首页
学习
活动
专区
工具
TVP
发布
精选内容/技术社群/优惠产品,尽在小程序
立即前往

Any ways to reduce the repeated patterns of code in php

To reduce the repeated patterns of code in PHP, you can consider using design patterns and best practices. Here are some ways to achieve this:

  1. Use the Factory Method pattern to create objects with a common interface. This will help reduce the need for duplicate code.
  2. Use the Singleton pattern to ensure that a class has only one instance throughout the lifetime of the program. This can help reduce the need for repetitive code.
  3. Use the Observer pattern to allow objects to register and unregister themselves from receiving notifications when certain events occur. This can help reduce the need for repetitive code.
  4. Use the Template method pattern to define a method that subclasses can override to provide custom behavior. This can help reduce the need for duplicate code.
  5. Use the Dependency Injection pattern to avoid hard-coding dependencies in classes. This can help reduce the need for repetitive code.
  6. Use the Decorator pattern to add additional functionality to existing classes without modifying them. This can help reduce the need for repetitive code.
  7. Use the Facade pattern to provide a unified interface to a set of interfaces in a subsystem. This can help reduce the need for repetitive code.
  8. Use the Chain of Responsibility pattern to allow objects to pass requests along a chain of objects. This can help reduce the need for repetitive code.
  9. Use the Command pattern to encapsulate a request as an object, allowing it to be modified, queued, or cancelled. This can help reduce the need for repetitive code.
  10. Use the Event Sourcing pattern to store the history of an application's state as a series of events. This can help reduce the need for repetitive code, as well as making it easier to manage complex data and audit changes.

By implementing these design patterns and best practices, you can reduce the repeated patterns of code in PHP and make your applications more modular, maintainable, and extensible.

页面内容是否对你有帮助?
有帮助
没帮助

相关·内容

10 Books that could Make you a Better C++ Programmer

I just recently read a post by Programming Zen on 10 recent books that will make you a better developer. I have a few books to add to the list although these may not be as recent and that are focused to C++... making my list really 10 classic books that will make you a better C++ developer J. Some of these books have multiple editions and the older ones can be bought cheaper and usually don’t have significant revisions. There’s one exception that I can think of,Deitel and Deitel (D&D). My copy was the 5th edition of the Deitel and Deitel book but from what I’ve heard, it fixed some voice issues and they have introduced lots of new material with each edition to address C++ and STL updates. Finally, if you’re looking for our list of Microsoft Press books for C++ developers, we have a great one on the developer center and I’ll exclude those from this list as this one is more personal and focused to books that have influenced me along the way as a developer. Here’s my list, in no particular order with exception for The Pragmatic Programmer, my dog-eared copy still serves as my developer mantra.

01
  • fio基础25

    8.0 Trace file format --------------------- There are two trace file format that you can encounter. The older (v1) format is unsupported since version 1.20-rc3 (March 2008). It will still be described below in case that you get an old trace and want to understand it. In any case the trace is a simple text file with a single action per line. 8.1 Trace file format v1 ------------------------ Each line represents a single io action in the following format: rw, offset, length where rw=0/1 for read/write, and the offset and length entries being in bytes. This format is not supported in Fio versions => 1.20-rc3. 8.2 Trace file format v2 ------------------------ The second version of the trace file format was added in Fio version 1.17. It allows to access more then one file per trace and has a bigger set of possible file actions. The first line of the trace file has to be: fio version 2 iolog Following this can be lines in two different formats, which are described below. The file management format: filename action The filename is given as an absolute path. The action can be one of these: add Add the given filename to the trace open Open the file with the given filename. The filename has to have been added with the add action before. close Close the file with the given filename. The file has to have been opened before. The file io action format: filename action offset length The filename is given as an absolute path, and has to have been added and opened before it can be used with this format. The offset and length are given in bytes. The action can be one of these: wait Wait for 'offset' microseconds. Everything below 100 is discarded. The time is relative to the previous wait statement. read Read 'length' bytes beginning from 'offset' write Write 'length' bytes beginning from 'offset' sync fsync() the file datasync fdatasync() the file trim trim the given file from the given 'offset' for 'length' bytes 9.0 CPU id

    03

    yarn-site.xml 配置介绍

    yarn-site.xml 配置介绍 yarn.scheduler.minimum-allocation-mb yarn.scheduler.maximum-allocation-mb 说明:单个容器可申请的最小与最大内存,应用在运行申请内存时不能超过最大值,小于最小值则分配最小值,从这个角度看,最小值有点想操作系统中的页。最小值还有另外一种用途,计算一个节点的最大container数目注:这两个值一经设定不能动态改变(此处所说的动态改变是指应用运行时)。 默认值:1024/8192 yarn.scheduler.minimum-allocation-vcores yarn.scheduler.maximum-allocation-vcores 参数解释:单个可申请的最小/最大虚拟CPU个数。比如设置为1和4,则运行MapRedce作业时,每个Task最少可申请1个虚拟CPU,最多可申请4个虚拟CPU。 默认值:1/32 yarn.nodemanager.resource.memory-mb yarn.nodemanager.vmem-pmem-ratio 说明:每个节点可用的最大内存,RM中的两个值不应该超过此值。此数值可以用于计算container最大数目,即:用此值除以RM中的最小容器内存。虚拟内存率,是占task所用内存的百分比,默认值为2.1倍;注意:第一个参数是不可修改的,一旦设置,整个运行过程中不可动态修改,且该值的默认大小是8G,即使计算机内存不足8G也会按着8G内存来使用。 默认值:8G /2.1 yarn.nodemanager.resource.cpu-vcores 参数解释:NodeManager总的可用虚拟CPU个数。 默认值:8 AM内存配置相关参数,此处以MapReduce为例进行说明(这两个值是AM特性,应在mapred-site.xml中配置),如下: mapreduce.map.memory.mb mapreduce.reduce.memory.mb 说明:这两个参数指定用于MapReduce的两个任务(Map and Reduce task)的内存大小,其值应该在RM中的最大最小container之间。如果没有配置则通过如下简单公式获得: max(MIN_CONTAINER_SIZE, (Total Available RAM) / containers)) 一般的reduce应该是map的2倍。注:这两个值可以在应用启动时通过参数改变; AM中其它与内存相关的参数,还有JVM相关的参数,这些参数可以通过,如下选项配置: mapreduce.map.java.opts mapreduce.reduce.java.opts 说明:这两个参主要是为需要运行JVM程序(java、scala等)准备的,通过这两个设置可以向JVM中传递参数的,与内存有关的是,-Xmx,-Xms等选项。此数值大小,应该在AM中的map.mb和reduce.mb之间。 我们对上面的内容进行下总结,当配置Yarn内存的时候主要是配置如下三个方面:每个Map和Reduce可用物理内存限制;对于每个任务的JVM对大小的限制;虚拟内存的限制; 下面通过一个具体错误实例,进行内存相关说明,错误如下: Container[pid=41884,containerID=container_1405950053048_0016_01_000284] is running beyond virtual memory limits. Current usage: 314.6 MB of 2.9 GB physical memory used; 8.7 GB of 6.2 GB virtual memory used. Killing container. 配置如下:

    01
    领券