AZ-305 Designing Microsoft Azure Infrastructure Solutions Exam

Recommend a caching solution for applications

Select and Configure Azure Caching Solutions

Azure applications often use caching to improve performance and scalability by storing frequently accessed data closer to the application. Common caching solutions include local in-memory caches, Azure Cache for Redis, and Azure CDN. Each solution addresses different workload characteristics: in-memory caches minimize latency within a single instance, Redis offers a distributed cache with persistence and geo-replication, and CDN accelerates delivery of static content globally. Choosing the right cache depends on your application's requirements for throughput, latency, data freshness, and geographic reach.

Evaluate application requirements by examining key factors:

• Throughput and latency: How many requests per second and how quickly must responses arrive?
• Data persistence and consistency: Is temporary staleness acceptable? Do you need durable storage or strict consistency?
• Geo-replication: Will users across regions benefit from a global cache layer?

Use these criteria to decide between:

• Local in-memory caching for ultra-low latency within a single service instance.
• Azure Cache for Redis for high-throughput, distributed caching with configurable persistence.
• Azure CDN for offloading static assets and reducing network round trips to origin servers.

When using Azure Cache for Redis, configure the cache to meet performance and resilience objectives:

• Choose an appropriate tier (Basic, Standard, Premium) based on workload size and feature needs.
• Define scaling boundaries by selecting shard counts and instance sizes to handle peak throughput.
• Configure data partitioning across shards for even load distribution and eviction policies (e.g., allkeys-lru) to manage memory.
• Enable persistence (RDB or AOF) and geo-replication for disaster recovery and global failover scenarios.

Implement robust application-tier caching patterns:

• Cache-aside: The application explicitly loads data into Redis and falls back to the database on a cache miss.
• Read-through/write-through: The cache automatically fetches or writes data to the backing store.
• Handle concurrency via optimistic or pessimistic locking to prevent stale updates and data collisions.
• Use the Circuit-Breaker pattern to detect cache failures and gracefully fall back to the original data store.

To maximize availability and responsiveness, combine shared caches with local private caches on each application instance. When retrieving data, check:

1. Local cache
2. Shared Redis cache
3. Original data store

This layered approach buffers cache outages and reduces load on the primary store, ensuring consistent performance under varied network and failure conditions.

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Conclusion

In summary, choosing the right caching solution for Azure applications involves assessing key factors such as throughput, latency, data persistence, consistency, and geo-replication needs. Solutions like local in-memory caches offer minimal latency within single instances while distributed caches like Azure Cache for Redis provide high throughput with persistent and geo-replicated configurations. Additionally, Azure CDN can effectively deliver static content globally at higher speeds. Configuration details including tiers, scaling boundaries, data partitioning, and eviction policies are critical to meeting performance targets. Implementing caching patterns like cache-aside, read-through/write-through, handling concurrency, and adopting circuit-breaker patterns can aid in maintaining cache efficiency and reliability. Combining local private caches with shared caches creates a resilient caching strategy that ensures smooth performance even under network or cache failures.