Design scalable database architectures for enterprise applications. Learn about sharding, replication, load balancing strategies, and how Fortune 500 companies handle millions of transactions per second.
Enterprise applications processing millions of users require sophisticated database architectures. Poor scaling decisions can cost companies millions in downtime and lost revenue. This guide covers proven patterns used by Netflix, Uber, and Amazon to scale their databases from thousands to billions of operations per day.
Add more power (CPU, RAM, storage) to existing servers.
Add more servers to distribute the load across multiple machines.
Split tables by rows across multiple databases. Each shard contains a subset of data.
-- Example: User sharding by ID range
-- Shard 1: users with id 1-1000000
-- Shard 2: users with id 1000001-2000000
-- Shard 3: users with id 2000001-3000000
-- Or by hash:
shard_id = hash(user_id) % num_shards
-- Route queries to appropriate shard
def get_user_shard(user_id):
return f"shard_{hash(user_id) % 4}"
Split tables by columns or related tables across different databases.
-- Example: E-commerce vertical sharding
-- Database 1: User authentication
CREATE TABLE users (id, email, password_hash);
-- Database 2: User profiles
CREATE TABLE user_profiles (user_id, name, bio, avatar);
-- Database 3: Orders and payments
CREATE TABLE orders (id, user_id, total, status);
CREATE TABLE payments (id, order_id, amount, gateway);
One write server, multiple read replicas.
Multiple write servers with bidirectional replication.
Multiple regions with local write masters.
Route write operations to master, reads to replicas.
Manage database connections efficiently with pools.
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