The key to improving performance of paging query is to choose the right method. 1. Use LIMIT and OFFSET to suit small data scenarios, but the performance declines significantly under big data; 2. Index-based "cursor" paging achieves stable performance through unique ordered fields, suitable for big data but does not support random page jumps; 3. Order BY is required to add to the paging under sorting and filtering, and use joint index optimization to avoid full table scanning; 4. Practical suggestions include limiting the maximum number of pages, aggregating data in advance, and checking the efficiency of ORM paging statements. Mastering these key points can effectively ensure paging performance and user experience.

When processing large-scale data, paging queries are an important means to improve performance and user experience. Directly returning thousands of records at one time is not only inefficient, but may also drag down databases and even front-end applications. A reasonable paging mechanism can effectively reduce the amount of data transmission and improve the response speed.

The following is a few common scenarios to talk about the key points and precautions for SQL pagination implementation.

Basic pagination using LIMIT and OFFSET

This is the most basic and common way of paging, and is suitable for most SQL databases (such as MySQL, PostgreSQL). The syntax is as follows:

 SELECT * FROM table_name ORDER BY id LIMIT 10 OFFSET 20;

• LIMIT controls how many pieces of data are returned per page
• OFFSET indicates how many records before skipping

Applicable scenarios:

• The data volume is not large (within tens of thousands)
• Backend management interface with low performance requirements

The problem is:

• When the offset is large (such as OFFSET 1000000 ), the performance will be significantly reduced because the database still needs to scan all previous rows before discarding

Efficient paging: Using index-based "cursor" method

When facing millions or even larger data sets, it is recommended to use a "cursor" paging method based on index fields such as auto-increment ID or timestamp. For example:

 SELECT * FROM table_name WHERE id > 1000 ORDER BY id LIMIT 10;

This method skips all previous records scans and starts reading directly from a certain location.

advantage:

• Stable performance, not affected by page numbers
• Especially suitable for infinite scrolling or API interfaces

Notes:

• There must be a unique and ordered field as the "cursor"
• Random page jumping is not supported (such as jumping directly from the first page to the tenth page)

How to deal with paging under sorting and filtering conditions?

In actual business, we often do not simply look up all data, but pagination after conditional filtering and sorting. At this time, you need to pay attention to the following points:

• Always add ORDER BY
  Otherwise, the order of results may be unstable, resulting in missed paging

• Joint index optimization
  If you often sort and filter by multiple fields combinations, you can create composite indexes to speed up

• Avoid full table scanning
  Try to enable the database to quickly locate the target data range through indexes instead of scanning one by one

Some practical tips and tips

• Don’t do the pagination too deeply. For example, if users turn to page 100, few people really need to look at the data on that page. They can limit the maximum number of pages or provide search functions.
• For report-type requirements, you can consider aggregating data in advance, or using materialized views to cache
• If you are using the ORM framework, remember to check whether the paging statements it generates are efficient. Some frameworks are implemented using OFFSET by default, and problems are likely to occur under big data.

Basically that's it. The pagination looks simple, but if you really need to do it quickly and stably, there are still many details to pay attention to.

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