Failure recovery model in big data using the checkpoint approach

Abstract

Distributed Stream Processing systems are becoming an increasingly crucial aspect of Big Data processing platforms as customers grow ever more reliant on their capacity to deliver fast access to fresh findings. As a result, the ability of a system to tolerate failure is necessary for making prompt judgments based on these data. By using checkpoint, these systems typically achieve fault tolerance and the capacity to automatically recover from partial failures. An innovative method for automatic runtime optimization of fault detection and tolerance methods have been developed and used in this work.

URN:NBN:sciencein.jist.2023.v11.564