How Companies Check Millions of Usernames in Milliseconds

Ever wondered how apps instantly tell you if a username is taken?

It looks like a simple check — but under the hood, it’s powered by a multi-layered system design that balances speed, correctness, and scale.

Let’s find out.

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Why Not Just Query the Database?

A naïve approach is to query the database for every keystroke or submit. That works for small traffic but becomes slow and expensive at scale because every check competes with other read and write workloads.

Instead, use a cascade that rejects as early as possible and confirms only when necessary.

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The layered architecture

1) Bloom Filter — Fast Pre-Check

A Bloom filter answers two things quickly:

• Definitely not present

• Maybe present

Why it helps:

• O(1) checks, very small memory footprint

• Eliminates the majority of impossible usernames without touching Redis or DB

Note: false positives are possible, false negatives are not. So a “maybe” must be confirmed downstream.

2) Redis Cache — The Speed Booster

After Bloom says “maybe”, requests go to Redis.

Keep recent and frequently checked usernames in Redis with short TTLs.

• Cache hit: instant yes/no

• Cache miss: go to the database and backfill Redis on the way out

3) Database — the source of truth

Ultimately, correctness depends on the database.

Under the hood:

• Unique index on username (usually backed by a B+ Tree)

• Guarantees no duplicates — even if two users see “available” at the same time

Schema tips:

• usernames table with columns: id, username (UNIQUE), created_at

• Normalize or store a canonical form to enforce case and Unicode rules consistently

4) Trie — Autocomplete for UX

This part is about experience, not correctness.

A Trie (prefix tree) makes typing feel magical:

• Input: te

• Suggestions: tea, team, teacup

Optimized for prefix searches, it’s why autocomplete feels instant.

This is separate from availability but improves perceived performance and usability.

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End‑to‑end flow

Here’s how the full pipeline works:

1. Client → API (debounced input or on submit)

2. Bloom filter

• Not present → return Available

• Maybe present → step 3

3. Redis

• Hit → return cached result

• Miss → step 4

4. Database

• Query by canonical username

• Update Redis with the result (short TTL for "available", slightly longer for "taken")

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Correctness & Race Conditions

• It always relies on the database’s unique index at write time. Even if two clients see “available,” only one insert will succeed.

• Treats the availability API as advisory — the signup API is authoritative via the DB constraint.

• In distributed systems, this tune Bloom/caches with careful TTLs.

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Conclusion

The Bloom Filter is a perfect example of how sometimes being “probably” right is better than being definitely right. It’s helping tech giants save billions while providing lightning-fast services to users worldwide.

Remember:

• Perfect accuracy isn’t always necessary

• Speed and efficiency often trump precision

• Simple solutions can solve billion-dollar problems

The next time you browse Chrome or Netflix, remember: there’s a tiny but mighty Bloom Filter working behind the scenes, saving companies billions while making your experience seamless.

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