At a glance
- Did you know
- A shared session store lets any app server handle a user's next request.
- Rule of thumb
- Keep sessions small and give them a TTL; they are read on every request.
- Watch out
- Every request reads it; an outage logs everyone out.
Its settings in ScaleMySystems
What you can change on it in the lab, and what each number does.
- Concurrency (slots)
- Calls it can work on at once (per instance when autoscaling). Needed slots ≈ calls per second × seconds each takes. Extra calls wait in the queue, then are rejected.
- Queue capacity
- Calls allowed to wait for a free slot. A bigger queue absorbs bursts but adds waiting time; 0 rejects straight away.
- vCPU
- CPU cores available. Needed ≈ calls per second × CPU per request ÷ 1000. Above about 90% busy it slows down and may crash.
- CPU per request (ms)
- CPU time each call burns. Part of what the component is; make the code cheaper to lower it.
- Latency (p50) (ms)
- Typical time one call takes here, before any calls it makes itself. Slots are held for this long.
- Latency (p99) (ms)
- The slowest 1% of calls. Callers must set their timeout above this or they cut off healthy calls.
- Timeout to dependencies (ms)
- How long it waits for each call it makes before giving up. Set it above the dependency’s p99 but low enough that a dead dependency does not hold every slot.
- Error rate
- Share of calls that fail on their own (bugs, declines, flaky tools). An agent that calls it on every step multiplies this.
- Quota (req/min)
- Calls allowed per minute (an API key or provider limit). Calls over it get 429s. Needed = calls per second × 60, with headroom.
Problems that start with it
- Distributed rate limiter: one counter store A global rate limiter keeps every client’s counter in one Redis node, and every request checks it. The node handles about 1,600 checks a second; traffic is 2,400.
- Shopping carts on one session store Every page reads or updates the cart in a session store with one vCPU, at 1,800 requests a second.
- Recommendations: the online feature store Every recommendation reads user and item features from an online feature store before the ranking model scores candidates. The store holds 6 reads at once at about 3 ms each; 2,500 requests arrive a second.
- Amazon storefront: from launch build to real traffic A storefront as first built: product pages read DynamoDB with no product cache, and the product, cart and checkout APIs, search, recommendations, the orders database, payments and fulfilment workers at an eighth of their real size. Shoppers arrive at real traffic and half of all requests fail, checkout included.
Full lesson coming soon
Lessons are being added one component at a time: how it works, its settings, a live exercise to break and fix it, interview questions and a short check. See the Circuit breaker lesson for what one looks like.