How Big Tech Ensures Lightning-Fast Username Checks Across the Globe

26 April 2025

@Bibhabendu Mukherjee

Searching for duplicate usernames in milliseconds is a core performance requirement for big tech platforms like Google, Facebook, Instagram, etc. What is the problem here "Username need to checked in real time"


1. Usernames Are Stored in High-Speed Key-Value Stores

  • Think Redis, Memcached, or even Cassandra for quick lookups.
  • These stores are optimized for constant time access, i.e., O(1) lookup time.

2. Data is Indexed and Sharded

  • Username data is indexed to allow fast searching.
  • Sharding splits the dataset across multiple machines (e.g., based on the first letter/hash of the username) to parallelize lookups.

3. Caching is Key

  • Frequently searched usernames (popular ones or during peak traffic) are cached in memory to avoid hitting the database repeatedly.
  • Example: Redis is used to store keys like username:john_doe -> true.

4. Bloom Filters for Early Rejection

  • Probabilistic data structures like Bloom Filters can quickly tell if a username definitely does not exist, reducing load on storage systems.
  • They are space-efficient and fast for read-heavy scenarios.

5. Backend Querying Only If Necessary

  • If the cache or bloom filter doesn't have the answer, only then a backend service (usually a microservice) queries the main user database.

This might involve SQL or NoSQL DBs like MySQL, PostgreSQL, DynamoDB, etc.

Here Write-Ahead operation should be performed means When a user signs up, a transaction-safe write operation is used to avoid race conditions.

[User Sign-up Form]
        |
        v
[API Gateway] ---> [Username Service (microservice)]
        |
        |--> [In-Memory Cache (Redis/Memcached)]
        |--> [Bloom Filter]
        |--> [Sharded DB (SQL or NoSQL)]


In the Write-Ahead Operation the system acquires a lock , here lock applied to the username (string). Same name can't be inserted twice it simply fails in silent

In SQL we can define a column line `username VARCHAR(255) UNIQUE` so that it also be unique.

INSERT INTO users(username) VALUES('codeaum')
ON CONFLICT DO NOTHING;


As there are lock system in row insertion that means the first request always get complete if 2 or more user comes in the same time. Now you might ask but how a database know when a username got inserted when it comes to a large scale like facebook, amazon , google etc,

[User 1 - India] --> Region-Asia App Server --> DB
[User 2 - US]    --> Region-US App Server   --> DB

Both servers try INSERT ... ON CONFLICT DO NOTHING
↓
[Central DB or Global Distributed DB]
↓
Only one gets inserted, the other is rejected.

Even if 2 users from different regions request the same username at the same time, the database is the single source of truth with a unique constraint that
Locks on insert and ensure global consistency

To perform this above steps the architecture holds some cool system like in-memory database Redis, Fast probabilistic Database Bloom filter, Global routing etc.

Importance of Redis and Bloom Filter

Redis and Bloom filters power fast username checks by combining speed and efficiency. Redis acts as a super-fast in-memory cache for exact username lookups. Bloom filters quickly tell if a username definitely doesn’t exist, avoiding unnecessary database hits. Together, they minimize latency and reduce backend load, ensuring millisecond responses.