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TL;DR

What "CRUD" means – Create / Read / Update / Delete – the four basic database ops that almost every app needs.

How you’ll see it in code – A tiny SQLite/SQL‑Alchemy snippet shows the ops on a local file, and a short Flask example turns those ops into an HTTP API you can hit with `curl` or Postman.

What to try next – Replace the SQLite backend with Postgres/MySQL if you want persistence across deployments, or hook up the Flask routes to your own front‑end.

1️⃣ Why CRUD matters

Every data‑centric app (todo list, inventory, blog) has to:

Operation What it does Typical endpoint

Create Add a new record `POST /items`

Read Retrieve data `GET /items/:id` or `GET /items`

Update Modify existing data `PUT /items/:id`

Delete Remove data `DELETE /items/:id`

If you can’t perform these four operations reliably, your app will feel stuck. That’s why a minimal CRUD backend is the foundation of any web or mobile application.

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2. What Is a Minimal CRUD Backend?

A minimal CRUD backend includes just enough code to:

Store data (usually in a database).

Expose endpoints that clients can hit.

Handle errors gracefully without unnecessary complexity.

Think of it as the skeleton: you get the bones, but you’re free to add muscles and skin later. This approach lets teams prototype quickly, iterate fast, and keep the codebase manageable.

Common Choices for a Minimal CRUD Backend

Stack Why It’s Minimal

Node.js + Express (or NestJS) with an in‑memory store or lightweight DB like SQLite JavaScript ecosystem is widely known; Express offers minimal routing.

Python + FastAPI Declarative syntax, automatic docs via OpenAPI.

Go + net/http Standard library only; no external dependencies.

Rust + Actix‑Web Strong type safety, compile‑time guarantees.

Pick the stack you’re comfortable with or one that fits your team's skill set.

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3. Designing the Service API

A RESTful design is straightforward and language-agnostic. Let’s define the endpoints:

HTTP Method Path Description

`POST` `/jobs` Submit a new job; returns job ID.

`GET` `/jobs/id` Get status & result of a job.

`DELETE` `/jobs/id` Cancel or delete a job (optional).

Payload Example

// POST /jobs

"operation": "factorial",
"payload":
"n": 1000000

Response Example

// 201 Created

"job_id": "abcd1234",
"status": "queued"

Later, polling:

GET /jobs/abcd1234
200 OK

"job_id": "abcd1234",
"status": "completed",
"result":
"value": "..." // large number or encoded string

Handling Large Results

Streaming: If the result is extremely large, return a URL to download the file or stream it in chunks.

Encoding: Base64 encode binary data if needed; otherwise, send raw bytes with proper content type.

Example Using HTTP Range Requests (Chunked Transfer)

If you support partial requests:

GET /jobs/abcd1234/result
Range: bytes=0-999999

Server responds with `206 Partial Content`, allowing clients to download large data progressively.

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5. Summary of Key Takeaways

Topic Recommendation

Parsing Use regex or parser combinators; handle nested parentheses; avoid recursion depth limits by iterative parsing.

Serialization Map `Tree` → `JSON`; use `Option`, arrays, and recursion; libraries can generate JSON automatically.

Query Language Design a simple prefix language; build an AST with variants; evaluate via pattern matching or function maps.

Large Data Stream JSON; chunk responses; use pagination; consider GraphQL for partial data retrieval.

By following these guidelines, you’ll be able to parse and serialize nested parentheses expressions into JSON, query them efficiently using a custom language, and handle large datasets without performance bottlenecks.
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