Harness massive filtering conditions: AnQiCMSarchiveFiltersThe Path to Performance Deep Analysis and Optimization of Tags

As an experienced website operations expert, I know that performance is the key to user experience and operational efficiency when managing a website with a large number of content models and filtering conditions.AnQiCMS with its high-performance architecture based on Go language is favored in the content management field.archiveFiltersHow does it perform under models with a large number of filtering conditions, and how should we optimize it?

archiveFiltersTag: A powerful assistant for complex filtering

In modern websites, especially in vertical industries such as e-commerce, real estate, recruitment, etc., users have increasingly refined needs for content filtering.An product list may need to be filtered by brand, price, color, size, material, etc., involving dozens of dimensions; a real estate information may involve conditions such as house type, area, location, orientation, floor, etc.archiveFiltersLabels are exactly for this "Multi-condition combination filtering" scenario.

From the AnQiCMS documentation, we learn that,archiveFiltersLabels are specifically used to generate a list of filtering conditions based on the various parameters of the document (i.e., fields defined in the custom content model).It can flexibly extract optional values of custom fields such as property types (residential, commercial), house sizes (single room, one bedroom and one living room), and generate corresponding filter links for each option.This greatly simplifies the development of front-end templates, allowing operators to easily build powerful filtering interfaces.

AnQiCMS supports users to customize content models according to business needs and add various types of fields, including single-line text, numbers, multi-line text, single choice, multiple choice, and dropdown selection.archiveFiltersThe power lies in its ability to intelligently parse these custom fields, generating corresponding filter options. When users select a filter condition on the frontend, they are actually sending a request to the server with specific query parameters such assex=男）的 request, the backend then processesarchiveListTags combined with these parameters to obtain and display the filtered document list.

Performance considerations under a large number of filtering conditions

When faced with a content model that has a large number of filtering conditions (for example, each condition has dozens or even hundreds of options),archiveFiltersThe performance of the tag becomes our focus. Its performance is mainly affected by the following aspects:

  1. The complexity of generating filtering conditions: archiveFilters
  2. Database query pressure:Although AnQiCMS is based on the Go language and has high-performance database operation capabilities, a large number of filtering conditions usually means that multiple queries need to be performed on the database (for example, querying all unique values for each filtering field), which will increase the database load.
  3. Front-end page rendering:The more filtering conditions generated, the more HTML elements the front-end page needs to render.This will increase the rendering burden on the browser side, especially for pages that contain complex CSS and JavaScript interactions, which may lead to slower page loading speeds and affect user experience.
  4. Concurrency advantages of Go language:Fortunately, AnQiCMS is developed based on Go language, and its 'high-performance architecture' utilizes Goroutine to implement asynchronous processing, enhancing the system's concurrent performance.This means that even with more filtering conditions, the system can schedule and process more efficiently, reducing the waiting time for individual requests.But concurrency cannot create resources out of thin air, and the final performance bottleneck may still appear in database response or CPU computation.

Optimization suggestions for AnQiCMS

After understandingarchiveFiltersThe working principle and potential performance bottlenecks, we can take a series of strategies to optimize its performance under a large number of filtering conditions:

One, backend configuration and data level optimization:

  • Simplify and optimize content model fields:Examine the custom fields in the existing content model, removing unnecessary or duplicate fields. Do not expose fields that do not have significant filtering meaning.archiveFiltersThe field design should be as close as possible to the dimensions most frequently used by users for filtering, avoiding being too comprehensive and pursuing being concise and refined.
  • Reasonably design the data source for filtering:For selection fields (radio, checkbox, dropdown), if the options are relatively fixed, they can be preset in the background to reducearchiveFiltersThe pressure of collecting all values in real-time from the database when the label is rendered on the front end.If certain filter values need to be dynamically generated, consider caching them, for example, caching the combinations of recently used or popular filter values.
  • Make full use of AnQiCMS static cache:AnQiCMS built-in static caching mechanism, for the filtering condition list that does not change frequently, the generated results can be cached statically.When the user visits again, it is directly read from the cache, greatly reducing the server's computing and database query costs.Regularly refresh the cache to ensure the real-time accuracy of the filtering conditions.

English、Front-end Template and User Experience Optimization:

  • Implement lazy loading or segmented loading of filtering conditions:For pages with many filtering conditions, consider not loading all filtering conditions at once.For example, only the most commonly used several filter conditions are displayed, and the rest are loaded asynchronously through the "Show More" button or when the user scrolls to a specific area.This can significantly improve the first screen loading speed.
  • Optimize front-end rendering performance:EnsurearchiveFiltersThe generated HTML structure of the label is concise and efficient, reducing unnecessary nesting and complex CSS selectors.Using front-end frameworks or libraries for efficient rendering and avoiding unnecessary DOM operations.
  • Provide intelligent search and keyword filtering:in addition to multi-dimensional filtering, combined with AnQiCMSarchiveListTagsqParameters, providing keyword search functionality. Often, users prefer to directly search for keywords rather than individually select filter conditions.
  • Designing a user-friendly filter interface:Group related filter conditions, collapse them, or use designs such as sidebars, pop-ups, etc., to avoid overly long pages and enhance the user experience during filtering.Although this is not directly related to backend performance, good UI/UX design can effectively enhance users' perception of the website's 'speediness'.

Three, Server and Architecture Level Optimization:

  • Ensure sufficient server resources:Although AnQiCMS is known for its lightweight and efficient design, facing large amounts of data and complex queries, sufficient CPU, memory, and high-speed storage (especially for databases) are still fundamental.
  • Database Optimization:Ensure that the database table (especially the custom field table) is indexed correctly.AnQiCMS as an enterprise-level CMS usually handles this part well, but if there are special customizations or performance bottlenecks, checking indexes is necessary.
  • Monitor and Analysis:Continuously monitor the website's page loading time, server response time, database query logs, etc., locate specific performance bottlenecks through data analysis, and optimize them accordingly.

In summary, the value of AnQiCMS'sarchiveFilters