Optimizing Container Images for Kubernetes

Introduction

Container images serve as the cornerstone for application deployment in Kubernetes, functioning as the core packaging format. These images form the foundation for pods and various Kubernetes objects, playing a pivotal role in maximizing the platform's capabilities. A well-constructed container image is characterized by its security, high performance, and focused functionality. Such images are adept at responding to Kubernetes-provided configuration data and instructions, and they are equipped with endpoints essential for deployments to monitor the application's internal state.

This guide delves into the art of creating top-tier container images, offering strategic insights and overarching goals that should influence your approach to containerization within Kubernetes environments. While our primary focus is on image development for Kubernetes, the principles and recommendations presented here are equally applicable to container deployment on other orchestration platforms and contexts.

When building container images for Kubernetes, understanding the defining characteristics of a proficient container image is crucial. What should be the objectives when designing new images? Identifying the most vital traits and behaviors is essential.

Qualities to Aim For in Container Images:

• Well-Defined Purpose: Each container image should serve a singular, focused function. Unlike virtual machines that bundle related functionalities, container images should emulate Unix utilities, excelling in one specific task. Complex functionalities can be managed externally, outside the container's scope.
• Configurable and Generic Design: Aim for reusable design in container images. The capability to adjust configurations at runtime is crucial, especially for testing images prior to production deployment. Such adaptable, generic images can be reconfigured in various ways without necessitating the creation of new images.
• Compact Image Size: In Kubernetes environments, smaller container images are advantageous. They expedite the download process on new nodes and typically contain fewer installed packages, enhancing security. Simplified container images also facilitate easier debugging by reducing software complexity.
• External Management of State: Given the volatile lifecycle of containers in clustered environments, it's vital to store application state externally. This approach ensures consistency, aids in service recovery and availability, and prevents data loss.
• Simplicity and Clarity: Strive to keep container images straightforward and comprehensible. Simplified images allow for direct inspection of configurations and logs, aiding in quicker troubleshooting. Consider container images as a means to package your application, rather than as complex machine configurations.
• Adherence to Containerized Software Best Practices: Container images should comply with the container model, avoiding traditional system administration practices like full init systems and daemonizing applications. Logging to stdout enables Kubernetes to manage log data, diverging from typical full operating system practices.
• Utilizing Kubernetes Features: Aligning with Kubernetes tooling is crucial. Implementing endpoints for liveness and readiness checks and adapting to configuration or environmental changes helps applications fully leverage the dynamic nature of Kubernetes deployments.

With a clear understanding of these key characteristics, we can delve into specific strategies to build highly functional and efficient container images for Kubernetes.

Reuse Minimal, Shared Base Layers

In the construction of container images, the choice of base images is foundational. Base images, either derived from a parent image or the 'scratch' layer (a blank slate without a filesystem), establish the fundamental operating system and essential functionalities. Container images are built by layering additional functionalities on top of these bases.

Directly starting from the scratch layer limits functionality significantly, as no standard utilities or filesystems are present. While scratch-based images are highly streamlined, they primarily serve in defining more complex base images. The general practice involves building container images upon a parent image, which provides a basic, reusable environment, eliminating the need to recreate an entire system for each new image.

Choosing the right base image is crucial, especially considering the bandwidth and startup time implications. For instance, while robust environments like Ubuntu offer familiarity, they also result in larger image sizes (often over 100MB). In contrast, Alpine Linux, with its minimal size (~5MB) and sufficient functionality, emerges as an efficient alternative for base images, striking a balance between utility and compactness.

For optimal efficiency, reusing the same parent image across multiple container images is advisable. This approach ensures that once the shared parent layer is downloaded on a machine, only the differing layers between images need downloading subsequently. Thus, standardizing on a common parent image not only streamlines development but also reduces data transfer requirements for new deployments, enhancing overall efficiency in containerized environments.

Managing Container Layers

Once you’ve selected a parent image, you can define your container image by adding additional software, copying files, exposing ports, and choosing processes to run. Certain instructions in the image configuration file (e.g., a Dockerfile if you are using Docker) will add additional layers to your image.

For many of the same reasons mentioned in the previous section, it’s important to be mindful of how you add layers to your images due to the resulting size, inheritance, and runtime complexity. To avoid building large, unwieldy images, it’s important to develop a good understanding of how container layers interact, how the build engine caches layers, and how subtle differences in similar instructions can have a big impact on the images you create.

Understanding Image Layers and Build Cache

Docker creates a new image layer each time it executes a RUN, COPY, or ADD instruction. If you build the image again, the build engine will check each instruction to see if it has an image layer cached for the operation. If it finds a match in the cache, it uses the existing image layer rather than executing the instruction again and rebuilding the layer.

This process can significantly shorten build times, but it is important to understand the mechanism used to avoid potential problems. For file copying instructions like COPY and ADD, Docker compares the checksums of the files to see if the operation needs to be performed again. For RUN instructions, Docker checks to see if it has an existing image layer cached for that particular command string.

While it might not be immediately obvious, this behavior can cause unexpected results if you are not careful. A common example of this is updating the local package index and installing packages in two separate steps. We will be using Ubuntu for this example, but the basic premise applies equally well to base images for other distributions:

Package installation example Dockerfile

FROM ubuntu:20.04
RUN apt -y update
RUN apt -y install nginx
. . .

Here, the local package index is updated in one RUN instruction (apt -y update) and Nginx is installed in another operation. This works without issue when it is first used. However, if the Dockerfile is updated later to install an additional package, there may be problems:

Package installation example Dockerfile

FROM ubuntu:20.04
RUN apt -y update
RUN apt -y install nginx php-fpm
. . .

We’ve added a second package to the installation command run by the second instruction. If a significant amount of time has passed since the previous image build, the new build might fail. That’s because the package index update instruction (RUN apt -y update) has not changed, so Docker reuses the image layer associated with that instruction. Since we are using an old package index, the version of the php-fpm package we have in our local records may no longer be in the repositories, resulting in an error when the second instruction is run.

To avoid this scenario, be sure to consolidate any steps that are interdependent into a single RUN instruction so that Docker will re-execute all of the necessary commands when a change occurs. In shell scripting, using the logical AND operator &&, which will execute multiple commands on the same line as long as they are each successful, is a good way to achieve this:

Package installation example Dockerfile

FROM ubuntu:20.04
RUN apt -y update && apt -y install nginx php-fpm
. . .

The instruction now updates the local package cache whenever the package list changes. An alternative would be to RUN an entire shell.sh script that contains multiple lines of instructions, but would have to be made available to the container first.

Reducing Image Layer Size by Tweaking RUN Instructions

The previous example demonstrates how Docker’s caching behavior can subvert expectations, but there are some other things to keep in mind about how RUN instructions interact with Docker’s layering system. As mentioned earlier, at the end of each RUN instruction, Docker commits the changes as an additional image layer. In order to exert control over the scope of the image layers produced, you can clean up unnecessary files by paying attention to the artifacts introduced by the commands you run.

In general, chaining commands together into a single RUN instruction offers a great deal of control over the layer that will be written. For each command, you can set up the state of the layer (apt -y update), perform the core command (apt install -y nginx php-fpm), and remove any unnecessary artifacts to clean up the environment before it’s committed. For example, many Dockerfiles chain rm -rf /var/lib/apt/lists/* to the end of apt commands, removing the downloaded package indexes, to reduce the final layer size:

Package installation example Dockerfile

FROM ubuntu:20.04
RUN apt -y update && apt -y install nginx php-fpm && rm -rf /var/lib/apt/lists/*
. . .

To further reduce the size of the image layers you are creating, trying to limit other unintended side effects of the commands you’re running can be helpful. For instance, in addition to the explicitly declared packages, apt also installs “recommended” packages by default. You can include --no-install-recommends to your apt commands to remove this behavior. You may have to experiment to find out if you rely on any of the functionality provided by recommended packages.

We’ve used package management commands in this section as an example, but these same principles apply to other scenarios. The general idea is to construct the prerequisite conditions, execute the minimum viable command, and then clean up any unnecessary artifacts in a single RUN command to reduce the overhead of the layer you’ll be producing.

Using Multi-stage Builds

Multi-stage builds were introduced in Docker 17.05, allowing developers to more tightly control the final runtime images they produce. Multi-stage builds allow you to divide your Dockerfile into multiple sections representing distinct stages, each with a FROM statement to specify separate parent images.

Earlier sections define images that can be used to build your application and prepare assets. These often contain build tools and development files that are needed to produce the application, but are not necessary to run it. Each subsequent stage defined in the file will have access to artifacts produced by previous stages.

The last FROM statement defines the image that will be used to run the application. Typically, this is a pared down image that installs only the necessary runtime requirements and then copies the application artifacts produced by previous stages.

This system allows you worry less about optimizing RUN instructions in the build stages since those container layers will not be present in the final runtime image. You should still pay attention to how instructions interact with layer caching in the build stages, but your efforts can be directed towards minimizing build time rather than final image size. Paying attention to instructions in the final stage is still important in reducing image size, but by separating the different stages of your container build, it’s easier to to obtain streamlined images without as much Dockerfile complexity.

Scoping Functionality at the Container and Pod Level

While the choices you make regarding container build instructions are important, broader decisions about how to containerize your services often have a more direct impact on your success. In this section, we’ll talk a bit more about how to best transition your applications from a more conventional environment to running on a container platform.

Containerizing by Function

Generally, it is good practice to package each piece of independent functionality into a separate container image.

This differs from common strategies employed in virtual machine environments where applications are frequently grouped together within the same image to reduce the size and minimize the resources required to run the VM. Since containers are lightweight abstractions that don’t virtualize the entire operating system stack, this tradeoff is less compelling on Kubernetes. So while a web stack virtual machine might bundle an Nginx web server with a Gunicorn application server on a single machine to serve a Django application, in Kubernetes these might be split into separate containers.

Designing containers that implement one discrete piece of functionality for your services offers a number of advantages. Each container can be developed independently if standard interfaces between services are established. For instance, the Nginx container could potentially be used to proxy to a number of different backends or could be used as a load balancer if given a different configuration.

Once deployed, each container image can be scaled independently to address varying resource and load constraints. By splitting your applications into multiple container images, you gain flexibility in development, organization, and deployment.

Combining Container Images in Pods

In Kubernetes, pods are the smallest unit that can be directly managed by the control plane. Pods consist of one or more containers along with additional configuration data to tell the platform how those components should be run. The containers within a pod are always scheduled on the same worker node in the cluster and the system automatically restarts failed containers. The pod abstraction is very useful, but it introduces another layer of decisions about how to bundle together the components of your applications.

Like container images, pods also become less flexible when too much functionality is bundled into a single entity. Pods themselves can be scaled using other abstractions, but the containers within cannot be managed or scaled independently. So, to continue using our previous example, the separate Nginx and Gunicorn containers should probably not be bundled together into a single pod. This way, they can be controlled and deployed separately.

However, there are scenarios where it does make sense to combine functionally different containers as a unit. In general, these can be categorized as situations where an additional container supports or enhances the core functionality of the main container or helps it adapt to its deployment environment. Some common patterns are:

• Sidecar: The secondary container extends the main container’s core functionality by acting in a supporting utility role. For example, the sidecar container might forward logs or update the filesystem when a remote repository changes. The primary container remains focused on its core responsibility, but is enhanced by the features provided by the sidecar.
• Ambassador: An ambassador container is responsible for discovering and connecting to (often complex) external resources. The primary container can connect to an ambassador container on well-known interfaces using the internal pod environment. The ambassador abstracts the backend resources and proxies traffic between the primary container and the resource pool.
• Adaptor: An adaptor container is responsible for normalizing the primary container’s interfaces, data, and protocols to align with the properties expected by other components. The primary container can operate using native formats and the adaptor container translates and normalizes the data to communicate with the outside world.

As you might have noticed, each of these patterns support the strategy of building standard, generic primary container images that can then be deployed in a variety contexts and configurations. The secondary containers help bridge the gap between the primary container and the specific deployment environment being used. Some sidecar containers can also be reused to adapt multiple primary containers to the same environmental conditions. These patterns benefit from the shared filesystem and networking namespace provided by the pod abstraction while still allowing independent development and flexible deployment of standardized containers.

Designing for Runtime Configuration

In the realm of containerized application development, designing for runtime configuration is a critical strategy to achieve a balance between creating standardized, reusable components and meeting the diverse demands of different runtime environments. This approach allows for the construction of versatile components whose behaviors are dynamically defined based on additional runtime configuration details. This flexibility is as relevant for container environments as it is for standalone applications.

To effectively implement runtime configuration, foresight is required throughout the application development and containerization processes. Applications should be architected to derive settings from various sources such as command line arguments, configuration files, or environment variables, enabling them to adapt when launched or reinitialized. This adaptive configuration logic is a crucial aspect of the code that must be established before the containerization phase.

For container configuration, particularly when drafting a Dockerfile, it's essential to incorporate elements that facilitate runtime customization. Containers can be equipped with various methods to receive runtime data. This includes mounting host files or directories as container volumes for file-based configurations, and passing environment variables into the container at startup. The use of Dockerfile instructions like CMD and ENTRYPOINT can be optimized to allow the passing of runtime configuration parameters as command line arguments.

In Kubernetes, which operates at a higher level of abstraction with pods rather than direct container management, there are additional tools for runtime configuration. Kubernetes ConfigMaps and Secrets are designed to define and inject configuration data into containers. ConfigMaps serve as flexible objects for storing and deploying configuration information that may vary across different environments or stages of testing. Secrets are used in a similar capacity but are tailored for managing sensitive data like passwords and API keys.

By mastering these runtime configuration mechanisms across various layers, developers can create adaptable and reusable components. These components are capable of adjusting to specific environmental parameters, greatly enhancing the flexibility and reusability of container images across diverse deployment scenarios. This adaptability not only streamlines the development process but also significantly expands the potential applications of a single container image.

Implementing Process Management with Containers

When transitioning to container-based environments, users often start by shifting existing workloads, with few or no changes, to the new system. They package applications in containers by wrapping the tools they are already using in the new abstraction. While it is helpful to use your usual patterns to get migrated applications up and running, dropping in previous implementations within containers can sometimes lead to ineffective design.

Treating Containers like Applications, Not Services

In container development, a common challenge arises when developers treat containers as if they were traditional service-oriented virtual machines. Implementing substantial service management features, such as running systemd services or daemonizing web servers inside containers, may align with standard practices in conventional computing environments. However, these practices can clash with the foundational principles of the container model.

In a containerized environment, the host system orchestrates container lifecycle events by sending signals to the process identified as Process ID (PID) 1 within the container. This PID 1, typically an init system in standard computing setups, is crucial because it's the only process directly managed by the host. When traditional init systems are employed within containers, they can obstruct direct control over the primary application by the host. While the host can manage the init system (start, stop, kill), it may not be able to directly influence the main application. This setup can lead to added complexity and sometimes unnecessary operational challenges.

A more streamlined approach in container architecture is to run the main application as PID 1. This simplification ensures that the primary application operates in the foreground and directly responds to the host's lifecycle management signals. For scenarios where multiple processes are necessary, PID 1 should be in charge of managing these subsequent processes. Certain applications, like Apache, are inherently designed to manage their worker processes. For others, employing a minimalistic init system (like dumb-init or tini) or a wrapper script can effectively manage process lifecycles. Regardless of the chosen method, it's imperative that the process designated as PID 1 within the container is designed to appropriately respond to termination signals from Kubernetes, ensuring smooth operation and management as per the container's environment requirements.

Managing Container Health in Kubernetes

In Kubernetes, effective container health management is crucial for maintaining robust and resilient applications. Kubernetes excels in managing the lifecycle of long-running processes and ensuring consistent access to applications, even amidst container restarts or changes in implementations. It achieves this by offloading the task of health monitoring and maintenance, utilizing its own toolkit to manage workload health efficiently.

To adeptly orchestrate container management, Kubernetes needs to assess the health and operational status of applications within containers. This is where liveness probes come into play. They are essentially network endpoints or commands designed to report on the health of the application. Kubernetes regularly checks these liveness probes to ascertain if the container is functioning correctly. Should a container fail to respond as expected, Kubernetes takes corrective action by restarting it to restore normal operations.

Additionally, Kubernetes employs readiness probes, which serve a slightly different purpose. While liveness probes assess health, readiness probes evaluate whether an application is prepared to handle incoming traffic. This distinction is particularly important for applications that require specific initialization procedures before they can start accepting connections. Readiness probes inform Kubernetes about the right time to incorporate a pod into a service or when to temporarily remove it, ensuring smooth traffic flow and service continuity.

Implementing these probes necessitates integrating endpoint definitions into the application and exposing them through the Docker image configuration. This integration allows Kubernetes to effectively monitor and manage container health, safeguarding against disruptions in service availability and ensuring that each component in the containerized environment performs optimally.

Conclusion

This guide has explored several key strategies essential for optimizing containerized applications within Kubernetes. To summarize, we highlighted the following tactics:

• Opt for Minimal and Shared Parent Images: This approach helps in constructing lightweight images, significantly reducing startup times.
• Employ Multi-Stage Builds: By doing so, you can distinctly separate the container's build phase from its runtime environment.
• Streamline Dockerfile Instructions: This leads to cleaner image layers and helps in circumventing potential caching errors.
• Isolate Functionality in Containers: Such isolation aids in achieving scalable and manageable containerization.
• Focus Pod Responsibilities: Designing pods with a single, clear purpose enhances their functionality.
• Incorporate Helper Containers: These can augment the primary container's capabilities or tailor them to specific deployment needs.
• Design for Runtime Configuration: Building with runtime adaptability in mind offers greater deployment flexibility.
• Run Primary Applications as PID 1: This ensures Kubernetes can effectively oversee container lifecycle events.
• Implement Health and Liveness Endpoints: Such endpoints allow Kubernetes to actively monitor container health.

As you navigate through the development and deployment of containerized applications, it's crucial to understand how these differ from traditional applications and to grasp their operational dynamics in a Kubernetes-managed cluster. Keeping these considerations in mind will not only help you sidestep common challenges but also fully leverage the extensive features and capabilities that Kubernetes offers, thereby enhancing the robustness and efficiency of your services.