A General Overview of Serverless Monitoring Tools
Serverless computing, like that found in platforms such as AWS Lambda, actually represent a new paradigm in 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. Such changes are particularly applicable to Lambda functions, but may also apply to just about any serverless computing environment.
In a traditional computing environment, it is necessary to monitor your servers and network, as well as other metrics to gauge performance. When you work in a serverless platform, like Lambda, these traditional metrics no longer matter. In this case, the vendor will be responsible to manage the network. server, and underlying infrastructure, while you are free to concentrate entirely upon the application code.
There are many out there who may be wondering why this is such an important point? When you use a serverless platform, you will be free to execute your code without having to think about the computing power of your underlying 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.
In AWS Lambda, all of these monitoring functions are actually hidden from you and handled automatically by the platform. The thing that you control in this platform is the application code, which you upload to Lambda as a function, and which is implemented and executed automatically in AWS as code. AWS primarily uses an application called CloudWatch to automatically monitor the performance of Lambda in the error free implementing of code and running of applications. If you want to further monitor application performance on Lambda, you can do so by using an application called X-Ray. Valuable insights for troubleshooting AWS Lambda errors and for correcting errors in code are stored in the Cloudwatch logs which can be consulted whenever you need to address errors.
Beginning work in a computing environment like Lambda can be quite a lot to get used to. Monitoring in Lambda is much different than monitoring in traditional applications. In this instance, you should take advantage of the natural, built in monitoring tools available to you in AWS such as CloudWatch, X-Ray, and the custom metrics that you will find.
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.