The growing popularity of serverless architectures has led to an increased need for solutions to the modern challenges of microservice observability—one of the most critical components for running high-performing, secure, and resilient serverless applications. Observability solutions have to break through the complexity of serverless systems, and with the right stack, observability enables not only fast and easy debugging of applications, but drives optimization and cost efficiency.

AWS re:Invent was back and BIG last week in Las Vegas. Approximately 50,000 AWS customers and partners got together in Las Vegas to learn, talk shop, and maybe attend a couple of parties here and there. Not only did Lumigo have a booth, but our own Saar Tochner, R&D Team Lead and AWS Community Builder gave a well-received talk on Lambda extensions.

“Testing your production environment” refers to the practice of running tests on production servers, using actual data from real users. Production testing doesn’t replace other methods like unit or integration testing. Instead, it extends them. Smoke testing is one approach that Lumigo has implemented to test our own production environments.

Managed services and serverless deployments have become increasingly popular tools in the software development process. This means that organizations are focusing less on infrastructure resources and more on the functionality and security of applications. Managed services—such as the applications like DynamoDB, Step Functions and API Gateway that are crucial to serverless architectures—come with associated costs.

Over the past few years, AWS has made incremental improvements to Lambda’s cold start performance. Nowadays, cold starts are much less an issue for most functions, unless you’re using Java or.Net Core.

At Lumigo, we keep improving the coverage and quality of our distributed tracing instrumentation to give you, through Lumigo’s transactions, the most accurate and intuitive representation of how your distributed system behaves. In this blog, we cover a recent development for the Amazon SQS instrumentation in Lumigo’s OpenTelemetry distro for Python, providing a seamless experience for a scenario that otherwise would result in confusing, broken transactions and lost insights.

Back in 2020, we covered the launch of Lambda Extensions and the subsequent release of the Lambda Logs API. These features aren’t designed for the average Lambda user. But they allow vendors to build better tools by giving them much-needed access to the Lambda execution environment.

Amazon CloudWatch monitors metrics of your Amazon Web Services (AWS) resources in real time and can trigger alarms when a metric goes above or below certain thresholds. Typically, Amazon CloudWatch sends out alarms by posting a message to an SNS (Amazon Simple Notification Service) topic, which distributes the message via several mediums, including email, SMS, and Lambda functions. Setting a CloudWatch alarm can be complex.

With serverless and containerized applications becoming a norm, workloads and integrations are spread across multiple cloud environments. As these apps become increasingly more distributed, monitoring also becomes more complicated with siloed and incomplete telemetry. This is where distributed tracing brings great value. It enables end-to-end visibility in your modern and complex application.

The internet of things is one of my favorite topics. IOT enables low-powered connected devices that opens gateways from the digital to the real world. While I love tinkering away with an Arduino sketch and the latest Espressif or Arduino board, there is always an air of frustration when trying to build out what at first seems like simple functionality using one of these “smart devices” because of the limited view we have into their operations.