Python is one of the most used languages among developers. There are many reasons why python is very famous among developers, which we will discuss in this blog. Due to the fame of python, it is used in many business applications, hence monitoring a python application is crucial. New Relic is one of the oldest monitoring tools for python monitoring. But New Relic competitors are growing rapidly; hence, if you do not like the New Relic user interface, many New Relic alternatives exist. All the Python scripts are monitored deeply and analyzed to make the developers’ life easy.
You can find the table of contents below if you want to skip to the good part!
General Python Performance Issues
Dynamic declaration of variable:
How New Relic interfaces with Python
How Scout APM differs from New Relic
How popular is Python?
Python is one of the most powerful languages. It provides all of the features of an object-oriented programming language. We can do almost everything using python, including building the backend, frontend, automation, scripting tasks, etc. Python is very easy to learn, so anyone from a non-coding background can also easily learn and build applications in python. Python is a very flexible language in terms of development, you can use it almost everywhere you want. Most of the famous applications that use every day use python for developing applications.
Also, you can perform AI, IoT, and machine learning things using python easily. You can control various objects using the internet easily. There are many courses freely available on the internet for learning python. Also, if you want to know more about why Python is the first choice for developers, you see this link.
General Python Performance Issues
This is the most obvious reason for developers who do not use python for development. Python is actually very slow compared to other languages like Java, C++, C, etc. There are many reasons why python is that slow in CPU processing tasks. We will discuss those issues in this part of the blog.
Dynamic declaration of variable:
In languages like C++, C, and Java, the declaration of a variable starts with the variable type like int, char, and then after the variable name. But in python, we declare the variable without stating any datatype, and we can also store any different kind of value to the same integer. In another programming language like C, when we declare a variable, it assigns a fixed size of memory to the variable and stores the value corresponding to that memory. But in python, when we declare a variable, it first creates a Pyobject, then creates a name and type code it to the datatype it was assigned. You can see that Python does more processes under the hood for creating the same variable that C creates in just a few steps.
Compilation at runtime
In other languages like Java or C++, the code is first converted into a machine-level language that can be understood by the machine, and then after that application runs. It means all the syntactical errors or import-related errors are caught before running the application hence it is the machine also to directly run a machine code.
But in the case of python, it does not compile its interpretation. It first converts the code into machine-level code. In python, code gets compiled during runtime, and hence it takes a longer time for the CPU to execute python code. The interpreter compiles and runs each line one by one hence it becomes slow at runtime.
Single threading
Python doesn’t allow multi-threading, so we cannot run multiple processes simultaneously. It has a mechanism called GIL, which stands for Global Interpret lock, which ensures that multiple processes are not executed simultaneously. This is because Python has a different kind of garbage collection, and due to maintaining that garbage collection, it has to implement GIL.
Single threading makes python slower than other languages.
We can improve the speed of your python code in several ways. You can refer to this Scout’s blog for improving the python speed.
How New Relic interfaces with Python
New Relic has a python agent that monitors your application easily. It monitors the python application on various levels, such as code level, business level, etc. For the installation of the New Relic agent for your python code, you have to first create a New Relic account and then install the New Relic using
Install New Relic
After installing the New Relic into your python application, you have to configure the application using New Relic’s license key. Now New Relic can monitor your python application all you have to do is just restart the application.
New Relic provides many benefits for monitoring your python application, like app performance index, overall view of the application, application monitoring, bug reporting, etc.
You can search in detail about the traces and error reports. New Relic shows where you perform the slowest and the reason behind it. You can see error logs, response time, etc., using the New Relic Python agent.
Although there are certain drawbacks of New Relic python agent, like memory leaks, not so much customization about the metrics, and you can not do precise alerting for your metrics.
How Scout APM differs from New Relic
Scout APM and New Relic are excellent at application performance monitoring. New Relic is one of the prominent products in the market. But there are some areas where ScoutAPM offers more than New Relic.
ScoutAPM provides features such as memory bloating detection, N+1 database queries, slow database queries, etc. It lets you compare the previous data with the current data. You can compare the last day’s and last week’s data in the Scout APM dashboard. You won’t find the memory breakdowns of the application, but in Scout APM, you can see the detailed breakdown of memory in addition to the timing metrics. ScoutAPM supports many programming languages like Ruby, Python, Elixir, PHP, NodeJS, and Open telemetry. Whenever you search for some items using SQL query in the dashboard, it will show you all the results related to that, not just a few.
Regarding python integration, you can install the python agent by installing the Scout APM module. Many libraries like ElasticSearch, jinja2, PyMongo, and Redis are detected and do not require any extra integration for starting the instrumentation. You can start error monitoring and exception monitoring and add alert bots on slack. You can track deployments, use custom metrics, and add various integration to your Scout application. You can drill down the error reports and traces of the application and find out the actual reason for the error. Scout smoothens the monitoring process by simplifying the integration and monitoring.
When you choose an APM tool, pricing is a crucial metric for companies. Scout APM’s monthly subscription starts from $161 per month, which is much less than New Relic with the same features.
Conclusion
We have discussed the benefits and popularity of python. Also, python has some issues at the architecture level, which could be solved by improved coding practices. New Relic and Scout provide a python monitoring agent to monitor your python application. Both more or less have the same functionalities. Scout APM makes things easier to use than New Relic’s complex UI. ScoutAPM supports all kinds of programming languages, from Python to PHP. Python monitoring using ScoutAPM is very easy to do. You can install it simply using one command and in-depth instrument your application.
The most important thing you can use Scout for free for 2 weeks without even showing your card details. So do not think so much just try ScoutAPM now, and ask yourself which helped your team to solve problems faster, Scout or New Relic.