---

title: Pagination in MongoDB- Scaler Topics
description: With this article by Scaler Topics we will learn all about MongoDB Pagination along with their examples and explanations.
category: MongoDB
author: Bharti Kumari
---

:::section{.abstract}
## Overview
This article provides an overview of `Pagination in MongoDB`, which is the process of dividing a large set of query results into smaller, more manageable chunks. Pagination is commonly used in web applications to display query results in a user-friendly manner. The article explains the various methods available in MongoDB for implementing pagination, including the use of the `limit()` and `skip()` methods. Additionally, the article discusses best practices for optimizing pagination performance in MongoDB, such as using indexes Overall, the article offers a comprehensive guide to implementing `Pagination in MongoDB.`

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:::section{.main}
## What is Pagination in MongoDB?
In MongoDB, `pagination` refers to the process of retrieving a subset of data from a collection in a structured manner. This is commonly used when working with large datasets, where you don't want to fetch all the data at once but only a portion of it. Pagination in MongoDB allows you to split the data into smaller, more manageable chunks. Pagination in MongoDB improves performance and reduces memory usage.

Pagination in MongoDB is achieved using two methods: `limit()` and `skip()`. The `limit()` method is used to specify the maximum number of documents to return in a single query, while the skip() method is used to specify the number of documents to skip before starting to return the data.

> For example, let's say you have a collection with 1000 documents and you want to retrieve 100 documents at a time. You can use the `limit()` method to specify that you want to retrieve 100 documents, and the `skip()` method to skip the first 0 documents in the collection for the first page, 100 documents for the second page, and so on.

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:::section{.main}
## How Pagination Works?

Pagination in MongoDB is a technique used to `divide a large dataset into smaller`, more manageable pieces that can be displayed or processed efficiently. The basic idea behind pagination is to retrieve a limited number of records from a dataset at a time instead of `retrieving` the entire dataset at once. Here's how Pagination in MongoDB typically works:

1. **Query the dataset:** The first step is to query the entire dataset using a database query or an `API call`. This will return all the records that match the specified search criteria.
1. **Determine the page size:** The next step is to determine the number of records that should be displayed on a single page. This is known as the "page size" and is usually set to a fixed number `(e.g., 10, 25, 50, or 100 records per page)`.
1. **Calculate the number of pages:** The total number of pages in the dataset can be calculated by dividing the total number of records by the page size. If the total number of records is not evenly divisible by the page size, the last page will have fewer records than the other pages.
1. **Retrieve the current page:** To retrieve a specific page, you need to calculate the offset of the first record on that page. The offset is calculated by multiplying the page number by the page size. For example, to retrieve `page 3` with a page size of 10, you would need to skip the first `20` records `(i.e., 2 * 10)` and retrieve the next `10` records.
1. **Display the current page:** Once you have retrieved the records for the current page, you can display them to the user or process them as needed.
1. **Repeat for additional pages:** If there are more pages in the dataset, you can repeat the process for each subsequent page until all the records have been retrieved.

`Pagination` in MongoDB is a powerful technique that can be used to improve the performance and usability of applications that work with large datasets. By breaking the dataset into smaller pieces, you can reduce the amount of data that needs to be retrieved, processed, and displayed at any given time, resulting in faster response times and a more `responsive` user interface.

It's important to note that Pagination in MongoDB can be resource-intensive for large datasets, especially when using the `skip() and limit() `methods in MongoDB or similar techniques in other databases. In these cases, it may be better to use other pagination techniques, such as cursor-based pagination, which can be more efficient for large datasets.

### a. Using Limit Method

The `limit()` method in MongoDB is used to specify the maximum number of documents to return in a query result. This method can be used in conjunction with other query methods, such as `find()`, `aggregate()`, and `count()`, to limit the number of documents returned by the query.

**Syntax**

db.collection.find().limit(n)

**Parameters**
`n:` Specifies the maximum number of documents to return. If n is not provided or is equal to 0, the query will return all matching documents.

**Example**
Suppose you have a collection named `books` that contain information about books. You can use the `limit()` method to retrieve the first 10 books from the collection as follows:

db.books.find().limit(10)

This will return the first 10 `books` in the books collection, according to the natural order of the documents in the collection.

It's important to note that the `limit()` method can be useful in managing large datasets, but it should be used with care. If the limit is set too high, it can negatively impact the query performance, so it's important to use the appropriate limit value based on the size of the dataset and the performance requirements of your application.

### b. Using Skip Method

The `skip()` method in MongoDB is used to skip a specified number of documents in a query result. This method can be used in conjunction with other query methods, such as `find()`, `aggregate()`, and `count()`, to skip a certain number of documents before returning the remaining documents in the query result.

**Syntax**

db.collection.find().skip(n)

**Parameters**
`n:` Specifies the number of documents to skip. If n is not provided or is equal to 0, the query will not skip any documents.

**Example**
Suppose you have a collection named `books` that contains information about books. You can use the `skip()` method to skip the first 10 books in the collection and return the remaining books as follows:

db.books.find().skip(10)

This will skip the first 10 books in the `books` collection and return the remaining books in the collection.

> It's important to note that the `skip()` method can be useful in managing large datasets, but it should be used with care. If the skip value is set too high, it can `negatively `impact the query performance, so it's important to use the appropriate skip value based on the size of the dataset and the performance requirements of your application. Additionally, the use of `skip()` with large collections may require an index to support efficient queries.

### c. Using ID Field

In MongoDB, the `_id` field is a mandatory field for every document in a collection, which serves as a unique identifier for that document. By default, MongoDB generates a unique `_id` field value for each new document, but you can also specify a custom **`_id`** value when inserting new documents.

**Syntax**

{ "_id": <value>, ... }

**Parameters**
`<value>:` Specifies the value of the `_id` field. This can be any valid BSON data type, such as a string, integer, or object.

### Example
Suppose you have a collection named **users** that contain information about users, and you want to insert a new user document with a custom `_id` value. You can do so as follows:

db. users.insertOne({
  "_id": "1234",
  "name": "John Doe",
  "email": "[e-mail]"
})

This will insert a new user document into the `users` collection with a custom `_id` value of "1234", along with the name and email fields. If you do not specify a custom `_id` value when inserting a new document, MongoDB will generate a unique `_id` value for the document.

It's important to note that the `_id` field is indexed by default in MongoDB and can be used for efficient queries and lookups. Additionally, MongoDB provides several special data types, such as `ObjectId`, which can be used for generating unique `_id` values with built-in support for indexing and sorting.

### d. Using Slice Operator
The `$slice` operator in MongoDB is used to return a subset of an array field in a document. This operator can be used in conjunction with the `find()` method to limit the number of elements returned in an array field.

**Syntax**

db. collection.find({}, { field: { $slice: <value> } })

**Parameters**

`<value>:` Specifies the slice value. This can be a positive integer to return the first `n` elements of the array, a negative integer to return the last `n` elements of the array or an array of two integers to skip the first `n` elements and return the next `m` elements of the array.

**Example**
Suppose you have a collection named **students** that contain information about students, and each student document has an array field named `grades` containing the grades of the student. You can use the `$slice` operator to retrieve a subset of the `grades` array field for each student as follows:

To retrieve the first 5 grades of each student:

db.students.find({}, { grades: { $slice: 5 } })

> It's important to note that the `$slice` operator can also be used in combination with other query methods, such as `sort()` and `limit()`, to further refine the query results. Additionally, the `**$slice`** operator is only applicable to array fields in a document and will not affect non-array fields.

### e. Using Range Queries

In MongoDB, range queries allow you to retrieve documents that fall within a specified range of values. You can perform range queries on any field in a document that contains a value that can be compared, such as numbers or dates.

**Syntax**

db. collection.find({ field: { $operator: value } })

**Parameters**

* **field:** Specifies the field name to perform the range query on.
* **$operator:** Specifies the comparison operator to use in the range query. This can be any of the following comparison operators:
* **$gt:** Matches values that are greater than the specified value.
* **$gte:** Matches values that are greater than or equal to the specified value.
* **$lt:** Matches values that are less than the specified value.
* **$lte:** Matches values that are less than or equal to the specified value.
* **Value:** Specifies the value to compare against in the range query.

**Example**

Suppose you have a collection named `sales` that contains information about sales transactions, and each sale document has a field named `amount` containing the transaction amount. You can use range queries to retrieve all sales transactions that fall within a specific range of transaction amounts as follows:

To retrieve all transactions greater than `$500`:

db. sales.find({ amount: { $gt: 500 } })

In the last example, the range query uses two comparison operators (`$gt` and `$lte`) to specify a range of transaction amounts to retrieve.

> It's important to note that you can also use range queries in conjunction with other query methods, such as `sort()` and `limit()`, to further refine the query results. Additionally, you can perform range queries on any field in a document that contains a value that can be compared, such as numbers or dates.

### f. Using Index Creation

In MongoDB, indexes are used to optimize query performance by providing a faster way to access data. Indexes can be created on one or more fields in a collection to improve the speed of queries that use those fields as criteria.

**Syntax**

db. collection.create index({ field: direction })

Where `collection` is the name of the collection to create the index on, `field` is the name of the field to index, and direction is the sorting `direction` for the index (1 for ascending order, -1 for descending order).

**Parameters**

**Collection:** Specifies the name of the collection to create the index on.

**Field:** Specifies the name of the field to index.

**direction:** Specifies the sorting direction for the index. This can be either 1 for ascending order or -1 for descending order.

**Example**
Suppose you have a collection named `users` that contains information about users, and each user document has a field named `username`. To create an index on the `username` field to improve the speed of queries that use the `username` field as a criterion, you can use the following command:

db. users.createIndex({ username: 1 })

In this example, the `createIndex()` method is called on the `users` collection with an object that specifies the `username` field to be indexed with a sorting direction of 1 `(i.e. ascending order)`.

It's important to note that creating indexes can have an impact on write performance and storage requirements, so you should only create indexes on fields that are frequently used as query criteria. Additionally, you can create compound indexes on multiple fields to further optimize query performance.

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:::section{.faq-section}
## FAQs

**Q.** What methods are available for implementing Pagination in MongoDB?

**A.** MongoDB provides several methods for implementing pagination, including the use of the `limit()` and `skip()` methods, as well as the aggregation pipeline and the cursor-based method.

**Q.** How do I optimize pagination performance in MongoDB?

**A** To optimize pagination performance in MongoDB, you can use indexes to speed up queries and reduce the number of network roundtrips. You can also limit the number of documents retrieved on each page and avoid using `skip()` with large offsets.

**Q.** What are the drawbacks of using the `skip()` method for Pagination in MongoDB?

**A** Using the `skip()` method for Pagination in MongoDB can be slow and resource-intensive when working with large datasets. This is because `skip()` requires the server to scan through all the documents in the collection until it reaches the desired offset.

**Q.** How can I use the cursor-based method for Pagination in MongoDB?

**A** To use the cursor-based method for Pagination in MongoDB, you can create a cursor with the `find()` method and use the `limit()` method to specify the number of documents to retrieve per page. You can then use the `next()` method to iterate through the cursor and retrieve the next set of documents.

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:::section{.summary}
## Conclusion
* **Pagination in MongoDB** is the process of dividing a large set of query results into smaller, more manageable chunks.
* MongoDB provides several methods for implementing pagination, including the use of the `limit()` and `skip()` methods, as well as the aggregation pipeline and the **cursor-based method**.
* To optimize pagination performance in MongoDB, you can use **indexes** to speed up queries and **reduce the number of network roundtrips**. You can also limit the number of documents retrieved on each page and avoid using `skip()` with large offsets.
* The **cursor-based** method is generally recommended for implementing Pagination in MongoDB, as it provides better performance and scalability than the `skip()` method.
* When working with large datasets, it's important to carefully consider the performance implications of pagination and choose the appropriate method for your use case.
* By following best practices and optimizing your queries, you can effectively implement `Pagination in MongoDB` and provide a seamless user experience for your applications.

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