Docker container monitoring involves tracking metrics to evaluate how containers are functioning. Container monitoring is critical for ensuring uptime and performance of containerized applications, as well as for container security.
Docker is a ubiquitous DevOps tool, providing containerization capabilities that include packing, shipping, and running applications within portable, lightweight, and insulated containers. Docker containers are particularly useful for enabling rapid deployment and scaling environments.
Containers are lighter weight than both physical and virtual machines, and their isolation offers additional security. They serve as miniature hosts that allow application components to run independently, but they also require complex configuration and networking. A Docker container is not an operating system or application, so traditional monitoring tools are insufficient for handling performance monitoring in Docker.
Docker monitoring requires the collection of various performance-related metrics from multiple components of a system, such as containers, hosts, and databases. Monitoring the performance of Docker containers is essential for detecting issues before they impact production and for ensuring the containers run smoothly.
Container monitoring tools capture metrics and offer visualization and analytic capabilities to track activity. Standard metrics covered by monitoring solutions include CPU usage and limit, memory usage and limit, and streaming logs provided in real time. IT teams can use information such as utilization ratios to decide when to scale up or down.
Monitoring the performance of Docker and Kubernetes containers requires ratios for memory and CPU. Containers that run HTTP servers require the collection of latency-related metrics and request counts.
A comprehensive container monitoring solution will take into account the different layers of a stack and the functional requirements of each layer. In addition to tracking numeric error data, the solution should offer text-based descriptions of the issue in words.
Containers add a new layer to your infrastructure, which necessitates the use of application performance management (APM) tools to enable the automatic discovery of all running containers and the capture of any changes to container deployments in real time.
Container orchestration platforms like Kubernetes assign the best suited host in a cluster to each container. As you scale or redeploy, the containers often shift to different hosts, and all this happens within the cluster. This means you need special tools to identify which host runs each container. As containers are isolated, this makes the monitoring and troubleshooting process easier.
You can set limits to the compute resources a container can use. This is important because if one container fully utilizes the resources, it will result in the underperformance of other containers competing for the same resources. In such instances, the cluster host doesn’t necessarily use all the resources, so regular resource allocation monitoring is insufficient.
If, for example, there is a memory failure in a container, monitoring the overall performance of a server might not reveal the slowness of the container.
Container logs differ from traditional application logs, as they are defined as
stdout console output streams. Docker containers collect and forward logs to their destination using a logging driver.
Each container running in the cluster may be running multiple processes, with each process using a different
stdout log stream. Monitoring application logs requires individually parsing and combining them. You must also be able to identify the origin of every log, and attach relevant metadata such as the container’s name and ID.
When you develop an application with a microservices architecture, each microservice is deployed in a separate container. You must be able to trace transactions through multiple microservices, given that distributed transactions travel through different services to get from the client to the database.
Containers add more layers that require monitoring—not only the physical hosts, but also all services running on hosts as well as all associated containers. The massive amount of components deployed in containerized workloads turns manual monitoring into an inefficient and time-consuming effort. You can avoid this issue by streamlining your container monitoring software with tools that offer top-level metrics.
A containerized environment is dynamic and subject to constant changes. Containers may move between hosts, services can be added and then removed, autoscaling processes add and remove instances as needed, and auto replication and failover may occur. All of these processes create an architecture that constantly changes.
To ensure that monitoring is not disrupted, you can automate service discovery. Automation does not require manual tracking of dependencies or connecting services when each change occurs. Additionally, service discovery can make it easier to accurately scale clusters of containers.
Containers are ephemeral by design. This means the data they hold often loses its value as time passes. A monitoring tool that offers data visualization features can help streamline analysis.
Monitoring tools usually provide a graphical interface that can help make it easier to spot dramatic changes or anomalies. There are also advanced monitoring solutions that pair machine learning (ML) with automated alerting. This can help ensure incidents are accurately, appropriately, and quickly reported to all relevant stakeholders.
Related content: Read our guide to Kubernetes monitoring tools
Calico offers powerful features for container monitoring and observability. These include:
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