column stores
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Author(s):  
Dai-Hai Ton That ◽  
Mohammadsaleh Gharehdaghi ◽  
Alexander Rasin ◽  
Tanu Malik


Author(s):  
Lujing Cen ◽  
Andreas Kipf ◽  
Ryan Marcus ◽  
Tim Kraska
Keyword(s):  


2021 ◽  
Vol 14 (6) ◽  
pp. 1080-1092
Author(s):  
Zhiqi Wang ◽  
Jin Xue ◽  
Zili Shao

Performance-monitoring timeseries systems such as Prometheus and InfluxDB play a critical role in assuring reliability and operationally. These systems commonly adopt a column-oriented storage model, by which timeseries samples from different time-series are separated, and all samples (with both numeric values and timestamps) in one timeseries are grouped into chunks and stored together. As a group of timeseries are often collected from the same source with the same timestamps, managing timestamps and metrics in a group manner provides more opportunities for query and insertion optimization but posts new challenges as well. Besides, for performance monitoring systems, to support better compression and efficient queries for most recent data that are most likely accessed by users, huge volumes of data are first cached in memory and then periodically flushed to disks. Periodic data flushing incurs high IO overhead, and simply discarding flushed data, which can still serve queries, not only is a waste but also brings huge memory reclamation cost. In this paper, we propose Heracles which integrates two techniques - (1) a new storage model, which enables efficient queries on compressed data by utilizing the shared timestamp column to easily locate corresponding metric values; (2) a novel two-level epoch-based memory manager, which allows the system to gradually flush and reclaim in-memory data while unreclaimed data can still serve queries. Heracles is implemented as a standalone module that can be easily integrated into existing performance monitoring timeseries systems. We have implemented a fully functional prototype with Heracles based on Prometheus tsdb, a representative open-source performance monitoring system, and conducted extensive experiments with real and synthetic timeseries data. Experimental results show that, compared with Prometheus, Heracles can improve the insertion throughput by 171%, and reduce the query latency and space usage by 32% and 30%, respectively, on average. Besides, to compare with other state-of-the-art storage techniques, we have integrated LevelDB (for LSM-tree-based structure) and Parquet (for column stores) into Prometheus tsdb, respectively, and experimental results show Heracles outperform these two integrations. We have released the open-source code of Heracles for public access.



Author(s):  
Alexander Slesarev ◽  
Evgeniy Klyuchikov ◽  
Kirill Smirnov ◽  
George Chernishev


2020 ◽  
Vol 126 ◽  
pp. 101732 ◽  
Author(s):  
Lena Wiese ◽  
Tim Waage ◽  
Michael Brenner


2020 ◽  
Vol 142 ◽  
pp. 112973 ◽  
Author(s):  
Maryam Mozaffari ◽  
Eslam Nazemi ◽  
Amir Masoud Eftekhari-Moghadam


Author(s):  
Prajwol Sangat ◽  
David Taniar ◽  
Christopher Messom
Keyword(s):  


Author(s):  
Prajwol Sangat ◽  
David Taniar ◽  
Maria Indrawan‐Santiago ◽  
Christopher Messom
Keyword(s):  


2019 ◽  
pp. 45-52
Author(s):  
Mohammed Eshtay ◽  
Azzam Sleit ◽  
Monther Aldwairi

NoSQL database systems have emerged and developed at an accelerating rate in the last years. Attractive properties such as scalability and performance, which are needed by many applications today, contributed to their increasing popularity. Time is very important aspect in many applications. Many NoSQL database systems do not offer built in management for temporal properties. In this paper, we discuss how we can embed temporal properties in NoSQL databases. We review and differentiate between the most popular NoSQL stores. Moreover, we propose various solutions to modify data models for embedding bitemporal properties in two of the most popular categories of NoSQL databases (Key-value stores and Column stores). In addition, we give examples of how to represent bitemporal properties using Redis Key-value store and Cassandra column oriented store. This work can be used as basis for designing and implementing temporal operators and temporal data management in NoSQL databases.



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