Intermediate everything in Backend Dev, page 5
Handling async errors in Express middleware
Await inside try/catch and call next(err) on failure, or wrap the handler in an async error adapter; never let a rejected Promise go uncaught.
Running independent requests with Promise.all and race
Start all requests then await Promise.all to get all results or fail fast on first rejection; use Promise.race when only the fastest settled result matters.
Output order of sync, microtask, and macrotask
C runs first synchronously, then B drains from the microtask queue, then A from the macrotask queue.
SemVer and the caret vs tilde range operators
MAJOR.MINOR.PATCH signals breaking/feature/fix; caret allows minor and patch updates, tilde allows only patch; use tilde for tighter control.
Layered structure for a scalable Express API
Routes map URLs, controllers handle HTTP, services hold business logic, data layer talks to the DB; keep each layer ignorant of HTTP except controllers.
Choosing between CommonJS and ES Modules
ESM is the standard with static import/export and top-level await; set type to module; CJS uses require and module.exports and is synchronous.
Why package-lock.json must be committed
Lockfile pins exact versions of the whole dependency tree including transitive deps; guarantees identical installs across machines and CI.
Offloading CPU-bound work with Worker Threads
Synchronous CPU work freezes the loop and all requests; offload to a Worker, communicate via messages or SharedArrayBuffer, use a pool.
nextTick vs setImmediate vs setTimeout(fn, 0)
NextTick is a microtask that drains before the loop continues; setImmediate runs in check; setTimeout(0) in timers.
Order of the Node.js event loop phases
Timers, pending callbacks, poll, check, close phases in order; I/O completion runs in poll.
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.
Modeling IoT data with tags and fields
Tags are indexed identifying metadata, fields are unindexed measured values, and tag cardinality drives memory.
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.
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