In Part 1 of this series, we discussed AWS Lambda functions and some key metrics for monitoring them. In this post, we’ll look at using Amazon’s native tooling to query those metrics. We’ll also show you how to collect logs and traces that provide further visibility into your Lambda functions. Amazon provides built-in monitoring functionality through CloudWatch and X-Ray.
In Part 2 of this series, we looked at how Amazon’s built-in monitoring services can help you get insights into all of your AWS Lambda functions. In this post, we’ll show you how to use Datadog to monitor all of the metrics emitted by Lambda, as well as function logs and performance data, to get a complete picture of your serverless applications. In this post, we will: Datadog integrates with AWS Lambda and other services such as Amazon API Gateway, S3, and DynamoDB.
AWS Lambda decouples the need to provision and maintain a runtime environment from running code, allowing developers to focus on applications rather than infrastructure. But, by abstracting away the underlying infrastructure of an application, serverless architectures introduce new challenges into monitoring and observability.
Since AWS Lambda was launched in 2014, serverless has transformed the way applications are built, deployed, and managed. By abstracting away the underlying infrastructure, developers are able to shift operational responsibilities to the cloud provider and focus on solving customer problems.
When an application in a Docker container emits logs, they are sent to the application’s stdout and stderr output streams. The container’s logging driver can access these streams and send the logs to a file, a log collector running on the host, or a log management service endpoint. In this post, we’ll explain how the driver you choose—and how you configure it—influences the performance of your containerized application and the reliability of your Docker logging.
To complement distributed tracing, runtime metrics, log analytics, synthetic testing, and real user monitoring, we’ve made another addition to the application developer’s toolkit to make troubleshooting performance issues even faster and simpler. Today, we’re excited to introduce Profiling—an always-on, production profiler that enables you to continuously analyze code-level performance across your entire environment, with minimal overhead.
Amazon Managed Streaming for Apache Kafka (MSK) is a fully managed service that allows developers to build highly available and scalable applications on Kafka. In addition to enabling developers to migrate their existing Kafka applications to AWS, Amazon MSK handles the provisioning and maintenance of Kafka and ZooKeeper nodes and automatically replicates data across multiple availability zones for high availability.
In Part 2, we showed you how to use Istio’s built-in features and integrations with third-party tools to visualize your service mesh, including the metrics that we introduced in Part 1. While Istio’s containerized architecture makes it straightforward to plug in different kinds of visualization software like Kiali and Grafana, you can get deeper visibility into your service mesh and reduce the time you spend troubleshooting by monitoring Istio with a single platform.
Istio is an open source service mesh that was released in 2017 as a joint project from Google, IBM, and Lyft. By abstracting the network routes between services from your application logic, Istio allows you to manage your network architecture without altering your application code. Istio makes it easier to implement canary deployments, circuit breakers, load balancing, and other architectural changes, while also offering service discovery, built-in telemetry, and transport layer security.
