Debugging a Failed CI/CD Pipeline: Why Docker Image Build Succeeds but Deploy to Kubernetes Fails

Introduction

Continuous Integration and Continuous Deployment (CI/CD) pipelines are essential for modern software development, allowing teams to automate testing, building, and deployment of their applications. However, when issues arise, it can be challenging to identify the root cause, especially when the Docker image build is successful, but the deployment to Kubernetes fails. In this post, we will explore the common reasons behind this issue, provide practical examples, and offer best practices and optimization tips to help you troubleshoot and fix the problem.

Understanding the CI/CD Pipeline

Before diving into the issue, let's review the basic components of a CI/CD pipeline:

Source Code: The application code stored in a version control system like Git.
CI/CD Tool: The tool used to automate the pipeline, such as Jenkins, GitLab CI/CD, or CircleCI.
Docker Image Build: The process of creating a Docker image from the application code.
Kubernetes Deployment: The process of deploying the Docker image to a Kubernetes cluster.

Common Reasons for Failure

There are several reasons why the Docker image build may succeed, but the deployment to Kubernetes fails. Some common causes include:

Image Size: The Docker image is too large, causing issues during deployment.
Incorrect Image Tag: The image tag is incorrect, leading to the wrong image being deployed.
Kubernetes Configuration: The Kubernetes configuration is incorrect, causing the deployment to fail.
Network Policies: The network policies are not configured correctly, blocking the deployment.

Image Size Issues

To demonstrate the issue with image size, let's consider an example using Docker and Kubernetes. Suppose we have a simple Node.js application with the following Dockerfile:

1# Use an official Node.js image as the base
2FROM node:14
3
4# Set the working directory to /app
5WORKDIR /app
6
7# Copy the package*.json files to the working directory
8COPY package*.json ./
9
10# Install the dependencies
11RUN npm install
12
13# Copy the application code to the working directory
14COPY . .
15
16# Expose the port
17EXPOSE 3000
18
19# Run the command to start the application
20CMD [ "npm", "start" ]

If the image size is too large, it may cause issues during deployment. To optimize the image size, we can use a multi-stage build:

1# Use an official Node.js image as the base for the build stage
2FROM node:14 AS build-stage
3
4# Set the working directory to /app
5WORKDIR /app
6
7# Copy the package*.json files to the working directory
8COPY package*.json ./
9
10# Install the dependencies
11RUN npm install
12
13# Copy the application code to the working directory
14COPY . .
15
16# Create a new stage for the production environment
17FROM node:14-alpine
18
19# Set the working directory to /app
20WORKDIR /app
21
22# Copy the application code from the build stage
23COPY --from=build-stage /app .
24
25# Expose the port
26EXPOSE 3000
27
28# Run the command to start the application
29CMD [ "npm", "start" ]

By using a multi-stage build, we can reduce the image size and improve the deployment process.

Incorrect Image Tag

Another common issue is using an incorrect image tag. To demonstrate this, let's consider an example using GitLab CI/CD. Suppose we have a .gitlab-ci.yml file with the following configuration:

1image: docker:latest
2
3stages:
4  - build
5  - deploy
6
7variables:
8  IMAGE_TAG: $CI_COMMIT_SHA
9
10build:
11  stage: build
12  script:
13    - docker build -t my-app:$IMAGE_TAG .
14  only:
15    - main
16
17deploy:
18  stage: deploy
19  script:
20    - kubectl apply -f deployment.yaml
21  only:
22    - main

In this example, the IMAGE_TAG variable is set to the commit SHA, which may not be the expected tag. To fix this, we can use a fixed tag or a tag that is generated based on the environment:

1image: docker:latest
2
3stages:
4  - build
5  - deploy
6
7variables:
8  IMAGE_TAG: latest
9
10build:
11  stage: build
12  script:
13    - docker build -t my-app:$IMAGE_TAG .
14  only:
15    - main
16
17deploy:
18  stage: deploy
19  script:
20    - kubectl apply -f deployment.yaml
21  only:
22    - main

By using a fixed tag, we can ensure that the correct image is deployed to Kubernetes.

Kubernetes Configuration

Kubernetes configuration issues can also cause the deployment to fail. To demonstrate this, let's consider an example using a deployment.yaml file:

1apiVersion: apps/v1
2kind: Deployment
3metadata:
4  name: my-app
5spec:
6  replicas: 3
7  selector:
8    matchLabels:
9      app: my-app
10  template:
11    metadata:
12      labels:
13        app: my-app
14    spec:
15      containers:
16      - name: my-app
17        image: my-app:latest
18        ports:
19        - containerPort: 3000

In this example, the replicas field is set to 3, which may not be the expected value. To fix this, we can update the replicas field to the correct value:

1apiVersion: apps/v1
2kind: Deployment
3metadata:
4  name: my-app
5spec:
6  replicas: 1
7  selector:
8    matchLabels:
9      app: my-app
10  template:
11    metadata:
12      labels:
13        app: my-app
14    spec:
15      containers:
16      - name: my-app
17        image: my-app:latest
18        ports:
19        - containerPort: 3000

By updating the replicas field, we can ensure that the correct number of replicas is deployed to Kubernetes.

Best Practices and Optimization Tips

To avoid common pitfalls and optimize the CI/CD pipeline, follow these best practices:

Use a multi-stage build to reduce the image size and improve the deployment process.
Use a fixed image tag or a tag that is generated based on the environment to ensure that the correct image is deployed.
Verify the Kubernetes configuration to ensure that the deployment is correct.
Use network policies to control traffic flow and improve security.
Monitor the pipeline to detect issues and optimize the process.

Common Pitfalls or Mistakes to Avoid

When working with CI/CD pipelines, it's essential to avoid common pitfalls, such as:

Not testing the pipeline before deploying to production.
Not monitoring the pipeline to detect issues and optimize the process.
Not using a version control system to manage changes to the pipeline.
Not using a CI/CD tool to automate the pipeline.

Conclusion

In conclusion, when the Docker image build is successful, but the deployment to Kubernetes fails, it's essential to identify the root cause and resolve the issue. By following the best practices and optimization tips outlined in this post, you can avoid common pitfalls and ensure a smooth deployment process. Remember to use a multi-stage build, a fixed image tag, and verify the Kubernetes configuration to ensure that the deployment is correct. By monitoring the pipeline and detecting issues early, you can optimize the process and improve the overall quality of your application.