Databases & Architecture

SQL, NoSQL, system design, microservices, APIs

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Test yourself: Top 30 advanced Databases & Architecture interview questions →Multiple choice, with the correct answer and why it is correct on every question. Free, no sign-in.

Advanced interview questions in Databases & Architecture, page 2

advanced1 min read

Mitigating a database shard hot spot

Short-term, add read replicas or cache the hot keys; long-term, fix the partition key with hashing, salting, or finer-grained splitting.

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Diagnosing degradation with normal CPU and memory

When CPU and memory look fine, sessions are waiting, not computing; examine wait statistics, lock and latch contention, I/O waits, and buffer pool hit ratio.

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Defense-in-depth against SQL injection

Beyond parameterization, apply least-privilege accounts, stored procedures, input allowlisting, and monitoring to shrink blast radius.

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Unit of Work / Session pattern in ORMs

The Unit of Work tracks new, dirty, and deleted objects, then flushes them as one batched transaction at commit.

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Fixing an ORM's inefficient aggregation query

Drop to raw SQL or a view for the heavy report, or restructure the ORM query and add indexes. Raw SQL is fast but couples to the schema; tuning keeps portability.

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Aurora vs Spanner architecture contrast

Aurora is single-writer with a shared distributed log-based storage and quorum, scaling reads; Spanner shards data with Paxos and TrueTime for global writes.

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Multi-region active-passive DR with Aurora

Async global replication to a passive region, promote and repoint traffic on failover, and fence the old primary to prevent split-brain.

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The small files problem in data lakes

Many tiny files create per-file overhead and metadata pressure, hurting scans; fix via compaction, batching writes, and tuning partitioning.

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Iceberg vs Delta Lake metadata and ACID

Iceberg uses a tree of metadata and manifest files with atomic pointer swaps and optimistic concurrency; Delta uses an ordered transaction log of JSON commits with optimistic concurrency.

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TSM-Tree vs LSM-Tree storage engines

Both buffer writes in memory and flush sorted immutable files, but TSM organizes by series and time with columnar, heavily compressed blocks tuned for ordered appends and range scans.

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Tuning HNSW for recall vs latency

ANN trades exactness for speed, and HNSW knobs M and efConstruction shape graph quality while efSearch trades query latency for recall at runtime.

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