Advanced Usage

Overview

This guide covers advanced patterns and techniques for using the Store adapter in production environments. Learn about error handling, performance optimization, monitoring, and architectural patterns that help you build robust, scalable applications using the Store adapter effectively.

Advanced patterns go beyond basic caching and Pub/Sub operations to address real-world challenges like fault tolerance, performance optimization, and architectural design. Understanding these patterns helps you build production-ready applications that handle failures gracefully, perform well under load, and scale effectively.

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Error Handling Strategies

Robust error handling ensures your application continues functioning even when the Store adapter encounters issues. These strategies help you build resilient systems that gracefully handle failures, retry transient errors, and fall back to alternative data sources when caching fails.

Error handling is critical in production environments where Redis might be temporarily unavailable or network issues might interrupt operations. These strategies ensure your application remains functional and provides a good user experience even when caching fails.

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Performance Optimization

Optimizing Store adapter performance involves understanding connection pooling, pipeline operations, and compression techniques. These optimizations help you build high-performance applications that scale well under load and efficiently use Redis resources.

Performance optimization is essential for production applications where every millisecond counts. Understanding these techniques helps you build fast, efficient applications that make optimal use of Redis resources.

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Monitoring and Observability

Monitoring and observability help you understand how your Store adapter is performing in production. Collecting metrics, implementing health checks, and tracking performance characteristics helps you identify issues early and optimize your Store usage effectively.

Good monitoring provides visibility into cache performance, error rates, and latency. This helps you optimize cache usage, identify bottlenecks, and ensure your caching strategy is working effectively.

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Architectural Patterns

Architectural patterns help you organize Store adapter usage effectively in your application. These patterns demonstrate how to implement repository patterns, combine caching strategies, and integrate event sourcing with caching. Understanding these patterns helps you build maintainable, scalable applications.

Good architectural patterns make your code more maintainable and easier to understand. They provide proven solutions to common problems and help you build robust applications that scale well.

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Multi-Tenant Patterns

Multi-tenant applications require tenant isolation to ensure data separation between tenants. These patterns demonstrate how to implement tenant isolation using key prefixes and tenant-scoped channels, enabling secure multi-tenant applications that share the same Redis infrastructure.

Tenant isolation is critical for SaaS applications where multiple tenants share the same infrastructure. These patterns ensure data separation while maintaining performance and scalability.

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Testing Strategies

Testing Store adapter usage requires strategies for mocking store operations, testing cache behavior, and verifying Pub/Sub functionality. These strategies help you write reliable tests that verify your Store usage works correctly without requiring a real Redis instance.

Good testing strategies ensure your Store usage works correctly and handles edge cases properly. They help you build reliable applications by catching issues early in the development process.

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Production Best Practices

Following production best practices ensures your Store adapter usage is reliable, performant, and maintainable. These practices cover TTL management, performance monitoring, and graceful degradation. Applying these practices helps you build production-ready applications that handle failures gracefully and perform well under load.

Production best practices prevent common issues like inappropriate TTL values, poor cache performance, and failure cascades. They ensure your Store usage works correctly in production environments and scales effectively.

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