Event Store

We propose an Event Store that will provide comprehensive event sourcing and data persistence capabilities, maintaining an immutable record (unchangeable history) of all detection events, judge decisions, and system interactions for audit compliance and analytical processing.

Purpose and System Role

The proposed Event Store will serve as the authoritative source of truth for all competition data, implementing event sourcing patterns (recording all changes as events) that capture every system interaction as discrete, immutable events. This approach will ensure complete audit trails for competition integrity, enable replay capabilities for dispute resolution, and provide rich datasets for performance analytics and system optimization.

By maintaining event-level granularity (detailed event tracking), the system will support complex queries, real-time aggregations, and historical analysis while ensuring data consistency across all integrated systems. The store will handle high-velocity event streams during peak competition periods while maintaining sub-millisecond write latency and horizontal scalability (ability to add more servers).

Architectural Possibilities

Several architectural approaches could serve the Event Store requirements, each offering distinct advantages based on deployment scale and operational requirements.

Distributed Streaming Architecture using event streaming platforms will provide high-throughput event ingestion with natural partitioning for horizontal scaling capabilities. Solutions such as Apache Kafka exemplify this approach, which will excel in large-scale deployments where event volumes reach millions of entries during peak competition periods.

Built-in Replication (automatic data copying) and durability guarantees will ensure data persistence even during hardware failures, making this suitable for mission-critical competition environments.

Message Queue Solutions (event buffering systems) such as RabbitMQ or Amazon SQS will offer simpler deployment models while maintaining reliable event handling capabilities. These solutions will prove particularly suitable for smaller-scale implementations where operational simplicity outweighs ultimate throughput requirements.

Message Routing Capabilities will enable flexible event distribution patterns that adapt to different venue configurations.

Streaming Database Approach using technologies like Redis Streams (in-memory data structure store) will provide a middle ground between simplicity and performance, offering stream processing capabilities with lower operational overhead. This approach will maintain event ordering and provide publish-subscribe capabilities while remaining lighter-weight than full distributed streaming platforms.

Reduced Complexity will make this option attractive for venues with limited technical infrastructure.

Custom Event Store implementation using PostgreSQL TimescaleDB extensions (time-series database optimizations) will optimize specifically for HYROX's unique event patterns and requirements. This approach will allow fine-tuned performance optimization while incorporating domain-specific features like competition-aware partitioning and specialized indexing strategies.

Purpose-built Design will enable optimal performance for HYROX-specific use cases while maintaining familiar SQL interfaces (database query language) for operational teams.

Implementation Patterns

Regardless of the underlying technology choice, the system will likely use CQRS (Command Query Responsibility Segregation - separate systems for writing and reading data) patterns with separate read and write models optimized for their respective use cases. Write operations will prioritize consistency and durability, while read operations will use materialized views (pre-calculated data summaries) and denormalized projections for optimal query performance.

Communication Protocols and APIs

Event ingestion will occur through various protocols depending on the chosen architecture. High-performance TCP connections (direct network links) with custom binary protocols will minimize serialization overhead (data conversion costs). REST APIs will provide query interfaces for historical data access, while GraphQL endpoints will enable flexible data exploration for analytics applications.

Streaming APIs will use Server-Sent Events (SSE - one-way live updates) and WebSocket connections (two-way live communication) for real-time event subscription with configurable filtering and aggregation capabilities. Batch export interfaces will support ETL processes (data pipeline operations) and integration with external analytics platforms using standard formats including Parquet and Avro (data storage formats).

Data Flow and Formats

Inbound events will undergo schema validation (data format checking), enrichment with contextual metadata, and partitioning based on temporal and spatial dimensions (time and location-based organization). The system will maintain multiple data representations including raw events, aggregated summaries, and projection tables optimized for specific query patterns.

Event ordering preservation will ensure causal consistency across distributed components, while deduplication mechanisms (duplicate removal) will handle potential duplicate events from retry scenarios. Compression algorithms will reduce storage footprint without impacting query performance, using dictionary encoding and columnar storage optimizations (data storage efficiency techniques).

Error Handling and Resilience

Robust error handling will include transaction-level consistency guarantees, automatic retry mechanisms for transient failures, and dead letter queues (problem event storage) for events requiring manual intervention. Backup and recovery procedures will ensure data durability with point-in-time recovery capabilities and cross-region replication (backup copies in different locations).

Monitoring systems will track ingestion rates, storage use, and query performance with automated alerting for anomalies. Circuit breaker patterns (automatic failure protection) will protect against cascading failures, while graceful degradation (reduced functionality) will maintain core functionality during peak load scenarios or partial system outages.