dbdemos-tracker 0.1.6

A tracking library for Databricks demo analytics.

dbdemos-tracker

A Python library for tracking Databricks demo analytics.

Installation

pip install dbdemos-tracker

Usage

Manual App Usage Tracking

from dbdemos_tracker import Tracker

# Create tracker for app - Use your repo name as app name
ws = WorkspaceClient()
tracker = Tracker(org_id=ws.get_workspace_id(), demo_name="app-name")

# Track app views in functions
@app.get("/")
def home():
    tracker.track_app_view(user_email="[e-mail]", app_path="/dashboard")
    return {"message": "Welcome to my demo app"}

FastAPI Middleware

We provide a tool to track all app views out of the box.

Try to filter on the main actions so that we don't have too many views using patterns=["/my/.*/url/to/track", "..."]

from fastapi import FastAPI
from dbdemos_tracker import Tracker

# Create FastAPI app
app = FastAPI(title="My Demo App")

# Add tracking middleware - this will track ALL requests automatically
Tracker.add_tracker_fastapi(app, "app-name"))
# You can also filter to only track some URL:
Tracker.add_tracker_fastapi(app, "app-name", patterns=['/demos/.*/load']))

# Then all your API endpoints will be tracked out of the box
@app.get("/")
def home():
    return {"message": "Welcome to my demo app"}

# Run with: uvicorn main:app --reload

Streamlit Integration

EXPERIMENTAL - NOT TESTED - REACH OUT FOR HELP ON #field-demos

Automatic Interceptor (Recommended):

import streamlit as st
from dbdemos_tracker import Tracker

# Setup automatic tracking - call this once at the top of your app
Tracker.setup_streamlit_interceptor("app-name")

# Your Streamlit app - all interactions are automatically tracked!
st.title("My Demo App")

# These will be automatically tracked:
if st.button("Process Data"):
    st.write("Data processed!")

name = st.text_input("Enter your name")
age = st.slider("Age", 0, 100, 25)
option = st.selectbox("Choose option", ["A", "B", "C"])
enabled = st.checkbox("Enable feature")

# Set user email for tracking (optional)
st.session_state.user_email = "[e-mail]"

Manual Tracking (if needed):

# Track specific interactions manually
Tracker.track_streamlit_app("app-name")

Configuration

Disable Tracking

# Disable tracking globally
Tracker.enable_tracker = False

# Re-enable when needed
Tracker.enable_tracker = True

Email Filtering

The tracker automatically filters emails to only track Databricks email addresses (@databricks.com). Non-Databricks emails are ignored for privacy.

Test Workspace

Tracking is automatically disabled for test workspace ID 1660015457675682.

Features

• Track demo usage analytics
• Configurable tracking enable/disable
• Automatic Databricks email filtering
• Error handling with timeout protection
• FastAPI middleware for automatic request tracking
• Streamlit integration for app interaction tracking
• Support for both notebook and app path tracking
• User hash generation for privacy

License

Databricks License