Yes, there are several ways to control task status persistence in Prefect 3.x. Here are the main approaches: 1. Using Environment Variables: You can set the following environment variable to globally disable task result persistence:

bash
PREFECT_TASKS_DEFAULT_PERSIST_RESULT=false

2. Flow-level Configuration: You can configure persistence at the flow level, which will apply to all tasks within that flow:

python
@flow(persist_result=False)
def my_flow():
    # Tasks in this flow won't persist results by default
    ...

3. Individual Task Configuration: You can also control persistence for specific tasks:

python
@task(persist_result=False)
def my_task():
    # This specific task won't persist its result
    ...

The order of precedence is: 1. Task-level settings (most specific) 2. Flow-level settings 3. Environment variables (most general) This means that if you set

persist_result=False

on a flow, all its tasks will inherit this setting unless they explicitly override it. Similarly, the environment variable setting will be used as a default unless overridden by flow or task-level configurations. This is particularly useful when: - You have tasks that produce large intermediate results that don't need to be stored - You want to reduce database load - You're running many tasks and don't need to track all their individual states Remember that even with result persistence disabled, Prefect will still track the basic execution state of your tasks (success/failure), just not their return values. Links for further reading: - Prefect Results Documentation - Server Database Configurations

Based on my research, I have to be honest: Prefect 3.x doesn't currently have a built-in way to store only failed task states while completely ignoring successful ones. However, I can suggest several approaches to help manage your database load: 1. Minimize State Information:

python
from prefect import flow, task

@task(persist_result=False,  # Don't store task results
      retries=0,  # Disable retry tracking if not needed
      cache_result_in_memory=True)  # Keep results in memory only
def your_task():
    ...

@flow(persist_result=False)
def your_high_volume_flow():
    for _ in range(3000):
        your_task()

2. Use Task Groups: Instead of creating 3000 individual tasks, you could group related tasks together to reduce the number of state entries:

python
@task
def batch_processor(items):
    results = []
    for item in items:
        try:
            # Process item
            results.append(("success", item))
        except Exception as e:
            results.append(("failed", item, str(e)))
    return results

@flow
def your_optimized_flow():
    # Process items in larger batches instead of individual tasks
    batch_size = 100
    items = range(3000)
    batches = [items[i:i + batch_size] for i in range(0, len(items), batch_size)]
    
    for batch in batches:
        batch_processor(batch)

3. Database Maintenance: - Regularly clean up old task runs using Prefect's built-in retention policies - Consider setting up a separate database instance for high-volume flows - Implement database partitioning strategies for your Prefect database 4. Custom Logging Solution: While Prefect will still store all states, you could implement your own logging system for failures:

python
from prefect import flow, task
import logging

# Configure your custom logger
failure_logger = logging.getLogger("failure_logger")
failure_logger.setLevel(logging.ERROR)

@task
def task_with_custom_error_logging():
    try:
        # Your task logic here
        pass
    except Exception as e:
        failure_logger.error(f"Task failed: {str(e)}")
        raise

Important Notes: - Prefect's state storage is fundamental to its observability and orchestration features - The database load from task states is usually not a major concern unless you're running extremely high-volume workflows - Consider reviewing your flow architecture - if you're creating 3000 tasks in 15 minutes regularly, you might benefit from restructuring into batch operations Alternative Considerations: 1. If database size is the primary concern, consider implementing a regular cleanup schedule for successful task runs 2. If you're using Prefect Cloud, contact support as they might have additional solutions for high-volume workflows 3. Consider using a different orchestration pattern for very high-volume, short-duration tasks where full observability isn't required Would you like me to elaborate on any of these approaches or explore other potential solutions for your specific use case?

Based on the research and especially the GitHub issues, I can suggest some concrete approaches to reduce I/O operations for your high-frequency task scenario: 1. Optimize Task State Handling:

python
from prefect import flow, task

@task(
    persist_result=False,  # Don't persist task results
    cache_result_in_memory=True,  # Keep results in memory only
    retries=0  # Disable retry tracking to reduce state changes
)
def your_task():
    # Your task logic here
    pass

@flow(
    persist_result=False,
    task_runner=ConcurrentTaskRunner(),  # Use concurrent task runner for better state management
)
def your_high_volume_flow():
    for _ in range(3000):
        your_task()

2. Configure Task Runner Settings:

python
from prefect import flow
from prefect.task_runners import ConcurrentTaskRunner

@flow(
    task_runner=ConcurrentTaskRunner(
        max_concurrent_tasks=100  # Adjust based on your system capabilities
    )
)
def your_flow():
    # Your flow logic here
    pass

3. Use Environment Variables for Global Settings:

bash
# Add these to your environment or .env file
export PREFECT_TASKS_DEFAULT_PERSIST_RESULT=false
export PREFECT_TASKS_DEFAULT_RETRY_DELAY_SECONDS=0
export PREFECT_LOGGING_LEVEL="ERROR"  # Reduce logging overhead

4. Optimize Memory Management:

python
@task
def your_task(data):
    try:
        result = process_data(data)
        del data  # Explicitly release input data
        return result
    finally:
        gc.collect()  # Force garbage collection after task completion

5. Consider Using Background Tasks:

python
from prefect import flow, task
from prefect.utilities.asyncio import sync_compatible

@task(persist_result=False)
async def async_task():
    # Your task logic here
    pass

@flow
@sync_compatible
async def your_flow():
    tasks = [async_task() for _ in range(3000)]
    await asyncio.gather(*tasks)

Key Points to Remember: - These optimizations focus on reducing I/O operations while maintaining essential task tracking - The system will still track task success/failure states but with minimal overhead - You'll still have visibility into failed tasks, which matches your requirement - The concurrent task runner can help batch state updates more efficiently Important Caveats: 1. You'll still see some database operations, but they should be significantly reduced 2. Make sure to monitor system memory usage as keeping results in memory could impact performance 3. Consider implementing a regular cleanup schedule for your Prefect database to prevent long-term growth Would you like me to elaborate on any of these approaches or explore other specific optimizations for your use case?

Let me break down exactly what

PREFECT_TASKS_DEFAULT_PERSIST_RESULT=false

does: 1. Primary Effect: - When set to

false

, Prefect will not store the actual return values (results) of tasks in the database - The task's state (success/failure) is still tracked, but not what the task returned 2. What IS Still Stored: - Task execution status (success/failure) - Task metadata (start time, end time, etc.) - Error information if the task fails 3. What is NOT Stored: - The actual data/values that tasks return - Intermediate results between tasks For example:

python
from prefect import flow, task

@task
def add_numbers(x, y):
    return x + y  # This result (the sum) won't be persisted

@task
def multiply_by_two(x):
    return x * 2  # This result won't be persisted

@flow
def my_flow():
    sum_result = add_numbers(5, 3)
    final_result = multiply_by_two(sum_result)
    return final_result

With `PREFECT_TASKS_DEFAULT_PERSIST_RESULT=false`: - You'll see in the UI that the tasks ran and succeeded - You won't be able to see the actual values (8 and 16) in the task results - The flow will still work correctly because results are passed between tasks in memory - You save database space and I/O operations This setting is particularly useful when: 1. Your tasks return large objects that would bloat the database 2. You have high-frequency tasks where storing all results would create too much database load 3. You care about task success/failure but don't need to inspect intermediate values 4. You're running into database size issues Remember: - This is a default setting - you can still override it for specific tasks where you do want to persist results - It doesn't affect the actual execution of your flows - just what gets stored in the database - It can significantly reduce database load in high-volume scenarios like yours with 3000 tasks

I have to be honest: Prefect 3.x doesn't currently provide a built-in way to completely disable storage of successful task states and metadata while only storing failed tasks. This is because task state tracking is fundamental to how Prefect manages workflow orchestration. However, I can suggest several approaches to minimize the impact: 1. Custom State Handler: You could implement a custom state handler that only processes failed states:

python
from prefect import flow, task
from prefect.states import Failed

def selective_state_handler(state, task_run, flow_run):
    if isinstance(state, Failed):
        # Process and store failed state
        return state
    else:
        # For successful states, do minimal processing
        return state

@task(on_completion=[selective_state_handler])
def your_task():
    # Your task logic here
    pass

2. Batch Processing with Error Logging: Instead of using individual tasks for each operation, you could batch process and only log errors:

python
from prefect import flow, task
import logging

error_logger = logging.getLogger("error_logger")

@task(persist_result=False)
def batch_processor(items):
    results = []
    errors = []
    
    for item in items:
        try:
            # Process item
            result = process_item(item)
            results.append(result)
        except Exception as e:
            # Only track failures
            errors.append({"item": item, "error": str(e)})
            error_logger.error(f"Failed to process item {item}: {str(e)}")
    
    if errors:
        raise Exception(f"Batch processing encountered {len(errors)} errors: {errors}")
    
    return results

@flow
def your_flow():
    # Process in larger batches instead of individual tasks
    batch_size = 100
    items = range(3000)
    batches = [items[i:i + batch_size] for i in range(0, len(items), batch_size)]
    
    for batch in batches:
        batch_processor(batch)

3. Regular Database Cleanup: If you can't avoid storing the states, you could implement a regular cleanup process:

python
from prefect.client import get_client
from prefect.states import Completed
import asyncio

async def cleanup_successful_tasks():
    async with get_client() as client:
        # Get task runs that completed successfully
        task_runs = await client.read_task_runs()
        for task_run in task_runs:
            if isinstance(task_run.state, Completed):
                # Delete successful task runs
                await client.delete_task_run(task_run.id)

Important Limitations to Consider: 1. These approaches might affect Prefect's ability to: - Track task dependencies - Provide full observability - Support certain retry mechanisms - Show accurate task history 2. The batch processing approach reduces individual task tracking but might make it harder to: - Identify specific failure points - Leverage Prefect's task-level features - Scale horizontally Alternative Suggestions: 1. Consider using a different pattern for very high-volume, low-observability needs: - Simple queue systems - Batch processing frameworks - Custom logging solutions 2. If you're using Prefect Cloud, you might want to: - Contact Prefect support for enterprise-level solutions - Discuss potential custom retention policies - Explore available API endpoints for selective state management Would you like me to elaborate on any of these approaches or explore other potential solutions for your specific use case?