LeanStore is a high-performance OLTP storage engine optimized for many-core CPUs and NVMe SSDs. Our goal is to achieve performance comparable to in-memory systems when the data set fits into RAM, while being able to fully exploit the bandwidth of fast NVMe SSDs for large data sets. While LeanStore is currently a research prototype, we hope to make it usable in production in the future.


Support for Very Large Data Sets on Directly-Attached NVMe Arrays

NVMe SSDs have become cheap (1 TB cost around 200 USD) and fast (achieving more than 2-7 GB/s bandwidth per device). Existing storage engines are not capable of exploiting such fast IO devices, in particular when multiple NVMe SSDs are combined in a single server. LeanStore has been designed from scratch for Directly-Attached NVMe Arrays to get the full performance from fast flash storage devices.

High In-Memory Performance

Using optimized index structures, LeanStore achieves very high in-memory performance and scalable synchronization techniques ensure that it scales very well on many-core CPUs. A lightweight buffer manager transparently keeps frequently-accessed data in RAM, while supporting arbitrarily-large data sets on SSD.

Embeddable Open Source Library

Similar to storage engines like RocksDB and WiredTiger, LeanStore can be embedded into applications by linking it as a library. It offers a C++ interface for basic data operations like point lookup, range lookup, insert, update, delete. Support for ACID transactions is currently under development. The source code is available under MIT license.


Prof. Viktor Leis, Adnan Alhomssi, Gabriel Haas

External Collaborators: Prof. Thomas Neumann, Prof. Alfons Kemper, Michael Haubenschild


Venue Publication Link
CIDR 2021 Contention and Space Management for B-Trees
Adnan Alhomssi, Viktor Leis
pdf - code
SIGMOD 2020 Rethinking Logging, Checkpoints, and Recovery for High-Performance Storage Engines
Michael Haubenschild, Caetano Sauer, Thomas Neumann, Viktor Leis
CIDR 2020 Exploiting Directly-Attached NVMe Arrays in DBMS
Gabriel Haas, Michael Haubenschild, Viktor Leis
IEEE Data Engineering Bulletin Optimistic Lock Coupling: A Scalable and Efficient General-Purpose Synchronization Method
Viktor Leis, Michael Haubenschild, Thomas Neumann
ICDE 2018 LeanStore: In-Memory Data Management Beyond Main Memory
Viktor Leis, Michael Haubenschild, Alfons Kemper, Thomas Neumann


Date Venue
April 2021 HPI Research Symposium 2021
April 2021 CMU Vaccination Database Seminar
March 2021 Dutch Seminar on Data Systems Design
July 2020 CWI Database Architectures Group
January 2020 TU Darmstadt
January 2020 CIDR
November 2019 University of Erlangen–Nuremberg
November 2019 University of Augsburg
April 2018 ICDE
April 2018 Imperial College London
March 2018 SAP HANA TechDays
August 2017 FGDB Symposium
April 2017 Columbia University
April 2017 Harvard University
April 2017 Massachusetts Institute of Technology
March 2017 Carnegie Mellon University


Contact us (see Team for emails) if you are interested in a thesis, student job, or a Ph.D. position!