InfluxQL transformation functions
InfluxQL transformation functions modify and return values in each row of queried data.
Missing InfluxQL functions
Some InfluxQL functions are in the process of being rearchitected to work with
the InfluxDB 3.0 storage engine. If a function you need is not here, check the
InfluxQL feature support page
for more information.
Must use aggregate or selector functions when grouping by time
Most transformation functions support GROUP BY
clauses that group by tags,
but do not directly support GROUP BY
clauses that group by time.
To use transformation functions with with a GROUP BY time()
clause, apply
an aggregate
or selector
function to the field_expression argument.
The transformation operates on the result of the aggregate or selector operation.
ABS()
Returns the absolute value of the field value.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply ABS()
to a field
SELECT
a ,
ABS ( a )
FROM numbers
LIMIT 6
time
a
abs
2023-01-01T00:00:00Z
0.33909108671076
0.33909108671076
2023-01-01T00:01:00Z
-0.774984088561186
0.774984088561186
2023-01-01T00:02:00Z
-0.921037167720451
0.921037167720451
2023-01-01T00:03:00Z
-0.73880754843378
0.73880754843378
2023-01-01T00:04:00Z
-0.905980032168252
0.905980032168252
2023-01-01T00:05:00Z
-0.891164752631417
0.891164752631417
Apply ABS()
to each field
SELECT ABS ( * ) FROM numbers LIMIT 6
time
abs_a
abs_b
2023-01-01T00:00:00Z
0.33909108671076
0.163643058925645
2023-01-01T00:01:00Z
0.774984088561186
0.137034364053949
2023-01-01T00:02:00Z
0.921037167720451
0.482943221384294
2023-01-01T00:03:00Z
0.73880754843378
0.0729732928756677
2023-01-01T00:04:00Z
0.905980032168252
1.77857552719844
2023-01-01T00:05:00Z
0.891164752631417
0.741147445214238
Apply ABS()
to time windows (grouped by time)
SELECT
ABS ( MEAN ( a ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
abs
2023-01-01T00:00:00Z
0.4345725888930678
2023-01-01T00:10:00Z
0.12861008519618367
2023-01-01T00:20:00Z
0.030168160597251192
2023-01-01T00:30:00Z
0.02928699660831855
2023-01-01T00:40:00Z
0.02211434600834538
2023-01-01T00:50:00Z
0.15530468657783394
ACOS()
Returns the arccosine (in radians) of the field value.
Field values must be between -1 and 1.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply ACOS()
to a field
SELECT
a ,
ACOS ( a )
FROM numbers
LIMIT 6
time
a
acos
2023-01-01T00:00:00Z
0.33909108671076
1.2248457522250173
2023-01-01T00:01:00Z
-0.774984088561186
2.4574862443115
2023-01-01T00:02:00Z
-0.921037167720451
2.741531473732281
2023-01-01T00:03:00Z
-0.73880754843378
2.4020955294179256
2023-01-01T00:04:00Z
-0.905980032168252
2.7044854502651114
2023-01-01T00:05:00Z
-0.891164752631417
2.6707024029338
Apply ACOS()
to each field
SELECT ACOS ( * ) FROM numbers LIMIT 6
time
acos_a
acos_b
2023-01-01T00:00:00Z
1.2248457522250173
1.7351786975993897
2023-01-01T00:01:00Z
2.4574862443115
1.433329416131427
2023-01-01T00:02:00Z
2.741531473732281
2.074809114132046
2023-01-01T00:03:00Z
2.4020955294179256
1.6438345403920092
2023-01-01T00:04:00Z
2.7044854502651114
2023-01-01T00:05:00Z
2.6707024029338
0.7360183965088304
Apply ACOS()
to time windows (grouped by time)
SELECT
ACOS ( MEAN ( a ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
acos
2023-01-01T00:00:00Z
2.0203599837582877
2023-01-01T00:10:00Z
1.441829029328407
2023-01-01T00:20:00Z
1.5406235882252437
2023-01-01T00:30:00Z
1.5415051418561052
2023-01-01T00:40:00Z
1.5486801779072885
2023-01-01T00:50:00Z
1.41486045205998
ASIN()
Returns the arcsine (in radians) of the field value.
Field values must be between -1 and 1.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply ASIN()
to a field
SELECT
a ,
ASIN ( a )
FROM numbers
LIMIT 6
time
a
asin
2023-01-01T00:00:00Z
0.33909108671076
0.34595057456987915
2023-01-01T00:01:00Z
-0.774984088561186
-0.8866899175166036
2023-01-01T00:02:00Z
-0.921037167720451
-1.1707351469373848
2023-01-01T00:03:00Z
-0.73880754843378
-0.8312992026230288
2023-01-01T00:04:00Z
-0.905980032168252
-1.133689123470215
2023-01-01T00:05:00Z
-0.891164752631417
-1.0999060761389035
Apply ASIN()
to each field
SELECT ASIN ( * ) FROM numbers LIMIT 6
time
asin_a
asin_b
2023-01-01T00:00:00Z
0.34595057456987915
-0.1643823708044932
2023-01-01T00:01:00Z
-0.8866899175166036
0.1374669106634696
2023-01-01T00:02:00Z
-1.1707351469373848
-0.5040127873371497
2023-01-01T00:03:00Z
-0.8312992026230288
-0.07303821359711259
2023-01-01T00:04:00Z
-1.133689123470215
2023-01-01T00:05:00Z
-1.0999060761389035
0.8347779302860662
Apply ASIN()
to time windows (grouped by time)
SELECT
ASIN ( MEAN ( a ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
asin
2023-01-01T00:00:00Z
-0.44956365696339134
2023-01-01T00:10:00Z
0.1289672974664895
2023-01-01T00:20:00Z
0.030172738569652847
2023-01-01T00:30:00Z
0.029291184938791334
2023-01-01T00:40:00Z
0.022116148887608062
2023-01-01T00:50:00Z
0.15593587473491674
ATAN()
Returns the arctangent (in radians) of the field value.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply ATAN()
to a field
SELECT
a ,
ATAN ( a )
FROM numbers
LIMIT 6
time
a
atan
2023-01-01T00:00:00Z
0.33909108671076
0.32692355076199897
2023-01-01T00:01:00Z
-0.774984088561186
-0.659300127490126
2023-01-01T00:02:00Z
-0.921037167720451
-0.7443170183837121
2023-01-01T00:03:00Z
-0.73880754843378
-0.6362993731936669
2023-01-01T00:04:00Z
-0.905980032168252
-0.7361091800814261
2023-01-01T00:05:00Z
-0.891164752631417
-0.727912249468035
Apply ATAN()
to each field
SELECT ATAN ( * ) FROM numbers LIMIT 6
time
atan_a
atan_b
2023-01-01T00:00:00Z
0.32692355076199897
-0.1622053541422186
2023-01-01T00:01:00Z
-0.659300127490126
0.13618613793696105
2023-01-01T00:02:00Z
-0.7443170183837121
-0.4499093121666581
2023-01-01T00:03:00Z
-0.6362993731936669
-0.07284417510130452
2023-01-01T00:04:00Z
-0.7361091800814261
1.0585985450688151
2023-01-01T00:05:00Z
-0.727912249468035
0.6378113578294793
Apply ATAN()
to time windows (grouped by time)
SELECT
ATAN ( MEAN ( a ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
atan
2023-01-01T00:00:00Z
-0.4099506966510045
2023-01-01T00:10:00Z
0.1279079463727065
2023-01-01T00:20:00Z
0.030159013397288013
2023-01-01T00:30:00Z
0.02927862748761639
2023-01-01T00:40:00Z
0.022110742100818606
2023-01-01T00:50:00Z
0.15407382461141705
ATAN2()
Returns the the arctangent of y/x
in radians.
ATAN2 ( expression_y , expression_x )
Arguments
expression_y : Expression to identify the y
numeric value or one or more
fields to operate on.
Can be a number literal, field key ,
constant, or wildcard (*
).
Supports numeric field types.
expression_x : Expression to identify the x
numeric value or one or more
fields to operate on.
Can be a number literal, field key ,
constant, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply ATAN2()
to a field divided by another field
SELECT ATAN2 ( a , b ) FROM numbers LIMIT 6
time
atan2
2023-01-01T00:00:00Z
2.0204217911794937
2023-01-01T00:01:00Z
-1.395783190047229
2023-01-01T00:02:00Z
-2.053731408859953
2023-01-01T00:03:00Z
-1.669248713922653
2023-01-01T00:04:00Z
-0.47112754043763505
2023-01-01T00:05:00Z
-0.8770454978291377
Apply ATAN2()
to each field divided by a numeric value
SELECT ATAN2 ( * , 2 ) FROM numbers LIMIT 6
time
atan2_a
atan2_b
2023-01-01T00:00:00Z
0.16794843225523703
-0.0816396675119722
2023-01-01T00:01:00Z
-0.36967737169970566
0.06841026268126137
2023-01-01T00:02:00Z
-0.4315666721698651
-0.2369359777533473
2023-01-01T00:03:00Z
-0.35385538623378937
-0.036470468100670846
2023-01-01T00:04:00Z
-0.4253376417906667
0.7268651162204586
2023-01-01T00:05:00Z
-0.41917415992493756
0.35488446257957357
Apply ATAN2()
to time windows (grouped by time)
SELECT
ATAN2 ( MEAN ( a ), MEAN ( b ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
atan2
2023-01-01T00:00:00Z
-1.278967897411707
2023-01-01T00:10:00Z
2.3520553840586773
2023-01-01T00:20:00Z
2.226497789888965
2023-01-01T00:30:00Z
3.0977773783018656
2023-01-01T00:40:00Z
2.9285769547942677
2023-01-01T00:50:00Z
0.9505419744107901
CEIL()
Returns the subsequent value rounded up to the nearest integer.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply CEIL()
to a field
SELECT
b ,
CEIL ( b )
FROM numbers
LIMIT 6
time
b
ceil
2023-01-01T00:00:00Z
-0.163643058925645
-0
2023-01-01T00:01:00Z
0.137034364053949
1
2023-01-01T00:02:00Z
-0.482943221384294
-0
2023-01-01T00:03:00Z
-0.0729732928756677
-0
2023-01-01T00:04:00Z
1.77857552719844
2
2023-01-01T00:05:00Z
0.741147445214238
1
Apply CEIL()
to each field
SELECT CEIL ( * ) FROM numbers LIMIT 6
time
ceil_a
ceil_b
2023-01-01T00:00:00Z
1
-0
2023-01-01T00:01:00Z
-0
1
2023-01-01T00:02:00Z
-0
-0
2023-01-01T00:03:00Z
-0
-0
2023-01-01T00:04:00Z
-0
2
2023-01-01T00:05:00Z
-0
1
Apply CEIL()
to time windows (grouped by time)
SELECT
CEIL ( MEAN ( b ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
ceil
2023-01-01T00:00:00Z
1
2023-01-01T00:10:00Z
-0
2023-01-01T00:20:00Z
-0
2023-01-01T00:30:00Z
-0
2023-01-01T00:40:00Z
-0
2023-01-01T00:50:00Z
1
COS()
Returns the cosine of the field value.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply COS()
to a field
SELECT
b ,
COS ( b )
FROM numbers
LIMIT 6
time
b
cos
2023-01-01T00:00:00Z
-0.163643058925645
0.9866403278718959
2023-01-01T00:01:00Z
0.137034364053949
0.9906254752128878
2023-01-01T00:02:00Z
-0.482943221384294
0.8856319645801471
2023-01-01T00:03:00Z
-0.0729732928756677
0.9973386305831397
2023-01-01T00:04:00Z
1.77857552719844
-0.20628737691395405
2023-01-01T00:05:00Z
0.741147445214238
0.7376943643170851
Apply COS()
to each field
SELECT COS ( * ) FROM numbers LIMIT 6
time
cos_a
cos_b
2023-01-01T00:00:00Z
0.9430573869206459
0.9866403278718959
2023-01-01T00:01:00Z
0.7144321674550146
0.9906254752128878
2023-01-01T00:02:00Z
0.6049946586273094
0.8856319645801471
2023-01-01T00:03:00Z
0.7392720891861374
0.9973386305831397
2023-01-01T00:04:00Z
0.616914561474936
-0.20628737691395405
2023-01-01T00:05:00Z
0.6285065034701617
0.7376943643170851
Apply COS()
to time windows (grouped by time)
SELECT
COS ( MEAN ( b ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
cos
2023-01-01T00:00:00Z
0.9914907269510592
2023-01-01T00:10:00Z
0.9918765457796455
2023-01-01T00:20:00Z
0.9997307399250498
2023-01-01T00:30:00Z
0.7850670342365872
2023-01-01T00:40:00Z
0.9947779847618986
2023-01-01T00:50:00Z
0.9938532355205111
CUMULATIVE_SUM()
Returns the running total of subsequent field values .
CUMULATIVE_SUM ( field_expression )
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, regular expression, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply CUMULATIVE_SUM()
to a field
SELECT CUMULATIVE_SUM ( b ) FROM numbers LIMIT 6
time
cumulative_sum
2023-01-01T00:00:00Z
-0.163643058925645
2023-01-01T00:01:00Z
-0.02660869487169601
2023-01-01T00:02:00Z
-0.5095519162559901
2023-01-01T00:03:00Z
-0.5825252091316577
2023-01-01T00:04:00Z
1.1960503180667823
2023-01-01T00:05:00Z
1.9371977632810204
Apply CUMULATIVE_SUM()
to each field
SELECT CUMULATIVE_SUM ( * ) FROM numbers LIMIT 6
time
cumulative_sum_a
cumulative_sum_b
2023-01-01T00:00:00Z
0.33909108671076
-0.163643058925645
2023-01-01T00:01:00Z
-0.43589300185042595
-0.02660869487169601
2023-01-01T00:02:00Z
-1.3569301695708769
-0.5095519162559901
2023-01-01T00:03:00Z
-2.095737718004657
-0.5825252091316577
2023-01-01T00:04:00Z
-3.001717750172909
1.1960503180667823
2023-01-01T00:05:00Z
-3.892882502804326
1.9371977632810204
Apply CUMULATIVE_SUM()
to field keys that match a regular expression
SELECT CUMULATIVE_SUM ( / [ ab ] / ) FROM numbers LIMIT 6
time
cumulative_sum_a
cumulative_sum_b
2023-01-01T00:00:00Z
0.33909108671076
-0.163643058925645
2023-01-01T00:01:00Z
-0.43589300185042595
-0.02660869487169601
2023-01-01T00:02:00Z
-1.3569301695708769
-0.5095519162559901
2023-01-01T00:03:00Z
-2.095737718004657
-0.5825252091316577
2023-01-01T00:04:00Z
-3.001717750172909
1.1960503180667823
2023-01-01T00:05:00Z
-3.892882502804326
1.9371977632810204
Apply CUMULATIVE_SUM()
to time windows (grouped by time)
SELECT
CUMULATIVE_SUM ( SUM ( b ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
cumulative_sum
2023-01-01T00:00:00Z
1.3054783385851743
2023-01-01T00:10:00Z
0.029980276948385454
2023-01-01T00:20:00Z
-0.20208529969578404
2023-01-01T00:30:00Z
-6.882005145666267
2023-01-01T00:40:00Z
-7.904410787756402
2023-01-01T00:50:00Z
-6.795080184131271
DERIVATIVE()
Returns the rate of change between subsequent field values
per unit
.
SELECT DERIVATIVE ( field_expression [, unit ])
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, regular expression, or wildcard (*
).
Supports numeric field types.
unit : Unit of time to use to calculate the rate of change.
Supports duration literals .
Default is 1s
(per second) .
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply DERIVATIVE()
to a field to calculate the per second change
SELECT DERIVATIVE ( b ) FROM numbers LIMIT 6
time
derivative
2023-01-01T00:01:00Z
0.005011290382993233
2023-01-01T00:02:00Z
-0.01033295975730405
2023-01-01T00:03:00Z
0.006832832141810439
2023-01-01T00:04:00Z
0.03085914700123513
2023-01-01T00:05:00Z
-0.017290468033070033
2023-01-01T00:06:00Z
-0.007557890705063634
Apply DERIVATIVE()
to a field to calculate the per 5 minute change
SELECT DERIVATIVE ( b , 5 m ) FROM numbers LIMIT 6
time
derivative
2023-01-01T00:01:00Z
1.5033871148979698
2023-01-01T00:02:00Z
-3.0998879271912148
2023-01-01T00:03:00Z
2.0498496425431316
2023-01-01T00:04:00Z
9.257744100370537
2023-01-01T00:05:00Z
-5.187140409921009
2023-01-01T00:06:00Z
-2.26736721151909
Apply DERIVATIVE()
to each field
SELECT DERIVATIVE ( * ) FROM numbers LIMIT 6
time
derivative_a
derivative_b
2023-01-01T00:01:00Z
-0.018567919587865765
0.005011290382993233
2023-01-01T00:02:00Z
-0.0024342179859877505
-0.01033295975730405
2023-01-01T00:03:00Z
0.0030371603214445152
0.006832832141810439
2023-01-01T00:04:00Z
-0.0027862080622411984
0.03085914700123513
2023-01-01T00:05:00Z
0.00024692132561391543
-0.017290468033070033
2023-01-01T00:06:00Z
0.016704951104985283
-0.007557890705063634
Apply DERIVATIVE()
to field keys that match a regular expression
SELECT DERIVATIVE ( / [ ab ] / ) FROM numbers LIMIT 6
time
derivative_a
derivative_b
2023-01-01T00:01:00Z
-0.018567919587865765
0.005011290382993233
2023-01-01T00:02:00Z
-0.0024342179859877505
-0.01033295975730405
2023-01-01T00:03:00Z
0.0030371603214445152
0.006832832141810439
2023-01-01T00:04:00Z
-0.0027862080622411984
0.03085914700123513
2023-01-01T00:05:00Z
0.00024692132561391543
-0.017290468033070033
2023-01-01T00:06:00Z
0.016704951104985283
-0.007557890705063634
Apply DERIVATIVE()
to time windows (grouped by time)
SELECT
DERIVATIVE ( MEAN ( b ), 1 m )
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
derivative
2023-01-01T00:10:00Z
-0.025809764002219633
2023-01-01T00:20:00Z
0.010434324849926194
2023-01-01T00:30:00Z
-0.06447854269326314
2023-01-01T00:40:00Z
0.05657514203880348
2023-01-01T00:50:00Z
0.021317362457152655
DIFFERENCE()
Returns the result of subtraction between subsequent field values .
DIFFERENCE ( field_expression )
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, regular expression, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply DIFFERENCE()
to a field
SELECT DIFFERENCE ( b ) FROM numbers LIMIT 6
time
difference
2023-01-01T00:01:00Z
0.300677422979594
2023-01-01T00:02:00Z
-0.619977585438243
2023-01-01T00:03:00Z
0.40996992850862635
2023-01-01T00:04:00Z
1.8515488200741077
2023-01-01T00:05:00Z
-1.0374280819842019
2023-01-01T00:06:00Z
-0.45347344230381803
Apply DIFFERENCE()
to each field
SELECT DIFFERENCE ( * ) FROM numbers LIMIT 6
time
difference_a
difference_b
2023-01-01T00:01:00Z
-1.114075175271946
0.300677422979594
2023-01-01T00:02:00Z
-0.14605307915926502
-0.619977585438243
2023-01-01T00:03:00Z
0.18222961928667092
0.40996992850862635
2023-01-01T00:04:00Z
-0.1671724837344719
1.8515488200741077
2023-01-01T00:05:00Z
0.014815279536834924
-1.0374280819842019
2023-01-01T00:06:00Z
1.002297066299117
-0.45347344230381803
Apply DIFFERENCE()
to field keys that match a regular expression
SELECT DIFFERENCE ( / [ ab ] / ) FROM numbers LIMIT 6
time
difference_a
difference_b
2023-01-01T00:01:00Z
-1.114075175271946
0.300677422979594
2023-01-01T00:02:00Z
-0.14605307915926502
-0.619977585438243
2023-01-01T00:03:00Z
0.18222961928667092
0.40996992850862635
2023-01-01T00:04:00Z
-0.1671724837344719
1.8515488200741077
2023-01-01T00:05:00Z
0.014815279536834924
-1.0374280819842019
2023-01-01T00:06:00Z
1.002297066299117
-0.45347344230381803
Apply DIFFERENCE()
to time windows (grouped by time)
SELECT
DIFFERENCE ( MEAN ( b ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
difference
2023-01-01T00:10:00Z
-0.2580976400221963
2023-01-01T00:20:00Z
0.10434324849926194
2023-01-01T00:30:00Z
-0.6447854269326314
2023-01-01T00:40:00Z
0.5657514203880348
2023-01-01T00:50:00Z
0.21317362457152655
ELAPSED()
Returns the difference between subsequent field value’s
timestamps in a specified unit
of time.
ELAPSED ( field_expression [, unit ])
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, regular expression, or wildcard (*
).
Supports all field types.
unit : Unit of time to return the elapsed time in.
Supports duration literals .
Default is 1ns
(nanoseconds) .
Notable behaviors
If the unit
is greater than the elapsed time between points, ELAPSED()
returns 0
.
ELAPSED()
supports the GROUP BY time()
clause but the query results aren’t very useful.
An ELAPSED()
query with a nested function and a GROUP BY time()
clause
returns the interval specified in the GROUP BY time()
clause.
Examples
The following examples use the
Random numbers sample data .
Apply ELAPSED()
to a field and return elapsed time in nanoseconds
SELECT ELAPSED ( b ) FROM numbers LIMIT 6
time
elapsed
2023-01-01T00:01:00Z
60000000000
2023-01-01T00:02:00Z
60000000000
2023-01-01T00:03:00Z
60000000000
2023-01-01T00:04:00Z
60000000000
2023-01-01T00:05:00Z
60000000000
2023-01-01T00:06:00Z
60000000000
Apply ELAPSED()
to a field and return elapsed time in seconds
SELECT ELAPSED ( b , 1 s ) FROM numbers LIMIT 6
time
elapsed
2023-01-01T00:01:00Z
60
2023-01-01T00:02:00Z
60
2023-01-01T00:03:00Z
60
2023-01-01T00:04:00Z
60
2023-01-01T00:05:00Z
60
2023-01-01T00:06:00Z
60
Apply ELAPSED()
to each field
SELECT ELAPSED ( * ) FROM numbers LIMIT 6
time
elapsed_a
elapsed_b
2023-01-01T00:01:00Z
60000000000
60000000000
2023-01-01T00:02:00Z
60000000000
60000000000
2023-01-01T00:03:00Z
60000000000
60000000000
2023-01-01T00:04:00Z
60000000000
60000000000
2023-01-01T00:05:00Z
60000000000
60000000000
2023-01-01T00:06:00Z
60000000000
60000000000
Apply ELAPSED()
to field keys that match a regular expression
SELECT ELAPSED ( / [ ab ] / , 1 s ) FROM numbers LIMIT 6
time
elapsed_a
elapsed_b
2023-01-01T00:01:00Z
60
60
2023-01-01T00:02:00Z
60
60
2023-01-01T00:03:00Z
60
60
2023-01-01T00:04:00Z
60
60
2023-01-01T00:05:00Z
60
60
2023-01-01T00:06:00Z
60
60
EXP()
Returns the exponential of the field value.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply EXP()
to a field
SELECT
a ,
EXP ( a )
FROM numbers
LIMIT 6
time
a
exp
2023-01-01T00:00:00Z
0.33909108671076
1.4036711951820788
2023-01-01T00:01:00Z
-0.774984088561186
0.460711111517308
2023-01-01T00:02:00Z
-0.921037167720451
0.39810592427186076
2023-01-01T00:03:00Z
-0.73880754843378
0.4776831901055915
2023-01-01T00:04:00Z
-0.905980032168252
0.40414561525252984
2023-01-01T00:05:00Z
-0.891164752631417
0.4101777188333968
Apply EXP()
to each field
SELECT EXP ( * ) FROM numbers LIMIT 6
time
exp_a
exp_b
2023-01-01T00:00:00Z
1.4036711951820788
0.8490450268435884
2023-01-01T00:01:00Z
0.460711111517308
1.14686755886191
2023-01-01T00:02:00Z
0.39810592427186076
0.6169648527893578
2023-01-01T00:03:00Z
0.4776831901055915
0.929625657322271
2023-01-01T00:04:00Z
0.40414561525252984
5.921415512753404
2023-01-01T00:05:00Z
0.4101777188333968
2.09834186598405
Apply EXP()
to time windows (grouped by time)
SELECT
EXP ( MEAN ( a ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
exp
2023-01-01T00:00:00Z
0.6475413743155294
2023-01-01T00:10:00Z
1.137246608416461
2023-01-01T00:20:00Z
1.030627830373793
2023-01-01T00:30:00Z
1.029720078241656
2023-01-01T00:40:00Z
1.0223606806499268
2023-01-01T00:50:00Z
1.1680137850180072
FLOOR()
Returns the subsequent value rounded down to the nearest integer.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply FLOOR()
to a field
SELECT
b ,
FLOOR ( b )
FROM numbers
LIMIT 6
time
b
floor
2023-01-01T00:00:00Z
-0.163643058925645
-1
2023-01-01T00:01:00Z
0.137034364053949
0
2023-01-01T00:02:00Z
-0.482943221384294
-1
2023-01-01T00:03:00Z
-0.0729732928756677
-1
2023-01-01T00:04:00Z
1.77857552719844
1
2023-01-01T00:05:00Z
0.741147445214238
0
Apply FLOOR()
to each field
SELECT FLOOR ( * ) FROM numbers LIMIT 6
time
floor_a
floor_b
2023-01-01T00:00:00Z
0
-1
2023-01-01T00:01:00Z
-1
0
2023-01-01T00:02:00Z
-1
-1
2023-01-01T00:03:00Z
-1
-1
2023-01-01T00:04:00Z
-1
1
2023-01-01T00:05:00Z
-1
0
Apply FLOOR()
to time windows (grouped by time)
SELECT
FLOOR ( SUM ( a ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
floor
2023-01-01T00:00:00Z
-5
2023-01-01T00:10:00Z
1
2023-01-01T00:20:00Z
0
2023-01-01T00:30:00Z
0
2023-01-01T00:40:00Z
0
2023-01-01T00:50:00Z
1
LN()
Returns the natural logarithm of the field value.
Field values must be greater than or equal to 0.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply LN()
to a field
SELECT
b ,
LN ( b )
FROM numbers
LIMIT 6
time
b
ln
2023-01-01T00:00:00Z
-0.163643058925645
2023-01-01T00:01:00Z
0.137034364053949
-1.98752355209665
2023-01-01T00:02:00Z
-0.482943221384294
2023-01-01T00:03:00Z
-0.0729732928756677
2023-01-01T00:04:00Z
1.77857552719844
0.5758127783016702
2023-01-01T00:05:00Z
0.741147445214238
-0.2995556920844895
Apply LN()
to each field
SELECT LN ( * ) FROM numbers LIMIT 6
time
ln_a
ln_b
2023-01-01T00:00:00Z
-1.0814865153308908
2023-01-01T00:01:00Z
-1.98752355209665
2023-01-01T00:02:00Z
2023-01-01T00:03:00Z
2023-01-01T00:04:00Z
0.5758127783016702
2023-01-01T00:05:00Z
-0.2995556920844895
Apply LN()
to time windows (grouped by time)
SELECT
LN ( SUM ( a ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
ln
2023-01-01T00:00:00Z
2023-01-01T00:10:00Z
0.25161504572793725
2023-01-01T00:20:00Z
-1.1983831026157092
2023-01-01T00:30:00Z
-1.2280265702380913
2023-01-01T00:40:00Z
-1.5089436474159283
2023-01-01T00:50:00Z
0.4402187212890264
LOG()
Returns the logarithm of the field value with base b
.
Field values must be greater than or equal to 0.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
b : Logarithm base to use in the operation.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply LOG()
to a field with a base of 3
SELECT
b ,
LOG ( b , 3 )
FROM numbers
LIMIT 6
time
b
log
2023-01-01T00:00:00Z
-0.163643058925645
2023-01-01T00:01:00Z
0.137034364053949
-1.8091219009630797
2023-01-01T00:02:00Z
-0.482943221384294
2023-01-01T00:03:00Z
-0.0729732928756677
2023-01-01T00:04:00Z
1.77857552719844
0.5241273780031629
2023-01-01T00:05:00Z
0.741147445214238
-0.2726673414946528
Apply LOG()
to each field with a base of 5
SELECT LOG ( * , 5 ) FROM numbers LIMIT 6
time
log_a
log_b
2023-01-01T00:00:00Z
-0.6719653532302217
2023-01-01T00:01:00Z
-1.2349178161776593
2023-01-01T00:02:00Z
2023-01-01T00:03:00Z
2023-01-01T00:04:00Z
0.3577725949246566
2023-01-01T00:05:00Z
-0.18612441633827553
Apply LOG()
to time windows (grouped by time)
SELECT
LOG ( SUM ( a ), 10 )
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
log
2023-01-01T00:00:00Z
2023-01-01T00:10:00Z
0.10927502592347751
2023-01-01T00:20:00Z
-0.5204511686721008
2023-01-01T00:30:00Z
-0.5333251630849791
2023-01-01T00:40:00Z
-0.6553258995757036
2023-01-01T00:50:00Z
0.1911845614863297
LOG2()
Returns the logarithm of the field value to the base 2.
Field values must be greater than or equal to 0.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply LOG2()
to a field
SELECT
b ,
LOG2 ( b )
FROM numbers
LIMIT 6
time
b
log2
2023-01-01T00:00:00Z
-0.163643058925645
2023-01-01T00:01:00Z
0.137034364053949
-2.8673903722598544
2023-01-01T00:02:00Z
-0.482943221384294
2023-01-01T00:03:00Z
-0.0729732928756677
2023-01-01T00:04:00Z
1.77857552719844
0.8307222397363156
2023-01-01T00:05:00Z
0.741147445214238
-0.4321675114403543
Apply LOG2()
to each field
SELECT LOG2 ( * ) FROM numbers LIMIT 6
time
log2_a
log2_b
2023-01-01T00:00:00Z
-1.560255232456162
2023-01-01T00:01:00Z
-2.8673903722598544
2023-01-01T00:02:00Z
2023-01-01T00:03:00Z
2023-01-01T00:04:00Z
0.8307222397363156
2023-01-01T00:05:00Z
-0.4321675114403543
Apply LOG2()
to time windows (grouped by time)
SELECT
LOG2 ( SUM ( a ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
log2
2023-01-01T00:00:00Z
2023-01-01T00:10:00Z
0.36300377868474476
2023-01-01T00:20:00Z
-1.7289013592288134
2023-01-01T00:30:00Z
-1.7716678429623767
2023-01-01T00:40:00Z
-2.1769455171078644
2023-01-01T00:50:00Z
0.6351013661101591
LOG10()
Returns the logarithm of the field value to the base 10.
Field values must be greater than or equal to 0.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply LOG10()
to a field
SELECT
b ,
LOG10 ( b )
FROM numbers
LIMIT 6
time
b
log10
2023-01-01T00:00:00Z
-0.163643058925645
2023-01-01T00:01:00Z
0.137034364053949
-0.8631705113283253
2023-01-01T00:02:00Z
-0.482943221384294
2023-01-01T00:03:00Z
-0.0729732928756677
2023-01-01T00:04:00Z
1.77857552719844
0.25007231222579585
2023-01-01T00:05:00Z
0.741147445214238
-0.1300953840950034
Apply LOG10()
to each field
SELECT LOG10 ( * ) FROM numbers LIMIT 6
time
log10_a
log10_b
2023-01-01T00:00:00Z
-0.46968362586098245
2023-01-01T00:01:00Z
-0.8631705113283253
2023-01-01T00:02:00Z
2023-01-01T00:03:00Z
2023-01-01T00:04:00Z
0.25007231222579585
2023-01-01T00:05:00Z
-0.1300953840950034
Apply LOG10()
to time windows (grouped by time)
SELECT
LOG10 ( SUM ( a ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
log10
2023-01-01T00:00:00Z
2023-01-01T00:10:00Z
0.10927502592347751
2023-01-01T00:20:00Z
-0.520451168672101
2023-01-01T00:30:00Z
-0.5333251630849791
2023-01-01T00:40:00Z
-0.6553258995757036
2023-01-01T00:50:00Z
0.19118456148632973
MOVING_AVERAGE()
Returns the rolling average across a window of subsequent field values .
MOVING_AVERAGE ( field_expression , N )
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, regular expression, or wildcard (*
).
Supports all field types.
N : Number of field values to use when calculating the moving average.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply MOVING_AVERAGE()
to a field
SELECT MOVING_AVERAGE ( a , 3 ) FROM numbers LIMIT 6
time
moving_average
2023-01-01T00:02:00Z
-0.4523100565236256
2023-01-01T00:03:00Z
-0.8116096015718056
2023-01-01T00:04:00Z
-0.8552749161074944
2023-01-01T00:05:00Z
-0.8453174444111498
2023-01-01T00:06:00Z
-0.5620041570439896
2023-01-01T00:07:00Z
-0.3569778402485757
Apply MOVING_AVERAGE()
to each field
SELECT MOVING_AVERAGE ( * , 3 ) FROM numbers LIMIT 6
time
moving_average_a
moving_average_b
2023-01-01T00:02:00Z
-0.4523100565236256
-0.16985063875199669
2023-01-01T00:03:00Z
-0.8116096015718056
-0.13962738340200423
2023-01-01T00:04:00Z
-0.8552749161074944
0.40755300431282615
2023-01-01T00:05:00Z
-0.8453174444111498
0.815583226512337
2023-01-01T00:06:00Z
-0.5620041570439896
0.9357989917743662
2023-01-01T00:07:00Z
-0.3569778402485757
0.15985821845558748
Apply MOVING_AVERAGE()
to field keys that match a regular expression
SELECT MOVING_AVERAGE ( / [ ab ] / , 3 ) FROM numbers LIMIT 6
time
moving_average_a
moving_average_b
2023-01-01T00:02:00Z
-0.4523100565236256
-0.16985063875199669
2023-01-01T00:03:00Z
-0.8116096015718056
-0.13962738340200423
2023-01-01T00:04:00Z
-0.8552749161074944
0.40755300431282615
2023-01-01T00:05:00Z
-0.8453174444111498
0.815583226512337
2023-01-01T00:06:00Z
-0.5620041570439896
0.9357989917743662
2023-01-01T00:07:00Z
-0.3569778402485757
0.15985821845558748
Apply MOVING_AVERAGE()
to time windows (grouped by time)
SELECT
MOVING_AVERAGE ( SUM ( a ), 3 )
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
moving_average
2023-01-01T00:20:00Z
-0.9193144769987766
2023-01-01T00:30:00Z
0.626884141339178
2023-01-01T00:40:00Z
0.27189834404638374
2023-01-01T00:50:00Z
0.6890200973149928
NON_NEGATIVE_DERIVATIVE()
Returns only non-negative rate of change between subsequent
field values .
Negative rates of change return null .
NON_NEGATIVE_DERIVATIVE ( field_expression [, unit ])
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, regular expression, or wildcard (*
).
Supports numeric field types.
unit : Unit of time to use to calculate the rate of change.
Supports duration literals .
Default is 1s
(per second) .
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply NON_NEGATIVE_DERIVATIVE()
to a field to calculate the per second change
SELECT NON_NEGATIVE_DERIVATIVE ( b ) FROM numbers LIMIT 6
time
non_negative_derivative
2023-01-01T00:01:00Z
0.005011290382993233
2023-01-01T00:03:00Z
0.006832832141810439
2023-01-01T00:04:00Z
0.03085914700123513
2023-01-01T00:08:00Z
0.0227877053636946
2023-01-01T00:10:00Z
0.001676063810538834
2023-01-01T00:11:00Z
0.014999637478226817
Apply NON_NEGATIVE_DERIVATIVE()
to a field to calculate the per 5 minute change
SELECT NON_NEGATIVE_DERIVATIVE ( b , 5 m ) FROM numbers LIMIT 6
time
non_negative_derivative
2023-01-01T00:01:00Z
1.5033871148979698
2023-01-01T00:03:00Z
2.0498496425431316
2023-01-01T00:04:00Z
9.257744100370537
2023-01-01T00:08:00Z
6.836311609108379
2023-01-01T00:10:00Z
0.5028191431616502
2023-01-01T00:11:00Z
4.499891243468045
Apply NON_NEGATIVE_DERIVATIVE()
to each field
SELECT NON_NEGATIVE_DERIVATIVE ( * ) FROM numbers LIMIT 6
time
non_negative_derivative_a
non_negative_derivative_b
2023-01-01T00:01:00Z
0.005011290382993233
2023-01-01T00:03:00Z
0.0030371603214445152
0.006832832141810439
2023-01-01T00:04:00Z
0.03085914700123513
2023-01-01T00:05:00Z
0.00024692132561391543
2023-01-01T00:06:00Z
0.016704951104985283
2023-01-01T00:08:00Z
0.0227877053636946
2023-01-01T00:09:00Z
0.018437240876186967
2023-01-01T00:10:00Z
0.001676063810538834
2023-01-01T00:11:00Z
0.014999637478226817
2023-01-01T00:13:00Z
0.006694752202850366
2023-01-01T00:14:00Z
0.011836797386191167
Apply NON_NEGATIVE_DERIVATIVE()
to field keys that match a regular expression
SELECT NON_NEGATIVE_DERIVATIVE ( / [ ab ] / ) FROM numbers LIMIT 6
time
non_negative_derivative_a
non_negative_derivative_b
2023-01-01T00:01:00Z
0.005011290382993233
2023-01-01T00:03:00Z
0.0030371603214445152
0.006832832141810439
2023-01-01T00:04:00Z
0.03085914700123513
2023-01-01T00:05:00Z
0.00024692132561391543
2023-01-01T00:06:00Z
0.016704951104985283
2023-01-01T00:08:00Z
0.0227877053636946
2023-01-01T00:09:00Z
0.018437240876186967
2023-01-01T00:10:00Z
0.001676063810538834
2023-01-01T00:11:00Z
0.014999637478226817
2023-01-01T00:13:00Z
0.006694752202850366
2023-01-01T00:14:00Z
0.011836797386191167
Apply NON_NEGATIVE_DERIVATIVE()
to time windows (grouped by time)
SELECT
NON_NEGATIVE_DERIVATIVE ( MEAN ( b ), 1 m )
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
non_negative_derivative
2023-01-01T00:20:00Z
0.010434324849926194
2023-01-01T00:40:00Z
0.05657514203880348
2023-01-01T00:50:00Z
0.021317362457152655
NON_NEGATIVE_DIFFERENCE()
Returns only non-negative result of subtraction between subsequent
field values .
Negative differences return null .
NON_NEGATIVE_DIFFERENCE ( field_expression )
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, regular expression, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply NON_NEGATIVE_DIFFERENCE()
to a field
SELECT NON_NEGATIVE_DIFFERENCE ( b ) FROM numbers LIMIT 6
time
non_negative_difference
2023-01-01T00:01:00Z
0.300677422979594
2023-01-01T00:03:00Z
0.40996992850862635
2023-01-01T00:04:00Z
1.8515488200741077
2023-01-01T00:08:00Z
1.367262321821676
2023-01-01T00:10:00Z
0.10056382863233004
2023-01-01T00:11:00Z
0.899978248693609
Apply NON_NEGATIVE_DIFFERENCE()
to each field
SELECT NON_NEGATIVE_DIFFERENCE ( * ) FROM numbers LIMIT 6
time
non_negative_difference_a
non_negative_difference_b
2023-01-01T00:01:00Z
0.300677422979594
2023-01-01T00:03:00Z
0.18222961928667092
0.40996992850862635
2023-01-01T00:04:00Z
1.8515488200741077
2023-01-01T00:05:00Z
0.014815279536834924
2023-01-01T00:06:00Z
1.002297066299117
2023-01-01T00:08:00Z
1.367262321821676
2023-01-01T00:09:00Z
1.106234452571218
2023-01-01T00:10:00Z
0.10056382863233004
2023-01-01T00:11:00Z
0.899978248693609
2023-01-01T00:13:00Z
0.401685132171022
2023-01-01T00:14:00Z
0.71020784317147
Apply NON_NEGATIVE_DIFFERENCE()
to field keys that match a regular expression
SELECT NON_NEGATIVE_DIFFERENCE ( / [ ab ] / ) FROM numbers LIMIT 6
time
non_negative_difference_a
non_negative_difference_b
2023-01-01T00:01:00Z
0.300677422979594
2023-01-01T00:03:00Z
0.18222961928667092
0.40996992850862635
2023-01-01T00:04:00Z
1.8515488200741077
2023-01-01T00:05:00Z
0.014815279536834924
2023-01-01T00:06:00Z
1.002297066299117
2023-01-01T00:08:00Z
1.367262321821676
2023-01-01T00:09:00Z
1.106234452571218
2023-01-01T00:10:00Z
0.10056382863233004
2023-01-01T00:11:00Z
0.899978248693609
2023-01-01T00:13:00Z
0.401685132171022
2023-01-01T00:14:00Z
0.71020784317147
Apply NON_NEGATIVE_DIFFERENCE()
to time windows (grouped by time)
SELECT
NON_NEGATIVE_DIFFERENCE ( MEAN ( b ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
non_negative_difference
2023-01-01T00:20:00Z
0.10434324849926194
2023-01-01T00:40:00Z
0.5657514203880348
2023-01-01T00:50:00Z
0.21317362457152655
POW()
Returns the field value to the power of x
.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
x : Power to raise to.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply POW()
to a field with a power of 3
SELECT
b ,
POW ( b , 3 )
FROM numbers
LIMIT 6
time
b
pow
2023-01-01T00:00:00Z
-0.163643058925645
-0.004382205777325515
2023-01-01T00:01:00Z
0.137034364053949
0.002573288422171338
2023-01-01T00:02:00Z
-0.482943221384294
-0.1126388541916811
2023-01-01T00:03:00Z
-0.0729732928756677
-0.0003885901893904874
2023-01-01T00:04:00Z
1.77857552719844
5.626222933751733
2023-01-01T00:05:00Z
0.741147445214238
0.4071119474284653
Apply POW()
to each field with a power of 5
SELECT POW ( * , 5 ) FROM numbers LIMIT 6
time
pow_a
pow_b
2023-01-01T00:00:00Z
0.004483135555212479
-0.00011735131084020357
2023-01-01T00:01:00Z
-0.2795528536239978
0.000048322282876973225
2023-01-01T00:02:00Z
-0.6628050073932118
-0.026271227986693114
2023-01-01T00:03:00Z
-0.22011853819169455
-0.000002069282189962477
2023-01-01T00:04:00Z
-0.6103699296012646
17.797604890097084
2023-01-01T00:05:00Z
-0.5620694808926487
0.22362640363833164
Apply POW()
to time windows (grouped by time)
SELECT
POW ( SUM ( a ), 10 )
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
pow
2023-01-01T00:00:00Z
2402278.159218532
2023-01-01T00:10:00Z
12.380844221267186
2023-01-01T00:20:00Z
0.000006244365466732681
2023-01-01T00:30:00Z
0.0000046424621235691315
2023-01-01T00:40:00Z
2.7973126174031977e-7
2023-01-01T00:50:00Z
81.6292140233699
ROUND()
Returns a field value rounded to the nearest integer.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply ROUND()
to a field
SELECT
b ,
ROUND ( b )
FROM numbers
LIMIT 6
time
b
round
2023-01-01T00:00:00Z
-0.163643058925645
-0
2023-01-01T00:01:00Z
0.137034364053949
0
2023-01-01T00:02:00Z
-0.482943221384294
-0
2023-01-01T00:03:00Z
-0.0729732928756677
-0
2023-01-01T00:04:00Z
1.77857552719844
2
2023-01-01T00:05:00Z
0.741147445214238
1
Apply ROUND()
to each field
SELECT ROUND ( * ) FROM numbers LIMIT 6
time
round_a
round_b
2023-01-01T00:00:00Z
0
-0
2023-01-01T00:01:00Z
-1
0
2023-01-01T00:02:00Z
-1
-0
2023-01-01T00:03:00Z
-1
-0
2023-01-01T00:04:00Z
-1
2
2023-01-01T00:05:00Z
-1
1
Apply ROUND()
to time windows (grouped by time)
SELECT
ROUND ( SUM ( a ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
round
2023-01-01T00:00:00Z
-4
2023-01-01T00:10:00Z
1
2023-01-01T00:20:00Z
0
2023-01-01T00:30:00Z
0
2023-01-01T00:40:00Z
0
2023-01-01T00:50:00Z
2
SIN()
Returns the sine of a field value.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply SIN()
to a field
SELECT
b ,
SIN ( b )
FROM numbers
LIMIT 6
time
b
sin
2023-01-01T00:00:00Z
-0.163643058925645
-0.1629136686003898
2023-01-01T00:01:00Z
0.137034364053949
0.13660588515594851
2023-01-01T00:02:00Z
-0.482943221384294
-0.4643877941052164
2023-01-01T00:03:00Z
-0.0729732928756677
-0.0729085450859347
2023-01-01T00:04:00Z
1.77857552719844
0.9784914502058565
2023-01-01T00:05:00Z
0.741147445214238
0.6751348197618099
Apply SIN()
to each field
SELECT SIN ( * ) FROM numbers LIMIT 6
time
sin_a
sin_b
2023-01-01T00:00:00Z
0.3326300722640741
-0.1629136686003898
2023-01-01T00:01:00Z
-0.6997047077914582
0.13660588515594851
2023-01-01T00:02:00Z
-0.7962295291135749
-0.4643877941052164
2023-01-01T00:03:00Z
-0.673406844448706
-0.0729085450859347
2023-01-01T00:04:00Z
-0.7870301289278495
0.9784914502058565
2023-01-01T00:05:00Z
-0.7778043295686337
0.6751348197618099
Apply SIN()
to time windows (grouped by time)
SELECT
SIN ( SUM ( a ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
sin
2023-01-01T00:00:00Z
0.933528830283535
2023-01-01T00:10:00Z
0.9597472276784815
2023-01-01T00:20:00Z
0.29712628761434723
2023-01-01T00:30:00Z
0.2887011711003489
2023-01-01T00:40:00Z
0.21934537994884437
2023-01-01T00:50:00Z
0.9998424824522808
SQRT()
Returns the square root of a field value.
Field values must be greater than or equal to 0.
Negative field values return null.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply SQRT()
to a field
SELECT
b ,
SQRT ( b )
FROM numbers
LIMIT 6
time
b
sqrt
2023-01-01T00:00:00Z
-0.163643058925645
2023-01-01T00:01:00Z
0.137034364053949
0.370181528515334
2023-01-01T00:02:00Z
-0.482943221384294
2023-01-01T00:03:00Z
-0.0729732928756677
2023-01-01T00:04:00Z
1.77857552719844
1.3336324558132349
2023-01-01T00:05:00Z
0.741147445214238
0.860899207349059
Apply SQRT()
to each field
SELECT SQRT ( * ) FROM numbers LIMIT 6
time
sqrt_a
sqrt_b
2023-01-01T00:00:00Z
0.5823152811928947
2023-01-01T00:01:00Z
0.370181528515334
2023-01-01T00:02:00Z
2023-01-01T00:03:00Z
2023-01-01T00:04:00Z
1.3336324558132349
2023-01-01T00:05:00Z
0.860899207349059
Apply SQRT()
to time windows (grouped by time)
SELECT
SQRT ( SUM ( a ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
sqrt
2023-01-01T00:00:00Z
2023-01-01T00:10:00Z
1.134063865909604
2023-01-01T00:20:00Z
0.5492555015405052
2023-01-01T00:30:00Z
0.5411746169982342
2023-01-01T00:40:00Z
0.4702589287652642
2023-01-01T00:50:00Z
1.2462130097934059
TAN()
Returns the tangent of the field value.
Arguments
field_expression : Expression to identify one or more fields to operate on.
Can be a field key ,
constant, or wildcard (*
).
Supports numeric field types.
Notable behaviors
Examples
The following examples use the
Random numbers sample data .
Apply TAN()
to a field
SELECT
b ,
TAN ( b )
FROM numbers
LIMIT 6
time
b
tan
2023-01-01T00:00:00Z
-0.163643058925645
-0.16511961248511045
2023-01-01T00:01:00Z
0.137034364053949
0.13789861917955581
2023-01-01T00:02:00Z
-0.482943221384294
-0.5243575352718546
2023-01-01T00:03:00Z
-0.0729732928756677
-0.07310309943905952
2023-01-01T00:04:00Z
1.77857552719844
-4.743341375725582
2023-01-01T00:05:00Z
0.741147445214238
0.9151958486043346
Apply TAN()
to each field
SELECT TAN ( * ) FROM numbers LIMIT 6
time
tan_a
tan_b
2023-01-01T00:00:00Z
0.3527145610408791
-0.16511961248511045
2023-01-01T00:01:00Z
-0.9793857830953787
0.13789861917955581
2023-01-01T00:02:00Z
-1.3160934857179802
-0.5243575352718546
2023-01-01T00:03:00Z
-0.9109052733075013
-0.07310309943905952
2023-01-01T00:04:00Z
-1.2757522322802637
-4.743341375725582
2023-01-01T00:05:00Z
-1.2375438046768912
0.9151958486043346
Apply TAN()
to time windows (grouped by time)
SELECT
TAN ( SUM ( a ))
FROM numbers
WHERE
time >= '2023-01-01T00:00:00Z'
AND time < '2023-01-01T01:00:00Z'
GROUP BY time ( 10 m )
time
tan
2023-01-01T00:00:00Z
-2.603968631156288
2023-01-01T00:10:00Z
3.4171098358131733
2023-01-01T00:20:00Z
0.31117972731464494
2023-01-01T00:30:00Z
0.30154101138968664
2023-01-01T00:40:00Z
0.22482036866737865
2023-01-01T00:50:00Z
56.3338223288096
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Thank you for being part of our community!
We welcome and encourage your feedback and bug reports for InfluxDB and this documentation.
To find support, use the following resources:
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