detect-foreign-keys

name	detect-foreign-keys
description	Identify foreign key relationships between tables using heuristics, value overlap analysis, and referential integrity checks

Detecting Foreign Keys

Purpose

This component skill guides systematic foreign key relationship detection in relational databases. Use it when:

• Multiple tables exist in the database and relationships are undocumented
• Need to understand table relationships before joining data
• Validating referential integrity between tables
• Identifying orphaned records that reference non-existent parent records
• Referenced by importing-data or cleaning-data skills requiring relationship analysis

Prerequisites

• Tables exist in database (relational database with SQL support)
• SQL query tool available (database CLI, IDE, or query interface)
• Table schemas have been examined (Phase 1 of understanding-data)
• Analysis workspace created

Foreign Key Detection Process

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.

---

Phase 1: Candidate Identification

Goal: Identify columns that are likely foreign keys based on naming patterns, data types, and uniqueness.

Identify Candidate FK Columns by Naming Convention

Common FK naming patterns:

• Columns ending in _id (e.g., customer_id, product_id)
• Columns ending in Id (e.g., customerId, productId)
• Columns named exactly id (but only in child tables)
• Columns starting with fk_ (e.g., fk_customer)
• Columns matching another table name (e.g., 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:

• List of candidate FK columns per table
• Note naming patterns observed
• Flag columns that might be composite keys (multiple FK columns in same table)

Identify Candidate PK Columns

Primary key characteristics:

• Named id, [table]_id, or similar
• INTEGER or TEXT type
• Likely to be unique
• Often the first column in the table

-- 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:

• Primary key columns per table
• Whether PKs are explicitly defined (pk=1) or inferred
• Tables without obvious primary keys

Match FK Candidates to Potential Parent Tables

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:

• FK candidate → Parent table mapping (e.g., orders.customer_id → customers.id)
• Confidence level:
  • High: Exact name match (e.g., customer_id → customer table)
  • Medium: Plural/singular variation (e.g., customer_id → customers table)
  • Low: Partial name match or ambiguous

---

Phase 2: Value Overlap Analysis

Goal: Validate FK candidates by checking if their values actually exist in the proposed parent table.

Check Value Overlap Percentage

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:

• 100% match: Strong FK relationship (perfect referential integrity)
• 95-99% match: Likely FK with some orphaned records
• 80-94% match: Possible FK with significant orphans (investigate)
• <80% match: Unlikely to be true FK (name coincidence or wrong parent table)

Document:

• Match percentage for each candidate relationship
• Count of orphaned FK values (values not in parent)
• Count of unused PK values (values not referenced by any FK)

Identify Orphaned Records

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:

• Sample orphaned values
• How many child records affected
• Whether orphaned values follow a pattern (all recent, specific category, etc.)

Check Reverse Overlap (Unused Parent Records)

-- 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:

• Count of unused parent records
• Whether this is expected (e.g., new customers with no orders yet)

---

Phase 3: Cardinality Assessment

Goal: Determine the relationship type (one-to-one, one-to-many, many-to-many).

Calculate FK → PK Cardinality

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:

• avg = 1.0, max = 1: One-to-one relationship
• avg > 1.0: One-to-many relationship (most common)
• Multiple FK columns referencing same parent: Potential many-to-many via junction table

Document:

• Relationship type (one-to-one, one-to-many)
• Average, min, max child records per parent
• Whether distribution is balanced or skewed

Identify Many-to-Many Relationships

Junction table characteristics:

• Table has 2+ foreign keys
• Few or no other columns besides FKs
• Composite primary key (both FKs together)

-- 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:

• Junction tables identified
• Which two (or more) tables they connect
• Cardinality of the many-to-many relationship

Check for Self-Referencing FKs

Hierarchical data pattern:

• Table has FK pointing to its own PK (e.g., 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:

• Self-referencing relationships
• Depth of hierarchy (max levels)
• Orphaned roots or cycles

---

Phase 4: Referential Integrity Validation

Goal: Quantify integrity violations and assess data quality impact.

Calculate Integrity Violation Rate

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, )  integrity_pct
 integrity_check;

Document:

• Total child records
• NULL FK percentage (records with no parent reference)
• Orphaned FK percentage (records referencing non-existent parent)
• Valid FK percentage (clean referential integrity)

Assess Impact of Integrity Violations

Business impact depends on:

• How joins will be used (INNER vs LEFT)
• Whether orphaned records are recent (data entry lag) or old (data quality issue)
• Whether NULL FKs are expected (optional relationships)

-- 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:

• Whether orphans are recent (may resolve soon) or old (permanent issue)
• Impact on analytical queries (e.g., "10% of orders will be excluded in INNER JOIN to customers")

Validate Composite Keys

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:

• Composite key relationships identified
• Integrity percentage for composite keys

---

Phase 5: Relationship Documentation

Goal: Create structured documentation of all discovered relationships for use in cleaning and analysis.

Create Relationship Catalog

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

Create Join Recommendations

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:

• LEFT JOIN: Preserves all 1,523 orders (15 will have NULL customer fields)
• INNER JOIN: Returns 1,508 orders (99.0% of total)
• Recommendation: Use LEFT JOIN, filter nulls in WHERE clause if needed

### 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

---

Integration with Other Skills

With 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)

With 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

With 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.

---

Common Pitfalls

DON'T:

• Assume naming conventions are always correct (validate with value overlap)
• Skip Phase 4 integrity validation - orphaned records break analyses
• Use INNER JOIN without understanding orphan impact
• Ignore NULL FKs - they may be legitimate or data quality issues

DO:

• Validate every candidate FK with value overlap analysis (Phase 2)
• Quantify integrity violations with exact counts and percentages
• Document both high-confidence and uncertain relationships
• Provide join recommendations based on integrity findings
• Feed FK findings back into cleaning-data scope

---

When to Re-Run

Re-run this skill when:

• New tables are added to the database
• Referential integrity violations are suspected
• Planning complex multi-table analyses
• Cleaning activities might have affected FK relationships
• Data loads introduce new orphaned records

---

Success Criteria

After completing this skill, you should have:

• ✅ Complete catalog of FK relationships with confidence levels
• ✅ Integrity percentages for each relationship
• ✅ Count and examples of orphaned records
• ✅ Cardinality assessment (one-to-one, one-to-many, many-to-many)
• ✅ Join recommendations (LEFT vs INNER, filters needed)
• ✅ Data quality implications documented
• ✅ Cleaning actions prioritized

This documentation feeds into importing-data quality reports and cleaning-data scope definitions, ensuring relationship-aware data quality management.