Firestore queries and indexes in GCP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ service_behavior

intermediate

Firestore Query Result Count

You have a Firestore collection named users with documents containing fields age (number) and city (string). You run this query:

db.collection('users').where('age', '>', 25).where('city', '==', 'Seattle').get()

What will be the number of documents returned if the collection has:
- 5 users aged 30 in Seattle
- 3 users aged 20 in Seattle
- 4 users aged 40 in New York
- 2 users aged 50 in Seattle?

A 10

B 5

C 2

D 7

💡 Hint

Remember the query filters on age > 25 and city == 'Seattle'.

The query filters users older than 25 and located in Seattle. Only 5 users aged 30 in Seattle and 2 users aged 50 in Seattle match both filters, so total is 7. The options show 7 as B, so the correct answer is B.

❓ Architecture

intermediate

Firestore Composite Index Requirement

You want to run this Firestore query:

db.collection('orders').where('status', '==', 'shipped').orderBy('date', 'desc').limit(10)

Which statement about indexes is true?

A A composite index on 'status' and 'date' is required for this query to work.

B Firestore automatically uses a single-field index on 'status' and a composite index on 'status' and 'date' is not needed.

C No indexes are needed because Firestore scans all documents.

D Only a single-field index on 'date' is required.

💡 Hint

Think about how Firestore handles queries with filters and orderBy on different fields.

Firestore requires a composite index when a query filters on one field and orders by another. Here, filtering on 'status' and ordering by 'date' needs a composite index on both fields.

❓ security

advanced

Firestore Query and Security Rules

Given this Firestore security rule:

allow read: if request.auth.uid == resource.data.ownerId;

What happens if a user runs a query filtering documents by ownerId equal to their UID?

A The query returns only documents where ownerId matches the user's UID.

B The query returns all documents regardless of ownerId because security rules do not filter query results.

C The query fails because Firestore requires an index on ownerId for security rules.

D The query returns documents but only those the user is authorized to read.

💡 Hint

Security rules filter access after the query runs, but queries must match rules to succeed.

Firestore evaluates security rules on each document returned by the query. If the query filters by ownerId equal to the user's UID, only authorized documents are returned.

✅ Best Practice

advanced

Optimizing Firestore Query Performance

You have a Firestore collection with millions of documents. You want to query documents where category is 'books' and price is less than 20, ordered by price. Which approach is best for performance?

A Use two separate queries: one filtering category and one filtering price, then merge results in code.

B Create a composite index on 'category' and 'price' and use a query filtering category and price with orderBy price.

C Query all documents and filter results in the application code.

D Create a single-field index on 'price' only and filter category in code.

💡 Hint

Indexes help Firestore quickly find matching documents without scanning all data.

A composite index on 'category' and 'price' supports filtering and ordering efficiently. Separate queries or client-side filtering cause performance issues.

🧠 Conceptual

expert

Firestore Query Limitations and Indexes

Which Firestore query is NOT possible without creating a custom composite index?

A Query documents where 'status' == 'active' and order by 'createdAt' descending.

B Query documents where 'category' == 'electronics' and 'price' < 500 ordered by 'price'.

C Query documents where 'age' > 30 and 'score' < 100.

D Query documents where 'type' == 'premium' ordered by 'lastLogin' ascending.

💡 Hint

Consider Firestore's support for range filters on multiple fields.

Firestore does not support range filters on multiple fields in a single query without a composite index. Option C uses two range filters (> and <) on different fields, which requires a composite index.