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Npm Time Series Database Libraries

Most Popular Npm Time Series Database Libraries

NameSizeLicenseAgeLast Published
smoothie24.47 kBMIT11 Years18 Jul 2022
timeseries-analysis16.37 kBMIT9 Years8 Dec 2014
dygraphs3.39 MBMIT9 Years16 Feb 2023
orchestrate25.2 kBApache-2.010 Years8 Nov 2016
gauss24.25 kBUNKNOWN12 Years22 Apr 2014
timestream6.38 kBMIT10 Years20 Feb 2015
pondjs21.18 MBBSD-3-Clause-LBNL8 Years7 Nov 2019
compute-pcorr3.98 kBMIT8 Years30 Nov 2014
timestream-aggregates3.24 kBMIT10 Years23 Jan 2015
react-timeseries-charts835.81 kBBSD-3-Clause-LBNL8 Years17 May 2019
timelines-chart264.97 kBMIT7 Years8 Feb 2023
redibox61.46 kBMIT7 Years23 Sep 2016
compute-covariance3.84 kBMIT8 Years29 Nov 2014
timestreamdb2.09 kBMIT10 Years9 Nov 2015
hoard28.37 kBMIT12 Years18 Aug 2011

When Are Time Series Database Libraries Useful

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.

In the context of JavaScript development with npm, developers often use time series database libraries to handle real-time analytics, event logging, monitoring, and trend analysis. These libraries greatly simplify the tasks of storing and retrieving complex time series data, thus allowing developers to focus on application logic.

What Functionalities Do Time Series Database Libraries Usually Have

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:

  1. Data Persistence: Stores time-stamped data persistently, retaining it in the event of a system crash or shutdown.

  2. Data Compression: Optimizes storage space by compressing the time-series data.

  3. High-Speed Writings: Allows rapid input of data, accommodating the high-speed nature of time-series data collection from numerous sources.

  4. Scalability: Scales operations to handle large amounts of data, coming from multiple sources at a rapid pace.

  5. Query functionality: Enables querying of data based on different time intervals, specific points in time, aggregates over time, etc.

  6. Data visualization: Some libraries may provide a feature to visualize the data, making it easier to analyze patterns and trends.

Gotchas/Pitfalls to Look Out For

While using time series database libraries in JavaScript via npm, developers should look out for several potential pitfalls:

  1. 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.

  2. 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.

  3. Library Maturity: Not all TSDB libraries are mature and fully featured. Some are still in experimental or early stages of development.

  4. Data Retention Policies: Some TSDB libraries apply automatic data retention policies, which could lead to unexpected data loss if not properly configured.

  5. 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.