🗂️ Replication vs Sharding — Scaling Your Data

Replication and sharding are two core strategies for scaling databases. Let’s break down what they are and when to use each.

📚 Replication

• What: Copying data across multiple servers.
• Why: Improves availability and fault tolerance.
• Types: Master-slave, master-master, synchronous, asynchronous.
• Pitfalls: Replication lag, split-brain scenarios, and consistency issues.

🧩 Sharding

• What: Splitting data across different servers (shards) by key.
• Why: Handles more data and traffic by distributing load.
• How: Range-based, hash-based, directory-based sharding.
• Pitfalls: Cross-shard queries are complex, rebalancing shards can be tricky.

⚖️ When to Use Which?

• Replication: For high availability and read scalability.
• Sharding: For write scalability and very large datasets.
• Combined: Many large systems use both for maximum scalability and reliability.

🛠️ Real-World Example

• Replication: MySQL master-slave setup for read-heavy workloads.
• Sharding: MongoDB or Cassandra for massive, distributed datasets.

🧠 Final Thoughts

Start with replication for simplicity, add sharding as your data and traffic grow. Monitor for replication lag and plan for shard rebalancing.