lilota

lilota is a lightweight Python library for executing long-running tasks in the background without the overhead of full-fledged task queue systems. 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
• Quick example
  • myscript.py
  • Create a worker instance
  • Register a background task
  • Integration of lilota
  • Define input and output models
  • Create a Lilota instance
  • 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

Quick example

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 have to create a script that has an instance of a worker (LilotaWorker). This worker registers one or several task-functions that can be executed later by a scheduler.

myscript.py

from dataclasses import dataclass
from lilota.worker import LilotaWorker


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


@dataclass
class AddOutput():
  sum: int


worker = LilotaWorker(
  db_url="postgresql+psycopg://postgres:postgres@localhost:5432/lilota_sample"
)


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


def main():
  worker.start()


if __name__ == "__main__":
  main()

Create a worker instance

worker = LilotaWorker(
  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

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

Integration of lilota

The script created above is passed to a Lilota instance for execution.

That instance can start multiple processes, each of which executes the script once. You can specify the number of workers for a Lilota instance. By default, it is set to cpu_count().

In this example, we use two model classes: one for input arguments and one for the output. These models should typically be defined in their own module and shared between Lilota and LilotaWorker.

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",
  script_path="sample/myscript.py",
  number_of_workers=8
)


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",
  script_path="sample/myscript.py",
  number_of_workers=8
)

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