Understanding Erlang's Concurrency Model

Lightweight Process Misuse

Each Erlang process is lightweight, but spawning thousands without supervision or proper messaging patterns can lead to memory exhaustion and scheduler imbalance.

loop() ->
    receive
        Msg ->
            %% Unbounded message processing
            loop()
    end.

Without control mechanisms or timeouts, such code can cause the process mailbox to grow indefinitely.

Message Queue Bloat

If a process receives messages faster than it can process them, its mailbox will grow, increasing GC time and memory use. This is common in logging or event collector processes.

Diagnosing System-Level Bottlenecks

Using observer and recon

Tools like observer and recon can help inspect process state, memory usage, and mailbox sizes. Look for long mailboxes and high reductions as signs of trouble.

observer:start().
recon:info(Pid).
recon:proc_window(reductions, 10).

Tracing Message Flow

Use dbg to trace inter-process communication and identify unexpected message patterns or high-frequency chatter.

dbg:tracer().
dbg:p(Pid, [send, receive]).

Common Runtime Pitfalls

State Leakage in GenServer

Improper handling of state in gen_server callbacks (e.g., growing state maps or lists without bounds) results in increasing memory footprint over time.

handle_cast({add_data, Data}, State) ->
    {noreply, [Data | State]}.

Ensure state is pruned or bounded periodically to avoid OOM errors.

Misconfigured Supervision Trees

Incorrect restart strategies (e.g., using one_for_one where rest_for_one is needed) can cause unintended cascading failures or leave important processes unrestarted.

Best Practices for Fault-Tolerant Erlang Systems

Use Backpressure Mechanisms

Implement mailbox size checks or use process registration with gproc to monitor and throttle message senders.

Cap Unbounded Data Structures

When storing data in memory (e.g., ETS tables, process state), implement eviction policies to prevent unbounded growth.

Apply Proper Timeout Strategies

Use timeouts in receive blocks and RPC calls to detect stalled processes early.

receive
    Msg -> handle(Msg)
after 5000 -> timeout_handler()
end.

Leverage Load Shedding

Drop low-priority messages under load, or reject requests in overloaded subsystems to maintain core availability.

Long-Term Architectural Considerations

Use Process Pools for Rate-Limited Resources

Spawning new processes for database or API access can overwhelm downstream services. Instead, use a fixed pool with queueing logic.

Distribute Load Across Nodes

In clustered deployments, uneven distribution of work can overload individual nodes. Use consistent hashing or registries to balance load.

Apply Circuit Breaker Patterns

When external systems fail, isolate and retry intelligently using circuit breakers or retry windows instead of flooding retries.

Conclusion

Erlang offers unmatched resilience and concurrency primitives, but mismanagement of lightweight processes, unchecked message queues, or misconfigured supervision trees can lead to hard-to-diagnose failures. Effective use of diagnostics tools like observer, combined with architectural best practices like backpressure, load shedding, and structured supervision, are essential to building and maintaining production-grade Erlang systems.

FAQs

1. How can I detect long message queues in production?

Use recon:proc_count(mailbox, N) to find processes with the longest mailboxes and observer for real-time monitoring.

2. What causes processes to leak memory in Erlang?

Typically unbounded growth in process state or large mailboxes that never get cleared. Review your gen_server state logic regularly.

3. Should I restart all children if one crashes?

Not always. Choose supervision strategies carefully—rest_for_one or one_for_all only when dependencies exist between processes.

4. What is the best way to handle overload conditions?

Implement throttling or shedding by monitoring mailbox sizes, and use circuit breakers to avoid flooding downstream services.

5. Is Erlang suitable for modern cloud-native systems?

Yes. With tools like Erlang/OTP, observer, and clustering support, Erlang is highly suited for building resilient microservices and distributed systems.