According to the storage model we can set up the corresponding database. Two SQL statements are supported for creating databases, as follows:
IoTDB > create database root.ln IoTDB > 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, it is not feasible to create root.ln.wf01 as database when two databases root.ln and root.sgcc exist. The system gives the corresponding error prompt as shown below:
IoTDB> CREATE DATABASE root.ln.wf01 Msg: 300: root.ln has already been created as database. IoTDB> create database root.ln.wf01 Msg: 300: root.ln has already been created as database.
The LayerName of database can only be characters, numbers, underscores. If you want to set it to pure numbers or contain other characters, you need to enclose the database name with backticks (``).
Besides, if deploy on Windows system, the LayerName is case-insensitive, which means it's not allowed to create databases root.ln and root.LN at the same time.
After creating the database, we can use the SHOW DATABASES statement and SHOW DATABASES <PathPattern> to view the databases. The SQL statements are as follows:
IoTDB> SHOW DATABASES IoTDB> 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
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.
IoTDB > DELETE DATABASE root.ln IoTDB > DELETE DATABASE root.sgcc // delete all data, all timeseries and all databases IoTDB > DELETE DATABASE root.**
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:
IoTDB> count databases IoTDB> count databases root.* IoTDB> count databases root.sgcc.* IoTDB> 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
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:
schema_region_group_extension_policy and data_region_group_extension_policy in iotdb-common.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.
The user can set any of the above heterogeneous parameters when creating a Database. The SQL statement is as follows:
CREATE DATABASE prefixPath (WITH databaseAttributeClause (COMMA? databaseAttributeClause)*)?
For example:
CREATE DATABASE root.db WITH SCHEMA_REPLICATION_FACTOR=1, DATA_REPLICATION_FACTOR=3, SCHEMA_REGION_GROUP_NUM=1, DATA_REGION_GROUP_NUM=2;
Users can adjust some heterogeneous parameters during the IoTDB runtime, as shown in the following SQL statement:
ALTER DATABASE prefixPath WITH databaseAttributeClause (COMMA? databaseAttributeClause)*
For example:
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:
The user can query the specific heterogeneous configuration of each Database, and the SQL statement is as follows:
SHOW DATABASES DETAILS prefixPath?
For example:
IoTDB> 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:
IoTDB supports storage-level TTL settings, which means it is able to delete old data automatically and periodically. The benefit of using TTL is that hopefully you can control the total disk space usage and prevent the machine from running out of disks. Moreover, the query performance may downgrade as the total number of files goes up and the memory usage also increase as there are more files. Timely removing such files helps to keep at a high query performance level and reduce memory usage.
The default unit of TTL is milliseconds. If the time precision in the configuration file changes to another, the TTL is still set to milliseconds.
The SQL Statement for setting TTL is as follow:
IoTDB> set ttl to root.ln 3600000
This example means that for data in root.ln, only 3600000 ms, that is, the latest 1 hour will remain, the older one is removed or made invisible.
IoTDB> set ttl to root.sgcc.** 3600000
It supports setting TTL for databases in a path. This example represents setting TTL for all databases in the root.sgcc path.
IoTDB> set ttl to root.** 3600000
This example represents setting TTL for all databases.
To unset TTL, we can use follwing SQL statement:
IoTDB> unset ttl to root.ln
After unset TTL, all data will be accepted in root.ln.
IoTDB> unset ttl to root.sgcc.**
Unset the TTL setting for all databases in the root.sgcc path.
IoTDB> unset ttl to root.**
Unset the TTL setting for all databases.
To Show TTL, we can use following SQL statement:
IoTDB> SHOW ALL TTL IoTDB> SHOW TTL ON DataBaseNames
The SHOW ALL TTL example gives the TTL for all databases. The SHOW TTL ON root.ln,root.sgcc,root.DB example shows the TTL for the three storage groups specified. Note: the TTL for databases that do not have a TTL set will display as null.
IoTDB> show all ttl +----------+-------+ | database|ttl(ms)| +---------+-------+ | root.ln|3600000| |root.sgcc| null| | root.DB|3600000| +----------+-------+
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.
Note: The device keyword in the following statements can be omitted.
The SQL syntax for creating a metadata template is as follows:
CREATE DEVICE TEMPLATE <templateName> ALIGNED? '(' <measurementId> <attributeClauses> [',' <measurementId> <attributeClauses>]+ ')'
Example 1: Create a template containing two non-aligned timeseires
IoTDB> create device template t1 (temperature FLOAT encoding=RLE, status BOOLEAN encoding=PLAIN compression=SNAPPY)
Example 2: Create a template containing a group of aligned timeseires
IoTDB> create device template t2 aligned (lat FLOAT encoding=Gorilla, lon FLOAT encoding=Gorilla)
The lat and lon measurements are aligned.
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:
IoTDB> set device template t1 to root.sg1.d1
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:
IoTDB> create timeseries using device template on root.sg1.d1
Example: Execute the following statement
IoTDB> set device template t1 to root.sg1.d1 IoTDB> set device template t2 to root.sg1.d2 IoTDB> create timeseries using device template on root.sg1.d1 IoTDB> create timeseries using device template on root.sg1.d2
Show the time series:
show timeseries root.sg1.**
+-----------------------+-----+-------------+--------+--------+-----------+----+----------+--------+-------------------+ | 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:
show devices root.sg1.**
+---------------+---------+ | devices|isAligned| +---------------+---------+ | root.sg1.d1| false| | root.sg1.d2| true| +---------------+---------+
The SQL statement looks like this:
IoTDB> show device templates
The execution result is as follows:
+-------------+ |template name| +-------------+ | t2| | t1| +-------------+
The SQL statement looks like this:
IoTDB> show nodes in device template t1
The execution result is as follows:
+-----------+--------+--------+-----------+ |child nodes|dataType|encoding|compression| +-----------+--------+--------+-----------+ |temperature| FLOAT| RLE| SNAPPY| | status| BOOLEAN| PLAIN| SNAPPY| +-----------+--------+--------+-----------+
IoTDB> show paths set device template t1
The execution result is as follows:
+-----------+ |child paths| +-----------+ |root.sg1.d1| +-----------+
IoTDB> show paths using device template t1
The execution result is as follows:
+-----------+ |child paths| +-----------+ |root.sg1.d1| +-----------+
To delete a group of timeseries represented by device template, namely deactivate the device template, use the following SQL statement:
IoTDB> delete timeseries of device template t1 from root.sg1.d1
or
IoTDB> deactivate device template t1 from root.sg1.d1
The deactivation supports batch process.
IoTDB> delete timeseries of device template t1 from root.sg1.*, root.sg2.*
or
IoTDB> 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.
The SQL Statement for unsetting device template is as follow:
IoTDB> 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.
The SQL Statement for dropping device template is as follow:
IoTDB> drop device template t1
Attention: Dropping an already set template is not supported.
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:
IoTDB> alter device template t1 add (speed FLOAT encoding=RLE, FLOAT TEXT encoding=PLAIN compression=SNAPPY)
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.
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:
IoTDB > create timeseries root.ln.wf01.wt01.status with datatype=BOOLEAN,encoding=PLAIN IoTDB > create timeseries root.ln.wf01.wt01.temperature with datatype=FLOAT,encoding=RLE IoTDB > create timeseries root.ln.wf02.wt02.hardware with datatype=TEXT,encoding=PLAIN IoTDB > create timeseries root.ln.wf02.wt02.status with datatype=BOOLEAN,encoding=PLAIN IoTDB > create timeseries root.sgcc.wf03.wt01.status with datatype=BOOLEAN,encoding=PLAIN IoTDB > create timeseries root.sgcc.wf03.wt01.temperature with datatype=FLOAT,encoding=RLE
From v0.13, you can use a simplified version of the SQL statements to create timeseries:
IoTDB > create timeseries root.ln.wf01.wt01.status BOOLEAN encoding=PLAIN IoTDB > create timeseries root.ln.wf01.wt01.temperature FLOAT encoding=RLE IoTDB > create timeseries root.ln.wf02.wt02.hardware TEXT encoding=PLAIN IoTDB > create timeseries root.ln.wf02.wt02.status BOOLEAN encoding=PLAIN IoTDB > create timeseries root.sgcc.wf03.wt01.status BOOLEAN encoding=PLAIN IoTDB > create timeseries root.sgcc.wf03.wt01.temperature FLOAT encoding=RLE
Notice that when in the CREATE TIMESERIES statement the encoding method conflicts with the data type, the system gives the corresponding error prompt as shown below:
IoTDB > create timeseries root.ln.wf02.wt02.status WITH DATATYPE=BOOLEAN, ENCODING=TS_2DIFF error: encoding TS_2DIFF does not support BOOLEAN
Please refer to Encoding for correspondence between data type and encoding.
The SQL statement for creating a group of timeseries are as follows:
IoTDB> CREATE ALIGNED TIMESERIES root.ln.wf01.GPS(latitude FLOAT encoding=PLAIN compressor=SNAPPY, longitude FLOAT encoding=PLAIN compressor=SNAPPY)
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.
To delete the timeseries we created before, we are able to use (DELETE | DROP) TimeSeries <PathPattern> statement.
The usage are as follows:
IoTDB> delete timeseries root.ln.wf01.wt01.status IoTDB> delete timeseries root.ln.wf01.wt01.temperature, root.ln.wf02.wt02.hardware IoTDB> delete timeseries root.ln.wf02.* IoTDB> drop timeseries root.ln.wf02.*
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:
IoTDB> show timeseries root.** IoTDB> 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,
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:
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:
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 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.
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>.IoTDB > COUNT TIMESERIES root.** IoTDB > COUNT TIMESERIES root.ln.** IoTDB > COUNT TIMESERIES root.ln.*.*.status IoTDB > COUNT TIMESERIES root.ln.wf01.wt01.status IoTDB > COUNT TIMESERIES root.** WHERE TIMESERIES contains 'sgcc' IoTDB > COUNT TIMESERIES root.** WHERE DATATYPE = INT64 IoTDB > COUNT TIMESERIES root.** WHERE TAGS(unit) contains 'c' IoTDB > COUNT TIMESERIES root.** WHERE TAGS(unit) = 'c' IoTDB > 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:
As can be seen, root is considered as LEVEL=0. So when you enter statements such as:
IoTDB > COUNT TIMESERIES root.** GROUP BY LEVEL=1 IoTDB > COUNT TIMESERIES root.ln.** GROUP BY LEVEL=2 IoTDB > COUNT TIMESERIES root.ln.wf01.* GROUP BY LEVEL=2
You will get following results:
+------------+-----------------+ | column|count(timeseries)| +------------+-----------------+ | root.sgcc| 2| |root.turbine| 1| | 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.
We can also add an alias, extra tag and attribute information while creating one timeseries.
The differences between tag and attribute are:
The SQL statements for creating timeseries with extra tag and attribute information are extended as follows:
create timeseries root.turbine.d1.s1(temprature) with datatype=FLOAT, encoding=RLE, compression=SNAPPY 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:
ALTER timeseries root.turbine.d1.s1 RENAME tag1 TO newTag1
ALTER timeseries root.turbine.d1.s1 SET newTag1=newV1, attr1=newV1
ALTER timeseries root.turbine.d1.s1 DROP tag1, tag2
ALTER timeseries root.turbine.d1.s1 ADD TAGS tag3=v3, tag4=v4
ALTER timeseries root.turbine.d1.s1 ADD ATTRIBUTES attr3=v3, attr4=v4
add alias or a new key-value if the alias or key doesn't exist, otherwise, update the old one with new value.
ALTER timeseries root.turbine.d1.s1 UPSERT ALIAS=newAlias TAGS(tag3=v3, tag4=v4) ATTRIBUTES(attr3=v3, attr4=v4)
SHOW TIMESERIES (<`PathPattern`>)? timeseriesWhereClause
returns all the timeseries information that satisfy the where condition and match the pathPattern. SQL statements are as follows:
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 (<`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:
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 :
IoTDB> count timeseries +-----------------+ |count(timeseries)| +-----------------+ | 6| +-----------------+ Total line number = 1 It costs 0.019s IoTDB> count timeseries root.** where TAGS(unit)='c' +-----------------+ |count(timeseries)| +-----------------+ | 2| +-----------------+ Total line number = 1 It costs 0.020s IoTDB> 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
containsfilter. In both case, the property in the where condition must be a tag.
create aligned timeseries
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:
IoTDB> 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：
IoTDB> 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.
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：
+------------+----------+ | 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
SHOW CHILD NODES pathPattern
Return all child nodes of the pathPattern.
Example：
+------------+ | child nodes| +------------+ | ln| +------------+
+------------+ | child nodes| +------------+ | wf01| | wf02| +------------+
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:
IoTDB > COUNT NODES root.** LEVEL=2 IoTDB > COUNT NODES root.ln.** LEVEL=2 IoTDB > COUNT NODES root.ln.wf01.** LEVEL=3 IoTDB > 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.
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:
IoTDB> show devices IoTDB> show devices root.ln.** IoTDB> 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:
IoTDB> show devices with database IoTDB> 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
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:
IoTDB> show devices IoTDB> count devices IoTDB> 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