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How to Optimize Your Apps with Java Java Apps_KelltonTech.png

Software consultants have been conducting optimization and performance analysis of enterprise IT applications developed on Java for above 10 years. However, there are few best industry practices that every software developer must follow. You can avoid some of the common troubleshooting practices with the help of them. 

First of all, you must understand the difference between client side and server side Java applications

In web programming, server-side applications are written using a code which runs on a server by using multiple languages like PHP, Java, C etc. the code is executed on the server using JavaScript. You can gain back-end access to databases, file systems, and servers.

Client-side applications are written in a code that runs on a client’s browser in a pattern similar to other languages that are executed by the browser. You can run active online content on web browsers. Client side or front-end applications are mostly developed on JavaScript along with HTML and CSS code. The scripts runs code after the web page is uploaded on a client’s desktop. 

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Server-side or back-end applications run scripts before loading the HTML code. PHP is a popular back-end software language in addition to Ruby on Rails, ASP.NET etc; these languages do not run on your computer, instead they run on the server that hosts your website and transfers the HTML code. 

Business applications developed in J2EE help create dynamic content that attracts more customers. However, few key points must be kept in mind to improve the performance of your Java applications. 

Define user requirements
Understanding customer’s requirements related to performance, availability, and scalability is crucial. Consider a customer’s demand is to develop an application that should be fast. How do you define the term ‘fast’? Would 1 second be fast or even 8 seconds can be termed as being ‘fast’. 

So as a developer, you must properly define the performance requirements for instance, 95% of login requests will respond within 2 seconds as measured by the server. 

Measuring response time is not guesswork
You have huge access to a wide variety of tools to optimize and analyze the performance of your code. Java comprises of tools like jvisualvm or jstat to help you.  Good profilers such as JProfiler or APM solutions will help in production analysis. 

Prioritize development tasks
A team of numerous Java developers optimizing code will not help you improve the application’s performance. Changes running parallel on the Javascript can destroy the entire application.

Take for instance, Developer X can do a good optimization on the code however simultaneously developer B deploys the code to check the performance of your application hence the work done by developer A goes unnoticed. 

Therefore, you must measure the performance of your application. Define all probable optimizations; prioritize these tasks balancing them with the risks involved to implement this change. 

Implement one optimization at a time and measure the application’s performance. Retract if there is no positive change in the performance analysis. 

Is your team limited by budget and time?  That’s a major reason why developers do not have time for analysis and performance testing in several projects. The reason behind this inefficiency is that every change reduces the level of performance. Hence, automating testing processes will help developers analyze real-time performance of the app. 

The need of the hour is a Continuous Delivery Pipeline. This pipeline breaks down the software delivery process into several stages. Each stage focuses on verifying quality of new features from different angles to validate new functionalities and prevent errors that might affect your users. The pipeline provides feedback to the development team and adds visibility into flow of changes to all users delivering new features.

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A continuous delivery pipeline is divided into the following stages:

  • Build automation and continuous integration
  • Test automation
  • Deployment automation

This method may not help avoid every issue related to app performance but it will definitely help you find the loopholes easily and that too at initial stages of development process that can be fixed gradually.  

Optimize only when necessary
What is the right time to tune your Java application? The correct answer is when all business needs are not being met by the existing app. Optimizing applications is harder when you are changing the code.  

Often, better performance leads to problems with better maintenance of the app. Once your requirements are fulfilled, you must stop tuning the application even though it is not fully optimized. 

Amdahl’s Law of Parallelization 
Gene Amdahl, a computer scientist defined theoretical speedup in latency of executing tasks at fixed workload that you can expect of a system with improvement of resources. Often, Amdahl's law is used in parallel computing to predict the theoretical speedup when using multiple processors.

For example, a program takes 20 hours working on a single processor core, while a specific part of that program takes an hour to execute cannot be parallelized, and the remaining 19 hours of execution will be parallelized regardless of the number of processors involved in the project. 

The more number of CPUs, developers have more Cores as a result applications can be developed at lower clock speeds using this architecture. However, developers need to acquire deep knowledge of threads, concurrency, locking etc. You can also use specialized languages such as Scala or Erlang to improve built-in concurrency support features. 

Scaling systems have evolved through the years. 

Scale up means to add more hardware to a single operating system. This approach, unfortunately has limited scope because you cannot install unlimited RAM/CPUs in a single operating system. In addition, this approach is not very cost-effective. 

Scale out refers to running applications on more than one operating system. It is a scalable and cheaper alternative but more complex for software applications. A developer needs to address issues related to clustering, data replication, and other methods of data storage and scalability.

Virtualization and cloud computing can become complicated when the horizontal scalability is automated and bidirectional i.e. hardware is added as well as removed on demand. 

Frameworks such as Hadoop MapReduce combine scalability and parallelism for developing applications; you can split the application logic into several components that run parallel to several nodes of that application. It improves the performance and scalability of the entire application. 

Retrieval of data from storage disks or a network is expensive. Caching architecture such as “Onion Skin Model” helps in improving the app’s performance. For example, pages of a web portal are generated through a web application which retrieves data from a stored database.

Caching the entire page in a proxy server will increase the page speed; however the only limitation is that you must post static content. Ideally, the page will refresh after every 15 minutes, but the solution is not viable for dynamic content. 

Distributed cache systems store the entire data in an in-memory database or optimize database caches to provide better SQL performance. 

Tools improving Java apps performance
VisualVM, a visual tool integrates many existing Java Development Kit software tools, lightweight memory, and other CPU profiling capabilities. It is designed for production and development and enhances monitoring and performance analysis capabilities for Java SE platform.

Eclipse Memory Analyzer is a quick Java heap analyzer and has rich features. It helps you identify memory leaks and reduces memory consumption.

Another standalone Java bundle is Java Out-of-Box Tool that installs startup (RC) scripts, modifies kernel parameters, rebuilds kernel, and reboots your system. In the first stage, the startup script modifies system tunables, which provides better “Out of The Box” behavior for the Java application.