InfluxQL Continuous Queries
See the equivalent InfluxDB v2 documentation: Process data with InfluxDB tasks.
Introduction
Continuous queries (CQ) are InfluxQL queries that run automatically and periodically on realtime data and store query results in a specified measurement.
| Basic Syntax | Advanced Syntax | CQ Management |
| Examples of Basic Syntax | Examples of Advanced Syntax | CQ Use Cases |
| Common Issues with Basic Syntax | Common Issues with Advanced Syntax | Further information |
Syntax
Basic syntax
CREATE CONTINUOUS QUERY <cq_name> ON <database_name>
BEGIN
<cq_query>
END
Description of basic syntax
The cq_query
The cq_query requires a
function,
an INTO clause,
and a GROUP BY time() clause:
SELECT <function[s]> INTO <destination_measurement> FROM <measurement> [WHERE <stuff>] GROUP BY time(<interval>)[,<tag_key[s]>]
Note: Notice that the
cq_querydoes not require a time range in aWHEREclause. InfluxDB automatically generates a time range for thecq_querywhen it executes the CQ. Any user-specified time ranges in thecq_query’sWHEREclause will be ignored by the system.
Schedule and coverage
Continuous queries operate on real-time data.
They use the local server’s timestamp, the GROUP BY time() interval, and
InfluxDB database’s preset time boundaries to determine when to execute and what time
range to cover in the query.
CQs execute at the same interval as the cq_query’s GROUP BY time() interval,
and they run at the start of the InfluxDB database’s preset time boundaries.
If the GROUP BY time() interval is one hour, the CQ executes at the start of
every hour.
When the CQ executes, it runs a single query for the time range between
now() and now() minus the
GROUP BY time() interval.
If the GROUP BY time() interval is one hour and the current time is 17:00,
the query’s time range is between 16:00 and 16:59.999999999.
Examples of basic syntax
The examples below use the following sample data in the transportation
database.
The measurement bus_data stores 15-minute resolution data on the number of bus
passengers and complaints:
name: bus_data
--------------
time passengers complaints
2016-08-28T07:00:00Z 5 9
2016-08-28T07:15:00Z 8 9
2016-08-28T07:30:00Z 8 9
2016-08-28T07:45:00Z 7 9
2016-08-28T08:00:00Z 8 9
2016-08-28T08:15:00Z 15 7
2016-08-28T08:30:00Z 15 7
2016-08-28T08:45:00Z 17 7
2016-08-28T09:00:00Z 20 7
Automatically downsampling data
Use a simple CQ to automatically downsample data from a single field and write the results to another measurement in the same database.
CREATE CONTINUOUS QUERY "cq_basic" ON "transportation"
BEGIN
SELECT mean("passengers") INTO "average_passengers" FROM "bus_data" GROUP BY time(1h)
END
cq_basic calculates the average hourly number of passengers from the
bus_data measurement and stores the results in the average_passengers
measurement in the transportation database.
cq_basic executes at one-hour intervals, the same interval as the
GROUP BY time() interval.
Every hour, cq_basic runs a single query that covers the time range between
now() and now() minus the GROUP BY time() interval, that is, the time
range between now() and one hour prior to now().
Annotated log output on the morning of August 28, 2016:
>
At **8:00** `cq_basic` executes a query with the time range `time >= '7:00' AND time < '08:00'`.
`cq_basic` writes one point to the `average_passengers` measurement:
>
name: average_passengers
------------------------
time mean
2016-08-28T07:00:00Z 7
>
At **9:00** `cq_basic` executes a query with the time range `time >= '8:00' AND time < '9:00'`.
`cq_basic` writes one point to the `average_passengers` measurement:
>
name: average_passengers
------------------------
time mean
2016-08-28T08:00:00Z 13.75
Here are the results:
> SELECT * FROM "average_passengers"
name: average_passengers
------------------------
time mean
2016-08-28T07:00:00Z 7
2016-08-28T08:00:00Z 13.75
Automatically downsampling data into another retention policy
Fully qualify
the destination measurement to store the downsampled data in a non-DEFAULT
retention policy (RP).
CREATE CONTINUOUS QUERY "cq_basic_rp" ON "transportation"
BEGIN
SELECT mean("passengers") INTO "transportation"."three_weeks"."average_passengers" FROM "bus_data" GROUP BY time(1h)
END
cq_basic_rp calculates the average hourly number of passengers from the
bus_data measurement and stores the results in the transportation database,
the three_weeks RP, and the average_passengers measurement.
cq_basic_rp executes at one-hour intervals, the same interval as the
GROUP BY time() interval.
Every hour, cq_basic_rp runs a single query that covers the time range between
now() and now() minus the GROUP BY time() interval, that is, the time
range between now() and one hour prior to now().
Annotated log output on the morning of August 28, 2016:
>
At **8:00** `cq_basic_rp` executes a query with the time range `time >= '7:00' AND time < '8:00'`.
`cq_basic_rp` writes one point to the `three_weeks` RP and the `average_passengers` measurement:
>
name: average_passengers
------------------------
time mean
2016-08-28T07:00:00Z 7
>
At **9:00** `cq_basic_rp` executes a query with the time range
`time >= '8:00' AND time < '9:00'`.
`cq_basic_rp` writes one point to the `three_weeks` RP and the `average_passengers` measurement:
>
name: average_passengers
------------------------
time mean
2016-08-28T08:00:00Z 13.75
Here are the results:
> SELECT * FROM "transportation"."three_weeks"."average_passengers"
name: average_passengers
------------------------
time mean
2016-08-28T07:00:00Z 7
2016-08-28T08:00:00Z 13.75
cq_basic_rp uses CQs and retention policies to automatically downsample data
and keep those downsampled data for an alternative length of time.
See the Downsampling and Data Retention
guide for an in-depth discussion about this CQ use case.
Automatically downsampling a database with backreferencing
Use a function with a wildcard (*) and INTO query’s
backreferencing syntax
to automatically downsample data from all measurements and numerical fields in
a database.
CREATE CONTINUOUS QUERY "cq_basic_br" ON "transportation"
BEGIN
SELECT mean(*) INTO "downsampled_transportation"."autogen".:MEASUREMENT FROM /.*/ GROUP BY time(30m),*
END
cq_basic_br calculates the 30-minute average of passengers and complaints
from every measurement in the transportation database (in this case, there’s only the
bus_data measurement).
It stores the results in the downsampled_transportation database.
cq_basic_br executes at 30 minutes intervals, the same interval as the
GROUP BY time() interval.
Every 30 minutes, cq_basic_br runs a single query that covers the time range
between now() and now() minus the GROUP BY time() interval, that is,
the time range between now() and 30 minutes prior to now().
Annotated log output on the morning of August 28, 2016:
>
At **7:30**, `cq_basic_br` executes a query with the time range `time >= '7:00' AND time < '7:30'`.
`cq_basic_br` writes two points to the `bus_data` measurement in the `downsampled_transportation` database:
>
name: bus_data
--------------
time mean_complaints mean_passengers
2016-08-28T07:00:00Z 9 6.5
>
At **8:00**, `cq_basic_br` executes a query with the time range `time >= '7:30' AND time < '8:00'`.
`cq_basic_br` writes two points to the `bus_data` measurement in the `downsampled_transportation` database:
>
name: bus_data
--------------
time mean_complaints mean_passengers
2016-08-28T07:30:00Z 9 7.5
>
[...]
>
At **9:00**, `cq_basic_br` executes a query with the time range `time >= '8:30' AND time < '9:00'`.
`cq_basic_br` writes two points to the `bus_data` measurement in the `downsampled_transportation` database:
>
name: bus_data
--------------
time mean_complaints mean_passengers
2016-08-28T08:30:00Z 7 16
Here are the results:
> SELECT * FROM "downsampled_transportation."autogen"."bus_data"
name: bus_data
--------------
time mean_complaints mean_passengers
2016-08-28T07:00:00Z 9 6.5
2016-08-28T07:30:00Z 9 7.5
2016-08-28T08:00:00Z 8 11.5
2016-08-28T08:30:00Z 7 16
Automatically downsampling data and configuring CQ time boundaries
Use an
offset interval
in the GROUP BY time() clause to alter both the CQ’s default execution time and
preset time boundaries.
CREATE CONTINUOUS QUERY "cq_basic_offset" ON "transportation"
BEGIN
SELECT mean("passengers") INTO "average_passengers" FROM "bus_data" GROUP BY time(1h,15m)
END
cq_basic_offsetcalculates the average hourly number of passengers from the
bus_data measurement and stores the results in the average_passengers
measurement.
cq_basic_offset executes at one-hour intervals, the same interval as the
GROUP BY time() interval.
The 15 minute offset interval forces the CQ to execute 15 minutes after the
default execution time; cq_basic_offset executes at 8:15 instead of 8:00.
Every hour, cq_basic_offset runs a single query that covers the time range
between now() and now() minus the GROUP BY time() interval, that is, the
time range between now() and one hour prior to now().
The 15 minute offset interval shifts forward the generated preset time boundaries in the
CQ’s WHERE clause; cq_basic_offset queries between 7:15 and 8:14.999999999 instead of 7:00 and 7:59.999999999.
Annotated log output on the morning of August 28, 2016:
>
At **8:15** `cq_basic_offset` executes a query with the time range `time >= '7:15' AND time < '8:15'`.
`cq_basic_offset` writes one point to the `average_passengers` measurement:
>
name: average_passengers
------------------------
time mean
2016-08-28T07:15:00Z 7.75
>
At **9:15** `cq_basic_offset` executes a query with the time range `time >= '8:15' AND time < '9:15'`.
`cq_basic_offset` writes one point to the `average_passengers` measurement:
>
name: average_passengers
------------------------
time mean
2016-08-28T08:15:00Z 16.75
Here are the results:
> SELECT * FROM "average_passengers"
name: average_passengers
------------------------
time mean
2016-08-28T07:15:00Z 7.75
2016-08-28T08:15:00Z 16.75
Notice that the timestamps are for 7:15 and 8:15 instead of 7:00 and 8:00.
Common issues with basic syntax
Handling time intervals with no data
CQs do not write any results for a time interval if no data fall within that time range.
Note that the basic syntax does not support using
fill()
to change the value reported for intervals with no data.
Basic syntax CQs ignore fill() if it’s included in the CQ query.
A possible workaround is to use the
advanced CQ syntax.
Resampling previous time intervals
The basic CQ runs a single query that covers the time range between now()
and now() minus the GROUP BY time() interval.
See the advanced syntax for how to configure the query’s
time range.
Backfilling results for older data
CQs operate on realtime data, that is, data with timestamps that occur
relative to now().
Use a basic
INTO query
to backfill results for data with older timestamps.
Missing tags in the CQ results
By default, all
INTO queries
convert any tags in the source measurement to fields in the destination
measurement.
Include GROUP BY * in the CQ to preserve tags in the destination measurement.
Advanced syntax
CREATE CONTINUOUS QUERY <cq_name> ON <database_name>
RESAMPLE EVERY <interval> FOR <interval>
BEGIN
<cq_query>
END
Description of advanced syntax
The cq_query
See Description of Basic Syntax.
Scheduling and coverage
CQs operate on real-time data. With the advanced syntax, CQs use the local
server’s timestamp, the information in the RESAMPLE clause, and the InfluxDB
server’s preset time boundaries to determine when to execute and what time range to
cover in the query.
CQs execute at the same interval as the EVERY interval in the RESAMPLE
clause, and they run at the start of InfluxDB’s preset time boundaries.
If the EVERY interval is two hours, InfluxDB executes the CQ at the top of
every other hour.
When the CQ executes, it runs a single query for the time range between
now() and now() minus the FOR interval in the RESAMPLE clause.
If the FOR interval is two hours and the current time is 17:00, the query’s
time range is between 15:00 and 16:59.999999999.
Both the EVERY interval and the FOR interval accept
duration literals.
The RESAMPLE clause works with either or both of the EVERY and FOR intervals
configured.
CQs default to the relevant
basic syntax behavior
if the EVERY interval or FOR interval is not provided (see the first issue in
Common Issues with Advanced Syntax
for an anomalous case).
Examples of advanced syntax
The examples below use the following sample data in the transportation database.
The measurement bus_data stores 15-minute resolution data on the number of bus
passengers:
name: bus_data
--------------
time passengers
2016-08-28T06:30:00Z 2
2016-08-28T06:45:00Z 4
2016-08-28T07:00:00Z 5
2016-08-28T07:15:00Z 8
2016-08-28T07:30:00Z 8
2016-08-28T07:45:00Z 7
2016-08-28T08:00:00Z 8
2016-08-28T08:15:00Z 15
2016-08-28T08:30:00Z 15
2016-08-28T08:45:00Z 17
2016-08-28T09:00:00Z 20
Configuring execution intervals
Use an EVERY interval in the RESAMPLE clause to specify the CQ’s execution
interval.
CREATE CONTINUOUS QUERY "cq_advanced_every" ON "transportation"
RESAMPLE EVERY 30m
BEGIN
SELECT mean("passengers") INTO "average_passengers" FROM "bus_data" GROUP BY time(1h)
END
cq_advanced_every calculates the one-hour average of passengers
from the bus_data measurement and stores the results in the
average_passengers measurement in the transportation database.
cq_advanced_every executes at 30-minute intervals, the same interval as the
EVERY interval.
Every 30 minutes, cq_advanced_every runs a single query that covers the time
range for the current time bucket, that is, the one-hour time bucket that
intersects with now().
Annotated log output on the morning of August 28, 2016:
>
At **8:00**, `cq_advanced_every` executes a query with the time range `WHERE time >= '7:00' AND time < '8:00'`.
`cq_advanced_every` writes one point to the `average_passengers` measurement:
>
name: average_passengers
------------------------
time mean
2016-08-28T07:00:00Z 7
>
At **8:30**, `cq_advanced_every` executes a query with the time range `WHERE time >= '8:00' AND time < '9:00'`.
`cq_advanced_every` writes one point to the `average_passengers` measurement:
>
name: average_passengers
------------------------
time mean
2016-08-28T08:00:00Z 12.6667
>
At **9:00**, `cq_advanced_every` executes a query with the time range `WHERE time >= '8:00' AND time < '9:00'`.
`cq_advanced_every` writes one point to the `average_passengers` measurement:
>
name: average_passengers
------------------------
time mean
2016-08-28T08:00:00Z 13.75
Here are the results:
> SELECT * FROM "average_passengers"
name: average_passengers
------------------------
time mean
2016-08-28T07:00:00Z 7
2016-08-28T08:00:00Z 13.75
Notice that cq_advanced_every calculates the result for the 8:00 time interval
twice.
First, it runs at 8:30 and calculates the average for every available data point
between 8:00 and 9:00 (8,15, and 15).
Second, it runs at 9:00 and calculates the average for every available data
point between 8:00 and 9:00 (8, 15, 15, and 17).
Because of the way InfluxDB
handles duplicate points
, the second result simply overwrites the first result.
Configuring time ranges for resampling
Use a FOR interval in the RESAMPLE clause to specify the length of the CQ’s
time range.
CREATE CONTINUOUS QUERY "cq_advanced_for" ON "transportation"
RESAMPLE FOR 1h
BEGIN
SELECT mean("passengers") INTO "average_passengers" FROM "bus_data" GROUP BY time(30m)
END
cq_advanced_for calculates the 30-minute average of passengers
from the bus_data measurement and stores the results in the average_passengers
measurement in the transportation database.
cq_advanced_for executes at 30-minute intervals, the same interval as the
GROUP BY time() interval.
Every 30 minutes, cq_advanced_for runs a single query that covers the time
range between now() and now() minus the FOR interval, that is, the time
range between now() and one hour prior to now().
Annotated log output on the morning of August 28, 2016:
>
At **8:00** `cq_advanced_for` executes a query with the time range `WHERE time >= '7:00' AND time < '8:00'`.
`cq_advanced_for` writes two points to the `average_passengers` measurement:
>
name: average_passengers
------------------------
time mean
2016-08-28T07:00:00Z 6.5
2016-08-28T07:30:00Z 7.5
>
At **8:30** `cq_advanced_for` executes a query with the time range `WHERE time >= '7:30' AND time < '8:30'`.
`cq_advanced_for` writes two points to the `average_passengers` measurement:
>
name: average_passengers
------------------------
time mean
2016-08-28T07:30:00Z 7.5
2016-08-28T08:00:00Z 11.5
>
At **9:00** `cq_advanced_for` executes a query with the time range `WHERE time >= '8:00' AND time < '9:00'`.
`cq_advanced_for` writes two points to the `average_passengers` measurement:
>
name: average_passengers
------------------------
time mean
2016-08-28T08:00:00Z 11.5
2016-08-28T08:30:00Z 16
Notice that cq_advanced_for will calculate the result for every time interval
twice.
The CQ calculates the average for the 7:30 time interval at 8:00 and at 8:30,
and it calculates the average for the 8:00 time interval at 8:30 and 9:00.
Here are the results:
> SELECT * FROM "average_passengers"
name: average_passengers
------------------------
time mean
2016-08-28T07:00:00Z 6.5
2016-08-28T07:30:00Z 7.5
2016-08-28T08:00:00Z 11.5
2016-08-28T08:30:00Z 16
Configuring execution intervals and CQ time ranges
Use an EVERY interval and FOR interval in the RESAMPLE clause to specify
the CQ’s execution interval and the length of the CQ’s time range.
CREATE CONTINUOUS QUERY "cq_advanced_every_for" ON "transportation"
RESAMPLE EVERY 1h FOR 90m
BEGIN
SELECT mean("passengers") INTO "average_passengers" FROM "bus_data" GROUP BY time(30m)
END
cq_advanced_every_for calculates the 30-minute average of
passengers from the bus_data measurement and stores the results in the
average_passengers measurement in the transportation database.
cq_advanced_every_for executes at one-hour intervals, the same interval as the
EVERY interval.
Every hour, cq_advanced_every_for runs a single query that covers the time
range between now() and now() minus the FOR interval, that is, the time
range between now() and 90 minutes prior to now().
Annotated log output on the morning of August 28, 2016:
>
At **8:00** `cq_advanced_every_for` executes a query with the time range `WHERE time >= '6:30' AND time < '8:00'`.
`cq_advanced_every_for` writes three points to the `average_passengers` measurement:
>
name: average_passengers
------------------------
time mean
2016-08-28T06:30:00Z 3
2016-08-28T07:00:00Z 6.5
2016-08-28T07:30:00Z 7.5
>
At **9:00** `cq_advanced_every_for` executes a query with the time range `WHERE time >= '7:30' AND time < '9:00'`.
`cq_advanced_every_for` writes three points to the `average_passengers` measurement:
>
name: average_passengers
------------------------
time mean
2016-08-28T07:30:00Z 7.5
2016-08-28T08:00:00Z 11.5
2016-08-28T08:30:00Z 16
Notice that cq_advanced_every_for will calculate the result for every time
interval twice.
The CQ calculates the average for the 7:30 interval at 8:00 and 9:00.
Here are the results:
> SELECT * FROM "average_passengers"
name: average_passengers
------------------------
time mean
2016-08-28T06:30:00Z 3
2016-08-28T07:00:00Z 6.5
2016-08-28T07:30:00Z 7.5
2016-08-28T08:00:00Z 11.5
2016-08-28T08:30:00Z 16
Configuring CQ time ranges and filling empty results
Use a FOR interval and fill() to change the value reported for time
intervals with no data.
Note that at least one data point must fall within the FOR interval for fill()
to operate.
If no data fall within the FOR interval the CQ writes no points to the
destination measurement.
CREATE CONTINUOUS QUERY "cq_advanced_for_fill" ON "transportation"
RESAMPLE FOR 2h
BEGIN
SELECT mean("passengers") INTO "average_passengers" FROM "bus_data" GROUP BY time(1h) fill(1000)
END
cq_advanced_for_fill calculates the one-hour average of passengers from the
bus_data measurement and stores the results in the average_passengers
measurement in the transportation database.
Where possible, it writes the value 1000 for time intervals with no results.
cq_advanced_for_fill executes at one-hour intervals, the same interval as the
GROUP BY time() interval.
Every hour, cq_advanced_for_fill runs a single query that covers the time
range between now() and now() minus the FOR interval, that is, the time
range between now() and two hours prior to now().
Annotated log output on the morning of August 28, 2016:
>
At **6:00**, `cq_advanced_for_fill` executes a query with the time range `WHERE time >= '4:00' AND time < '6:00'`.
`cq_advanced_for_fill` writes nothing to `average_passengers`; `bus_data` has no data
that fall within that time range.
>
At **7:00**, `cq_advanced_for_fill` executes a query with the time range `WHERE time >= '5:00' AND time < '7:00'`.
`cq_advanced_for_fill` writes two points to `average_passengers`:
>
name: average_passengers
------------------------
time mean
2016-08-28T05:00:00Z 1000 <------ fill(1000)
2016-08-28T06:00:00Z 3 <------ average of 2 and 4
>
[...]
>
At **11:00**, `cq_advanced_for_fill` executes a query with the time range `WHERE time >= '9:00' AND time < '11:00'`.
`cq_advanced_for_fill` writes two points to `average_passengers`:
>
name: average_passengers
------------------------
2016-08-28T09:00:00Z 20 <------ average of 20
2016-08-28T10:00:00Z 1000 <------ fill(1000)
>
At 12:00, cq_advanced_for_fill executes a query with the time range WHERE time >= '10:00' AND time < '12:00'.
cq_advanced_for_fill writes nothing to average_passengers; bus_data has no data
that fall within that time range.
Here are the results:
> SELECT * FROM "average_passengers"
name: average_passengers
------------------------
time mean
2016-08-28T05:00:00Z 1000
2016-08-28T06:00:00Z 3
2016-08-28T07:00:00Z 7
2016-08-28T08:00:00Z 13.75
2016-08-28T09:00:00Z 20
2016-08-28T10:00:00Z 1000
Note:
fill(previous)doesn’t fill the result for a time interval if the previous value is outside the query’s time range. See Frequently Asked Questions for more information.
Common issues with advanced syntax
If the EVERY interval is greater than the GROUP BY time() interval
If the EVERY interval is greater than the GROUP BY time() interval, the CQ
executes at the same interval as the EVERY interval and runs a single query
that covers the time range between now() and now() minus the EVERY
interval (not between now() and now() minus the GROUP BY time() interval).
For example, if the GROUP BY time() interval is 5m and the EVERY interval
is 10m, the CQ executes every ten minutes.
Every ten minutes, the CQ runs a single query that covers the time range
between now() and now() minus the EVERY interval, that is, the time
range between now() and ten minutes prior to now().
This behavior is intentional and prevents the CQ from missing data between execution times.
If the FOR interval is less than the execution interval
If the FOR interval is less than the GROUP BY time() interval or, if
specified, the EVERY interval, InfluxDB returns the following error:
error parsing query: FOR duration must be >= GROUP BY time duration: must be a minimum of <minimum-allowable-interval> got <user-specified-interval>
To avoid missing data between execution times, the FOR interval must be equal
to or greater than the GROUP BY time() interval or, if specified, the EVERY
interval.
Currently, this is the intended behavior. GitHub Issue #6963 outlines a feature request for CQs to support gaps in data coverage.
Continuous query management
Only admin users are allowed to work with CQs. For more on user privileges, see Authentication and Authorization.
Listing continuous queries
List every CQ on an InfluxDB instance with:
SHOW CONTINUOUS QUERIES
SHOW CONTINUOUS QUERIES groups results by database.
Examples
The output shows that the telegraf and mydb databases have CQs:
> SHOW CONTINUOUS QUERIES
name: _internal
---------------
name query
name: telegraf
--------------
name query
idle_hands CREATE CONTINUOUS QUERY idle_hands ON telegraf BEGIN SELECT min(usage_idle) INTO telegraf.autogen.min_hourly_cpu FROM telegraf.autogen.cpu GROUP BY time(1h) END
feeling_used CREATE CONTINUOUS QUERY feeling_used ON telegraf BEGIN SELECT mean(used) INTO downsampled_telegraf.autogen.:MEASUREMENT FROM telegraf.autogen./.*/ GROUP BY time(1h) END
name: downsampled_telegraf
--------------------------
name query
name: mydb
----------
name query
vampire CREATE CONTINUOUS QUERY vampire ON mydb BEGIN SELECT count(dracula) INTO mydb.autogen.all_of_them FROM mydb.autogen.one GROUP BY time(5m) END
Deleting continuous queries
Delete a CQ from a specific database with:
DROP CONTINUOUS QUERY <cq_name> ON <database_name>
DROP CONTINUOUS QUERY returns an empty result.
Examples
Drop the idle_hands CQ from the telegraf database:
> DROP CONTINUOUS QUERY "idle_hands" ON "telegraf"`
>
Altering continuous queries
CQs cannot be altered once they’re created.
To change a CQ, you must DROP and reCREATE it with the updated settings.
Continuous query statistics
If query-stats-enabled is set to true in your influxdb.conf or using the INFLUXDB_CONTINUOUS_QUERIES_QUERY_STATS_ENABLED environment variable, data will be written to _internal with information about when continuous queries ran and their duration.
Information about CQ configuration settings is available in the Configuration documentation.
Note:
_internalhouses internal system data and is meant for internal use. The structure of and data stored in_internalcan change at any time. Use of this data falls outside the scope of official InfluxData support.
Continuous query use cases
Downsampling and Data Retention
Use CQs with InfluxDB database retention policies (RPs) to mitigate storage concerns. Combine CQs and RPs to automatically downsample high precision data to a lower precision and remove the dispensable, high precision data from the database.
See the Downsampling and data retention guide for a detailed walkthrough of this common use case.
Precalculating expensive queries
Shorten query runtimes by pre-calculating expensive queries with CQs. Use a CQ to automatically downsample commonly queried, high precision data to a lower precision. Queries on lower precision data require fewer resources and return faster.
Tip: Pre-calculate queries for your preferred graphing tool to accelerate the population of graphs and dashboards.
Substituting for a HAVING clause
InfluxQL does not support HAVING clauses.
Get the same functionality by creating a CQ to aggregate the data and querying
the CQ results to apply the HAVING clause.
Note: InfluxQL supports subqueries which also offer similar functionality to
HAVINGclauses. See Data Exploration for more information.
Example
InfluxDB does not accept the following query with a HAVING clause.
The query calculates the average number of bees at 30 minute intervals and
requests averages that are greater than 20.
SELECT mean("bees") FROM "farm" GROUP BY time(30m) HAVING mean("bees") > 20
To get the same results:
1. Create a CQ
This step performs the mean("bees") part of the query above.
Because this step creates CQ you only need to execute it once.
The following CQ automatically calculates the average number of bees at
30 minutes intervals and writes those averages to the mean_bees field in the
aggregate_bees measurement.
CREATE CONTINUOUS QUERY "bee_cq" ON "mydb" BEGIN SELECT mean("bees") AS "mean_bees" INTO "aggregate_bees" FROM "farm" GROUP BY time(30m) END
2. Query the CQ results
This step performs the HAVING mean("bees") > 20 part of the query above.
Query the data in the measurement aggregate_bees and request values of the mean_bees field that are greater than 20 in the WHERE clause:
SELECT "mean_bees" FROM "aggregate_bees" WHERE "mean_bees" > 20
Substituting for nested functions
Some InfluxQL functions support nesting of other functions. Most do not. If your function does not support nesting, you can get the same functionality using a CQ to calculate the inner-most function. Then simply query the CQ results to calculate the outer-most function.
Note: InfluxQL supports subqueries which also offer the same functionality as nested functions. See Data Exploration for more information.
Example
InfluxDB does not accept the following query with a nested function.
The query calculates the number of non-null values
of bees at 30 minute intervals and the average of those counts:
SELECT mean(count("bees")) FROM "farm" GROUP BY time(30m)
To get the same results:
1. Create a CQ
This step performs the count("bees") part of the nested function above.
Because this step creates a CQ you only need to execute it once.
The following CQ automatically calculates the number of non-null values of bees at 30 minute intervals
and writes those counts to the count_bees field in the aggregate_bees measurement.
CREATE CONTINUOUS QUERY "bee_cq" ON "mydb" BEGIN SELECT count("bees") AS "count_bees" INTO "aggregate_bees" FROM "farm" GROUP BY time(30m) END
2. Query the CQ results
This step performs the mean([...]) part of the nested function above.
Query the data in the measurement aggregate_bees to calculate the average of the
count_bees field:
SELECT mean("count_bees") FROM "aggregate_bees" WHERE time >= <start_time> AND time <= <end_time>
Further information
To see how to combine two InfluxDB features, CQs, and retention policies, to periodically downsample data and automatically expire the dispensable high precision data, see Downsampling and data retention.
Kapacitor, InfluxData’s data processing engine, can do the same work as continuous queries in InfluxDB databases.
To learn when to use Kapacitor instead of InfluxDB and how to perform the same CQ functionality with a TICKscript, see examples of continuous queries in Kapacitor.
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