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Fault tolerance is critical for distributed stream processing systems, yetachieving error-free fault tolerance often incurs substantial performanceoverhead. We present AF-Stream, a distributed stream processing system thataddresses the trade-off between performance and accuracy in fault tolerance.AF-Stream builds on a notion called approximate fault tolerance, whose idea isto mitigate backup overhead by adaptively issuing backups, while ensuring thatthe errors upon failures are bounded with theoretical guarantees. Specifically,AF-Stream allows users to specify bounds on both the state divergence and theloss of non-backup streaming items. It issues state and item backups only whenthe bounds are reached. Our AF-Stream design provides an extensible programmingmodel for incorporating general streaming algorithms as well as exports onlyfew threshold parameters for configuring approximation fault tolerance.Furthermore, we formally prove that AF-Stream preserves high algorithm-specificaccuracy of streaming algorithms, and in particular the convergence guaranteesof online learning. Experiments 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.

On the Performance and Convergence of Distributed Stream Processing via Approximate Fault Tolerance