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Andrey Avtomonov
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python/klo-sl/demos/complex_cte_join.yaml
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python/klo-sl/demos/complex_cte_join.yaml
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# Complex CTE Runtime Join Demo
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#
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# Demonstrates:
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# 1. Two SQL sources with internal CTEs (customer_lifetime_value, churn_risk)
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# 2. Both join to `customers` at the source level (many_to_one)
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# 3. `customers` joins to `regions` (many_to_one)
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# 4. A query requesting measures from BOTH SQL sources + dimensions from `regions`
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# triggers chasm trap detection and aggregate locality
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#
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# Join graph:
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# customer_lifetime_value --m2o--> customers --m2o--> regions
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# churn_risk --m2o--> customers --m2o--> regions
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# --- Table sources ---
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- name: regions
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table: public.regions
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grain: [id]
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columns:
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- name: id
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type: number
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- name: name
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type: string
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- name: continent
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type: string
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- name: customers
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table: public.customers
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grain: [id]
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columns:
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- name: id
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type: number
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- name: name
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type: string
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- name: segment
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type: string
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- name: region_id
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type: number
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- name: signed_at
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type: time
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role: time
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- name: arr
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type: number
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joins:
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- to: regions
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"on": region_id = regions.id
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relationship: many_to_one
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- name: orders
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table: public.orders
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grain: [id]
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columns:
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- name: id
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type: number
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- name: customer_id
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type: number
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- name: amount
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type: number
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- name: created_at
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type: time
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role: time
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joins:
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- to: customers
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"on": customer_id = customers.id
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relationship: many_to_one
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- name: order_items
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table: public.order_items
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grain: [id]
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columns:
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- name: id
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type: number
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- name: order_id
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type: number
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- name: quantity
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type: number
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- name: unit_price
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type: number
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joins:
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- to: orders
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"on": order_id = orders.id
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relationship: many_to_one
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# --- SQL source: Customer Lifetime Value (uses internal CTEs) ---
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- name: customer_lifetime_value
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description: |
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Customer lifetime value estimate using monthly revenue cohort analysis.
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Internal CTEs aggregate orders+order_items by month, then compute
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active_months and avg_mrr per customer before estimating LTV.
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sql: |
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WITH monthly_revenue AS (
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SELECT
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o.customer_id,
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DATE_TRUNC('month', o.created_at) AS month,
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SUM(oi.quantity * oi.unit_price) AS mrr
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FROM orders o
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JOIN order_items oi ON o.id = oi.order_id
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GROUP BY o.customer_id, DATE_TRUNC('month', o.created_at)
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),
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cohort_stats AS (
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SELECT
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customer_id,
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MIN(month) AS first_month,
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COUNT(DISTINCT month) AS active_months,
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AVG(mrr) AS avg_mrr
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FROM monthly_revenue
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GROUP BY customer_id
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)
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SELECT
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cs.customer_id,
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cs.first_month,
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cs.active_months,
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cs.avg_mrr,
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cs.avg_mrr * cs.active_months * 1.2 AS ltv_estimate
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FROM cohort_stats cs
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grain: [customer_id]
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columns:
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- name: customer_id
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type: number
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- name: first_month
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type: time
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- name: active_months
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type: number
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- name: avg_mrr
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type: number
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- name: ltv_estimate
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type: number
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joins:
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- to: customers
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"on": customer_id = customers.id
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relationship: many_to_one
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measures:
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- name: avg_ltv
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expr: avg(ltv_estimate)
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description: "Average customer lifetime value"
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- name: total_ltv
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expr: sum(ltv_estimate)
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description: "Total lifetime value across customers"
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- name: avg_active_months
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expr: avg(active_months)
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description: "Average number of active months per customer"
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# --- SQL source: Churn Risk (uses internal CTEs) ---
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- name: churn_risk
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description: |
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Customer churn risk score combining recency, frequency, and support burden.
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Internal CTEs compute rfm_scores from orders and ticket_counts from a
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support table before producing a weighted composite score.
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sql: |
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WITH rfm_scores AS (
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SELECT
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customer_id,
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EXTRACT(DAY FROM NOW() - MAX(created_at)) AS days_since_last_order,
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COUNT(*) AS order_frequency,
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AVG(amount) AS avg_order_value
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FROM orders
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GROUP BY customer_id
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),
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ticket_counts AS (
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SELECT
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customer_id,
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COUNT(*) AS open_tickets,
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AVG(EXTRACT(DAY FROM resolved_at - created_at)) AS avg_resolution_days
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FROM support_tickets
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WHERE status = 'open'
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GROUP BY customer_id
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)
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SELECT
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r.customer_id,
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r.days_since_last_order,
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r.order_frequency,
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COALESCE(t.open_tickets, 0) AS open_tickets,
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CASE
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WHEN r.days_since_last_order > 180 THEN 0.9
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WHEN r.days_since_last_order > 90 THEN 0.6
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ELSE 0.2
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END * 0.4
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+ CASE
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WHEN r.order_frequency < 2 THEN 0.8
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WHEN r.order_frequency < 5 THEN 0.4
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ELSE 0.1
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END * 0.3
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+ CASE
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WHEN COALESCE(t.open_tickets, 0) > 3 THEN 0.9
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WHEN COALESCE(t.open_tickets, 0) > 1 THEN 0.5
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ELSE 0.1
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END * 0.3 AS score,
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CASE
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WHEN r.avg_order_value < 100 THEN 'SMB'
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WHEN r.avg_order_value < 1000 THEN 'Mid-Market'
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ELSE 'Enterprise'
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END AS customer_type
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FROM rfm_scores r
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LEFT JOIN ticket_counts t ON r.customer_id = t.customer_id
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grain: [customer_id]
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columns:
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- name: customer_id
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type: number
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- name: days_since_last_order
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type: number
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- name: order_frequency
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type: number
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- name: open_tickets
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type: number
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- name: score
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type: number
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- name: customer_type
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type: string
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joins:
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- to: customers
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"on": customer_id = customers.id
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relationship: many_to_one
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measures:
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- name: avg_risk
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expr: avg(score)
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description: "Average churn risk score"
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- name: high_risk_count
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expr: count(customer_id)
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filter: "score > 0.7"
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description: "Number of high-risk customers"
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60
python/klo-sl/demos/run_complex_cte_join.sh
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60
python/klo-sl/demos/run_complex_cte_join.sh
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@ -0,0 +1,60 @@
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#!/usr/bin/env bash
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# Complex CTE Runtime Join Demo
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#
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# Shows how two SQL sources with internal CTEs (customer_lifetime_value, churn_risk)
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# are joined at runtime through the join graph to a dimension table (regions),
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# triggering chasm trap detection and aggregate locality.
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set -euo pipefail
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cd "$(dirname "$0")/.."
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MODEL="demos/complex_cte_join.yaml"
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echo "============================================"
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echo " Demo 1: Chasm Trap — Two CTE metrics + regions dimension"
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echo "============================================"
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echo ""
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echo "Query: Average LTV and average churn risk by region,"
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echo " for customers with churn score > 0.7"
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echo ""
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echo '{
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"measures": ["customer_lifetime_value.avg_ltv", "churn_risk.avg_risk"],
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"dimensions": ["regions.name"],
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"filters": ["churn_risk.score > 0.7"]
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}' | uv run python -m semantic_layer.cli --model "$MODEL" --json --plan
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echo ""
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echo "============================================"
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echo " Demo 2: Single CTE metric enriched with regions"
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echo "============================================"
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echo ""
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echo "Query: LTV breakdown by region and customer segment,"
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echo " only customers with 6+ active months"
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echo ""
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echo '{
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"measures": [
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"customer_lifetime_value.avg_ltv",
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"customer_lifetime_value.avg_active_months",
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{"expr": "count(customer_lifetime_value.customer_id)", "name": "customer_count"}
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],
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"dimensions": ["regions.name", "customers.segment"],
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"filters": ["customer_lifetime_value.active_months >= 6"]
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}' | uv run python -m semantic_layer.cli --model "$MODEL" --json --plan
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echo ""
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echo "============================================"
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echo " Demo 3: Runtime aggregation on CTE columns + cross-source join"
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echo "============================================"
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echo ""
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echo "Query: P90 churn score and max LTV by region continent"
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echo ""
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echo '{
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"measures": [
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{"expr": "percentile(churn_risk.score, 0.9)", "name": "p90_churn"},
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{"expr": "max(customer_lifetime_value.ltv_estimate)", "name": "max_ltv"}
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],
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"dimensions": ["regions.continent"]
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}' | uv run python -m semantic_layer.cli --model "$MODEL" --json --plan
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