Snitch: Putting consistency back into S3

Consistency in data storage is crucial, with classical transactional databases ensuring that once a transaction commits, changes are visible to all. However, according to the CAP theorem, maintaining consistency often sacrifices availability in network partition scenarios. Amazon's S3 prioritizes availability, adopting an eventual consistency model, which can lead to visibility delays post-commitment and potential data loss, particularly problematic when S3 is the primary data source. At Box, this issue manifests in their Analytics Infrastructure, relying on S3 as the source of truth, leading to duplicate entries due to eventual consistency. They counter this by implementing Snitch, a filesystem extending S3AFileSystem, which maintains a consistent meta-store in DynamoDB to monitor and resolve consistency issues. Snitch enhances reliability by performing additional checks and re-implementing certain functionalities to optimize performance, despite initial bottlenecks from DynamoDB's partition throttling. The system requires all components interacting with S3 to use Snitch to prevent metastore corruption, achieved by configuring the core-site.xml file to ensure Snitch's omnipresence across the Hadoop ecosystem. Over a year, Snitch has successfully mitigated numerous eventual consistency incidents, enhancing data reliability within Box's analytics operations.

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