Question 1

How do you identify slow queries in a production database?

Accepted Answer

We enable slow query logging and use tools like pg_stat_statements or MySQL Performance Schema to capture slow-running queries. We then analyze their execution plans using EXPLAIN to identify missing indexes or inefficient joins.

Question 2

What is the difference between normalization and denormalization?

Accepted Answer

Normalization organizes data to minimize redundancy and protect integrity, which is ideal for write-heavy transactional systems. Denormalization groups data to speed up read operations, which is highly beneficial for complex reporting and analytics.

Question 3

When should we use Redis caching instead of database optimization?

Accepted Answer

Redis caching is best for frequently accessed, static data to bypass database hits entirely. However, database optimization like indexing is still necessary for dynamic queries and write operations that cannot be cached.

Question 4

How do you handle database migrations with zero downtime?

Accepted Answer

We use a multi-phase migration strategy, such as the expand-contract pattern, where database changes are backward-compatible. This allows the application to run smoothly while data is migrated incrementally in the background.

Question 5

What is connection pooling and why do we need PgBouncer?

Accepted Answer

Each database connection consumes memory and CPU, which can exhaust resources during traffic spikes. PgBouncer manages a pool of reusable connections, allowing thousands of client requests to share a small set of actual database connections.

Question 6

How do you determine if we need sharding or read replicas?

Accepted Answer

If your application has high read traffic, read replicas are the easiest way to distribute the load. Sharding is required when your dataset is too large to fit on a single server or write operations exceed single-node capacity.

Question 7

Can you optimize NoSQL databases like MongoDB?

Accepted Answer

Yes, NoSQL databases require careful schema design and indexing just like relational databases. We optimize MongoDB by designing efficient document schemas, creating compound indexes, and optimizing aggregation pipelines.

Question 8

What metrics do you monitor to ensure database health?

Accepted Answer

We monitor key performance indicators including CPU utilization, memory usage, disk I/O operations, active connection counts, and replication lag. We also track query latency percentiles to ensure consistent user experiences.

How can we help?

Database Design & Optimization
FAQ

Helpful
information

Getting Started

Timeline

Pricing

Support

Still have questions?

Database Design & OptimizationFAQ

Helpfulinformation