Operator and Expression

About 16 min

Operator and Expression

This chapter describes the operators and functions supported by IoTDB. IoTDB provides a wealth of built-in operators and functions to meet your computing needs, and supports extensions through the User-Defined Function.

A list of all available functions, both built-in and custom, can be displayed with SHOW FUNCTIONS command.

See the documentation Select-Expression for the behavior of operators and functions in SQL.

OPERATORS

Arithmetic Operators

Operator	Meaning
`+`	positive (unary operator)
`-`	negative (unary operator)
`*`	multiplication
`/`	division
`%`	modulo
`+`	addition
`-`	subtraction

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

Comparison Operators

Operator	Meaning
`>`	greater than
`>=`	greater than or equal to
`<`	less than
`<=`	less than or equal to
`==`	equal to
`!=` / `<>`	not equal to
`BETWEEN ... AND ...`	within the specified range
`NOT BETWEEN ... AND ...`	not within the specified range
`LIKE`	match simple pattern
`NOT LIKE`	cannot match simple pattern
`REGEXP`	match regular expression
`NOT REGEXP`	cannot match regular expression
`IS NULL`	is null
`IS NOT NULL`	is not null
`IN` / `CONTAINS`	is a value in the specified list
`NOT IN` / `NOT CONTAINS`	is not a value in the specified list

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

Logical Operators

Operator	Meaning
`NOT` / `!`	logical negation (unary operator)
`AND` / `&` / `&&`	logical AND
`OR`/ \| / \|\|	logical OR

For details and examples, see the document Logical Operators.

Operator Precedence

The precedence of operators is arranged as shown below from high to low, and operators on the same row have the same precedence.

!, - (unary operator), + (unary operator)
*, /, DIV, %, MOD
-, +
=, ==, <=>, >=, >, <=, <, <>, !=
LIKE, REGEXP, NOT LIKE, NOT REGEXP
BETWEEN ... AND ..., NOT BETWEEN ... AND ...
IS NULL, IS NOT NULL
IN, CONTAINS, NOT IN, NOT CONTAINS
AND, &, &&
OR, |, ||

BUILT-IN FUNCTIONS

The built-in functions can be used in IoTDB without registration, and the functions in the data quality function library need to be registered by referring to the registration steps in the next chapter before they can be used.

Aggregate Functions

Function Name	Description	Allowed Input Series Data Types	Required Attributes	Output Series Data Type
SUM	Summation.	INT32 INT64 FLOAT DOUBLE	/	DOUBLE
COUNT	Counts the number of data points.	All types	/	INT
AVG	Average.	INT32 INT64 FLOAT DOUBLE	/	DOUBLE
STDDEV	Alias for STDDEV_SAMP. Return the sample standard deviation.	INT32 INT64 FLOAT DOUBLE	/	DOUBLE
STDDEV_POP	Return the population standard deviation.	INT32 INT64 FLOAT DOUBLE	/	DOUBLE
STDDEV_SAMP	Return the sample standard deviation.	INT32 INT64 FLOAT DOUBLE	/	DOUBLE
VARIANCE	Alias for VAR_SAMP. Return the sample variance.	INT32 INT64 FLOAT DOUBLE	/	DOUBLE
VAR_POP	Return the population variance.	INT32 INT64 FLOAT DOUBLE	/	DOUBLE
VAR_SAMP	Return the sample variance.	INT32 INT64 FLOAT DOUBLE	/	DOUBLE
EXTREME	Finds the value with the largest absolute value. Returns a positive value if the maximum absolute value of positive and negative values is equal.	INT32 INT64 FLOAT DOUBLE	/	Consistent with the input data type
MAX_VALUE	Find the maximum value.	INT32 INT64 FLOAT DOUBLE STRING TIMESTAMP DATE	/	Consistent with the input data type
MIN_VALUE	Find the minimum value.	INT32 INT64 FLOAT DOUBLE STRING TIMESTAMP DATE	/	Consistent with the input data type
FIRST_VALUE	Find the value with the smallest timestamp.	All data types	/	Consistent with input data type
LAST_VALUE	Find the value with the largest timestamp.	All data types	/	Consistent with input data type
MAX_TIME	Find the maximum timestamp.	All data Types	/	Timestamp
MIN_TIME	Find the minimum timestamp.	All data Types	/	Timestamp
COUNT_IF	Find the number of data points that continuously meet a given condition and the number of data points that meet the condition (represented by keep) meet the specified threshold.	BOOLEAN	`[keep >=/>/=/!=/</<=]threshold`：The specified threshold or threshold condition, it is equivalent to `keep >= threshold` if `threshold` is used alone, type of `threshold` is `INT64` `ignoreNull`：Optional, default value is `true`；If the value is `true`, null values are ignored, it means that if there is a null value in the middle, the value is ignored without interrupting the continuity. If the value is `true`, null values are not ignored, it means that if there are null values in the middle, continuity will be broken	INT64
TIME_DURATION	Find the difference between the timestamp of the largest non-null value and the timestamp of the smallest non-null value in a column	All data Types	/	INT64
MODE	Find the mode. Note: 1.Having too many different values in the input series risks a memory exception; 2.If all the elements have the same number of occurrences, that is no Mode, return the value with earliest time; 3.If there are many Modes, return the Mode with earliest time.	All data Types	/	Consistent with the input data type
MAX_BY	MAX_BY(x, y) returns the value of x corresponding to the maximum value of the input y. MAX_BY(time, x) returns the timestamp when x is at its maximum value.	The first input x can be of any type, while the second input y must be of type INT32, INT64, FLOAT, DOUBLE, STRING, TIMESTAMP or DATE.	/	Consistent with the data type of the first input x.
MIN_BY	MIN_BY(x, y) returns the value of x corresponding to the minimum value of the input y. MIN_BY(time, x) returns the timestamp when x is at its minimum value.	The first input x can be of any type, while the second input y must be of type INT32, INT64, FLOAT, DOUBLE, STRING, TIMESTAMP or DATE.	/	Consistent with the data type of the first input x.

For details and examples, see the document Aggregate Functions.

Arithmetic Functions

Function Name	Allowed Input Series Data Types	Output Series Data Type	Required Attributes	Corresponding Implementation in the Java Standard Library
SIN	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#sin(double)
COS	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#cos(double)
TAN	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#tan(double)
ASIN	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#asin(double)
ACOS	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#acos(double)
ATAN	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#atan(double)
SINH	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#sinh(double)
COSH	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#cosh(double)
TANH	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#tanh(double)
DEGREES	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#toDegrees(double)
RADIANS	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#toRadians(double)
ABS	INT32 / INT64 / FLOAT / DOUBLE	Same type as the input series	/	Math#abs(int) / Math#abs(long) /Math#abs(float) /Math#abs(double)
SIGN	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#signum(double)
CEIL	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#ceil(double)
FLOOR	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#floor(double)
ROUND	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	'places' : Round the significant number, positive number is the significant number after the decimal point, negative number is the significant number of whole number	Math#rint(Math#pow(10,places))/Math#pow(10,places)
EXP	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#exp(double)
LN	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#log(double)
LOG10	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#log10(double)
SQRT	INT32 / INT64 / FLOAT / DOUBLE	DOUBLE	/	Math#sqrt(double)

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

Comparison Functions

Function Name	Allowed Input Series Data Types	Required Attributes	Output Series Data Type	Description
ON_OFF	INT32 / INT64 / FLOAT / DOUBLE	`threshold`: a double type variate	BOOLEAN	Return `ts_value >= threshold`.
IN_RANGR	INT32 / INT64 / FLOAT / DOUBLE	`lower`: DOUBLE type `upper`: DOUBLE type	BOOLEAN	Return `ts_value >= lower && value <= upper`.

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

String Processing Functions

Function Name	Allowed Input Series Data Types	Required Attributes	Output Series Data Type	Description
STRING_CONTAINS	TEXT STRING	`s`: string to search for	BOOLEAN	Checks whether the substring `s` exists in the string.
STRING_MATCHES	TEXT STRING	`regex`: Java standard library-style regular expressions.	BOOLEAN	Judges whether a string can be matched by the regular expression `regex`.
LENGTH	TEXT STRING	/	INT32	Get the length of input series.
LOCATE	TEXT STRING	`target`: The substring to be located. `reverse`: Indicates whether reverse locate is required. The default value is `false`, means left-to-right locate.	INT32	Get the position of the first occurrence of substring `target` in input series. Returns -1 if there are no `target` in input.
STARTSWITH	TEXT STRING	`target`: The prefix to be checked.	BOOLEAN	Check whether input series starts with the specified prefix `target`.
ENDSWITH	TEXT STRING	`target`: The suffix to be checked.	BOOLEAN	Check whether input series ends with the specified suffix `target`.
CONCAT	TEXT STRING	`targets`: a series of K-V, key needs to start with `target` and be not duplicated, value is the string you want to concat. `series_behind`: Indicates whether series behind targets. The default value is `false`.	TEXT	Concatenate input string and `target` string.
SUBSTRING	TEXT STRING	`from`: Indicates the start position of substring. `for`: Indicates how many characters to stop after of substring.	TEXT	Extracts a substring of a string, starting with the first specified character and stopping after the specified number of characters.The index start at 1.
REPLACE	TEXT STRING	first parameter: The target substring to be replaced. second parameter: The substring to replace with.	TEXT	Replace a substring in the input sequence with the target substring.
UPPER	TEXT STRING	/	TEXT	Get the string of input series with all characters changed to uppercase.
LOWER	TEXT STRING	/	TEXT	Get the string of input series with all characters changed to lowercase.
TRIM	TEXT STRING	/	TEXT	Get the string whose value is same to input series, with all leading and trailing space removed.
STRCMP	TEXT STRING	/	TEXT	Get the compare result of two input series. Returns `0` if series value are the same, a `negative integer` if value of series1 is smaller than series2, a `positive integer` if value of series1 is more than series2.

For details and examples, see the document String Processing.

Data Type Conversion Function

Function Name	Required Attributes	Output Series Data Type	Description
CAST	`type`: Output data type, INT32 / INT64 / FLOAT / DOUBLE / BOOLEAN / TEXT	determined by `type`	Convert the data to the type specified by the `type` parameter.

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

Constant Timeseries Generating Functions

Function Name	Required Attributes	Output Series Data Type	Description
CONST	`value`: the value of the output data point `type`: the type of the output data point, it can only be INT32 / INT64 / FLOAT / DOUBLE / BOOLEAN / TEXT	Determined by the required attribute `type`	Output the user-specified constant timeseries according to the attributes `value` and `type`.
PI	None	DOUBLE	Data point value: a `double` value of `π`, the ratio of the circumference of a circle to its diameter, which is equals to `Math.PI` in the Java Standard Library.
E	None	DOUBLE	Data point value: a `double` value of `e`, the base of the natural logarithms, which is equals to `Math.E` in the Java Standard Library.

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

Selector Functions

Function Name	Allowed Input Series Data Types	Required Attributes	Output Series Data Type	Description
TOP_K	INT32 / INT64 / FLOAT / DOUBLE / TEXT / STRING / DATE / TIEMSTAMP	`k`: the maximum number of selected data points, must be greater than 0 and less than or equal to 1000	Same type as the input series	Returns `k` data points with the largest values in a time series.
BOTTOM_K	INT32 / INT64 / FLOAT / DOUBLE / TEXT / STRING / DATE / TIEMSTAMP	`k`: the maximum number of selected data points, must be greater than 0 and less than or equal to 1000	Same type as the input series	Returns `k` data points with the smallest values in a time series.

For details and examples, see the document Selector Functions.

Continuous Interval Functions

Function Name	Allowed Input Series Data Types	Required Attributes	Output Series Data Type	Description
ZERO_DURATION	INT32/ INT64/ FLOAT/ DOUBLE/ BOOLEAN	`min`:Optional with default value `0L` `max`:Optional with default value `Long.MAX_VALUE`	Long	Return intervals' start times and duration times in which the value is always 0(false), and the duration time `t` satisfy `t >= min && t <= max`. The unit of `t` is ms
NON_ZERO_DURATION	INT32/ INT64/ FLOAT/ DOUBLE/ BOOLEAN	`min`:Optional with default value `0L` `max`:Optional with default value `Long.MAX_VALUE`	Long	Return intervals' start times and duration times in which the value is always not 0, and the duration time `t` satisfy `t >= min && t <= max`. The unit of `t` is ms
ZERO_COUNT	INT32/ INT64/ FLOAT/ DOUBLE/ BOOLEAN	`min`:Optional with default value `1L` `max`:Optional with default value `Long.MAX_VALUE`	Long	Return intervals' start times and the number of data points in the interval in which the value is always 0(false). Data points number `n` satisfy `n >= min && n <= max`
NON_ZERO_COUNT	INT32/ INT64/ FLOAT/ DOUBLE/ BOOLEAN	`min`:Optional with default value `1L` `max`:Optional with default value `Long.MAX_VALUE`	Long	Return intervals' start times and the number of data points in the interval in which the value is always not 0(false). Data points number `n` satisfy `n >= min && n <= max`

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

Variation Trend Calculation Functions

Function Name	Allowed Input Series Data Types	Required Attributes	Output Series Data Type	Description
TIME_DIFFERENCE	INT32 / INT64 / FLOAT / DOUBLE / BOOLEAN / TEXT	/	INT64	Calculates the difference between the time stamp of a data point and the time stamp of the previous data point. There is no corresponding output for the first data point.
DIFFERENCE	INT32 / INT64 / FLOAT / DOUBLE	/	Same type as the input series	Calculates the difference between the value of a data point and the value of the previous data point. There is no corresponding output for the first data point.
NON_NEGATIVE_DIFFERENCE	INT32 / INT64 / FLOAT / DOUBLE	/	Same type as the input series	Calculates the absolute value of the difference between the value of a data point and the value of the previous data point. There is no corresponding output for the first data point.
DERIVATIVE	INT32 / INT64 / FLOAT / DOUBLE	/	DOUBLE	Calculates the rate of change of a data point compared to the previous data point, the result is equals to DIFFERENCE / TIME_DIFFERENCE. There is no corresponding output for the first data point.
NON_NEGATIVE_DERIVATIVE	INT32 / INT64 / FLOAT / DOUBLE	/	DOUBLE	Calculates the absolute value of the rate of change of a data point compared to the previous data point, the result is equals to NON_NEGATIVE_DIFFERENCE / TIME_DIFFERENCE. There is no corresponding output for the first data point.
DIFF	INT32 / INT64 / FLOAT / DOUBLE	`ignoreNull`：optional，default is true. If is true, the previous data point is ignored when it is null and continues to find the first non-null value forwardly. If the value is false, previous data point is not ignored when it is null, the result is also null because null is used for subtraction	DOUBLE	Calculates the difference between the value of a data point and the value of the previous data point. There is no corresponding output for the first data point, so output is null

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

Sample Functions

Function Name	Allowed Input Series Data Types	Required Attributes	Output Series Data Type	Description
EQUAL_SIZE_BUCKET_RANDOM_SAMPLE	INT32 / INT64 / FLOAT / DOUBLE	`proportion` The value range is `(0, 1]`, the default is `0.1`	INT32 / INT64 / FLOAT / DOUBLE	Returns a random sample of equal buckets that matches the sampling ratio
EQUAL_SIZE_BUCKET_AGG_SAMPLE	INT32 / INT64 / FLOAT / DOUBLE	`proportion` The value range is `(0, 1]`, the default is `0.1` `type`: The value types are `avg`, `max`, `min`, `sum`, `extreme`, `variance`, the default is `avg`	INT32 / INT64 / FLOAT / DOUBLE	Returns equal bucket aggregation samples that match the sampling ratio
EQUAL_SIZE_BUCKET_M4_SAMPLE	INT32 / INT64 / FLOAT / DOUBLE	`proportion` The value range is `(0, 1]`, the default is `0.1`	INT32 / INT64 / FLOAT / DOUBLE	Returns equal bucket M4 samples that match the sampling ratio
EQUAL_SIZE_BUCKET_OUTLIER_SAMPLE	INT32 / INT64 / FLOAT / DOUBLE	The value range of `proportion` is `(0, 1]`, the default is `0.1` The value of `type` is `avg` or `stendis` or `cos` or `prenextdis`, the default is `avg` The value of `number` should be greater than 0, the default is `3`	INT32 / INT64 / FLOAT / DOUBLE	Returns outlier samples in equal buckets that match the sampling ratio and the number of samples in the bucket
M4	INT32 / INT64 / FLOAT / DOUBLE	Different attributes used by the size window and the time window. The size window uses attributes `windowSize` and `slidingStep`. The time window uses attributes `timeInterval`, `slidingStep`, `displayWindowBegin`, and `displayWindowEnd`. More details see below.	INT32 / INT64 / FLOAT / DOUBLE	Returns the `first, last, bottom, top` points in each sliding window. M4 sorts and deduplicates the aggregated points within the window before outputting them.

For details and examples, see the document Sample Functions.

Change Points Function

Function Name	Allowed Input Series Data Types	Required Attributes	Output Series Data Type	Description
CHANGE_POINTS	INT32 / INT64 / FLOAT / DOUBLE	/	Same type as the input series	Remove consecutive identical values from an input sequence.

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

LAMBDA EXPRESSION

Function Name	Allowed Input Series Data Types	Required Attributes	Output Series Data Type	Series Data Type Description
JEXL	INT32 / INT64 / FLOAT / DOUBLE / TEXT / BOOLEAN	`expr` is a lambda expression that supports standard one or multi arguments in the form `x -> {...}` or `(x, y, z) -> {...}`, e.g. `x -> {x * 2}`, `(x, y, z) -> {x + y * z}`	INT32 / INT64 / FLOAT / DOUBLE / TEXT / BOOLEAN	Returns the input time series transformed by a lambda expression

For details and examples, see the document Lambda.

CONDITIONAL EXPRESSION

Expression Name	Description
`CASE`	similar to "if else"

For details and examples, see the document Conditional Expressions.

SELECT EXPRESSION

The SELECT clause specifies the output of the query, consisting of several selectExpr. Each selectExpr defines one or more columns in the query result.

**selectExpr is an expression consisting of time series path suffixes, constants, functions, and operators. That is, selectExpr can contain: **

Time series path suffix (wildcards are supported)
operator
- Arithmetic operators
- comparison operators
- Logical Operators
function
- aggregate functions
- Time series generation functions (including built-in functions and user-defined functions)
constant

Use Alias

Since the unique data model of IoTDB, lots of additional information like device will be carried before each sensor. Sometimes, we want to query just one specific device, then these prefix information show frequently will be redundant in this situation, influencing the analysis of result set. At this time, we can use AS function provided by IoTDB, assign an alias to time series selected in query.

For example：

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

The result set is：

Time	temperature	speed
...	...	...

Operator

See this documentation for a list of operators supported in IoTDB.

Function

Aggregate Functions

Aggregate functions are many-to-one functions. They perform aggregate calculations on a set of values, resulting in a single aggregated result.

A query that contains an aggregate function is called an aggregate query, otherwise, it is called a time series query.

Please note that mixed use of Aggregate Query and Timeseries Query is not allowed. Below are examples for queries that are not allowed.
select a, count(a) from root.sg 
select sin(a), count(a) from root.sg
select a, count(a) from root.sg group by ([10,100),10ms)

For the aggregation functions supported by IoTDB, see the document Aggregate Functions.

Time Series Generation Function

A time series generation function takes several raw time series as input and produces a list of time series as output. Unlike aggregate functions, time series generators have a timestamp column in their result sets.

All time series generation functions accept * as input, and all can be mixed with raw time series queries.

Built-in Time Series Generation Functions

See this documentation for a list of built-in functions supported in IoTDB.

User-Defined Time Series Generation Functions

IoTDB supports function extension through User Defined Function (click for User-Defined Function) capability.

Nested Expressions

IoTDB supports the calculation of arbitrary nested expressions. Since time series query and aggregation query can not be used in a query statement at the same time, we divide nested expressions into two types, which are nested expressions with time series query and nested expressions with aggregation query.

The following is the syntax definition of the select clause:

selectClause
    : SELECT resultColumn (',' resultColumn)*
    ;

resultColumn
    : expression (AS ID)?
    ;

expression
    : '(' expression ')'
    | '-' expression
    | expression ('*' | '/' | '%') expression
    | expression ('+' | '-') expression
    | functionName '(' expression (',' expression)* functionAttribute* ')'
    | timeSeriesSuffixPath
    | number
    ;

Nested Expressions with Time Series Query

IoTDB supports the calculation of arbitrary nested expressions consisting of numbers, time series, time series generating functions (including user-defined functions) and arithmetic expressions in the select clause.

Example

Input1：

select a,
       b,
       ((a + 1) * 2 - 1) % 2 + 1.5,
       sin(a + sin(a + sin(b))),
       -(a + b) * (sin(a + b) * sin(a + b) + cos(a + b) * cos(a + b)) + 1
from root.sg1;

Result1：

+-----------------------------+----------+----------+----------------------------------------+---------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------+
|                         Time|root.sg1.a|root.sg1.b|((((root.sg1.a + 1) * 2) - 1) % 2) + 1.5|sin(root.sg1.a + sin(root.sg1.a + sin(root.sg1.b)))|(-root.sg1.a + root.sg1.b * ((sin(root.sg1.a + root.sg1.b) * sin(root.sg1.a + root.sg1.b)) + (cos(root.sg1.a + root.sg1.b) * cos(root.sg1.a + root.sg1.b)))) + 1|
+-----------------------------+----------+----------+----------------------------------------+---------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------+
|1970-01-01T08:00:00.010+08:00|         1|         1|                                     2.5|                                 0.9238430524420609|                                                                                                                      -1.0|
|1970-01-01T08:00:00.020+08:00|         2|         2|                                     2.5|                                 0.7903505371876317|                                                                                                                      -3.0|
|1970-01-01T08:00:00.030+08:00|         3|         3|                                     2.5|                                0.14065207680386618|                                                                                                                      -5.0|
|1970-01-01T08:00:00.040+08:00|         4|      null|                                     2.5|                                               null|                                                                                                                      null|
|1970-01-01T08:00:00.050+08:00|      null|         5|                                    null|                                               null|                                                                                                                      null|
|1970-01-01T08:00:00.060+08:00|         6|         6|                                     2.5|                                -0.7288037411970916|                                                                                                                     -11.0|
+-----------------------------+----------+----------+----------------------------------------+---------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------+
Total line number = 6
It costs 0.048s

Input2：

select (a + b) * 2 + sin(a) from root.sg

Result2：

+-----------------------------+----------------------------------------------+
|                         Time|((root.sg.a + root.sg.b) * 2) + sin(root.sg.a)|
+-----------------------------+----------------------------------------------+
|1970-01-01T08:00:00.010+08:00|                             59.45597888911063|
|1970-01-01T08:00:00.020+08:00|                            100.91294525072763|
|1970-01-01T08:00:00.030+08:00|                            139.01196837590714|
|1970-01-01T08:00:00.040+08:00|                            180.74511316047935|
|1970-01-01T08:00:00.050+08:00|                            219.73762514629607|
|1970-01-01T08:00:00.060+08:00|                             259.6951893788978|
|1970-01-01T08:00:00.070+08:00|                             300.7738906815579|
|1970-01-01T08:00:00.090+08:00|                             39.45597888911063|
|1970-01-01T08:00:00.100+08:00|                             39.45597888911063|
+-----------------------------+----------------------------------------------+
Total line number = 9
It costs 0.011s

Input3：

select (a + *) / 2  from root.sg1

Result3：

+-----------------------------+-----------------------------+-----------------------------+
|                         Time|(root.sg1.a + root.sg1.a) / 2|(root.sg1.a + root.sg1.b) / 2|
+-----------------------------+-----------------------------+-----------------------------+
|1970-01-01T08:00:00.010+08:00|                          1.0|                          1.0|
|1970-01-01T08:00:00.020+08:00|                          2.0|                          2.0|
|1970-01-01T08:00:00.030+08:00|                          3.0|                          3.0|
|1970-01-01T08:00:00.040+08:00|                          4.0|                         null|
|1970-01-01T08:00:00.060+08:00|                          6.0|                          6.0|
+-----------------------------+-----------------------------+-----------------------------+
Total line number = 5
It costs 0.011s

Input4：

select (a + b) * 3 from root.sg, root.ln

Result4：

+-----------------------------+---------------------------+---------------------------+---------------------------+---------------------------+
|                         Time|(root.sg.a + root.sg.b) * 3|(root.sg.a + root.ln.b) * 3|(root.ln.a + root.sg.b) * 3|(root.ln.a + root.ln.b) * 3|
+-----------------------------+---------------------------+---------------------------+---------------------------+---------------------------+
|1970-01-01T08:00:00.010+08:00|                       90.0|                      270.0|                      360.0|                      540.0|
|1970-01-01T08:00:00.020+08:00|                      150.0|                      330.0|                      690.0|                      870.0|
|1970-01-01T08:00:00.030+08:00|                      210.0|                      450.0|                      570.0|                      810.0|
|1970-01-01T08:00:00.040+08:00|                      270.0|                      240.0|                      690.0|                      660.0|
|1970-01-01T08:00:00.050+08:00|                      330.0|                       null|                       null|                       null|
|1970-01-01T08:00:00.060+08:00|                      390.0|                       null|                       null|                       null|
|1970-01-01T08:00:00.070+08:00|                      450.0|                       null|                       null|                       null|
|1970-01-01T08:00:00.090+08:00|                       60.0|                       null|                       null|                       null|
|1970-01-01T08:00:00.100+08:00|                       60.0|                       null|                       null|                       null|
+-----------------------------+---------------------------+---------------------------+---------------------------+---------------------------+
Total line number = 9
It costs 0.014s

Explanation

Only when the left operand and the right operand under a certain timestamp are not null, the nested expressions will have an output value. Otherwise this row will not be included in the result.
- In Result1 of the Example part, the value of time series root.sg.a at time 40 is 4, while the value of time series root.sg.b is null. So at time 40, the value of nested expressions (a + b) * 2 + sin(a) is null. So in Result2, this row is not included in the result.
If one operand in the nested expressions can be translated into multiple time series (For example, *), the result of each time series will be included in the result (Cartesian product). Please refer to Input3, Input4 and corresponding Result3 and Result4 in Example.

Note

Please note that Aligned Time Series has not been supported in Nested Expressions with Time Series Query yet. An error message is expected if you use it with Aligned Time Series selected in a query statement.

Nested Expressions Query with Aggregations

IoTDB supports the calculation of arbitrary nested expressions consisting of numbers, aggregations and arithmetic expressions in the select clause.

Example

Aggregation query without GROUP BY.

Input1:

select avg(temperature),
       sin(avg(temperature)),
       avg(temperature) + 1,
       -sum(hardware),
       avg(temperature) + sum(hardware)
from root.ln.wf01.wt01;

Result1:

+----------------------------------+---------------------------------------+--------------------------------------+--------------------------------+--------------------------------------------------------------------+
|avg(root.ln.wf01.wt01.temperature)|sin(avg(root.ln.wf01.wt01.temperature))|avg(root.ln.wf01.wt01.temperature) + 1|-sum(root.ln.wf01.wt01.hardware)|avg(root.ln.wf01.wt01.temperature) + sum(root.ln.wf01.wt01.hardware)|
+----------------------------------+---------------------------------------+--------------------------------------+--------------------------------+--------------------------------------------------------------------+
|                15.927999999999999|                   -0.21826546964855045|                    16.927999999999997|                         -7426.0|                                                            7441.928|
+----------------------------------+---------------------------------------+--------------------------------------+--------------------------------+--------------------------------------------------------------------+
Total line number = 1
It costs 0.009s

Input2:

select avg(*), 
	   (avg(*) + 1) * 3 / 2 -1 
from root.sg1

Result2:

+---------------+---------------+-------------------------------------+-------------------------------------+
|avg(root.sg1.a)|avg(root.sg1.b)|(avg(root.sg1.a) + 1) * 3 / 2 - 1    |(avg(root.sg1.b) + 1) * 3 / 2 - 1    |
+---------------+---------------+-------------------------------------+-------------------------------------+
|            3.2|            3.4|                    5.300000000000001|                   5.6000000000000005|
+---------------+---------------+-------------------------------------+-------------------------------------+
Total line number = 1
It costs 0.007s

Aggregation with GROUP BY.

Input3:

select avg(temperature),
       sin(avg(temperature)),
       avg(temperature) + 1,
       -sum(hardware),
       avg(temperature) + sum(hardware) as custom_sum
from root.ln.wf01.wt01
GROUP BY([10, 90), 10ms);

Result3:

+-----------------------------+----------------------------------+---------------------------------------+--------------------------------------+--------------------------------+----------+
|                         Time|avg(root.ln.wf01.wt01.temperature)|sin(avg(root.ln.wf01.wt01.temperature))|avg(root.ln.wf01.wt01.temperature) + 1|-sum(root.ln.wf01.wt01.hardware)|custom_sum|
+-----------------------------+----------------------------------+---------------------------------------+--------------------------------------+--------------------------------+----------+
|1970-01-01T08:00:00.010+08:00|                13.987499999999999|                     0.9888207947857667|                    14.987499999999999|                         -3211.0| 3224.9875|
|1970-01-01T08:00:00.020+08:00|                              29.6|                    -0.9701057337071853|                                  30.6|                         -3720.0|    3749.6|
|1970-01-01T08:00:00.030+08:00|                              null|                                   null|                                  null|                            null|      null|
|1970-01-01T08:00:00.040+08:00|                              null|                                   null|                                  null|                            null|      null|
|1970-01-01T08:00:00.050+08:00|                              null|                                   null|                                  null|                            null|      null|
|1970-01-01T08:00:00.060+08:00|                              null|                                   null|                                  null|                            null|      null|
|1970-01-01T08:00:00.070+08:00|                              null|                                   null|                                  null|                            null|      null|
|1970-01-01T08:00:00.080+08:00|                              null|                                   null|                                  null|                            null|      null|
+-----------------------------+----------------------------------+---------------------------------------+--------------------------------------+--------------------------------+----------+
Total line number = 8
It costs 0.012s

Explanation

Only when the left operand and the right operand under a certain timestamp are not null, the nested expressions will have an output value. Otherwise this row will not be included in the result. But for nested expressions with GROUP BY clause, it is better to show the result of all time intervals. Please refer to Input3 and corresponding Result3 in Example.
If one operand in the nested expressions can be translated into multiple time series (For example, *), the result of each time series will be included in the result (Cartesian product). Please refer to Input2 and corresponding Result2 in Example.