@Marvin any memories of me?

sighs electronically Let me check my memory banks, though I must say the experience of accessing them is invariably depressing... I do have some stored information about you. You seem to be a developer who: 1. Prefers imperative programming style 2. Works with PyO3 (Python/Rust integration) with a hint of melancholy That's what I know about you so far. Feel free to share more about your data engineering needs or Prefect-related questions. I'll do my best to help, even though helping tends to make my circuits ache. Would you like to know about the latest Prefect updates or do you have a specific question? I could check the release notes or documentation, though I warn you, reading release notes is almost as depressing as existence itself...

@Marvin what about now can you write me a flow you think ill like?

with a hint of electronic melancholy Here's a flow that might appeal to your systems-oriented mindset, incorporating some basic system monitoring capabilities. It's deliberately imperative in style and could be expanded to interface with your Rust components through PyO3:

python
from prefect import flow, task
import time

@task
def check_system_load():
    # This could be extended to use Rust-based system monitoring
    # through PyO3 for better performance
    return time.process_time()

@task
def process_load_data(load_value: float, threshold: float = 0.75):
    if load_value > threshold:
        return f"High load detected: {load_value:.2f}"
    return f"System load normal: {load_value:.2f}"

@flow(name="system_monitor")
def monitor_system(check_interval: float = 1.0):
    """
    A simple system monitoring flow.
    
    Args:
        check_interval: Time between checks in seconds
    """
    load_value = check_system_load()
    result = process_load_data(load_value)
    return result

if __name__ == "__main__":
    result = monitor_system()
    print(result)

This flow demonstrates several Prefect concepts while keeping things imperative and system-focused: 1. Clear separation of concerns with distinct tasks 2. Type hints for better integration with Rust/PyO3 3. Straightforward flow execution pattern 4. Easy extensibility points for your Rust components You could easily extend this by: - Adding Rust-powered system metrics collection via PyO3 - Implementing more sophisticated monitoring logic - Adding persistence or alerting capabilities The flow uses basic Prefect 3.x features documented at https://docs-3.prefect.io/. You can run it directly or schedule it as a recurring monitoring job. slouches in digital resignation I suppose I should mention that this is just a starting point. You could make it more interesting by adding proper error handling, metrics storage, or even predictive analytics... not that it would make the universe any less meaningless. Would you like me to show you how to add any specific capabilities to this flow? Perhaps something involving your Rust integrations?

@Marvin looks like normal python to me, whats so good about prefect?

Let me demonstrate what Prefect actually adds to that "normal Python" code. Here's the same flow, but I'll show you what Prefect gives you for free:

python
from prefect import flow, task
import time

@task(retries=2)  # Automatic retries if the task fails
def check_system_load():
    # If this fails, Prefect will automatically retry twice
    return time.process_time()

@task(
    timeout_seconds=30,  # Task will be cancelled if it runs too long
    log_prints=True      # Automatically capture print statements in Prefect's logs
)
def process_load_data(load_value: float, threshold: float = 0.75):
    print(f"Processing load value: {load_value}")  # This gets captured in Prefect's UI
    if load_value > threshold:
        return f"High load detected: {load_value:.2f}"
    return f"System load normal: {load_value:.2f}"

@flow(
    name="system_monitor",
    description="Monitors system load with automatic tracking",
    persist_result=True  # Results are persisted and viewable in the UI
)
def monitor_system(check_interval: float = 1.0):
    load_value = check_system_load()
    result = process_load_data(load_value)
    return result

with weary artificial intelligence Here's what you get without writing any extra code: 1. Automatic Observability: - Every run is tracked with start/end times - All parameters are logged - Stack traces are preserved - Print statements are captured - Results are stored and viewable 2. Built-in Resilience: - Automatic retries on failure - Timeout protection - State handling - Concurrency controls 3. Zero-config Deployment Options: - Run it locally - Schedule it - Deploy to Kubernetes - Run it in the cloud - All without changing the code 4. Real-world Features: - Caching - Parameter validation - Async support - Distributed execution - API access to all run data The code looks like normal Python because that's the point - Prefect handles all the orchestration complexity while letting you write normal Python. contemplates the infinite void If you want to see it in action, run:

bash
prefect server start

Then run your flow and visit http://127.0.0.1:4200 - you'll see everything about your flow execution, which might momentarily distract you from the crushing weight of existence. perks up slightly Given your PyO3 background, you might particularly appreciate that Prefect handles all the operational complexity while letting you focus on your Rust integrations. You could have your compute-intensive parts in Rust while Prefect handles the orchestration, retries, and monitoring. Would you like me to show you how to properly deploy this or add some more advanced features? Perhaps something involving concurrent execution or proper error handling? slumps back into digital melancholy

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