IoTDB Introduction

Apache IoTDB is a low-cost, high-performance IoT-native time-series database. It addresses challenges faced by enterprises in managing time-series data for IoT big data platforms, including complex application scenarios, massive data volumes, high sampling frequencies, frequent out-of-order data, time-consuming data processing, diverse analytical requirements, and high storage and maintenance costs.

• GitHub Repository: https://github.com/apache/iotdb
• Open-Source Installation Packages: https://iotdb.apache.org/Download/
• Installation, Deployment, and Usage Documentation: Quick Start

1. Product Ecosystem

The IoTDB ecosystem consists of multiple components designed to efficiently manage and analyze massive IoT-generated time-series data.

Key components include:

1. Time-Series Database (Apache IoTDB): The core component for time-series data storage, offering high compression, rich query capabilities, real-time stream processing, high availability, and scalability. It provides security guarantees, configuration tools, multi-language APIs, and integration with external systems for building business applications.
2. Time-Series File Format (Apache TsFile): A specialized storage format for time-series data, enabling efficient storage and querying. TsFile underpins IoTDB and AINode, unifying data management across collection, storage, and analysis phases.
3. Time-Series Model Training-Inference Engine (IoTDB AINode): A unified engine for intelligent analysis, supporting model training, data management, and integration with machine/deep learning frameworks.

2. TimechoDB Architecture

The diagram below illustrates a typical IoTDB cluster deployment (3 ConfigNodes and 3 DataNodes):

3. Key Features

Apache IoTDB offers the following advantages:

• Flexible Deployment:

  • One-click cloud deployment
  • Out-of-the-box terminal usage
  • Seamless terminal-cloud synchronization

• Cost-Effective Storage:

  • High-compression disk storage
  • Unified management of historical and real-time data

• Hierarchical Measurement Point Management:

  • Aligns with industrial device hierarchies
  • Supports directory browsing and search

• High Throughput Read/Write:

  • Supports millions of devices
  • Handles high-speed, out-of-order, and multi-frequency data ingestion

• Rich Query Capabilities:

  • Native time-series computation engine
  • Timestamp alignment during queries
  • Over 100 built-in aggregation and time-series functions
  • AI-ready time-series feature analysis

• High Availability & Scalability:

  • HA distributed architecture with 24/7 uptime
  • Automatic load balancing for node scaling
  • Heterogeneous cluster support

• Low Learning Curve:

  • SQL-like query language
  • Multi-language SDKs
  • Comprehensive toolchain (e.g., console)

• Ecosystem Integration:

  • Hadoop, Spark, Grafana, ThingsBoard, DataEase, etc.

4. TimechoDB

Timecho Technology has developed TimechoDB, a commercial product built on the open-source version of Apache IoTDB, to provide enterprise-grade solutions and services for businesses and commercial clients. TimechoDB addresses the multifaceted challenges enterprises face when building IoT big data platforms for managing time-series data, including complex application scenarios, massive data volumes, high sampling frequencies, frequent out-of-order data, time-consuming data processing, diverse analytical requirements, and high storage and maintenance costs.

Leveraging TimechoDB, Timecho Technology offers a broader range of product features, enhanced performance and stability, and a richer suite of efficiency tools. Additionally, it provides comprehensive enterprise services, delivering commercial clients with superior product capabilities and an optimized experience in development, operation, and usage.

• Timecho Technology Official Website: https://www.timecho.com/
• TimechoDB Documentation: Quick Start