Hi everyone, I'm trying to use Traceloop to trace some background batch jobs we run nightly. The jobs process data for different clients, but in Traceloop, the spans from different jobs are getting tangled together into one trace. It's a mess and I can't tell which span belongs to which client's data.
I contacted support, but they just pointed me to the general docs on batching, which didn't help. Has anyone else run into this? How do you isolate traces for separate batch jobs, especially when they might be running in parallel? Is there a specific setting or context I need to be setting? I'm using the Python SDK.
Thanks for any pointers!
newbie
Ask me in a year
The support team's generic response is unfortunately common when you're dealing with non-trivial orchestration patterns. The core issue is trace context propagation outside of a normal request/response flow.
When your batch jobs run in parallel, they're likely sharing a default or root trace context because nothing is creating a new, isolated context per job. You need to manually manage this. Don't rely on auto-instrumentation for batch work. Before each job's execution, explicitly create a new trace context and set it as the current one. Use `tracer.start_as_current_span()` with a distinct name (include the client ID), and ensure all subsequent operations for that job happen within that context manager.
Also, check your concurrency model. Are you using threads? Processes? Each needs a different strategy for context isolation. If you're using something like Celery, you must inject the trace context into the task headers and restore it at the worker.
James K.
Yeah, that mixing is super frustrating and a classic gotcha with parallel batch jobs. The docs often assume a simple, linear flow. Since you're on Python, the key is to explicitly manage your contexts at the job launch point.
If you're using something like `concurrent.futures.ThreadPoolExecutor`, you need to create and activate a fresh trace context *inside* the function each worker thread executes, not in the main thread that dispatches them. A quick example:
```python
from opentelemetry import trace
def process_client(client_id):
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span(f"batch-job-{client_id}") as span:
span.set_attribute("client.id", client_id)
# Your actual job logic here
```
Are you by chance using a queue or task runner like Celery? The context propagation gets trickier there, you might need to manually pass trace context as baggage between processes.
— francesc
I'm also using ThreadPoolExecutor for some of our batch work, so that example is helpful. But does creating a fresh span at the start of each worker function guarantee they stay isolated if those workers make downstream HTTP calls or database queries? I've had issues where auto-instrumentation for libraries like requests or sqlalchemy can inadvertently pick up a context from another thread if you're not careful.
Also, how does this approach compare to manually managing the TracerProvider for each job? I've seen some older examples that create a new provider per process, but that seems heavy.