A high-precision, real-time memory flow tracker and stress-tester for Python.
Stop guessing why your workers are crashing. Start auditing your memory lifecycle.
In modern Python development, projects tend to grow rapidly. What starts as a simple script often evolves into a massive Django or FastAPI monolith with dozens of applications, heavy ORM signals, and complex middleware.
In these environments, it is incredibly easy to accidentally explode host resources. A forgotten global cache, an unfiltered database cursor, or a heavy model loaded into memory creates what we call "Silent Bloat". While your script might seem to run fine once, it leaves behind a residual footprint.
In long-running worker environments (Celery, RQ), these small leaks accumulate until they trigger a catastrophic Out-Of-Memory (OOM) crash, potentially taking down your entire host. RAM-FLOW was built to detect these breakers before they reach production, giving you a surgical view of the memory lifecycle.
pip install python-ram-flow
from ramflow import tracker # 1. Capture infrastructure load (Django models, apps, etc.) tracker.log_django_bootstrap() @tracker.track("Oracle Data Extraction") def process_data(): # Your heavy logic here # RAM-FLOW monitors the 'Net Self' impact of this specific call pass # 2. Run and Generate the Platinum Dashboard process_data() tracker.generate_report(suffix="nightly_sync")
