Summary of several methods for removing duplicate data in SQL (window function for data deduplication)

catalogueMethod 1: distinctMethod 2: groupbyMethod 3: Window functionWhen using SQL to extract and analyze data, we often encounter scenarios where data is duplicated, requiring us to perform data deduplication and subsequent analysis.Taking the sales report of a certain e-commerce company as an example, we commonly use distinct or group by statements to deduplicate data

When using SQL to extract and analyze data, we often encounter scenarios where data is duplicated, requiring us to perform data deduplication and subsequent analysis.

Taking the sales report of a certain e-commerce company as an example, we commonly use distinct or group by statements to deduplicate data. Today, we introduce a new method that uses window functions to deduplicate data.

Field Explanation

Visitor ID: A customer who enters the store to browse for treasures

Browsing time: The date when the visitor entered the store's browsing page

Frequent browsing: The length of time visitors enter the store's browsing page

Now it is necessary to know each visitor in the store and the corresponding browsing date (each visitor browsing multiple times on the same day is counted as a record)

Problem solving ideas

Method 1: distinct

The SQL is written as follows:

Select distinct visitor ID, browsing time from Taobao's daily sales data table;

Query results:

When using the distinct statement to remove duplicates in multiple fields, two special points need to be noted:

1) The distinct syntax stipulates that for single field and multi field deduplication, it must be placed before the first query field.

2) If multiple column fields in a table are deduplicated, the process of deduplication is to treat the multiple fields as a whole. For example, in the above example, we deduplicate the visitor ID and browsing time as a whole, rather than deduplicating the visitor ID separately and then deduplicating the name separately. Therefore, the same visitor ID will correspond to different browsing times.

Method 2: groupby

The SQL is written as follows:

Select visitor ID, browse time from Taobao Daily Sales Data Table groupby visitor ID, browse time;

Query results:

Groupby groups the visitor ID and browsing time, and after grouping and summarizing, changes the number of rows in the table. Each row only has one category. Using groupby here will keep the visitor ID and browsing time as one category, and duplicate ones will not be displayed.

Method 3: Window function

When using window functions for deduplication, it is slightly more complex than distinct and groupby. Window functions do not reduce the number of rows in the original table, but instead group and sort the fields. Detailed explanation of window functions

The basic syntax of window functions is as follows:

< Window Functions> Over (partitionby< column name for grouping> orderby< column name for sorting>)

According to the requirements of the question, we will determine each visitor and their corresponding browsing date. We will group the visitor ID, browsing time, and sort the browsing time in seconds.

The SQL is written as follows:

Select visitor ID, browse time, row_ Number () over (partition by visitor ID, browse time order by browse time (seconds)) as ranking from Taobao Daily Sales Data Table;

Query results:

The window function queries are grouped by each customer and browsing date. If there are several views on the same day, it will be sorted based on the number of likes, filtered and ranked as 1 to obtain each visitor and corresponding browsing date.

The SQL is written as follows:

Select visitor ID, browse time, row_ Number () over (partition by visitor ID, browse time order by browse time (seconds)) as ranking from Taobao Daily Sales Data Table;

Query results:

Did you get the three operations to remove duplicates? Welcome to add your deduplication method in the comments section~

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