Node.js with Yarn
Best practices for Dockerfile for Node.js with Yarn
π³ Annotated Dockerfile for Node.js with Yarn:
# Use Node.js LTS as the base image for consistency and long-term support
FROM node:lts-jod AS base
# Stage 1: Install production dependencies using Yarn
FROM base AS deps
# Set working directory
WORKDIR /app
# Copy only package definition files first
COPY package.json yarn.lock .yarnrc.yml ./
# Enable corepack
RUN corepack enable
# Install only production dependencies
# Use yarn install --immutable for deterministic builds
RUN --mount=type=cache,id=yarn,target=/usr/local/share/.cache/yarn \
yarn install --immutable
# Stage 2: Build the application
FROM base AS build
WORKDIR /app
# Copy package definitions to maintain consistent build context
COPY package.json yarn.lock .yarnrc.yml ./
# Enable corepack
RUN corepack enable
# Install all dependencies (dev + prod) for building the app
RUN --mount=type=cache,id=yarn,target=/usr/local/share/.cache/yarn \
yarn install --immutable
# Copy entire source code
COPY . .
# Run build script defined in your package.json
RUN yarn build
# Stage 3: Create the final lightweight production image
FROM base
# Set working directory
WORKDIR /app
# Copy only production dependencies (no dev dependencies)
COPY --from=deps /app/node_modules /app/node_modules
# Copy compiled application output (dist directory)
COPY --from=build /app/dist /app/dist
# Explicitly set environment to production
ENV NODE_ENV production
# Default command to run your Node.js application
CMD ["node", "./dist/index.js"]
π Why these are best practices:
β Multi-stage builds
- Smaller final images: Dependencies and build tools are discarded after use, reducing container size.
- Security: Fewer files and tools mean a smaller attack surface.
β Caching Yarn modules
- Faster builds: Reusing the Yarn cache reduces install times significantly.
- Lower CI/CD overhead: Speeds up continuous integration and deployment workflows.
β Using --frozen-lockfile flag
- Deterministic builds: Ensures exact versions from yarn.lock are used.
- Fails if yarn.lock needs to be updated, preventing unexpected changes.
β Separating dependencies and build stages
- Clear separation of concerns: Each stage serves a single purpose, making it easier to debug and optimize.
- Improved cache efficiency: Changes in code don't trigger unnecessary reinstallation of unchanged dependencies.
β Minimal runtime image
- Performance and security: Only the essential runtime code is present, limiting potential vulnerabilities.
- Lower resource consumption: Optimized resource usage in production deployments.
π Additional Dockerfile best practices you can adopt:
Use a non-root user
For enhanced security, run your app as a non-root user:
FROM base
# Create a non-root user
RUN useradd -m appuser
WORKDIR /app
COPY --from=deps /app/node_modules /app/node_modules
COPY --from=build /app/dist /app/dist
ENV NODE_ENV production
# Switch to non-root user
USER appuser
CMD ["node", "./dist/index.js"]
Use HEALTHCHECK directive
Allows Docker to monitor container health automatically.
HEALTHCHECK --interval=30s --timeout=3s \
CMD curl -f http://localhost:3000/health || exit 1
Use explicit .dockerignore
Prevent copying unnecessary files into your image.
Example .dockerignore
node_modules
dist
coverage
.git
Dockerfile
docker-compose.yml
README.md
*.log
Set resource limits explicitly
When deploying containers, always set CPU and memory limits to avoid resource starvation or instability.
Example in Kubernetes or Docker Compose (outside Dockerfile)
resources:
limits:
cpu: 1000m
memory: 1Gi
Consider using Distroless or Alpine images
Switch to even lighter-weight base images if you're comfortable handling potential compatibility issues:
FROM node:22-alpine AS base
Or distroless:
FROM gcr.io/distroless/nodejs22-debian12 AS final
By following these annotations and best practices, your Docker images become faster to build, more secure, smaller, and easier to maintainβideal for modern production workflows.
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