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RFC: Support for external observability providers - Tracer #2030

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@Vandita2020

Description

@Vandita2020

Is this related to an existing feature request or issue?

Issue: #1433
Logger RFC: #2014
Metrics RFC: #2015

Which AWS Lambda Powertools utility does this relate to?

Tracer

Summary

This RFC is one of the three that defines the format when setting up loggers, metrics and traces for better integration with other observability providers.

This RFC is specifically for the Tracer. Currently, we have undocumented BaseProvider for Tracer, but we need to decide more on what minimum features the BaseProvider should support. The RFC discusses on the features that could be a part of custom tracer for users to integrate other Observability providers easily.

Use case

The use case for this utility would be for developers who want to use other observability providers to trace their application, other than AWS X-Ray.

Proposal

Current tracer experience

The Powertools’ tracer utility is essentially a wrapper for the AWS X-Ray SDK. Some key features of this utility include auto capturing cold start as annotation, auto capturing responses or full exceptions as metadata, and auto-disabling when not running in AWS Lambda environment. Tracer also auto patches supported modules by AWS X-Ray.

from aws_lambda_powertools import Tracer
from aws_lambda_powertools.utilities.typing import LambdaContext

# Current experience in using metrics should not change
tracer = Tracer(service="ServerlessAirline")

def collect_payment(charge_id: str) -> str:
    return f"dummy payment collected for charge: {charge_id}"

@tracer.capture_lambda_handler
def handler(event: dict, context: LambdaContext) -> str:
    charge_id = event.get("charge_id", "")
    return collect_payment(charge_id=charge_id)
JSON output
{
    "trace_id": "1-5e367daf-6c7f6d9f6c3a6e5800c7d42d",
    "id": "e986a861d4590d97",
    "name": "payment",
    "start_time": 1580441546.023,
    "end_time": 1580441552.983,
    "http": {
        "request": {
            "method": "GET",
            "url": "https://api.example.com/",
            "client_ip": "192.168.1.1",
            "user_agent": "Mozilla/5.0",
        },
        "response": {
            "status": 200,
            "content_length": 1024,
            "headers": {
                "Content-Type": "application/json"
            }
        }
    },
    "subsegments": [
        {
            "id": "3b3b3d8ba74fa7fe",
            "name": "my-subsegment",
            "start_time": 1580441548.023,
            "end_time": 1580441551.983,
            "http": {
                "request": {
                    "method": "POST",
                    "url": "https://api.example.com/submit",
                    "headers": {
                        "Content-Type": "application/json",
                        "Authorization": "Bearer abc123"
                    },
                    "body": "{\"data\": \"example\"}"
                },
                "response": {
                    "status": 200,
                    "content_length": 128,
                    "headers": {
                        "Content-Type": "application/json"
                    }
                }
            },
            "annotations": {
                "example": "annotation"
            }
        }
    ],
    "annotations": {
        "example": "annotation"
    },
    "metadata": {
        "example": "metadata"
    }
}

Tracer proposal

We propose a new parameter to the existing tracer utility that developers can use to specify which observability provider they would like their traces to be pushed to. The below code snippet is a rudimentary look at how this utility can be used and how it will function. Out of the box, we will support DataDog. Other providers TBD

from aws_lambda_powertools import Tracer
from aws_lambda_powertools.utilities.typing import LambdaContext

tracer = Tracer(service="ServerlessAirline", format=Tracer.DATADOG)

def collect_payment(charge_id: str) -> str:
    return f"dummy payment collected for charge: {charge_id}"

@tracer.capture_lambda_handler
def handler(event: dict, context: LambdaContext) -> str:
    charge_id = event.get("charge_id", "")
    return collect_payment(charge_id=charge_id)
JSON output
{
   "trace_id": "3541457326329954564",
   "span_id": "467508042476235233",
   "parent_id": "3541457326329954564",
   "name": "payment",
   "resource": "GET /api",
   "start": 1647370203.4475,
   "duration": 0.0325,
   "service": "serverlessAirline",
   "type": "web",
   "meta": {
       "http": {
           "method": "GET",
           "url": "http://localhost:8000/api",
           "status_code": 200
       }
   }
}
Bring your own provider

If you would like to use an observability provider not supported out of the box, or define their own tracer functions, we will define an interface that the customer can implement and pass in to the Tracer class.

classDiagram
    class BaseProvider {
        +start_span() -> Span
        +end_span() -> Span
        +put_annotation(key: str, value: Union[str, numbers.Number, bool]) -> None
        +put_metadata(key: str, value: Any, namespace: str = "default") -> None
    }
    class CustomTracerProvider {
        +start_span() -> Span
        +end_span() -> Span
        +put_annotation(key: str, value: Union[str, numbers.Number, bool]) -> None
        +put_metadata(key: str, value: Any, namespace: str = "default") -> None
    }
    BaseProvider <|-- CustomTracerProvider
Loading

Example

from aws_lambda_powertools.tracing.tracer import Tracer
from aws_lambda_powertools.utilities.typing import LambdaContext
from aws_lambda_powertools.tracing.base import BaseProvider
from contextlib import contextmanager
import threading
import asyncio

class CustomTracerProvider(BaseProvider):
    _thread_local = threading.local()
    
    @contextmanager
    def trace_context(self):
        # when we enter this context, we start a new span and store its context
        if not hasattr(self._thread_local, "trace_id"):
            self._thread_local.trace_id = self.start_span()
        try:
            yield
        finally:
            self.end_span(self._thread_local.trace_id)
    
    @contextmanager
    def trace(self, span:"Span",parent_context: Optional[context_api.Context] = None) -> None:
        # when we enter this we start a child span with the given parent context
        try:
            self.start_span(self, span, parent_context=parent_context)
            yield 
        finally:
            self.end_span(self, span)
    
            
    def start_span(self, span, parent_context: Optional[context_api.Context] = None) -> span:
        """This method is proposed as a solution as it exists for other providers
        This method is responsible for starting the trace. This might involve initializing some data structures,
        connecting to an external service, or performing some other setup work"""
        return span()
    
    def end_span(self, span):
        """This method is proposed as a solution as it exists for other providers.
        This method is responsible for ending the tracing of a span. This might involve finalizing data structures,
        sending data to an external service, or performing some other cleanup work"""
    
    def put_annotation(self, key: str, value: Union[str, numbers.Number, bool]) -> None:
        """Annotate current active trace entity with a key-value pair."""
        
    def put_metadata(self, key: str, value: Any, namespace: str = "default") -> None:
        """Add metadata to the current active trace entity."""
    
    def add_exception(self, exception):
        """Add an exception to trace entities."""
    
    def ignore_endpoint(self, hostname: Optional[str] = None, urls: Optional[List[str]] = None):
        """To ignore the endpoints you don't want requests to be traced, 
        perhaps due to the volume of calls or sensitive URLs. """
    
    def inject_context(self,context):
        """To inject missing context/information like service name"""
    
    def capture_method_async(self, method:Callable, capture_response: Optional[Union[bool, str]] = None, capture_error: Optional[Union[bool, str]] = None):
        """To capture async method"""

tracer = Tracer({serviceName: 'serverlessAirline'},provider=CustomTracerProvider())

@tracer.capture_method_async
async def collect_payment_async(charge_id: str) -> str:
    tracer.put_annotation(key="PaymentId", value=charge_id)
    await asyncio.sleep(0.5)
    return f"dummy payment collected for charge: {charge_id}"
    
@tracer.capture_method
def collect_payment(charge_id: str) -> str:
    return f"dummy payment collected for charge: {charge_id}"

@tracer.capture_lambda_handler   
def handler(event: dict, context: LambdaContext) -> str:
    charge_id = event.get("charge_id", "")
    
    with tracer.provider.trace(span="charge"):
        return collect_payment(charge_id=charge_id)

The five methods defined above are a combination of methods that already exist in the BaseProvider and the ones that are most common in other observability providers.

The current BaseProvider does support most of the features used in the major observability providers. There are couple of differences I noticed while researching through the other Observability providers.

  1. There is difference in nomenclature used to define data that gets received from services, Powertools call them segments whereas other observability providers call them span.
  2. Observability providers like Datadog, Lumigo, NewRelic provides an option to start and end tracing through their start_span and end_span methods, whereas in Powertools we do not have such methods. The possible reason could be that AWS X-Ray anyway keeps track of how the request flows within the application from the start and if it doesn’t support any service, it gives an option to add a subsegment there to keep track of it. Whereas for most of the other providers we need to mention it to start and end tracing. We will add those methods so people can utilize them when using other providers with this capability.

Out of scope

Sending traces from Powertools to the customer's desired observability platform will not be in the scope of this project. The implementation should only support modifying the output of the Tracer so that the customer can push them to their platform of choice.

Potential challenges

We need to determine which platforms we want to support out-of-the-box (apart from Datadog).

Dependencies and Integrations

We will have to integrate with (and thus, have a dependency on) Datadog and any other platforms we decide to support out-of-the-box.

Alternative solutions

No response

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