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.

Practice

1. What is the main purpose of indexes in Firestore queries?

easy

A. To make queries faster and more efficient

B. To store large files like images

C. To backup data automatically

D. To encrypt data for security

Solution

1. Step 1: Understand what indexes do in Firestore

   Indexes help Firestore find data quickly without scanning all documents.

2. Step 2: Relate indexes to query performance

   By using indexes, queries run faster and use less resources.

3. Final Answer:

   To make queries faster and more efficient -> Option A

4. Quick Check:

   Indexes speed up queries = D [OK]

Hint: Indexes speed up data search in Firestore queries [OK]

Common Mistakes:

• Thinking indexes store files
• Confusing indexes with backups
• Assuming indexes encrypt data

2. Which of the following is the correct syntax to create a simple Firestore query filtering by a field age equal to 30?

easy

A. db.collection('users').filter('age', '=', 30)

B. db.collection('users').where('age', '==', 30)

C. db.collection('users').query('age' == 30)

D. db.collection('users').find('age', 30)

Solution

1. Step 1: Recall Firestore query syntax

   Firestore uses where(field, operator, value) to filter data.

2. Step 2: Match syntax with options

   db.collection('users').where('age', '==', 30) uses where('age', '==', 30), which is correct syntax.

3. Final Answer:

   db.collection('users').where('age', '==', 30) -> Option B

4. Quick Check:

   Correct Firestore filter syntax = B [OK]

Hint: Use .where(field, '==', value) for filtering in Firestore [OK]

Common Mistakes:

• Using .filter instead of .where
• Using == inside query string
• Using .find which is not Firestore syntax

3. Given this Firestore query:

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

What must you do to avoid an error when running this query?

medium

A. Remove the orderBy clause

B. Create a single-field index on date only

C. No index needed, Firestore handles it automatically

D. Create a composite index on status and date

Solution

1. Step 1: Understand query with multiple filters and sorting

   This query filters by status and sorts by date.

2. Step 2: Know Firestore index requirements

   Firestore requires a composite index for queries with multiple filters or sorting on different fields.

3. Final Answer:

   Create a composite index on status and date -> Option D

4. Quick Check:

   Composite index needed for multiple filters + orderBy = A [OK]

Hint: Use composite indexes for queries with filters and sorting [OK]

Common Mistakes:

• Assuming single-field index is enough
• Thinking Firestore auto-creates all indexes
• Removing orderBy to fix error

4. You wrote this Firestore query:

db.collection('products').where('price', '>', 100).orderBy('name')

But it gives an error about missing index. What is the best fix?

medium

A. Change price filter to ==

B. Remove the orderBy clause

C. Create a composite index on price (ascending) and name (ascending)

D. Create a single-field index on name only

Solution

1. Step 1: Identify query components causing index error

   The query filters by price > 100 and orders by name.

2. Step 2: Understand Firestore index needs for range filter + orderBy

   Firestore requires a composite index on both fields with correct sort order.

3. Final Answer:

   Create a composite index on price (ascending) and name (ascending) -> Option C

4. Quick Check:

   Composite index needed for range filter + orderBy = C [OK]

Hint: Create composite index matching filter and orderBy fields [OK]

Common Mistakes:

• Removing orderBy instead of indexing
• Changing filter operator incorrectly
• Creating only single-field index

5. You want to query a Firestore collection events to find documents where type is 'conference' and attendees is greater than 100, sorted by date ascending. Which index should you create to support this query?

hard

A. Composite index on type (ascending), attendees (ascending), and date (ascending)

B. Single-field index on date only

C. Composite index on type (descending) and date (ascending)

D. No index needed, Firestore supports this query by default

Solution

1. Step 1: Analyze query filters and sorting

   The query filters by type == 'conference' and attendees > 100, and sorts by date ascending.

2. Step 2: Determine required index type

   Multiple filters plus sorting require a composite index including all these fields with correct order.

3. Final Answer:

   Composite index on type (ascending), attendees (ascending), and date (ascending) -> Option A

4. Quick Check:

   Composite index covers all filters and orderBy fields = A [OK]

Hint: Include all filter and orderBy fields in composite index [OK]

Common Mistakes:

• Creating only single-field index
• Using wrong sort order in index
• Assuming no index needed