Design patterns and anti-patterns#
This section collects common design patterns and anti-patterns for writing Ray applications.
- Pattern: Using nested tasks to achieve nested parallelism
- Pattern: Using generators to reduce heap memory usage
- Pattern: Using ray.wait to limit the number of pending tasks
- Pattern: Using resources to limit the number of concurrently running tasks
- Pattern: Using asyncio to run actor methods concurrently
- Pattern: Using an actor to synchronize other tasks and actors
- Pattern: Using a supervisor actor to manage a tree of actors
- Pattern: Using pipelining to increase throughput
- Anti-pattern: Returning ray.put() ObjectRefs from a task harms performance and fault tolerance
- Anti-pattern: Calling ray.get on task arguments harms performance
- Anti-pattern: Calling ray.get in a loop harms parallelism
- Anti-pattern: Calling ray.get unnecessarily harms performance
- Anti-pattern: Processing results in submission order using ray.get increases runtime
- Anti-pattern: Fetching too many objects at once with ray.get causes failure
- Anti-pattern: Over-parallelizing with too fine-grained tasks harms speedup
- Anti-pattern: Redefining the same remote function or class harms performance
- Anti-pattern: Passing the same large argument by value repeatedly harms performance
- Anti-pattern: Closure capturing large objects harms performance
- Anti-pattern: Using global variables to share state between tasks and actors
- Anti-pattern: Serializing ray.ObjectRef out of band
- Anti-pattern: Forking new processes in application code