1. Introduction
Hi, in this tutorial we are going to create a project that allow us to upload a video using a endpoint HTTP and then convert it to another resolution format using another service , think of this as a process that a video platform like youtube o vimeo does when an user upload a video on their servers, in order to accomplish that we are going to use the following AWS services:
S3 Simple Storage Service:
We will use this service to store the videos in the original format and also in a compresses format, we are going to create two different buckets for each purpose.
Elastic Container Registry:
This service is for store our docker container images that will be use on ECS and lambda services.
ECS Elastic Container Service:
We will deploy our API http using ECS,this allows us to run our application in a scalable way without going into the complexity of managing the infrastructure manually, we will use containers to package our API service,
Lambda Serverless functions:
We will use this service to trigger a event/function when a video in the original format is uploaded to a bucket, this will obtain the video and convert it to another resolution ( using ffmpeg ) after save it on a different output bucket.
AFTER CREATED AND USE/TEST THE SERVICES, BE SURE TO DELETE THAT SERVICES FROM THE AWS DASHBOARD IN ORDER TO AVOID UNWANTED CHARGES.
- Introduction
- Create user on AWS IAM
- Create S3 buckets
- Create API service
- Create ECR container image repository
- Create ECS Elastic Container Service
- Update API service
- Create lambda container
- Create lambda service
- Conclusion
Diagram architecture
Requirements
- AWS account - https://aws.amazon.com/
- Docker - https://docs.docker.com/engine/install/
- Go - https://go.dev/doc/install
- AWS CLI - https://aws.amazon.com/cli/
We need to create an account in AWS in order to follow this tutorial,
Our services, API will use golang, so we need that on our machine.
We use docker to create our images, that images will be upload to ECR, and run on ECS and Lambda services.
Besides we will create most of the AWS resources using the web dashboard, we must install the AWS cli to upload the docker image.
Create user on AWS IAM
We need to create an user for this tutorial in AWS, this user will have an ACCESS KEY and a SECRET KEY that we will need later to connect to the AWS services that required our project, also we should limit the permissions and acces to this user to the specifics services that we need, this is very important for security reasons and for prevent any unwanted action on our account.
On the search box of AWS dashboard search for IAM and click on the first option that appears.
On IAM section go to.
Sidebar -> Access management -> Users -> Add user
On name put a name "aws-tutorial" or anything you want is not important at this point, after click on Next.
We now need to add the permissions that this user will have, we are going to add manually this settings, so check that option on the dasboard.
We need to add the following permissions policies:
AmazonS3FullAccess
This is required for our API services to upload files on S3 bucket.
AmazonEC2ContainerRegistryFullAccess
This is required to upload our docker images to the ECR service
Use the search box and select both policies, after that click on next.
Create acces key
After succesfuly create the user, enter on detail section of the user previously created.
Go to
Security credentials -> Create access key
On use cases, "SELECT OTHER"( AWS list other options and alternatives that we do not care at this moment ), click on Next, you can put a description if you
want, after that click on Create access key.
After that you see the access key and private key on the dashboard, copy that on some secure place of your own, this is because is the last time that AWS shows your private key, we need those values when we create our API and lambda services.
Create S3 buckets
Our project needs two buckets, one for the original video upload and another for the compress video files, for that go to the AWS dashboard and search for S3, click on the first option that appears.
Click on create bucket, a new form configuration appears, most of the options should work fine on default values, but we need to change the following options:
AWS Region: You can select any region you want, in this tutorial we are going to use "us-east-2 Oregon" region.
Bucket name: this must be a unique name, for this tutorial choose something that has the input name on it, for example "bucket-input", this semantic helps later when we create a trigger in the lambda server configuration.
We mantain the select option for "block all public access", we are going to access the bucket only by ours services, so we do not need to make it public.
After that click on create bucket.
For the second bucket who is the recipe for the compress videos, repeat the same steps as before, only remember to change the name for something like "bucket-output".
Create API service
For this tutorial we use golang to develop our API, we will create two endpoints, one that is in charge to received a file from a client and later save it on the bucket-input and another to show the API version text.
First a new project folder on your machine, name it ecs-tutorial, inside that folder run the following command.
go mod init ecs-tutorial
Install dependencies
go get github.com/aws/aws-sdk-go@v1.50.35
go get github.com/joho/godotenv@v1.5.1
go get github.com/go-chi/chi
aws-sdk: This is the AWS sdk that allow us to connect to different AWS services, in this case we are going to connect to S3 storage.
godotenv: This library allow us to use environment file (.env), we are going to use it for store our AWS credentials, version, port etc.
chi: This is a lightweight router HTTP, we are going to use it for create our API endpoints.
Start creating a .env file with this content.
AWS_S3_BUCKET=your-bucket-name
AWS_ACCESS_KEY=your-access-key
AWS_SECRET__KEY=your-secret-key
AWS_REGION=your-region
PORT=80
Replaces the values with your own, the bucket the we use in this service is the "input" bucket that we created before.
Create a main.go file with the following content.
package main
import (
"log"
"net/http"
"os"
"github.com/go-chi/chi"
"github.com/go-chi/chi/middleware"
"github.com/joho/godotenv"
)
func uploadFileHandler(w http.ResponseWriter, r *http.Request) {
w.Write([]byte("upload"))
}
func main() {
err := godotenv.Load()
if err != nil {
panic("Error loading .env file")
}
r := chi.NewRouter()
r.Use(middleware.Logger)
r.Get("/", func(w http.ResponseWriter, r *http.Request) {
w.Write([]byte("version 0.0.1"))
})
r.Post("/upload", uploadFileHandler)
http.ListenAndServe(":"+os.Getenv("PORT"), r)
}
In the main function we first obtain the environment variables defined in the .env file, if that fails panic and stop the process,
after create a chi router object, we use the middleware logger for log server request info, that is helpful it on the terminal, lastly create two endpoints, one for the root path that show the version of the application, and another for the upload file,
at the moment funcion uploadFileHandler is return a hardcoded string "upload", later we will add the logic to save the file on the S3 bucket.
Create ECR container image repository
Create docker image
Before upload the image, we must create a docker image of our application, for that matter create a Dockerfile file on the ecs folder.
Dockerfile
# syntax=docker/dockerfile:1
# Build the application from source
FROM golang:1.22 AS build-stage
WORKDIR /app
COPY go.mod go.sum ./
RUN go mod download
COPY *.go ./
RUN CGO_ENABLED=0 GOOS=linux go build -o /ecs-api
FROM gcr.io/distroless/base-debian11 AS build-release-stage
WORKDIR /
COPY --from=build-stage /ecs-api /ecs-api
COPY .env ./
EXPOSE 80
ENTRYPOINT ["/ecs-api"]
We use the multi build docker buildkit feature in this definition,
first we create a image call build-stage, in this setup we use the official golang 1.21 version, next copy the go.mod and go.sum files and all the files .go that we have in our project, finally create the binary of our application using the go build command.
The second image is the release image, we use a distroless base image, this is a minimal image that only contains the necessary components to run our application, this has the advantege to be more secure and lightweight, next copy the binary create on the previous step, the .env file, and expose the port 80 ( this is the value the we have to define on the .env file ), the ENTRYPOINT value is the command that executes our binary.
Create image
docker build -t ecs-api .
Run the API server on background and expose service on port 8080
docker run -d -p 8080:80 ecs-api
Test service using curl
curl localhost:8080
That sould return the version of the API server.
Create ECR repository
On the AWS dashboard search for ECR and click on the first option that appears on the results,
select private access for the image and choose any name, for other options use the default values and click on Create Repository
We need to install the AWS cli on our machine, this is necessary to authenticate to our AWS account and upload the image on the registry.
So follow instructions on this page according to your OS.
https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html
Upload image to image registry
Go to the details of the repository that we created before, click on button "View push commands", this shows the commands that we need to execute on our machine to authenticate and upload our image in the registry.
You should see something like this.
aws ecr get-login-password --region us-west-2 | docker login --username AWS --password-stdin 99999.dkr.ecr.us-west-2.amazonaws.com
docker build -t ecs-api .
docker tag ecs-api yourawsuri:v0.0.1
docker push yourawsuri:v0.0.1
After a successful push, we should see the image in the ECR aws dashboard, copy the value of the image URI , we need this when we create a ECS service, so copy that value on some place.
ECS - Create Elastic Container Service
ECS, or Amazon Elastic Container Service, is a fully managed container orchestration service provided by AWS. It allows developers to easily run, stop, and manage containers on a cluster. This service abstracts away the complexity of managing the underlying infrastructure, such as provisioning and scaling EC2 instances.
Go to the ECS section on AWS dashboard using the search box, click on Create Cluster, select any name you want, for example something like "ecs-tutorial",
in the Infrastructure section select the option AWS Fargate, this is a serveless feature that allow us not worry about the overhead of the maintance of the machines, mantain the other options with the default values and click on "Create".
Create task definition
We need to create a Task definition, this allow us to create a configuration for our Docker containers, such as CPU, memory, networking , image URI, etc.
Before that we must generate a "Task execution role", for that matter go to
IAM -> Access management -> Roles
Select AWS service as en entity type, in the use case select "Elasctic Container Service" -> Elastic Container Service Task click on Next.
In the permissions policies search box, type or copy "AmazonECSTaskExecutionRolePolicy", select that policy and click on Next.
This permission is required by our ECS cluster to create and have access to other AWS services.
Enter a role name and click on Create role, remember that value , because we will need that on next section.
On the ECS dashboard, click on Task Definitions sidebar, after go to Create new Task Definition,
This is a very long form, for most of the options we use the default value, but we need to change the following:
Task definition family: Put a name for the task definition, for example "aws-tutorial-task"
Infrastructure requirements: On Launch type select AWS Fargate Serverless,
Operating system/Architecture: Select Linux/x86_64, this is the architecture of our docker image.
Task size: Because this is a tutorial we are going to use the minimum values, so put 1GB for memory and 0.5 vCPU, change this values if you need more resources for your application.
Task execution role: Select the role that we created before, this is required for pulls them image for ECR, save logs to CloudWatch and other actions that required ECS to work properly.
Container details
Image URI: Select the URI of the image the we upload to ECR before.
Port mappings: Check this specific values.
port: 80
Protocol: TCP
App protocol: HTTP
Leaves the other containers configurations with the default values.
Storage, Monitoring and Tags are optional, so we can skip that sections and click on Create
Create service ECS
On ECS dashboard, select the cluster the we created before, go on service tabs and click on "Create" button.
Environment configuration:
Application type: Choose Service option, this is a best fit for our type of API long running application.
Task definition: On Family select the Task definition that we created in the previous step,
revisiion should selecte the last version of our task,
Service Name: Put a name for the service, for example "aws-tutorial-service"
Service type; Choose replica ,this allow us to run multiple copies of our application, and ECS will manage the load balancing and scaling for us.
Networking
On this section leave the VPC with the default values
Security group: Our services run on HTTP port 80, for that reasons we must create a new config with and onbound rule.
Make sure that public IP on turned on, this is because we are not going to use a Load Balancer in this tutorial, so we need to access our service using the public IP of the service.
Leave other options with default values and click on "Create"
Wait a moment and go to the cluster section, click on the Task tab service and go to the task attached to the service.
On the task detail, look up for the Public IP , you can test using curl
curl http://yourip/
That should return the api version response of our API service.
Update API service
In order to upload a file to a S3 bucket we need to update our handler uploadFileHandler with the following code.
import (
"bytes"
"context"
"fmt"
"io"
"log"
"net/http"
"os"
"time"
"github.com/aws/aws-sdk-go/aws"
"github.com/aws/aws-sdk-go/aws/awserr"
"github.com/aws/aws-sdk-go/aws/credentials"
"github.com/aws/aws-sdk-go/aws/request"
"github.com/aws/aws-sdk-go/aws/session"
"github.com/aws/aws-sdk-go/service/s3"
"github.com/go-chi/chi"
"github.com/go-chi/chi/middleware"
"github.com/joho/godotenv"
)
func uploadFileHandler(w http.ResponseWriter, r *http.Request) {
// 10MB maximum file size
err := r.ParseMultipartForm(10 << 20)
if err != nil {
http.Error(w, "Failed to parse multipart form", http.StatusBadRequest)
return
}
file, fileHeader, err := r.FormFile("file")
if err != nil {
http.Error(w, "Failed to retrieve file from form data", http.StatusBadRequest)
return
}
defer file.Close()
fileBytes := bytes.Buffer{}
_, err = io.Copy(&fileBytes, file)
if err != nil {
http.Error(w, "Failed to read file content", http.StatusInternalServerError)
return
}
err = uploadToS3(fileHeader.Filename, bytes.NewReader(fileBytes.Bytes()))
if err != nil {
http.Error(w, "Failed to upload file to S3", http.StatusInternalServerError)
return
}
w.WriteHeader(http.StatusOK)
w.Write([]byte("File uploaded successfully"))
}
func uploadToS3(name string, fileContent io.ReadSeeker) error {
bucket := os.Getenv("AWS_S3_BUCKET")
key := os.Getenv("AWS_ACCESS_KEY")
secret := os.Getenv("AWS_SECRET_KEY")
timeout := 1000 * time.Second
sess := session.Must(session.NewSession(&aws.Config{
Region: aws.String(os.Getenv("AWS_REGION")),
Credentials: credentials.NewStaticCredentials(key, secret, ""),
}))
svc := s3.New(sess)
ctx := context.Background()
var cancelFn func()
if timeout > 0 {
ctx, cancelFn = context.WithTimeout(ctx, timeout)
}
if cancelFn != nil {
defer cancelFn()
}
_, err := svc.PutObjectWithContext(ctx, &s3.PutObjectInput{
Bucket: aws.String(bucket),
Body: fileContent,
Key: aws.String(name),
ContentDisposition: aws.String("attachment"),
})
if err != nil {
if aerr, ok := err.(awserr.Error); ok && aerr.Code() == request.CanceledErrorCode {
fmt.Fprintf(os.Stderr, "upload canceled due to timeout, %v\n", err)
return err
} else {
fmt.Fprintf(os.Stderr, "failed to upload object, %v\n", err)
return err
}
}
return nil
}
On the handler code, we first set a file size limit of 10MB , after we check if the value "form-data" exists on the request and response a specific message if an error exists.
Next we copy the value of file content on a variable on type Buffer bytes, check for errors and if everything is ok call a function named
uploadToS3, this function recieve two parameters.
name: this is the name of the file that the client upload.
fileContent: this is a file object content that we are going to upload to S3, we need to convert the bytes to a Reader type in order to implement the io.ReadSeeker interface required by the AWS SDK.
We create a new AWS session using the AWS_ACCESS_KEY and the AWS_SECRET_KEY environment variables, next create a context with a timeout of 60 seconds that we use when we call the PutObjectWithContext method, this receives a bucket name, file content and the name of file, after that we check if the operation fails and returns and error value or nil if everything is ok.
Back in uploadFileHandler we check for the response for the uploadToS3 function, if an error exists we response a 500 status code and a message, otherwise we response a 200 status code and a message that the file was uploaded successfully.
Upload new image to ECR
docker build -t ecs-api .
docker tag ecs-api youtawsuri:v0.0.2
docker push youtawsuri:v0.0.2
Update ECS Task Definition
Back in the ECS dashboard, we should create a new task definition with the new image version, for that matter go to Task Definition -> Create New Revision, search the container config and change the value of the Image URI with the new image version.
After that, go to Cluster -> Service -> Update Service.
On this section, make sure that the revision value is the latest task definition we create before and click on Update,
after a few seconds we should see the new version deployed.
Check on the task detail the public IP and test using curl
curl -X POST -F "file=@./yourimage.png" http://yourip/upload
If this works correctly, you can see the file upload on the S3 input-bucket aws dasboard.
Create lambda container
We are going to create a container that will be running on a lambda function in AWS, this code will be called when we upload a video file on the input aws bucket.
Create a new folder call lambda on your machine, go to that folder and run this command.
Init project
go mod init lambda-tutorial
Install dependencies
go get github.com/aws/aws-lambda-go@v1.46.0
go get github.com/aws/aws-sdk-go@v1.50.35
go get github.com/joho/godotenv@v1.5.1
Create a main.go file with the following content.
package main
import (
"context"
"github.com/aws/aws-lambda-go/events"
"github.com/aws/aws-lambda-go/lambda"
)
func handler(ctx context.Context, event events.APIGatewayProxyRequest) (events.APIGatewayProxyResponse, error) {
response := events.APIGatewayProxyResponse{
StatusCode: 200,
Body: "Hello lambda",
}
return response, nil
}
func main() {
lambda.Start(handler)
}
On main function we call the Star method of the lambda sdk, this method requires a function argument call "handler" , it will be executed when the lambda function is invoked, this function uses a method of the AWS sdk called APIGatewayProxyResponse to create a response using a status code of 200 and a text response "Hello lambda".
Next, create a Dockerfile with the following content.
FROM golang:1.22 as build
WORKDIR /lambda
COPY go.mod go.sum ./
COPY main.go .
RUN go build -tags lambda.norpc -o main main.go
FROM public.ecr.aws/lambda/provided:al2023
COPY --from=build /lambda/main ./main
ENTRYPOINT [ "./main" ]
We use a multi-stage approach, first we define a build stage using a golang:1.22 image, copy the required files for the service go.mod, go.sum, main.go and build the binary of our lambda function, in the release stage we use an AWS official lambda image, this is required to work in the context of a lambda function service, copy the binary from the build stage en set the entrypoint command executing the binary.
Build container image
docker build -t lambda-tutorial .
Create container to test
docker run -d -p 9000:8080 \
--entrypoint /usr/local/bin/aws-lambda-rie \
lambda-tutorial ./main
Test using curl
curl -XPOST "http://localhost:9000/2015-03-31/functions/function/invocations" -d '{}'
The sould return this
{"statusCode":200,"headers":null,"multiValueHeaders":null,"body":"Hello lambda"}
Upload image to ECR
On the ECR AWS dashboard, create a new private repository called "lambda-tutorial" or something similar.
Build and upload the image to the registry, check view push commands on detail image.
docker build -t lambda-tutorial:v0.0.1 .
docker tag lambda-tutorial:v0.0.1 yourURI/lambda-tutorial:v0.0.1
docker push lambda-tutorial:v0.0.1 yourURI/lambda-tutorial:v0.0.1
Create lambda service
AWS Lambda is a serverless computing service that allows you to run code without provisioning or managing servers. It enables you to execute your code in response to various events, such as changes to data in S3 buckets, updates to DynamoDB tables, HTTP requests via API Gateway, or custom events generated by your applications.
Create policy
Go to IAM -> Access management -> Policies -> Create policy
On policiy editor section select JSON and paste this code
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"logs:PutLogEvents",
"logs:CreateLogGroup",
"logs:CreateLogStream"
],
"Resource": "arn:aws:logs:*:*:*"
},
{
"Effect": "Allow",
"Action": [
"s3:GetObject"
],
"Resource": "arn:aws:s3:::*/*"
}
]
}
This definition allow the lambda function to access to the Watch logs and S3 AWS services require by our lambda application.
Click on next, put a name like "lambda-tutorial-policy" and press on Create policy button.
Create role
Go to IAM -> Access management -> Roles -> Create role, select AWS service as entity type, in the use case select Lambda and click on next, on the permissions section search the policy that we created before "lambda-tutorial-policy" select it and click on next, in role detail select a name for the role, for example "lambda-tutorial-role" finally click on Create role.
Create lambda function
On the AWS dashboard go to the lambda section, make sure to select the same region that we used in the S3 bucked setup (in our case "us-west-2 - Oregon"), this is neccesary to make our trigger configuration works properly.
Select container image, put any function name, in the image URI you can type the value by hand or click in the "browse images" to select the version image ECR.
On Change default execution role, select Use an existing role and choose the execution role that we created before "lambda-tutorial-role".
Click on create, wait a moment until AWS finish the creation of the lambda ( you should see a message ), in order to check our container application , go to Test section and click on "Test" button, this should return the response "Hello lambda" that we define on the lambda function handler.
Add trigger s3
We need to configure our lambda to execute when a file is upload to a S3 bucket, for that reason go to the lambda detail and click on Add Trigger,
On Trigger configuration, select S3 as a trigger type, in the bucket select the S3 created on the first section of this tutorial, be sure to select the input bucket type, leave default values on the other options and click on Add.
Update dockerfile
We need to install the ffmepg in our image configuration , this is require to compress the video uploaded in the input-bucket, for that matter update Dockerfile with this content.
FROM golang:1.22 as build
WORKDIR /helloworld
COPY go.mod go.sum ./
COPY main.go .
RUN go build -tags lambda.norpc -o main main.go
FROM public.ecr.aws/lambda/provided:al2023
RUN dnf -y install wget && dnf -y install tar && dnf -y install xz && \
cd /usr/local/bin && \
mkdir ffmpeg && \
cd ffmpeg && \
wget https://www.johnvansickle.com/ffmpeg/old-releases/ffmpeg-4.2.1-amd64-static.tar.xz && \
tar xvf ffmpeg-4.2.1-amd64-static.tar.xz && \
ln -s /usr/local/bin/ffmpeg/ffmpeg-4.2.1-amd64-static/ffmpeg /usr/bin/ffmpeg
COPY .env .env
COPY --from=build /helloworld/main ./main
ENTRYPOINT [ "./main" ]
On the lambda image, we add a new RUN command, that will install some dependencies.
wget: required to download ffmpeg binaray file.
tar, xz: required to extract the file.
After installed those dependencies, we download the ffmpeg binary file using wget, extract the file and create a symbolic link to the /usr/bin/ffmpeg path, this is required in order to call the ffmpeg from our golang code.
Add a .env file with the definition of the required environment variables
AWS_S3_BUCKET=your-output-bucket
AWS_ACCESS_KEY=your-access-key
AWS_SECRET__KEY=your-secret-key
AWS_REGION=your-region
Update lambda/main.go
func convertFile(inputFile string, outputFile string) error {
out, err := exec.Command("ffmpeg", "-i", inputFile, "-vf", "scale=640:480", outputFile).Output()
if err != nil {
fmt.Printf("ffmpeg command error: %s", err)
return err
}
fmt.Println("Command Successfully Executed")
return nil
}
In this function we received as parameters the path string of the input and output video files that we use in a ffmepg call, this command compress the original video and save on the outputFile path, this is a very basic command that we use for this tutorial, ffmpeg has a lot of options and configurations for different resolutions and formats, you can check this link for more information.
Finally check for an error command and return that value of nil is everything is ok.
https://img.ly/blog/ultimate-guide-to-ffmpeg/#video-properties
func handlerTriggerS3Bucket(ctx context.Context, s3Event events.S3Event) error {
err := godotenv.Load()
if err != nil {
log.Fatal("Error loading .env file")
}
bucketOutput := os.Getenv("AWS_S3_BUCKET")
key := os.Getenv("AWS_ACCESS_KEY")
secret := os.Getenv("AWS_SECRET_KEY")
region := os.Getenv("AWS_REGION")
timeout := 1000 * time.Second
sess := session.Must(session.NewSession(&aws.Config{
Region: aws.String(region),
Credentials: credentials.NewStaticCredentials(key, secret, ""),
}))
svc := s3.New(sess)
var cancelFn func()
if timeout > 0 {
ctx, cancelFn = context.WithTimeout(ctx, timeout)
}
if cancelFn != nil {
defer cancelFn()
}
for _, record := range s3Event.Records {
bucket := record.S3.Bucket.Name
key := record.S3.Object.URLDecodedKey
obj, err := svc.GetObject(&s3.GetObjectInput{
Bucket: aws.String(bucket),
Key: aws.String(key),
})
if err != nil {
log.Printf("error getting head of object %s/%s: %s", bucket, key, err)
return err
}
headOutput, err := svc.HeadObject(&s3.HeadObjectInput{
Bucket: &bucket,
Key: &key,
})
if err != nil {
log.Printf("error getting head of object %s/%s: %s", bucket, key, err)
return err
}
log.Printf("successfully retrieved %s/%s of type %s", bucket, key, *headOutput.ContentType)
var buf bytes.Buffer
if _, err := io.Copy(&buf, obj.Body); err != nil {
fmt.Fprintln(os.Stderr, "Error reading file:", err)
return err
}
fmt.Println("File size (bytes):", buf.Len())
inputFile := "/tmp/input-" + key
outputFile := "/tmp/output-" + key
err = os.WriteFile(inputFile, buf.Bytes(), 0644)
if err != nil {
fmt.Fprintln(os.Stderr, err)
return err
}
err = convertFile(inputFile, outputFile)
if err != nil {
fmt.Fprintln(os.Stderr, err)
return err
}
fileToUpload, err := os.ReadFile(outputFile)
if err != nil {
fmt.Fprintln(os.Stderr, err)
return err
}
_, err = svc.PutObjectWithContext(ctx, &s3.PutObjectInput{
Bucket: aws.String(bucketOutput),
Body: bytes.NewReader(fileToUpload),
Key: aws.String(key),
ContentDisposition: aws.String("attachment"),
})
if err != nil {
if aerr, ok := err.(awserr.Error); ok && aerr.Code() == request.CanceledErrorCode {
fmt.Fprintf(os.Stderr, "upload canceled due to timeout, %v\n", err)
return err
} else {
fmt.Fprintf(os.Stderr, "failed to upload object, %v\n", err)
return err
}
}
}
return nil
}
func main() {
lambda.Start(handlerTriggerS3Bucket)
}
handlerTriggerS3Bucket is the function that will be called when a new file is upload in the input-bucket, first we created some helper variables that access to the values defined in the .env file, important thing, make sure that the value of AWS_S3_BUCKET is the correct output-bucket, to prevent a infinity loop lambda call in our AWS services.
After create a new session using the AWS sdk, this is the same code as the ECS container the we did in the previous section,
next we obtain the value of the record/file using the events.S3EventRecord type, call the GetObject method in order to retreived the object from S3.
Next call HeadObject method to get metadata of the object, this is only for debug purposes, we will see that value later in the CloudWatch logs.
We need to create a buffer variable to copy the value of the object S3 object, if no error happens on that call, we create and save a new file on the /tmp folder, next call the converFile function created previously that runs the ffmpeg command, if no error ocurred we read the output file that is created also in the /tmp folder and uploads the video compress in the output-bucket.
On main function we update the function trigger that is called on the lambda.Start method.
Build and upload the image to the registry, check the commands on the AWS dasboard ECR.
docker build -t lambda-tutorial:v0.0.2 .
docker tag lambda-tutorial:v0.0.1 yourURI/lambda-tutorial:v0.0.2
docker push yourURI/lambda-tutorial:v0.0.2
Update lambda image
Go to the AWS lambda and look up for the update image button
On Container Image setting -> Browse images, look for the lambda image and select the latest version, in this case v0.0.2, click on "Save", wait for a few seconds and we should see a success message.
Before test, we must to update the configuration of our lambda function, go to Configuration -> General configuration -> Edit.
Change the memory value to 256 MB and the timeout to 1 minute, this is required because the ffmpeg command is a complex operation and requires more resources than the default values, keep in mind that depending of the operation and size of the video file you should change this values accordingly.
Click on Save and wait a few seconds for the changes to take effect.
Test
We are going to use a video sample from this page
https://file-examples.com/index.php/sample-video-files/sample-mp4-files/
Select the 640x360 - size 3MB file , that should work fine with our configuration.
We can test using our API ECS service using curl
curl -X POST -F "file=@./yourvideo.mp4" http://yourip/upload
You can also use the S3 dasboard and upload directly in the input-bucket
S3 -> your-bucket -> Upload
After upload the video, the lambda function should be triggered, compress and upload the video in the output-bucket, you can check the CloudWatch logs for more information about the process.
Go to
Lambda -> your-lambda-function -> Monitor -> View logs in CloudWatch
Check logs
You also should see a new compress video file on your output-bucket, you can download and check the resolution and size of the file.
Conclusion
In this tutorial we learned how to use different AWS services to create a scalable and serverless application, we
create a API service using ECS, a lambda function that compress a video files, and a S3 bucket that store the original and compress video files.
Make sure to delete all the resources that we created in this tutorial.
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