Category: Amazon ElastiCache

AWS recently announced, in partnership with Google Cloud and the Valkey community, the general availability of Valkey General Language Independent Driver for the Enterprise (GLIDE) 2.0, the latest release. Valkey GLIDE is multi-language client library designed for reliability and performance. In this post, we discuss what Valkey GLIDE is and its key benefits, and then dive into its new enhancements.

Amazon Aurora, Amazon DynamoDB, and Amazon ElastiCache are popular choices for developers powering critical workloads, including global commerce platforms, financial systems, and real-time analytics applications. To enhance productivity, developers are supplementing everyday tasks with AI-assisted tools that understand context, suggest improvements, and help reason through system configurations. Model Context Protocol (MCP) is at the helm of this revolution, rapidly transforming how developers integrate AI assistants into their development pipelines. In this post, we explore the core concepts behind MCP and demonstrate how new AWS MCP servers can accelerate your database development through natural language prompts.

As companies expand globally, they must be able to architect highly available and fault-tolerant systems across multiple AWS Regions. With such scale, a company can find itself in this position when designing a caching solution across its multi-Region infrastructure. In this post, we dive deep into how to use Amazon ElastiCache for Valkey, a fully managed in-memory data store with Redis OSS and Valkey compatibility, and the Amazon ElastiCache for Valkey Global Datastore feature set.

Habby is a game studio that creates interactive entertainment to connect players worldwide. We adopted Valkey GLIDE, a client library for Amazon ElastiCache for Valkey and Redis OSS, to address our system challenges. Our system uses the Amazon ElastiCache for Redis OSS publish/subscribe (Pub/Sub) functionality for the chat message sending. However, we faced challenges with connection stability during infrastructure changes, such as instance scaling, Redis OSS version upgrades, and hardware failures. This post describes our messaging system architecture and explains how we improved system reliability by using Valkey GLIDE as the client communicating with Amazon ElastiCache.

In this post, we explore the basics of integrating a Spring Boot application with ElastiCache to enable caching. Amazon ElastiCache is a fully managed, Valkey-, Memcached-, and Redis OSS-compatible service that delivers real-time, cost-optimized performance for modern applications with 99.99% SLA availability. ElastiCache speeds up application performance, scaling to millions of operations per second with microsecond response time.

Modern applications are built as a group of microservices, and the latency for one component can impact the performance of the entire system. Monitoring latency is critical for maintaining optimal performance, enhancing user experience, and maintaining system reliability. In this post, we explore ways to monitor latency, detect anomalies, and troubleshoot high-latency issues effectively for your self-designed (node-based) ElastiCache clusters.

Valkey is an open-source, distributed, in-memory key-value data store that offers high-performance data retrieval and storage capabilities, making it an ideal choice for scalable, low-latency modern application development. Originating as a fork of Redis OSS following recent licensing changes, Valkey maintains full compatibility with its predecessor while providing high performance alternative for its developers. Valkey […]

Today, we are adding support for Valkey 8.0 on Amazon ElastiCache. ElastiCache version 8.0 for Valkey brings faster scaling for ElastiCache Serverless and memory optimizations for node-based clusters. In this post, we discuss these improvements and how you can benefit from them.

In this post, we show you how to use Amazon ElastiCache as a write-through cache for an application that uses an Amazon Keyspaces (for Apache Cassandra) table to store data about book awards. We use a Cassandra Python client driver to access Amazon Keyspaces programmatically and a Redis client to connect to the ElastiCache cluster.

When clients start running queries on new Amazon Aurora replicas, they will notice a longer runtime for the first few times that queries are run; this is due to the cold cache of the replica. As the database runs more queries, the cache gets populated and the clients notice faster runtimes. In this post, we focus on how to address the cold cache so clients that are connecting through a load-balanced endpoint get a consistent experience regardless of whether the replicas are automatically or manually scaled. In addition, we also look at other caching solutions such as Amazon ElastiCache, a fully managed Memcached, Redis, and Valkey compatible service, that can further improve the overall experience for latency-sensitive applications and, in some situations (such as higher cache hits), lead to less frequent auto-scaling events of the Aurora read replicas.