During the last years NoSQL databases have been developed to ad-dress the needs of tremendous performance, reliability and horizontal scalability.
NoSQL time series databases (TSDBs) have risen to combine valuable NoSQL properties with characteristics of time series data encountering many use-cases. Solutions offer the efficient handling of data volume and frequency related to time series.
Developers and decision makers struggle with the choice of a TSDB among a large variety of solutions. Up to now no comparison exists focusing on the specific features and qualities of those heterogeneous applications.
This paper aims to deliver two frameworks for the comparison of TSDBs, firstly with a focus on features and secondly on quality. Furthermore, we apply and evaluate the frameworks on up to seven open-source TSDBs such as InfluxDB and OpenTSDB.
We come to the result that the investigated TSDBs differ mainly in support- and extension related points. They share performance-enhancing techniques, time-related query capabilities and data schemas optimized for the handling of time-series data.
Table of Contents
- Abstract
- Introduction
- Background
- Distributed Systems and NoSQL Databases
- Time Series and Time Series Data
- Time Series Databases (TSDBs)
- Time Series Data Implications
- Drawbacks of Relational Databases
- Benefits of NoSQL Databases
- Architecture of Time Series Databases
- Related Work
- Comparison Frameworks
- Feature-oriented
- Quality-oriented
- Application
- Feature-oriented
- Applied to open-source TSDBs
- Quality-oriented
- Applied to InfluxDB
- Applied to OpenTSDB
- Comparison Overview
- Results
- Conclusions
- References
Objectives and Key Themes
This paper aims to provide a comprehensive comparison of NoSQL time series databases (TSDBs). The objective is to develop and apply frameworks for comparing these databases based on their features and quality attributes, addressing the lack of such comparative analysis in existing literature. The study evaluates several open-source TSDBs to test the validity and effectiveness of the proposed frameworks.
- Comparison of NoSQL Time Series Databases
- Development of Feature-Oriented and Quality-Oriented Comparison Frameworks
- Evaluation of Open-Source TSDBs
- Analysis of TSDB Characteristics and Capabilities
- Identification of Strengths and Weaknesses of Different TSDBs
Chapter Summaries
Introduction: This chapter introduces the increasing importance of time-series data in various fields, highlighting the limitations of traditional databases in handling large volumes of such data. It emphasizes the rise of NoSQL databases and their suitability for time-series data, leading to the development of specialized TSDBs. The chapter establishes the need for a comprehensive comparison of these TSDBs, focusing on the lack of existing literature and the contribution of this paper in addressing this gap. It presents a concise overview of the paper's structure and its main contributions.
Background: This section provides essential background information on distributed systems, NoSQL databases, and time-series databases. It explores the characteristics of time-series data, the drawbacks of using relational databases for time-series data, and the advantages of using NoSQL databases as a foundation for TSDBs. Specific examples of existing NoSQL databases (like Cassandra and HBase) are mentioned and their suitability for time-series data is discussed, along with the challenges and specific design adaptations needed. The chapter culminates with an explanation of the application of TSDBs in domains such as smart grids, illustrating the practical context and importance of the research.
Architecture of Time Series Databases: This chapter delves into the architectural considerations of TSDBs, likely exploring different design choices and their implications for scalability, performance, and query capabilities. It probably discusses specific architectural patterns and their suitability for handling time-series data. The discussion may cover topics such as data storage, indexing strategies, and query processing methods optimized for time-series data. Specific examples of different architectures might be included to highlight the diversity of approaches within the field.
Related Work: This chapter reviews existing literature on time series databases, identifying the gap in comprehensive comparative studies. It positions the current work within the existing research landscape, highlighting its novelty and significance in addressing the limitations of previous studies. This section likely discusses prior attempts at comparing TSDBs and pinpoints their shortcomings, paving the way for a more robust comparison approach by the authors.
Comparison Frameworks: This chapter introduces the two main frameworks developed in the paper: a feature-oriented framework and a quality-oriented framework. The chapter would likely detail the specific criteria and metrics used within each framework to assess different TSDBs. This includes a clear description of how each framework is designed to capture the essential features and qualities of TSDBs, focusing on the methodologies and rationale behind their development. A detailed explanation of the selection criteria and their justification would be provided.
Application: This chapter demonstrates the application of the developed comparison frameworks to several open-source TSDBs. It likely presents a detailed case study, applying the feature-oriented framework to multiple TSDBs and the quality-oriented framework to at least InfluxDB and OpenTSDB. The chapter would include a systematic analysis, highlighting the strengths and weaknesses of each database based on the selected criteria and metrics in the previous chapter. The outcomes would probably lead into the discussion of the relative merits of different TSDBs based on their features and quality attributes.
Comparison Overview: This section synthesizes the results from the application of the comparison frameworks. It may offer a comparative overview of the different TSDBs analyzed, highlighting similarities and differences, strengths, and weaknesses, as evidenced by the analysis in the application chapter. This chapter brings together the various aspects of the comparison to paint a coherent picture of the landscape of NoSQL time-series databases.
Results: This chapter presents the findings of the study, likely showing the results of applying the comparison frameworks. The results would probably be presented in a clear and concise manner, possibly using tables or graphs to summarize the key findings. Specific examples and data might be presented to support the overall conclusions of the study.
Keywords
NoSQL, Time Series Database (TSDB), Comparison Framework, OpenTSDB, InfluxDB, System Architecture, Distributed System
Frequently Asked Questions: A Comprehensive Comparison of NoSQL Time Series Databases
What is the main topic of this paper?
This paper provides a comprehensive comparison of NoSQL Time Series Databases (TSDBs). It focuses on developing and applying frameworks to compare these databases based on their features and quality attributes, addressing a gap in existing literature.
What are the key objectives of this research?
The main objectives are to compare NoSQL TSDBs, develop feature-oriented and quality-oriented comparison frameworks, evaluate open-source TSDBs, analyze TSDB characteristics and capabilities, and identify the strengths and weaknesses of different TSDBs.
What frameworks were developed for the comparison?
Two frameworks were developed: a feature-oriented framework and a quality-oriented framework. These frameworks define specific criteria and metrics to assess different TSDBs based on their features and quality attributes.
Which databases were evaluated in this study?
The study evaluates several open-source TSDBs. Specific examples mentioned include InfluxDB and OpenTSDB, with the feature-oriented framework applied to multiple TSDBs and the quality-oriented framework specifically applied to InfluxDB and OpenTSDB.
What is the structure of the paper?
The paper is structured as follows: Abstract, Introduction, Background (including Distributed Systems, NoSQL Databases, Time Series Data, TSDBs, and their implications), Architecture of Time Series Databases, Related Work, Comparison Frameworks (Feature-oriented and Quality-oriented), Application of the Frameworks, Comparison Overview, Results, Conclusions, and References.
What background information is provided?
The background section covers distributed systems, NoSQL databases, time-series data, the drawbacks of relational databases for time-series data, and the advantages of using NoSQL databases for TSDBs. It also discusses the application of TSDBs in various domains.
How are the comparison frameworks applied?
The feature-oriented framework is applied to multiple open-source TSDBs, while the quality-oriented framework is specifically applied to InfluxDB and OpenTSDB. A systematic analysis is conducted to highlight the strengths and weaknesses of each database.
What are the key findings of the study (Results)?
The results section presents the findings of applying the comparison frameworks, summarizing key findings likely through tables or graphs. Specific examples and data support the overall conclusions.
What are the main conclusions of the paper?
The conclusions summarize the overall findings of the comparative analysis, highlighting the insights gained regarding the strengths and weaknesses of different NoSQL TSDBs based on the applied frameworks. This section likely reflects on the implications of the findings for future research and practical application.
What are the keywords associated with this paper?
The keywords include NoSQL, Time Series Database (TSDB), Comparison Framework, OpenTSDB, InfluxDB, System Architecture, and Distributed System.
What is the intended audience of this paper?
The intended audience is likely researchers and professionals in the fields of database systems, distributed systems, and data management. The detailed technical analysis and comparative approach suggest a focus on an audience with a strong technical background.
- Quote paper
- Kevin Rudolph (Author), 2015, A Comparison of NoSQL Time Series Databases, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/299975
