Optimizing Firestore Queries for Performance and Cost Efficiency

Saturday, February 1, 2025

Firestore is designed for scalability and real-time updates, but inefficient queries can lead to increased read costs and slower performance. Since Firestore charges per document read, write, and delete, optimizing queries is essential for keeping costs low and performance high.

This article covers:

How Firestore query costs work

Common mistakes that increase costs

Techniques to optimize query performance

Understanding Firestore Query Costs

Firestore pricing is based on:

Reads: Every time a document is retrieved, it counts as a read—even if no document matches the query.

Writes: Each time a document is created, updated, or deleted.

Deletes: Charged separately, though usually minimal.

Costly Mistakes to Avoid

Unindexed Queries: Queries that require Firestore to scan an entire collection instead of using an index.

Fetching Too Much Data: Retrieving entire documents when only a few fields are needed.

Large Collection Scans: Queries without proper filters that fetch too many documents at once.

Techniques to Optimize Firestore Queries

1. Use Indexes for Faster Queries

Firestore automatically creates single-field indexes, but compound indexes (for multiple fields) must be added manually.

Use Firestore’s index suggestions in the Firebase Console.

Avoid unnecessary indexes for rarely queried fields.

2. Fetch Only Necessary Data

Firestore retrieves entire documents by default, but you can store frequently accessed data separately to reduce read costs.

Instead of fetching an entire user document with unnecessary fields:

Language: javascript

import { getFirestore, doc, getDoc } from "firebase/firestore";

const db = getFirestore();
const userRef = doc(db, "users", "userId");
const userSnap = await getDoc(userRef);

if (userSnap.exists()) {
  const { name, email } = userSnap.data();
  console.log("User:", name, email);
}

✅ Only retrieves the needed fields (name, email) instead of processing a large document.

3. Implement Pagination

Instead of fetching an entire collection, use .limit() and .startAfter() to load data in chunks:

Language: javascript

import { collection, query, orderBy, startAfter, limit, getDocs } from "firebase/firestore";

const db = getFirestore();
const usersRef = collection(db, "users");
const q = query(usersRef, orderBy("createdAt"), startAfter(lastVisibleDoc), limit(10));
const querySnapshot = await getDocs(q);

✅ This reduces the number of reads per query and improves loading speed.

4. Use Cursors for Efficient Filtering

Cursors (.startAt(), .endAt(), .startAfter()) help optimize range queries:

Language: javascript

import { where, orderBy, startAt, limit, getDocs } from "firebase/firestore";

const db = getFirestore();
const transactionsRef = collection(db, "transactions");
const q = query(transactionsRef, where("amount", ">", 100), orderBy("amount"), startAt(500), limit(10));
const querySnapshot = await getDocs(q);

✅ Retrieves transactions over $500 efficiently by using an index.

5. Structure Data for Query Performance

When designing your Firestore database:

Use subcollections for related data instead of keeping everything in one large collection.

Precompute values instead of performing expensive real-time calculations.

Denormalize data when necessary to avoid unnecessary joins.

Example: Optimizing a Query

Before (Inefficient Query)

Fetching all users and filtering on the client:

Language: javascript

const usersRef = collection(db, "users");
const querySnapshot = await getDocs(usersRef);

const filteredUsers = querySnapshot.docs.filter(doc => doc.data().isActive);

❌ Problem: Retrieves all users, increasing read costs unnecessarily.

After (Optimized Query)

Fetching only active users directly from Firestore:

Language: javascript

import { collection, query, where, getDocs } from "firebase/firestore";

const db = getFirestore();
const q = query(collection(db, "users"), where("isActive", "==", true));
const querySnapshot = await getDocs(q);

✅ Improvement: Reduces the number of documents read by filtering at the database level.

Conclusion

Optimizing Firestore queries keeps your app fast and cost-effective. Key takeaways:

✅ Use indexes to speed up queries.

✅ Fetch only necessary fields to reduce data transfer.

✅ Implement pagination instead of loading entire collections.

✅ Use cursors for range queries.

✅ Structure data efficiently with subcollections and precomputed values.

By applying these techniques, you'll enhance performance, reduce Firestore costs, and create a more scalable app. 🚀