lilota 0.10.0

A lightweight solution for long-running tasks

lilota

lilota is a lightweight Python library for executing long-running tasks in the background without the overhead of full-fledged task queue systems like Celery or RabbitMQ. While those tools are powerful and valuable, lilota focuses on scenarios where a simpler approach is sufficient.

It is designed for simple, asynchronous task execution with minimal setup and overhead.

• lilota
  • Features
  • When to use lilota
  • Installation
  • Simple mode and Cluster mode
  • Quick example (Simple mode)
    • Define input and output models
    • Create a lilota instance
    • Register a background task
    • Start lilota
    • Schedule a task
    • Retrieve task information including the output (if available)
  • Documentation
  • Examples

Features

• Run long-running tasks
• Simple API and minimal configuration and setup
• Persistent task state stored in a database
• No message broker required
• Suitable for applications that use background jobs, i.e. web applications.

When to use lilota

Use lilota when your application needs to run tasks that take time, such as:

• image or file processing
• report generation
• sending emails
• heavy computations

Instead of blocking the request, lilota lets you start the task in the background.

Installation

pip install lilota

Simple mode and Cluster mode

lilota supports two modes: Simple mode and Cluster mode.

In Simple mode one scheduler and one worker is started. The scheduler is responsible for scheduling the tasks and the worker executes the tasks. A worker is executing only one task at a time. More information can be found here.

If you want to execute multiple tasks in parallel you have to run lilota in Cluster mode. Here you have one scheduler and several workers. More information can be found here.

Quick example (Simple mode)

This example demonstrates how to add two numbers using a function that runs in the background.

This could, of course, also be a function that generates a report or performs a heavy computation. For simplicity, we will just add two numbers.

First, we define a class used to pass input arguments to the background task. Here, we call this model AddInput, which has two properties: a and b.

We also define an output model called AddOutput. This model is populated with the result of the computation and stored in the database, where it can later be retrieved.

Here is the full example:

from dataclasses import dataclass
from lilota.core import Lilota
from lilota.models import Task
import time


@dataclass
class AddInput():
    a: int
    b: int


@dataclass
class AddOutput():
  sum: int


lilota = Lilota(
  db_url="postgresql+psycopg://postgres:postgres@localhost:5432/lilota_sample"
)

@lilota.register("add", input_model=AddInput, output_model=AddOutput)
def add(data: AddInput) -> AddOutput:
  return AddOutput(sum=data.a + data.b)


def main():
  # Start lilota
  lilota.start()

  # Schedule a task
  task_id = lilota.schedule("add", AddInput(a=2, b=3))

  # Wait one second because Lilota runs in the background and decides 
  # when to pick up a task. This is normally not needed. We do it 
  # here because we want to wait until the task has been executed.
  time.sleep(1)

  # Retrieve task information from the database and print the result
  task: Task = lilota.get_task_by_id(task_id)
  print(f"We add the numbers 2 and 3: ")
  print(task.output)


if __name__ == "__main__":
  main()

Define input and output models

• Input and output models are optional
• You do not have to use dataclasses for these models. You can use any serializable model, such as pydantic models.
• It is only important that the models are serializable, since they are stored in the database.
• lilota uses a ModelProtocol. To comply with it, you only need to define an as_dict method. A full example using pydantic can be found here: 3-add-two-numbers-using-pydantic.py
• lilota also supports passing a TaskProgress instance to the task function. This can be used to update progress information in the database. It is important to set set_progress_manually=True when creating the lilota instance. A full example can be found here: 5-setting-task-progress-manually.py

Create a lilota instance

lilota = Lilota(
  db_url="postgresql+psycopg://postgres:postgres@localhost:5432/lilota_sample"
)

In this example we use a url to a postgres database. lilota uses SQLAlchemy and therefore all databases that are supported by SQLAlchemy can be used here.

Register a background task

@lilota.register("add", input_model=AddInput, output_model=AddOutput)
def add(data: AddInput) -> AddOutput:
  return AddOutput(sum=data.a + data.b)

Start lilota

lilota.start()

Schedule a task

task_id = lilota.schedule("add", AddInput(a=2, b=3))

The schedule function creates a task entry in the database and starts executing it immediately. The ID of the stored task is returned.

Retrieve task information including the output (if available)

task: Task = lilota.get_task_by_id(task_id)
add_output = AddOutput(**task.output)
print(add_output.sum)

Documentation

https://tobiasroessler.github.io/lilota/

Examples

Example	URL
A simple "Hello World" example	1-hello-world.py
Add two numbers using an input and an output model	2-add-two-numbers.py
Add two numbers using a pydantic input and an output model	3-add-two-numbers-using-pydantic.py
Database access inside the task function	4-using-db-inside-task.py
Set the task progress manually in a task function	5-setting-task-progress-manually.py