Taskturbine

Taskturbine is a cross-language durable task framework for Rust & Python, using postgres for task storage.

What are durable tasks?

Durable tasks are operations that are resilient to failure and interruptions. Instead of having to manually manage retries, state and scheduling, you express your logic as a workflow of operations or functions. Each 'step' in a durable task will store its result, and will resume from the last completed step.

import os
from taskturbine import TaskturbineApp, Config, TaskContext

config = Config(
    database_url=os.getenv("DB_URL"),
    app_module="testapp:app",
    worker_concurrency=4,
)
app = TaskturbineApp(config)


@app.register_task("hello-world")
def hello_world(ctx: TaskContext) -> None:
    logger.info(f"Hello world! {ctx.params_bytes.decode()}")

    # We start off defining the steps in our task.
    @ctx.step(name="compute_user_data")
    def compute_user_data(ctx: TaskContext) -> Payload:
        params = ctx.params
        # do some compute/query and returns a dict value.
        return {"name": params["name"], "id": uuid.uuid4().hex, "started": time.time()}
        
    @ctx.step(name="send-email")
    def send_email(ctx: TaskContext, user: Payload, event: Payload) -> Payload:
        email.send_message("hello-user", data={"user": user, "event": event})

    @ctx.step(name="process-complete")
    def process_complete(ctx: TaskContext, user: Payload, event: Payload) -> Payload:
        # Do more IO
        return user

    # Once we have defined our steps, we wire them together with control flow.
    # Steps have their return values stored and if a step/task fails, it will
    # resume from the last completed step.
    # Any logic in the task body will be run *each* time the task is executed.
    user = compute_user_data(ctx)
    assert user, "User must exist"
    send_email(ctx, user)

    completed = process_complete(ctx, user, event)

    print("Completed:", completed)

Taskturbine provides a simple durable cross-platform task framework for Rust, and Python. Using postgres as both a queue and state storage allows taskturbine to be operationally simple yet provide powerful features like retries, scheduling, workload separation, external synchronization and more.

Installation

For python:

uv add taskturbine

For rust:

cargo add taskturbine

Concepts

Application logic is defined as tasks. Tasks are functions that are composed of multiple operations or steps. Tasks execute their steps in the order they are defined, and the result of each step is stored. Should a task or step fail for any reason, a subsequent run can be scheduled. When the subsequent run is started, the application state is reconstructed from stored checkpoints, and any completed steps are skipped. Tasks can also sleep for a period of time, or wait for an event to be emit elsewhere in your application logic. Event payloads are also stored allowing you to build race free synchronization logic and workflows.

As an application grows, you'll likely want to isolate different workloads from each other. To facilitate this, tasks can be spawned into a named channel. Workers claim and execute tasks from one or more channels. This enables you to have different groups of workers for different workloads or customers.

Glossary

• usecase The client application that a task belongs to. A single taskturbine database can be shared by multiple applications if required.
• channel Channels enable you to separate workloads within a usecase. For example, you may want many workers processing high-priority tasks, and fewer processing lower priority work.
• task A workflow or task that should be executed durably.
• step An incremental operation or side-effect that can succeed or fail. Steps act as error and persistence boundaries for your tasks. Steps that complete are not retried or run multiple times.
• checkpoint As steps are completed, checkpoints are created.
• run An attempt to execute a task. Each run can read checkpoints from previous runs, allowing tasks to resume where they left off.
• event Tasks can be suspended until named events are emit by the application. Events are ideal for waiting on webhooks, or other tasks to complete.
• wait When a task is waiting for an event, it records a wait.

This diagram connects each of the concepts and glossary terms together:

erDiagram
  Usecase ||--|{ Task : "contains many"
  Usecase ||--|{ Channel : "contains many"
  Task ||--o{ Run : "has many"
  Task ||--|| Channel : "spawned on"
  Worker ||--|{ Channel : "consumes from"
  Task ||--o{ Step : "zero or more"
  Step ||--o| Checkpoint : "one or none" 
  Task ||--o| Wait : "wait for event"
  Event ||--o{ Wait : "arrived event"

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Command line tools

The taskturbine-cli package provides a command line tool for managing and interacting with a taskturbine application. With taskturbine-cli you can:

• Spawn tasks
• Emit events
• Perform cleanup operations
• Clear all stored tasks + events
• Create schema

Installing the command line tool

You can build and install the CLI tool with:

cargo build -p taskturbine-cli

# Assuming that ~/.local/bin is on $PATH
mv ./target/release/taskturbine-cli ~/.local/bin/

# You need to set TASKTURBINE_DATABASE_URL to a DSN for postgres
# Emit help
taskturbine-cli -h

The CLI tool provides a simple interface for spawning tasks, emitting events, cancel tasks and perform cleanup operations. You can also use the CLI tool to create schema with migrate, clear all state with clear.

Comparisons

This project is quite new, if you're looking for more mature solutions, consider the following:

• Absurd Directly inspired this library. The schema used in taskturbine was derived from absurd's. The naming of the core storage interface is also modelled after absurd's stored procedures.
• Cadence One of the original projects in durable execution. It has inspired many systems since then.
• Temporal in addition to durable execution, temporal integrates deeply with the runtime environment to help build detemerministic workflows.
• Inngest a self-hostable event driven workflow system. It's self-hostable, can be run locally.
• DBOS A more mature implementation of durable workflows on top of Postgres.

Project History

The schema and library interfaces were inspired by Absurd. However, instead of leaning on stored procedures, the 'core' library interface is implemented as a rust crate, and libraries are provided for both Rust and Python, with support for more languages planned in the future.

License

Apache 2.0 License

Copyright 2025 earendil-works

Copyright 2025 Mark Story