dagster-celery
Package for using Celery as Dagster's execution engine.
Decision gist · record as of 2026-08-14
Yes, if you need to scale Dagster beyond a single machine or integrate with existing Celery infrastructure. The package is actively maintained, has no known vulnerabilities, and carries a permissive license. Install only if you have a Celery broker (RabbitMQ, Redis, etc.) already running or planned; it adds complexity that single-machine deployments do not need.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Celery broker (e.g.
- RabbitMQ, Redis) and worker processes running separately; Dagster must be Python 3.10 or later.
- Low friction installation with a pure-Python wheel and four runtime dependencies (celery, click, dagster, importlib-metadata).
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows use in commercial and private projects without copyleft obligations, making it suitable for enterprise deployments.
last release 2026-08-14 (0 days) · last repo commit 2026-08-13 · 15,996 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,912,612 downloads/mo, #2,454 on PyPI
Alternatives
Verify before relying
pip install dagster-celery
import dagster as dg
from dagster_celery import celery_executor
@dg.job(executor_def=celery_executor)
def my_job():
pass- Whether the package includes built-in configuration helpers or requires manual Celery broker setup.
- Performance characteristics and recommended worker count for typical workloads.
- Support for Celery task routing, priority queues, or other advanced Celery features.
What it is and what it does
dagster-celery is a Dagster integration that plugs Celery in as the execution engine for running Dagster assets and jobs. Instead of executing tasks sequentially or on a single machine, this package lets you distribute work across a cluster of Celery workers, enabling parallel execution and horizontal scaling of your data pipelines.
The package acts as a bridge between Dagster's declarative asset model and Celery's distributed task queue. When you configure a Dagster job or asset with the celery_executor, each task runs as a Celery job on an available worker node. This is useful for production deployments where you need to scale beyond a single machine or isolate compute-heavy workloads. The package is maintained as part of the main Dagster project and is actively developed.
Use it for
- Scale Dagster asset materialization across multiple machines in production environments.
- Isolate long-running or resource-intensive data transformations on dedicated worker nodes.
- Integrate Dagster pipelines with existing Celery infrastructure and message brokers.
- Enable parallel execution of independent Dagster tasks within a single job run.
- Deploy Dagster in cloud-native environments where distributed execution is required.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to scale Dagster beyond a single machine or integrate with existing Celery infrastructure.
The package is actively maintained, has no known vulnerabilities, and carries a permissive license. Install only if you have a Celery broker (RabbitMQ, Redis, etc.) already running or planned; it adds complexity that single-machine deployments do not need.
Install
dagster-celery on PyPI
Before you install
Low friction installation with a pure-Python wheel and four runtime dependencies (celery, click, dagster, importlib-metadata). The package is actively maintained with a release on 2026-08-14 and no known vulnerabilities.
Requires Celery broker (e.g. RabbitMQ, Redis) and worker processes running separately; Dagster must be Python 3.10 or later.
License in practice
Apache-2.0 permissive license allows use in commercial and private projects without copyleft obligations, making it suitable for enterprise deployments.
Quickstart
pip install dagster-celery
import dagster as dg
from dagster_celery import celery_executor
@dg.job(executor_def=celery_executor)
def my_job():
passVerify before relying
- Whether the package includes built-in configuration helpers or requires manual Celery broker setup.
- Performance characteristics and recommended worker count for typical workloads.
- Support for Celery task routing, priority queues, or other advanced Celery features.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesceleryclickdagsterimportlib-metadata |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,912,612 / month, #2,454 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dagster_celery-0.29.18-py3-none-any.whl
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See also dagster-celery-k8s · dagster · dagster-cloud-cli · dagster-docker · dagster-dg-core · dagster-webserver · dagster-aws · dagster-spark · dagster-shell · dagster-pyspark