Identifying missing indexes in MongoDB - Step-by-Step Execution

Concept Flow - Identifying missing indexes

Run slow query

↓

Check query plan

↓

Look for COLLSCAN (collection scan)?

No→Index used, no missing index

Yes↓

Analyze query fields

↓

Suggest index on those fields

↓

Create index

↓

Query runs faster

This flow shows how to find queries that do not use indexes and then create indexes to speed them up.

Execution Sample

MongoDB

db.collection.find({age: {$gt: 30}}).explain('executionStats')

This command runs a query and shows how MongoDB executes it, including whether it uses an index.

Execution Table

Step	Action	Query Plan Detail	Index Used?	Result
1	Run query with explain	COLLSCAN	No	Full collection scan, slow
2	Identify filter field	Filter on 'age' field	N/A	No index on 'age'
3	Suggest index	Create index on 'age'	N/A	Index created
4	Run query again with explain	IXSCAN on 'age'	Yes	Index used, faster query
5	Exit	Query optimized	Yes	Execution stops

💡 After creating index on 'age', query uses index and runs faster, so no missing index remains

Variable Tracker

Variable	Start	After Step 1	After Step 3	After Step 4	Final
Index on 'age'	None	None	Created	Created	Created
Query plan	N/A	COLLSCAN	N/A	IXSCAN	IXSCAN

Key Moments - 2 Insights

Why does the query show COLLSCAN in the plan at first?

Because there is no index on the 'age' field yet, MongoDB scans the whole collection (COLLSCAN) as shown in execution_table step 1.

How do we know the index creation fixed the problem?

After creating the index, the query plan changes to IXSCAN (index scan) in step 4, meaning the index is used and the query is faster.

Visual Quiz - 3 Questions

Look at the execution_table, what is the query plan detail at step 1?

A COLLSCAN

B IXSCAN

C FETCH

D SORT

💡 Hint

Check the 'Query Plan Detail' column in execution_table row with Step 1

At which step is the index on 'age' created?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the 'Index Used?' and 'Action' columns in execution_table for index creation

If the index was not created, what would the query plan show at step 4?

A IXSCAN

B COLLSCAN

C FETCH

D SORT

💡 Hint

Refer to execution_table step 1 and 4 to see difference after index creation

Concept Snapshot

Identifying missing indexes in MongoDB:
- Run query with .explain('executionStats')
- Check if query plan shows COLLSCAN (collection scan)
- If yes, no index is used on filter fields
- Create index on those fields
- Query then uses IXSCAN (index scan) and runs faster

Full Transcript

To identify missing indexes in MongoDB, first run your query with explain('executionStats') to see how MongoDB executes it. If the query plan shows COLLSCAN, it means MongoDB scans the whole collection because no suitable index exists. Next, analyze which fields are used in the query filter and create an index on those fields. After creating the index, run the query again with explain. Now the query plan should show IXSCAN, meaning the index is used and the query runs faster. This process helps find and fix missing indexes to improve query performance.

Practice

1. What is the main reason to create indexes in MongoDB collections?

easy

A. To store more data in the database

B. To make queries run faster by helping MongoDB find data quickly

C. To backup the database automatically

D. To encrypt the data for security

Solution

1. Step 1: Understand the purpose of indexes

   Indexes are special data structures that help MongoDB quickly locate data without scanning the entire collection.

2. Step 2: Compare options with index purpose

   Only To make queries run faster by helping MongoDB find data quickly correctly states that indexes speed up queries by helping find data faster.

3. Final Answer:

   To make queries run faster by helping MongoDB find data quickly -> Option B

4. Quick Check:

   Indexes improve query speed = A [OK]

Hint: Indexes speed up data search, not storage or security [OK]

Common Mistakes:

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

2. Which MongoDB command lists all indexes on a collection named users?

easy

A. db.users.showIndexes()

B. db.users.getIndex()

C. db.users.findIndexes()

D. db.users.listIndexes()

Solution

1. Step 1: Recall the correct method to list indexes

   The correct MongoDB method to list indexes on a collection is listIndexes().

2. Step 2: Check each option's validity

   Only db.users.listIndexes() is a valid MongoDB command; others are incorrect or do not exist.

3. Final Answer:

   db.users.listIndexes() -> Option D

4. Quick Check:

   List indexes command = listIndexes() [OK]

Hint: Use listIndexes() to see all indexes on a collection [OK]

Common Mistakes:

• Using getIndex() which is not a MongoDB method
• Trying findIndexes() which does not exist
• Confusing showIndexes() with listIndexes()

3. Given the query db.orders.find({ customerId: 123 }) and the collection has no index on customerId, what will explain('executionStats') likely show?

medium

A. A COLLSCAN (collection scan) with high number of documents examined

B. An IXSCAN (index scan) using the customerId index

C. A syntax error because explain cannot be used here

D. No documents returned because no index exists

Solution

1. Step 1: Understand query behavior without index

   Without an index on customerId, MongoDB must scan the entire collection (COLLSCAN) to find matching documents.

2. Step 2: Interpret explain output

   The explain('executionStats') will show a COLLSCAN and a high number of documents examined, indicating no index was used.

3. Final Answer:

   A COLLSCAN (collection scan) with high number of documents examined -> Option A

4. Quick Check:

   No index means COLLSCAN = B [OK]

Hint: No index means full collection scan in explain output [OK]

Common Mistakes:

• Expecting an index scan without an index
• Thinking explain causes syntax error
• Assuming no documents returned without index

4. You notice a slow query on db.products.find({ category: 'books', price: { $lt: 20 } }). The collection has an index only on category. What is the best fix?

medium

A. Rewrite the query to remove the price filter

B. Remove the existing index on category

C. Add a compound index on { category: 1, price: 1 }

D. Add an index only on price

Solution

1. Step 1: Analyze existing index and query filters

   The query filters on both category and price, but only category is indexed, so the price filter is not optimized.

2. Step 2: Choose the best index to optimize query

   A compound index on { category: 1, price: 1 } allows MongoDB to efficiently filter by both fields, speeding up the query.

3. Final Answer:

   Add a compound index on { category: 1, price: 1 } -> Option C

4. Quick Check:

   Compound index matches query fields = D [OK]

Hint: Use compound indexes for queries filtering multiple fields [OK]

Common Mistakes:

• Removing useful indexes
• Adding index on only one field when query filters multiple
• Changing query to remove filters instead of indexing

5. A MongoDB collection logs has slow queries filtering by userId and sorting by timestamp. Which index best improves performance?

hard

A. A compound index on { userId: 1, timestamp: -1 }

B. An index on { timestamp: 1 }

C. An index on { userId: 1 }

D. No index needed; increase RAM instead

Solution

1. Step 1: Understand query pattern with filter and sort

   The query filters by userId and sorts by timestamp, so both fields affect performance.

2. Step 2: Select index that supports filter and sort

   A compound index on { userId: 1, timestamp: -1 } supports efficient filtering by userId and sorting by timestamp in descending order.

3. Final Answer:

   A compound index on { userId: 1, timestamp: -1 } -> Option A

4. Quick Check:

   Compound index matches filter and sort = C [OK]

Hint: Compound index with sort field improves filter + sort queries [OK]

Common Mistakes:

• Using single-field index ignoring sort
• Choosing wrong sort order in index
• Ignoring indexing and blaming hardware