Common Concepts

1. SQL Dialect Related Concepts

1.1 sql_dialect

IoTDB supports two time-series data models (SQL dialects), both managing devices and measurement points:

• Tree Model: Organizes data in a hierarchical path structure, where each path represents a measurement point of a device.
• Table Model: Organizes data in a relational table format, where each table corresponds to a type of device.

Each dialect comes with its own SQL syntax and query patterns tailored to its data model.

1.2 Schema

Schema refers to the metadata structure of the database, which can follow either a tree or table format. It includes definitions such as measurement point names, data types, and storage configurations.

1.3 Device

A device corresponds to a physical device in a real-world scenario, typically associated with multiple measurement points.

1.4 Timeseries

Also referred to as: physical quantity, time series, timeline, point, signal, metric, measurement value, etc. A measurement point is a time series consisting of multiple data points arranged in ascending timestamp order. It typically represents a collection point that periodically gathers physical quantities from its environment.

1.5 Encoding

Encoding is a compression technique that represents data in binary form, improving storage efficiency. IoTDB supports multiple encoding methods for different types of data. For details, refer to: Compression and Encoding 。

1.6 Compression

After encoding, IoTDB applies additional compression techniques to further reduce data size and improve storage efficiency. Various compression algorithms are supported. For details, refer to: Compression and Encoding。

2. Distributed System Related Concepts

IoTDB supports distributed deployments, typically in a 3C3D cluster model (3 ConfigNodes, 3 DataNodes), as illustrated below:

2.1 Key Concepts

• Nodes (ConfigNode, DataNode*, AINode*)
• Regions (SchemaRegion, DataRegion)
• Replica Groups

Below is an introduction to these concepts.

2.2 Nodes

An IoTDB cluster consists of three types of nodes, each with distinct responsibilities:

• ConfigNode (Management Node) Manages cluster metadata, configuration, user permissions, schema, and partitioning. It also handles distributed scheduling and load balancing. All ConfigNodes are replicated for high availability.
• DataNode (Storage and Computation Node) Handles client requests, stores data, and executes computations.
• AINode (Analytics Node) Provides machine learning capabilities, allowing users to register pre-trained models and perform inference via SQL. It includes built-in time-series models and common ML algorithms for tasks like prediction and anomaly detection.

2.3 Data Partitioning

IoTDB divides schema and data into Regions, which are managed by DataNodes.

• SchemaRegion: Stores schema information (devices and measurement points). Regions with the same RegionID across different DataNodes serve as replicas.
• DataRegion: Stores time-series data for a subset of devices over a specified time period. Regions with the same RegionID across different DataNodes act as replicas.

For more details, see Cluster Data Partitioning

2.4 Replica Groups

Replica groups ensure high availability by maintaining multiple copies of schema and data. The recommended replication configurations are:

Category	Configuration Item	Standalone Recommended	Cluster Recommended
Metadata	schema_replication_factor	1	3
Data	data_replication_factor	1	2

3. Deployment Related Concepts

IoTDB has two operation modes: standalone mode and cluster mode.

3.1 Standalone Mode

An IoTDB standalone instance includes 1 ConfigNode and 1 DataNode, i.e., 1C1D.

• Features: Easy for developers to install and deploy, with low deployment and maintenance costs and convenient operations.
• Use Cases: Scenarios with limited resources or low high-availability requirements, such as edge servers.
• Deployment Method: Stand-Alone Deployment

3.2 Dual-Active Mode

Dual-Active Deployment is a feature of TimechoDB, where two independent instances synchronize bidirectionally and can provide services simultaneously. If one instance stops and restarts, the other instance will resume data transfer from the breakpoint.

An IoTDB Dual-Active instance typically consists of 2 standalone nodes, i.e., 2 sets of 1C1D. Each instance can also be a cluster.

• Features: The high-availability solution with the lowest resource consumption.
• Use Cases: Scenarios with limited resources (only two servers) but requiring high availability.
• Deployment Method: Dual-Active Deployment

3.3 Cluster Mode

An IoTDB cluster instance consists of 3 ConfigNodes and no fewer than 3 DataNodes, typically 3 DataNodes, i.e., 3C3D. If some nodes fail, the remaining nodes can still provide services, ensuring high availability of the database. Performance can be improved by adding DataNodes.

• Features: High availability, high scalability, and improved system performance by adding DataNodes.
• Use Cases: Enterprise-level application scenarios requiring high availability and reliability.
• Deployment Method: Cluster Deployment

3.4 Feature Summary

Dimension	Stand-Alone Mode	Dual-Active Mode	Cluster Mode
Use Cases	Edge-side deployment, low high-availability requirements	High-availability services, disaster recovery scenarios	High-availability services, disaster recovery scenarios
Number of Machines Required	1	2	≥3
Security and Reliability	Cannot tolerate single-point failure	High, can tolerate single-point failure	High, can tolerate single-point failure
Scalability	Can expand DataNodes to improve performance	Each instance can be scaled as needed	Can expand DataNodes to improve performance
Performance	Can scale with the number of DataNodes	Same as one of the instances	Can scale with the number of DataNodes

• Notes: The deployment steps for Stand-Alone Mode and Cluster Mode are similar (adding ConfigNodes and DataNodes one by one), with differences only in the number of replicas and the minimum number of nodes required to provide services.