Data Structures and Algorithms
Overview of the data structures and algorithms section—foundations for performance, correctness, and system design.
This section covers the data structures and algorithms that software engineers rely on when reasoning about performance, memory, and correctness. Content here focuses on practical understanding: when a structure fits a problem, what costs you pay, and how those choices surface in larger systems.
Why this section exists
System design articles often assume familiarity with hashing, trees, queues, and graph traversal. Distributed topics such as consistent hashing and database sharding rest on the same foundations. This area of the knowledge base is where those building blocks will live.
What to expect
Articles will emphasise:
- Mental models — how a structure behaves under insert, lookup, and resize
- Complexity in context — big-O as a starting point, plus constants and locality
- Connections upward — links into architecture, scalability, and databases when an algorithm becomes a system technique
Related reading
While DSA-specific pages grow, these articles apply core algorithmic ideas at system scale:
- Consistent Hashing — ring placement and remapping
- Database Sharding — partitioning keys and load balance
- Scaling Writes — bottlenecks that often reduce to contention and data layout