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Initial open-source release

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Andrey Avtomonov 2026-05-10 23:12:26 +02:00
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python/klo-sl/demos/run_complex_cte_join.sh Executable file
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#!/usr/bin/env bash
# Complex CTE Runtime Join Demo
#
# Shows how two SQL sources with internal CTEs (customer_lifetime_value, churn_risk)
# are joined at runtime through the join graph to a dimension table (regions),
# triggering chasm trap detection and aggregate locality.
set -euo pipefail
cd "$(dirname "$0")/.."
MODEL="demos/complex_cte_join.yaml"
echo "============================================"
echo " Demo 1: Chasm Trap — Two CTE metrics + regions dimension"
echo "============================================"
echo ""
echo "Query: Average LTV and average churn risk by region,"
echo " for customers with churn score > 0.7"
echo ""
echo '{
"measures": ["customer_lifetime_value.avg_ltv", "churn_risk.avg_risk"],
"dimensions": ["regions.name"],
"filters": ["churn_risk.score > 0.7"]
}' | uv run python -m semantic_layer.cli --model "$MODEL" --json --plan
echo ""
echo "============================================"
echo " Demo 2: Single CTE metric enriched with regions"
echo "============================================"
echo ""
echo "Query: LTV breakdown by region and customer segment,"
echo " only customers with 6+ active months"
echo ""
echo '{
"measures": [
"customer_lifetime_value.avg_ltv",
"customer_lifetime_value.avg_active_months",
{"expr": "count(customer_lifetime_value.customer_id)", "name": "customer_count"}
],
"dimensions": ["regions.name", "customers.segment"],
"filters": ["customer_lifetime_value.active_months >= 6"]
}' | uv run python -m semantic_layer.cli --model "$MODEL" --json --plan
echo ""
echo "============================================"
echo " Demo 3: Runtime aggregation on CTE columns + cross-source join"
echo "============================================"
echo ""
echo "Query: P90 churn score and max LTV by region continent"
echo ""
echo '{
"measures": [
{"expr": "percentile(churn_risk.score, 0.9)", "name": "p90_churn"},
{"expr": "max(customer_lifetime_value.ltv_estimate)", "name": "max_ltv"}
],
"dimensions": ["regions.continent"]
}' | uv run python -m semantic_layer.cli --model "$MODEL" --json --plan