FlightTracker: Social Graph Consistency at Scale | Xiao Shi and Scott Pruett

Facebook delivers fresh personalized content by performing a large number of reads to our online data store TAO. TAO is read optimized and handles billions of queries per second. To achieve high efficiency, organizational scalability, and optimize for different workloads, Facebook has traditionally relied on asynchronous replication which presents consistency challenges: application developers must handle or build guardrails against potentially stale reads. Historically, TAO alleviated this burden by providing read-your-writes (RYW) consistency by default using write-through caching. This strategy fell short as our social graph ecosystem expanded to include global secondary indexing as well as other backend data stores.
We built FlightTracker to manage consistency for the social graph at Facebook. By decomposing the consistency problem, FlightTracker provides uniform semantics across heterogenous data stores. FlightTracker allowed us to evolve beyond write-through caching and fixed communication patterns for higher reliability for TAO. FlightTracker maintains the efficiency, latency, and availability advantages of async replication, preserves the decoupling of our systems, and achieves organization scalability. Through FlightTracker, we provide user-centric RYW as a baseline while allowing select use cases such as realtime pub-sub systems like GraphQL Subscriptions to obtain higher levels of consistency.
This talk will introduce how FlightTracker provides RYW consistency at scale and focus on how FlightTracker works for global secondary indexing systems. For more details, please refer to our blog post or our paper in OSDI’20.
Read more in Scott and Xiao's blog post, FlightTracker: Social Graph Consistency at Scale at atscaleconfere...