Based on the research and GitHub issues, I can provide some strategies for managing memory in Prefect. Here's a comprehensive approach: 1. Task Result Management: - Set

persist_result=False

and

cache_result_in_memory=False

on tasks when possible - Be cautious with large data objects being passed between tasks, as they can remain in memory - Consider using external storage for large intermediate results 2. Database Connection Management: - Scale down the number of active database connections when possible - Implement connection pooling with appropriate timeout settings - Close connections explicitly when they're no longer needed 3. Best Practices for Large Data Handling: - Use streaming approaches when possible instead of loading entire datasets into memory - Implement garbage collection calls strategically in your flows - Consider breaking large flows into smaller sub-flows that can release memory between executions 4. Code Example for Memory-Conscious Flow:

python
from prefect import flow, task
import gc

@task(persist_result=False, cache_result_in_memory=False)
def process_data(data):
    # Process your data
    result = data.compute()  # If using dask or similar
    return result

@flow
def memory_conscious_flow():
    # Process in chunks or streams when possible
    for chunk in data_chunks:
        result = process_data(chunk)
        # Do something with result
        
        # Explicitly clean up
        del result
        gc.collect()

5. Infrastructure Considerations: - If using Kubernetes, set appropriate memory limits and requests - Monitor memory usage with appropriate observability tools - Consider using worker pools with memory-based scaling policies 6. For Large DataFrame Operations: - Consider using Dask DataFrames instead of Pandas for large datasets - Use

del

statements strategically after large operations - Implement checkpointing for very large data processing pipelines Important Links: - Memory Management Blog Post - Infrastructure Cleanup Jobs - Related GitHub Issue Discussion Note: While these strategies can help manage memory usage, there's currently no built-in automatic memory cleanup mechanism in Prefect. The best approach is to be proactive about memory management in your flow design and implementation. Would you like me to elaborate on any of these strategies or provide more specific examples for your use case?

Based on your code and the research, I can see several potential issues and solutions to address the memory retention problem. Here's a comprehensive approach to fix this: 1. Modify Your Current Code Instead of using

submit().result()

directly, which might keep references to the data in memory, try this pattern:

python
from prefect.utilities.asyncutils import sync_compatible
import gc
import psutil

@sync_compatible
async def process_groups(groups):
    for min_uc_id, max_uc_id in groups:
        # Submit the task
        future = group_matched_components.submit(
            enricehd_parquet_files,
            min_uc_id=min_uc_id,
            max_uc_id=max_uc_id
        )
        
        # Get the result and immediately clear it
        result = await future
        future = None  # Clear the future reference
        
        # Clear any references to the result
        del result
        gc.collect()
        
        # Optional: Force Python to return memory to the OS
        import ctypes
        libc = ctypes.CDLL("libc.so.6")
        libc.malloc_trim(0)
        
        # Log memory usage for debugging
        process = psutil.Process()
        print(f"Memory usage: {process.memory_info().rss / 1024 / 1024 / 1024:.2f} GB")
        
        await asyncio.sleep(5)  # Async sleep instead of time.sleep

2. Task Configuration Modify your task definition to minimize memory retention:

python
@task(
    persist_result=False,
    cache_result_in_memory=False,
    retries=0  # Disable retries if you don't need them
)
def enricehd_parquet_files(min_uc_id, max_uc_id):
    # Your processing logic here
    pass

3. Data Processing Strategies If you're processing large parquet files, consider these modifications:

python
@task(persist_result=False, cache_result_in_memory=False)
def enricehd_parquet_files(min_uc_id, max_uc_id):
    try:
        # Read and process in chunks if possible
        with pq.ParquetFile(path) as pf:
            for batch in pf.iter_batches():
                # Process each batch
                result = process_batch(batch)
                # Write results immediately to storage
                save_results(result)
                del result
                gc.collect()
    finally:
        # Ensure cleanup happens
        gc.collect()

4. Additional Memory Management Techniques a. Use a context manager for memory-intensive operations:

python
from contextlib import contextmanager

@contextmanager
def memory_manager():
    try:
        yield
    finally:
        gc.collect()
        import ctypes
        libc = ctypes.CDLL("libc.so.6")
        libc.malloc_trim(0)

# Usage in your code
with memory_manager():
    result = group_matched_components.submit(...).result()

b. Consider implementing a memory monitor:

python
def check_memory_usage():
    process = psutil.Process()
    memory_gb = process.memory_info().rss / 1024 / 1024 / 1024
    if memory_gb > 45:  # Alert if memory usage is too high
        logger.warning(f"High memory usage detected: {memory_gb:.2f} GB")

5. System-Level Considerations - If possible, set a memory limit for your Python process using

resource.setrlimit

- Consider using a memory profiler to identify specific memory leaks - Monitor memory usage patterns to identify if there are specific operations causing the retention 6. Alternative Approach If the above solutions don't help, consider restructuring your code to use streaming or chunking:

python
@task
def process_group_chunked(min_uc_id, max_uc_id, chunk_size=1000):
    for chunk_start in range(min_uc_id, max_uc_id, chunk_size):
        chunk_end = min(chunk_start + chunk_size, max_uc_id)
        # Process smaller chunk
        process_chunk(chunk_start, chunk_end)
        gc.collect()

The persistent high memory usage despite garbage collection suggests that either: 1. There are still references to your data somewhere in the Prefect runtime

2. The Python interpreter isn't returning memory to the operating system 3. There might be memory fragmentation To debug this further: - Could you share what type of data

enricehd_parquet_files

is processing? - What is the typical size of the data being processed in each iteration? - Are you using any particular executor (Dask, Ray, etc.)? This information would help me provide more specific recommendations for your use case.

Ah, this helps explain the memory retention issue. When using Ray with Dask, there are some specific memory management considerations we need to address. Let me help you optimize this code: 1. First, let's modify your task to be more memory-efficient:

python
from prefect import task, get_run_logger
import dask.dataframe as dd
import gc
import ray
from ray.util.dask import RayDaskCallback
import numpy as np

@task(
    persist_result=False,
    cache_result_in_memory=False,
    retries=0  # Disable retries unless needed
)
def calculate_adaptive_bins(
    parquet_paths: List[str], 
    target_records_per_bin: int = 1_500_000
) -> List[Tuple[int, int]]:
    """
    Calculate adaptive bins with improved memory management.
    """
    logger = get_run_logger()
    
    try:
        # Configure Dask for better memory handling
        with dask.config.set({
            'distributed.worker.memory.target': 0.6,  # Target 60% memory usage
            'distributed.worker.memory.spill': 0.7,   # Spill to disk at 70%
            'distributed.worker.memory.pause': 0.8,   # Pause processing at 80%
            'distributed.worker.memory.terminate': 0.95  # Terminate at 95%
        }):
            # Read only necessary columns
            ddf = dd.read_parquet(
                parquet_paths,
                columns=['uc_id'],
                engine='pyarrow'
            )
            
            <http://logger.info|logger.info>("Converting uc_id to long for memory optimization...")
            ddf['uc_id'] = dd.to_numeric(ddf['uc_id'], errors='coerce').astype('int64')
            
            with RayDaskCallback():
                # Calculate total records in chunks to avoid memory spike
                total_records = ddf.shape[0].compute()
                optimal_bins = max(1, round(total_records / target_records_per_bin))
                
                <http://logger.info|logger.info>(f"Total records: {total_records:,}")
                <http://logger.info|logger.info>(f"Calculated bins: {optimal_bins}")
                
                # Calculate quantiles with better memory handling
                quantile_points = np.linspace(0, 1, optimal_bins + 1)
                quantiles = ddf['uc_id'].quantile(quantile_points).compute()
                
                # Clear ddf as soon as possible
                del ddf
                gc.collect()
                
                # Generate bins
                bins = [
                    (
                        int(quantiles[i]),
                        int(quantiles[i + 1]) + 1 if i == optimal_bins - 1 else int(quantiles[i + 1])
                    )
                    for i in range(optimal_bins)
                ]
                
                <http://logger.info|logger.info>(f"Calculated {len(bins)} uc_id bins")
                
                # Clear quantiles
                del quantiles
                gc.collect()
                
                return bins
                
    finally:
        # Cleanup
        ray.util.dask.shutdown_ray_dask()
        gc.collect()

2. Add a Ray memory monitor (create a new file

memory_utils.py

): ```python import psutil import ray import logging from typing import Optional class RayMemoryMonitor: def __init__(self, threshold_gb: float = 45.0, logger: Optional[logging.Logger] = None): self.threshold_gb = threshold_gb self.logger = logger or logging.getLogger(name) def check_memory(self): process = psutil.Process() memory_gb = process.memory_info().rss / 1024 / 1024 / 1024 if memory_gb > self.threshold_gb: self.logger.warning(f"High memory usage detected: {memory_gb:.2f} GB") # Get Ray memory usage ray_memory = ray.available_resources().get("memory", 0) self.logger.warning(f"Ray available memory: {ray_memory / 1024 / 1024 / 1024:.2f} GB") # Suggest garbage collection gc.collect()

# Optional: Force Python to return memory to the OS import ctypes libc = ctypes.CDLL("libc.so.6") libc.malloc_trim(0)

3. *Configure Ray properly* before running your flow:

```python
@flow
def your_flow():
    # Configure Ray with memory limits
    ray.init(
        _memory=80 * 1024 * 1024 * 1024,  # 80GB max memory
        object_store_memory=40 * 1024 * 1024 * 1024,  # 40GB for object store
        _plasma_directory="/tmp",  # Use disk for spillover
        runtime_env={
            "env_vars": {
                "MALLOC_TRIM_THRESHOLD_": "65536"  # More aggressive memory trimming
            }
        }
    )
    
    try:
        memory_monitor = RayMemoryMonitor(threshold_gb=45.0)
        
        # Your existing code
        bins = calculate_adaptive_bins(parquet_paths, target_records_per_bin=1_500_000)
        
        # Monitor memory after bin calculation
        memory_monitor.check_memory()
        
        # Rest of your flow
        for min_uc_id, max_uc_id in bins:
            result = group_matched_components.submit(
                enricehd_parquet_files,
                min_uc_id=min_uc_id,
                max_uc_id=max_uc_id
            )
            # Process result
            
    finally:
        # Cleanup Ray
        ray.shutdown()

4. Additional Recommendations: - Consider using Ray's object spilling feature to automatically move objects to disk when memory pressure is high:

python
ray.init(
    object_spilling_config={
        "type": "filesystem",
        "params": {
            "directory_path": "/path/to/spill"
        }
    }
)

- If possible, process your parquet files in chunks:

python
def count_records_in_chunks(parquet_paths):
    total = 0
    for path in parquet_paths:
        pf = pq.ParquetFile(path)
        total += pf.metadata.num_rows
    return total

- Monitor Ray dashboard (typically at

localhost:8265

) during execution to track memory usage The main causes of your memory retention might be: 1. Ray's object store holding onto references 2. Dask dataframe operations not releasing memory immediately 3. Python's memory fragmentation when dealing with large datasets Would you like me to: 1. Provide more specific Ray configuration options for your use case? 2. Show how to implement chunked processing of the parquet files? 3. Explain how to set up more detailed memory monitoring? Let me know which aspect you'd like me to elaborate on further.

For Prefect 3.x, here's the recommended way to set up your deployment with the environment variable:

python
from prefect.deployments import Deployment
from prefect.infrastructure import DockerContainer

# Define your Docker container with the environment variable
docker_container = DockerContainer(
    image="your-image:tag",
    env={
        "MALLOC_TRIM_THRESHOLD_": "65536"
    }
)

# Create your deployment
deployment = Deployment(
    name="your-deployment-name",
    flow=your_flow,
    infrastructure=docker_container,
)

# Apply the deployment
deployment.apply()

Or if you're using a YAML configuration file (

deployment.yaml

):

yaml
deployments:
- name: your-deployment-name
  version: null
  tags: []
  description: null
  schedule: null
  flow_name: your_flow
  entrypoint: path/to/your/flow.py:your_flow
  parameters: {}
  work_pool:
    name: your-work-pool
    work_queue_name: default
  infrastructure:
    type: docker-container
    env:
      MALLOC_TRIM_THRESHOLD_: "65536"

The environment variable will be available to your flow when it runs in the Docker container. This should help with memory management by making memory trimming more aggressive. Would you like me to: 1. Show how to verify the environment variable is set correctly in your container? 2. Provide additional memory-related environment variables that might be helpful? 3. Explain how to combine this with other Docker container optimizations?

Yes, that configuration looks correct! The environment variable

MALLOC_TRIM_THRESHOLD_

is properly placed under the

env

section of the

job_variables

. However, I notice you're setting it to "0" - this is different from the "65536" we discussed earlier. Let me explain the implications: - `MALLOC_TRIM_THRESHOLD_=0`: Most aggressive setting - tells the allocator to try to release memory back to the OS immediately whenever possible. This can lead to: - More frequent memory trimming operations - Potentially better memory usage but with some CPU overhead - Good for cases where memory pressure is high and CPU isn't a bottleneck - `MALLOC_TRIM_THRESHOLD_=65536`: More balanced setting - only triggers memory trimming when the free memory exceeds 64KB. This: - Reduces the frequency of trim operations - Balances CPU usage with memory management - Generally recommended for most use cases Your configuration is valid and will work, but you might want to experiment with both values to see which works better for your specific workload. Here's the complete configuration with both options commented:

yaml
- name: "generate_creatordb-prod"
  entrypoint: "generate_creatordb_flow.py:generate_creatordb_flow"
  work_pool:
    name: main-pool
    job_variables:
      image: "{{ build-image.image }}"
      image_pull_policy: "Always"
      env:
        prefect_results_default_storage_block: "s3-bucket/workflows-s3-cache"
        # Option 1: Most aggressive memory trimming (current setting)
        MALLOC_TRIM_THRESHOLD_: "0"
        # Option 2: Balanced memory trimming (uncomment to use)
        # MALLOC_TRIM_THRESHOLD_: "65536"
      volumes:
        - /prefect_data:/prefect_data
  tags:
    - prod
    - "{{ $PREFECT_DEPLOYMENT_IMAGE_TAG }}"
  description: "Production deployment for generate_creatordb (version: $PREFECT_DEPLOYMENT_IMAGE_TAG)"
  parameters:
    environment: "prod"
  schedule:
    cron: "0 2 * * 0"
    timezone: "Europe/Paris"

You might also want to consider adding these complementary memory-related environment variables:

yaml
env:
  prefect_results_default_storage_block: "s3-bucket/workflows-s3-cache"
  MALLOC_TRIM_THRESHOLD_: "0"
  # Optional additional memory-related settings
  PYTHONMALLOC: "malloc"  # Use system malloc instead of Python's
  MALLOC_ARENA_MAX: "2"   # Limit number of memory pools
  PYMALLOC_DEBUG: "1"     # Enable debug mode for memory allocations (development only)

Would you like me to: 1. Explain how to monitor the effectiveness of these memory settings? 2. Show how to add memory metrics collection to your flow? 3. Provide guidance on tuning these values based on your workload?