Imports

Problem

What becomes confusing, fragile, or impossible without understanding imports? 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

Imports belongs to “WebAssembly as a boundary”. imports is learned by comparing readable, constrained, and deliberately clever implementations against same evidence.

For imports, 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: Solve imports three ways using identical fixture: 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: Run all three implementations against: Compare one million calls versus one batched call.
4. Failure case: Force every implementation through: Read beyond agreed memory region and validate bounds in host wrapper.

RESULT
All versions preserve Benchmark separates startup, crossing, and compute; batch reduces crossing overhead.; comparison records code size, predictability, and debugging cost. 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 ("imports", 'baseline');
CREATE INDEX observations_topic ON observations(topic);
EXPLAIN QUERY PLAN SELECT result FROM observations WHERE topic = "imports";
SELECT result FROM observations WHERE topic = "imports";

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

Independent exercise

Compare three versions: imports

1. Create second case from blank file: Run all three implementations against: Compare one million calls versus one batched call.
2. Then create controlled failure: Force every implementation through: 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

• All versions preserve Benchmark separates startup, crossing, and compute; batch reduces crossing overhead.; comparison records code size, predictability, and debugging cost.
• Query plan reports use of observations_topic; final query prints baseline.
• Controlled imports failure produces captured evidence; repair restores stated invariant without hiding error.

Hints

Solution

Attempt both exercises before opening reference approach.