Developer's Guide

To develop an application, if you plan to use TDengine as a tool for time-series data processing, there are several things to do:

1. Determine the connection method to TDengine. No matter what programming language you use, you can always use the REST interface, but you can also use connectors unique to each programming language for convenient connections.
2. Based on your application scenario, determine the data model. Depending on the characteristics of the data, decide whether to create one or multiple databases; distinguish between static tags and collected metrics, establish the correct supertables, and create subtables.
3. Decide on the method of inserting data. TDengine supports data insertion using standard SQL, but also supports Schemaless mode insertion, which allows data to be written directly without manually creating tables.
4. Based on business requirements, determine which SQL queries need to be written.
5. If you want to perform lightweight real-time statistical analysis based on time-series data, including various monitoring dashboards, it is recommended to use the streaming computing capabilities of TDengine 3.0, instead of deploying complex streaming computing systems like Spark or Flink.
6. If your application has modules that need to consume inserted data and you want to be notified when new data is inserted, it is recommended to use the data subscription feature provided by TDengine, without the need to deploy Kafka or other messaging queue software.
7. In many scenarios (such as vehicle management), applications need to obtain the latest status of each data collection point, so it is recommended to use TDengine's Cache feature, instead of deploying separate caching software like Redis.
8. If you find that TDengine's functions do not meet your requirements, you can use User Defined Functions (UDF) to solve the problem.

This section is organized in the order mentioned above. For ease of understanding, TDengine provides example code for each feature and each supported programming language, located at Example Code. All example codes are guaranteed to be correct by CI, scripts located at Example Code CI. If you want to learn more about using SQL, check out the SQL Manual. If you want to learn more about using various connectors, read the Connector Reference Guide. If you also want to integrate TDengine with third-party systems, such as Grafana, please refer to Third-Party Tools.

If you encounter any problems during the development process, please click "Report Issue" at the bottom of each page to submit an Issue directly on GitHub.

📄️ Connecting to TDengine

TDengine provides a rich set of application development interfaces. To facilitate users in quickly developing their applications, TDengine supports connectors for multiple programming languages. The official connectors include support for C/C++, Java, Python, Go, Node.js, C#, Rust, Lua (community contribution), and PHP (community contribution). These connectors support connecting to the TDengine cluster using the native interface (taosc) and REST interface (not supported in some languages yet). Community developers have also contributed several unofficial connectors, such as ADO.NET connector, Lua connector, and PHP connector. Additionally, TDengine can directly call the REST API provided by taosadapter for data writing and querying operations.

📄️ Running SQL Statements

TDengine provides comprehensive support for the SQL language, allowing users to query, insert, and delete data using familiar SQL syntax. TDengine's SQL also supports database and table management operations, such as creating, modifying, and deleting databases and tables. TDengine extends standard SQL by introducing features unique to time-series data processing, such as aggregation queries, downsampling, and interpolation queries, to adapt to the characteristics of time-series data. These extensions enable users to process time-series data more efficiently and perform complex data analysis and processing. For specific supported SQL syntax, please refer to TDengine SQL

📄️ Schemaless Ingestion

In IoT applications, to achieve functions such as automated management, business analysis, and device monitoring, it is often necessary to collect a large number of data items. However, due to reasons such as application logic upgrades and hardware adjustments of the devices themselves, the data collection items may change frequently. To address this challenge, TDengine provides a schemaless writing method, aimed at simplifying the data recording process.

📄️ Parameter Binding

When inserting data using parameter binding, it can avoid the resource consumption of SQL syntax parsing, thereby significantly improving the write performance. The reasons why parameter binding can improve writing efficiency include:

📄️ Managing Consumers

TDengine provides data subscription and consumption interfaces similar to those of message queue products. In many scenarios, by adopting TDengine's time-series big data platform, there is no need to integrate additional message queue products, thus simplifying application design and reducing maintenance costs. This chapter introduces the related APIs and usage methods for data subscription with various language connectors. For basic information on data subscription, please refer to Data Subscription

📄️ User-Defined Functions

Introduction to UDF

📄️ Ingesting Data Efficiently

This section describes how to write data to TDengine efficiently.