Ray is a general-purpose distributed system designed to integrate data processing libraries into distributed applications, with a robust distributed memory manager. Dask-on-Ray is a community-contributed plugin that enables running Dask task graphs on Ray clusters. The blog post compares the memory management and performance of Ray versus Dask with its built-in scheduler, highlighting recent features in Ray such as object spilling and better control of memory usage. The comparison focuses on architectural differences between Dask and Dask-on-Ray, including the location of the object store, configuration parameters, and system reliability under memory pressure. Microbenchmarks demonstrate that Dask-on-Ray can scale sorting to 10x larger datasets than with Dask's built-in scheduler, while Ray's shared-memory object store provides a more precise measurement of total memory usage on each node. The design differences between Dask and Dask-on-Ray lead to varying performance characteristics, particularly when dealing with large datasets and memory-intensive workloads.