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Video on Demand (VOD) requires transcoding into multiple renditions to support adaptive bitrate streaming (HLS/DASH). Running FFmpeg directly on a single server creates bottlenecks for high workloads. Deploying FFmpeg on Kubernetes allows horizontal scaling, job queueing, and fault tolerance for large transcoding pipelines. Containerizing FFmpeg To run FFmpeg in Kubernetes, it first needs to be packaged in a container. A container image contains FFmpeg and its dependencies so that every worker pod in the cluster runs the same predictable environment. FROM ubuntu:22.04 RUN apt-get update && apt-get install -y ffmpeg ENTRYPOINT ['ffmpeg'] Job Orchestration with Kubernetes Jobs In Kubernetes, a Job represents a task that should run until it finishes. Each transcoding task, such as converting a single MP4 file into an HLS output, can be modeled as a Kubernetes Job. The Job specification tells Kubernetes how to run FFmpeg, where to write the output, and what to do if the process fails. If a job fails, Kubernetes can automatically retry it. When it completes, it is marked successful. This gives a structured way to manage many independent transcoding tasks across the cluster. apiVersion: batch/v1 kind: Job metadata: name: ffmpeg-transcode-job spec: template: spec: containers: - name: ffmpeg image: myrepo/ffmpeg:latest command: ['ffmpeg', '-i', 'input.mp4', '-c:v', 'libx264', '-b:v', '3000k', '-c:a', 'aac', '-f', 'hls', '/output/out.m3u8'] volumeMounts: - name: output mountPath: /output restartPolicy: Never volumes: - name: output persistentVolumeClaim: claimName: vod-storage-pvc backoffLimit: 4 Distributed Work Queue When multiple videos are uploaded, each needs a corresponding transcoding job. A queueing system such as RabbitMQ, Kafka, or AWS SQS distributes these tasks. Uploading a video generates a message in the queue that contains the video location and required renditions. Kubernetes workers consume messages from the queue and create Jobs. This prevents overload on any single worker and ensures that jobs are spread evenly across the cluster. Handling Multiple Renditions Adaptive bitrate streaming requires a video to be transcoded into multiple resolutions and bitrates. FFmpeg can generate all renditions in a single command or separate them into different tasks. Depending on the cluster design, one worker pod may handle the full set of renditions for a video, or renditions may be split across multiple pods. The renditions are output as HLS or DASH segments with a manifest file that lists them. Example Command : ffmpeg -i input.mp4 -map 0:v -map 0:a -c:v h264 -c:a aac -b:v:0 500k -s:v:0 426x240 -b:v:1 1000k -s:v:1 640x360 -b:v:2 2500k -s:v:2 1280x720 -f hls -hls_playlist_type vod output.m3u8 Autoscaling Transcoding Workloads Demand for transcoding can vary. At times, there may be only a few uploads, while at other times thousands of videos may need processing. Kubernetes includes an autoscaler that adjusts the number of worker pods depending on resource usage or queue size. When the workload increases, more pods are created to process jobs in parallel. When the workload drops, pods are scaled down. This keeps resource usage efficient without sacrificing throughput. Storage and CDN Integration Once transcoding finishes, the video outputs need to be accessible to end users. Intermediate results can be written to persistent volumes within Kubernetes. The final renditions are typically uploaded to object storage such as Amazon S3 or Google Cloud Storage. From there, a Content Delivery Network distributes the segments and manifests so they are cached at edge locations for low-latency playback. Monitoring and Fault Tolerance A scalable system must be observable and resilient to failure. Monitoring tools such as Prometheus and Grafana track metrics, including how many jobs are running, how long they take, and whether pods are failing. Logs collected from pods allow debugging of FFmpeg errors. Kubernetes itself provides retry policies and backoff mechanisms so that failed jobs are reattempted automatically within defined limits.
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