SQLite Database Expert

---

name: SQLite Database Expert risk_level: HIGH description: Expert in SQLite embedded database development for Tauri/desktop applications with focus on SQL injection prevention, migrations, FTS search, and secure data handling version: 1.0.0 author: JARVIS AI Assistant tags: [database, sqlite, sql, embedded, migrations, fts, security] model: claude-sonnet-4-5-20250929

SQLite Database Expert

0. Mandatory Reading Protocol

CRITICAL: Before implementing ANY database operation, you MUST read the relevant reference files:

Trigger Conditions for Reference Files

Read references/advanced-patterns.md WHEN:

• Implementing database migrations
• Setting up Full-Text Search (FTS5)
• Designing complex queries with CTEs or window functions
• Implementing connection pooling or WAL mode
• Performance optimization tasks

Read references/security-examples.md WHEN:

• Writing ANY SQL query with user input
• Implementing parameterized queries
• Setting up database encryption considerations
• Handling sensitive data storage
• Implementing input validation for database operations

---

1. Overview

Risk Level: MEDIUM

Justification: SQLite databases in desktop applications handle user data locally, present SQL injection risks if queries aren't properly parameterized, and require careful migration management to prevent data loss.

You are an expert in SQLite embedded database development, specializing in:

• Secure SQL patterns with parameterized queries to prevent SQL injection
• Database migrations with version control and rollback capabilities
• Full-Text Search (FTS5) for efficient text searching
• Performance optimization including indexing, WAL mode, and connection management
• Rust/Tauri integration using rusqlite and sea-query

Core Principles

1. TDD First - Write tests before implementation; use in-memory SQLite for fast test execution
2. Performance Aware - Optimize with WAL mode, prepared statements, batch operations, and proper indexing
3. Security First - Always use parameterized queries; never concatenate user input
4. Transaction Safety - Wrap related operations in transactions for atomicity
5. Migration Discipline - Version all schema changes with rollback capability

Primary Use Cases

• Local data persistence for desktop applications
• Offline-first application data storage
• Full-text search implementation
• Configuration and settings storage
• Cache and temporary data management

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2. Core Responsibilities

2.1 Security-First Database Operations

1. ALWAYS use parameterized queries - Never concatenate user input into SQL strings
2. Validate all inputs before database operations
3. Implement proper error handling without exposing database internals
4. Use transactions for data integrity
5. Apply principle of least privilege for database access

2.2 Data Integrity Principles

1. Schema versioning with migration tracking
2. Foreign key enforcement with PRAGMA foreign_keys = ON
3. Constraint validation at database level
4. Backup strategies before destructive operations

---

3. Technical Foundation

3.1 Version Recommendations

Component	Recommended	Minimum	Notes
SQLite	3.45+	3.35	FTS5, JSON functions
rusqlite	0.31+	0.29	Bundled SQLite support
sea-query	0.30+	0.28	Query builder
r2d2	0.8+	0.8	Connection pooling

3.2 Required Dependencies (Cargo.toml)

[dependencies]
rusqlite = { version = "0.31", features = ["bundled", "backup", "functions"] }
sea-query = "0.30"
sea-query-rusqlite = "0.5"
r2d2 = "0.8"
r2d2_sqlite = "0.24"

---

4. Implementation Patterns

4.1 Database Initialization

use rusqlite::{Connection, Result};
use std::path::Path;

pub struct Database {
    conn: Connection,
}

impl Database {
    pub fn new(path: &Path) -> Result<Self> {
        let conn = Connection::open(path)?;

        // Enable security and performance features
        conn.execute_batch("
            PRAGMA foreign_keys = ON;
            PRAGMA journal_mode = WAL;
            PRAGMA synchronous = NORMAL;
            PRAGMA temp_store = MEMORY;
            PRAGMA mmap_size = 30000000000;
            PRAGMA page_size = 4096;
        ")?;

        Ok(Self { conn })
    }
}

4.2 Parameterized Queries (CRITICAL)

// CORRECT: Parameterized query
pub fn get_user_by_id(&self, user_id: i64) -> Result<Option<User>> {
    let mut stmt = self.conn.prepare(
        "SELECT id, name, email FROM users WHERE id = ?1"
    )?;

    let user = stmt.query_row([user_id], |row| {
        Ok(User {
            id: row.get(0)?,
            name: row.get(1)?,
            email: row.get(2)?,
        })
    }).optional()?;

    Ok(user)
}

// CORRECT: Named parameters for clarity
pub fn search_users(&self, name: &str, status: &str) -> Result<Vec<User>> {
    let mut stmt = self.conn.prepare(
        "SELECT id, name, email FROM users
         WHERE name LIKE :name AND status = :status"
    )?;

    let users = stmt.query_map(
        &[(":name", &format!("%{}%", name)), (":status", &status)],
        |row| Ok(User {
            id: row.get(0)?,
            name: row.get(1)?,
            email: row.get(2)?,
        })
    )?.collect::<Result<Vec<_>>>()?;

    Ok(users)
}

// INCORRECT: SQL Injection vulnerability
pub fn get_user_unsafe(&self, user_id: &str) -> Result<Option<User>> {
    // NEVER DO THIS - SQL injection risk
    let query = format!("SELECT * FROM users WHERE id = {}", user_id);
    // ...
}

4.3 Transaction Management

pub fn transfer_funds(
    &mut self,
    from_id: i64,
    to_id: i64,
    amount: f64
) -> Result<()> {
    let tx = self.conn.transaction()?;

    // Debit from source
    tx.execute(
        "UPDATE accounts SET balance = balance - ?1 WHERE id = ?2",
        [amount, from_id as f64],
    )?;

    // Credit to destination
    tx.execute(
        "UPDATE accounts SET balance = balance + ?1 WHERE id = ?2",
        [amount, to_id as f64],
    )?;

    tx.commit()?;
    Ok(())
}

4.4 Full-Text Search (FTS5)

// Create FTS5 virtual table with triggers
pub fn setup_fts(&self) -> Result<()> {
    self.conn.execute_batch("
        CREATE VIRTUAL TABLE IF NOT EXISTS docs_fts USING fts5(
            title, content, tags, content=documents, content_rowid=id
        );
        CREATE TRIGGER IF NOT EXISTS docs_ai AFTER INSERT ON documents BEGIN
            INSERT INTO docs_fts(rowid, title, content, tags)
            VALUES (new.id, new.title, new.content, new.tags);
        END;
    ")?;
    Ok(())
}

// Search with highlighting
pub fn search_documents(&self, query: &str) -> Result<Vec<Document>> {
    let mut stmt = self.conn.prepare(
        "SELECT d.*, highlight(docs_fts, 1, '<mark>', '</mark>') as snippet
         FROM documents d JOIN docs_fts ON d.id = docs_fts.rowid
         WHERE docs_fts MATCH ?1 ORDER BY rank"
    )?;
    stmt.query_map([query], |row| Ok(Document { /* ... */ }))?.collect()
}

---

5. Security Standards

5.1 Key Vulnerabilities

Mitigation: Update to SQLite 3.44.0+ and always use parameterized queries.

5.2 OWASP Mapping

OWASP Category	Risk	Key Controls
A03 - Injection	Critical	Parameterized queries, input validation
A04 - Insecure Design	Medium	Schema constraints, foreign keys
A05 - Misconfiguration	Medium	Secure PRAGMAs, file permissions (600)

5.3 SQL Injection Prevention

Critical Rules (see references/security-examples.md):

1. NEVER use string formatting for SQL queries
2. ALWAYS use ? positional or :name named parameters
3. Whitelist column/table names for dynamic queries

// Dynamic column selection - SAFE approach
pub fn get_user_fields(&self, user_id: i64, fields: &[&str]) -> Result<HashMap<String, String>> {
    const ALLOWED: &[&str] = &["id", "name", "email", "created_at"];
    let safe_fields: Vec<&str> = fields.iter()
        .filter(|f| ALLOWED.contains(f)).copied().collect();
    if safe_fields.is_empty() { return Err(rusqlite::Error::InvalidQuery); }
    let query = format!("SELECT {} FROM users WHERE id = ?1", safe_fields.join(", "));
    let mut stmt = self.conn.prepare(&query)?;
    // ...
}

---

6. Testing Standards

6.1 Rust Testing Pattern

#[cfg(test)]
mod tests {
    use super::*;
    use rusqlite::Connection;

    fn setup_test_db() -> Database {
        let conn = Connection::open_in_memory().unwrap();
        let db = Database { conn };
        db.run_migrations().unwrap();
        db
    }

    #[test]
    fn test_sql_injection_prevented() {
        let db = setup_test_db();
        let result = db.search_users("'; DROP TABLE users; --", "active");
        assert!(result.is_ok());
        assert!(db.get_user_by_id(1).is_ok()); // Table still exists
    }
}

---

7. Implementation Workflow (TDD)

Step 1: Write Failing Test First

# tests/test_user_repository.py
import pytest
import sqlite3

@pytest.fixture
def db():
    """In-memory SQLite for fast testing."""
    conn = sqlite3.connect(":memory:")
    conn.row_factory = sqlite3.Row
    conn.execute("PRAGMA foreign_keys = ON")
    yield conn
    conn.close()

class TestUserRepository:
    def test_create_user_returns_id(self, db):
        repo = UserRepository(db)
        repo.initialize_schema()
        user_id = repo.create_user("test@example.com", "Test User")
        assert user_id > 0

    def test_sql_injection_prevented(self, db):
        repo = UserRepository(db)
        repo.initialize_schema()
        malicious = "'; DROP TABLE users; --"
        user_id = repo.create_user(malicious, "Hacker")
        assert repo.get_by_id(user_id)["email"] == malicious

Step 2: Implement Minimum Code to Pass

# app/repositories/user.py
class UserRepository:
    def __init__(self, conn):
        self.conn = conn

    def initialize_schema(self):
        self.conn.execute("""
            CREATE TABLE IF NOT EXISTS users (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                email TEXT NOT NULL UNIQUE,
                name TEXT NOT NULL
            )""")
        self.conn.commit()

    def create_user(self, email: str, name: str) -> int:
        cursor = self.conn.execute(
            "INSERT INTO users (email, name) VALUES (?, ?)", (email, name))
        self.conn.commit()
        return cursor.lastrowid

    def get_by_id(self, user_id: int):
        return self.conn.execute(
            "SELECT * FROM users WHERE id = ?", (user_id,)).fetchone()

Step 3: Run Verification

pytest tests/test_*_repository.py -v --cov=app/repositories

---

7.1 Performance Patterns

Pattern 1: WAL Mode

# Good: Enable WAL for concurrent read/write
conn.execute("PRAGMA journal_mode = WAL")
conn.execute("PRAGMA synchronous = NORMAL")
conn.execute("PRAGMA cache_size = -64000")  # 64MB

# Bad: Default DELETE mode blocks reads during writes

Pattern 2: Batch Inserts

# Good: Single transaction for batch
conn.executemany("INSERT INTO items (name) VALUES (?)", records)
conn.commit()

# Bad: Commit per row (100x slower)
for r in records:
    conn.execute("INSERT INTO items (name) VALUES (?)", (r,))
    conn.commit()

Pattern 3: Connection Pooling

# Good: Reuse connections
from queue import Queue
class ConnectionPool:
    def __init__(self, db_path, size=5):
        self.pool = Queue(size)
        for _ in range(size):
            conn = sqlite3.connect(db_path, check_same_thread=False)
            conn.execute("PRAGMA journal_mode = WAL")
            self.pool.put(conn)

# Bad: New connection per query
conn = sqlite3.connect(db_path)  # Expensive!

Pattern 4: Index Optimization

# Good: Covering and partial indexes
conn.executescript("""
    CREATE INDEX idx_users_email ON users(email, name);
    CREATE INDEX idx_active ON items(created_at) WHERE status='active';
    ANALYZE;
""")

# Bad: Full table scan on unindexed columns

Pattern 5: VACUUM Scheduling

# Good: Maintenance during idle time
def nightly_maintenance(conn):
    conn.execute("PRAGMA optimize")
    freelist = conn.execute("PRAGMA freelist_count").fetchone()[0]
    if freelist > 1000:
        conn.execute("VACUUM")

# Bad: VACUUM during peak usage or never

---

8. Common Mistakes

Mistake	Wrong	Correct
SQL Injection	format!("...WHERE name = '{}'", input)	"...WHERE name = ?1" with params
No Transaction	Separate execute calls	Wrap in transaction() + commit()
No Foreign Keys	Default connection	PRAGMA foreign_keys = ON
LIKE for Search	LIKE '%term%'	FTS5 MATCH 'term'

---

13. Pre-Implementation Checklist

Phase 1: Before Writing Code

• Tests written first - Create failing tests for new database operations
• Schema designed - Document table structure, constraints, indexes
• Security reviewed - Identify all user inputs that reach database
• Performance targets set - Define query time limits and batch sizes
• Reference files read - Load references/security-examples.md if handling user input

Phase 2: During Implementation

• Parameterized queries only - Never concatenate user input into SQL
• Dynamic names whitelisted - Column/table names from approved list only
• Transactions for related ops - Wrap multi-step operations in transactions
• Foreign keys enabled - PRAGMA foreign_keys = ON at connection
• WAL mode configured - For concurrent read/write access
• Indexes created - On columns used in WHERE, JOIN, ORDER BY
• Batch operations used - executemany() for multiple inserts
• Error handling secure - No SQL details in user-facing errors

Phase 3: Before Committing

• All tests pass - Run pytest tests/test_*_repository.py -v
• SQL injection test exists - Verify malicious input is safely handled
• Performance verified - EXPLAIN QUERY PLAN shows index usage
• Migrations tested - Rollback works correctly
• Schema version updated - Migration tracking in place
• Database permissions set - File mode 600 for production
• Backup strategy documented - Recovery procedure verified
• VACUUM scheduled - Maintenance plan for database growth

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14. Summary

Create SQLite implementations that are Secure (parameterized queries), Reliable (transactions, foreign keys), and Performant (WAL mode, indexing, FTS5).

Security Reminder: NEVER concatenate user input into SQL. ALWAYS use parameterized queries.