Best Practices

Patterns and conventions for building maintainable, efficient applications with firestore-orm.

These recommendations cover repository lifecycle, query efficiency, data hygiene, and hook design. Each one includes a working example and the reasoning behind it.

1. Initialize repositories once

Create repository instances once and reuse them throughout your application. Don’t create new instances inside every function.

// ❌ Bad - Creates a new instance every time
export function getUserRepository() {
  return FirestoreRepository.withSchema<User>(db, 'users', userSchema);
}

// ✅ Good - Single instance, reused everywhere
export const userRepo = FirestoreRepository.withSchema<User>(db, 'users', userSchema);

Why: Repository construction is lightweight, but recreating instances repeatedly is unnecessary and makes hook management inconsistent — hooks registered with on() live on the instance, so a fresh instance loses every listener you attached to the previous one.

2. Organize repositories in a centralized module

Keep all repository instances and their shared hooks in one module so wiring stays discoverable and each collection has a single source of truth.

repositories/index.ts

import { db } from '../config/firebase';
import { FirestoreRepository } from '@reggieofarrell/firestore-orm';
import * as schemas from '../schemas';

export const userRepo = FirestoreRepository.withSchema<schemas.User>(
  db,
  'users',
  schemas.userSchema,
);

export const orderRepo = FirestoreRepository.withSchema<schemas.Order>(
  db,
  'orders',
  schemas.orderSchema,
);

export const productRepo = FirestoreRepository.withSchema<schemas.Product>(
  db,
  'products',
  schemas.productSchema,
);

// Setup common hooks
userRepo.on('afterCreate', async user => {
  await auditLog.record('user_created', user);
});

orderRepo.on('afterCreate', async order => {
  await notificationService.sendOrderConfirmation(order);
});

3. Use cursor-based pagination over offset

For large datasets, cursor-based pagination is significantly more efficient than offset pagination. paginate(pageSize, cursor?) requires a prior orderBy() and returns { items, nextCursor, hasMore }; pass the previous page’s nextCursor back in to advance.

// ✅ Good - Cursor-based (scales well)
const { items, nextCursor, hasMore } = await userRepo
  .query()
  .orderBy('createdAt', 'desc')
  .paginate(20, lastCursor);

// ❌ Avoid - Offset-based (expensive for large page numbers)
const result = await userRepo.query().orderBy('createdAt', 'desc').offsetPaginate(100, 20); // Skip 1980 docs to reach page 100

Why: Offset pagination requires Firestore to scan and skip every document before your offset, while cursor pagination jumps directly to the starting position. See Queries for the full pagination API.

4. Use query updates for bulk operations

When updating multiple documents based on a condition, use query().update() instead of fetching and then updating.

// ✅ Good - Single query, batched writes
await orderRepo
  .query()
  .where('status', '==', 'pending')
  .where('createdAt', '<', cutoffDate)
  .update({ status: 'expired' });

// ❌ Less efficient - Two operations
const orders = await orderRepo
  .query()
  .where('status', '==', 'pending')
  .where('createdAt', '<', cutoffDate)
  .get();

await orderRepo.bulkUpdate(orders.map(o => ({ id: o.id, data: { status: 'expired' } })));

Note: query().update() and query().delete() do not run lifecycle hooks — they issue batched writes directly. If you rely on beforeUpdate/afterUpdate side effects, fetch and use bulkUpdate (or per-document update) instead. See Lifecycle hooks.

5. Add timestamps consistently

Always add createdAt and updatedAt timestamps to track the data lifecycle. Note the required top-level id: z.string() in the schema — every firestore-orm schema must declare it.

const userSchema = z.object({
  id: z.string(),
  name: z.string(),
  email: z.string().email(),
  createdAt: z.string().datetime(),
  updatedAt: z.string().datetime(),
});

// On create
await userRepo.create({
  ...data,
  createdAt: new Date().toISOString(),
  updatedAt: new Date().toISOString(),
});

// On update
await userRepo.update(id, {
  ...data,
  updatedAt: new Date().toISOString(),
});

If you prefer to store native Firestore timestamps instead of ISO strings, see Timestamps for the millisecond converter and the hook-based write conversion pattern.

6. Handle composite index errors gracefully

Firestore requires composite indexes for certain query combinations. The ORM surfaces a FirestoreIndexError with a clear message and a link to create the missing index.

try {
  const results = await orderRepo
    .query()
    .where('status', '==', 'pending')
    .where('total', '>', 100)
    .orderBy('createdAt', 'desc')
    .get();
} catch (error) {
  if (error instanceof FirestoreIndexError) {
    console.log(error.toString());
    // Logs a formatted message with a link to create the index.
    // Click the link, wait 1-2 minutes for the index to build, then retry the query.
  }
}

See Error handling for the full error taxonomy and Troubleshooting for index-related tips.

7. Use transactions for critical operations

Any operation requiring consistency across multiple documents should use a transaction. runInTransaction passes a transaction-scoped repository; do all reads with getForUpdateInTransaction before any writes.

// ✅ Atomic transfer
await accountRepo.runInTransaction(async (tx, repo) => {
  const from = await repo.getForUpdateInTransaction(tx, fromId);
  const to = await repo.getForUpdateInTransaction(tx, toId);

  if (from.balance < amount) {
    throw new Error('Insufficient funds');
  }

  await repo.updateInTransaction(tx, fromId, {
    balance: from.balance - amount,
  });

  await repo.updateInTransaction(tx, toId, {
    balance: to.balance + amount,
  });
});

See Transactions for the complete transaction-scoped API.

8. Use streaming for large data exports

When processing large datasets (exports, migrations, batch jobs), use query().stream() to iterate lazily and avoid loading everything into memory.

// ✅ Memory efficient
const csvStream = createWriteStream('users.csv');
csvStream.write('name,email,status\n');

for await (const user of userRepo.query().stream()) {
  csvStream.write(`${user.name},${user.email},${user.status}\n`);
}

csvStream.end();

9. Structure hooks for reusability

Keep hooks focused and modular. Avoid putting complex business logic directly inside a hook — delegate to a dedicated service so the logic stays testable.

// ✅ Good - Focused, testable
class UserNotificationService {
  async sendWelcomeEmail(user: User) {
    // Email logic here
  }
}

const notificationService = new UserNotificationService();

userRepo.on('afterCreate', async user => {
  await notificationService.sendWelcomeEmail(user);
});

// ❌ Bad - Business logic coupled to the hook
userRepo.on('afterCreate', async user => {
  const template = await db.collection('templates').doc('welcome').get();
  const emailService = new EmailService(config);
  await emailService.send({
    to: user.email,
    subject: template.data().subject,
    body: template.data().body.replace('{{name}}', user.name),
  });
  await db.collection('email_logs').add({ userId: user.id, type: 'welcome' });
});

See Lifecycle hooks for the full event list and payload shapes.