Detailed Component Design

1. Short code generation

This is the most critical design decision. We need ~7-character codes that are globally unique and not guessable. Two viable strategies:

Base62 encoding of an ID — a central counter (or a distributed one via Twitter Snowflake) generates a monotonically increasing integer; we Base62-encode it. A 7-character Base62 string handles 62⁷ ≈ 3.5 trillion URLs. The downside is that sequential IDs produce predictable codes, making enumeration easy.

Pre-generated token pool — a background job pre-generates random Base62 codes, stores them in a "tokens" table, and atomically marks them used on demand. This avoids the hot counter bottleneck and produces unpredictable codes. It's the preferred approach at scale.

2. Redirect service (the hot path)

The redirect path is entirely read-only and has three tiers: CDN edge (fastest), Redis in-memory cache (fast), and DB (slowest but always correct). On every cache miss, the layer below is queried and the result is backfilled upward. Click events are emitted asynchronously — the redirect response is never blocked on analytics writes.

3. Database schema

The core urls table holds code (PK, indexed), long_url, user_id, created_at, expires_at, and is_active. A separate clicks table (or a time-series store like ClickHouse/TimescaleDB) stores code, timestamp, referrer, user_agent, country, and ip_hash. The tokens table for pre-generated codes holds token and used_at.

4. Analytics pipeline

Click events flow into Kafka. A stream processor (Flink or Lambda) aggregates them — total clicks, unique clicks by day, top referrers — and writes results to a read-optimised analytics store. Raw events are never queried directly.

5. Expiry & cleanup

A scheduled worker scans the urls table for rows where expires_at < now() and marks them inactive. Redis TTLs are set to match the URL's expiry. This prevents serving stale redirects after expiry without scanning the DB on every request.

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