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authorDaniel Baumann <daniel.baumann@progress-linux.org>2024-04-19 02:57:58 +0000
committerDaniel Baumann <daniel.baumann@progress-linux.org>2024-04-19 02:57:58 +0000
commitbe1c7e50e1e8809ea56f2c9d472eccd8ffd73a97 (patch)
tree9754ff1ca740f6346cf8483ec915d4054bc5da2d /collectors/python.d.plugin/pandas/pandas.conf
parentInitial commit. (diff)
downloadnetdata-be1c7e50e1e8809ea56f2c9d472eccd8ffd73a97.tar.xz
netdata-be1c7e50e1e8809ea56f2c9d472eccd8ffd73a97.zip
Adding upstream version 1.44.3.upstream/1.44.3upstream
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
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+# netdata python.d.plugin configuration for pandas
+#
+# This file is in YaML format. Generally the format is:
+#
+# name: value
+#
+# There are 2 sections:
+# - global variables
+# - one or more JOBS
+#
+# JOBS allow you to collect values from multiple sources.
+# Each source will have its own set of charts.
+#
+# JOB parameters have to be indented (using spaces only, example below).
+
+# ----------------------------------------------------------------------
+# Global Variables
+# These variables set the defaults for all JOBs, however each JOB
+# may define its own, overriding the defaults.
+
+# update_every sets the default data collection frequency.
+# If unset, the python.d.plugin default is used.
+update_every: 5
+
+# priority controls the order of charts at the netdata dashboard.
+# Lower numbers move the charts towards the top of the page.
+# If unset, the default for python.d.plugin is used.
+# priority: 60000
+
+# penalty indicates whether to apply penalty to update_every in case of failures.
+# Penalty will increase every 5 failed updates in a row. Maximum penalty is 10 minutes.
+# penalty: yes
+
+# autodetection_retry sets the job re-check interval in seconds.
+# The job is not deleted if check fails.
+# Attempts to start the job are made once every autodetection_retry.
+# This feature is disabled by default.
+# autodetection_retry: 0
+
+# ----------------------------------------------------------------------
+# JOBS (data collection sources)
+#
+# The default JOBS share the same *name*. JOBS with the same name
+# are mutually exclusive. Only one of them will be allowed running at
+# any time. This allows autodetection to try several alternatives and
+# pick the one that works.
+#
+# Any number of jobs is supported.
+#
+# All python.d.plugin JOBS (for all its modules) support a set of
+# predefined parameters. These are:
+#
+# job_name:
+# name: myname # the JOB's name as it will appear on the dashboard
+# # dashboard (by default is the job_name)
+# # JOBs sharing a name are mutually exclusive
+# update_every: 1 # the JOB's data collection frequency
+# priority: 60000 # the JOB's order on the dashboard
+# penalty: yes # the JOB's penalty
+# autodetection_retry: 0 # the JOB's re-check interval in seconds
+#
+# Additionally to the above, example also supports the following:
+#
+# chart_configs: [<dictionary>] # an array for chart config dictionaries.
+#
+# ----------------------------------------------------------------------
+# AUTO-DETECTION JOBS
+
+# Some example configurations, enable this collector, uncomment and example below and restart netdata to enable.
+
+# example pulling some hourly temperature data, a chart for today forecast (mean,min,max) and another chart for current.
+# temperature:
+# name: "temperature"
+# update_every: 5
+# chart_configs:
+# - name: "temperature_forecast_by_city"
+# title: "Temperature By City - Today Forecast"
+# family: "temperature.today"
+# context: "pandas.temperature"
+# type: "line"
+# units: "Celsius"
+# df_steps: >
+# pd.DataFrame.from_dict(
+# {city: requests.get(f'https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lng}&hourly=temperature_2m').json()['hourly']['temperature_2m']
+# for (city,lat,lng)
+# in [
+# ('dublin', 53.3441, -6.2675),
+# ('athens', 37.9792, 23.7166),
+# ('london', 51.5002, -0.1262),
+# ('berlin', 52.5235, 13.4115),
+# ('paris', 48.8567, 2.3510),
+# ('madrid', 40.4167, -3.7033),
+# ('new_york', 40.71, -74.01),
+# ('los_angeles', 34.05, -118.24),
+# ]
+# }
+# );
+# df.describe(); # get aggregate stats for each city;
+# df.transpose()[['mean', 'max', 'min']].reset_index(); # just take mean, min, max;
+# df.rename(columns={'index':'city'}); # some column renaming;
+# df.pivot(columns='city').mean().to_frame().reset_index(); # force to be one row per city;
+# df.rename(columns={0:'degrees'}); # some column renaming;
+# pd.concat([df, df['city']+'_'+df['level_0']], axis=1); # add new column combining city and summary measurement label;
+# df.rename(columns={0:'measurement'}); # some column renaming;
+# df[['measurement', 'degrees']].set_index('measurement'); # just take two columns we want;
+# df.sort_index(); # sort by city name;
+# df.transpose(); # transpose so its just one wide row;
+# - name: "temperature_current_by_city"
+# title: "Temperature By City - Current"
+# family: "temperature.current"
+# context: "pandas.temperature"
+# type: "line"
+# units: "Celsius"
+# df_steps: >
+# pd.DataFrame.from_dict(
+# {city: requests.get(f'https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lng}&current_weather=true').json()['current_weather']
+# for (city,lat,lng)
+# in [
+# ('dublin', 53.3441, -6.2675),
+# ('athens', 37.9792, 23.7166),
+# ('london', 51.5002, -0.1262),
+# ('berlin', 52.5235, 13.4115),
+# ('paris', 48.8567, 2.3510),
+# ('madrid', 40.4167, -3.7033),
+# ('new_york', 40.71, -74.01),
+# ('los_angeles', 34.05, -118.24),
+# ]
+# }
+# );
+# df.transpose();
+# df[['temperature']];
+# df.transpose();
+
+# example showing a read_csv from a url and some light pandas data wrangling.
+# pull data in csv format from london demo server and then ratio of user cpus over system cpu averaged over last 60 seconds.
+# example_csv:
+# name: "example_csv"
+# update_every: 2
+# chart_configs:
+# - name: "london_system_cpu"
+# title: "London System CPU - Ratios"
+# family: "london_system_cpu"
+# context: "pandas"
+# type: "line"
+# units: "n"
+# df_steps: >
+# pd.read_csv('https://london.my-netdata.io/api/v1/data?chart=system.cpu&format=csv&after=-60', storage_options={'User-Agent': 'netdata'});
+# df.drop('time', axis=1);
+# df.mean().to_frame().transpose();
+# df.apply(lambda row: (row.user / row.system), axis = 1).to_frame();
+# df.rename(columns={0:'average_user_system_ratio'});
+# df*100;
+
+# example showing a read_json from a url and some light pandas data wrangling.
+# pull data in json format (using requests.get() if json data is too complex for pd.read_json() ) from london demo server and work out 'total_bandwidth'.
+# example_json:
+# name: "example_json"
+# update_every: 2
+# chart_configs:
+# - name: "london_system_net"
+# title: "London System Net - Total Bandwidth"
+# family: "london_system_net"
+# context: "pandas"
+# type: "area"
+# units: "kilobits/s"
+# df_steps: >
+# pd.DataFrame(requests.get('https://london.my-netdata.io/api/v1/data?chart=system.net&format=json&after=-1').json()['data'], columns=requests.get('https://london.my-netdata.io/api/v1/data?chart=system.net&format=json&after=-1').json()['labels']);
+# df.drop('time', axis=1);
+# abs(df);
+# df.sum(axis=1).to_frame();
+# df.rename(columns={0:'total_bandwidth'});
+
+# example showing a read_xml from a url and some light pandas data wrangling.
+# pull weather forecast data in xml format, use xpath to pull out temperature forecast.
+# example_xml:
+# name: "example_xml"
+# update_every: 2
+# line_sep: "|"
+# chart_configs:
+# - name: "temperature_forcast"
+# title: "Temperature Forecast"
+# family: "temp"
+# context: "pandas.temp"
+# type: "line"
+# units: "celsius"
+# df_steps: >
+# pd.read_xml('http://metwdb-openaccess.ichec.ie/metno-wdb2ts/locationforecast?lat=54.7210798611;long=-8.7237392806', xpath='./product/time[1]/location/temperature', parser='etree')|
+# df.rename(columns={'value': 'dublin'})|
+# df[['dublin']]|
+
+# example showing a read_sql from a postgres database using sqlalchemy.
+# note: example assumes a running postgress db on localhost with a netdata users and password netdata.
+# sql:
+# name: "sql"
+# update_every: 5
+# chart_configs:
+# - name: "sql"
+# title: "SQL Example"
+# family: "sql.example"
+# context: "example"
+# type: "line"
+# units: "percent"
+# df_steps: >
+# pd.read_sql_query(
+# sql='\
+# select \
+# random()*100 as metric_1, \
+# random()*100 as metric_2 \
+# ',
+# con=create_engine('postgresql://localhost/postgres?user=netdata&password=netdata')
+# );