System Design Interview Question Frequency Analysis (2026)

System design is the round that most candidates feel least prepared for, and most preparation advice is based on what the author personally experienced, not aggregate data. LeakCode changes that. Across 1,722 tagged system design reports in our database, five thematic clusters account for the majority of what companies actually ask. Here is what the frequency breakdown looks like, and what it means for how you should prepare.

One important caveat before the numbers: system design rounds are the most underreported segment in interview databases. Candidates find it easy to write "I was asked to implement a binary tree traversal" but much harder to write up a 45-minute system design session in full. This means the 1,722 tagged rows likely represent a small sample of the total system design volume. The thematic proportions, however, are consistent across sources and year-over-year, giving us confidence in the relative rankings even if the absolute count is understated. See our source methodology page for how LeakCode handles this reporting artifact.

Theme 1: Storage and data modeling (the most common cluster)

The single largest cluster of system design questions involves storage decisions: what kind of database to use, how to model data for a given access pattern, how to handle schema evolution, and how to make tradeoffs between relational and non-relational storage. This theme shows up in roughly a third of all tagged system design reports in the LeakCode database.

The specific framing varies: sometimes it is "design the persistence layer for X," sometimes it is embedded in a broader scenario where the candidate is expected to surface the storage question themselves. At Google, reports suggest interviewers often push candidates to justify their database choice under load. At Amazon, the framing often includes cost constraints and DynamoDB's single-table design patterns. At Microsoft, SQL Server versus Cosmos DB tradeoffs appear frequently in the reports LeakCode has indexed.

For prep: you should be able to articulate, in real time, the decision process between relational, document, wide-column, and time-series storage. The interview is not testing whether you know the answer; it is testing whether you have a structured framework for getting there.

Theme 2: Scale and capacity estimation

The second most frequent cluster involves scale: what happens at 10x load, how do you horizontally partition this service, and what are the back-of-envelope numbers that inform those decisions. This theme appears in roughly 28% of tagged reports in the LeakCode database.

At Meta, this cluster is particularly dense in reports. Meta's scale is genuinely different from most companies, and interviewers appear to use scale questions to calibrate whether a candidate has intuition for what "billions of events per day" actually means in practice. Reports indexed by LeakCode from Meta system design rounds consistently include capacity math as a first step, not an afterthought.

At Google, scale questions are often paired with consistency tradeoffs: if you partition this service, what do you give up? The CAP theorem framing is explicit in a significant portion of Google system design reports in the LeakCode database.

For prep: practice doing capacity estimation out loud. Choose numbers that are defensible, not impressive. Interviewers at FAANG have seen candidates throw around figures that contradict their own design. Consistency between your numbers and your architecture is what gets remembered.

Theme 3: Caching strategy

Caching questions appear in roughly 22% of tagged system design rounds in the LeakCode database, making them the third most common theme. The range is wide: in-process caches, distributed caches, read-through versus write-through versus write-behind, TTL strategy, and cache invalidation on data mutation.

What makes caching questions interesting in aggregate is how often they surface as a follow-up rather than the primary prompt. A candidate is asked to design a service, describes a database-backed read path, and then the interviewer says "what if that query runs a million times a minute?" From that point, the real interview is about whether the candidate can reason through caching without being led step by step.

The reports indexed by LeakCode show that Amazon interviewers are especially likely to ask about cache consistency: what happens if a write lands in the database but the cache still has the old value, and who gets served stale data in the interim? This is not an abstract question at Amazon's scale. Candidates who treat cache invalidation as obvious tend to get pushed harder in follow-up. Visit the LeakCode system design topic page to filter questions specifically by this theme.

Theme 4: Queuing and async processing

Message queues, event streams, and async job processing appear in roughly 18% of system design reports in the LeakCode database. The framing is usually a system that cannot do everything synchronously: a payment that must trigger downstream services, a notification that should not block the primary write, or a batch job that must be resilient to worker failure.

Meta and Google both have significant queuing-related system design reports in the LeakCode database. At Meta, queue-based architectures appear in contexts involving user-generated events at massive fan-out. At Google, the framing often involves pipeline durability: what happens to an in-flight message if a worker crashes?

The key insight from the LeakCode data: interviewers are not asking you to compare specific technologies. They are asking whether you understand at-least-once versus at-most-once versus exactly-once delivery semantics, and whether you can reason about the tradeoffs in context. A candidate who jumps to naming a technology without describing the delivery guarantee they need tends to get probed harder.

Theme 5: Real-time and streaming systems

The fifth major theme in system design, appearing in roughly 14% of tagged reports, involves real-time data: live dashboards, feed ranking systems that must react to new content, collaborative editing, or any system where latency between write and read visibility is a design constraint rather than an implementation detail.

Real-time questions are especially concentrated in reports from candidates interviewing for senior-level roles. At Google and Meta, real-time system design questions often overlap with the scale theme: the challenge is not just delivering data in real time but doing so to millions of concurrent consumers. LeakCode's data shows these questions skew toward L5+ equivalent roles, which is consistent with the depth of infrastructure knowledge they require. For context on how level maps to interview content, see our post on Google L4 versus L5 interview differences.

For prep: real-time questions reward candidates who think in terms of push versus pull, event sourcing, and backpressure. The worst answers treat real-time as "add WebSockets" without addressing what happens when the client cannot consume events as fast as they arrive.

What the frequency data means for your prep

The five themes above, storage, scale, caching, queuing, and real-time, together account for the significant majority of system design content in the LeakCode database. There is meaningful tail beyond these five: API design, authentication and authorization architecture, search and ranking systems, and monitoring and observability each appear in a non-trivial number of reports. But if you are optimizing preparation time, the five themes above are where to start.

A few things the aggregate data makes clear that isolated advice often gets wrong:

• Technology naming is not the answer. Reports that describe weak system design feedback consistently note that the candidate named technologies without justifying choices. The theme is consistent across all five clusters.
• Follow-up depth separates candidates. The first part of a system design question is often a table-stakes opener. The real differentiation happens in how a candidate responds to follow-up constraints. LeakCode's reports show this pattern especially clearly at Google and Meta.
• Underreporting biases the surface area. The 1,722 system design entries in LeakCode are a floor, not a ceiling. Companies ask system design in rounds that candidates do not always explicitly label as such. See how LeakCode classifies round types for the full methodology.