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High-performance, scalable time-series database designed for Industrial IoT (IIoT) scenarios
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taosdata/TDengine
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English |简体中文 |TDengine Cloud |Learn more about TSDB
- Introduction
- Documentation
- Prerequisites
- Building
- Packaging
- Installation
- Running
- Testing
- Releasing
- Workflow
- Coverage
- Contributing
TDengine is an open source, high-performance, cloud native and AI poweredtime-series database 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: 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: 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: 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: 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: 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: 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: 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, pleasecheck here. The easiest way to experience TDengine is throughTDengine Cloud. For the latest TDengine component TDgpt, please refer toTDgpt README for details.
For user manual, system design and architecture, please refer toTDengine Documentation (TDengine 文档)
You can choose to install TDengine viacontainer,installation package,Kubernetes or tryfully managed service 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,Go Connector,Python Connector,Node.js Connector,C# Connector,Rust Connector.
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 TSDB-Enterprise edition.
If you want to compile taosAdapter or taosKeeper, you need to install Go 1.23 or above.
Install required tools on Linux
sudo apt-get updatesudo apt-get install -y gcc cmake build-essential git libjansson-dev \ libsnappy-dev liblzma-dev zlib1g-dev pkg-config
sudo yum updateyum install -y epel-release gcc gcc-c++ make cmake git perl dnf-plugins-core yum config-manager --set-enabled powertoolsyum install -y zlib-static xz-devel snappy-devel jansson-devel pkgconfig libatomic-static libstdc++-static
Install required tools on macOS
Please install the dependencies withbrew.
brew install argp-standalone gflags pkgconfig
Not available for TDengine TSDB-OSS.
Clone the repository to the target machine:
git clone https://github.com/taosdata/TDengine.gitcd TDengineTDengine 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 usecmake .. -DBUILD_TOOLS=true to make them be compiled with TDengine.
TDengine requiresGCC 9.3.1 or higher andCMake 3.18.0 or higher for building.
Detailed steps to build on Linux
You can run the bash scriptbuild.sh to build both TDengine and taosTools including taosBenchmark and taosdump as below:
./build.sh
It equals to execute following commands:
mkdir debug&&cd debugcmake .. -DBUILD_TOOLS=true -DBUILD_CONTRIB=truemake
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:
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:
cmake .. -DCPUTYPE=aarch64&& cmake --build.
Detailed steps to build on macOS
Please install XCode command line tools and cmake. Verified with XCode 11.4+ on Catalina and Big Sur.
mkdir debug&&cd debugcmake ..&& 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.
Not available for TDengine TSDB-OSS.
The TDengine TSDB-OSS installer can NOT be created by this repository only, due to some component dependencies. We are still working on this improvement.
Detailed steps to install on Linux
After building successfully, TDengine can be installed by:
sudo make install
Installing from source code will also configure service management for TDengine. Users can also choose toinstall from packages for it.
Detailed steps to install on macOS
After building successfully, TDengine can be installed by:
sudo make install
Not available for TDengine TSDB-OSS.
Detailed steps to run on Linux
To start the service after installation on linux, in a terminal, use:
sudo systemctl start taosd
Then users can use the TDengine CLI to connect the TDengine server. In a terminal, use:
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 betaosd.exe)
./build/bin/taosd -c test/cfg
In another terminal, use the TDengine CLI to connect the server:
./build/bin/taos -c test/cfg
Option-c test/cfg specifies the system configuration file directory.
Detailed steps to run on macOS
To start the service after installation on macOS, double-click the /applications/TDengine to start the program, or in a terminal, use:
sudo launchctl start com.tdengine.taosd
Then users can use the TDengine CLI to connect the TDengine server. In a terminal, use:
taos
If TDengine CLI connects the server successfully, welcome messages and version info are printed. Otherwise, an error message is shown.
Not available for TDengine TSDB-OSS.
For how to run different types of tests on TDengine, please seeTesting TDengine.
For the complete list of TDengine Releases, please seeReleases.
TDengine build check workflow can be found in thisGithub Action. More workflows will be available soon.
Latest TDengine test coverage report can be found oncoveralls.io
How to run the coverage report locally?
To create the test coverage report (in HTML format) locally, please run following commands:cd testsbash 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.
Please follow thecontribution guidelines to contribute to TDengine.
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