When it comes to creating a high-performance production environment, it is becoming increasingly clear that Docker can be an important key to success. The benefits of container use have been taking the application performance world by storm. Since Docker launched in 2013, more than 800 million Docker containers have been pulled from the public Docker Hub.
Docker Adoption Rates Continue to Spread Like Wildfire
Container use is soaring, according to a recent survey conducted by O’Reilly Media and Ruxit, a division of Dynatrace, which shows 93% of respondents are now using or are planning to use container technology. The cloud ecosystem supporting Docker is also rapidly growing with AWS announcing their container service enhancements and major cloud stacks like OpenStack supporting Docker as a key technology for application delivery.
But without a proven approach to monitoring your container environment’s success can be derailed quickly. This need was confirmed in the O’Reilly/Ruxit survey, as 46% of respondents identified monitoring as a key challenge in production environments.
Production Environments Require a Different Approach
Starting out with Docker, many people begin in smaller controlled environments, as with anything new. But technology trailblazers have taken Docker far beyond this and most technology experts quickly learn that the simple, seamless application delivery process Docker offers makes it an obvious great match for more complex continuous delivery production environments.
In Docker-based environments, continuous-integration and continuous-deployment processes must be adapted so they seamlessly support a push and pull of images to and from a registry. Docker-specific automation technology is evolving quickly, with numerous new features and improvements introduced with each new release. The tools involved in building, deploying and operating containers typically include Docker’s own tools, but may also include third-party tools.
How Monitoring Plays into Container Deployments
In these fast-paced dynamic deployment scenarios with numerous automation tools at work, monitoring is more crucial than ever. Your monitoring approach needs to allow you to track communication between tools and validate results of the automation process. In this way, monitoring can identify inconsistencies and shortcomings in tool configuration in your production environment to ensure it is performing as expected. Application monitoring is key to confirming whether or not the automated process chain results in shippable applications that perform as expected.
There is No Self-Driving Container Infrastructure – Yet
One of the big reasons for adopting containerization is it allows you to evolve from using cumbersome and challenging application architectures to lightweight, flexible microservices.
Some tools are very well suited to handle coordination and communication between containers hosting microservices. They make it easier to deploy and scale these environments - adding containers to clusters of hosts and registering the containers with load balancers. These tools even handle failovers and redeployments of broken containers to maintain the required number of containers in service.
Despite all this functionality however, solutions to some key orchestration challenges are still fairly new. They support the logistics of scaling, but require input about when and how individual services should be scaled. This information is typically accessible through application monitoring because it can offer deep, real-time insights into services – including inbound and outbound service communications with other services.
Having this high-quality performance data is key to determining the impact that adding and removing containers has to the response times and performance of each service. As a consequence, monitoring tools are now a part of the feedback loop with orchestration tools. Monitoring tools drive the tweaking of orchestration configurations (i.e., when to reduce or increase the number of containers).
Innovating the Future of Monitoring
Highly dynamic and scalable microservices environments require monitoring that scales.
Monitoring solutions need to autonomously adjust to changing environment configurations. Manual configuration is as impractical as manual deployment and orchestration. Auto-discovery and self-learning of performance baselines, as well as highly dynamic dashboarding capabilities, have made many traditional monitoring solutions obsolete.
As these environments scale dynamically, monitoring solutions need to keep up with them. This makes SaaS-based solutions ideal candidates for monitoring Docker environments. In cases where SaaS is not an option, a “feels-like-SaaS” approach to on-premise monitoring is the solution of choice.
When deploying Docker and related container technologies in production, monitoring is particularly important for understanding and proving whether or not your applications are working properly. The increasing number of Docker-related tools and projects required to provide basic infrastructure to run distributed applications creates a whole new set of monitoring requirements that go well beyond classic metrics-only driven approaches. Visualizing and understanding the dynamics of container environments is at least as equally important as performance metrics. The dynamics and scalability requirements call for a new set of monitoring tools as retrofitted classic monitoring tools fail to deliver innovation on the core challenges of these environments.
Alois Mayr is a Developer Advocate at Ruxit.
