Scala with SBT
Best practices for Dockerfile for Scala with SBT
🐳 Annotated Dockerfile for Scala with SBT:
🔍 Why these are best practices:
✅ Multi-stage builds
- Reduces final image size.
- Separates build environment from runtime environment.
- Eliminates SBT, Scala compiler, and build tools from production image.
✅ SBT dependency caching
- Uses Docker's build cache to avoid downloading dependencies repeatedly.
- Caches Ivy2, Coursier, and SBT directories for faster builds.
- Dramatically improves build times in CI/CD pipelines.
✅ SBT assembly for fat JAR
- Creates a single, self-contained JAR with all dependencies.
- Simplifies deployment with minimal runtime requirements.
- Ensures consistent behavior across environments.
✅ JRE-only runtime image
- Uses minimal JRE instead of full JDK for smaller runtime image.
- Reduces attack surface by excluding development tools.
- Improves startup times and resource utilization.
✅ Security best practices
- Runs the application as a non-root user.
- Follows principle of least privilege for enhanced security.
- Sets appropriate file ownership and permissions.
🚀 Additional Dockerfile best practices you can adopt:
Skip tests during build
For faster builds when tests are run separately:
Optimize JVM for containers
Fine-tune JVM settings for containerized environments:
Add health checks
Monitor application health:
Use .dockerignore
Exclude unnecessary files from your Docker build context:
Configure for different environments
Use build arguments to customize your builds:
Enable GraalVM native-image for Scala
For Scala 3 projects that support native compilation:
Implement proper signal handling
Ensure your container responds to orchestration signals:
Use a distroless base image for minimal runtime
Further reduce attack surface with distroless:
By following these practices, you'll create Docker images for your Scala applications that are secure, efficient, and optimized for both development and production environments. These approaches help minimize build times, reduce image sizes, and ensure consistent behavior across different deployment environments, leveraging Scala's JVM foundation for robust containerized applications.
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