[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-6030":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":44,"readmeContent":45,"aiSummary":46,"trendingCount":16,"starSnapshotCount":16,"syncStatus":17,"lastSyncTime":47,"discoverSource":48},6030,"TDengine","taosdata\u002FTDengine","taosdata","High-performance, scalable time-series database designed for Industrial IoT (IIoT) scenarios","https:\u002F\u002Ftdengine.com",null,"C",24899,4994,680,402,0,2,16,52,10,87.2,"GNU Affero General Public License v3.0",false,"main",true,[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"bigdata","cloud-native","cluster","connected-vehicles","database","distributed","financial-analysis","industrial-iot","iot","metrics","monitoring","scalability","sql","tdengine","time-series","time-series-database","tsdb","2026-06-12 04:00:27","\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Ftdengine.com\" target=\"_blank\">\n  \u003Cimg\n    src=\"tdengine-logo.svg\"\n    alt=\"TDengine\"\n    width=\"500\"\n  \u002F>\n  \u003C\u002Fa>\n\u003C\u002Fp>\n\u003Cdiv align=\"center\">\n\n[![TDengine Release Build](https:\u002F\u002Fgithub.com\u002Ftaosdata\u002FTDengine\u002Factions\u002Fworkflows\u002Ftdengine-release-build.yml\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Ftaosdata\u002FTDengine\u002Factions\u002Fworkflows\u002Ftdengine-release-build.yml)\n[![Coverage 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align=\"center\">\n\nEnglish | [简体中文](README-CN.md) | [TDengine Cloud](https:\u002F\u002Fcloud.tdengine.com) | [Learn more about TSDB](https:\u002F\u002Ftdengine.com\u002Ftime-series-database\u002F)\n\n\u003Cbr \u002F>\n\u003C\u002Fdiv>\n\n# Table of Contents\n\n- [1. Introduction](#1-introduction)\n- [2. Documentation](#2-documentation)\n- [3. Prerequisites](#3-prerequisites)\n  - [3.1 Prerequisites on Linux](#31-prerequisites-on-linux)\n    - [3.1.1 For Ubuntu](#311-for-ubuntu)\n    - [3.1.2 For CentOS](#312-for-centos)\n  - [3.2 Prerequisites on macOS](#32-prerequisites-on-macos)\n  - [3.3 Prerequisites on Windows](#33-prerequisites-on-windows)\n  - [3.4 Clone the repo](#34-clone-the-repo)\n- [4. Building](#4-building)\n  - [4.1 Build on Linux](#41-build-on-linux)\n  - [4.2 Build on macOS](#42-build-on-macos)\n  - [4.3 Build on Windows](#43-build-on-windows)\n- [5. Packaging](#5-packaging)\n- [6. Installation](#6-installation)\n  - [6.1 Install on Linux](#61-install-on-linux)\n  - [6.2 Install on macOS](#62-install-on-macos)\n  - [6.3 Install on Windows](#63-install-on-windows)\n- [7. Running](#7-running)\n  - [7.1 Run TDengine on Linux](#71-run-tdengine-on-linux)\n  - [7.2 Run TDengine on macOS](#72-run-tdengine-on-macos)\n  - [7.3 Run TDengine on Windows](#73-run-tdengine-on-windows)\n- [8. Testing](#8-testing)\n- [9. Releasing](#9-releasing)\n- [10. Workflow](#10-workflow)\n- [11. Coverage](#11-coverage)\n- [12. Contributing](#12-contributing)\n\n# 1. Introduction\n\nTDengine is an open source, high-performance, cloud native and AI powered [time-series database](https:\u002F\u002Ftdengine.com\u002Ftsdb\u002F) 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:\n\n- **[High Performance](https:\u002F\u002Ftdengine.com\u002Ftdengine\u002Fhigh-performance-time-series-database\u002F)**: 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.\n\n- **[Simplified Solution](https:\u002F\u002Ftdengine.com\u002Ftdengine\u002Fsimplified-time-series-data-solution\u002F)**: 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.\n\n- **[Cloud Native](https:\u002F\u002Ftdengine.com\u002Ftdengine\u002Fcloud-native-time-series-database\u002F)**: 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.\n\n- **[AI Powered](https:\u002F\u002Ftdengine.com\u002Ftdengine\u002Ftdgpt\u002F)**: 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, anomaly detection, imputation and classification.\n\n- **[Ease of Use](https:\u002F\u002Ftdengine.com\u002Ftdengine\u002Feasy-time-series-data-platform\u002F)**: 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.\n\n- **[Easy Data Analytics](https:\u002F\u002Ftdengine.com\u002Ftdengine\u002Ftime-series-data-analytics-made-easy\u002F)**: 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.\n\n- **[Open Source](https:\u002F\u002Ftdengine.com\u002Ftdengine\u002Fopen-source-time-series-database\u002F)**: 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.\n\nFor a full list of TDengine competitive advantages, please [check here](https:\u002F\u002Ftdengine.com\u002Ftdengine\u002F). The easiest way to experience TDengine is through [TDengine Cloud](https:\u002F\u002Fcloud.tdengine.com). For the latest TDengine component TDgpt, please refer to [TDgpt README](.\u002Ftools\u002Ftdgpt\u002FREADME.md) for details.\n\n# 2. Documentation\n\nFor user manual, system design and architecture, please refer to [TDengine Documentation](https:\u002F\u002Fdocs.tdengine.com) ([TDengine 文档](https:\u002F\u002Fdocs.taosdata.com))\n\nYou can choose to install TDengine via [container](https:\u002F\u002Fdocs.tdengine.com\u002Fget-started\u002Fdeploy-in-docker\u002F), [installation package](https:\u002F\u002Fdocs.tdengine.com\u002Fget-started\u002Fdeploy-from-package\u002F), [Kubernetes](https:\u002F\u002Fdocs.tdengine.com\u002Foperations-and-maintenance\u002Fdeploy-your-cluster\u002F#kubernetes-deployment) or try [fully managed service](https:\u002F\u002Fcloud.tdengine.com\u002F) without installation. This quick guide is for developers who want to contribute, build, release and test TDengine by themselves.\n\nFor contributing\u002Fbuilding\u002Ftesting TDengine Connectors, please check the following repositories: [JDBC Connector](https:\u002F\u002Fgithub.com\u002Ftaosdata\u002Ftaos-connector-jdbc), [Go Connector](https:\u002F\u002Fgithub.com\u002Ftaosdata\u002Fdriver-go), [Python Connector](https:\u002F\u002Fgithub.com\u002Ftaosdata\u002Ftaos-connector-python), [Node.js Connector](https:\u002F\u002Fgithub.com\u002Ftaosdata\u002Ftaos-connector-node), [C# Connector](https:\u002F\u002Fgithub.com\u002Ftaosdata\u002Ftaos-connector-dotnet), [Rust Connector](https:\u002F\u002Fgithub.com\u002Ftaosdata\u002Ftaos-connector-rust).\n\n# 3. Prerequisites\n\nAt the moment, TDengine server supports running on Linux\u002FMacOS systems. Any application can also choose the RESTful interface provided by taosAdapter to connect the taosd service. TDengine supports X64\u002FARM64 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.\n\nStarting from version 3.1.0.0, TDengine supports the Windows system exclusively in its TSDB-Enterprise edition.\n\nIf you want to compile taosAdapter or taosKeeper, you need to install Go 1.23 or above.\n\n## 3.1 Prerequisites on Linux\n\n\u003Cdetails>\n\n\u003Csummary>Install required tools on Linux\u003C\u002Fsummary>\n\n### 3.1.1 For Ubuntu\n\nVerified on Ubuntu 18.04, 20.04, 22.04.\n\n```bash\nsudo apt-get update\nsudo apt-get install -y gcc cmake build-essential git libjansson-dev \\\n  libsnappy-dev liblzma-dev zlib1g-dev pkg-config libtool autoconf automake groff\n```\n\n### 3.1.2 For CentOS\n\nVerified on CentOS 8.\n\n```bash\nsudo yum update\nyum install -y epel-release gcc gcc-c++ make cmake git perl dnf-plugins-core autoconf automake libtool groff\nyum config-manager --set-enabled powertools\nyum install -y zlib-static xz-devel snappy-devel jansson-devel pkgconfig libatomic-static libstdc++-static \n```\n\n\u003C\u002Fdetails>\n\n## 3.2 Prerequisites on macOS\n\n\u003Cdetails>\n\n\u003Csummary>Install required tools on macOS\u003C\u002Fsummary>\n\nPlease install the dependencies with [brew](https:\u002F\u002Fbrew.sh\u002F).\n\n```bash\nbrew install argp-standalone gflags pkgconfig\n```\n\n\u003C\u002Fdetails>\n\n## 3.3 Prerequisites on Windows\n\nNot available for TDengine TSDB-OSS.\n\n## 3.4 Clone the repo\n\nClone the repository to the target machine:\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Ftaosdata\u002FTDengine.git\ncd TDengine\n```\n\n\u003C\u002Fdetails>\n\n# 4. Building\n\nTDengine 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.\n\nTDengine requires [GCC](https:\u002F\u002Fgcc.gnu.org\u002F) 9.3.1 or higher and [CMake](https:\u002F\u002Fcmake.org\u002F) 3.18.0 or higher for building.\n\n## 4.1 Build on Linux\n\n\u003Cdetails>\n\n\u003Csummary>Detailed steps to build on Linux\u003C\u002Fsummary>\n\nYou can run the bash script `build.sh` to build both TDengine and taosTools including taosBenchmark and taosdump as below:\n\n```bash\n.\u002Fbuild.sh\n```\n\nIt equals to execute following commands:\n\n```bash\nmkdir debug && cd debug\ncmake .. -DBUILD_TOOLS=true -DBUILD_CONTRIB=true\nmake\n```\n\nIf you want to compile taosAdapter, you need to add the `-DBUILD_HTTP=false` option.\n\nIf you want to compile taosKeeper, you need to add the `-DBUILD_KEEPER=true` option.\n\nYou can use Jemalloc as memory allocator instead of glibc:\n\n```bash\ncmake .. -DJEMALLOC_ENABLED=ON\n```\n\nTDengine build script can auto-detect the host machine's architecture on x86, x86-64, arm64 platform.\nYou can also specify architecture manually by CPUTYPE option:\n\n```bash\ncmake .. -DCPUTYPE=aarch64 && cmake --build .\n```\n\n\u003C\u002Fdetails>\n\n## 4.2 Build on macOS\n\n\u003Cdetails>\n\n\u003Csummary>Detailed steps to build on macOS\u003C\u002Fsummary>\n\nPlease install XCode command line tools and cmake. Verified with XCode 11.4+ on Catalina and Big Sur.\n\n```shell\nmkdir debug && cd debug\ncmake .. && cmake --build .\n```\n\nIf you want to compile taosAdapter, you need to add the `-DBUILD_HTTP=false` option.\n\nIf you want to compile taosKeeper, you need to add the `-DBUILD_KEEPER=true` option.\n\n\u003C\u002Fdetails>\n\n## 4.3 Build on Windows\n\nNot available for TDengine TSDB-OSS.\n\n# 5. Packaging\n\nThe TDengine TSDB-OSS installer can NOT be created by this repository only, due to some component dependencies. We are still working on this improvement.\n\n# 6. Installation\n\n## 6.1 Install on Linux\n\n\u003Cdetails>\n\n\u003Csummary>Detailed steps to install on Linux\u003C\u002Fsummary>\n\nAfter building successfully, TDengine can be installed by:\n\n```bash\nsudo make install\n```\n\nInstalling from source code will also configure service management for TDengine. Users can also choose to [install from packages](https:\u002F\u002Fdocs.tdengine.com\u002Fget-started\u002Fdeploy-from-package\u002F) for it.\n\n\u003C\u002Fdetails>\n\n## 6.2 Install on macOS\n\n\u003Cdetails>\n\n\u003Csummary>Detailed steps to install on macOS\u003C\u002Fsummary>\n\nAfter building successfully, TDengine can be installed by:\n\n```bash\nsudo make install\n```\n\n\u003C\u002Fdetails>\n\n## 6.3 Install on Windows\n\nNot available for TDengine TSDB-OSS.\n\n# 7. Running\n\n## 7.1 Run TDengine on Linux\n\n\u003Cdetails>\n\n\u003Csummary>Detailed steps to run on Linux\u003C\u002Fsummary>\n\nTo start the service after installation on linux, in a terminal, use:\n\n```bash\nsudo systemctl start taosd\n```\n\nThen users can use the TDengine CLI to connect the TDengine server. In a terminal, use:\n\n```bash\ntaos\n```\n\nIf TDengine CLI connects the server successfully, welcome messages and version info are printed. Otherwise, an error message is shown.\n\nIf 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`)\n\n```bash\n.\u002Fbuild\u002Fbin\u002Ftaosd -c test\u002Fcfg\n```\n\nIn another terminal, use the TDengine CLI to connect the server:\n\n```bash\n.\u002Fbuild\u002Fbin\u002Ftaos -c test\u002Fcfg\n```\n\nOption `-c test\u002Fcfg` specifies the system configuration file directory.\n\n\u003C\u002Fdetails>\n\n## 7.2 Run TDengine on macOS\n\n\u003Cdetails>\n\n\u003Csummary>Detailed steps to run on macOS\u003C\u002Fsummary>\n\nTo start the service after installation on macOS, double-click the \u002Fapplications\u002FTDengine to start the program, or in a terminal, use:\n\n```bash\nsudo launchctl start com.tdengine.taosd\n```\n\nThen users can use the TDengine CLI to connect the TDengine server. In a terminal, use:\n\n```bash\ntaos\n```\n\nIf TDengine CLI connects the server successfully, welcome messages and version info are printed. Otherwise, an error message is shown.\n\n\u003C\u002Fdetails>\n\n## 7.3 Run TDengine on Windows\n\nNot available for TDengine TSDB-OSS.\n\n# 8. Testing\n\nFor how to run different types of tests on TDengine, please see [Testing TDengine](.\u002Ftests\u002FREADME.md).\n\n# 9. Releasing\n\nFor the complete list of TDengine Releases, please see [Releases](https:\u002F\u002Fgithub.com\u002Ftaosdata\u002FTDengine\u002Freleases).\n\n# 10. Workflow\n\nTDengine build check workflow can be found in this [Github Action](https:\u002F\u002Fgithub.com\u002Ftaosdata\u002FTDengine\u002Factions\u002Fworkflows\u002Ftaosd-ci-build.yml). More workflows will be available soon.\n\n# 11. Coverage\n\nLatest TDengine test coverage report can be found on [coveralls.io](https:\u002F\u002Fcoveralls.io\u002Fgithub\u002Ftaosdata\u002FTDengine)\n\n\u003Cdetails>\n\n\u003Csummary>How to run the coverage report locally?\u003C\u002Fsummary>\nTo create the test coverage report (in HTML format) locally, please run following commands:\n\n```bash\ncd tests\nbash setup-lcov.sh -v 1.16 && .\u002Frun_local_coverage.sh -b main -c task \n# on main branch and run cases in longtimeruning_cases.task \n# for more information about options please refer to .\u002Frun_local_coverage.sh -h\n```\n\n> **NOTE**:\n> Please note that the -b and -i options will recompile TDengine with the -DCOVER=true option, which may take a amount of time.\n\n\u003C\u002Fdetails>\n\n# 12. Contributing\n\nPlease follow the [contribution guidelines](CONTRIBUTING.md) to contribute to TDengine.\n","TDengine 是一个专为工业物联网（IIoT）场景设计的高性能、可扩展的时间序列数据库。它支持SQL查询，具有分布式架构，能够处理大规模的数据集，并提供实时数据写入和查询功能。TDengine 采用C语言开发，具备出色的性能和低延迟特性，适用于需要高效存储与分析时间序列数据的应用场景，如工业监控、车联网、金融分析等。其开源性质及AGPLv3许可证使得开发者可以自由地使用和贡献代码。","2026-06-11 03:05:24","top_language"]