Avoiding ClickHouse Fan Traps : A Technical Guide for High-Performance Analytics

Shiv Iyer ChistaDATA, ClickHouse, ClickHouse Performance, ClickHouse Performance IO, ClickHouse Query Parser, ClickHouse Query Performance, ClickHouse Query Profiler, ClickHouse Search, OLAP

Avoiding Fan Traps in ClickHouse: A Technical Guide for High-Performance Analytics

Fan traps are one of the most insidious data modeling issues in analytical databases, and ClickHouse is no exception. While ClickHouse’s columnar architecture and blazing-fast performance make it ideal for OLAP workloads, fan traps can silently corrupt your aggregations, leading to incorrect business insights. This comprehensive guide explores how to identify, prevent, and resolve fan traps in ClickHouse environments.

Understanding Fan Traps in ClickHouse Context

A fan trap occurs when joining tables with one-to-many relationships creates a Cartesian product effect, inflating aggregation results. In ClickHouse’s distributed, columnar environment, these issues can be particularly problematic due to the scale of data processing.

The Anatomy of a ClickHouse Fan Trap

Consider this typical e-commerce scenario with three tables:

-- Customers table
CREATE TABLE customers (
    customer_id UInt64,
    customer_name String,
    registration_date Date
) ENGINE = MergeTree()
ORDER BY customer_id;

-- Orders table  
CREATE TABLE orders (
    order_id UInt64,
    customer_id UInt64,
    order_date Date,
    order_amount Decimal(10,2)
) ENGINE = MergeTree()
ORDER BY (customer_id, order_date);

-- Support tickets table
CREATE TABLE support_tickets (
    ticket_id UInt64,
    customer_id UInt64,
    created_date Date,
    priority String
) ENGINE = MergeTree()
ORDER BY (customer_id, created_date);

This problematic query creates a fan trap:

-- PROBLEMATIC: Creates fan trap
SELECT 
    c.customer_name,
    COUNT(o.order_id) as total_orders,
    COUNT(t.ticket_id) as total_tickets,
    SUM(o.order_amount) as total_revenue
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id
LEFT JOIN support_tickets t ON c.customer_id = t.customer_id
GROUP BY c.customer_name;

ClickHouse-Specific Solutions

1. Subquery Approach with ClickHouse Optimizations

ClickHouse excels at subquery optimization. This approach leverages ClickHouse’s ability to parallelize independent subqueries:

SELECT 
    c.customer_name,
    COALESCE(order_stats.total_orders, 0) as total_orders,
    COALESCE(order_stats.total_revenue, 0) as total_revenue,
    COALESCE(ticket_stats.total_tickets, 0) as total_tickets,
    COALESCE(ticket_stats.high_priority_tickets, 0) as high_priority_tickets
FROM customers c
LEFT JOIN (
    SELECT 
        customer_id,
        COUNT(*) as total_orders,
        SUM(order_amount) as total_revenue
    FROM orders
    GROUP BY customer_id
) order_stats ON c.customer_id = order_stats.customer_id
LEFT JOIN (
    SELECT 
        customer_id,
        COUNT(*) as total_tickets,
        countIf(priority = 'High') as high_priority_tickets
    FROM support_tickets
    GROUP BY customer_id
) ticket_stats ON c.customer_id = ticket_stats.customer_id;

2. Using ClickHouse’s DISTINCT Optimization

ClickHouse’s columnar storage makes DISTINCT operations highly efficient:

SELECT 
    c.customer_name,
    COUNT(DISTINCT o.order_id) as total_orders,
    COUNT(DISTINCT t.ticket_id) as total_tickets,
    -- Be careful with SUM and DISTINCT
    sumDistinct(o.order_amount) as total_revenue
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id
LEFT JOIN support_tickets t ON c.customer_id = t.customer_id
GROUP BY c.customer_name;

Warning: sumDistinct() sums unique values, not the sum per customer. For revenue calculations, use the subquery approach instead.

3. Leveraging ClickHouse’s Window Functions

ClickHouse’s window functions can elegantly solve fan traps:

WITH customer_metrics AS (
    SELECT DISTINCT
        c.customer_id,
        c.customer_name,
        COUNT(o.order_id) OVER (PARTITION BY c.customer_id) as total_orders,
        SUM(o.order_amount) OVER (PARTITION BY c.customer_id) as total_revenue,
        COUNT(t.ticket_id) OVER (PARTITION BY c.customer_id) as total_tickets
    FROM customers c
    LEFT JOIN orders o ON c.customer_id = o.customer_id
    LEFT JOIN support_tickets t ON c.customer_id = t.customer_id
)
SELECT 
    customer_name,
    total_orders,
    total_revenue,
    total_tickets
FROM customer_metrics;

4. ClickHouse Materialized Views for Performance

For frequently accessed metrics, create materialized views to pre-aggregate data:

-- Create materialized view for customer order metrics
CREATE MATERIALIZED VIEW customer_order_metrics
ENGINE = SummingMergeTree()
ORDER BY customer_id
AS SELECT
    customer_id,
    COUNT(*) as order_count,
    SUM(order_amount) as total_revenue
FROM orders
GROUP BY customer_id;

-- Create materialized view for customer ticket metrics  
CREATE MATERIALIZED VIEW customer_ticket_metrics
ENGINE = SummingMergeTree()
ORDER BY customer_id
AS SELECT
    customer_id,
    COUNT(*) as ticket_count,
    countIf(priority = 'High') as high_priority_count
FROM support_tickets
GROUP BY customer_id;

-- Query without fan traps
SELECT 
    c.customer_name,
    COALESCE(om.order_count, 0) as total_orders,
    COALESCE(om.total_revenue, 0) as total_revenue,
    COALESCE(tm.ticket_count, 0) as total_tickets,
    COALESCE(tm.high_priority_count, 0) as high_priority_tickets
FROM customers c
LEFT JOIN customer_order_metrics om ON c.customer_id = om.customer_id
LEFT JOIN customer_ticket_metrics tm ON c.customer_id = tm.customer_id;

Advanced ClickHouse Techniques

Using ARRAY JOIN for Complex Relationships

For scenarios with nested data structures, ClickHouse’s ARRAY JOIN can help avoid fan traps:

-- Example with nested customer data
CREATE TABLE customer_events (
    customer_id UInt64,
    order_ids Array(UInt64),
    ticket_ids Array(UInt64),
    event_date Date
) ENGINE = MergeTree()
ORDER BY (customer_id, event_date);

-- Avoid fan trap with ARRAY JOIN
SELECT 
    customer_id,
    COUNT(DISTINCT order_id) as unique_orders,
    COUNT(DISTINCT ticket_id) as unique_tickets
FROM customer_events
ARRAY JOIN order_ids as order_id, ticket_ids as ticket_id
GROUP BY customer_id;

Leveraging ClickHouse’s dictGet() Function

For dimension lookups, use dictionaries to avoid unnecessary joins:

-- Create dictionary for customer data
CREATE DICTIONARY customer_dict (
    customer_id UInt64,
    customer_name String,
    registration_date Date
)
PRIMARY KEY customer_id
SOURCE(CLICKHOUSE(HOST 'localhost' PORT 9000 TABLE 'customers'))
LIFETIME(MIN 300 MAX 600)
LAYOUT(HASHED());

-- Query without joining customer table
SELECT 
    dictGet('customer_dict', 'customer_name', customer_id) as customer_name,
    COUNT(*) as total_orders,
    SUM(order_amount) as total_revenue
FROM orders
GROUP BY customer_id;

Performance Optimization Strategies

1. Proper Table Engine Selection

Choose appropriate table engines based on your use case:

-- For frequently updated aggregations
CREATE TABLE customer_metrics (
    customer_id UInt64,
    metric_date Date,
    order_count UInt64,
    ticket_count UInt64,
    revenue Decimal(15,2)
) ENGINE = SummingMergeTree()
PARTITION BY toYYYYMM(metric_date)
ORDER BY (customer_id, metric_date);

-- For immutable analytical data
CREATE TABLE customer_analytics (
    customer_id UInt64,
    analysis_date Date,
    total_orders UInt64,
    total_tickets UInt64,
    lifetime_value Decimal(15,2)
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(analysis_date)
ORDER BY customer_id;

2. Optimize JOIN Order and Conditions

ClickHouse processes JOINs left-to-right, so order matters:

-- Optimized JOIN order (smallest table first)
SELECT 
    c.customer_name,
    order_metrics.total_orders,
    ticket_metrics.total_tickets
FROM (
    SELECT customer_id, COUNT(*) as total_orders
    FROM orders 
    WHERE order_date >= today() - INTERVAL 30 DAY
    GROUP BY customer_id
) order_metrics
LEFT JOIN customers c ON order_metrics.customer_id = c.customer_id
LEFT JOIN (
    SELECT customer_id, COUNT(*) as total_tickets
    FROM support_tickets
    WHERE created_date >= today() - INTERVAL 30 DAY
    GROUP BY customer_id
) ticket_metrics ON c.customer_id = ticket_metrics.customer_id;

3. Use PREWHERE for Early Filtering

Leverage ClickHouse’s PREWHERE clause for optimal performance:

SELECT 
    customer_id,
    COUNT(DISTINCT order_id) as orders,
    COUNT(DISTINCT ticket_id) as tickets
FROM (
    SELECT 
        c.customer_id,
        o.order_id,
        t.ticket_id
    FROM customers c
    LEFT JOIN orders o ON c.customer_id = o.customer_id
    LEFT JOIN support_tickets t ON c.customer_id = t.customer_id
    PREWHERE c.registration_date >= '2024-01-01'
)
GROUP BY customer_id;

Monitoring and Detection

Creating Fan Trap Detection Queries

Implement monitoring queries to detect potential fan traps:

-- Detect potential fan traps by comparing row counts
WITH base_counts AS (
    SELECT 
        COUNT(*) as customer_count
    FROM customers
),
joined_counts AS (
    SELECT 
        COUNT(*) as joined_row_count,
        COUNT(DISTINCT c.customer_id) as distinct_customers
    FROM customers c
    LEFT JOIN orders o ON c.customer_id = o.customer_id
    LEFT JOIN support_tickets t ON c.customer_id = t.customer_id
)
SELECT 
    bc.customer_count,
    jc.joined_row_count,
    jc.distinct_customers,
    jc.joined_row_count / bc.customer_count as multiplication_factor
FROM base_counts bc, joined_counts jc;

Automated Testing Framework

-- Create test to validate aggregation accuracy
CREATE OR REPLACE VIEW fan_trap_test AS
WITH 
direct_orders AS (
    SELECT customer_id, COUNT(*) as order_count
    FROM orders GROUP BY customer_id
),
direct_tickets AS (
    SELECT customer_id, COUNT(*) as ticket_count  
    FROM support_tickets GROUP BY customer_id
),
joined_result AS (
    SELECT 
        c.customer_id,
        COUNT(DISTINCT o.order_id) as joined_order_count,
        COUNT(DISTINCT t.ticket_id) as joined_ticket_count
    FROM customers c
    LEFT JOIN orders o ON c.customer_id = o.customer_id
    LEFT JOIN support_tickets t ON c.customer_id = t.customer_id
    GROUP BY c.customer_id
)
SELECT 
    'PASS' as test_status
FROM direct_orders do
FULL OUTER JOIN direct_tickets dt ON do.customer_id = dt.customer_id
FULL OUTER JOIN joined_result jr ON COALESCE(do.customer_id, dt.customer_id) = jr.customer_id
WHERE COALESCE(do.order_count, 0) = COALESCE(jr.joined_order_count, 0)
  AND COALESCE(dt.ticket_count, 0) = COALESCE(jr.joined_ticket_count, 0)
HAVING COUNT(*) = (SELECT COUNT(DISTINCT customer_id) FROM customers);

Best Practices for ClickHouse Fan Trap Prevention

1. Data Modeling Guidelines

  • Use star schema patterns with fact tables and dimension tables
  • Implement proper partitioning strategies based on query patterns
  • Create dedicated aggregation tables for frequently accessed metrics
  • Leverage ClickHouse’s specialized engines like SummingMergeTree for pre-aggregated data

2. Query Development Workflow

  1. Identify relationships before writing queries
  2. Start with single-table aggregations then join results
  3. Use EXPLAIN to understand query execution plans
  4. Test with known datasets to validate results
  5. Implement automated testing for critical business metrics

3. Performance Monitoring

  • Monitor query execution times for performance degradation
  • Track memory usage for queries with multiple JOINs
  • Set up alerts for queries that exceed expected row counts
  • Regular review of query patterns and optimization opportunities

Conclusion

Fan traps in ClickHouse can significantly impact both query performance and result accuracy. By understanding the root causes and implementing the solutions outlined in this guide, you can build robust analytical queries that scale with your data growth. Remember that prevention is always better than correction—invest time in proper data modeling and query design patterns to avoid fan traps from the start.

The key to success with ClickHouse analytics is leveraging its strengths: columnar storage, parallel processing, and specialized engines. When combined with proper fan trap prevention techniques, you can build high-performance analytical systems that deliver accurate insights at scale.

Further Reading:

Open Source Data Warehousing and Analytics

Implementing Data Level Security on ClickHouse: Complete Technical Guide

ClickHouse ReplacingMergeTree Explained

Building Fast Data Loops in ClickHouse®

Connecting ClickHouse® to Apache Kafka®

References:

How Anthropic is using ClickHouse to scale observability for the AI era

About Shiv Iyer 262 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.
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