Writing custom cloudwatch metrics




Metrics measure the performance of your system. Several AWS services provide free metrics, such as the CPU usage of an Amazon EC2 instance. You can create Amazon CloudWatch alarms based on metrics and send Amazon SNS messages when the alarm state changes. You can use this mechanism to implement elastic scaling if the message is […]


With that conceptual information out of the way, let’s look at the simplest way to create a custom metric. Writing metric data points into CloudWatch is how you create a metric. There isn’t a separate operation to create a metric and then write data points into it.


Writing to CloudWatch To write metrics to CloudWatch from Python code, first, we have to create an instance of CloudWatch client. For that, we must import the boto library and write the following ...


Pairing the data from AWS within other applications you’re already using is a good way to squeeze the most from CloudWatch metrics. To see what this would look like, the free trial offered when you sign up for Scalyr allows you to run queries and reports with sample data before plugging in live data. That’s yet another good practice to adhere to as an engineer in order to gauge ...


All the widgets have graphs/line charts based on custom metrics. I defined these custom metrics from metric-filters being defined on the CloudWatch log group. For every custom metric, I want to set the unit to, for example, milliseconds, seconds, hours etc. CloudWatch console somehow shows all the metric units to be counts only.