According to the storage model selected before, we can create corresponding timeseries in the two storage groups 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 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.*
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, storage group 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|storage group|dataType|encoding|compression| tags| attributes| +-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+ |root.sgcc.wf03.wt01.temperature| null| root.sgcc| FLOAT| RLE| SNAPPY| null| null| | root.sgcc.wf03.wt01.status| null| root.sgcc| BOOLEAN| PLAIN| SNAPPY| 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"}| | root.ln.wf02.wt02.hardware| null| root.ln| TEXT| PLAIN| SNAPPY| null| null| | root.ln.wf02.wt02.status| null| root.ln| BOOLEAN| PLAIN| SNAPPY| null| null| | root.ln.wf01.wt01.temperature| null| root.ln| FLOAT| RLE| SNAPPY| null| null| | root.ln.wf01.wt01.status| null| root.ln| BOOLEAN| PLAIN| SNAPPY| null| null| +-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+ Total line number = 7 It costs 0.016s +-----------------------------+-----+-------------+--------+--------+-----------+----+----------+ | timeseries|alias|storage group|dataType|encoding|compression|tags|attributes| +-----------------------------+-----+-------------+--------+--------+-----------+----+----------+ | root.ln.wf02.wt02.hardware| null| root.ln| TEXT| PLAIN| SNAPPY|null| null| | root.ln.wf02.wt02.status| null| root.ln| BOOLEAN| PLAIN| SNAPPY|null| null| |root.ln.wf01.wt01.temperature| null| root.ln| FLOAT| RLE| SNAPPY|null| null| | root.ln.wf01.wt01.status| null| root.ln| BOOLEAN| PLAIN| SNAPPY|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 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:
IoTDB > COUNT TIMESERIES root.** IoTDB > COUNT TIMESERIES root.ln.** IoTDB > COUNT TIMESERIES root.ln.*.*.status IoTDB > COUNT TIMESERIES root.ln.wf01.wt01.status
Besides, 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>
.
For example, if there are several timeseries (use show timeseries
to show all timeseries):
+-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+ | timeseries| alias|storage group|dataType|encoding|compression| tags| attributes| +-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+ |root.sgcc.wf03.wt01.temperature| null| root.sgcc| FLOAT| RLE| SNAPPY| null| null| | root.sgcc.wf03.wt01.status| null| root.sgcc| BOOLEAN| PLAIN| SNAPPY| 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"}| | root.ln.wf02.wt02.hardware| null| root.ln| TEXT| PLAIN| SNAPPY| {"unit":"c"}| null| | root.ln.wf02.wt02.status| null| root.ln| BOOLEAN| PLAIN| SNAPPY| {"description":"test1"}| null| | root.ln.wf01.wt01.temperature| null| root.ln| FLOAT| RLE| SNAPPY| null| null| | root.ln.wf01.wt01.status| null| root.ln| BOOLEAN| PLAIN| SNAPPY| null| null| +-------------------------------+--------+-------------+--------+--------+-----------+-------------------------------------------+--------------------------------------------------------+ Total line number = 7 It costs 0.004s
Then the Metadata Tree will be as below:
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`>)? WhereClause
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 unit=c show timeseries root.ln.** where description contains 'test1'
The results are shown below respectly:
+--------------------------+-----+-------------+--------+--------+-----------+------------+----------+ | timeseries|alias|storage group|dataType|encoding|compression| tags|attributes| +--------------------------+-----+-------------+--------+--------+-----------+------------+----------+ |root.ln.wf02.wt02.hardware| null| root.ln| TEXT| PLAIN| SNAPPY|{"unit":"c"}| null| +--------------------------+-----+-------------+--------+--------+-----------+------------+----------+ Total line number = 1 It costs 0.005s +------------------------+-----+-------------+--------+--------+-----------+-----------------------+----------+ | timeseries|alias|storage group|dataType|encoding|compression| tags|attributes| +------------------------+-----+-------------+--------+--------+-----------+-----------------------+----------+ |root.ln.wf02.wt02.status| null| root.ln| BOOLEAN| PLAIN| SNAPPY|{"description":"test1"}| null| +------------------------+-----+-------------+--------+--------+-----------+-----------------------+----------+ Total line number = 1 It costs 0.004s
COUNT TIMESERIES (<`PathPattern`>)? WhereClause COUNT TIMESERIES (<`PathPattern`>)? WhereClause 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 unit = c count timeseries root.** where 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 unit = c +-----------------+ |count(timeseries)| +-----------------+ | 2| +-----------------+ Total line number = 1 It costs 0.020s IoTDB> count timeseries root.** where 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
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:
+--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+ | timeseries|alias|storage group|dataType|encoding|compression| tags| attributes| +--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+ |root.sg1.d1.s1| null| root.sg1| INT32| RLE| SNAPPY|{"tag1":"v1","tag2":"v2"}|{"attr2":"v2","attr1":"v1"}| |root.sg1.d1.s2| null| root.sg1| DOUBLE| GORILLA| SNAPPY|{"tag4":"v4","tag3":"v3"}|{"attr4":"v4","attr3":"v3"}| +--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+
Support query:
IoTDB> show storage group where tag1='v1' Msg: 401: Error occurred while parsing SQL to physical plan: line 1:19 mismatched input 'where' expecting {<EOF>, ';'} IoTDB> show timeseries where tag1='v1' +--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+ | timeseries|alias|storage group|dataType|encoding|compression| tags| attributes| +--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+ |root.sg1.d1.s1| null| root.sg1| INT32| RLE| SNAPPY|{"tag1":"v1","tag2":"v2"}|{"attr2":"v2","attr1":"v1"}| +--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+
The above operations are supported for timeseries tag, attribute updates, etc.