.
By specifying the ExecutionModel.recommendedBatchSize, the configuration can be fine-tuned.
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- ExecutionModel.scala
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- val batchedExecutionModulus: Int
Always equal to
recommendedBatchSize-1.Always equal to
recommendedBatchSize-1.Provided for convenience.
- Definition Classes
- BatchedExecution → ExecutionModel
- def nextFrameIndex(current: Int): Int
Returns the next frame index in the run-loop.
Returns the next frame index in the run-loop.
If the returned integer is zero, then the next cycle in the run-loop should execute asynchronously.
- Definition Classes
- BatchedExecution → ExecutionModel
- def productElementNames: Iterator[String]
- Definition Classes
- Product
- val recommendedBatchSize: Int
Recommended batch size used for breaking synchronous loops in asynchronous batches.
Recommended batch size used for breaking synchronous loops in asynchronous batches. When streaming value from a producer to a synchronous consumer it's recommended to break the streaming in batches as to not hold the current thread or run-loop indefinitely.
This is rounded to the next power of 2, because then for applying the modulo operation we can just do:
val modulus = recommendedBatchSize - 1 // ... nr = (nr + 1) & modulus
For the JVM the default value can be adjusted with:
java -Dmonix.environment.batchSize=2048 \ ...- Definition Classes
- BatchedExecution → ExecutionModel
This is the API documentation for the Monix library.
Package Overview
monix.execution exposes lower level primitives for dealing with asynchronous execution:
Atomictypes, as alternative tojava.util.concurrent.atomicmonix.catnap exposes pure abstractions built on top of the Cats-Effect type classes:
monix.eval is for dealing with evaluation of results, thus exposing Task and Coeval.
monix.reactive exposes the
Observablepattern:Observableimplementationsmonix.tail exposes Iterant for purely functional pull based streaming:
BatchandBatchCursor, the alternatives to Scala'sIterableandIteratorrespectively that we are using within Iterant's encodingYou can control evaluation with type you choose - be it Task, Coeval, cats.effect.IO or your own as long as you provide correct cats-effect or cats typeclass instance.