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

Jupyter InfluxDB 2.0 Flux integration
https://github.com/bonitoo-io/ipython-flux

flux influxdb ipython jupyter magic python

Last synced: 20 days ago
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Jupyter InfluxDB 2.0 Flux integration

• Host: GitHub
• URL: https://github.com/bonitoo-io/ipython-flux
• Owner: bonitoo-io
• License: mit
• Created: 2020-07-21T14:31:24.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-11-06T20:50:09.000Z (about 2 years ago)
• Last Synced: 2024-11-13T15:56:27.552Z (about 2 months ago)
• Topics: flux, influxdb, ipython, jupyter, magic, python
• Language: Python
• Homepage:
• Size: 896 KB
• Stars: 6
• Watchers: 3
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

        
============

ipython-flux

============

.. image:: https://circleci.com/gh/bonitoo-io/ipython-flux.svg?style=svg

    :target: https://circleci.com/gh/bonitoo-io/ipython-flux

:Author: Robert Hajek, Bonitoo.io

Introduces a %flux (or %%flux) magic.

Connect to a InfluxDB and run Flux commands within IPython or IPython Notebook.

.. image:: https://raw.github.com/bonitoo-io/ipython-flux/master/examples/example.png

   :width: 600px

   :alt: screenshot of ipython-flux in the Notebook

Examples

--------

.. code-block:: python

    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:

.. code-block:: python

    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``:

.. code-block:: python

    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.  

.. code-block:: python

    In [1]: %flux --persist  --bucket my-bucket --measurement  --tags tag_column1,tag_column2

.. _Pandas: http://pandas.pydata.org/

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 ``

    Run Flux from file at this path

``-x`` / ``--close `` 

    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

.. code-block:: python

    In [1]: %load_ext flux

Development

-----------

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