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Creating Success with Containers in Production

Monitoring is the key to outstanding performance with Docker
Alois Mayr

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.

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Creating Success with Containers in Production

Monitoring is the key to outstanding performance with Docker
Alois Mayr

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.

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...