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2025-01-19 Update From: SLTechnology News&Howtos shulou NAV: SLTechnology News&Howtos > Internet Technology >
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This article mainly introduces "how to use NBI visualization + influxDB time series database to build the big data analysis platform of the Internet of things". In the daily operation, I believe that many people have doubts about how to use NBI visualization + influxDB time series database to build the big data analysis platform of the Internet of things. The editor consulted all kinds of materials and sorted out simple and useful operation methods. I hope it will be helpful for you to answer the doubt of "how to use NBI visualization + influxDB time series database to build big data analysis platform of the Internet of things"! Next, please follow the editor to study!
What is a time series database
Let's first introduce what is time series data. Time series data is a series of data based on time. Connecting these data points into lines in time coordinates can be made into multi-latitude reports in the past, revealing their trends, regularity, and anomalies; in the future, we can do big data analysis, machine learning, and realize prediction and early warning.
Time series database is the database that stores time series data, and it needs to support basic functions such as fast writing of time series data, persistence, multi-latitude aggregate query and so on.
Compared with the traditional database, only the current value of the data is recorded, while the temporal database records all the historical data. At the same time, the query of time series data always takes time as the filtering condition.
The scenario of time series database
All those who have time series data generation, and need to show their historical trends, periodic laws, anomalies, and further predict and analyze the future are all suitable scenarios for the time series database.
In the direction of industrial Internet of things environment monitoring, due to the requirements of the industry, it is necessary to store the working condition data. For example, customers have 20000 monitoring points in each factory area, a collection cycle of 500ms, a total of 20 factory areas. This adds up to an astonishing 26 trillion data points a year. Assuming each point 50Byte, the total amount of data will reach 1p (if each server has 10T of hard disk, then a total of more than 100servers will be needed). These data should not only be generated in real time and written into storage, but also support rapid query, visual display, and help managers analyze and make decisions; and they can also be used to do big data analysis, find deep-seated problems, and help enterprises save energy and reduce emissions. Increase efficiency. The final customer adopted the influxDB time series database scheme to help him solve the problem.
In the Internet scenario, there are also a large number of time series data generated. For example, in order to ensure the user's experience, every network stutter and network delay of the user will be recorded in Baidu Tiangong's time series database. The report is directly generated by the time series database for technical products to analyze, find and solve problems as soon as possible, and ensure the user's experience.
What is InfluxDB?
InfluxDB is an open source sequential data developed by InfluxData. It is written by Go and focuses on querying and storing sequential data with high performance. InfluxDB is widely used in storage system monitoring data, IoT industry real-time data and other scenarios.
Key features of Influxdb
1. Support for query syntax similar to SQL
two。 Provides direct access to Http Api
3. Store more than 1 billion levels of time series data
4. Flexible data retention policy that can be defined to the Database level (only the hottest data is retained)
5. Built-in management interface and CMD
6. Aggregate query with flying speed
7. Aggregate queries by different time periods
8. Built-in continuous query function to regularly calculate the data of a specified period of time and insert it into the specified table, which can be understood as collecting data at a fixed time.
9. Scale horizontally to support cluster mode
Introduction to the scheme:
Technical Architecture:
Introduction to the NBI visualization platform:
As a new generation of self-service and exploratory analysis tool, NBI big data visual analysis platform always focuses on the new products which are simple, easy to use and emphasize interactive analysis from the point of view of users. We integrate all aspects of data analysis (data preparation, self-service data modeling, exploratory analysis, authority control) into the system to enable enterprises to manage and analyze data orderly and safely.
Product features:
Case presentation:
At this point, the study on "how to use NBI visualization + influxDB time series database to build big data analysis platform of the Internet of things" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!
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