Compilation

Problem

What becomes confusing, fragile, or impossible without understanding compilation? This lesson answers that through explanation, a worked example, two runnable exercises, and a reference solution. No teacher-supplied worksheet is required.

WebAssembly brought a portable low-level compilation target to the browser; SQLite brought a mature relational engine into one embedded file.

Concept explanation

Compilation belongs to “WebAssembly as a boundary”. compilation must be connected to layer below by tracing representation, ownership, control, and failure. WASM is explicit boundary between host calls and linear memory; speed depends on workload and crossing cost.

For compilation, trace concrete input, state transition, output, and failure through an explicit low-level or relational boundary where memory, crossings, schemas, queries, and persistence costs can be measured.

JavaScript calls into a WASM module whose linear memory and host imports form an explicit boundary. SQLite executes queries over pages, indexes, and a planner. Apply that model to supplied normal, boundary, and failure cases; each case below names its input and expected evidence.

Example bank

Compare normal, boundary, failure, and cross-layer cases. Predict each observation before revealing the explanation.

Worked example

Start from supplied lab.sql. Focus: Trace compilation end-to-end while running: Call tiny exported arithmetic function and inspect module exports/memory.

1. Run: sqlite3 :memory: < lab.sql
2. Save baseline evidence. Inspect module exports, memory, JS/WASM crossings, schemas, indexes, transactions, query plans, OPFS files, and recovery.
3. Boundary case: At every compilation boundary, label owner and representation during: Compare one million calls versus one batched call.
4. Failure case: Locate first layer where evidence diverges during: Read beyond agreed memory region and validate bounds in host wrapper.

RESULT
Trace explains Benchmark separates startup, crossing, and compute; batch reduces crossing overhead. without skipping any conversion, queue, process, protocol, or storage boundary. Starter-level baseline: Query plan reports use of observations_topic; final query prints baseline.

Starter material

Create lab.sql, paste this exact content, then run the command below.

CREATE TABLE observations (id INTEGER PRIMARY KEY, topic TEXT NOT NULL, result TEXT NOT NULL);
INSERT INTO observations(topic, result) VALUES ("compilation", 'baseline');
CREATE INDEX observations_topic ON observations(topic);
EXPLAIN QUERY PLAN SELECT result FROM observations WHERE topic = "compilation";
SELECT result FROM observations WHERE topic = "compilation";

STOP / CLEANUPIn-memory SQLite exits after script. Stop any local web server with Ctrl+C.

Independent exercise

Remove one abstraction: compilation

1. Create second case from blank file: At every compilation boundary, label owner and representation during: Compare one million calls versus one batched call.
2. Then create controlled failure: Locate first layer where evidence diverges during: Read beyond agreed memory region and validate bounds in host wrapper.
3. Use WASM debugger · SQLite CLI · EXPLAIN QUERY PLAN · OPFS to prove behavior, then repair controlled failure.
4. Compare result against supplied acceptance checks and reference approach before marking complete.

Expected result

• Trace explains Benchmark separates startup, crossing, and compute; batch reduces crossing overhead. without skipping any conversion, queue, process, protocol, or storage boundary.
• Query plan reports use of observations_topic; final query prints baseline.
• Controlled compilation failure produces captured evidence; repair restores stated invariant without hiding error.

Hints

Solution

Attempt both exercises before opening reference approach.