Data Partitioning and Replication
Explore key concepts of data partitioning strategies such as range, hash, consistent hashing, and directory-based methods, alongside replication models like single-leader, multi-leader, and leaderless replication. Understand how these approaches distribute data, handle failures, and balance consistency with availability. This lesson equips you with the knowledge to apply partitioning and replication effectively in designing scalable, fault-tolerant data layers.
In the previous lesson, we introduced scaling strategies as the response to growth, focusing on two core approaches: vertical scaling (scaling up a single machine) and horizontal scaling (scaling out across multiple machines). These form the foundation for all capacity-related design decisions.
In this lesson, we build on that foundation by exploring what happens after horizontal scaling the compute layer, and how the data layer must evolve using partitioning and replication to handle continued scale, reliability, and performance demands.
Data partitioning strategies
Partitioning divides a dataset so that each node owns a distinct subset of rows or documents. The partitioning method determines how evenly the load is distributed and how efficiently different query patterns execute.
Several strategies exist, each with distinct trade-offs in distribution uniformity, query flexibility, and operational complexity.
Range-based partitioning: The data is split based on a range that does not overlap. Old partitions can easily be archived to serve queries for newer ranges more efficiently. This approach is simple to implement and supports efficient range queries, but it is vulnerable to
hotspots A hotspot refers to a specific partition that experiences disproportionately high traffic, read or write requests, compared to other partitions, causing performance bottlenecks.
Hash-based partitioning: A hash function is applied to each key and assigns the result to a shard. This distributes keys uniformly, reducing hotspot risk significantly. The trade-off is that range queries ...