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.

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.

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.

scholarly journals Intelligent Indexing—Boosting Performance in Database Applications by Recognizing Index Patterns

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1348
Author(s):  
Alberto Arteta Albert ◽  
Nuria Gómez Blas ◽  
Luis Fernando de Mingo López
Keyword(s):  
Data Mining ◽  
Database System ◽  
Database Management System ◽  
Index Structure ◽  
Index Structures ◽  
Database Applications ◽  
Data Mining Algorithms ◽  
Speed Up ◽  
Overall Performance ◽  
Mining Algorithms

An issue that most databases face is the static and manual character of indexing operations. This old-fashioned way of indexing database objects is proven to affect the database performance to some degree, creating downtime and a possible impact in the performance that is usually solved by manually running index rebuild or defrag operations. Many data mining algorithms can speed up by using appropriate index structures. Choosing the proper index largely depends on the type of query that the algorithm performs against the database. The statistical analyzers embedded in the Database Management System are neither always accurate enough to automatically determine when to use an index nor to change its inner structure. This paper provides an algorithm that targets those indexes that are causing performance issues on the databases and then performs an automatic operation (defrag, recreation, or modification) that can boost the overall performance of the Database System. The effectiveness of proposed algorithm has been evaluated with several experiments developed and show that this approach consistently leads to a better resulting index configuration. The downtime of having a damaged, fragmented, or inefficient index is reduced by increasing the chances for the optimizer to be using the proper index structure.

scholarly journals CoroBase

2020 ◽  
Vol 14 (3) ◽  
pp. 431-444
Author(s):  
Yongjun He ◽  
Jiacheng Lu ◽  
Tianzheng Wang
Keyword(s):  
Data Structures ◽  
Main Memory ◽  
Data Prefetching ◽  
Backward Compatibility ◽  
Transaction Models ◽  
Main Memory Database ◽  
Hide Data ◽  
Rich Data ◽  
Software Prefetching ◽  
Database Engine

Data stalls are a major overhead in main-memory database engines due to the use of pointer-rich data structures. Lightweight coroutines ease the implementation of software prefetching to hide data stalls by overlapping computation and asynchronous data prefetching. Prior solutions, however, mainly focused on (1) individual components and operations and (2) intra-transaction batching that requires interface changes, breaking backward compatibility. It was not clear how they apply to a full database engine and how much end-to-end benefit they bring under various workloads. This paper presents CoroBase, a main-memory database engine that tackles these challenges with a new coroutine-to-transaction paradigm. Coroutine-to-transaction models transactions as coroutines and thus enables inter-transaction batching, avoiding application changes but retaining the benefits of prefetching. We show that on a 48-core server, CoroBase can perform close to 2x better for read-intensive workloads and remain competitive for workloads that inherently do not benefit from software prefetching.

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