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We present Sparse Numerical Array-Based Range Filters (SNARF), a learned range filter that efficiently supports range queries for numerical data. SNARF creates a model of the data distribution to map the keys into a bit array which is stored in a compressed form. The model along with the compressed bit array which constitutes SNARF are used to answer membership queries. We evaluate SNARF on multiple synthetic and real-world datasets as a stand-alone filter and by integrating it into RocksDB. For range queries, SNARF provides up to 50x better false positive rate than state-of-the-art range filters, such as SuRF and Rosetta, with the same space usage. We also evaluate SNARF in RocksDB as a filter replacement for filtering requests before they access on-disk data structures. For RocksDB, SNARF can improve the execution time of the system up to 10x compared to SuRF and Rosetta for certain read-only workloads.
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HQ-Filter: Hierarchy-Aware Filter For Empty-Resulting Queries in Interactive Exploration
Modern visual data exploration systems are designed as client-server applications where the front-end interface generates a large number of queries to the back-end which are handled by a database server. As data exploration being a trial and error process, a significant amount of these queries return an empty result, which does not change the state of the visualization. These requests still add a significant overhead on network communication, request handling, and data processing. Moreover, given the virtually unlimited query space, it is impractical to enumerate and send all empty (or all non-empty) queries to the client to filter them. This paper introduces HQ-Filter, a hierarchy-aware filter for empty resulting queries, which utilizes the hierarchical nature of the data to construct a configurable and probabilistic filter. HQ-Filter can filter out empty-resulting queries at the client-side with a minimal size and processing overhead. HQ-Filter is applied to two existing data exploration systems for geospatial data, UCR-Star and Cloudberry. In both cases, it can successfully eliminate hundreds of queries per user which results in up-to 66% increase in server capacity by providing up to 15x speedup for average response time and up to 90% decrease in the server workload.
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- PAR ID:
- 10293603
- Date Published:
- Journal Name:
- The IEEE International Conference on Mobile Data Management, MDM
- Page Range / eLocation ID:
- 49 to 58
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/MDM52706.2021.00019
