Composite index requirements in Firebase - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

What happens if a Firestore query requires a composite index that does not exist?

You run a Firestore query that filters on two fields and orders by a third. The required composite index is missing. What will Firestore do?

A The query returns an empty result set without error.

B Firestore automatically creates the composite index in the background and runs the query.

C The query runs but ignores the order by clause.

D Firestore throws an error with a direct link to create the missing composite index.

💡 Hint

Think about how Firestore helps developers create missing indexes.

Firestore detects missing composite indexes needed for queries and returns an error with a direct link to create the index in the Firebase console.

🧠 Conceptual

intermediate

Why are composite indexes required for some Firestore queries?

Firestore requires composite indexes for queries that:

• Filter on multiple fields
• Order by fields not included in single-field indexes
• Use range filters on multiple fields

Which reason best explains why composite indexes are needed?

A To speed up queries by pre-sorting data on multiple fields together.

B To reduce Firestore storage costs by combining indexes.

C To allow queries to run without any network calls.

D To automatically backup data before queries run.

💡 Hint

Think about how indexes help databases find data faster.

Composite indexes speed up queries by indexing multiple fields together, allowing Firestore to quickly find and sort matching documents.

📝 Syntax

advanced

Which Firestore index definition JSON correctly defines a composite index on fields 'status' (ascending) and 'createdAt' (descending)?

Choose the valid JSON snippet for a composite index on 'status' ascending and 'createdAt' descending.

A { "collectionGroup": "orders", "queryScope": "COLLECTION", "fields": [{"fieldPath": "status", "order": "ASC"}, {"fieldPath": "createdAt", "order": "DESC"}] }

B { "collection": "orders", "fields": [{"fieldPath": "status", "order": "ASCENDING"}, {"fieldPath": "createdAt", "order": "DESCENDING"}] }

C { "collectionGroup": "orders", "queryScope": "COLLECTION", "fields": [{"fieldPath": "status", "order": "ASCENDING"}, {"fieldPath": "createdAt", "order": "DESCENDING"}] }

D { "collectionGroup": "orders", "queryScope": "COLLECTION", "fields": [{"fieldPath": "status", "order": "ascending"}, {"fieldPath": "createdAt", "order": "descending"}] }

💡 Hint

Check the exact property names and values Firestore expects in index definitions.

Firestore index JSON uses 'collectionGroup', 'queryScope', and 'fields' with 'fieldPath' and 'order' properties. The order values must be uppercase 'ASCENDING' or 'DESCENDING'.

❓ optimization

advanced

How can you minimize the number of composite indexes needed for multiple queries on the same collection?

You have several queries on a Firestore collection filtering and ordering by different combinations of fields. What is the best way to reduce the total composite indexes you must create?

A Create separate composite indexes for each query to avoid conflicts.

B Create one composite index that includes all fields used in any query, ordered by frequency of use.

C Use only single-field indexes and avoid composite indexes entirely.

D Create composite indexes only for the most complex queries and let others run without indexes.

💡 Hint

Think about how composite indexes can cover multiple queries if designed carefully.

A single composite index covering all fields used in queries can serve multiple queries if the order of fields matches query requirements, reducing the total indexes needed.

🔧 Debug

expert

Why does this Firestore query fail despite having a composite index defined?

Given a composite index on fields 'category' (ascending) and 'price' (descending), this query filters where category = 'books' and orders by price ascending. Why does it fail?

db.collection('products').where('category', '==', 'books').orderBy('price', 'asc')

A The composite index order for 'price' is descending but the query orders ascending, causing a mismatch.

B Firestore does not support ordering by fields with equality filters.

C The query must include an orderBy on 'category' before ordering by 'price'.

D The composite index must include a filter on 'price' as well.

💡 Hint

Check if the order directions in the query match the index definition.

Firestore requires the order directions in the query to match the composite index exactly. Here, the index orders 'price' descending but the query orders ascending, causing failure.