A Look at the 6 Best Python Error Monitoring Tools in 2022
A Look at the 6 Best Python Error Monitoring Tools in 2022
Errors are the necessary evils of software development. They bring to your attention critical information about what’s wrong with your application and what needs fixing before your end-users suffer.
Error monitoring tools offer significant help in this cause by aggregating all the errors and issues your applications (and their end users) are struggling with under one roof and providing valuable insights to resolve these and optimize performance. As a result, these tools play a crucial role in enabling organizations and developers to build and deploy robust and reliable software with confidence.
In this post, we’ll start by getting a sense of the significance of error monitoring tools. Then after a brief discussion about the essential factors to consider before getting one such tool for yourself, we’ll take a look at the top 6 error monitoring tools for Python applications to get in 2022. Finally, before we wrap up, we’ll go through how easy it is to integrate an error monitoring tool into your Python application.
Here’s an outline of what we’ll be covering so you can easily navigate or skip ahead in the post –
- Why Use a Python Error Monitoring Solution?
- How to Evaluate Python Error Monitoring Tools
- Our List of the Best Python Error Monitoring Tools
- Choose the Right Python Error Monitoring Tool for Your Business
Why Use a Python Error Monitoring Solution?
Stack Overflow trends (December 2021)
When a programming language like Python is so extensively deployed — in hundreds of thousands of applications running across the internet, it becomes crucial for the community to have frameworks and tools that can effectively monitor their status, health, and performance. A critical aspect of this automated application surveillance is error monitoring.
24 x 7 Manual Monitoring isn’t Viable
We all know the enormous impact that even the most minor errors can have in bringing down the best applications. However, having your applications manually monitored 24 x 7 for errors (and their causes) isn’t an option; it is wasteful in terms of time, energy, and resources. This is precisely what error monitoring tools are here to help with.
Error monitoring tools like Scout APM allow you to track all the errors and issues in your application in an automated fashion. They provide you with all the information you’ll need to understand the error and solve it. This includes all the contextual information (date, time, browser, OS version, session data, request parameters, user information, etc.), error types and frequency, corresponding end-point, stack trace, the specific line of erroneous code, and much more. With these tools in place, you can rest assured that all of your uncaught errors will not go unnoticed and would rather be tracked, unraveled, alerted about, debugged, and eventually resolved.
Automated Peace of Mind
Moreover, equipped with 24 x 7 real-time alerting mechanisms, these tools can also have you informed right away about any hindrances. Furthermore, they provide you with peace of mind, knowing that no issue in your application will go unnoticed, allowing you to resolve errors before the user catches wind of anything. Below is an example of what an example interface in Scout looks like.
Here, you can see information about the errors tracked in the last 24 hours visualized — grouped based on their type and corresponding endpoints triggered. Furthermore, there’s also a list of all the issues, with information about the endpoints and controllers, error spike rates, number of occurrences, followed by an option to dig deeper into each error. This clear breakdown of application errors turns out to be pretty handy for development teams to decide how they should allocate their resources and what they should work on next.
Ease of Access
With the convenience of error monitoring tools, product managers and other administrators no longer need to log into a server through a terminal and run a bunch of commands to discover and learn more about errors plaguing their applications. All they need to do is sign into a fancy web UI that has customizable dashboards that present trends and insights about your application’s status through fancy graph visualizations.
This can make application performance trends super accessible to everyone in the organization and simplify collaboration across teams and boost morale.
Support Customers Before they Seek Support
Another essential virtue of these tools is reducing the reliance on expensive customer support systems that operate on error reports from users. When most of your application’s issues and errors are notified to you automatically through error monitoring tools, you know exactly what went wrong and where you messed up. As a result, you can start working on resolving the error before most users experience any oddity.
Consequently, no user needs to go through the painful process of reporting an error, and you don’t have to worry too much about technical support for trivial, otherwise easily solvable issues.
This is all the more reason for organizations to be one step ahead in the game and have their own setups in place that preemptively save users from a bad experience.
How to Evaluate Python Error Monitoring Tools
For people considering investing in error monitoring solutions for your applications, there are several different tools in the market you can look into. Finding the right fit for your application’s requirements can get overwhelming. To make it easier for you to shortlist, below is a list of the different criteria you need to consider carefully before deciding.
Important Criteria to Consider
- Features and functionality: You want a toolkit equipped with the best and broadest set of features
- Ease of use: A tool that offers the best features but isn’t intuitive and easy to work with does no good.
- Technical support: For support with any issues you might face with the tool in the future.
Based on these criteria, we have compiled a list of the top 6 error monitoring tools in the market (in no particular order). We hope this can help you figure out what tool would best fit your team.
Our List of the Best Python Error Monitoring Tools
Scout APM is one of the top solutions in Application Performance Monitoring (APM). Their error monitoring tool comes as a handy addon to its well-established, renowned application monitoring toolkit. Scout APM is recognized as a ‘high performer’ for winter 2022 (in the APM tools category) and boasts an outstanding 4.6-star rating (out of 5) on G2 (from 58 reviews).
Along with an intuitive, easy-to-use dashboard, Scout’s error monitoring tool comes with additional features like – full stack traces, error contexts, Github integration, customizable real-time alerts, and always-on support — to name a few. With Scout, your organization can also benefit from comprehensive data and trends about your application’s health and performance, apart from the already nifty error monitoring tool. This enables your team to holistically evaluate the (good or bad) performance of your application, in addition to gaining valuable insights from the error monitoring trends – all under one dashboard.
Moreover, with different tier plans, Scout provides a cost-effective solution for your application’s monitoring requirements. For enterprise-level support, Scout APM provides even more.
This makes Scout APM an excellent contender – a complete package for all your application’s monitoring needs. Below is a full list of its features, sourced from Scout’s website.
- Full-stack traces for debugging where things went wrong
- Contextual information about the request and its params
- Real-time monitoring with error rates
- Intelligent error grouping (based on type and endpoint)
- Github integrations for detailed backtraces (and commit information)
- Real-time alerts via email, Slack, Splunk, PagerDuty, OpsGenie, WebHooks, etc.
- Search Functionality
For more information about how (easy it is) to integrate Scout’s error monitoring into your Python application, read till the end of the post or directly jump to the relevant section.
Bugsnag is a stability monitoring platform, recently acquired by SmartBear, that ships with a powerful Python real-time error monitoring solution. Used by several popular mid-sized companies like Slack, Lyft, Pandora, etc., it is another excellent tool that gets the job done and more.
Apart from a beautiful dashboard, context-rich error information, and support for integrating live alerts through different channels (e.g., Slack, PagerDuty, etc.), product managers and engineers find the feature to group errors based on the users pretty useful. This makes it highly convenient for the team to locate and identify errors reported by end-users. Bugsnag also boasts an excellent 4.4-star rating on G2 (from 29 reviews). Below is a snapshot of their user-friendly dashboard view.
Bugsnag Python Error Monitoring Dashboard
- Detailed exception reports through stack traces with code snippets
- Customizable error reports
- Diagnostic request information for better error understanding
- Support for popular Python libraries (e.g., Django, Flask, Celery, etc.)
- Application health benchmarking and error prioritization
- Integration with Python’s logging framework
- Two-way issue tracker sync with ticketing systems like Jira
- Generous ‘lite’ plan for hobbyists to get started with
Rollbar is another top-rated error monitoring solution that sells itself as a continuous code improvement platform. With a superb 4.5 stars average rating from 117 reviews, G2 has recognized Rollbar as a leader in the Bug Tracking Software category for Winter 2022.
Rollbar offers intelligent AI-assisted default grouping of errors to simplify the clutter of redundant errors. This technology can identify patterns from different stack traces to condense noisy error streams into simplified, easy-to-work-with grouped alerts. Additionally, there’s also support for custom grouping rule options as well.
With Rollbar, development teams also have a lot of flexibility with full access to their monitored data through Rollbar Query Language (RQL) – a structured SQL-like interface to fetch Rollbar data in any way, shape, or form. This enables organizations to uncover new insights by aggregating custom records and metrics relevant to their applications’ functioning. Furthermore, perhaps most importantly, Rollbar’s aesthetic dashboards are known for neatly conveying the most critical metrics that summarize how well your application is doing. Below are two views of their product sourced from their website.
Rollbar Python Error Monitoring Dashboard Views
- Real-time error notifications with all the critical information made available in one simple view
- Automatic error grouping using Rollbar’s machine learning-based technology to avoid alert fatigue
- Telemetry data about the timeline of browser events leading up to the error (e.g., page loads, user actions, network activity, and console logs)
- Rollbar Query Language (RQL) for querying Rollbar’s data in any customized format, with whatever tools you like to work with.
- Thorough coverage of each error — including contextual metadata such as stack traces, request headers, application state, staging environment, browsers, and OS versions, affected users with IP addresses, deployed code versions, etc.
- Convenient issue tracking integration with tools like Jira
Airbrake is another error monitoring and performance management tool that was recently acquired by LogicMonitor. It sells itself as a lightweight, developer-centric solution to your error monitoring requirements. With support for deployment tracking, customizable error grouping, code quality trends and insights, and other APM features, Airbrake is an excellent tool for tracking uncaught errors.
Even though there is room for more design-friendly, fancier UI dashboards, Airbrake more than makes up for it with its attention to detail in capturing all the essential metrics in one view.
Below is a snapshot of the Airbrake platform, sourced from their website.
AirBrake Python Error Monitoring Dashboard Views
- Custom notifications for prioritizing critical errors
- Offers a ‘hotspots’ dashboard view for localizing application pain points
- Easily integrates Slack, GitHub, Bitbucket, Gitlab, JIRA, and other tools into your triage workflows
- Breadcrumbs error trails, error grouping, scope information, search and filter functionality, and code snapshots for better debugging
- Deployment tracking support and overall application quality trends
Sentry is another competitive error monitoring + performance monitoring tool used by more than 80K organizations across 146 countries globally. It’s popularity and success have also enabled G2 to recognize Sentry as a leader in the Bug Tracking Software category for Winter 2022. Some well-known organizations that leverage Sentry for their application monitoring needs are Atlassian, Github, Disney, Reddit, Microsoft, and many more.
Sentry offers an elegant and easy-to-understand interface with a comprehensive analysis and breakdown of each issue – with all the information you might ever require (and more) to make necessary amends. Apart from the ton of features (listed below) that it offers, it also allows you to build your custom dashboards — to plot whatever data you want (through custom queries) in whatever visualization form you like. Additionally, it bolsters team productivity in development workflows by integrating with most project management, visualization, and communication platforms such as Asana, Trello, Slack, Microsoft Teams, Bitbucket, Github, and whatnot. One of the best things about Sentry is that they have also provided access to an online sandbox environment to play around with their error monitoring tool. People new to error and performance monitoring realms will find this option to explore their product (without having to pay or set up anything) pretty helpful. Below is a snapshot of their Issues dashboard from their sandbox environment.
Sentry’s Error Monitoring Dashboard (from their sandbox environment)
However, one apparent caveat with Sentry is that some people have found customer support to be a little difficult to reach and get help from.
- Support for popular Python web frameworks (such as Flask, Django, Bottle, Celery, etc.) and cloud service providers (AWS Lambda, Google Cloud, and Microsoft Azure).
- Custom tags for labeling and reproducing different errors
- Complete stack traces with additional error trails (breadcrumbs; through SQL queries, debug logs, and corresponding network requests)
- Access to raw event data from Sentry using Discover (Sentry’s query builder).
- Support for more than 30 languages apart from Python
- Deployment version tracking using the Releases tab with data about the number of crashes, issues, and user adoption
- Issue assignment to developers for eliminating ambiguity in responsibility (Issue Owners) + rules for automated assignment
- Cross-project view to see all organization issues in one place.
Last on this list, but not least by any means, Raygun is another popular application monitoring platform that you can get your hands on. It offers a suite of monitoring solutions, including crash (error) reporting, real-user monitoring, and application performance monitoring for building reliable software applications.
Raygun excels in what can be regarded as the bread and butter of error monitoring tools through a lightweight, easy to setup SDK, breadcrumb-based event trails, error stack traces, HTTP request headers, issue tracking, integration with Slack, and much more. Developers and product managers also highly appreciate the additional features like deployment tracking and user experience monitoring for a wholesome evaluation of how releases ebb and flow in performance and how end-users receive them. The user experience monitoring feature to track down the user experience of individual users for identifying and getting more context (browser, OS versions, session information, etc.) about customer grievances is a fan favorite. Even though UI-wise, Raygun’s dashboard design is perhaps a little aesthetically outdated, it is systematic, neat, and straightforward enough to clearly convey all the relevant information about your application’s errors.
Raygun Python Error Monitoring Dashboard View
- Real-time error monitoring through rich, contextual diagnostics taking you down to the exact line of erroneous code
- Deployment tracking to observe the impact of each release on software quality through metrics such as page speed, load times, user satisfaction, etc.
- Customer experience monitoring for identifying individual users that faced issues while using your application + accessing their complete activity and session details to triage support requests
- Advanced priority-based error sorting (e.g., users affected and the number of occurrences) and filtering using parameters such as date, location, OS, browser, and other custom tags.
- Integration with code hosting platforms (like Github, Bitbucket, and Gitlab), communication apps (like Slack), and project management tools (like Jira, Zendesk, and Asana)
Enable Error Monitoring in Your Python Application
Now that you have gotten a glimpse of what all these error monitoring tools have to offer, what they have in common, and how they differ, let us look at an example of how easy it is to plug most of these into our applications. We’ll see how easily you can enable Scout APM’s error monitoring tool into your Python application in a few steps.
- Install the Python dependency using PIP.
pip install scout-apm --upgrade
- Initialize Scout configuration in your config file like so –
from scout_apm.api import Config Config.set( # ... other configuration options monitor=True, errors_enabled=True, # Defaults to true since v2.23.0 )
And enable the environment variables like so –
export SCOUT_MONITOR=True export SCOUT_ERRORS_ENABLED=True
- Deploy this updated code and contact us at firstname.lastname@example.org to enable the service!
You can find more information about integrating Scout into your Python application here.
Choose the Right Python Error Monitoring Tool for Your Business
As Python applications scale to cater to millions of users worldwide, the vital role of these monitoring tools is only going to grow significantly. To prevent getting bogged down by the overwhelming burden of extinguishing unprecedented fires across your applications 24 x 7, consider investing in a robust error monitoring tool that can take care of all this for you.
If you care about optimizing performance and value your organization’s and your developers’ time — time that could otherwise go wasted being on the constant lookout for issues, troubleshooting, figuring out where things went wrong, and debugging, this decision should be a no-brainer.
With Scout APM, you get actionable insights from both performance monitoring and error monitoring – all under one dashboard, so you can spend less time fixing bugs and more time building stuff. Get a good sense of how APM and error monitoring can help your business by getting started with a 14-day free trial (no credit card needed) or schedule a demo with one of our experts today!