How do you handle database sharding and partitioning in distributed backend systems?
Database sharding and partitioning are essential techniques in managing large amounts of data in distributed backend systems. Both techniques allow for horizontal scaling, improved performance, and high availability. Sharding Sharding involves splitting the data across multiple servers, known as shards. Each shard contains a subset of the data, and together, they form the complete dataset. This distribution helps distribute the workload and allows for better utilization of resources. To implement sharding, you need to: Determine a shard key that evenly distributes the data. The shard key is a unique identifier that determines which shard the data belongs to. Use a consistent hashing algorithm to assign data to shards. This algorithm ensures an even distribution of data across the shards. Implement a metadata store to track the location of data. This store keeps track of which shard each piece of data belongs to. Update your application’s data access layer to handle sharding. Your application needs to be aware of the sharding strategy and query the correct