python performance profiling

This article mainly introduces the simple performance test results of common python web frameworks (including django, flask, bottle, tornado ), for more information about the performance of django, flask, bottle, and tornado frameworks. The performance of django is completely speechless. Django, flask, and bottle are

Python learning notes (threading interface performance stress test), pythonthreading Another week Last week's progress: multi-process Learning Practice today The initially designed interface performance stress test code is as follows: 1 #! /Usr/bin/env python 2 #-*-coding: utf_8-*-3 4 import threading 5 import requests

Build a python high-performance framework gevent Development Environment on CentOS 6.31. Upgrade python 2.6 to python 2.7.For business needs, upgrade python from 2.6.6 to 2.7.10 before installation. Refer to blog:Http://blog.csdn.net/tao_627/article/details/46928899 Install

configuration file will look more uniform than opening in the script4. Browser viewChrome refreshes the GHIPTE Web page to seeGhipte, servers, ec2-54-201-82-69, weblog (custom), HTTP, the following monitoring graphs appearWe can generate 200 status codes using Ab-c 100-n http://localhost/Use the Refresh Ghipte browser page to generate a 304 status code650) this.width=650; "src=" Http://s3.51cto.com/wyfs02/M01/5C/0B/wKioL1UaHCijRVkoAAMpNTmsVaU913.jpg "title=" Manual httpcode.png "width=" 650 "he

the child interpreter resides.4) in the sub-process, the binary data is deserialized with pickle, which is reverted to a Python object.5) Introduce a Python module that contains the GCD function.6) Each sub-process computes its input data in parallel.7) Serialize the result of the operation and turn it into bytes.8) Copy these bytes through the socket into the main process.9) The main process performs a de

Tags: python mysqldb dbutil sqlobject pooleddbFirst introduce the following mysqldb, Dbutil, Sqlobject:(1) MySQLdb is the interface for Python to connect MySQL database, it implements the Python database API specification V2.0, based on the MySQL C API. In addition to MySQLdb, Python can also be oursql, pymysql, Myconn

Today, the Python dictionary has been specifically tested for performance results in comparison with various methods.The test code is as follows:1 defdict_traverse ():2 fromTimeImportClock3My_dict = {'name':'Jim',' Age':' -','Height':'180cm','Weight':'60kg'}4 5T_start =clock ()6 forKeyinchMy_dict:#worst-performing notation. No optimization7 Print 'Type01.01:%s---%s'%(Key, My_dict[key])8T1

Locating program Performance BottlenecksThe prerequisite for code optimization is to understand where the performance bottleneck is, where the main time of the program is consumed, and for more complex code to be located with tools, Python has built-in rich performance analysis tools such as Profile,cprofile and hotsho

times-fold difference in performance. Remember that the string is very large, do not use s=s+a[i] this way, because the constant creation of memory space to add, not only the program is very card, greatly consumes the resources, but also very slow. ____________________________________________________________ In Python: Import time# First Way, appenda=[]t=time.time () for I in Range (1000000): a.a

in the processor at any given time.The Gil solves the problem:To take advantage of multicore, Python supports multi-threading, but there are data integrity and state synchronization issues between threads, while Gil solves data integrity and state synchronization issues between multithreading (loads locks on lines running on the interpreter, ensuring that only one thread is running in the interpreter at a time).Effects of Gil:A loads lock on a thread

Python 3.6 Performance Testing Framework Locust installation and use, pythonlocust Background Build and use of Python3.6 Performance Testing Framework Locust Basic Python version: python3.6 Development Tool: pycharm Installation and configuration of Locust Click "File"> "setting" Click "setting" to enter the settings

Share the simple Performance Test Results of common python web frameworks (including django, flask, bottle, tornado) and djangoflask Tested the simplest performance of django, flask, bottle, and tornado frameworks. The performance of django is completely speechless. Django, flask, and bottle are all started using gunic

Tags: django-mysql-host[Email protected] ~]# tail-20000/tmp/python/alldjango-mysql.py#!/bin/usr/bin pythonImport Os,datetime,paramikoImport Tab,sys,multiprocessing,timeSys.path.append ('/tmp/python/django-1.5.1/django/bin/myweb ')os.environ[' django_settings_module '] = ' myweb.settings 'From Pyweb.models import Filesystem,men_cpu,tablespace#hosts =[' 192.168.1.10 ', ' 192.168.1.11 ', ' 192.168.1.13 ', ' 19

resource exhaustion problem. Python's socket processing is not suitable for dealing with these types of situations, specifically, we found that in our own Python code, each action has made multiple system calls and context switches, which adds enormous overhead to the system. Improved performance: Mix python with C After combing the code, I chose to port the mos

Google's own search solution, Linux and Mac should be able to run directly.Using cTYPES to invoke C-language functionsOr the above example, we require a 2 number of the and. So you can write that on Windowsint Add (intint num2) { return num1 + num2;}Then it compiles the file into a dynamic-link library, which requires the CL commandThe CL command requires Visual Studio to be installed, and if it is already installed, use this method to configure the environment variable HTTP://HI.BAIDU.COM/

Python to optimize NumPy package performance tutorial, pythonnumpy NumPy is the foundation of many scientific software packages in Python. It provides a special data type ndarray, Which is optimized in vector computing. This object is the core of most algorithms in scientific numerical computing. Compared with native Python