https://github.com/garyd203/xraysink
Instrument asyncio Python for distributed tracing with AWS X-Ray.
https://github.com/garyd203/xraysink
asyncio asyncio-python aws-xray distributed-tracing hacktoberfest python python3
Last synced: 3 months ago
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Instrument asyncio Python for distributed tracing with AWS X-Ray.
- Host: GitHub
- URL: https://github.com/garyd203/xraysink
- Owner: garyd203
- License: apache-2.0
- Created: 2020-06-21T22:44:30.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-07-08T11:48:01.000Z (about 1 year ago)
- Last Synced: 2025-06-30T00:34:12.195Z (3 months ago)
- Topics: asyncio, asyncio-python, aws-xray, distributed-tracing, hacktoberfest, python, python3
- Language: Python
- Homepage:
- Size: 457 KB
- Stars: 23
- Watchers: 2
- Forks: 3
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# xraysink
Extra AWS X-Ray instrumentation to use distributed tracing with asyncio Python
libraries that are not (yet) supported by the official
[aws_xray_sdk](https://github.com/aws/aws-xray-sdk-python) library.
## What Problem Does xraysink Solve?
`aws_xray_sdk` is the standard library to collect trace data from your Python
code and send the trace data to the
[AWS X-Ray distributed tracing tool](https://aws.amazon.com/xray/). However,
if you have asyncio Python code, then there are some gaps and occasional
bugs in the functionality provided by that library. `xraysink` plugs those gaps.
It can be a bit confusing using two libraries together, so here's a high-level
breakdown of which library will help you do what:
* Add tracing to HTTP requests handled by FastAPI (or another async Python
web framework): `xraysink` (via [middleware](#fastapi))
* Add tracing to background (non-HTTP-request) functions written as async
Python functions: `xraysink` (via [xray_task_async](#background-jobstasks)
decorator)
* Everything else: [aws_xray_sdk](https://github.com/aws/aws-xray-sdk-python)
## Integrations Supported
* Generic ASGI-compatible tracing middleware for *any* ASGI-compliant web
framework. This has been tested with:
- [aiohttp server](https://docs.aiohttp.org/en/stable/)
- [FastAPI](https://fastapi.tiangolo.com/)
* asyncio [Task's](https://docs.python.org/3/library/asyncio-task.html)
* Background jobs/tasks
## Installation
xraysink is distributed as a standard python package through
[pypi](https://pypi.org/), so you can install it with your favourite Python
package manager. For example:
pip install xraysink
## How to use
`xraysink` augments the functionality provided by `aws_xray_sdk`. Before
using the tools in `xraysink`, you first need to configure `aws_xray_sdk` -
this will probably involve calling `xray_recorder.configure()` when your
process starts, and optionally `aws_xray_sdk.core.patch()`.
Extra instrumentation provided by `xraysink` is described below.
### FastAPI
Instrument incoming requests in your FastAPI web server by adding the
`xray_middleware` to your app. For example, you could do:
from starlette.middleware.base import BaseHTTPMiddleware
from xraysink.asgi.middleware import xray_middleware
# Standard asyncio X-Ray configuration, customise as you choose
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](https://docs.python.org/3/library/asyncio-task.html)
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 aws_xray_sdk.core import xray_recorder
from xraysink.context import AsyncContext # NB: Use the AsyncContext from xraysink
# Use the fixed AsyncContext when configuring X-Ray,
# and customise other configuration as you choose.
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:
from aws_xray_sdk.core import xray_recorder
from xraysink.tasks import xray_task_async
# Standard asyncio X-Ray configuration, customise as you choose
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)
# Start your background task using your scheduling system of choice :)
schedule_recurring_task(cleanup_stale_tokens)
If your background task functions are called from a function that is already
instrumented (eg. send an email immediately after handling a request), then
the background task will appear as a child segment of that trace. In this case,
you must ensure you use the non-buggy `AsyncContext` when configuring the recorder
(ie. `from xraysink.context import AsyncContext`)
### CloudWatch Logs integration
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})
1. 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 as part of your process
initialisation (eg. in the same place where you call
`xray_recorder.configure`).
set_xray_log_group("/example/service-name")
Note that this feature relies on undocumented functionality, and is
[not yet](https://github.com/aws/aws-xray-sdk-python/issues/188)
supported by the official Python SDK.
## Licence
This project uses the Apache 2.0 licence, to make it compatible with
[aws_xray_sdk](https://github.com/aws/aws-xray-sdk-python), the
primary library for integrating with AWS X-Ray.