Clustering, Fault Tolerance and Load Balancing

Apache CouchDB is a NoSQL document-oriented database that provides various features for scalability and high availability. Here’s a brief overview of Clustering, Fault tolerance, and Load balancing under Apache CouchDB:

Clustering: Clustering refers to the process of grouping multiple servers to work together as a single system to handle a large amount of data. In Apache CouchDB, clustering is achieved through a technique called replication, where data is replicated across multiple servers to ensure data availability and reduce the risk of data loss. CouchDB supports both active and passive replication, where active replication allows data to be updated in real-time while passive replication provides a backup mechanism to ensure data availability in case of server failure.
Fault tolerance: Fault tolerance refers to the ability of a system to continue functioning even in the event of component failures. Apache CouchDB provides fault tolerance through its clustering mechanism, where if one server fails, the data can still be accessed through another server in the cluster. Additionally, CouchDB provides automatic failover, where if a primary server fails, another server in the cluster will automatically take over the primary role, ensuring continuous data availability.
Load balancing: Load balancing refers to the process of distributing incoming requests across multiple servers to ensure that no single server is overloaded. Apache CouchDB supports load balancing through various techniques such as round-robin, where requests are distributed evenly across multiple servers, or weighted round-robin, where servers with higher capacity are given a higher weight to handle more requests. Additionally, CouchDB supports a technique called reverse proxying, where a proxy server is used to distribute requests across multiple CouchDB instances.

In summary, Apache CouchDB provides various features for clustering, fault tolerance, and load balancing, making it a scalable and reliable choice for managing large amounts of data.