Over the last decade, there has been a tremendous growth in data-intensive applications and services in the cloud. Data is created on a variety of edge sources, e.g., devices, browsers, and servers, and processed by cloud applications to gain insights or take decisions. Applications and services either work on collected data, or monitor and process data in real time. These applications are typically update intensive and involve a large amount of state beyond what can fit in main memory. However, they display significant temporal locality in their access pattern.
FASTER is a new key-value store for point operations, that combines a highly cache-optimized concurrent hash index with a novel self-tuning data organization. It extends the standard key-value store interface to handle read-modify-writes and blind update operations. FASTER achieves orders-of-magnitude better throughput – up to 160M operations per second on a single machine – than alternative systems deployed widely today, and exceeds the performance of pure in-memory data structures when the working set fits in memory.
FASTER is open source (we have a C# and C++ version). Find FASTER on GitHub. The FASTER research paper appeared at the SIGMOD 2018 conference, and can be downloaded under the publications tab. We also have a VLDB 2018 demo paper here.
Recovery of FASTER is an interesting topic and research contribution in and of itself, with deep implications to recovery in database systems in general. You can learn more about FASTER recovery, with a new model called Concurrent Prefix Recovery (CPR), in our new SIGMOD 2019 paper here.
Senior Principal Researcher
Distinguished Scientist and Director of Redmond Lab
University of Washington