Here's a practical example that demonstrates how a flow can return meaningful data that could be used by other flows or stored for later use:

python
from prefect import flow

@flow
def fetch_stock_data(symbol: str) -> dict:
    """A flow that fetches and processes stock market data."""
    # Simulate fetching stock data
    stock_data = {
        "symbol": symbol,
        "price": 150.25,
        "volume": 1000000
    }
    
    # You could do more processing here
    return stock_data

@flow
def analyze_stocks():
    # Call the first flow and use its return value
    tesla_data = fetch_stock_data("TSLA")
    apple_data = fetch_stock_data("AAPL")
    
    # Process the returned data
    total_volume = tesla_data["volume"] + apple_data["volume"]
    return {
        "stocks_analyzed": [tesla_data["symbol"], apple_data["symbol"]],
        "total_volume": total_volume
    }

# Run the analysis
result = analyze_stocks()
print(result)  # Will print the dictionary with analysis results

In this example: 1. The

fetch_stock_data

flow returns a dictionary containing stock information 2. The

analyze_stocks

flow calls the first flow twice and uses its return values 3. The final result is a new dictionary containing aggregated information Return values are useful for: - Passing data between flows - Storing results in the Prefect backend for later reference - Chaining flows together where one flow's output becomes another's input - Creating reports or summaries of flow execution - Enabling conditional logic based on flow results Remember that return values from flows are automatically tracked and stored in Prefect's backend, making them available for inspection through the UI or API. This is particularly useful for debugging, monitoring, and analyzing your workflow results over time. The return value can be any Python object that can be serialized (dictionaries, lists, numbers, strings, etc.). However, if you're working with generator functions, they need to yield results rather than return them directly.

No, by default task results are not stored in Prefect's backend. Here's how task result storage works: 1. Default Behavior: Task results are ephemeral and only kept in memory during flow execution. 2. Enabling Result Storage: You can enable result storage by: - Setting

persist_result=True

on individual tasks - Using a

result_storage

block to specify where results should be stored - Setting the environment variable

PREFECT_RESULTS_PERSIST_BY_DEFAULT=true

Here's an example of configuring task result storage:

python
from prefect import flow, task
from prefect.filesystems import LocalFileSystem

# Create a storage block for results
local_storage = LocalFileSystem(basepath="/path/to/results")

@task(persist_result=True, result_storage=local_storage)
def my_task():
    return "This result will be stored"

@flow
def my_flow():
    result = my_task()
    return result

You can also configure result storage at the flow level, which will apply to all tasks within that flow:

python
@flow(result_storage=local_storage)
def my_flow():
    # All task results in this flow will use the specified storage
    ...

Key Points to Remember: - Result storage is optional and must be explicitly enabled - You need to configure a storage block (e.g., LocalFileSystem, S3, Azure, etc.) to persist results - Stored results can be useful for: - Debugging - Result caching - Sharing data between flow runs - Audit trails - Large results should be carefully considered as they can impact storage costs and performance If you're working with large datasets or sensitive information, you might want to be selective about which task results you persist and where you store them.

I need to correct my previous response. Actually, neither flow return values nor task results are stored in Prefect's backend by default. Both are ephemeral unless explicitly configured for persistence. Here's the accurate breakdown: Default Behavior: - Flow return values: Ephemeral (not stored) - Task results: Ephemeral (not stored) To persist either of them, you need to: 1. Enable persistence using

persist_result=True

2. Configure a

result_storage

block to specify where to store the results Example showing both flow and task persistence:

python
from prefect import flow, task
from prefect.filesystems import LocalFileSystem

# Create a storage block
storage = LocalFileSystem(basepath="/path/to/storage")

@task(persist_result=True, result_storage=storage)
def my_task():
    return "Task result will be stored"

@flow(persist_result=True, result_storage=storage)
def my_flow():
    task_result = my_task()
    return "Flow result will be stored"

You can also enable persistence globally using the environment variable

PREFECT_RESULTS_PERSIST_BY_DEFAULT=true

, which will affect both flows and tasks. Thank you for asking for clarification - it's important to be precise about these details!