Amazon ElastiCache

1. Introduction

Amazon ElastiCache makes it easy to set up, manage, and scale distributed in-memory cache in the AWS Cloud. It provides a high performance, resizable, and cost-effective in-memory cache, while removing complexity associated with deploying and managing a distributed cache environment. With Amazon ElastiCache Serverless, you can create a highly available cache in under a minute without infrastructure provisioning or configuration, and never have to worry about capacity. Amazon ElastiCache is Valkey-, Memcached-, and Redis OSS-compatible.

2. Supported Engines

Amazon ElastiCache is now compatible with three in‑memory caching engines:

• Memcached:

  • A simple, multi-threaded caching solution ideal for read-heavy use cases.
  • Managed as a pool of independent nodes that scale horizontally, with features such as automatic node replacement and Auto Discovery to adjust dynamically to your application’s needs.

• Redis (ElastiCache for Redis OSS):

  • Offers a richer feature set with advanced data structures (lists, sets, sorted sets, and hashes), persistence options, replication, and pub/sub messaging.
  • Supports high availability with a primary node and read replicas, as well as automatic failover using a Multi-AZ configuration.
  • Provides additional security features such as encryption (both in transit and at rest), Redis AUTH, and Role-Based Access Control (RBAC).

• Valkey: Valkey is a fork of Redis OSS 7.2, created by the Linux Foundation and community contributors after Redis changed its licensing. It is an open source, in-memory, high performance, key-value datastore. It is a drop-in replacement for Redis OSS. It can be used for a variety of workloads such as caching, session stores, and message queues, and can act as a primary database. Valkey can run as either a standalone daemon or in a cluster, with options for replication and high availability.

2.1. Redis vs Memcached vs Valkey

Below is a side-by-side comparison table of the three engines—Memcached, Redis, and Valkey:

Feature	Memcached	Redis	Valkey
Origin / History	- One of the oldest and most widely adopted in-memory caches. - Historically the gold standard for web caching.	- Open-source, started as an advanced key-value store with more complex data structures.- Long track record of community adoption.	- Forked from Redis OSS 7.2 in March 2024 after Redis Inc. changed its open-source model. - Overseen by the Linux Foundation, ensuring an open, community-driven project.
Primary Use Case	- Primarily used for object caching to offload databases. - Focused on simple key-value caching.	- In-memory data store with advanced data structures. - Can act as a cache or a primary database for certain workloads.	- Drop-in replacement for Redis OSS with additional performance optimizations. - Suitable for caching, session stores, message queues, or as a primary database.
Data Structures	- Simple key-value pairs (strings only).	- Rich data types (strings, hashes, lists, sets, sorted sets, bitmaps, HyperLogLogs, streams, geospatial).	- Same advanced data structures as Redis (sorted sets, hashes, bit arrays, HyperLogLogs, streams, geospatial, etc.).
Persistence	- No built-in persistence; purely in-memory, ephemeral.	- Offers RDB (snapshotting) and AOF (Append Only File) persistence options.	- Similar to Redis, supports point-in-time backups and disk persistence. - Primary-replica architecture for high availability.
Replication & High Availability	- No native replication; each node is standalone and treated as expendable.	- Supports asynchronous replication (primary-replica), enabling Multi-AZ setups and failover.	- Also supports primary-replica architecture with asynchronous replication. - Designed to support high availability (multi-node, cluster setups).
Multi-threading	- Multithreaded, can effectively utilize multiple CPU cores.	- Primarily single-threaded event loop (though newer versions have some threading for I/O), but historically less multi-core usage.	- Valkey 7.2 is similar to Redis, but Valkey 8.0 introduces a new I/O threading architecture. - Up to 230% higher throughput and 70% lower latency than Valkey 7.2.
Scalability	- Horizontal scaling by adding/removing nodes; treated like a pool (similar to an EC2 Auto Scaling group).	- Scales via clustering and sharding across multiple nodes with failover.	- Offers clustering, replication, and high availability options. - Can scale vertically or horizontally to meet demand.
Advanced Features	- Basic atomic increment/decrement counters only.	- Pub/Sub messaging, geospatial queries, sorting, ranking, complex data manipulations, Lua scripting.	- Same feature set as Redis (e.g., Pub/Sub, geospatial, advanced data structures) plus improvements in performance and memory usage. - Ideal for advanced real-time use cases such as geospatial queries and ML workloads.
Security & Compliance	- Typically used for simpler caching scenarios; does not offer advanced security or compliance features out of the box.	- Offers encryption in transit and at rest (in managed services), suitable for PCI DSS, HIPAA, FedRAMP use cases.	- Like Redis, supports encryption and compliance (in managed AWS services) for enterprise use.
Licensing & Community	- Open source but historically developed outside the Linux Foundation’s stewardship.	- Initially open source, but newer Redis Inc. versions have changed licenses.	- Fully open source under the BSD 3-Clause license. - Stewarded by the Linux Foundation and a vibrant open source community. - Ensures license stability and community-driven development.
AWS Managed Options	- Amazon ElastiCache for Memcached: fully managed, auto-scaling, node replacement, easy setup.	- Amazon ElastiCache for Redis: fully managed, widely used for caching, sessions, pub/sub, and more.	- Amazon ElastiCache for Valkey: drop-in Redis replacement with improved performance.
Pricing / Cost	- Available under ElastiCache. Pricing depends on node type and usage.	- ElastiCache for Redis pricing is higher than Memcached, but still pay-as-you-go.	- ElastiCache for Valkey: ~20–33% lower cost vs. other supported engines in certain modes.
Ideal Use Cases	- Pure caching with minimal overhead. - Need simple, fast, distributed caches.	- Applications needing advanced data structures. - Leaderboards, real-time analytics, pub/sub, geospatial, session store, etc.	- All Redis-like use cases plus improved performance. - Caching, real-time analytics, ephemeral or persistent store. - Suitable for open source enthusiasts seeking assured license continuity and community development.

3. Cluster Formation

Amazon ElastiCache is organized into clusters. With Memcached, a cluster consists of multiple independent nodes forming a distributed cache pool. In contrast, a Redis cluster is built around a single primary node with optional read replicas for scaling reads and ensuring high availability.

4. Network & Security

• VPC Integration:
  You can deploy your cache clusters within an Amazon Virtual Private Cloud (VPC) for network isolation and enhanced security.
• Security Features:
  In addition to VPC security, ElastiCache supports encryption (both at rest and in transit), and for Redis, it provides authentication mechanisms and role-based access controls.

5. Monitoring

Amazon ElastiCache integrates with Amazon CloudWatch, providing detailed metrics (e.g., CPU usage, memory consumption, cache hits/misses, and replication lag) that help monitor the health and performance of your cache nodes.

6. Scaling

For Memcached, you can add or remove nodes to adjust capacity. For Redis, scaling can be achieved by adding read replicas (for read scaling) or sharding simple key/counter workloads across multiple clusters since complex data types do not shard automatically.

7. Pricing Models

Amazon ElastiCache offers both on-demand and reserved node pricing models. Reserved nodes provide a significant discount compared to on‑demand pricing in exchange for a one- or three-year commitment, helping to optimize cost based on predictable workloads.

8. Use Cases

ElastiCache is a versatile service with a range of applications:

• Database Query Caching: Offload read requests from relational or NoSQL databases, thereby improving response times.
• Session Stores: Cache user sessions to enhance the performance and scalability of web applications.
• Real-Time Leaderboards: Utilize Redis’ sorted sets to maintain real-time ranking systems.
• Rate Limiting and Atomic Counters: Use atomic operations for reliable, high-speed counters and to throttle API requests.
• Time-Series Data Processing: Rapidly process and analyze time-stamped data for real-time analytics.

9. Conclusion

For further information, please refer to the official documentation.