Comparison of top observability and debugging tools to help you monitor Python in AWS Lambda.

Organizations are constantly trying to maintain pace with users' expectations and desires from a digital experience. These users expect an experience that constantly changes based on their preferences and behavior, which means innovating quickly and improving software is critical to user happiness and driving business success.

AWS Fargate is a serverless compute engine for containers that work with both Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS). With Fargate handling instance provisioning and scaling, users don’t have to worry about spinning up instances when their applications need resources. While this has many benefits, it’s not without its share of challenges which can limit its applicability to a wide variety of use cases.

I’ve had a great conversation with a buddy of mine who is launching a new service, and while he is not a technical person, he came up to me asking about serverless and if it could have an actual impact on his startup. Naturally, I got very excited about the topic and proceeded to list all the benefits of serverless technology and how decentralized technology has revolutionized the industry, so on so forth. After a 15-minute monologue, the guy stops me and politely asks me the question again.

Monitoring AWS Lambda performance plays a crucial part in your everyday AWS Lambda usage. Monitoring helps you identify any performance issues, and it can also send you alerts and notify you of anything you might need to know. The world is slowly getting to a point where machines and computers will be flawless, but until then, if we let them perform various tasks for us, we could at least monitor their performance.

Serverless computing is on the rise, having already earned the mantle of “The Next Big Thing”. For developers, serverless computing means less concern regarding infrastructure when deploying code, as all computing resources are dynamically provisioned by the cloud provider. Pricing is generally on a pay-as-you-use model and is based on resources consumed – which is in line with modern business principles of “on-demand”, flexibility and rapid scaling.

Open Telemetry represents an effort to combine distributed tracing, metrics and logging into a single set of system components and language-specific libraries. Recently, OpenTelemetry became a CNCF incubating project, but it already enjoys quite a significant community and vendor support. OpenTelemetry defines itself as “an observability framework for cloud-native software”, although it should be able to cover more than what we know as “cloud-native software”.

Systems run into problems all the time. To keep things running smoothly, we need to have an error monitoring and logging system to help us discover and resolve whatever issue that may arise as soon as possible. The bigger the system the more challenging it becomes to monitor it and pinpoint the issue. And with serverless systems with 100s of services running concurrently, monitoring and troubleshooting are even more challenging tasks.

Here is a quick round up of the newest product features, resources, and events!

While we know the many benefits of going serverless – reduced costs via pay-per-use pricing models, less operational burden/overhead, instant scalability, increased automation – the challenges of going serverless are often not addressed as comprehensively. The understandable concerns over migrating can stop any architectural decisions and actions being made for fear of getting it wrong and not having the right resources.