temporal-python

E-commerce order processing implemented twice: once as a hand-rolled pipeline, once as a Temporal workflow. The point is the diff between them.

The pipeline

check inventory -> process payment -> reserve inventory -> ship -> notify customer

Any step can fail. In traditional_implementation.py that means bespoke retry loops, manual state tracking, and compensation logic that has to be kept in sync by hand. In temporal_workflows.py the same pipeline becomes OrderProcessingWorkflow: each step is an activity with a RetryPolicy, state is persisted after every activity, and a crashed worker resumes exactly where it stopped.

What the Temporal version shows:

• Automatic state persistence and recovery
• Per-activity retry policies with exponential backoff
• Activity timeouts and heartbeats
• Saga-style compensation (rollback_payment, rollback_inventory) when a later step fails
• OrderApprovalWorkflow: a long-running workflow that waits on a human signal

Layout

File	Purpose
traditional_implementation.py	Baseline pipeline without a workflow engine
temporal_activities.py	Activity definitions (OrderProcessingActivities)
temporal_workflows.py	OrderProcessingWorkflow, OrderApprovalWorkflow
temporal_worker.py	Worker that registers workflows and activities
temporal_client.py / simple_temporal_client.py	Start workflows and query results
*_fixed.py	Standalone variants that run without the package-relative imports

Run it

pip install temporalio
temporal server start-dev            # local Temporal server + UI on :8233

python temporal_worker_fixed.py      # terminal 1
python temporal_client_fixed.py      # terminal 2

Kill the worker mid-order and restart it: the workflow picks up from the last completed activity. Open http://localhost:8233 to watch the event history.

A walkthrough deck is in Temporal Demo_ E-commerce Order Processing x Arsh - README.pdf.