ClickHouse is a column-oriented database built for fast analytical queries over large datasets. As the engine behind an enterprise data warehouse and Apache Superset dashboards it performs very well, as long as you work with its design and not against it.
Why it fits analytics
- Columnar storage and compression mean queries read only the columns they need.
- Vectorized execution makes large aggregations fast.
- MergeTree engines support ordering, partitioning and deduplication strategies suited to CDC data.
Patterns that help
Pre-aggregate before joining
Joining two large, detailed tables and then aggregating can exhaust memory. Aggregate each side in a CTE first, then join the smaller results.
WITH cy AS (
SELECT item_code, sum(qty) AS qty, avg(rate) AS rate
FROM gold.purchases WHERE fiscal_year = 2026
GROUP BY item_code
),
py AS (
SELECT item_code, avg(rate) AS rate
FROM gold.purchases WHERE fiscal_year = 2025
GROUP BY item_code
)
SELECT cy.item_code, (py.rate - cy.rate) * cy.qty AS saving
FROM cy LEFT JOIN py USING (item_code);Use ARRAY JOIN deliberately
ARRAY JOIN expands arrays into rows. It is useful for generating date or category expansions without large cross joins, which keeps memory use predictable.
Order keys follow filters
Choose ORDER BY keys that match the most common dashboard filters, such as date, plant and item, so ClickHouse can skip data it does not need.
Handle CDC duplicates
With ReplacingMergeTree, query with FINAL or deduplicate in the silver layer. Do not assume merges have already run.
Takeaway
ClickHouse rewards thoughtful modeling. Pre-aggregation, good ordering keys and an explicit deduplication strategy are usually enough to keep dashboards fast and stable.
Fiaz Anjum
Senior Data Engineering & Business Intelligence Professional | Digital Transformation Specialist
- ClickHouse
- Performance
- Data Warehouse