Monitor key metrics for Amazon Aurora
A number of AWS customers have been asking us for improved visibility into their Amazon RDS performance. Today we're happy to oblige that request by bringing in monitoring support for the Aurora database (DB) engine - Amazon's MySQL and PostgreSQL-compatible relational database.
In recent years, Aurora has emerged as one of the most popular DB engines in the AWS cloud. It runs a broad set of demanding workloads and has gained immense popularity among a number of prominent enterprises. With our added support for Aurora, you can collect valuable performance metrics and metadata for your Aurora DB clusters to gain complete visibility into your databases' performance.
Start visualizing everything in your Aurora environment with Site24x7.
- Identify stat trends for query throughput, connections, performance and resource usage.
- Compare current metrics with past values to establish baselines.
- Build custom dashboards to extract insights; set up alerts to troubleshoot common issues.
- And much more.
Get the most out of your existing AWS integration
Understanding the state of your application's underlying components and infrastructure is necessary to ensure its reliability. This is where performance metrics and resource utilization stats come in - the more metrics you track, the greater insight you'll have into the behavior and health of your systems.
To help you in this regard, we introduced monitoring support two weeks ago for Amazon's fully managed queue service, SQS. Today we're going one step further and bringing in more than a dozen metrics across an array of already supported AWS services for a more comprehensive view. Use these additional metrics to get a better understanding of your AWS environment.
Since systems are built hierarchically, let's start with the server instances first.
Evaluate the performance of your server instances better by monitoring the amount of data being written to or from your instance store volumes with disk I/0 stats, and identifying drops and fluctuations in network traffic volume with connectivity indicators. If you're switching your instance type from T2 Standard to T2 Unlimited, you can keep track of credits with the CPU Surplus Credit Balance and CPU Surplus Credits Charged metrics.
The next set of metrics concerns DB instance performance.
Apart from the essential metric dimensions - space, latency, throughput, and input/output operations per second (IOPS) for storage volumes, as well as CPU, memory, and swap for DB instances - you can now observe and receive alerts on 14 other metrics concerned with burst performance, MySQL read replicas, PostgreSQL DB engine, network traffic, and buffer cache performance.
If you're more of a NoSQL kind of person, then our expanded visibility into DynamoDB will help you gain more clarity. Metrics on Global Secondary Index (GSI) creation, DynamoDB streams, Global tables, TTL deletions, and other key performance metrics can help you correctly provision appropriate throughput for your DB tables.
We've also included a couple other metrics for crucial application components like SNS and Lambda. Now you can monitor SMS success rate for your active topics, learn when writes to dead letter queues are failing, and immediately identify slow record processing for your Lambda functions.
You can track, visualize, and alert on all the newly added metrics if you're already monitoring their associated resource.
That's all we have for now, but keep your suggestions and feedback coming so we can improve our AWS monitoring capabilities even more. If you have any questions or concerns, get in touch with us at firstname.lastname@example.org.