Python Monitoring

What is Python Monitoring?

Let Scout do the hard work of discovering performance issues in your Python Monitoring applications so you don't have to. Scout is the only Python Monitoring Application Performance Monitoring (APM) product that identifies and prioritizes performance and stability issues such as N+1 database queries, slow database queries, bloat memory, performance abnormalities, and more in Python applications.

Scout is easy to install (it's just a Python package) and monitors popular Python frameworks like Django, Flask, Celery, and more, with almost no configuration.

Key features of Python Monitoring

Automatically reports key Python monitoring metrics

After you install the Python Monitoring package, Scout automatically tracks your performance KPIs. In a dashboard, you'll see:

• Response time (mean and 95th percentile)
• Apdex Score
• Throughput (requests per minute)
• Web Transactions
• Error rate
• Memory usage

Instruments many Python Monitoring libraries automatically

Scout instrumentes a wide variety of Python libraries - in most cases, there's no need to add custom instrumentation. Some of the Scout Instruments libraries automatically:

Datastores

• SQLAlchemy
• ElasticSearch
• Mongo
• Redis

Structures and libraries

• Bottle
• CherryPy
• Celery
• Django
• Dramatiq
• Falcon
• Flask
• Huey
• Nameko
• Pyramid
• RQ
• Starlette
• Jinja
• Urllib3/HTTP

Bloat memory detection

Scout calculates the memory boost caused by web or background jobs by displaying the exact transactions that lead to bloat memory. Scout also tracks object allocations so you can work to minimize or eliminate unnecessary overhead on your application.

N+1 Detection

SQLAlchemy makes database ORM super easy. But it also makes it much easier to query N+1 to your code. Scout covers you by detecting N+1 queries and even listing how much time you could save by fixing them.

See your code in Scout through GitHub integration

Scout collects detailed traces of slow requests and, along with the GitHub integration, shows the exact line of code responsible for the slow response time.

Track user-specific issues with custom context

Append context-customized data to requests tracked by Scout. Use Trace Explorer to search and filter based on this data.

Digest Emails

Receive daily or weekly summary emails summarizing the performance of your apps, as well as highlights of outliers and endpoints that may need developer attention.

Easy installation via PyPI

Install Scout like any other normal Python package. It's safe to run Scout alongside your existing APM library.

Low overhead

The monitoring agent uses only a portion of the capabilities of existing application monitoring agents, providing high-fidelity tracking details.

System Requirements

.PHP

Linux or MacOS.