Service Replication involves running multiple identical copies (replicas) of a service across different nodes (imagine servers) in the distributed system. If one replica fails, the others can continue to handle requests, thus preventing a single point of failure.
A) Vertical Scaling: This involves increasing the resources (CPU, RAM, storage) of a single machine. While it can improve performance, it doesn't address fault tolerance. If that single machine fails, the entire service is down.
B) Data Partitioning: This strategy involves dividing data across multiple nodes. While this can improve performance and distribute load, it doesn't inherently prevent service failure. It's more about data management than ensuring continuous service availability.
Data Partitioning (also known as sharding) is often a database design technique where a large table is divided into smaller, more manageable pieces called partitions. Each partition contains a subset of the data, typically split based on a specific criteria or key (date of creation e.g.)
Let's imagine you have a massive e-commerce database with a "Sales" table that has millions of records. This table becomes challenging to manage and query efficiently as it grows. Data partitioning helps solve this problem.
D) Monolithic Architecture: This involves building an application as a single, unified unit. This creates a single point of failure; if any part of the application fails, the entire application is affected.