Actors vs Queues and Logs Choosing the right building block for asynchronous workloads is critical. Actors excel at managing in-memory state and orchestrating concurrent work. Their mailboxes are ephemeral and rely on best-effort delivery. If a process crashes, in-flight messages may be lost. For workflows that demand durable delivery, ordering or replay, a dedicated queue or log is the safer option. The diagram below contrasts direct actor messaging with using a persistent queue or log. Read more...

Backpressure and Flow Control A common misconception is that actor systems automatically handle unlimited message rates. In reality, every actor has finite processing capacity. Unbounded mailboxes may lead to increased memory usage and eventually to process failure. Strategies inside Proto.Actor Bounded mailboxes limit how many messages an actor can queue. Throttling or work pulling lets actors request work only when ready. Batching aggregates many messages into one processing unit. When external queues help If producers outrun consumers for extended periods, or if you need durable buffering, place a persistent queue or log in front of the actor system. Read more...

Proto.Actor vs Erlang and Akka Proto.Actor builds on ideas popularised by Erlang/OTP and Akka but aims to provide a minimal, cross-language runtime. Similarities Actor model: all three use message passing, supervision and isolation. Failure handling: supervisors restart failed actors instead of sharing memory. Differences Language support: Erlang targets the BEAM VM; Akka focuses on JVM and Scala/Java; Proto.Actor provides implementations for .NET and Go with similar APIs. Minimalism: Proto. Read more...