Monitoring

Data for monitoring

Now we monitor two types of events for monitoring: request and error. First type is all requests, second is failed requests only.

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:

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

  • fields

    fields	description
    request_id	request id
    execution_time	request execution time

  ClickHouse JSON `data` field Example:

  {
      "service": "luna-admin",
      "route": "POST:/accounts",
      "status_code": 200,
      "request_id": "1536751345,6a5c2191-3e9b-f5a4-fc45-3abf43625c5f",
      "execution_time": 123.45
  }
  

• Errors series.

  Triggered on failed request. Each point contains error_code of luna error.

  InfluxDB:

  • tags

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

  • fields

    fields	description
    request_id	request id

  ClickHouse JSON `data` field Example:

  {
      "service": "luna-admin",
      "route": "POST:/accounts",
      "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”.

Aggregated statistics

For statistical purposes monitoring data is being aggregated and downsampled. Data includes average response time, total responses count, 95 and 99 percentiles, errors counts, sdk estimators usage. To enable aggregation you need to set MONITORING_AGGREGATION setting to 1

InfluxDB:

To perform this task influx tasks are used. Each task runs around 1am Moscow time, fetches all points from the previous day and performs required calculations. To change schedule either edit flux scripts in base_scripts/flux before creating tasks or edit tasks in the Influx UI after creating them. Resulting data is stored in the bucket luna_monitoring_aggregated, its created along with the tasks. To create tasks run use storages tool provided by VisionLabs.

ClickHouse

ClickHouseDB has a built-in mechanism for data aggregation called Materialised views, which allows collecting statistics on the fly.

Classes

Module contains points for monitoring.

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

Class fo storing an additional data for monitoring.

class luna_admin.crutches_on_wheels.cow.monitoring.points.InfluxFormatter

Format any point filed into inline format

class luna_admin.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

Return type:

dict

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

Parameters:

• _type (type) – field type

• _field (str) – field name

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

Return type:

str

Returns:

string format of the field

class luna_admin.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_admin.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_admin.crutches_on_wheels.cow.monitoring.points.getRoute(resource, method)

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

Returns:

{resource}”

Return type:

“{method}

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

Context manager for timing execution time.

Parameters:

• monitoringData (DataForMonitoring) – container for saving result

• fieldName (str) – field name

Module implement base class for monitoring

class luna_admin.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.

Parameters:

points (Iterable[TypeVar(TPoint, bound= BaseMonitoringPoint)]) – point

Return type:

None

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_admin.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_admin.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.

Parameters:

points (Iterable[BaseMonitoringPoint]) – points

Return type:

None

clearBuffer()

Clear monitoring buffer

Return type:

list[str]

static convertFieldsToInfluxLineProtocol(fields)

Convert field value to influx line protocol format

Parameters:

fields (dict) – dict with values to convert

Return type:

str

Returns:

line protocol string

generatePointStr(point)

Generate string from point

Parameters:

point (BaseMonitoringPoint) – point

Return type:

str

Returns:

influx line protocol string

async stopMonitoring()

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

Return type:

None

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

Container for influx 2.x settings