#EXPORT CLOUDWATCH METRICS TO PROMETHEUS INSTALL#
#EXPORT CLOUDWATCH METRICS TO PROMETHEUS CODE#

Show All Lambda functions and Show Lambda. For convenience, two search expressions are available out-of-the-box. Once the Asserts exporter is set up, all the Lambda functions and other services they interact with will be discovered automatically. Once you have installed the exporter, Asserts will automatically discover the Lambda functions and start surfacing the Assertions.

#EXPORT CLOUDWATCH METRICS TO PROMETHEUS INSTALL#

To monitor your AWS Lambda applications with Asserts, you must install the Asserts Exporter which will export the metrics to Asserts Cloud. In the next section of this blog post, we will see how Asserts makes it extremely easy to monitor the CheckoutService Lambda function, automatically identifies the Key Performance Indicators (KPIs), surfaces assertions based on SAAFE model, and makes it very easy to define and track the SLOs driven by business requirements. Defining the SLOs and tracking their compliance is another bunch of work that is not straightforward. You will need to learn PromQL, setup the AlertManager, create alert rules with possibly different thresholds for different functions.

#EXPORT CLOUDWATCH METRICS TO PROMETHEUS CODE#

This makes it possible to manage Monitoring as Code The CloudWatch metrics can be exported to Prometheus using exporters like AWS CloudWatch Exporter, YACE or using AWS Kinesis Data Firehose and Grafana can be used to create Dashboards. Alternatively we could use the widely used combination of Prometheus and Grafana. Now that the metrics and the requirements have been identified, monitoring the metrics can be achieved in CloudWatch itself.

Response time should be within 3s 99.9% of the time.

Checkout needs to be available 99.9% time, and.

The business requirement is to track the availability and responsiveness of the CheckoutService Lambda every week.

So let’s identify the business requirements and define the SLOs based on them. SLOs help align monitoring with business objectives and prevent alert fatigue. If you set up alerts based on errors or latency degradations, it could lead to alert fatigue. As an SRE, you would like to be alerted only when there is an impact on business. Systems operating at scale have errors in some parts all the time. But it's not as straightforward as it might seem. So it would seem that we should define thresholds for the metrics like latency and error rate and be notified when there are deviations.

In our example, we know the Lambda function and services that need monitoring. Like any other application, the service owner of a Lambda function will care about availability and responsiveness. Also, for some analytics, the Checkout Lambda publishes messages to a few SQS Queues Architecture for our example Important Metrics Request, Error, and Duration (RED) Resource Metrics What to monitor? Suppose that the DiscountService is a microservice running on Kubernetes, and the CheckoutService Lambda function invokes the DiscountService.

We also have a DiscountService that’s used to find any discounts available for the cart at the time of checkout. Let’s take the example of an online shopping cart where the checkout is implemented as a Lambda function invoked through an API Gateway. An example - Online Shopping Cart Checkout as Lambda You don’t have to pay for the idle time of your servers. AWS Lambda enables a pay-as-you-use model. It allows an event-driven model wherein Lambda functions are invoked in response to events, e.g., requests to an API Gateway, messages from an SQS Queue, or changes in DynamoDB or S3. It supports popular languages and runtimes like JavaScript/NodeJS, Python, Go and Java. AWS Lambda allows you to write functions and run them as a scalable service.

Export cloudwatch metrics to prometheus