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Scalability Strategies

Scaling is not just about adding more servers; it's about designing systems that can grow gracefully.

Vertical vs. Horizontal Scaling

Vertical Scaling (Scale Up)

Adding more power (CPU, RAM, Disk) to an existing machine.

  • Pros: Simple implementation, no code changes usually required.
  • Cons: Hard hardware limits, single point of failure, exponential cost.
  • Use Case: Early stage startups, database masters (initially).

Horizontal Scaling (Scale Out)

Adding more machines to the pool of resources.

  • Pros: Theoretically infinite scale, redundancy/fault tolerance, linear cost.
  • Cons: Increased complexity (data consistency, load balancing, distributed transactions).
  • Use Case: Web servers, distributed databases (Cassandra, MongoDB).

Load Balancing

Distributes incoming network traffic across a group of backend servers.

Types

  • L4 (Transport Layer): Routes based on IP and Port. Fast, simple, unaware of content.
  • L7 (Application Layer): Routes based on URL, Cookies, Headers. Smarter, can do SSL termination, but more CPU intensive.

Algorithms

  • Round Robin: Sequential distribution.
  • Least Connections: Sends to server with fewest active connections.
  • IP Hash: Maps client IP to a specific server (sticky sessions).

Caching

Storing copies of data in a temporary storage location for faster access.

Strategies

  • Cache-Aside (Lazy Loading): App checks cache. If miss, reads DB, writes to cache.
  • Write-Through: App writes to cache and DB synchronously. High consistency, higher write latency.
  • Write-Back (Write-Behind): App writes to cache only. Cache syncs to DB asynchronously. High write throughput, risk of data loss.

Eviction Policies

  • LRU (Least Recently Used): Discards the least recently used items first.
  • LFU (Least Frequently Used): Counts how often an item is needed.
  • FIFO (First In First Out): Simple queue.

Database Sharding

Partitioning data across multiple machines.

Horizontal Sharding

Splitting rows. E.g., Users 1-1M on DB1, 1M-2M on DB2.

  • Pros: Infinite data growth.
  • Cons: Complex queries (joins across shards), rebalancing data is painful (Consistent Hashing helps).

Vertical Sharding

Splitting columns/tables. E.g., User Profile table on DB1, User Photos table on DB2.

  • Pros: Simple to implement initially.
  • Cons: Limited by the growth of a single feature.