Data Replication in Distributed Databases

WHAT?

Data replication is a fundamental concept in the world of distributed databases. It involves maintaining copies of the same data on multiple machines connected through a network. This seemingly simple practice carries significant benefits and complexities, making it a crucial aspect of modern database management.

WHY?

Reducing Latency

By placing data copies geographically closer to users, latency in accessing that data is minimized. This enhances user experience, especially in applications that require real-time or low-latency responses.

Increasing Availability

Data replication ensures that even if some machines in the network fail, the system can continue functioning. This improves overall system availability and reliability, critical for mission-critical applications.

Scaling Read Throughput

Replicating data enables the distribution of read queries across multiple machines, increasing the overall read throughput. This is essential for handling large volumes of read requests in high-demand applications.

HOW?

When it comes to replicating data in distributed databases, there are three popular strategies:

Single-Leader

In this approach, one node acts as the primary leader, responsible for handling write operations. Other nodes replicate data from the leader. This method simplifies write coordination but can create a single point of failure.

Multi-Leader

Multiple nodes can independently accept write operations, and changes are propagated between them. This approach improves fault tolerance and can handle write-intensive workloads but introduces the complexity of conflict resolution.

Leaderless Replication

In leaderless replication, there is no designated leader. All nodes can accept write operations and must synchronize data among themselves. This strategy enhances fault tolerance and scalability but requires robust conflict resolution mechanisms.