released Wednesday a new Application Performance Monitoring (APM) extension to its platform that extends the company’s cloud monitoring platform to quickly identify code-level issues. This offering has been specifically built to help devops teams troubleshoot issues in complex, distributed applications that use hybrid clouds, microservices or containers.
Datadog’s APM solution supports a variety of use cases, including performance optimization and outage diagnosis to reduce the impact of costly downtime for large-scale users.
The solution can be installed quickly via a hosted SaaS solution that works out of the box; offers automatic tracing of individual requests from end to end across hosts and services and flame graphs for identifying the most frequently used code paths. It also offers customizable dashboards for data aggregation and correlation; and comes with a built-in collaboration between teams spanning dev and ops organizations. It offers smart alerting via email, SMS and cloud-based collaboration tools; machine-learning based anomaly detection; and transparent tag-based aggregation of performance data from microservices, containers and ephemeral hosts.
“The pace of scale and dynamism of the infrastructure and application environment are accelerating, with the advent of containers, microservices, autoscaling and software-defined ‘everything’ stressing the capabilities of existing APM tools1,” wrote Cameron Haight, Vice President with Gartner’s IT Systems, Security and Risk research group in a March 2016 report. In a June 2016 report2 Haight notes that, “many, if not most, commercial APM tools do not have the ability to collect hundreds of data points on potentially thousands of microservices at high rates of frequency in order to ‘paint a picture’ of the environment in total.”
“Having a single platform to monitor both our dynamic infrastructure and the code-level performance of our applications will give us a much richer context for making decisions,” said Valentino Volonghi, chief technology officer at AdRoll. “As a Datadog customer, we’re excited to see the company continue to move fast and provide solutions for modern architectures and workflows.”
In August, Datadog integrated with AWS Lambda to serve thousands of customers ranging from hyper-scale startups to enterprise-grade software companies, which positions the company at the forefront of emerging technologies like Docker and serverless architectures.
AWS Lambda reduces the operational complexities of building and running applications, freeing developers to focus on building applications instead of managing infrastructure. AWS Lambda functions can be triggered via application program interface (API), through in-app actions, or by AWS events, such as a new object being added to an Amazon Simple Storage Service (Amazon S3) bucket.
The difficulty with monitoring serverless functions is exactly that: the servers that run the code are not exposed to the developers. This means traditional monitoring methods that rely on a host-based agent do not apply to AWS Lambda functions. Now with Datadog’s AWS Lambda integration, developers can emit custom metrics specific to their AWS Lambda functions, right from the functions themselves, on top of monitoring the metrics provided via Amazon CloudWatch.
Once collected, these metrics can then be used to create visualizations and alerts alongside operational data from the rest of the infrastructure environment for actionable insight.
Datadog expanded in July support for the Microsoft Azure cloud platform. Following the release of Microsoft Azure Resource Manager (ARM) – the latest way to deploy virtual machines on the platform – Datadog will now support Microsoft Azure SQL Database as well as cloud servers deployed through ARM. This will empower large enterprise companies running their infrastructure in the cloud to correlate metrics from these new services with application performance.