Concept scope at a glance
Dashboards that slow down as the customer base grows
A SaaS analytics product works well at launch, then slows as data volume and tenants increase. Queries that took milliseconds now take seconds, and costs climb with them. This concept looks at how to design for growth from the start.
Measure first, then choose the architecture
We would profile the slowest queries, agree performance targets, and then design an aggregation layer, caching strategy and ingestion pipeline to meet them. Load tests would run before launch, not after the first complaint.
- 1BaselineProfile real queries and set clear performance budgets.
- 2ModelDesign the data model and a pre-aggregation layer for common questions.
- 3BuildImplement the API, caching and background jobs.
- 4ProveRun load tests at expected and peak volumes, with monitoring in place.
What we would decide, and why
Set performance budgets before building
Agree what "fast" means in numbers so every decision can be checked against it.
Pre-compute what users ask for every day
Keep the heavy work off the request path.
Isolate tenants by design
One heavy customer should never slow down everyone else.
What success would look like
This is a concept brief, so there are no results to report. These are the measures we would track if it were built, and what we would check first.
- p95 query time against the agreed budgetMeasured in load tests and production monitoring
- Error rate under peak loadTracked per release
- Infrastructure cost per active accountCompare before and after
What we would check first
- Do the test volumes match real customer data?
- What are the failure modes under overload?
- What does the design cost at 10x scale?
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