AWS Database Blog

In our previous post, Capture and tune resource utilization metrics for Amazon RDS for SQL Server,’ we demonstrated how to use Amazon RDS Enhanced Monitoring and Amazon RDS Performance Insights to diagnose and debug CPU utilization bottlenecks for Amazon Relational Database Service (Amazon RDS) for SQL Server. Aside from CPU and memory, I/O performance is critical for overall database performance. It’s important to understand the I/O requirements of a SQL Server workload, which is dependent on various factors like query access patterns, database schema, and state of database maintenance. Understanding your workload’s, I/O patterns can guide you in selecting the optimal storage type for your RDS instance, balancing performance needs with cost-effectiveness. In this post, we demonstrate how you can use Amazon RDS monitoring tools along with SQL Server monitoring capabilities to capture, diagnose, and resolve I/O issues on an RDS for SQL Server instance.

It’s November, which means that AWS re:Invent 2024 is just around the corner! We’ve summarized a list of re:Invent sessions that include Amazon DynamoDB. We encourage readers who are interested in learning more about DynamoDB to bookmark this list to streamline how you schedule your conference week in Las Vegas this year.

This is a guest post written by Jon Haddad an Apache Cassandra committer specializing in performance tuning, fixing broken clusters, and cost optimization. In this post, we discuss the basics of improving the performance of Amazon EBS with Cassandra to take advantage of the operational benefits. We explore some basic tools used by Cassandra operators to gain insight into key performance metrics. You can then apply these metrics to modify key operating system (OS) tuneables and Cassandra configuration. Finally, we review benchmarks on performance gains by implementing best practices for Amazon EBS.

In this post, we provide a step-by-step guide to update the endpoint name for a new Amazon RDS instance while keeping the existing endpoint name, along with key considerations for this process.

With Oracle Multitenant, you can consolidate standalone databases by either creating them as PDBs or migrating them to PDBs. Performance Insights has introduced a new PDB dimension to help you visualize and analyze the distribution of the load on individual PDBs within the CDB on a RDS for Oracle instance. Now, you can slice the database load metric by the PDB and SQL dimensions to identify the top queries running on each of the PDBs. In this post, we will discuss how to identify resource-intensive SQL queries at a PDB level on a visual dashboard in Performance Insights.

Channel Corporation is a B2B software as a service (SaaS) startup that operates the all-in-one artificial intelligence (AI) messenger Channel Talk. In Part 1 of this series, we introduced our motivation for NoSQL adoption, technical problems with business growth, and considerations for migration from PostgreSQL to Amazon DynamoDB. In this post, we share our experience integrating with other services to solve areas that couldn’t be addressed with DynamoDB alone.

Channel Corporation is a B2B software as a service (SaaS) startup that operates the all-in-one artificial intelligence (AI) messenger Channel Talk. This two-part blog series starts by presenting the motivation and considerations for migrating from RDBMS to NoSQL. In this post, we discuss the motivation behind Channel Corporation’s architecture modernization with Amazon DynamoDB, the reason behind choosing DynamoDB, and the four major considerations before migrating from Amazon Relational Database Service (Amazon RDS) for PostgreSQL.

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.

Amazon DynamoDB is ideal for storing chat history and metadata due to its scalability and low latency. DynamoDB can efficiently store chat history, allowing quick access to past interactions. User-specific metadata, such as preferences and session information, can be stored to personalize responses and manage active sessions, enhancing the overall chatbot experience.In this post, we explore how to design an optimal schema for chatbots, whether you’re building a small proof of concept application or deploying a large-scale production system.

Amazon DynamoDB, Amazon Bedrock, and LangChain can provide a powerful combination for building robust, context-aware chatbots. In this post, we explore how to use LangChain with DynamoDB to manage conversation history and integrate it with Amazon Bedrock to deliver intelligent, contextually aware responses. We break down the concepts behind the DynamoDB chat connector in LangChain, discuss the advantages of this approach, and guide you through the essential steps to implement it in your own chatbot.