Data Structures for System Design Interviews
Practice this topic in a realistic system design interview
In this chapter, we'll explore the data structures that come up most often in system design interviews. For each one, we'll look at how it works, what operations it's designed to make efficient, and the trade-offs you need to understand when deciding where to use it in a real system.
In coding interviews, you usually choose the right data structure and build an efficient algorithm around it. But in system design interviews, the focus is a little different. Instead of implementing data structures, we try to understand how they're used inside real systems and why certain technologies are better suited for specific workloads.
For example, Cassandra, a wide-column database, uses an LSM-tree-based storage engine to handle write-heavy workloads efficiently. Relational databases commonly use B-tree-based indexes to organize data and make lookups faster. Understanding these underlying data structures helps you choose the right technology for your system and explain the trade-offs behind your decisions.
You won't typically need to implement these data structures in a system design interview. But you should know how they organize data, which operations they make efficient, and where their limitations come from.
With that in mind, let's start with one of the most common operations in any system: looking up data by key.