taskops 0.1.5

A flexible task scheduling and execution framework

Task Processor

A flexible and extensible task processing system that allows you to schedule, execute, and monitor tasks with ease.

Features

• Flexible Task Scheduling: Support for recurring and one-time tasks
• Extensible Plugin System: Easy integration with various services and databases
• Robust Error Handling: Built-in retry mechanisms and error logging
• YAML Configuration: Simple task configuration using YAML files
• Comprehensive Logging: Detailed logging with rotation and retention policies
• Type Safety: Full type hints and validation using Pydantic

Installation

From GitHub (Recommended)

pip install git+https://github.com/sharik709/task-processor.git

From TestPyPI

pip install -i https://test.pypi.org/simple/ taskops

From PyPI

pip install taskops

Quick Start

1. Create a task configuration file (tasks.yaml):

tasks:
  - name: "backup_database"
    command: "pg_dump mydb > backup.sql"
    schedule:
      type: "recurring"
      interval: "1d"
    retry:
      max_attempts: 3
      delay: 300  # 5 minutes

2. Run the task processor:

taskops --config-dir /path/to/config

Configuration

Task Configuration

Tasks can be configured using YAML files. Each task can have the following properties:

name: "task_name"          # Unique identifier for the task
command: "command_to_run"  # Shell command to execute
schedule:
  type: "recurring"        # or "one-time"
  interval: "1h"          # for recurring tasks (e.g., "1m", "1h", "1d")
  start_time: "2024-02-20T10:00:00"  # for one-time tasks
retry:
  max_attempts: 3         # Maximum number of retry attempts
  delay: 60              # Delay between retries in seconds

Command Line Options

taskops [OPTIONS]

Options:
  --config-dir TEXT     Directory containing task configuration files
  --log-dir TEXT       Directory for log files
  --max-log-files INT  Maximum number of log files to keep per task
  --help              Show this message and exit

Python API Usage

You can also use the task-processor directly in your Python code:

from datetime import datetime
from task_processor import Task, Schedule, RetryConfig, TaskScheduler, LogManager, LogConfig

# Initialize logging (optional but recommended)
log_config = LogConfig(log_dir="./logs")
log_manager = LogManager(log_config)

# Create a task scheduler with logging
scheduler = TaskScheduler(log_manager=log_manager)

# Define a recurring task
recurring_task = Task(
    name="data_processing",
    command="python process_data.py",
    schedule=Schedule(
        type="recurring",
        interval="1h"  # Run every hour
    ),
    retry=RetryConfig(
        max_attempts=3,
        delay=60  # Retry after 60 seconds
    )
)

# Define a one-time task
one_time_task = Task(
    name="database_cleanup",
    command="python cleanup_db.py",
    schedule=Schedule(
        type="one-time",
        start_time=datetime(2024, 5, 1, 3, 0, 0)  # Run at 3 AM on May 1, 2024
    ),
    retry=RetryConfig(
        max_attempts=2,
        delay=300  # Retry after 5 minutes
    )
)

# Add tasks to the scheduler
scheduler.add_task(recurring_task)
scheduler.add_task(one_time_task)

# Start the scheduler (this will block and run until stopped)
try:
    print("Task scheduler is running. Press Ctrl+C to stop.")
    scheduler.run()
except KeyboardInterrupt:
    print("Shutting down scheduler...")
    scheduler.stop()

For a complete working example, see example_task_processor.py.

Development

Setup

1. Clone the repository:

git clone https://github.com/sharik709/task-processor.git
cd task-processor

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install development dependencies:

pip install -r requirements.txt

Running Tests

pytest

For coverage report:

pytest --cov=task_processor --cov-report=term-missing

Contributing

See CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.