base on High-performance, scalable time-series database designed for Industrial IoT (IIoT) scenarios <p align="center"> <a href="https://tdengine.com" target="_blank"> <img src="docs/assets/tdengine.svg" alt="TDengine" width="500" /> </a> </p> [](https://github.com/taosdata/TDengine/actions/workflows/tdengine-release-build.yml) [](https://coveralls.io/github/taosdata/TDengine?branch=3.0) [](https://github.com/feici02/TDengine/commits/main/) <br /> [](https://github.com/taosdata/TDengine/releases) [](https://github.com/taosdata/TDengine/blob/main/LICENSE) [](https://bestpractices.coreinfrastructure.org/projects/4201) <br /> [](https://twitter.com/tdenginedb) [](https://www.youtube.com/@tdengine) [](https://discord.com/invite/VZdSuUg4pS) [](https://www.linkedin.com/company/tdengine) [](https://stackoverflow.com/questions/tagged/tdengine) English | [简体中文](README-CN.md) | [TDengine Cloud](https://cloud.tdengine.com) | [Learn more about TSDB](https://tdengine.com/time-series-database/) # Table of Contents 1. [Introduction](#1-introduction) 1. [Documentation](#2-documentation) 1. [Prerequisites](#3-prerequisites) - [3.1 Prerequisites On Linux](#31-prerequisites-on-linux) - [3.2 Prerequisites On macOS](#32-prerequisites-on-macos) - [3.3 Prerequisites On Windows](#33-prerequisites-on-windows) - [3.4 Clone the repo](#34-clone-the-repo) 1. [Building](#4-building) - [4.1 Build on Linux](#41-build-on-linux) - [4.2 Build on macOS](#42-build-on-macos) - [4.3 Build On Windows](#43-build-on-windows) 1. [Packaging](#5-packaging) 1. [Installation](#6-installation) - [6.1 Install on Linux](#61-install-on-linux) - [6.2 Install on macOS](#62-install-on-macos) - [6.3 Install on Windows](#63-install-on-windows) 1. [Running](#7-running) - [7.1 Run TDengine on Linux](#71-run-tdengine-on-linux) - [7.2 Run TDengine on macOS](#72-run-tdengine-on-macos) - [7.3 Run TDengine on Windows](#73-run-tdengine-on-windows) 1. [Testing](#8-testing) 1. [Releasing](#9-releasing) 1. [Workflow](#10-workflow) 1. [Coverage](#11-coverage) 1. [Contributing](#12-contributing) # 1. Introduction TDengine is an open source, high-performance, cloud native and AI powered [time-series database](https://tdengine.com/tsdb/) designed for Internet of Things (IoT), Connected Cars, and Industrial IoT. It enables efficient, real-time data ingestion, processing, and analysis of TB and even PB scale data per day, generated by billions of sensors and data collectors. TDengine differentiates itself from other time-series databases with the following advantages: - **[High Performance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: TDengine is the only time-series database to solve the high cardinality issue to support billions of data collection points while out performing other time-series databases for data ingestion, querying and data compression. - **[Simplified Solution](https://tdengine.com/tdengine/simplified-time-series-data-solution/)**: Through built-in caching, stream processing, data subscription and AI agent features, TDengine provides a simplified solution for time-series data processing. It reduces system design complexity and operation costs significantly. - **[Cloud Native](https://tdengine.com/tdengine/cloud-native-time-series-database/)**: Through native distributed design, sharding and partitioning, separation of compute and storage, RAFT, support for kubernetes deployment and full observability, TDengine is a cloud native Time-Series Database and can be deployed on public, private or hybrid clouds. - **[AI Powered](https://tdengine.com/tdengine/tdgpt/)**: Through the built in AI agent TDgpt, TDengine can connect to a variety of time series foundation model, large language model, machine learning and traditional algorithms to provide time series data forecasting, anomly detection, imputation and classification. - **[Ease of Use](https://tdengine.com/tdengine/easy-time-series-data-platform/)**: For administrators, TDengine significantly reduces the effort to deploy and maintain. For developers, it provides a simple interface, simplified solution and seamless integrations for third party tools. For data users, it gives easy data access. - **[Easy Data Analytics](https://tdengine.com/tdengine/time-series-data-analytics-made-easy/)**: Through super tables, storage and compute separation, data partitioning by time interval, pre-computation and AI agent, TDengine makes it easy to explore, format, and get access to data in a highly efficient way. - **[Open Source](https://tdengine.com/tdengine/open-source-time-series-database/)**: TDengine’s core modules, including cluster feature and AI agent, are all available under open source licenses. It has gathered 23.7k stars on GitHub. There is an active developer community, and over 730k running instances worldwide. For a full list of TDengine competitive advantages, please [check here](https://tdengine.com/tdengine/). The easiest way to experience TDengine is through [TDengine Cloud](https://cloud.tdengine.com). For the latest TDengine component TDgpt, please refer to [TDgpt README](./tools/tdgpt/README.md) for details. # 2. Documentation For user manual, system design and architecture, please refer to [TDengine Documentation](https://docs.tdengine.com) ([TDengine 文档](https://docs.taosdata.com)) You can choose to install TDengine via [container](https://docs.tdengine.com/get-started/deploy-in-docker/), [installation package](https://docs.tdengine.com/get-started/deploy-from-package/), [Kubernetes](https://docs.tdengine.com/operations-and-maintenance/deploy-your-cluster/#kubernetes-deployment) or try [fully managed service](https://cloud.tdengine.com/) without installation. This quick guide is for developers who want to contribute, build, release and test TDengine by themselves. For contributing/building/testing TDengine Connectors, please check the following repositories: [JDBC Connector](https://github.com/taosdata/taos-connector-jdbc), [Go Connector](https://github.com/taosdata/driver-go), [Python Connector](https://github.com/taosdata/taos-connector-python), [Node.js Connector](https://github.com/taosdata/taos-connector-node), [C# Connector](https://github.com/taosdata/taos-connector-dotnet), [Rust Connector](https://github.com/taosdata/taos-connector-rust). # 3. Prerequisites At the moment, TDengine server supports running on Linux/MacOS systems. Any application can also choose the RESTful interface provided by taosAdapter to connect the taosd service. TDengine supports X64/ARM64 CPU, and it will support MIPS64, Alpha64, ARM32, RISC-V and other CPU architectures in the future. Right now we don't support build with cross-compiling environment. Starting from version 3.1.0.0, TDengine supports the Windows system exclusively in its Enterprise edition. If you want to compile taosAdapter or taosKeeper, you need to install Go 1.23 or above. ## 3.1 Prerequisites on Linux <details> <summary>Install required tools on Linux</summary> ### For Ubuntu 18.04、20.04、22.04 ```bash sudo apt-get update sudo apt-get install -y gcc cmake build-essential git libjansson-dev \ libsnappy-dev liblzma-dev zlib1g-dev pkg-config ``` ### For CentOS 8 ```bash sudo yum update yum install -y epel-release gcc gcc-c++ make cmake git perl dnf-plugins-core yum config-manager --set-enabled powertools yum install -y zlib-static xz-devel snappy-devel jansson-devel pkgconfig libatomic-static libstdc++-static ``` </details> ## 3.2 Prerequisites on macOS <details> <summary>Install required tools on macOS</summary> Please install the dependencies with [brew](https://brew.sh/). ```bash brew install argp-standalone gflags pkgconfig ``` </details> ## 3.3 Prerequisites on Windows Not available for community edition. ## 3.4 Clone the repo Clone the repository to the target machine: ```bash git clone https://github.com/taosdata/TDengine.git cd TDengine ``` </details> # 4. Building TDengine provide a few useful tools such as taosBenchmark (was named taosdemo) and taosdump. They were part of TDengine. By default, TDengine compiling does not include taosTools. You can use `cmake .. -DBUILD_TOOLS=true` to make them be compiled with TDengine. TDengine requires [GCC](https://gcc.gnu.org/) 9.3.1 or higher and [CMake](https://cmake.org/) 3.13.0 or higher for building. ## 4.1 Build on Linux <details> <summary>Detailed steps to build on Linux</summary> You can run the bash script `build.sh` to build both TDengine and taosTools including taosBenchmark and taosdump as below: ```bash ./build.sh ``` It equals to execute following commands: ```bash mkdir debug && cd debug cmake .. -DBUILD_TOOLS=true -DBUILD_CONTRIB=true make ``` If you want to compile taosAdapter, you need to add the `-DBUILD_HTTP=false` option. If you want to compile taosKeeper, you need to add the `-DBUILD_KEEPER=true` option. You can use Jemalloc as memory allocator instead of glibc: ```bash cmake .. -DJEMALLOC_ENABLED=ON ``` TDengine build script can auto-detect the host machine's architecture on x86, x86-64, arm64 platform. You can also specify architecture manually by CPUTYPE option: ```bash cmake .. -DCPUTYPE=aarch64 && cmake --build . ``` </details> ## 4.2 Build on macOS <details> <summary>Detailed steps to build on macOS</summary> Please install XCode command line tools and cmake. Verified with XCode 11.4+ on Catalina and Big Sur. ```shell mkdir debug && cd debug cmake .. && cmake --build . ``` If you want to compile taosAdapter, you need to add the `-DBUILD_HTTP=false` option. If you want to compile taosKeeper, you need to add the `-DBUILD_KEEPER=true` option. </details> ## 4.3 Build on Windows Not available for community edition. # 5. Packaging The TDengine community installer can NOT be created by this repository only, due to some component dependencies. We are still working on this improvement. # 6. Installation ## 6.1 Install on Linux <details> <summary>Detailed steps to install on Linux</summary> After building successfully, TDengine can be installed by: ```bash sudo make install ``` Installing from source code will also configure service management for TDengine. Users can also choose to [install from packages](https://docs.tdengine.com/get-started/deploy-from-package/) for it. </details> ## 6.2 Install on macOS <details> <summary>Detailed steps to install on macOS</summary> After building successfully, TDengine can be installed by: ```bash sudo make install ``` </details> ## 6.3 Install on Windows Not available for community edition. # 7. Running ## 7.1 Run TDengine on Linux <details> <summary>Detailed steps to run on Linux</summary> To start the service after installation on linux, in a terminal, use: ```bash sudo systemctl start taosd ``` Then users can use the TDengine CLI to connect the TDengine server. In a terminal, use: ```bash taos ``` If TDengine CLI connects the server successfully, welcome messages and version info are printed. Otherwise, an error message is shown. If you don't want to run TDengine as a service, you can run it in current shell. For example, to quickly start a TDengine server after building, run the command below in terminal: (We take Linux as an example, command on Windows will be `taosd.exe`) ```bash ./build/bin/taosd -c test/cfg ``` In another terminal, use the TDengine CLI to connect the server: ```bash ./build/bin/taos -c test/cfg ``` Option `-c test/cfg` specifies the system configuration file directory. </details> ## 7.2 Run TDengine on macOS <details> <summary>Detailed steps to run on macOS</summary> To start the service after installation on macOS, double-click the /applications/TDengine to start the program, or in a terminal, use: ```bash sudo launchctl start com.tdengine.taosd ``` Then users can use the TDengine CLI to connect the TDengine server. In a terminal, use: ```bash taos ``` If TDengine CLI connects the server successfully, welcome messages and version info are printed. Otherwise, an error message is shown. </details> ## 7.3 Run TDengine on Windows Not available for community edition. # 8. Testing For how to run different types of tests on TDengine, please see [Testing TDengine](./tests/README.md). # 9. Releasing For the complete list of TDengine Releases, please see [Releases](https://github.com/taosdata/TDengine/releases). # 10. Workflow TDengine build check workflow can be found in this [Github Action](https://github.com/taosdata/TDengine/actions/workflows/taosd-ci-build.yml). More workflows will be available soon. # 11. Coverage Latest TDengine test coverage report can be found on [coveralls.io](https://coveralls.io/github/taosdata/TDengine) <details> <summary>How to run the coverage report locally?</summary> To create the test coverage report (in HTML format) locally, please run following commands: ```bash cd tests bash setup-lcov.sh -v 1.16 && ./run_local_coverage.sh -b main -c task # on main branch and run cases in longtimeruning_cases.task # for more information about options please refer to ./run_local_coverage.sh -h ``` > **NOTE:** > Please note that the -b and -i options will recompile TDengine with the -DCOVER=true option, which may take a amount of time. </details> # 12. Contributing Please follow the [contribution guidelines](CONTRIBUTING.md) to contribute to TDengine. ", Assign "at most 3 tags" to the expected json: {"id":"1043","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"