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new 880544a Add documentation for testing on Kubernetes (#23)
880544a is described below
commit 880544aa51fe3175cd68dd8c391dd30b2c86dc5d
Author: Vakaris Baškirov <[email protected]>
AuthorDate: Mon Oct 7 17:27:35 2024 +0300
Add documentation for testing on Kubernetes (#23)
* add Dockerfile and docs, modify example
* doc fixes
---
docs/testing-on-k8s.md | 71 ++++++++++++++++++++++++++++++++++++++++++++++++++
examples/tips.py | 5 +---
k8s/Dockerfile | 34 ++++++++++++++++++++++++
3 files changed, 106 insertions(+), 4 deletions(-)
diff --git a/docs/testing-on-k8s.md b/docs/testing-on-k8s.md
new file mode 100644
index 0000000..f6e2666
--- /dev/null
+++ b/docs/testing-on-k8s.md
@@ -0,0 +1,71 @@
+# Testing in Kubernetes
+
+This guide explains how to test DataFusion Ray on Kubernetes during
development. It assumes you have an existing Kubernetes cluster.
+
+## 1. Deploy the KubeRay Operator
+
+To manage Ray clusters, you need to deploy the KubeRay operator using Helm.
This step is required once per Kubernetes cluster.
+
+```shell
+helm repo add kuberay https://ray-project.github.io/kuberay-helm/
+helm repo update
+
+# Install the Custom Resource Definitions (CRDs) and KubeRay operator
+helm install kuberay-operator kuberay/kuberay-operator
+
+# Verify that the operator is running in the `default` namespace.
+kubectl get pods
+
+# Example output:
+# NAME READY STATUS RESTARTS AGE
+# kuberay-operator-7fbdbf8c89-pt8bk 1/1 Running 0 27s
+```
+
+You can customize the operator's settings (e.g., resource limits and
requests). For basic testing, the default configuration should suffice.
+For more details and customization options, refer to the [KubeRay Helm Chart
documentation](https://github.com/ray-project/kuberay-helm/tree/main/helm-chart/kuberay-operator).
+
+## 2. Build a Custom Docker Image
+You need to build a custom Docker image containing your local development copy
of DataFusion Ray rather than using the default PyPi release.
+
+Run the following command to build your Docker image:
+
+```shell
+docker build -t [YOUR_IMAGE_NAME]:[YOUR_TAG] -f k8s/Dockerfile .
+```
+After building the image, push it to a container registry accessible by your
Kubernetes cluster.
+
+## 3. Deploy a RayCluster
+Next, deploy a RayCluster using the custom image.
+
+```shell
+helm repo update
+helm install datafusion-ray kuberay/ray-cluster \
+ --set 'image.repository=[YOUR_REPOSITORY]' \
+ --set 'image.tag=[YOUR_TAG]' \
+ --set 'imagePullPolicy=Always'
+```
+Make sure you replace *[YOUR_REPOSITORY]* and *[YOUR_TAG]* with your actual
container registry and image tag values.
+
+You can further customize RayCluster settings (such as resource allocations,
autoscaling, and more).
+For full configuration options, refer to the [RayCluster Helm Chart
documentation](https://github.com/ray-project/kuberay-helm/tree/main/helm-chart/ray-cluster).
+
+## 4. Port Forwarding
+
+To access Ray's dashboard, set up port forwarding between your local machine
and the Ray cluster's head node:
+
+```shell
+kubectl port-forward service/raycluster-kuberay-head-svc 8265:8265
+```
+
+This makes Ray’s dashboard and API available at `http://127.0.0.1:8265`.
+
+
+## 5. Run an Example
+From the examples directory in your project, you can run a sample job using
the following commands:
+
+```
+export RAY_ADDRESS="http://127.0.0.1:8265";
+ray job submit --working-dir ./examples/ -- python3 tips.py
+```
+
+### Expected output:
diff --git a/examples/tips.py b/examples/tips.py
index e72425a..a4e499e 100644
--- a/examples/tips.py
+++ b/examples/tips.py
@@ -22,11 +22,8 @@ from datafusion_ray import DatafusionRayContext
SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__))
-# Start a local cluster
-ray.init(resources={"worker": 1})
-
# Connect to a cluster
-# ray.init()
+ray.init()
# Create a context and register a table
ctx = DatafusionRayContext(2)
diff --git a/k8s/Dockerfile b/k8s/Dockerfile
new file mode 100644
index 0000000..9fb4531
--- /dev/null
+++ b/k8s/Dockerfile
@@ -0,0 +1,34 @@
+FROM rayproject/ray:2.37.0.cabc24-py312
+
+RUN sudo apt update && \
+ sudo apt install -y curl build-essential
+
+# Intall Rust
+RUN curl https://sh.rustup.rs -sSf | sh -s -- --default-toolchain stable -y
+
+WORKDIR /home/ray
+
+# install dependencies
+COPY requirements-in.txt /home/ray/
+RUN python3 -m venv venv && \
+ source venv/bin/activate && \
+ pip3 install -r requirements-in.txt
+
+# add sources
+RUN mkdir /home/ray/src
+RUN mkdir /home/ray/datafusion_ray
+COPY src /home/ray/src/
+COPY datafusion_ray /home/ray/datafusion_ray/
+COPY pyproject.toml /home/ray/
+COPY Cargo.* /home/ray/
+COPY build.rs /home/ray/
+COPY README.md /home/ray/
+
+# build datafusion_ray
+RUN source venv/bin/activate && \
+ source /home/ray/.cargo/env && \
+ maturin build --release
+
+FROM rayproject/ray:2.37.0.cabc24-py312
+COPY --from=0
/home/ray/target/wheels/datafusion_ray-0.6.0-cp38-abi3-manylinux_2_35_x86_64.whl
/home/ray/datafusion_ray-0.6.0-cp38-abi3-manylinux_2_35_x86_64.whl
+RUN pip3 install
/home/ray/datafusion_ray-0.6.0-cp38-abi3-manylinux_2_35_x86_64.whl
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