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• Running ffmpeg in Docker environment on AWS EC2 [duplicate]

  5 mai 2024, par must

  I want to use FFMPEG inside my Java application.
I want to instal ffmpeg in environment where this app is running.

  


  My current Dockerfile :

  


  # Stage 1: Build the application
FROM maven:3.8.4-openjdk-17 AS build
WORKDIR /app
COPY pom.xml .
COPY src ./src
RUN mvn clean install -Dmaven.test.skip=true

# Stage 2: Run the application
FROM openjdk:17
WORKDIR /app
COPY --from=build /app/target/application.jar ./app.jar
EXPOSE 8080
CMD ["java", "-jar", "-Dspring.profiles.active=pr", "app.jar"]


  


  I simply built a docker image by running docker buildx build --platform linux/amd64 -t repo/app:1.0 .

  


  Then in AWS Console I run docker run and pull built image.

  


  Everyone writes about adding

  


  RUN apt-get -y update && apt-get -y upgrade && apt-get install -y --no-install-recommends ffmpeg


  


  but I cannot do it as I'm building image on MacOS and I do no have apt-get command.

  


  When I was trying to pull some image form DockerHub FFMPEG could not be found.

  


  I tried this one : https://hub.docker.com/r/jrottenberg/ffmpeg

  


  and declared DockerFile as :

  


  # Stage 1: Build the application
FROM maven:3.8.4-openjdk-17 AS build
WORKDIR /app
COPY pom.xml .
COPY src ./src
RUN mvn clean install -Dmaven.test.skip=true

# Stage 2: Install ffmpeg
FROM jrottenberg/ffmpeg:latest AS ffmpeg

# Stage 3: Run the application
FROM openjdk:17
WORKDIR /app
COPY --from=build /app/target/application.jar ./app.jar
COPY --from=ffmpeg /usr/local/bin/ffmpeg /usr/local/bin/ffmpeg

EXPOSE 8080
CMD ["java", "-jar", "-Dspring.profiles.active=pr", "app.jar"]


  


  but still it did not work. I get :

  


  ffmpeg: error while loading shared libraries: libavdevice.so.58: cannot open shared object file: No such file or directory


  


  I'm using Amazon Linux on EC2.

  


  Can someone get me on the right track ?

  


• Why does fluent-ffmpeg only work when it throws the error Output stream closed

  29 mars 2024, par volume one

  I am using fluent-ffmpeg to process a video file (and then upload that to Amazon S3). The code is very straightforward but it only works if :

  


  

  • pipe option {end: true} is set in .output()

  


  • which has a side-effect that causes the following console log output

  


  


  


  Processing : 19.261847354642416% done Processing :
32.365144874807335% done Processing : 48.80978326261429% done Processing : 78.35771917058617%
Processing : 91.49377493455148% done Processing :
99.91264359125745% done An error occurred : Output stream closed

  


  


  Despite that error, it seems the file is generated correctly and it gets uploaded to Amazon S3 fine.

  


  This is the fluent-ffmpeg code :

  


  import {PassThrough} from 'node:stream';
import FFMpeg from 'fluent-ffmpeg';

let PassThroughStream = new PassThrough();

             FFMpeg('/testvideo.mp4')
                    .videoCodec('libx264')
                    .audioCodec('libmp3lame')
                    .size(`640x480`)
                    // Stream output requires manually specifying output formats
                    .format('mp4')
                    .outputOptions('-movflags dash')
                    .on('progress', function (progress) {
                        console.log('Processing: ' + progress.percent + '% done');
                    })
                    .on('error', function (err) {
                        console.log('An error occurred: ' + err.message);
                    })
                    .on('end', function () {
                        console.log('FFMpeg Processing finished!');
                    })
                    .output(PassThroughStream, {end: true})
                    .run();

   // Now upload to S3
    try {
          await s3Upload({
              AWSS3Client: 'mys3client',
              Bucket: 'publicbucket,
              ACL: "public-read",
              ContentType: 'video/mp4',
              Key: 'whoever/whatever.mp4',
              Body: PassThroughStream
           });
    } catch (error) {
                    console.log(`s3Upload error`, error)
                }


  


  If I set the pipe output() option to {end: false} then there is no error from fluent-ffmpeg and I get "Processing: 100% done FFMpeg Processing finished!" as the final console log.

  


  BUT the problem is that the s3Upload() does not do anything. There are no errors. Just no activity.

  


  I feel very uncomfortable letting fluent-ffmpeg end in an error even if the code itself does the job intended. It will also cause testing to fail. What could be the issue ?

  


  The command line code is : ffmpeg -i https:/xxxbucket.s3.amazonaws.com/14555/file-example.mp4 -acodec libmp3lame -vcodec libx264 -filter:v scale=w=trunc(oh*a/2)*2:h=480 -f mp4 -movflags dash pipe:1

  


• Failed to run the ffmpeg binary : The process "ffmpeg exceeded the timeout of 60 seconds

  6 janvier 2024, par Sufian The Geek

  I am using FSPOSTER wordpress plugin to share videos on instagram as a reels and story. I have installed wordpress on amazon aws ec2 t2.micro. FsPoster required FFmpeg plugin to work with the videos.

  


  Whenever I share the video, I get error after 60 seconds. There are some videos that are easily getting shared as a reel and story. I looked into the plugin code and changed every single timeout function value to 300 seconds including php.ini file. There are some libraries like symfony in the plugin, I also changed the timeout 60 sec to 300 seconds. Restarted the server and all but still getting the error.

  


  


  Failed to run the ffmpeg binary : The process "'ffmpeg' -v error -y -i '/opt/bitnami/wordpress/wp-content/uploads/2024/01/videoWater-First-18.mp4' -vf 'crop=w=1280:h=720:x=0:y=0' -metadata:s:v rotate="" -f mp4 -c:v libx264 -preset fast -crf 24 -strict -2 -c:a aac -b:a 96k '/tmp/fs_65994ac0ddf8b.mp4'" exceeded the timeout of 60 seconds.

  


  


  I am using bitnami wordpress on ec3 aws t2.micro that has 1 vcpu and 1 GB ram. I also switched to 2 vcpu and 8 gb ram but still this timeout issue.