Everything in Backend Dev, page 9
Problems the Lakehouse architecture solves
Lakehouse adds ACID transactions, schema enforcement, and time travel on cheap object storage.
Why choose Kafka over a REST endpoint for ingestion
Kafka buffers spikes, decouples producers from consumers, replays and fans out durably.
Cutting managed database costs without breaking SLOs
Pool connections, prune and tune indexes, offload reads, tier or partition cold data, right-size storage IOPS.
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.
Diagnosing and optimizing a slow production query
Read the EXPLAIN ANALYZE plan, find the costly node, then fix via indexing, rewrite, or stats.
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.
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.
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.
The Volcano iterator model of query execution
Each operator exposes open/next/close, parents pull tuples from children, uniform composable interface, pipelined low memory.
Phantom reads and how serializable prevents them
New rows matching a predicate appear between reads; classic Repeatable Read locks existing rows not ranges; Serializable uses range or predicate locks.
Choosing a time-series database for metrics
High-ingest timestamped writes, time-window queries, retention and downsampling, time-optimized compression.
Diagnosing database latency layer by layer
Split total time into pool-wait, query execution, and ORM-generated query patterns; use metrics at each layer.
Sessionizing clickstream events into sessions
Order events per user, split on inactivity gap, assign session ids, pick event or session grain.
Vectorized query execution and its speedups
Process column batches per operator call, amortize per-tuple overhead, use cache locality and SIMD.
The buffer pool's role in database IO
Caches pages, serves reads from RAM, buffers dirty writes flushed later, uses eviction like LRU.
Predicate pushdown and why it speeds queries
Apply WHERE conditions at the scan or remote source, prune partitions and rows early, shrink data movement.
Denormalization: trading write cost for read speed
Duplicate or precompute data to avoid joins, accept harder writes and consistency risk, justify by read-heavy access.
Upgrading a stateful Flink job without losing state
Take a savepoint, stop with drain, deploy new jar, restore from savepoint with matching operator UIDs.
Tuning a database connection pool
Max size, min idle, connection and max-lifetime timeouts; size from cores and latency, not guesswork.
The N+1 query problem and how to fix it
One query for a list plus one per item for its relation, fix with eager loading or a batched join.
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