DEV.co
Open-Source Databases · taosdata

TDengine

TDengine is an open-source, high-performance time-series database optimized for IoT and industrial scenarios, capable of handling billions of data points at scale. It combines data ingestion, storage, and stream processing in a single system with built-in caching and AI-powered analytics features.

Source: GitHub — github.com/taosdata/TDengine
25k
GitHub stars
5k
Forks
C
Primary language
AGPL-3.0
License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

FieldValue
Repositorytaosdata/TDengine
Ownertaosdata
Primary languageC
LicenseAGPL-3.0 — OSI-approved
Stars25k
Forks5k
Open issues426
Latest releasever-3.4.1.6 (2026-04-30)
Last updated2026-07-08
Sourcehttps://github.com/taosdata/TDengine

What TDengine is

Written in C with native distributed architecture, TDengine supports SQL queries, RAFT-based clustering, Kubernetes deployment, and compute-storage separation. It addresses high-cardinality workloads and includes built-in stream processing, data subscription, and agent-based AI integration.

Quickstart

Get the TDengine source

Clone the repository and explore it locally.

terminalbash
git clone https://github.com/taosdata/TDengine.gitcd TDengine# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

High-volume IoT and sensor data ingestion

Billions of devices sending metrics at high frequency; TDengine's architecture is optimized for many-to-one write patterns and high cardinality time-series data typical of connected devices, vehicles, and industrial equipment.

Real-time monitoring and anomaly detection

Continuous ingestion of operational metrics with sub-second latency requirements; built-in stream processing and AI agent capabilities enable anomaly detection, forecasting, and alerting without external pipeline dependencies.

Cloud-native deployment at scale

Organizations needing distributed, Kubernetes-ready time-series storage with separation of compute and storage; native sharding and partitioning support reduces operational complexity compared to managed alternatives.

Implementation considerations

  • First build requires `-DBUILD_CONTRIB=ON` to download external dependencies (xxhash, zstd, lz4); subsequent builds can omit this flag. Linux is the primary build and deployment platform.
  • Clustering and distributed setup require RAFT consensus and proper configuration for availability; test failover scenarios in staging before production deployment.
  • AGPL-3.0 license requires legal review if integrating into proprietary systems or cloud SaaS offerings; modifications to core code trigger copyleft obligations.
  • Memory and disk requirements scale with data volume and retention policy; monitoring and capacity planning are essential for large-scale deployments.
  • AI agent (TDgpt) integration relies on external LLMs and foundation models; separate API keys, rate limits, and cost considerations apply.

When to avoid it — and what to weigh

  • AGPL-3.0 license incompatibility with closed-source products — Any derivative works or modifications must be released under AGPL-3.0; organizations requiring proprietary modifications or integration into closed-source products must review licensing terms carefully or license separately.
  • Windows-primary deployments — Open-source builds are primarily Linux/macOS; Windows support in the open-source tree is explicitly limited. Use Linux as the production target.
  • Simple, single-node, low-scale deployments — TDengine's architecture complexity (clustering, RAFT, distribution) adds operational overhead; simpler single-node alternatives may be more appropriate for small, stateless, or non-critical use cases.
  • Relational OLTP workloads — TDengine is purpose-built for time-series append-heavy workloads, not general-purpose transactional databases; complex JOINs, updates, and multi-entity relationships belong in a traditional RDBMS.

License & commercial use

Licensed under AGPL-3.0 (GNU Affero General Public License v3.0). Core modules including clustering and AI agent are open-source under this license. AGPL-3.0 is a copyleft license requiring derivative works and network-based service deployments to be released under the same license.

AGPL-3.0 permits commercial use of unmodified binaries. However, any modifications, derivative distributions, or network-exposed services must publish source code under AGPL-3.0. Organizations building proprietary extensions or offering TDengine as a service component must publish modified code or obtain separate commercial licensing. Requires legal review before use in closed-source products or managed services.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

SignalAssessment
MaintenanceActive
DocumentationAdequate
License clarityClear
Deployment complexityHigh
DEV.co fitGood
Assessment confidenceHigh
Security considerations

Written in C (memory safety not enforced by language); security considerations include buffer overflow, injection, and credential management typical of systems languages. CII Best Practices badge suggests some hardening. Unknown: penetration testing results, vulnerability disclosure policy, encryption-at-rest/in-transit status, authentication/authorization model maturity, and history of security advisories.

Alternatives to consider

InfluxDB

Mature, widely-adopted time-series database with simpler operational model and permissive BSL/commercial licensing; less complex clustering but may have lower ingestion throughput on high-cardinality workloads.

Prometheus + long-term storage (Thanos, Cortex)

Industry-standard open-source monitoring stack with strong Kubernetes integration and mature ecosystem; better for metrics-focused workloads but requires external components for high-volume streaming ingestion.

TimescaleDB (PostgreSQL extension)

Purpose-built time-series on top of PostgreSQL with strong ACID guarantees and relational query support; simpler for workloads requiring cross-entity JOINs and transactional consistency.

Software development agency

Build on TDengine with DEV.co software developers

Review AGPL-3.0 licensing implications, test build and deployment on Linux, and validate cluster failover scenarios. Contact legal if modifications or SaaS integration planned.

Talk to DEV.co

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

Related on DEV.co

Explore the category and the services that help you build with it.

TDengine FAQ

Can I use TDengine in a proprietary product?
Only if you do not modify the core code. If you modify or extend TDengine, you must release modifications under AGPL-3.0 or obtain separate commercial licensing. Network-exposed deployments (SaaS) also trigger AGPL-3.0 obligations. Requires legal review.
What is the difference between TDengine open-source and TDengine Cloud?
Open-source TDengine is AGPL-3.0 licensed and self-hosted. TDengine Cloud is a fully managed service (separate licensing/SLA). The README mentions TDengine Cloud as an option but does not specify pricing, feature parity, or commercial terms.
Does TDengine support Windows?
Limited support. Open-source builds are primarily Linux and macOS. Windows support in the open-source tree is explicitly limited per the README. Use Linux for production deployments.
What are the minimum system requirements?
Linux (Ubuntu 18.04+, CentOS 7+) or macOS 10.15+. x86_64 or ARM64 CPU, 4 GB RAM, 2 GB free disk. CMake >= 3.21 required for builds. Python 3 and Go 1.23+ optional for specific components.

Work with a software development agency

DEV.co helps companies turn open-source tools like TDengine into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source databases stack.

Ready to evaluate TDengine?

Review AGPL-3.0 licensing implications, test build and deployment on Linux, and validate cluster failover scenarios. Contact legal if modifications or SaaS integration planned.