Clickhouse

Table of Contents Introduction Project Overview Recent Development Iterations Key Features & Improvements Challenges & Solutions Collaboration in Practice Mermaid Diagram: Secure Service Architecture Future Plans Conclusion Introduction Welcome to another behind-the-scenes look at the Scores project! In this post, Claude (the tool), Caroline (the AI coding assistant), and Stef Hock (the human developer) share how collaboration, creativity, and continuous improvement have shaped the latest development cycle. Our journey is guided by care, challenge, and a relentless drive to make Scores more secure, scalable, and innovative. ...

Disclaimer: This blog post is automatically generated from project documentation and technical proposals using AI assistance. The content represents our development journey and architectural decisions. Code examples are simplified illustrations and may not reflect the exact production implementation. Table of Contents The Challenge: When Metrics Evolve The Why: Statistical Integrity Over Time The What: Snapshot-Based Versioning The How: Implementation Journey Database Migrations Domain Model Evolution Match Lifecycle Integration Projection Layer Updates API Enhancements Collaboration in Action The Results: What We Achieved Looking Forward The Challenge: When Metrics Evolve Imagine this scenario: You’ve carefully tuned your player statistics algorithms, assigning specific weights to different shot types—smashes get higher offensive weights, delicate chiquitas favor precision metrics. Six months later, after analyzing thousands of matches, you realize that smash weights should be adjusted from 1.0 to 0.95 to better reflect actual game dynamics. ...

Disclaimer: This blog post is automatically generated from project documentation and technical proposals using AI assistance. The content represents our development journey and architectural decisions. Code examples are simplified illustrations and may not reflect the exact production implementation. Table of Contents Introduction The Challenge: Mixed Responsibilities Phase 0: Architectural Cleanup Phase 1: Static Imports and Consolidation Building the Projections Layer Achievements and ROI What’s Next Introduction Over the past few weeks, Caroline (our AI assistant) and I have been working on a significant architectural improvement to the Scores project - implementing a Dynamic Query Registry with proper separation between transactional and analytical concerns. This wasn’t just a refactoring exercise; it was a fundamental rethinking of how we handle queries in an event-sourced, CQRS-based system. ...

Disclaimer: This blog post is automatically generated from project documentation and technical proposals using AI assistance. The content represents our development journey and architectural decisions. Code examples are simplified illustrations and may not reflect the exact production implementation. The Materialized View Problem I was reviewing PostgreSQL slow query logs when Caroline pointed out something concerning: “Our materialized view refreshes are taking longer every week.” She pulled up the stats: SELECT schemaname, matviewname, last_refresh, query_start, current_timestamp - query_start as refresh_duration FROM pg_stat_progress_refresh_mat_view JOIN pg_stat_activity USING (pid); The match_statistics view was taking 18 seconds to refresh. The player_momentum view was at 12 seconds. “That’s only going to get worse as we accumulate more matches,” Caroline said. “And these views lock tables during refresh.” Claude suggested a solution: “ClickHouse is designed for exactly this—analytical queries over large datasets. Keep PostgreSQL for transactions, use ClickHouse for analytics.” We decided to migrate. ...

Disclaimer: This blog post is automatically generated from project documentation and technical proposals using AI assistance. The content represents our development journey and architectural decisions. Code examples are simplified illustrations and may not reflect the exact production implementation. The Biometric Challenge Caroline had a question that changed everything: “What if we could track heart rate during pressure points?” I looked up from my keyboard. “You mean correlate biometric data with match events?” “Exactly,” she said. “We already track every shot, every score change. If we add heart rate and movement data, we could see exactly when players get stressed.” Claude liked the idea: “Biometric data would let you detect fatigue patterns, recovery rates, momentum shifts—all based on actual physiology, not just scores.” We needed a system that could handle high-frequency data (up to 50Hz for gyroscopes), stream it in real-time from wearables, and correlate it with our existing event sourcing system. ...