Distributed Counter — Detailed

flowchart TB
  subgraph Write[Increment path]
    EVT[Event sources]
    SHARD[N micro-counters per key<br/>shard by event hash]
    LOCAL[Local batch + flush]
    REDIS[(Redis counters per shard)]
    EXACT[(Durable per-shard ledger)]
  end

  subgraph Read[Read path]
    AGG([Aggregator: sum across shards])
    CACHE[(Approx cache 1s-10s)]
    API[Counter API]
  end

  subgraph Approx[Approximate fallback]
    HLL[HyperLogLog uniques]
    CMS[CMS hot keys]
  end

  subgraph Recon[Reconciliation]
    BATCH[Batch totals - exact]
    DRIFT[Drift alarm]
  end

  Write --> Read
  Approx --- Write
  Recon --- Write

    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 EVT,SHARD,LOCAL,API,HLL,CMS,BATCH,DRIFT service;
    class EXACT,CACHE datastore;
    class REDIS cache;
    class AGG compute;

Why shard a counter

• A single hot key (e.g. tweet likes for a celeb) bottlenecks updates.
• Split into N counters; sum on read.
• Trade-off: read fan-out vs write contention.

Approximate vs exact

• For "views/likes at scale" approximate (HLL, CMS) is fine.
• Money counters must be exact → use ledger + read-side cached aggregate.

Glossary & fundamentals

Concepts referenced in this design. Each row links to its canonical page; the tag column shows whether it is a high-level (HLD) or low-level (LLD) concept.

Tag	Concept	What it is	Page
HLD	Sharding	horizontal partitioning across nodes	database-sharding
HLD	Probabilistic data structures	Bloom, HLL, Count-Min, MinHash, t-digest	probabilistic-data-structures
HLD	Event sourcing + CQRS	commands -> events; separate read model	event-sourcing-cqrs