How to configure CommonsPool2TargetSource in spring?

This become pain in my neck!!! I have three queries. 1)I want to configure CommonsPool2TargetSource in my project for pooling of my custom POJO class. What I have done so far : MySpringBeanConfig class : @Configuration @EnableWebMvc @ComponentScan(basePackages = {"com.redirect.controller","com.redirect.business","com.redirect.dao.impl","com.redirect.model"}) @EnableTransactionManagement @PropertySource("classpath:" + JioTUConstant.SYSTEM_PROPERTY_FILE_NAME + ".properties") @Import({JioTUCouchbaseConfig.class,JioTUJmsConfig.class}) public class JioTUBeanConfig extends WebMvcConfigurerAdapter {

2021-06-14 05:16:49    分类:问答    java   spring   pool   proxyfactory

python no output when using pool.map_async

I am experiencing very strange issues while working with the data inside my function that gets called by pool.map. For example, the following code works as expected... import csv import multiprocessing import itertools from collections import deque cur_best = 0 d_sol = deque(maxlen=9) d_names = deque(maxlen=9) **import CSV Data1** def calculate(vals): #global cur_best sol = sum(int(x[2]) for x in vals) names = [x[0] for x in vals] print(", ".join(names) + " = " + str(sol)) def process(): pool = multiprocessing.Pool(processes=4) prod = itertools.product(([x[2], x[4], x[10]] for x in Data1))

2021-06-12 06:11:54    分类:问答    python   map   multiprocessing   pool   itertools

python multiprocessing, manager initiates process spawn loop

I have a simple python multiprocessing script that sets up a pool of workers that attempt to append work-output to a Manager list. The script has 3 call stacks: - main calls f1 that spawns several worker processes that call another function g1. When one attempts to debug the script (incidentally on Windows 7/64 bit/VS 2010/PyTools) the script runs into a nested process creation loop, spawning an endless number of processes. Can anyone determine why? I'm sure I am missing something very simple. Here's the problematic code: - import multiprocessing import logging manager = multiprocessing

2021-06-11 06:53:55    分类:问答    python   process   multiprocessing   python-multiprocessing   pool

python multiprocessing.Pool kill *特定* 长时间运行或挂起的进程(python multiprocessing.Pool kill *specific* long running or hung process)

问题 我需要执行许多并行数据库连接和查询的池。 我想使用 multiprocessing.Pool 或 concurrent.futures ProcessPoolExecutor。 蟒蛇 2.7.5 在某些情况下,查询请求花费的时间太长或永远不会完成(挂起/僵尸进程)。 我想从已超时的 multiprocessing.Pool 或 concurrent.futures ProcessPoolExecutor 中终止特定进程。 这是一个如何杀死/重新生成整个进程池的示例,但理想情况下我会尽量减少 CPU 抖动,因为我只想杀死一个特定的长时间运行的进程,该进程在超时秒后没有返回数据。 出于某种原因,在返回并完成所有结果后,下面的代码似乎无法终止/加入进程池。 它可能与超时发生时杀死工作进程有关,但是池会在他们被杀死时创建新的工作人员并且结果如预期。 from multiprocessing import Pool import time import numpy as np from threading import Timer import thread, time, sys def f(x): time.sleep(x) return x if __name__ == '__main__': pool = Pool(processes=4, maxtasksperchild=4)

2021-06-11 05:19:04    分类:技术分享    python   process   timeout   multiprocessing   pool

分析 python 多处理池(Profiling a python multiprocessing pool)

问题 我正在尝试在多处理池中的每个进程上运行 cProfile.runctx(),以了解我的源中的多处理瓶颈是什么。 这是我正在尝试做的一个简化示例: from multiprocessing import Pool import cProfile def square(i): return i*i def square_wrapper(i): cProfile.runctx("result = square(i)", globals(), locals(), "file_"+str(i)) # NameError happens here - 'result' is not defined. return result if __name__ == "__main__": pool = Pool(8) results = pool.map_async(square_wrapper, range(15)).get(99999) print results 不幸的是,尝试在探查器中执行“result = square(i)”不会影响调用它的范围内的“result”。 我怎样才能在这里完成我想要做的事情? 回答1 试试这个: def square_wrapper(i): result = [None] cProfile.runctx("result[0] = square(i)"

2021-06-09 12:25:35    分类:技术分享    python   profiling   multiprocessing   pool

池或不池 java 加密服务提供程序(To Pool or not to Pool java crypto service providers)

问题 解决方案 MessageDigest => 根据需要经常创建新实例 KeyFactory => 使用单个共享实例 SecureRandom => 使用 StackObjectPool 密码 => 使用 StackObjectPool 问题 我在编写安全框架时经常会遇到一个难题:“合并还是不合并” 基本上这个问题分为两个“组”: 第 1 组: SecureRandom因为对nextBytes(...)的调用是同步的,它可能成为 WebApp/多线程应用程序的瓶颈第 2 组:加密服务提供商,例如MessageDigest 、 Signature 、 Cipher 、 KeyFactory ……(因为getInstance()的成本?) 你有什么意见 ? 你对这些问题的习惯是什么? 编辑 09/07/2013 我终于花时间自己测试@Qwerky Share课程,我发现结果非常......令人惊讶。 该课程缺乏我主要关注的问题:像 GenericObjectPool 或 StackObjectPool 这样的池。 所以我重新设计了课程以测试所有 4 个替代方案: 具有同步要点的单个共享实例每个循环内的新实例(我对您可以将摘要创建拉到循环外的情况不感兴趣)gist 通用对象池:要点堆栈对象池:要点 我不得不将循环次数降低到 100000,因为 1M 在池上花费了太多时间。

2021-06-09 10:29:29    分类:技术分享    java   pool

Thread Pool vs Many Individual Threads

I'm in the middle of a problem where I am unable decide which solution to take. The problem is a bit unique. Lets put it this way, i am receiving data from the network continuously (2 to 4 times per second). Now each data belongs to a different, lets say, group. Now, lets call these groups, group1, group2 and so on. Each group has a dedicated job queue where data from the network is filtered and added to its corresponding group for processing. At first I created a dedicated thread per group which would take data from the job queue, process it and then goes to blocking state (using Linked

2021-06-05 12:21:18    分类:问答    java   multithreading   pool   pooling   spawning

python prime crunching: processing pool is slower?

So I've been messing around with python's multiprocessing lib for the last few days and I really like the processing pool. It's easy to implement and I can visualize a lot of uses. I've done a couple of projects I've heard about before to familiarize myself with it and recently finished a program that brute forces games of hangman. Anywho, I was doing an execution time compairison of summing all the prime numbers between 1 million and 2 million both single threaded and through a processing pool. Now, for the hangman cruncher, putting the games in a processing pool improved execution time by

2021-06-05 07:44:38    分类:问答    python   multiprocessing   pool

如何获得 Python 多处理池要完成的“工作”量?(How to get the amount of “work” left to be done by a Python multiprocessing Pool?)

问题 到目前为止,每当我需要使用多处理时,我都是通过手动创建“进程池”并与所有子进程共享工作队列来实现的。 例如: from multiprocessing import Process, Queue class MyClass: def __init__(self, num_processes): self._log = logging.getLogger() self.process_list = [] self.work_queue = Queue() for i in range(num_processes): p_name = 'CPU_%02d' % (i+1) self._log.info('Initializing process %s', p_name) p = Process(target = do_stuff, args = (self.work_queue, 'arg1'), name = p_name) 通过这种方式,我可以向队列中添加内容,这些内容将被子进程使用。 然后我可以通过检查Queue.qsize()监控处理的Queue.qsize() : while True: qsize = self.work_queue.qsize() if qsize == 0: self._log.info('Processing finished') break

2021-06-05 07:03:37    分类:技术分享    python   process   parallel-processing   multiprocessing   pool

Python multiprocessing Pool map and imap

I have a multiprocessing script with pool.map that works. The problem is that not all processes take as long to finish, so some processes fall asleep because they wait until all processes are finished (same problem as in this question). Some files are finished in less than a second, others take minutes (or hours). If I understand the manual (and this post) correctly, pool.imap is not waiting for all the processes to finish, if one is done, it is providing a new file to process. When I try that, the script is speeding over the files to process, the small ones are processed as expected, the

2021-06-03 15:13:05    分类:问答    multiprocessing   cpu-usage   python-3.5   pool