More in Backend Dev — page 29
CSP: Model Concurrency with Message Passing
CSP treats concurrency as isolated processes talking through channels, not threads fighting over shared memory. It shaped Go, Erlang, and occam. Engineers often retrofit shared-state patterns into channel-based code and reintroduce race conditions.
go.mod: Root of Go Module Identity
A go.mod file anchors a Go module, declaring its canonical path and dependencies to turn a directory into a versioned unit. Every project needs one at its root, and the path dictates how others import your packages.
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.
WHAT IT TESTS: Linking forms to anomaly prevention and join overhead. OUTLINE: 1NF atomic values; 2NF no partial dependencies; 3NF no transitive dependencies; prevents update anomalies but adds join overhead. RED FLAG: Jargon without linking to anomalies.
What is the difference between DDL and DML in SQL?
WHAT IT TESTS: Your grasp of the schema-versus-data boundary. ANSWER OUTLINE: DDL shapes schema with CREATE or ALTER; DML handles row-level data with SELECT, INSERT, UPDATE, or DELETE. RED FLAG: Labeling SELECT as DDL or insisting DDL never affects data.
Explain ACID properties and why they matter for banking or e-commerce
WHAT IT TESTS: Mapping ACID to real failure modes in finance. ANSWER OUTLINE: Define each as a failure-handling guarantee; show how partial commits cause double-spending. RED FLAG: Vague definitions that skip Isolation levels or Durability details.
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.
The N+1 Query Problem
N+1 means fetching one record, then looping to query its relations one by one. It explodes latency in ORM code that looks innocent, turning a page load into hundreds of round-trips. The fix is eager loading, yet developers often miss it until production melts.
Leaderless Replication: No Master, No Bottleneck
Leaderless replication lets any node accept writes, skipping a single leader bottleneck. Systems like Dynamo stay available during partitions, reconciling conflicts with vector clocks later.
Volcano Model: Pipelined Query Execution
Volcano makes every query operator a generator yielding one tuple per call. Scans, joins, and sorts stream data upward through open-next-close interfaces without materializing intermediates. The hidden cost is millions of virtual calls that stall modern CPUs.
Database Joins: Nested, Hash, Sort-Merge
A join matches rows by trading memory for speed. Nested loops use indexes; hash joins load large sets into RAM; sort-merge streams sorted data. The optimizer hides its choice, so a missing index can force a disk-spilling hash join.
SQL JOIN: Match Rows Across Tables
A SQL JOIN matches rows across tables on a shared key to build one logical record. You use it when orders need customer names or posts need authors. The footgun is that INNER JOIN silently drops rows with missing keys, making data seem to vanish.
ORM: The Virtual Object Database Layer
ORM converts data between relational databases and object-oriented program memory, creating a virtual object database inside your code. The footgun is designing object models that ignore the relational structure, forcing awkward translations you never see.
How SQL Queries Become Abstract Syntax Trees
An AST turns a flat SQL string into a tree of operations the database can reason about. The parser builds this tree before execution planning. Do not confuse it with the raw parse tree, which keeps punctuation and formatting the AST strips away.
Second Normal Form (2NF)
2NF ensures every non-prime attribute depends on the entire candidate key, not just part of it. It only matters when a relation has a composite key. The footgun is assuming single-attribute keys automatically satisfy 2NF.