Eager Workflows & Asynchronous Tasks

Eager Workflows & Asynchronous Tasks

The system provides robust mechanisms for managing task execution, allowing developers to choose between immediate, synchronous processing (Eager Workflows) and deferred, non-blocking operations (Asynchronous Tasks). This dual approach optimizes application responsiveness and resource utilization by ensuring that critical, short-lived operations execute promptly, while long-running or I/O-bound tasks do not block the main application thread.

Eager Workflows

Eager Workflows execute immediately and synchronously within the current process. They are ideal for operations where the result is required without delay, or for tasks that are computationally inexpensive and complete quickly.

Purpose: Eager Workflows ensure that operations critical to the immediate user experience or subsequent synchronous logic complete before the calling function proceeds. This guarantees predictable execution order and simplifies error handling for direct operations.

Core Features:

• Direct Execution: Tasks run as standard function calls.
• Immediate Results: The calling context receives the result or exception directly upon completion.
• Simplified Debugging: Execution flow is linear and easy to trace.

Implementation: To define an eager workflow, apply the @eager_workflow decorator to a function. This decorator ensures the function executes synchronously when called.

from my_task_system import eager_workflow

@eager_workflow
def process_user_input(data: dict) -> dict:
    """
    Processes user input immediately and returns a transformed result.
    """
    print(f"Processing input eagerly: {data}")
    # Simulate some quick processing
    processed_data = {k: v.upper() for k, v in data.items()}
    return {"status": "completed", "result": processed_data}

# Calling an eager workflow executes it directly
result = process_user_input({"name": "Alice", "action": "login"})
print(f"Eager workflow result: {result}")
# Expected output:
# Processing input eagerly: {'name': 'Alice', 'action': 'login'}
# Eager workflow result: {'status': 'completed', 'result': {'name': 'ALICE', 'action': 'LOGIN'}}

Common Use Cases:

• API Request Validation: Validating incoming data before processing.
• Authentication & Authorization Checks: Verifying user credentials and permissions.
• Small Database Transactions: Updating a user's last login time.
• Real-time UI Updates: Operations that directly impact the immediate user interface.

Asynchronous Tasks

Asynchronous Tasks execute in the background, decoupled from the calling process. They are designed for long-running operations, I/O-bound tasks (e.g., network requests, file operations), or tasks that do not require an immediate response from the caller.

Purpose: Asynchronous Tasks prevent the main application thread from blocking, improving responsiveness and scalability. They enable applications to initiate complex operations without waiting for their completion, freeing up resources to handle other requests or tasks.

Core Features:

• Non-blocking Execution: Tasks are submitted to a queue and processed by dedicated workers.
• Deferred Results: Results are not immediately available; they must be retrieved later.
• Scalability: Task processing can be distributed across multiple workers or machines.
• Reliability: Tasks can be retried automatically on failure, and their state can be monitored.
• Scheduling: Tasks can be scheduled for future execution or at recurring intervals.

Implementation: To define an asynchronous task, use the @async_task decorator. When a function decorated with @async_task is called, it does not execute immediately. Instead, it returns a TaskHandle object, and the actual execution is deferred to a background worker.

from my_task_system import async_task, get_task_result, TaskHandle
import time

@async_task
def generate_report(user_id: str, period: str) -> dict:
    """
    Generates a complex report in the background.
    This operation might take a significant amount of time.
    """
    print(f"Starting report generation for user {user_id} for {period}...")
    time.sleep(5)  # Simulate a long-running operation
    report_data = {"user": user_id, "period": period, "metrics": {"sales": 12345, "profit": 6789}}
    print(f"Report generation completed for user {user_id}.")
    return {"status": "completed", "report": report_data}

# Submitting an asynchronous task returns a TaskHandle
task_handle: TaskHandle = generate_report("user_123", "Q3-2023")
print(f"Report generation task submitted with ID: {task_handle.task_id}")

# The main application can continue processing other requests
print("Application continues processing other tasks...")

# To retrieve the result, use get_task_result with the TaskHandle
# This call will block until the task is complete or a timeout occurs
try:
    final_report = get_task_result(task_handle, timeout=10)
    print(f"Retrieved report: {final_report}")
except TimeoutError:
    print(f"Task {task_handle.task_id} timed out while waiting for result.")
except Exception as e:
    print(f"Task {task_handle.task_id} failed with error: {e}")

Common Use Cases:

• Email Notifications: Sending welcome emails, password resets, or marketing campaigns.
• Image/Video Processing: Resizing, watermarking, or encoding media files.
• Data Imports/Exports: Processing large CSV files or generating complex reports.
• Third-party API Integrations: Calling external services that might have high latency.
• Batch Processing: Running nightly jobs for data aggregation or cleanup.

Key Features and Capabilities

The system's task management utility offers several capabilities that enhance both eager and asynchronous workflows:

• Task Definition: Both @eager_workflow and @async_task decorators allow defining functions as specific types of tasks.
• Task Queuing (Asynchronous): Asynchronous tasks are automatically placed into a robust task queue, ensuring reliable delivery to workers.
• Worker Pool Management (Asynchronous): Dedicated worker processes or threads consume tasks from the queue and execute them. The system manages worker lifecycle and resource allocation.
• Result Management:
  • Eager: Results are returned directly.
  • Asynchronous: A TaskHandle object is returned immediately. The get_task_result(handle, timeout) function allows blocking retrieval of the final result or exception.
• Error Handling & Retries (Asynchronous): Asynchronous tasks can be configured with retry policies (e.g., number of retries, backoff strategy) to handle transient failures. Exceptions raised within an asynchronous task are captured and can be retrieved via get_task_result.
• Task State Monitoring (Asynchronous): The TaskHandle provides methods to check the current status of a task (e.g., is_pending(), is_running(), is_completed(), is_failed()).
• Scheduled Tasks (Asynchronous): The system supports scheduling asynchronous tasks to run at specific times or recurring intervals using a schedule_task function (not shown in example, but conceptually available).

Integration Patterns

The task management utility integrates seamlessly with common application architectures:

• Web Frameworks: Use asynchronous tasks to offload long-running operations from web request handlers. For example, a user registration endpoint can submit an email verification task asynchronously, returning an immediate success response to the client without waiting for the email to send.
• Data Processing Pipelines: Integrate asynchronous tasks to process data in stages. A data ingestion service might submit a parsing task, which then submits a validation task, and so on, allowing for parallel and fault-tolerant processing.
• Microservices: Asynchronous tasks serve as a reliable communication mechanism between services, enabling eventual consistency and decoupling service dependencies.

Considerations & Best Practices

• Choosing Between Eager and Asynchronous:
  • Eager: Use for operations that are fast, critical for immediate feedback, or require synchronous error handling.
  • Asynchronous: Use for operations that are slow, I/O-bound, can tolerate eventual consistency, or benefit from retries and background processing.
• Performance Implications:
  • Eager: Can block the main thread, potentially impacting responsiveness if tasks are too long.
  • Asynchronous: Introduces overhead for queuing and worker management but significantly improves overall system throughput and responsiveness by offloading work.
• Idempotency (Asynchronous): Design asynchronous tasks to be idempotent where possible. This means that executing the same task multiple times with the same inputs produces the same result and does not cause unintended side effects. This is crucial for retry mechanisms.
• Error Handling:
  • Eager: Standard Python try-except blocks are sufficient.
  • Asynchronous: Implement robust error handling within the task function itself. Ensure that get_task_result is used with appropriate try-except blocks to catch exceptions propagated from the worker.
• Resource Management: Monitor worker resource usage (CPU, memory) to ensure the worker pool is adequately sized for the task load. Overloading workers can lead to task backlogs and performance degradation.
• Task Granularity: Break down complex operations into smaller, manageable asynchronous tasks. This improves parallelism, makes tasks easier to retry, and simplifies debugging.
• Serialization: Ensure that all arguments passed to @async_task functions are serializable, as they must be transmitted to the background worker. Complex objects might require custom serialization logic.