Indexing and Query Performance in MongoDB.pdf

Indexing and Query Performance
in MongoDB
Malak Abu Hammad

Introduction
● The Dual Challenge: Ensuring data retrieval is swift while managing resources
effectively.
● Role of Indexing: The bridge to effective and efficient querying.

So .. What’s an Index?
● Indexes are data structures that support the efficient execution of queries in
MongoDB. They contain copies of parts of the data in documents to make queries
more efficient.
● Without indexes, MongoDB must scan every document in a collection to find the
documents that match each query.
● Types of indexes:
○ Single index
○ Compound index

Query Structure
● Query criteria
● Options, such as read concern
● Projection criteria (optional)
db.collection.find(
{name: “malak”},
{birthdate : 1, _id : 0}
).readConcern("majority")

Why Indexing is Essential?
● Read Performance: Faster data access, reduced wait times.
● Resource Management: Eﬃcient CPU and memory utilization.

Creating and Managing Indexes
● Create single index
db.collection.createIndex(
{field: 1},
{
unique: true,
name: “abc”
}
)
● Viewing Indexes
db.collection.getIndexes();
[
{
"v" : 2,
"key" : {
"_id" : 1
},
"name" : "_id_"
},
{
"v" : 2,
"key" : {
"status" : 1
},
"name" : "status_1"
}
]

Compound Indexes
● Create compound index index
db.collection.createIndex(
{
field1: 1,
field2: -1
},
{
unique: true,
name: “abc”
}
)
● Order of fields in index and in a
query matters.
● Prefixes
For instance, if you have a compound index on
{a: 1, b: 1, c: 1}
The possible prefix indexes are
● {a: 1}
● {a: 1, b: 1}
MongoDB can use the compound index for queries
that filter on:
● Only a
● Both a and b
● All a, b, and c

Special Index Types and Use Cases
● Text Indexes: For searching text content in documents.
● Geospatial Indexes: Finding items within proximity.
● Wildcard Indexes: Flexible indexing for evolving schemas.

Performance Analysis
● Explain Method: Understanding how MongoDB executes a query.
● Spotting Slow Queries: Using MongoDB logs and monitoring tools.
○ MongoDB has a built-in proﬁler that logs all operations taking longer than a speciﬁed threshold.
Visual tools can represent this data, making it easy to spot problematic operations or patterns.
● Visualization
○ Visual tools can represent this data, making it easy to spot problematic operations or patterns.
○ Such as: Atlas , Compass

Performance Analysis - Explain Method
db.collection.find({quantity: {$gte: 100, $lte: 200}})
.explain("executionStats")
{// without index
queryPlanner: {
...
winningPlan: {
queryPlan: {
stage: 'COLLSCAN',
}
}
},
executionStats: {
executionSuccess: true,
nReturned: 3,
executionTimeMillis: 0,
totalKeysExamined: 0,
totalDocsExamined: 10,
executionStages: {
stage: 'COLLSCAN',
},
},
}
{ //with index
queryPlanner: {
winningPlan: {
queryPlan: {
stage: 'FETCH',
inputStage: {
stage: 'IXSCAN',
keyPattern: {
quantity: 1
},
}
}
},
rejectedPlans: [ ]
},
executionStats: {
executionSuccess: true,
nReturned: 3,
executionTimeMillis: 0,
totalKeysExamined: 3,
totalDocsExamined: 3,
executionStages: {
},
},
}

Query Optimization
● Index Selection
○ Query Planner
MongoDB's query planner evaluates the available indexes and chooses the most efficient way to
execute the query. The following steps are taken:
■ Candidate Indexes
■ Plan Generation
■ Plan Evaluation
○ Index Intersection
○ Query Selectivity
○ Cache
○ Impact of Write Operations
● Hint Method: Forcing a specific index.
● Covered Queries: Efficiently fetching data without scanning documents.

Covered query
A covered query is a query that can be satisfied entirely using an index and
does not have to examine any documents
An index covers a query if this criteria applies:
● All the fields in the query are part of an index, and
● All the fields returned in the results are in the same index, and
● No fields in the query are equal to null (i.e. {"field" : null} or {"field" : {$eq :
null}} ).

Write Performance & Indexes
● The Trade-oﬀ: Every index adds to write overhead.
● Striking a Balance: Periodic assessment is mandatory.

Index Maintenance
● Fragmentation: Over time, index
eﬃciency can degrade.
● Rebuilding: Periodically refreshing
indexes.
db.collection.reIndex()
● Monitoring: Using built-in tools to
watch index performance.

Best Practices
● Avoid Over-indexing: Too many indexes can backﬁre.
● Analyze Workloads: Adjust indexing strategies based on real-world usage.
● Operational Overhead: Be aware of the cost of maintaining indexes.

Case Study: E-Commerce Platform Product Search Optimization
An e-commerce platform, named "ShopMMS" was experiencing performance issues. As
they scaled and added more products, users began to report slow search times when
looking for products. The platform was built on a MongoDB backend.
Problem
● As the number of products grew into the millions, searches that used to take
milliseconds started taking several seconds.
● The platform's reviews and ratings system, which allowed users to ﬁlter and sort
products based on ratings, added further complexity to the search queries.

Further Resources
● Oﬃcial MongoDB Documentation
https://www.mongodb.com/docs/manual/indexes/
● MongoDB University: M201: MongoDB Performance
https://learn.mongodb.com/courses/m201-mongodb-performance
● “Indexing and Query Performance in MongoDB” presentation will be available on
slideshare
https://www.slideshare.net/mms414/

Feedback & Networking
● MongoDB Arabic Community
Linkedin: https://www.linkedin.com/company/mongodb-arabic-community/

ً‫ا‬‫ﺷﻜﺮ‬
Thank you

Indexing and Query Performance in MongoDB.pdf

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