Author: Meet Bhagdev

This post shows how to build a semantic cache using vector search on Amazon ElastiCache for Valkey. As detailed in the Impact section of this post, our experiments with semantic caching reduced LLM inference cost by up to 86 percent and improved average end-to-end latency for queries by up to 88 percent.

Amazon Keyspaces (for Apache Cassandra) is a scalable, highly available, and managed Apache Cassandra-compatible database service. With Amazon Keyspaces, you can run your Cassandra workloads on AWS using the same Cassandra application code and developer tools that you use today. You don’t have to provision, patch, or manage servers, and you don’t have to install, […]

The Amazon Keyspaces team had a great time meeting with many of you at AWS re:Invent 2022. It was particularly exciting to experience re:Invent at its peak in-person attendance. The team enjoyed listening to your feedback and use cases, and understanding what you want us to build next. We work backwards from these conversations and feedback […]

Amazon DocumentDB (with MongoDB compatibility) is a scalable, highly durable, and fully managed database service for operating mission-critical MongoDB workloads. We recently announced support for AWS Graviton2 instances for Amazon DocumentDB. AWS Graviton2 processors are custom built by AWS using 64-bit Arm Neoverse cores and feature always-on, fully encrypted DDR4 memory and 50% faster per-core […]

In this post, we show you how to integrate Amazon DocumentDB with Amazon ES so you can run full text search queries over your Amazon DocumentDB data. Specifically, we show you how to use an AWS Lambda function to stream events from your Amazon DocumentDB cluster’s change stream to an Amazon ES domain so you can run full text search queries on the data.

Amazon DocumentDB (with MongoDB compatibility) is a fast, scalable, highly available, and fully managed document database service that supports MongoDB workloads. You can use the same MongoDB 3.6 application code, drivers, and tools to run, manage, and scale workloads on Amazon DocumentDB without having to worry about managing the underlying infrastructure. As a document database, Amazon DocumentDB makes it easy to store, query, and index JSON data. AWS built Amazon DocumentDB to uniquely solve your challenges around availability, performance, reliability, durability, scalability, backup, and more. In doing so, we built several tools, like the profiler, to help you run analyze your workload on Amazon DocumentDB. The profiler gives you the ability to log the time and details of slow-running operations on your cluster. In this post, we show you how to use the profiler in Amazon DocumentDB to analyze slow-running queries to identify bottlenecks and improve individual query performance and overall cluster performance.

Amazon DocumentDB (with MongoDB compatibility) is a fast, scalable, highly available, and fully managed document database service that supports MongoDB workloads. You can use the same MongoDB 3.6, 4.0 or 5.0 application code, drivers, and tools to run, manage, and scale workloads on Amazon DocumentDB without having to worry about managing the underlying infrastructure. As […]