Flux query basics
Most Flux queries follow the same basic structure. Familiarize yourself with the basic concepts and steps to use when querying data with Flux.
Basic query structure
The majority of basic Flux queries include the following steps:
from(bucket: "example-bucket") // ── Source
|> range(start: -1d) // ── Filter on time
|> filter(fn: (r) => r._field == "foo") // ── Filter on column values
|> group(columns: ["sensorID"]) // ── Shape
|> mean() // ── Process
Source
Flux input functions retrieve data from a data source. All input functions return a stream of tables.
Flux supports multiple data sources including, time series databases (such as InfluxDB and Prometheus), relational databases (such as MySQL and PostgreSQL), CSV, and more.
- For more information about supported data sources, see Query data sources.
- For a list of input functions, see Function types and categories – Inputs.
Filter
Filter functions iterate over and evaluate each input row to see if it matches specified conditions. Rows that meet the conditions are included in the function output. Rows that do not meet the specified conditions are dropped.
Flux provides the following primary filter functions:
range()
: filter data based on time.filter()
: filter data based on column values.filter()
uses a predicate function defined in thefn
parameter to evaluate input rows. Each row is passed into the predicate function as a record,r
, containing key-value pairs for each column in the row.
Other filter functions are also available. For more information, see Function types and categories – Filters.
Shape data
Many queries need to modify the structure of data to prepare it for processing. Common data-shaping tasks include regrouping data by column values or by time or pivoting column values into rows.
Functions that reshape data include the following:
group()
: modify group keyswindow()
: modify_start
and_stop
values of rows to group data by timepivot()
: pivot column values into rowsdrop()
: drop specific columnskeep()
: keep specific columns and drop all others
Process
Processing data can take on many forms, and includes the following types of operations:
- Aggregate data: aggregate all rows of an input table into a single row. For information, see Function types and categories - Aggregates.
- Select specific data points: return specific rows from each input table. For example, return the first or last row, the row with the highest or lowest value, and more. For information, see Function types and categories - Selectors.
- Rewrite rows: use
map()
to rewrite each input row. Transform values with mathematic operations, process strings, dynamically add new columns, and more. - Send notifications: evaluate data and use Flux notification endpoint functions to send notifications to external services. For information, see Function types and categories- Notification endpoints.
aggregateWindow helper function
aggregateWindow()
is a helper function
that both shapes and processes data.
The function windows and groups data by time, and then applies an aggregate
or selector function to the restructured tables.
Write a basic query
Use InfluxDB sample data to write a basic Flux query that queries data, filters the data by time and column values, and then applies an aggregate.
Use the InfluxDB Data Explorer or the Flux REPL to build and execute the following basic query.
Import the
influxdata/influxdb/sample
package and use thesample.data()
function to load theairSensor
sample dataset.import "influxdata/influxdb/sample" sample.data(set: "airSensor")
sample.data()
returns data as if it was queried from InfluxDB. To actually query data from InfluxDB, replacesample.data()
with thefrom()
function.Pipe the returned data forward into
range()
to filter the data by time. Return data from the last hour.import "influxdata/influxdb/sample" sample.data(set: "airSensor") |> range(start: -1h)
Use
filter()
to filter rows based on column values. In this example, return only rows that include values for theco
field. The field name is stored in the_field
column.import "influxdata/influxdb/sample" sample.data(set: "airSensor") |> range(start: -1h) |> filter(fn: (r) => r._field == "co")
Use
mean()
to calculate the average value in each input table. Because InfluxDB groups data by series,mean()
returns a table for each uniquesensor_id
containing a single row with the average value in the_value
column.import "influxdata/influxdb/sample" sample.data(set: "airSensor") |> range(start: -1h) |> filter(fn: (r) => r._field == "co") |> mean()
Use
group()
to restructure tables into a single table:import "influxdata/influxdb/sample" sample.data(set: "airSensor") |> range(start: -1h) |> filter(fn: (r) => r._field == "co") |> mean() |> group()
Results from this basic query should be similar to the following:
_start
and _stop
columns have been omitted.
_field | _measurement | sensor_id | _value |
---|---|---|---|
co | airSensors | TLM0100 | 0.42338714381053716 |
co | airSensors | TLM0101 | 0.4223251339463061 |
co | airSensors | TLM0102 | 0.8543452859060252 |
co | airSensors | TLM0103 | 0.2782783780205422 |
co | airSensors | TLM0200 | 4.612143110484339 |
co | airSensors | TLM0201 | 0.297474366047375 |
co | airSensors | TLM0202 | 0.3336370208486757 |
co | airSensors | TLM0203 | 0.4948166816959906 |
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