Kubernetes Jobによるバッチシステムのリソース最適化 / AbemaTV DevCon 2018 TrackB Session B6
Report
AbemaTV, Inc.Follow
Oct. 13, 2018•0 likes•4,496 views
Kubernetes Jobによるバッチシステムのリソース最適化 / AbemaTV DevCon 2018 TrackB Session B6
Oct. 13, 2018•0 likes•4,496 views
Report
Technology
AbemaTVの動画トランスコードを支えるバッチシステムが抱えていた問題と、Kubernetes Jobを使った改善策についてお話します。
AbemaTV, Inc.Follow
More Related Content
What's hot
gRPC or Rest, why not both?Mohammad Murad
How happy they became with H2O/mruby and the future of HTTPIchito Nagata
Cilium - BPF & XDP for containersDocker, Inc.
BPF: Next Generation of Programmable DatapathThomas Graf
Kubernetes at Datadog ScaleDocker, Inc.
Kernel Recipes 2017 - EBPF and XDP - Eric LeblondAnne Nicolas
Similar to Kubernetes Jobによるバッチシステムのリソース最適化 / AbemaTV DevCon 2018 TrackB Session B6
Orchestrating the execution of workflows for media streaming service and even...Shuen-Huei Guan
Introduction to GstreamerRand Graham
Haproxy - zastosowaniaŁukasz Jagiełło
![[KubeCon EU 2020] containerd Deep Dive](https://cdn.slidesharecdn.com/ss_thumbnails/containerd-deep-dive-200819115038-thumbnail.jpg?width=384&height=256&fit=bounds)
[KubeCon EU 2020] containerd Deep DiveAkihiro Suda
Research on Audio and Video StreamingNyros Technologies
ARAI How to Preserve and Provide 4K Contents OnlineFIAT/IFTA
![[KubeCon EU 2020] containerd Deep Dive](https://cdn.slidesharecdn.com/ss_thumbnails/containerd-deep-dive-200819115038-thumbnail.jpg?width=2048&height=1162&fit=bounds)
More from AbemaTV, Inc.
世界の動画技術動向を見据えた AbemaTV の向かう先 / AbemaTV DevCon 2018 TrackA Final SessionAbemaTV, Inc.
「72時間ホンネテレビ」の負荷対策とその裏側 / AbemaTV DevCon 2018 TrackA Session A1AbemaTV, Inc.
Keynote：AbemaTVのエンジニア組織論と今後の技術戦略 / AbemaTV DevCon 2018 TrackA KeynoteAbemaTV, Inc.
AbemaTVの広告システムと、データサイエンスの広告事業への貢献 / AbemaTV DevCon 2018 TrackA Session A4AbemaTV, Inc.
AbemaTV における推薦システム / AbemaTV DevCon 2018 TrackB Session B7AbemaTV, Inc.
ExoPlayerで最適な視聴体験を届けるために / AbemaTV DevCon 2018 TrackB Session B4AbemaTV, Inc.
Recently uploaded
Mitigating Third-Party Risks: Best Practices for CISOs in Ensuring Robust Sec...TrustArc
ISO Survey 2022: ISO 27001 certificates (ISMS)Andrey Prozorov, CISM, CIPP/E, CDPSE. LA 27001
Navigating the FutureOnBoard
Supplier Sourcing_Cathy.pptxCatarinaTorrenuevaMa
Keynote: Two years at the British Library... and counting / Alan Danskin (Bri...CILIP MDG
Data Formats: Reading and writing JSON – XML - YAMLCSUC - Consorci de Serveis Universitaris de Catalunya
Kubernetes Jobによるバッチシステムのリソース最適化 / AbemaTV DevCon 2018 TrackB Session B6
- 1.
- 2. ! "
- 3.
- 5. ! !
- 6. 1080p! 720p! 480p" 240p"360p" 180p" HLS MPEG2-TS 1080p! 720p! 480p" 240p"360p" 180p" MPEG-DASH Fragmented MP4 # $ Thumbnail % DRM packaging
- 8. Worker 1 Worker 2 Worker 3 Queue ∠
- 9. Task1Task2Task3 Worker Pod1 Worker Pod2 Worker Pod3 Node (GCE VM Instance) Enqueue task ID Store task details Pull tasks Transcode Request Kubernetes Task1 Task2 program: XXX type: master status: inqueue program: YYY type: mbr status: inqueue ∠
- 10. Task1Task2Task3 Worker Pod1 Worker Pod2 Worker Pod3 Node (GCE VM Instance) Enqueue task ID Store task details Pull tasks Transcode Request Kubernetes Task1 Task2 program: XXX type: master status: inqueue program: YYY type: mbr status: inqueue ∠
- 11. Worker Pod 1 Worker Pod 2 Worker Pod 3 vCPUx5, Mem 8GB vCPUx5, Mem 8GB vCPUx5, Mem 8GB メタデータ生成 必要リソース小 240pトランスコード 必要リソース中 1080pトランスコード 必要リソース大 Kubernetes Node (vCPUx16, Mem 25GB)
- 15. kind: Pod spec: containers: - name: transcoder image: transcoder resources: requests: memory: “500Mi" cpu: "1" limits: memory: “8Gi" cpu: "5"
- 16. Kubernetes Worker Pod 1 use: vCPUx1, Mem “500Mi” Worker Pod 2 use: vCPUx5, Mem “8Gi” Node (vCPUx16, Mem 25GB) … Kubernetes Worker Pod 2 use: vCPUx1, Mem “500Mi” Worker Pod 1 use: vCPUx5, Mem “8Gi” Node (vCPUx16, Mem 25GB) …
- 17. Kubernetes Worker Pod 1 use: vCPUx8, Mem 10GB Worker Pod 2 use: vCPUx4, Mem 8GB Worker Pod 3 use: vCPUx4, Mem 8GB Worker Pod 4 use: vCPUx8, Mem 10GB Node (vCPUx16, Mem 32GB) Node Evict
- 18. Kubernetes Worker Pod 1 use: vCPUx8, Mem 10GB Worker Pod 2 use: vCPUx4, Mem 8GB Worker Pod 3 use: vCPUx4, Mem 8GB Worker Pod 4 use: vCPUx8, Mem 10GB Node (vCPUx16, Mem 32GB) Node Evict
- 20. High-perf Workers vCPUx4 Mem 8GB. Low-perf Workers vCPUx1 Mem 2GB. Kubernetes ex) metadata, 180p, 240p ex) 720p, 1080p Trancode ∠ ∠Transcode Request
- 21.
- 23. MBR Queue ∠ Transcode Queue ∠ DRM Queue ∠ Metadata Generator vCPUx1 Mem 2GB. MBR Transcoder vCPUx4 Mem 8GB. DRM Packager vCPUx2 Mem 4GB. Kubernetes
- 24.
- 26. Job Pod
- 27. Task1 Task2 program: XXX type: master status: complete program: YYY type: mbr status: running Create JobStore task details Kubernetes Transcode 1080p vCPUx8, Mem 10GB DRM packaging vCPUx1, Mem 1GB Transcode 240p vCPUx2, Mem 2GB Transcode 480p vCPUx4, Mem 4GB Transcode Manager
- 28. Task1 Task2 program: XXX type: master status: complete program: YYY type: mbr status: running Create JobStore task details Kubernetes Transcode 1080p vCPUx8, Mem 10GB DRM packaging vCPUx1, Mem 1GB Transcode 240p vCPUx2, Mem 2GB Transcode 480p vCPUx4, Mem 4GB Transcode Manager
- 29. ∠
- 31. ! Video Metadata " Thumbnail ! MPEG2-TS MBR ! FMP4 MBR # DRM packaging 1080p 1080p 720p 720p 480p 480p 360p 360p 240p 240p 180p 180p MPEG2-TS MBR FMP4 MBR
- 32. ! Video Metadata " Thumbnail ! MPEG2-TS MBR ! FMP4 MBR # DRM packaging 1080p 1080p 720p 720p 480p 480p 360p 360p 240p 240p 180p 180p MPEG2-TS MBR FMP4 MBR
- 33. kind: Job spec: template: spec: containers: - name: transcoder image: abema-transcoder command: [“transcoder”, …] restartPolicy: OnFailure backOffLimit: 3
- 34. ! Video Metadata " Thumbnail ! MPEG2-TS MBR ! FMP4 MBR # DRM packaging 1080p 1080p 720p 720p 480p 480p 360p 360p 240p 240p 180p 180p MPEG2-TS MBR FMP4 MBR
- 35. kind: Job spec: template: spec: containers: - name: transcoder image: abema-transcoder command: [“transcoder”, …] parallelism: 3 completions: 3
- 36. Job MBR Manager Job 1080p Job 720p Job 480p Job 360p Job 240p Job 180p
- 37. import ( “k8s.io/client-go/kubernetes" "k8s.io/client-go/pkg/watch" ) wi, err := client.BatchV1().Jobs(..).Watch(..) defer wi.Stop() for { r, _ := <- wi.ResultChan() switch r.Type { case watch.Modified: // ... case watch.Deleted: // ... case watch.Error: // … } (k8s.io/client-go/kubernetes) Clientset.BatchV1().Jobs().Watch()
- 38. https://kubernetes.io/docs/concepts/workloads/controllers/ jobs-run-to-completion/#job-patterns A single Job to create a pod which then creates other pods, acting as a sort of custom controller for those pods. This allows the most flexibility, but may be somewhat complicated to get started with and offers less integration with Kubernetes. $ %
- 39. https://kubernetes.io/docs/concepts/workloads/controllers/ jobs-run-to-completion/#job-patterns A single Job to create a pod which then creates other pods, acting as a sort of custom controller for those pods. This allows the most flexibility, but may be somewhat complicated to get started with and offers less integration with Kubernetes. $ %
- 42.
- 43. THANK YOU