SQL Manual

DATABASE MANAGEMENT

For more details, see document Operate-Metadata.

Create Database

  1. IoTDB > create database root.ln
  2. IoTDB > create database root.sgcc
  3. IoTDB> CREATE DATABASE root.ln.wf01
  4. Msg: 300: root.ln has already been created as database.
  5. IoTDB> create database root.ln.wf01
  6. Msg: 300: root.ln has already been created as database.

Show Databases

  1. IoTDB> SHOW DATABASES
  2. IoTDB> SHOW DATABASES root.**

Delete Database

  1. IoTDB > DELETE DATABASE root.ln
  2. IoTDB > DELETE DATABASE root.sgcc
  3. // delete all data, all timeseries and all databases
  4. IoTDB > DELETE DATABASE root.**

Count Databases

  1. IoTDB> count databases
  2. IoTDB> count databases root.*
  3. IoTDB> count databases root.sgcc.*
  4. IoTDB> count databases root.sgcc

Setting up heterogeneous databases (Advanced operations)

Set heterogeneous parameters when creating a Database

  1. 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

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

Show heterogeneous databases

  1. SHOW DATABASES DETAILS

TTL

Set TTL

  1. IoTDB> set ttl to root.ln 3600000
  2. IoTDB> set ttl to root.sgcc.** 3600000
  3. IoTDB> set ttl to root.** 3600000

Unset TTL

  1. IoTDB> unset ttl to root.ln
  2. IoTDB> unset ttl to root.sgcc.**
  3. IoTDB> unset ttl to root.**

Show TTL

  1. IoTDB> SHOW ALL TTL
  2. IoTDB> SHOW TTL ON StorageGroupNames

SCHEMA TEMPLATE

For more details, see document Operate-Metadata.

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Create template(s1 int, s2 float) on root.sgSQL Manual - 图2open in new window

Create device root.sg.d1

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Create Schema Template

Example 1: Create a template containing two non-aligned timeseires

  1. IoTDB> create schema template t1 (temperature FLOAT encoding=RLE, status BOOLEAN encoding=PLAIN compression=SNAPPY)

Example 2: Create a template containing a group of aligned timeseires

  1. IoTDB> create schema template t2 aligned (lat FLOAT encoding=Gorilla, lon FLOAT encoding=Gorilla)

The lat and lon measurements are aligned.

Set Schema Template

  1. IoTDB> set schema template t1 to root.sg1.d1

Activate Schema Template

  1. IoTDB> set schema template t1 to root.sg1.d1
  2. IoTDB> set schema template t2 to root.sg1.d2
  3. IoTDB> create timeseries using schema template on root.sg1.d1
  4. IoTDB> create timeseries using schema template on root.sg1.d2

Show Schema Template

  1. IoTDB> show schema templates
  2. IoTDB> show nodes in schema template t1
  3. IoTDB> show paths set schema template t1
  4. IoTDB> show paths using schema template t1

Deactivate Schema Template

  1. IoTDB> delete timeseries of schema template t1 from root.sg1.d1
  2. IoTDB> deactivate schema template t1 from root.sg1.d1
  3. IoTDB> delete timeseries of schema template t1 from root.sg1.*, root.sg2.*
  4. IoTDB> deactivate schema template t1 from root.sg1.*, root.sg2.*

Unset Schema Template

  1. IoTDB> unset schema template t1 from root.sg1.d1

Drop Schema Template

  1. IoTDB> drop schema template t1

Alter Schema Template

  1. IoTDB> alter schema template t1 add (speed FLOAT encoding=RLE, FLOAT TEXT encoding=PLAIN compression=SNAPPY)

TIMESERIES MANAGEMENT

For more details, see document Operate-Metadata.

Create Timeseries

  1. IoTDB > create timeseries root.ln.wf01.wt01.status with datatype=BOOLEAN,encoding=PLAIN
  2. IoTDB > create timeseries root.ln.wf01.wt01.temperature with datatype=FLOAT,encoding=RLE
  3. IoTDB > create timeseries root.ln.wf02.wt02.hardware with datatype=TEXT,encoding=PLAIN
  4. IoTDB > create timeseries root.ln.wf02.wt02.status with datatype=BOOLEAN,encoding=PLAIN
  5. IoTDB > create timeseries root.sgcc.wf03.wt01.status with datatype=BOOLEAN,encoding=PLAIN
  6. 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:
  1. IoTDB > create timeseries root.ln.wf01.wt01.status with datatype=BOOLEAN,encoding=PLAIN
  2. IoTDB > create timeseries root.ln.wf01.wt01.temperature with datatype=FLOAT,encoding=RLE
  3. IoTDB > create timeseries root.ln.wf02.wt02.hardware with datatype=TEXT,encoding=PLAIN
  4. IoTDB > create timeseries root.ln.wf02.wt02.status with datatype=BOOLEAN,encoding=PLAIN
  5. IoTDB > create timeseries root.sgcc.wf03.wt01.status with datatype=BOOLEAN,encoding=PLAIN
  6. IoTDB > create timeseries root.sgcc.wf03.wt01.temperature with datatype=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:
  1. IoTDB > create timeseries root.ln.wf02.wt02.status WITH DATATYPE=BOOLEAN, ENCODING=TS_2DIFF
  2. error: encoding TS_2DIFF does not support BOOLEAN

Create Aligned Timeseries

  1. IoTDB> CREATE ALIGNED TIMESERIES root.ln.wf01.GPS(latitude FLOAT encoding=PLAIN compressor=SNAPPY, longitude FLOAT encoding=PLAIN compressor=SNAPPY)

Delete Timeseries

  1. IoTDB> delete timeseries root.ln.wf01.wt01.status
  2. IoTDB> delete timeseries root.ln.wf01.wt01.temperature, root.ln.wf02.wt02.hardware
  3. IoTDB> delete timeseries root.ln.wf02.*
  4. IoTDB> drop timeseries root.ln.wf02.*

Show Timeseries

  1. IoTDB> show timeseries root.**
  2. IoTDB> show timeseries root.ln.**
  3. IoTDB> show timeseries root.ln.** limit 10 offset 10
  4. IoTDB> show timeseries root.ln.** where timeseries contains 'wf01.wt'
  5. IoTDB> show timeseries root.ln.** where dataType=FLOAT

Count Timeseries

  1. IoTDB > COUNT TIMESERIES root.**
  2. IoTDB > COUNT TIMESERIES root.ln.**
  3. IoTDB > COUNT TIMESERIES root.ln.*.*.status
  4. IoTDB > COUNT TIMESERIES root.ln.wf01.wt01.status
  5. IoTDB > COUNT TIMESERIES root.** WHERE TIMESERIES contains 'sgcc' 
  6. IoTDB > COUNT TIMESERIES root.** WHERE DATATYPE = INT64
  7. IoTDB > COUNT TIMESERIES root.** WHERE TAGS(unit) contains 'c' 
  8. IoTDB > COUNT TIMESERIES root.** WHERE TAGS(unit) = 'c' 
  9. IoTDB > COUNT TIMESERIES root.** WHERE TIMESERIES contains 'sgcc' group by level = 1
  10. IoTDB > COUNT TIMESERIES root.** GROUP BY LEVEL=1
  11. IoTDB > COUNT TIMESERIES root.ln.** GROUP BY LEVEL=2
  12. IoTDB > COUNT TIMESERIES root.ln.wf01.* GROUP BY LEVEL=2

Tag and Attribute Management

  1. create timeseries root.turbine.d1.s1(temprature) with datatype=FLOAT, encoding=RLE, compression=SNAPPY tags(tag1=v1, tag2=v2) attributes(attr1=v1, attr2=v2)
  • Rename the tag/attribute key
  1. ALTER timeseries root.turbine.d1.s1 RENAME tag1 TO newTag1
  • Reset the tag/attribute value
  1. ALTER timeseries root.turbine.d1.s1 SET newTag1=newV1, attr1=newV1
  • Delete the existing tag/attribute
  1. ALTER timeseries root.turbine.d1.s1 DROP tag1, tag2
  • Add new tags
  1. ALTER timeseries root.turbine.d1.s1 ADD TAGS tag3=v3, tag4=v4
  • Add new attributes
  1. 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.

  1. 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
  1. SHOW TIMESERIES (<`PathPattern`>)? timeseriesWhereClause

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

  1. ALTER timeseries root.ln.wf02.wt02.hardware ADD TAGS unit=c
  2. ALTER timeseries root.ln.wf02.wt02.status ADD TAGS description=test1
  3. show timeseries root.ln.** where TAGS(unit)='c'
  4. show timeseries root.ln.** where TAGS(description) contains 'test1'
  • count timeseries using tags
  1. COUNT TIMESERIES (<`PathPattern`>)? timeseriesWhereClause
  2. 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:

  1. count timeseries
  2. count timeseries root.** where TAGS(unit)='c'
  3. count timeseries root.** where TAGS(unit)='c' group by level = 2

create aligned timeseries

  1. 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:

  1. IoTDB> show timeseries
  2. +--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+--------+-------------------+
  3. |    timeseries|alias|     database|dataType|encoding|compression|                     tags|                 attributes|deadband|deadband parameters|
  4. +--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+--------+-------------------+
  5. |root.sg1.d1.s1| null|     root.sg1|   INT32|     RLE|     SNAPPY|{"tag1":"v1","tag2":"v2"}|{"attr2":"v2","attr1":"v1"}|    null|               null|
  6. |root.sg1.d1.s2| null|     root.sg1|  DOUBLE| GORILLA|     SNAPPY|{"tag4":"v4","tag3":"v3"}|{"attr4":"v4","attr3":"v3"}|    null|               null|
  7. +--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+--------+-------------------+

Support query：

  1. IoTDB> show timeseries where TAGS(tag1)='v1'
  2. +--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+--------+-------------------+
  3. |    timeseries|alias|     database|dataType|encoding|compression|                     tags|                 attributes|deadband|deadband parameters|
  4. +--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+--------+-------------------+
  5. |root.sg1.d1.s1| null|     root.sg1|   INT32|     RLE|     SNAPPY|{"tag1":"v1","tag2":"v2"}|{"attr2":"v2","attr1":"v1"}|    null|               null|
  6. +--------------+-----+-------------+--------+--------+-----------+-------------------------+---------------------------+--------+-------------------+

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

NODE MANAGEMENT

For more details, see document Operate-Metadata.

Show Child Paths

  1. SHOW CHILD PATHS pathPattern

Show Child Nodes

  1. SHOW CHILD NODES pathPattern

Count Nodes

  1. IoTDB > COUNT NODES root.** LEVEL=2
  2. IoTDB > COUNT NODES root.ln.** LEVEL=2
  3. IoTDB > COUNT NODES root.ln.wf01.** LEVEL=3
  4. IoTDB > COUNT NODES root.**.temperature LEVEL=3

Show Devices

  1. IoTDB> show devices
  2. IoTDB> show devices root.ln.**
  3. IoTDB> show devices root.ln.** where device contains 't'
  4. IoTDB> show devices with database
  5. IoTDB> show devices root.ln.** with database

Count Devices

  1. IoTDB> show devices
  2. IoTDB> count devices
  3. IoTDB> count devices root.ln.**

INSERT & LOAD DATA

Insert Data

For more details, see document Write-Delete-Data.

Use of INSERT Statements

  • Insert Single Timeseries
  1. IoTDB > insert into root.ln.wf02.wt02(timestamp,status) values(1,true)
  2. IoTDB > insert into root.ln.wf02.wt02(timestamp,hardware) values(1, 'v1')
  • Insert Multiple Timeseries
  1. IoTDB > insert into root.ln.wf02.wt02(timestamp, status, hardware) VALUES (2, false, 'v2')
  2. IoTDB > insert into root.ln.wf02.wt02(timestamp, status, hardware) VALUES (3, false, 'v3'),(4, true, 'v4')
  • Use the Current System Timestamp as the Timestamp of the Data Point
  1. IoTDB > insert into root.ln.wf02.wt02(status, hardware) values (false, 'v2')

Insert Data Into Aligned Timeseries

  1. IoTDB > create aligned timeseries root.sg1.d1(s1 INT32, s2 DOUBLE)
  2. IoTDB > insert into root.sg1.d1(time, s1, s2) aligned values(1, 1, 1)
  3. IoTDB > insert into root.sg1.d1(time, s1, s2) aligned values(2, 2, 2), (3, 3, 3)
  4. IoTDB > select * from root.sg1.d1

Load External TsFile Tool

For more details, see document Import-Export-Tool.

Load with SQL

  1. Load a single tsfile by specifying a file path (absolute path).
  • load '/Users/Desktop/data/1575028885956-101-0.tsfile'
  • load '/Users/Desktop/data/1575028885956-101-0.tsfile' verify=true
  • load '/Users/Desktop/data/1575028885956-101-0.tsfile' verify=false
  • load '/Users/Desktop/data/1575028885956-101-0.tsfile' sglevel=1
  • load '/Users/Desktop/data/1575028885956-101-0.tsfile' onSuccess=delete
  • load '/Users/Desktop/data/1575028885956-101-0.tsfile' verify=true sglevel=1
  • load '/Users/Desktop/data/1575028885956-101-0.tsfile' verify=false sglevel=1
  • load '/Users/Desktop/data/1575028885956-101-0.tsfile' verify=true onSuccess=none
  • load '/Users/Desktop/data/1575028885956-101-0.tsfile' verify=false sglevel=1 onSuccess=delete
  1. Load a batch of files by specifying a folder path (absolute path).
  • load '/Users/Desktop/data'
  • load '/Users/Desktop/data' verify=false
  • load '/Users/Desktop/data' verify=true
  • load '/Users/Desktop/data' verify=true sglevel=1
  • load '/Users/Desktop/data' verify=false sglevel=1 onSuccess=delete

Load with Script

  1. ./load-rewrite.bat -f D:\IoTDB\data -h 192.168.0.101 -p 6667 -u root -pw root

DELETE DATA

For more details, see document Write-Delete-Data.

Delete Single Timeseries

  1. IoTDB > delete from root.ln.wf02.wt02.status where time<=2017-11-01T16:26:00;
  2. IoTDB > delete from root.ln.wf02.wt02.status where time>=2017-01-01T00:00:00 and time<=2017-11-01T16:26:00;
  3. IoTDB > delete from root.ln.wf02.wt02.status where time < 10
  4. IoTDB > delete from root.ln.wf02.wt02.status where time <= 10
  5. IoTDB > delete from root.ln.wf02.wt02.status where time < 20 and time > 10
  6. IoTDB > delete from root.ln.wf02.wt02.status where time <= 20 and time >= 10
  7. IoTDB > delete from root.ln.wf02.wt02.status where time > 20
  8. IoTDB > delete from root.ln.wf02.wt02.status where time >= 20
  9. IoTDB > delete from root.ln.wf02.wt02.status where time = 20
  10. IoTDB > delete from root.ln.wf02.wt02.status where time > 4 or time < 0
  11. Msg: 303: Check metadata error: For delete statement, where clause can only contain atomic
  12. expressions like : time > XXX, time <= XXX, or two atomic expressions connected by 'AND'
  13. IoTDB > delete from root.ln.wf02.wt02.status

Delete Multiple Timeseries

  1. IoTDB > delete from root.ln.wf02.wt02 where time <= 2017-11-01T16:26:00;
  2. IoTDB > delete from root.ln.wf02.wt02.* where time <= 2017-11-01T16:26:00;
  3. IoTDB> delete from root.ln.wf03.wt02.status where time < now()
  4. Msg: The statement is executed successfully.

Delete Time Partition (experimental)

  1. IoTDB > DELETE PARTITION root.ln 0,1,2

QUERY DATA

For more details, see document Query-Data.

  1. SELECT [LAST] selectExpr [, selectExpr] ...
  2.     [INTO intoItem [, intoItem] ...]
  3.     FROM prefixPath [, prefixPath] ...
  4.     [WHERE whereCondition]
  5.     [GROUP BY {
  6.         ([startTime, endTime), interval [, slidingStep]) |
  7.         LEVEL = levelNum [, levelNum] ... |
  8.         TAGS(tagKey [, tagKey] ... ) |
  9.         VARIATION(expression[,delta][,ignoreNull=true/false]) |
  10.         CONDITION(expression,[keep>/>=/=/</<=]threshold[,ignoreNull=true/false]) |
  11.         SESSION(timeInterval) |
  12.         COUNT(expression, size[,ignoreNull=true/false])
  13.     }]
  14.     [HAVING havingCondition]
  15.     [ORDER BY sortKey {ASC | DESC}]
  16.     [FILL ({PREVIOUS | LINEAR | constant})]
  17.     [SLIMIT seriesLimit] [SOFFSET seriesOffset]
  18.     [LIMIT rowLimit] [OFFSET rowOffset]
  19.     [ALIGN BY {TIME | DEVICE}]

Basic Examples

Select a Column of Data Based on a Time Interval

  1. IoTDB > select temperature from root.ln.wf01.wt01 where time < 2017-11-01T00:08:00.000

Select Multiple Columns of Data Based on a Time Interval

  1. IoTDB > select status, temperature from root.ln.wf01.wt01 where time > 2017-11-01T00:05:00.000 and time < 2017-11-01T00:12:00.000;

Select Multiple Columns of Data for the Same Device According to Multiple Time Intervals

  1. IoTDB > select status,temperature from root.ln.wf01.wt01 where (time > 2017-11-01T00:05:00.000 and time < 2017-11-01T00:12:00.000) or (time >= 2017-11-01T16:35:00.000 and time <= 2017-11-01T16:37:00.000);

Choose Multiple Columns of Data for Different Devices According to Multiple Time Intervals

  1. IoTDB > select wf01.wt01.status,wf02.wt02.hardware from root.ln where (time > 2017-11-01T00:05:00.000 and time < 2017-11-01T00:12:00.000) or (time >= 2017-11-01T16:35:00.000 and time <= 2017-11-01T16:37:00.000);

Order By Time Query

  1. IoTDB > select * from root.ln.** where time > 1 order by time desc limit 10;

SELECT CLAUSE

Use Alias

  1. IoTDB > select s1 as temperature, s2 as speed from root.ln.wf01.wt01;

Nested Expressions

Nested Expressions with Time Series Query
  1. IoTDB > select a,
  2.        b,
  3.        ((a + 1) * 2 - 1) % 2 + 1.5,
  4.        sin(a + sin(a + sin(b))),
  5.        -(a + b) * (sin(a + b) * sin(a + b) + cos(a + b) * cos(a + b)) + 1
  6. from root.sg1;
  8. IoTDB > select (a + b) * 2 + sin(a) from root.sg
  10. IoTDB > select (a + *) / 2  from root.sg1
  12. IoTDB > select (a + b) * 3 from root.sg, root.ln
Nested Expressions query with aggregations
  1. IoTDB > select avg(temperature),
  2.        sin(avg(temperature)),
  3.        avg(temperature) + 1,
  4.        -sum(hardware),
  5.        avg(temperature) + sum(hardware)
  6. from root.ln.wf01.wt01;
  8. IoTDB > select avg(*), 
  9.        (avg(*) + 1) * 3 / 2 -1 
  10. from root.sg1
  12. IoTDB > select avg(temperature),
  13.        sin(avg(temperature)),
  14.        avg(temperature) + 1,
  15.        -sum(hardware),
  16.        avg(temperature) + sum(hardware) as custom_sum
  17. from root.ln.wf01.wt01
  18. GROUP BY([10, 90), 10ms);

Last Query

  1. IoTDB > select last status from root.ln.wf01.wt01
  2. IoTDB > select last status, temperature from root.ln.wf01.wt01 where time >= 2017-11-07T23:50:00
  3. IoTDB > select last * from root.ln.wf01.wt01 order by timeseries desc;
  4. IoTDB > select last * from root.ln.wf01.wt01 order by dataType desc;

WHERE CLAUSE

Time Filter

  1. IoTDB > select s1 from root.sg1.d1 where time > 2022-01-01T00:05:00.000;
  2. IoTDB > select s1 from root.sg1.d1 where time = 2022-01-01T00:05:00.000;
  3. IoTDB > select s1 from root.sg1.d1 where time >= 2022-01-01T00:05:00.000 and time < 2017-11-01T00:12:00.000;

Value Filter

  1. IoTDB > select temperature from root.sg1.d1 where temperature > 36.5;
  2. IoTDB > select status from root.sg1.d1 where status = true;
  3. IoTDB > select temperature from root.sg1.d1 where temperature between 36.5 and 40;
  4. IoTDB > select temperature from root.sg1.d1 where temperature not between 36.5 and 40;
  5. IoTDB > select code from root.sg1.d1 where code in ('200', '300', '400', '500');
  6. IoTDB > select code from root.sg1.d1 where code not in ('200', '300', '400', '500');
  7. IoTDB > select code from root.sg1.d1 where temperature is null;
  8. IoTDB > select code from root.sg1.d1 where temperature is not null;

Fuzzy Query

  • Fuzzy matching using Like
  1. IoTDB > select * from root.sg.d1 where value like '%cc%'
  2. IoTDB > select * from root.sg.device where value like '_b_'
  • Fuzzy matching using Regexp
  1. IoTDB > select * from root.sg.d1 where value regexp '^[A-Za-z]+$'
  2. IoTDB > select * from root.sg.d1 where value regexp '^[a-z]+$' and time > 100

GROUP BY CLAUSE

  • Aggregate By Time without Specifying the Sliding Step Length
  1. IoTDB > select count(status), max_value(temperature) from root.ln.wf01.wt01 group by ([2017-11-01T00:00:00, 2017-11-07T23:00:00),1d);
  • Aggregate By Time Specifying the Sliding Step Length
  1. IoTDB > select count(status), max_value(temperature) from root.ln.wf01.wt01 group by ([2017-11-01 00:00:00, 2017-11-07 23:00:00), 3h, 1d);
  • Aggregate by Natural Month
  1. IoTDB > select count(status) from root.ln.wf01.wt01 group by([2017-11-01T00:00:00, 2019-11-07T23:00:00), 1mo, 2mo);
  2. IoTDB > select count(status) from root.ln.wf01.wt01 group by([2017-10-31T00:00:00, 2019-11-07T23:00:00), 1mo, 2mo);
  • Left Open And Right Close Range
  1. IoTDB > select count(status) from root.ln.wf01.wt01 group by ((2017-11-01T00:00:00, 2017-11-07T23:00:00],1d);
  • Aggregation By Variation
  1. IoTDB > select __endTime, avg(s1), count(s2), sum(s3) from root.sg.d group by variation(s6)
  2. IoTDB > select __endTime, avg(s1), count(s2), sum(s3) from root.sg.d group by variation(s6, ignoreNull=false)
  3. IoTDB > select __endTime, avg(s1), count(s2), sum(s3) from root.sg.d group by variation(s6, 4)
  4. IoTDB > select __endTime, avg(s1), count(s2), sum(s3) from root.sg.d group by variation(s6+s5, 10)
  • Aggregation By Condition
  1. IoTDB > select max_time(charging_status),count(vehicle_status),last_value(soc) from root.** group by condition(charging_status=1,KEEP>=2,ignoringNull=true)
  2. IoTDB > select max_time(charging_status),count(vehicle_status),last_value(soc) from root.** group by condition(charging_status=1,KEEP>=2,ignoringNull=false)
  • Aggregation By Session
  1. IoTDB > select __endTime,count(*) from root.** group by session(1d)
  2. IoTDB > select __endTime,sum(hardware) from root.ln.wf02.wt01 group by session(50s) having sum(hardware)>0 align by device
  • Aggregation By Count
  1. IoTDB > select count(charging_stauts), first_value(soc) from root.sg group by count(charging_status,5) 
  2. IoTDB > select count(charging_stauts), first_value(soc) from root.sg group by count(charging_status,5,ignoreNull=false)
  • Aggregation By Level
  1. IoTDB > select count(status) from root.** group by level = 1
  2. IoTDB > select count(status) from root.** group by level = 3
  3. IoTDB > select count(status) from root.** group by level = 1, 3
  4. IoTDB > select max_value(temperature) from root.** group by level = 0
  5. IoTDB > select count(*) from root.ln.** group by level = 2
  • Aggregate By Time with Level Clause
  1. IoTDB > select count(status) from root.ln.wf01.wt01 group by ((2017-11-01T00:00:00, 2017-11-07T23:00:00],1d), level=1;
  2. IoTDB > select count(status) from root.ln.wf01.wt01 group by ([2017-11-01 00:00:00, 2017-11-07 23:00:00), 3h, 1d), level=1;
  • Aggregation query by one single tag
  1. IoTDB > SELECT AVG(temperature) FROM root.factory1.** GROUP BY TAGS(city);
  • Aggregation query by multiple tags
  1. IoTDB > SELECT avg(temperature) FROM root.factory1.** GROUP BY TAGS(city, workshop);
  • Downsampling Aggregation by tags based on Time Window
  1. IoTDB > SELECT avg(temperature) FROM root.factory1.** GROUP BY ([1000, 10000), 5s), TAGS(city, workshop);

HAVING CLAUSE

Correct:

  1. IoTDB > select count(s1) from root.** group by ([1,11),2ms), level=1 having count(s2) > 1
  2. IoTDB > select count(s1), count(s2) from root.** group by ([1,11),2ms) having count(s2) > 1 align by device

Incorrect:

  1. IoTDB > select count(s1) from root.** group by ([1,3),1ms) having sum(s1) > s1
  2. IoTDB > select count(s1) from root.** group by ([1,3),1ms) having s1 > 1
  3. IoTDB > select count(s1) from root.** group by ([1,3),1ms), level=1 having sum(d1.s1) > 1
  4. IoTDB > select count(d1.s1) from root.** group by ([1,3),1ms), level=1 having sum(s1) > 1

FILL CLAUSE

PREVIOUS Fill

  1. IoTDB > select temperature, status from root.sgcc.wf03.wt01 where time >= 2017-11-01T16:37:00.000 and time <= 2017-11-01T16:40:00.000 fill(previous);

LINEAR Fill

  1. IoTDB > select temperature, status from root.sgcc.wf03.wt01 where time >= 2017-11-01T16:37:00.000 and time <= 2017-11-01T16:40:00.000 fill(linear);

Constant Fill

  1. IoTDB > select temperature, status from root.sgcc.wf03.wt01 where time >= 2017-11-01T16:37:00.000 and time <= 2017-11-01T16:40:00.000 fill(2.0);
  2. IoTDB > select temperature, status from root.sgcc.wf03.wt01 where time >= 2017-11-01T16:37:00.000 and time <= 2017-11-01T16:40:00.000 fill(true);

LIMIT and SLIMIT CLAUSES (PAGINATION)

Row Control over Query Results

  1. IoTDB > select status, temperature from root.ln.wf01.wt01 limit 10
  2. IoTDB > select status, temperature from root.ln.wf01.wt01 limit 5 offset 3
  3. IoTDB > select status,temperature from root.ln.wf01.wt01 where time > 2017-11-01T00:05:00.000 and time< 2017-11-01T00:12:00.000 limit 2 offset 3
  4. IoTDB > select count(status), max_value(temperature) from root.ln.wf01.wt01 group by ([2017-11-01T00:00:00, 2017-11-07T23:00:00),1d) limit 5 offset 3

Column Control over Query Results

  1. IoTDB > select * from root.ln.wf01.wt01 where time > 2017-11-01T00:05:00.000 and time < 2017-11-01T00:12:00.000 slimit 1
  2. IoTDB > select * from root.ln.wf01.wt01 where time > 2017-11-01T00:05:00.000 and time < 2017-11-01T00:12:00.000 slimit 1 soffset 1
  3. IoTDB > select max_value(*) from root.ln.wf01.wt01 group by ([2017-11-01T00:00:00, 2017-11-07T23:00:00),1d) slimit 1 soffset 1

Row and Column Control over Query Results

  1. IoTDB > select * from root.ln.wf01.wt01 limit 10 offset 100 slimit 2 soffset 0

ORDER BY CLAUSE

Order by in ALIGN BY TIME mode

  1. IoTDB > select * from root.ln.** where time <= 2017-11-01T00:01:00 order by time desc;

Order by in ALIGN BY DEVICE mode

  1. IoTDB > select * from root.ln.** where time <= 2017-11-01T00:01:00 order by device desc,time asc align by device;
  2. IoTDB > select * from root.ln.** where time <= 2017-11-01T00:01:00 order by time asc,device desc align by device;
  3. IoTDB > select * from root.ln.** where time <= 2017-11-01T00:01:00 align by device;
  4. IoTDB > select count(*) from root.ln.** group by ((2017-11-01T00:00:00.000+08:00,2017-11-01T00:03:00.000+08:00],1m) order by device asc,time asc align by device

Order by arbitrary expressions

  1. IoTDB > select score from root.** order by score desc align by device
  2. IoTDB > select score,total from root.one order by base+score+bonus desc
  3. IoTDB > select score,total from root.one order by total desc
  4. IoTDB > select base, score, bonus, total from root.** order by total desc NULLS Last,
  5.                                   score desc NULLS Last,
  6.                                   bonus desc NULLS Last,
  7.                                   time desc align by device
  8. IoTDB > select min_value(total) from root.** order by min_value(total) asc align by device
  9. IoTDB > select min_value(total),max_value(base) from root.** order by max_value(total) desc align by device
  10. IoTDB > select score from root.** order by device asc, score desc, time asc align by device

ALIGN BY CLAUSE

Align by Device

  1. IoTDB > select * from root.ln.** where time <= 2017-11-01T00:01:00 align by device;

INTO CLAUSE (QUERY WRITE-BACK)

  1. IoTDB > select s1, s2 into root.sg_copy.d1(t1), root.sg_copy.d2(t1, t2), root.sg_copy.d1(t2) from root.sg.d1, root.sg.d2;
  2. IoTDB > select count(s1 + s2), last_value(s2) into root.agg.count(s1_add_s2), root.agg.last_value(s2) from root.sg.d1 group by ([0, 100), 10ms);
  3. IoTDB > select s1, s2 into root.sg_copy.d1(t1, t2), root.sg_copy.d2(t1, t2) from root.sg.d1, root.sg.d2 align by device;
  4. IoTDB > select s1 + s2 into root.expr.add(d1s1_d1s2), root.expr.add(d2s1_d2s2) from root.sg.d1, root.sg.d2 align by device;
  • Using variable placeholders:
  1. IoTDB > select s1, s2
  2. into root.sg_copy.d1(::), root.sg_copy.d2(s1), root.sg_copy.d1(${3}), root.sg_copy.d2(::)
  3. from root.sg.d1, root.sg.d2;
  5. IoTDB > select d1.s1, d1.s2, d2.s3, d3.s4
  6. into ::(s1_1, s2_2), root.sg.d2_2(s3_3), root.${2}_copy.::(s4)
  7. from root.sg;
  9. IoTDB > select * into root.sg_bk.::(::) from root.sg.**;
  11. IoTDB > select s1, s2, s3, s4
  12. into root.backup_sg.d1(s1, s2, s3, s4), root.backup_sg.d2(::), root.sg.d3(backup_${4})
  13. from root.sg.d1, root.sg.d2, root.sg.d3
  14. align by device;
  16. IoTDB > select avg(s1), sum(s2) + sum(s3), count(s4)
  17. into root.agg_${2}.::(avg_s1, sum_s2_add_s3, count_s4)
  18. from root.**
  19. align by device;
  21. IoTDB > select * into ::(backup_${4}) from root.sg.** align by device;
  23. IoTDB > select s1, s2 into root.sg_copy.d1(t1, t2), aligned root.sg_copy.d2(t1, t2) from root.sg.d1, root.sg.d2 align by device;

OPERATOR

For more details, see document Operator-and-Expression.

Arithmetic Operators

For details and examples, see the document Arithmetic Operators and Functions.

  1. select s1, - s1, s2, + s2, s1 + s2, s1 - s2, s1 * s2, s1 / s2, s1 % s2 from root.sg.d1

Comparison Operators

For details and examples, see the document Comparison Operators and Functions.

  1. # Basic comparison operators
  2. select a, b, a > 10, a <= b, !(a <= b), a > 10 && a > b from root.test;
  4. # `BETWEEN ... AND ...` operator
  5. select temperature from root.sg1.d1 where temperature between 36.5 and 40;
  6. select temperature from root.sg1.d1 where temperature not between 36.5 and 40;
  8. # Fuzzy matching operator: Use `Like` for fuzzy matching
  9. select * from root.sg.d1 where value like '%cc%'
  10. select * from root.sg.device where value like '_b_'
  12. # Fuzzy matching operator: Use `Regexp` for fuzzy matching
  13. select * from root.sg.d1 where value regexp '^[A-Za-z]+$'
  14. select * from root.sg.d1 where value regexp '^[a-z]+$' and time > 100
  15. select b, b like '1%', b regexp '[0-2]' from root.test;
  17. # `IS NULL` operator
  18. select code from root.sg1.d1 where temperature is null;
  19. select code from root.sg1.d1 where temperature is not null;
  21. # `IN` operator
  22. select code from root.sg1.d1 where code in ('200', '300', '400', '500');
  23. select code from root.sg1.d1 where code not in ('200', '300', '400', '500');
  24. select a, a in (1, 2) from root.test;

Logical Operators

For details and examples, see the document Logical Operators.

  1. select a, b, a > 10, a <= b, !(a <= b), a > 10 && a > b from root.test;

BUILT-IN FUNCTIONS

For more details, see document Operator-and-Expression.

Aggregate Functions

For details and examples, see the document Aggregate Functions.

  1. select count(status) from root.ln.wf01.wt01;
  3. select count_if(s1=0 & s2=0, 3), count_if(s1=1 & s2=0, 3) from root.db.d1;
  4. select count_if(s1=0 & s2=0, 3, 'ignoreNull'='false'), count_if(s1=1 & s2=0, 3, 'ignoreNull'='false') from root.db.d1;
  6. select time_duration(s1) from root.db.d1;

Arithmetic Functions

For details and examples, see the document Arithmetic Operators and Functions.

  1. select s1, sin(s1), cos(s1), tan(s1) from root.sg1.d1 limit 5 offset 1000;
  2. select s4,round(s4),round(s4,2),round(s4,-1) from root.sg1.d1;

Comparison Functions

For details and examples, see the document Comparison Operators and Functions.

  1. select ts, on_off(ts, 'threshold'='2') from root.test;
  2. select ts, in_range(ts, 'lower'='2', 'upper'='3.1') from root.test;

String Processing Functions

For details and examples, see the document String Processing.

  1. select s1, string_contains(s1, 's'='warn') from root.sg1.d4;
  2. select s1, string_matches(s1, 'regex'='[^\\s]+37229') from root.sg1.d4;
  3. select s1, length(s1) from root.sg1.d1
  4. select s1, locate(s1, "target"="1") from root.sg1.d1
  5. select s1, locate(s1, "target"="1", "reverse"="true") from root.sg1.d1
  6. select s1, startswith(s1, "target"="1") from root.sg1.d1
  7. select s1, endswith(s1, "target"="1") from root.sg1.d1
  8. select s1, s2, concat(s1, s2, "target1"="IoT", "target2"="DB") from root.sg1.d1
  9. select s1, s2, concat(s1, s2, "target1"="IoT", "target2"="DB", "series_behind"="true") from root.sg1.d1
  10. select s1, substring(s1 from 1 for 2) from root.sg1.d1
  11. select s1, replace(s1, 'es', 'tt') from root.sg1.d1
  12. select s1, upper(s1) from root.sg1.d1
  13. select s1, lower(s1) from root.sg1.d1
  14. select s3, trim(s3) from root.sg1.d1
  15. select s1, s2, strcmp(s1, s2) from root.sg1.d1
  16. select strreplace(s1, "target"=",", "replace"="/", "limit"="2") from root.test.d1
  17. select strreplace(s1, "target"=",", "replace"="/", "limit"="1", "offset"="1", "reverse"="true") from root.test.d1
  18. select regexmatch(s1, "regex"="\d+\.\d+\.\d+\.\d+", "group"="0") from root.test.d1
  19. select regexreplace(s1, "regex"="192\.168\.0\.(\d+)", "replace"="cluster-$1", "limit"="1") from root.test.d1
  20. select regexsplit(s1, "regex"=",", "index"="-1") from root.test.d1
  21. select regexsplit(s1, "regex"=",", "index"="3") from root.test.d1

Data Type Conversion Function

For details and examples, see the document Data Type Conversion Function.

  1. SELECT cast(s1 as INT32) from root.sg

Constant Timeseries Generating Functions

For details and examples, see the document Constant Timeseries Generating Functions.

  1. select s1, s2, const(s1, 'value'='1024', 'type'='INT64'), pi(s2), e(s1, s2) from root.sg1.d1;

Selector Functions

For details and examples, see the document Selector Functions.

  1. select s1, top_k(s1, 'k'='2'), bottom_k(s1, 'k'='2') from root.sg1.d2 where time > 2020-12-10T20:36:15.530+08:00;

Continuous Interval Functions

For details and examples, see the document Continuous Interval Functions.

  1. select s1, zero_count(s1), non_zero_count(s2), zero_duration(s3), non_zero_duration(s4) from root.sg.d2;

Variation Trend Calculation Functions

For details and examples, see the document Variation Trend Calculation Functions.

  1. select s1, time_difference(s1), difference(s1), non_negative_difference(s1), derivative(s1), non_negative_derivative(s1) from root.sg1.d1 limit 5 offset 1000; 
  3. SELECT DIFF(s1), DIFF(s2) from root.test;
  4. SELECT DIFF(s1, 'ignoreNull'='false'), DIFF(s2, 'ignoreNull'='false') from root.test;

Sample Functions

For details and examples, see the document Sample Functions.

  1. select equal_size_bucket_random_sample(temperature,'proportion'='0.1') as random_sample from root.ln.wf01.wt01;
  2. select equal_size_bucket_agg_sample(temperature, 'type'='avg','proportion'='0.1') as agg_avg, equal_size_bucket_agg_sample(temperature, 'type'='max','proportion'='0.1') as agg_max, equal_size_bucket_agg_sample(temperature,'type'='min','proportion'='0.1') as agg_min, equal_size_bucket_agg_sample(temperature, 'type'='sum','proportion'='0.1') as agg_sum, equal_size_bucket_agg_sample(temperature, 'type'='extreme','proportion'='0.1') as agg_extreme, equal_size_bucket_agg_sample(temperature, 'type'='variance','proportion'='0.1') as agg_variance from root.ln.wf01.wt01;
  3. select equal_size_bucket_m4_sample(temperature, 'proportion'='0.1') as M4_sample from root.ln.wf01.wt01;
  4. select equal_size_bucket_outlier_sample(temperature, 'proportion'='0.1', 'type'='avg', 'number'='2') as outlier_avg_sample, equal_size_bucket_outlier_sample(temperature, 'proportion'='0.1', 'type'='stendis', 'number'='2') as outlier_stendis_sample, equal_size_bucket_outlier_sample(temperature, 'proportion'='0.1', 'type'='cos', 'number'='2') as outlier_cos_sample, equal_size_bucket_outlier_sample(temperature, 'proportion'='0.1', 'type'='prenextdis', 'number'='2') as outlier_prenextdis_sample from root.ln.wf01.wt01;
  6. select M4(s1,'timeInterval'='25','displayWindowBegin'='0','displayWindowEnd'='100') from root.vehicle.d1
  7. select M4(s1,'windowSize'='10') from root.vehicle.d1

Change Points Function

For details and examples, see the document Time-Series.

  1. select change_points(s1), change_points(s2), change_points(s3), change_points(s4), change_points(s5), change_points(s6) from root.testChangePoints.d1

DATA QUALITY FUNCTION LIBRARY

For more details, see document Operator-and-Expression.

Data Quality

For details and examples, see the document Data-Quality.

  1. # Completeness
  2. select completeness(s1) from root.test.d1 where time <= 2020-01-01 00:00:30
  3. select completeness(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:01:00
  5. # Consistency
  6. select consistency(s1) from root.test.d1 where time <= 2020-01-01 00:00:30
  7. select consistency(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:01:00
  9. # Timeliness
  10. select timeliness(s1) from root.test.d1 where time <= 2020-01-01 00:00:30
  11. select timeliness(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:01:00
  13. # Validity
  14. select Validity(s1) from root.test.d1 where time <= 2020-01-01 00:00:30
  15. select Validity(s1,"window"="15") from root.test.d1 where time <= 2020-01-01 00:01:00
  17. # Accuracy
  18. select Accuracy(t1,t2,t3,m1,m2,m3) from root.test

Data Profiling

For details and examples, see the document Data-Profiling.

  1. # ACF
  2. select acf(s1) from root.test.d1 where time <= 2020-01-01 00:00:05
  4. # Distinct
  5. select distinct(s2) from root.test.d2
  7. # Histogram
  8. select histogram(s1,"min"="1","max"="20","count"="10") from root.test.d1
  10. # Integral
  11. select integral(s1) from root.test.d1 where time <= 2020-01-01 00:00:10
  12. select integral(s1, "unit"="1m") from root.test.d1 where time <= 2020-01-01 00:00:10
  14. # IntegralAvg
  15. select integralavg(s1) from root.test.d1 where time <= 2020-01-01 00:00:10
  17. # Mad
  18. select mad(s0) from root.test
  19. select mad(s0, "error"="0.01") from root.test
  21. # Median
  22. select median(s0, "error"="0.01") from root.test
  24. # MinMax
  25. select minmax(s1) from root.test
  27. # Mode
  28. select mode(s2) from root.test.d2
  30. # MvAvg
  31. select mvavg(s1, "window"="3") from root.test
  33. # PACF
  34. select pacf(s1, "lag"="5") from root.test
  36. # Percentile
  37. select percentile(s0, "rank"="0.2", "error"="0.01") from root.test
  39. # Quantile
  40. select quantile(s0, "rank"="0.2", "K"="800") from root.test
  42. # Period
  43. select period(s1) from root.test.d3
  45. # QLB
  46. select QLB(s1) from root.test.d1
  48. # Resample
  49. select resample(s1,'every'='5m','interp'='linear') from root.test.d1
  50. select resample(s1,'every'='30m','aggr'='first') from root.test.d1
  51. select resample(s1,'every'='30m','start'='2021-03-06 15:00:00') from root.test.d1
  53. # Sample
  54. select sample(s1,'method'='reservoir','k'='5') from root.test.d1
  55. select sample(s1,'method'='isometric','k'='5') from root.test.d1
  57. # Segment
  58. select segment(s1, "error"="0.1") from root.test
  60. # Skew
  61. select skew(s1) from root.test.d1
  63. # Spline
  64. select spline(s1, "points"="151") from root.test
  66. # Spread
  67. select spread(s1) from root.test.d1 where time <= 2020-01-01 00:00:30
  69. # Stddev
  70. select stddev(s1) from root.test.d1
  72. # ZScore
  73. select zscore(s1) from root.test

Anomaly Detection

For details and examples, see the document Anomaly-Detection.

  1. # IQR
  2. select iqr(s1) from root.test
  4. # KSigma
  5. select ksigma(s1,"k"="1.0") from root.test.d1 where time <= 2020-01-01 00:00:30
  7. # LOF
  8. select lof(s1,s2) from root.test.d1 where time<1000
  9. select lof(s1, "method"="series") from root.test.d1 where time<1000
  11. # MissDetect
  12. select missdetect(s2,'minlen'='10') from root.test.d2
  14. # Range
  15. select range(s1,"lower_bound"="101.0","upper_bound"="125.0") from root.test.d1 where time <= 2020-01-01 00:00:30
  17. # TwoSidedFilter
  18. select TwoSidedFilter(s0, 'len'='5', 'threshold'='0.3') from root.test
  20. # Outlier
  21. select outlier(s1,"r"="5.0","k"="4","w"="10","s"="5") from root.test
  23. # MasterTrain
  24. select MasterTrain(lo,la,m_lo,m_la,'p'='3','eta'='1.0') from root.test
  26. # MasterDetect
  27. select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='repair','p'='3','k'='3','eta'='1.0') from root.test
  28. select MasterDetect(lo,la,m_lo,m_la,model,'output_type'='anomaly','p'='3','k'='3','eta'='1.0') from root.test

Frequency Domain

For details and examples, see the document Frequency-Domain.

  1. # Conv
  2. select conv(s1,s2) from root.test.d2
  4. # Deconv
  5. select deconv(s3,s2) from root.test.d2
  6. select deconv(s3,s2,'result'='remainder') from root.test.d2
  8. # DWT
  9. select dwt(s1,"method"="haar") from root.test.d1
  11. # FFT
  12. select fft(s1) from root.test.d1
  13. select fft(s1, 'result'='real', 'compress'='0.99'), fft(s1, 'result'='imag','compress'='0.99') from root.test.d1
  15. # HighPass
  16. select highpass(s1,'wpass'='0.45') from root.test.d1
  18. # IFFT
  19. select ifft(re, im, 'interval'='1m', 'start'='2021-01-01 00:00:00') from root.test.d1
  21. # LowPass
  22. select lowpass(s1,'wpass'='0.45') from root.test.d1

Data Matching

For details and examples, see the document Data-Matching.

  1. # Cov
  2. select cov(s1,s2) from root.test.d2
  4. # DTW
  5. select dtw(s1,s2) from root.test.d2
  7. # Pearson
  8. select pearson(s1,s2) from root.test.d2
  10. # PtnSym
  11. select ptnsym(s4, 'window'='5', 'threshold'='0') from root.test.d1
  13. # XCorr
  14. select xcorr(s1, s2) from root.test.d1 where time <= 2020-01-01 00:00:05

Data Repairing

For details and examples, see the document Data-Repairing.

  1. # TimestampRepair
  2. select timestamprepair(s1,'interval'='10000') from root.test.d2
  3. select timestamprepair(s1) from root.test.d2
  5. # ValueFill
  6. select valuefill(s1) from root.test.d2
  7. select valuefill(s1,"method"="previous") from root.test.d2
  9. # ValueRepair
  10. select valuerepair(s1) from root.test.d2
  11. select valuerepair(s1,'method'='LsGreedy') from root.test.d2
  13. # MasterRepair
  14. select MasterRepair(t1,t2,t3,m1,m2,m3) from root.test
  16. # SeasonalRepair
  17. select seasonalrepair(s1,'period'=3,'k'=2) from root.test.d2
  18. select seasonalrepair(s1,'method'='improved','period'=3) from root.test.d2

Series Discovery

For details and examples, see the document Series-Discovery.

  1. # ConsecutiveSequences
  2. select consecutivesequences(s1,s2,'gap'='5m') from root.test.d1
  3. select consecutivesequences(s1,s2) from root.test.d1
  5. # ConsecutiveWindows
  6. select consecutivewindows(s1,s2,'length'='10m') from root.test.d1

Machine Learning

For details and examples, see the document Machine-Learning.

  1. # AR
  2. select ar(s0,"p"="2") from root.test.d0
  4. # Representation
  5. select representation(s0,"tb"="3","vb"="2") from root.test.d0
  7. # RM
  8. select rm(s0, s1,"tb"="3","vb"="2") from root.test.d0

LAMBDA EXPRESSION

For details and examples, see the document Lambda.

  1. select jexl(temperature, 'expr'='x -> {x + x}') as jexl1, jexl(temperature, 'expr'='x -> {x * 3}') as jexl2, jexl(temperature, 'expr'='x -> {x * x}') as jexl3, jexl(temperature, 'expr'='x -> {multiply(x, 100)}') as jexl4, jexl(temperature, st, 'expr'='(x, y) -> {x + y}') as jexl5, jexl(temperature, st, str, 'expr'='(x, y, z) -> {x + y + z}') as jexl6 from root.ln.wf01.wt01;
  2. ## CONDITIONAL EXPRESSION
  4. For details and examples, see the document [Conditional Expressions](https://iotdb.apache.org/UserGuide/V1.2.x/Operators-Functions/Conditional.html).

select T, P, case when 1000=1050 then “bad temperature” when P<=1000000 or P>=1100000 then “bad pressure” end as result from root.test1

select str, case when str like “%cc%” then “has cc” when str like “%dd%” then “has dd” else “no cc and dd” end as result from root.test2

select count(case when x<=1 then 1 end) as (-∞,1], count(case when 1<x and x<=3 then 1 end) as (1,3], count(case when 3<x and x<=7 then 1 end) as (3,7], count(case when 7<x then 1 end) as (7,+∞) from root.test3

select x, case x when 1 then “one” when 2 then “two” else “other” end from root.test4

select x, case x when 1 then true when 2 then false end as result from root.test4

select x, case x when 1 then 1 when 2 then 222222222222222 when 3 then 3.3 when 4 then 4.4444444444444 end as result from root.test4

  2. ## TRIGGER
  4. For more details, see document [Database-Programming]($2603439d83e4de79.md).
  6. ### Create Trigger

// Create Trigger createTrigger : CREATE triggerType TRIGGER triggerName=identifier triggerEventClause ON pathPattern AS className=STRING_LITERAL uriClause? triggerAttributeClause? ;

triggerType : STATELESS | STATEFUL ;

triggerEventClause : (BEFORE | AFTER) INSERT ;

uriClause : USING URI uri ;

uri : STRING_LITERAL ;

triggerAttributeClause : WITH LR_BRACKET triggerAttribute (COMMA triggerAttribute)* RR_BRACKET ;

triggerAttribute : key=attributeKey operator_eq value=attributeValue ;

  2. ### Drop Trigger

// Drop Trigger dropTrigger : DROP TRIGGER triggerName=identifier ;

  2. ### Show Trigger

SHOW TRIGGERS

  2. ## CONTINUOUS QUERY (CQ)
  4. For more details, see document [Operator-and-Expression]($42104b379c76ebf5.md).

CREATE (CONTINUOUS QUERY | CQ) [RESAMPLE [EVERY ] [BOUNDARY ] [RANGE [, end_time_offset]] ] [TIMEOUT POLICY BLOCKED|DISCARD] BEGIN SELECT CLAUSE INTO CLAUSE FROM CLAUSE [WHERE CLAUSE] [GROUP BY([, ]) [, level = ]] [HAVING CLAUSE] [FILL {PREVIOUS | LINEAR | constant}] [LIMIT rowLimit OFFSET rowOffset] [ALIGN BY DEVICE] END

  2. ### Configuring execution intervals

CREATE CONTINUOUS QUERY cq1 RESAMPLE EVERY 20s BEGIN SELECT max_value(temperature) INTO root.ln.wf02.wt02(temperature_max), root.ln.wf02.wt01(temperature_max), root.ln.wf01.wt02(temperature_max), root.ln.wf01.wt01(temperature_max) FROM root.ln.. GROUP BY(10s) END

  2. ### Configuring time range for resampling

CREATE CONTINUOUS QUERY cq2 RESAMPLE RANGE 40s BEGIN SELECT max_value(temperature) INTO root.ln.wf02.wt02(temperature_max), root.ln.wf02.wt01(temperature_max), root.ln.wf01.wt02(temperature_max), root.ln.wf01.wt01(temperature_max) FROM root.ln.. GROUP BY(10s) END

  2. ### Configuring execution intervals and CQ time ranges

CREATE CONTINUOUS QUERY cq3 RESAMPLE EVERY 20s RANGE 40s BEGIN SELECT max_value(temperature) INTO root.ln.wf02.wt02(temperature_max), root.ln.wf02.wt01(temperature_max), root.ln.wf01.wt02(temperature_max), root.ln.wf01.wt01(temperature_max) FROM root.ln.. GROUP BY(10s) FILL(100.0) END

  2. ### Configuring end\_time\_offset for CQ time range

CREATE CONTINUOUS QUERY cq4 RESAMPLE EVERY 20s RANGE 40s, 20s BEGIN SELECT max_value(temperature) INTO root.ln.wf02.wt02(temperature_max), root.ln.wf02.wt01(temperature_max), root.ln.wf01.wt02(temperature_max), root.ln.wf01.wt01(temperature_max) FROM root.ln.. GROUP BY(10s) FILL(100.0) END

  2. ### CQ without group by clause

CREATE CONTINUOUS QUERY cq5 RESAMPLE EVERY 20s BEGIN SELECT temperature + 1 INTO root.precalculated_sg.::(temperature) FROM root.ln.. align by device END

  2. ### CQ Management
  4. #### Listing continuous queries

SHOW (CONTINUOUS QUERIES | CQS) 

  2. #### Dropping continuous queries

DROP (CONTINUOUS QUERY | CQ) 

  2. #### Altering continuous queries
  4. CQs can't be altered once they're created. To change a CQ, you must `DROP` and re`CREATE` it with the updated settings.
  6. ## USER-DEFINED FUNCTION (UDF)
  8. For more details, see document [Operator-and-Expression]($42104b379c76ebf5.md).
  10. ### UDF Registration

CREATE FUNCTION AS (USING URI URI-STRING)?

  2. ### UDF Deregistration

DROP FUNCTION 

  2. ### UDF Queries

SELECT example() from root.sg.d1 SELECT example(s1, ) from root.sg.d1 SELECT example(, ) from root.sg.d1

SELECT example(s1, ‘key1’=’value1’, ‘key2’=’value2’), example(*, ‘key3’=’value3’) FROM root.sg.d1; SELECT example(s1, s2, ‘key1’=’value1’, ‘key2’=’value2’) FROM root.sg.d1;

SELECT s1, s2, example(s1, s2) FROM root.sg.d1; SELECT , example() FROM root.sg.d1 DISABLE ALIGN; SELECT s1 example( / s1 + s2) FROM root.sg.d1; SELECT s1, s2, s1 + example(s1, s2), s1 - example(s1 + example(s1, s2) / s2) FROM root.sg.d1;

  2. ### Show All Registered UDFs

SHOW FUNCTIONS

  2. ## ADMINISTRATION MANAGEMENT
  4. For more details, see document [Operator-and-Expression]($42104b379c76ebf5.md).
  6. ### SQL Statements
  8. -   Create User

CREATE USER ;
Eg: IoTDB > CREATE USER thulab ‘pwd’;

  2. -   Delete User

DROP USER ;
Eg: IoTDB > DROP USER xiaoming;

  2. -   Create Role

CREATE ROLE ;
Eg: IoTDB > CREATE ROLE admin;

  2. -   Delete Role

DROP ROLE ;
Eg: IoTDB > DROP ROLE admin;

  2. -   Grant User Privileges

GRANT USER PRIVILEGES ON ;
Eg: IoTDB > GRANT USER tempuser PRIVILEGES INSERT_TIMESERIES, DELETE_TIMESERIES on root.ln., root.sgcc.; Eg: IoTDB > GRANT USER tempuser PRIVILEGES CREATE_ROLE;

  2. -   Grant User All Privileges

GRANT USER PRIVILEGES ALL; Eg: IoTDB > GRANT USER tempuser PRIVILEGES ALL;

  2. -   Grant Role Privileges

GRANT ROLE PRIVILEGES ON ;
Eg: IoTDB > GRANT ROLE temprole PRIVILEGES INSERT_TIMESERIES, DELETE_TIMESERIES ON root.sgcc., root.ln.; Eg: IoTDB > GRANT ROLE temprole PRIVILEGES CREATE_ROLE;

  2. -   Grant Role All Privileges

GRANT ROLE PRIVILEGES ALL ON ;
Eg: IoTDB > GRANT ROLE temprole PRIVILEGES ALL;

  2. -   Grant User Role

GRANT TO ;
Eg: IoTDB > GRANT temprole TO tempuser;

  2. -   Revoke User Privileges

REVOKE USER PRIVILEGES ON ;
Eg: IoTDB > REVOKE USER tempuser PRIVILEGES DELETE_TIMESERIES on root.ln.**; Eg: IoTDB > REVOKE USER tempuser PRIVILEGES CREATE_ROLE;

  2. -   Revoke User All Privileges

REVOKE USER PRIVILEGES ALL; Eg: IoTDB > REVOKE USER tempuser PRIVILEGES ALL;

  2. -   Revoke Role Privileges

REVOKE ROLE PRIVILEGES ON ;
Eg: IoTDB > REVOKE ROLE temprole PRIVILEGES DELETE_TIMESERIES ON root.ln.**; Eg: IoTDB > REVOKE ROLE temprole PRIVILEGES CREATE_ROLE;

  2. -   Revoke All Role Privileges

REVOKE ROLE PRIVILEGES ALL;
Eg: IoTDB > REVOKE ROLE temprole PRIVILEGES ALL;

  2. -   Revoke Role From User

REVOKE FROM ; Eg: IoTDB > REVOKE temprole FROM tempuser;

  2. -   List Users

LIST USER Eg: IoTDB > LIST USER

  2. -   List User of Specific Role

LIST USER OF ROLE ; Eg: IoTDB > LIST USER OF ROLE roleuser;

  2. -   List Roles

LIST ROLE Eg: IoTDB > LIST ROLE

  2. -   List Roles of Specific User

LIST ROLE OF USER ;
Eg: IoTDB > LIST ROLE OF USER tempuser;

  2. -   List All Privileges of Users

LIST PRIVILEGES USER ;
Eg: IoTDB > LIST PRIVILEGES USER tempuser;

  2. -   List Related Privileges of Users(On Specific Paths)

LIST PRIVILEGES USER ON ; Eg: IoTDB> LIST PRIVILEGES USER tempuser ON root.ln., root.ln.wf01.; +————+—————————————————-+ | role| privilege| +————+—————————————————-+ | | root.ln. : ALTER_TIMESERIES| |temprole|root.ln.wf01. : CREATE_TIMESERIES| +————+—————————————————-+ Total line number = 2 It costs 0.005s IoTDB> LIST PRIVILEGES USER tempuser ON root.ln.wf01.wt01.; +————+—————————————————-+ | role| privilege| +————+—————————————————-+ | | root.ln. : ALTER_TIMESERIES| |temprole|root.ln.wf01.** : CREATE_TIMESERIES| +————+—————————————————-+ Total line number = 2 It costs 0.005s

  2. -   List All Privileges of Roles

LIST PRIVILEGES ROLE Eg: IoTDB > LIST PRIVILEGES ROLE actor;

  2. -   List Related Privileges of Roles(On Specific Paths)

LIST PRIVILEGES ROLE ON ;
Eg: IoTDB> LIST PRIVILEGES ROLE temprole ON root.ln., root.ln.wf01.wt01.; +—————————————————-+ | privilege| +—————————————————-+ |root.ln.wf01. : CREATE_TIMESERIES| +—————————————————-+ Total line number = 1 It costs 0.005s IoTDB> LIST PRIVILEGES ROLE temprole ON root.ln.wf01.wt01.; +—————————————————-+ | privilege| +—————————————————-+ |root.ln.wf01.** : CREATE_TIMESERIES| +—————————————————-+ Total line number = 1 It costs 0.005s

  2. -   Alter Password

ALTER USER SET PASSWORD ; Eg: IoTDB > ALTER USER tempuser SET PASSWORD ‘newpwd’;

  2. ### Operations restricted by non root users
  4. At present, the following SQL statements supported by iotdb can only be operated by the `root` user, and no corresponding permission can be given to the new user.
  6. #### TsFile Management
  8. -   Load TsFiles

Eg: IoTDB > load ‘/Users/Desktop/data/1575028885956-101-0.tsfile’

  2. -   remove a tsfile

Eg: IoTDB > remove ‘/Users/Desktop/data/data/root.vehicle/0/0/1575028885956-101-0.tsfile’

  2. -   unload a tsfile and move it to a target directory

Eg: IoTDB > unload ‘/Users/Desktop/data/data/root.vehicle/0/0/1575028885956-101-0.tsfile’ ‘/data/data/tmp’

  2. #### Delete Time Partition (experimental)

Eg: IoTDB > DELETE PARTITION root.ln 0,1,2

  2. #### Continuous Query,CQ

Eg: IoTDB > CREATE CONTINUOUS QUERY cq1 BEGIN SELECT max_value(temperature) INTO temperature_max FROM root.ln.. GROUP BY time(10s) END

  2. #### Maintenance Command
  4. -   FLUSH

Eg: IoTDB > flush

  2. -   MERGE

Eg: IoTDB > MERGE Eg: IoTDB > FULL MERGE

  2. -   CLEAR CACHE

Eg: IoTDB > CLEAR CACHE

  2. -   SET STSTEM TO READONLY / WRITABLE

Eg: IoTDB > SET STSTEM TO READONLY / WRITABLE

  2. -   Query abort

Eg: IoTDB > KILL QUERY 1

  2. #### Watermark Tool
  4. -   Watermark new users

Eg: IoTDB > grant watermark_embedding to Alice

  2. -   Watermark Detection

Eg: IoTDB > revoke watermark_embedding from Alice ```