Distributed Counter — Simple

Problem statement (interviewer prompt)

Design a high-throughput distributed counter for things like like / view counts: 1M+ increments/second across millions of distinct counters, sub-second reads, eventually consistent OK. Avoid hot-key bottlenecks via sharded counters or probabilistic estimation (HLL).

flowchart LR
  E[Event source]
  SHARD[Sharded counter<br/>per key shard]
  AGG([Aggregator])
  API[Read API]
  E --> SHARD --> AGG --> API

    classDef client fill:#dbeafe,stroke:#1e40af,stroke-width:1px,color:#0f172a;
    classDef edge fill:#cffafe,stroke:#0e7490,stroke-width:1px,color:#0f172a;
    classDef service fill:#fef3c7,stroke:#92400e,stroke-width:1px,color:#0f172a;
    classDef datastore fill:#fee2e2,stroke:#991b1b,stroke-width:1px,color:#0f172a;
    classDef cache fill:#fed7aa,stroke:#9a3412,stroke-width:1px,color:#0f172a;
    classDef queue fill:#ede9fe,stroke:#5b21b6,stroke-width:1px,color:#0f172a;
    classDef compute fill:#d1fae5,stroke:#065f46,stroke-width:1px,color:#0f172a;
    classDef storage fill:#e5e7eb,stroke:#374151,stroke-width:1px,color:#0f172a;
    classDef external fill:#fce7f3,stroke:#9d174d,stroke-width:1px,color:#0f172a;
    classDef obs fill:#f3e8ff,stroke:#6b21a8,stroke-width:1px,color:#0f172a;
    class E,SHARD,API service;
    class AGG compute;