xraysink 1.2.0

Instrument asyncio Python for distributed tracing with AWS X-Ray.

xraysink (aka xray-asyncio)

Extra AWS X-Ray instrumentation to use distributed tracing with asyncio Python libraries that are not (yet) supported by the official aws_xray_sdk library.

Integrations Supported

• Generic ASGI-compatible tracing middleware for any ASGI-compliant web framework. This has been tested with:
  • aiohttp server
  • FastAPI
• asyncio Task's
• Background jobs/tasks

Installation

xraysink is distributed as a standard python package through pypi, so you can install it with your favourite Python package manager. For example:

pip install xraysink 

How to use

FastAPI

Instrument incoming requests in your FastAPI web server by adding the xray_middleware. For example:

# Basic asyncio X-Ray configuration
xray_recorder.configure(context=AsyncContext(), service="my-cute-little-service")

# Create a FastAPI app with various middleware
app = FastAPI()
app.add_middleware(MyTracingDependentMiddleware)  # Any middleware that is added earlier will have the X-Ray tracing context available to it
app.add_middleware(BaseHTTPMiddleware, dispatch=xray_middleware)

Asyncio Tasks

If you start asyncio Task's from a standard request handler, then the AWS X-Ray SDK will not correctly instrument any outgoing requests made inside those Tasks.

Use the fixed AsyncContext from xraysink as a drop-in replacement, like so:

from xraysink.context import AsyncContext  # Use the AsyncContext from xraysink
from aws_xray_sdk.core import xray_recorder

xray_recorder.configure(context=AsyncContext(use_task_factory=True))

Background Jobs/Tasks

If your process starts background tasks that make network calls (eg. to the database or an API in another service), then each execution of one of those tasks should be treated as a new X-Ray trace. Indeed, if you don't do so then you will likely get context_missing errors.

An async function that implements a background task can be easily instrumented using the @xray_task_async() decorator, like so:

# Basic asyncio X-Ray configuration
xray_recorder.configure(context=AsyncContext(), service="my-cute-little-service")

# Any call to this function will start a new X-Ray trace
@xray_task_async()
async def cleanup_stale_tokens():
    await database.get_table("tokens").delete(age__gt=1)

schedule_recurring_task(cleanup_stale_tokens)

Process-Level Configuration

You can link your X-Ray traces to your CloudWatch Logs log records, which enhances the integration with AWS CLoudWatch ServiceLens. Take the following steps:

1. Put the X-Ray trace ID into every log message. There is no convention for how to do this (it just has to appear verbatim in the log message somewhere), but if you are using structured logging then the convention is to use a field called traceId. Here's an example

   trace_id = xray_recorder.get_trace_entity().trace_id
   logging.getLogger("example").info("Hello World!", extra={"traceId": trace_id})
   

2. Explicitly set the name of the CloudWatch Logs log group associated with your process. There is no general way to detect the Log Group from inside the process, hence it requires manual configuration.

   set_xray_log_group("/example/service-name")
   

Note that this feature relies on undocumented functionality, and is not yet supported by the official Python SDK.

Licence

This project uses the Apache 2.0 licence, to make it compatible with aws_xray_sdk, the primary library for integrating with AWS X-Ray.