Intermediate everything in Backend Dev, page 6
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.
Synchronous vs asynchronous replication trade-offs
Sync waits for replica ack giving zero data loss but higher latency; async acks immediately, faster but risks losing recent writes on failover.
Choosing a good shard key and avoiding hot spots
High cardinality, even write distribution, query alignment; monotonic keys send all writes to one shard.
When a graph database beats relational or document stores
Deeply connected data, variable-depth traversals, fraud or recommendation paths, index-free adjacency.
SQL isolation levels and the anomalies they prevent
Read Uncommitted allows dirty reads; Read Committed blocks them; Repeatable Read blocks non-repeatable reads; Serializable blocks phantoms.
Zero-downtime schema migration on a hot table
Expand-migrate-contract phases, dual-write and backfill, decouple deploys from migrations.
The analysis phase: tokenizers and token filters
Analysis turns raw text into index terms via a tokenizer that splits text into tokens then token filters that transform them, like lowercasing or stemming.
Vector embeddings and vector databases
An embedding is a learned dense vector capturing semantic meaning, and vector DBs use ANN indexes like HNSW for fast similarity search that relational B-trees cannot provide.
Cache-aside pattern pros and cons
App reads cache, on miss loads DB and populates, invalidates on write; pros are resilience and lean cache, cons are stale windows and app-managed invalidation.
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