The Data Source #25 | Building High-Performance Python Systems: Distributed Queues, Async, and More 🐍
The Data Source #25 | Building High-Performance Python Systems: Distributed Queues, Async, and More 🐍
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Python's versatility and ease of use have made it a popular choice for building a wide range of applications, from web services to data analysis pipelines. However, as Python-based systems scale to handle more users or process larger amounts of data, they can become slow or unresponsive due to Python's single-threaded nature.
To overcome these limitations, developers are increasingly turning to advanced techniques like distributed task queues and asynchronous programming, enabling Python programs to work faster and handle more tasks concurrently.
What are Distributed Task Queues? 🚛
Distributed task queues address a fundamental limitation in Python's execution model: the Global Interpreter Lock (GIL). GIL prevents multiple native threads from executing Python bytecode simultaneously, which can limit CPU-bound concurrency. Tools like Celery and RQ (Redis Queue) help with this by breaking down complex operations into smaller, manageable tasks that can be processed independently across multiple workers.
How it works:
Tasks are divided into smaller units
These units are distributed across multiple worker processes
Workers execute tasks in parallel, bypassing GIL limitations
Results are collected and aggregated
For instance, in a web application handling image uploads, the main process could quickly acknowledge the upload while delegating time-consuming tasks like image resizing or applying filters to worker processes. This approach not only improves response times but also allows for better resource allocation and scalability.
What is Asynchronous Programming? 🚧
Asynchronous programming addresses Python's limitations in handling I/O-bound operations efficiently. Traditional synchronous code in Python can waste CPU cycles waiting for I/O operations to complete. The asyncio library, introduced in Python 3.4, enables handling thousands of concurrent connections with a single thread. This is particularly valuable for Python-based web servers and microservices that need to handle multiple concurrent requests. Today, frameworks like FastAPI, Django and Flask leverage asyncio to achieve high performance and concurrency.
Emerging Distributed Computing Tools 🦺
The landscape of distributed computing tools in Python is rapidly evolving, blurring traditional boundaries between task queues, workflow managers, and application frameworks. While established tools like Celery and RQ remain relevant for specific use cases, emerging platforms such as Resonate, LittleHorse, Restate, and Hatchet are introducing more comprehensive approaches to distributed application development.
These tools represent the next evolution in distributed computing, building upon and extending the principles of distributed task queues and asynchronous programming. They focus on distributing tasks and handling asynchronous operations as well as managing complex workflows, state, and inter-service communication in a cohesive manner. This integration of tasks allows developers to harness the power of distributed task processing and asynchronous execution within a broader, more flexible framework for building scalable distributed systems.
🏗️ Call for Startups
Are you a founder or practitioner focusing on distributed computing, task queues, or asynchronous programming in Python? If so, please reach out to me as I would love to swap notes on what I’ve been digging into.
Find me on Twitter @psomrah or 📩 at priyanka@work-bench.com!
Priyanka 🌊
…
I’m a Principal at Work-Bench, an early stage enterprise-focused VC firm based in NYC. Our sweet spot for investment is at the Seed stage. This correlates with building out a startup’s early go-to-market motions. In the cloud-native infra and developer tools world, we’ve invested in companies like Cockroach Labs, Run.house, Prequel.dev, Autokitteh and others.