Interview Questions
SQLAlchemy Core provides SQL abstraction layer, direct table operations. ORM provides object-relational mapping, domain model abstraction. Core offers better performance, more control. ORM provides higher-level abstractions, easier object manipulation. Consider use case requirements for choosing.
Use connection pools (SQLAlchemy's Pool, psycopg2's pool). Configure pool size, overflow, timeout. Handle connection recycling, cleanup. Consider concurrent access patterns, resource limits. Implement proper error handling and monitoring.
Use migration tools (Alembic, Django migrations). Version control migrations, test before deployment. Handle data migrations separately. Consider rollback strategies, large table modifications. Document migration steps and dependencies.
Use transaction context managers, explicit commit/rollback. Handle atomic operations, savepoints. Implement proper error handling. Consider isolation levels, deadlock prevention. Manage transaction scope and nesting.
Use indexing, query analysis tools. Optimize JOIN operations, limit result sets. Consider query execution plans. Implement caching strategies. Monitor query performance. Use database-specific optimization features.
Use parameterized queries, ORM query builders. Never concatenate SQL strings. Validate input data. Consider escape sequences, prepared statements. Implement proper access controls and input sanitization.
Define relationships (one-to-many, many-to-many) using relationship() in SQLAlchemy or ForeignKey in Django. Handle lazy loading, eager loading. Consider cascade operations, backref relationships. Manage relationship lifecycle.
Use migration tools with version control. Implement forward/backward migrations. Track schema changes in source control. Consider database branching strategies. Handle schema conflicts and dependencies.
Handle specific database exceptions, implement retry logic. Log errors appropriately. Consider transaction rollback, connection recovery. Implement proper cleanup. Provide meaningful error messages.
Use sharding keys, implement routing logic. Handle cross-shard queries, transactions. Consider data distribution, rebalancing. Implement proper shard management. Handle shard failures and recovery.
Implement regular backups, verify backup integrity. Handle incremental backups, point-in-time recovery. Consider backup automation, retention policies. Test recovery procedures. Document backup/restore processes.
Implement exponential backoff, max retry attempts. Handle connection timeouts, dead connections. Consider circuit breaker pattern. Implement proper logging and monitoring. Handle cleanup properly.
Use caching layers (Redis, Memcached). Implement cache invalidation strategies. Handle cache consistency. Consider cache warming, eviction policies. Implement proper error handling for cache failures.
Configure master-slave replication, handle failover. Implement read/write splitting. Consider replication lag, consistency requirements. Handle replication errors and recovery. Monitor replication status.
Implement batched operations, progress tracking. Handle data validation, rollback capability. Consider performance impact, downtime requirements. Test migration procedures. Document migration steps.
Monitor query performance, connection usage. Log slow queries, errors. Implement performance metrics collection. Consider monitoring tools integration. Handle alert threshold configuration.
Use proper isolation levels, row-level locking. Handle deadlock detection, prevention. Consider optimistic vs pessimistic locking. Implement proper transaction boundaries. Handle concurrent access patterns.
Implement zero-downtime migrations. Handle backward compatibility. Use temporary tables for large changes. Consider rollback procedures. Test migration scripts. Document change procedures.
Use test databases, fixtures. Implement transaction rollback for tests. Handle database isolation. Consider performance testing. Test migration scripts. Implement proper cleanup procedures.
Implement proper authentication, authorization. Use least privilege principle. Handle sensitive data encryption. Consider audit logging. Implement access control policies. Handle security patches.
Use schema versioning, data versioning. Handle version conflicts. Implement upgrade/downgrade procedures. Consider backward compatibility. Document version dependencies.
Use connection pools, proper cleanup. Handle connection lifecycle. Implement health checks. Consider connection timeouts. Handle connection leaks. Monitor connection usage.
Handle schema flexibility, denormalization. Implement proper indexing strategies. Consider consistency models. Handle scaling operations. Implement proper error handling. Monitor performance.
Use configuration files, environment variables. Handle different environments. Implement secure credential management. Consider configuration versioning. Document configuration requirements.
Monitor query performance, implement indexing. Use query optimization tools. Consider database configuration tuning. Handle connection pooling optimization. Monitor resource usage.
Track data changes, user actions. Implement audit tables. Handle audit log maintenance. Consider compliance requirements. Implement proper retention policies. Handle audit log security.
Implement high availability solutions, automatic failover. Handle failover detection, recovery. Consider data consistency. Implement proper monitoring. Document failover procedures.
Monitor growth trends, resource usage. Plan scaling strategies. Consider performance requirements. Implement monitoring tools. Document capacity requirements. Handle growth projections.
Schedule maintenance windows, implement automation. Handle index maintenance, statistics updates. Consider performance impact. Implement proper monitoring. Document maintenance procedures.