What to Expect in the Grafana Labs System Design Interview

Expect one system design interview, and expect the problem to come from observability. Observability means understanding a running system from its metrics, logs, and traces. Grafana Labs calls its exercise non abstract system design: your design must include real numbers, not only component diagrams. Candidates report problems about time series storage, log search, and query performance at large scale. This answer lists the common question types and works through one example.

Quick Overview

The Non Abstract Style

Most companies accept a diagram of boxes with arrows. Here, every box needs a number attached. How many samples arrive per second? How many bytes is one sample? How many machines does ingestion need? Interviewers ask you to justify each number with simple arithmetic. Practice doing this arithmetic out loud, slowly and clearly. An estimate that is wrong but reasoned is fine. A design with no numbers is not.

The High Cardinality Problem

Cardinality is the number of unique label combinations in your data. One metric with a user id label can create millions of separate series. Each unique series costs memory and index entries. Naive designs fail exactly here, and interviewers know it. Talk about per tenant limits, dropping high cardinality labels, and pre aggregation. A tenant is one customer whose data is isolated from others.

A Worked Example: Design a Metrics Storage System

Requirements. Ingest ten million samples per second from many customers. Keep data for about a year. Recent data must be fast to query. State these numbers first, and confirm them with the interviewer before designing anything.

Write path. Agents send samples to ingesters. An ingester is a service that receives and buffers recent samples in memory. Every two hours, ingesters write compressed blocks to object storage. Object storage is cheap storage for files, in the style of Amazon S3. This split keeps recent data fast and old data cheap.

Read path. A querier receives each dashboard query. It fans out, meaning it sends the query to many ingesters and storage blocks at once. Results are merged and cached. Old data is downsampled: stored at lower resolution to make long range queries cheap.

Reliability. Replicate each incoming sample to three ingesters. Replication means keeping copies on separate machines so one failure loses nothing. Place replicas in different zones.

Cardinality control. Enforce per tenant series limits at the ingesters. Reject or aggregate data past the limit, and report it clearly to the customer.

Connect the Design to the Product

You are not expected to know the company's internal designs. But naming the real problem behind a choice helps. For example, Loki indexes only labels, not full log text, because full text indexes are expensive at scale. Explaining why a choice like that saves money shows exactly the judgment they want.

What Interviewers Evaluate

Three things decide the result. First, whether your numbers are reasonable and you can defend them. Second, whether you state trade offs plainly: memory against cost, freshness against query speed. Third, whether you design for failure from the start rather than after a hint.

How to Prepare

• Learn the building blocks. Queues, caches, replication, and partitioning appear in every question here. Grokking the System Design Interview covers all of them with examples.
• Go deeper on storage systems. Time series and log systems reward extra depth. Grokking the Advanced System Design Interview studies real distributed storage designs.
• Practice estimation daily. Five minutes of capacity arithmetic each day for two weeks changes this interview completely.
• See the full process. The stages around this one are described in What is the Grafana Labs interview process like? Behavioral themes are in Top Grafana Labs behavioral interview questions. The motivation question is in How to answer "Why do you want to work at Grafana Labs?"