Performance & Optimization Interview Questions

Key techniques include: Query caching, field-level caching, query batching, cache normalization. Implement proper fetch policies, query deduplication. Features automatic garbage collection and cache persistence.

Query batching combines multiple queries into single request. Uses Apollo Link Batch. Reduces network requests, improves performance. Best for multiple simultaneous queries. Consider timeout configuration and request ordering.

Field-level caching stores individual field values. Uses unique field identifiers, custom field policies. Enables granular cache control, reduces redundant requests. Improves performance for frequently accessed data.

Memory optimization through cache eviction, garbage collection. Implement proper cache policies, data retention. Monitor memory usage, clean unused data. Consider cache size limits and cleanup strategies.

Query deduplication eliminates duplicate network requests. Automatically combines identical queries. Reduces server load, improves client performance. Enable/disable through client configuration.

Query prefetching loads data before needed. Use client.query() or preloadQuery(). Improves perceived performance, reduces loading time. Consider resource usage and timing strategies.

Large query optimization through pagination, field selection. Implement fragments, query splitting. Use proper fetch policies, cache strategies. Consider data requirements and network impact.

Cache normalization flattens nested data structures. Uses unique identifiers, reference handling. Reduces data duplication, improves cache efficiency. Enables consistent data updates.

Performance monitoring through Apollo Studio, metrics collection. Track query performance, error rates. Analyze cache effectiveness, network usage. Consider client-side and server-side metrics.

Mutation optimization through optimistic updates, proper cache updates. Implement batch mutations, efficient cache writes. Consider update strategies and rollback mechanisms.

Fetch policies control cache/network behavior. Different policies affect performance differently. Balance between data freshness and performance. Consider use case requirements and network conditions.

Efficient pagination through cursor-based approach, proper cache configuration. Implement field policies, merge functions. Consider data loading strategies and UX requirements.

Network optimization through proper link configuration, request batching. Implement retry strategies, timeout handling. Consider error handling and network conditions.

Bundle optimization through proper imports, code splitting. Remove unused features, implement lazy loading. Consider build configuration and dependency management.

Cache policies control data freshness, network requests. Balance between performance and data accuracy. Configure based on data requirements and user experience.

Real-time optimization through proper subscription handling, cache updates. Implement efficient data sync, update strategies. Consider WebSocket configuration and connection management.

Network reduction through proper query design, field selection. Implement fragments, query composition. Consider data requirements and bandwidth usage.

Client-side optimization through cache policies, computed fields. Implement efficient data access patterns. Consider data volume and computation cost.

Field policies affect cache behavior, data access patterns. Impact on read/write performance. Consider custom field handling and cache efficiency.

Efficient cache persistence through proper storage strategy, serialization. Implement selective persistence, cleanup. Consider storage limits and performance impact.