A telemetry service ingests a flood of events — 300 per second, and 80% of them are writes that must be stored. A read-cache can't help you here (there's nothing to cache), and your SQL database is drowning. Pick a datastore actually built for this write volume.
Requirements
Functional
·Service accepts a continuous stream of telemetry events and durably stores each one
Non-functional
·~300 events/sec
·80% writes, 20% reads
·p99 latency ≤ 200ms
·99% availability
Constraints
·Budget: $7/hr or less
·A read-cache does not help — nothing here is read twice
Out of scope
·Event replay/reprocessing
·Real-time aggregation
TrafficRequests per second — the rate of incoming traffic this level throws at your system.
~300 r/s · write-heavy
20% reads · 80% writesWhat fraction of requests are reads (fetches) vs writes (updates) — this decides which components sit on the hot path.
Win conditions
p99 latency99% of requests finish at or under this time — a stricter tail-latency bar than p95 that catches worst-case slowness.≤ 200ms
availabilityThe share of legitimate requests that succeeded, out of everything sent your way. 99% still means 1 in 100 users saw an error.≥ 99%
throughputHow many requests per second your system actually completed — not requested, completed.≥ 250 r/s
error rateThe share of requests that failed outright: timeouts, drops, or capacity overload.≤ 2%
costWhat your architecture costs to run per hour, based on the instances and components you've wired up.≤ $7/hr
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Components
Client
ClientWhere traffic originates.
Compute
API GatewayFront door — the app tier requests pass through.
500 r/s · 20ms · $2/hr
Backend
350 r/s · 25ms · $2/hr
Lvl 7
Storage
SQL DatabaseRelational store. Durable, but the lowest throughput.
200 r/s · 50ms · $3/hr
NoSQL DB3× the throughput of SQL — at a premium. Pay for it only when the load demands it.
600 r/s · 30ms · $4/hr
Redis CacheIn-memory read-cache — absorbs repeated reads. Useless for writes.