Backpressure & Load Regulation Patterns
Keep systems stable under overload using backpressure, rate limiting, and load shedding patterns built on OTP primitives.
Backpressure & Load Regulation Patterns is a free Erlang OTP: Distributed & Fault-Tolerant Systems Programming lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Erlang OTP: Distributed & Fault-Tolerant Systems Programming learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
What Is Backpressure?
When work arrives faster than a system can process it, queues grow and latency explodes. Backpressure is the practice of signalling producers to slow down so the system stays stable instead of melting down.
The Unbounded Queue Problem
An async gen_server:cast never blocks the caller, so its mailbox can grow without limit under load — the system runs out of memory before it ever rejects work.
Synchronous Calls as Backpressure
Using gen_server:call instead of cast makes the caller wait until the server is ready, naturally throttling producers to the server speed.
Reply = gen_server:call(worker, {process, Job}, 5000).Bounded Queues
Maintain an explicit counter of in-flight work and reject or block new requests once a limit is reached, rather than letting the mailbox grow.
handle_call({job, J}, _From, #{n := N} = S) when N < 100 ->
{reply, ok, S#{n := N + 1}};
handle_call({job, _}, _From, S) ->
{reply, {error, overloaded}, S}.Load Shedding
Under extreme load, the best move can be to shed low-priority work fast — returning an error immediately — to protect the system for high-priority requests.
Rate Limiting with Tokens
A token-bucket limiter grants a fixed number of permits per interval; requests beyond that wait or fail. This caps throughput predictably.
case take_token(Bucket) of
ok -> do_work();
empty -> {error, rate_limited}
end.Measuring Pressure
The mailbox length of a bottleneck process is a live pressure gauge. Monitor it and trigger shedding before it grows dangerous.
process_info(Worker, message_queue_len).Pooling for Throughput
A pool of worker processes (e.g. via poolboy) bounds concurrency: requests queue for a free worker, giving natural backpressure with controlled parallelism.
Circuit Breakers
When a downstream dependency is failing, a circuit breaker opens to stop sending requests for a cooldown period, preventing pileups and giving the dependency time to recover.
Timeouts as Protection
Always give gen_server:call a finite timeout. An unbounded wait lets a stuck server block callers indefinitely; a timeout converts a hang into a fast, recoverable error.
case catch gen_server:call(srv, req, 2000) of
{ok, R} -> R;
_ -> {error, busy}
end.Choosing a Strategy
Combine patterns: synchronous calls for natural throttling, bounded queues to cap memory, rate limiting for fairness, and load shedding plus circuit breakers as last-resort protection.
Quick Check
Test your load regulation knowledge.
Recap
You learned to keep systems stable under overload:
- Backpressure signals producers to slow down
- Synchronous
callthrottles naturally; bounded queues cap memory - Rate limiting enforces fairness; load shedding protects priority work
- Worker pools bound concurrency; circuit breakers stop downstream pileups
- Monitor mailbox length as a live pressure gauge
Frequently asked questions
Is the “Backpressure & Load Regulation Patterns” lesson free?
Yes — the full text of “Backpressure & Load Regulation Patterns” is free to read here on the web, and the Erlang OTP: Distributed & Fault-Tolerant Systems Programming course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Erlang OTP: Distributed & Fault-Tolerant Systems Programming course, upgrade to CoddyKit PRO.
What will I learn in “Backpressure & Load Regulation Patterns”?
Keep systems stable under overload using backpressure, rate limiting, and load shedding patterns built on OTP primitives. You practise Erlang OTP: Distributed & Fault-Tolerant Systems Programming with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Erlang OTP: Distributed & Fault-Tolerant Systems Programming?
No prior experience is required. Erlang OTP: Distributed & Fault-Tolerant Systems Programming on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Backpressure & Load Regulation Patterns” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Erlang OTP: Distributed & Fault-Tolerant Systems Programming lesson?
Yes. Every Erlang OTP: Distributed & Fault-Tolerant Systems Programming lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Designing for High Availability
- Distributed Consensus Patterns
- Erlang OTP Case Studies
- Backpressure & Load Regulation Patterns