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The Dulin Report

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On Amazon Prime Video’s move to a monolith May 14, 2023 One size does not fit all: neither cloud nor on-prem Apr 10, 2023 Comparing AWS SQS, SNS, and Kinesis: A Technical Breakdown for Enterprise Developers Feb 11, 2023 Stop Shakespearizing Sep 16, 2022 Using GNU Make with JavaScript and Node.js to build AWS Lambda functions Sep 4, 2022 Monolithic repository vs a monolith Aug 23, 2022 Keep your caching simple and inexpensive Jun 12, 2022 Java is no longer relevant May 29, 2022 There is no such thing as one grand unified full-stack programming language May 27, 2022 Best practices for building a microservice architecture Apr 25, 2022 TypeScript is a productivity problem in and of itself Apr 20, 2022 In most cases, there is no need for NoSQL Apr 18, 2022 Node.js and Lambda deployment size restrictions Mar 1, 2021 Should we abolish Section 230 ? Feb 1, 2021 TDWI 2019: Architecting Modern Big Data API Ecosystems May 30, 2019 Microsoft acquires Citus Data Jan 26, 2019 Which AWS messaging and queuing service to use? Jan 25, 2019 Using Markov Chain Generator to create Donald Trump's state of union speech Jan 20, 2019 Let’s talk cloud neutrality Sep 17, 2018 A conservative version of Facebook? Aug 30, 2018 TypeScript starts where JavaScript leaves off Aug 2, 2017 Design patterns in TypeScript: Chain of Responsibility Jul 22, 2017 I built an ultimate development environment for iPad Pro. Here is how. Jul 21, 2017 Rather than innovating Walmart bullies their tech vendors to leave AWS Jun 27, 2017 Emails, politics, and common sense Jan 14, 2017 Don't trust your cloud service until you've read the terms Sep 27, 2016 I am addicted to Medium, and I am tempted to move my entire blog to it Sep 9, 2016 What I learned from using Amazon Alexa for a month Sep 7, 2016 Amazon Alexa is eating the retailers alive Jun 22, 2016 In search for the mythical neutrality among top-tier public cloud providers Jun 18, 2016 What can we learn from the last week's salesforce.com outage ? May 15, 2016 Why it makes perfect sense for Dropbox to leave AWS May 7, 2016 Managed IT is not the future of the cloud Apr 9, 2016 JavaScript as the language of the cloud Feb 20, 2016 Our civilization has a single point of failure Dec 16, 2015 Operations costs are the Achille's heel of NoSQL Nov 23, 2015 IT departments must transform in the face of the cloud revolution Nov 9, 2015 Setting Up Cross-Region Replication of AWS RDS for PostgreSQL Sep 12, 2015 Top Ten Differences Between ActiveMQ and Amazon SQS Sep 5, 2015 Ten Questions to Consider Before Choosing Cassandra Aug 8, 2015 The Three Myths About JavaScript Simplicity Jul 10, 2015 Big Data is not all about Hadoop May 30, 2015 Smart IT Departments Own Their Business API and Take Ownership of Data Governance May 13, 2015 Guaranteeing Delivery of Messages with AWS SQS May 9, 2015 We Need a Cloud Version of Cassandra May 7, 2015 Building a Supercomputer in AWS: Is it even worth it ? Apr 13, 2015 Ordered Sets and Logs in Cassandra vs SQL Apr 8, 2015 Exploration of the Software Engineering as a Profession Apr 8, 2015 Finding Unused Elastic Load Balancers Mar 24, 2015 Where AWS Elastic BeanStalk Could be Better Mar 3, 2015 Trying to Replace Cassandra with DynamoDB ? Not so fast Feb 2, 2015 Why I am Tempted to Replace Cassandra With DynamoDB Nov 13, 2014 How We Overcomplicated Web Design Oct 8, 2014 Infrastructure in the cloud vs on-premise Aug 25, 2014 Cassandra: a key puzzle piece in a design for failure Aug 18, 2014 Cassandra: Lessons Learned Jun 6, 2014

Java is no longer relevant

May 29, 2022

Why I no longer use Java for backend services



I spent 18 years of my career advocating and using Java for all of my projects. However, in the past six years, my use of Java has dropped to practically zero.



Each new programming language and platform is meant to solve a particular set of problems. The prolonged use of tools that no longer solve relevant problems makes solving contemporary issues harder. When Java first came out in 1995, it set out to solve specific problems that are no longer relevant 27 years later.



Platform independence in 1995 was strategically crucial to any company trying to compete with Microsoft, whose Windows operating system was dominant. By convincing developers to use a toolchain that could produce executable artifacts that could be distributed to many platforms, competing operating systems could gain new applications.



The way Java achieved platform independence was by using the concept of byte-code. The Java compiler never produced the final binary executable for any particular hardware and operating system combination. Instead, it produced byte-code, a higher-level binary code than machine code. This byte-code would then execute in a Java Virtual Machine (JVM) in its final deployment environment. The JVM, in turn, would dynamically translate byte-code into native code at runtime.



The JVM itself had to be installed on the operating system to run Java applications. Initially, all major operating systems enthusiastically embraced it. In theory, if all new applications were built in Java, all operating systems that could run a JVM would automatically be able to run them.



Sun Microsystems (the original company behind Java) built out what they called Abstract Windowing Toolkit (AWT) and later Swing to support platform-independent user interfaces. They built abstractions over most common UI artifacts such as lists, buttons, windows, frames, tabs, dialogs, and pop-ups.



The cracks in the Java-on-the-desktop idea began to form early on. The problem was that Java prevented developers from taking advantage of native operating system capabilities by abstracting GUI artifacts. Desktop Java applications would never look as polished as native, and they would never perform as well either.



Microsoft built their proprietary JVM. When Apple launched macOS X, they initially included support for Java as one of the primary languages and runtime environments for MacOS X applications. Though AWT was part of the JVMs, by promoting their native UI frameworks, both Apple and Microsoft sabotaged the idea of a platform-independent UI.



The growth of Java on the desktop was therefore stunted from the beginning. Today, except for a few niche products such as Integrated Development Environments (IDEs) and legacy enterprise applications still eking out their daily survival, Java on the desktop is effectively dead. IDEs, by their very nature, must be platform-independent and allow developers to use whatever development environment they find appropriate for their productivity. Enterprise desktop applications written in Java could be implemented as full-stack, at least theoretically



Over time, the mistakes made by Oracle, legal battles over JVM distribution, security incidents, and the rise of smartphones and modern web browsers destroyed the idea that platform-independent UIs can be built in Java. As of today, the only user-facing platform still running Java natively is Android, and platform independence means running Java apps on various Android devices — if such a thing is even possible.



Java’s prospects on the servers looked much better. Platform independent networking, concurrency, and distributed computing capabilities proved valuable. As is typical in most situations, a developer could write and test backend code in a platform-independent manner on their computer. They could then deploy their work to the server, which could be a UNIX server. 



Typically a Java server would run a JEE (Java Enterprise Edition) application server. A single application server would run multiple applications at a time. The application components would take the form of servlets and Enterprise Java Beans. The application itself would be packaged as an Enterprise Application Archive (EAR) and deployed to the application server utilizing its command line. It made sense in the late 1990s and the first decade of the 2000s when there was no such thing as a public cloud, on-premise servers were expensive shared resources, and application server licenses were sold by CPU.



JEE application servers were expensive, bloated, and resource-intensive. They also had proprietary features. Rather than being tied to an operating system, Java server components would get linked to an application server. One of the projects I worked on was porting a trading system backend out of WebLogic to run as a simple Java process.



The rise of containers such as Docker put an end to the idea that JVM was the only way to run platform-independent backend code. One of the problems with JVM was that over time, different versions of JVMs became incompatible with one another. So, developers used Docker to control the version of JVM. However, Docker itself raised some questions about the need for the use of Java and JVM at all — if I can run Docker on my development machine, use whatever language I want, and then deploy this Docker container on the server, why do I need a JVM at all?



The decline of on-premise servers and the rise of server-less public cloud put the final nail in the coffin of JEE application servers. If I can deploy my docker containers in the form of AWS Lambda functions or AWS ECS services, I need neither Java nor JEE servers. I can pick whatever language works best for my productivity and my application’s performance. I can write my code once, build a container, and deploy it anywhere I want.



Platform independence was not the only problem Java solved. Java addressed many of the complexities of C and C++. Java streamlined object-oriented programming. Though relevant in 1995 and the first decade of the 2000s, today very few applications have more than 2 levels of class hierarchy and modern languages like Swift, Go, and Rust address complexities of C, C++, and now Java in much better ways.




Final thoughts




Though Java was my primary way of earning a living from about 1997 to 2015, it has long outlived the problems it solved. Java’s issues are being solved now by modern tools like Docker. Except for a few niche use cases, I no longer use Java for my projects.