Improving Relational Database Upon the Arrival of Storage Hardware with Built-in Transparent Compression

Qiao, Yifan; Chen, Xubin; Hao, Jingpeng; Li, Jiangpeng; Wu, Qi; Wang, Jingqiang; Liu, Yang; Zhang, Tong

doi:10.1109/nas51552.2021.9605481

Cited by 4 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the amplified database size deteriorates the performance of MyRocks in terms of TPS and write amplification, whereas optimized MySQL suffers less from space amplification by leveraging our effective space-optimizing techniques. Adding to the claim that B-tree can achieve similar write amplification than LSM tree [13], experiment result shown in Fig. IV-A1 implies that B-tree-based storage engines do achieve even smaller space and write amplification than LSM tree.…”

Section: ) Comparison With Myrocksmentioning

confidence: 92%

A Case for Space Compaction of B-Tree Nodes on Flash Storage

Lee

2023

IEEE Access

View full text Add to dashboard Cite

It is well known that the in-use space in B-tree nodes is only two-thirds, on average. Such low space utilization is detrimental to flash storage in terms of cost as well as performance. In particular, as the database grows, the logical space waste in B-tree nodes will exacerbate the physical write amplification inside flash devices, worsening the write performance and eventually degrading transaction throughput. To address this problem, we propose two space compaction techniques for B-tree nodes: space reservation and data redistribution. The space reservation technique dynamically reserves the free space per index to prevent node split caused by future updates. Meanwhile, data redistribution redistributes records to a neighboring node before the split occurs. Experimental results using MySQL show that our approach can reduce the in-use database space by up to 40%, doubling the throughput. In addition, our approach outperforms MyRocks with less space. Our experimental results confirm that space compaction techniques obsolete on hard disks are very rewarding on flash storage.

show abstract

Section: ) Comparison With Myrocksmentioning

confidence: 92%

A Case for Space Compaction of B-Tree Nodes on Flash Storage

Lee

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Related work: Computational storage [14] enables data-intensive applications by offloading computation to storage devices. Data compression [15] is a suitable task for this, but it faces challenges such as memory constraints, data dependency, bandwidth and latency trade-offs, and power efficiency. Previous works apply LZ77 to various hardware scenarios.…”

Section: Implementation and Evaluationmentioning

confidence: 99%

ASIC design of LZ77 compressor for computational storage drives

Huang,

Song,

Guo

et al. 2023

Electronics Letters

View full text Add to dashboard Cite

The advent of cloud computing, 5G, and artificial intelligence technology has spurred the exponential growth of big data and the need for data compression. To alleviate the CPU burden, computational engines for data compression integrated into network cards and storage devices, giving rise to computational storage. However, computational storage faces several challenges, such as limited memory resources for large hash tables, data dependency for parallel encoding and decoding, and stringent requirements for bandwidth, latency, and power efficiency. Here, an application‐specific integrated circuit‐based LZ77 solution that aims to address these challenges is proposed. Our solution reduces the hash table resource consumption and the bandwidth fluctuations, while achieving high compression ratios. The solution is implemented using Taiwan Semiconductor Manufacturing Company Limited 12 nm technology and demonstrate that a single LZ77 engine can achieve a 4 GB/s throughput at 1 GHz clock frequency. By using four engines, a computational storage drive to achieve 16 GB/s compression bandwidth is enabled. The solution achieves a comparable compression ratio as the standard LZ77 algorithm with a negligible compression ratio loss of 5%, balancing efficiency and effectiveness.

show abstract

“…Traditionally, search mechanisms make use of various index structures such as binary tree, hash, B+-tree, trie, skip list, ordered index, and so on [1,2,[5][6][7][8]. Learned index is a recently developed idea that integrates machine learning algorithms into index structures to improve search performance and to reduce the memory footprint for index structures [9].…”

Section: Learned Indexmentioning

confidence: 99%

“…Many data structures have been developed to enhance performance and to reduce memory usage of search. Typical examples include B+-tree [5], trie [6], radix tree [7], ordered index [8], and so on. Recently, a new approach, called learned index, has gained attention [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

An Empirical Study of Segmented Linear Regression Search in LevelDB

et al. 2023

View full text Add to dashboard Cite

The purpose of this paper is proposing a novel search mechanism, called SLR (Segmented Linear Regression) search, based on the concept of learned index. It is motivated by our observation that a lot of big data, collected and used by previous studies, have a linearity property, meaning that keys and their stored locations show a strong linear correlation. This observation leads us to design SLR search where we apply segmentation into the well-known machine learning algorithm, linear regression, for identifying a location from a given key. We devise two segmentation techniques, equal-size and error-aware, with the consideration of both prediction accuracy and segmentation overhead. We implement our proposal in LevelDB, Google’s key-value store, and verify that it can improve search performance by up to 12.7%. In addition, we find that the equal-size technique provides efficiency in training while the error-aware one is tolerable to noisy data.

show abstract

Improving Relational Database Upon the Arrival of Storage Hardware with Built-in Transparent Compression

Cited by 4 publications

References 16 publications

A Case for Space Compaction of B-Tree Nodes on Flash Storage

A Case for Space Compaction of B-Tree Nodes on Flash Storage

ASIC design of LZ77 compressor for computational storage drives

An Empirical Study of Segmented Linear Regression Search in LevelDB

Contact Info

Product

Resources

About