A Python handler for logityme.
LogiTyme is a Python package used to track the time spent on each function, custom functions, and the entire Python Program
- Python package repo link: https://pypi.org/project/LogiTyme/
- Install LogiTyme via pip:
To install the LogiTyme package, use the following pip command
pip install LogiTyme
- Verifify the Installation:
After installation, you can verify it by importing LogiTyme in a python script
import LogiTyme print(LogiTyme.__version__)
Simple example on how to use:
from LogiTyme import LogiTyme
logityme = LogiTyme(env="local")
logityme.StartReport()
def slow_function(n):
result = 0
for i in range(n):
for j in range(n):
result += i*j
print(result)
return result
slow_function(500)
logityme.LogiFuncStart(name="for-loop")
re = 0
for i in range(500):
for j in range(500):
re += i * j
print(re)
logityme.LogiFuncEnd()
logityme.GenerateReport()Resulted Output:
Performance Analysis
1. Introduction:
This report presents the findings of performance analysis conducted on the python program 'test_main.py'. This purpose of the analysis is to give insights of time consumed by the program and provide recommendations for optimizing the programs's performance
2. Methodolgy:
The program was profiled using cprofile mmodile to collect data on exection time. The collected data was analyzed to identify functions consuming the most time.
3. Results:
- Started the program at: 2024-06-14 14:25:01.708068
- Ended the program at: 2024-06-14 14:25:07.886945
- Total Execution Time: 6.179 seconds
- memory consumed: 0.0203MB
4. Functions Results:
+---------------+---------------+
| Function Name | Time Consumed |
+---------------+---------------+
| slow_function | 3.024 secs |
| for-loop | 2.65 secs |
+---------------+---------------+
5. inBuilt-functions Time-Consumed Report:
+----------------------------------+---------------+
| Function Name | Time Consumed |
+----------------------------------+---------------+
| <built-in method builtins.print> | 5.253 secs |
| <built-in method nt.stat> | 0.001 secs |
+----------------------------------+---------------+
6. Environment Suggestions:
- Short tasks (less than 5 minutes):
-- GCP (Cloud Functions, Compute Engine, GKE, Cloud Run) or AWS (Lambda, EC2, ECS, Step Function, Glue):
Both are well-suited for tasks that complete quickly.
-- Azure Functions (Consumption Plan, VM, AKS, Container Instances):
Good choice for short tasks
7. Code Optimization:
+---------------+---------------+
| Function Name | Time Consumed |
+---------------+---------------+
| slow_function | 3.024 secs |
| for-loop | 2.65 secs |
+---------------+---------------+
The above function "slow_function" is in the 1 position for having highest amount of time in the entire program. Since the function took 3.024 secs is less then 300 seconds (i.e < 5 mins). The function is quite optimized
The above function "for-loop" is in the 2 position for having highest amount of time in the entire program. Since the function took 2.65 secs is less then 300 seconds (i.e < 5 mins). The function is quite optimized
8. Conclusion:
The analysis revealed areas for potential optimization in the Python program 'test_main.py'. By implementing the recommendations outlined in this report, the program's performance can be improved, leading to better overall efficency
0.0.2 / 0.0.3 / 0.0.4 / 0.0.5 / 0.0.6- Launching LogiTyme
- Functions Included:
- StartReport: used to start the process of logging the time for you python program.
- GenerateReport: used to end the logging process and generate a report based on each function used in the code.
Now this will start process the logged data and generate a report based on the time spent in each function used in your code.
The generated report will provide insights into the performance if different functions
- env To run the code in local machine or in cloud machine
- env = "local" / "cloud"
- LogiFuncStart & LogiFuncEnd: used to log time for custom code.
- Functions Included:
- Launching LogiTyme
- Created By: Aravind Kumar Vemula
- Twitter: https://x.com/AravindKumarV09
- Github: https://github.com/lmas3009
