⚠️ Top 10 Mistakes Businesses Make When Using Firestore (And How to Fix Them)

Firestore is a powerful, serverless NoSQL database -- but it's not foolproof.

We've helped dozens of companies avoid costly Firestore mistakes that lead to high bills, slow apps, or even data loss.

Here are the top 10 mistakes we see again and again -- and how to fix them like a pro.

🚫 1. Using Firestore Like a SQL Database

Mistake: Trying to do complex joins or relational queries

Impact: High read costs and poor performance

• ✅Fix:

• Denormalize your data
• Use subcollections or duplication for fast access
• Lean into NoSQL design

🧱 2. Over-Nesting Data

Mistake: Deep nesting (3+ levels) inside a document

Impact: Difficult updates, large document sizes

• ✅Fix:

• Keep nesting shallow
• Use subcollections for related items

📚 3. Keeping Too Much in a Single Document

Mistake: Storing arrays of thousands of items in one doc

Impact: Hits [1MB document limit](https://firebase.google.com/docs/firestore/quotas) and fails at scale

• ✅Fix:

• Break into multiple docs or subcollections
• Paginate or load only what's needed

📈 4. Not Using Indexes Properly

Mistake: Ignoring custom or composite indexes

Impact: Query errors or slow reads

• ✅Fix:

• Use Firebase console or `firestore.indexes.json`
• Avoid full scans -- index what you query

📊 5. Reading More Data Than Needed

Mistake: Fetching entire documents/collections

Impact: Costly reads and bandwidth waste

• ✅Fix:

• Use `.select()` to project only required fields
• Use `.limit()` and paginate with cursors

🔒 6. Weak Security Rules

Mistake: `allow read, write: if true` during production

Impact: Public data leaks

• ✅Fix:

• Always scope rules to `request.auth.uid`
• Use Firestore Simulator to test rules

💵 7. No Cost Monitoring or Budgeting

Mistake: No tracking on reads/writes

Impact: Surprise billing

• ✅Fix:

• Use GCP budget alerts
• Set Firestore usage dashboards in console

🧪 8. Testing in Production

Mistake: Running tests on live Firestore DB

Impact: Real user data corruption

• ✅Fix:

• Use separate `dev`, `staging`, `prod` environments
• Prefix or segment collections per env

🕵️ 9. No Data Validation Logic

Mistake: Relying solely on frontend for validation

Impact: Corrupt or unexpected data saved

• ✅Fix:

• Use Firestore rules or Cloud Functions for backend validation
• Consider Firestore schema validators in code

⚙️ 10. Over-Reliance on Triggers

Mistake: Using Cloud Functions for every write event

Impact: Cold starts, delays, higher costs

• ✅Fix:

• Batch actions or use Pub/Sub for heavy work
• Avoid trigger chains (function calling another)

✅ Firestore Mistakes: Visual Summary

Let's create a chart showing common mistakes vs impact level:

📉 Firestore Mistakes and Their Severity

The chart shows the severity and frequency of common Firestore mistakes, helping developers prioritize which issues to address first.

✅ Final CTA

> 💡 Not sure if you're using Firestore efficiently?

> We help businesses audit, optimize, and scale their Firebase apps -- without the costly mistakes.