Data, Storage & Async

Paved Path

• Datastore: Firestore (Native mode)
• Files: Firebase Storage (GCS)
• Cache: none initially
• Search: Algolia (near‑real‑time sync)
• Async jobs: Cloud Tasks
• Eventing: Pub/Sub
• Backups: Daily Firestore exports; 30‑day retention
• PII governance: Lean (tag collections, least‑privilege IAM, default encryption)

import * as functions from 'firebase-functions';
import * as admin from 'firebase-admin';
import algoliasearch from 'algoliasearch';

admin.initializeApp();
const client = algoliasearch(process.env.ALGOLIA_APP_ID!, process.env.ALGOLIA_API_KEY!);
const index = client.initIndex('users');

export const onUserWrite = functions.firestore
  .document('users/{id}')
  .onWrite(async (change, context) => {
    const id = context.params.id;
    if (!change.after.exists) return index.deleteObject(id);
    const data = change.after.data()!;
    await index.saveObject({ objectID: id, ...data });
  });

import { CloudTasksClient } from '@google-cloud/tasks';
const client = new CloudTasksClient();
export async function enqueue(url: string, payload: any) {
  const [res] = await client.createTask({
    parent: client.queuePath('proj', 'loc', 'queue'),
    task: { httpRequest: { httpMethod: 'POST', url, body: Buffer.from(JSON.stringify(payload)).toString('base64') } }
  });
  return res.name;
}

LLM Notes

• When modeling Firestore, optimize for access patterns. Prefer flat collections with indexes over deep nesting.