import { DocumentDataply } from 'document-dataply';

// 1. Define Document Type (Schema)
type UserProfile = {
  name: string;
  age: number;
  tags: string[];
}

async function main() {
  // 2. Create instance and connect to file
  const db = DocumentDataply.Define<UserProfile>()
    .Options({ wal: 'my-database.wal' })
    .Open('my-database.db');
  
  await db.init();

  // 3. Create Index (Specify 'name' field to improve search speed)
  await db.migration(1, async (tx) => {
    await db.createIndex('idx_name', { type: 'btree', fields: ['name'] }, tx);
    console.log('Index created successfully');
  });

  // 4. Insert Single Document
  await db.insert({
    name: 'IU',
    age: 30,
    tags: ['Singer', 'Actor']
  });

  // 5. Query Data
  const query = db.select({
    name: 'IU' 
  });

  // Fetch results as an array at once
  const results = await query.drain();
  console.log(results); 
  // Output: [{ _id: 1, name: 'IU', age: 30, tags: ['Singer', 'Actor'] }]

  // 6. Close the connection
  await db.close();
}

main();

const largeData = Array.from({ length: 10000 }, (_, i) => ({
  name: `User_${i}`,
  age: Math.floor(Math.random() * 50),
  tags: ['imported']
}));

// Much faster than individual inserts
await db.insertBatch(largeData);

const activeSeniors = await db.select({
  age: { gte: 65 },
  status: { equal: 'active' },
  name: { like: 'John%' }
}).drain();

// Single Upsert: Inserts if _id is missing/not found, updates if _id exists
await db.upsert({ _id: 1, name: 'John Doe', age: 31 });

// Batch Upsert: Process multiple inserts and updates in a single batch
await db.upsertBatch([
  { name: 'New User', age: 20 },                    // Inserted with auto _id
  { _id: 1, name: 'John Doe Updated', age: 32 }    // Updated existing document
]);