#concurrency

#concurrency

The most remarkable thing you might notice when coming to Clojure from Java is that it is homoiconic, which means the code is written in the form of the language's data structures. This practice, also known as code as data, results in very consistent syntax with a limited number of keywords and constructs. It also creates a meta-programming model using "syntax-aware" code templates (called macros).

The Python standard library provides the asyncio module to facilitate writing high-performance concurrent code. By leveraging async/await syntax, it provides a high level API for creating and managing event loops, coroutines, tasks, and performing asynchronous I/O operations. It is used as a foundation for Python asynchronous frameworks that provide high-performance network and web-servers, database connection libraries, distributed task queues, etc. Multiple libraries and frameworks, such as FastAPI and aiohttp, are built on top of asyncio.

Goroutines are lightweight threads that can run concurrently, allowing for multiple tasks to be performed simultaneously without the overhead of traditional threads.

AWS Lambda allows for concurrent execution of programs, leveraging Goroutines for non-blocking I/O bound tasks, enhancing performance similar to Node.js asynchronous handling.

Approachable concurrency in Swift 6.2 simplifies concurrent programming, making the compiler more predictable and reducing overwhelming errors and warnings not related to real issues.

Swift 6.1 introduces type-wide global actor inference control, supporting nonisolated declarations for protocols, enhancing concurrency management and simplifying the Swift development experience.

A common problem when building Python GUI applications is the interface locking up when attempting to perform long-running background tasks.