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# Data Modeling

## 1. Database Management

### 1.1 Create Database

According to the storage model we can set up the corresponding database. Two SQL statements are supported for creating databases, as follows:

```sql
create database root.ln;
create database root.sgcc;
```

We can thus create two databases using the above two SQL statements.

It is worth noting that 1 database is recommended.

When the path itself or the parent/child layer of the path is already created as database, the path is then not allowed to be created as database.

For example, when the databases root.ln and root.sgcc already exist, creating the database root.ln.wf01 is not allowed. The system will return the corresponding error message as shown below:

```sql
CREATE DATABASE root.ln.wf01;
Msg: org.apache.iotdb.jdbc.IoTDBSQLException: 501: root.ln has already been created as a database
```

Similarly, when the database root.db.test already exists, creating the database root.db is not allowed either. The system will return the corresponding error message as shown below:

```sql
CREATE DATABASE root.db;
Msg: org.apache.iotdb.jdbc.IoTDBSQLException: 529: Some children of root.db have already been created as datab
```

Database Node Naming Rules:
1. Node names may contain: **Chinese/English letters, Digits (0-9), Underscore(\_)、Period (.)、Backtick(\`)**
2. The entire name must be enclosed in **backticks (\`)** if:
    - It consists solely of digits (e.g., 12345)
    - It contains special characters (. or \_) that may cause ambiguity (e.g., db.01, \_temp)
3. Escaping Backticks:
   If the node name itself contains a backtick (\`), use **two consecutive backticks(\`\`)** to represent a single backtick. Example: To name a node as \`db123\`\` (containing one backtick), write it as \`db123\`\`\`.

Besides, if deploy on Windows or macOS system, the LayerName is case-insensitive, which means it's not allowed to create databases `root.ln` and `root.LN` at the same time.

### 1.2 Show Databases

After creating the database, we can use the [SHOW DATABASES](../SQL-Manual/SQL-Manual_apache) statement and [SHOW DATABASES \<PathPattern>](../SQL-Manual/SQL-Manual_apache) to view the databases. The SQL statements are as follows:

```sql
SHOW DATABASES;
SHOW DATABASES root.**;
```

The result is as follows:

```
+-------------+----+-------------------------+-----------------------+-----------------------+
|database| ttl|schema_replication_factor|data_replication_factor|time_partition_interval|
+-------------+----+-------------------------+-----------------------+-----------------------+
|    root.sgcc|null|                        2|                      2|                 604800|
|      root.ln|null|                        2|                      2|                 604800|
+-------------+----+-------------------------+-----------------------+-----------------------+
Total line number = 2
It costs 0.060s
```

### 1.3 Delete Database

User can use the `DELETE DATABASE <PathPattern>` statement to delete all databases matching the pathPattern. Please note the data in the database will also be deleted. 

```sql
DELETE DATABASE root.ln;
DELETE DATABASE root.sgcc;
// delete all data, all timeseries and all databases;
DELETE DATABASE root.**;
```

### 1.4 Count Databases

User can use the `COUNT DATABASE <PathPattern>` statement to count the number of databases. It is allowed to specify `PathPattern` to count the number of databases matching the `PathPattern`.

SQL statement is as follows:

```sql
count databases;
count databases root.*;
count databases root.sgcc.*;
count databases root.sgcc;
```

The result is as follows:

```
+-------------+
|     database|
+-------------+
|    root.sgcc|
| root.turbine|
|      root.ln|
+-------------+
Total line number = 3
It costs 0.003s

+-------------+
|     database|
+-------------+
|            3|
+-------------+
Total line number = 1
It costs 0.003s

+-------------+
|     database|
+-------------+
|            3|
+-------------+
Total line number = 1
It costs 0.002s

+-------------+
|     database|
+-------------+
|            0|
+-------------+
Total line number = 1
It costs 0.002s

+-------------+
|     database|
+-------------+
|            1|
+-------------+
Total line number = 1
It costs 0.002s
```

### 1.5 Setting up heterogeneous databases (Advanced operations)

Under the premise of familiar with IoTDB metadata modeling, 
users can set up heterogeneous databases in IoTDB to cope with different production needs.

Currently, the following database heterogeneous parameters are supported:

| Parameter                 | Type    | Description                                   |
| ------------------------- | ------- | --------------------------------------------- |
| TTL                       | Long    | TTL of the Database                           |
| SCHEMA_REPLICATION_FACTOR | Integer | The schema replication number of the Database |
| DATA_REPLICATION_FACTOR   | Integer | The data replication number of the Database   |
| SCHEMA_REGION_GROUP_NUM   | Integer | The SchemaRegionGroup number of the Database  |
| DATA_REGION_GROUP_NUM     | Integer | The DataRegionGroup number of the Database    |

Note the following when configuring heterogeneous parameters:

+ TTL and TIME_PARTITION_INTERVAL must be positive integers.
+ SCHEMA_REPLICATION_FACTOR and DATA_REPLICATION_FACTOR must be smaller than or equal to the number of deployed DataNodes.
+ The function of SCHEMA_REGION_GROUP_NUM and DATA_REGION_GROUP_NUM are related to the parameter `schema_region_group_extension_policy` and `data_region_group_extension_policy` in iotdb-system.properties configuration file. Take DATA_REGION_GROUP_NUM as an example:
    If `data_region_group_extension_policy=CUSTOM` is set, DATA_REGION_GROUP_NUM serves as the number of DataRegionGroups owned by the Database.
    If `data_region_group_extension_policy=AUTO`, DATA_REGION_GROUP_NUM is used as the lower bound of the DataRegionGroup quota owned by the Database. That is, when the Database starts writing data, it will have at least this number of DataRegionGroups.

Users can set any heterogeneous parameters when creating a Database, or adjust some heterogeneous parameters during a stand-alone/distributed IoTDB run.

#### Set heterogeneous parameters when creating a Database

The user can set any of the above heterogeneous parameters when creating a Database. The SQL statement is as follows:

```sql
CREATE DATABASE prefixPath (WITH databaseAttributeClause (COMMA? databaseAttributeClause)*)?
```

For example:

```sql
CREATE DATABASE root.db WITH SCHEMA_REPLICATION_FACTOR=1, DATA_REPLICATION_FACTOR=3, SCHEMA_REGION_GROUP_NUM=1, DATA_REGION_GROUP_NUM=2;
```

#### Adjust heterogeneous parameters at run time

Users can adjust some heterogeneous parameters during the IoTDB runtime, as shown in the following SQL statement:

```sql
ALTER DATABASE prefixPath WITH databaseAttributeClause (COMMA? databaseAttributeClause)*
```

For example:

```sql
ALTER DATABASE root.db WITH SCHEMA_REGION_GROUP_NUM=1, DATA_REGION_GROUP_NUM=2;
```

Note that only the following heterogeneous parameters can be adjusted at runtime:

+ SCHEMA_REGION_GROUP_NUM
+ DATA_REGION_GROUP_NUM

#### Show heterogeneous databases

The user can query the specific heterogeneous configuration of each Database, and the SQL statement is as follows:

```sql
SHOW DATABASES DETAILS prefixPath?
```

For example:

```sql
SHOW DATABASES DETAILS
+--------+--------+-----------------------+---------------------+---------------------+--------------------+-----------------------+-----------------------+------------------+---------------------+---------------------+
|Database|     TTL|SchemaReplicationFactor|DataReplicationFactor|TimePartitionInterval|SchemaRegionGroupNum|MinSchemaRegionGroupNum|MaxSchemaRegionGroupNum|DataRegionGroupNum|MinDataRegionGroupNum|MaxDataRegionGroupNum|
+--------+--------+-----------------------+---------------------+---------------------+--------------------+-----------------------+-----------------------+------------------+---------------------+---------------------+
|root.db1|    null|                      1|                    3|            604800000|                   0|                      1|                      1|                 0|                    2|                    2|
|root.db2|86400000|                      1|                    1|            604800000|                   0|                      1|                      1|                 0|                    2|                    2|
|root.db3|    null|                      1|                    1|            604800000|                   0|                      1|                      1|                 0|                    2|                    2|
+--------+--------+-----------------------+---------------------+---------------------+--------------------+-----------------------+-----------------------+------------------+---------------------+---------------------+
Total line number = 3
It costs 0.058s
```

The query results in each column are as follows:

+ The name of the Database
+ The TTL of the Database
+ The schema replication number of the Database
+ The data replication number of the Database
+ The time partition interval of the Database
+ The current SchemaRegionGroup number of the Database
+ The required minimum SchemaRegionGroup number of the Database
+ The permitted maximum SchemaRegionGroup number of the Database
+ The current DataRegionGroup number of the Database
+ The required minimum DataRegionGroup number of the Database
+ The permitted maximum DataRegionGroup number of the Database

### 1.6 TTL

IoTDB supports setting data retention time (TTL) at the device level, allowing the system to automatically and periodically delete old data to effectively control disk space and maintain high query performance and low memory usage. TTL is set in milliseconds by default. Once data expires, it cannot be queried or written, but physical deletion is delayed until compaction. Please note that changes to TTL may temporarily affect data queryability, and if TTL is reduced or removed, previously invisible data due to TTL may reappear.

Important notes:
- TTL is set in milliseconds and is not affected by the time precision in the configuration file.
- Changes to TTL may affect data queryability.
- The system will eventually remove expired data, but there may be a delay.
- TTL determines data expiration based on the data point timestamp, not the ingestion time.
- The system supports setting up to 1000 TTL rules. When the limit is reached, existing rules must be removed before new ones can be added.

#### TTL Path Rule
The path can only be prefix paths (i.e., the path cannot contain \* , except \*\* in the last level).
This path will match devices and also allows users to specify paths without asterisks as specific databases or devices.
When the path does not contain asterisks, the system will check if it matches a database; if it matches a database, both the path and path.\*\* will be set at the same time. Note: Device TTL settings do not verify the existence of metadata, i.e., it is allowed to set TTL for a non-existent device.
```
qualified paths：
root.**
root.db.**
root.db.group1.**
root.db
root.db.group1.d1

unqualified paths：
root.*.db
root.**.db.*
root.db.*
```
#### TTL Applicable Rules
When a device is subject to multiple TTL rules, the more precise and longer rules are prioritized. For example, for the device "root.bj.hd.dist001.turbine001", the rule "root.bj.hd.dist001.turbine001" takes precedence over "root.bj.hd.dist001.\*\*", and the rule "root.bj.hd.dist001.\*\*" takes precedence over "root.bj.hd.**".
#### Set TTL
The set ttl operation can be understood as setting a TTL rule, for example, setting ttl to root.sg.group1.** is equivalent to mounting ttl for all devices that can match this path pattern.
The unset ttl operation indicates unmounting TTL for the corresponding path pattern; if there is no corresponding TTL, nothing will be done.
If you want to set TTL to be infinitely large, you can use the INF keyword.
The SQL Statement for setting TTL is as follow:
```sql
set ttl to pathPattern 360000;
```
Set the Time to Live (TTL) to a pathPattern of 360,000 milliseconds; the pathPattern should not contain a wildcard (\*) in the middle and must end with a double asterisk (\*\*). The pathPattern is used to match corresponding devices.
To maintain compatibility with older SQL syntax, if the user-provided pathPattern matches a database (db), the path pattern is automatically expanded to include all sub-paths denoted by path.\*\*.
For instance, writing "set ttl to root.sg 360000" will automatically be transformed into "set ttl to root.sg.\*\* 360000", which sets the TTL for all devices under root.sg. However, if the specified pathPattern does not match a database, the aforementioned logic will not apply. For example, writing "set ttl to root.sg.group 360000" will not be expanded to "root.sg.group.\*\*" since root.sg.group does not match a database.
It is also permissible to specify a particular device without a wildcard (*).
#### Unset TTL

To unset TTL, we can use follwing SQL statement:

```sql
unset ttl from root.ln
```

After unset TTL, all data will be accepted in `root.ln`.
```sql
unset ttl from root.sgcc.**
```

Unset the TTL in the `root.sgcc` path.

New syntax
```sql
unset ttl from root.**
```

Old syntax
```sql
unset ttl to root.**
```
There is no functional difference between the old and new syntax, and they are compatible with each other.
The new syntax is just more conventional in terms of wording.

Unset the TTL setting for all path pattern.

#### Show TTL

To Show TTL, we can use following SQL statement:

show all ttl

```sql
SHOW ALL TTL
+--------------+--------+
|          path|     TTL|
|       root.**|55555555|
| root.sg2.a.**|44440000|
+--------------+--------+
```

show ttl on pathPattern
```sql
SHOW TTL ON root.db.**;
+--------------+--------+
|          path|     TTL|
|    root.db.**|55555555|
|  root.db.a.**|44440000|
+--------------+--------+
```

The SHOW ALL TTL example gives the TTL for all path patterns.
The SHOW TTL ON pathPattern shows the TTL for the path pattern specified.

Display devices' ttl
```sql
show devices
+---------------+---------+---------+
|         Device|IsAligned|      TTL|
+---------------+---------+---------+
|root.sg.device1|    false| 36000000|
|root.sg.device2|     true|      INF|
+---------------+---------+---------+
```
All devices will definitely have a TTL, meaning it cannot be null. INF represents infinity.

## 2. Device Template

IoTDB supports the device template function, enabling different entities of the same type to share metadata, reduce the memory usage of metadata, and simplify the management of numerous entities and measurements.

![](/img/%E6%A8%A1%E6%9D%BF.png)

![](/img/templateEN.jpg)

### 2.1 Create Device Template

The SQL syntax for creating a metadata template is as follows:

```sql
CREATE DEVICE TEMPLATE <templateName> ALIGNED? '(' <measurementId> <attributeClauses> [',' <measurementId> <attributeClauses>]+ ')'
```

**Example 1:** Create a template containing two non-aligned timeseries

```sql
create device template t1 (temperature FLOAT, status BOOLEAN)
```

**Example 2:** Create a template containing a group of aligned timeseries

```sql
create device template t2 aligned (lat FLOAT, lon FLOAT)
```

The` lat` and `lon` measurements are aligned.

When creating a template, the system will automatically assign default encoding and compression methods, requiring no manual specification. If your business scenario requires custom adjustments, you may refer to the following example:

```sql
create device template t1 (temperature FLOAT encoding=RLE, status BOOLEAN encoding=PLAIN compression=SNAPPY)
```
For a full list of supported data types and corresponding encoding methods, please refer to [Compression & Encoding](../Technical-Insider/Encoding-and-Compression.md)。


### 2.2 Set Device Template

After a device template is created, it should be set to specific path before creating related timeseries or insert data.

**It should be ensured that the related database has been set before setting template.**

**It is recommended to set device template to database path. It is not suggested to set device template to some path above database**

**It is forbidden to create timeseries under a path setting s tedeviceplate. Device template shall not be set on a prefix path of an existing timeseries.**

The SQL Statement for setting device template is as follow:

```sql
set device template t1 to root.sg1.d1
```

### 2.3 Activate Device Template

After setting the device template, with the system enabled to auto create schema, you can insert data into the timeseries. For example, suppose there's a database root.sg1 and t1 has been set to root.sg1.d1, then timeseries like root.sg1.d1.temperature and root.sg1.d1.status are available and data points can be inserted.


**Attention**: Before inserting data or the system not enabled to auto create schema, timeseries defined by the device template will not be created. You can use the following SQL statement to create the timeseries or activate the  templdeviceate, act before inserting data:

```sql
create timeseries using device template on root.sg1.d1
```

**Example:** Execute the following statement

```sql
set device template t1 to root.sg1.d1;
set device template t2 to root.sg1.d2;
create timeseries using device template on root.sg1.d1;
create timeseries using device template on root.sg1.d2;
```

Show the time series:

```sql
show timeseries root.sg1.**
````

```sql
+-----------------------+-----+-------------+--------+--------+-----------+----+----------+--------+-------------------+
|             timeseries|alias|     database|dataType|encoding|compression|tags|attributes|deadband|deadband parameters|
+-----------------------+-----+-------------+--------+--------+-----------+----+----------+--------+-------------------+
|root.sg1.d1.temperature| null|     root.sg1|   FLOAT|     RLE|     SNAPPY|null|      null|    null|               null|
|     root.sg1.d1.status| null|     root.sg1| BOOLEAN|   PLAIN|     SNAPPY|null|      null|    null|               null|
|        root.sg1.d2.lon| null|     root.sg1|   FLOAT| GORILLA|     SNAPPY|null|      null|    null|               null|
|        root.sg1.d2.lat| null|     root.sg1|   FLOAT| GORILLA|     SNAPPY|null|      null|    null|               null|
+-----------------------+-----+-------------+--------+--------+-----------+----+----------+--------+-------------------+
```

Show the devices:

```sql
show devices root.sg1.**
````

```sql
+---------------+---------+
|        devices|isAligned|
+---------------+---------+
|    root.sg1.d1|    false|
|    root.sg1.d2|     true|
+---------------+---------+
````

### 2.4 Show Device Template

- Show all device templates

The SQL statement looks like this:

```sql
show device templates
```

The execution result is as follows:

```sql
+-------------+
|template name|
+-------------+
|           t2|
|           t1|
+-------------+
```

- Show nodes under in device template

The SQL statement looks like this:

```sql
show nodes in device template t1
```

The execution result is as follows:

```sql
+-----------+--------+--------+-----------+
|child nodes|dataType|encoding|compression|
+-----------+--------+--------+-----------+
|temperature|   FLOAT|     RLE|     SNAPPY|
|     status| BOOLEAN|   PLAIN|     SNAPPY|
+-----------+--------+--------+-----------+
```

- Show the path prefix where a device template is set

```sql
show paths set device template t1
```

The execution result is as follows:

```sql
+-----------+
|child paths|
+-----------+
|root.sg1.d1|
+-----------+
```

- Show the path prefix where a device template is used (i.e. the time series has been created)

```sql
show paths using device template t1
```

The execution result is as follows:

```sql
+-----------+
|child paths|
+-----------+
|root.sg1.d1|
+-----------+
```

### 2.5 Deactivate device Template

To delete a group of timeseries represented by device template, namely deactivate the device template, use the following SQL statement:

```sql
delete timeseries of device template t1 from root.sg1.d1
```

or

```sql
deactivate device template t1 from root.sg1.d1
```

The deactivation supports batch process. 

```sql
delete timeseries of device template t1 from root.sg1.*, root.sg2.*
```

or

```sql
deactivate device template t1 from root.sg1.*, root.sg2.*
```

If the template name is not provided in sql, all template activation on paths matched by given path pattern will be removed.

### 2.6 Unset Device Template

The SQL Statement for unsetting device template is as follow:

```sql
unset device template t1 from root.sg1.d1
```

**Attention**: It should be guaranteed that none of the timeseries represented by the target device template exists, before unset it. It can be achieved by deactivation operation.

### 2.7 Drop Device Template

The SQL Statement for dropping device template is as follow:

```sql
drop device template t1
```

**Attention**: Dropping an already set template is not supported.

### 2.8 Alter Device Template

In a scenario where measurements need to be added, you can modify the  template to add measurements to all devicesdevice using the device template.

The SQL Statement for altering device template is as follow:

```sql
alter device template t1 add (speed FLOAT)
```

**When executing data insertion to devices with device template set on related prefix path and there are measurements not present in this device template, the measurements will be auto added to this device template.**

## 3. Timeseries Management

### 3.1 Create Timeseries

According to the storage model selected before, we can create corresponding timeseries in the two databases respectively. The SQL statements for creating timeseries are as follows:

```sql
create timeseries root.ln.wf01.wt01.status with datatype=BOOLEAN;
create timeseries root.ln.wf01.wt01.temperature with datatype=FLOAT;
create timeseries root.ln.wf02.wt02.hardware with datatype=TEXT;
create timeseries root.ln.wf02.wt02.status with datatype=BOOLEAN;
create timeseries root.sgcc.wf03.wt01.status with datatype=BOOLEAN;
create timeseries root.sgcc.wf03.wt01.temperature with datatype=FLOAT;
```

From v0.13, you can use a simplified version of the SQL statements to create timeseries:

```sql
create timeseries root.ln.wf01.wt01.status BOOLEAN;
create timeseries root.ln.wf01.wt01.temperature FLOAT;
create timeseries root.ln.wf02.wt02.hardware TEXT;
create timeseries root.ln.wf02.wt02.status BOOLEAN;
create timeseries root.sgcc.wf03.wt01.status BOOLEAN;
create timeseries root.sgcc.wf03.wt01.temperature FLOAT;
```

When creating a timeseries, the system will automatically assign default encoding and compression methods, requiring no manual specification. If your business scenario requires custom adjustments, you may refer to the following example:

```sql
create timeseries root.sgcc.wf03.wt01.temperature FLOAT encoding=PLAIN compressor=SNAPPY
```

Note that if you manually specify an encoding method that is incompatible with the data type, the system will return an error message, as shown below:

```sql
create timeseries root.ln.wf02.wt02.status WITH DATATYPE=BOOLEAN, ENCODING=TS_2DIFF;
error: encoding TS_2DIFF does not support BOOLEAN
```

For a full list of supported data types and corresponding encoding methods, please refer to [Compression & Encoding](../Technical-Insider/Encoding-and-Compression.md)。

### 3.2 Create Aligned Timeseries

The SQL statement for creating a group of timeseries are as follows:

```sql
CREATE ALIGNED TIMESERIES root.ln.wf01.GPS(latitude FLOAT, longitude FLOAT)
```

You can set different datatype, encoding, and compression for the timeseries in a group of aligned timeseries

It is also supported to set an alias, tag, and attribute for aligned timeseries.

### 3.3 Delete Timeseries

To delete the timeseries we created before, we are able to use `(DELETE | DROP) TimeSeries <PathPattern>` statement.

The usage are as follows:

```sql
delete timeseries root.ln.wf01.wt01.status;
delete timeseries root.ln.wf01.wt01.temperature, root.ln.wf02.wt02.hardware;
delete timeseries root.ln.wf02.*;
drop timeseries root.ln.wf02.*;
```

### 3.4 Show Timeseries

* SHOW LATEST? TIMESERIES pathPattern? whereClause? limitClause?

    There are four optional clauses added in SHOW TIMESERIES, return information of time series 

Timeseries information includes: timeseries path, alias of measurement, database it belongs to, data type, encoding type, compression type, tags and attributes.

Examples:

* SHOW TIMESERIES

    presents all timeseries information in JSON form

* SHOW TIMESERIES <`PathPattern`> 

    returns all timeseries information matching the given <`PathPattern`>. SQL statements are as follows:

```sql
show timeseries root.**;
show timeseries root.ln.**;
```

The results are shown below respectively:

```
+-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+--------+-------------------+
|                     timeseries|   alias|     database|dataType|encoding|compression|                                       tags|                                              attributes|deadband|deadband parameters|
+-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+--------+-------------------+
|root.sgcc.wf03.wt01.temperature|    null|    root.sgcc|   FLOAT|     RLE|     SNAPPY|                                       null|                                                    null|    null|               null|
|     root.sgcc.wf03.wt01.status|    null|    root.sgcc| BOOLEAN|   PLAIN|     SNAPPY|                                       null|                                                    null|    null|               null|
|             root.turbine.d1.s1|newAlias| root.turbine|   FLOAT|     RLE|     SNAPPY|{"newTag1":"newV1","tag4":"v4","tag3":"v3"}|{"attr2":"v2","attr1":"newV1","attr4":"v4","attr3":"v3"}|    null|               null|
|     root.ln.wf02.wt02.hardware|    null|      root.ln|    TEXT|   PLAIN|     SNAPPY|                                       null|                                                    null|    null|               null|
|       root.ln.wf02.wt02.status|    null|      root.ln| BOOLEAN|   PLAIN|     SNAPPY|                                       null|                                                    null|    null|               null|
|  root.ln.wf01.wt01.temperature|    null|      root.ln|   FLOAT|     RLE|     SNAPPY|                                       null|                                                    null|    null|               null|
|       root.ln.wf01.wt01.status|    null|      root.ln| BOOLEAN|   PLAIN|     SNAPPY|                                       null|                                                    null|    null|               null|
+-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+--------+-------------------+
Total line number = 7
It costs 0.016s

+-----------------------------+-----+-------------+--------+--------+-----------+----+----------+--------+-------------------+
|                   timeseries|alias|     database|dataType|encoding|compression|tags|attributes|deadband|deadband parameters|
+-----------------------------+-----+-------------+--------+--------+-----------+----+----------+--------+-------------------+
|   root.ln.wf02.wt02.hardware| null|      root.ln|    TEXT|   PLAIN|     SNAPPY|null|      null|    null|               null|
|     root.ln.wf02.wt02.status| null|      root.ln| BOOLEAN|   PLAIN|     SNAPPY|null|      null|    null|               null|
|root.ln.wf01.wt01.temperature| null|      root.ln|   FLOAT|     RLE|     SNAPPY|null|      null|    null|               null|
|     root.ln.wf01.wt01.status| null|      root.ln| BOOLEAN|   PLAIN|     SNAPPY|null|      null|    null|               null|
+-----------------------------+-----+-------------+--------+--------+-----------+----+----------+--------+-------------------+
Total line number = 4
It costs 0.004s
```

* SHOW TIMESERIES LIMIT INT OFFSET INT

    returns all the timeseries information start from the offset and limit the number of series returned. For example,

```sql
show timeseries root.ln.** limit 10 offset 10
```

* SHOW TIMESERIES WHERE TIMESERIES contains 'containStr'

    The query result set is filtered by string fuzzy matching based on the names of the timeseries. For example:

```sql
show timeseries root.ln.** where timeseries contains 'wf01.wt'
```

The result is shown below:

```
+-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+--------+-------------------+
|                     timeseries|   alias|     database|dataType|encoding|compression|                                       tags|                                              attributes|deadband|deadband parameters|
+-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+--------+-------------------+
|  root.ln.wf01.wt01.temperature|    null|      root.ln|   FLOAT|     RLE|     SNAPPY|                                       null|                                                    null|    null|               null|
|       root.ln.wf01.wt01.status|    null|      root.ln| BOOLEAN|   PLAIN|     SNAPPY|                                       null|                                                    null|    null|               null|
+-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+--------+-------------------+
Total line number = 2
It costs 0.016s
```

* SHOW TIMESERIES WHERE DataType=type

    The query result set is filtered by data type. For example:

```sql
show timeseries root.ln.** where dataType=FLOAT
```

The result is shown below:

```
+-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+--------+-------------------+
|                     timeseries|   alias|     database|dataType|encoding|compression|                                       tags|                                              attributes|deadband|deadband parameters|
+-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+--------+-------------------+
|root.sgcc.wf03.wt01.temperature|    null|    root.sgcc|   FLOAT|     RLE|     SNAPPY|                                       null|                                                    null|    null|               null|
|             root.turbine.d1.s1|newAlias| root.turbine|   FLOAT|     RLE|     SNAPPY|{"newTag1":"newV1","tag4":"v4","tag3":"v3"}|{"attr2":"v2","attr1":"newV1","attr4":"v4","attr3":"v3"}|    null|               null|
|  root.ln.wf01.wt01.temperature|    null|      root.ln|   FLOAT|     RLE|     SNAPPY|                                       null|                                                    null|    null|               null|
+-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+--------+-------------------+
Total line number = 3
It costs 0.016s

```

* SHOW TIMESERIES WHERE TAGS(KEY) = VALUE
* SHOW TIMESERIES WHERE TAGS(KEY) CONTAINS VALUE

  The query result set is filtered by tags. For example:

```sql
show timeseries root.ln.** where TAGS(unit)='c';
show timeseries root.ln.** where TAGS(description) contains 'test1';
```

The query results are as follows:

```
+--------------------------+-----+-------------+--------+--------+-----------+------------+----------+--------+-------------------+
|                timeseries|alias|     database|dataType|encoding|compression|        tags|attributes|deadband|deadband parameters|
+--------------------------+-----+-------------+--------+--------+-----------+------------+----------+--------+-------------------+
|root.ln.wf02.wt02.hardware| null|      root.ln|    TEXT|   PLAIN|     SNAPPY|{"unit":"c"}|      null|    null|               null|
+--------------------------+-----+-------------+--------+--------+-----------+------------+----------+--------+-------------------+
Total line number = 1
It costs 0.005s

+------------------------+-----+-------------+--------+--------+-----------+-----------------------+----------+--------+-------------------+
|              timeseries|alias|     database|dataType|encoding|compression|                   tags|attributes|deadband|deadband parameters|
+------------------------+-----+-------------+--------+--------+-----------+-----------------------+----------+--------+-------------------+
|root.ln.wf02.wt02.status| null|      root.ln| BOOLEAN|   PLAIN|     SNAPPY|{"description":"test1"}|      null|    null|               null|
+------------------------+-----+-------------+--------+--------+-----------+-----------------------+----------+--------+-------------------+
Total line number = 1
It costs 0.004s

```


* SHOW LATEST TIMESERIES

    all the returned timeseries information should be sorted in descending order of the last timestamp of timeseries

It is worth noting that when the queried path does not exist, the system will return no timeseries.  


### 3.5 Count Timeseries

IoTDB is able to use `COUNT TIMESERIES <Path>` to count the number of timeseries matching the path. SQL statements are as follows:

* `WHERE` condition could be used to fuzzy match a time series name with the following syntax: `COUNT TIMESERIES <Path> WHERE TIMESERIES contains 'containStr'`.
* `WHERE` condition could be used to filter result by data type with the syntax: `COUNT TIMESERIES <Path> WHERE DataType=<DataType>'`.
* `WHERE` condition could be used to filter result by tags with the syntax: `COUNT TIMESERIES <Path> WHERE TAGS(key)='value'` or `COUNT TIMESERIES <Path> WHERE TAGS(key) contains 'value'`.
* `LEVEL` could be defined to show count the number of timeseries of each node at the given level in current Metadata Tree. This could be used to query the number of sensors under each device. The grammar is: `COUNT TIMESERIES <Path> GROUP BY LEVEL=<INTEGER>`.


```sql
COUNT TIMESERIES root.**;
COUNT TIMESERIES root.ln.**;
COUNT TIMESERIES root.ln.*.*.status;
COUNT TIMESERIES root.ln.wf01.wt01.status;
COUNT TIMESERIES root.** WHERE TIMESERIES contains 'sgcc' ;
COUNT TIMESERIES root.** WHERE DATATYPE = INT64;
COUNT TIMESERIES root.** WHERE TAGS(unit) contains 'c' ;
COUNT TIMESERIES root.** WHERE TAGS(unit) = 'c' ;
COUNT TIMESERIES root.** WHERE TIMESERIES contains 'sgcc' group by level = 1;
```

For example, if there are several timeseries (use `show timeseries` to show all timeseries):

```
+-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+--------+-------------------+
|                     timeseries|   alias|     database|dataType|encoding|compression|                                       tags|                                              attributes|deadband|deadband parameters|
+-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+--------+-------------------+
|root.sgcc.wf03.wt01.temperature|    null|    root.sgcc|   FLOAT|     RLE|     SNAPPY|                                       null|                                                    null|    null|               null|
|     root.sgcc.wf03.wt01.status|    null|    root.sgcc| BOOLEAN|   PLAIN|     SNAPPY|                                       null|                                                    null|    null|               null|
|             root.turbine.d1.s1|newAlias| root.turbine|   FLOAT|     RLE|     SNAPPY|{"newTag1":"newV1","tag4":"v4","tag3":"v3"}|{"attr2":"v2","attr1":"newV1","attr4":"v4","attr3":"v3"}|    null|               null|
|     root.ln.wf02.wt02.hardware|    null|      root.ln|    TEXT|   PLAIN|     SNAPPY|                               {"unit":"c"}|                                                    null|    null|               null|
|       root.ln.wf02.wt02.status|    null|      root.ln| BOOLEAN|   PLAIN|     SNAPPY|                    {"description":"test1"}|                                                    null|    null|               null|
|  root.ln.wf01.wt01.temperature|    null|      root.ln|   FLOAT|     RLE|     SNAPPY|                                       null|                                                    null|    null|               null|
|       root.ln.wf01.wt01.status|    null|      root.ln| BOOLEAN|   PLAIN|     SNAPPY|                                       null|                                                    null|    null|               null|
+-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+--------+-------------------+
Total line number = 7
It costs 0.004s
```

Then the Metadata Tree will be as below:

<center><img style="width:100%; max-width:600px; margin-left:auto; margin-right:auto; display:block;" src="/img/github/69792176-1718f400-1201-11ea-861a-1a83c07ca144.jpg"></center>

As can be seen, `root` is considered as `LEVEL=0`. So when you enter statements such as:

```sql
COUNT TIMESERIES root.** GROUP BY LEVEL=1;
COUNT TIMESERIES root.ln.** GROUP BY LEVEL=2;
COUNT TIMESERIES root.ln.wf01.* GROUP BY LEVEL=2;
```

You will get following results:

```
+------------+-----------------+
|      column|count(timeseries)|
+------------+-----------------+
|   root.sgcc|                2|
|     root.ln|                4|
+------------+-----------------+
Total line number = 3
It costs 0.002s

+------------+-----------------+
|      column|count(timeseries)|
+------------+-----------------+
|root.ln.wf02|                2|
|root.ln.wf01|                2|
+------------+-----------------+
Total line number = 2
It costs 0.002s

+------------+-----------------+
|      column|count(timeseries)|
+------------+-----------------+
|root.ln.wf01|                2|
+------------+-----------------+
Total line number = 1
It costs 0.002s
```

> Note: The path of timeseries is just a filter condition, which has no relationship with the definition of level.

### 3.6 Tag and Attribute Management

We can also add an alias, extra tag and attribute information while creating one timeseries.

The differences between tag and attribute are:

* Tag could be used to query the path of timeseries, we will maintain an inverted index in memory on the tag: Tag -> Timeseries
* Attribute could only be queried by timeseries path : Timeseries -> Attribute

The SQL statements for creating timeseries with extra tag and attribute information are extended as follows:

```sql
create timeseries root.turbine.d1.s1(temprature) with datatype=FLOAT tags(tag1=v1, tag2=v2) attributes(attr1=v1, attr2=v2)
```

The `temprature` in the brackets is an alias for the sensor `s1`. So we can use `temprature` to replace `s1` anywhere.

> IoTDB also supports using AS function to set alias. The difference between the two is: the alias set by the AS function is used to replace the whole time series name, temporary and not bound with the time series; while the alias mentioned above is only used as the alias of the sensor, which is bound with it and can be used equivalent to the original sensor name.

> Notice that the size of the extra tag and attribute information shouldn't exceed the `tag_attribute_total_size`.

We can update the tag information after creating it as following:

* Rename the tag/attribute key

```sql
ALTER timeseries root.turbine.d1.s1 RENAME tag1 TO newTag1
```

* Reset the tag/attribute value

```sql
ALTER timeseries root.turbine.d1.s1 SET newTag1=newV1, attr1=newV1
```

* Delete the existing tag/attribute

```sql
ALTER timeseries root.turbine.d1.s1 DROP tag1, tag2
```

* Add new tags

```sql
ALTER timeseries root.turbine.d1.s1 ADD TAGS tag3=v3, tag4=v4
```

* Add new attributes

```sql
ALTER timeseries root.turbine.d1.s1 ADD ATTRIBUTES attr3=v3, attr4=v4
```

* Upsert alias, tags and attributes

> add alias or a new key-value if the alias or key doesn't exist, otherwise, update the old one with new value.

```sql
ALTER timeseries root.turbine.d1.s1 UPSERT ALIAS=newAlias TAGS(tag3=v3, tag4=v4) ATTRIBUTES(attr3=v3, attr4=v4)
```

* Show timeseries using tags. Use TAGS(tagKey) to identify the tags used as filter key

```sql
SHOW TIMESERIES (<`PathPattern`>)? timeseriesWhereClause
```

returns all the timeseries information that satisfy the where condition and match the pathPattern. SQL statements are as follows:

```sql
ALTER timeseries root.ln.wf02.wt02.hardware ADD TAGS unit=c;
ALTER timeseries root.ln.wf02.wt02.status ADD TAGS description=test1;
show timeseries root.ln.** where TAGS(unit)='c';
show timeseries root.ln.** where TAGS(description) contains 'test1';
```

The results are shown below respectly:

```
+--------------------------+-----+-------------+--------+--------+-----------+------------+----------+--------+-------------------+
|                timeseries|alias|     database|dataType|encoding|compression|        tags|attributes|deadband|deadband parameters|
+--------------------------+-----+-------------+--------+--------+-----------+------------+----------+--------+-------------------+
|root.ln.wf02.wt02.hardware| null|      root.ln|    TEXT|   PLAIN|     SNAPPY|{"unit":"c"}|      null|    null|               null|
+--------------------------+-----+-------------+--------+--------+-----------+------------+----------+--------+-------------------+
Total line number = 1
It costs 0.005s

+------------------------+-----+-------------+--------+--------+-----------+-----------------------+----------+--------+-------------------+
|              timeseries|alias|     database|dataType|encoding|compression|                   tags|attributes|deadband|deadband parameters|
+------------------------+-----+-------------+--------+--------+-----------+-----------------------+----------+--------+-------------------+
|root.ln.wf02.wt02.status| null|      root.ln| BOOLEAN|   PLAIN|     SNAPPY|{"description":"test1"}|      null|    null|               null|
+------------------------+-----+-------------+--------+--------+-----------+-----------------------+----------+--------+-------------------+
Total line number = 1
It costs 0.004s
```

- count timeseries using tags

```sql
COUNT TIMESERIES (<`PathPattern`>)? timeseriesWhereClause;
COUNT TIMESERIES (<`PathPattern`>)? timeseriesWhereClause GROUP BY LEVEL=<INTEGER>;
```

returns all the number of timeseries that satisfy the where condition and match the pathPattern. SQL statements are as follows:

```sql
count timeseries;
count timeseries root.** where TAGS(unit)='c';
count timeseries root.** where TAGS(unit)='c' group by level = 2;
```

The results are shown below respectly :

```
count timeseries
+-----------------+
|count(timeseries)|
+-----------------+
|                6|
+-----------------+
Total line number = 1
It costs 0.019s
count timeseries root.** where TAGS(unit)='c'
+-----------------+
|count(timeseries)|
+-----------------+
|                2|
+-----------------+
Total line number = 1
It costs 0.020s
count timeseries root.** where TAGS(unit)='c' group by level = 2
+--------------+-----------------+
|        column|count(timeseries)|
+--------------+-----------------+
|  root.ln.wf02|                2|
|  root.ln.wf01|                0|
|root.sgcc.wf03|                0|
+--------------+-----------------+
Total line number = 3
It costs 0.011s
```

> Notice that, we only support one condition in the where clause. Either it's an equal filter or it is an `contains` filter. In both case, the property in the where condition must be a tag.

create aligned timeseries

```sql
create aligned timeseries root.sg1.d1(s1 INT32 tags(tag1=v1, tag2=v2) attributes(attr1=v1, attr2=v2), s2 DOUBLE tags(tag3=v3, tag4=v4) attributes(attr3=v3, attr4=v4))
```

The execution result is as follows:

```sql
show timeseries
+--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+--------+-------------------+
|    timeseries|alias|     database|dataType|encoding|compression|                     tags|                 attributes|deadband|deadband parameters|
+--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+--------+-------------------+
|root.sg1.d1.s1| null|     root.sg1|   INT32|     RLE|     SNAPPY|{"tag1":"v1","tag2":"v2"}|{"attr2":"v2","attr1":"v1"}|    null|               null|
|root.sg1.d1.s2| null|     root.sg1|  DOUBLE| GORILLA|     SNAPPY|{"tag4":"v4","tag3":"v3"}|{"attr4":"v4","attr3":"v3"}|    null|               null|
+--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+--------+-------------------+
```

Support query：

```sql
show timeseries where TAGS(tag1)='v1'
+--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+--------+-------------------+
|    timeseries|alias|     database|dataType|encoding|compression|                     tags|                 attributes|deadband|deadband parameters|
+--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+--------+-------------------+
|root.sg1.d1.s1| null|     root.sg1|   INT32|     RLE|     SNAPPY|{"tag1":"v1","tag2":"v2"}|{"attr2":"v2","attr1":"v1"}|    null|               null|
+--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+--------+-------------------+
```

The above operations are supported for timeseries tag, attribute updates, etc.

## 4. Path query

### 4.1 Path

A `path` is an expression that conforms to the following constraints:

```sql
path       
    : nodeName ('.' nodeName)*
    ;
    
nodeName
    : wildcard? identifier wildcard?
    | wildcard
    ;
    
wildcard 
    : '*' 
    | '**'
    ;
```

### 4.2 NodeName

- The parts of a path separated by `.` are called node names (`nodeName`).
- For example, `root.a.b.c` is a path with a depth of 4 levels, where `root`, `a`, `b`, and `c` are all node names.

#### Constraints

- **Reserved keyword `root`**: `root` is a reserved keyword that is only allowed at the beginning of a path. If `root` appears in any other level, the system will fail to parse it and report an error.
- **Character support**: Except for the first level (`root`), other levels support the following characters:
  - Letters (`a-z`, `A-Z`)
  - Digits (`0-9`)
  - Underscores (`_`)
  - UNICODE Chinese characters (`\u2E80` to `\u9FFF`)
- **Case sensitivity**: On Windows systems, path node names in the database are case-insensitive. For example, `root.ln` and `root.LN` are considered the same path.

### 4.3 Special Characters (Backquote)

If special characters (such as spaces or punctuation marks) are needed in a `nodeName`, you can enclose the node name in Backquote (`). For more information on the use of backticks, please refer to [Backquote](../SQL-Manual/Syntax-Rule.md#reverse-quotation-marks).

### 4.4 Path Pattern

To make it more convenient and efficient to express multiple time series, IoTDB provides paths with wildcards `*` and `**`. Wildcards can appear in any level of a path.

- **Single-level wildcard (`\*`)**: Represents one level in a path.
  - For example, `root.vehicle.*.sensor1` represents paths with a depth of 4 levels, prefixed by `root.vehicle` and suffixed by `sensor1`.
- **Multi-level wildcard (`\**`)**: Represents one or more levels (`*`+).
  - For example:
    - `root.vehicle.device1.**` represents all paths with a depth of 4 or more levels, prefixed by `root.vehicle.device1`.
    - `root.vehicle.**.sensor1` represents paths with a depth of 4 or more levels, prefixed by `root.vehicle` and suffixed by `sensor1`.

**Note**: `*` and `**` cannot be placed at the beginning of a path.

### 4.5 Show Child Paths

```sql
SHOW CHILD PATHS pathPattern
```

Return all child paths and their node types of all the paths matching pathPattern.

node types: ROOT -> DB INTERNAL -> DATABASE -> INTERNAL -> DEVICE -> TIMESERIES


Example：

* return the child paths of root.ln：show child paths root.ln

```
+------------+----------+
| child paths|node types|
+------------+----------+
|root.ln.wf01|  INTERNAL|
|root.ln.wf02|  INTERNAL|
+------------+----------+
Total line number = 2
It costs 0.002s
```

> get all paths in form of root.xx.xx.xx：show child paths root.xx.xx

### 4.6 Show Child Nodes

```sql
SHOW CHILD NODES pathPattern
```

Return all child nodes of the pathPattern.

Example：

* return the child nodes of root：show child nodes root

```
+------------+
| child nodes|
+------------+
|          ln|
+------------+
```

* return the child nodes of root.ln：show child nodes root.ln

```
+------------+
| child nodes|
+------------+
|        wf01|
|        wf02|
+------------+
```

### 4.7 Count Nodes

IoTDB is able to use `COUNT NODES <PathPattern> LEVEL=<INTEGER>` to count the number of nodes at
 the given level in current Metadata Tree considering a given pattern. IoTDB will find paths that
  match the pattern and counts distinct nodes at the specified level among the matched paths.
  This could be used to query the number of devices with specified measurements. The usage are as
   follows:

```sql
COUNT NODES root.** LEVEL=2;
COUNT NODES root.ln.** LEVEL=2;
COUNT NODES root.ln.wf01.** LEVEL=3;
COUNT NODES root.**.temperature LEVEL=3;
```

As for the above mentioned example and Metadata tree, you can get following results:

```
+------------+
|count(nodes)|
+------------+
|           4|
+------------+
Total line number = 1
It costs 0.003s

+------------+
|count(nodes)|
+------------+
|           2|
+------------+
Total line number = 1
It costs 0.002s

+------------+
|count(nodes)|
+------------+
|           1|
+------------+
Total line number = 1
It costs 0.002s

+------------+
|count(nodes)|
+------------+
|           2|
+------------+
Total line number = 1
It costs 0.002s
```

> Note: The path of timeseries is just a filter condition, which has no relationship with the definition of level.

### 4.8 Show Devices

* SHOW DEVICES pathPattern? (WITH DATABASE)? devicesWhereClause? limitClause?

Similar to `Show Timeseries`, IoTDB also supports two ways of viewing devices:

* `SHOW DEVICES` statement presents all devices' information, which is equal to `SHOW DEVICES root.**`.
* `SHOW DEVICES <PathPattern>` statement specifies the `PathPattern` and returns the devices information matching the pathPattern and under the given level.
* `WHERE` condition supports `DEVICE contains 'xxx'`  to do a fuzzy query based on the device name.

SQL statement is as follows:

```sql
show devices;
show devices root.ln.**;
show devices root.ln.** where device contains 't';
```

You can get results below:

```
+-------------------+---------+
|            devices|isAligned|
+-------------------+---------+
|  root.ln.wf01.wt01|    false|
|  root.ln.wf02.wt02|    false|
|root.sgcc.wf03.wt01|    false|
|    root.turbine.d1|    false|
+-------------------+---------+
Total line number = 4
It costs 0.002s

+-----------------+---------+
|          devices|isAligned|
+-----------------+---------+
|root.ln.wf01.wt01|    false|
|root.ln.wf02.wt02|    false|
+-----------------+---------+
Total line number = 2
It costs 0.001s
```

`isAligned` indicates whether the timeseries under the device are aligned.

To view devices' information with database, we can use `SHOW DEVICES WITH DATABASE` statement.

* `SHOW DEVICES WITH DATABASE` statement presents all devices' information with their database.
* `SHOW DEVICES <PathPattern> WITH DATABASE` statement specifies the `PathPattern` and returns the 
    devices' information under the given level with their database information.

SQL statement is as follows:

```sql
show devices with database;
show devices root.ln.** with database;
```

You can get results below:

```
+-------------------+-------------+---------+
|            devices|     database|isAligned|
+-------------------+-------------+---------+
|  root.ln.wf01.wt01|      root.ln|    false|
|  root.ln.wf02.wt02|      root.ln|    false|
|root.sgcc.wf03.wt01|    root.sgcc|    false|
|    root.turbine.d1| root.turbine|    false|
+-------------------+-------------+---------+
Total line number = 4
It costs 0.003s

+-----------------+-------------+---------+
|          devices|     database|isAligned|
+-----------------+-------------+---------+
|root.ln.wf01.wt01|      root.ln|    false|
|root.ln.wf02.wt02|      root.ln|    false|
+-----------------+-------------+---------+
Total line number = 2
It costs 0.001s
```

### 4.9 Count Devices

* COUNT DEVICES /<PathPattern/>

The above statement is used to count the number of devices. At the same time, it is allowed to specify `PathPattern` to count the number of devices matching the `PathPattern`.

SQL statement is as follows:

```sql
show devices;
count devices;
count devices root.ln.**;
```

You can get results below:

```
+-------------------+---------+
|            devices|isAligned|
+-------------------+---------+
|root.sgcc.wf03.wt03|    false|
|    root.turbine.d1|    false|
|  root.ln.wf02.wt02|    false|
|  root.ln.wf01.wt01|    false|
+-------------------+---------+
Total line number = 4
It costs 0.024s

+--------------+
|count(devices)|
+--------------+
|             4|
+--------------+
Total line number = 1
It costs 0.004s

+--------------+
|count(devices)|
+--------------+
|             2|
+--------------+
Total line number = 1
It costs 0.004s
```