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5 Reasons You Must Use Logs During Python Debugging

4 min read

Managing log events from your applications and infrastructure is one of the most important processes for launching your application. Log management has a lot of steps—log transformation, log collection, aggregation, parsing, search, and analysis. Managing logs does not end there. You need to archive and eventually dispose of the logs you don’t need anymore.

Typically, in a large system, all subsystems produce logs that need to be collected in one place for the team to search and analyze events. This helps the team find out about issues and crashes and get a clear understanding of user behavior.

This applies to any Python application. Log management is a key component of debugging your Python application. 

Here are a few reasons to use proper log management for your software.

What went wrong

Once a Python application is deployed and used by users, there can be scenarios where it behaves in an unusual way. You might receive a few complaints from users. The first step is to find out what went wrong. The logs may not give you the exact piece of code that is causing an issue.

A well-thought log strategy will definitely help you find the scenario that is causing your Python application to misbehave.

Helps analyze the issue

Having meaningful logs in your Python application enables you to analyze issues with ease. Once you know the issue you might want to know: 

  • How often is the issue being seen?
  • How many users are affected?
  • Is this a corner case?

Analyzing an issue helps you make decisions and prioritize issues based on the impact they have.

Helps fix trouble-causing issues

Often, we face situations where we know there is an issue but don’t know how to fix it. Taking a deeper dive into the logs can help find the exact problem and might help you figure out a fix.

This is not the usual scenario but can be useful in a few cases.

Understand user behavior

Long response times to APIs, gateway timeouts, and issues with caches are a few of the problems users face whith Python applications. The problem can be the result of the inefficient implementation, DB querying, or resource utilization.

Logs help you monitor and analyze requests and response times in real-time to find the piece of code or the unoptimized DB query that is not performing up to the mark.

Can build alert system over logs

Building an alert system that runs various analyses and queries on the gathered logs to find out if the Python application is working as expected is a great automated setup to have.

Logs from the Python application and other applications are collected in one place and indexed in a data store that is efficient at running queries. A set of queries created by developers to know the health of the system run automatically and periodically, and raise the alarm if things go out of hand.


Here are a few tools I use for debugging and log management:

Lightrun Cloud

Lightrun Cloud is a free real-time debugging tool that supports Java and Python applications. The tool is easy to set up and use, and logs can be added on the fly without going through the whole CI/CD cycle. It helps developers get logs from a monolith app, microservices, Kubernetes, Docker Swarm, serverless, and more. 

Lightrun is ISO 27001 certified and guarantees the privacy of your code. It can be run on all cloud platforms like AWS, Microsoft Azure, and GCP.

With a minimal footprint, Lightrun can help you add logs to your live Python app. Lightrun is more than just log management. With it, you can extract variables, values, performance metrics, arguments, etc. In short, Lightrun lets you debug your application remotely. 

AWS CloudWatch Logs

Aws Cloudwatch Logs

CloudWatch is one of the great tools built by the AWS team. If you are using the AWS infrastructure, this service comes with many options to manage all other services.

CloudWatch Logs is used to gather logs from various sources of AWS, including EC2, Route 53, Lambda, Python applications, and more. It helps you analyze and monitor the logs in real-time.

CloudWatch Logs is a highly scalable service. The tool can be used to view, search, and analyze logs in one console. The CloudWatch Log query language enables you to filter the logs based on the desired fields when searching for an error. You can create visualizations in the CloudWatch dashboards as well. 



ELK is a very popular stack for collecting, searching and visualizing logs. It is a combination of three open-source software: 

  • ElasticSearch: ElasticSearch is used for storing, searching, and filtering logs.
  • Logstash: Logstash is used to collect data from your (Python) applications. Then, the data is transformed into the desired format and stashed to ElasticSearch.
  • Kibana: Kibana is a visualization tool that uses ElasticSearch under the hood to create beautiful views and dashboards for your queries.

ELK is one of the most widely used tools for log collection and analysis in the industry. Kibana is a powerful query language that helps you analyze logs with unbelievable ease.


Log management is a crucial part of your software architecture. It enables developers and system administrators to know the health of each sub-system. In this article, we discussed the importance of log management, and then we listed tools that collect, monitor, and analyze logs for Python applications. These apply to software in general as well.

Logs help in debugging and fixing issues and more. They can be used to raise warnings to avert issues and to prepare the team. Log strategy and policy are an important part of the process and are often taken up seriously by developers and system administrators to form a good log management scenario.

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