How to Model Data Effectively in ClickHouse

Why does ClickHouse need a different modeling approach?

Column-oriented storage, append-only writes, and aggressive compression demand schemas that minimize random updates, leverage ordering keys, and favor wide fact tables over many joins.

Which engine should I pick for analytics?

Use MergeTree when you need custom partitioning and primary keys, ReplacingMergeTree to deduplicate rows, and AggregatingMergeTree to pre-aggregate metrics.

How do I model an ecommerce fact table?

Create a wide Orders fact table with denormalized customer and product attributes to reduce joins. Split infrequently queried text columns into separate tables if they bloat storage.

Example wide Orders table

CREATE TABLE OrdersWide
(
id UInt64,
order_date Date,
customer_id UInt64,
customer_name String,
total_amount Decimal(10,2),
product_ids Array(UInt64),
quantities Array(UInt8)
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(order_date)
ORDER BY (order_date, id);


How do I handle high-cardinality dimensions like products?

Store the raw dimension in a lookup table using ReplacingMergeTree and embed surrogate keys in the fact table. Use LowCardinality(String) for text values that compress well.

What partition key works best?

Partition by coarse grain columns—month or week on order_date—to balance mutation cost and query pruning. Never partition by high-cardinality columns such as customer_id.

When should I pre-aggregate data?

Pre-aggregate when queries repeatedly group by the same keys with large scans. Use AggregatingMergeTree to store running sums and counts.

Pre-aggregated daily revenue table

CREATE TABLE DailyRevenue
(
order_date Date,
total_amount AggregateFunction(sum, Decimal(10,2))
)
ENGINE = AggregatingMergeTree()
PARTITION BY order_date
ORDER BY order_date;


How do materialized views speed up queries?

Materialized views automatically keep derived tables in sync. Create one that writes to DailyRevenue whenever Orders receives new data.

What are common ClickHouse modeling mistakes?

First, over-normalizing tables causes expensive joins. Second, choosing a primary key that doesn’t match query filters leads to full scans.