Why Denormalization is Not the Answer to Reducing Joins in ClickHouse

Reducing query times and optimizing performance are critical goals when working with ClickHouse, a fast, open-source, columnar database. ClickHouse is optimized for fast analytical queries on large datasets, but its join performance is often a bottleneck. A common reaction is to denormalize tables to reduce joins—but this approach is often ineffective or even counterproductive in ClickHouse due to its unique architecture.

This article explores why denormalization is not the ideal solution and outlines better alternatives.

Denormalization at ClickHouse

ClickHouse is built for speed, not transactional consistency. Its primary goal is to execute analytical queries as fast as possible, even on massive datasets. Since ClickHouse is optimized for large scans and aggregations rather than point lookups, it struggles with traditional joins.

Denormalization combines data from multiple tables into one, reducing the need for joins during queries. This approach can boost performance in certain databases, but in ClickHouse, the story is more nuanced. To understand why, we need to rethink how ClickHouse processes data compared to traditional databases.

Forget What You Learned About Normalization at Traditional Databases

Normalization is the standard practice in traditional relational databases like MySQL or PostgreSQL. Data is split into multiple tables to eliminate redundancy, ensure consistency, and simplify maintenance. Joins are then used to combine this data at query time. However, these databases are row-based — they store data row-by-row on disk. Joins are expensive because the database must repeatedly read and combine entire rows, even if you only need a few columns. ClickHouse works differently. It's a columnar database, meaning data is stored by columns rather than rows. This design allows:

• Faster reads — Only the needed columns are scanned, reducing I/O.
• Better compression — Similar values in a column compress more efficiently.
• Parallel processing — Queries can process different columns simultaneously.

This architecture already reduces the overhead traditional databases face with joins. In many cases, ClickHouse's join optimizations — e.g., hash joins, parallel hash joins, partial merge join, etc. — are more than sufficient without resorting to denormalization. Denormalizing in ClickHouse can actually introduce new problems. It inflates data size, impacts compression, slows down inserts, and increases the load on the MergeTree engine. Instead, techniques like materialized views, dictionaries, and pre-joined data provide faster performance without the downsides. The key takeaway: What works in traditional databases doesn't always translate to ClickHouse. To get the best performance, think in columns, not rows, and rethink denormalization altogether.

Why Denormalization is Problematic in ClickHouse

Denormalization is often recommended in traditional relational databases to improve read performance, especially for analytical workloads. However, in ClickHouse, denormalization can introduce more problems than it solves. Let's explore ClickHouse's architecture and understand why denormalization might not be the best approach.

ClickHouse Architecture Overview

ClickHouse is designed as a columnar database optimized for fast analytical queries. Unlike traditional row-based databases, it stores data by columns, allowing it to read only the necessary columns to answer a query, minimizing I/O. It supports data compression, parallel processing, and distributed querying out of the box. ClickHouse achieves its speed with features like MergeTree tables, which handle massive data ingestion and partitioning, and by minimizing the number of rows scanned during queries. However, it's not optimized for frequent updates or complex joins — which brings us to the core of the denormalization problem. Side note: If you want to explore this further, please take a look at the ClickHouse Architecture Overview.

Storage Overhead in a Columnar Database

Denormalization involves merging multiple tables into one wide table to avoid joins. This leads to storing redundant data. In a columnar format like ClickHouse, redundancy doesn't come cheap. Each column is compressed individually, but repeating data across columns leads to a larger overall dataset. Compression ratios suffer when data lacks distinct patterns or is highly repetitive, causing increased disk usage and longer backup/restore times.

Data Ingestion and Update Complexity

ClickHouse is built for append-only workloads with minimal updates. When data is denormalized, any change in a single entity (e.g., product name or user info) requires rewriting the entire denormalized dataset. This inflates ingestion costs and slows down real-time pipelines. Keeping such datasets fresh becomes a complex, resource-intensive task.

Query Performance Trade-offs

While denormalization may simplify some query patterns, it's not a free performance boost. Wide tables with many columns mean more data needs to be read from disk, even if only a few columns are relevant to a query. Moreover, ClickHouse's ability to process arrays and nested data structures allows efficient handling of normalized data without sacrificing performance. Joins are indeed slower than in-memory databases, but ClickHouse offers optimizations like JOIN algorithms, dictionary tables, and materialized views to handle them more effectively.

Lesson Learned

Denormalization in ClickHouse is not a performance hack — it's a workaround. It's usually recommended because joins aren't as fast as in traditional row-based systems. However, this trade-off leads to bloated storage, complex ingestion workflows, and slower queries on wide tables.

Better Alternatives to Denormalization

ClickHouse offers several powerful alternatives that optimize performance without sacrificing data integrity.

Materialized Views

Materialized views allow you to precompute and store the results of complex queries. By aggregating or transforming data in advance, you can avoid expensive joins at query time. This is especially useful for dashboards or frequently accessed reports. Example: Let's say you track website visits (visits table) and want to count daily visitors per country:

Now, querying daily_visits_mv is fast, avoiding repeated aggregation on the raw visits table. Advantage: Avoids duplicating raw data while still optimizing query speed.

Dictionary Tables

Dictionaries are in-memory key-value maps that provide ultra-fast lookups — perfect for replacing small, frequently joined dimension tables. For example, mapping user IDs to names or product IDs to categories can be handled efficiently with dictionaries, drastically reducing join overhead. Example: Imagine you have a users table and frequently join it with orders for user names. Instead, create a dictionary:

Then, join data via the dictionary in queries:

This skips a full table join and speeds up lookups. Advantage: Joins become near-instant for small reference tables.

Projections

Projections are ClickHouse's way of optimizing data storage and access paths. They let you define alternative data layouts, including pre-joined or pre-aggregated structures, without touching the original table. This speeds up specific query patterns while keeping the underlying data normalized and clean.

Example: For a sales table, you often analyze total revenue per product category. Let's define a projection to speed that up:

Now, queries that match this pattern automatically use the projection:

Advantage: No separate table management—ClickHouse uses projections automatically.

Tune Join Settings

ClickHouse offers various join optimizations, from controlling memory limits to selecting the right join algorithm (merge, hash, or partial_merge). Fine-tuning these settings based on your dataset and query pattern can often yield significant performance improvements without altering your schema. Example: Suppose you have two large tables, orders, and customers, joined on customer_id. You can adjust ClickHouse's join behavior to improve performance:

Then run the query:

This limits memory usage while still handling large joins efficiently.

Pre-joining/Pre-aggregation Data

In ETL workflows, you can pre-join or pre-aggregate data before loading it into ClickHouse. This is particularly useful for large datasets where joins are predictable and repetitive. By preparing the data upstream, you save ClickHouse from doing the same work repeatedly during queries. Example: Let's say you repeatedly join orders and products. Instead of running the join every query, create a pre-joined table during ETL:

This keeps your source tables normalized but speeds up reporting queries on a pre-joined version.

Case Studies

Example 1: Denormalization Attempt in an E-Commerce Analytics System

Scenario: An e-commerce company implemented ClickHouse to analyze customer behavior and sales data. Their initial schema was normalized, with separate tables for orders, customers, and products. To reduce the number of joins in their queries, they decided to denormalize the schema by creating a single order_details table that combined data from all three tables. Implementation:

• The order_details table included redundant data such as customer names, product names, and product categories for each order.
• This reduced the need for joins in queries like "Get total sales by product category." Challenges Faced:
• Storage Overhead: The order_details table grew significantly in size due to data duplication. For example, customer names and product details were repeated for every order.
• Data Consistency: When a product name or category was updated, the team had to update all related rows in the order_details table, leading to write amplification and potential inconsistencies.
• Query Performance: While some queries were faster, others became slower due to the increased table size. For example, aggregating data by customer or product required scanning larger datasets.
• Scalability Issues: As the dataset grew, the denormalized table became harder to manage, and the system struggled to handle concurrent writes and queries.

Outcome: The team realized that denormalization introduced more problems than it solved. They reverted to a normalized schema and instead used ClickHouse's materialized views to pre-aggregate data for common queries. This approach reduced the need for joins while maintaining data consistency and storage efficiency.

Example 2: Pre-Aggregation in a Financial Analytics Platform

Scenario: A financial analytics platform used ClickHouse to process and analyze large volumes of transaction data. Their initial schema included normalized tables for transactions, accounts, and users. Queries often required joining these tables to generate reports, which impacted performance. Alternative Approach: Instead of denormalizing, the team implemented pre-aggregating the data before loading it into ClickHouse. They created a table called daily_transaction_summary with precomputed daily transaction totals by account and user. Implementation:

• The daily_transaction_summary table stored aggregated data such as account_id, user_id, transaction_date, and total_amount.
• Queries that previously required joins to calculate daily totals could now directly query the materialized view. Benefits Observed:
• Improved Query Performance: Queries were significantly faster because they avoided joins and operated on precomputed data.
• Reduced Storage Overhead: Unlike denormalization, pre-aggregation minimized data duplication by storing only the necessary aggregated values.
• Scalability: The system could handle larger datasets and more concurrent users without performance degradation.

Outcome: The financial analytics platform achieved better performance and scalability without resorting to denormalization.

Lesson Learned

These case studies demonstrate that denormalization may seem like an attractive solution for reducing joins in ClickHouse, but it often comes with significant drawbacks. By leveraging ClickHouse's native features and optimizing schema design, data engineers can achieve better performance and scalability without compromising data integrity.

Practical Recommendations

Evaluate Query Patterns and Data Volume

Before considering denormalization, investigate whether joins are genuinely causing performance issues. In many cases, optimizing query patterns or indexes solves the problem.

Combine Techniques When Necessary

For example, you can use materialized views for aggregates and dictionaries for dimension lookups. This hybrid approach reduces joins without creating denormalized tables.

Monitor Performance Continuously

Leverage ClickHouse's query profiler (system.query_log) and system tables (system.metrics, system.parts) to track slow queries, memory usage, and bottlenecks.

The Case for Denormalization

This article has shown the downside of relying on denormalization and provided other alternatives. However, there are cases when denormalization is a good strategy for reducing joins and improving performance.

When to Denormalize Data in ClickHouse

Denormalization in ClickHouse shines when analytical queries require fewer joins. ClickHouse performs best when queries avoid joins, especially on large datasets, so merging multiple tables into a single wide table can significantly boost performance. This approach works particularly well for immutable or append-only data, such as logs, events, or time-series data, where updates are rare or nonexistent. Additionally, when pre-aggregated data is essential — like in dashboards or reporting scenarios — a denormalized structure reduces query complexity and speeds up response times.

Storage efficiency, while generally a concern with denormalization, is less problematic in ClickHouse due to its powerful compression algorithms. Denormalization can be a strong option if storage costs are less important than achieving faster query performance.

When Not to Denormalize Data in ClickHouse

Despite its performance advantages, denormalization isn’t suitable for every scenario. If your data undergoes frequent updates or deletions — like user profiles or product details — a normalized structure is far more efficient. Updating a denormalized table requires rewriting entire rows, which is costly and slow, whereas normalized tables allow partial updates to specific fields without touching unrelated data.

Data integrity is another critical consideration. Normalized tables inherently maintain better data consistency and avoid duplication errors. In a denormalized model, duplicating data across rows increases the risk of inconsistency, especially when changes are needed. Furthermore, while denormalized tables can simplify queries, extensive tables with many columns may lead to slower reads when querying only a subset of the data.

Lastly, complex data relationships — such as many-to-many associations or hierarchical structures — are more maintainable in a normalized schema. Denormalization can oversimplify these relationships, leading to bloated rows and harder-to-maintain data models.

Comparison: Normalization vs. Denormalization in ClickHouse

Conclusion

While effective in traditional databases, denormalization introduces more problems than it solves in ClickHouse. Its columnar storage, append-only nature, and MergeTree engine make denormalization expensive in terms of storage, ingestion, and query performance. Rather than denormalizing, data engineers should embrace ClickHouse's strengths — using materialized views, dictionaries, projections, and join optimizations — to achieve fast, maintainable, and scalable analytics. For some workflows, pre-joining or pre-aggregating data before loading is a solid strategy to avoid the downside of denormalization. By understanding how ClickHouse works under the hood, engineers can build performant data models without falling back on denormalization as a shortcut.

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Source

• ClickHouse Joins Under the Hood - Hash Join, Parallel Hash Join, Grace Hash Join
• ClickHouse Joins Under the Hood - Full Sorting Merge Join, Partial Merge Join
• ClickHouse Joins Under the Hood - Direct Join
• ClickHouse Architecture Overview