In this video, you’ll see how to monitor Amazon Relational Database Service
(Amazon RDS) and Aurora databases on Amazon Managed Grafana.
With this capability, you can visualize critical database-level metrics in Grafana,
create custom dashboards to monitor database health, and set alerts to
identify potential performance issues.
We’ll begin in Amazon RDS.
Let’s view the database we want to monitor in Grafana.
We’ll be monitoring this Aurora PostgreSQL cluster, which has one
writer and one reader instance.
Let’s go to Amazon Managed Grafana to set up a workspace where we’ll be
able to view our database metrics.
We’ll give the workspace a name and move on.
We’ll use AWS IAM Identity Center for authentication.
Let’s leave the Permission type set to Service managed so that permissions
will be automatically provisioned.
Next, we’ll configure the service managed permission settings.
We’ll retain the default selection and use Grafana to monitor resources in our
current account rather than in our Organizational Units.
Next, we’ll select our data source, which is Amazon CloudWatch,
where metrics from RDS and Aurora databases are published automatically.
We’ll use Amazon Simple Notification Service (SNS) as the notification
channel for these metrics.
Let’s review the settings and create the workspace.
Here’s the workspace we created.
Let’s drill down.
We’re required to assign a new user or group to this workspace to make sure we can log in.
For demonstration purposes, we’ve already created a user in IAM Identity Center.
Let’s assign this user to the workspace.
Since we’ll be creating a new dashboard in this workspace, let’s promote this
user from “viewer” to “admin.”
Now let’s go back and use the workspace URL to log in as this user.
The first thing we need to do here is create an AWS data source.
We’ll leave the service set to CloudWatch and select the region
where we created the Aurora cluster.
In this case, it’s US East (N. Virginia).
We’ll add the data source, and then take a look at its settings.
Let’s save and test the data source to make sure it’s working properly.
Our data source is working.
Next, let’s take a look at our dashboards.
The Amazon RDS dashboard contains the metrics from our Aurora cluster.
Let’s import it.
In this view, we can see our cluster-level metrics.
We’re capturing database connections, RAM, and CPU utilization.
Let’s take a look at the instance-level metrics.
We have two instances, and we are capturing the memory and
CPU utilization for both of them.
We’re also tracking storage space, read and write throughput, and latency.
We can also see the IOPS (input/output operations per second).
Let’s explore our metrics using the “Explore” option on the sidebar.
First, we’ll save this dashboard.
Let’s explore the CloudWatch data source we added earlier.
We’ll specify the AWS/RDS namespace.
That’s where the metrics for RDS and Aurora clusters are written by default.
In this case, let’s take a look at the CPU utilization metric.
Notice that the RDS CPU is high here, almost 50 percent.
We can capture these metrics and add them to a dashboard.
Let’s create our own dashboard.
Let’s add a new panel.
We’ll capture the same CPU utilization metric we specified in the Explore view.
We can customize the time frame for the metrics.
Let’s display the metric for the last hour.
Let’s give the panel a title.
Now we’ll apply our changes to save this panel.
We can add additional panels as needed.
We can also add an alert rule or notification that will let us know if CPU utilization
exceeds a specified threshold.
We’ll add a notification channel.
The notification type and topic are populated with the information we
provided when we created the workspace.
We can also use third-party notifiers.
The Amazon Managed Grafana User Guide provides a list of supported
notifiers and more detail about setting up alert notifications.
You’ve just seen how to monitor Amazon RDS and Amazon Aurora
databases on Amazon Managed Grafana.
You can learn more about this topic in the description and links for this video.
Thanks for watching. Now it’s your turn to try.