Optimizing Multi-Stage Dockerfiles for Java EE Apps
When building Java EE applications with multi-stage Dockerfiles, there are several optimization techniques you can use to further reduce the size of your final Docker image and improve the build process.
Leveraging Build Dependencies
One common optimization is to leverage build dependencies only in the build stage and exclude them from the final runtime stage. For example, you can use a separate build image that includes all the necessary build tools, such as a Maven or Gradle image, and then copy the built artifacts to a smaller runtime image.
## Build stage
FROM maven:3.8.2-openjdk-11 AS build
COPY . /app
WORKDIR /app
RUN mvn clean package
## Runtime stage
FROM openjdk:11-jdk-slim
COPY --from=build /app/target/*.jar app.jar
ENTRYPOINT ["java", "-jar", "app.jar"]
Utilizing Multi-Stage Cache
Docker's multi-stage build feature also allows you to leverage the build cache to speed up subsequent builds. By organizing your Dockerfile in a way that maximizes cache reuse, you can significantly reduce the build time.
## Base stage
FROM maven:3.8.2-openjdk-11 AS base
WORKDIR /app
## Copy dependencies
COPY pom.xml .
RUN mvn dependency:go-offline
## Build stage
FROM base AS build
COPY . .
RUN mvn clean package
## Runtime stage
FROM openjdk:11-jdk-slim
COPY --from=build /app/target/*.jar app.jar
ENTRYPOINT ["java", "-jar", "app.jar"]
In this example, the pom.xml
file is copied and the dependencies are downloaded in the base stage. This ensures that the dependency resolution step is cached, speeding up subsequent builds.
Optimizing the Runtime Image
Another optimization technique is to use a smaller base image for the runtime stage. For example, you can use the openjdk:11-jdk-slim
image instead of the full openjdk:11-jdk
image, which can significantly reduce the final image size.
## Runtime stage
FROM openjdk:11-jdk-slim
COPY --from=build /app/target/*.jar app.jar
ENTRYPOINT ["java", "-jar", "app.jar"]
Additionally, you can use techniques like multi-stage builds, Alpine-based images, or distroless images to further optimize the runtime image size.
By applying these optimization techniques, you can create highly optimized Docker images for your Java EE applications, reducing the image size and improving the build process.