AWS RDS Monitoring

RDS monitoring involves tracking performance metrics for Amazon Relational Database Service, a web service designed to make it easier for DBAs to set up, run, and scale cloud-based relational databases.

Many companies choose to use Amazon Web Services (AWS) as their cloud-based computing provider due to the benefits the platform offers. It’s designed to be an industry-standard, flexible, and easy-to-use service providing cost-effective relational database options with resizable capacity. It also includes management tools for common administrative database tasks.

Databases are integral to the core functionality of many businesses and organizations, so it’s important to monitor them and ensure they operate optimally.

AWS provides the ability to scale—even to the point of thousands of database requests per second—and makes it easier for DBAs to manage terabytes of data. However, large or complex databases need to be monitored to ensure service availability and performance aren’t affected.

This is why it’s important for DBAs to have robust Amazon RDS monitoring capabilities in place. With the right tool, AWS RDS monitoring can simplify the process of managing cloud-based relational databases by combining the capabilities of multiple tools into a centralized RDS monitoring solution.

RDS monitoring helps maintain the availability and performance of Amazon RDS instances and other AWS solutions. RDS monitoring provides DBAs with data associated with different aspects of their AWS solutions, so they can better troubleshoot issues before they impact the performance of the database or web-based service.

Amazon RDS monitoring tools can track several valuable performance metrics, which can be broken into different categories:

• Availability

These metrics allow DBAs to know whether the RDS instances or cluster endpoints are accessible from client-side interactions, whether there are issues with specific instances (such as stops, starts, failovers, or deletions), and whether there are failovers of multi-AZ instances.

• Cost

These metrics allow DBAs to track the monthly cost of each RDS instance.

• Health and Performance

These metrics allow DBAs to keep track of the hardware and capacity used by the Amazon RDS instances, monitoring specifically for metrics like CPU, disk space, and memory usage. This also extends to latency for database queries, latency for disk read/write times, the number of active database connections, and whether there are errors or warnings detected in log files.

• Manageability

These metrics allow DBAs to monitor changes made to the RDS instances’ option groups, parameters, properties, security groups, and tags as well as who made them.

• Recoverability

These metrics allow DBAs to check whether each RDS instance is being properly backed up and whether individual databases have been successfully backed up.

• Security

These metrics allow DBAs to know which users are connecting to each database instance, and they allow them to monitor the queries each user is running.

It’s important to establish baselines for each of these metrics, as they can often be indicative of latent issues capable of having a drastic impact on the RDS and web services.

Many companies and organizations rely on databases and AWS platform services to perform their routine business-critical functions. For this reason, it’s especially important for DBAs and technology support services staff to ensure all Amazon RDS instances are actively monitored.

By establishing a strong and proactive approach to AWS RDS monitoring, DBAs will have the tools to identify issues before they become fully realized problems, ultimately saving the company time and money. Effective monitoring is the cornerstone of IT operations, providing real-time visibility into system performance and health. It enables proactive identification of anomalies and performance issues, facilitating swift troubleshooting and accurate root cause analysis (RCA) to minimize downtime and ensure system reliability.

AWS RDS is easy to use and is a low-administration platform, but this means proper AWS RDS monitoring practices can be easily overlooked. Selecting the right RDS monitoring tool and implementing a proactive database monitoring policy is the smart decision for supporting overall organizational efficiency and peace of mind.

RDS monitoring tools make it straightforward for database admins to monitor and track vital performance metrics. By tracking performance data related to network throughput, I/O read and write, and client connections across all RDS instances, DBAs will have the information they need to make informed decisions about troubleshooting performance issues and ensuring resources are properly allocated and provisioned.

Amazon RDS sends out event notifications when the database instance’s state changes, such as in the case of a multi-AZ failover. RDS monitoring tools allow you to stay on top of these event notifications to better ensure database uptime and performance service levels by allowing you to subscribe to event categories. The most important ones to subscribe to include the following:

• Failover
• Failure
• Low storage
• Notification
• Recovery

RDS monitoring software can also help you establish enforceable baselines and critical thresholds by measuring how the instances perform during different traffic patterns and load conditions. Extending monitoring to historical database instance helps generate an archive of performance data over time, which DBAs can leverage to identify ongoing performance patterns and recurring anomalies or issues.

SolarWinds^® Database Performance Analyzer (DPA) is an intuitive and flexible solution offering DBAs visibility into their databases as they extend or shift their infrastructure to cloud-based services and platforms. DPA provides a unique approach to monitoring by using wait-based analysis strategy combined with other health metrics to quickly pinpoint performance problems and anomalies.

From a single unified dashboard, DPA can help you quickly examine performance issues, using at-a-click drill-downs to identify the source of bottlenecks and the root cause of anomalous behavior. In addition, DPA uses algorithmic machine learning to automatically “understand” normal database behavior patterns.

DPA also includes strong anomaly detection capabilities, using spikes in performance to improve performance analysis. Overall, DPA is designed to offer in-depth information about how your AWS RDS instances are performing, and it helps you eliminate inefficiencies, so you can reduce resource requirements and lower cloud compute costs.