Replication and Partition Strategies in Distributed Systems and Usecases

Authors: Arjun Reddy Lingala

DOI: https://doi.org/10.5281/zenodo.14880884

Short DOI: https://doi.org/g84934

Country: USA

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Abstract: Modern computing heavily relies on distributed systems to ensure scalability, fault tolerance, and high availability. Two fundamental techniques that enhance distributed architectures are replication and partitioning. Replication involves maintaining multiple copies of data to improve fault tolerance and accessibility, whereas partitioning divides datasets to enhance scalability and load distribution. This paper provides an in-depth examination of these strategies, highlighting their benefits, trade- offs, and real-world applications. Various replication models such as synchronous and asynchronous replication, multi-leader replication, and leader-less replication are explored. Similarly, partitioning techniques including horizontal and vertical partitioning, range-based, hash-based, partitioning using secondary indexes, rebalancing partitions and its challenges, and impact on system performance. Additionally this study discusses the implications of replication and partitioning as two separate aspects which deep dives into replication considering that the data is fit into a single server, and partitioning considering that the replications is handled well. This study highlights the importance of replication and partitioning without considering the impact on one another and later user can take appropriate way based on the system guarantees that they want for their system optimal performance. This paper serves as a comprehensive resource for system architects, engineers, and researchers striving to optimize distributed systems in an era of growing data complexity.

Keywords: Scaling, Replication, Partitioning, Distributed systems, Consistent hashing, Consensus, Coordination, Leader- follower, Replication lag, Latency, Throughput

Paper Id: 232133

Published On: 2021-11-07

Published In: Volume 9, Issue 6, November-December 2021

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