Main-memory index structures with fixed-size partial keys

2001 ◽  
Author(s):  
Philip Bohannon ◽  
Peter Mcllroy ◽  
Rajeev Rastogi
Keyword(s):  
Main Memory ◽  
Index Structures ◽  
Fixed Size

Main-memory index structures with fixed-size partial keys

2001 ◽  
Vol 30 (2) ◽  
pp. 163-174 ◽  
Author(s):  
Philip Bohannon ◽  
Peter Mcllroy ◽  
Rajeev Rastogi
Keyword(s):  
Main Memory ◽  
Index Structures ◽  
Fixed Size

scholarly journals Selective caching: a persistent memory approach for multi-dimensional index structures

2021 ◽  
Author(s):  
Muhammad Attahir Jibril ◽  
Philipp Götze ◽  
David Broneske ◽  
Kai-Uwe Sattler
Keyword(s):  
Main Memory ◽  
Index Structure ◽  
Index Structures ◽  
Cloud Infrastructure ◽  
General Technique ◽  
Persistent Memory ◽  
The Cost ◽  
Cloud Applications ◽  
Memory Layout ◽  
Analytical Index

AbstractAfter the introduction of Persistent Memory in the form of Intel’s Optane DC Persistent Memory on the market in 2019, it has found its way into manifold applications and systems. As Google and other cloud infrastructure providers are starting to incorporate Persistent Memory into their portfolio, it is only logical that cloud applications have to exploit its inherent properties. Persistent Memory can serve as a DRAM substitute, but guarantees persistence at the cost of compromised read/write performance compared to standard DRAM. These properties particularly affect the performance of index structures, since they are subject to frequent updates and queries. However, adapting each and every index structure to exploit the properties of Persistent Memory is tedious. Hence, we require a general technique that hides this access gap, e.g., by using DRAM caching strategies. To exploit Persistent Memory properties for analytical index structures, we propose selective caching. It is based on a mixture of dynamic and static caching of tree nodes in DRAM to reach near-DRAM access speeds for index structures. In this paper, we evaluate selective caching on the OLAP-optimized main-memory index structure Elf, because its memory layout allows for an easy caching. Our experiments show that if configured well, selective caching with a suitable replacement strategy can keep pace with pure DRAM storage of Elf while guaranteeing persistence. These results are also reflected when selective caching is used for parallel workloads.

Study and Optimization of T-Tree Index in Main Memory Database

2013 ◽  
Vol 427-429 ◽  
pp. 2531-2535 ◽  
Author(s):  
Feng Dong Sun ◽  
Quan Guo ◽  
Lan Wang
Keyword(s):  
High Performance ◽  
Main Memory ◽  
Memory Access ◽  
Index Structure ◽  
Index Structures ◽  
Clock Speed ◽  
Main Memory Database ◽  
Tree Index ◽  
Overall Performance

The bottleneck is not the disk I/O but CUP clock speed faster than the memory speed in main memory database .In order to achieve high performance in main memory database ,it is a good approach to design new index structures to improve the memory access speed .This chapter presents a T-tree index structure and its algorithms in main memory database firstly .Then presents two results on Optimization of T-tree index ,including T-tail tree and TTB-tree. Our results indicate that the T-Tree provides good overall performance in main memory.

Optimization of T-Tree Index of Main Memory Database in Critical Application

2010 ◽  
Vol 40-41 ◽  
pp. 206-211
Author(s):  
Zhi Lin Zhu
Keyword(s):  
Data Structures ◽  
High Performance ◽  
Main Memory ◽  
Ongoing Study ◽  
Index Structure ◽  
Index Structures ◽  
Main Memory Database ◽  
Data Structures And Algorithms ◽  
Tree Index ◽  
Overall Performance

One approach to achieving high performance in the DBMS in the critical application is to store the database in main memory rather than on disk. One can then design new data structures and algorithms oriented towards increasing the efficiency of the main memory database -MMDB. In this paper we present some results on index structures from an ongoing study of MMDB. We propose a new index structure, the T-tail Tree. We give the main algorithm of the T-tail Tree and the performance of these algorithms. Our results indicate that T-tail Tree provides good overall performance in main memory.

AgiMonitor: a remote database monitoring tool for main memory database

2014 ◽  
Author(s):  
Huazhuang Yao ◽  
Yongyan Wang ◽  
Shuai Wang ◽  
Kun Li ◽  
Chao Guo
Keyword(s):  
Main Memory ◽  
Monitoring Tool ◽  
Main Memory Database

Feasibility of Longest Prefix Matching using Learned Index Structures

2021 ◽  
Vol 48 (4) ◽  
pp. 45-48
Author(s):  
Shunsuke Higuchi ◽  
Junji Takemasa ◽  
Yuki Koizumi ◽  
Atsushi Tagami ◽  
Toru Hasegawa
Keyword(s):  
Machine Learning ◽  
Data Structure ◽  
Index Structures ◽  
Packet Forwarding ◽  
Fundamental Idea ◽  
Search Operation ◽  
Longest Prefix Matching ◽  
Ip Packet ◽  
Computation Speed

This paper revisits longest prefix matching in IP packet forwarding because an emerging data structure, learned index, is recently presented. A learned index uses machine learning to associate key-value pairs in a key-value store. The fundamental idea to apply a learned index to an FIB is to simplify the complex longest prefix matching operation to a nearest address search operation. The size of the proposed FIB is less than half of an existing trie-based FIB while it achieves the computation speed nearly equal to the trie-based FIB. Moreover, the computation speed of the proposal is independent of the length of IP prefixes, unlike trie-based FIBs.

scholarly journals Efficient Retrieval of Music Recordings Using Graph-Based Index Structures

Signals ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 336-352
Author(s):  
Frank Zalkow ◽  
Julian Brandner ◽  
Meinard Müller
Keyword(s):  
Nearest Neighbor ◽  
Response Times ◽  
Negative Impact ◽  
Nearest Neighbor Search ◽  
Index Structure ◽  
Search Problem ◽  
Index Structures ◽  
Music Retrieval ◽  
Retrieval Systems ◽  
Music Recordings

Flexible retrieval systems are required for conveniently browsing through large music collections. In a particular content-based music retrieval scenario, the user provides a query audio snippet, and the retrieval system returns music recordings from the collection that are similar to the query. In this scenario, a fast response from the system is essential for a positive user experience. For realizing low response times, one requires index structures that facilitate efficient search operations. One such index structure is the K-d tree, which has already been used in music retrieval systems. As an alternative, we propose to use a modern graph-based index, denoted as Hierarchical Navigable Small World (HNSW) graph. As our main contribution, we explore its potential in the context of a cross-version music retrieval application. In particular, we report on systematic experiments comparing graph- and tree-based index structures in terms of the retrieval quality, disk space requirements, and runtimes. Despite the fact that the HNSW index provides only an approximate solution to the nearest neighbor search problem, we demonstrate that it has almost no negative impact on the retrieval quality in our application. As our main result, we show that the HNSW-based retrieval is several orders of magnitude faster. Furthermore, the graph structure also works well with high-dimensional index items, unlike the tree-based structure. Given these merits, we highlight the practical relevance of the HNSW graph for music information retrieval (MIR) applications.

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