grafane 0.5

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Grafane

A very opinionated influxdb client that uses the official python client and is very much inspired in grafana's query builder.

Setup

pip install grafane

In order to query influxdb this library expects the following environment variables to be set:

• INFLUXDB_HOST: Defaults to 0.0.0.0
• INFLUXDB_PORT: Defaults to 8086
• INFLUXDB_DB: Defaults to metrics
• INFLUXDB_USER: Defaults to admin
• INFLUXDB_USER_PASSWORD: Defaults to admin123

Drop measurement

c = Grafane(metric='test')
c.drop_measurement() # Drops test from influxdb

Write

With:

points = [
    {
        'fields': {
            'value': 1.2,
            'value2': 1.3,
        },
        'tags': {
            'tag1': 'value1',
                    'tag2': 'value2'
        }
    },
    {
        'fields': {
            'value': 1.86,
            'value2': 2.3,
        },
        'tags': {
            'tag1': 'value2',
                    'tag2': 'value1'
        }
    },
    {
        'fields': {
            'value': 1.4,
            'value2': 1.1,
        },
        'tags': {
            'tag1': 'value3',
                    'tag2': 'value2'
        }
    },
    {
        'fields': {
            'value': 1.8,
            'value2': 1.95,
        },
        'tags': {
            'tag1': 'value1',
                    'tag2': 'value2'
        }
    },
]

You can do either do multiple single queries:

from grafane import Grafane
c = Grafane(metric='generic') # Metric defaults to generic
for p in points:
	c.report(p['fields'], p['tags'])

Or a single query with multiple points:

c.report_points(points)

if you don't provide time for a point it defaults to:

>>> datetime.utcnow().replace(tzinfo=pytz.utc)
datetime.datetime(2019, 2, 8, 19, 32, 38, 788003, tzinfo=<UTC>)

Read

Select

In [6]: c.select(fields='value')                                                                                                                                                                            

In [7]: c.execute_query()                                                                                                                                                                                   
Out[7]: 
[{'time': '2019-02-10T20:37:13.786477056Z', 'value': 1.2},
 {'time': '2019-02-10T20:37:13.786508032Z', 'value': 1.86},
 {'time': '2019-02-10T20:37:13.786518016Z', 'value': 1.4},
 {'time': '2019-02-10T20:37:13.786535936Z', 'value': 1.8}]

Select multiple fields

In [16]: c.select(fields=['value', 'value2'])                                                                                                                                                               

In [17]: c.execute_query()                                                                                                                                                                                  
Out[17]: 
[{'time': '2019-02-10T20:42:37.22864512Z', 'value': 1.2, 'value2': 1.3},
 {'time': '2019-02-10T20:42:37.228871936Z', 'value': 1.86, 'value2': 2.3},
 {'time': '2019-02-10T20:42:37.228883968Z', 'value': 1.4, 'value2': 1.1},
 {'time': '2019-02-10T20:42:37.22889216Z', 'value': 1.8, 'value2': 1.95}]

Select w/ aggregation

In [18]: c.select(fields='value', aggregation='sum')                                                                                                                                                        

In [19]: c.execute_query()                                                                                                                                                                                  
Out[19]: [{'time': '1970-01-01T00:00:00Z', 'sum': 6.26}]

Select multiple fields w/ aggregation

In [20]: c.select(fields=['value', 'value2'], aggregation=['sum', 'mean'])                                                                                                                                  

In [21]: c.execute_query()                                                                                                                                                                                  
Out[21]: [{'time': '1970-01-01T00:00:00Z', 'sum': 6.26, 'mean': 1.6625}]

Group aggregated results in time blocks

In [22]: c.select(fields=['value', 'value2'], aggregation=['sum', 'mean'])                                                                                                                                  

In [23]: c.time_block('1m')                                                                                                                                                                                 

In [24]: c.execute_query()                                                                                                                                                                                  
Out[24]: 
[{'time': '2019-02-10T20:42:00Z', 'sum': 6.26, 'mean': 1.6625},
 {'time': '2019-02-10T20:43:00Z', 'sum': None, 'mean': None},
 {'time': '2019-02-10T20:44:00Z', 'sum': None, 'mean': None},
 {'time': '2019-02-10T20:45:00Z', 'sum': None, 'mean': None},
 {'time': '2019-02-10T20:46:00Z', 'sum': None, 'mean': None},
 {'time': '2019-02-10T20:47:00Z', 'sum': None, 'mean': None}]

When grouping time blocks, in order to avoid empty rows you need to fill results with None

In [29]: c.select(fields=['value', 'value2'], aggregation=['sum', 'mean'])                                                                                                                                  

In [30]: c.time_block('1m')                                                                                                                                                                                 

In [31]: c.fill_with('none')                                                                                                                                                                                

In [32]: c.execute_query()                                                                                                                                                                                  
Out[32]: [{'time': '2019-02-10T20:42:00Z', 'sum': 6.26, 'mean': 1.6625}]

Group aggregated results by tag values

In [34]: c.select(fields=['value', 'value2'], aggregation=['sum', 'mean'])                                                                                                                                  

In [35]: c.group_by('tag1')                                                                                                                                                                                 

In [36]: c.execute_query()                                                                                                                                                                                  
Out[36]: 
[{'tags': {'tag1': 'value1'},
  'time': '1970-01-01T00:00:00Z',
  'sum': 3,
  'mean': 1.625},
 {'tags': {'tag1': 'value2'},
  'time': '1970-01-01T00:00:00Z',
  'sum': 1.86,
  'mean': 2.3},
 {'tags': {'tag1': 'value3'},
  'time': '1970-01-01T00:00:00Z',
  'sum': 1.4,
  'mean': 1.1}]