How to Denormalize Data in ClickHouse

Why denormalize data in ClickHouse?

Denormalization trades extra storage for faster scans. ClickHouse excels at reading contiguous columns, so pre-joining customer, order, and product details eliminates run-time JOINs and slashes latency for dashboards.

What are the main denormalization strategies?

Pre-joined wide tables

Create a single MergeTree table that stores customer, order, and product columns together. Ideal for real-time dashboards that read raw facts.

Aggregated summary tables

Store pre-calculated totals, counts, or averages using GROUP BY. Dashboards can hit tiny tables without heavy aggregation.

Arrays & Nested types

ClickHouse lets you embed repeating fields (e.g., order_items.product_id) as parallel arrays, keeping one row per order.

How to build a wide table with a one-time backfill?

Use INSERT INTO wide_table SELECT ... JOIN ... to copy historical data. This populates the denormalized structure before new data arrives.

INSERT INTO OrdersWide
SELECT o.id, c.name, c.email, o.order_date, o.total_amount,
p.id AS product_id, p.name AS product_name, p.price, oi.quantity
FROM Orders o
JOIN Customers c ON c.id = o.customer_id
JOIN OrderItems oi ON oi.order_id = o.id
JOIN Products p ON p.id = oi.product_id;

How to keep the wide table updated automatically?

Create a MATERIALIZED VIEW that watches the normalized fact table (OrderItems) and inserts joined rows into OrdersWide whenever new events arrive.

CREATE MATERIALIZED VIEW mv_orders_wide TO OrdersWide AS
SELECT o.id, c.name, c.email, o.order_date, o.total_amount,
p.id AS product_id, p.name AS product_name, p.price, oi.quantity
FROM OrderItems oi
JOIN Orders o ON o.id = oi.order_id
JOIN Customers c ON c.id = o.customer_id
JOIN Products p ON p.id = oi.product_id;

How to refresh summary tables incrementally?

Insert only new or changed rows into an aggregate table using the SIMPLE_AGGREGATE_SUMMARIZATION pattern: INSERT INTO summary SELECT ... WHERE order_date > max_date.

Best practices for denormalized schemas

Pick the narrowest data types, compress strings with codecs, and partition by time to speed merges. Use VERSIONED COLLAPSING if late-arriving events occur.

How to query denormalized tables efficiently?

Always list only required columns; ClickHouse reads them lazily. Filter by partition keys (e.g., order_date) to avoid full-table scans.