before picking Redis or Kafka, decide what the product must do, how much load it sees, and which failure hurts most

mental picture: it is like planning a restaurant: menu, rush hour, seating, and delivery orders decide the kitchen before anyone buys ovens

start with use cases: who does what, and which actions are read-heavy, write-heavy, private, public, or time-sensitive

turn vague scale into rough numbers: users, requests per second, storage growth, hot objects, and latency targets

exactly

draw the simplest end-to-end path first: client, edge, app service, data store, async workers, external dependencies

then stress the path: what gets slow first, what gets expensive first, what goes down if one box dies

only after that do components make sense, because every component is answering a specific pressure

that is the trap

the fancy diagram is usually the last step, not the first step

adding a cache helps reads but creates freshness problems

splitting services clarifies ownership but adds network failure

sharding increases capacity but makes queries and operations uglier

jumping straight to named technologies makes the design look confident while hiding the actual assumptions

the read path

the write path

the thing that is allowed to be stale

the thing that absolutely is not

yeah. most system design is boring on purpose

say the product behavior, estimate the pressure, name the bottleneck, then pick the simplest thing that handles it

perfect

draw the pressure, then choose the machinery