Monitoring

Data for monitoring

There are two types of events that are monitored: request (all requests) and error (failed requests only).

Every event is a point in the time series. The point is represented using the following data:

• series name (e.g. requests and errors)

• start request time (timestamp of the request start)

The tag is an indexed data in storage. It is represented as a dictionary, where

• keys - string tag names,

• values - string, integer or float.

The field is a non-indexed data in storage. It is represented as a dictionary, where

• keys - string field names,

• values - string, integer or float.

‘Requests’ series. Saving data for ‘Requests’ is triggered on every request. Each point contains data about the corresponding request (execution time and etc.).

• tags

  tag name	description
  service	always “luna-licenses”
  route	concatenation of a request method and a request resource (GET:/version)
  status_code	HTTP status code of response

• fields

  fields	description
  request_id	request ID
  execution_time	request execution time

‘Errors’ series. Saving data for ‘Errors’ is triggered when a request fails. Each point contains error_code of LUNA error.

• tags

  tag name	description
  service	always “luna-licenses”
  route	concatenation of a request method and a request resource (GET:/version)
  status_code	HTTP status code of response
  error_code	LUNA error code

• fields

  fields	description
  request_id	request ID

Every handler can add additional tags or fields.

‘Licensing’ series. Triggered on license check when starting the service as well as scheduled daily at midnight. Each point contains license check data.

• tags

  tag name	description
  service	always “luna-licenses”
  license_status	license status (“ok”, “warning”, “error”)

• fields

  fields	description
  license_period_rest	number of days before the license expires
  warnings	license warning messages
  errors	license error messages

Database

Monitoring is implemented as data sending to an influx database. You can specify your database credentials in configuration file in section “monitoring”.

Classes

Module contains points for monitoring.

class luna_licenses.crutches_on_wheels.cow.monitoring.points.BaseMonitoringPoint(eventTime)

Abstract class for points

eventTime

event time as timestamp

Type:

float

abstract property fields: Dict[str, int | float | str]

Get fields from point. We supposed that fields are not indexing data

Returns:

dict with fields.

abstract property tags: Dict[str, int | float | str]

Get tags from point. We supposed that tags are indexing data

Returns:

dict with tags.

class luna_licenses.crutches_on_wheels.cow.monitoring.points.BaseRequestMonitoringPoint(requestId, resource, method, requestTime, service, statusCode)

Base class for point which is associated with requests.

requestId

request id

Type:

str

route

concatenation of a request method and a request resource

Type:

str

service

service name

Type:

str

statusCode

status code of a request response.

Type:

int

property fields: Dict[str, int | float | str]

Get fields

Returns:

“request_id”

Return type:

dict with following keys

property tags: Dict[str, int | float | str]

Get tags

Returns:

“route”, “service”, “status_code”

Return type:

dict with following keys

class luna_licenses.crutches_on_wheels.cow.monitoring.points.DataForMonitoring(tags=<factory>, fields=<factory>)

Class fo storing an additional data for monitoring.

class luna_licenses.crutches_on_wheels.cow.monitoring.points.InfluxFormatter

Format any point filed into inline format

class luna_licenses.crutches_on_wheels.cow.monitoring.points.MonitoringPointInfluxFormatBuilder(name, bases, namespace, /, **kwargs)

Complement point class with explicit fields formatting function for the sake of better performance

To perform type format building target class must have ‘fields’ property return value annotated with TypedDict.

Target class might be configured via ‘Config’ class. Available options:

extraFields: whether class should handle additional fields or not

>>> from typing import TypedDict
>>>
>>> class MonitoringFields(TypedDict):
...     field1: str
...     field2: int
...     field3: float
...     field4: bool
>>>
>>> class BasePoint(BaseMonitoringPoint, metaclass=MonitoringPointInfluxFormatBuilder):
...
...     def __init__(self, fields: dict):
...         self._fields = fields
...
...     @property
...     def tags(self):
...         return {}
...
...     @property
...     def fields(self) -> MonitoringFields:
...         return self._fields
...
>>> class TestPointNoExtra(BasePoint):
...
...     class Config:
...         extraFields = False
...
>>> class TestPointWithExtra(BasePoint):
...     class Config:
...         extraFields = True
>>>
>>>
>>> point1 = TestPointNoExtra({"field1": "data", "field2": 1, "field3": 1.0, "field4": False})
>>> point2 = TestPointWithExtra({"field1": "data", "field2": 1, "field3": 1.0, "field4": False, "extra": True})
>>> print(point1.convertFieldsToInfluxLineProtocol())
field1="data",field2=1i,field3=1.000000,field4=False
>>> print(point2.convertFieldsToInfluxLineProtocol())
field1="data",field2=1i,field3=1.000000,field4=False,extra=True

classmethod buildInfluxFormats(annotations, extraFields)

Build map with influx formats for corresponding fields

Return type:

dict

Parameters:

• annotations (dict) – point fields type annotations

• extraFields (bool) – whether point uses extra fields or not

Returns:

dict of fields with their format

static convertFieldsToInfluxLineProtocolNoExtra(point)

Convert point fields into influx line protocol format without extra fields

static convertFieldsToInfluxLineProtocolWithExtra(point)

Convert point fields into influx line protocol format with extra fields

static getTypeFormat(_type, _field, extraFields)

Get field type format

Return type:

str

Parameters:

• _type (type) – field type

• _field (str) – field name

• extraFields (bool) – whether point uses extra fields or not

Returns:

string format of the field

class luna_licenses.crutches_on_wheels.cow.monitoring.points.RequestErrorMonitoringPoint(requestId, resource, method, errorCode, service, requestTime, statusCode, additionalTags=None, additionalFields=None)

Request monitoring point is suspended for monitoring requests errors (error codes)

errorCode

error code

Type:

int

additionalTags

additional tags which was specified for the request

Type:

dict

additionalFields

additional fields which was specified for the request

Type:

dict

property fields: Dict[str, int | float | str]

Get fields.

Returns:

dict with base fields and additional tags

series: str = 'errors'

series “errors”

property tags: Dict[str, int | float | str]

Get tags.

Returns:

dict with base tags, “error_code” and additional tags

class luna_licenses.crutches_on_wheels.cow.monitoring.points.RequestMonitoringPoint(requestId, resource, method, executionTime, requestTime, service, statusCode, additionalTags=None, additionalFields=None)

Request monitoring point is suspended for monitoring all requests and measure a request time etc.

executionTime

execution time

Type:

float

additionalTags

additional tags which was specified for the request

Type:

dict

additionalFields

additional fields which was specified for the request

Type:

dict

property fields: Dict[str, int | float | str]

Get fields.

Returns:

dict with base fields, “execution_time” and additional tags

series: str = 'requests'

series “request”

property tags: Dict[str, int | float | str]

Get tags.

Returns:

dict with base tags and additional tags

luna_licenses.crutches_on_wheels.cow.monitoring.points.getRoute(resource, method)

Return type:

str

Get a request route, concatenation of a request method and a request resource :param resource: resource :param method: method

Returns:

{resource}”

Return type:

“{method}

luna_licenses.crutches_on_wheels.cow.monitoring.points.monitorTime(monitoringData, fieldName)

Context manager for timing execution time.

Parameters:

• monitoringData – container for saving result

• fieldName – field name

Module implement base class for monitoring

class luna_licenses.crutches_on_wheels.cow.monitoring.manager.LunaMonitoringManager(settings, name, pluginManager)

Monitoring manager. Sends data to the monitoring storage and monitoring plugins. .. attribute:: settings

monitoring storage settings

async close()

Stop monitoring.

Return type:

None

flushPoints(points)

Flush point to monitoring.

Return type:

None

Parameters:

points – point

async initialize()

Initialize monitoring

Return type:

None

async probe()

Check that we can connect to the monitoring service with current configuration. Can be used without initialization

class luna_licenses.crutches_on_wheels.cow.monitoring.manager.MonitoringSettings(*args, **kwargs)

Monitoring settings protocol

class InfluxCredentials

Monitoring credentials

Module contains classes for sending a data to an influx monitoring.

class luna_licenses.crutches_on_wheels.cow.monitoring.influx_adapter.InfluxMonitoringAdapter(settings, flushingPeriod)

Influx 2.x adaptor. Suspended to send points to an influxdb

bucket

influx bucket name

Type:

str

addPointsToBuffer(points)

Add points to buffer.

Return type:

None

Parameters:

points – points

clearBuffer()

Clear monitoring buffer

Return type:

list[str]

static convertFieldsToInfluxLineProtocol(fields)

Convert field value to influx line protocol format

Return type:

str

Parameters:

fields – dict with values to convert

Returns:

line protocol string

generatePointStr(point)

Generate string from point

Return type:

str

Parameters:

point – point

Returns:

influx line protocol string

async stopMonitoring()

Stop monitoring (cancel all request and stop getting new).

Return type:

None

class luna_licenses.crutches_on_wheels.cow.monitoring.influx_adapter.InfluxSettings(url, bucket, organization, token)

Container for influx 2.x settings