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, referred to as 1C1D.

• Features: Easy for developers to install and deploy, with lower deployment and maintenance costs, and convenient operation.
• Applicable scenarios: Situations with limited resources or where high availability is not a critical requirement, such as edge servers.
• Deployment method:Stand-Alone Deployment

3.2 Cluster Mode

An IoTDB cluster instance consists of 3 ConfigNodes and no fewer than 3 DataNodes, typically 3 DataNodes, referred to as 3C3D. In the event of partial node failures, the remaining nodes can still provide services, ensuring high availability of the database service, and the database performance can be improved with the addition of nodes.

• Features: High availability and scalability, with the ability to enhance system performance by adding DataNodes.
• Applicable scenarios: Enterprise-level application scenarios that require high availability and reliability.
• Deployment method: Cluster Deployment

3.3 Summary of Features

Dimension	Stand-Alone Mode	Cluster Mode
Applicable Scenario	Edge deployment, low requirement for high availability	High-availability business, disaster recovery scenarios, etc.
Number of Machines Required	1	≥3
Security and Reliability	Cannot tolerate single-point failure	High, can tolerate single-point failure
Scalability	Scalable by adding DataNodes to improve performance	Scalable by adding DataNodes to improve performance
Performance	Scalable with the number of DataNodes	Scalable with the number of DataNodes

• The deployment steps for standalone mode and cluster mode are similar (adding ConfigNodes and DataNodes one by one), with the only differences being the number of replicas and the minimum number of nodes required to provide services.