![]() This article references metric terminology introduced in our Monitoring 101 series, which provides a framework for metric collection and alerting.Ĭount of executed statements (sent by client only)Ĭount of executed statements (sent by client or executed within stored programs) And the version of MariaDB (10.0.17) available on RDS at the time of this writing is not fully compatible with the MySQL Workbench tool or the sys schema, both of which are detailed in Part 2 of this series. For instance, Aurora has auto-scaling storage and therefore does not expose a metric tracking free storage space. Most of the performance metrics outlined here also apply to Aurora and MariaDB on RDS, but there are some key differences between the database engines. Part 2 of this series explains how to collect both types of metrics. ![]() MySQL metrics, on the other hand, must be accessed from the database instance itself. RDS metrics (as opposed to MySQL metrics) are available through Amazon CloudWatch, and are available regardless of which database engine you use. In this article we’ll highlight key metrics in each of the above areas that together give you a detailed view of your database’s performance. ![]() Users of MySQL on RDS have access to hundreds of metrics, but it’s not always easy to tell what you should focus on. To keep your applications running smoothly, it is important to understand and track performance metrics in the following areas: This article focuses on the original RDS database engine: MySQL. RDS users can choose from several relational database engines, including MySQL, Oracle, SQL Server, Postgres, MariaDB, and Amazon Aurora, a new MySQL-compatible database built for RDS. What is RDS?Īmazon Relational Database Service (RDS) is a hosted database service in the AWS cloud. Part 2 is about collecting metrics from both RDS and MySQL, and Part 3 details how to monitor MySQL on RDS with Datadog. This post is part 1 of a 3-part series about monitoring MySQL on Amazon RDS.
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