We present three iterations of our video rendering engine Stitcher as development went from prototype to production and limitations of time and memory were identified. We also discuss Vidder our internal video editing tool that uses Stitcher for video rendering.

1. PRESENTED BY
Redis as Job Cache in an Auto-Scaling
Distributed Video Rendering Pipeline
Peter Karp
BuzzFeed, Staff Software Engineer

2. PRESENTED BY
Introduction
BuzzFeed creates ~100 hours of video content a week.
Many video production tasks are repetitive and tedious to do manually.
● Captions
● Adding overlay text to videos (translation, localization).
● Inserting promos, ads or slugs.
● Compilations of related short videos.
● Cut-downs to meet platform time limits.
Short-form videos created using custom editing tool - Vidder!

3. PRESENTED BY
Three iterations:
1. Prototype renderer as backend on a web server.
2. Production v.1 on microservice.
3. Production v.2 distributed multiprocessing with auto-scaling.
● Decompose rendering into smaller jobs.
● Run jobs on multiple microservers coordinated by a job cache in
Redis.
Problem: Rendering video is slow

4. PRESENTED BY
Stitcher v.2
Video Rendering Farm
Assemble video clips, images and text together into a video using a simple
json syntax, the Stitcher schema language – StitcherSL.
● Overlay (composite) text, images, video
● Join (stitch) video
● Trim, Crop, Resize, Position
● Captions - rendered between a start/end time

5. PRESENTED BY
Stitcher v.2
Tech Stack
● Python 3.6
● moviepy
● ffmpeg
● marshmallow
● Redis

6. PRESENTED BY
Stitcher v.2
Redis Job Cache Requirements
● Access to job info in a distributed microservice architecture
● Persist render jobs while jobs are running
● Long-term persistence not needed
○ Remove data after job is finished

7. How many have shot videos?

8. How many have
edited video
content?

9. Cutting physical media

10. Using complex interfaces

11. PRESENTED BY
Vidder
A Simplified Media Editing
Interface

12. PRESENTED BY
Vidder
A Simplified Media Editing
Interface

13. Stitcher schema language
Describes how to combine media into a video
● Clips
○ Video
○ Image
○ Text
● Schedules
○ Sequence
○ Parallel

14. Stitcher schema language - clips
● Video
{"clip": "video", "src": "final.mp4", "in_t": "00:00:05", "out_t": "00:00:07", "width": 1920, "height":
1080}
● Image
{"clip": "img", "src": "homesick_1920x1080_720.png", "dur": 5, "width": 1920, "height": 1080}
● Text
{"clip": "text", "txt": "The End", "dur": 5, "font": "Helvetica", "fontsize": 100, "color": "blue"}

15. Stitcher schema language - schedules
● Sequence
{"schedule": "seq", "clips": [ … ]}
● Parallel
{"schedule": "par", "clips": [ … ]}

16. Stitcher schema language - nesting
Schedules are clips too and can be nested!
{"schedule": "seq", "clips": [
{"schedule": "par", "clips": [
{"clip": "video", "src": "beer-ts.mp4", "in_t": "7.92", "out_t": "10.38"},
{"clip": "text", "txt": "Text and Video", "dur": 2.46, "fontsize": 78}
]},
{"schedule": "par", "clips": [
{"clip": "video", "src": "mars1-ts.mp4", "in_t": "0.00", "out_t": "3.33"},
{"clip": "text", "txt": "Text and Video again", "dur": 3.33, "fontsize": 78}
]}
]}

17. Stitcher schema language - nesting

18. Stitcher Architecture Evolution
1. Prototype - render in backend of a web server.
2. Production v.1 on microservice - render in a dedicated service.
3. Production v.2 distributed multiprocessing with auto-scaling.
● Decompose rendering into small tasks.
● Run jobs on auto-scaling microservice coordinated by Redis job cache.

19. Stitcher 0.1 Prototype
media_stitcher
S3
POST
https://stitcher.buzzfeed.com/media-stitcher/conform
{"schedule": "seq", "clips": [ … ]}
Stitcher System
Render engine

20. PRESENTED BY
Stitcher Prototype Demo
● Select 3 videos with search terms
in the slots.
● Select a clip from each video
using the offset + duration in the
slot.
● Use Stitcher to generate the
custom video
● Over 100K possible videos
25 search terms
~50 videos per search term
9 different offsets (10 - 90%)
9 different durations (1 - 9 seconds)
Video Slot Machine

21. Stitcher v.1
● Production ready.
● Separate servers for handling the api requests and rendering.
● Used as the video rendering engine for Vidder

22. Stitcher v.1
media_stitcher_apiVidder
qr_media_renderer
Render engine
S3
POST
https://stitcher.buzzfeed.com/media-stitcher/conform
{"schedule": "seq", "clips": [ … ]}
Stitcher System
Message Queue

23. qr_media_renderer microservice
msg.touch
Render engine
Validate input
Event handler
Message Queue
Task Complete()
Every 30
sec
multiprocessing.Pool
Cancel
msg.finish()
S3
webhook

24. PRESENTED BY
Vidder + Stitcher
Hitting some system limits

25. Stitcher v.2
● Distributed multi-server execution
● Coordination of tasks provided by a Redis Job Cache.
● Enhanced performance with auto-scaling microservice rendering.
● Larger projects are possible in Vidder
● Takes advantage of the independence between Vidder cells
○ Captions
○ Audio track

26. Media
Renderer
Cell-3
Render-1
Render-2
Render-3
Render-4
Stitcher
payload
{
Cell-1,
Cell-2,
Cell-3,
Cell-4,
}
Message queue
Redis
Job
Cach
e
Media
Renderer
Cell-1
Media
Renderer
Cell-2
Media
Renderer
Cell-4
Stitcher
S3Client Webhook
Media Stitcher
Media Stitcher
Various
Source
URLs
Media
Stitcher
Api
Stitcher v.2
1
2
4
3
5a 5b 5c 5d
67
9,
10
11
12
8

27. qr_media_renderer microservice
msg.touch
Render engine
Validate input
Event handler
Message Queue
Task Complete()
Every 30
sec
multiprocessing.Pool
Cancel
msg.finish()
S3

28. PRESENTED BY
Stitcher v.2
Auto-scaling

29. PRESENTED BY
Stitcher v.2
Redis Job Cache
1. Initialize cache for new job
○ Add unique job_id to jobs set - sadd
○ Add list of task status to status::{job_id} - rpush
2. Mark task with status (“success” or “error”)
○ Set task_id status to status::{job_id} - lset
○ Return True if all tasks set status - lrange
3. Remove job_id from jobs set - srem

30. PRESENTED BY
Stitcher v.2
Redis Job Cache
● No jobs - jobs set empty
○ {"jobs": []}
● One job in jobs set - 4 tasks “started”
○ "jobs": [{"job_id": "641049e2-4ab9-490e-b90d-fbeb46b18f0f", "size": "4", "status": ["started",
"started", "started", "started"], "started": 4}]}
● One job in jobs set - 2 tasks completed: “success”
○ "jobs": [{"job_id": "641049e2-4ab9-490e-b90d-fbeb46b18f0f", "size": "4", "status":
["started", "success", "started", "success"], "started": 2}]}

31. PRESENTED BY
Stitcher v.2
Video Rendering Farm
Stitcher v.1 vs v.2 Comparison
V.1 Rendering
Proved value of an automated
rendering pipeline
● Sequential Processing
● All cells processed on one instance
● Intermediate batching of 8-cells at a time
● 2 GB instance
● Long videos hit 15 minutes nsq timeout
V.2 Rendering
Render longer jobs faster on smaller instances
● Fast Parallel Processing
● One Vidder cell processed per instance
● No special-case handling
● 1 GB instance
● Single cells unlikely to hit limit
● Sub-divide jobs to decrease time

32. PRESENTED BY
Stitcher v.2
Increased Performance
Parallel Render Processing for
better performance
Job distributed across multiple
rendering servers

33. PRESENTED BY
Automated and assisted editing can
increase productivity in a high volume
production environment.

34. PRESENTED BY
Conclusion
Scaling video rendering tasks in a high-volume
production environment is challenging. Stitcher
makes it easier. Redis makes it faster.

35. Thank you!

36. PRESENTED BY