Name
Abstract | ||
|---|---|---|
SummaryStore is an approximate time-series store, designed for analytics, capable of storing large volumes of time-series data (~1 petabyte) on a single node; it preserves high degrees of query accuracy and enables near real-time querying at unprecedented cost savings. SummaryStore contributes time-decayed summaries, a novel abstraction for summarizing data streams, along with an ingest algorithm to continually merge the summaries for efficient range queries; in conjunction, it returns reliable error estimates alongside the approximate answers, supporting a range of machine learning and analytical workloads. We successfully evaluated SummaryStore using real-world applications for forecasting, outlier detection, and Internet traffic monitoring; it can summarize aggressively with low median errors, 0.1 to 10%, for different workloads. Under range-query microbenchmarks, it stored 1PB synthetic stream data (10241TB streams), on a single node, using roughly 10 TB (100x compaction) with 95%-ile error below 5% and median cold-cache query latency of 1.3s (worst case latency under 70s).
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Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3132747.3132758 | SOSP '17: ACM SIGOPS 26th Symposium on Operating Systems Principles
Shanghai
China
October, 2017 |
Field | DocType | ISBN |
Query optimization,Data mining,Anomaly detection,Data stream mining,Computer science,Petabyte,Range query (data structures),Real-time computing,Latency (engineering),Analytics,Internet traffic | Conference | 978-1-4503-5085-3 |
Citations | PageRank | References |
4 | 0.38 | 30 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nitin Agrawal | 1 | 999 | 56.74 |
| Ashish Vulimiri | 2 | 187 | 8.44 |