June 2016 Summaries

Apache Tomcat is an open-source HTTP web server developed by the Apache Software Foundation, widely used for serving Java applications across various operating systems. It implements key Java technologies such as Servlet, JavaServer Pages, Java Expression Language, and WebSocket, and consists of components like Catalina, Coyote, Jasper, and Cluster for efficient operation. Monitoring Tomcat's performance is essential, as poor performance impacts end-user experience, and integrating with Datadog allows for comprehensive metric collection and visualization. Datadog pulls metrics into an out-of-the-box dashboard, categorizing them into general, thread pool, servlet, and JSP metrics, offering insights into server resource consumption and performance. For example, metrics like `tomcat.servlet.processing.time` provide critical insights into user experience and infrastructure efficiency. Datadog's integration facilitates quick identification of performance issues and can be easily configured, enhancing the monitoring capabilities for Apache Tomcat users.

Gunicorn is a UNIX-compatible WSGI HTTP server used to run Python applications, developed in 2010 by Benoit Chesneau. It's often implemented with a reverse proxy server like NGINX and can serve Python applications compatible with frameworks like Django and Flask. Datadog's out-of-the-box Gunicorn dashboard provides information on resource utilization, work metrics, latency, throughput, HTTP status codes, and more. The integration collects several categories of metrics including latency, throughput, HTTP status codes, and resource utilization. By monitoring Gunicorn alongside NGINX and custom metrics from your web application, you can investigate issues as they arise and set up targeted alerts to adjust configurations when needed.

Gunicorn is an open-source WSGI HTTP server developed in 2010 by Benoit Chesneau, commonly used to run Python applications, and can be easily configured, lightweight, and scalable. It's often implemented with a reverse proxy server like NGINX, which handles requests for static resources and passes on the dynamic portion of the request to Gunicorn. Datadog provides an out-of-the-box Gunicorn dashboard that shows information such as resource utilization, work metrics, and request duration, allowing users to monitor Gunicorn's performance alongside other elements of their infrastructure. The integration collects metrics including latency, throughput, HTTP status codes, and resource utilization, enabling users to investigate issues as they arise, set up targeted alerts, and start monitoring Gunicorn.

Postfix, an open-source mail transfer agent (MTA) first released in 1997, remains a popular choice for organizations seeking a cost-effective email routing and delivery solution that also provides protection against issues such as spambots and malware. Datadog integrates with Postfix to enable comprehensive monitoring of email service performance by tracking key metrics like the size of incoming, active, and deferred queues. Monitoring these metrics helps establish a baseline for queue sizes and allows users to set alerts for deviations that may indicate performance issues. Datadog's integration enhances Postfix by not only collecting and visualizing metrics but also by correlating them with other systems, detecting outliers, and alerting on abnormal behavior. Users can easily configure Datadog to monitor Postfix and other infrastructure components by deploying the Datadog Agent, with additional support offered for other SMTP-based services like AWS SES.

Jean-Mathieu Saponaro discusses the challenges of monitoring Docker-powered services, which operate with a higher number of containers compared to traditional services and often involve dynamic infrastructure managed by orchestrators like Kubernetes. The article introduces Datadog's Autodiscovery feature, which automatically tracks container locations and gathers detailed metrics, enabling continuous monitoring despite the shifting nature of service hosts. Autodiscovery works by listening to Docker events and applying predefined configuration templates to containers based on their images or labels, using variables such as %%host%% and %%port%% to adapt to changing infrastructure. The system can be seamlessly integrated into Kubernetes or DC/OS environments and is enabled automatically for Datadog Agents deployed as Docker containers. For those using other platforms, a simple setup involving configuration templates and enabling Autodiscovery is required. The Docker-certified Datadog Agent image, available on the Docker Store, offers a straightforward way to leverage this feature, allowing users to easily monitor dynamic containerized services.

Mashape has partnered with Datadog to integrate their API management platform, Kong, with Datadog's monitoring and analytics capabilities. Through this partnership, users can now monitor Kong's usage and performance metrics using Datadog. The integration includes a plugin that allows logging of API metrics such as request count, request size, response status, and latency to the local Datadog Agent. Additionally, the updated Datadog Agent collects Kong's connection and database details. This partnership aims to provide developers with better API management by leveraging the strengths of both platforms.

Mashape has partnered with Datadog to integrate their API management platform Kong with the monitoring and analytics tool. Through this integration, developers can now monitor Kong's usage and performance metrics in real-time, allowing for better API management and optimization. The integration is made possible through a plugin-oriented architecture of Kong, which allows for easy configuration and setup of authentication, security, and traffic controls. The Datadog plugin provides a straightforward way to collect full set of metrics available for monitoring Kong, including request count, response size, latency, and more. This partnership aims to provide developers with better API management tools, making it easier to monitor and optimize their APIs.

The integration of CloudHealth and Datadog aims to provide end-to-end visibility across every dimension of cloud environments, enabling organizations to make informed decisions regarding provisioning and rightsizing in the cloud. This partnership offers benefits such as rightsizing analysis, visualization of key assets, and advanced performance metrics per asset. Many joint customers have reported significant time and cost savings through this integration.

CloudHealth Technologies has announced an integration with Datadog, bringing end-to-end visibility and actionable insights to cloud infrastructure management. The partnership provides customers with a Rightsizing analysis tool that uses granular data from the Datadog Agent to recommend instance sizing and cost optimization. CloudHealth's visualization capabilities allow users to zero-in on key assets, track trends, and explore performance data in more detail using either the CloudHealth or Datadog consoles. This integration aims to help organizations manage their cloud environments, reduce costs, and optimize services by providing a comprehensive view of their cloud infrastructure.

You can monitor DynDNS updates with Datadog by setting up a read-only user in your DynDNS portal and adding the credentials to the Datadog integrations page. This allows you to visualize changes across your zones, analyze Queries Per Second (QPS) made by zone or record type, and set custom parameters for alerting. By monitoring these metrics, such as dyn.changes and dyn.qps, you can gain insight into how your DNS is performing and stay ahead of issues before they impact users. The flexibility of dynamic DNS systems can make them difficult to monitor at scale, but tools like Datadog provide a way to do so easily.

JMX (Java Management Extensions) is a mechanism for managing and monitoring Java applications, system objects, and devices. It represents resources as MBean (Managed Bean) objects, providing a window to read and interact with the running application. JConsole is the canonical graphical monitoring tool for applications running in the JVM, but it has limitations due to its resource-intensive nature and requirement of X11. The Datadog Agent provides an alternative solution with tools like Jmxterm and JMXFetch, which offer lightweight, full-featured JMX consoles without external dependencies. Jmxterm allows users to connect to monitor Java applications, while JMXFetch enables the collection of metrics and provides a comprehensive view of system data. These tools can be used to implement ongoing monitoring, spot trends, and diagnose issues in Java applications with minimal setup.

Mesos is an open source cluster manager that allocates resources such as CPU and RAM for distributed applications like Spark, Hadoop, and Kafka. It was built to run high-scale applications and can be used with container technologies like Docker. Datadog has integrated Mesos into its monitoring platform, allowing users to see metrics from all nodes in the cluster, including resource utilization, task execution, and application performance. This integration enables comprehensive dashboards for each service, providing insights into scaling events, resource allocation, and more. With this integration, users can monitor their entire cluster in one place, eliminating the need for ad hoc command line queries or separate web UIs for individual technologies.

Amazon Simple Email Service (SES) is an affordable and scalable email service that integrates with various AWS services such as EC2 instances, Elastic Beanstalk, AWS SNS, AWS S3, AWS KMS, CloudTrail, and the Management Console. Datadog integrates with AWS SES to provide monitoring of SES metrics like maximum emails sent in a 24-hour period, total emails sent in the last 24 hours, hard bounces, complaints, delivery attempts, and rejected send attempts. By visualizing these metrics over time, users can gain insights into trends in their SES performance and collaborate with their team to address any abnormalities. Integration of AWS SES with Datadog is simple and can be done by verifying AWS credentials and enabling the SES checkbox under 'Limit metric collection'.

SES provides a flexible SMTP or API-based email solution that can easily scale to any size. Its affordability, reliability, and native integration with other AWS services make SES a highly popular outbound mass email service. When integrated with Datadog, users can monitor their SES metrics according to their respective importance to filter out noise. The available metrics include maximum number of emails sent in a 24 hour period, total number of emails sent in the past 24 hours, number of hard bounces, number of complaints, delivery attempts, and rejected send attempts. Users can create visualizations to keep track of these metrics over time, giving them better insight into trends in their SES performance and allowing for collaboration with their team. Integrating AWS SES with Datadog is a straightforward process that requires verifying AWS credentials and enabling the integration, after which Datadog will automatically begin gathering metrics.

Amazon Web Services (AWS) Virtual Private Cloud (VPC) is an isolated section of AWS that allows users to allocate resources within a custom-defined virtual network. Datadog has introduced an AWS VPC integration, enabling monitoring of all components within the VPC alongside EC2-classic components. Benefits of using VPC include running instances on single-tenant hardware, more granular filtering, and additional access control layers. Once the AWS integration is enabled in Datadog, statuses of VPC services are collected automatically and displayed alongside metrics from EC2-classic instances within a dashboard. Users can add tags to their hosts for filtering purposes and gain insights into app performance by correlating VPC metrics with other infrastructure metrics. Customizable monitoring options are available for VPCs in Datadog, allowing users to set custom monitors and assign additional tags. To start monitoring VPCs, enable the AWS integration tile or sign up for a Datadog trial.

A new integration between Rollbar and Datadog has been released, allowing users to extend their error monitoring options. This integration enables the syndication of exceptions, errors, and code deployments as events within a user's Datadog event stream. By connecting Rollbar and Datadog, users can improve their monitoring capabilities by receiving notifications for various events, filtering notifications based on specific criteria, searching for exceptions in graphs, discussing exceptions with team members, and ultimately spending less time debugging issues. The integration aims to streamline monitoring efforts and reduce noise in daily workflows. For more information on the integration process, users can refer to Rollbar's integration docs or check out their full list of integrations.

This is a guest post by Mike Smith @themlsmith, Head of Growth at Rollbar, announcing the release of a new integration with Datadog to extend error monitoring options. The integration allows users to syndicate exceptions, errors and code deployments as events within their Datadog event stream, providing improved notification filtering, graph searching and team discussion capabilities. By bringing Rollbar data into Datadog, users can streamline their monitoring efforts, reduce noise in their workflow, and spend less time debugging issues.

AWS Virtual Private Cloud (VPC) is an isolated section of Amazon Web Services that allows allocation of AWS resources within a custom-defined virtual network, providing benefits such as single-tenant hardware, fine-grained egress and ingress filtering, and access control. Datadog's AWS VPC integration enables monitoring of all components within the VPC alongside EC2-classic components, allowing for correlation with other infrastructure metrics and gaining insight into app performance. With this integration, customers can automatically collect statuses of their VPC services, add custom tags, and set up custom monitors to alert on changes. The integration is available through a setup tile in Datadog's integrations list or by signing up for a trial, enabling comprehensive monitoring of VPCs with a flexible service.