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Identify foreign key relationships between tables using heuristics, value overlap analysis, and referential integrity checks
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| name | detect-foreign-keys |
| description | Identify foreign key relationships between tables using heuristics, value overlap analysis, and referential integrity checks |
This component skill guides systematic foreign key relationship detection in relational databases. Use it when:
Create a TodoWrite checklist for the 5-phase FK detection process:
Phase 1: Candidate Identification - pending
Phase 2: Value Overlap Analysis - pending
Phase 3: Cardinality Assessment - pending
Phase 4: Referential Integrity Validation - pending
Phase 5: Relationship Documentation - pending
Mark each phase as you complete it. Document all findings in structured format.
Goal: Identify columns that are likely foreign keys based on naming patterns, data types, and uniqueness.
Common FK naming patterns:
_id (e.g., customer_id, product_id)Id (e.g., customerId, productId)id (but only in child tables)fk_ (e.g., fk_customer)customer in orders table)-- List all columns across all tables
SELECT
m.name as table_name,
p.name as column_name,
p.type as column_type
FROM sqlite_master m
JOIN pragma_table_info(m.name) p
WHERE m.type = 'table'
AND m.name NOT LIKE 'sqlite_%'
AND (
p.name LIKE '%_id'
OR p.name LIKE '%Id'
OR p.name LIKE 'fk_%'
OR p.name = 'id'
)
ORDER BY m.name, p.name;
Document:
Primary key characteristics:
id, [table]_id, or similar-- Find columns likely to be primary keys
SELECT
m.name as table_name,
p.name as column_name,
p.type as column_type,
p.pk as is_primary_key
FROM sqlite_master m
JOIN pragma_table_info(m.name) p
WHERE m.type = 'table'
AND m.name NOT LIKE 'sqlite_%'
AND (
p.pk = 1 -- Explicitly defined PK
OR p.name = 'id'
OR p.name = m.name || '_id'
)
ORDER BY m.name;
Document:
Heuristic: A column named customer_id likely references a table named customers or customer.
-- Cross-reference FK column names with table names
-- (Pseudo-query - implement with string matching logic)
-- For each FK candidate like 'customer_id':
-- 1. Strip suffix (_id, Id)
-- 2. Look for table named 'customers', 'customer', or similar
-- 3. Record as potential relationship
Document:
orders.customer_id → customers.id)customer_id → customer table)customer_id → customers table)Goal: Validate FK candidates by checking if their values actually exist in the proposed parent table.
For each candidate FK relationship identified in Phase 1:
-- Calculate what percentage of FK values exist in parent table
WITH fk_values AS (
SELECT DISTINCT child_fk_column as value
FROM child_table
WHERE child_fk_column IS NOT NULL
),
pk_values AS (
SELECT DISTINCT parent_pk_column as value
FROM parent_table
WHERE parent_pk_column IS NOT NULL
),
overlap AS (
SELECT COUNT(*) as matching_count
FROM fk_values fk
WHERE fk.value IN (SELECT value FROM pk_values)
)
SELECT
(SELECT COUNT(*) FROM fk_values) as total_fk_values,
(SELECT COUNT(*) FROM pk_values) as total_pk_values,
overlap.matching_count,
ROUND(100.0 * overlap.matching_count / (SELECT COUNT(*) FROM fk_values), 2) as match_percentage
FROM overlap;
Interpret match percentage:
Document:
For relationships with <100% match:
-- Find child records with FK values that don't exist in parent
SELECT
child_table.rowid,
child_table.child_fk_column as orphaned_value,
COUNT(*) OVER (PARTITION BY child_table.child_fk_column) as occurrences
FROM child_table
LEFT JOIN parent_table ON child_table.child_fk_column = parent_table.parent_pk_column
WHERE parent_table.parent_pk_column IS NULL
AND child_table.child_fk_column IS NOT NULL
LIMIT 20;
Document:
-- Find parent records not referenced by any child
SELECT
parent_table.parent_pk_column as unused_pk_value,
COUNT(*) as occurrence_count
FROM parent_table
LEFT JOIN child_table ON parent_table.parent_pk_column = child_table.child_fk_column
WHERE child_table.child_fk_column IS NULL
AND parent_table.parent_pk_column IS NOT NULL
LIMIT 20;
Document:
Goal: Determine the relationship type (one-to-one, one-to-many, many-to-many).
How many child records per parent record?
-- Average number of child records per parent
SELECT
COUNT(*) as total_child_records,
COUNT(DISTINCT child_fk_column) as distinct_fk_values,
ROUND(1.0 * COUNT(*) / NULLIF(COUNT(DISTINCT child_fk_column), 0), 2) as avg_children_per_parent,
MIN(child_count) as min_children,
MAX(child_count) as max_children
FROM child_table
CROSS JOIN (
SELECT
child_fk_column as fk,
COUNT(*) as child_count
FROM child_table
WHERE child_fk_column IS NOT NULL
GROUP BY child_fk_column
);
Interpret cardinality:
Document:
Junction table characteristics:
-- Find tables with multiple FK candidates (potential junction tables)
SELECT
table_name,
COUNT(*) as fk_column_count,
GROUP_CONCAT(column_name, ', ') as fk_columns
FROM (
SELECT
m.name as table_name,
p.name as column_name
FROM sqlite_master m
JOIN pragma_table_info(m.name) p
WHERE m.type = 'table'
AND m.name NOT LIKE 'sqlite_%'
AND (p.name LIKE '%_id' OR p.name LIKE 'fk_%')
)
GROUP BY table_name
HAVING COUNT(*) >= 2
ORDER BY fk_column_count DESC;
Document:
Hierarchical data pattern:
employee.manager_id → employee.id)-- Find columns that might reference the same table
SELECT
table_name,
column_name,
type
FROM (
SELECT
m.name as table_name,
p.name as column_name,
p.type as type
FROM sqlite_master m
JOIN pragma_table_info(m.name) p
WHERE m.type = 'table'
AND m.name NOT LIKE 'sqlite_%'
AND (
p.name LIKE 'parent_%'
OR p.name LIKE 'manager_%'
OR p.name LIKE '%_parent_id'
)
);
Document:
Goal: Quantify integrity violations and assess data quality impact.
For each confirmed FK relationship:
-- Comprehensive referential integrity check
WITH integrity_check AS (
SELECT
COUNT(*) as total_child_records,
COUNT(child_fk_column) as non_null_fk_count,
COUNT(*) - COUNT(child_fk_column) as null_fk_count,
SUM(CASE WHEN p.parent_pk_column IS NULL AND c.child_fk_column IS NOT NULL THEN 1 ELSE 0 END) as orphaned_count
FROM child_table c
LEFT JOIN parent_table p ON c.child_fk_column = p.parent_pk_column
)
SELECT
total_child_records,
non_null_fk_count,
null_fk_count,
ROUND(100.0 * null_fk_count / total_child_records, 2) as null_fk_pct,
orphaned_count,
ROUND(100.0 * orphaned_count / non_null_fk_count, 2) as orphaned_pct,
non_null_fk_count - orphaned_count as valid_fk_count,
ROUND(100.0 * (non_null_fk_count - orphaned_count) / non_null_fk_count, 2) as integrity_pct
FROM integrity_check;
Document:
Business impact depends on:
-- Analyze orphaned records by recency
SELECT
CASE
WHEN date_column >= date('now', '-7 days') THEN 'Last 7 days'
WHEN date_column >= date('now', '-30 days') THEN 'Last 30 days'
WHEN date_column >= date('now', '-90 days') THEN 'Last 90 days'
ELSE 'Older than 90 days'
END as recency,
COUNT(*) as orphaned_count
FROM child_table c
LEFT JOIN parent_table p ON c.child_fk_column = p.parent_pk_column
WHERE p.parent_pk_column IS NULL
AND c.child_fk_column IS NOT NULL
AND c.date_column IS NOT NULL
GROUP BY recency
ORDER BY MIN(c.date_column);
Document:
If multiple columns together form a FK:
-- Check integrity for composite FK
WITH composite_fk_values AS (
SELECT DISTINCT
child_table.fk_column1,
child_table.fk_column2
FROM child_table
WHERE child_table.fk_column1 IS NOT NULL
AND child_table.fk_column2 IS NOT NULL
),
composite_pk_values AS (
SELECT DISTINCT
parent_table.pk_column1,
parent_table.pk_column2
FROM parent_table
)
SELECT
COUNT(*) as total_composite_fk_values,
SUM(CASE WHEN pk.pk_column1 IS NULL THEN 1 ELSE 0 END) as orphaned_count
FROM composite_fk_values fk
LEFT JOIN composite_pk_values pk
ON fk.fk_column1 = pk.pk_column1
AND fk.fk_column2 = pk.pk_column2;
Document:
Goal: Create structured documentation of all discovered relationships for use in cleaning and analysis.
Document each confirmed relationship:
## Foreign Key Relationships
### High Confidence Relationships (>95% integrity)
#### orders.customer_id → customers.id
- **Relationship Type:** Many-to-one
- **Child Table:** orders (1,523 rows)
- **Parent Table:** customers (342 rows)
- **Match Percentage:** 98.2%
- **Cardinality:** Avg 4.5 orders per customer (min: 1, max: 47)
- **NULL FKs:** 12 rows (0.8%)
- **Orphaned FKs:** 15 rows (1.0%)
- **Recommended Join:** LEFT JOIN (to preserve orphaned orders)
- **Cleaning Action:** Investigate 15 orphaned orders, flag for review
### Medium Confidence Relationships (80-95% integrity)
#### products.category_id → categories.id
- **Relationship Type:** Many-to-one
- **Child Table:** products (856 rows)
- **Parent Table:** categories (24 rows)
- **Match Percentage:** 87.3%
- **Cardinality:** Avg 35.7 products per category (min: 2, max: 142)
- **NULL FKs:** 89 rows (10.4%)
- **Orphaned FKs:** 20 rows (2.4%)
- **Recommended Join:** INNER JOIN (if categorized products only needed)
- **Cleaning Action:** Exclude or recategorize 20 orphaned products
### Low Confidence / Unconfirmed (<80% integrity)
#### transactions.merchant_id → merchants.id
- **Relationship Type:** Uncertain
- **Match Percentage:** 67.8%
- **Issue:** Large number of orphaned merchant_id values
- **Recommendation:** Verify with data owner - may be wrong parent table
For each relationship:
## Join Recommendations
### orders ⟶ customers
**Recommended SQL:**
```sql
-- Use LEFT JOIN to preserve all orders (including orphans)
SELECT
o.*,
c.customer_name,
c.customer_segment
FROM orders o
LEFT JOIN customers c ON o.customer_id = c.id;
-- Alternative: INNER JOIN if orphans should be excluded
SELECT
o.*,
c.customer_name
FROM orders o
INNER JOIN customers c ON o.customer_id = c.id;
-- Note: Excludes 15 orders (1.0%) with invalid customer_id
Join Impact:
### Document Data Quality Implications
```markdown
## Data Quality Implications
### Orphaned Records Summary
Total orphaned records across all relationships: 35 (2.1% of all child records)
| Child Table | FK Column | Orphan Count | % of Child Table | Impact |
|-------------|-----------|--------------|------------------|--------|
| orders | customer_id | 15 | 1.0% | Low - recent orders, may resolve |
| products | category_id | 20 | 2.4% | Medium - affects category analysis |
### Recommended Cleaning Actions
**High Priority:**
1. products.category_id orphans (20 rows) - CREATE placeholder category "Uncategorized" or exclude from analysis
2. orders.customer_id orphans (15 rows) - FLAG for customer service review
**Medium Priority:**
3. NULL customer_id in orders (12 rows) - Investigate if legitimate (guest checkout?) or data entry error
### Analysis Limitations
Due to referential integrity issues:
- Customer-level aggregations will exclude 1.0% of orders (if using INNER JOIN)
- Category-level product analysis may be incomplete (2.4% of products uncategorized)
- Time-series trends should use LEFT JOIN to preserve all records
importing-data (Phase 5: Quality Assessment)After importing tables, run FK detection to include in quality report:
## Foreign Key Relationships (from detect-foreign-keys skill)
High Confidence:
- orders.customer_id → customers.id (98% integrity, 15 orphans)
- ...
Medium Confidence:
- products.category_id → categories.id (87% integrity, 20 orphans)
cleaning-data (Phase 1: Scope Definition)Use FK findings to inform cleaning scope:
## Referential Integrity Issues
From detect-foreign-keys analysis:
- **orders.customer_id:** 15 orphaned records (1.0%) - Priority: HIGH
- Recommended action: Flag for review, preserve with LEFT JOIN
understanding-data (Phase 4: Relationship Identification)This skill provides the systematic process for Phase 4:
## Phase 4: Relationship Identification
Use the `detect-foreign-keys` component skill to systematically identify and validate all foreign key relationships.
DON'T:
DO:
Re-run this skill when:
After completing this skill, you should have:
This documentation feeds into importing-data quality reports and cleaning-data scope definitions, ensuring relationship-aware data quality management.