How to Apply Best Practices in ClickHouse

What are the essential ClickHouse best practices for high-performance analytics?

ClickHouse best practices ensure high-performance analytics by optimizing schema design, queries, and server settings.

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Description

Why do ClickHouse best practices matter?

Following best practices prevents slow queries, excess storage, and outages. They keep analytical workloads fast even as data grows.

How should I model ecommerce data?

Create wide, denormalized tables for read speed. Use MergeTree engines with appropriate primary keys like (order_date, id) to cluster daily inserts.

Is partitioning by day or month better?

Daily partitions (toYYYYMMDD(order_date)) speed deletes and drop-partition operations for GDPR compliance.Monthly partitions reduce partition count when data is small.

What are must-use table engines?

Use ReplacingMergeTree for upserts, AggregatingMergeTree for pre-aggregated rollups, and ReplicatedMergeTree for HA clusters.

How do I load data efficiently?

Insert in batches of 10k–100k rows via INSERT VALUES or INSERT SELECT.Avoid single-row inserts; they cause many parts and slow merges.

Which settings boost query speed?

Set max_threads to CPU cores, max_block_size to 65,536, and use_uncompressed_cache to 1 for hot columns.

When should I use materialized views?

Create materialized views to pre-aggregate KPIs like daily revenue.They reduce heavy group-by queries at runtime.

How can I monitor and troubleshoot?

Query system tables: system.parts for part count, system.merges for merge status, and system.query_log for slow queries.

Best practice checklist

✅ Denormalize, ✅ Partition wisely, ✅ Batch inserts, ✅ Tune settings, ✅ Use materialized views, ✅ Monitor system tables.

Why How to Apply Best Practices in ClickHouse is important

How to Apply Best Practices in ClickHouse Example Usage


-- Detect days where revenue dropped vs. 7-day average
WITH avg7 AS (
    SELECT order_date, revenue,
           avg(revenue) OVER (ORDER BY order_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS avg_7d
    FROM daily_revenue
)
SELECT order_date, revenue, avg_7d
FROM avg7
WHERE revenue < 0.8 * avg_7d;

How to Apply Best Practices in ClickHouse Syntax


-- Create an Orders fact table with daily partitions and order_date clustering
CREATE TABLE Orders
(
    id UInt64,
    customer_id UInt64,
    order_date Date,
    total_amount Decimal(12,2)
)
ENGINE = MergeTree
PARTITION BY toYYYYMMDD(order_date)
ORDER BY (order_date, id);

-- Materialized view for daily revenue
CREATE MATERIALIZED VIEW daily_revenue
ENGINE = SummingMergeTree
PARTITION BY order_date
ORDER BY order_date AS
SELECT order_date, sum(total_amount) AS revenue
FROM Orders
GROUP BY order_date;

Common Mistakes

Mistake: Normalizing tables as in OLTP systems. Why wrong: Joins across billions of rows slow ClickHouse. Fix: Store flattened, wide tables or use materialized views.
Mistake: Loading one row per INSERT. Why wrong: Generates many small parts, hurts merges. Fix: Batch 10k–100k rows per INSERT or use <code>clickhouse-client --query="INSERT INTO ... FORMAT CSV"</code>.