01
Dashboards freezing for seconds
PostgreSQL or MySQL timing out on COUNT(DISTINCT) over millions of event rows.
Slow dashboard aggregations and expensive warehouse queries on user data
ClickHouse real-time analytics architecture and migration
Serve customer-facing dashboards from ClickHouse instead of an OLTP database or a per-query-billed warehouse: model for your queries, ingest from Kafka, pre-aggregate on write.
Symptoms
Signs your platform has this problem
If several of these sound familiar, the plan below is where we would start.
02
Expensive warehouse bills
Customer-facing traffic running repeated analytical queries against Snowflake.
03
Stale data
Batch ingestion delaying data by hours before it reaches dashboards.
Remediation plan
How we fix it, step by step
Each phase ends with a measurement, so you can see what changed before the next one starts.
01
Schema and MergeTree modeling
Designing MergeTree tables with sorting keys that match your dashboard queries.
02
Kafka ingestion
Streaming events from Kafka into ClickHouse continuously.
03
Materialized views
Pre-aggregating hourly and daily rollups as data is written.
04
API cutover
Pointing dashboard APIs at ClickHouse and retiring the old queries.
Technical checklist
Remediation checklist
What we check before a change goes to production:
- Model ClickHouse tables with ORDER BY keys that match real queries
- Ingest from Kafka with the Kafka table engine or ClickPipes
- Create materialized views that pre-aggregate metrics at write time
- Move user-facing queries off the warehouse
What we measure
We take a baseline first and report the same measurements after each change, from your own monitoring — evidence, not promised results.
- Query latency
- Dashboard queries at p95 under real concurrency
- Monthly cost
- Current database or warehouse cost vs ClickHouse
- Data freshness
- Time from event to visible in the dashboard
Related service
Cloud & DevOps
Cloud cost optimization, Kubernetes platforms, and CI/CD that make deploys boring — savings and reliability measured in your dashboards, not our deck.
Explore Cloud & DevOpsQuestions
Questions about this remediation
ClickHouse is built for high-concurrency, low-latency queries from web applications, and its capacity-based pricing doesn't grow with every query your users run.
It can consume Kafka topics directly through the Kafka table engine (or ClickPipes on ClickHouse Cloud) and merges inserted parts in the background. Insert in batches rather than row by row.
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Want an engineer to look at this with you?
Send us the symptoms and any metrics you have. We'll reply within one business day, set up a call and agree what to measure before anything changes.