When not to index in MongoDB

Introduction

Indexes help find data faster, but sometimes they slow things down or use too much space. Knowing when not to add an index saves resources and keeps your database fast.

When the collection is very small and queries are fast without indexes.

When the field has many repeated values, so the index won't help much.

When you write data very often and indexing slows down inserts or updates.

When the field is rarely used in queries or sorting.

When storage space is limited and indexes take too much room.

Syntax

MongoDB

No specific syntax because this is about deciding NOT to create an index.

Indexes are created with db.collection.createIndex() in MongoDB.

Choosing not to index is a decision based on data and query patterns.

Examples

This means you skip db.collection.createIndex({status: 1}) to save space and write speed.

MongoDB

// No index created on 'status' because it has few unique values
// and queries on it are rare.

For small collections, scanning all documents is quick, so no index needed.

MongoDB

// No index on 'description' field because collection is small
// and full collection scans are fast.

Sample Program

This example shows a situation where you choose not to create an index on a rarely used field in a small collection.

MongoDB

use shopDB
// Suppose 'orders' collection is small and 'notes' field is rarely queried
// We decide NOT to create an index on 'notes'
// So no createIndex command is run here

// To check existing indexes:
db.orders.getIndexes()

Important Notes

Too many indexes slow down data writes because each index must be updated.

Indexes use extra disk space, so avoid indexing fields with low uniqueness.

Always monitor query performance and adjust indexes as your data grows.

Summary

Indexes speed up queries but can slow down writes and use space.

Don't index small collections or fields rarely used in queries.

Skip indexing fields with many repeated values to save resources.

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