B+-tree index optimization by exploiting internal parallelism of flash-based solid state drives

Roh, Hyun Seog; Park, Sang‐Hyun; Kim, Sungho; Shin, Mincheol; Lee, Sang‐Won

doi:10.14778/2095686.2095688

Cited by 57 publications

(24 citation statements)

References 17 publications

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“…Typically, the data transfers from/to the multiple packages on the same channel get serialized. However, data transfers can be interleaved with other operations (e.g., reading data from a page to the data register) on other packages sharing the same channel [10,29]. This interleaving provides package-level parallelism.…”

Section: Parallelism In Ssd Architecturementioning

confidence: 99%

“…However, as we will show later, this design pattern aids in instrumenting patterns 1-4. Plane-level parallelism: Earlier works [18,29] have shown that intermingling small reads and small writes affects plane-level parallelism, leading to a performance drop of up to 1.3X in throughput compared to issuing consecutive small reads followed by consecutive small writes. However, the above design patterns already dictate that we issue large writes (DP2) and small reads (small page reads for lookup requests; DP1), which already ensure that small reads and small writes are not intermingled by default.…”

Section: Extracting Parallelismmentioning

confidence: 99%

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Design patterns for tunable and efficient SSD-based indexes

Anand¹,

Gember-Jacobson

Engstrom

2014

Proceedings of the Tenth ACM/IEEE Symposium on Architectures for Networking and Communications Systems

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A number of data-intensive systems require using random hashbased indexes of various forms, e.g., hash tables, Bloom filters, and locality sensitive hash tables. In this paper, we present general SSD optimization techniques that can be used to design a variety of such indexes while ensuring higher performance and easier tunability than specialized state-of-the-art approaches. We leverage two key SSD innovations: a) rearranging the data layout on the SSD to combine multiple read requests into one page read, and b) intelligently reordering requests to exploit inherent parallelism in the architecture of SSDs. We build three different indexes using these techniques, and we conduct extensive studies showing their superior performance, lower CPU/memory footprint, and tunability compared to state-of-the-art systems.

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Section: Parallelism In Ssd Architecturementioning

confidence: 99%

Section: Extracting Parallelismmentioning

confidence: 99%

Design patterns for tunable and efficient SSD-based indexes

Anand¹,

Gember-Jacobson

Engstrom

2014

Proceedings of the Tenth ACM/IEEE Symposium on Architectures for Networking and Communications Systems

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“…Chen et al [2] studied on how to uncover internal parallelism features of SSDs and revealed that exploiting internal parallelism can significantly improve I/O performance. In the study of [6], researchers focused on finding an efficient way to generate parallel I/Os to access SSDs. By assessing different methods to create parallel I/O, authors suggest a new I/O request method and design a new B+-tree variant called PIO B-tree to exploit internal parallelism of SSDs.…”

Section: Related Workmentioning

confidence: 99%

Scan and Join Optimization by Exploiting Internal Parallelism of Flash-Based Solid State Drives

Lai

Fan

Meng

2013

Web-Age Information Management

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Abstract. Nowadays, flash-based solid state drives (SSDs) are gradually replacing hard disk drives (HDDs) as the primary non-volatile storage in both desktop and enterprise applications because of their potential to speed up performance and reduce power consumption. However, database query processing engines are designed based on the fundamental characteristics of HDDs, so they may not benefit immediately from SSDs. Previous researches on optimizing database query processing on SSDs have mainly focused on leveraging the high random data access performance of SSDs and avoiding slow random writes whenever possible. However, they fail to exploit the rich internal parallelism of SSDs. In this paper, we focus on exploiting rich internal parallelism of SSDs to optimize scan and join operators. Firstly, we detect internal parallelism of SSDs seemed as black boxes. Then we propose a parallel table scan operator called ParaScan to take full advantage of internal parallelism of SSDs. Based on ParaScan, we also present an efficient parallel join operator called ParaHashJoin to accelerate database query processing. Experimental results on TPC-H datasets show that our ParaScan on SSD significantly outperforms the traditional table scan on SSD by 1X, and ParaHashJoin is 1.5X faster than traditional hash join operator especially when join selectivity is small.

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“…With the rapid development of flash memory and solid state drives (SSDs), optimizing B+-tree for flash memory has attracted many researchers in recent years [1][2][3][4]. Implementing B+-tree on SSD can get better read performance because of the fast read speed of flash memory, but the writing operations caused by B+-tree will worsen the overall time performance, due to the poor random write speed of flash memory.…”

Section: Introductionmentioning

confidence: 99%

“…The pages inside huge leaf nodes are stored on HDD or SSD according to the states of these pages, while the interior nodes are stored on SSD. (3) We conduct experiments on a real hybrid storage system consisting of one HDD, a low-end SSD, and a high-end SSD. The results show that the HybridB tree is superior to both the B+-tree on HDD and the B+-tree on SSD/HDD, in terms of time performance, SSD write count, and buffer costs.…”

Section: Introductionmentioning

confidence: 99%

Optimizing B+-tree for hybrid storage systems

Jin

Yang

Yue

2014

Distrib Parallel Databases

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Flash-memory-based solid state drives (SSD) have been widely used in computer systems. Due to the high price and some specific features of SSD such as asymmetric read/write speeds and limited erasure endurance, it has been a very common solution, e.g., in modern data centers, to use hybrid storage systems involving SSD and traditional hard disks (HDD). However, the SSD/HDD-based hybrid storage systems introduce some new problems in the indexing schemes for data management. In this paper, we propose a new B+-tree-based index for such hybrid storage systems, which is called HybridB tree. The HybridB tree aims to reduce the random writes to SSD while keeping high time performance and low buffer costs. Particularly, we introduce a new design called huge leaf to avoid the splits and merges on B+-tree. A huge leaf node contains two or more leaf nodes in different states. We place the leaf nodes on HDD or SSD according to their current states, and dynamically adapt the states of leaf nodes when they are read or updated. After a detailed explanation on the structure and operations of the HybridB tree, we give a theoretical analysis on the costs of the HybridB tree. Then, we conduct experiments on two TPC-C traces, using a real hybrid storage system including one HDD and two SSDs, and compare the performance of our proposal with two implementations of B+-tree, namely the B+-tree on HDD and the B+-tree on SSD/HDD. The results show that our proposal has the best time performance and the fewest buffer costs. Moreover, our proposal is able to effectively reduce the random writes to SSD.

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B+-tree index optimization by exploiting internal parallelism of flash-based solid state drives

Cited by 57 publications

References 17 publications

Design patterns for tunable and efficient SSD-based indexes

Design patterns for tunable and efficient SSD-based indexes

Scan and Join Optimization by Exploiting Internal Parallelism of Flash-Based Solid State Drives

Optimizing B+-tree for hybrid storage systems

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