Every Flux query needs the following:
1. Define your data source
from() function defines an InfluxDB data source.
It requires a
For this example, use
telegraf/autogen, a combination of the default database and retention policy provided by the TICK stack.
2. Specify a time range
Flux requires a time range when querying time series data. “Unbounded” queries are very resource-intensive and as a protective measure, Flux will not query the database without a specified range.
Use the pipe-forward operator (
|>) to pipe data from your data source into the
function, which specifies a time range for your query.
It accepts two properties:
Ranges can be relative using negative durations
or absolute using timestamps.
Example relative time ranges
// Relative time range with start only. Stop defaults to now. from(bucket:"telegraf/autogen") |> range(start: -1h) // Relative time range with start and stop from(bucket:"telegraf/autogen") |> range(start: -1h, stop: -10m)
Relative ranges are relative to “now.”
Example absolute time range
from(bucket:"telegraf/autogen") |> range(start: 2018-11-05T23:30:00Z, stop: 2018-11-06T00:00:00Z)
Use the following:
For this guide, use the relative time range,
-15m, to limit query results to data from the last 15 minutes:
from(bucket:"telegraf/autogen") |> range(start: -15m)
3. Filter your data
Pass your ranged data into the
filter() function to narrow results based on data attributes or columns.
filter() function has one parameter,
fn, which expects an anonymous function
with logic that filters data based on columns or attributes.
Records or rows are passed into the
filter() function as an object (
The anonymous function takes the object and evaluates it to see if it matches the defined filters.
AND relational operator to chain multiple filters.
// Pattern (r) => (r.objectProperty comparisonOperator comparisonExpression) // Example with single filter (r) => (r._measurement == "cpu") // Example with multiple filters (r) => (r._measurement == "cpu") and (r._field != "usage_system" )
Use the following:
For this example, filter by the
cpu measurement, the
usage_system field, and the
cpu-total tag value:
from(bucket:"telegraf/autogen") |> range(start: -15m) |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system" and r.cpu == "cpu-total" )
4. Yield your queried data
yield() function to output the filtered tables as the result of the query.
from(bucket:"telegraf/autogen") |> range(start: -15m) |> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system" and r.cpu == "cpu-total" ) |> yield()
Chronograf and the
influxCLI automatically assume a
yield()function at the end of each script in order to output and visualize the data. Best practice is to include a
yield()function, but it is not always necessary.
You have now queried data from InfluxDB using Flux. This is a barebones query that can now be transformed in other ways.