Intermediate everything in Backend Dev, page 15
Go Escape Analysis Chooses Stack Over Heap
Go escape analysis is the compiler pass that decides whether a variable lives on the stack or heap. It avoids heap allocations when data stays local, but any pointer that outlives its function escapes. The footgun is assuming small values never allocate.
Structs: Go's Plain Memory vs Rust's Ownership
Structs bundle named fields into a custom type. Use them when tuples or maps collapse under many values. The footgun is assuming the same syntax means the same rules: Go zero-values fields silently, while Rust demands explicit initialization unless you derive…
Primitive Scalars: Go vs Rust
Go's int grows with the architecture; Rust fixes sizes like i32 at compile time. Use Go's int for loops and Rust's i32 for counters, but both require explicit casts to mix. Assuming Go's int is 64-bit breaks 32-bit builds, and Rust's as truncates silently.
The C Application Binary Interface
An ABI is the contract a library exposes for in-process machine code access. You see this whenever a compiled program calls into a compiled library at the binary level.
FFI Error Handling: Translation and Unwinding
FFI error handling is a translation layer: foreign errors must become Rust Results before safe code sees them, or you risk UB. You do this in -sys wrappers around C libraries. The footgun: foreign exceptions unwinding across boundary without -unwind ABI is UB.
TCP Listeners: Go vs Rust
Go spins up TCP listeners and handles connections with lightweight goroutines—1 million costs ~500MB—but GC pauses introduce 2-5ms latency. Rust trades boilerplate for zero-cost safety and consistent sub-100µs response times without garbage collection.
go vet: Catch Bugs Compilers Allow
go vet catches suspicious constructs the compiler ignores, like Printf argument mismatches. Run it in CI to spot concurrency and formatting bugs early. It relies on heuristics, so a clean report does not guarantee correctness and false positives can occur.
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.
1NF atomic values; 2NF no partial dependencies; 3NF no transitive dependencies; prevents update anomalies but adds join overhead.
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.
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