How to Add FFmpeg Support to AWS Lambda Using Docker: A Step-by-Step Guide

AWS Lambda provides a powerful serverless computing environment that lets developers run code without provisioning or managing servers. In many of those cases, there is also a limitation-that FFmpeg, one of the highly utilized tools to process multimedia, is not natively supported. It can be a roadblock when working with applications needing video or audio manipulation in a Lambda environment.

You’ll find only a few resources online if you search for a solution, and the guides might be outdated. In this article, I’ll walk you through a practical solution for Dockerizing your AWS Lambda function with FFmpeg support. By following along with this guide and using the below Dockerfile, you’ll easily overcome this obstacle and have your Lambda function FFmpeg-ready in no time.

Why FFmpeg isn’t supported natively in AWS Lambda?

The AWS Lambda execution environment is very optimized and stripped down; it doesn’t have a lot of tools pre-installed, one of which is FFmpeg. This makes it very tough to do anything that involves video encoding, transcoding, or audio processing. Generally speaking, AWS Lambda Layers bring a great way of customizing, but they don’t completely solve this problem when working with libraries that depend on system-level binaries, such as FFmpeg.

Solution: Dockerizing Your AWS Lambda Function

Using a custom container image is perhaps the best way to add FFmpeg to your Lambda function. Docker will help you bundle your application along with all of its dependencies, including FFmpeg in one package. Here I will give an overview of how you can set up your Docker environment to make FFmpeg work seamlessly with AWS Lambda.

Step-by-Step Guide to Dockerizing Your AWS Lambda with FFmpeg Support

Step 1: Create Your Dockerfile

The Dockerfile below outlines the steps needed to create a custom AWS Lambda image with FFmpeg support. The image is based on Amazon Linux 2023, which is compatible with AWS Lambda. It installs the necessary packages, FFmpeg, and the AWS Lambda Runtime Interface Client.

# Use the Amazon Linux 2023 base image
FROM public.ecr.aws/lambda/provided:al2023

# Install necessary system packages
RUN dnf update -y && \
    dnf install -y \
        python3 \
        python3-pip \
        gcc \
        git \
        unzip \
        xz \
        wget \
        tar \
    dnf clean all

# Install ffmpeg
RUN wget https://johnvansickle.com/ffmpeg/releases/ffmpeg-release-amd64-static.tar.xz && \
    tar -xf ffmpeg-release-amd64-static.tar.xz --strip-components=1 -C /usr/local/bin && \
    ln -sf /usr/local/bin/ffmpeg /usr/bin/ffmpeg && \
    rm -f ffmpeg-release-amd64-static.tar.xz

# Install the AWS Lambda Runtime Interface Client (RIC)
RUN pip3 install awslambdaric

# Set work directory
WORKDIR ${LAMBDA_TASK_ROOT}

# Copy the function code and requirements file into the container
COPY . ${LAMBDA_TASK_ROOT}

# Install Python dependencies
RUN pip3 install --trusted-host pypi.python.org -r requirements.txt

# Set the entrypoint to the Lambda Runtime Interface Client
ENTRYPOINT ["python3", "-m", "awslambdaric"]

# Set the CMD to the Lambda function handler
CMD ["lambda_function.lambda_handler"]

Step 2: Breaking Down the Dockerfile

1. Base Image:
We use the base image `FROM public.ecr.aws/lambda/provided:al2023` with Amazon Linux 2023 to make it compatible with AWS Lambda.

2. System Package Installation:
The `dnf install` command installs necessary packages in the system, including Python, GCC, among other useful utilities.

3. FFmpeg Installation:
We use the static build of FFmpeg from a trusted source and extract it into the `/usr/local/bin` directory. A symbolic link is created to make FFmpeg available system-wide.

4. AWS Lambda Runtime Interface Client (RIC):
The `awslambdaric` package is installed to enable the custom runtime for AWS Lambda.

5. Working Directory and File Copy:
The code for the Lambda function and its dependencies are copied into the working directory of the container. (`LAMBDA_TASK_ROOT`).

6. Python Dependencies:
Dependencies listed in `requirements.txt` are installed using `pip`.

7. Entrypoint and Command:
The `ENTRYPOINT` says to start the Lambda Runtime Interface Client, and `CMD` provides the default command to run the Lambda function handler at `lambda_function.lambda_handler`.

Step 3: Build and Push Your Docker Image

Next, build your Docker image using the following command:

docker build -t my-lambda-image .

Once the image is built, you need to push it to the Amazon Elastic Container Registry (ECR) or any other container registry supported by AWS Lambda:

# Tag the image
docker tag my-lambda-image:latest <account-id>.dkr.ecr.<region>.amazonaws.com/my-lambda-image:latest

# Login to ECR
aws ecr get-login-password --region <region> | docker login --username AWS --password-stdin <account-id>.dkr.ecr.<region>.amazonaws.com

# Push the image
docker push <account-id>.dkr.ecr.<region>.amazonaws.com/my-lambda-image:latest

Step 4: Create Your Lambda Function Using the Custom Image

1. Open the AWS Lambda console.
2. Click on the Create function and select Container image.
3. Deploy your function with the selected image from ECR.

Benefits of Using a Custom Docker Image

• Full Control: You will have total control over the environment; thus, you can include all the required libraries and binaries.
• Scalability: Utilize serverless scaling through AWS Lambda without functionality loss.
• Better Debugging: The development in the Docker environment mirrors the production setup, which makes debugging a lot easier.

Common Use Cases of FFmpeg in AWS Lambda

• Video/Audio Encoding: Transcode video/audio files into different formats
• Thumbnail Generation: On-the-fly video thumbnails for web applications
• Audio Extraction: Extract audio streams out of videos for further processing or analysis

Conclusion

Integrating FFmpeg into AWS Lambda opens a whole new dimension for its multimedia processing in serverless applications. Dockerizing your Lambda function makes it very easy to include FFmpeg and other complicated dependencies that are otherwise unsupported. The approach also solves compatibility issues and further amplifies your capability in building powerful, scalable serverless applications.