Analysis of Indexing Structures for Immutable Data.

In emerging applications such as blockchains and collaborative data analytics, there are strong demands for data immutability, multi-version accesses, and tamper-evident controls. To provide efficient support for lookup and merge operations, three new index structures for immutable data, namely Merkle Patricia Trie (MPT), Merkle Bucket Tree(MBT), and Pattern-Oriented-Split Tree (POS-Tree), have been proposed. Although these structures have been adopted in real applications, there is no systematic evaluation of their pros and cons in the literature, making it difficult for practitioners to choose the right index structure for their applications. To alleviate the above problem, we present a comprehensive analysis of the existing index structures for immutable data, and evaluate both their asymptotic and empirical performance. Specifically, we show that MPT, MBT, and POS-Tree are all instances of a recently proposed framework, dubbed Structurally Invariant and Reusable Indexes (SIRI). We propose to evaluate the SIRI instances on their index performance and deduplication capability. We establish the worst-case guarantees of each index, and experimentally evaluate all indexes in a wide variety of settings. Based on our theoretical and empirical analysis, we conclude that POS-Tree is a favorable choice for indexing immutable data.