Intermediate interview questions in Databases & Architecture, page 4
How databases implement GROUP BY aggregation
Hash aggregation builds a hash table keyed by group holding running aggregates; sort aggregation orders rows then aggregates adjacent groups; optimizer picks based on data and memory.
Logical vs physical query plans and the optimizer
Logical plan says what (relational algebra, no algorithms); physical plan says how (specific operators); cost-based optimizer enumerates physical options and picks the cheapest using statistics.
Optimizing queries on a billion-row fact table
Partition to prune scans, index for selective lookups, materialize views to precompute aggregates; each adds write or maintenance cost.
Diagnosing and optimizing a slow production query
Read the EXPLAIN ANALYZE plan, find the costly node, then fix via indexing, rewrite, or stats.
Designing an HA/DR strategy for an OLTP database
Sync standby in-region for zero data loss, async cross-region for DR, automated failover with a quorum.
Cutting managed database costs without breaking SLOs
Pool connections, prune and tune indexes, offload reads, tier or partition cold data, right-size storage IOPS.
Why choose Kafka over a REST endpoint for ingestion
Kafka buffers spikes, decouples producers from consumers, replays and fans out durably.
Problems the Lakehouse architecture solves
Lakehouse adds ACID transactions, schema enforcement, and time travel on cheap object storage.
Modeling IoT data with tags and fields
Tags are indexed identifying metadata, fields are unindexed measured values, and tag cardinality drives memory.
Diagnosing high Redis eviction and cache misses
Use INFO memory and stats to confirm pressure, check fragmentation ratio, pick LFU over LRU for skewed access, set sane TTLs.
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