Comprehensive Guide for ClickHouse Horizontal Scaling and Capacity Planning

Introduction

Calculating your company’s required real-time analytics capacity on ClickHouse involves several steps and considerations. It’s important to assess current and projected data volumes, query complexity, and the expected concurrency of queries. Here’s a structured approach to estimate the capacity.

Runbook for ClickHouse Horizontal Scaling and Capacity Planning

Understand Data Volume and Velocity

1. Current Data Volume: Determine the size of your current datasets that will be stored in ClickHouse.
2. Data Growth Projection: Estimate the expected growth of your data over time. Consider factors like new data sources, increase in transaction volumes, etc.
3. Data Ingestion Rate: Assess how fast data is coming into your system and needs to be available for querying.

Assess Query Characteristics

1. Query Complexity: Analyze the complexity of the queries that will be run. Complex queries with multiple joins, aggregations, and subqueries require more resources.
2. Query Frequency: Determine how often these queries will be executed.
3. Concurrency Requirements: Estimate the number of concurrent queries the system needs to support.

Determine Performance Requirements

1. Response Time: Define the acceptable response time for your queries.
2. Real-time Analytics Requirement: For real-time analytics, understand the maximum delay acceptable from data ingestion to query availability.

Hardware Considerations

1. CPU: ClickHouse is CPU-intensive, especially for query processing. The number of cores and their speed will impact query performance.
2. Memory: Adequate RAM is crucial for caching and query processing.
3. Storage: Assess the type (SSD vs HDD), capacity, and I/O capabilities of your storage solution. ClickHouse benefits from fast storage for both reading and writing operations.
4. Network: Ensure your network can handle the data throughput without becoming a bottleneck.

Plan for Redundancy and High Availability

1. Replication: Factor in the need for replicas for high availability and load balancing.
2. Backup and Recovery: Consider the capacity needed for backups and an efficient recovery plan.

Consider ClickHouse Specifics

1. Sharding: Decide on a sharding strategy based on your data distribution and query patterns.
2. Compression: ClickHouse provides excellent compression. Factor this in when estimating storage requirements.

Use Benchmarking and Testing

1. Benchmarking: Use similar datasets and query loads to benchmark performance on a smaller scale.
2. Load Testing: Simulate the expected load on the system to identify potential bottlenecks and capacity limits.

Consult with ChistaDATA ClickHouse Performance Experts

1. ChistaDATA ClickHouse Performance Engineering: Committed to building High-Performance ClickHouse Infrastructure for WebScale

Monitoring and Scalability

1. Ongoing Monitoring: Continuously monitor the system’s performance and scale resources as needed.
2. Scalability Plan: Have a plan for scaling up your infrastructure as your data and query complexity grows.

Conclusion

Calculating real-time analytics capacity for ClickHouse is an iterative process that combines understanding your data and query patterns, performance requirements, and system capabilities. It’s essential to start with a solid baseline and be prepared to adjust as you monitor real-world performance. Regular benchmarking, testing, and consultation with experts can greatly assist in accurately determining the needed capacity.

To read more about horizontal scaling in ClickHouse, do consider reading the following article:

• ClickHouse Horizontal Scaling: Optimal Read-Write Split Configuration and Execution
• ClickHouse Horizontal Scaling: Setting up 6-node ClickHouse Infrastructure with Replication & Sharding
• ClickHouse Horizontal Scaling: Implementing Parallel Replicas with Dynamic Shards
• ClickHouse Horizontal Scaling: Sharding and Resharding Strategies