Scaling the Social Graph: How Facebook's TAO Manages Distributed Reads
Explore how Facebook's TAO handles billions of social graph edges using a read-through cache and write-behind MySQL sharding to achieve sub-millisecond latency.
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Explore how Facebook's TAO handles billions of social graph edges using a read-through cache and write-behind MySQL sharding to achieve sub-millisecond latency.
Learn how to use cursor-based pagination in the Facebook Graph API to prevent data drift and duplicate records when retrieving dynamic datasets.
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Goal We want to retrieve user profile data for thousands of users in a single operation. The operation must finish in under 1 second when the service receives 200 concurrent requests. Context Under heavy load, the average round‑trip time for a single Graph API call spikes to 300 ms, causing the overall pipeline to exceed the SLA. Two documented approaches ex