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By having multi-stage builds, the first stage can build the Golang binary using a larger Docker image as the base. In the second stage, the newly built binary can be.
Two of the announcements at DockerCon 2017 directly relevant to Java developers are:. Docker Multi-stage build. This blog explains the purpose of Docker multi-stage build and provide examples of how they help us generate smaller and more efficient Java Docker images. Just show me the code:. What is the issue? Building a Docker image for a Java application typically involves building the application and package the generated artifact into an image. A Java developer would likely use Maven or Gradle to build a JAR or WAR file.
If you are using the Maven base image to build the application then it will download the required dependencies from the configured repositories and keep them in the image. The number of JARs in the local repository could be significant depending upon the number of dependencies in the pom.xml.
This could leave a lot of cruft in the image. Let’s take a look at a sample Dockerfile. FROM maven:3.5-jdk-8 COPY src /usr/src/myapp/src COPY pom.xml /usr/src/myapp RUN mvn -f /usr/src/myapp/pom.xml clean package ENV WILDFLYVERSION 10.1.0.Final ENV WILDFLYHOME /usr RUN cd $WILDFLYHOME && curl tar zx && mv $WILDFLYHOME/wildfly-$WILDFLYVERSION $WILDFLYHOME/wildfly RUN cp /usr/src/myapp/target/people-1.0-SNAPSHOT.war $WILDFLYHOME/wildfly/standalone/deployments/people.war EXPOSE 8080 CMD '/usr/wildfly/bin/standalone.sh', '-b', '0.0.0.0'.
In this Dockerfile:. maven:3.5-jdk-8 is used as the base image. Application source code is copied to the image. Maven is used to build the application artifact.
WildFly is downloaded and installed. Generated artifact is copied to the deployments directory of WildFly. Finally, WildFly is started There are several issues with this kind of flow:. Using maven as the base image restricts on what functionality is available in the image. This requires WildFly to be downloaded and configured explicitly.
Building the artifact downloads all Maven dependencies. These stay in the image and are not needed at runtime. This causes an unnecessary bloat in the image size at runtime.
Change in WildFly version will require to update the Dockerfile. This would’ve been much easier if we could use the jboss/wildfly base image by itself. In addition, unit tests may run before packaging the artifact and integration tests after the image is created. The test dependencies and results is again not needed to live in the production image. There are other ways to build the Docker image.
For example, splitting the Dockerfile into two files. The first file will then build the artifact and copy the artifact to a common location using volume mapping. The second file will then pick up the generated artifact and then use the lean base image.
This approach has also has issues where multiple Dockerfiles need to be maintained separately. Additional, there is an out-of-band hand-off between the two Dockerfiles. Let’s see how these issues are resolved with multi-stage build.
What are Docker multi-stage build? Multi-stage build allows multiple FROM statements in a Dockerfile. The instructions following each FROM statement and until the next one, creates an intermediate image. The final FROM statement is the final base image. Artifacts from intermediate stages can be copied using COPY -from=, starting from 0 for the first base image.
The artifacts not copied over are discarded. This allows to keep the final image lean and only include the relevant artifacts.
FROM syntax is updated to specify stage name using as. In this Dockerfile:.
There are two FROM instructions. This means this is a two-stage build. maven:3.5-jdk-8 is the base image for the first build. This is used to build the WAR file for the application. The first stage is named as BUILD.
jboss/wildfly:10.1.0.Final is the second and the final base image for the build. WAR file generated in the first stage is copied over to this stage using COPY -from syntax. The file is directly copied in the WildFly deployments directory. Let’s take a look at what are some of the advantages of this approach. Advantages of Docker multi-stage build. One Dockerfile has the entire build process defined. There is no need to have separate Dockerfiles and then coordinate transfer of artifact between “build” Dockerfile and “run” Dockerfile using volume mapping.
Base image for the final image can be chosen appropriately to meet the runtime needs. This helps with reduction of the overall size of the runtime image. Additionally, the cruft from build time is discarded during intermediate stage. Standard WildFly base image is used instead of downloading and configuring the distribution manually. This makes it a lot easier to update the image if a newer tag is released. Size of the image built using a single Dockerfile is 816MB.
In contrast, the size of the image built using multi-stage build is 584MB. So, using a multi-stage helps create a much smaller image. Is this a typical way of building Docker image? Are there other ways by which the image size can be reduced? Sure, you can use docker-maven-plugin as shown at to build/test the image locally and then push to repo. But this mechanism allows you to generate and package artifact without any other dependency, including Java.
Sure, maven:jdk-8-alpine image can be used to create a smaller image. But then you’ll have to create or find a WildFly image built using jdk-8-alpine, or something similar, as well. But the cruft, such as maven repository, two Dockerfiles, sharing of artifact using volume mapping or some other similar technique would still be there. There are other ways to craft your build cycle. But if you are using Dockerfile to build your artifact then you should seriously consider multi-stage builds. Read more discussion in.
As mentioned earlier, the complete code for this is available at. Sign up for to get a DockerCon 2017 recap. When doing the single-stage build the dependencies downloaded by maven will not be kept into the image because the.m2 folder is declared as a VOLUME in the maven image Dockerfile. Your point is still valid, the image is larger, but not because of the maven downloads. In both single-stage and multi-stage examples here, mvn clean package is called at image build-time, thus offering no chance of mounting an external.m2 folder so that downloading dependencies can be skipped. What I’m saying is that these approaches have the drawback of a bigger build time. Using the maven image for build, but by mounting the code and the.m2 folder and running “mvn clean package” on a container started from this image allows for caching all the maven dowloads.
Docker does help us to become effective data scientists.For the past 5 years, I have heard lots of buzz about docker containers. It seemed like all my software engineering friends are using them for developing applications. I wanted to figure out how this technology could make me more effective but I found tutorials online either too detailed: elucidating features I would never use as a data scientist, or too shallow: not giving me enough information to help me understand how to be effective with Docker quickly. This quick primer on docker helps to learn the things you need to know to quickly get started.
The Multi-Stage feature allows a single Dockerfile to contain multiple stages in order to produce the desired, optimised, Docker Image. Previously, the problem would have been solved with two Dockerfiles. One file would have the steps to build the binary and artifacts using a development container, the second would be optimised for production and not include the development tools. By removing development tooling in the production image, you reproduce the attack surface and improve the deployment time. Sample Code Start by deploying a sample. This currently using a two staged Docker Build approach.
This scenario will create a new Dockerfile that allows the image to be built using a single command. Git clone Multi-Stage Dockerfile Using the editor, create a Multi-Stage Dockerfile. The first stage using the Golang SDK to build a binary. The second stage copies the resulting binary into a optimised Docker Image.
# First Stage FROM golang:1.6-alpine RUN mkdir /app ADD. /app/ WORKDIR /app RUN CGOENABLED=0 GOOS=linux go build -a -installsuffix cgo -o main. # Second Stage FROM alpine EXPOSE 80 CMD '/app' # Copy from first stage COPY -from=0 /app/main /app There are currently discussions about improving the syntax that you can follow at. With the new syntax for the Dockerfile in place, the build process is identical to previously. Task Create the desired Docker Image using the build command below.
Docker build -f Dockerfile.multi -t golang-app. The result will be two images.
One untagged that was used for the first stage and the second, smaller image, our target image. Docker images If you receive the error, COPY -from=0 /build/out /app/ Unknown flag: from, it means you're running an older version of Docker without the multi-stage support.
Step 1 of this scenario upgrades the current Docker version. Help Katacoda offerings an Interactive Learning Environment for Developers. This course uses a command line and a pre-configured sandboxed environment for you to use. Below are useful commands when working with the environment. Cd Change directory ls List directory echo 'contents' Write contents to a file cat Output contents of file Vim In the case of certain exercises you will be required to edit files or text.
The best approach is with Vim. Vim has two different modes, one for entering commands (Command Mode) and the other for entering text (Insert Mode). You need to switch between these two modes based on what you want to do. The basic commands are: i In Command Mode Change into Insert Mode, you can now insert and edit text in the file esc In Insert Mode Change into Command Mode, you can now execute commands:wq In Command Mode Save and Exit.