diff options
Diffstat (limited to 'collectors/python.d.plugin/pandas')
| -rw-r--r-- | collectors/python.d.plugin/pandas/metadata.yaml | 310 | ||||
| -rw-r--r-- | collectors/python.d.plugin/pandas/pandas.conf | 4 |
2 files changed, 311 insertions, 3 deletions
diff --git a/collectors/python.d.plugin/pandas/metadata.yaml b/collectors/python.d.plugin/pandas/metadata.yaml new file mode 100644 index 000000000..28a1d3b21 --- /dev/null +++ b/collectors/python.d.plugin/pandas/metadata.yaml @@ -0,0 +1,310 @@ +plugin_name: python.d.plugin +modules: + - meta: + plugin_name: python.d.plugin + module_name: pandas + monitored_instance: + name: Pandas + link: https://learn.netdata.cloud/docs/data-collection/generic-data-collection/structured-data-pandas + categories: + - data-collection.generic-data-collection + icon_filename: pandas.png + related_resources: + integrations: + list: [] + info_provided_to_referring_integrations: + description: "" + keywords: + - pandas + - python + most_popular: false + overview: + data_collection: + metrics_description: | + [Pandas](https://pandas.pydata.org/) is a de-facto standard in reading and processing most types of structured data in Python. + If you have metrics appearing in a CSV, JSON, XML, HTML, or [other supported format](https://pandas.pydata.org/docs/user_guide/io.html), + either locally or via some HTTP endpoint, you can easily ingest and present those metrics in Netdata, by leveraging the Pandas collector. + + This collector can be used to collect pretty much anything that can be read by Pandas, and then processed by Pandas. + + More detailed information can be found in the Netdata documentation [here](https://learn.netdata.cloud/docs/data-collection/generic-data-collection/structured-data-pandas). + method_description: | + The collector uses [pandas](https://pandas.pydata.org/) to pull data and do pandas-based preprocessing, before feeding to Netdata. + supported_platforms: + include: [] + exclude: [] + multi_instance: true + additional_permissions: + description: "" + default_behavior: + auto_detection: + description: "" + limits: + description: "" + performance_impact: + description: "" + setup: + prerequisites: + list: + - title: Python Requirements + description: | + This collector depends on some Python (Python 3 only) packages that can usually be installed via `pip` or `pip3`. + + ```bash + sudo pip install pandas requests + ``` + + Note: If you would like to use [`pandas.read_sql`](https://pandas.pydata.org/docs/reference/api/pandas.read_sql.html) to query a database, you will need to install the below packages as well. + + ```bash + sudo pip install 'sqlalchemy<2.0' psycopg2-binary + ``` + configuration: + file: + name: python.d/pandas.conf + description: "" + options: + description: | + There are 2 sections: + + * Global variables + * One or more JOBS that can define multiple different instances to monitor. + + The following options can be defined globally: priority, penalty, autodetection_retry, update_every, but can also be defined per JOB to override the global values. + + Additionally, the following collapsed table contains all the options that can be configured inside a JOB definition. + + Every configuration JOB starts with a `job_name` value which will appear in the dashboard, unless a `name` parameter is specified. + folding: + title: Config options + enabled: true + list: + - name: chart_configs + description: an array of chart configuration dictionaries + default_value: "[]" + required: true + - name: chart_configs.name + description: name of the chart to be displayed in the dashboard. + default_value: None + required: true + - name: chart_configs.title + description: title of the chart to be displayed in the dashboard. + default_value: None + required: true + - name: chart_configs.family + description: "[family](https://learn.netdata.cloud/docs/data-collection/chart-dimensions-contexts-and-families#family) of the chart to be displayed in the dashboard." + default_value: None + required: true + - name: chart_configs.context + description: "[context](https://learn.netdata.cloud/docs/data-collection/chart-dimensions-contexts-and-families#context) of the chart to be displayed in the dashboard." + default_value: None + required: true + - name: chart_configs.type + description: the type of the chart to be displayed in the dashboard. + default_value: None + required: true + - name: chart_configs.units + description: the units of the chart to be displayed in the dashboard. + default_value: None + required: true + - name: chart_configs.df_steps + description: a series of pandas operations (one per line) that each returns a dataframe. + default_value: None + required: true + - name: update_every + description: Sets the default data collection frequency. + default_value: 5 + required: false + - name: priority + description: Controls the order of charts at the netdata dashboard. + default_value: 60000 + required: false + - name: autodetection_retry + description: Sets the job re-check interval in seconds. + default_value: 0 + required: false + - name: penalty + description: Indicates whether to apply penalty to update_every in case of failures. + default_value: yes + required: false + - name: name + description: Job name. This value will overwrite the `job_name` value. 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. + default_value: "" + required: false + examples: + folding: + enabled: true + title: Config + list: + - name: Temperature API Example + folding: + enabled: true + description: example pulling some hourly temperature data, a chart for today forecast (mean,min,max) and another chart for current. + config: | + 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}¤t_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(); + - name: API CSV Example + folding: + enabled: true + description: example showing a read_csv from a url and some light pandas data wrangling. + config: | + 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; + - name: API JSON Example + folding: + enabled: true + description: example showing a read_json from a url and some light pandas data wrangling. + config: | + 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'}); + - name: XML Example + folding: + enabled: true + description: example showing a read_xml from a url and some light pandas data wrangling. + config: | + 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']]| + - name: SQL Example + folding: + enabled: true + description: example showing a read_sql from a postgres database using sqlalchemy. + config: | + 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') + ); + troubleshooting: + problems: + list: [] + alerts: [] + metrics: + folding: + title: Metrics + enabled: false + description: | + This collector is expecting one row in the final pandas DataFrame. It is that first row that will be taken + as the most recent values for each dimension on each chart using (`df.to_dict(orient='records')[0]`). + See [pd.to_dict()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_dict.html)." + availability: [] + scopes: + - name: global + description: | + These metrics refer to the entire monitored application. + labels: [] + metrics: [] diff --git a/collectors/python.d.plugin/pandas/pandas.conf b/collectors/python.d.plugin/pandas/pandas.conf index ca523ed36..74a7da3e9 100644 --- a/collectors/python.d.plugin/pandas/pandas.conf +++ b/collectors/python.d.plugin/pandas/pandas.conf @@ -61,9 +61,7 @@ update_every: 5 # # Additionally to the above, example also supports the following: # -# num_lines: 4 # the number of lines to create -# lower: 0 # the lower bound of numbers to randomly sample from -# upper: 100 # the upper bound of numbers to randomly sample from +# chart_configs: [<dictionary>] # an array for chart config dictionaries. # # ---------------------------------------------------------------------- # AUTO-DETECTION JOBS |