taosdata/TDengine

High-performance, scalable time-series database designed for Industrial IoT (IIoT) scenarios

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Summary Information

Updated 36 minutes ago
Added to GitGenius on July 10th, 2024
Created on July 11th, 2019
Open Issues & Pull Requests: 446 (+0)
Number of forks: 5,018
Total Stargazers: 25,081 (+0)
Total Subscribers: 674 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 359.9 days
Mean response time: 331.1 days
90th percentile: 721.9 days
Tracked items: 1,920

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 93% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. 73% of tracked open issues have had no activity in three months, so the open count overstates what is actively being worked. Only 5% of issues opened in the past year have been closed. Three people close 89% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 410
New in 7 days: 1
Closed in 7 days: 0
Avg open age: 535 days
Stale 30+ days: 393
Stale 90+ days: 383

Recent activity

Opened in 7 days: 1
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 1

Top labels

  • bug (2,594)
  • help wanted (1,753)
  • question (1,682)
  • enhancement (445)
  • performance (186)
  • good first issue (62)
  • improvement (50)
  • community (27)

Detailed Description

TDengine is an open-source, high-performance time-series database written in C and designed specifically for Industrial IoT, connected vehicles, and IoT applications. The project is hosted at https://tdengine.com and addresses the challenge of efficiently ingesting, processing, and analyzing massive volumes of time-series data generated by billions of sensors and data collection points, handling TB to PB scale data per day.

The repository emphasizes several core technical capabilities. TDengine claims to be the only time-series database that solves the high cardinality issue while supporting billions of data collection points, with particular strength in data ingestion performance, query speed, and data compression efficiency. The system incorporates built-in caching, stream processing, data subscription, and AI agent features to provide a simplified solution for time-series data processing. Its cloud-native architecture includes native distributed design, sharding and partitioning, separation of compute and storage, RAFT consensus, Kubernetes deployment support, and full observability. The database supports SQL queries and includes an AI-powered component called TDgpt that connects to time-series foundation models, large language models, and machine learning algorithms for forecasting, anomaly detection, imputation, and classification tasks.

The repository documentation indicates comprehensive deployment options including container-based installation, installation packages, Kubernetes deployment, and a fully managed cloud service. The build system uses CMake with version 3.21 or higher required, and the project supports Linux, macOS, and limited Windows builds, with Linux being the primary and recommended platform. The codebase includes Python 3 for testing frameworks and optionally supports Go 1.23 and above for building components like taosAdapter and taosKeeper. The project maintains build acceleration through ccache support and uses Conan 2.x for the taos-gen component. The README emphasizes that initial source builds require the BUILD_CONTRIB flag to download and build external dependencies including xxhash, zstd, and lz4, with subsequent builds able to reuse cached artifacts.