ElastiCache

Overview

AWS ElastiCache provides managed Redis or Memcached services, serving as in-memory databases that deliver high performance with low latency. Similar to how RDS manages relational databases, ElastiCache handles the operational complexities of caching solutions. AWS manages all aspects including OS maintenance, patching, optimization, configuration, monitoring, failure recovery, and backups.

Redis Cluster mode

Redis Cluster Mode refers to a distributed implementation of Redis that automatically partitions data across multiple nodes in a cluster. It provides high availability and horizontal scaling while maintaining the simplicity and performance Redis is known for. While using Redis with cluster mode enabled, there are some limitations: - You cannot manually promote any of the replica nodes to primary. - Multi-AZ is required. - You can only change the structure of a cluster, the node type, and the number of nodes by restoring from a backup.

All the nodes in a Redis cluster (cluster mode enabled or cluster mode disabled) must reside in the same region.

Example usages

Database Cache Pattern

In this architecture, applications first query ElastiCache for data. When cache misses occur, the application retrieves data from the primary database (typically RDS), then stores it in ElastiCache for future use. This pattern significantly reduces database load for read-intensive workloads. However, implementing an effective cache invalidation strategy becomes crucial to maintain data freshness.

User Session Store Pattern

ElastiCache excels at managing user session data in distributed applications. When users authenticate with any application instance, their session data is written to ElastiCache. This allows other application instances to retrieve the session data, enabling seamless user experiences across multiple application servers and making applications truly stateless.

Redis vs Memcached Comparison

Redis Capabilities

Redis offers robust features for enterprise applications. It supports Multi-AZ deployments with automatic failover capabilities and read replicas for enhanced read scaling and high availability. Data durability is ensured through AOF (Append-Only File) persistence, complemented by comprehensive backup and restore functionality. Redis also provides advanced data structures including Sets and Sorted Sets.

Memcached Features

Memcached focuses on simplicity and multi-threaded performance. It supports data partitioning through multi-node sharding but doesn’t provide replication for high availability. Being non-persistent by design, it offers basic backup and restore capabilities through a serverless approach. Its multi-threaded architecture makes it particularly efficient for specific use cases.

Caching Strategies

Lazy Loading (Cache-Aside)

This strategy loads data into the cache only when necessary. Its advantages include efficient cache space utilization and resilience to node failures. However, it introduces additional latency on cache misses due to the required database roundtrip. Data staleness can occur when database updates aren’t immediately reflected in the cache.

Write-Through Caching

Write-through caching updates both the database and cache simultaneously. This approach ensures cache consistency and enables quick reads. However, it introduces write latency due to the dual update requirement. New data remains unavailable until explicitly added to the database, though this limitation can be mitigated by combining with lazy loading.

Cache Management

Eviction Policies and TTL

Cache entries can be removed through: - Explicit deletion - Memory pressure-based eviction (LRU) - Time-to-live (TTL) expiration TTL proves particularly valuable for time-sensitive data such as leaderboards, comments, and activity streams, with durations ranging from seconds to days. Memory pressure and frequent evictions indicate a need for cache scaling.

Implementation Considerations

Implementing ElastiCache requires significant application code modifications to properly handle caching logic. Developers must carefully consider caching strategies, data consistency requirements, and failure scenarios. The chosen caching pattern should align with the application’s specific needs regarding data freshness, read/write patterns, and performance requirements.

The investment in proper cache implementation pays off through reduced database load, improved application response times, and enhanced scalability. However, this requires careful consideration of cache invalidation strategies, error handling, and monitoring to ensure optimal performance.