PhilDB: The time series database with built-in change logging
- Published
- Accepted
- Subject Areas
- Data Science, Databases
- Keywords
- time series, database, logging, Python, data science
- Copyright
- © 2016 MacDonald
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. PhilDB: The time series database with built-in change logging. PeerJ PrePrints 4:e1488v2 https://doi.org/10.7287/peerj.preprints.1488v2
Abstract
PhilDB is an open-source time series database that supports storage of time series datasets that are dynamic, that is it records updates to existing values in a log as they occur. PhilDB eases loading of data for the user by utilising an intelligent data write method. It preserves existing values during updates and abstracts the update complexity required to achieve logging of data value changes. It implements fast reads to make it practical to select data for analysis. Recent open-source systems have been developed to indefinitely store long-period high-resolution time series data without change logging. Unfortunately such systems generally require a large initial installation investment before use because they are designed to operate over a cluster of servers to achieve high-performance writing of static data in real time. In essence, they have a 'big data' approach to storage and access. Other open-source projects for handling time series data that avoid the 'big data' approach are also relatively new and are complex or incomplete. None of these systems gracefully handle revision of existing data while tracking values that changed. Unlike 'big data' solutions, PhilDB has been designed for single machine deployment on commodity hardware, reducing the barrier to deployment. PhilDB takes a unique approach to meta-data tracking; optional attribute attachment. This facilitates scaling the complexities of storing a wide variety of data. That is, it allows time series data to be loaded as time series instances with minimal initial meta-data, yet additional attributes can be created and attached to differentiate the time series instances when a wider variety of data is needed. PhilDB was written in Python, leveraging existing libraries. While some existing systems come close to meeting the needs PhilDB addresses, none cover all the needs at once. PhilDB was written to fill this gap in existing solutions. This paper explores existing time series database solutions, discusses the motivation for PhilDB, describes the architecture and philosophy of the PhilDB software, and performs a simple evaluation between InfluxDB, PhilDB, and SciDB.
Author Comment
This is an updated submission to PeerJ for review after responding to a major revision request.
Supplemental Information
Results and scripts for reproducing
Results used in the paper, with the scripts used to produce/for reproducing the results and figures with basic instructional README file. Mostly Python scripts with an IPython notebook for producing the figures from the results (text files).