Bladerunner: Stream Processing at Scale for a Live View of Backend Data Mutations at the Edge

https://dl.acm.org/doi/pdf/10.1145/3477132.3483572

Problem statement

• Users of Facebook services access the social graph from around the world

• Challenges on data liveness

  • The social graph is constantly mutating and providing every one of our end users with fresh and up-to-date data is challenging

  • Challenges

    • Mutability and relevance

      • Parts of the social graph can mutate at a high rate and some contents are time sensitive

    • Rapid changing foci

      • Users change the focus of their interest frequently

    • Legacy substrate

      • Client devices have limited processing power and storage capacity

• Polling solution

  • Client device issuing initial query when needed and then periodically polling server for updates.

    • Wastes network bandwidth and client battery

    • Leads to many costly queries to server

      • Range queries, intersection queries that are costly

    • Polling interval introduces extra latency

• Bladerunner architecture

Query data and Establish subscription

Issue mutation and trigger publish

Event fanout and data delivery

• Deep Dive - Pylon

  • Simple pub/sub system that delivers published messages to BRASSes across regions

  • 
• Deep Dive - BRASS

  • responsible for application-specific stream processing

  • 
• Deep Dive - BURST

  • application-layer protocol developed to support long running streams between client devices and BRASSes

  • 
• Deep Dive - Failure Handling and Delivery Guarantee

  • BRASS and BURST are designed to provide flexibility in choosing different levels of delivery guarantees, while most applications on Bladernner today only require best effort delivery

• Use case

  • Comments for live videos

• Production experience - live video comments

  • Huge efficiency wins after switching from polling to Bladerunner, besides reduced latency in comment delivery

• Other learnings

  • Explicitly specify liveness requirement for product services

  • Ranking?

    • Polling: batchify

    • Bladerunner: wait between latest items v.s. more engaging items in product experience