When not to index in MongoDB - Cheat Sheet & Quick Revision

Recall & Review

beginner

Why should you avoid indexing fields with very few unique values in MongoDB?

Because indexes on fields with low uniqueness (like boolean fields) do not improve query performance much and can add unnecessary overhead.

beginner

What is a downside of indexing fields that are rarely used in queries?

Indexing rarely queried fields wastes storage and slows down write operations without benefiting read performance.

intermediate

Why might you avoid indexing very large fields or fields with large data size?

Because large fields increase index size, which can slow down index maintenance and use more disk space.

beginner

How can too many indexes affect MongoDB write performance?

Each index must be updated on writes, so having many indexes slows down insert, update, and delete operations.

intermediate

When is it not recommended to create an index on a field that changes frequently?

Because frequent changes cause constant index updates, which can degrade write performance.

Which type of field is generally NOT a good candidate for indexing in MongoDB?

A Fields with many unique values

B Fields frequently used in queries

C Fields with very few unique values

D Fields used in sorting

Fields with very few unique values (like booleans) do not benefit much from indexing and add overhead.

What happens if you create too many indexes on a MongoDB collection?

A Storage space decreases

B Read operations become slower

C Indexes automatically merge

D Write operations become slower

Each index must be updated on writes, so too many indexes slow down inserts, updates, and deletes.

Why should you avoid indexing fields that rarely appear in queries?

A They waste resources without benefit

B They reduce storage needs

C They improve query speed

D They speed up writes

Indexing rarely queried fields wastes storage and slows writes without improving query performance.

Which field characteristic can make an index very large and slow to maintain?

A Fields with large data size

B Fields with short strings

C Fields with boolean values

D Small numeric fields

Large fields increase index size, making index updates slower and using more disk space.

When is it NOT recommended to index a field that changes frequently?

A When it is used in queries

B When frequent updates cause overhead

C When it is rarely updated

D When it is a primary key

Frequent updates cause constant index maintenance, which slows down write performance.

Explain why indexing fields with low uniqueness can be harmful in MongoDB.

Think about how useful an index is if many documents share the same value.

Describe situations when you should avoid creating indexes to keep MongoDB efficient.

Consider both read and write performance impacts.

Practice

1. Which of the following is a good reason not to create an index on a MongoDB collection?

easy

A. The collection is very small and queries are fast without indexes.

B. The field is frequently used in queries to filter data.

C. The collection has millions of documents and needs fast lookups.

D. The field has many unique values and is often sorted.

Solution

1. Step 1: Understand index impact on small collections

   Indexes add overhead and use space, so on very small collections, queries are already fast without them.

2. Step 2: Compare with other options

   The other options describe good reasons to create indexes: frequent filtering, large collections, and fields with many unique values used in sorting.

3. Final Answer:

   The collection is very small and queries are fast without indexes. -> Option A

4. Quick Check:

   Small collections often don't need indexes [OK]

Hint: Avoid indexing tiny collections to save space and speed [OK]

Common Mistakes:

• Thinking all collections always need indexes
• Ignoring index overhead on small data
• Confusing frequent query fields with rare ones

2. Which MongoDB command correctly creates an index on the email field of the users collection?

easy

A. db.users.create_index(email)

B. db.users.createIndex({email: 1})

C. db.users.index({email: 'asc'})

D. db.users.addIndex('email')

Solution

1. Step 1: Recall MongoDB index creation syntax

   The correct method is createIndex with a document specifying field and order (1 for ascending).

2. Step 2: Check each option

   db.users.createIndex({email: 1}) matches the correct syntax. The distractors use wrong method names or formats: create_index(email) (incorrect casing, underscore, and argument style), index({email: 'asc'}) (non-existent method), addIndex('email') (wrong method).

3. Final Answer:

   db.users.createIndex({email: 1}) -> Option B

4. Quick Check:

   Use createIndex({field: 1}) syntax [OK]

Hint: Use createIndex({field: 1}) to create ascending index [OK]

Common Mistakes:

• Using wrong method names like create_index
• Passing field name as string instead of object
• Confusing ascending with string 'asc'

3. Given a collection logs with a field status that has only two values: 'success' or 'fail', what is the likely effect of creating an index on status?

medium

A. The index will use extra space but provide no significant speedup.

B. The index will cause errors because of low cardinality.

C. Queries filtering by status will be much faster due to the index.

D. The index will automatically compress the data to save space.

Solution

1. Step 1: Understand low cardinality fields and indexing

   Fields with few distinct values (low cardinality) like 'success'/'fail' do not benefit much from indexes because many documents share the same value.

2. Step 2: Analyze options

   Claims of much faster queries overestimate benefits for low cardinality. No errors occur from low cardinality indexes. Automatic data compression does not happen. The index uses extra space with no significant speedup.

3. Final Answer:

   The index will use extra space but provide no significant speedup. -> Option A

4. Quick Check:

   Low cardinality fields rarely benefit from indexes [OK]

Hint: Avoid indexing fields with few unique values [OK]

Common Mistakes:

• Assuming all indexes speed up queries
• Believing low cardinality causes errors
• Thinking indexes compress data automatically

4. You created an index on the category field of a MongoDB collection, but queries filtering by category are still slow. What is a likely cause?

medium

A. MongoDB does not support indexing on string fields.

B. The index was created with the wrong field name syntax.

C. The category field has very few distinct values causing low index efficiency.

D. The collection is empty, so indexes do not work.

Solution

1. Step 1: Consider index usefulness on low distinct values

   If category has few unique values, the index won't help much because many documents share the same value.

2. Step 2: Evaluate other options

   Wrong field name syntax is unlikely if the index exists; MongoDB supports indexing on string fields; empty collections have no data to slow down queries.

3. Final Answer:

   The category field has very few distinct values causing low index efficiency. -> Option C

4. Quick Check:

   Low distinct values reduce index speedup [OK]

Hint: Check field cardinality if index doesn't speed queries [OK]

Common Mistakes:

• Assuming all indexes always speed queries
• Believing MongoDB can't index strings
• Thinking empty collections cause index failure

5. You have a MongoDB collection orders with 100 documents. The status field has values 'pending', 'shipped', 'delivered'. You want to optimize queries filtering by status. What is the best indexing strategy?

hard

A. Create multiple indexes on each possible status value separately.

B. Create a single-field index on status to speed up queries.

C. Create a compound index on status and orderDate even if orderDate is rarely queried.

D. Do not create an index because the collection is small and status has few distinct values.

Solution

1. Step 1: Analyze collection size and field cardinality

   The collection is very small (100 documents), and status has only three distinct values, so indexing will add overhead without much benefit.

2. Step 2: Evaluate indexing options

   Create a single-field index on status to speed up queries. creates an index but likely unnecessary here. Create a compound index on status and orderDate even if orderDate is rarely queried. adds complexity with rarely used field. Create multiple indexes on each possible status value separately. is invalid as MongoDB does not create separate indexes per value.

3. Final Answer:

   Do not create an index because the collection is small and status has few distinct values. -> Option D

4. Quick Check:

   Small collections and low cardinality fields often don't need indexes [OK]

Hint: Skip indexes on small collections with few unique values [OK]

Common Mistakes:

• Creating unnecessary indexes on small data
• Misunderstanding compound index benefits
• Trying to index each value separately