Improving integration performance with API pagination

When dealing with large datasets, loading all the data at once can impact efficiency, leading to slow response times, increased network traffic, and a frustrating user experience.

In this article, you’ll learn how you can leverage Digibee to address these challenges by implementing pagination techniques into your integrations to enhance overall application performance.

The challenges of unpaginated data

Without effective pagination, applications face several issues:

• Slow load times: Users experience delays, affecting their interaction with the application.

• Performance degradation: Applications may become unresponsive or crash due to excessive data loads.

• Increased network traffic: Handling large volumes of data in single requests can degrade performance.

• Time-outs and errors: High data volumes can lead to timeouts and out-of-memory errors.

Consider a customer management dashboard that needs to display thousands of records. Loading all entries at once would be inefficient and slow. Now, imagine an e-commerce platform that attempts to load all product listings in a single request. Both scenarios highlight the importance of pagination. By breaking down large datasets into smaller chunks, pagination can significantly improve performance and user experience.

Considerations for implementing pagination

• General requirements: Evaluate data volume and time limit when deciding the appropriate pagination approach.

• Server-side vs. client-side pagination: Decide whether to implement pagination on the server side, where the server returns only the required subset, or on the client side, where the entire dataset is loaded and paginated within the application.

• Parallel processing: For large datasets and tight deadlines, use parallel processing techniques to accelerate data handling.

• Event-driven architecture (EDA): Use paginated queries in conjunction with EDA to enhance data processing and responsiveness.

Putting theory into practice

For this example, you’ll focus on REST API pagination, a method commonly used in e-commerce platforms. In these scenarios, users navigate product catalogs using "Next" and "Previous" buttons, ensuring that only a portion of products is loaded per request.

While you will explore this specific API example in detail, it's important to note that pagination can also be applied in various contexts, as each integration scenario presents a unique use case.

Key concepts:

• Offset: Specifies the starting point for data retrieval.

• Limit: Defines the number of records to fetch in each request.

In practice, the offset will correspond to the page number, and the limit will represent the page size.

Example API request:

GET /products?page=1&pageSize=20

Setting up the solution

The diagram below offers a high-level overview of a proposed our architecture.

Pipeline implementation

This section explores how pagination is implemented in a pipeline.

1. API and validation: The pipeline is exposed as a REST API that accepts page and pageSize as query parameters. A Validator connector ensures these parameters are valid and throws an error if parameters are invalid or missing. Additionally, the pipeline can impose a pageSize limit to maintain optimal performance.

2. Calculating values: The pipeline calculates the skip value (offset), which determines the starting point for data retrieval, based on page and pageSize. The skip value is computed by multiplying the page number by the page size and then subtracting the page size from the result.

For instance, if the user requests page 3 with a page size of 50, the calculation would be (3 * 50) - 50, which equals 100. This value determines the starting point for data retrieval. Meanwhile, the limit value is simply the pageSize.

1. Data retrieval: The pipeline queries the database using skip and limit to retrieve a specific data subset.

2. Final Response: The final response may include information such as the retrieved data from the database, row count, page size, and even a hasMoreData flag indicating if more data exists.

Example:

• User requests page 4 with a pageSize of 3.

• The pipeline retrieves data starting from record 9 (calculated skip) and fetches 3 records (defined by pageSize).

• The response includes the retrieved data (3 user records) along with rowCount (3), pageSize (3), and hasMoreData (true, indicating more data).

How response time is impacted by pagination

Graph #1

Graph #2

1. Without pagination (graph #1): Retrieving all records at once overloads the server, increasing processing time.

2. With pagination (graph #2): Improved response times by fetching data in manageable chunks.

Finding the right balance

While pagination significantly improves performance, it’s important to avoid extremes. Querying records both excessively large and overly small may also lead to excessive server overhead and inefficient network traffic. The key is to determine the optimal chunk size for your server. Experiment with different page sizes to identify the most efficient configuration for your specific use case.

Final thoughts

In a world of exponentially growing data, API pagination helps retrieve and display data effectively. You can explore more possibilities in our Documentation Portal, Digibee Academy for courses on Pagination, or visit our Blog to discover more resources and insights.

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