Toward high-performance distributed stream processing via approximate fault tolerance

Abstract

Fault tolerance is critical for distributed stream processing systems, yet achieving error-free fault tolerance often incurs substantial performance overhead. We present AF-Stream , a distributed stream processing system that addresses the trade-off between performance and accuracy in fault tolerance. AF-Stream builds on a notion called approximate fault tolerance , whose idea is to mitigate backup overhead by adaptively issuing backups, while ensuring that the errors upon failures are bounded with theoretical guarantees. Our AF-Stream design provides an extensible programming model for incorporating general streaming algorithms, and also exports only few threshold parameters for configuring approximation fault tolerance. Experiments on Amazon EC2 show that AF-Stream maintains high performance (compared to no fault tolerance) and high accuracy after multiple failures (compared to no failures) under various streaming algorithms.