Python applications have proven to be top notch when dealing with complex scientific or numeric problems. As Python applications become more dynamic and complex, there’s a need to monitor performance for better troubleshooting.
Developers would want to monitor Python performance for several reasons. For instance, you’d want to be the first to notice any likelihood of your application crashing. Also, you can get real-time reports/metrics about events from your application.
We’ll explore the top Python metrics to monitor and the different metric types for Python monitoring. First, let’s look at the importance of monitoring Python performance.
Pros of Monitoring Python Performance
Monitoring Python performance is as important as building the application itself. The process can become quickly cumbersome as you scale your application. Therefore, it’s important for monitoring to be automated as opposed to the traditional manual method.
Tools like SolarWinds® Observability make it easier to monitor Python performance. Some advantages of using automated tools include:
- Get real-time data on top Python metrics like response time, memory usage, errors, and so on.
- Track web transactions.
- Make sure the application is running optimally.
- View trends and spot abnormalities in your application.
Now that we’ve seen the advantages of monitoring Python performance, let’s explore the top Python metrics to monitor.
Top Python Metrics to Monitor
Below are the top Python metrics you won’t want to miss out on monitoring.
1. Response Time
Nobody wants a slow application. Response time is the average time an application’s server takes to return the results of a user’s request. The lower the response time, the quicker requests are processed.
If the response time is very high, users’ requests will be processed slowly. This will cause a lag and a poor user experience. Users tend to abandon applications with a high response time.
For better Python performance, a response time between 0.1 seconds to one second is acceptable because users don’t typically notice this small of a delay. However, a response time of more than one second is problematic as users may abandon your application.
2. Request Latency
Since response time has to do with the time the application server takes to respond to a request from a user, how about the time it takes the request to get to the server? Ideally, when a user sends a request to the server, it gets the request immediately. However, this isn’t always the case.
Request latency is the average delay in time for a user’s request to get to the server. Requests from users can be delayed by the network and other features before getting to the server. Python performance is affected by latency, especially when it becomes high.
When latency time becomes high, requests take a longer time to return a response. This will affect the overall performance of the application.
3. Unhandled Errors (Exceptions)
How do Python applications handle errors? What happens to the performance of your application when errors aren’t handled properly?
An exception is a type of error that disrupts the normal flow of instructions during the execution of a program. For instance, if a user tries to fetch data that isn’t present in the database, an exception will occur even though your codes are correct. If this error isn’t attended to, it can crash the user’s version of the application.
Developers have to write codes that handle exceptions. These programs let the application know what steps to take once it encounters an exception. To use the example above, we can write a program to make it so whenever a user requests data not in the database, a message is sent to the user rather than the application completely shutting down.
Sometimes the application throws an exception unexpectedly, such as when developers ship in new features to users. Unhandled exceptions affect the performance of your application, and as mentioned, they can crash applications when they’re unattended to.
4. Queued Time and Queue Size
When users send lots of requests to an application’s server, the server may not be able to attend to them all at once. Instead of sending a message that the server can’t address the requests, the server queues the requests. Once queued, requests will be attended to on a first-come, first-serve basis.
A large queue size or higher response time depicts an underlying problem in applications’ performance. Queue time affects when a request’s response will get to a user. In other words, response to the user’s request is faster if the request spends less time in the queue, compared to when it spends more time in the queue.
5. CPU Usage
The CPU (central processing unit) fulfills the user’s instructions. High processor usage can cause applications to respond slowly, freeze, or shut down.
Monitoring CPU usage involves taking processing time into account. Slow processing can have multiple causes, so you should use automated monitoring tools with real-time results on the current CPU usage.
6. System Memory Usage
Python applications won’t run at their full capacity with low memory. Monitoring memory usage is important to guarantee the performance of an application.
Memory monitoring tools provide in-depth reports on the utilization of memory. As a result, the performance of your application isn’t hindered by memory utilization.
Memory utilization is the average available memory in use at any given moment. The memory utilization of your system is an important factor, as it allows the system to run multiple tasks simultaneously. For example, you won’t want your hosting system to fail in hosting your web application because of low memory.
7. File Descriptor Usage
The number of files opened in your application affects your application’s performance. This is why file descriptors—a low-level facility provided by the OS kernel—keep track of all the opened files in the system.
However, this tends to pose a problem as more opened files will affect the performance of the application. For this reason, file descriptor usage in your application has to be monitored continuously.
8. Disk Usage
Disk usage is an important factor for application performance. Disk space refers to space available for storage. More disk space means you can scale up your application efficiently.
If you don’t know your disk usage and try to scale up your application with no available free space, your application will shut down unexpectedly and your users won’t be able to access them anymore.
Traces for Metrics Monitoring
Monitoring a Python application’s performance is easier with tracing. Automated monitoring tools provide transaction traces. Detailed transaction traces allow developers to visualize application bottlenecks.
Also, with traces, you can tell when there’s an abnormality in your application. You can add custom tracing spans across any Python metric.
Maximum Observability With Traces and Metrics
Monitoring a Python application’s performance is best with metrics and traces. The developer has access to real-time reports on the performance of their application and detailed traces from their application.
These traces tend to form a pattern over time that can be used to detect abnormalities in the future. With constant traces pulled out from real-time metrics, teams can now achieve maximum observability.
The SolarWinds Observability SaaS (formerly known as SolarWinds Observability) provides maximum observability for teams to monitor Python performance. Sign up for a free trial to start getting real-time metrics and traces.
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This post was written by Ukpai Ugochi. Ukpai is a full stack JavaScript developer (MEVN), and she contributes to FOSS in her free time. She loves to share knowledge about her transition from marine engineering to software development to encourage people who love software development and don’t know where to begin.