Optimizing B+-tree for hybrid storage systems

Jin, Peiquan; Yang, Peihui; Yue, Lihua

doi:10.1007/s10619-014-7157-7

Cited by 28 publications

(3 citation statements)

References 20 publications

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“…Although this work focuses on optimizing the performance of the buffer manager on HHD-based databases, some techniques are also friendly to SSD-based databases. In the future, we will improve the AMG-Buffer design to make it applicable to different kinds of storage, such as magnetic disks, SSDs, hybrid storage [31], and persistent memory [32].…”

Section: Discussionmentioning

confidence: 99%

Adaptive Multi-Grained Buffer Management for Database Systems

Wang

Jin

2021

Future Internet

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The traditional page-grained buffer manager in database systems has a low hit ratio when only a few tuples within a page are frequently accessed. To handle this issue, this paper proposes a new buffering scheme called the AMG-Buffer (Adaptive Multi-Grained Buffer). AMG-Buffer proposes to use two page buffers and a tuple buffer to organize the whole buffer. In this way, the AMG-Buffer can hold more hot tuples than a single page-grained buffer. Further, we notice that the tuple buffer may cause additional read I/Os when writing dirty tuples into disks. Thus, we introduce a new metric named clustering rate to quantify the hot-tuple rate in a page. The use of the tuple buffer is determined by the clustering rate, allowing the AMG-Buffer to adapt to different workloads. We conduct experiments on various workloads to compare the AMG-Buffer with several existing schemes, including LRU, LIRS, CFLRU, CFDC, and MG-Buffer. The results show that AMG-Buffer can significantly improve the hit ratio and reduce I/Os compared to its competitors. Moreover, the AMG-Buffer achieves the best performance on a dynamic workload as well as on a large data set, suggesting its adaptivity and scalability to changing workloads.

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Section: Discussionmentioning

confidence: 99%

Adaptive Multi-Grained Buffer Management for Database Systems

Wang

Jin

2021

Future Internet

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“…Due to the superior randomness of B + trees and the performance of range queries, it has become the technology of choice for all commercial databases, even for distributed databases [6]. There are many literatures on B + tree optimization, such as concurrency control [7], targeted optimization of new hardware like Solid State Drive (SSD) [8,9,10,11] and so on.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Indexing Structure for Multidimensional Categorical Range Aggregation Query

Yang¹,

Zhao²,

Li³

et al. 2019

KSII TIIS

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Categorical range aggregation, which is conceptually equivalent to running a range aggregation query separately on multiple datasets, returns the query result on each dataset. The challenge is when the number of dataset is as large as hundreds or thousands, it takes a lot of computation time and I/O. In previous work, only a single dimension of the range restriction has been solved, and in practice, more applications are being used to calculate multiple range restriction statistics. We proposed MCRI-Tree, an index structure designed to solve multi-dimensional categorical range aggregation queries, which can utilize main memory to maximize the efficiency of CRA queries. Specifically, the MCRI-Tree answers any query in O(nk n-1) I/Os (where n is the number of dimensions, and k denotes the maximum number of pages covered in one dimension among all the n dimensions during a query). The practical efficiency of our technique is demonstrated with extensive experiments.

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“…HeteroDrive [11] is an example for such a system. HybridB tree, introduced in [12], is a B + tree based index for SSD / HDD-based hybrid storage systems which reduces the random writes to the SSD without sacrificing performance. In [13], caching policies for hybrid database storage systems consisting of SSDs and HDDs are introduced.…”

Section: Related Workmentioning

confidence: 99%

Icarus: Towards a multistore database system

Vogt

Stiemer

Schuldt

2017

2017 IEEE International Conference on Big Data (Big Data)

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The last years have seen a vast diversification on the database market. In contrast to the "one-size-fits-all" paradigm according to which systems have been designed in the past, today's database management systems (DBMS) are tuned for particular workloads. This has led to DBMSs optimized for high performance, high throughput read / write workloads in online transaction processing (OLTP) and systems optimized for complex analytical queries (OLAP). However, this approach reaches a limit when systems have to deal with mixed workloads that are neither pure OLAP nor pure OLTP workloads. In such cases, multistores are increasingly gaining popularity. Rather than supporting one single database paradigm and addressing one particular workload, multistores encompass several DBMSs that store data in different schemas and allow to route requests on a per-query level to the most appropriate system. In this paper, we introduce the multistore ICARUS. In our evaluation based on a workload that combines OLTP and OLAP elements, we show that ICARUS is able to speed-up queries up to a factor of three by properly routing queries to the best underlying DBMS.

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Optimizing B+-tree for hybrid storage systems

Cited by 28 publications

References 20 publications

Adaptive Multi-Grained Buffer Management for Database Systems

Adaptive Multi-Grained Buffer Management for Database Systems

An Efficient Indexing Structure for Multidimensional Categorical Range Aggregation Query

Icarus: Towards a multistore database system

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