Monitoring

Data for monitoring

  Now we monitor two types of events for monitoring: request and error. The first type is all requests, second is failed requests only. Every event is a point in the time series. The point is represented as the union of the following data:

• series name (now requests and errors)

• start request time

• tags, indexed data in storage, dictionary: keys - string tag names, values - string, integer, float

• fields, non-indexed data in storage, dictionary: keys - string tag names, values - string, integer, float

‘Requests’ series. Triggered on every request. Each point contains a data about corresponding request (execution time and etc).

Requests series tags

tag name	description
service	“luna-python-matcher” or “luna-matcher-proxy”
route	concatenation of a request method and a request resource (POST:/matcher/faces)
status_code	http status code of response

Requests series fields

fields	description
request_id	request id
execution_time	request execution time

‘Matching-Process’ series. Triggered on every match action. Each point contains data about matching performance.

Matching-Process series tags

tag name	description
service	“luna-python-matcher” or “luna-matcher-proxy”
matching_type	type of matching
matching_candidate	candidate for matching (events_candidates, faces_candidates, attributes_candidates)

Matching-Process series additional tags

tag name	resource	description	service
matcher	match_face	preferred matcher	luna-matcher-proxy
matcher	match_body	preferred matcher	luna-matcher-proxy

Matching-Process series fields

fields	description
request_id	request id
matching_time	time taken to perform matching

Matching-Process series additional fields

fields	matching_type	description	service
list_id	match_face	list id used for cached list matching	luna-python-matcher
load_descriptor_time	match_body	time taken to load descriptor before matching *	luna-python-matcher
load_descriptor_time	match_face	time taken to load descriptor before matching *	luna-python-matcher
enrich_match_result_time	match_face	time taken to enrich cached list matching result with data	luna-python-matcher
enrich_match_result_time	match_face	time taken to enrich matching result with data	luna-matcher-proxy
enrich_match_result_time	match_body	time taken to enrich matching result with data	luna-matcher-proxy

* For events reference showed only if events_ids or external_ids not specified in candidates filters

‘Errors’ series. Triggered on failed request. Each point contains error_code of luna error.

Errors series tags

tag name	description
service	“luna_python_matcher” or “luna-matcher-proxy”
route	concatenation of a request method and a request resource (POST:/matcher)
status_code	http status code of response
error_code	luna error code

Errors series fields

fields	description
request_id	request id

Every handler can add additional tags or fields.

Database

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

Classes

Module contains points for monitoring.

class luna_python_matcher.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_python_matcher.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

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

Class fo storing an additional data for monitoring.

class luna_python_matcher.crutches_on_wheels.cow.monitoring.points.InfluxFormatter

Format any point filed into inline format

class luna_python_matcher.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

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_python_matcher.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_python_matcher.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

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_python_matcher.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_python_matcher.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_python_matcher.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_python_matcher.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_python_matcher.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

Returns:

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

Container for influx 2.x settings