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This is a medium difficulty System Design question, commonly asked during Technical Screen rounds at Databricks.

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This question is commonly asked for Software Engineer candidates at Databricks during technical interviews.

Design KV store with sliding-window QPS metrics | Databricks Interview Question

Q: Design KV store with sliding-window QPS metrics

This question evaluates designing an in-memory key–value store with sliding-window throughput metrics, testing competencies in data structures, concurrency, time-series aggregation, and real-time performance measurement.

Problem

Design an in-memory key–value store that supports basic operations and can report the average operation load over a recent time window.

Functional requirements

KV operations
- PUT(key, value) stores/overwrites a value.
- GET(key) retrieves the value (or null /not found).
Load/throughput metric query
- Provide a function that can answer, for a given time window (e.g., the last 10 minutes ):
  - Average GETs per second during that window
  - Average PUTs per second during that window
- Example query: “In the past 10 minutes, what is the average GET QPS and PUT QPS?”

Clarifications to resolve with the interviewer (state assumptions if not provided)

Is the window always “last X” (sliding window) vs. “between start/end timestamps”? Assume sliding window: last X .
Maximum window size / retention (e.g., up to 1 year). Assume queries can be up to 1 year .
Exactness vs approximation. Assume exact counts are preferred if feasible.
Concurrency expectations. Assume multi-threaded callers.

Non-functional goals

KV operations should be low latency.
Metric queries should be efficient for common windows (minutes/hours) and not require scanning raw logs.
Memory usage should be bounded given a maximum retention.

Problem

Design an in-memory key–value store that supports basic operations and can report the average operation load over a recent time window.

Functional requirements

KV operations

PUT(key, value) stores/overwrites a value.
GET(key) retrieves the value (or null /not found).

Load/throughput metric query

Provide a function that can answer, for a given time window (e.g., the last 10 minutes ):
- Average GETs per second during that window
- Average PUTs per second during that window
Example query: “In the past 10 minutes, what is the average GET QPS and PUT QPS?”

Clarifications to resolve with the interviewer (state assumptions if not provided)

Is the window always “last X” (sliding window) vs. “between start/end timestamps”? Assume sliding window: last X .

Maximum window size / retention (e.g., up to 1 year). Assume queries can be up to 1 year .

Exactness vs approximation. Assume exact counts are preferred if feasible.

Concurrency expectations. Assume multi-threaded callers.

Non-functional goals

KV operations should be low latency.

Metric queries should be efficient for common windows (minutes/hours) and not require scanning raw logs.

Memory usage should be bounded given a maximum retention.

Design KV store with sliding-window QPS metrics

Quick Overview

Problem

Functional requirements

Clarifications to resolve with the interviewer (state assumptions if not provided)

Non-functional goals

Solution

Comments (0)

Design KV store with sliding-window QPS metrics

Quick Overview

Problem

Functional requirements

Clarifications to resolve with the interviewer (state assumptions if not provided)

Non-functional goals

Solution

Comments (0)