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About TDengine
TDengine is an open-source, high-performance, cloud-native time-series database designed specifically for Industrial IoT (IIoT), Connected Cars, and DevOps scenarios. Developed by TAOS Data in China, TDengine features the innovative "Super Table" concept that provides templates for data collection points with both metric schemas and tag schemas for static attributes. With AI-powered capabilities through TDgpt, TDengine enables forecasting and anomaly detection in single SQL statements, supporting billions of data collection points while maintaining high performance for data ingestion, querying, and compression.
GreptimeDB vs. TDengine
| Feature/Aspect | GreptimeDB | TDengine |
|---|---|---|
| Data Model | Unified Observability Database | Industrial IoT Time-Series Database |
| Value Model | Multi-Value (supports complex data structures) | Multi-Value with Super Table templates |
| Multi-model Support | Metrics, Logs & Traces in one database | Time-series data with IoT-specific optimizations |
| Ingestion Protocols | SQL gRPC InfluxDB Line Protocol Prometheus Remote Storage OpenTelemetry HTTP API | MQTT OPC-UA RESTful API SQL INSERT Industrial protocols |
| Query Languages | SQL & PromQL (dual interface) | Standard SQL with time-series functions |
| Data Retention | Flexible TTL policies with tiered storage | Configurable retention with automatic deletion |
| Continuous Aggregation | Built-in SQL aggregation, Pipeline ETL engine & Flow streaming computation | Stream processing with caching and data subscription |
| Use Cases | Unified observability, real-time analytics, IoT monitoring, edge computing | Industrial IoT, smart manufacturing, connected vehicles, energy monitoring |
| Architecture | Cloud-native distributed with compute-storage separation | Distributed design with Super Table templates |
| Storage Format | Apache Parquet (columnar, compressed) | Native time-series optimized storage |
| Storage Scalability | Object storage integration with unlimited capacity | Distributed storage with automatic sharding |
| High Availability | Native clustering with automatic failover | Multi-replica deployment with data synchronization |
| License | Apache 2.0 | AGPL v3.0 (core), Commercial (enterprise) |
| Deployment Options | Single-node, cluster, Kubernetes-native, edge-to-cloud with unified API | On-premise, cloud, hybrid deployment with Kubernetes |
| Operational Complexity | Single unified system with simplified Kubernetes operations | Requires understanding of Super Table concepts |
| Written Language | Rust (memory safety, performance) | C (system-level performance) |
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