Running on Kubernetes

Daft can run on Kubernetes using the Daft quickstart Helm chart. This allows you to test drive both simple single-node jobs and distributed Ray-based workloads on your Kubernetes cluster without any additional dependencies.

Prerequisites

Kubernetes 1.19+
Helm 3.x
kubectl configured to access your cluster

Simple Mode

Run a quick Daft job on Kubernetes using the native runner:

=== "🐍 Python"

```python
# Create a script file: my_script.py
# /// script
# dependencies = ["daft"]
# ///
import daft

df = daft.from_pydict({
    "a": [3, 2, 5, 6, 1, 4],
    "b": [True, False, False, True, True, False]
})

df = df.where(df["b"]).sort(df["a"])
print(df.collect())
```

Deploy to Kubernetes:

bash

# Install the job with your script
helm install my-job oci://ghcr.io/eventual-inc/daft/quickstart \
  --set-file job.script=my_script.py

# View logs
kubectl logs -f job/my-job-quickstart-job

# Cleanup
helm uninstall my-job

Distributed Mode

Run the same Daft job on a Ray cluster with multiple workers:

=== "🐍 Python"

```python
# Create a script file: distributed_script.py
# /// script
# dependencies = ["daft", "ray[client]==2.46.0"]
# ///
import daft
import ray

ray.init(runtime_env={"pip": ["daft"]})

df = daft.from_pydict({
    "a": [3, 2, 5, 6, 1, 4],
    "b": [True, False, False, True, True, False]
})

df = df.where(df["b"]).sort(df["a"])
print(df.collect())
```

Deploy with Ray cluster:

bash

# Install with Ray cluster (3 workers)
helm install distributed-job oci://ghcr.io/eventual-inc/daft/quickstart \
  --set distributed=true \
  --set worker.replicas=3 \
  --set-file job.script=distributed_script.py

# Access Ray Dashboard
kubectl port-forward service/distributed-job-quickstart-head 8265:8265
# Open http://localhost:8265 in your browser

# View job logs
kubectl logs -f job/distributed-job-quickstart-job

# Cleanup
helm uninstall distributed-job

What's Next?

Visit the Helm chart documentation to try out your own Daft workloads on Kubernetes.