Everything in Databases & Architecture, page 7
Relational versus wide-column for a news feed
Relational gives flexible joins but read-time fan-out; Cassandra precomputes per-user feed rows for fast writes-side fan-out.
Polymorphic associations and referential integrity
A single column can't FK two tables, so integrity is unenforced; alternatives use exclusive arcs or per-type tables.
3NF versus BCNF and the overlapping-key gap
BCNF requires every determinant be a superkey; 3NF allows exceptions for prime attributes.
Adjacency List versus Nested Set for hierarchies
Adjacency list is simple writes but recursive reads; nested set is fast subtree reads but costly writes.
Modeling one-to-many versus many-to-many relationships
One-to-many uses a foreign key on the many side; many-to-many needs a junction table with two foreign keys.
Normalizing a flat orders table to 3NF
Split repeating data, remove partial dependencies, remove transitive dependencies, define keys.
When to intentionally denormalize a schema
Identify read-heavy join cost, duplicate or precompute data, and own the consistency burden.
Diagnosing and fixing the N+1 query problem
Define the 1 parent plus N child queries, fix via JOIN or batched IN, and ORM eager loading.
Read Committed versus Serializable isolation levels
Name the four levels, map each anomaly (dirty read, non-repeatable read, phantom) to the level that blocks it.
Materialization and Pipelining
Two query-execution strategies: materialization writes each operator's full output to disk before the next reads it, while pipelining streams tuples operator-to-operator without intermediate storage.
What is the difference between WHERE and HAVING in SQL?
Tests SQL execution order and aggregation. A strong answer states WHERE filters rows before grouping, HAVING filters groups after aggregation, and gives an aggregate example that WHERE cannot evaluate.
Find users who never placed an order and explain JOIN choice
This tests SQL anti-joins and NULL semantics. A strong answer uses LEFT JOIN with IS NULL or NOT EXISTS, explains why NOT IN is risky with NULLs, and why NOT EXISTS is preferred. Red flag: using INNER JOIN or ignoring NULLs.
Explain database indexes, the classic data structure, and write-heavy trade-offs
Tests the read-write trade-off of indexing. A strong answer names B-Trees, explains they avoid full scans, and notes that inserts, updates, and deletes must update the index, adding write amplification and storage cost. Red flag: claiming indexes are free.
Describe 1NF, 2NF, 3NF, normalization's purpose, and its performance trade-off.
1NF atomic values; 2NF no partial dependencies; 3NF no transitive dependencies; prevents update anomalies but adds join overhead.
What is the difference between DDL and DML in SQL?
DDL shapes schema with CREATE or ALTER; DML handles row-level data with SELECT, INSERT, UPDATE, or DELETE.
Explain ACID properties and why they matter for banking or e-commerce
Define each as a failure-handling guarantee; show how partial commits cause double-spending.
What is the difference between primary, foreign, and unique keys?
This tests relational integrity basics. Answer: primary keys identify rows, foreign keys reference tables, and unique keys are alternate candidates. Red flag: saying unique keys are just for indexing or omitting a non-PK example like email.
Relevance Ranking: Sorting Results by Likely Usefulness
Relevance ranking orders results by how well they satisfy query intent, not just keyword overlap. It powers ecommerce, documentation, and log search. The footgun is chasing click-through over task completion, which surfaces popular but wrong answers.
Backpressure: Slow the Producer or Crash
Backpressure is a feedback signal telling upstream to slow down when downstream cannot keep up. You see it in stream processors like Flink or Kafka where a slow consumer risks memory exhaustion. Ignore it and queues grow until the service crashes.
Stream-Table Duality: Two Views of One Dataset
A table is a snapshot; a stream is the changelog that built it. The same data can be viewed either way: tables answer what is true now, while streams capture every change that led there. Treating them as separate systems is the expensive footgun.
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