Temporary File Manager Technical Documentation

1. Overview

Purpose: TempFileManager is a core component in the AIECS system responsible for managing the lifecycle of temporary files. This module solves critical issues in temporary file management such as storage leaks, concurrent access conflicts, and resource waste through time-based automatic cleanup mechanisms and thread-safe design, providing reliable file resource management infrastructure for the entire system.

Core Value:

Automatic Cleanup: Intelligent cleanup mechanism based on file age, preventing storage space leaks
Thread Safety: Uses lock mechanisms to ensure safe operations in multi-threaded environments
Resource Optimization: Timely release of temporary files that are no longer needed, optimizing storage usage
Simple and Easy to Use: Provides concise API interfaces, reducing usage complexity
Observability: Complete logging and state tracking functionality

2. Problem Background & Design Motivation

2.1 Business Pain Points

The following key challenges are faced in AI application development:

Storage Leak Issues: Temporary files are created but forgotten to clean up, causing continuous disk space growth
Concurrent Access Conflicts: Simultaneous operations on file registry in multi-threaded environments lead to data inconsistency
Complex Resource Management: Manual management of temporary file lifecycles is error-prone
Performance Impact: Large numbers of temporary files affect file system performance
Debugging Difficulties: Lack of tracking mechanisms for file creation and cleanup
Security Risks: Sensitive data may remain in temporary files

2.2 Design Motivation

Based on the above pain points, a time-driven temporary file management solution was designed:

Automated Management: Automatically triggers cleanup through time thresholds, reducing manual intervention
Thread-Safe Design: Uses lock mechanisms to ensure data consistency in concurrent environments
Resource Isolation: Centralizes temporary file management for easier monitoring and cleanup
Configurability: Supports custom cleanup strategies and storage paths
Fault Tolerance: Retains file records when cleanup fails, supporting retry mechanisms

3. Architecture Positioning & Context

3.1 System Architecture Diagram

graph TB
    subgraph "Application Layer"
        A[AI Tools] --> B[Data Processing Tools]
        B --> C[File Processing Tools]
        C --> D[Report Generation Tools]
    end
    
    subgraph "File Management Layer"
        E[TempFileManager] --> F[File Registry]
        E --> G[Cleanup Scheduler]
        E --> H[Thread Lock Mechanism]
    end
    
    subgraph "Storage Layer"
        I[Temporary File Directory] --> J[Data Files]
        I --> K[Cache Files]
        I --> L[Log Files]
    end
    
    subgraph "Infrastructure Layer"
        M[File System] --> N[Disk Storage]
        O[Operating System] --> P[File Permissions]
        Q[Monitoring System] --> R[Storage Metrics]
    end
    
    A --> E
    B --> E
    C --> E
    D --> E
    
    E --> I
    F --> M
    G --> O
    H --> Q

3.2 Upstream and Downstream Dependencies

Upstream Callers:

AI tool classes (chart tools, data processing tools, etc.)
File processing services
Report generation services
Cache management systems

Downstream Dependencies:

Operating system file system
Python standard library (os, time, threading)
Logging system

Peer Components:

Configuration management system
Monitoring and metrics collection
Error handling mechanism

3.3 Data Flow

sequenceDiagram
    participant T as Tool
    participant TFM as TempFileManager
    participant FS as File System
    participant L as Logging System

    T->>TFM: Register temporary file
    TFM->>FS: Check file existence
    TFM->>TFM: Record file information
    TFM->>L: Log registration
    
    Note over TFM: Periodic cleanup check
    
    TFM->>TFM: Check file age
    TFM->>FS: Delete expired files
    TFM->>L: Log cleanup
    TFM->>T: Return cleanup result

4. Core Features & Use Cases

4.1 File Registration and Tracking

Feature Description: Register temporary files with the manager, record creation time, and track file status.

Core Features:

Automatic path normalization
File existence validation
Thread-safe registration
Detailed logging

Usage Scenarios:

from aiecs.tools.temp_file_manager import TempFileManager
import tempfile
import os

# Create temporary file manager
temp_manager = TempFileManager(
    base_dir="/tmp/aiecs_temp",
    max_age=3600  # Clean up after 1 hour
)

# Create temporary file
with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as f:
    f.write('{"data": "temporary content"}')
    temp_file_path = f.name

# Register temporary file
temp_manager.register_file(temp_file_path)
print(f"File registered: {temp_file_path}")

# Check if file is being managed
if temp_manager.is_temp_file(temp_file_path):
    print("File is being managed")
    age = temp_manager.get_file_age(temp_file_path)
    print(f"File age: {age:.2f} seconds")

Real-world Application Cases:

Data Processing Tools: Register intermediate processing result files
Chart Generation Tools: Manage generated image files
Report Tools: Track temporary report files
Cache Systems: Manage cache data files

4.2 Automatic Cleanup Mechanism

Feature Description: Automatically cleans up expired temporary files based on file age, preventing storage space leaks.

Core Features:

Time-based cleanup strategy
Force cleanup option
Batch cleanup operations
Error handling and retry

Usage Scenarios:

import time
from aiecs.tools.temp_file_manager import TempFileManager

# Create short-term temporary file manager
short_term_manager = TempFileManager(
    base_dir="/tmp/short_term",
    max_age=300  # Clean up after 5 minutes
)

# Create multiple temporary files
temp_files = []
for i in range(5):
    with tempfile.NamedTemporaryFile(mode='w', suffix=f'_{i}.txt', delete=False) as f:
        f.write(f"Temporary content {i}")
        temp_files.append(f.name)
        short_term_manager.register_file(f.name)

print(f"Registered {len(temp_files)} temporary files")

# Wait for files to expire
print("Waiting for files to expire...")
time.sleep(310)  # Wait more than 5 minutes

# Execute cleanup
removed_count = short_term_manager.cleanup()
print(f"Cleaned up {removed_count} expired files")

# Force cleanup all files
remaining_count = short_term_manager.clear_all()
print(f"Force cleaned {remaining_count} remaining files")

Real-world Application Cases:

Scheduled Cleanup Tasks: Regularly clean up expired temporary files
Memory Pressure Handling: Force cleanup when memory is insufficient
System Maintenance: Clean up all temporary files during system maintenance
Storage Monitoring: Trigger cleanup when storage usage is too high

4.3 File Status Query

Feature Description: Provides file age query and status check functionality, supporting file lifecycle monitoring.

Core Features:

File age calculation
Registration status check
Relative path support
Real-time status updates

Usage Scenarios:

from aiecs.tools.temp_file_manager import TempFileManager
import tempfile
import time

# Create file manager
manager = TempFileManager("/tmp/query_test", max_age=1800)

# Create and register file
with tempfile.NamedTemporaryFile(mode='w', suffix='.log', delete=False) as f:
    f.write("Log content")
    log_file = f.name

manager.register_file(log_file)

# Query file status
print(f"Is file managed: {manager.is_temp_file(log_file)}")
print(f"File age: {manager.get_file_age(log_file):.2f} seconds")

# Query again after waiting
time.sleep(10)
print(f"File age after 10 seconds: {manager.get_file_age(log_file):.2f} seconds")

# Check if file is approaching expiration
age = manager.get_file_age(log_file)
if age > manager.max_age * 0.8:  # Exceeded 80% of lifetime
    print("File is about to expire, recommend processing early")

Real-world Application Cases:

File Monitoring: Monitor temporary file lifecycles
Performance Optimization: Optimize processing strategies based on file age
Debugging Support: View file status during debugging
Resource Planning: Plan storage resources based on file usage

4.4 Batch Operation Support

Feature Description: Supports batch registration, querying, and cleanup of temporary files, improving operation efficiency.

Core Features:

Batch file registration
Batch status querying
Batch cleanup operations
Operation result statistics

Usage Scenarios:

from aiecs.tools.temp_file_manager import TempFileManager
import tempfile
import os

# Create file manager
manager = TempFileManager("/tmp/batch_test", max_age=600)

# Batch create and register files
file_paths = []
for i in range(10):
    with tempfile.NamedTemporaryFile(mode='w', suffix=f'_batch_{i}.txt', delete=False) as f:
        f.write(f"Batch content {i}")
        file_paths.append(f.name)
        manager.register_file(f.name)

print(f"Batch registered {len(file_paths)} files")

# Batch check file status
managed_files = []
for file_path in file_paths:
    if manager.is_temp_file(file_path):
        age = manager.get_file_age(file_path)
        managed_files.append((file_path, age))
        print(f"File {os.path.basename(file_path)}: {age:.2f} seconds")

print(f"Number of files being managed: {len(managed_files)}")

# Batch cleanup
removed_count = manager.cleanup()
print(f"Batch cleaned {removed_count} files")

Real-world Application Cases:

Batch Data Processing: Process large numbers of temporary data files
System Initialization: Batch clean up old files at system startup
Storage Maintenance: Regularly batch clean up storage space
Test Environment: Batch clean up test files after testing

5. API Reference

5.1 TempFileManager Class

Constructor

def __init__(self, base_dir: str, max_age: int = 3600)

Parameters:

base_dir (str, required): Base directory path for temporary files
max_age (int, optional): Maximum lifetime of temporary files (seconds), default is 3600 (1 hour)

Exceptions:

OSError: If base directory cannot be created

Core Methods

register_file

def register_file(self, file_path: str) -> None

Function: Register temporary file and record creation time Parameters:

file_path (str, required): File path to register

Behavior:

Automatically converts to absolute path
Validates file existence
Thread-safely records file information
Logs debug information

cleanup

def cleanup(self, force: bool = False) -> int

Function: Clean up expired temporary files Parameters:

force (bool, optional): Whether to force cleanup all files, default is False

Returns:

int: Number of files cleaned up

Behavior:

Decides whether to clean up based on file age
Thread-safely executes cleanup operations
Logs cleanup results
Retains file records when cleanup fails

get_file_age

def get_file_age(self, file_path: str) -> Optional[float]

Function: Get age of registered file Parameters:

file_path (str, required): File path

Returns:

Optional[float]: File age (seconds), returns None if file is not registered

Behavior:

Automatically converts to absolute path
Calculates difference between file registration time and current time
Returns precise age value

is_temp_file

def is_temp_file(self, file_path: str) -> bool

Function: Check if file is registered as temporary file Parameters:

file_path (str, required): File path

Returns:

bool: Returns True if file is registered, otherwise False

Behavior:

Automatically converts to absolute path
Checks if file is in registry
Does not validate if file actually exists

clear_all

def clear_all(self) -> int

Function: Clean up all registered temporary files Returns:

int: Number of files cleaned up

Behavior:

Equivalent to cleanup(force=True)
Cleans up all registered files
Returns number of files cleaned up

5.2 Attributes

base_dir

base_dir: str

Type: str Description: Base directory path for temporary files

max_age

max_age: int

Type: int Description: Maximum lifetime of temporary files (seconds)

files

files: Dict[str, float]

Type: Dict[str, float] Description: Dictionary mapping file paths to creation times

lock

lock: Lock

Type: threading.Lock Description: Lock object for thread-safe operations

6. Technical Implementation Details

6.1 Thread Safety Mechanism

Lock Design:

def __init__(self, base_dir: str, max_age: int = 3600):
    self.lock = Lock()  # Create thread lock

def register_file(self, file_path: str) -> None:
    with self.lock:  # Use context manager to ensure proper lock release
        self.files[abs_path] = time.time()

Concurrency Control Strategy:

Use threading.Lock to protect shared data structures
All modification operations are performed under lock protection
Use context manager to ensure proper lock release
Design patterns to avoid deadlocks

6.2 Path Processing Mechanism

Path Normalization:

def register_file(self, file_path: str) -> None:
    abs_path = os.path.abspath(file_path)  # Convert to absolute path
    if not os.path.isfile(abs_path):      # Validate file existence
        logger.warning(f"Attempted to register non-existent file: {abs_path}")
        return

Path Processing Features:

Automatically converts to absolute path to avoid duplicate registration
File existence validation prevents invalid registration
Unified path format for easier management
Supports both relative and absolute path input

6.3 Time Management Strategy

Timestamp Recording:

def register_file(self, file_path: str) -> None:
    with self.lock:
        self.files[abs_path] = time.time()  # Record current timestamp

Age Calculation:

def get_file_age(self, file_path: str) -> Optional[float]:
    abs_path = os.path.abspath(file_path)
    if abs_path in self.files:
        return time.time() - self.files[abs_path]  # Calculate age
    return None

Time Management Features:

Uses time.time() to get high-precision timestamps
Real-time file age calculation
Supports millisecond-level time precision
Automatically handles timezone issues

6.4 Error Handling Mechanism

Layered Error Handling:

def cleanup(self, force: bool = False) -> int:
    for file_path in files_to_remove:
        try:
            if os.path.exists(file_path):
                os.remove(file_path)
                logger.debug(f"Removed temporary file: {file_path}")
            del self.files[file_path]
        except Exception as e:
            logger.error(f"Failed to remove temporary file {file_path}: {e}")
            # Retain file record for retry

Error Handling Strategy:

Retains records when file deletion fails
Detailed error logging
Single file errors do not affect overall cleanup
Supports subsequent retry mechanisms

6.5 Memory Management

Data Structure Design:

self.files: Dict[str, float] = {}  # Lightweight dictionary storage

Memory Optimization Strategy:

Only stores necessary file paths and timestamps
Timely cleanup of records for deleted files
Avoids storing file content to reduce memory usage
Uses efficient data structures

7. Configuration & Deployment

7.1 Environment Variable Configuration

Basic Configuration:

# Temporary file configuration
TEMP_FILE_BASE_DIR=/tmp/aiecs_temp
TEMP_FILE_MAX_AGE=3600
TEMP_FILE_CLEANUP_INTERVAL=300

# Logging configuration
LOG_LEVEL=INFO
LOG_FORMAT=json
LOG_FILE=/var/log/aiecs/temp_file_manager.log

# Storage configuration
MAX_TEMP_FILES=10000
TEMP_FILE_SIZE_LIMIT=104857600  # 100MB

Advanced Configuration:

# Performance configuration
TEMP_FILE_CLEANUP_BATCH_SIZE=100
TEMP_FILE_CLEANUP_THREADS=4
TEMP_FILE_CLEANUP_TIMEOUT=30

# Security configuration
TEMP_FILE_SECURE_DELETE=true
TEMP_FILE_PERMISSION_MASK=0600
TEMP_FILE_OWNER_CHECK=true

# Monitoring configuration
ENABLE_TEMP_FILE_METRICS=true
METRICS_BACKEND=prometheus
PROMETHEUS_PORT=9090

7.2 Dependency Management

Core Dependencies:

# requirements.txt
# Uses Python standard library, no additional dependencies needed

Optional Dependencies:

# requirements-optional.txt
psutil>=5.9.0  # System resource monitoring
watchdog>=2.1.0  # File system monitoring

Development Dependencies:

# requirements-dev.txt
pytest>=7.0.0
pytest-mock>=3.10.0
pytest-cov>=4.0.0
black>=23.0.0
mypy>=1.0.0

7.3 Deployment Configuration

Docker Configuration:

FROM python:3.9-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

# Create temporary file directory
RUN mkdir -p /tmp/aiecs_temp && chmod 755 /tmp/aiecs_temp

COPY . .
CMD ["python", "-m", "aiecs.tools.temp_file_manager"]

Kubernetes Configuration:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: temp-file-manager
spec:
  replicas: 1
  selector:
    matchLabels:
      app: temp-file-manager
  template:
    metadata:
      labels:
        app: temp-file-manager
    spec:
      containers:
      - name: temp-file-manager
        image: aiecs/temp-file-manager:latest
        env:
        - name: TEMP_FILE_BASE_DIR
          value: "/tmp/aiecs_temp"
        - name: TEMP_FILE_MAX_AGE
          value: "3600"
        volumeMounts:
        - name: temp-storage
          mountPath: /tmp/aiecs_temp
        resources:
          requests:
            memory: "128Mi"
            cpu: "100m"
          limits:
            memory: "256Mi"
            cpu: "200m"
      volumes:
      - name: temp-storage
        emptyDir: {}

7.4 Monitoring Configuration

Prometheus Metrics:

from prometheus_client import Counter, Histogram, Gauge

# Define monitoring metrics
temp_files_registered = Counter('temp_files_registered_total', 'Total registered temporary files')
temp_files_cleaned = Counter('temp_files_cleaned_total', 'Total cleaned temporary files')
temp_files_active = Gauge('temp_files_active', 'Currently active temporary files')
temp_file_age = Histogram('temp_file_age_seconds', 'Age of temporary files when cleaned')
cleanup_duration = Histogram('temp_file_cleanup_duration_seconds', 'Time spent on cleanup operations')

Health Check:

def health_check():
    """Check temporary file manager health status"""
    try:
        # Check base directory
        if not os.path.exists(manager.base_dir):
            return {"status": "unhealthy", "error": "Base directory not accessible"}
        
        # Check file count
        active_files = len(manager.files)
        if active_files > MAX_TEMP_FILES:
            return {"status": "degraded", "warning": f"Too many temp files: {active_files}"}
        
        return {
            "status": "healthy",
            "active_files": active_files,
            "base_dir": manager.base_dir,
            "max_age": manager.max_age,
            "timestamp": time.time()
        }
    except Exception as e:
        return {
            "status": "unhealthy",
            "error": str(e),
            "timestamp": time.time()
        }

8. Maintenance & Troubleshooting

8.1 Monitoring Metrics

Key Metrics:

Number of registered temporary files
Number of cleaned files
Average file age
Cleanup operation duration
Cleanup failure rate

Monitoring Dashboard:

# Grafana query examples
# Temporary file count trend
temp_files_active

# Cleanup operation frequency
rate(temp_files_cleaned_total[5m])

# File age distribution
histogram_quantile(0.95, rate(temp_file_age_seconds_bucket[5m]))

# Cleanup operation duration
histogram_quantile(0.95, rate(cleanup_duration_seconds_bucket[5m]))

8.2 Common Issues and Solutions

8.2.1 File Cleanup Failure

Symptoms:

“Failed to remove temporary file” errors in logs
Temporary file count continuously growing
Insufficient disk space

Troubleshooting Steps:

Check file permissions: ls -la /tmp/aiecs_temp/
Check disk space: df -h /tmp
Check if file is in use: lsof /tmp/aiecs_temp/filename
View detailed error logs

Solution:

# Check file permissions
import os
import stat

def check_file_permissions(file_path):
    try:
        file_stat = os.stat(file_path)
        permissions = stat.filemode(file_stat.st_mode)
        print(f"File permissions: {permissions}")
        
        # Try to modify permissions
        os.chmod(file_path, 0o666)
        return True
    except Exception as e:
        print(f"Permission check failed: {e}")
        return False

# Force cleanup
def force_cleanup_with_retry(manager, max_retries=3):
    for attempt in range(max_retries):
        try:
            removed_count = manager.cleanup(force=True)
            print(f"Successfully cleaned {removed_count} files")
            break
        except Exception as e:
            print(f"Cleanup attempt {attempt + 1} failed: {e}")
            if attempt == max_retries - 1:
                print("All cleanup attempts failed")

8.2.2 High Memory Usage

Symptoms:

Process memory usage continuously growing
System response slowing down
Out of memory errors

Troubleshooting Steps:

Check file registry size: len(manager.files)
Monitor memory usage: ps aux | grep python
Analyze file path lengths
Check for memory leaks

Solution:

# Memory usage analysis
import psutil
import sys

def analyze_memory_usage(manager):
    process = psutil.Process()
    memory_info = process.memory_info()
    
    print(f"Process memory usage: {memory_info.rss / 1024 / 1024:.2f} MB")
    print(f"Registered file count: {len(manager.files)}")
    
    # Calculate memory used by file paths
    total_path_length = sum(len(path) for path in manager.files.keys())
    print(f"Total file path length: {total_path_length} characters")
    
    # Clean up file records that haven't been used for a long time
    current_time = time.time()
    old_files = [
        path for path, creation_time in manager.files.items()
        if current_time - creation_time > manager.max_age * 2
    ]
    
    for path in old_files:
        del manager.files[path]
    
    print(f"Cleaned up {len(old_files)} expired file records")

8.2.3 Concurrent Access Issues

Symptoms:

File registry data inconsistency
Incomplete cleanup operations
Thread safety issues

Troubleshooting Steps:

Check lock usage
Analyze concurrent access patterns
View thread safety logs
Test concurrent scenarios

Solution:

# Concurrent testing
import threading
import time

def test_concurrent_access(manager, num_threads=10, files_per_thread=100):
    def worker(thread_id):
        for i in range(files_per_thread):
            file_path = f"/tmp/test_{thread_id}_{i}.txt"
            with open(file_path, 'w') as f:
                f.write(f"Test content {thread_id}_{i}")
            manager.register_file(file_path)
    
    threads = []
    for i in range(num_threads):
        thread = threading.Thread(target=worker, args=(i,))
        threads.append(thread)
        thread.start()
    
    for thread in threads:
        thread.join()
    
    print(f"Concurrent test completed, registered files: {len(manager.files)}")
    
    # Verify data consistency
    expected_files = num_threads * files_per_thread
    if len(manager.files) != expected_files:
        print(f"Warning: File count mismatch, expected {expected_files}, actual {len(manager.files)}")

8.3 Performance Optimization

Batch Operation Optimization:

def batch_cleanup(manager, batch_size=100):
    """Batch cleanup optimization"""
    current_time = time.time()
    files_to_remove = []
    
    with manager.lock:
        for file_path, creation_time in list(manager.files.items()):
            if current_time - creation_time > manager.max_age:
                files_to_remove.append(file_path)
                if len(files_to_remove) >= batch_size:
                    break
    
    # Batch delete
    removed_count = 0
    for file_path in files_to_remove:
        try:
            if os.path.exists(file_path):
                os.remove(file_path)
            del manager.files[file_path]
            removed_count += 1
        except Exception as e:
            logger.error(f"Batch cleanup failed {file_path}: {e}")
    
    return removed_count

Memory Optimization:

def optimize_memory_usage(manager):
    """Optimize memory usage"""
    # Clean up records for non-existent files
    existing_files = {}
    for file_path in list(manager.files.keys()):
        if os.path.exists(file_path):
            existing_files[file_path] = manager.files[file_path]
        else:
            logger.debug(f"Cleaning up record for non-existent file: {file_path}")
    
    manager.files = existing_files
    logger.info(f"Memory optimization completed, cleaned up {len(manager.files) - len(existing_files)} invalid records")

8.4 Log Analysis

Log Configuration:

import logging

# Configure temporary file manager logger
temp_logger = logging.getLogger('aiecs.tools.temp_file_manager')
temp_logger.setLevel(logging.INFO)

# Add file handler
file_handler = logging.FileHandler('/var/log/aiecs/temp_file_manager.log')
file_handler.setFormatter(logging.Formatter(
    '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
))
temp_logger.addHandler(file_handler)

Key Log Patterns:

# Find cleanup operation logs
grep "Cleaned up" /var/log/aiecs/temp_file_manager.log | tail -100

# Analyze cleanup failures
grep "Failed to remove" /var/log/aiecs/temp_file_manager.log | wc -l

# Monitor file registration
grep "Registered temporary file" /var/log/aiecs/temp_file_manager.log | tail -50

9. Visualizations

9.1 File Lifecycle Diagram

flowchart TD
    A["File Creation"] --> B["Register to Manager"]
    B --> C["Record Creation Time"]
    C --> D["Periodic Age Check"]
    D --> E{File Expired?}
    E -->|No| F["Continue Monitoring"]
    E -->|Yes| G["Execute Cleanup"]
    F --> D
    G --> H["Delete File"]
    H --> I["Remove Registration Record"]
    I --> J["Log Cleanup"]
    
    K["Force Cleanup"] --> G
    L["System Shutdown"] --> M["Clean Up All Files"]
    M --> G

9.2 System Architecture Diagram

graph TB
    subgraph "Application Layer"
        A["AI Tools"] --> B["Data Processing Tools"]
        B --> C["File Processing Tools"]
        C --> D["Report Generation Tools"]
    end
    
    subgraph "File Management Layer"
        E["TempFileManager"] --> F["File Registry"]
        E --> G["Cleanup Scheduler"]
        E --> H["Thread Lock Mechanism"]
    end
    
    subgraph "Storage Layer"
        I["Temporary File Directory"] --> J["Data Files"]
        I --> K["Cache Files"]
        I --> L["Log Files"]
    end
    
    subgraph "Infrastructure Layer"
        M["File System"] --> N["Disk Storage"]
        O["Operating System"] --> P["File Permissions"]
        Q["Monitoring System"] --> R["Storage Metrics"]
    end
    
    A --> E
    B --> E
    C --> E
    D --> E
    
    E --> I
    F --> M
    G --> O
    H --> Q

9.3 Concurrent Access Diagram

sequenceDiagram
    participant T1 as Thread 1
    participant T2 as Thread 2
    participant TFM as TempFileManager
    participant FS as File System

    T1->>TFM: Register file A
    TFM->>TFM: Acquire lock
    TFM->>FS: Check file existence
    TFM->>TFM: Record file information
    TFM->>TFM: Release lock
    
    T2->>TFM: Register file B
    TFM->>TFM: Acquire lock
    TFM->>FS: Check file existence
    TFM->>TFM: Record file information
    TFM->>TFM: Release lock
    
    Note over TFM: Cleanup check
    
    TFM->>TFM: Acquire lock
    TFM->>TFM: Check file age
    TFM->>FS: Delete expired files
    TFM->>TFM: Release lock

9.4 Performance Monitoring Diagram

xychart-beta
    title "Temporary File Count Trend"
    x-axis ["Jan", "Feb", "Mar", "Apr", "May"]
    y-axis "File Count" 0 --> 1000
    line [100, 150, 120, 180, 200]
    bar [120, 180, 140, 220, 250]

10. Version History

v1.0.0 (2024-01-15)

New Features:

Implemented basic temporary file management functionality
Supported file registration and tracking
Added time-based automatic cleanup mechanism
Implemented thread-safe operations

Technical Features:

Dictionary-based file registry
Uses threading.Lock to ensure thread safety
Simple age calculation and cleanup logic

v1.1.0 (2024-02-01)

New Features:

Added file status query functionality
Implemented batch operation support
Enhanced error handling mechanism
Added detailed logging

Performance Optimizations:

Optimized path processing logic
Improved memory usage efficiency
Enhanced concurrent performance

v1.2.0 (2024-03-01)

New Features:

Added force cleanup functionality
Implemented cleanup result statistics
Supported configurable parameters
Integrated monitoring and metrics collection

Monitoring Enhancements:

Prometheus metrics integration
Detailed performance analysis
Health check interface

v1.3.0 (2024-04-01) [Planned]

Planned Features:

Add file size limits
Implement intelligent cleanup strategies
Support distributed file management
Add file compression functionality

Architecture Optimizations:

Plugin-based architecture
Cloud-native integration
Auto-scaling

Appendix

B. Example Code

C. Technical Support

Technical Documentation: https://docs.aiecs.com
Issue Reporting: https://github.com/aiecs/issues
Community Discussion: https://discord.gg/aiecs

Temporary File Manager Technical Documentation

1. Overview

2. Problem Background & Design Motivation

2.1 Business Pain Points

2.2 Design Motivation

3. Architecture Positioning & Context

3.1 System Architecture Diagram

3.2 Upstream and Downstream Dependencies

3.3 Data Flow

4. Core Features & Use Cases

4.1 File Registration and Tracking

4.2 Automatic Cleanup Mechanism

4.3 File Status Query

4.4 Batch Operation Support

5. API Reference

5.1 TempFileManager Class

Constructor

Core Methods

register_file

cleanup

get_file_age

is_temp_file

clear_all

5.2 Attributes

base_dir

max_age

files

lock

6. Technical Implementation Details

6.1 Thread Safety Mechanism

6.2 Path Processing Mechanism

6.3 Time Management Strategy

6.4 Error Handling Mechanism

6.5 Memory Management

7. Configuration & Deployment

7.1 Environment Variable Configuration

7.2 Dependency Management

7.3 Deployment Configuration

7.4 Monitoring Configuration

8. Maintenance & Troubleshooting

8.1 Monitoring Metrics

8.2 Common Issues and Solutions

8.2.1 File Cleanup Failure

8.2.2 High Memory Usage

8.2.3 Concurrent Access Issues

8.3 Performance Optimization

8.4 Log Analysis

9. Visualizations

9.1 File Lifecycle Diagram

9.2 System Architecture Diagram

9.3 Concurrent Access Diagram

9.4 Performance Monitoring Diagram

10. Version History

v1.0.0 (2024-01-15)

v1.1.0 (2024-02-01)

v1.2.0 (2024-03-01)

v1.3.0 (2024-04-01) [Planned]

Appendix

A. Related Documentation

B. Example Code

C. Technical Support