Database replication is the process of copying and storing data from one database (primary) to one or more other databases (secondary replicas). It ensures data availability, reliability, and performance across different servers or locations. Replication helps prevent data loss by having backups ready in case of system failure, ensures that data is accessible more quickly from closer servers, and allows applications to scale better by balancing read requests across multiple databases.
In short, it’s a crucial technique for improving system resilience and data safety, while optimizing performance in high-demand applications.
Replication of data becomes complex when the data changes over time. This is because any change needs to be reflected across all copies of the data. There are three main approaches to handling these changes in distributed databases: single-leader, multi-leader, and leaderless replication. Each of these methods has its own advantages and disadvantages, and most distributed databases use one of these approaches to manage data consistency across multiple nodes.
Single-Leader Replication
Single-leader replication is a method used in databases to ensure that data is consistent across multiple copies (replicas) of the database. Here’s how it works:
One replica is designated as the leader (also known as the primary or master). When a client (user or system) wants to write or make changes to the database (e.g., adding, updating, or deleting data), it must send the request to this leader. The leader processes the write operation and saves the changes to its local storage.
Once the leader has successfully made the changes, it sends the updated data to all the other replicas, known as followers (or secondaries). The followers receive this data and update their local copies, ensuring they remain in sync with the leader.
The key point in single-leader replication is that all writes happen on the leader, while reads can happen on either the leader or the followers. This ensures consistency, as the leader is the single source of truth for any data changes.
This method ensures data consistency, but it also means that if the leader goes down, the system might have to promote a follower to become the new leader to continue processing write requests.
Synchronous Versus Asynchronous Replication
There are two main ways to share changes with replica nodes:
1. Synchronous Replication: In this method, the primary node waits for confirmation from all secondary nodes before telling the client the operation was successful. It ensures all replicas are up to date. However, if one replica fails or takes too long to respond, it causes delays, leading to slower responses for the client.
2. Asynchronous Replication: Here, the primary node doesn’t wait for replicas to confirm before reporting success to the client. This speeds things up but comes with a risk: if the primary node fails, any data not copied to the replicas might be lost.
In short, synchronous replication focuses on data accuracy across all replicas, while asynchronous prioritizes speed and availability, but may risk data loss in case of failure. It’s a balance between consistency and availability.
Multi-Leader Replication
Multi-Leader Replication is a replication strategy where multiple database nodes can accept write operations simultaneously, unlike single-leader replication, where only one node handles writes. This is useful for systems spread across multiple regions, providing better availability and performance.
When data is written to one leader, it is asynchronously replicated to other leaders, ensuring that all nodes eventually sync up with the changes.
Leaderless Replication
Leaderless Replication is a method of data replication where there is no single leader or master node that coordinates all writes and reads. Instead, every node in the system is equal and can handle both read and write operations. When a client sends a write request, it can be sent to multiple nodes, and the data is written to several nodes simultaneously. This approach increases fault tolerance, as any node can process requests, and the system can keep functioning even if some nodes fail.
Here’s how it works in simple terms:
• Writes: When data is written, it is sent to multiple nodes. Not all nodes need to immediately acknowledge the write for it to be considered successful. Instead, it waits for a subset of nodes (called a quorum) to confirm.
• Reads: When a client reads data, it may query several nodes. The client or system will check if the data is consistent across the nodes and return the most up-to-date version.
The tradeoff with leaderless replication is that while it provides high availability and fault tolerance, it can introduce data consistency challenges. If different nodes have different versions of the data, conflict resolution strategies must be used to reconcile the differences. Examples of databases that use leaderless replication are Amazon DynamoDB and Apache Cassandra.
