How can ClickHouse improve its columnar storage format to further reduce disk space usage?

Introduction

To improve ClickHouse's columnar storage format and reduce disk space usage, we need to analyze the current system, identify pain points, and propose innovative solutions. I'll approach this by examining user segments, analyzing pain points, generating solutions, and evaluating their potential impact. Let's dive in.

Step 1

Clarifying Questions

• Looking at ClickHouse's position as a high-performance analytical database, I'm thinking about the scale of data we're dealing with. Could you provide more context on the typical data volumes our users are managing, and how this has changed recently?

Why it matters: Determines the urgency and scale of our optimization efforts Expected answer: Data volumes growing exponentially, reaching petabyte scale Impact on approach: Would focus on scalable, future-proof solutions

• Considering ClickHouse's columnar storage is already quite efficient, I'm curious about the specific use cases driving this improvement initiative. Can you share more about the industries or applications where disk space is becoming a critical constraint?

Why it matters: Helps tailor solutions to specific user needs and pain points Expected answer: IoT, financial services, and e-commerce generating massive datasets Impact on approach: Would prioritize solutions targeting high-volume, time-series data

• Given ClickHouse's open-source nature, I'm wondering about the balance between community contributions and internal development. How much of our improvement efforts should focus on empowering the community versus developing proprietary enhancements?

Why it matters: Influences the development strategy and resource allocation Expected answer: 70% internal focus, 30% community enablement Impact on approach: Would design solutions with clear APIs and extension points

• Considering the competitive landscape, I'm interested in understanding how our storage efficiency compares to other columnar databases. Do we have benchmarks against systems like Apache Parquet or Google BigQuery?

Why it matters: Helps set realistic goals and identify areas for differentiation Expected answer: Comparable or slightly better than competitors in most scenarios Impact on approach: Would focus on innovative approaches beyond standard optimizations