Analytics Stack (Superset + JupyterLab) on Kind

This document explains how to provision and access Apache Superset and JupyterLab in a local Kind Kubernetes cluster for analytics and ML exploration of NoETL executions.

Both tools are deployed into a dedicated Kubernetes namespace analytics and can access the existing Postgres database (and other in-cluster services) via cluster DNS. Postgres manifests are kept separate in the postgres namespace.

Prerequisites

• Local Kind cluster created for this project
• Postgres deployed to the cluster (provided in this repo)
• Taskfile CLI installed (go-task)

Quick checks and setup:

# Create Kind cluster (if not present)
task kind:local:cluster-create

# Deploy Postgres (required for Superset metadata and your analytics sources)
task postgres:k8s:deploy

Provisioning

Deploy the analytics stack (Superset + JupyterLab):

task analytics:k8s:deploy

This applies the following manifests:

• ci/manifests/analytics/namespace.yaml
• Superset: ci/manifests/analytics/superset/{secret.yaml,deployment.yaml,service.yaml}
• JupyterLab: ci/manifests/analytics/jupyterlab/{secret.yaml,deployment.yaml,service.yaml}

Remove the analytics stack:

task analytics:k8s:remove

Access

For local Kind usage we expose NodePorts.

• Superset UI: http://localhost:30888

  • Default admin: admin / admin
  • Change these in ci/manifests/analytics/superset/secret.yaml

• JupyterLab UI: http://localhost:30999

  • Default token: noetl
  • Change token in ci/manifests/analytics/jupyterlab/secret.yaml

If the ports conflict on your host, update nodePort values in the corresponding Service manifests and re-apply.

Kind port mappings (host ↔ cluster)

The Kind cluster is configured to forward these NodePorts to the same ports on your localhost:

• Superset: 30888 → 30888
• JupyterLab: 30999 → 30999

Configuration lives in ci/kind/config.yaml under extraPortMappings and is applied when the cluster is created. If your Kind cluster existed before these mappings were added, recreate it to apply:

task kind:local:cluster-delete
task kind:local:cluster-create

If you change NodePort values in the Service manifests, also update ci/kind/config.yaml accordingly and recreate the cluster so the new host mappings take effect.

Connectivity to Postgres and Other Databases

Superset uses the existing Postgres as its metadata database. The connection string is configured via the secret and uses cross-namespace DNS:

postgresql+psycopg2://demo:[email protected]:5432/demo_noetl

From JupyterLab notebooks, you can connect to the same Postgres using the same DNS name. Example (Python):

import sqlalchemy as sa

engine = sa.create_engine(
    "postgresql+psycopg2://demo:[email protected]:5432/demo_noetl"
)

with engine.connect() as conn:
    rows = conn.execute(sa.text("select now()"))
    print(list(rows))

Install libraries inside the Jupyter environment as needed, for example:

pip install psycopg2-binary sqlalchemy pandas

To connect to other in-cluster databases/services, use their Kubernetes service DNS names in the form:

<service>.<namespace>.svc.cluster.local

Changing Credentials and Secrets

• Superset admin credentials and SQLALCHEMY_DATABASE_URI are in ci/manifests/analytics/superset/secret.yaml.
• JupyterLab access token is in ci/manifests/analytics/jupyterlab/secret.yaml.

Edit the files as needed and redeploy:

task analytics:k8s:deploy

Note: Current values are for local development only. Rotate in real environments.

Troubleshooting

Basic checks:

# Ensure correct kubectl context
task kubectl:local:context-set-kind

# Check pods
kubectl get pods -n analytics

# Wait for readiness (example for Superset)
kubectl wait --for=condition=ready pod -l app=superset -n analytics --timeout=180s

# Inspect logs
kubectl logs -n analytics deploy/superset
kubectl logs -n analytics deploy/jupyterlab

If Superset fails to start, verify that the Postgres service is reachable from the analytics namespace:

kubectl run -it --rm pgcheck --image=postgres:17 -n analytics -- bash
apt-get update && apt-get install -y dnsutils
nslookup postgres.postgres.svc.cluster.local
psql "postgresql://demo:[email protected]:5432/demo_noetl" -c 'select 1'

Files and Tasks Reference

• Manifests under ci/manifests/analytics/
• Taskfile: ci/taskfile/analytics.yml
• Top-level shortcuts in taskfile.yml:
  • task analytics:k8s:deploy
  • task analytics:k8s:remove

Execution analysis notebook (Jupyter)

An exploratory dashboard-style notebook is provided to analyze NoETL playbook executions stored in Postgres:

• Location in repo: tests/fixtures/notebooks/regression_dashboard.py
• It is a Jupyter-friendly Python script with #%% cells (works in JupyterLab, VS Code, PyCharm).
• What it shows:
  • Recent runs overview and final statuses
  • KPIs (success/error counts, median duration)
  • Failures by step (top offenders)
  • Slowest steps by duration percentiles
  • Drill-down timeline (Gantt-like) for a selected execution

Dependencies (install inside JupyterLab or your local venv):

pip install pandas sqlalchemy psycopg2-binary plotly

Connectivity:

• By default, it reads DB params from tests/fixtures/credentials/pg_local.json (in-cluster DNS).
• It has an automatic fallback to localhost:54321 (Kind NodePort) with demo:demo user for demo_noetl DB.

How to run:

1. Open JupyterLab at http://localhost:30999.
2. Upload tests/fixtures/notebooks/regression_dashboard.py (or mount your repo into the pod).
3. Open it and execute the cells. Adjust parameters at the top (playbook path, lookback window) as needed.

Or run locally in your IDE with a Jupyter kernel; ensure Postgres is reachable on localhost:54321.