How does Wavefront handle high-cardinality metrics compared to Prometheus? [Advanced]
Answer
Wavefront-style platforms are designed for high-cardinality dimensional metrics at large ingest scale, while vanilla Prometheus is more sensitive to high series cardinality because each Prometheus server has local memory and TSDB limits. That said, both require disciplined tag and label design.
Technical explanation
Prometheus cardinality directly affects scrape, memory, disk, and query performance on each server or remote backend.
Wavefront/DX OpenExplore is built as a centralized streaming analytics backend, so it can handle larger dimensional datasets, but cost and query performance still depend on data shape.
Neither platform should receive unbounded request IDs or raw user IDs as metric dimensions.
Hands-on example
Design example: allow tags such as service, cluster, region, endpoint_template, and status_class. Reject tags such as request_id, session_id, email, full_url, and stacktrace. Track top metrics by points per second and unique tag combinations monthly.
Check how well your resume matches the role with our free resume checker— match score, ATS check, and the skills you're missing.
More Observability interview questions
- What is observability, and how is it different from traditional monitoring? [Basic]
- What are the three pillars of observability (metrics, logs, traces)? [Basic]
- What is the difference between monitoring and observability in practice? [Basic]
- What are the four golden signals of monitoring? [Basic]
- What is the difference between the USE method and the RED method? [Basic]
- When would you use the USE method versus the RED method? [Basic]
- What is an SLI, an SLO, and an SLA, and how do they relate? [Basic]
- How do you choose good SLIs for a service? [Basic]