ClickHouse Migration Guide: Supercharging Your Data Analytics

In our journey to optimize data analytics performance, we recently migrated from a traditional relational database to ClickHouse. This column-oriented DBMS has dramatically improved our query speeds and analytical capabilities. Here's our experience and a guide to help you make a similar transition.

What is ClickHouse?

ClickHouse is an open-source, column-oriented database management system designed for online analytical processing (OLAP). Created by Yandex for their Yandex.Metrica web analytics service, ClickHouse is specifically engineered to handle massive amounts of data and provide lightning-fast query responses for analytical workloads.

Key Features of ClickHouse

• Column-oriented storage - Optimized for analytical queries that typically process only a few columns but many rows
• Blazing-fast performance - Processes billions of rows and dozens of gigabytes of data per server per second
• Linear scalability - Scales horizontally across multiple servers with minimal overhead
• Real-time data ingestion - Supports high-speed data insertion with minimal delay
• SQL support - Familiar SQL interface with extensions for analytical workloads
• Data compression - Advanced compression algorithms that significantly reduce storage requirements
• Fault tolerance - Built-in replication and sharding capabilities

Why We Migrated from MySQL to ClickHouse

Our analytics platform was previously built on MySQL, which served us well for many years. However, as our data volume grew exponentially and our analytical needs became more complex, we started experiencing significant performance bottlenecks:

• Analytical queries were taking minutes or even hours to complete
• Complex aggregations and grouping operations were putting excessive load on our servers
• Real-time analytics became practically impossible
• Storage costs were increasing rapidly as we needed to maintain multiple indices
• Scaling vertically became prohibitively expensive

After evaluating several alternatives, we chose ClickHouse for its exceptional performance characteristics and compatibility with our existing SQL-based workflows. The results have been remarkable:

• Queries that took minutes now complete in seconds or milliseconds
• Storage requirements reduced by approximately 70% due to efficient compression
• Ability to perform complex analytical operations in real-time
• Significantly lower infrastructure costs despite handling more data

Migration Process: Step by Step

1. Assessment and Planning

Before diving into the migration, we conducted a thorough assessment of our existing database and analytics requirements:

• Identified tables and queries that would benefit most from ClickHouse's architecture
• Analyzed query patterns to optimize schema design
• Determined which data needed real-time access versus batch processing
• Established performance benchmarks to measure success

2. Schema Design

ClickHouse's column-oriented nature requires a different approach to schema design. We focused on:

-- Example ClickHouse table schema
CREATE TABLE events
(
    event_date Date,
    event_time DateTime,
    user_id UInt64,
    event_type String,
    page_url String,
    country String,
    browser String,
    os String,
    duration_ms UInt32
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_date)
ORDER BY (event_date, user_id)
SAMPLE BY user_id;

Key considerations in our schema design:

• Choosing the right table engine - MergeTree for most analytics data
• Effective partitioning strategy - Usually by date for time-series data
• Optimizing the primary key - Based on common query patterns
• Appropriate data types - Using specialized types like LowCardinality for categorical data

3. Data Migration

We used a phased approach for data migration:

# Export data from MySQL to CSV
mysql -e "SELECT * FROM analytics.events" -N | \
  sed 's/\t/","/g;s/^/"/;s/$/"/;s/\n//g' > events.csv

# Import data into ClickHouse
cat events.csv | clickhouse-client --query="INSERT INTO events FORMAT CSV"

For larger datasets, we used ClickHouse's built-in integration with MySQL:

-- Create a MySQL dictionary source
CREATE DICTIONARY mysql_source
(
    id UInt64,
    name String
)
PRIMARY KEY id
SOURCE(MYSQL(host 'mysql-host' port 3306 user 'user' password 'password' db 'database' table 'table'))
LIFETIME(MIN 300 MAX 600)
LAYOUT(FLAT());

4. Application Changes

Adapting our application to work with ClickHouse required several changes:

prepare("SELECT COUNT(*) as count, country FROM events GROUP BY country");
$stmt->execute();
$results = $stmt->fetchAll(PDO::FETCH_ASSOC);

// After: ClickHouse connection
$clickhouse = new ClickHouseDB\Client(['host' => 'localhost', 'port' => 8123, 'username' => 'default', 'password' => '']);
$results = $clickhouse->select(
    "SELECT COUNT(*) as count, country FROM events GROUP BY country"
)->rows();

5. Testing and Optimization

After the initial migration, we focused on testing and optimization:

• Ran benchmark tests comparing query performance before and after migration
• Identified and optimized slow queries
• Adjusted schema design based on real-world usage patterns
• Implemented materialized views for common query patterns

-- Creating a materialized view for faster aggregations
CREATE MATERIALIZED VIEW events_daily_mv
ENGINE = SummingMergeTree()
PARTITION BY toYYYYMM(event_date)
ORDER BY (event_date, country, event_type)
AS SELECT
    event_date,
    country,
    event_type,
    count() AS events_count,
    sum(duration_ms) AS total_duration
FROM events
GROUP BY event_date, country, event_type;

Challenges and Solutions

Challenge 1: SQL Compatibility

While ClickHouse supports SQL, there are some differences from standard SQL and MySQL:

• Limited JOIN performance - ClickHouse is not optimized for complex JOINs
• No UPDATE/DELETE in place - Data modifications work differently
• Different function names - Many functions have ClickHouse-specific names

Solution: We denormalized our schema where appropriate and adapted our queries to use ClickHouse's syntax and functions. For data that required frequent updates, we maintained a hybrid approach, keeping some data in MySQL.

Challenge 2: Data Consistency

ClickHouse prioritizes performance over ACID transactions, which presented challenges for maintaining data consistency.

Solution: We implemented application-level consistency checks and used atomic inserts where possible. For critical data requiring strong consistency, we kept it in our transactional database and synchronized with ClickHouse.

Challenge 3: Migration Downtime

Migrating large datasets without significant downtime was challenging.

Solution: We implemented a dual-write approach during migration, writing to both MySQL and ClickHouse simultaneously while gradually shifting read operations to ClickHouse.

mysqlRepository->saveEvent($eventData);
    
    // Write to ClickHouse (new)
    $clickhouseSuccess = $this->clickhouseRepository->saveEvent($eventData);
    
    // Log any inconsistencies for reconciliation
    if ($mysqlSuccess !== $clickhouseSuccess) {
        $this->logger->warning('Data consistency issue detected', [
            'event' => $eventData,
            'mysql_success' => $mysqlSuccess,
            'clickhouse_success' => $clickhouseSuccess
        ]);
    }
    
    return $mysqlSuccess || $clickhouseSuccess;
}

Performance Comparison

The performance improvements after migrating to ClickHouse were substantial:

Query Type	MySQL (s)	ClickHouse (s)	Improvement
Count by day (30 days)	4.32	0.18	24x faster
Unique users per day	12.75	0.31	41x faster
Funnel analysis	67.21	1.24	54x faster
Retention analysis	145.32	2.18	67x faster

Best Practices for ClickHouse

1. Schema Design

• Design your schema with analytical queries in mind
• Use appropriate data types (e.g., LowCardinality for categorical data)
• Choose partitioning strategies based on your query patterns
• Denormalize where it makes sense for analytics

2. Query Optimization

• Use materialized views for common aggregations
• Leverage ClickHouse's specialized functions for analytics
• Avoid complex JOINs where possible
• Use FINAL keyword sparingly as it can impact performance

3. Infrastructure

• Allocate sufficient memory for query processing
• Use SSD storage for better performance
• Implement proper monitoring and alerting
• Consider distributed ClickHouse for very large datasets

Conclusion

Migrating from MySQL to ClickHouse has been transformative for our analytics capabilities. While the migration process required careful planning and execution, the performance benefits have far outweighed the effort involved. Our queries are now orders of magnitude faster, enabling real-time analytics that were previously impossible.

If your organization deals with large volumes of analytical data and is experiencing performance issues with traditional databases, ClickHouse could be the solution you're looking for. The column-oriented architecture, combined with powerful aggregation capabilities, makes it ideal for data-intensive analytical workloads.

Have you migrated to ClickHouse or are considering it? I'd love to hear about your experiences and answer any questions in the comments below.