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

• tags

  tag name	description
  service	always “lambda-<lambda-id>”
  route	concatenation of a request method and a request resource (POST:/main)
  status_code	http status code of response

• fields

  fields	description
  request_id	request id
  execution_time	request execution time

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

• tags

  tag name	description
  service	always “lambda-<lambda-id>”
  route	concatenation of a request method and a request resource (POST:/main)
  status_code	http status code of response
  error_code	luna error code

• fields

  fields	description
  request_id	request id

Custom Monitoring

For base monitoring usage information see monitoring.

It is possible to create custom monitoring points. For example, there is a need to send data about how long it took to download or process images.

It is possible to specify your own series, tags and fields, but there will always be a mandatory tag “service” with the “lambda-<lambda-id>” value.

For add custom monitoring point follow steps:

1. Add file monitoring_points.py to lambda archive with the following content:

   monitoring_points.py

   from luna_lambda_tools.public.monitoring import CustomMonitoringPoint
   
   
   class TestMonitoringPoint(CustomMonitoringPoint):
       """Test monitoring point"""
   
       series = "test_monitoring"
   

   There are several rules for this file:

   • The number of points can be any.

   • Each point class name may be specify by any unique name.

   • Each point class must be inherited by CustomMonitoringPoint

   • Each point class must contain series attribute to specify monitoring series.

2. Set monitoring points from monitoring_points.py to lambda_main.py and specify tags nad fields:

   lambda_main.py

   import asyncio
   from time import time
   
   from luna_lambda_tools import StandaloneLambdaRequest
   from monitoring_points import TestMonitoringPoint
   
   
   async def main(request: StandaloneLambdaRequest) -> dict:
       # start execution time
       stt = time()
   
       # do some logic
       await asyncio.sleep(1)
   
       # send monitoring point with execution time
       request.sendToMonitoring(
           (TestMonitoringPoint(pointTags={"lambda_type": "standalone"}, pointFields={"execution_time": time() - stt}),)
       )
       return {"result": "lambda result"}
   

   request example

   from luna3.luna_lambda.luna_lambda import LambdaApi
   
   SERVER_ORIGIN = "http://lambda_address:lambda_port"  # Replace by your values before start
   SERVER_API_VERSION = 1
   lambdaApi = LambdaApi(origin=SERVER_ORIGIN, api=SERVER_API_VERSION)
   lambdaId, accountId = "your_lambda_id", "your_account_id"  # Replace by your values before start
   
   
   def makeRequest():
       reply = lambdaApi.proxyLambdaPost(lambdaId=lambdaId, path="main", accountId=accountId)
       return reply
   
   
   if __name__ == "__main__":
       response = makeRequest()
       print(response.json)
   

   There are several rules for this file:

   • Enable monitoring. See INFLUX_MONITORING setting in basic principles of configuration.

   • Send points using request.sendToMonitoring function. If monitoring is unavailable, the points will not be sent without any errors.

   • Specify tags by set dictionary with tags to pointTags named argument.

   • Specify fileds by set dictionary with fields to pointFields named argument.

Database

Monitoring is implemented as data sending to an influx database.