apache/iotdb
Iotdb
Apache IoTDB is a time-series database designed for the Internet of Things, purpose-built to ingest high-volume data from millions of low-power devices and store timestamp-value pairs with configurable data types and encoding schemes. It organizes time series data and device metadata in a tree-like hierarchy, enabling efficient management of complex industrial sensor networks.
The database supports rich querying capabilities, including time-aligned data retrieval across multiple devices, time-based aggregation like downsampling, and frequency-domain signal analysis. It provides high-throughput read and write operations while compressing stored data with high-ratio algorithms to reduce hardware storage costs. Data can be imported from and exported to external files for backup or transfer.
IoTDB integrates with big data ecosystems such as Hadoop, Spark, Flink, and Grafana for processing, analysis, and visualization. It offers flexible deployment options across edge and cloud environments with one-click setup and data synchronization between nodes.
Features
- Time Series Data Storage - Collects and persists timestamp-value pairs from industrial devices with configurable data types and encoding schemes.
- Big Data Processing - Connect to Hadoop, Spark, Flink, and Grafana to process, analyze, and visualize time-series data.
- High-Throughput Ingestion Pipelines - Ingest and retrieve data from millions of concurrent device connections at high throughput.
- High-Volume Data Ingestion - Accepts writes from millions of low-power devices at high speed, enabling real-time data collection from industrial sensors.
- Time-Series Aggregations - Joins time-series data from multiple devices and sensors, and performs time-based aggregation like downsampling.
- Time-Series Query Analytics - Aligns data across devices, transforms signals in the frequency domain, and aggregates along time dimensions.
- Time-Series SQL Querying - Retrieves stored time series by path, supports filtering by time range and selecting multiple series in a single query.
- Hadoop Integrations - Connect with Hadoop, Spark, and Grafana to analyze and visualize time-series data.
- Time-Series Compression - Compress time series data with high-ratio algorithms to reduce hardware storage costs.
- Edge-to-Cloud Synchronization - Installs with one click on cloud platforms, provides terminal access tools, and synchronizes data between edge and cloud nodes.
- Time Series Data Loading - Loads time series data from external files into the database for storage and analysis.
- Time Series Export - Writes stored time series data to external files for backup or transfer to other systems.
- Hierarchical Time Series Organizations - Structures time series data in a tree-like directory for efficient management of complex device and measurement hierarchies.
- Flexible - Installs on cloud platforms or terminal devices with one-click setup and synchronizes data between them.
- IoT Device Tree Hierarchies - Manages IoT device metadata as a tree structure and supports wildcard-based fuzzy matching for flexible queries.
- Data Processing - Provides frequency-based encoding and visualization sampling algorithms.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
