Atlas Ad-Serving Server - Advertising at scale at Facebook
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Facebook's Atlas is an ad server that also allows ad buyers to measure, target, and optimize digital and mobile ads across digital (i.e., not just on Facebook). Atlas operates separately from Facebook, does not access personal information from the social network or share marketing data with Facebook.
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Atlas Ad-Serving Server - Advertising at scale at Facebook
- 2. Rebuilding Atlas – Advertising at Scale at Facebook Jason McHugh, Software Engineer | March, 2015
- 3. Outline • Ad serving technology • History of Atlas • The acquisition • Challenges • Architecture • Lessons learned
- 4. Advertising
- 5. Advertising • Advertising a huge industry with massive budgets • Digital advertising is the fastest growing advertising medium • More time spent with digital media than TV • Time spent on mobile exceeded TV Source: eMarketer, 2014; Flurry, 2014
- 6. Third Party Ad Serving
- 7. How Third Party Ad Serving Works Advertiser
- 8. How Third Party Ad Serving Works Advertiser Campaigns Click-Through URLs Atlas Creative Concepts
- 10. Serving An Ad Atlas You, me, or other random human
- 13. History of Atlas • 1997 – Atlas started life as Avenue A • 2003 – Avenue A becomes aQuantive • 2007 – aQuantive was purchased by Microsoft for $6 billion dollars • 2012 – Microsoft took a $6.2 billion dollar writedown • 2013 – Facebook acquired Atlas in April • 2014 – Atlas New publically launched at Ad Week in September
- 14. Challenges
- 15. • Ad Tech Stack • Architecture • Data Model and databases • Data flows • Deployment Challenge: Understand the System One Single DB Instance
- 16. Challenge: Huge Product • Third party ad server • Advertiser and publisher negotiations – RFP • Search management • Search optimization • Email tracking • Custom analysis and reporting • Rich media including video • Franchise Management
- 17. Challenge: No Lift and Shift • Lift and Shift • Common approach after acquisition • Take as much as exists at the time of the purchase • Move to your data centers and then evolve • Not possible here • Non-open compute hardware • Usage of Microsoft close-source technologies
- 18. Architecture • Holistic view of the logical architecture • Detail a piece of the physical architecture
- 22. Logical Architecture - Focused
- 23. Physical Architecture – Ad Delivery
- 24. Physical Architecture – Data Processing Pipeline
- 25. Scribe • Large-scale, high throughput message queue • Not lossless but guarantees are excellent and perfect for us • Decouples producers from consumers • Persistent for n days • Sharded consumption • Checkpoint streams
- 26. Physical Architecture – Data Processing Pipeline
- 27. Detailed Look – Stream Processing • Mini workflows • Scalability is hard • Message queues can be costly • Repeatable re-execution
- 34. Lessons Learned
- 35. Mistake – Minimize the www/hack tier
- 36. Mistake – Minimize the www/hack tier • Minimize the code in www tier to ensure a higher level of availability • Hack and the www tier have come a long way in 2 years • Huge improvements in availability • API changes hit two separate systems • Couldn’t leverage all the improvements and investments in hack
- 37. Mistake – Looking ahead • Lesson • Look ahead to where the industry is going • Or look to where an organization (or group) will invest efforts • Plan to meet them there
- 38. Questions?