Tag: 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.

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.

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.

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 […]

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.

Alight Solutions is a leading cloud-based human capital technology and services provider that has focused its operations on integrated benefits administration, healthcare navigation, and employee experience solutions. In this post, we share how Alight Solutions transformed their caching infrastructure using ElastiCache while maintaining strict performance requirements, achieving over 60% cost reduction, 70-80% reduction in operational overhead, migration of gigabytes of data with sub-0.5 millisecond performance for millions of users, and a 99.99% reduction in incident rate.

In this post we show you how to build a secure Amazon ElastiCache for Valkey cluster using Terraform, implementing best practices and comprehensive security controls including encryption, authentication, and network isolation.

Vector search for Amazon ElastiCache is now generally available. You can now use ElastiCache to index, search, and update billions of high-dimensional vector embeddings from popular providers like Amazon Bedrock, Amazon SageMaker, Anthropic, and OpenAI—with latencies as low as microseconds and up to 99% recall.

In April 2024, AWS announced support for Valkey, a community-driven fork of Redis born out of a shared belief that critical infrastructure software should be vendor neutral and open source. In this post, we share how, just over a year in, we remain fully committed to the Valkey project and announce support for the latest version with Amazon ElastiCache version 8.1 for Valkey. We explore the benefits of Valkey through real-world examples the benefits of the latest innovations, including a new hash table with additional memory efficiencies, support for Bloom filters, observability enhancements, and new functionality.

Amazon ElastiCache now supports Bloom filters: a fast, memory-efficient, probabilistic data structure that lets you quickly insert items and check whether items exist. In this post, we discuss two real-world use cases demonstrating how Bloom filters work in ElastiCache, the best-practices to implement, and how you can save at least 90% in memory and cost compared to alternative implementations. Bloom filters are available in ElastiCache version 8.1 for Valkey in all AWS Regions and at no additional cost.