Niv Dayan
Postdoctoral Researcher
Harvard University

I am a postdoctoral researcher at the Data Systems Lab (DASLab) and at the Institute for Applied Computational Science (IACS), both at Harvard University. I develop data structures for key-value stores, flash memory, and data exploration.


Self-Designing Key-Value Stores

The rising proportion of writes in application workloads has made write-optimized indexes such as LSM-trees resurge in popularity. The cost of such indexes is measured along three dimensions: the work needed to insert a key-value pair (write-amplification), the work needed for a lookup (read-amplification), and the amount of main memory and secondary storage needed to store the index (space-amplification). In this project, we identify the optimal trade-off curves among these cost metrics and show how to navigate them to find the best point for a given application. See Monkey.

Predictable Flash Memory Performance

Although flash memory has risen as a popular secondary storage medium, its performance is difficult to predict. The reason is that internally, flash memory is subject to a complex set of constraints. Storage units must be erased before they are updated, erases have a bigger granularity than writes, and each storage unit has a limited lifetime in terms of erases. A flash translation layer (FTL) hides these constraints and exposes a simple block interface to the application. The problem is that the FTL's behind-the-scene work impacts performance. In this project, we redesign the FTL so as to eliminate bottlenecks and make the performance of flash devices predictable. See GeckoFTL, EagleTree, and the vision.

Tractable Big Data Exploration

Our era’s scientific Wonders of the World such as the Large Synoptic Survey Telescope (LSST) will produce vast quantities of data. The manner in which this data is organized will determine the efficiency in which different kinds of queries can be processed. The problem is that a scientist does not necessarily know in advance which queries will run, because the next question about the data often depends on the results of a previous query. Thus, being able to efficiently process a broad range of queries from an evolving scientific workload is crucial. In this project, we develop data structures that enable a broader range of queries to be answered efficiently so that scientific exploration can remain tractable. See Data Canopy.

Selected Publications

Dostoevsky: Better Space-Time Trade-Offs for LSM-Tree Based Key-Value Stores via Adaptive Removal of Superfluous Merging.
Niv Dayan, Stratos Idreos.
SIGMOD 2018.

Coconut: A Scalable Bottom-Up Approach for Building Data Series Indexes.
Haridimos Kondylakis, Niv Dayan, Kostas Zoumpatianos, Themis Palpanas.
VLDB 2018.

Monkey: Optimal Navigable Key-Value Store.
Niv Dayan, Manos Athanassoulis, Stratos Idreos.
SIGMOD 2017. Best of SIGMOD 2017. Selected as one of the four best papers of SIGMOD 2017.

Data Canopy: Accelerating Exploratory Statistical Analysis.
Abdul Wasay, Xinding Wei, Niv Dayan, Stratos Idreos.
SIGMOD 2017.

GeckoFTL: Scalable Flash Translation Techniques for Very Large Flash Devices.
Niv Dayan, Philippe Bonnet, Stratos Idreos.
SIGMOD 2016.

Past and Future Steps for Adaptive Storage Data Systems: From Shallow to Deep Adaptivity.
Stratos Idreos, Manos Athanassoulis, Niv Dayan, Demi Guo, Mike S. Kester, Lukas Maas, Kostas Zoumpatianos.
Birte@VLDB 2016.

EagleTree: Exploring the Design Space of SSD-Based Algorithms.
Niv Dayan, Martin Kjaer Svendsen, Matias Bjoerling, Philippe Bonnet, Luc Bouganim.
Demo@VLDB 2013.

The Necessary Death of the Block Device Interface.
Matias Bjoerling, Philippe Bonnet, Luc Bouganim, Niv Dayan.
CIDR 2013.


My CV can be downloaded here.

Niv Dayan
Harvard SEAS - DASlab
33 Oxford Street
136 Maxwell Dworkin
Cambridge, MA 02138