Everything in Databases & Architecture, page 2
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.
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.
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.
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.
Inverted index in search engines
An inverted index maps each term to the list of documents containing it, making keyword lookup O(1)-ish instead of scanning every document.
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.
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.
Exactly-once semantics in stream processing
Exactly-once means each event affects state once despite retries, it is hard because of failures between processing and committing, and you achieve it via idempotency or atomic…
Clickstream architecture for real-time and batch
Ingest events into a log like Kafka, fan out to a real-time path for dashboards and a batch path to a lake for ad-hoc analysis.
Schema-on-read in data lakes
Structure is applied at query time not ingest, enabling flexible raw storage and ML, but costing query-time validation and risking data swamps.
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