A Closer Look at Serverless Monitoring Tools
AWS Lambda and other forms of serverless computing platforms actually represent a completely new way of doing things in the world of computing. Through the process of virtualizing the hardware server, serverless computing actually removes the necessity of a host server from the equation entirely. Because of the major differences between traditional and serverless computing, the user is forced to rethink several important pieces of the puzzle, including their use of monitoring functions. These changes are particularly important in AWS Lambda, but also apply in any other serverless computing environment.
When you are working in a traditional computing environment, there are numerous performance metrics to monitor, particularly the performance of the server and the network. Whenever you are working in a new, serverless environment like AWS Lambda, these metrics will no longer be of importance to you. This is because they will be monitored by the vendor, who manages the server and underlying infrastructure, while you are able to concentrate on your application code entirely.
At first, you may wonder why this would be so important for you? Serverless computing systems allow you to execute and monitor your code without having to concentrate on your computing power and servers. To ensure that you always have enough computing power to execute your code, AWS Lambda always scales the available computing capacity to your needs.
All of these functions are actually hidden from you and handled automatically by the AWS Lambda platform. As user, the thing you control in this system is the application code, which you begin by uploading into Lambda as a function and is then implemented in AWS as code. An application called CloudWatch is the default program for monitoring Lambda for errors in running code. In Lambda, AWS also allows you to monitor application performance by using an application called X-Ray. Whenever it is necessary to address errors in Lambda, you can consult the CloudWatch logs, in which all applicable error information is stored and from which you can derive valuable insights for correcting problems and errors in code.
Beginning work in a computing environment like Lambda can be quite a lot to get used to. When you are monitoring Lambda, it can be quite different from more traditional applications. Therefore, you will need to leverage the already built in monitoring features in AWS like X-Ray, CloudWatch, and custom metrics that are available to you.
If you would like to find out more about serverless monitoring tools in Lambda and AWS, the best thing you can do is is take a moment to visit the website of a software developer who offers these tools online. All you need to do to get started is search the web for serverless monitoring tools, python error handling, and the AWS pricing calculator.