Design Live Sports Score System

Real-time sports scores with low-latency updates, fanout, caching, and resilience.

How the design evolves

Stage 1: Partner ingestion + durable event log

Build a reliable ingest path with ordering, dedupe, and a durable event log you can replay.

What was missing: Durability, ordering, and replay for partner events.

Why that's risky: Without an append-only log, bugs/outages permanently lose or corrupt score updates.

What gets added: Edge/WAF, partner ingestion API, event stream (partitioned by match), event log retention, snapshot builder + snapshot DB.

Trade-offs: Eventual consistency between ingest and snapshot read model; operational overhead of streaming.

Stage 2: Read path at scale (CDN + cache + API)

Serve millions of reads safely using CDN + cache; keep DB off the critical fan traffic path.

What was missing: Read scalability.

Why that's risky: Finals traffic melts the DB and increases tail latency.

What gets added: CDN in front of read API + hot cache; snapshot DB as the source of truth for reads.

Trade-offs: Cache invalidation.

Stage 3: Realtime delivery (WS/SSE + pub/sub)

Fan out ordered deltas to connected clients using realtime gateways and a pub/sub layer.

What was missing: Realtime delivery.

Why that's risky: Polling at scale is expensive, laggy, and causes thundering herds.

What gets added: Realtime LB + gateways, delta publisher, and a pub/sub layer for efficient fanout.

Trade-offs: Connection management + backpressure; ordering guarantees per match require careful partitioning.

Stage 4: Production hardening (multi-region + ops)

Make the system resilient: retries/DLQ, observability, and multi-region failover for big matches.

What was missing: Operational safety and regional resilience.

Why that's risky: One region outage or a bad partner event can cause global downtime or incorrect scores.

What gets added: Multi-region deploy, regional caches, DB replicas, retry/DLQ path, and observability with SLO alerts.

Trade-offs: Cost + complexity of multi-region replication and operational tooling.

Frequently asked questions

How do you handle late corrections from official feeds?

Write compensating events with higher sequence/version; consumers rebuild snapshots; clients receive correction deltas.

What happens if a partner sends malformed or poison-pill events during a final?

Validate at ingestion, quarantine failures into a DLQ, and use delayed retries with alerting. Keep the stream moving by skipping/isolating bad partitions and replay once fixed.

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Use clear names so your design intent is easy to understand.
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"Order API", "Restaurant Service"
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