Optimization method for search box keyword filtering in Vue development

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Release: 2023-06-30 14:12:01
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How to optimize the search box keyword filtering issue in Vue development

In Vue development, the search box is a common functional requirement. When users enter keywords in the search box, we usually need to filter the relevant data to display matching results. However, with large amounts of data and frequent updates, keyword filtering can become inefficient or unstable. This article will introduce some optimization methods to improve the performance and user experience of search box keyword filtering in Vue development.

  1. Avoid frequent calculations
    Vue is a framework based on a responsive system. When the data changes, the related views will automatically update. However, recalculating the filter results each time when the keywords in the search box change may cause performance issues. In order to avoid frequent calculations, we can use debounce or throttling to control the trigger frequency of the filter function.

Anti-shake means to wait for a certain period of time and then perform the corresponding operation if the event is not triggered again. In the search box, we can use the debounce function of the lodash library to delay triggering the filter function to ensure that the user completes the input before filtering.

Throttling means to only perform an operation once within a certain period of time. In the search box, we can use the throttle function of the lodash library to control the triggering frequency of the filter function, for example, perform a filter operation every 500 milliseconds. This can reduce unnecessary calculations and improve performance.

  1. Use virtual list
    If the amount of data is very large, filtering the entire data directly may cause the page to freeze or load slowly. In this case, we can use virtual lists to optimize performance.

Virtual list is a technology that only renders data within the visible area. When the user scrolls the page, only the data in the currently visible area will be rendered, and other data will only retain the appearance structure without actual rendering. This can greatly reduce the number of DOMs and improve the rendering performance of the page.

When using virtual lists, we can use some ready-made Vue plug-ins, such as vue-virtual-scroller or vue-virtual-scroll-list, etc. These plug-ins provide convenient APIs and configuration options, allowing us to quickly implement virtual list functionality.

  1. Using indexing and caching
    In order to further improve filtering performance, we can use indexing and caching to speed up search operations.

Indexing refers to preprocessing data and establishing some form of data structure to find and filter data faster. For example, we can use a Trie tree or inverted index to establish a mapping relationship between keywords and data, so that we can quickly locate data containing specific keywords.

Caching refers to caching the filtering results to avoid repeated calculations. When the keyword changes, we can first check whether the corresponding result already exists in the cache. If it exists, the cached result will be used directly without repeated calculations. In Vue, we can use the computed attribute or the watch attribute to handle the caching of filter results.

  1. Lazy loading of data
    When the amount of data is very large, loading all the data at once may cause the page to be too large and affect performance. At this point, we can use lazy loading to reduce the load on the page.

Lazy loading means loading data only when needed. In the search box, we can set a threshold. When the user enters a keyword and exceeds the threshold, data loading and filtering operations will be performed. This can avoid loading a large amount of data at one time, reduce the load of the page, and improve performance.

Summary:
In Vue development, optimizing search box keyword filtering is an important part of improving user experience and performance. By avoiding frequent calculations, using virtual lists, using indexes and caches, and lazy loading of data, we can improve the performance and user experience of search box filtering in the presence of large amounts of data and frequent updates.

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