# TDengine — High-Performance Time-Series Database for IoT

> TDengine is a purpose-built time-series database optimized for IoT and industrial data. It delivers 10x compression and 10x query speed over general-purpose databases.

## Install

Save as a script file and run:

# TDengine — High-Performance Time-Series Database for IoT

## Quick Use
```bash
docker run -d -p 6030:6030 -p 6041:6041 tdengine/tdengine
taos  # connect via CLI
# CREATE DATABASE demo;
# USE demo;
# CREATE STABLE meters (ts TIMESTAMP, voltage FLOAT) TAGS (location BINARY(64));
```

## Introduction
TDengine is an open-source time-series database built in C, designed from scratch for IoT, telemetry, and industrial monitoring. It combines a database engine, caching, stream processing, and message queuing in a single package, eliminating the need to deploy Kafka, Redis, and a TSDB separately.

## What TDengine Does
- Ingests billions of data points per day with microsecond timestamps
- Compresses time-series data 10x or more using column-oriented storage
- Runs SQL queries with built-in time-window functions (INTERVAL, SLIDING, FILL)
- Supports super-tables that group millions of devices under one schema with tags
- Includes built-in stream processing for real-time aggregation

## Architecture Overview
TDengine uses a vnode-based architecture where each virtual node owns a subset of time-series data. Data is written append-only in column format with aggressive compression (delta-of-delta, simple-8b, and dictionary encoding). A management node coordinates metadata. Clustering is native — add nodes and TDengine redistributes vnodes automatically. The query engine pushes computation to data nodes for parallel execution.

## Self-Hosting & Configuration
- Install via Docker, apt/yum packages, or compile from source on Linux/macOS
- Configure taosd.cfg for memory limits, WAL, and replication factor
- Use taosAdapter (port 6041) for RESTful and WebSocket client access
- Enable authentication with ALTER USER and per-database privileges
- Scale horizontally by adding data nodes to the cluster with CREATE DNODE

## Key Features
- Super-table model groups millions of devices under a single schema with tags
- Built-in stream processing replaces external Flink/Spark for common aggregations
- Data retention policies automatically drop expired partitions
- Schemaless writing accepts InfluxDB Line Protocol, OpenTSDB, and JSON
- Native connectors for Python, Java, Go, Rust, Node.js, and C#

## Comparison with Similar Tools
- **InfluxDB** — single-node OSS; TDengine scales natively with clustering
- **TimescaleDB** — Postgres extension; TDengine is purpose-built with higher ingest speed
- **QuestDB** — fast for analytics; TDengine adds built-in stream processing and caching
- **ClickHouse** — general OLAP; TDengine is optimized for time-series with super-table semantics
- **Apache IoTDB** — Java-based TSDBfor IoT; TDengine is written in C for lower resource usage

## FAQ
**Q: Can TDengine replace Kafka for IoT data?**
A: For many IoT pipelines yes. TDengine includes built-in message queuing and stream processing, so a separate Kafka layer is not needed.

**Q: Does TDengine support SQL?**
A: Yes. TDengine uses a SQL dialect with extensions for time-series operations like INTERVAL, FILL, and PARTITION BY.

**Q: How does the super-table model work?**
A: You define one super-table with a schema and tags. Each device gets a child table inheriting that schema. Queries across millions of child tables use tag-based filtering for speed.

**Q: What are the hardware requirements?**
A: A single node can handle 100K+ inserts per second on modest hardware (4 cores, 8 GB RAM). Larger workloads scale linearly by adding nodes.

## Sources
- https://github.com/taosdata/TDengine
- https://docs.tdengine.com

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Source: https://tokrepo.com/en/workflows/2b6840a2-399f-11f1-9bc6-00163e2b0d79
Author: Script Depot