absurd 0.0.7

A Python client for the Absurd SQL-based durable execution workflow system

Absurd Python Client

A Python client for the Absurd SQL-based durable execution workflow system. This library provides a Python interface to interact with Absurd's PostgreSQL-based task queue and workflow engine.

About Absurd

Absurd is a SQL-based durable execution workflow system that uses PostgreSQL as the backend. It provides features such as:

• Task queuing and processing
• Durable task execution that survives crashes
• Event-driven workflow coordination
• Checkpoint-based state management
• Retry strategies and failure handling
• Workflow run tracking
• Support for complex workflow patterns
• Multi-tenant support (Phase 13)

Table of Contents

• Features
• Installation
• Quick Start
• Usage Examples
  • Basic Task Processing
  • Task Management
  • Event Handling
  • Checkpoint Management
  • Workflow Tracking
  • Connection Management
  • Retry Strategies
  • Cancellation Rules
  • Long-Running Tasks
• Error Handling
• Configuration
• Highway DSL Integration
• API Reference
• Contributing
• License
• Support

Features

Core Functionality

• Task Management: Spawn, claim, complete, and fail tasks with comprehensive options
• Event Handling: Emit and wait for events for cross-workflow coordination
• Checkpoint Management: Save and restore state for long-running tasks
• Retry Strategies: Configure exponential and fixed retry backoff
• Cancellation Rules: Set time limits to automatically cancel tasks
• Workflow Tracking: Monitor complex workflow executions (Phase 10)
• Connection Pooling: Built-in support for efficient database connection management
• Security: SQL injection protected queue names and table identifiers

Advanced Features

• Multi-tenancy: Tenant-aware operations (Phase 13)
• Run Status Management: Detailed tracking of task run states
• Claim Extension: Extend worker claims for long-running operations
• Scheduling: Delayed task execution and rate limiting
• Cleanup: Automated cleanup of completed tasks and events

Installation

Prerequisites

Before installing the Python Absurd client, you'll need:

• Python 3.8 or higher
• PostgreSQL with the Absurd extension installed
• Psycopg3 library for PostgreSQL connectivity

Install from PyPI

pip install absurd

Install from Source

git clone https://github.com/rodmena-limited/python-absurd-client.git
cd python-absurd-client
pip install .

Development Installation

pip install -e ".[dev]"

Quick Start

Here's a simple example that demonstrates the core functionality:

import psycopg
from absurd_client import AbsurdClient

# Create a client instance
client = AbsurdClient(queue_name="my_queue")

# Connect to your PostgreSQL database
with psycopg.connect("your_connection_string") as conn:
    # Create the queue (optional - done automatically when spawning tasks)
    client.create_queue(conn)
    
    # Spawn a new task
    task_id, run_id, workflow_run_id = client.spawn_task(
        conn=conn,
        task_name="process_data",
        params={"input": "data"},
        headers={"priority": "high", "region": "us-east-1"},
        retry_strategy={
            "kind": "exponential",
            "base_seconds": 30,
            "factor": 2.0,
            "max_seconds": 3600
        },
        max_attempts=3,
        cancellation={
            "max_delay": 3600,      # Max 1 hour delay before starting
            "max_duration": 7200    # Max 2 hours total execution time
        }
    )

    print(f"Spawned task: {task_id}, run: {run_id}, workflow run: {workflow_run_id}")

    # Claim and process tasks
    claimed_tasks = client.claim_task(conn, qty=1, claim_timeout=60)
    for task_data in claimed_tasks:
        run_id, task_id, attempt, task_name, params, retry_strategy, max_attempts, headers, *_ = task_data

        try:
            # Process the task
            result = process_task(task_name, params)

            # Mark as completed
            client.complete_task(conn, run_id, result)
            print(f"Task {task_id} completed successfully")
        except Exception as e:
            # Mark as failed
            client.fail_task(conn, run_id, str(e))
            print(f"Task {task_id} failed: {e}")

Usage Examples

Basic Task Processing

import psycopg
from absurd_client import AbsurdClient

def process_task(task_name, params):
    """Simulate task processing"""
    if task_name == "echo":
        return {"output": f"Processed: {params.get('message', '')}", "status": "success"}
    return {"result": "unknown", "status": "error"}

# Create client
client = AbsurdClient(queue_name="default_queue")

# Connect to database and process tasks
with psycopg.connect("your_connection_string") as conn:
    # Spawn a task
    task_id, run_id, workflow_run_id = client.spawn_task(
        conn=conn,
        task_name="echo",
        params={"message": "Hello, World!"}
    )

    print(f"Spawned task: {task_id}")

    # Claim and process tasks
    claimed_tasks = client.claim_task(conn, qty=1)
    for task_data in claimed_tasks:
        run_id, task_id, attempt, task_name, params, *_ = task_data

        try:
            result = process_task(task_name, params)
            client.complete_task(conn, run_id, result)
            print(f"Completed task {task_id}")
        except Exception as e:
            client.fail_task(conn, run_id, str(e))
            print(f"Failed task {task_id}: {e}")

Task Management

The client provides comprehensive task management capabilities:

from absurd_client import AbsurdClient

client = AbsurdClient(queue_name="task_queue")

with psycopg.connect("your_connection_string") as conn:
    # Spawn a task
    task_id, run_id, workflow_run_id = client.spawn_task(
        conn=conn,
        task_name="complex_task",
        params={"data": "value", "options": {"verbose": True}}
    )

    # Get detailed task status
    task_status = client.get_task_status(conn, task_id)
    print(f"Task state: {task_status['state']}")
    
    # Get detailed run status
    run_status = client.get_run_status(conn, run_id)
    print(f"Run state: {run_status['state']}")
    
    # Schedule the task for future execution
    from datetime import datetime, timedelta
    future_time = datetime.now() + timedelta(minutes=10)
    client.schedule_task(conn, run_id, future_time)
    
    # Cancel a task if needed
    # client.cancel_task(conn, run_id)  # Only works if task is pending or sleeping

Event Handling

Use events for cross-workflow coordination:

from absurd_client import AbsurdClient, AbsurdSleepError

client = AbsurdClient(queue_name="event_queue")

def task_that_waits_for_event(conn, run_id, task_id):
    """A task that waits for an event"""
    try:
        payload = client.wait_for_event(
            conn=conn,
            run_id=run_id,
            event_name="data_ready",
            timeout_seconds=3600,  # 1 hour timeout
            task_id=task_id,
            step_name="waiting_step"
        )
        return {"status": "received", "payload": payload}
    except AbsurdSleepError:
        # This is expected - the orchestrator should handle this
        # by marking the task as sleeping
        raise

def task_that_emits_event(conn):
    """A task that processes data and emits an event"""
    # Simulate data processing
    result = {"processed_data": "some_value"}

    # Emit the event that other tasks are waiting for
    client.emit_event(
        conn=conn,
        event_name="data_ready",
        payload=result
    )

    return {"status": "event_emitted", "data": result}

# In a real orchestrator, you would handle AbsurdSleepError like this:
def process_task_with_event_handling(conn, run_id, task_id):
    try:
        return task_that_waits_for_event(conn, run_id, task_id)
    except AbsurdSleepError as e:
        # Mark the run as sleeping until the event occurs
        client.set_run_sleeping(conn, e.run_id, e.event_name)
        # Return without completing the task - it will resume when event occurs
        return None

Checkpoint Management

For long-running tasks that need to save state:

import time
from absurd_client import AbsurdClient

client = AbsurdClient(queue_name="long_running_queue")

def long_running_task(conn, run_id, task_id):
    """Simulate a long-running task with checkpoints"""
    # Check if we have a checkpoint from a previous attempt
    checkpoint = client.get_checkpoint(conn, task_id, "progress")

    start_step = 1
    if checkpoint and checkpoint.get("state"):
        start_step = checkpoint["state"].get("current_step", 1)

    for step in range(start_step, 6):  # 5 steps total
        # Simulate work
        time.sleep(2)  # Simulate processing

        # Save checkpoint
        client.set_checkpoint(
            conn=conn,
            task_id=task_id,
            step_name="progress",
            state={"current_step": step, "completed": f"step_{step}"},
            owner_run=run_id
        )

        # Extend claim if needed for long operations
        client.extend_claim(conn, run_id, extend_by_seconds=60)

    return {"status": "completed", "steps": 5}

with psycopg.connect("your_connection_string") as conn:
    # Spawn the long-running task
    task_id, run_id, workflow_run_id = client.spawn_task(
        conn=conn,
        task_name="long_running_task",
        params={"work": "complex_job"}
    )

    # Claim and process
    claimed_tasks = client.claim_task(conn)
    for task_data in claimed_tasks:
        run_id, task_id, *_ = task_data
        try:
            result = long_running_task(conn, run_id, task_id)
            client.complete_task(conn, run_id, result)
        except Exception as e:
            client.fail_task(conn, run_id, str(e))

Workflow Tracking

Track complex workflows across multiple tasks:

from absurd_client import AbsurdClient
import uuid
from datetime import datetime

client = AbsurdClient(queue_name="workflow_tracking_queue")

def run_data_pipeline():
    with psycopg.connect("your_connection_string") as conn:
        # Create a workflow run to track this pipeline
        workflow_run_id = client.create_workflow_run(
            conn=conn,
            workflow_name="data_pipeline",
            workflow_version="1.0.0",
            inputs={"source": "s3://bucket/data", "target": "warehouse"},
            created_by="pipeline_system",
            tags={"environment": "production", "priority": "high"}
        )

        # Update workflow status
        client.update_workflow_run_status(
            conn=conn,
            workflow_run_id=workflow_run_id,
            status="running",
            started_at=datetime.now()
        )

        # Spawn the extraction task
        extract_task_id, extract_run_id, _ = client.spawn_task(
            conn=conn,
            task_name="extract_data",
            params={"workflow_run_id": workflow_run_id, "source": "s3://bucket/data"},
            headers={"workflow_run_id": str(workflow_run_id)}
        )

        # Process tasks (in real app, this would be a separate worker loop)
        claimed_tasks = client.claim_task(conn)
        for task_data in claimed_tasks:
            run_id, task_id, attempt, task_name, params, *_ = task_data
            
            try:
                # Simulate processing
                result = {"processed": f"Data from {params.get('source')}"}
                
                # Complete the task
                client.complete_task(conn, run_id, result)
                
                # Update workflow status when needed
                client.update_workflow_run_status(
                    conn=conn,
                    workflow_run_id=workflow_run_id,
                    status="completed",
                    result=result,
                    completed_at=datetime.now(),
                    task_count=1
                )
            except Exception as e:
                client.fail_task(conn, run_id, str(e))
                
                # Update workflow status to failed
                client.update_workflow_run_status(
                    conn=conn,
                    workflow_run_id=workflow_run_id,
                    status="failed",
                    error={
                        "type": "ProcessingError",
                        "message": str(e),
                        "timestamp": datetime.now().isoformat()
                    }
                )

run_data_pipeline()

Connection Management

For production applications, use connection pooling:

from psycopg_pool import ConnectionPool
from absurd_client import AbsurdClient
import psycopg

# Create a connection pool
pool = ConnectionPool("your_connection_string", min_size=2, max_size=10)

client = AbsurdClient(queue_name="pooled_queue")

def process_task_with_pooling():
    with pool.connection() as conn:
        # Spawn a task
        task_id, run_id, workflow_run_id = client.spawn_task(
            conn=conn,
            task_name="pooled_task",
            params={"data": "value"}
        )

        # Process any claimed tasks
        claimed_tasks = client.claim_task(conn, qty=5)
        for task_data in claimed_tasks:
            run_id, task_id, attempt, task_name, params, *_ = task_data

            try:
                result = {"processed": params}
                client.complete_task(conn, run_id, result)
            except Exception as e:
                client.fail_task(conn, run_id, str(e))

# Example with context manager for safety
def process_task_safely():
    try:
        with pool.connection() as conn:
            task_id, run_id, workflow_run_id = client.spawn_task(
                conn=conn,
                task_name="safe_task",
                params={"data": "value"}
            )
            # Process the task...
    except psycopg.OperationalError as e:
        print(f"Database connection error: {e}")
    except psycopg.Error as e:
        print(f"PostgreSQL error: {e}")

Retry Strategies

Handle failures with configurable retry strategies:

from absurd_client import AbsurdClient, spawn_retry_task

client = AbsurdClient(queue_name="retry_queue")

def unreliable_task():
    """A task that sometimes fails"""
    import random
    if random.random() < 0.7:  # 70% chance of failure
        raise Exception("Random failure for demonstration")
    return {"status": "success"}

with psycopg.connect("your_connection_string") as conn:
    # Spawn with retry strategy using convenience function
    task_id, run_id, workflow_run_id = spawn_retry_task(
        client=client,
        conn=conn,
        task_name="unreliable_task",
        params={},
        max_attempts=5,
        retry_kind="exponential",
        base_seconds=10,
        factor=2.0,
        max_seconds=3600  # Max 1 hour between retries
    )

    # Or spawn directly with retry strategy
    task_id, run_id, workflow_run_id = client.spawn_task(
        conn=conn,
        task_name="another_unreliable_task",
        params={},
        max_attempts=3,
        retry_strategy={
            "kind": "fixed",
            "base_seconds": 60  # Fixed 1 minute between retries
        }
    )

    # Process the task
    claimed_tasks = client.claim_task(conn)
    for task_data in claimed_tasks:
        run_id, task_id, *_ = task_data

        try:
            result = unreliable_task()
            client.complete_task(conn, run_id, result)
        except Exception as e:
            # The retry logic is handled by Absurd
            client.fail_task(conn, run_id, str(e))

Cancellation Rules

Create tasks with automatic cancellation:

from absurd_client import AbsurdClient, spawn_cancellable_task

client = AbsurdClient(queue_name="cancellable_queue")

with psycopg.connect("your_connection_string") as conn:
    # Spawn a task that can be cancelled if it takes too long
    task_id, run_id, workflow_run_id = spawn_cancellable_task(
        client=client,
        conn=conn,
        task_name="potentially_long_task",
        params={"data": "value"},
        max_delay_seconds=3600,      # Max 1 hour of delay before starting
        max_duration_seconds=7200    # Max 2 hours total duration
    )

    # Or spawn directly with cancellation rules
    task_id, run_id, workflow_run_id = client.spawn_task(
        conn=conn,
        task_name="another_long_task",
        params={"data": "value"},
        cancellation={
            "max_delay": 1800,      # Max 30 minutes of delay
            "max_duration": 3600    # Max 1 hour total execution time
        }
    )

    # Manually cancel a task if needed
    # client.cancel_task(conn, run_id)  # Only works for pending/sleeping tasks

Long-Running Tasks

For tasks that need to extend their processing time:

from absurd_client import AbsurdClient
import time

client = AbsurdClient(queue_name="long_task_queue")

def long_processing_task(conn, run_id, task_id):
    """A task that extends its claim as needed"""
    for i in range(10):  # 10 steps of processing
        time.sleep(10)  # Simulate work
        
        # Extend the claim by 60 seconds for each chunk of work
        client.extend_claim(conn, run_id, extend_by_seconds=60)
        
        # Save progress checkpoint
        client.set_checkpoint(
            conn=conn,
            task_id=task_id,
            step_name="progress",
            state={"step": i, "total": 10},
            owner_run=run_id
        )
    
    return {"status": "completed", "steps": 10}

with psycopg.connect("your_connection_string") as conn:
    task_id, run_id, workflow_run_id = client.spawn_task(
        conn=conn,
        task_name="long_processing",
        params={}
    )
    
    claimed_tasks = client.claim_task(conn)
    for task_data in claimed_tasks:
        run_id, task_id, *_ = task_data
        result = long_processing_task(conn, run_id, task_id)
        client.complete_task(conn, run_id, result)

Error Handling

The client includes comprehensive error handling for various scenarios:

AbsurdSleepError

The AbsurdSleepError is a special exception that signals when a run enters SLEEPING state to wait for an event:

from absurd_client import AbsurdClient, AbsurdSleepError

client = AbsurdClient(queue_name="error_handling_queue")

try:
    payload = client.wait_for_event(
        conn=conn,
        run_id=run_id,
        event_name="data_ready",
        timeout_seconds=3600,
        task_id=task_id,
        step_name="waiting_step"
    )
except AbsurdSleepError as e:
    # The orchestrator should catch this and mark the run as sleeping
    print(f"Run {e.run_id} sleeping, waiting for event '{e.event_name}'")
    # In a real orchestrator, this would mark the run as sleeping
    # and free the worker thread to process other tasks

# In the orchestrator, you would typically handle it like this:
def handle_task_processing():
    try:
        # Process task here
        pass
    except AbsurdSleepError as e:
        # Mark the run as sleeping
        client.set_run_sleeping(conn, e.run_id, e.event_name)
        # Free worker thread - return from function without completing task
        return

Database Errors

The client handles database errors appropriately:

from absurd_client import AbsurdClient

client = AbsurdClient(queue_name="my_queue")

try:
    # Attempt to complete a task
    client.complete_task(conn, run_id, result)
except Exception as e:
    # Handle database errors or other issues
    print(f"Failed to complete task: {e}")
    # Handle appropriately based on your application needs

Validation Errors

Queue names are validated to prevent SQL injection:

try:
    # This will raise ValueError if queue_name is invalid
    client = AbsurdClient(queue_name="invalid-queue-name!")  # Invalid character
except ValueError as e:
    print(f"Invalid queue name: {e}")

# Valid queue names contain only letters, numbers, and underscores
# and must start with a letter or underscore
client = AbsurdClient(queue_name="valid_queue_name")  # Valid

Configuration

Environment Variables

The client supports the following environment variables:

• ABSURD_DEFAULT_QUEUE: Default queue name when not specified (default: absurd_default)
• ABSURD_WORKER_ID: Default worker ID (default: absurd_worker_1)

Queue Name Validation

Queue names must follow these rules to prevent SQL injection:

• Must contain only letters, numbers, and underscores
• Must start with a letter or underscore
• Should not be overly long (recommendation: under 30 characters)

Highway DSL Integration

The highway-dsl package is a Python-based domain-specific language for defining complex workflows that fully supports Absurd. Highway DSL provides a fluent API for building workflows with features such as:

• Rich Operators: Task, Condition (if/else), Parallel, ForEach, While, Wait, Switch, EmitEvent, and WaitForEvent operators
• Scheduling: Built-in support for cron-based schedules, start dates, and catchup configuration
• Event-Driven Features: First-class support for event emission and waiting for cross-workflow coordination
• Error Handling: Retry policies, timeout policies, and callback hooks for production-grade workflows
• YAML/JSON Interoperability: Workflows can be defined in Python and exported to YAML or JSON
• Mermaid Diagram Generation: Visualize workflows with generated Mermaid diagrams

Example of defining a workflow with Highway DSL:

from highway_dsl import WorkflowBuilder, RetryPolicy
from datetime import timedelta

# Create a complex ETL workflow
workflow = (
    WorkflowBuilder("data_pipeline")
    .set_schedule("0 2 * * *")  # Daily at 2 AM
    .set_start_date(datetime.now())
    .add_tags("etl", "production")
    .set_max_active_runs(1)
    .set_default_retry_policy(RetryPolicy(
        max_retries=3,
        delay=timedelta(seconds=30),
        backoff_factor=2.0
    ))
    .task("extract", "etl.extract_data", result_key="raw_data")
    .task("transform", "etl.transform_data", args=["{{raw_data}}"], result_key="transformed_data")
    .parallel("process_branches", {
        "branch_a": lambda w: w.task("process_a", "etl.process_part_a", args=["{{transformed_data}}"]),
        "branch_b": lambda w: w.task("process_b", "etl.process_part_b", args=["{{transformed_data}}"])
    })
    .task("load", "etl.load_data", args=["{{transformed_data}}"])
    .build()
)

# Export to YAML for use with Absurd
print(workflow.to_yaml())

# Generate Mermaid diagram
print(workflow.to_mermaid())

With highway-dsl, you can define complex workflow patterns using a clear, fluent syntax and export them in formats compatible with Absurd's PostgreSQL-based workflow engine.

API Reference

AbsurdClient Class

Constructor

AbsurdClient(queue_name: str | None = None, worker_id: str | None = None)

Creates a new AbsurdClient instance.

Parameters:

• queue_name: Name of the queue to use (defaults to environment variable ABSURD_DEFAULT_QUEUE or "absurd_default")
• worker_id: ID of the worker (defaults to environment variable ABSURD_WORKER_ID or "absurd_worker_1")

Core Methods

create_queue(conn: psycopg.Connection) -> None Creates the Absurd queue if it doesn't exist.

spawn_task(conn: psycopg.Connection, task_name: str, params: dict, options: dict | None = None, headers: dict | None = None, retry_strategy: dict | None = None, max_attempts: int | None = None, cancellation: dict | None = None, workflow_run_id: uuid.UUID | None = None) -> tuple[uuid.UUID, uuid.UUID, uuid.UUID] Spawns a new task in the Absurd queue.

Returns: (task_id, run_id, workflow_run_id)

claim_task(conn: psycopg.Connection, worker_id: str | None = None, claim_timeout: int = 30, qty: int = 1) -> list[tuple[Any, ...]] Claims tasks from the Absurd queue.

complete_task(conn: psycopg.Connection, run_id: uuid.UUID, result: dict[str, Any] | None = None) -> None Marks a task as completed.

fail_task(conn: psycopg.Connection, run_id: uuid.UUID, reason: str | dict[str, Any], retry_at: datetime | None = None) -> None Marks a task as failed.

cancel_task(conn: psycopg.Connection, run_id: uuid.UUID) -> bool Manually cancels a pending or sleeping task.

Event Handling Methods

emit_event(conn: psycopg.Connection, event_name: str, payload: dict[str, Any] | None = None) -> None Emits an event and wakes any runs waiting for it.

wait_for_event(conn: psycopg.Connection, run_id: uuid.UUID, event_name: str, timeout_seconds: int | None = None, task_id: uuid.UUID | None = None, step_name: str | None = None) -> Any Waits for an event using Absurd's sleep/wake mechanism.

set_run_sleeping(conn: psycopg.Connection, run_id: uuid.UUID, event_name: str) -> None Marks a run as SLEEPING waiting for an event.

Checkpoint Management Methods

set_checkpoint(conn: psycopg.Connection, task_id: uuid.UUID, step_name: str, state: dict[str, Any], owner_run: uuid.UUID, extend_claim_by: int | None = None) -> None Sets a checkpoint for a task with optional claim extension.

get_checkpoint(conn: psycopg.Connection, task_id: uuid.UUID, step_name: str, include_pending: bool = False) -> dict[str, Any] | None Gets a checkpoint for a task.

get_all_checkpoints(conn: psycopg.Connection, task_id: uuid.UUID, run_id: uuid.UUID) -> list[dict[str, Any]] Gets all checkpoints for a task.

extend_claim(conn: psycopg.Connection, run_id: uuid.UUID, extend_by_seconds: int) -> None Extends the claim timeout for a long-running task.

Workflow Tracking Methods

create_workflow_run(conn: psycopg.Connection, workflow_name: str, workflow_version: str, inputs: dict[str, Any] | None = None, absurd_run_id: uuid.UUID | None = None, created_by: str | None = None, tags: dict[str, Any] | None = None, workflow_hash: str | None = None) -> uuid.UUID Creates a new workflow_run record to track workflow execution.

update_workflow_run_status(conn: psycopg.Connection, workflow_run_id: uuid.UUID, status: str, result: dict[str, Any] | None = None, error: dict[str, Any] | None = None, started_at: datetime | None = None, completed_at: datetime | None = None, task_count: int | None = None) -> None Updates workflow_run status and metadata.

Status and Utility Methods

get_task_status(conn: psycopg.Connection, task_id: uuid.UUID) -> dict[str, Any] | None Gets detailed task status information.

get_run_status(conn: psycopg.Connection, run_id: uuid.UUID) -> dict[str, Any] | None Gets detailed run status information.

get_checkpoints_for_run(conn: psycopg.Connection, run_id: uuid.UUID) -> dict[str, Any] Gets all checkpoints for a specific run.

save_checkpoint_for_run(conn: psycopg.Connection, run_id: uuid.UUID, step_name: str, data: Any, task_id: uuid.UUID | None = None) -> None Sets a checkpoint for a specific step.

get_run_checkpoint(conn: psycopg.Connection, run_id: uuid.UUID, step_name: str) -> Any Gets a specific checkpoint for a run.

Scheduling and Management Methods

schedule_task(conn: psycopg.Connection, run_id: uuid.UUID, wake_at: datetime) -> None Schedules a task to run at a specific time.

cleanup_tasks(conn: psycopg.Connection, ttl_seconds: int, limit: int = 1000) -> int Cleans up old completed tasks.

cleanup_events(conn: psycopg.Connection, ttl_seconds: int, limit: int = 1000) -> int Cleans up old events.

Helper Functions

spawn_retry_task(client: AbsurdClient, conn: psycopg.Connection, task_name: str, params: dict[str, Any], max_attempts: int = 3, retry_kind: str = "exponential", base_seconds: int = 30, factor: float = 2.0, max_seconds: int | None = None) -> tuple[uuid.UUID, uuid.UUID, uuid.UUID] Convenience function to spawn a task with retry strategy.

spawn_cancellable_task(client: AbsurdClient, conn: psycopg.Connection, task_name: str, params: dict[str, Any], max_delay_seconds: int | None = None, max_duration_seconds: int | None = None) -> tuple[uuid.UUID, uuid.UUID, uuid.UUID] Convenience function to spawn a task with cancellation rules.

get_absurd_client(queue_name: str | None = None, worker_id: str | None = None) -> AbsurdClient Returns the singleton AbsurdClient instance.

Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for your changes
5. Run the test suite (pytest)
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

Please make sure your code follows the project's coding standards and includes appropriate documentation and tests.

License

This project is licensed under the terms specified in the LICENSE file.

Support

For support, please open an issue in the GitHub repository. For questions about usage or integration, feel free to create a discussion topic.

For commercial support or enterprise features, please contact the maintainers through the GitHub repository.