Merging upstream version 1.45.3+dfsg.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 211 insertions, 0 deletions

diff --git a/src/collectors/python.d.plugin/pandas/pandas.conf b/src/collectors/python.d.plugin/pandas/pandas.conf
new file mode 100644
index 000000000..74a7da3e9
--- /dev/null
+++ b/src/collectors/python.d.plugin/pandas/pandas.conf
@@ -0,0 +1,211 @@
+# 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')
+#             );