Examples

In [1]: %load_ext flux

In [2]: %%flux http://localhost:9999 --token "my-token" --org my-org
   ...: from(bucket: "apm_metricset")
   ...:   |> range(start: v.timeRangeStart, stop: v.timeRangeStop)
   ...:   |> filter(fn: (r) => r["_measurement"] == "apm_metricset")
   ...:   |> filter(fn: (r) => r["_field"] == "samples_system.process.cpu.total.norm.pct")
   ...:
Out[2]: ...

After the first connection, connect info can be omitted:

In [3]: %flux
   ...: from(bucket: "apm_metricset")
   ...:   |> range(start: v.timeRangeStart, stop: v.timeRangeStop)
   ...:   |> filter(fn: (r) => r["_measurement"] == "apm_metricset")
   ...:   |> filter(fn: (r) => r["_field"] == "samples_system.process.cpu.total.norm.pct")

Out[8]: ...

If no connect string is supplied, %flux will use environment variables INFLUXDB_V2_URL, INFLUXDB_V2_ORG, INFLUXDB_V2_TOKEN to create connection into InfluxDB.

Ordinary IPython assignment works for single-line %flux queries:

In [12]: result = %flux from(bucket: "my-bucket")  |> range(start: 0)

The << operator captures query results in a local variable, and can be used in multi-line %%flux:

In [19]: %%flux my_dataset <<
    ...: from(bucket: "my-bucket")
    ...: |> range(start: -30m)
    ...: |> filter(fn: (r) => r["_measurement"] == "cpu")
    ...: |> filter(fn: (r) => r["_field"] == "usage_idle" or r["_field"] == "usage_system" or r["_field"] == "usage_user")
    ...: |> filter(fn: (r) => r["cpu"] == "cpu-total")
    ...: |> drop(columns: ["_start", "_stop", "_result", "_measurement", "table", "_result"])
    ...: |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")

The result of the Flux command is automatically converted into Pandas dataframe. It is often useful to use Flux functions fieldsAsCol() or pivot() to convert data containing multiple timeseries into one dataset.

Persist dataframe

The --persist argument, with the name of a DataFrame object in memory will create a measurement in the database from the named DataFrame.

In [1]: %flux --persist <data_frame_variable_name> --bucket my-bucket --measurement <new measurement name> --tags tag_column1,tag_column2

Options

-l / --connections

List all active connections

-t / --token

InfluxDB token

-o / --org

InfluxDB org

--timeout

InfluxDB query timeout in milliseconds (default timeout is 10_000 ms)

-f / --file <path>

Run Flux from file at this path

-x / --close <session-name>

Close named connection

Persist options

-p / --persist

Create a measurement in the database from the named DataFrame

-b / --bucket

target bucket name

-T / --tags

comma separated list of columns that will be stored as tags, rest of columns will be stored as fields

-m / --measurement

optional, target measurement name, if not specified measurement is taken from dataframe name

Installing

Install the lastest release with:

pip install ipython-flux

or download from https://github.com/bonitoo-io/ipython-flux and:

cd ipython-flux
sudo python setup.py install

Enable IPython flux magic extension in Jupyter notebook using

In [1]: %load_ext flux

Development

https://github.com/bonitoo-io/ipython-flux

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