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Database Scalability
Database Scalability
Database Scalability
Database Scalability
Database Scalability
Database Scalability
Database Scalability
Database Scalability
Database Scalability
Database Scalability
Database Scalability
Database Scalability
Database Scalability
Scalability Options
Database scalability options range from simple to complex. Start with the simplest approach and evolve.
Vertical Scaling
Upgrade to a larger server with more CPU, RAM, and storage.
AWS RDS instance upgrade
resource "aws_db_instance" "main" {
instance_class = "db.r6g.8xlarge" # 32 vCPU, 256GB RAM
allocated_storage = 5000 # 5TB SSD
}
Simple but has a cost ceiling and hardware limits.
Read Replicas
Offload read traffic to replicas:
class DatabaseRouter:
def init(self, primary, replicas):
self.primary = primary
self.replicas = replicas
def get_conn(self, write=False):
if write:
return self.primary
return random.choice(self.replicas)
Route reads to replicas, writes to primary
db_router.get_conn(write=True).execute("INSERT INTO ...")
results = db_router.get_conn(write=False).execute("SELECT ...")
Effective for read-heavy workloads. Does not help with write scaling.
Caching
Reduce database load with in-memory caching:
def get_user(user_id):
user = cache.get(f"user:{user_id}")
if not user:
user = db.query("SELECT * FROM users WHERE id = %s", user_id)
cache.setex(f"user:{user_id}", 3600, json.dumps(user))
return user
Horizontal Scaling (Sharding)
Distribute data across multiple database servers:
class ShardManager:
def init(self, shards):
self.shards = shards
def get_shard(self, customer_id):
return self.shards[hash(customer_id) % len(self.shards)]
Most complex. Use tools like Vitess, Citus, or CockroachDB.
Scaling Decision Tree
Is DB overloaded?
├── Read-heavy? → Add read replicas
├── Write-heavy?
│ ├── Can you cache? → Add Redis/memcached
│ └── Cache insufficient? → Shard
└── Both? → Scale vertically first, then shard
Conclusion
Scale vertically first (simple). Add read replicas for read loads. Add caching for repeated queries. Shard only when necessary. Monitor your bottleneck before choosing a strategy. Most applications never need sharding.
See also: Database Horizontal Scaling Strategies, Read Replicas: Scaling Reads, Replication Lag, and Failover, Redis Caching Patterns.
See also: Database Horizontal Scaling Strategies, Read Replicas: Scaling Reads, Replication Lag, and Failover, Database Capacity Planning: Sizing, Growth Forecasting, and Scaling
See also: Database Horizontal Scaling Strategies, Read Replicas: Scaling Reads, Replication Lag, and Failover, Database Capacity Planning: Sizing, Growth Forecasting, and Scaling
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