What methods can be used to increase the throughput and resilience of a distributed database as the load grows?

The following methods are used to increase throughput and resilience of a distributed database as load grows:

• Sharding — horizontal partitioning of data into parts (shards) stored on different nodes. This reduces the load on each individual server and increases overall throughput.

• Replication — creating copies of data on multiple nodes to enhance fault tolerance and distribute read load.

• Load balancing — distributing requests among cluster nodes for resource utilization.

• Caching — using caches (e.g., Redis, Memcached) to reduce database access frequency.

• Query and index optimization — reducing operation execution time by proper index configuration and SQL query optimization.

• Using asynchronous operations and message queues — to offload the database and improve system responsiveness.

Example of sharding in Java using Spring Data:

@Document(collection = "users")
public class User {
    @Id
    private String id;
    private String shardKey; // e.g., region or user type
    private String name;
    // getters and setters
}

// Connection configuration to different shards can be implemented via AbstractRoutingDataSource

By combining these methods, you can scale the database and increase its resilience as load increases.