Making a Scalable and Fault-Tolerant Database System: Data Partitioning and Data Replication

A scalable and fault-tolerant database system can be achieved through data partitioning and replication, which allows databases to handle vast amounts of data and numerous client requests by distributing the workload across multiple machines. In key-value stores, partitions are independent (key, value) pairs, while in relational databases, partitions are sets of rows with common values in partitioning columns. Sharding involves splitting data into shards distributed among different nodes to manage space requirements and client requests effectively. Data replication further enhances fault tolerance by storing each shard on multiple nodes across different locations, ensuring resilience against node failures. Consistent hashing, a technique used by databases like ScyllaDB and Cassandra, ensures even distribution of partitions and workload by mapping partition keys to a circle of tokens, which represent shards. This method facilitates balanced storage and request distribution across nodes, accommodating changes in the number of nodes while maintaining efficient data management and replication.