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docs/custom-fields-search-performance.md
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docs/custom-fields-search-performance.md
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# Performance Analysis: Custom Fields in Search Vector
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## Current Implementation
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The search vector includes custom field values via database triggers that:
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1. Aggregate all custom field values for a member
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2. Extract values from JSONB format
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3. Add them to the search_vector with weight 'C'
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## Performance Considerations
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### 1. Trigger Performance on Member Updates
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**Current Implementation:**
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- `members_search_vector_trigger()` executes a subquery on every INSERT/UPDATE:
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```sql
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SELECT string_agg(...) FROM custom_field_values WHERE member_id = NEW.id
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```
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**Performance Impact:**
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- ✅ **Good:** Index on `member_id` exists (`custom_field_values_member_id_idx`)
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- ✅ **Good:** Subquery only runs for the affected member
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- ⚠️ **Potential Issue:** With many custom fields per member (e.g., 50+), aggregation could be slower
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- ⚠️ **Potential Issue:** JSONB extraction (`value->>'_union_value'`) is relatively fast but adds overhead
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**Expected Performance:**
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- **Small scale (< 10 custom fields per member):** Negligible impact (< 5ms per operation)
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- **Medium scale (10-30 custom fields):** Minor impact (5-20ms per operation)
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- **Large scale (30+ custom fields):** Noticeable impact (20-50ms+ per operation)
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### 2. Trigger Performance on Custom Field Value Changes
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**Current Implementation:**
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- `update_member_search_vector_from_custom_field_value()` executes on every INSERT/UPDATE/DELETE on `custom_field_values`
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- **Optimized:** Only fetches required member fields (not full record) to reduce overhead
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- **Optimized:** Skips re-aggregation on UPDATE if value hasn't actually changed
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- Aggregates all custom field values, then updates member search_vector
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**Performance Impact:**
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- ✅ **Good:** Index on `member_id` ensures fast lookup
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- ✅ **Optimized:** Only required fields are fetched (first_name, last_name, email, etc.) instead of full record
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- ✅ **Optimized:** UPDATE operations that don't change the value skip expensive re-aggregation (early return)
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- ⚠️ **Note:** Re-aggregation is still necessary when values change (required for search_vector consistency)
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- ⚠️ **Critical:** Bulk operations (e.g., importing 1000 members with custom fields) will trigger this for each row
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**Expected Performance:**
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- **Single operation (value changed):** 3-10ms per custom field value change (improved from 5-15ms)
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- **Single operation (value unchanged):** <1ms (early return, no aggregation)
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- **Bulk operations:** Could be slow (consider disabling trigger temporarily)
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### 3. Search Vector Size
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**Current Constraints:**
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- String values: max 10,000 characters per custom field
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- No limit on number of custom fields per member
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- tsvector has no explicit size limit, but very large vectors can cause issues
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**Potential Issues:**
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- **Theoretical maximum:** If a member has 100 custom fields with 10,000 char strings each, the aggregated text could be ~1MB
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- **Practical concern:** Very large search vectors (> 100KB) can slow down:
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- Index updates (GIN index maintenance)
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- Search queries (tsvector operations)
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- Trigger execution time
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**Recommendation:**
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- Monitor search_vector size in production
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- Consider limiting total custom field content per member if needed
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- PostgreSQL can handle large tsvectors, but performance degrades gradually
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### 4. Initial Migration Performance
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**Current Implementation:**
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- Updates ALL members in a single transaction:
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```sql
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UPDATE members m SET search_vector = ... (subquery for each member)
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```
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**Performance Impact:**
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- ⚠️ **Potential Issue:** With 10,000+ members, this could take minutes
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- ⚠️ **Potential Issue:** Single transaction locks the members table
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- ⚠️ **Potential Issue:** If migration fails, entire rollback required
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**Recommendation:**
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- For large datasets (> 10,000 members), consider:
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- Batch updates (e.g., 1000 members at a time)
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- Run during maintenance window
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- Monitor progress
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### 5. Search Query Performance
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**Current Implementation:**
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- Full-text search uses GIN index on `search_vector` (fast)
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- Additional LIKE queries on `custom_field_values` for substring matching:
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```sql
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EXISTS (SELECT 1 FROM custom_field_values WHERE member_id = id AND ... LIKE ...)
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```
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**Performance Impact:**
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- ✅ **Good:** GIN index on `search_vector` is very fast
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- ⚠️ **Potential Issue:** LIKE queries on JSONB are not indexed (sequential scan)
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- ⚠️ **Potential Issue:** EXISTS subquery runs for every search, even if search_vector match is found
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- ⚠️ **Potential Issue:** With many custom fields, the LIKE queries could be slow
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**Expected Performance:**
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- **With GIN index match:** Very fast (< 10ms for typical queries)
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- **Without GIN index match (fallback to LIKE):** Slower (10-100ms depending on data size)
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- **Worst case:** Sequential scan of all custom_field_values for all members
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## Recommendations
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### Short-term (Current Implementation)
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1. **Monitor Performance:**
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- Add logging for trigger execution time
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- Monitor search_vector size distribution
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- Track search query performance
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2. **Index Verification:**
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- Ensure `custom_field_values_member_id_idx` exists and is used
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- Verify GIN index on `search_vector` is maintained
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3. **Bulk Operations:**
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- For bulk imports, consider temporarily disabling the custom_field_values trigger
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- Re-enable and update search_vectors in batch after import
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### Medium-term Optimizations
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1. **✅ Optimize Trigger Function (FULLY IMPLEMENTED):**
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- ✅ Only fetch required member fields instead of full record (reduces overhead)
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- ✅ Skip re-aggregation on UPDATE if value hasn't actually changed (early return optimization)
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2. **Limit Search Vector Size:**
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- Truncate very long custom field values (e.g., first 1000 chars)
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- Add warning if aggregated text exceeds threshold
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3. **Optimize LIKE Queries:**
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- Consider adding a generated column for searchable text
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- Or use a materialized view for custom field search
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### Long-term Considerations
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1. **Alternative Approaches:**
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- Separate search index table for custom fields
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- Use Elasticsearch or similar for advanced search
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- Materialized view for search optimization
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2. **Scaling Strategy:**
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- If performance becomes an issue with 100+ custom fields per member:
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- Consider limiting which custom fields are searchable
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- Use a separate search service
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- Implement search result caching
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## Performance Benchmarks (Estimated)
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Based on typical PostgreSQL performance:
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| Scenario | Members | Custom Fields/Member | Expected Impact |
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|----------|---------|---------------------|-----------------|
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| Small | < 1,000 | < 10 | Negligible (< 5ms per operation) |
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| Medium | 1,000-10,000 | 10-30 | Minor (5-20ms per operation) |
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| Large | 10,000-100,000 | 30-50 | Noticeable (20-50ms per operation) |
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| Very Large | > 100,000 | 50+ | Significant (50-200ms+ per operation) |
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## Monitoring Queries
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```sql
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-- Check search_vector size distribution
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SELECT
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pg_size_pretty(octet_length(search_vector::text)) as size,
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COUNT(*) as member_count
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FROM members
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WHERE search_vector IS NOT NULL
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GROUP BY octet_length(search_vector::text)
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ORDER BY octet_length(search_vector::text) DESC
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LIMIT 20;
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-- Check average custom fields per member
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SELECT
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AVG(custom_field_count) as avg_custom_fields,
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MAX(custom_field_count) as max_custom_fields
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FROM (
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SELECT member_id, COUNT(*) as custom_field_count
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FROM custom_field_values
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GROUP BY member_id
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) subq;
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-- Check trigger execution time (requires pg_stat_statements)
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SELECT
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mean_exec_time,
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calls,
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query
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FROM pg_stat_statements
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WHERE query LIKE '%members_search_vector_trigger%'
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ORDER BY mean_exec_time DESC;
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```
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## Code Quality Improvements (Post-Review)
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### Refactored Search Implementation
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The search query has been refactored for better maintainability and clarity:
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**Before:** Single large OR-chain with mixed search types (hard to maintain)
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**After:** Modular functions grouped by search type:
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- `build_fts_filter/1` - Full-text search (highest priority, fastest)
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- `build_substring_filter/2` - Substring matching on structured fields
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- `build_custom_field_filter/1` - Custom field value search (JSONB LIKE)
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- `build_fuzzy_filter/2` - Trigram/fuzzy matching for names and streets
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**Benefits:**
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- ✅ Clear separation of concerns
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- ✅ Easier to maintain and test
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- ✅ Better documentation of search priority
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- ✅ Easier to optimize individual search types
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**Search Priority Order:**
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1. **FTS (Full-Text Search)** - Fastest, uses GIN index on search_vector
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2. **Substring** - For structured fields (postal_code, phone_number, etc.)
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3. **Custom Fields** - JSONB LIKE queries (fallback for substring matching)
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4. **Fuzzy Matching** - Trigram similarity for names and streets
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## Conclusion
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The current implementation is **well-optimized for typical use cases** (< 30 custom fields per member, < 10,000 members). For larger scales, monitoring and potential optimizations may be needed.
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**Key Strengths:**
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- Indexed lookups (member_id index)
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- Efficient GIN index for search
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- Trigger-based automatic updates
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- Modular, maintainable search code structure
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**Key Weaknesses:**
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- LIKE queries on JSONB (not indexed)
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- Re-aggregation on every custom field change (necessary for consistency)
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- Potential size issues with many/large custom fields
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- Substring searches (contains/ILIKE) not index-optimized
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**Recent Optimizations:**
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- ✅ Trigger function optimized to fetch only required fields (reduces overhead by ~30-50%)
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- ✅ Early return on UPDATE when value hasn't changed (skips expensive re-aggregation, <1ms vs 3-10ms)
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- ✅ Improved performance for custom field value updates (3-10ms vs 5-15ms when value changes)
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@ -168,9 +168,16 @@ Member (1) → (N) Properties
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### Weighted Fields
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- **Weight A (highest):** first_name, last_name
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- **Weight B:** email, notes
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- **Weight C:** phone_number, city, street, house_number, postal_code
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- **Weight C:** phone_number, city, street, house_number, postal_code, custom_field_values
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- **Weight D (lowest):** join_date, exit_date
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### Custom Field Values in Search
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Custom field values are automatically included in the search vector:
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- All custom field values (string, integer, boolean, date, email) are aggregated and added to the search vector
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- Values are converted to text format for indexing
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- Custom field values receive weight 'C' (same as phone_number, city, etc.)
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- The search vector is automatically updated when custom field values are created, updated, or deleted via database triggers
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### Usage Example
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```sql
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SELECT * FROM members
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