Using incremental page numbers for pagination scales poorly, so to solve this, Shopify uses relative cursor pagination to deliver faster queries.
Overview
The article discusses the challenges of traditional pagination using incremental page numbers and introduces relative cursor pagination as a more efficient alternative. It highlights performance improvements, implementation strategies, and the impact of these changes on Shopify's API.
What You'll Learn
1
How to implement relative cursor pagination in your applications
2
Why relative cursor pagination is more efficient than traditional pagination
3
How to handle sorting and skipping records effectively using cursors
Prerequisites & Requirements
- Understanding of pagination concepts and SQL queries
- Familiarity with API development and database indexing(optional)
Key Questions Answered
What are the performance implications of using large offsets in pagination?
Using large offsets in pagination can significantly slow down queries, as demonstrated by tests showing that an offset of 100,000 took 2,221.60 ms, while lower offsets took considerably less time. This inefficiency arises because the database must process and discard a large number of records, leading to timeouts and resource contention.
How does relative cursor pagination improve performance?
Relative cursor pagination improves performance by allowing requests to continue from the last retrieved record rather than starting from an offset. This method reduces the number of records the database needs to process, resulting in faster query times, as shown by tests indicating a performance improvement of over 400 times when using last IDs instead of offsets.
What challenges arise when sorting by non-unique fields?
Sorting by non-unique fields can lead to skipped records when requesting subsequent pages. To avoid this, it's essential to use a secondary sort column with a unique value, such as an ID, ensuring that all records are retrieved correctly without omissions.
How does Shopify facilitate the use of relative cursors for clients?
Shopify simplifies the use of relative cursors by generating URLs for next and previous pages, which are included in the Link header of API responses. This approach allows clients to navigate through paginated results without manually calculating the next cursor position.
Key Statistics & Figures
Query time at offset 100,000
2,221.60 ms
This demonstrates the inefficiency of using large offsets in pagination.
Performance improvement using last ID over offset
over 400 times faster
This highlights the significant efficiency gains achieved through relative cursor pagination.
Average performance improvement on /admin/products.json endpoint
11 times faster
This indicates the effectiveness of relative cursors compared to traditional pagination methods.
Technologies & Tools
Database
SQL
Used for querying product records in the Shopify database.
Backend
REST API
Facilitates communication between clients and the Shopify server for product data retrieval.
Key Actionable Insights
1Implement relative cursor pagination in your API to enhance performance and scalability.This approach is particularly beneficial for applications dealing with large datasets, as it reduces query times and prevents timeouts associated with high offsets.
2Ensure proper indexing on fields used for sorting to maintain query performance.Without appropriate indexes, queries can become slower than traditional pagination methods, negating the benefits of cursor-based pagination.
3Educate your team on the importance of using unique fields for sorting to avoid data inconsistencies.This practice helps prevent skipped records and ensures that all relevant data is retrieved during pagination.
Common Pitfalls
1
Failing to implement unique sorting fields can lead to skipped records during pagination.
When sorting by non-unique fields, subsequent queries may miss records, causing inconsistencies in data retrieval. Always use a unique identifier as a secondary sort field to ensure all records are captured.
Related Concepts
Pagination Strategies
Database Indexing
API Performance Optimization