This article describes a set of best practices for building containers. These practices cover a wide range of goals, from shortening the build time, to creating smaller and more resilient images, with the aim of making containers easier to build (for example, with Cloud Build), and easier to run in Kubernetes Engine.
These best practices are not of equal importance. For example, you might successfully run a production workload without some of them, but others are fundamental. In particular, the importance of the security-related best practices is subjective. Whether you implement them depends on your environment and constraints.
To get the most out of this article, you need some knowledge of Docker and Kubernetes. Some best practices discussed here also apply to Windows containers, but most assume that you are working with Linux containers. Advice about running and operating containers is available in Best Practices for Operating Containers.
Package a single application per container
When you start working with containers, it's a common mistake to treat them as virtual machines that can run many different things simultaneously. A container can work this way, but doing so reduces most of the advantages of the container model. For example, take a classic Apache/MySQL/PHP stack: you might be tempted to run all the components in a single container. However, the best practice is to use two or three different containers: one for Apache, one for MySQL, and potentially one for PHP if you are running PHP-FPM.
Because a container is designed to have the same lifecycle as the application it hosts, each of your containers should contain only one application. When a container starts, so should the application, and when the application stops, so should the container. The following diagram shows this best practice.
If you have multiple applications in a container, they might have different lifecycles, or be in different states. For instance, you might end up with a container that is running, but with one of its core components crashed or unresponsive. Without an additional health check, the overall container management system (Docker or Kubernetes) cannot tell whether the container is healthy. In the case of Kubernetes, it means that your container will not be restarted by default if needed.
You might see the following actions in public images, but do not follow their example:
- Using a process management system such as supervisord to manage one or several applications in the container.
- Using a bash script as an entrypoint in the container, and making it spawn several applications as background jobs. For the proper use of bash scripts in containers, see Properly handle PID 1, signal handling, and zombie processes.
Properly handle PID 1, signal handling, and zombie processes
Linux signals are the main way to control the lifecycle of processes inside a
container. In line with the previous best practice, in order to tightly link the
lifecycle of your application to the container it is in, ensure that your
application properly handles Linux signals. The most important Linux signal is
SIGTERM because it terminates a process. Your application might also receive a
SIGKILL signal, which is used to kill the process non-gracefully, or a SIGINT
signal, which is sent when you type
Ctrl+C and is usually treated like
Process identifiers (PIDs) are unique identifiers that the Linux kernel gives to each process. PIDs are namespaced, meaning that a container has its own set of PIDs that are mapped to PIDs on the host system. The first process launched when starting a Linux kernel has the PID 1. For a normal operating system, this process is the init system, for example, systemd or SysV. Similarly, the first process launched in a container gets PID 1. Docker and Kubernetes use signals to communicate with the processes inside containers, most notably to terminate them. Both Docker and Kubernetes can only send signals to the process that has PID 1 inside a container.
In the context of containers, PIDs and Linux signals create two problems to consider.
Problem 1: How the Linux kernel handles signals
The Linux kernel handles signals differently for the process that has PID 1 than it does for other processes. Signal handlers are not automatically registered for this process, meaning that signals such as SIGTERM or SIGINT will have no effect by default. By default, you must kill processes by using SIGKILL, preventing any graceful shutdown. Depending on your application, using SIGKILL can result in user-facing errors, interrupted writes (for data stores), or unwanted alerts in your monitoring system.
Problem 2: How classic init systems handle orphaned processes
Classic init systems such as systemd are also used to remove (reap) orphaned, zombie processes. Orphaned processes—processes whose parents have died—are reattached to the process that has PID 1, which should reap them when they die. A normal init system does that. But in a container, this responsibility falls on whatever process has PID 1. If that process does not properly handle the reaping, you risk running out of memory or some other resources.
These problems have several common solutions, outlined in the following sections.
Solution 1: Run as PID 1 and register signal handlers
This solution addresses only the first problem. It is valid if your application spawns child processes in a controlled way (which is often the case), avoiding the second problem.
The easiest way to implement this solution is to launch your process with the
ENTRYPOINT instructions in your Dockerfile. For example, in the
following Dockerfile, nginx is the first and only process to be launched.
FROM debian:9 RUN apt-get update && \ apt-get install -y nginx EXPOSE 80 CMD [ "nginx", "-g", "daemon off;" ]
Sometimes, you might need to prepare the environment in your container for your
process to run properly. In this case, the best practice is to have the
container launch a shell script when starting. This shell script is tasked with
preparing the environment and launching the main process. However, if you take
this approach, the shell script has PID 1, not your process, which is why
you must use the built-in
exec command to launch the process from the shell
exec command replaces the script with the program you want. Your
process then inherits PID 1.
Solution 2: Use a specialized init system
As you would in a more classic Linux environment, you can also use an init system to deal with those problems. However, normal init systems such as systemd or SysV are too complex and large for just this purpose, which is why we recommend that you use an init system such as tini that is created especially for containers.
If you use a specialized init system, the init process has PID 1 and does the following:
- Registers the correct signal handlers.
- Makes sure that signals work for your application.
- Reaps any eventual zombie processes.
You can use this solution in Docker itself by using the
--init option of the
docker run command. To use this solution in Kubernetes, you must install the
init system in your container image and use it as entrypoint for your
Optimize for the Docker build cache
The Docker build cache can greatly accelerate the building of container images. Images are built layer by layer, and in a Dockerfile, each instruction creates a layer in the resulting image. During a build, when possible, Docker reuses a layer from a previous build and skips a potentially costly step. Docker can use its build cache only if all previous build steps used it. While this behavior is usually a good thing that makes builds go faster, you need to consider a few cases.
For example, to fully benefit from the Docker build cache, you must position
the build steps that change often at the bottom of the Dockerfile. If you put
them at the top, Docker cannot use its build cache for the other
build steps that are changing less often. Because a new Docker image is usually
built for each new version of your source code, add the source code to the image
as late as possible in the Dockerfile. In the following diagram, you can see
that if you are changing
STEP 1, Docker can reuse only the layers from the
FROM debian:9 step. If you change
STEP 3, however, Docker can reuse the
STEP 1 and
Reusing layers has another consequence: if a build step relies on any kind
of cache stored on the local file system, this cache must be generated in
the same build step. If this cache is not being generated, your build step might
be executed with an out-of-date cache coming from a previous build. You see this
behavior most commonly with package managers such as apt or yum: you must
update your repositories in the same
RUN command as your package installation.
If you change the second
RUN step in the following Dockerfile, the
update command is not rerun, leaving you with an out-of-date apt cache.
FROM debian:9 RUN apt-get update RUN apt-get install -y nginx
Instead, merge the two commands in a single
FROM debian:9 RUN apt-get update && \ apt-get install -y nginx
Remove unnecessary tools
To protect your applications from attackers, try to reduce the attack surface of your application by removing any unnecessary tools. For example, remove utilities like netcat, which you can use to create a reverse shell inside your system. If netcat is not present in the container, the attacker has to find another way.
This best practice is true for any workload, even if it is not containerized. The difference is that it is much simpler to implement with containers than it is with classic virtual machines or bare-metal servers.
Some of those tools might be useful for debugging. For example, if you push this best practice far enough, exhaustive logs, tracing, profiling, and Application Performance Management systems become almost mandatory. In effect, you can no longer rely on local debugging tools because they are often highly privileged.
File system content
The first part of this best practice deals with the content of the container image. Keep as few things as possible in your image. If you can compile your application into a single statically linked binary, adding this binary to the scratch image allows you to get a final image that contains only your application and nothing else. By reducing the number of tools packaged in your image, you reduce what a potential attacker can do in your container. For more information, see Build the smallest image possible.
File system security
Having no tools in your image is not sufficient: you must prevent potential attackers from installing their own tools. You can combine two methods here:
Avoid running as root inside the container: this method offers a first layer of security and could prevent, for example, attackers from modifying root-owned files using a package manager embedded in your image (such as
apk). For this method to be useful, you must disable or uninstall the
sudocommand. This topic is more broadly covered in Avoid running as root.
Launch the container in read-only mode, which you do by using the
--read-onlyflag from the
docker runcommand or by using the readOnlyRootFilesystem option in Kubernetes. You can enforce this in Kubernetes by using a PodSecurityPolicy.
Build the smallest image possible
Building a smaller image offers advantages such as faster upload and download times, which is especially important for the cold start time of a pod in Kubernetes: the smaller the image, the faster the node can download it. However, building a small image can be difficult because it's easy to inadvertently include build dependencies or unoptimized layers in your final image.
Use the smallest base image possible
The base image is the one referenced in the
FROM instruction in your
Dockerfile. Every other instruction in the Dockerfile builds on top of this
image. The smaller the base image, the smaller the resulting image is, and the
more quickly it can be downloaded. For example, the
image is 71 MB smaller than the
You can even use the scratch base image, which is an empty image on which you can build your own runtime environment. If your application is a statically linked binary, using the scratch base image is easily done:
FROM scratch COPY mybinary /mybinary CMD [ "/mybinary" ]
The distroless project provides you with minimal base images for a number of different languages. The images contain only the runtime dependencies for the language, but do not include many tools you would expect in a Linux distribution (shells, package managers, and so on).
Reduce the amount of clutter in your image
To reduce the size of your image, install only what is strictly needed inside it. It might be tempting to install extra packages, and then remove them at a later step. However, this approach is not sufficient. Because each instruction of the Dockerfile creates a layer, removing data from the image in a later step than the step that created it does not reduce the size of the overall image (the data is still there, just hidden in a deeper layer). Consider this example:
|Bad Dockerfile||Good Dockerfile|
In the bad version of the Dockerfile, the
[buildpackage] and files in
/var/lib/apt/lists/* still exist in the layer corresponding to the first
RUN. This layer is part of the image and must be uploaded and downloaded with
the rest, even if the data it contains is not accessible in the resulting image.
In the good version of the Dockerfile, everything is done in a single layer that
contains only your built application. The
[buildpackage] and files in
/var/lib/apt/lists/* don't exist anywhere in the resulting image, not even
hidden in a deeper layer.
For more information on image layers, see Optimize for the Docker build cache.
Another great way to reduce the amount of clutter in your image is to use multi-staged builds (introduced in Docker 17.05). Multi-stage builds allow you to build your application in a first "build" container and use the result in another container, while using the same Dockerfile.
In the following Dockerfile, the
hello binary is built in a first container
and injected in a second one. Because the second container is based on
the resulting image contains only the
hello binary and not the source file and
object files needed during the build. The binary must be statically linked in
order to work without the need for any outside library in the scratch image.
FROM golang:1.10 as builder WORKDIR /tmp/go COPY hello.go ./ RUN CGO_ENABLED=0 go build -a -ldflags '-s' -o hello FROM scratch CMD [ "/hello" ] COPY --from=builder /tmp/go/hello /hello
Try to create images with common layers
If you must download a Docker image, Docker first checks whether you already have some of the layers that are in the image. If you do have those layers, they aren't downloaded. This situation can occur if you previously downloaded another image that has the same base as the image you are currently downloading. The result is that the amount of data downloaded is much less for the second image.
At an organizational level, you can take advantage of this reduction by providing your developers with a set of common, standard, base images. Your systems must download each base image only once. After the initial download, only the layers that make each image unique are needed. In effect, the more your images have in common, the faster they are to download.
Use vulnerability scanning in Container Registry
Software vulnerabilities are a well-understood problem in the world of bare-metal servers and virtual machines. A common way to address these vulnerabilities is to use a centralized inventory system that lists the packages installed on each server. Subscribe to the vulnerability feeds of the upstream operating systems to be informed when a vulnerability affects your servers, and patch them accordingly.
However, because containers are supposed to be immutable (see statelessness and immutability of containers for more details), do not patch them in place in case of a vulnerability. The best practice is to rebuild the image, patches included, and redeploy it. Containers have a much shorter lifecycle and a less well-defined identity than servers. So using a similar centralized inventory system is a poor way to detect vulnerabilities in containers.
To help you address this problem, Container Registry has a vulnerability scanning feature. When enabled, this feature identifies package vulnerabilities for your container images. Images are scanned when they are uploaded to Container Registry and whenever there is an update to the vulnerability database. You can act on the information reported by this feature in several ways:
- Create a cron-like job that lists vulnerabilities and triggers the process to fix them, where a fix exists,
- As soon as a vulnerability is detected, use the Cloud Pub/Sub integration to trigger the patching process that your organization uses.
We recommend automating the patching process and relying on the existing continuous integration pipeline initially used to build the image. If you are confident in your continuous deployment pipeline, you might also want to automatically deploy the fixed image when ready. However, most people prefer a manual verification step before deployment. The following process achieves that:
- Store your images in Container Registry and enable vulnerability scanning.
- Configure a job that regularly fetches new vulnerabilities from Container Registry and triggers a rebuild of the images if needed.
- When the new images are built, have your Continuous Deployment system deploy them to a staging environment.
- Manually check the staging environment for problems.
- If no problems are found, manually trigger the deployment to production.
Properly tag your images
Docker images are generally identified by two components: their name and their
tag. For example, for the
google/cloud-sdk:193.0.0 image, "google/cloud-sdk"
is the name and "193.0.0" is the tag. The tag "latest" is used by default if you
do not provide one in your Docker commands. The name/tag pair is unique at any
given time. However, you can reassign a tag to a different image as needed.
When you build an image, it's up to you to tag it properly. Follow a coherent and consistent tagging policy. Document your tagging policy so that image users can easily understand it.
Container images are a way of packaging and releasing a piece of software. Tagging the image allows users to identify a specific version of your software in order to download it. For this reason, tightly link the tagging system on container images to the release policy of your software.
Tagging using semantic versioning
A common way of releasing software is to "tag" (as in the
git tag command) a
particular version of the source code with a version number. The
Semantic Versioning Specification
provides a clean way of handling version numbers. In this system, your software
has a three-part version number: "X.Y.Z", where:
- X is the major version, incremented only for incompatible API changes.
- Y is the minor version, incremented for new features.
- Z is the patch version, incremented for bug fixes.
Every increment in the minor or patch version number must be for a backward-compatible change.
If you use this system, or a similar one, tag your images according to the following policy:
- The "latest" tag always refers to the most recent (possibly stable) image. This tag is moved as soon as a new image is created.
- The "X.Y.Z" tag refers to a specific version of your software. Do not move it to another image.
- The "X.Y" tag refers to the latest patch release of the "X.Y" minor branch of your software. It is moved when a new patch version is released.
- The "X" tag refers to the latest patch release of the latest minor release of the "X" major branch. It is moved when either a new patch version or a new minor version is released.
Using this policy offers users the flexibility to choose which version of your software they want to use. They can pick a specific "X.Y.Z" version and be guaranteed that the image will never change, or they can get updates automatically by choosing a less specific tag.
Tagging using the Git commit hash
If you have an advanced continuous delivery system and you release your software often, you probably don't use version numbers as described in the Semantic Versioning Specification. In this case, a common way of handling version numbers is to use the Git commit SHA-1 hash (or a short version of it) as the version number. By design, the Git commit hash is immutable and references a specific version of your software.
You can use this commit hash as a version number for your software, but also as a tag for the Docker image built from this specific version of your software. Doing so makes Docker images traceable: because in this case the image tag is immutable, you instantly know which specific version of your software is running inside a given container. In your Continuous Delivery pipeline, automate the update of the version number used for your deployments.
Carefully consider whether to use a public image
One of the great advantages of Docker is the sheer number of publicly available images, for all kinds of software. These images allow you to get started quickly. However, when you are designing a container strategy for your organization, you will likely have constraints that publicly provided images will not be able to meet. Here are a few examples of constraints that might render the use of public images impossible:
- You want to control exactly what is inside your images.
- You don't want to depend on an external repository.
- You want to strictly control vulnerabilities in your production environment.
- You want the same base operating system in every image.
The response to all those constraints is the same, and is unfortunately costly: you must build your own images. Building your own images is fine for a limited number of images, but this number has a tendency to grow quickly. To have any chance of managing such a system at scale, think about using the following:
- An automated way to build images, in a reliable way, even for images that are built rarely. Build triggers in Cloud Build are a good way to achieve that.
- A standardized base image. Google provides some base images that you can use.
- An automated way to propagate updates to the base image to "child" images.
- A way to address vulnerabilities in your images. For more information, see Use vulnerability analysis in Container Registry.
- A way to enforce your internal standards on images created by the different teams in your organization.
Several tools are available to help you enforce policies on the images that you build and deploy:
- container-diff can analyze the content of images and even compare two images between them.
- container-structure-test can test whether the content of an image complies with a set of rules that you define.
- Grafeas is an artifact metadata API, where you store metadata about your images to later check whether those images comply with your policies.
- Kubernetes has admission controllers that you can use to check a number of prerequisites before deploying a workload in Kubernetes.
- Kubernetes also has pod security policies, that you can use to enforce the use of security options in the cluster.
You might also want to adopt a hybrid system: using a public image such as Debian or Alpine as the base image and building everything on top of that image. Or you might want to use public images for some of your noncritical images, and build your own images for other cases. Those questions have no right or wrong answers, but you have to address them.
A note about licenses
Before you include third-party libraries and packages in your Docker image, ensure that the respective licenses allow you to do so. Third-party licenses might also impose restrictions on redistribution, which apply when you publish a Docker image to a public registry.
- Learn about Best Practices for Operating Containers.
- Build your first containers with Cloud Build.
- Spin up your first Kubernetes Engine cluster.
- Speed up your Cloud Build builds.
- Preparing a Kubernetes Engine Environment for Production.
- Docker has its own set of best practices, some of which are covered in this document.
Try out other Google Cloud Platform features for yourself. Have a look at our tutorials.