Abstract
The emerging decentralized storage systems (DSSs), such as InterPlanetary File System (IPFS), Storj, and Sia, provide people with a new storage model. Instead of being centrally managed, the data are sliced up and distributed across the nodes of the network. Furthermore, each data object is uniquely identified by a cryptographic hash (ObjectId) and can only be retrieved by ObjectId. Compared with the search functions provided by the existing centralized storage systems, the application scenarios of the DSSs are subject to certain restrictions. In this paper, we first apply decentralized B+Tree and HashMap to the DSSs to provide keyword search. Both indexes are kept in blocks. Since these blocks may be scattered on multiple nodes, we ensure that all operations involve as few blocks as possible to reduce network cost and response time. In addition, the version control and version merging algorithms are designed to effectively organize the indexes and facilitate data integration. The experimental results prove that our indexes have excellent availability and scalability.
Highlights
With the rapid development of internet technology, centralized storage has become an important business model in our daily life
Centralized storage systems depend on a trusted third party, which inevitably inherits the single point of failure drawback
The experiment is performed in a real InterPlanetary File System (IPFS) cluster and the results show that the two indexes provide excellent availability and scalability
Summary
With the rapid development of internet technology, centralized storage has become an important business model in our daily life. The emerging DSSs, such as InterPlanetary File System (IPFS) [1], Storj [2], and Sia [3], can provide people with a new storage model They are built on a peer-to-peer (p2p) network, so there is no need to rely on third-party platforms. Electronics 2020, 9, 2041 structures to adapt to different scenarios Inspired by this point, this paper aims to design a global index based on block storage to provide keyword search. If the index occupies multiple blocks, it must be effectively organized so that all operations can involve as few blocks as possible Driven by these demands, this paper chooses B+Tree and HashMap as the main index structures. From the index type perspective, B+Tree and HashMap denote representatives of the tree and hash structures, respectively These indexes have lower height and a reasonable number of branches, which is conducive to query and update.
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