Forest of Distributed B+Tree Based on Key-Value Store for Big-Set Problem

Abstract

In many big-data systems, the amount of data is growing rapidly. Many systems have to store big-sets: the sets with a large number of items. Efficiently storing a large number of big-sets to support high rate updating and querying is a challenging problem in data storage systems. Nowadays, distributed key-value stores play important roles in building large-scale systems with many advantages. They support horizontal scalability, low-latency, high throughput when manipulating small or medium key-value pairs. Unfortunately, when working with big-set data structure, they do not work well and most of them are not scalable with a large number of big sets. In this research, we analyze the difficulty in storing big-sets using key-value stores. An architecture called “Forest of distributed $B^{+}Tree$ ” and algorithms are proposed to build NoSql data store for storing big data structures such as set, dictionary. The big-sets are split into multiple small sets of limited size and stored in key-value stores. A Multi-level meta-data is also proposed and used to reduce the complexity in writing operations of big-sets when using key-value stores from O(N) to O(log(N)). This research can store larger number of items in a set than Cassandra and Google BigTable. Parts of big set in this research is distributed while a row in Google BigTable only has a limited size and must be fit in a server. Experiment results show that proposed system has better read performance than Cassandra. The proposed architecture may potentially be used in various applications such as storage system for data from sensors in the Internet of Things (IoT) systems, commercial transaction storages and social networks.