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Table of Contents

1. Introduction
2. Documentation
3. Prerequisites
   • 3.1 Prerequisites On Linux
   • 3.2 Prerequisites On macOS
   • 3.3 Prerequisites On Windows
   • 3.4 Clone the repo
4. Building
   • 4.1 Build on Linux
   • 4.2 Build on macOS
   • 4.3 Build On Windows
5. Packaging
6. Installation
   • 6.1 Install on Linux
   • 6.2 Install on macOS
   • 6.3 Install on Windows
7. Running
   • 7.1 Run TDengine on Linux
   • 7.2 Run TDengine on macOS
   • 7.3 Run TDengine on Windows
8. Testing
9. Releasing
10. Workflow
11. Coverage
12. Contributing

1. Introduction

TDengine is an open source, high-performance, cloud native and AI powered time-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, please check here. The easiest way to experience TDengine is through TDengine Cloud. For the latest TDengine component TDgpt, please refer to TDgpt README for details.

2. Documentation

For user manual, system design and architecture, please refer to TDengine Documentation (TDengine 文档)

You can choose to install TDengine via container, installation package, Kubernetes or try fully 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.

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

sudo apt-get update
sudo apt-get install -y gcc cmake build-essential git libjansson-dev \
  libsnappy-dev liblzma-dev zlib1g-dev pkg-config

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 

3.2 Prerequisites on macOS

brew install argp-standalone gflags pkgconfig

3.3 Prerequisites on Windows

Not available for community edition.

3.4 Clone the repo

Clone the repository to the target machine:

git clone https://github.com/taosdata/TDengine.git
cd TDengine

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 9.3.1 or higher and CMake 3.18.0 or higher for building.

4.1 Build on Linux

./build.sh

mkdir debug && cd debug
cmake .. -DBUILD_TOOLS=true -DBUILD_CONTRIB=true
make

cmake .. -DJEMALLOC_ENABLED=ON

cmake .. -DCPUTYPE=aarch64 && cmake --build .

4.2 Build on macOS

mkdir debug && cd debug
cmake .. && cmake --build .

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

sudo make install

6.2 Install on macOS

sudo make install

6.3 Install on Windows

Not available for community edition.

7. Running

7.1 Run TDengine on Linux

sudo systemctl start taosd

taos

./build/bin/taosd -c test/cfg

./build/bin/taos -c test/cfg

7.2 Run TDengine on macOS

sudo launchctl start com.tdengine.taosd

taos

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.

9. Releasing

For the complete list of TDengine Releases, please see Releases.

10. Workflow

TDengine build check workflow can be found in this Github Action. More workflows will be available soon.

11. Coverage

Latest TDengine test coverage report can be found on coveralls.io

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

12. Contributing

Please follow the contribution guidelines to contribute to TDengine.