Revisiting Concurrency in High-Performance NoSQL Databases


Yuvraj Patel, University of Wisconsin-Madison; Mohit Verma, NVIDIA; Andrea Arpaci-Dusseau and Remzi Arpaci-Dusseau, University of Wisconsin-Madison


We measure the performance of five popular databases and show that single-node performance does not scale while hosting data on high-performance storage systems (e.g., Flash-based SSDs). We then analyze each system, unveiling techniques each system uses to increase concurrent performance; our taxonomy places said approaches into six different categories (thread architecture, batching, granularity, partitioning, scheduling and low-level efficiency) and thus points towards possible remedies that can scale the system. Finally, we introduce Xyza, a modified version of MongoDB that uses a wide range of classic and novel techniques to improve performance under concurrent, write-heavy workloads. Empirical analysis reveals that Xyza is 2x to 3x faster than MongoDB and scales well (up to 32 processing cores).

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@inproceedings {216896,
author = {Yuvraj Patel and Mohit Verma and Andrea C. Arpaci-Dusseau and Remzi H. Arpaci-Dusseau},
title = {Revisiting Concurrency in High-Performance NoSQL Databases},
booktitle = {10th {USENIX} Workshop on Hot Topics in Storage and File Systems (HotStorage 18)},
year = {2018},
address = {Boston, MA},
url = {},
publisher = {{USENIX} Association},