As application architecture moves from monoliths to microservices, observability has become a growing challenge. The services that make up a distributed application, and the many dependencies and communication pathways between them, are difficult to govern and observe. You can get more control and visibility of your application by including a service mesh—a layer of infrastructure that manages traffic among microservices.

Datadog is pleased to announce the availability of Synthetics, a whole new layer of visibility on the Datadog platform. By monitoring your applications and API endpoints via simulated user requests, Synthetics helps you ensure uptime, identify regional issues, track application performance, and manage your SLAs and SLOs. By unifying Synthetics with your metrics, traces, and logs, Datadog allows you to observe how all your systems are performing as experienced by your users.

Since its release in 1995, PHP has been one of the most popular server-side languages for building web applications. It supports a wide range of web servers, databases, and operating systems. PHP developers use popular frameworks like Laravel, Symfony, and Zend to deploy and manage sites that serve high volumes of traffic. To help you monitor PHP performance, identify bottlenecks, and optimize your users’ experience, we’re pleased to announce APM & distributed tracing for PHP.

C# logs can be an invaluable resource for optimizing application performance and debugging errors. But it’s not easy to extract the full potential of your logs if they’re not providing enough context around each error, or if they’re written in a format that’s difficult to parse.

With the growing number of customers monitoring Azure in Datadog, we saw a great opportunity to help our users get faster visibility across their dynamic cloud environments. We’re happy to announce the release of new out-of-the-box dashboards for several Azure services.

Amazon MQ is a cloud-based, AWS-managed service for the popular Apache ActiveMQ message broker. It offers many advantages of being part of the AWS ecosystem as AWS automatically provisions and maintains infrastructure components from services including EC2 and EBS.

Amazon Elastic Container Service (ECS) is an orchestration service for Docker containers running within the Amazon Web Services (AWS) cloud. You can declare the components of a container-based infrastructure, and ECS will deploy, maintain, and remove those components automatically. The resulting ECS cluster lends itself to a microservice architecture where containers are scaled and scheduled based on need.

In Part 1, we introduced a number of key metrics that you can use for ECS monitoring. Monitoring ECS involves paying attention to two levels of abstraction: the status of your services, tasks, and containers, as well as the resource use from the underlying compute and storage infrastructure, monitored per EC2 host or Docker container. In this post, we’ll survey some techniques you can use to monitor both levels of your ECS deployment.

As we explained in Part 1, it’s important to monitor task status and resource use at the level of ECS constructs like clusters and services, while also paying attention to what’s taking place within each host or container. In this post, we’ll show you how Datadog can help you: Automatically collect metrics from every layer of your ECS deployment, Track data from your ECS cluster, plus its hosts and running services in dashboards, and more.