August 2019 Summaries

As a developer advocate for Lumigo, I'm pleased to announce the release of a new Serverless framework plugin that makes it easier for developers to get started with observability in their serverless applications. The plugin can be installed using npm and requires only a few lines of configuration to get up and running. It currently supports Node.js and Python, but we're open to adding support for other language runtimes if there's demand. With this plugin, developers can start monitoring their functions at runtime and gain valuable insights into the performance and behavior of their applications.

Amazon SQS is a lightweight, fully-managed message queueing service that decouples and scales microservices, serverless applications, and distributed systems. AWS Lambda offers a fully-managed serverless runtime, enabling developers to run code in multiple languages without provisioning servers. By configuring an SQS queue as an event source for a Lambda function, the function can automatically trigger and process messages from the queue, scaling with the workload. This setup provides several benefits, including fewer moving parts, reduced costs, improved fault tolerance, and enhanced user experience. However, it also introduces some limitations, such as visibility timeouts and SQS triggers being charged at regular prices. To mitigate these issues, developers can implement strategies like increasing concurrency limits, setting reserved concurrency, or using backpressure control in front of the SQS queue. Additionally, understanding failure modes like partial failures, SQS over-scaling, and SQS over-pulling is crucial to ensure a reliable system. By embracing curiosity and experimenting with learning experiments in AWS, developers can mitigate risks and create a scalable and resilient system.

AWS CloudWatch Logs is a powerful tool for monitoring AWS Lambda functions, but it can be tricky to use effectively. To get the most out of CloudWatch Logs, you need to understand how it works, including how logs are organized into log groups and streams, and how pricing and limitations apply. One key feature is subscription filters, which allow you to send serverless logs to Amazon Kinesis for better performance and easier management at scale. Another important feature is metric filters, which enable you to define custom metrics, such as latency of a Lambda function. CloudWatch Logs Insights also provides a SQL-like query capability, allowing you to analyze log messages using ad-hoc queries. However, there are limitations to these features, including the inability to query logs across multiple log groups and the lack of support for querying logs from multiple AWS accounts. To get the most out of CloudWatch Logs, it's essential to adopt good logging practices, such as writing structured logs, sampling debug logs in production, and using tools like dazn-lambda-powertools to simplify logging and correlation.