ER-Store: A Hybrid Storage Mechanism with Erasure Coding and Replication in Distributed Database Systems

In distributed database systems, as cluster scales grow, efficiency and availability become critical considerations. In a cluster, a common approach to high availability is using replication, but this is inefficient due to its low storage utilization. Erasure coding can provide data reliability while ensuring high storage utilization. However, due to the large number of coding and decoding operations required by the CPU, it is not suitable for some frequently updated data. In order to optimize the storage efficiency of the data in the distributed system without affecting the availability of the data, this paper proposes a data temperature recognition algorithm that can distinguish data tablets and divides data tablets into three types, cold, warm, and hot, according to the frequency of access. Combining three replicas and erasure coding technology, ER-store is proposed, a hybrid storage mechanism for different data types. At the same time, we combined the read-write separation architecture of the distributed database system to design the data temperature conversion cycle, which reduces the computational overhead caused by frequent updates of erasure coding technology. We have implemented this design on the CBase database system based on the read-write separation architecture, and the experimental results show that it can save 14.6%–18.3% of the storage space while meeting the efficient access performance of the system.

A Review on Fault Tolerance in Distributed Database

In this paper, we study about the different types of fault tolerance techniques which are used in various distributed database systems. The main focus of this research is about how the data are storedin the servers, fault detection techniques and the recovery techniques used. A fault can occur for many reasons. For example, system failure, resource failure, network between the server’s failure and any other reasons. These faults must be emphasis in order to make sure the system can work smoothly without any problem. A proper failure detector and a reliable fault tolerance technique can avoid loss and at once save the system from fail.

A Survey On Fragmentation In Distributed Database Systems

One of the most critical aspects of distributed database design and management is fragmentation. If the fragmentation is done properly, we can expect to achieve better throughput from such systems. The primary concern of DBMS design is the fragmentation and allocation of the underlying database. The distribution of data across various sites of computer networks involves making proper fragmentation and placement decisions. The first phase in the process of distributing a database is fragmentation which clusters information into fragments. This process is followed by the allocation phase which distributes, and if necessary, replicates the generated fragments among the nodes of a computer network. The use of data fragmentation to improve performance is not new and commonly appears in file design and optimization literature. An efficient functionality of any distributed database system is highly dependent on its proper design in terms of adopted fragmentation and allocation methods. Fragmentations of large, global databases are performed by dividing the database horizontally, vertically or combination of both. In order to enable the distributed database systems to work efficiently, the fragments have to be allocated across the available sites in such a way that reduces communication cost of data.In this article, we have tried to describe the existing methods of database fragmentation and have an overview of the existing methods. Finally, we conclude with suggestions for using machine learning to solve the overlap problem in fragments.