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 Extract all the juice from your queries with these 8 tricks
Dec 26, 2025

Extract all the juice from your queries with these 8 tricks

If your Clickhouse cluster is an orange, then your queries are the juicing tool that you need to extract all the precious sweet nectar that is the data you need to provide value to your shareholders.

One of the most complex aspects of any data warehouse, and generally data engineering as a whole, is how to extract the absolute last bit of optimization from your environment as possible. If your Clickhouse cluster is an orange, then your queries are the juicing tool that you need to extract all the precious sweet nectar that is the data you need to provide value to your shareholders.

In this blog post, we’re going to explore how you can maintain your juicing tools and optimize your queries to be as fast and juicy as possible.

What makes an efficient query?

An efficient query in Clickhouse is one that leverages the database’s columnar storage and parallel processing capabilities to their fullest potential. The key factors that determine query efficiency include proper data organization, optimal use of indexes, and understanding how Clickhouse processes data internally. These fundamentals lay the groundwork for the optimization techniques we’ll explore in detail.

Let’s break down these fundamentals before diving into specific optimization techniques. Understanding the core concepts of how Clickhouse operates will help us make better decisions when writing and optimizing our queries. This foundation is crucial for implementing the performance-enhancing strategies we’ll discuss throughout this article.

Clickhouse architecture

At its core, Clickhouse’s architecture is designed for high-performance analytics on large-scale datasets. The system employs a distributed, column-oriented approach where data is organized into tables that are partitioned and distributed across multiple nodes. This architecture enables parallel processing and efficient data compression, which are crucial factors in query performance.

The MergeTree Table Engine

The MergeTree engine family is Clickhouse’s most powerful and versatile table engine. It provides key features like data partitioning, primary key indexing, and efficient data storage with compression. The engine gets its name from the process of periodically merging data parts in the background, which helps maintain optimal performance during both writes and reads. Understanding how MergeTree works is crucial because it directly impacts how you should structure your queries for maximum efficiency.

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While MergeTree is Clickhouse’s flagship table engine, it’s worth exploring other available engines as each serves specific use cases. Some notable alternatives include:

  • ReplacingMergeTree - Useful for deduplication of rows with the same primary key
  • SummingMergeTree - Automatically aggregates data during background merges
  • AggregatingMergeTree - Perfect for pre-aggregated data storage and complex aggregation scenarios

How Architecture Affects Queries

The distributed architecture of Clickhouse has several direct implications for query performance. When writing queries, it’s essential to consider how data is partitioned and distributed across nodes, as this affects how efficiently the system can retrieve and process information. The way data is organized in columns rather than rows means that queries that select fewer columns will generally perform better, as they need to scan less data from disk.

Optimization 1. Use Primary Keys and Indexes Wisely

The first fundamental concept for optimizing Clickhouse queries is the strategic use of primary keys and indexes. Unlike traditional databases, Clickhouse’s primary keys don’t enforce uniqueness but instead create sparse indexes that help accelerate data retrieval. Properly designed primary keys should reflect your most common query patterns, considering the order of columns and their cardinality. When combined with PREWHERE clauses and efficient partitioning strategies, well-structured primary keys can dramatically reduce the amount of data that needs to be processed.

Cache Primary Keys

Caching primary keys in Clickhouse is a crucial optimization technique that can significantly improve query performance. When primary key values are cached in memory, Clickhouse can quickly determine which data parts need to be read without performing disk operations. This is particularly effective for queries that frequently filter on primary key columns, as it reduces I/O operations and speeds up data retrieval.

Here’s an example of how to implement primary key caching in Clickhouse:

ALTER TABLE your_table 
MODIFY SETTING primary_key_lazy_load = 1,
			         use_primary_key_cache = 1,
			         prewarm_primary_key_cache = 1

This configuration ensures that frequently accessed primary key values remain in memory, reducing the need for repeated disk reads and improving overall query performance. However, it’s important to balance memory usage with your system’s available resources to prevent performance degradation.

Optimization 2. Optimize Your Data Skipping Patterns

Data skipping is one of Clickhouse’s most powerful performance features, allowing the system to bypass entire chunks of data that aren’t relevant to your query. To leverage this effectively, you should structure your queries to take advantage of partitioning keys and primary key order. The key is to design your data model so that commonly filtered columns are placed at the beginning of your primary key, enabling Clickhouse to skip irrelevant data parts quickly.

Let’s look at an example of how to implement effective data skipping patterns:

-- Example table with optimized primary key order
CREATE TABLE events (
    date Date,
    user_id UInt32,
    event_type String,
    payload String
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(date)
ORDER BY (date, user_id, event_type);

In this structure, queries filtering by date and user_id will benefit from Clickhouse’s ability to skip irrelevant data parts, significantly reducing query execution time.

Optimization 3. Leverage PREWHERE for Efficient Filtering

The PREWHERE clause is a Clickhouse-specific optimization that can dramatically improve query performance when filtering large datasets. Similar to WHERE, but executed earlier in the query pipeline, PREWHERE helps Clickhouse read only the columns needed for filtering before accessing other columns. This selective reading of data can significantly reduce I/O operations and speed up query execution.

Here’s how to effectively use PREWHERE in your queries:

SELECT 
    user_id,
    sum(amount) as total_amount
FROM transactions
PREWHERE date >= '2024-01-01'
WHERE user_id > 1000
GROUP BY user_id;

Optimization 4. Use proper I/O formats

Choosing the right input and output formats in Clickhouse is crucial for optimizing query performance and data transfer efficiency. The format you select can significantly impact both the speed of data loading and the resource utilization of your queries. While formats like Native and RowBinary offer excellent performance for bulk operations, JSON and CSV formats provide better compatibility with external systems.

Here’s an example of how different formats can affect query performance:

-- Using Native format for maximum performance
SELECT * FROM table FORMAT Native;

-- Using JSONEachRow for better compatibility
SELECT * FROM table FORMAT JSONEachRow;

Optimization 5. Use views

Views in Clickhouse are powerful tools for query optimization and code reusability. They can help encapsulate complex logic, improve query maintainability, and potentially enhance performance through query result caching. Materialized views, in particular, can dramatically speed up analytical queries by pre-computing and storing commonly accessed aggregations.

Here’s an example of how to create and use views effectively:

-- Create a regular view
CREATE VIEW daily_sales AS
SELECT 
    toDate(timestamp) as date,
    sum(amount) as total_sales
FROM transactions
GROUP BY date;

-- Create a materialized view
CREATE MATERIALIZED VIEW sales_by_category
ENGINE = SummingMergeTree()
ORDER BY (category_id)
AS SELECT 
    category_id,
    sum(amount) as category_total
FROM transactions
GROUP BY category_id;

Optimization 6. Understand and Use Parallel Processing

One of Clickhouse’s most powerful features is its ability to process queries in parallel across multiple cores and servers. Understanding how to structure your queries to take advantage of this parallelism can significantly improve performance. This includes using distributed tables effectively and ensuring your queries are written to allow for parallel execution whenever possible.

Here’s an example of how to leverage parallel processing in your queries:

-- Using distributed tables for parallel processing
SELECT 
    user_id,
    count() as event_count
FROM events_distributed
GROUP BY user_id
SETTINGS max_threads = 8;

Optimization 7. Use Materialized Views Strategically

While materialized views can significantly improve query performance by pre-calculating results, they come with storage and maintenance overhead. The key is to identify frequently-used query patterns that involve complex calculations or aggregations, and create materialized views only for these specific cases. This ensures you get the maximum benefit while minimizing resource usage.

Here’s an example of strategic materialized view usage:

-- Create a materialized view for frequently accessed daily metrics
CREATE MATERIALIZED VIEW daily_metrics
ENGINE = SummingMergeTree()
ORDER BY (date, metric_type)
AS SELECT 
    toDate(timestamp) as date,
    metric_type,
    count() as event_count,
    sum(value) as total_value
FROM raw_events
GROUP BY date, metric_type;

Optimization 8. Monitor and Analyze Query Performance

Regular monitoring and analysis of query performance is essential for maintaining optimal database operations. By using Clickhouse’s system tables and monitoring tools, you can identify bottlenecks, track resource usage, and make data-driven decisions about query optimization. This proactive approach helps ensure your queries continue to perform efficiently as your data volume grows and usage patterns evolve.

Here’s how to use Clickhouse’s system tables to monitor query performance:

-- Check query execution times and resource usage
SELECT 
    query,
    query_duration_ms,
    memory_usage,
    read_rows,
    written_rows
FROM system.query_log
WHERE type = 'QueryFinish'
ORDER BY query_duration_ms DESC
LIMIT 10;

Conclusion

Optimizing Clickhouse queries is an ongoing journey that requires understanding the database’s architecture, careful planning of data structures, and continuous monitoring of performance metrics. By following these eight optimization techniques, you can significantly improve your query performance and make the most of Clickhouse’s powerful features. Remember that the key to success lies not just in implementing these optimizations, but in regularly reviewing and adjusting them based on your evolving data patterns and business needs. If you’re struggling with your query performance, schedule a call with us right now and get a free query optimization plan.