The Coming Death of Denormalization: How Differential Dataflow Will Save Us All

The Original Sin: Why We Denormalized in the First Place

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SELECT 
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    u.name,
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    u.email,
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    COUNT(o.id) as order_count,
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    SUM(p.price * oi.quantity) as total_spent,
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    AVG(p.rating) as avg_product_rating
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FROM users u
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JOIN orders o ON u.id = o.user_id
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JOIN order_items oi ON o.id = oi.order_id
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JOIN products p ON oi.product_id = p.id
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WHERE o.created_at > NOW() - INTERVAL '30 days'
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GROUP BY u.id, u.name, u.email

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CREATE TABLE user_stats (
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    user_id INT PRIMARY KEY,
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    name VARCHAR(255),
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    email VARCHAR(255),
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    order_count INT,
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    total_spent DECIMAL(10,2),
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    avg_product_rating DECIMAL(3,2),
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    last_updated TIMESTAMP
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);

The Curse of Manual Maintenance

1. •Insert the order
2. •Insert the order items
3. •Update the user_stats table
4. •Pray that nothing fails in between

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def update_user_stats(user_id):
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    # Oh god, here we go again
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    stats = calculate_user_stats(user_id)  # Does the 4 joins
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    # What if this fails?
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    db.execute("""
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        UPDATE user_stats 
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        SET order_count = %s,
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            total_spent = %s,
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            avg_product_rating = %s,
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            last_updated = NOW()
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        WHERE user_id = %s
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    """, (stats['order_count'], stats['total_spent'], 
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          stats['avg_product_rating'], user_id))
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    # What if the user placed another order while we were calculating?
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    # What if we're running this in parallel?
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    # What if... what if... what if...

Enter Differential Dataflow: The Savior We Don't Deserve

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// Define your view declaratively
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let user_stats = users
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    .join(&orders)
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    .join(&order_items)
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    .join(&products)
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    .reduce(|_key, vals, output| {
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        // Your aggregation logic here
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        // This looks like batch processing code
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        // But it runs incrementally!
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    });

1. •Figures out exactly which parts of the computation are affected
2. •Computes only the deltas (changes)
3. •Updates the materialized view with perfect consistency
4. •Does all of this faster than you can blink

The False Prophets and the True Messiah

The Pretenders to the Throne

1. •Your original denormalization nightmare
2. •A half-implemented "modern" solution that only works for 20% of your use cases

Enter Feldera: The Chosen One

Why This Changes Everything

• •Take that heinous denormalized table that powers your dashboard and replace it with a real-time materialized view
• •Maintain perfect consistency across ALL your derived data, not just the simple stuff
• •Delete 90% of your data synchronization code
• •Stop settling for eventual consistency. Sure, it's "acceptable" – in the same way that a root canal is "acceptable." But what if you could have hard consistency AND better performance? Why make the tradeoff when you don't have to?

The Inevitable Rise

The New World Order

• •You can have arbitrarily complex views that update in real-time
• •Join performance is a solved problem because joins are pre-computed
• •You never have to choose between normalization and performance
• •Your materialized views are always, always consistent
• •You can sleep at night without dreaming about data synchronization bugs

The Death Rattle of Denormalization

Epilogue: A Prayer for the Future