If you’re looking to monitor and enhance your troubleshooting capabilities for your Python projects, you’re in the right place. In this article, we’ll break down some of the best Python monitoring tools you can use today.
Here is our list of the best Python monitoring tools:
- DataDog Python Application Performance Ideal for dev teams and businesses that need feature-rich monitoring with prebuilt and customizable templates.
- Retrace APM Provides code level tracing and easy to use tools for isolation.
- New Relic Automatically maps out your network to discovery dependencies.
- AppDynamics A full feature performance monitor that works with Python and dozens of other apps.
- Scout APM Very lightweight and simple to install.
- Dynatrace Provides detailed topology of your applications with geographic data.
- Atatus Drills down right to the source code level from the dashboard to highlight issues.
- Prometheus Open-source application monitor with full HTTP API integrations.
- Sentry.io Cloud-based APM with Breadcrumb event tracking features.
- Metricly Uses anomaly detection to help prevent false positives.
The best Python monitoring tools:
Datadog Application Performance Manager works with a wide variety of code and applications, including Python. DataDog excels in displaying all of the critical alerts and metrics without sacrificing ease of use or cutting out core features. This tool acts as a cloud monitoring service, meaning you won’t have to spin up a virtual machine or install this on a server.
Through a completely customizable dashboard, the raw application data is sorted into actionable metrics that can easily be interpreted at a glance for end-to-end visibility into your Python applications performance. Insights such as total requests, total errors, latency distribution, and a total number of queries are all made accessible both in real-time and historically.
Searching for traces is surprisingly fast and requires no sampling at all. Searching by tag brings in live data from every trace in real-time. Each trace captures the lifespan of the request from front end clients, down to databases. This makes finding and isolating problems in your Python code simple, no matter what end of the request you’re on.
All log data is auto-correlated for every single trace that comes through your application. This data can automatically be sorted by webpage, country, pages, or users to help understand who is being impacted by a specific problem. This can be achieved by searching through out-of-the-box tags and doesn’t require learning a query language.
To dig down into the code level DataDog utilizes continuous profiling to constantly measure all of your code all the time. This breaks down and color codes issues and insights by the exact line of code that is impacting the applications. Whether this is analyzing memory usage, or discovering a line of code is using more CPU than needed, all of these insights are brought together in a way that makes understanding the problem easier on a visual level.
To test your code before deployment you can use DataDog Synthetics to simulate real traffic patterns on your application from around the world to see how your code holds up. The results from this test automatically correlate issues on the front end with specific areas of code for quick remediation.
You can test out DataDog and all of its features for free for 14 days.
Retrace APM combines real-time monitoring of your Python applications with holistic logging and interprets that data to provide you with actionable troubleshooting steps. Retrace is another SaaS-based performance monitor that allows for low overhead and fast onboarding. Through an agent, Retrace automatically detects all code running on your server, and then pulls that data into its dashboards.
Built-in dashboards provide a fast look into your application’s performance historically, and in real-time. Get an exact look at what the most popular queries are, average response time, and the total number of requested actions. From the main dashboard, you can discover memory leaks, find bottlenecks in your stacks, and identify slow database queries all without having to leave the main dashboard.
Along with hard metrics like resource consumption, Retrace provides data on the user level with Apdex user satisfaction ratings and isolates slow dependencies and services where problems arise. This information is aggregated and put into your Retrace App Score that rates the overall performance of your application on all levels.
Code-level tracing is easy to navigate, but not quite as intuitive as DataDogs tag-based searching. Retrace makes it easy to measure your Python apps in developments, QA, and production.
You can test out Retrace free through a 14-day trial.
New Relic APM provides key data through the Overview Dashboard that allows you to quickly toggle between general information to detailed geographical details and insights. New Relic provides data on response time, web transactions, error rates, and total requests per minute to your Python application. All insights link directly to the transaction traces to provide even deeper insight in just a few clicks.
New Relics supports multiple platforms, so it won’t matter if your application is in the cloud, on a server, or in a hybrid environment. New Relic does a great job of making it easy to compare new and old code after a deployment, and highlighting changes in code where new problems were created.
While there are a number of pre-configured metrics, you’ll have the freedom to create your own for events like login attempts or registrations. Those newly created metrics can automatically link into a fully customizable dashboard, making this ideal if you want to measure and monitor specific functions of your Python application.
Features in New Relic like cross-application tracing allow for deep insights across multiple tiers and services to provide total visibility into your traces. This tracing also automatically maps each tier into an application topology map that makes understanding the relationships between tiers easy.
Support for all major frameworks comes right out of the box and includes CherryPy, Django, Bottle, Pylons, Flask, and Web2py. New Relic has pricing tiers that start completely free and increase as your reporting and monitoring needs to, making this an ideal choice for smaller dev teams who are on a budget.
AppDynamics by Cisco is a full-service application manager that supports many different types of applications, software, and integrations. For Python monitoring, AppDynamics provides powerful insights and easy to use topology maps to let you know where there is an issue, and exactly where it’s coming from.
You can monitor your Python applications in real-time and drill down into call stacks right from the main dashboard. AppDynamics makes visualizing issues in the stack and application dependencies simple with automatic application topology. As the agent works its way through your code, a full map of the entire stack is created that allows you to interact with any part of your Python code.
While resource metrics are important, AppDynamics also can aggregate business transaction performance and user satisfaction to better understand how technical issues impact real people and the bottom line. When issues are detected, AppDynamics has a feature called Runbook Automation which allows you to create scripts that execute when certain thresholds, events, or actions take place.
If something can’t be automatically fixed via a script, AppDynamics has intelligent alerting that can be configured to alert you or your team through email or third-party integration. Alerting can also be set up for when baseline performance drifts, or an SLA appears that it’s going to be broken soon based on past data.
There are four overall health statuses that can be automatically applied to your application based on performance. Critical, warning, normal, and unknown are all states the APM can enter. When these health status change you can choose to be alerted. Alerts are highly configurable which helps avoid redundant alerts and notification fatigue.
You can start using AppDynamics for free through a 15-day trial.
Scout APM is an easy to install performance monitor that brings the most critical monitoring information to light. Scout installs just like a Python package meaning it’s up and running with almost no configuration in just a few minutes.
Scout can dig deep and provide insights that some APM miss such as N+1 database query performance, memory bloat, and anomalous app behavior. Once installed, out-of-the-box KPIs are generated into the dashboard to report on critical information such as Apdex score, requests per minute, error rate, memory usage, and response time.
The Memory Bloat Detection works by passively calculating the increase in memory caused by background jobs and isolating the exact transactions that lead to memory bloat. Object allocation is also tracked to help you cut down and eliminate unneeded overhead in your application.
Alerts are flexible and customizable and even come with a daily or weekly email digest to summarize how your app is performing. This digest also includes anomalies and endpoints that might need developer attention.
Surprisingly this entire system runs on a very small footprint and takes up very little resources. This is due to Scout’s tracing algorithms working more with anomaly requests and decreasing analysis on faster areas of the Python code.
Python libraries such as Django, Flask are already included and eliminate the need to add custom instrumentation. In total Scout supports up to 14 different python libraries and four different types of datastores.
Pricing for Scout APM starts at $129.00 (£98.90) per month.
Dynatrace also supports a vast number of applications for performance monitoring and works well with Python-based code. The Dynatrace platform really shines when it comes to its visualizations. Dependencies are mapped out using Smartscape technology to automatically build out a map of your dependent applications and services.
This mapping provides a great overview of your apps’ network but also allows you to see traces as they move through your topology. This map is interactive and gives you the flexibility to highlight any node and drill down further to view more granular details. Much of this monitoring is done on the process level, giving you a view into exactly which processes are making calls and which processes are being called on.
Dynatrace also offers web monitoring which gives you an interactive geographical map that allows you to view your application from your customers’ perspective. If your applications require constant access from across the globe, web monitoring makes it easy to view areas that might be experiencing performance issues or outages. From this same dashboard, you can not only visualize the error but understand the circumstances that caused that error to take place.
You can test out Dynatrace for Python performance monitoring for 15 days through a free trial.
Atatus provides full visibility into your Python performance and allows you to view and understand your application dependencies and related services. Atatus deploys through a simple agent and can capture all requests to your Python applications without needing to change or modify your source code. This tool paints a clear picture of how all of your methods, external requests, and database statements impact your users’ experience.
The Atatus dashboard provides all of the basic metrics you’d expect but is also very customer focused. The insights highlight and prioritize issues that are likely to impact user experience and conversions over other areas of your application.
A table view breakdown helps highlight bottlenecks in your database queries and third-party calls. This allows you to sort and sift through all SQL and NoSQL queries to identify problems with query time in which operations are being impacted.
All errors in your Python application are tracked and captured with full stack trace, allowing you to toggle from top-level insights straight to the highlighted source code that was involved in the issue. On the proactive side, alerts can be configured to be sent via emails, Slack, PagerDuty, or webhook directly into your own custom solution.
Pricing for Atatus starts at $49.00 (£37.57) per month. While there is currently no free trial available you can request a fully interactive demo through their website.
Prometheus is 100% open source and community-driven, meaning it’s completely free to use and was inspired by Google’s Borgmon monitoring system.
Monitoring can be done on multiple layers including internally, as a service, through server libraries, and provide business logic insights. Prometheus uses a functional query language called PromQL that allows for data to be queried in real-time. Expressions can be displayed in graph format, broken down into spreadsheet view, or sent elsewhere for processing through the HTTP API integration.
To graph out your data you’ll need to utilize another tool such as Grafana. Once installed you can set Prometheus as a data source to start pulling in metrics live. While open-source platforms like Grafana have a collection of shared dashboards, this is still going to take you time to set up, test, and implement.
Alerting rules are defined on conditions and are based on the Prometheus expression language. While there are alerting templates, any type of customization or changes is going to be a bit cumbersome to manually integrate.
These are not out of the box solutions and require some knowledge of the platform which would take time. This is fairly common with open source platforms. If you have the time to tinker with Prometheus and don’t mind relying on the open-source community for help, this is a solid free alternative for Python monitoring.
Sentry is a lightweight and fast way to implement performance monitoring directly into your Python project. Installation is quick and just requires an install of the Sentry SDK. With support for Django, Flash, Bottle, Celery, AWS Lambda, RQ, and Pyramid there is ample support for whatever Python library you use.
Minutes after installation Sentry begins identifying poor performing API calls and creating logs of code-related errors for you or your team to review. Sentry can analyze local variables right in the stack for errors just as if you were in a development environment.
A unique feature of Sentry is its Breadcrumb error feedback. This allows you to see the exact events that lead up to an error or specific event. This feature alone allows you to get an understanding of end-user behavior, and isolate the root cause of the problem without spending hours attempting to replicate it yourself.
You can try out Sentry in a limited capacity completely for free. Paid plans start at $26.00 (£19.93) per month.
Metricly is a cloud-based application monitor designed to monitor Python applications across multiple dashboards and allows for viewing of both the application and the source code level.
Metricly also utilizes anomaly detection to help stay ahead of the curve and put you in a more proactive position. Over time, Metricly understands how your users and applications behave with each other to build a pattern.
If this pattern is broken in a way that causes problems you can choose to be alerted or script a response action to deal with that condition. This is a great alternative to using static thresholds which might falsely alert on events you’re well aware of.
You can test out Metricly through a 21-day free trial.
Choosing a Python monitoring tool
There’s clearly plenty of Python monitoring tools to choose from. So which one is right for you? DataDog Java Performance Monitor will likely be the best fit for most dev teams and businesses. It’s out of the box solutions and easy to use integrations make it the most comprehensive and feature-rich product for Python performance monitoring.
How do you currently monitor your Python-based applications? Let us know in the comments below.