Seek pagination is even more robust, as it returns consistent ordering even when new items are added to the table. Seek pagination can be more complicated for the backend to implement, however. It can also get thrown off when items are removed from the database. Subsequent queries would not result in expansive database hits, and will be served very quickly from the application server’s memory.

Cursor-based pagination is more efficient than offset-based pagination when dealing with rapidly changing data, as it ensures data stability and avoids potential duplication or omission of items. It also simplifies navigation through the data and provides a consistent experience, even if items are added or removed during pagination. Since it uses sequentially ordered keys, keyset pagination can provide a consistent performance and replicable results, irrespective of the data set’s size or its rate of updates. This, however, makes the method unsuitable for data that’s not sequential.

GraphQL Pagination Libraries

In conclusion, implementing effective API pagination is essential for providing efficient and user-friendly access to large datasets. Pagination typically involves the use of parameters, such as offset and limit or cursor-based tokens, to control the size and position of the data subset to be retrieved. These parameters determine the starting point and the number of records to include on each page. Pagination comes in handy where you want to show a large data set in manageable chunks. However, need to be careful in how you implement it, in order to serve both your users and search engines effectively.

In scenarios where data has a temporal aspect, time-based pagination can be useful. It involves using time-related parameters, such as “start_time” and “end_time,” to specify a time range for retrieving data. In this article, we will discuss the best practices for implementing API pagination, ensuring that developers can handle large datasets effectively and deliver data in a manageable and efficient manner.

API pagination best practices

There are a lot of different ways to implement the pagination algorithm. Depending on the method used, both performance and the user experience will be affected. This method might combine elements of offset, cursor, keyset, page-based, and time-based pagination, depending on the data set’s nature and the application’s requirements.

Once users are on your site, then they have the opportunity to interact with other pages as structured by your pagination. While these alternative pagination methods aren’t as prevalent as standard techniques, they offer unique ways to enhance user experience and engagement. When considering one of these pagination in web application methods, always prioritize usability testing to ensure it aligns with the expectations and preferences of your target audience. Now, let’s look at an example where we use the parameters by which we can/want to give out unique content, and it’s important for us to keep such filtered pages in the index.

Combined pagination

Pagination thus helps to limit the number of results to help keep network traffic in check. We call the createPageButtons() function before the showPage() function. First, we retrieve all the pagination buttons using the document.querySelectorAll and assign them to the pageButtons variable. Next, we create a click event listener and attach it to the page buttons. When a button is clicked, the event listener’s callback function gets executed. If the index is within the range, the toggle keyword applies the hidden class (which we’ll define in our CSS code) to the item, effectively hiding it.

• With such a large volume of data, the recovery process can quickly become resource-intensive.
• Pagination also makes it easier for a user to find the information they are looking for.
• However, excessively large page sizes may make it challenging for users to find specific records or navigate through the data efficiently.
• Time-based pagination is a type of keyset pagination used to navigate a data set based on time criteria.

This logic should reside in your application, and will work with both database-driven and middle-tier pagination algorithms. Modern frameworks and database drivers allow efficient in-memory traversal of data on the application server level or middle-tier level. However, because this approach could be combined with the caching solution on the application server, it should be considered for small to medium data sets. Unless the returning result set is guaranteed to be very small, any web application with search capabilities must have pagination. For instance, if the result set is less then 30 rows, pagination may be optional. However, if it’s bigger then 100 rows, pagination is highly recommended, and if it’s bigger then 500 rows, pagination is practically required.

Unlocking the Power of API Pagination: Best Practices and Strategies

If you are not snapshotting the original data set, then this is just a fact of life. In his free time he loves to watch soccer matches, go on long runs in  parks, and explore local restaurants. When building customer-facing integrations (i.e. product integrations), you’ll be dealing with a diverse set of 3rd-party APIs that use different types of pagination. This can make your integration development process all the more complicated and time consuming. For example, consider a developer that wants to program a request to the Spotify API to retrieve catalog information about a specific artist’s albums. Using pagination and filtering could be helpful, especially if the artist has a large discography.

To summarize, we’ve looked at what API pagination is, as well as some of the most common methods of implementation. These include offset pagination, keyset pagination, and seek pagination. We also discuss some of the merits and shortcomings of each approach to help you decide which approach is best for your API designs. Keyset pagination, also known as range-based pagination, relies on sorting and filtering based on the properties of the data. Instead of relying on page numbers or offsets, the API request includes the criteria for retrieving the following data set. Cursor-based pagination uses a cursor, such as an ID or a timestamp, to mark the position from which the next set of data should be fetched.

Are there situations where infinite scroll is more beneficial than traditional pagination on mobile interfaces?

Small data sets do work on effectively on offset pagination but large realtime datasets do require cursor pagination. Keyset pagination, or the seek method, is tailored for scenarios that involve large or frequently-updated data sets. It involves using a specific field (or set of fields) as a key to paginate through the data set. This key is usually a unique and sequentially ordered column—for example, a timestamp or an auto-incrementing ID. Firstly, we create a createPageButtons() function that will store the logic to create our buttons.

Queries just happen to be the easiest to implement as well as consistent, so we’re going to stick with that approach. Keyset pagination uses the filter values of the previous page to determine the next set of items. It’s also stateless on the server side and works regardless of custom sort_by parameters. API queries to dense databases could potentially return millions, if not billions, of results.

How Should You Paginate a Web API?

In addition, the pressure on server resources increases, potentially leading to slowdowns or even system crashes. Pagination allows you to retrieve data (REST resources) in an efficient way that doesn’t strain the backend or frontend. More specifically, it allows you to partition data into digestible segments. This helps ensures smooth data transactions, which prevents server strain and enhances client experiences. The benefits of this approach is that it doesn’t require additional backend logic.