perftracker 1.1.0

PerfTracker is a lightweight function performance monitoring framework

PerfTracker

PerfTracker is a Python performance tracking package designed to assist developers in monitoring and tracking the execution time of their functions. It provides a straightforward and efficient way to measure function performance, enabling you to optimize your code effectively.

Features

• Ease of Use: Simply add a decorator to your functions.
• Flexibility: Can be used without decorator for manual tracking.
• Detailed Statistics: PerfTracker provides comprehensive statistics about your function's performance. It allows you to get the execution times and calculate the average calls per minute over a certain period. This information can be crucial in identifying bottlenecks in your code and optimizing it.
• No Dependencies: PerfTracker does not require any 3rd party packages to function.
• Debug Logging: Logs function execution deltas in real time.

  [09:48:58] DEBUG [perftracker] levelup.levelup.message_handler took 0.0017ms to complete.
  [09:49:18] DEBUG [perftracker] levelup.levelup.save_cache took 0.001099ms to complete.
  

Disclaimer

This package uses a global variable _perf for the performance model. It uses a key schema of module_name.function_name to avoid interference when multiple packages use it for tracking.

Installation

You can install PerfTracker using pip, the Python package installer. Run the following command in your terminal:

pip install perftracker

Methods

PerfTracker provides several methods to track and retrieve function performance data:

• perf(max_entries=None): A decorator to measure and record the execution time of a function. If max_entries is set, it will limit the number of records kept for the function to this value.
• get_stats(): Returns the current Performance instance, which contains all recorded performance data.
• Performance.add(function, exe_time, max_entries=None): Adds an execution time record for a function. You can use this method to manually add execution time records.
• Performance.get(function): Returns the execution time records for a function. This can be useful if you want to analyze the performance data further.
• Performance.cpm(function, time_delta): Calculates the average calls per minute (CPM) of a function over a certain period. This can give you an idea of how frequently a function is called.
• Performance.avg_time(function, time_delta): Calculate the average time a function takes to execute over a certain period. This can help you identify slow functions that may need optimization.

Usage

Using PerfTracker is straightforward. Here are some examples:

Basic Usage

from perftracker import perf, get_stats

@perf(max_entries=100)
def my_function():
    # Your code here...

# Get performance statistics
stats = get_stats()

Retrieving Execution Time Records

from perftracker import perf, get_stats

@perf(max_entries=100)
def my_function():
    # Your code here...

# Get performance statistics
stats = get_stats()

# Retrieve execution time records
records = stats.get(my_function)

Calculating Calls Per Minute

from perftracker import perf, get_stats
from datetime import timedelta

@perf(max_entries=100)
def my_function():
    # Your code here...

# Get performance statistics
stats = get_stats()

# Calculate calls per minute for the last hour
cpm = stats.cpm(my_function, timedelta(hours=1))

Tracking Things Manually

from perftracker import get_stats
from time import perf_counter

def some_function():
    stats = get_stats()
    start_time = perf_counter()

    ...  # Some code that takes time...

    end_time = perf_counter()
    delta = end_time - start_time

    # Add time manually, the 'add' method can take strings too
    stats.add("my_custom_function_key", delta)

Full Usage

from perftracker import perf, get_stats
from datetime import timedelta
from time import sleep
import random

@perf(max_entries=100)
def my_function():
    # Your code here...

# Run the code a few times
for _ in range(10):
    my_function()
    sleep(random.random())

# Get performance statistics
stats = get_stats()

# Get a list of FTime objects (FTime represents when a function was called)
records = stats.get(my_function)

# Get first time the function was called
record = records[0]

# (float) Time this particular entry took to execute in ms
execution_time = record.exe_time

# (datetime.datetime) in UTC timezone naive, the time when this entry was created
timestamp = record.timestamp

# Calls per minute (float)
cpm = stats.cpm(my_function)

# Average execution time (float)
avg_exe_timne = stats.avg_time(my_function)

Discord Bot Cog Usage

PerfTracker can also be used in a Discord bot environment to help you track down bottlenecks in your bot's performance. Here is an example of how you can use PerfTracker in a Discord bot cog:

from discord.ext import commands
from perftracker import perf, get_stats

class MyCog(commands.Cog):
    def __init__(self, bot):
        self.bot = bot

    @perf(max_entries=100)
    async def slow_function(self, ctx):
        # Your sussy code here...

    @commands.command()
    async def performance(self, ctx):
        stats = get_stats()
        records = stats.get(self.slow_function)
        if records:
            avg_time = stats.avg_time(self.slow_function)
            await ctx.send(f"Average execution time for {function_name}: {avg_time}ms")
        else:
            await ctx.send(f"No performance records found for {function_name}")

def setup(bot):
    bot.add_cog(MyCog(bot))

In this example, slow_function is a function in a Discord bot cog that is tracked by PerfTracker. The performance command can be used to retrieve the average execution time of the specific command.

You can replace slow_function with any function in your bot that you want to track. The performance statistics are stored in memory and will be lost when the bot is restarted.

Please note that this example assumes you have basic knowledge of how to create and use cogs in a Discord bot. If you are unfamiliar with this, you can read the Discord.py Cogs guide.

Performance Comparison

PerfTracker is designed to have minimal overhead, allowing you to monitor your program's performance without significantly affecting its execution time. The following graph compares the overhead of PerfTracker with cProfile, another popular performance tracking package.

During this test, PerfTracker consistently has less overhead than cProfile, making it a more efficient choice for performance tracking in Python. The code used for this comparison can be found Here

Contributing

We welcome contributions to PerfTracker. If you have a feature request, bug report, or want to improve the code, please feel free to submit a Pull Request.

License

PerfTracker is licensed under the terms of the MIT license. This means you are free to use, modify, and distribute the code, as long as you include the original copyright and license notice.