Programming Tutorials

  Your metrics dashboard is screaming. Latency on the payment processing node has spiked to 5000ms. Memory usage is climbing vertically. The logs are paradoxically silent. You know a GenServer is stuck, or a message queue is overflowing, but you don't know   which   one. In most runtimes, your only move is to capture a heap dump and restart the service, severing active connections and losing in-flight state. The BEAM (Erlang VM) is different. It was designed for systems that cannot go down. You can surgically attach a remote shell to the running cluster, identify the rogue process, inspect its internal state, and even trace function calls in real-time—all without stopping the world. Here is how to safely diagnose a zombie process in a high-throughput production environment. The Root Cause: Mailboxes and Reductions To fix a stuck BEAM node, you must understand how it breaks. Mailbox Overflow:  Every process (Actor) has a mailbox. If a GenServer receives  cast ...

  In production BEAM environments, few errors trigger paging alerts as consistently as the dreaded GenServer timeout: ** (exit) { {:timeout, {GenServer, :call, [MyServer, :request, 5000]}}, [{GenServer, :call, 3, [file: 'lib/gen_server.ex', line: 1013]}, ...] } When this occurs under high load, it rarely happens in isolation. It usually signals a cascading failure where a "singleton" process has become a bottleneck, causing callers to pile up, memory usage to spike, and eventually leading to a node restart. Increasing the default 5000ms timeout is almost never the correct solution. It only delays the inevitable crash. You need to diagnose the mailbox congestion and architecturally decouple the ingestion of requests from their processing. The Root Cause: The Actor Model Bottleneck To fix this, we must understand the mechanics of  GenServer.call/3 . Synchronous Block:  The calling process sends a message to the target process and enters a receive loop, setting a tim...