Episode 48 — Container fundamentals: runtimes and the image/container boundary
In Episode forty eight, titled “Container fundamentals: runtimes and the image/container boundary,” we build a clean mental model of containers as processes packaged with their dependencies, because that framing prevents many of the beginner misunderstandings that show up on the CompTIA Linux+ exam and in real troubleshooting. Containers are not magical mini computers, and they are not simply compressed archives of files either, they are a way to run a process with a controlled view of the system and a predictable set of libraries and configuration. That idea becomes especially important when you start mixing container workloads with traditional host services, because you need to know what the host is responsible for and what the container is responsible for. When your mental model is correct, you can predict why a container stopped, why data disappeared, or why resource usage looks different than expected. The goal of this episode is to make containers feel logical by understanding boundaries, lifecycles, and the runtime mechanisms that make them work.
A foundational distinction is that images are templates while containers are running instances, and confusing the two leads to many avoidable mistakes. An image is a packaged filesystem and metadata that describes how to start a workload, while a container is what you get when that image is instantiated and a process is launched under a container runtime. The image is static in the sense that it represents a build artifact, and the container is dynamic in the sense that it has runtime state such as process activity, networking, and a writable layer. This is why you can create multiple containers from the same image, just as you can bake multiple loaves of bread from the same recipe, and each instance can have different runtime behavior even though the template is identical. When you troubleshoot, you need to know whether you are dealing with a build problem in the image or an execution problem in the container instance. That separation is one of the most exam relevant concepts because it clarifies what is persistent and what is ephemeral.
Layered images are a key reason containers are efficient, and understanding layers helps you reason about storage usage and why updates can be fast or slow depending on what changed. A layered image is built as a stack of filesystem changes, where each layer represents an incremental modification such as adding a package, copying application code, or changing configuration. Storage is efficient because layers can be shared across images, meaning if two images share a common base layer, the host can store that shared layer once and reference it from both images. The practical implication is that large base layers can be reused, and small application layers can be swapped frequently, which makes iterative builds and deployments faster. It also means that small changes can invalidate cache layers and cause rebuilds to pull or store more data than you expect if the layering strategy is poor. At exam level, the key is recognizing that layers affect storage and reuse, and at operational level it helps you predict why disk usage grows over time.
Under the hood, namespaces and cgroups are the mechanisms that make containers feel isolated and controlled on a shared host, and they are central to understanding why containers are lightweight. Namespaces provide isolation by giving a process its own view of certain system resources, such as process identifiers, networking, and filesystem mounts, so the process behaves as if it is running in its own environment. Control groups, commonly called cgroups, provide resource control by limiting and accounting for CPU, memory, and other resources, which allows multiple workloads to coexist without one starving the others. The container runtime uses these kernel features to shape the process’s perspective and constraints without requiring a separate kernel per workload. This is an important point because it explains why containers start quickly and use fewer resources than full virtual machines, which must emulate hardware boundaries and often run separate operating systems. When you remember that containers are still host kernel processes, the behavior of performance, visibility, and limits becomes more predictable.
By default, a container filesystem is separate from the host filesystem, which is both a safety feature and a source of surprise if you expect files to persist after the container lifecycle ends. The container typically has its own writable layer on top of the image layers, and changes made inside the container live in that writable space unless you deliberately connect host storage into the container. This is why you can install a package in a running container and see it work, but when you remove and recreate the container from the original image, that change is gone because it never became part of a new image. That separation protects the host from accidental modifications and helps make containers reproducible, but it also means you must plan persistence intentionally for anything that should survive restarts. At exam level, the important idea is that container filesystems are isolated by default, and persistence is not automatic. Once you accept that default behavior, “where did my data go” becomes a question you can answer quickly.
Bind mounts and volumes are the primary ways to persist important data, and the difference between them often comes down to control, portability, and how tightly you want the container to couple to host paths. A bind mount connects a specific host path into the container, which can be very direct and convenient, but it also means the container depends on that exact host directory structure being present and correct. A volume is a managed storage area that is typically controlled by the container platform, which can make it easier to move workloads between hosts or to manage lifecycle separately from container instances. Both approaches solve the same core problem, which is that the container’s writable layer is not a durable storage strategy for important state. The operational mindset is to treat application state as separate from application code, and to decide explicitly where that state should live. When you do this intentionally, containers become repeatable and data becomes durable.
Container lifecycle states help you interpret what you are seeing when a workload behaves unexpectedly, because containers are not simply “on” or “off,” they move through meaningful phases like created, running, stopped, and removed. Created means the container is defined and has its filesystem prepared, but the main process is not executing yet, which is useful for understanding scenarios where configuration is present but nothing is running. Running means the main process is active, and you can expect resource usage, logs, and network activity depending on what the process does. Stopped means the process ended, which can be clean or due to failure, and that difference matters for diagnosis and restart decisions. Removed means the container instance no longer exists, and at that point any runtime writable data not stored in persistent storage is effectively gone. Understanding these lifecycle states keeps you from assuming a container disappeared mysteriously when in reality it followed a normal lifecycle path.
A particularly important concept is why containers exit quickly when the main process ends, because this behavior surprises people who treat containers like miniature servers. A container is designed to run a workload, and that workload is defined by a primary process, so when that process ends, the container has nothing left to do and it stops. If the main process is a short lived command, the container will naturally start and then exit almost immediately, which is expected behavior, not necessarily a failure. This design encourages single purpose containers that do one job well, rather than containers that mimic full operating systems running many background services. It also ties directly into observability, because the container’s logs and exit status usually reflect the main process outcome, which can be used to understand why it stopped. At exam level, remember that container lifetime is tied to the main process, and at operational level this explains many “it keeps stopping” reports.
Security boundaries matter, and one of the most important exam level insights is that root inside a container can still create risk for the host if other controls are weak or misconfigured. Containers provide isolation, but they share the host kernel, which means container escape vulnerabilities and excessive privileges can have serious consequences. Running as root inside the container can also interact with mounted host paths in powerful ways, because filesystem permissions and ownership can become complicated when container identity maps to host identity. This is why container security is not simply “containers are safe,” it is about reducing privileges, controlling what is mounted, and being careful with capabilities that allow deep interaction with the host. Even without advanced security topics, it is important to remember that containers are not full virtualization boundaries by default. The safe mindset is to treat containers as a convenience and isolation mechanism, not as a guarantee of strong separation.
Consider a scenario where an application running in a container loses data after a restart, and you must decide whether a volume or a bind mount is the right persistence choice. If the data is application state that must survive container recreation, storing it outside the container writable layer is mandatory, and that pushes you toward either a volume or a bind mount depending on how you want to manage it. A bind mount can be appropriate when you want explicit control over the exact host location, such as integrating with existing directories, backups, or host level monitoring, but it increases coupling to host paths. A volume can be a safer default when you want container managed persistence that is easier to relocate and less dependent on specific host directory layouts. The key is that you decide based on operational needs like portability and clarity, not based on habit. When you match the persistence method to the data’s importance and the deployment environment, you avoid the painful lesson of “stateless by accident.”
It is also important to avoid treating containers like full virtual machines with many background services, because that mindset tends to produce bloated images, confusing lifecycles, and harder troubleshooting. Containers work best when they run a focused workload, such as a single application process or a tightly related set of processes designed to run together. When you try to pack a container with multiple daemons, you recreate the complexity of a full system without the management expectations that come with it, and the container lifecycle semantics start to fight you. You also increase your attack surface and make updates more complex, because more software means more dependencies and more patching obligations. Operationally, a multi service container can obscure which component failed and why the container stopped, because the container’s life is tied to one main process even if other processes exist. The exam does not expect you to design container platforms, but it does expect you to understand that containers are not intended to be full system replacements.
A memory hook that captures the image versus container boundary is that an image is the recipe, and a container is the meal, because it makes the relationship intuitive without oversimplifying the mechanics. A recipe describes ingredients and steps, which parallels how an image describes filesystem layers and a default command to run, while a meal is what you experience in the moment, which parallels a running container with its active process and runtime state. You can make multiple meals from one recipe, just as you can run multiple containers from one image, and each one can have its own runtime differences such as environment variables, network attachments, or persistent storage connections. This hook also reminds you that changing the meal does not change the recipe, meaning changes inside a running container do not automatically become part of the image unless you build a new image. When you remember this, troubleshooting becomes clearer because you know whether you are addressing a template issue or an instance issue.
To mini review what makes containers lightweight, the core idea is that containers do not require a separate operating system kernel per workload, and they reuse the host kernel while providing isolated views and resource limits through namespaces and cgroups. That reuse reduces overhead in memory and startup time, which is why containers can be created and started quickly compared to full virtual machines. The layered image model also contributes because common layers can be shared, reducing storage duplication and making distribution more efficient. This is not magic, it is engineering tradeoffs: you get efficiency and fast startup, but you rely on the host kernel as a shared boundary. Understanding that tradeoff is what allows you to use containers appropriately rather than assuming they provide the same isolation guarantees as full virtualization. At exam level, lightweight means shared kernel plus isolation features, and that is the phrase to keep in your head.
To conclude Episode forty eight, one safe use case for containers is packaging an application and its dependencies so it runs consistently across development, testing, and production without relying on the host to provide the exact same library versions. This works well when the application can be treated as mostly stateless, with important data stored in a volume or bind mount so that container instances can be replaced without losing state. It is also safe when you treat the container as a controlled process with limited privileges and clear storage boundaries, rather than as a general purpose system. The image provides the repeatable template, the container provides the runtime instance, and the host provides the kernel features that make isolation and resource control possible. When you keep these boundaries clear, containers become a practical operational tool rather than a confusing abstraction. The exam wants you to recognize these fundamentals, and real systems reward you for applying them consistently.
