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| 21 | + |
| 22 | +# Common Concepts |
| 23 | + |
| 24 | +## Sql_dialect Related Concepts |
| 25 | + |
| 26 | +| Concept | Meaning | |
| 27 | +| ----------------------- | ------------------------------------------------------------ | |
| 28 | +| sql_dialect | IoTDB supports two time-series data models (SQL dialects), both managing devices and measurement points. Tree: Manages data in a hierarchical path manner, where one path corresponds to one measurement point of a device. Table: Manages data in a relational table manner, where one table corresponds to a category of devices. | |
| 29 | +| Schema | Schema is the data model information of the database, i.e., tree structure or table structure. It includes definitions such as the names and data types of measurement points. | |
| 30 | +| Device | Corresponds to a physical device in an actual scenario, usually containing multiple measurement points. | |
| 31 | +| Timeseries | Also known as: physical quantity, time series, timeline, point location, semaphore, indicator, measurement value, etc. It is a time series formed by arranging multiple data points in ascending order of timestamps. Usually, a Timeseries represents a collection point that can periodically collect physical quantities of the environment it is in. | |
| 32 | +| Encoding | Encoding is a compression technique that represents data in binary form to improve storage efficiency. IoTDB supports various encoding methods for different types of data. For more detailed information, please refer to:[Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md) | |
| 33 | +| Compression | After data encoding, IoTDB uses compression technology to further compress binary data to enhance storage efficiency. IoTDB supports multiple compression methods. For more detailed information, please refer to: [Encoding-and-Compression](../Technical-Insider/Encoding-and-Compression.md) | |
| 34 | + |
| 35 | +## Distributed Related Concepts |
| 36 | + |
| 37 | +The following figure shows a common IoTDB 3C3D (3 ConfigNodes, 3 DataNodes) cluster deployment pattern: |
| 38 | + |
| 39 | +<img src="https://alioss.timecho.com/docs/img/Cluster-Concept03.png" alt="" style="width: 60%;"/> |
| 40 | + |
| 41 | +IoTDB's cluster includes the following common concepts: |
| 42 | + |
| 43 | +- Nodes（ConfigNode、DataNode、AINode） |
| 44 | +- Region（SchemaRegion、DataRegion） |
| 45 | +- Replica Groups |
| 46 | + |
| 47 | +The above concepts will be introduced in the following text. |
| 48 | + |
| 49 | +### Nodes |
| 50 | + |
| 51 | +IoTDB cluster includes three types of nodes (processes): ConfigNode (management node), DataNode (data node), and AINode (analysis node), as shown below: |
| 52 | + |
| 53 | +- ConfigNode: Manages cluster node information, configuration information, user permissions, metadata, partition information, etc., and is responsible for the scheduling of distributed operations and load balancing. All ConfigNodes are fully backed up with each other, as shown in ConfigNode-1, ConfigNode-2, and ConfigNode-3 in the figure above. |
| 54 | +- DataNode: Serves client requests and is responsible for data storage and computation, as shown in DataNode-1, DataNode-2, and DataNode-3 in the figure above. |
| 55 | +- AINode: Provides machine learning capabilities, supports the registration of trained machine learning models, and allows model inference through SQL calls. It has already built-in self-developed time-series large models and common machine learning algorithms (such as prediction and anomaly detection). |
| 56 | + |
| 57 | +### Data Partitioning |
| 58 | + |
| 59 | +In IoTDB, both metadata and data are divided into small partitions, namely Regions, which are managed by various DataNodes in the cluster. |
| 60 | + |
| 61 | +- SchemaRegion: Metadata partition, managing the metadata of a part of devices and measurement points. SchemaRegions with the same RegionID on different DataNodes are mutual replicas, as shown in SchemaRegion-1 in the figure above, which has three replicas located on DataNode-1, DataNode-2, and DataNode-3. |
| 62 | +- DataRegion: Data partition, managing the data of a part of devices for a certain period of time. DataRegions with the same RegionID on different DataNodes are mutual replicas, as shown in DataRegion-2 in the figure above, which has two replicas located on DataNode-1 and DataNode-2. |
| 63 | +- For specific partitioning algorithms, please refer to: [Data Partitioning](../Technical-Insider/Cluster-data-partitioning.md) |
| 64 | + |
| 65 | +### Replica Groups |
| 66 | + |
| 67 | +The number of replicas for data and metadata can be configured. The recommended configurations for different deployment modes are as follows, where multi-replication can provide high-availability services. |
| 68 | + |
| 69 | +| Category | Parameter | Stand-Alone Recommended Configuration | Cluster Recommended Configuration | |
| 70 | +| :----- | :------------------------ | :----------- | :----------- | |
| 71 | +| Schema | schema_replication_factor | 1 | 3 | |
| 72 | +| Data | data_replication_factor | 1 | 2 | |
| 73 | + |
| 74 | +## Deployment Related Concepts |
| 75 | + |
| 76 | +IoTDB has two operating modes: Stand-Alone mode and Cluster mode. |
| 77 | + |
| 78 | +### Stand-Alone Mode |
| 79 | + |
| 80 | +An IoTDB Stand-Alone instance includes 1 ConfigNode and 1 DataNode, i.e., 1C1D; |
| 81 | + |
| 82 | + |
| 83 | +- **Features**：Easy for developers to install and deploy, with low deployment and maintenance costs and convenient operations. |
| 84 | +- **Applicable Scenarios**：Scenarios with limited resources or low requirements for high availability, such as edge-side servers. |
| 85 | +- **Deployment Method**：[Stand-Alone-Deployment](../Deployment-and-Maintenance/Stand-Alone-Deployment_apache.md) |
| 86 | + |
| 87 | +### Cluster Mode |
| 88 | + |
| 89 | +An IoTDB cluster instance consists of 3 ConfigNodes and no less than 3 DataNodes, usually 3 DataNodes, i.e., 3C3D; when some nodes fail, the remaining nodes can still provide services, ensuring the high availability of the database service, and the database performance can be improved with the addition of nodes. |
| 90 | + |
| 91 | +- **Features**：High availability and scalability, and the system performance can be improved by adding DataNodes. |
| 92 | +- **Applicable Scenarios**：Enterprise-level application scenarios requiring high availability and reliability. |
| 93 | +- **Deployment Method**：[Cluster-Deployment](../Deployment-and-Maintenance/Cluster-Deployment_apache.md) |
| 94 | + |
| 95 | +### Summary of Features |
| 96 | + |
| 97 | +| Dimension | Stand-Alone Mode | Cluster Mode | |
| 98 | +| ------------ | ---------------------------- | ------------------------ | |
| 99 | +| Applicable Scenarios | Edge-side deployment, scenarios with low requirements for high availability | High-availability business, disaster recovery scenarios, etc. | |
| 100 | +| Number of Machines Required | 1 | ≥3 | |
| 101 | +| Security and Reliability | Cannot tolerate single-point failures | High, can tolerate single-point failures | |
| 102 | +| Scalability | Can expand DataNodes to improve performance | Can expand DataNodes to improve performance | |
| 103 | +| Performance | Can be expanded with the number of DataNodes | Can be expanded with the number of DataNodes | |
| 104 | + |
| 105 | +- The deployment steps for single-machine mode and cluster mode are similar (adding ConfigNodes and DataNodes one by one), with only the number of replicas and the minimum number of nodes that can provide services being different. |
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