docs: Add comprehensive qualified names example

Created examples/qualified_names.sql demonstrating table alias support across all SQL clauses.

Examples include:
- Basic CTE with aliases in WHERE and SELECT
- ORDER BY with qualified column names
- GROUP BY with qualified columns and aggregations
- Nested CTEs with multi-level alias usage
- String method calls with qualified names
- Complex queries combining all clauses with aliases

All 7 examples tested and working correctly.

Note: HAVING clause currently requires unqualified column names.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

Author: TimelordUK

examples/qualified_names.sql

@@ -0,0 +1,136 @@
+-- Qualified Names and Table Aliases
+-- Demonstrates table alias support across SQL clauses (WHERE, SELECT, ORDER BY, GROUP BY)
+
+-- Example 1: Basic CTE with alias in WHERE and SELECT
+-- Shows qualified column references (t.column_name)
+WITH data AS (
+    SELECT value as id, value * 10 as amount, value % 3 as category
+    FROM RANGE(1, 10)
+)
+SELECT t.id, t.amount, t.category
+FROM data t
+WHERE t.amount > 30;
+GO
+
+-- Example 2: CTE with alias in ORDER BY
+-- Demonstrates sorting by qualified column names
+WITH products AS (
+    SELECT
+        value as product_id,
+        value * 100 as price,
+        CASE WHEN value % 2 = 0 THEN 'even' ELSE 'odd' END as type
+    FROM RANGE(1, 10)
+)
+SELECT p.product_id, p.price, p.type
+FROM products p
+WHERE p.price >= 300
+ORDER BY p.price DESC;
+GO
+
+-- Example 3: CTE with alias in GROUP BY
+-- Shows aggregation with qualified column references
+WITH sales AS (
+    SELECT
+        value % 4 as region,
+        value % 3 as product_category,
+        value * 100 as revenue
+    FROM RANGE(1, 24)
+)
+SELECT
+    s.region,
+    s.product_category,
+    COUNT(*) as count,
+    SUM(s.revenue) as total_revenue,
+    AVG(s.revenue) as avg_revenue
+FROM sales s
+GROUP BY s.region, s.product_category
+ORDER BY s.region, s.product_category;
+GO
+
+-- Example 4: Nested CTEs with aliases
+-- Demonstrates multiple CTEs with qualified names at each level
+WITH base AS (
+    SELECT value as id, value % 5 as bucket
+    FROM RANGE(1, 25)
+),
+enriched AS (
+    SELECT b.id, b.bucket, b.id * 10 as score
+    FROM base b
+    WHERE b.bucket IN (0, 1, 2)
+),
+aggregated AS (
+    SELECT
+        e.bucket,
+        COUNT(*) as count,
+        SUM(e.score) as total_score,
+        AVG(e.score) as avg_score
+    FROM enriched e
+    GROUP BY e.bucket
+)
+SELECT a.bucket, a.count, a.total_score, a.avg_score
+FROM aggregated a
+ORDER BY a.avg_score DESC;
+GO
+
+-- Example 5: Complex filtering with string methods
+-- Shows qualified names with method calls
+WITH records AS (
+    SELECT
+        value as id,
+        CASE
+            WHEN value % 3 = 0 THEN 'type.category.A'
+            WHEN value % 3 = 1 THEN 'type.category.B'
+            ELSE 'type.category.C'
+        END as classification
+    FROM RANGE(1, 20)
+)
+SELECT r.id, r.classification
+FROM records r
+WHERE r.classification.Contains('category.B');
+GO
+
+-- Example 6: Multiple aggregations with HAVING
+-- Demonstrates qualified names in HAVING clause
+-- Note: With RANGE(1, 50) and % 5, each customer has 10 transactions
+WITH transactions AS (
+    SELECT
+        value % 5 as customer_id,
+        value * 50 as transaction_amount
+    FROM RANGE(1, 100)
+)
+SELECT
+    t.customer_id,
+    COUNT(*) as transaction_count,
+    SUM(t.transaction_amount) as total_amount
+FROM transactions t
+GROUP BY t.customer_id
+HAVING COUNT(*) >= 10
+ORDER BY total_amount DESC;
+GO
+
+-- Example 7: Combining all clauses with aliases
+-- Full-featured query using qualified names throughout
+WITH raw_data AS (
+    SELECT
+        value as id,
+        value % 10 as category,
+        value * 25 as value,
+        CASE
+            WHEN value % 2 = 0 THEN 'even'
+            ELSE 'odd'
+        END as parity
+    FROM RANGE(1, 100)
+)
+SELECT
+    r.category,
+    r.parity,
+    COUNT(*) as record_count,
+    SUM(r.value) as category_total,
+    AVG(r.value) as category_average,
+    MIN(r.value) as min_value,
+    MAX(r.value) as max_value
+FROM raw_data r
+WHERE r.value >= 125 AND r.category < 8
+GROUP BY r.category, r.parity
+ORDER BY r.category ASC, category_total DESC;
+GO