AWS EKS Fargate in the Video Conversion Project

Project overview

One of our long term customers has raised a requirement for a simple, robust, scalable, and cost-effective video transcoding solution to complement their existing services. With the customer’s R&D team, we’ve designed and built a serverless video transcoding platform that utilizes Fargate for EKS, SQS, and Lambda to deliver this service to the end-users.

All phases of the project were delivered to the customer as IaC (Infrastructure as Code, Terraform) and Jenkins pipelines code. All infrastructure-related artifacts are managed in Git.

The video transcoding project opened a new line of business for our customers, allowing them to provide a better, faster, and cheaper service to their platform’s end user.

Solution design

Since the Lambda function has a maximum timeout of 15 minutes and the client had its own docker image for this service already, our choice fell on the EKS Fargate, a serverless solution based on Kubernetes.

Also, we have chosen the AWS EKS Fargate to implement the transcoding platform because with EKS Fargate it is possible to easily build a serverless environment that does not need to be maintained or monitored, everything is managed by the AWS cloud. By utilizing this AWS service we do not need to keep constantly running instances, Fargate instances will be connected automatically exactly when they are needed.

There are also some specifics when working with AWS EKS Fargate that we have had to take into account during this project:

  • Separate EKS NodeGroup for the operation of service pods is required;

  • Additional steps are required to use IAM roles;

  • There is no standard out of the box solution for logging;

Here is the additional list of AWS EKS Fargate limitations from the AWS website:

  • There is a maximum of 4 vCPU and 30Gb memory per pod;

  • Currently, there is no support for stateful workloads that require persistent volumes or file systems;

  • you cannot run DaemonSets, Privileged pods, or pods that use HostNetwork or HostPort;

  • the only load balancer you can use is an Application Load Balancer

In the end, our final high-level design scheme was as follows:

  1. The client calls the Gateway API and uploads the source video to the S3 Bucket

  2. The Gateway API creates a message in SQS with a parameter that contains the path to the downloaded video file.

  3. SQS is triggering a Lambda Function, which creates a Kubernetes job and creates a message in the second SQS.

  4. The application takes the message from SQS, receives the path to the file from the parameter, transcodes it, loads the already converted video file into the S3 Bucket, and removes the message from the queue.

  5. If the job result is unsuccessful or execution timeout has happened, a message from the "Running" queue enters the dead letter queue for further analysis.

Picture 1 - Solution Design Schematics

Lambda function

Python was chosen as the programming language for the Lambda function.

As we said earlier, the following tasks were assigned to our lambda function:

  • read the parameter from the SQS message that triggered it;

  • create a message in the “Running” queue with the parameter that was received from the message trigger;

  • create Kubernetes batch-job;

We planned to make a kubectl analogy from the Lambda function, for this we decided to use Kubernetes Python Client. For work with SQS we used the PIP package “boto3”.

First we need get parameter from SQS message, so we written following function:

# Get input value from SQS message

def get_input_value(event):

message = (event["Records"][0]["messageAttributes"]["input"])

key = message.get('stringValue')

return key

Where “input” is the name of a parameter created by API Gateway and the content path to the video file in S3 Bucket.

Second, we need to create a message in the “Running” queue with the same parameter.

# Create message in SQS Running

def sqs_running_create_message(inputFile):

sqs = boto3.client('sqs', region_name=awsRegion)

message = sqs.send_message(





'input': {

'StringValue': inputFile,

'DataType': 'String'




print("Sent message to Running SQS")

key = (message["MessageId"])

print("Message ID: " + key)

return key

Values “awsRegion” and “sqsRunningUrl” were stored in the Lambda environment.

After we read the parameter from the SQS message and created the message in the “Running” queue, we are ready to create a Kubernetes job.

def create_job_object(event):

jobName = "transcoder-" + randomString()

namespace = "default"

inputFile = get_input_value(event)

messageId = sqs_running_create_message(inputFile)

# Configureate Pod template container

container = client.V1Container(


image=ecrURL + ":" + ecrTag,



'name': 'SQS_QUEUE_URL',

'value': sqsRunningUrl



'name': 'AWS_REGION',

'value': awsRegion



'name': 'AWS_S3_BUCKET',

'value': s3bucket



# Create and configure a spec section

template = client.V1PodTemplateSpec(

metadata=client.V1ObjectMeta(labels={"app": "transcoder"}),

spec=client.V1PodSpec(restart_policy="Never", containers=[container]))

# Create the specification of deployment

spec = client.V1JobSpec(




# Instantiate the job object

job = client.V1Job(





print("Creating job: "+ str(jobName))

print("Image Tag: " + str(ecrTag))

print("Input: " + str(inputFile))

return job

Values “ecrURL” and “ecrTag” we stored in Lambda environment.

As you can see, we add variables to the pod environment variables “'SQS_QUEUE_URL” and “AWS_REGION” so that after successful conversion the application can remove the message from the queue, also we add the “AWS_S3_BUCKET” variable.

We tested the python script locally and it worked perfectly, but after loading the code into the Lambda function we encountered the following problem - the Python package “Kubernetes” was missing in the Lambda Python. To avoid this problem, we created a virtual environment, installed the “Kubernetes” package, and uploaded the contents of the virtual environment into the Lambda function. After that, the Python script successfully started working in the Lambda function.

And so, the Lambda function works, it reads the parameter from the SQS message, creates a message in the “Running” queue and creates a Kubernetes job, so we went on to test Kubernetes job. And we saw that the server is constantly restarting, we looked at the logs and saw that the application does not have access to the SQS, although the IAM role that the Fargate uses has an attached policy with permission to this SQS. We began to examine in more detail and noticed that the application is trying to access the address "", which is the address for receiving metadata for EC2 instances, and since we have Fargate, this did not work for us. We looked at all the possible endpoints to get metadata, there are 3 of them in all, this is for EC2 Instance, Lambda and ECS Fargate (unfortunately this does not work for EKS Fargate). We could use “AWS_ACCESS_KEY_ID” and “AWS_SECRET_ACCESS_KEY” inside the container, but it is unsafe and we do not recommend anyone to do this. We decided to use AWS OpenID Connect and Kubernetes service account.

OpenID Connect

In the AWS console, go to our EKS Cluster and copy the value of the variable "OpenID Connect provider URL"

Go to the “AIM” -> “Identity providers” and create a new provider.

  • Provider Type - “OpenID Connect”

  • Provider URL - Value from “OpenID Connect provider URL”

  • Audience - “”

And lastly, it remains to create IAM Role with the following contents in the Trust Policy:


















Where you need to replace the following:

  • AWS_REGION - your AWS Region

  • AWS_ACCOUNT_ID - your AWS Account ID


In the future, you can attach all the necessary policies to this AIM Role.

Kubernetes Service Account