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
• Write a basic query

Basic query structure

The majority of basic Flux queries include the following steps:

• Source
• Filter
• Shape
• Process

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 the fn 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 keys
• window(): modify _start and _stop values of rows to group data by time
• pivot(): pivot column values into rows
• drop(): drop specific columns
• keep(): 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.

{{% note %}}

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. {{% /note %}}

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.

{{% note %}} Use the InfluxDB Data Explorer or the Flux REPL to build and execute the following basic query. {{% /note %}}

1. Import the influxdata/influxdb/sample package and use the sample.data() function to load the airSensor sample dataset.

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   import "influxdata/influxdb/sample"
   
   sample.data(set: "airSensor")
   

   {{% note %}} sample.data() returns data as if it was queried from InfluxDB. To actually query data from InfluxDB, replace sample.data() with the from() function. {{% /note %}}

2. Pipe the returned data forward into range() to filter the data by time. Return data from the last hour.

   jsCopy

   import "influxdata/influxdb/sample"
   
   sample.data(set: "airSensor")
       |> range(start: -1h)
   

3. Use filter() to filter rows based on column values. In this example, return only rows that include values for the co field. The field name is stored in the _field column.

   jsCopy

   import "influxdata/influxdb/sample"
   
   sample.data(set: "airSensor")
       |> range(start: -1h)
       |> filter(fn: (r) => r._field == "co")
   

4. Use mean() to calculate the average value in each input table. Because InfluxDB groups data by series, mean() returns a table for each unique sensor_id containing a single row with the average value in the _value column.

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   import "influxdata/influxdb/sample"
   
   sample.data(set: "airSensor")
       |> range(start: -1h)
       |> filter(fn: (r) => r._field == "co")
       |> mean()
   

5. Use group() to restructure tables into a single table:

   jsCopy

   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:

{{% note %}} _start and _stop columns have been omitted. {{% /note %}}

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