Best Practices - Module 3: Index Management¶

Index Schema Design¶

Field Configuration¶

Use Descriptive Names: Choose clear, descriptive field names that reflect their purpose
Minimize Field Count: Only include fields that will be searched, filtered, or retrieved
Optimize Field Attributes: Only enable necessary attributes (searchable, filterable, sortable, facetable)
Choose Appropriate Data Types: Use the most specific data type for each field

Key Field Best Practices¶

Single Value Keys: Always use single-value, unique identifiers as key fields
Stable Keys: Choose keys that won't change over the document's lifetime
String Keys: Use string type for key fields, even for numeric identifiers
URL-Safe Keys: Ensure key values are URL-safe for REST API operations

Field Attribute Optimization¶

Searchable: Only for fields that need full-text search
Filterable: For fields used in filter expressions and facets
Sortable: For fields used in orderBy expressions
Facetable: For fields used in faceted navigation
Retrievable: Set to false for fields that don't need to be returned in results

Performance Optimization¶

Storage Efficiency¶

Minimize Index Size: Smaller indexes perform better and cost less
Optimize Field Storage: Use appropriate field types and avoid unnecessary attributes
Consider Field Length: Longer text fields consume more storage and processing
Remove Unused Fields: Regularly review and remove fields that aren't being used

Query Performance¶

Design for Query Patterns: Structure your index to support your most common queries
Use Appropriate Analyzers: Choose analyzers that match your content and language
Optimize Facetable Fields: Limit the number of facetable fields to improve performance
Consider Field Ordering: Place frequently queried fields earlier in the schema

Scaling Considerations¶

Plan for Growth: Design indexes to handle expected data volume growth
Monitor Resource Usage: Track storage, memory, and query performance metrics
Right-Size Service Tier: Choose appropriate service tier for your workload
Partition Strategy: Understand how partitions affect performance and cost

Data Management¶

Document Structure¶

Consistent Schema: Maintain consistent document structure across all documents
Handle Missing Fields: Plan for documents with missing or null field values
Normalize Data: Ensure consistent data formats and values
Validate Input: Implement data validation before indexing

Document Operations¶

Batch Operations: Use batch operations for better performance when possible
Unique Document Keys: Ensure document keys are unique across the entire index
Handle Updates Properly: Use merge operations for partial updates
Manage Document Versions: Plan for document versioning if needed

Data Quality¶

Clean Data: Remove or fix malformed data before indexing
Consistent Formatting: Ensure consistent date, number, and text formatting
Handle Special Characters: Properly encode special characters and Unicode
Validate Required Fields: Ensure required fields are present and valid

Security and Access Control¶

Authentication and Authorization¶

Use Managed Identity: Prefer managed identity over API keys when possible
Secure API Keys: Store API keys securely and rotate them regularly
Principle of Least Privilege: Grant minimum necessary permissions
Monitor Access: Implement logging and monitoring for access patterns

Data Protection¶

Sensitive Data: Avoid indexing sensitive or personally identifiable information
Data Encryption: Ensure data is encrypted in transit and at rest
Access Logging: Enable audit logging for compliance and security monitoring
Network Security: Use private endpoints and firewall rules when appropriate

Compliance¶

Data Retention: Implement appropriate data retention policies
Right to be Forgotten: Plan for data deletion requirements
Geographic Restrictions: Consider data residency requirements
Audit Trails: Maintain audit trails for compliance purposes

Operational Excellence¶

Monitoring and Alerting¶

Health Monitoring: Implement comprehensive health monitoring
Performance Metrics: Track key performance indicators
Error Monitoring: Monitor and alert on error rates and types
Capacity Planning: Monitor resource utilization trends

Maintenance Procedures¶

Regular Reviews: Periodically review index schema and usage patterns
Performance Tuning: Regularly optimize based on usage patterns
Index Rebuilding: Plan for periodic index rebuilds when necessary
Schema Evolution: Plan for schema changes and migrations

Backup and Recovery¶

Configuration Backup: Maintain backups of index configurations
Data Export: Implement procedures for data export and backup
Recovery Testing: Regularly test recovery procedures
Disaster Recovery: Plan for disaster recovery scenarios

Development and Testing¶

Development Workflow¶

Environment Separation: Use separate indexes for development, testing, and production
Version Control: Store index definitions in version control systems
Automated Testing: Implement automated tests for index operations
Documentation: Maintain comprehensive documentation

Testing Strategies¶

Schema Validation: Test index schema with representative data
Performance Testing: Test with production-like data volumes
Load Testing: Test under expected load conditions
Error Handling: Test error scenarios and recovery procedures

Deployment Practices¶

Blue-Green Deployments: Use blue-green deployments for zero-downtime updates
Gradual Rollouts: Implement gradual rollouts for major changes
Rollback Plans: Always have rollback plans for deployments
Change Management: Implement proper change management processes

Common Anti-Patterns to Avoid¶

Schema Design Anti-Patterns¶

❌ Over-Attribution: Making all fields searchable, filterable, and sortable
❌ Generic Field Names: Using vague names like "field1", "data", "content"
❌ Wrong Data Types: Using string for numeric data or dates
❌ Compound Keys: Using multiple fields as composite keys

Performance Anti-Patterns¶

❌ Excessive Fields: Including too many unnecessary fields
❌ Large Text Fields: Indexing very large text fields without consideration
❌ Inappropriate Analyzers: Using wrong analyzers for content type
❌ Ignoring Metrics: Not monitoring performance and resource usage

Operational Anti-Patterns¶

❌ No Backup Strategy: Not having backup and recovery procedures
❌ Hardcoded Values: Embedding environment-specific values in configurations
❌ No Monitoring: Not implementing proper monitoring and alerting
❌ Manual Processes: Relying on manual processes for routine operations

Index Lifecycle Management¶

Planning Phase¶

Requirements Analysis: Thoroughly analyze search requirements
Data Analysis: Understand source data structure and quality
Performance Requirements: Define performance and scalability requirements
Resource Planning: Plan for required resources and costs

Development Phase¶

Iterative Design: Use iterative approach for schema design
Prototype Testing: Test with representative data samples
Performance Validation: Validate performance with realistic loads
Documentation: Document design decisions and rationale

Production Phase¶

Gradual Rollout: Deploy gradually with monitoring
Performance Monitoring: Continuously monitor performance metrics
User Feedback: Collect and analyze user feedback
Optimization: Continuously optimize based on usage patterns

Maintenance Phase¶

Regular Reviews: Conduct regular reviews of performance and usage
Schema Evolution: Plan and implement schema changes as needed
Capacity Management: Monitor and manage capacity requirements
End-of-Life Planning: Plan for index retirement when necessary

Checklist for Production Readiness¶

Schema Design¶

[ ] All field names are descriptive and consistent
[ ] Field attributes are optimized for actual usage
[ ] Data types are appropriate for content
[ ] Key field is properly configured
[ ] Analyzers are appropriate for content and language

Performance¶

[ ] Index size is optimized
[ ] Query performance meets requirements
[ ] Resource utilization is within acceptable limits
[ ] Scaling strategy is defined

Security¶

[ ] Authentication and authorization are properly configured
[ ] Sensitive data is not indexed
[ ] Access logging is enabled
[ ] Network security is implemented

Operations¶

[ ] Monitoring and alerting are configured
[ ] Backup and recovery procedures are defined
[ ] Documentation is complete and current
[ ] Team is trained on operational procedures

Testing¶

[ ] Schema has been tested with representative data
[ ] Performance testing has been completed
[ ] Error handling has been tested
[ ] Recovery procedures have been tested

By following these best practices, you'll create robust, performant, and maintainable search indexes that scale with your needs and provide excellent search experiences for your users.