A holistic scalability strategy for time series databases following cascading polyglot persistence
Visualitza/Obre
Cita com:
hdl:2117/372829
Tipus de documentArticle
Data publicació2022-08-18
EditorMultidisciplinary Digital Publishing Institute (MDPI)
Condicions d'accésAccés obert
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Reconeixement 4.0 Internacional
ProjecteIoTwins - Distributed Digital Twins for industrial SMEs: a big-data platform (EC-H2020-857191)
Abstract
Time series databases aim to handle big amounts of data in a fast way, both when introducing new data to the system, and when retrieving it later on. However, depending on the scenario in which these databases participate, reducing the number of requested resources becomes a further requirement. Following this goal, NagareDB and its Cascading Polyglot Persistence approach were born. They were not just intended to provide a fast time series solution, but also to find a great cost-efficiency balance. However, although they provided outstanding results, they lacked a natural way of scaling out in a cluster fashion. Consequently, monolithic approaches could extract the maximum value from the solution but distributed ones had to rely on general scalability approaches. In this research, we proposed a holistic approach specially tailored for databases following Cascading Polyglot Persistence to further maximize its inherent resource-saving goals. The proposed approach reduced the cluster size by 33%, in a setup with just three ingestion nodes and up to 50% in a setup with 10 ingestion nodes. Moreover, the evaluation shows that our scaling method is able to provide efficient cluster growth, offering scalability speedups greater than 85% in comparison to a theoretically 100% perfect scaling, while also ensuring data safety via data replication.
CitacióGarcia, C.; Becerra, Y.; Cucchietti, F. A holistic scalability strategy for time series databases following cascading polyglot persistence. "Big data and cognitive computing", 18 Agost 2022, vol. 6, núm. 3, article 86, p. 1-30.
ISSN2504-2289
Versió de l'editorhttps://www.mdpi.com/2504-2289/6/3/86
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