Monitoring

Data for monitoring

Now we monitor several types of events:

• request, all HTTP requests.

• error, all failed HTTP requests.

• subtask execution, some messages about a process of executing a task.

• subtask errors, all errors, generated during subtask processing.

• subtask final, messages about the end of subtask processing.

• worker requests, all messages, generated during subtask sending to workers.

• worker queue, some messages about a transportation task through a worker queue.

Every event is a point in the time series. The point is represented as a 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 HTTP request. Each point contains data about the corresponding request (execution time and etc).

• tags

  tag name	description
  service	“luna-tasks” or “luna-tasks-worker”
  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. Triggered on failed HTTP request. Each point contains error_code of luna error.

• tags

  tag name	description
  service	“luna-tasks” or “luna-tasks-worker”
  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

‘Subtask errors’ series. Triggered on an error in a pipeline of a task processing.

• tags

  tag name	description
  service	always “luna-tasks-worker”
  task_id	task id
  subtask_id	subtask id
  task_type	task type (1, 2, 3, …)
  error_code	luna error code

• fields

  fields	description
  error_code	luna error code

‘subtask execution’ series. Triggered on an error in a pipeline of a task processing.

• tags

  tag name	description
  service	always “luna-tasks-worker”
  task_id	task id
  subtask_id	subtask id
  task_type	task type (1, 2, 3, …)

• fields

  fields	description
  save_result_time	time of saving result in image-store (all task type)
  linked_faces	approximate linked faces count (linker task)
  events	removed event ids count (gc task)
  events_face_descriptors	removed face descriptors count (gc task)
  events_body_descriptors	removed body descriptors count (gc task)
  faces	removed face ids count (gc task)
  faces_face_descriptors	removed face descriptors count (gc task)
  match_time	matching time (crossmatching, clustering, roc task)
  clusterization_report_build_time	build report time (reporter task)
  track_id_collecting	track id (clustering task)
  export_build_time	export build time (exporter task)

‘subtask final’ series. Triggered on an end of subtask execution.

• tags

  tag name	description
  service	always “luna-tasks-worker”
  task_id	task id
  subtask_id	subtask id
  task_type	task type (1, 2, 3, …)
  status	task status (1, 2, 3, 4, 5)

• fields

  fields	description
  execution_time	execution subtask time

‘worker queue’ series. Triggered on a getting task from the worker queue.

• tags

  tag name	description
  service	always “luna-tasks-worker”
  task_id	task id
  subtask_id	subtask id
  task_type	task type (1, 2, 3, …)

• fields

  fields	description
  transport_time	time between receiving task of a worker and start execution

‘worker requests’ series. Triggered after sending subtask to a worker.

• tags

  tag name	description
  service	always “luna-tasks-worker”
  task_id	task id
  subtask_id	subtask id

• fields

  fields	description
  transport_time	initialize sending a task subtasks and send subtask to thr worker execution

It can be to 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_tasks.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_tasks.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_tasks.crutches_on_wheels.cow.monitoring.points.DataForMonitoring(tags=<factory>, fields=<factory>)

Class fo storing an additional data for monitoring.

class luna_tasks.crutches_on_wheels.cow.monitoring.points.InfluxFormatter

Format any point filed into inline format

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

Container for influx 2.x settings