A Distributed Data Storage Strategy Based on LOPs

Distributed data management requires data partitioning and deployment at the data storage level, and data querying requires the configuration and integration of query subresults at each site. The data partitioning strategy is closely related to the overhead of the distributed system. It is necessary to determine the appropriate data partitioning strategy and update strategy according to the application. This paper proposes a widely distributed storage and processing scheme for a distributed linear order partition (DLOP) based on time stamps. This scheme proposes two kinds of partition strategy based on the characteristics of an "equivalent division" of a linear order partition (LOP), namely, partitioning based on time interval equilibrium and partitioning based on query expectation. Each site in the distributed system is uniformly configured with an index-based data query mechanism to complete the distributed management of data. The corresponding experiments verify the practicability and efficiency of the proposed storage strategy and show that the proposed method is effective for the self-scalability of the data scale and reduces the cluster hardware configuration requirements.