Every developer has that one side project they've been meaning to build for years. For most of us, it sits in a notes app somewhere, occasionally haunting us at 2 AM. The reason it never gets built usually isn't lack of skill — it's that the effort-to-payoff ratio feels wrong.
I want to talk about a specific instance of this problem: building custom tooling around SQLite. And more importantly, how AI-assisted development is collapsing the timeline on projects that used to feel unreasonable for a solo developer.
The Problem: SQLite Is Deceptively Simple
SQLite is everywhere. It's in your phone, your browser, probably your toaster. It's the most deployed database engine in the world. And because it's "just a file," developers tend to assume working with it programmatically should be straightforward.
It's not.
The moment you try to build anything beyond basic CRUD — a query analyzer, a migration tool, a custom SQL dialect layer — you slam into a wall. SQLite's SQL dialect has quirks that diverge from PostgreSQL and MySQL in subtle, painful ways. The type system is dynamic (technically "type affinity"). ALTER TABLE support has historically been limited. And parsing SQL correctly is genuinely hard.
-- This is valid SQLite but would fail in PostgreSQL
CREATE TABLE users (
id INTEGER PRIMARY KEY, -- implicitly ROWID alias
name TEXT,
age TEXT -- yes, you can store an integer here and SQLite won't complain
);
-- Type affinity means this succeeds silently
INSERT INTO users (id, name, age) VALUES (1, 'Alan', 'not a number');
If you're building a tool that needs to understand SQLite SQL — not just pass it through, but actually parse, validate, or transform it — you need a parser that handles these quirks correctly.
Why Writing a SQL Parser Is a Nightmare
I've attempted this twice over the years. Both times, I got about 60% of the way there and gave up. Here's why.
SQL isn't a simple language to parse. The grammar is context-sensitive in places, keywords can double as identifiers, and every database engine has its own extensions. For SQLite specifically:
-
Keywords aren't reserved the way you'd expect. In SQLite, you can use most keywords as identifiers without quoting them. Try parsing
SELECT group FROM order WHERE select = 1— it's valid SQLite. - Expression parsing is recursive and complex. Subqueries can appear almost anywhere an expression can.
- The official grammar is defined in a Lemon parser file (SQLite uses its own parser generator), which isn't exactly plug-and-play for other languages.
# A naive tokenizer falls apart quickly
def tokenize(sql):
tokens = []
i = 0
while i < len(sql):
# Handle string literals
if sql[i] == "'":
j = i + 1
while j < len(sql):
if sql[j] == "'":
if j + 1 < len(sql) and sql[j + 1] == "'":
j += 2 # escaped quote: '' inside a string
continue
break
j += 1
tokens.append(('STRING', sql[i:j+1]))
i = j + 1
# Now handle identifiers, numbers, operators...
# This gets ugly FAST
# What about [bracket-quoted identifiers]? "double-quoted" ones?
# What about blob literals like X'1234'?
else:
# ... hundreds more lines
i += 1
return tokens
That snippet covers maybe 5% of what a real tokenizer needs. And tokenizing is the easy part — actually building an AST from tokens is where the real pain lives.
The Traditional Approach: Use an Existing Parser
The standard advice is "don't write your own parser." Fair enough. Here are your options:
- Use SQLite's own parser by embedding SQLite as a C library and hooking into its internals. Works, but you're writing C bindings and dealing with SQLite's internal representation, which isn't designed for external consumption.
-
Use a general SQL parser library like
sqlparser-rs(Rust) orsqlparse(Python). These work for common SQL but often choke on SQLite-specific syntax. - Use ANTLR with a SQLite grammar. This is actually reasonable, but ANTLR grammars for SQLite tend to be community-maintained and lag behind SQLite releases.
Each of these gets you partway there. None of them give you exactly what you want: a clean, hackable parser that understands SQLite's full dialect and produces a useful AST in your language of choice.
How AI Changes the Build-vs-Wait Calculation
Here's where things get interesting. I recently tackled a SQLite tooling project that I'd been putting off for years. The difference this time? I used AI coding assistants throughout the process.
Let me be specific about what helped and what didn't.
What AI is genuinely good at here
Boilerplate generation. Writing the hundredth pattern-matching branch for a SQL token type is mind-numbing work. AI assistants are excellent at generating these repetitive structures once you establish the pattern:
// After establishing the pattern for a few token types,
// AI can reliably generate the rest
fn parse_statement(&mut self) -> Result<Statement, ParseError> {
match self.peek_token() {
Some(Token::Select) => self.parse_select(),
Some(Token::Insert) => self.parse_insert(),
Some(Token::Update) => self.parse_update(),
Some(Token::Delete) => self.parse_delete(),
Some(Token::Create) => self.parse_create(),
Some(Token::Drop) => self.parse_drop(),
Some(Token::Alter) => self.parse_alter(),
Some(Token::With) => self.parse_with(), // CTE support
Some(Token::Explain) => self.parse_explain(),
Some(Token::Pragma) => self.parse_pragma(), // SQLite-specific
Some(Token::Attach) => self.parse_attach(), // SQLite-specific
Some(Token::Detach) => self.parse_detach(), // SQLite-specific
Some(tok) => Err(ParseError::UnexpectedToken(tok.clone())),
None => Err(ParseError::UnexpectedEof),
}
}
Test case generation. This was the biggest win. Writing comprehensive test cases for a SQL parser is brutal — you need to cover edge cases you'd never think of. AI assistants are surprisingly good at generating adversarial SQL test cases.
Documentation lookup. Instead of tabbing back and forth to the SQLite docs, I could ask about specific behavior and get relevant answers (though I always verified against the official docs).
What AI is NOT good at here
Architectural decisions. The AI couldn't tell me whether to use a recursive descent parser or a Pratt parser for expressions. I had to make that call based on experience. (I went with recursive descent with Pratt parsing for expressions — the hybrid approach handles SQL's precedence rules well.)
Subtle semantic correctness. AI-generated code would sometimes handle SQL syntax that looks right but violates SQLite's actual behavior. You absolutely need to test against real SQLite to catch these.
Knowing when to stop. AI will happily help you gold-plate a feature nobody needs. The discipline of "good enough" still has to come from you.
The Practical Takeaway
If you've been sitting on a tooling project for years because the effort felt disproportionate, it might be time to revisit that calculation. Here's my rough framework:
- Identify the repetitive parts. These are where AI gives you the biggest speed multiplier. Parser branches, test cases, serialization code.
- Keep the architecture decisions human. Sketch your design on paper before touching an AI assistant. You need to know where you're going.
- Test against the real thing. For database tooling specifically, always validate against the actual database engine. Don't trust the parser — trust the database.
- Timebox ruthlessly. AI makes it easy to add "one more feature." Set a deadline and ship.
The project that would have taken me a year of weekends took about three months. Not because AI wrote the code for me — it didn't. It wrote maybe 30-40% of the code, and I rewrote a good chunk of that. But it eliminated the drudgery that had been killing my motivation for years.
Sometimes the bottleneck on a side project isn't the hard parts. It's the boring parts. And that's exactly where AI coding tools earn their keep.
Resources
- SQLite's official SQL syntax diagrams — the authoritative reference
- SQLite's Lemon parser generator — if you want to understand how SQLite itself parses SQL
- The sqlparser-rs crate — a solid starting point if you're working in Rust
- SQLite's quirks page — required reading before building any SQLite tooling
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