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Strategic activity mapping for software architects May 25, 2025 The future is bright Mar 30, 2025 Comparing AWS SQS, SNS, and Kinesis: A Technical Breakdown for Enterprise Developers Feb 11, 2023 Should today’s developers worry about AI code generators taking their jobs? Dec 11, 2022 Scripting languages are tools for tying APIs together, not building complex systems Jun 8, 2022 Java is no longer relevant May 29, 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 A year of COVID taught us all how to work remotely Feb 10, 2021 What programming language to use for a brand new project? Feb 18, 2020 Microsoft acquires Citus Data Jan 26, 2019 The religion of JavaScript Nov 26, 2018 Teleportation can corrupt your data Sep 29, 2018 Let’s talk cloud neutrality Sep 17, 2018 What does a Chief Software Architect do? Jun 23, 2018 TypeScript starts where JavaScript leaves off Aug 2, 2017 Node.js is a perfect enterprise application platform Jul 30, 2017 Design patterns in TypeScript: Chain of Responsibility Jul 22, 2017 Rather than innovating Walmart bullies their tech vendors to leave AWS Jun 27, 2017 TDWI 2017, Chicago, IL: Architecting Modern Big Data API Ecosystems May 30, 2017 Copyright in the 21st century or how "IT Gurus of Atlanta" plagiarized my and other's articles Mar 21, 2017 Online grocers have an additional burden to be reliable Jan 5, 2017 Don't trust your cloud service until you've read the terms Sep 27, 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 JEE in the cloud era: building application servers Apr 22, 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 IT departments must transform in the face of the cloud revolution Nov 9, 2015 We Live in a Mobile Device Notification Hell Aug 22, 2015 What Every College Computer Science Freshman Should Know Aug 14, 2015 Book Review: "Shop Class As Soulcraft" By Matthew B. Crawford Jul 5, 2015 Attracting STEM Graduates to Traditional Enterprise IT Jul 4, 2015 Your IT Department's Kodak Moment Jun 17, 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 What can Evernote Teach Us About Enterprise App Architecture Apr 2, 2015 Microsoft and Apple Have Everything to Lose if Chromebooks Succeed Mar 31, 2015 On apprenticeship Feb 13, 2015 Wall St. wakes up to underinvestment in OMS Aug 21, 2014 Cassandra: Lessons Learned Jun 6, 2014

Scripting languages are tools for tying APIs together, not building complex systems

June 8, 2022

There are two general categories of programming languages: compiled, and interpreted.



A program written in a compiled language must first be converted into native machine code to run. This step is called compilation.



A program written in an interpreted language does not need to be converted into native code. The interpreter parses the program and executes it in real-time. It is possible to use the interpreter interactively as a calculator or debugger.



In some cases, the interpreter may store the program in the form of intermediate code in memory and compile parts of it into native code in-real-time in a process called Just-in-time Compilation.



The interpreter may save the intermediate bytecode for faster loading times during future execution as an optimization. Python, for example, saves *.pyc files with compiled bytecode of the imported modules. 



Similarly, though technically a compiled language, Java produces *.class files with bytecode. Java is not a strictly interpreted language and is outside of this post's scope.



An interpreted language is typically a higher-level language. It may explicitly represent data structures, such as lists, tuples, and dictionaries (maps). It may also have higher-level abstractions over algorithms. It does not have to be loosely typed, though most interpreted languages are.



Under most circumstances, a program written in an interpreted language is slower than compiled. However, program performance needs to be balanced against developer productivity. The very purpose of an interpreted language is to hide complex algorithms from the developer and make programs available to run immediately after every change. A program being executed is in the same language that it is written in.



To paraphrase Mel Conway, an interpreted language is an application language, not algorithm (aka platform ) language. Interpreted languages are best used as a "glue" that ties algorithms and APIs together — not to implement algorithms.



There is a reason why many modern interpreted languages are also referred to as "scripting." Scripting is automating existing APIs and algorithms and combining them into new APIs and algorithms. Scripting is about controlling or orchestrating existing tools, APIs, and algorithms.








A reader may rightfully ask, "Oleg, what are you getting at?"



What makes me think of these things early in the mornings is the proliferation of JavaScript as an enterprise application language. I believe using JS as a hammer to solve every problem will hound enterprise IT for decades.



JavaScript is an interpreted scripting language. It's a tool for tying things together — DOM objects on a web page and APIs.



I love JavaScript as a tool for quickly building simple apps that tie cloud APIs together.



I'm not too fond of JavaScript for the religion it cultivates among its fans who are in denial about its limitations and feel the need to use it to solve every problem, however inappropriate it might be for the task.