AWS Database Blog

Tag: Valkey

Real-time personalized recommendations with Amazon SageMaker and Valkey

Amazon receives millions of visits every day, and earning each customer’s trust visit after visit is the foundation that the store is built on. A meaningful part of that trust comes down to whether the recommendations we surface feel relevant and whether they reflect what the customer actually cares about in the moment. In this post, we describe an architecture that makes it achievable. Amazon SageMaker hosts a sentence transformer model on a managed endpoint and turns customer query text into dense semantic vectors. Valkey is an open source, in-memory data store with built-in vector search. It’s available on AWS through Amazon ElastiCache and Amazon MemoryDB. In our architecture, we use Amazon-managed Valkey to store the product catalog as a vector index.

How HotelTrader cut inter-AZ cost 95% and latency by 49% with Valkey GLIDE on Amazon ElastiCache

In this post, you learn how HotelTrader reduced inter-availability zone data transfer costs by 95% and improved average latency by 49% by migrating from the Redis Lettuce client to Valkey GLIDE on Amazon ElastiCache. The post walks through how HotelTrader identified hidden cross-AZ data transfer costs in their multi-AZ ElastiCache cluster, implemented Valkey GLIDE’s AZ-affinity read strategy to route requests to local replicas, optimized throughput with request batching, and executed a zero-downtime migration using A/B testing over 15 days.

Announcing Valkey 9.0 for Amazon ElastiCache

Amazon ElastiCache now supports Valkey 9.0. This brings the latest community-driven innovations from the Valkey open source project to address the performance and capability requirements of applications as they grow more data-intensive and latency-sensitive, such as real-time analytics, AI-driven retrieval, and high-throughput caching. In this post, we explore how these enhancements help customers build faster applications, streamline architectures, and support new real-time and AI-driven workloads.

Announcing aggregations on Amazon ElastiCache

Amazon ElastiCache now supports aggregation queries, so you can filter, group, transform, and summarize data directly in your cache with a single query. This post walks through the use cases that aggregations unlock, and shows how they work by building a faceted browsing engine using Amazon ElastiCache for Valkey.

Valkey turns two

Two years ago, Valkey emerged as a community-driven response to the need for a truly open, vendor-neutral alternative to Redis. In this post, we’ll look back at two years of progress, highlighting the rapid adoption of Valkey, the innovations delivered by the community, and what these developments mean for the future of modern caching and […]

Migrating to Amazon ElastiCache for Valkey: Best practices and a customer success story

In this post, we provide a guide to migrating from Redis OSS to ElastiCache for Valkey, incorporating different migration strategies and AWS best practices. Additionally, we highlight a customer’s successful migration to Valkey, which maintained their robust performance standards while achieving a 20% reduction in ElastiCache cluster costs.

MaiCoin case study: Blue/green upgrade from Amazon ElastiCache Redis to Valkey

MaiCoin is a leading cryptocurrency exchange and brokerage platform in Taiwan. The MaiCoin platform previously ran on a set of Amazon ElastiCache deployment clusters on Redis OSS. This post explores MaiCoin’s practical approaches using RedisShake for migrating from Amazon ElastiCache for Redis OSS to Amazon ElastiCache for Valkey using blue/green deployment strategies.

Build persistent memory for agentic AI applications with Mem0 Open Source, Amazon ElastiCache for Valkey, and Amazon Neptune Analytics

Today, we’re announcing a new integration between Mem0 Open Source, Amazon ElastiCache for Valkey, and Amazon Neptune Analytics to provide persistent memory capabilities to agentic AI applications. This integration solves a critical challenge when building agentic AI applications: without persistent memory, agents forget everything between conversations, making it impossible to deliver personalized experiences or complete multi-step tasks effectively. In this post, we show how you can use this new Mem0 integration.