Save the Data: A New Approach to Database Management in Solid-State Drives

The ever-increasing workload of data centers calls for new ways to store and access data. Researchers from the Daegu Gyeongbuk Institute of Science and Technology, Korea, have developed a new approach to manage databases in solid state drives, providing marked performance improvements in read/write delays and offloading database computation tasks from CPUs to increase efficiency and reduce power consumption.

On the left, Prof. Sungjin Lee from the Department of Information and Communication Engineering at DGIST. In the center, first-author Sunsu Im, who has a Master’s degree and works at the same department. On the right, second-author Jinwook Bae, who has a Master’s degree as well.

Data centers, the backbone of many Internet-based services and applications, are facing increasing demands, which calls for new database and data storage technologies

As Web services, cloud storage, and big-data services continue expanding and finding their way into our lives, the gigantic hardware infrastructures they rely on—known as data centers—need to be improved to keep up with the current demand. One promising solution for improving the performance and reducing the energy load associated with reading and writing large amounts of data is to confer storage devices with some computational capabilities and offload part of the data read/write process from CPUs.


In a recent study presented at the 2020 USENIX Annual Technical Conference, researchers from Daegu Gyeongbuk Institute of Science and Technology (DGIST), Korea, describe a new way of implementing a key–value store in solid state drives (SSDs), which offers many advantages over a more widely used method.


A key–value store (also known as key–value database) is a way of storing, managing, and retrieving data in the form of key–value pairs. The most common way to implement one is through the use of a hash function, an algorithm that can quickly match a given key with its associated stored data to achieve fast read/write access.


One of the main problems of implementing a hash-based key–value store is that the random nature of the hash function occasionally leads to long delays (latency) in read/write operations. To solve this problem, the researchers from DGIST implemented a different paradigm, called “log-structured merge-tree (LSM).” This approach relies on ordering the data hierarchically, therefore putting an upper bound on the maximum latency.


In their implementation, nicknamed “PinK,” they addressed the most serious limitations of LSM-based key–value stores for SSDs. With its optimized memory use, guaranteed maximum delays, and hardware accelerators for offloading certain sorting tasks from the CPU, PinK represents a novel and effective take on data storage for SSDs in data centers. Professor Sungjin Lee, who led the study, remarks: “Key–value store is a widely used fundamental infrastructure for various applications, including Web services, artificial intelligence applications, and cloud systems. We believe that PinK could greatly improve the user-perceived performance of such services.


So far, experimental results confirm the performance gains offered by this new implementation and highlight the potential of letting storage devices compute some operations by themselves. “We believe that our study gives a good direction of how computational storage devices should be designed and built and what technical issues we should address for efficient in-storage computing,” Prof Lee concludes.




Junsu Im1, Jinwook Bae1, Chanwoo Chung2, Arvind2 and Sungjin Lee1*

Title of original paper

PinK: High-speed In-storage Key-value Store with Bounded Tails


Proceedings of the 2020 USENIX Annual Technical Conference




1Daegu Gyeongbuk Institute of Science and Technology (DGIST)  

2Massachusetts Institute of Technology


*Corresponding author’s email: [email protected].kr



About Daegu Gyeongbuk Institute of Science and Technology (DGIST)

Daegu Gyeongbuk Institute of Science and Technology (DGIST) is a well-known and respected research institute located in Daegu, Republic of Korea. Established in 2004 by the Korean Government, the main aim of DGIST is to promote national science and technology, as well as to boost the local economy.

With a vision of “Changing the world through convergence", DGIST has undertaken a wide range of research in various fields of science and technology. DGIST has embraced a multidisciplinary approach to research and undertaken intensive studies in some of today's most vital fields. DGIST also has state-of-the-art-infrastructure to enable cutting-edge research in materials science, robotics, cognitive sciences, and communication engineering.





About the author

Sungjin Lee received a BE degree in Electrical Engineering from Korea University in 2005 and MS and PhD degrees in Computer Science and Engineering from Seoul National University in 2007 and 2013, respectively. He is currently an Assistant Professor at DGIST in South Korea. Before joining DGIST, he was a postdoctoral associate in the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT), Cambridge, USA. His current research interests include storage systems, operating systems, and system software.