This is archived documentation for InfluxData product versions that are no longer maintained. For newer documentation, see the latest InfluxData documentation.
InfluxQL is an SQL-like query language for interacting with data in InfluxDB.
The following sections detail InfluxQL’s SELECT statement and useful query syntax
for exploring your data.
| The Basics: | Limit and Sort Query Results: | General Tips on Query Syntax: |
| The SELECT statement | ORDER BY time DESC | Time Syntax |
| The WHERE clause | The LIMIT and SLIMIT Clauses | Regular Expressions |
| The GROUP BY clause | The OFFSET and SOFFSET Clauses | Data Types and Cast Operations |
| The INTO clause | Merge Behavior | |
| Multiple Statements | ||
| Subqueries |
Sample Data
This document uses publicly available data from the National Oceanic and Atmospheric Administration’s (NOAA) Center for Operational Oceanographic Products and Services. See the Sample Data page to download the data and follow along with the example queries in the sections below.
Start by logging into the Influx CLI:
$ influx -precision rfc3339 -database NOAA_water_database
Connected to http://localhost:8086 version 1.2.x
InfluxDB shell 1.2.x
>Next, get acquainted with this subsample of the data in the h2o_feet measurement:
name: h2o_feet
-———————————–
time
level description
location
water_level
2015-08-18T00:00:00Z
between 6 and 9 feet
coyote_creek
8.12
2015-08-18T00:00:00Z
below 3 feet
santa_monica
2.064
2015-08-18T00:06:00Z
between 6 and 9 feet
coyote_creek
8.005
2015-08-18T00:06:00Z
below 3 feet
santa_monica
2.116
2015-08-18T00:12:00Z
between 6 and 9 feet
coyote_creek
7.887
2015-08-18T00:12:00Z
below 3 feet
santa_monica
2.028
The data in the h2o_feet measurement
occur at six-minute time intervals.
The measurement has one tag key
(location) which has two tag values:
coyote_creek and santa_monica.
The measurement also has two fields:
level description stores string field values
and water_level stores float field values.
All of these data is in the NOAA_water_database database.
Disclaimer: The
level descriptionfield isn’t part of the original NOAA data - we snuck it in there for the sake of having a field key with a special character and string field values.
The basic SELECT statement
The SELECT statement queries data from a particular measurement or measurements.
Tired of reading? Check out this InfluxQL Short:
Syntax
SELECT <field_key>[,<field_key>,<tag_key>] FROM <measurement_name>[,<measurement_name>]Description of Syntax
The SELECT statement requires a SELECT clause and a FROM clause.
SELECT clause
The SELECT clause supports several formats for specifying data:
SELECT *
Returns all fields and tags.
SELECT "<field_key>"
Returns a specific field.
SELECT "<field_key>","<field_key>"
Returns more than one field.
SELECT "<field_key>","<tag_key>"
Returns a specific field and tag.
The SELECT clause must specify at least one field when it includes a tag.
SELECT "<field_key>"::field,"<tag_key>"::tag
Returns a specific field and tag.
The ::[field | tag] syntax specifies the identifier’s type.
Use this syntax to differentiate between field keys and tag keys that have the same name.
Other supported features: Arithmetic Operations, Functions, Basic Cast Operations, Regular Expressions
FROM clause
The FROM clause supports several formats for specifying a measurement(s):
FROM <measurement_name>
Returns data from a single measurement.
If you’re using the CLI InfluxDB queries the measurement in the
USEd
database and the DEFAULT retention policy.
If you’re using the HTTP API InfluxDB queries the
measurement in the database specified in the db query string parameter
and the DEFAULT retention policy.
FROM <measurement_name>,<measurement_name>
Returns data from more than one measurement.
FROM <database_name>.<retention_policy_name>.<measurement_name>
Returns data from a fully qualified measurement.
Fully qualify a measurement by specifying its database and retention policy.
FROM <database_name>..<measurement_name>
Returns data from a measurement in a user-specified database and the DEFAULT
retention policy.
Other supported features: Regular Expressions
Quoting
Identifiers must be double quoted if they contain characters other than [A-z,0-9,_], if they
begin with a digit, or if they are an InfluxQL keyword.
While not always necessary, we recommend that you double quote identifiers.
Note: The quoting syntax for queries differs from the line protocol. Please review the rules for single and double-quoting in queries.
Examples
Example 1: Select all fields and tags from a single measurement
> SELECT * FROM "h2o_feet"
name: h2o_feet
--------------
time level description location water_level
2015-08-18T00:00:00Z below 3 feet santa_monica 2.064
2015-08-18T00:00:00Z between 6 and 9 feet coyote_creek 8.12
[...]
2015-09-18T21:36:00Z between 3 and 6 feet santa_monica 5.066
2015-09-18T21:42:00Z between 3 and 6 feet santa_monica 4.938
The query selects all fields and
tags from the h2o_feet
measurement.
If you’re using the CLI be sure to enter
USE NOAA_water_database before you run the query.
The CLI queries the data in the USEd database and the
DEFAULT retention policy.
If you’re using the HTTP API be sure to set the
db query string parameter
to NOAA_water_database.
If you do not set the rp query string parameter, the HTTP API automatically
queries the database’s DEFAULT retention policy.
Example 2: Select specific tags and fields from a single measurement
> SELECT "level description","location","water_level" FROM "h2o_feet"
name: h2o_feet
--------------
time level description location water_level
2015-08-18T00:00:00Z below 3 feet santa_monica 2.064
2015-08-18T00:00:00Z between 6 and 9 feet coyote_creek 8.12
[...]
2015-09-18T21:36:00Z between 3 and 6 feet santa_monica 5.066
2015-09-18T21:42:00Z between 3 and 6 feet santa_monica 4.938
The query selects the level description field, the location tag, and the
water_level field.
Note that the SELECT clause must specify at least one field when it includes
a tag.
Example 3: Select specific tags and fields from a single measurement, and provide their identifier type
> SELECT "level description"::field,"location"::tag,"water_level"::field FROM "h2o_feet"
name: h2o_feet
--------------
time level description location water_level
2015-08-18T00:00:00Z below 3 feet santa_monica 2.064
2015-08-18T00:00:00Z between 6 and 9 feet coyote_creek 8.12
[...]
2015-09-18T21:36:00Z between 3 and 6 feet santa_monica 5.066
2015-09-18T21:42:00Z between 3 and 6 feet santa_monica 4.938
The query selects the level description field, the location tag, and the
water_level field from the h2o_feet measurement.
The ::[field | tag] syntax specifies if the
identifier is a field or tag.
Use ::[field | tag] to differentiate between an identical field key and tag key .
That syntax is not required for most use cases.
Example 4: Select all fields from a single measurement
> SELECT *::field FROM "h2o_feet"
name: h2o_feet
--------------
time level description water_level
2015-08-18T00:00:00Z below 3 feet 2.064
2015-08-18T00:00:00Z between 6 and 9 feet 8.12
[...]
2015-09-18T21:36:00Z between 3 and 6 feet 5.066
2015-09-18T21:42:00Z between 3 and 6 feet 4.938
The query selects all fields from the h2o_feet measurement.
The SELECT clause supports combining the * syntax with the :: syntax.
Example 5: Select a specific field from a measurement and perform basic arithmetic
> SELECT ("water_level" * 2) + 4 from "h2o_feet"
name: h2o_feet
--------------
time water_level
2015-08-18T00:00:00Z 20.24
2015-08-18T00:00:00Z 8.128
[...]
2015-09-18T21:36:00Z 14.132
2015-09-18T21:42:00Z 13.876
The query multiplies water_level’s field values by two and adds four to those
values.
Note that InfluxDB follows the standard order of operations.
See Mathematical Operators
for more on supported operators.
Example 6: Select all data from more than one measurement
> SELECT * FROM "h2o_feet","h2o_pH"
name: h2o_feet
--------------
time level description location pH water_level
2015-08-18T00:00:00Z below 3 feet santa_monica 2.064
2015-08-18T00:00:00Z between 6 and 9 feet coyote_creek 8.12
[...]
2015-09-18T21:36:00Z between 3 and 6 feet santa_monica 5.066
2015-09-18T21:42:00Z between 3 and 6 feet santa_monica 4.938
name: h2o_pH
------------
time level description location pH water_level
2015-08-18T00:00:00Z santa_monica 6
2015-08-18T00:00:00Z coyote_creek 7
[...]
2015-09-18T21:36:00Z santa_monica 8
2015-09-18T21:42:00Z santa_monica 7
The query selects all fields and tags from two measurements: h2o_feet and
h2o_pH.
Separate multiple measurements with a comma (,).
Example 7: Select all data from a fully qualified measurement
> SELECT * FROM "NOAA_water_database"."autogen"."h2o_feet"
name: h2o_feet
--------------
time level description location water_level
2015-08-18T00:00:00Z below 3 feet santa_monica 2.064
2015-08-18T00:00:00Z between 6 and 9 feet coyote_creek 8.12
[...]
2015-09-18T21:36:00Z between 3 and 6 feet santa_monica 5.066
2015-09-18T21:42:00Z between 3 and 6 feet santa_monica 4.938
The query selects data in the NOAA_water_database, the autogen retention
policy, and the measurement h2o_feet.
In the CLI, fully qualify a measurement to query data in a database other
than the USEd database and in a retention policy other than the
DEFAULT retention policy.
In the HTTP API, fully qualify a measurement in place of using the db
and rp query string parameters if desired.
Example 8: Select all data from a measurement in a particular database
> SELECT * FROM "NOAA_water_database".."h2o_feet"
name: h2o_feet
--------------
time level description location water_level
2015-08-18T00:00:00Z below 3 feet santa_monica 2.064
2015-08-18T00:00:00Z between 6 and 9 feet coyote_creek 8.12
[...]
2015-09-18T21:36:00Z between 3 and 6 feet santa_monica 5.066
2015-09-18T21:42:00Z between 3 and 6 feet santa_monica 4.938
The query selects data in the NOAA_water_database, the DEFAULT retention
policy, and the h2o_feet measurement.
The .. indicates the DEFAULT retention policy for the specified database.
In the CLI, specify the database to query data in a database other than the
USEd database.
In the HTTP API, specify the database in place of using the db query
string parameter if desired.
Common Issues with the SELECT statement
Issue 1: Selecting tag keys in the SELECT clause
A query requires at least one field key
in the SELECT clause to return data.
If the SELECT clause only includes a single tag key or several tag keys, the
query returns an empty response.
This behavior is a result of how the system stores data.
Example
The following query returns no data because it specifies a single tag key (location) in
the SELECT clause:
> SELECT "location" FROM "h2o_feet"
>
To return any data associated with the location tag key, the query’s SELECT
clause must include at least one field key (water_level):
> SELECT "water_level","location" FROM "h2o_feet" LIMIT 3
name: h2o_feet
time water_level location
---- ----------- --------
2015-08-18T00:00:00Z 8.12 coyote_creek
2015-08-18T00:00:00Z 2.064 santa_monica
[...]
2015-09-18T21:36:00Z 5.066 santa_monica
2015-09-18T21:42:00Z 4.938 santa_monica
The WHERE clause
The WHERE filters data based on
fields,
tags, and/or
timestamps.
Tired of reading? Check out this InfluxQL Short:
Syntax
SELECT_clause FROM_clause WHERE <conditional_expression> [(AND|OR) <conditional_expression> [...]]
Description of Syntax
The WHERE clause supports conditional_expressions on fields, tags, and
timestamps.
fields
field_key <operator> ['string' | boolean | float | integer]
The WHERE clause supports comparisons against string, boolean, float,
and integer field values.
Single quote string field values in the WHERE clause.
Queries with unquoted string field values or double quoted string field values
will not return any data and, in most cases,
will not return an error.
Supported operators:= equal to<> not equal to!= not equal to> greater than>= greater than or equal to< less than<= less than or equal to
Other supported features: Arithmetic Operations, Regular Expressions
tags
tag_key <operator> ['tag_value']
Single quote tag values in
the WHERE clause.
Queries with unquoted tag values or double quoted tag values will not return
any data and, in most cases,
will not return an error.
Supported operators:= equal to<> not equal to!= not equal to
Other supported features: Regular Expressions
timestamps
For most SELECT statements, the default time range is between 1677-09-21 00:12:43.145224194 and 2262-04-11T23:47:16.854775806Z UTC.
For SELECT statements with a GROUP BY time() clause, the default time
range is between 1677-09-21 00:12:43.145224194 UTC and now().
The Time Syntax section on this page
details how to specify alternative time ranges in the WHERE clause.
Examples
Example 1: Select data that have specific field key-values
> SELECT * FROM "h2o_feet" WHERE "water_level" > 8
name: h2o_feet
--------------
time level description location water_level
2015-08-18T00:00:00Z between 6 and 9 feet coyote_creek 8.12
2015-08-18T00:06:00Z between 6 and 9 feet coyote_creek 8.005
[...]
2015-09-18T00:12:00Z between 6 and 9 feet coyote_creek 8.189
2015-09-18T00:18:00Z between 6 and 9 feet coyote_creek 8.084
The query returns data from the h2o_feet
measurement with
field values of water_level
that are greater than eight.
Example 2: Select data that have a specific string field key-value
> SELECT * FROM "h2o_feet" WHERE "level description" = 'below 3 feet'
name: h2o_feet
--------------
time level description location water_level
2015-08-18T00:00:00Z below 3 feet santa_monica 2.064
2015-08-18T00:06:00Z below 3 feet santa_monica 2.116
[...]
2015-09-18T14:06:00Z below 3 feet santa_monica 2.999
2015-09-18T14:36:00Z below 3 feet santa_monica 2.907
The query returns data from the h2o_feet measurement with field values of
level description that equal the below 3 feet string.
InfluxQL requires single quotes around string field values in the WHERE
clause.
Example 3: Select data that have a specific field key-value and perform basic arithmetic
> SELECT * FROM "h2o_feet" WHERE "water_level" + 2 > 11.9
name: h2o_feet
--------------
time level description location water_level
2015-08-29T07:06:00Z at or greater than 9 feet coyote_creek 9.902
2015-08-29T07:12:00Z at or greater than 9 feet coyote_creek 9.938
2015-08-29T07:18:00Z at or greater than 9 feet coyote_creek 9.957
2015-08-29T07:24:00Z at or greater than 9 feet coyote_creek 9.964
2015-08-29T07:30:00Z at or greater than 9 feet coyote_creek 9.954
2015-08-29T07:36:00Z at or greater than 9 feet coyote_creek 9.941
2015-08-29T07:42:00Z at or greater than 9 feet coyote_creek 9.925
2015-08-29T07:48:00Z at or greater than 9 feet coyote_creek 9.902
2015-09-02T23:30:00Z at or greater than 9 feet coyote_creek 9.902
The query returns data from the h2o_feet measurement with field values of
water_level plus two that are greater than 11.9.
Note that InfluxDB follows the standard order of operations
See Mathematical Operators
for more on supported operators.
Example 4: Select data that have a specific tag key-value
> SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica'
name: h2o_feet
--------------
time water_level
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
[...]
2015-09-18T21:36:00Z 5.066
2015-09-18T21:42:00Z 4.938
The query returns data from the h2o_feet measurement where the
tag key location is set to santa_monica.
InfluxQL requires single quotes around tag values in the WHERE clause.
Example 5: Select data that have specific field key-values and tag key-values
> SELECT "water_level" FROM "h2o_feet" WHERE "location" <> 'santa_monica' AND (water_level < -0.59 OR water_level > 9.95)
name: h2o_feet
--------------
time water_level
2015-08-29T07:18:00Z 9.957
2015-08-29T07:24:00Z 9.964
2015-08-29T07:30:00Z 9.954
2015-08-29T14:30:00Z -0.61
2015-08-29T14:36:00Z -0.591
2015-08-30T15:18:00Z -0.594
The query returns data from the h2o_feet measurement where the tag key
location is not set to santa_monica and where the field values of
water_level are either less than -0.59 or greater than 9.95.
The WHERE clause supports the operators AND and OR, and supports
separating logic with parentheses.
Example 6: Select data that have specific timestamps
> SELECT * FROM "h2o_feet" WHERE time > now() - 7d
The query returns data from the h2o_feet measurement that have timestamps
within the past seven days.
The Time Syntax section on this page
offers in-depth information on supported time syntax in the WHERE clause.
Common Issues with the WHERE Clause
Issue 1: A WHERE clause query unexpectedly returns no data
In most cases, this issue is the result of missing single quotes around tag values or string field values. Queries with unquoted or double quoted tag values or string field values will not return any data and, in most cases, will not return an error.
The first two queries in the code block below attempt to specify the tag value
santa_monica without any quotes and with double quotes.
Those queries return no results.
The third query single quotes santa_monica (this is the supported syntax)
and returns the expected results.
> SELECT "water_level" FROM "h2o_feet" WHERE "location" = santa_monica
> SELECT "water_level" FROM "h2o_feet" WHERE "location" = "santa_monica"
> SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica'
name: h2o_feet
--------------
time water_level
2015-08-18T00:00:00Z 2.064
[...]
2015-09-18T21:42:00Z 4.938
The first two queries in the code block below attempt to specify the string
field value at or greater than 9 feet without any quotes and with double
quotes.
The first query returns an error because the string field value includes
white spaces.
The second query returns no results.
The third query single quotes at or greater than 9 feet (this is the
supported syntax) and returns the expected results.
> SELECT "level description" FROM "h2o_feet" WHERE "level description" = at or greater than 9 feet
ERR: error parsing query: found than, expected ; at line 1, char 86
> SELECT "level description" FROM "h2o_feet" WHERE "level description" = "at or greater than 9 feet"
> SELECT "level description" FROM "h2o_feet" WHERE "level description" = 'at or greater than 9 feet'
name: h2o_feet
--------------
time level description
2015-08-26T04:00:00Z at or greater than 9 feet
[...]
2015-09-15T22:42:00Z at or greater than 9 feet
The GROUP BY clause
The GROUP BY clause groups query results by a user-specified
set of tags or a time interval.
| GROUP BY tags | |||
| GROUP BY time intervals: | Basic Syntax | Advanced Syntax | GROUP BY time intervals and fill() |
GROUP BY tags
GROUP BY <tag> queries group query results by a user-specified set of tags.
Tired of reading? Check out this InfluxQL Short:
Syntax
SELECT_clause FROM_clause [WHERE_clause] GROUP BY [* | <tag_key>[,<tag_key]]
Description of Syntax
GROUP BY *
Groups results by all tags
GROUP BY <tag_key>
Groups results by a specific tag
GROUP BY <tag_key>,<tag_key>
Groups results by more than one tag.
The order of the tag keys is irrelevant.
If the query includes a WHERE clause the GROUP BY
clause must appear after the WHERE clause.
Other supported features: Regular Expressions
Examples
Example 1: Group query results by a single tag
> SELECT MEAN("water_level") FROM "h2o_feet" GROUP BY "location"
name: h2o_feet
tags: location=coyote_creek
time mean
---- ----
1970-01-01T00:00:00Z 5.359342451341401
name: h2o_feet
tags: location=santa_monica
time mean
---- ----
1970-01-01T00:00:00Z 3.530863470081006
The query uses an InfluxQL function
to calculate the average water_level for each
tag value of location in
the h2o_feet measurement.
InfluxDB returns results in two series: one for each tag value of location.
Note: In InfluxDB, epoch 0 (
1970-01-01T00:00:00Z) is often used as a null timestamp equivalent. If you request a query that has no timestamp to return, such as an aggregation function with an unbounded time range, InfluxDB returns epoch 0 as the timestamp.
Example 2: Group query results by more than one tag
> SELECT MEAN("index") FROM "h2o_quality" GROUP BY location,randtag
name: h2o_quality
tags: location=coyote_creek, randtag=1
time mean
---- ----
1970-01-01T00:00:00Z 50.69033760186263
name: h2o_quality
tags: location=coyote_creek, randtag=2
time mean
---- ----
1970-01-01T00:00:00Z 49.661867544220485
name: h2o_quality
tags: location=coyote_creek, randtag=3
time mean
---- ----
1970-01-01T00:00:00Z 49.360939907550076
name: h2o_quality
tags: location=santa_monica, randtag=1
time mean
---- ----
1970-01-01T00:00:00Z 49.132712456344585
name: h2o_quality
tags: location=santa_monica, randtag=2
time mean
---- ----
1970-01-01T00:00:00Z 50.2937984496124
name: h2o_quality
tags: location=santa_monica, randtag=3
time mean
---- ----
1970-01-01T00:00:00Z 49.99919903884662
The query uses an InfluxQL function to calculate the average index for
each combination of the location tag and the randtag tag in the
h2o_quality measurement.
Separate multiple tags with a comma in the GROUP BY clause.
Example 3: Group query results by all tags
> SELECT MEAN("index") FROM "h2o_quality" GROUP BY *
name: h2o_quality
tags: location=coyote_creek, randtag=1
time mean
---- ----
1970-01-01T00:00:00Z 50.55405446521169
name: h2o_quality
tags: location=coyote_creek, randtag=2
time mean
---- ----
1970-01-01T00:00:00Z 50.49958856271162
name: h2o_quality
tags: location=coyote_creek, randtag=3
time mean
---- ----
1970-01-01T00:00:00Z 49.5164137518956
name: h2o_quality
tags: location=santa_monica, randtag=1
time mean
---- ----
1970-01-01T00:00:00Z 50.43829082296367
name: h2o_quality
tags: location=santa_monica, randtag=2
time mean
---- ----
1970-01-01T00:00:00Z 52.0688508894012
name: h2o_quality
tags: location=santa_monica, randtag=3
time mean
---- ----
1970-01-01T00:00:00Z 49.29386362086556
The query uses an InfluxQL function
to calculate the average index for every possible
tag combination in the h2o_quality
measurement.
Note that the query results are identical to the results of the query in Example 2
where we explicitly specified the location and randtag tag keys.
This is because the h2o_quality measurement only has two tag keys.
GROUP BY time intervals
GROUP BY time() queries group query results by a user-specified time interval.
Basic GROUP BY time() Syntax
Syntax
SELECT <function>(<field_key>) FROM_clause WHERE <time_range> GROUP BY time(<time_interval>),[tag_key] [fill(<fill_option>)]
Description of Basic Syntax
Basic GROUP BY time() queries require an InfluxQL function
in the SELECT clause and a time range in the
WHERE clause.
Note that the GROUP BY clause must come after the WHERE clause.
time(time_interval)
The time_interval in the GROUP BY time() clause is a
duration literal.
It determines how InfluxDB groups query results over time.
For example, a time_interval of 5m groups query results into five-minute
time groups across the time range specified in the WHERE clause.
fill(<fill_option>)
fill(<fill_option>) is optional.
It changes the value reported for time intervals that have no data.
See GROUP BY time intervals and fill()
for more information.
Coverage:
Basic GROUP BY time() queries rely on the time_interval and on InfluxDB’s
preset time boundaries to determine the raw data included in each time interval
and the timestamps returned by the query.
Examples of Basic Syntax
The examples below use the following subsample of the sample data:
> SELECT "water_level","location" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z'
name: h2o_feet
--------------
time water_level location
2015-08-18T00:00:00Z 8.12 coyote_creek
2015-08-18T00:00:00Z 2.064 santa_monica
2015-08-18T00:06:00Z 8.005 coyote_creek
2015-08-18T00:06:00Z 2.116 santa_monica
2015-08-18T00:12:00Z 7.887 coyote_creek
2015-08-18T00:12:00Z 2.028 santa_monica
2015-08-18T00:18:00Z 7.762 coyote_creek
2015-08-18T00:18:00Z 2.126 santa_monica
2015-08-18T00:24:00Z 7.635 coyote_creek
2015-08-18T00:24:00Z 2.041 santa_monica
2015-08-18T00:30:00Z 7.5 coyote_creek
2015-08-18T00:30:00Z 2.051 santa_monica
Example 1: Group query results into 12 minute intervals
> SELECT COUNT("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m)
name: h2o_feet
--------------
time count
2015-08-18T00:00:00Z 2
2015-08-18T00:12:00Z 2
2015-08-18T00:24:00Z 2
The query uses an InfluxQL function
to count the number of water_level points with the tag
location = coyote_creek and it group results into 12 minute intervals.
The result for each timestamp
represents a single 12 minute interval.
The count for the first timestamp covers the raw data between 2015-08-18T00:00:00Z
and up to, but not including, 2015-08-18T00:12:00Z.
The count for the second timestamp covers the raw data between 2015-08-18T00:12:00Z
and up to, but not including, 2015-08-18T00:24:00Z.
Example 2: Group query results into 12 minutes intervals and by a tag key
> SELECT COUNT("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m),"location"
name: h2o_feet
tags: location=coyote_creek
time count
---- -----
2015-08-18T00:00:00Z 2
2015-08-18T00:12:00Z 2
2015-08-18T00:24:00Z 2
name: h2o_feet
tags: location=santa_monica
time count
---- -----
2015-08-18T00:00:00Z 2
2015-08-18T00:12:00Z 2
2015-08-18T00:24:00Z 2
The query uses an InfluxQL function
to count the number of water_level points.
It groups results by the location tag and into 12 minute intervals.
Note that the time interval and the tag key are separated by a comma in the
GROUP BY clause.
The query returns two series of results: one for each
tag value of the location tag.
The result for each timestamp represents a single 12 minute interval.
The count for the first timestamp covers the raw data between 2015-08-18T00:00:00Z
and up to, but not including, 2015-08-18T00:12:00Z.
The count for the second timestamp covers the raw data between 2015-08-18T00:12:00Z
and up to, but not including, 2015-08-18T00:24:00Z.
Common Issues with Basic Syntax
Issue 1: Unexpected timestamps and values in query results
With the basic syntax, InfluxDB relies on the GROUP BY time() interval
and on the system’s preset time boundaries to determine the raw data included
in each time interval and the timestamps returned by the query.
In some cases, this can lead to unexpected results.
Example
Raw data:
> SELECT "water_level" FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:18:00Z'
name: h2o_feet
--------------
time water_level
2015-08-18T00:00:00Z 8.12
2015-08-18T00:06:00Z 8.005
2015-08-18T00:12:00Z 7.887
2015-08-18T00:18:00Z 7.762
Query and Results:
The following query covers a 12-minute time range and groups results into 12-minute time intervals, but it returns two results:
> SELECT COUNT("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-08-18T00:06:00Z' AND time < '2015-08-18T00:18:00Z' GROUP BY time(12m)
name: h2o_feet
time count
---- -----
2015-08-18T00:00:00Z 1 <----- Note that this timestamp occurs before the start of the query's time range
2015-08-18T00:12:00Z 1
Explanation:
InfluxDB uses preset round-number time boundaries for GROUP BY intervals that are
independent of any time conditions in the WHERE clause.
When it calculates the results, all returned data must occur within the query’s
explicit time range but the GROUP BY intervals will be based on the preset
time boundaries.
The table below shows the preset time boundary, the relevant GROUP BY time() interval, the
points included, and the returned timestamp for each GROUP BY time()
interval in the results.
| Time Interval Number | Preset Time Boundary | GROUP BY time() Interval | Points Included | Returned Timestamp |
|---|---|---|---|---|
| 1 | time >= 2015-08-18T00:00:00Z AND time < 2015-08-18T00:12:00Z | time >= 2015-08-18T00:06:00Z AND time < 2015-08-18T00:12:00Z | 8.005 | 2015-08-18T00:00:00Z |
| 2 | time >= 2015-08-12T00:12:00Z AND time < 2015-08-18T00:24:00Z | time >= 2015-08-12T00:12:00Z AND time < 2015-08-18T00:18:00Z | 7.887 | 2015-08-18T00:12:00Z |
The first preset 12-minute time boundary begins at 00:00 and ends just before
00:12.
Only one raw point (8.005) falls both within the query’s first GROUP BY time() interval and in that
first time boundary.
Note that while the returned timestamp occurs before the start of the query’s time range,
the query result excludes data that occur before the query’s time range.
The second preset 12-minute time boundary begins at 00:12 and ends just before
00:24.
Only one raw point (7.887) falls both within the query’s second GROUP BY time() interval and in that
second time boundary.
The advanced GROUP BY time() syntax allows users to shift
the start time of InfluxDB’s preset time boundaries.
Example 3
in the Advanced Syntax section continues with the query shown here;
it shifts forward the preset time boundaries by six minutes such that
InfluxDB returns:
name: h2o_feet
time count
---- -----
2015-08-18T00:06:00Z 2
Advanced GROUP BY time() Syntax
Syntax
SELECT <function>(<field_key>) FROM_clause WHERE <time_range> GROUP BY time(<time_interval>,<offset_interval>),[tag_key] [fill(<fill_option>)]
Description of Advanced Syntax
Advanced GROUP BY time() queries require an InfluxQL function
in the SELECT clause and a time range in the
WHERE clause.
Note that the GROUP BY clause must come after the WHERE clause.
time(time_interval,offset_interval)
See the Basic GROUP BY time() Syntax
for details on the time_interval.
The offset_interval is a
duration literal.
It shifts forward or back InfluxDB’s preset time boundaries.
The offset_interval can be positive or negative.
fill(<fill_option>)
fill(<fill_option>) is optional.
It changes the value reported for time intervals that have no data.
See GROUP BY time intervals and fill()
for more information.
Coverage:
Advanced GROUP BY time() queries rely on the time_interval, the offset_interval
, and on InfluxDB’s preset time boundaries to determine the raw data included in each time interval
and the timestamps returned by the query.
Examples of Advanced Syntax
The examples below use the following subsample of the sample data:
> SELECT "water_level" FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:54:00Z'
name: h2o_feet
--------------
time water_level
2015-08-18T00:00:00Z 8.12
2015-08-18T00:06:00Z 8.005
2015-08-18T00:12:00Z 7.887
2015-08-18T00:18:00Z 7.762
2015-08-18T00:24:00Z 7.635
2015-08-18T00:30:00Z 7.5
2015-08-18T00:36:00Z 7.372
2015-08-18T00:42:00Z 7.234
2015-08-18T00:48:00Z 7.11
2015-08-18T00:54:00Z 6.982
Example 1: Group query results into 18 minute intervals and shift the preset time boundaries forward
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-08-18T00:06:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(18m,6m)
name: h2o_feet
time mean
---- ----
2015-08-18T00:06:00Z 7.884666666666667
2015-08-18T00:24:00Z 7.502333333333333
2015-08-18T00:42:00Z 7.108666666666667
The query uses an InfluxQL function
to calculate the average water_level, grouping results into 18 minute
time intervals, and offsetting the preset time boundaries by six minutes.
The time boundaries and returned timestamps for the query without the offset_interval adhere to InfluxDB’s preset time boundaries. Let’s first examine the results without the offset:
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-08-18T00:06:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(18m)
name: h2o_feet
time mean
---- ----
2015-08-18T00:00:00Z 7.946
2015-08-18T00:18:00Z 7.6323333333333325
2015-08-18T00:36:00Z 7.238666666666667
2015-08-18T00:54:00Z 6.982
The time boundaries and returned timestamps for the query without the
offset_interval adhere to InfluxDB’s preset time boundaries:
| Time Interval Number | Preset Time Boundary | GROUP BY time() Interval | Points Included | Returned Timestamp |
|---|---|---|---|---|
| 1 | time >= 2015-08-18T00:00:00Z AND time < 2015-08-18T00:18:00Z | time >= 2015-08-18T00:06:00Z AND time < 2015-08-18T00:18:00Z | 8.005,7.887 | 2015-08-18T00:00:00Z |
| 2 | time >= 2015-08-18T00:18:00Z AND time < 2015-08-18T00:36:00Z | <— same | 7.762,7.635,7.5 | 2015-08-18T00:18:00Z |
| 3 | time >= 2015-08-18T00:36:00Z AND time < 2015-08-18T00:54:00Z | <— same | 7.372,7.234,7.11 | 2015-08-18T00:36:00Z |
| 4 | time >= 2015-08-18T00:54:00Z AND time < 2015-08-18T01:12:00Z | time = 2015-08-18T00:54:00Z | 6.982 | 2015-08-18T00:54:00Z |
The first preset 18-minute time boundary begins at 00:00 and ends just before
00:18.
Two raw points (8.005 and 7.887) fall both within the first GROUP BY time() interval and in that
first time boundary.
Note that while the returned timestamp occurs before the start of the query’s time range,
the query result excludes data that occur before the query’s time range.
The second preset 18-minute time boundary begins at 00:18 and ends just before
00:36.
Three raw points (7.762 and 7.635 and 7.5) fall both within the second GROUP BY time() interval and in that
second time boundary. In this case, the boundary time range and the interval’s time range are the same.
The fourth preset 18-minute time boundary begins at 00:54 and ends just before
1:12:00.
One raw point (6.982) falls both within the fourth GROUP BY time() interval and in that
fourth time boundary.
The time boundaries and returned timestamps for the query with the
offset_interval adhere to the offset time boundaries:
| Time Interval Number | Offset Time Boundary | GROUP BY time() Interval | Points Included | Returned Timestamp |
|---|---|---|---|---|
| 1 | time >= 2015-08-18T00:06:00Z AND time < 2015-08-18T00:24:00Z | <— same | 8.005,7.887,7.762 | 2015-08-18T00:06:00Z |
| 2 | time >= 2015-08-18T00:24:00Z AND time < 2015-08-18T00:42:00Z | <— same | 7.635,7.5,7.372 | 2015-08-18T00:24:00Z |
| 3 | time >= 2015-08-18T00:42:00Z AND time < 2015-08-18T01:00:00Z | <— same | 7.234,7.11,6.982 | 2015-08-18T00:42:00Z |
| 4 | time >= 2015-08-18T01:00:00Z AND time < 2015-08-18T01:18:00Z | NA | NA | NA |
The six-minute offset interval shifts forward the preset boundary’s time range
such that the boundary time ranges and the relevant GROUP BY time() interval time ranges are
always the same.
With the offset, each interval performs the calculation on three points, and
the timestamp returned matches both the start of the boundary time range and the
start of the GROUP BY time() interval time range.
Note that offset_interval forces the fourth time boundary to be outside
the query’s time range so the query returns no results for that last interval.
Example 2: Group query results into 12 minute intervals and shift the preset time boundaries back
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-08-18T00:06:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(18m,-12m)
name: h2o_feet
time mean
---- ----
2015-08-18T00:06:00Z 7.884666666666667
2015-08-18T00:24:00Z 7.502333333333333
2015-08-18T00:42:00Z 7.108666666666667
The query uses an InfluxQL function
to calculate the average water_level, grouping results into 18 minute
time intervals, and offsetting the preset time boundaries by -12 minutes.
Note: The query in Example 2 returns the same results as the query in Example 1, but the query in Example 2 uses a negative
offset_intervalinstead of a positiveoffset_interval. There are no performance differences between the two queries; feel free to choose the most intuitive option when deciding between a positive and negativeoffset_interval.
The time boundaries and returned timestamps for the query without the offset_interval adhere to InfluxDB’s preset time boundaries. Let’s first examine the results without the offset:
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-08-18T00:06:00Z' AND time <= '2015-08-18T00:54:00Z' GROUP BY time(18m)
name: h2o_feet
time mean
---- ----
2015-08-18T00:00:00Z 7.946
2015-08-18T00:18:00Z 7.6323333333333325
2015-08-18T00:36:00Z 7.238666666666667
2015-08-18T00:54:00Z 6.982
The time boundaries and returned timestamps for the query without the
offset_interval adhere to InfluxDB’s preset time boundaries:
| Time Interval Number | Preset Time Boundary | GROUP BY time() Interval | Points Included | Returned Timestamp |
|---|---|---|---|---|
| 1 | time >= 2015-08-18T00:00:00Z AND time < 2015-08-18T00:18:00Z | time >= 2015-08-18T00:06:00Z AND time < 2015-08-18T00:18:00Z | 8.005,7.887 | 2015-08-18T00:00:00Z |
| 2 | time >= 2015-08-18T00:18:00Z AND time < 2015-08-18T00:36:00Z | <— same | 7.762,7.635,7.5 | 2015-08-18T00:18:00Z |
| 3 | time >= 2015-08-18T00:36:00Z AND time < 2015-08-18T00:54:00Z | <— same | 7.372,7.234,7.11 | 2015-08-18T00:36:00Z |
| 4 | time >= 2015-08-18T00:54:00Z AND time < 2015-08-18T01:12:00Z | time = 2015-08-18T00:54:00Z | 6.982 | 2015-08-18T00:54:00Z |
The first preset 18-minute time boundary begins at 00:00 and ends just before
00:18.
Two raw points (8.005 and 7.887) fall both within the first GROUP BY time() interval and in that
first time boundary.
Note that while the returned timestamp occurs before the start of the query’s time range,
the query result excludes data that occur before the query’s time range.
The second preset 18-minute time boundary begins at 00:18 and ends just before
00:36.
Three raw points (7.762 and 7.635 and 7.5) fall both within the second GROUP BY time() interval and in that
second time boundary. In this case, the boundary time range and the interval’s time range are the same.
The fourth preset 18-minute time boundary begins at 00:54 and ends just before
1:12:00.
One raw point (6.982) falls both within the fourth GROUP BY time() interval and in that
fourth time boundary.
The time boundaries and returned timestamps for the query with the
offset_interval adhere to the offset time boundaries:
| Time Interval Number | Offset Time Boundary | GROUP BY time() Interval | Points Included | Returned Timestamp |
|---|---|---|---|---|
| 1 | time >= 2015-08-17T23:48:00Z AND time < 2015-08-18T00:06:00Z | NA | NA | NA |
| 2 | time >= 2015-08-18T00:06:00Z AND time < 2015-08-18T00:24:00Z | <— same | 8.005,7.887,7.762 | 2015-08-18T00:06:00Z |
| 3 | time >= 2015-08-18T00:24:00Z AND time < 2015-08-18T00:42:00Z | <— same | 7.635,7.5,7.372 | 2015-08-18T00:24:00Z |
| 4 | time >= 2015-08-18T00:42:00Z AND time < 2015-08-18T01:00:00Z | <— same | 7.234,7.11,6.982 | 2015-08-18T00:42:00Z |
The negative 12-minute offset interval shifts back the preset boundary’s time range
such that the boundary time ranges and the relevant GROUP BY time() interval time ranges are always the
same.
With the offset, each interval performs the calculation on three points, and
the timestamp returned matches both the start of the boundary time range and the
start of the GROUP BY time() interval time range.
Note that offset_interval forces the first time boundary to be outside
the query’s time range so the query returns no results for that first interval.
Example 3: Group query results into 12 minute intervals and shift the preset time boundaries forward
This example is a continuation of the scenario outlined in Common Issues with Basic Syntax.
> SELECT COUNT("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-08-18T00:06:00Z' AND time < '2015-08-18T00:18:00Z' GROUP BY time(12m,6m)
name: h2o_feet
time count
---- -----
2015-08-18T00:06:00Z 2
The query uses an InfluxQL function
to count the number of water_level points, grouping results into 12 minute
time intervals, and offsetting the preset time boundaries by six minutes.
The time boundaries and returned timestamps for the query without the offset_interval adhere to InfluxDB’s preset time boundaries. Let’s first examine the results without the offset:
> SELECT COUNT("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-08-18T00:06:00Z' AND time < '2015-08-18T00:18:00Z' GROUP BY time(12m)
name: h2o_feet
time count
---- -----
2015-08-18T00:00:00Z 1
2015-08-18T00:12:00Z 1
The time boundaries and returned timestamps for the query without the
offset_interval adhere to InfluxDB’s preset time boundaries:
| Time Interval Number | Preset Time Boundary | GROUP BY time() Interval | Points Included | Returned Timestamp |
|---|---|---|---|---|
| 1 | time >= 2015-08-18T00:00:00Z AND time < 2015-08-18T00:12:00Z | time >= 2015-08-18T00:06:00Z AND time < 2015-08-18T00:12:00Z | 8.005 | 2015-08-18T00:00:00Z |
| 2 | time >= 2015-08-12T00:12:00Z AND time < 2015-08-18T00:24:00Z | time >= 2015-08-12T00:12:00Z AND time < 2015-08-18T00:18:00Z | 7.887 | 2015-08-18T00:12:00Z |
The first preset 12-minute time boundary begins at 00:00 and ends just before
00:12.
Only one raw point (8.005) falls both within the query’s first GROUP BY time() interval and in that
first time boundary.
Note that while the returned timestamp occurs before the start of the query’s time range,
the query result excludes data that occur before the query’s time range.
The second preset 12-minute time boundary begins at 00:12 and ends just before
00:24.
Only one raw point (7.887) falls both within the query’s second GROUP BY time() interval and in that
second time boundary.
The time boundaries and returned timestamps for the query with the
offset_interval adhere to the offset time boundaries:
| Time Interval Number | Offset Time Boundary | GROUP BY time() Interval | Points Included | Returned Timestamp |
|---|---|---|---|---|
| 1 | time >= 2015-08-18T00:06:00Z AND time < 2015-08-18T00:18:00Z | <— same | 8.005,7.887 | 2015-08-18T00:06:00Z |
| 2 | time >= 2015-08-18T00:18:00Z AND time < 2015-08-18T00:30:00Z | NA | NA | NA |
The six-minute offset interval shifts forward the preset boundary’s time range
such that the preset boundary time range and the relevant GROUP BY time() interval time range are the
same.
With the offset, the query returns a single result, and the timestamp returned
matches both the start of the boundary time range and the start of the GROUP BY time() interval
time range.
Note that offset_interval forces the second time boundary to be outside
the query’s time range so the query returns no results for that second interval.
GROUP BY time intervals and fill()
fill() changes the value reported for time intervals that have no data.
Syntax
SELECT <function>(<field_key>) FROM_clause WHERE <time_range> GROUP BY time(time_interval,[<offset_interval])[,tag_key] [fill(<fill_option>)]
Description of Syntax
By default, a GROUP BY time() interval with no data reports null as its
value in the output column.
fill() changes the value reported for time intervals that have no data.
Note that fill() must go at the end of the GROUP BY clause if you’re
GROUP(ing) BY several things (for example, both tags and a time interval).
fill_option
Any numerical value Reports the given numerical value for time intervals with no data.
linear
Reports the results of linear interpolation for time intervals with no data.
none
Reports no timestamp and no value for time intervals with no data.
null
Reports null for time intervals with no data but returns a timestamp. This is the same as the default behavior.
previous
Reports the value from the previous time interval for time intervals with no data.
Examples
Example 1: fill(100) Example 2: fill(linear) Example 3: fill(none) Example 4: fill(null) Example 5: fill(previous)
Without fill(100):
> SELECT MAX("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-09-18T16:00:00Z' AND time <= '2015-09-18T16:42:00Z' GROUP BY time(12m)
name: h2o_feet
--------------
time max
2015-09-18T16:00:00Z 3.599
2015-09-18T16:12:00Z 3.402
2015-09-18T16:24:00Z 3.235
2015-09-18T16:36:00Z
With fill(100):
> SELECT MAX("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-09-18T16:00:00Z' AND time <= '2015-09-18T16:42:00Z' GROUP BY time(12m) fill(100)
name: h2o_feet
--------------
time max
2015-09-18T16:00:00Z 3.599
2015-09-18T16:12:00Z 3.402
2015-09-18T16:24:00Z 3.235
2015-09-18T16:36:00Z 100
fill(100) changes the value reported for the time interval with no data to 100.
Without fill(linear):
> SELECT MEAN("tadpoles") FROM "pond" WHERE time >= '2016-11-11T21:00:00Z' AND time <= '2016-11-11T22:06:00Z' GROUP BY time(12m)
name: pond
time mean
---- ----
2016-11-11T21:00:00Z 1
2016-11-11T21:12:00Z
2016-11-11T21:24:00Z 3
2016-11-11T21:36:00Z
2016-11-11T21:48:00Z
2016-11-11T22:00:00Z 6
With fill(linear):
> SELECT MEAN("tadpoles") FROM "pond" WHERE time >= '2016-11-11T21:00:00Z' AND time <= '2016-11-11T22:06:00Z' GROUP BY time(12m) fill(linear)
name: pond
time mean
---- ----
2016-11-11T21:00:00Z 1
2016-11-11T21:12:00Z 2
2016-11-11T21:24:00Z 3
2016-11-11T21:36:00Z 4
2016-11-11T21:48:00Z 5
2016-11-11T22:00:00Z 6
fill(linear) changes the value reported for the time interval with no data
to the results of linear interpolation.
Note: The data in Example 2 are not in
NOAA_water_database. We had to create a dataset with less regular data to work withfill(linear).
Without fill(none):
> SELECT MAX("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-09-18T16:00:00Z' AND time <= '2015-09-18T16:42:00Z' GROUP BY time(12m)
name: h2o_feet
--------------
time max
2015-09-18T16:00:00Z 3.599
2015-09-18T16:12:00Z 3.402
2015-09-18T16:24:00Z 3.235
2015-09-18T16:36:00Z
With fill(none):
> SELECT MAX("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-09-18T16:00:00Z' AND time <= '2015-09-18T16:42:00Z' GROUP BY time(12m) fill(none)
name: h2o_feet
--------------
time max
2015-09-18T16:00:00Z 3.599
2015-09-18T16:12:00Z 3.402
2015-09-18T16:24:00Z 3.235
fill(none) reports no value and no timestamp for the time interval with no data.
Without fill(null):
> SELECT MAX("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-09-18T16:00:00Z' AND time <= '2015-09-18T16:42:00Z' GROUP BY time(12m)
name: h2o_feet
--------------
time max
2015-09-18T16:00:00Z 3.599
2015-09-18T16:12:00Z 3.402
2015-09-18T16:24:00Z 3.235
2015-09-18T16:36:00Z
With fill(null):
> SELECT MAX("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-09-18T16:00:00Z' AND time <= '2015-09-18T16:42:00Z' GROUP BY time(12m) fill(null)
name: h2o_feet
--------------
time max
2015-09-18T16:00:00Z 3.599
2015-09-18T16:12:00Z 3.402
2015-09-18T16:24:00Z 3.235
2015-09-18T16:36:00Z
fill(null) reports null as the value for the time interval with no data.
That result matches the result of the query without fill(null).
Without fill(previous):
> SELECT MAX("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-09-18T16:00:00Z' AND time <= '2015-09-18T16:42:00Z' GROUP BY time(12m)
name: h2o_feet
--------------
time max
2015-09-18T16:00:00Z 3.599
2015-09-18T16:12:00Z 3.402
2015-09-18T16:24:00Z 3.235
2015-09-18T16:36:00Z
With fill(previous):
> SELECT MAX("water_level") FROM "h2o_feet" WHERE "location"='coyote_creek' AND time >= '2015-09-18T16:00:00Z' AND time <= '2015-09-18T16:42:00Z' GROUP BY time(12m) fill(previous)
name: h2o_feet
--------------
time max
2015-09-18T16:00:00Z 3.599
2015-09-18T16:12:00Z 3.402
2015-09-18T16:24:00Z 3.235
2015-09-18T16:36:00Z 3.235
fill(previous) changes the value reported for the time interval with no data to 3.235,
the value from the previous time interval.
Common issues with fill()
Issue 1: fill() when no data fall within the query’s time range
Currently, queries ignore fill() if no data fall within the query’s time range.
This is the expected behavior. An open
feature request on GitHub
proposes that fill() should force a return of values even if the query’s time
range covers no data.
Example
The following query returns no data because water_level has no points within
the query’s time range.
Note that fill(800) has no effect on the query results.
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location" = 'coyote_creek' AND time >= '2015-09-18T22:00:00Z' AND time <= '2015-09-18T22:18:00Z' GROUP BY time(12m) fill(800)
>
Issue 2: fill(previous) when the previous result falls outside the query’s time range
fill(previous) doesn’t fill the result for a time interval if the previous
value is outside the query’s time range.
Example
The following query covers the time range between 2015-09-18T16:24:00Z and 2015-09-18T16:54:00Z.
Note that fill(previous) fills the result for 2015-09-18T16:36:00Z with the
result from 2015-09-18T16:24:00Z.
> SELECT MAX("water_level") FROM "h2o_feet" WHERE location = 'coyote_creek' AND time >= '2015-09-18T16:24:00Z' AND time <= '2015-09-18T16:54:00Z' GROUP BY time(12m) fill(previous)
name: h2o_feet
--------------
time max
2015-09-18T16:24:00Z 3.235
2015-09-18T16:36:00Z 3.235
2015-09-18T16:48:00Z 4
The next query shortens the time range in the previous query.
It now covers the time between 2015-09-18T16:36:00Z and 2015-09-18T16:54:00Z.
Note that fill(previous) doesn’t fill the result for 2015-09-18T16:36:00Z with the
result from 2015-09-18T16:24:00Z; the result for 2015-09-18T16:24:00Z is outside the query’s
shorter time range.
> SELECT MAX("water_level") FROM "h2o_feet" WHERE location = 'coyote_creek' AND time >= '2015-09-18T16:36:00Z' AND time <= '2015-09-18T16:54:00Z' GROUP BY time(12m) fill(previous)
name: h2o_feet
--------------
time max
2015-09-18T16:36:00Z
2015-09-18T16:48:00Z 4
Issue 3: fill(linear) when the previous or following result falls outside the query’s time range
fill(linear) doesn’t fill the result for a time interval with no data if the
previous result or the following result is outside the query’s time range.
Example
The following query covers the time range between 2016-11-11T21:24:00Z and
2016-11-11T22:06:00Z. Note that fill(linear) fills the results for the
2016-11-11T21:36:00Z time interval and the 2016-11-11T21:48:00Z time interval
using the values from the 2016-11-11T21:24:00Z time interval and the
2016-11-11T22:00:00Z time interval.
> SELECT MEAN("tadpoles") FROM "pond" WHERE time > '2016-11-11T21:24:00Z' AND time <= '2016-11-11T22:06:00Z' GROUP BY time(12m) fill(linear)
name: pond
time mean
---- ----
2016-11-11T21:24:00Z 3
2016-11-11T21:36:00Z 4
2016-11-11T21:48:00Z 5
2016-11-11T22:00:00Z 6
The next query shortens the time range in the previous query.
It now covers the time between 2016-11-11T21:36:00Z and 2016-11-11T22:06:00Z.
Note that fill() previous doesn’t fill the results for the 2016-11-11T21:36:00Z
time interval and the 2016-11-11T21:48:00Z time interval; the result for
2016-11-11T21:24:00Z is outside the query’s shorter time range and InfluxDB
cannot perform the linear interpolation.
> SELECT MEAN("tadpoles") FROM "pond" WHERE time >= '2016-11-11T21:36:00Z' AND time <= '2016-11-11T22:06:00Z' GROUP BY time(12m) fill(linear)
name: pond
time mean
---- ----
2016-11-11T21:36:00Z
2016-11-11T21:48:00Z
2016-11-11T22:00:00Z 6
Note: The data in Issue 3 are not in
NOAA_water_database. We had to create a dataset with less regular data to work withfill(linear).
The INTO clause
The INTO clause writes query results to a user-specified measurement.
Syntax
SELECT_clause INTO <measurement_name> FROM_clause [WHERE_clause] [GROUP_BY_clause]
Description of Syntax
The INTO clause supports several formats for specifying a measurement:
INTO <measurement_name>
Writes data to the specified measurement.
If you’re using the CLI InfluxDB writes the data to the measurement in the
USEd
database and the DEFAULT retention policy.
If you’re using the HTTP API InfluxDB writes the data to the
measurement in the database specified in the db query string parameter
and the DEFAULT retention policy.
INTO <database_name>.<retention_policy_name>.<measurement_name>
Writes data to a fully qualified measurement.
Fully qualify a measurement by specifying its database and retention policy.
INTO <database_name>..<measurement_name>
Writes data to a measurement in a user-specified database and the DEFAULT
retention policy.
INTO <database_name>.<retention_policy_name>.:MEASUREMENT FROM /<regular_expression>/
Writes data to all measurements in the user-specified database and
retention policy that match the regular expression in the FROM clause.
:MEASUREMENT is a backreference to each measurement matched in the FROM clause.
Examples
Example 1: Rename a database
> SELECT * INTO "copy_NOAA_water_database"."autogen".:MEASUREMENT FROM "NOAA_water_database"."autogen"./.*/ GROUP BY *
name: result
time written
---- -------
0 76290
Directly renaming a database in InfluxDB is not possible, so a common use for the INTO clause is to move data from one database to another.
The query above writes all data in the NOAA_water_database and autogen retention policy to the copy_NOAA_water_database database and the autogen retention policy.
The backreference syntax (:MEASUREMENT) maintains the source measurement names in the destination database.
Note that both the copy_NOAA_water_database database and its autogen retention policy must exist prior to running the INTO query.
See Database Management
for how to manage databases and retention policies.
The GROUP BY * clause preserves tags in the source database as tags in the destination database.
The following query does not maintain the series context for tags; tags will be stored as fields in the destination database (copy_NOAA_water_database):
SELECT * INTO "copy_NOAA_water_database"."autogen".:MEASUREMENT FROM "NOAA_water_database"."autogen"./.*/
When moving large amounts of data, we recommend sequentially running INTO queries for different measurements and using time boundaries in the WHERE clause.
This prevents your system from running out of memory.
The codeblock below provides sample syntax for those queries:
SELECT *
INTO <destination_database>.<retention_policy_name>.<measurement_name>
FROM <source_database>.<retention_policy_name>.<measurement_name>
WHERE time > now() - 100w and time < now() - 90w GROUP BY *
SELECT *
INTO <destination_database>.<retention_policy_name>.<measurement_name>
FROM <source_database>.<retention_policy_name>.<measurement_name>}
WHERE time > now() - 90w and time < now() - 80w GROUP BY *
SELECT *
INTO <destination_database>.<retention_policy_name>.<measurement_name>
FROM <source_database>.<retention_policy_name>.<measurement_name>
WHERE time > now() - 80w and time < now() - 70w GROUP BY *
Example 2: Write the results of a query to a measurement
> SELECT "water_level" INTO "h2o_feet_copy_1" FROM "h2o_feet" WHERE "location" = 'coyote_creek'
name: result
------------
time written
1970-01-01T00:00:00Z 7604
> SELECT * FROM "h2o_feet_copy_1"
name: h2o_feet_copy_1
---------------------
time water_level
2015-08-18T00:00:00Z 8.12
[...]
2015-09-18T16:48:00Z 4
The query writes its results a new measurement: h2o_feet_copy_1.
If you’re using the CLI, InfluxDB writes the data to
the USEd database and the DEFAULT retention policy.
If you’re using the HTTP API, InfluxDB writes the
data to the database and retention policy specified in the db and rp
query string parameters.
If you do not set the rp query string parameter, the HTTP API automatically
writes the data to the database’s DEFAULT retention policy.
The response shows the number of points (7605) that InfluxDB writes to h2o_feet_copy_1.
The timestamp in the response is meaningless; InfluxDB uses epoch 0
(1970-01-01T00:00:00Z) as a null timestamp equivalent.
Example 3: Write the results of a query to a fully qualified measurement
> SELECT "water_level" INTO "where_else"."autogen"."h2o_feet_copy_2" FROM "h2o_feet" WHERE "location" = 'coyote_creek'
name: result
------------
time written
1970-01-01T00:00:00Z 7604
> SELECT * FROM "where_else"."autogen"."h2o_feet_copy_2"
name: h2o_feet_copy_2
---------------------
time water_level
2015-08-18T00:00:00Z 8.12
[...]
2015-09-18T16:48:00Z 4
The query writes its results to a new measurement: h2o_feet_copy_2.
InfluxDB writes the data to the where_else database and to the autogen
retention policy.
Note that both where_else and autogen must exist prior to running the INTO
query.
See Database Management
for how to manage databases and retetion policies.
The response shows the number of points (7605) that InfluxDB writes to h2o_feet_copy_2.
The timestamp in the response is meaningless; InfluxDB uses epoch 0
(1970-01-01T00:00:00Z) as a null timestamp equivalent.
Example 4: Write aggregated results to a measurement (downsampling)
> SELECT MEAN("water_level") INTO "all_my_averages" FROM "h2o_feet" WHERE "location" = 'coyote_creek' AND time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m)
name: result
------------
time written
1970-01-01T00:00:00Z 3
> SELECT * FROM "all_my_averages"
name: all_my_averages
---------------------
time mean
2015-08-18T00:00:00Z 8.0625
2015-08-18T00:12:00Z 7.8245
2015-08-18T00:24:00Z 7.5675
The query aggregates data using an
InfluxQL function and a GROUP BY
time() clause.
It also writes its results to the all_my_averages measurement.
The response shows the number of points (3) that InfluxDB writes to all_my_averages.
The timestamp in the response is meaningless; InfluxDB uses epoch 0
(1970-01-01T00:00:00Z) as a null timestamp equivalent.
The query is an example of downsampling: taking higher precision data,
aggregating those data to a lower precision, and storing the lower precision
data in the database.
Downsampling is a common use case for the INTO clause.
Example 5: Write aggregated results for more than one measurement to a different database (downsampling with backreferencing)
> SELECT MEAN(*) INTO "where_else"."autogen".:MEASUREMENT FROM /.*/ WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:06:00Z' GROUP BY time(12m)
name: result
time written
---- -------
1970-01-01T00:00:00Z 5
> SELECT * FROM "where_else"."autogen"./.*/
name: average_temperature
time mean_degrees mean_index mean_pH mean_water_level
---- ------------ ---------- ------- ----------------
2015-08-18T00:00:00Z 78.5
name: h2o_feet
time mean_degrees mean_index mean_pH mean_water_level
---- ------------ ---------- ------- ----------------
2015-08-18T00:00:00Z 5.07625
name: h2o_pH
time mean_degrees mean_index mean_pH mean_water_level
---- ------------ ---------- ------- ----------------
2015-08-18T00:00:00Z 6.75
name: h2o_quality
time mean_degrees mean_index mean_pH mean_water_level
---- ------------ ---------- ------- ----------------
2015-08-18T00:00:00Z 51.75
name: h2o_temperature
time mean_degrees mean_index mean_pH mean_water_level
---- ------------ ---------- ------- ----------------
2015-08-18T00:00:00Z 63.75
The query aggregates data using an
InfluxQL function and a GROUP BY
time() clause.
It aggregates data in every measurement that matches the regular expression
in the FROM clause and writes the results to measurements with the same name in the
where_else database and the autogen retention policy.
Note that both where_else and autogen must exist prior to running the INTO
query.
See Database Management
for how to manage databases and retention policies.
The response shows the number of points (5) that InfluxDB writes to the where_else
database and the autogen retention policy.
The timestamp in the response is meaningless; InfluxDB uses epoch 0
(1970-01-01T00:00:00Z) as a null timestamp equivalent.
The query is an example of downsampling with backreferencing.
It takes higher precision data from more than one measurement,
aggregates those data to a lower precision, and stores the lower precision
data in the database.
Downsampling with backreferencing is a common use case for the INTO clause.
Common Issues with the INTO clause
Issue 1: Missing data
If an INTO query includes a tag key in the SELECT clause, the query converts tags in the current
measurement to fields in the destination measurement.
This can cause InfluxDB to overwrite points that were previously differentiated
by a tag value.
The
Frequently Asked Questions
document describes that behavior in detail.
To preserve tags in the current measurement as tags in the destination measurement,
GROUP BY the relevant tag key or GROUP BY * in the INTO query.
Issue 2: Automating queries with the INTO clause
The INTO clause section in this document shows how to manually implement
queries with an INTO clause.
See the Continuous Queries
documentation for how to automate INTO clause queries on realtime data.
Among other uses,
Continuous Queries automate the downsampling process.
ORDER BY time DESC
By default, InfluxDB returns results in ascending time order; the first point
returned has the oldest timestamp and
the last point returned has the most recent timestamp.
ORDER BY time DESC reverses that order such that InfluxDB returns the points
with the most recent timestamps first.
Syntax
SELECT_clause [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] ORDER BY time DESC
Description of Syntax
ORDER by time DESC must appear after the GROUP BY clause
if the query includes a GROUP BY clause.
ORDER by time DESC must appear after the WHERE clause
if the query includes a WHERE clause and no GROUP BY clause.
Examples
Example 1: Return the newest points first
> SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' ORDER BY time DESC
name: h2o_feet
time water_level
---- -----------
2015-09-18T21:42:00Z 4.938
2015-09-18T21:36:00Z 5.066
[...]
2015-08-18T00:06:00Z 2.116
2015-08-18T00:00:00Z 2.064
The query returns the points with the most recent timestamps from the
h2o_feet measurement first.
Without ORDER by time DESC, the query would return 2015-08-18T00:00:00Z
first and 2015-09-18T21:42:00Z last.
Example 2: Return the newest points first and include a GROUP BY time() clause
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:42:00Z' GROUP BY time(12m) ORDER BY time DESC
name: h2o_feet
time mean
---- ----
2015-08-18T00:36:00Z 4.6825
2015-08-18T00:24:00Z 4.80675
2015-08-18T00:12:00Z 4.950749999999999
2015-08-18T00:00:00Z 5.07625
The query uses an InfluxQL function
and a time interval in the GROUP BY clause
to calculate the average water_level for each twelve-minute
interval in the query’s time range.
ORDER BY time DESC returns the most recent 12-minute time intervals
first.
Without ORDER BY time DESC, the query would return
2015-08-18T00:00:00Z first and 2015-08-18T00:36:00Z last.
The LIMIT and SLIMIT clauses
LIMIT and SLIMIT limit the number of
points and the number of
series returned per query.
The LIMIT Clause
LIMIT <N> returns the first N points from the specified measurement.
Syntax
SELECT_clause [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] LIMIT <N>
Description of Syntax
N specifies the number of points to return from the specified measurement.
If N is greater than the number of points in a measurement, InfluxDB returns
all points from that series.
Note that the LIMIT clause must appear in the order outlined in the syntax above.
Examples
Example 1: Limit the number of points returned
> SELECT "water_level","location" FROM "h2o_feet" LIMIT 3
name: h2o_feet
time water_level location
---- ----------- --------
2015-08-18T00:00:00Z 8.12 coyote_creek
2015-08-18T00:00:00Z 2.064 santa_monica
2015-08-18T00:06:00Z 8.005 coyote_creek
The query returns the three oldest points (determined by timestamp) from the
h2o_feet measurement.
Example 2: Limit the number points returned and include a GROUP BY clause
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:42:00Z' GROUP BY *,time(12m) LIMIT 2
name: h2o_feet
tags: location=coyote_creek
time mean
---- ----
2015-08-18T00:00:00Z 8.0625
2015-08-18T00:12:00Z 7.8245
name: h2o_feet
tags: location=santa_monica
time mean
---- ----
2015-08-18T00:00:00Z 2.09
2015-08-18T00:12:00Z 2.077
The query uses an InfluxQL function
and a GROUP BY clause
to calculate the average water_level for each tag and for each twelve-minute
interval in the query’s time range.
LIMIT 2 requests the two oldest twelve-minute averages (determined by timestamp).
Note that without LIMIT 2, the query would return four points per series;
one for each twelve-minute interval in the query’s time range.
The SLIMIT Clause
SLIMIT <N> returns every point from <N> series in the specified measurement.
Syntax
SELECT_clause [INTO_clause] FROM_clause [WHERE_clause] GROUP BY *[,time(<time_interval>)] [ORDER_BY_clause] SLIMIT <N>
Description of Syntax
N specifies the number of series to return from the specified measurement.
If N is greater than the number of series in a measurement, InfluxDB returns
all series from that measurement.
There is an ongoing issue that requires queries with SLIMIT to include GROUP BY *.
Note that the SLIMIT clause must appear in the order outlined in the syntax above.
Examples
Example 1: Limit the number of series returned
> SELECT "water_level" FROM "h2o_feet" GROUP BY * SLIMIT 1
name: h2o_feet
tags: location=coyote_creek
time water_level
---- -----
2015-08-18T00:00:00Z 8.12
2015-08-18T00:06:00Z 8.005
2015-08-18T00:12:00Z 7.887
[...]
2015-09-18T16:12:00Z 3.402
2015-09-18T16:18:00Z 3.314
2015-09-18T16:24:00Z 3.235
The query returns all water_level points from one of the series associated
with the h2o_feet measurement.
Example 2: Limit the number of series returned and include a GROUP BY time() clause
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:42:00Z' GROUP BY *,time(12m) SLIMIT 1
name: h2o_feet
tags: location=coyote_creek
time mean
---- ----
2015-08-18T00:00:00Z 8.0625
2015-08-18T00:12:00Z 7.8245
2015-08-18T00:24:00Z 7.5675
2015-08-18T00:36:00Z 7.303
The query uses an InfluxQL function
and a time interval in the GROUP BY clause
to calculate the average water_level for each twelve-minute
interval in the query’s time range.
SLIMIT 1 requests a single series
associated with the h2o_feet measurement.
Note that without SLIMIT 1, the query would return results for the two series
associated with the h2o_feet measurement: location=coyote_creek and
location=santa_monica.
LIMIT and SLIMIT
LIMIT <N> followed by SLIMIT <N> returns the first <N> points from <N> series in the specified measurement.
Syntax
SELECT_clause [INTO_clause] FROM_clause [WHERE_clause] GROUP BY *[,time(<time_interval>)] [ORDER_BY_clause] LIMIT <N1> SLIMIT <N2>
Description of Syntax
N1 specifies the number of points to return per measurement.
If N1 is greater than the number of points in a measurement, InfluxDB returns all points from that measurement.
N2 specifies the number of series to return from the specified measurement.
If N2 is greater than the number of series in a measurement, InfluxDB returns all series from that measurement.
There is an ongoing issue that requires queries with LIMIT and SLIMIT to include GROUP BY *.
Note that the LIMIT and SLIMIT clauses must appear in the order outlined in the syntax above.
Examples
Example 1: Limit the number of points and series returned
> SELECT "water_level" FROM "h2o_feet" GROUP BY * LIMIT 3 SLIMIT 1
name: h2o_feet
tags: location=coyote_creek
time water_level
---- -----------
2015-08-18T00:00:00Z 8.12
2015-08-18T00:06:00Z 8.005
2015-08-18T00:12:00Z 7.887
The query returns the three oldest points (determined by timestamp) from one
of the series associated with the
measurement h2o_feet.
Example 2: Limit the number of points and series returned and include a GROUP BY time() clause
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:42:00Z' GROUP BY *,time(12m) LIMIT 2 SLIMIT 1
name: h2o_feet
tags: location=coyote_creek
time mean
---- ----
2015-08-18T00:00:00Z 8.0625
2015-08-18T00:12:00Z 7.8245
The query uses an InfluxQL function
and a time interval in the GROUP BY clause
to calculate the average water_level for each twelve-minute
interval in the query’s time range.
LIMIT 2 requests the two oldest twelve-minute averages (determined by
timestamp) and SLIMIT 1 requests a single series
associated with the h2o_feet measurement.
Note that without LIMIT 2 SLIMIT 1, the query would return four points
for each of the two series associated with the h2o_feet measurement.
The OFFSET and SOFFSET Clauses
OFFSET and SOFFSET paginates points and series returned.
| The OFFSET clause | The SOFFSET clause |
The OFFSET clause
OFFSET <N> paginates N points in the query results.
Syntax
SELECT_clause [INTO_clause] FROM_clause [WHERE_clause] [GROUP_BY_clause] [ORDER_BY_clause] LIMIT_clause OFFSET <N> [SLIMIT_clause]
Description of Syntax
N specifies the number of points to paginate.
The OFFSET clause requires a LIMIT clause.
Using the OFFSET clause without a LIMIT clause can cause inconsistent
query results.
Note: InfluxDB returns no results if the
WHEREclause includes a time range and theOFFSETclause would cause InfluxDB to return points with timestamps outside of that time range.
Examples
Example 1: Paginate points
> SELECT "water_level","location" FROM "h2o_feet" LIMIT 3 OFFSET 3
name: h2o_feet
time water_level location
---- ----------- --------
2015-08-18T00:06:00Z 2.116 santa_monica
2015-08-18T00:12:00Z 7.887 coyote_creek
2015-08-18T00:12:00Z 2.028 santa_monica
The query returns the fourth, fifth, and sixth points from the h2o_feet measurement.
If the query did not include OFFSET 3, it would return the first, second,
and third points from that measurement.
Example 2: Paginate points and include several clauses
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:42:00Z' GROUP BY *,time(12m) ORDER BY time DESC LIMIT 2 OFFSET 2 SLIMIT 1
name: h2o_feet
tags: location=coyote_creek
time mean
---- ----
2015-08-18T00:12:00Z 7.8245
2015-08-18T00:00:00Z 8.0625
This example is pretty involved, so here’s the clause-by-clause breakdown:
The SELECT clause specifies an InfluxQL function.
The FROM clause specifies a single measurement.
The WHERE clause specifies the time range for the query.
The GROUP BY clause groups results by all tags (*) and into 12-minute intervals.
The ORDER BY time DESC clause returns results in descending timestamp order.
The LIMIT 2 clause limits the number of points returned to two.
The OFFSET 2 clause excludes the first two averages from the query results.
The SLIMIT 1 clause limits the number of series returned to one.
Without OFFSET 2, the query would return the first two averages of the query results:
name: h2o_feet
tags: location=coyote_creek
time mean
---- ----
2015-08-18T00:36:00Z 7.303
2015-08-18T00:24:00Z 7.5675
The SOFFSET clause
SOFFSET <N> paginates N series in the query results.
Syntax
SELECT_clause [INTO_clause] FROM_clause [WHERE_clause] GROUP BY *[,time(time_interval)] [ORDER_BY_clause] [LIMIT_clause] [OFFSET_clause] SLIMIT_clause SOFFSET <N>
Description of Syntax
N specifies the number of series to paginate.
The SOFFSET clause requires an SLIMIT clause.
Using the SOFFSET clause without an SLIMIT clause can cause inconsistent
query results.
There is an ongoing issue that requires queries with SLIMIT to include GROUP BY *.
Note: InfluxDB returns no results if the
SOFFSETclause paginates through more than the total number of series.
Examples
Example 1: Paginate series
> SELECT "water_level" FROM "h2o_feet" GROUP BY * SLIMIT 1 SOFFSET 1
name: h2o_feet
tags: location=santa_monica
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
[...]
2015-09-18T21:36:00Z 5.066
2015-09-18T21:42:00Z 4.938
The query returns data for the series associated with the h2o_feet
measurement and the location = santa_monica tag.
Without SOFFSET 1, the query returns data for the series associated with the
h2o_feet measurement and the location = coyote_creek tag.
Example 2: Paginate series and include all clauses
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:42:00Z' GROUP BY *,time(12m) ORDER BY time DESC LIMIT 2 OFFSET 2 SLIMIT 1 SOFFSET 1
name: h2o_feet
tags: location=santa_monica
time mean
---- ----
2015-08-18T00:12:00Z 2.077
2015-08-18T00:00:00Z 2.09
This example is pretty involved, so here’s the clause-by-clause breakdown:
The SELECT clause specifies an InfluxQL function.
The FROM clause specifies a single measurement.
The WHERE clause specifies the time range for the query.
The GROUP BY clause groups results by all tags (*) and into 12-minute intervals.
The ORDER BY time DESC clause returns results in descending timestamp order.
The LIMIT 2 clause limits the number of points returned to two.
The OFFSET 2 clause excludes the first two averages from the query results.
The SLIMIT 1 clause limits the number of series returned to one.
The SOFFSET 1 clause paginates the series returned.
Without SOFFSET 1, the query would return the results for a different series:
name: h2o_feet
tags: location=coyote_creek
time mean
---- ----
2015-08-18T00:12:00Z 7.8245
2015-08-18T00:00:00Z 8.0625
Time Syntax
For most SELECT statements, the default time range is between 1677-09-21 00:12:43.145224194 and 2262-04-11T23:47:16.854775806Z UTC.
For SELECT statements with a GROUP BY time() clause, the default time
range is between 1677-09-21 00:12:43.145224194 UTC and now().
The following sections detail how to specify alternative time ranges in the SELECT
statement’s WHERE clause.
| Absolute Time | Relative Time | Common Issues with Time Syntax |
Tired of reading? Check out this InfluxQL Short:
Absolute Time
Specify absolute time with date-time strings and epoch time.
Syntax
SELECT_clause FROM_clause WHERE time <operator> ['<rfc3339_date_time_string>' | '<rfc3339_like_date_time_string>' | <epoch_time>] [AND ['<rfc3339_date_time_string>' | '<rfc3339_like_date_time_string>' | <epoch_time>] [...]]
Description of Syntax
Supported operators
= equal to<> not equal to!= not equal to> greater than>= greater than or equal to< less than<= less than or equal to
Currently, InfluxDB does not support using OR with absolute time in the WHERE
clause. See the Frequently Asked Questions
document and the GitHub Issue
for more information.
rfc3339_date_time_string
'YYYY-MM-DDTHH:MM:SS.nnnnnnnnnZ'
.nnnnnnnnn is optional and is set to .000000000 if not included.
The RFC3339 date-time string requires single quotes.
rfc3339_like_date_time_string
'YYYY-MM-DD HH:MM:SS.nnnnnnnnn'
HH:MM:SS.nnnnnnnnn.nnnnnnnnn is optional and is set to 00:00:00.000000000 if not included.
The RFC3339-like date-time string requires single quotes.
epoch_time
Epoch time is the amount of time that has elapsed since 00:00:00 Coordinated Universal Time (UTC), Thursday, 1 January 1970.
By default, InfluxDB assumes that all epoch timestamps are in nanoseconds. Include a duration literal at the end of the epoch timestamp to indicate a precision other than nanoseconds.
Basic Arithmetic
All timestamp formats support basic arithmetic.
Add (+) or subtract (-) a time from a timestamp with a duration literal.
Note that InfluxQL requires a whitespace between the + or - and the
duration literal.
Examples
Example 1: Specify a time range with RFC3339 date-time strings
> SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00.000000000Z' AND time <= '2015-08-18T00:12:00Z'
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
The query returns data with timestamps between August 18, 2015 at 00:00:00.000000000 and
August 18, 2015 at 00:12:00.
The nanosecond specification in the first timestamp (.000000000)
is optional.
Note that the single quotes around the RFC3339 date-time strings are required.
Example 2: Specify a time range with RFC3339-like date-time strings
> SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18' AND time <= '2015-08-18 00:12:00'
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
The query returns data with timestamps between August 18, 2015 at 00:00:00 and August 18, 2015 at 00:12:00. The first date-time string does not include a time; InfluxDB assumes the time is 00:00:00.
Note that the single quotes around the RFC3339-like date-time strings are required.
Example 3: Specify a time range with epoch timestamps
> SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= 1439856000000000000 AND time <= 1439856720000000000
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
The query returns data with timestamps that occur between August 18, 2015 at 00:00:00 and August 18, 2015 at 00:12:00. By default InfluxDB assumes epoch timestamps are in nanoseconds.
Example 4: Specify a time range with second-precision epoch timestamps
> SELECT "water_level" FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= 1439856000s AND time <= 1439856720s
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 2.116
2015-08-18T00:12:00Z 2.028
The query returns data with timestamps that occur between August 18, 2015
at 00:00:00 and August 18, 2015 at 00:12:00.
The s duration literal at the
end of the epoch timestamps indicate that the epoch timestamps are in seconds.
Example 5: Perform basic arithmetic on an RFC3339-like date-time string
> SELECT "water_level" FROM "h2o_feet" WHERE time > '2015-09-18T21:24:00Z' + 6m
name: h2o_feet
time water_level
---- -----------
2015-09-18T21:36:00Z 5.066
2015-09-18T21:42:00Z 4.938
The query returns data with timestamps that occur at least six minutes after
September 18, 2015 at 21:24:00.
Note that the whitespace between the + and 6m is required.
Example 6: Perform basic arithmetic on an epoch timestamp
> SELECT "water_level" FROM "h2o_feet" WHERE time > 24043524m - 6m
name: h2o_feet
time water_level
---- -----------
2015-09-18T21:24:00Z 5.013
2015-09-18T21:30:00Z 5.01
2015-09-18T21:36:00Z 5.066
2015-09-18T21:42:00Z 4.938
The query returns data with timestamps that occur at least six minutes before
September 18, 2015 at 21:24:00.
Note that the whitespace between the - and 6m is required.
Relative time
Use now() to query data with timestamps relative to the server’s current timestamp.
Syntax
SELECT_clause FROM_clause WHERE time <operator> now() [[ - | + ] <duration_literal>] [(AND|OR) now() [...]]
Description of Syntax
now() is the Unix time of the server at the time the query is executed on that server.
The whitespace between - or + and the duration literal is required.
Supported operators
= equal to<> not equal to!= not equal to> greater than>= greater than or equal to< less than<= less than or equal to
duration_literal
u or µ microsecondsms millisecondss secondsm minutesh hoursd daysw weeks
Examples
Example 1: Specify a time range with relative time
> SELECT "water_level" FROM "h2o_feet" WHERE time > now() - 1h
The query returns data with timestamps that occur within the past hour.
The whitespace between - and 1h is required.
Example 2: Specify a time range with absolute time and relative time
> SELECT "level description" FROM "h2o_feet" WHERE time > '2015-09-18T21:18:00Z' AND time < now() + 1000d
name: h2o_feet
time level description
---- -----------------
2015-09-18T21:24:00Z between 3 and 6 feet
2015-09-18T21:30:00Z between 3 and 6 feet
2015-09-18T21:36:00Z between 3 and 6 feet
2015-09-18T21:42:00Z between 3 and 6 feet
The query returns data with timestamps that occur between September 18, 2015
at 21:18:00 and 1000 days from now().
The whitespace between + and 1000d is required.
Common Issues with Time Syntax
Issue 1: Using OR with absolute time
Currently, InfluxDB does not support using OR with absolute time
in the WHERE clause.
See the Frequently Asked Questions document for more information.
Issue 2: Querying data that occur after now() with a GROUP BY time() clause
Most SELECT statements have a default time range between 1677-09-21 00:12:43.145224194 and 2262-04-11T23:47:16.854775806Z UTC.
For SELECT statements with a GROUP BY time() clause, the default time
range is between 1677-09-21 00:12:43.145224194 UTC and now().
To query data with timestamps that occur after now(), SELECT statements with
a GROUP BY time() clause must provide an alternative upper bound in the
WHERE clause.
Example
Use the CLI to write a point to the NOAA_water_database that occurs after now():
> INSERT h2o_feet,location=santa_monica water_level=3.1 1587074400000000000
Run a GROUP BY time() query that covers data with timestamps between
2015-09-18T21:30:00Z and now():
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location"='santa_monica' AND time >= '2015-09-18T21:30:00Z' GROUP BY time(12m) fill(none)
name: h2o_feet
time mean
---- ----
2015-09-18T21:24:00Z 5.01
2015-09-18T21:36:00Z 5.002
Run a GROUP BY time() query that covers data with timestamps between
2015-09-18T21:30:00Z and 180 weeks from now():
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location"='santa_monica' AND time >= '2015-09-18T21:30:00Z' AND time <= now() + 180w GROUP BY time(12m) fill(none)
name: h2o_feet
time mean
---- ----
2015-09-18T21:24:00Z 5.01
2015-09-18T21:36:00Z 5.002
2020-04-16T22:00:00Z 3.1
Note that the WHERE clause must provide an alternative upper bound to
override the default now() upper bound. The following query merely resets
the lower bound to now() such that the query’s time range is between
now() and now():
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location"='santa_monica' AND time >= now() GROUP BY time(12m) fill(none)
>
Issue 3: Configuring the returned timestamps
The CLI returns timestamps in
nanosecond epoch format by default.
Specify alternative formats with the
precision <format> command.
The HTTP API returns timestamps
in RFC3339 format by default.
Specify alternative formats with the
epoch query string parameter.
Regular Expressions
InfluxQL supports using regular expressions when specifying:
- field keys and tag keys in the
SELECTclause - measurements in the
FROMclause - tag values and string field values in the
WHEREclause. - tag keys in the
GROUP BYclause
Currently, InfluxQL does not support using regular expressions to match
non-string field values in the
WHERE clause,
databases, and
retention polices.
Note: Regular expression comparisons are more computationally intensive than exact string comparisons; queries with regular expressions are not as performant as those without.
Syntax
SELECT /<regular_expression_field_key>/ FROM /<regular_expression_measurement>/ WHERE [<tag_key> <operator> /<regular_expression_tag_value>/ | <field_key> <operator> /<regular_expression_field_value>/] GROUP BY /<regular_expression_tag_key>/
Description of Syntax
Regular expressions are surrounded by / characters and use
Golang’s regular expression syntax.
Supported operators:=~ matches against!~ doesn’t match against
Examples
Example 1: Use a regular expression to specify field keys and tag keys in the SELECT clause
> SELECT /l/ FROM "h2o_feet" LIMIT 1
name: h2o_feet
time level description location water_level
---- ----------------- -------- -----------
2015-08-18T00:00:00Z between 6 and 9 feet coyote_creek 8.12
The query selects all field keys
and tag keys that include an l.
Note that the regular expression in the SELECT clause must match at least one
field key in order to return results for a tag key that matches the regular
expression.
Currently, there is no syntax to distinguish between regular expressions for
field keys and regular expressions for tag keys in the SELECT clause.
The syntax /<regular_expression>/::[field | tag] is not supported.
Example 2: Use a regular expression to specify field keys with a function in the SELECT clause
> SELECT DISTINCT(/level/) FROM "h2o_feet" WHERE "location" = 'santa_monica' AND time >= '2015-08-18T00:00:00.000000000Z' AND time <= '2015-08-18T00:12:00Z'
name: h2o_feet
time distinct_level description distinct_water_level
---- -------------------------- --------------------
2015-08-18T00:00:00Z below 3 feet 2.064
2015-08-18T00:00:00Z 2.116
2015-08-18T00:00:00Z 2.028
The query uses an InfluxQL function
to return the distinct field values
for every field key that contains the word level.
Example 3: Use a regular expression to specify measurements in the FROM clause
> SELECT MEAN("degrees") FROM /temperature/
name: average_temperature
time mean
---- ----
1970-01-01T00:00:00Z 79.98472932232272
name: h2o_temperature
time mean
---- ----
1970-01-01T00:00:00Z 64.98872722506226
The query uses an InfluxQL function
to calculate the average degrees for every measurement in the NOAA_water_database
database that contains the word temperature.
Example 4: Use a regular expression to specify tag values in the WHERE clause
> SELECT MEAN(water_level) FROM "h2o_feet" WHERE "location" =~ /[m]/ AND "water_level" > 3
name: h2o_feet
time mean
---- ----
1970-01-01T00:00:00Z 4.47155532049926
The query uses an InfluxQL function
to calculate the average water_level where the tag value of location
includes an m and water_level is greater than three.
Example 5: Use a regular expression to specify a tag with no value in the WHERE clause
> SELECT * FROM "h2o_feet" WHERE "location" !~ /./
>
The query selects all data from the h2o_feet measurement where the location
tag has no value.
Every data point in the NOAA_water_database has a tag value for location.
It’s possible to perform this same query without a regular expression. See the Frequently Asked Questions document for more information.
Example 6: Use a regular expression to specify a tag with a value in the WHERE clause
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location" =~ /./
name: h2o_feet
time mean
---- ----
1970-01-01T00:00:00Z 4.442107025822523
The query uses an InfluxQL function
to calculate the average water_level across all data that have a tag value for
location.
Example 7: Use a regular expression to specify a field value in the WHERE clause
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location" = 'santa_monica' AND "level description" =~ /between/
name: h2o_feet
time mean
---- ----
1970-01-01T00:00:00Z 4.47155532049926
The query uses an InfluxQL function
to calculate the average water_level for all data where the field value of
level description includes the word between.
Example 8: Use a regular expresssion to specify tag keys in the GROUP BY clause
> SELECT FIRST("index") FROM "h2o_quality" GROUP BY /l/
name: h2o_quality
tags: location=coyote_creek
time first
---- -----
2015-08-18T00:00:00Z 41
name: h2o_quality
tags: location=santa_monica
time first
---- -----
2015-08-18T00:00:00Z 99
The query uses an InfluxQL function
to select the first value of index for every tag that includes the letter l
in its tag key.
Data Types and Cast Operations
The SELECT clause supports specifying a field’s type and basic cast
operations with the :: syntax.
| Data Types | Cast Operations |
Data types
Field values can be floats, integers, strings, or booleans.
The :: syntax allows users to specify the field’s type in a query.
Note: Generally, it is not necessary to specify the field value type in the
SELECTclause. In most cases, InfluxDB rejects any writes that attempt to write a field value to a field that previously accepted field values of a different type.It is possible for field value types to differ across shard groups. In these cases, it may be necessary to specify the field value type in the
SELECTclause. Please see the Frequently Asked Questions document for more information on how InfluxDB handles field value type discrepancies.
Syntax
SELECT_clause <field_key>::<type> FROM_clause
Description of syntax.
type can be float, integer, string, or boolean.
In most cases, InfluxDB returns no data if the field_key does not store data of the specified
type. See Cast Operations for more information.
Example
> SELECT "water_level"::float FROM "h2o_feet" LIMIT 4
name: h2o_feet
--------------
time water_level
2015-08-18T00:00:00Z 8.12
2015-08-18T00:00:00Z 2.064
2015-08-18T00:06:00Z 8.005
2015-08-18T00:06:00Z 2.116
The query returns values of the water_level field key that are floats.
Cast Operations
The :: syntax allows users to perform basic cast operations in queries.
Currently, InfluxDB supports casting field values from integers to
floats or from floats to integers.
Syntax
SELECT_clause <field_key>::<type> FROM_clause
Description of Syntax
type can be float or integer.
InfluxDB returns no data if the query attempts to cast an integer or float to a string or boolean.
Examples
Example 1: Cast float field values to integers
> SELECT "water_level"::integer FROM "h2o_feet" LIMIT 4
name: h2o_feet
--------------
time water_level
2015-08-18T00:00:00Z 8
2015-08-18T00:00:00Z 2
2015-08-18T00:06:00Z 8
2015-08-18T00:06:00Z 2
The query returns the integer form of water_level’s float field values.
Example 2: Cast float field values to strings (this functionality is not supported)
> SELECT "water_level"::string FROM "h2o_feet" LIMIT 4
>
The query returns no data as casting a float field value to a string is not yet supported.
Merge Behavior
In InfluxDB, queries merge series automatically.
Example
The h2o_feet measurement in the NOAA_water_database is part of two series.
The first series is made up of the h2o_feet measurement and the location = coyote_creek tag.
The second series is made of up the h2o_feet measurement and the location = santa_monica tag.
The following query automatically merges those two series when it calculates the average water_level:
> SELECT MEAN("water_level") FROM "h2o_feet"
name: h2o_feet
--------------
time mean
1970-01-01T00:00:00Z 4.442107025822521
If you want the average water_level for the first series only, specify the relevant tag in the WHERE clause:
> SELECT MEAN("water_level") FROM "h2o_feet" WHERE "location" = 'coyote_creek'
name: h2o_feet
--------------
time mean
1970-01-01T00:00:00Z 5.359342451341401
If you want the average water_level for each individual series, include a GROUP BY clause:
> SELECT MEAN("water_level") FROM "h2o_feet" GROUP BY "location"
name: h2o_feet
tags: location=coyote_creek
time mean
---- ----
1970-01-01T00:00:00Z 5.359342451341401
name: h2o_feet
tags: location=santa_monica
time mean
---- ----
1970-01-01T00:00:00Z 3.530863470081006
Multiple Statements
Separate multiple SELECT statements in a query with a semicolon (;).
Examples:
In InfluxDB’s CLI:
> SELECT MEAN("water_level") FROM "h2o_feet"; SELECT "water_level" FROM "h2o_feet" LIMIT 2
name: h2o_feet
time mean
---- ----
1970-01-01T00:00:00Z 4.442107025822522
name: h2o_feet
time water_level
---- -----------
2015-08-18T00:00:00Z 8.12
2015-08-18T00:00:00Z 2.064
With InfluxDB’s HTTP API:
{
"results": [
{
"statement_id": 0,
"series": [
{
"name": "h2o_feet",
"columns": [
"time",
"mean"
],
"values": [
[
"1970-01-01T00:00:00Z",
4.442107025822522
]
]
}
]
},
{
"statement_id": 1,
"series": [
{
"name": "h2o_feet",
"columns": [
"time",
"water_level"
],
"values": [
[
"2015-08-18T00:00:00Z",
8.12
],
[
"2015-08-18T00:00:00Z",
2.064
]
]
}
]
}
]
}
Subqueries
A subquery is a query that is nested in the FROM clause of another query.
Use a subquery to apply a query as a condition in the enclosing query.
Subqueries offer functionality similar to nested functions and SQL
[HAVING clauses](https://en.wikipedia.org/wiki/Having_(SQL)).
Syntax
SELECT_clause FROM ( SELECT_statement ) [...]
Description of Syntax
InfluxDB performs the subquery first and the main query second.
The main query surrounds the subquery and requires at least the SELECT clause and the FROM clause.
The main query supports all clauses listed in this document.
The subquery appears in the main query’s FROM clause, and it requires surrounding parentheses.
The subquery supports all clauses listed in this document.
InfluxQL supports multiple nested subqueries per main query. Sample syntax for multiple subqueries:
SELECT_clause FROM ( SELECT_clause FROM ( SELECT_statement ) [...] ) [...]
Examples
Example 1: Calculate the SUM() of several MAX() values
> SELECT SUM("max") FROM (SELECT MAX("water_level") FROM "h2o_feet" GROUP BY "location")
name: h2o_feet
time sum
---- ---
1970-01-01T00:00:00Z 17.169
The query returns the sum of the maximum water_level values across every tag value of location.
InfluxDB first performs the subquery; it calculates the maximum value of water_level for each tag value of location:
> SELECT MAX("water_level") FROM "h2o_feet" GROUP BY "location"
name: h2o_feet
tags: location=coyote_creek
time max
---- ---
2015-08-29T07:24:00Z 9.964
name: h2o_feet
tags: location=santa_monica
time max
---- ---
2015-08-29T03:54:00Z 7.205
Next, InfluxDB performs the main query and calculates the sum of those maximum values: 9.964 + 7.205 = 17.169.
Notice that the main query specifies max, not water_level, as the field key in the SUM() function.
Example 2: Calculate the MEAN() difference between two fields
> SELECT MEAN("difference") FROM (SELECT "cats" - "dogs" AS "difference" FROM "pet_daycare")
name: pet_daycare
time mean
---- ----
1970-01-01T00:00:00Z 1.75
The query returns the average of the differences between the number of cats and dogs in the pet_daycare measurement.
InfluxDB first performs the subquery.
The subquery calculates the difference between the values in the cats field and the values in the dogs field,
and it names the output column difference:
> SELECT "cats" - "dogs" AS "difference" FROM "pet_daycare"
name: pet_daycare
time difference
---- ----------
2017-01-20T00:55:56Z -1
2017-01-21T00:55:56Z -49
2017-01-22T00:55:56Z 66
2017-01-23T00:55:56Z -9
Next, InfluxDB performs the main query and calculates the average of those differences.
Notice that the main query specifies difference as the field key in the MEAN() function.
Example 3: Calculate several MEAN() values and place a condition on those mean values
> SELECT "all_the_means" FROM (SELECT MEAN("water_level") AS "all_the_means" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m) ) WHERE "all_the_means" > 5
name: h2o_feet
time all_the_means
---- -------------
2015-08-18T00:00:00Z 5.07625
The query returns all mean values of the water_level field that are greater than five.
InfluxDB first performs the subquery.
The subquery calculates MEAN() values of water_level from 2015-08-18T00:00:00Z through 2015-08-18T00:30:00Z and groups the results into 12-minute intervals.
It also names the output column all_the_means:
> SELECT MEAN("water_level") AS "all_the_means" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m)
name: h2o_feet
time all_the_means
---- -------------
2015-08-18T00:00:00Z 5.07625
2015-08-18T00:12:00Z 4.950749999999999
2015-08-18T00:24:00Z 4.80675
Next, InfluxDB performs the main query and returns only those mean values that are greater than five.
Notice that the main query specifies all_the_means as the field key in the SELECT clause.
Example 4: Calculate the SUM() of several DERIVATIVE() values
> SELECT SUM("water_level_derivative") AS "sum_derivative" FROM (SELECT DERIVATIVE(MEAN("water_level")) AS "water_level_derivative" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m),"location") GROUP BY "location"
name: h2o_feet
tags: location=coyote_creek
time sum_derivative
---- --------------
1970-01-01T00:00:00Z -0.4950000000000001
name: h2o_feet
tags: location=santa_monica
time sum_derivative
---- --------------
1970-01-01T00:00:00Z -0.043999999999999595
The query returns the sum of the derivative of average water_level values for each tag value of location.
InfluxDB first performs the subquery.
The subquery calculates the derivative of average water_level values taken at 12-minute intervals.
It performs that calculation for each tag value of location and names the output column water_level_derivative:
> SELECT DERIVATIVE(MEAN("water_level")) AS "water_level_derivative" FROM "h2o_feet" WHERE time >= '2015-08-18T00:00:00Z' AND time <= '2015-08-18T00:30:00Z' GROUP BY time(12m),"location"
name: h2o_feet
tags: location=coyote_creek
time water_level_derivative
---- ----------------------
2015-08-18T00:12:00Z -0.23800000000000043
2015-08-18T00:24:00Z -0.2569999999999997
name: h2o_feet
tags: location=santa_monica
time water_level_derivative
---- ----------------------
2015-08-18T00:12:00Z -0.0129999999999999
2015-08-18T00:24:00Z -0.030999999999999694
Next, InfluxDB performs the main query and calculates the sum of the water_level_derivative values for each tag value of location.
Notice that the main query specifies water_level_derivative, not water_level or derivative, as the field key in the SUM() function.
Common Issues with Subqueries
Issue 1: Multiple SELECT statements in a subquery
InfluxQL supports multiple nested subqueries per main query:
SELECT_clause FROM ( SELECT_clause FROM ( SELECT_statement ) [...] ) [...]
------------------ ----------------
Subquery 1 Subquery 2
InfluxQL does not support multiple SELECT statements per subquery:
SELECT_clause FROM (SELECT_statement; SELECT_statement) [...]
The system returns a parsing error if a subquery includes multiple SELECT statements.