Apache IoTDB
Open-source time series database at the Apache Software Foundation; available for free download and use on GitHub
TimechoDB
Enterprise-grade product based on Apache IoTDB, offering enhanced performance, expanded features and customization
Timecho Workbench
Tool with GUI for managing TimechoDB connections, metadata, and data.
Embedded-Edge-Cloud Solution
Free Download from Apache or on GitHub
- Developed Independently: innovative data files and distributed systems
- Apache Top-Level Project: the only Top-Level project in ASF for time series databases.
- Theory to Practice: 30+ patents in time series data management and has 10+ papers accepted at top-tier database conferences.
- Wide Set of Deployment Scenarios: embedded, edge and cloud deployments; standalone and distributed modes.
- Designed for IoT: optimized data models, storage engine and consensus protocols for IoT scenarios.
- High-Performance Capabilities: proven track record of managing billions of time series, handling tens of millions of data points per second, and achieving 10:1 lossless compression ratio.
Easier and Faster
Professional, Comprehensive, and Support with SLAs
- Flexible Data Collection: supports over 150 data acquisition protocols/device models and is compatible with third-party data acquisition modules.
- Active-Active Architecture: achieves high availability and low resource utilization through a two-node setup, providing real-time hot backup.
- System Monitoring: supports 134 monitoring metrics and allows flexible configuration of monitoring schemes with high availability.
- HMI: industrial automation monitoring system with features such as screen/device configuration, animation effects, real-time alarms, and historical/real-time trends.
- Diverse Views: allows users to assign different path names to the same time series, enabling different classifications and organizations of time series from various business perspectives.
- Data Security: provides audit logs and system whitelisting functionality, enabling fast query of user behavior and visualized configuration modifications.
- Stable Compatibility: certified by multiple mainstream CPUs, operating systems (OS), and other qualifications assessments.
- Feature Comparison
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- Stand-Alone Deployment
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- Cluster Deployment
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- Dual Active Deployment
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- Docker Deployment
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- Measurement Management
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- Data Writing
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- Data Querying
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- Continuous Querying
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- Triggers
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- User-Defined Function
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- Database Administration
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- Data Synchronisation
- File Synchronization Only, No Built-in Plugins
- Real-time Synchronization + File Synchronization, Rich Built-in Plugins
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- Stream Processing
- Framework Only, No Built-in Plugins
- Framework with Rich Built-in Plugins
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- Tiered Storage
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- Views
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- Whitelisting
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- Audit Logs
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- Workbench
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- Cluster Management Tools
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- System Monitoring Tools
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- Domestic Compatibility Certification
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- Best Practices
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- Usage Training
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Designed for Apache IoTDB and TimechoDB
One-Stop Solution for Managing Time-Series Data
- Data Connection: Configure server access, login credentials, and establish connections all in one place. It supports connections to multiple instances of TimechoDB.
- Database Management: View data models, perform self-service queries, preview data, and manage permissions. It includes a built-in query command database and visual displays for configuring permissions.
- Monitoring Management: Visualize monitoring metrics, connection information, audit logs, and distributed cluster management.
- Data Dashboard: Create statistics, real-time charts, data computations, and subscribe to alerts, enabling effective data visualization.
Best Practices
Serving over 1000 backbone and industrial leading enterprises
Changan Automobile - Built a smart platform for querying massive amounts of connected vehicle data and remote diagnostics
Solution Breakthroughs:
- Data structure ensuring high scalability, low cost, and high stability: Achieved seamless scaling without interrupting normal system operation, enabling rapid capacity expansion and reduction of management and operational costs.
- Achieved tens of millions of writes per second: Enabled writing and processing of billions of tags from multiple devices, maintaining a stable high-speed writing rate regardless of the data volume.
- Enabled efficient queries for connected vehicle scenarios: Supported efficient detailed and latest value queries.
- Met real-time analysis needs: Improved batch reading speed for real-time analysis.
Business Effects:
- Currently stores over 150 million time series and enables overall data insertion rate of tens of millions points per second.
- With optimized data schema, a single machine replaced the functionality of 25 previous nodes and maintained high stability.
- Achieved millisecond-level response-times for single vehicle time range queries (real-time queries) and latest point queries (latest vehicle status queries).
- Efficiently continuously processed streams of data for 400–500 signals per vehicle within a three-month period, improving data query efficiency for the diagnostic system from minutes to seconds under the same hardware resource conditions.
MCC Cisdi Group - Built the core part of the Water and Soil Cloud Industrial IoT Platform for data development governance
Solution Breakthroughs:
- End-to-end data management: Managed data collection, data writing, data storage, and performed characteristic data processing, data querying, data computation, and data analysis based on business requirements.
- Addressed existing pain points in the data flow: Provided technical optimization solutions for cluster management, dual-active management, MPP scheduling, and operation instructions in the data flow.
- Individualized for metallurgical scenarios: Industry-specific optimization of aggregation function libraries, equipment time alignment, scene triggers, custom rule downsampling, and real-time data tool platforms.
- High compression ratio: Using various compression methods to significantly improve data compression ratio on historical data, enabling storage of more historical data under the same memory conditions.
Business Effects:
- Increased number of written data points by 132%.
- Reduced storage costs by 50%.
- Accelerated query speed by 100%.
- Data retention of more than 2 years.
