--- sidebar_label: TDengine + Telegraf + Grafana title: Quickly Build IT DevOps Visualization System with TDengine + Telegraf + Grafana --- ## Background TDengine is a big data platform designed and optimized for IoT (Internet of Things), Vehicle Telematics, Industrial Internet, IT DevOps, etc. by TAOSData. Since it opened its source code 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 persistent 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 of configuration files. The architecture is as follows. ![IT-DevOps-Solutions-Telegraf.png](/img/IT-DevOps-Solutions-Telegraf.png) ## Installation steps ### Installing Telegraf, Grafana and TDengine To install Telegraf, Grafana, and TDengine, please refer to the relevant official documentation. ### Telegraf Please refer to the [official documentation](https://portal.influxdata.com/downloads/). ### Grafana Please refer to the [official documentation](https://grafana.com/grafana/download). ### TDengine Download the latest TDengine-server 2.4.0.x or above from the [Downloads](http://taosdata.com/cn/all-downloads/) page on the TAOSData website and install it. ## Data Connection Setup ### Download TDengine plug-in to grafana plug-in directory ```bash 1. wget -c https://github.com/taosdata/grafanaplugin/releases/download/v3.1.3/tdengine-datasource-3.1.3.zip 2. sudo unzip tdengine-datasource-3.1.3.zip -d /var/lib/grafana/plugins/ 3. sudo chown grafana:grafana -R /var/lib/grafana/plugins/tdengine 4. echo -e "[plugins]\nallow_loading_unsigned_plugins = tdengine-datasource\n" | sudo tee -a /etc/grafana/grafana.ini 5. sudo systemctl restart grafana-server.service ``` ### Modify /etc/telegraf/telegraf.conf 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://:6041/influxdb/v1/write?db=" method = "POST" timeout = "5s" username = "" password = "" data_format = "influx" influx_max_line_bytes = 250 ``` Then restart telegraf: ```bash sudo systemctl start telegraf ``` ### Importing the Dashboard 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- v0.1.0.json`, download the dashboard JSON file and import it. You will then see the dashboard in the following screen. ![IT-DevOps-Solutions-telegraf-dashboard.png](/img/IT-DevOps-Solutions-telegraf-dashboard.png) ## Wrap-up The above demonstrates how to quickly build a IT DevOps visualization system. Thanks to the new schemaless protocol parsing feature in TDengine version 2.4.0.0 and the powerful ecological software adaptation capability, 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.