What are the advantages and limitations of using distributed consistency algorithms in back-end systems?
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Distributed consistency algorithms in back-end systems offer several advantages and limitations. These algorithms provide fault tolerance by replicating data across multiple nodes, ensuring consistency across replicas . They also allow for high-performance data durability by utilizing non-volatile memory (NVM) . However, it is challenging to tie NVM memory persistency models to existing data consistency frameworks . The trade-offs between performance, durability, and programmer intuition need to be carefully considered when designing these algorithms . Additionally, achieving and detecting a globally consistent state is crucial for distributed systems, but centralised and deterministic approaches are not scalable or fault-tolerant . Epidemic-based paradigms, which are decentralised computations, offer scalability and resilience but have probabilistic and non-explicit convergence detection . Improved distributed consistency algorithms can regulate the selection process of cluster main nodes, allowing for flexibility and efficiency in switching and recovery . Overall, the advantages of distributed consistency algorithms include fault tolerance, high performance, and scalability, while limitations include the complexity of tying memory persistency models and trade-offs between performance and durability.
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