Monitoring

Monitoring is implemented as sending data to the influx database.

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 (requests or errors)

• timestamp of the request start

• tags

• fields

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.

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-api”
  account_id	account id or none
  route	concatenation of a request method and a request resource (POST:/extractor)
  status_code	http status code of response

• fields

  fields	description
  request_id	request id
  execution_time	request execution time

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-api”
  account_id	account ID or none
  route	concatenation of a request method and a request resource (POST:/extractor)
  status_code	http status code of response
  error_code	LUNA PLATFRORM error code

• fields

  fields	description
  request_id	request id

Every handler can add additional tags or fields. For example, handler of resource /handlers/{handlerId}/events adds tag handler_id.

For a resource “/detector”, “/extractor”, “/handlers/{handler_id}/events” there are additional fields:

• fields

  fields	description
  task_execution_time	sdk task execution time
  results_queue_transport_time	transport time by results queue
  detector_queue_transport_time	transport time by detector queue
  extractor_queue_transport_time	transport time by extractor queue
  warp_attributes_estimator_queue_transport_time	transport time by warp attributes queue
  detector_stage_time	detector stage time
  extractor_stage_time	extractor stage time
  warp_estimator_stage_time	warp estimator stage time
  faces_detect_time	face detect time
  descriptors_extract_time	descriptors extract time
  basic_attributes_extract_time	basic attributes extract time
  gaze_direction_estimation_time	gaze direction estimation time
  head_pose_estimation_time	head pose execution time
  eyes_attributes_estimation_time	eyes attributes execution time
  ags_estimation_time	ags estimation time
  quality_estimation_time	quality estimation time
  emotions_estimation_time	emotions estimation time
  mouth_attributes_estimation_time	mouth attributes estimation time
  save_samples_time	save samples to image store time
  save_face_attributes_time	save face attributes to luna-faces time
  load_samples_time	load samples from image-store time
  load_images_for_processing_time	load images for processing time

Database

You can set your database credentials in configuration file in section “monitoring”.

Plugins

You can create your plugin for sending monitoring data. See plugins

Classes

Module contains points for monitoring’s.

class luna_api.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.

Return type:

Dict[str, Union[int, float, str]]

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

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

Returns:

dict with tags.

Return type:

Dict[str, Union[int, float, str]]

class luna_api.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

requestTime

a request processing start timestamp

Type:

float

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

Return type:

Dict[str, Union[int, float, str]]

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

Get tags

Returns:

“route”, “service”, “status_code”

Return type:

dict with following keys

Return type:

Dict[str, Union[int, float, str]]

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

Class fo storing an additional data for monitoring.

class luna_api.crutches_on_wheels.cow.monitoring.points.InfluxFormatter

Format any point filed into inline format

class luna_api.crutches_on_wheels.cow.monitoring.points.MonitroingPointInfluxFormatBuilder(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=MonitroingPointInfluxFormatBuilder):
...
...     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

Parameters:

• annotations (dict) – point fields type annotations

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

Returns:

dict of fields with their format

Return type:

dict

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

Parameters:

• _type (type) – field type

• _field (str) – field name

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

Returns:

string format of the field

Return type:

str

class luna_api.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

Return type:

Dict[str, Union[int, float, str]]

series: str = 'errors'

series “errors”

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

Get tags.

Returns:

dict with base tags, “error_code” and additional tags

Return type:

Dict[str, Union[int, float, str]]

class luna_api.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 and 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

Return type:

Dict[str, Union[int, float, str]]

series: str = 'requests'

series “request”

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

Get tags.

Returns:

dict with base tags and additional tags

Return type:

Dict[str, Union[int, float, str]]

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

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

Returns:

{resource}”

Return type:

“{method}

Return type:

str

luna_api.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_api.crutches_on_wheels.cow.monitoring.manager.LunaMonitoringManager(settings, 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.

Parameters:

points – point

Return type:

None

async initialize()

Initialize monitoring

Return type:

None

class luna_api.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_api.crutches_on_wheels.cow.monitoring.influx_adapter.BaseMonitoringAdapter(settings, flushingPeriod)

Base monitoring adapter.

backgroundScheduler

runner for periodic flushing monitoring points

Type:

AsyncIOScheduler

_buffer

list of buffering points which is waiting sending to influx

Type:

List[BaseRequestMonitoringPoint]

flushingPeriod

period of flushing points (in seconds)

Type:

float

logger

logger

Type:

Logger

_influxSettings

current influx settings

Type:

InfluxSettings

_job

sending monitoring data job

Type:

Job

addPointsToBuffer(points)

Add points to buffer.

Parameters:

points – points

Return type:

None

static convertFieldsToInfluxLineProtocol(fields)

Convert field value to influx line protocol format

Parameters:

fields – dict with values to convert

Retruns:

line protocol string

Return type:

str

generatePointStr(point)

Generate string from point

Parameters:

point – point

Returns:

influx line protocol string

Return type:

str

initializeScheduler()

Start the loop for sending data from the buffer to monitoring.

Return type:

None

stopScheduler()

Stop monitoring.

Return type:

None

updateFlushingPeriod(newPeriod)

Update flushing period :param newPeriod: new period

class luna_api.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

initializeMonitoring()

Initialize monitoring.

Return type:

None

async stopMonitoring()

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

Return type:

None

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

Container for influx 2.x settings