|smoothie||24.47 kB||MIT||11 Years||18 Jul 2022|
|timeseries-analysis||16.37 kB||MIT||9 Years||8 Dec 2014|
|dygraphs||3.39 MB||MIT||9 Years||16 Feb 2023|
|orchestrate||25.2 kB||Apache-2.0||10 Years||8 Nov 2016|
|gauss||24.25 kB||UNKNOWN||12 Years||22 Apr 2014|
|timestream||6.38 kB||MIT||10 Years||20 Feb 2015|
|pondjs||21.18 MB||BSD-3-Clause-LBNL||8 Years||7 Nov 2019|
|compute-pcorr||3.98 kB||MIT||8 Years||30 Nov 2014|
|timestream-aggregates||3.24 kB||MIT||10 Years||23 Jan 2015|
|react-timeseries-charts||835.81 kB||BSD-3-Clause-LBNL||8 Years||17 May 2019|
|timelines-chart||264.97 kB||MIT||7 Years||8 Feb 2023|
|redibox||61.46 kB||MIT||7 Years||23 Sep 2016|
|compute-covariance||3.84 kB||MIT||8 Years||29 Nov 2014|
|timestreamdb||2.09 kB||MIT||10 Years||9 Nov 2015|
|hoard||28.37 kB||MIT||12 Years||18 Aug 2011|
Time Series Database (TSDB) libraries are essential when dealing with data that is indexed and tracked over a time period. This type of data structure is frequently used in various sectors including finance, medical, weather forecast, and science research, where data points are monitored over time.
Time series database libraries, especially those available in the npm ecosystem, provide a wide array of functionalities, allowing developers to efficiently process and analyze time-based data. Some of those functionalities include:
Data Persistence: Stores time-stamped data persistently, retaining it in the event of a system crash or shutdown.
Data Compression: Optimizes storage space by compressing the time-series data.
High-Speed Writings: Allows rapid input of data, accommodating the high-speed nature of time-series data collection from numerous sources.
Scalability: Scales operations to handle large amounts of data, coming from multiple sources at a rapid pace.
Query functionality: Enables querying of data based on different time intervals, specific points in time, aggregates over time, etc.
Data visualization: Some libraries may provide a feature to visualize the data, making it easier to analyze patterns and trends.
Handling Large Data: Time Series Databases are designed to handle a lot of data arriving in a very quick succession. It is essential to ensure the library being used can scale and handle data volume efficiently.
Data Accuracy: Most time-series DBs aim to provide high throughput, sometime this comes at the cost of data precision. Depending on your application necessity, you need to keep an eye out for this trade-off.
Library Maturity: Not all TSDB libraries are mature and fully featured. Some are still in experimental or early stages of development.
Data Retention Policies: Some TSDB libraries apply automatic data retention policies, which could lead to unexpected data loss if not properly configured.
Read/Write Rates: Timeseries data usually has high write rates but lower read rates. If your use case involves high read rates, performance might deteriorate.
Remember to thoroughly read and understand the library documentation, and consider your specific requirements and constraints when selecting a TSDB library for your project.