Project: TDengine (App Definition and Development / Database)
File: 01-telegraf.md

title: IT Visualization with TDengine + Telegraf + Grafana sidebar_label: TDengine + Telegraf + Grafana description: This document describes how to create an IT visualization system by integrating TDengine with Telegraf and Grafana. TDengine is a big data platform designed and optimized for IoT (Internet of Things), Vehicle Telemetry, Industrial Internet, IT DevOps and other applications. Since it was open-sourced in July 2019, it has won the favor of a large number of time-series data developers with its innovative data modeling design, convenient installation, easy-to-use programming interface, and powerful data writing and query performance. IT DevOps metric data usually are time sensitive, for example: System resource metrics: CPU, memory, IO, bandwidth, etc. Software system metrics: health status, number of connections, number of requests, number of timeouts, number of errors, response time, service type, and other business-related metrics. Current mainstream IT DevOps system usually include a data collection module, a data persistent module, and a visualization module; Telegraf and Grafana are one of the most popular data collection modules and visualization modules, respectively. The data persistence module is available in a wide range of options, with OpenTSDB or InfluxDB being the most popular. TDengine, as an emerging time-series big data platform, has the advantages of high performance, high reliability, easy management and easy maintenance. This article introduces how to quickly build a TDengine + Telegraf + Grafana based IT DevOps visualization system without writing even a single line of code and by simply modifying a few lines in configuration files. The architecture is as follows. To install Telegraf, Grafana, and TDengine, please refer to the relevant official documentation. Please refer to the . Please refer to the . Download and install the . Please refer to For the configuration method, add the following text to `/etc/telegraf/telegraf.conf`, where `database name` should be the name where you want to store Telegraf data in TDengine, `TDengine server/cluster host`, `username` and `password` please fill in the actual TDengine values. ```text [[outputs.http]] url = "http://<TDengine server/cluster host>:6041/influxdb/v1/write?db=<database name>" method = "POST" timeout = "5s" username = "<TDengine's username>" password = "<TDengine's password>" data_format = "influx" ``` Then restart telegraf: ```bash sudo systemctl start telegraf ``` Log in to the Grafana interface using a web browser at `IP:3000`, with the system's initial username and password being `admin/admin`. Click on the gear icon on the left and select `Plugins`, you should find the TDengine data source plugin icon. Click on the plus icon on the left and select `Import` to get the data from `https://github.com/taosdata/grafanaplugin/blob/master/examples/telegraf/grafana/dashboards/telegraf-dashboard-v3.json` (for TDengine 3.0. for TDengine 2.x, please use `telegraf-dashboard-v2.json`), download the dashboard JSON file and import it. You will then see the dashboard in the following screen. The above demonstrates how to quickly build a IT DevOps visualization system. Thanks to the schemaless protocol parsing feature in TDengine and ability to integrate easily with a large software ecosystem, users can build an efficient and easy-to-use IT DevOps visualization system in just a few minutes. Please refer to the official documentation and product implementation cases for other features.