Monitoring

Data for monitoring

Comparison of data formats for Clickhouse and InfluxDB:

InfluxDB: Each event is presented as a “point” in a time series. The structure of a point includes:

• series name

• start event 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

Clickhouse: In Clickhouse, the data structure resembles that of a traditional SQL table. Each event is represented as a record, where:

• The `time` field contains the record’s creation timestamp;

• The `data` field contains a JSON object with all the information that would otherwise be distributed across tags and fields in InfluxDB.

Important: In Clickhouse, there is no differentiation between “tags” and “fields”—all data is consolidated into a single JSON object within the data field.

Monitoring series

The structure and the meaning of each monitoring series remain consistent. However, for Clickhouse, data from tags and fields are merged into a single JSON object under the data field. Below are examples for each series:

• Requests series.

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

  InfluxDB:

  Requests series tags

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

  Requests series fields

  fields	description
  request_id	request ID
  execution_time	request execution time

  ClickHouse JSON `data` field Example:

  {
      "service": "luna-python-matcher",
      "route": "POST:/matcher/faces",
      "status_code": 200,
      "request_id": "1536751345,6a5c2191-3e9b-f5a4-fc45-3abf43625c5f",
      "execution_time": 120
  }
  

• ‘matching_process’ series.

  Triggered on every match action. Each point contains data about matching performance.

  InfluxDB:

  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_batch_size	match_face	list count 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

  ClickHouse JSON `data` field Example:

  {
      "service": "luna-python-matcher",
      "matching_type": "face_matching",
      "matching_candidate": "faces_candidates",
      "matcher": "match_face",
      "request_id": "1536751345,6a5c2191-3e9b-f5a4-fc45-3abf43625c5f",
      "matching_time": 20,
      "list_batch_size": 1000,
      "load_descriptor_time": 15,
      "enrich_match_result_time": 5
  }
  

  * 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.

  InfluxDB:

  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

  ClickHouse JSON `data` field Example:

  {
      "service": "luna-python-matcher",
      "route": "POST:/sdk",
      "status_code": 400,
      "error_code": 13037,
      "request_id": "1536751345,6a5c2191-3e9b-f5a4-fc45-3abf43625c5f",
  }
  

Every handler can add additional tags or fields.

Database

You can refer to documentation for influx database and clickhouse database to compare the databases and choose what benefit your needs more. Note that clickhouse might be the better choice for aggregation You can setup 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.

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_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

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

Get tags

Returns:

“route”, “service”, “status_code”

Return type:

dict with following keys

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

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_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

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_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

series: str = 'requests'

series “request”

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

Get tags.

Returns:

dict with base tags and additional tags

luna_python_matcher.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_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, 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_python_matcher.crutches_on_wheels.cow.monitoring.manager.MonitoringSettings(*args, **kwargs)

Monitoring settings protocol

class ClickhouseCredentials

Monitoring credentials

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

Container for influx 2.x settings