InfluxDB access via IPython
Project description
- Author:
Robert Hajek, Bonitoo.io
Introduces a %flux (or %%flux) magic.
Connect to a InfluxDB and run Flux commands within IPython or IPython Notebook.
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
News
0.0.6
Release date: 06-11-2022
#5 - Fix setup.py formatting
0.0.5
Release date: 25-03-2022
0.0.4
Release date: 18-08-2020
#1: Fixed token argument starts with “-”
0.0.3
Release date: 12-08-2020
Updated tox.ini and requirement dependencies
0.0.2
Release date: 6-08-2020
Fixed connection creation from os enviroment variables
Added persist dataframe into InfluxDB
0.0.1
Release date: 21-07-2020
Initial release
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for ipython_flux-0.0.6-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1d6b18530f99a5f7293f9cc609780a3df1ae815a0ec0c7ad7d6e108d97fb5983 |
|
MD5 | 1ea7c9690b4aa81b509cb973485a9f4a |
|
BLAKE2b-256 | 47644b82a4b60682e73d07e9ffc2b3b2e973475299b169d0356d556d29cd0974 |