dagster-celery-k8s
A Dagster integration for celery-k8s-executor
Decision gist · record as of 2026-08-14
Yes, if you are running Dagster in production on Kubernetes and need distributed, scalable task execution. Install it as part of your Dagster orchestration stack when you have Celery and Kubernetes infrastructure already in place. Not needed for local development or single-machine deployments. No known security vulnerabilities and actively maintained.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (supports up to 3.14).
- Assumes Dagster, Celery, and Kubernetes infrastructure are already configured and running.
- Active maintenance with a release on 2026-08-14 and low install friction.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 licensed under permissive terms, allowing commercial use, modification, and distribution with minimal restrictions.
last release 2026-08-14 (0 days) · last repo commit 2026-08-13 · 15,996 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,920,540 downloads/mo, #3,430 on PyPI
Alternatives
Verify before relying
pip install dagster-celery-k8s
from dagster_celery_k8s import celery_k8s_job_executor
from dagster import job
@job(executor_def=celery_k8s_job_executor)
def my_pipeline():
pass- Whether this package is intended as a standalone installation or primarily as a dependency of dagster-k8s or dagster-celery.
- Performance characteristics and scaling limits when running large numbers of concurrent tasks across Kubernetes nodes.
- Whether additional configuration beyond the executor definition is required to connect to an existing Celery broker and Kubernetes cluster.
What it is and what it does
dagster-celery-k8s is an integration package that bridges Dagster's data orchestration framework with Celery task queue execution on Kubernetes. It provides an executor that allows Dagster jobs to distribute individual asset computations and task runs across a Kubernetes cluster using Celery as the task queue backend, enabling horizontal scaling and multi-tenant isolation.
The package is part of Dagster's ecosystem and is used when you want to run Dagster pipelines in production on Kubernetes infrastructure. Rather than running all tasks in a single process or on a single machine, this executor queues tasks to Celery workers deployed as Kubernetes pods, allowing you to scale compute independently and run many pipelines concurrently across your cluster. It is typically installed alongside dagster, dagster-celery, and dagster-k8s as part of a larger orchestration deployment.
Use it for
- Running Dagster data pipelines at scale across a Kubernetes cluster with multiple worker nodes and dynamic pod scheduling.
- Isolating pipeline workloads in a multi-tenant Dagster deployment where different teams or projects need separate compute resources.
- Distributing long-running asset computations (ETL, model training, report generation) across containerized workers to reduce latency.
- Integrating Dagster with existing Celery infrastructure and Kubernetes deployments already in place at your organization.
- Enabling CI/CD best practices by running asset definitions and tests in ephemeral Kubernetes pods provisioned on demand.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are running Dagster in production on Kubernetes and need distributed, scalable task execution.
Install it as part of your Dagster orchestration stack when you have Celery and Kubernetes infrastructure already in place. Not needed for local development or single-machine deployments. No known security vulnerabilities and actively maintained.
Install
dagster-celery-k8s on PyPI
Before you install
Active maintenance with a release on 2026-08-14 and low install friction. Depends on dagster, dagster-celery, and dagster-k8s, all of which are core Dagster components. Suitable for production use in environments already running Dagster.
Requires Python 3.10 or later (supports up to 3.14). Assumes Dagster, Celery, and Kubernetes infrastructure are already configured and running.
License in practice
Apache-2.0 licensed under permissive terms, allowing commercial use, modification, and distribution with minimal restrictions.
Quickstart
pip install dagster-celery-k8s
from dagster_celery_k8s import celery_k8s_job_executor
from dagster import job
@job(executor_def=celery_k8s_job_executor)
def my_pipeline():
passVerify before relying
- Whether this package is intended as a standalone installation or primarily as a dependency of dagster-k8s or dagster-celery.
- Performance characteristics and scaling limits when running large numbers of concurrent tasks across Kubernetes nodes.
- Whether additional configuration beyond the executor definition is required to connect to an existing Celery broker and Kubernetes cluster.
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 | 3 packagesdagster-celerydagster-k8sdagster |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,920,540 / month, #3,430 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dagster_celery_k8s-0.29.18-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “dagster celery kubernetes executor”
- dagster-celery-k8sIntegrates Dagster data orchestration with Celery task queue and…
- dagster-celeryIntegrates Celery as a distributed task execution backend for Dagster…
- dagster-k8sIntegrates Dagster data pipeline orchestration with Kubernetes,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Distributed Computing packages
gRPC Python is an HTTP/2-based RPC framework that enables you to define and call remote procedures across network boundaries using protocol buffers for serialization.
Install it if you need RPC communication in a distributed system or are integrating with existing gRPC services.
execnet lets you spawn and communicate with Python interpreters across local processes, remote hosts, and different platforms, using a simple API for task distribution and inter-process messaging.
However, the aging maintenance status (275 days since last release) means you should verify it meets your concurrency and performance needs before committing to a…
Cloudpickle extends Python's standard pickle module to serialize lambda functions, interactively-defined functions and classes, and other constructs that the default pickle cannot handle, making it suitable for cluster computing and remote code execution.
Install it if you need to serialize lambda functions, interactively-defined code, or non-standard Python constructs for cluster computing or distributed execution.
Provides a unified, open()-compatible Python API for streaming large files from remote storage (S3, GCS, Azure, HDFS, SFTP, HTTP) and local filesystems, with transparent compression support.
Install it if you work with large files on cloud storage or remote systems and want to avoid writing boilerplate around multiple SDKs.
Portalocker provides cross-platform file locking with support for exclusive and shared locks, plus Redis-based distributed locks and process-aware PID file locking.
Install it if you need file or process coordination; the optional extras (pywin32, redis) are only required for specific lock types.
Ray is a distributed computing framework that scales Python applications from a single machine to multi-node clusters, providing abstractions for parallel tasks, stateful actors, and shared objects.
See also dagster-celery · dagster-k8s · dagster · dagster-docker · dagster-cloud-cli · dagster-webserver · dagster-dg-core · dagster-aws · dagster-shell · kestra