How to query duplicate data in oracle

In Oracle, querying duplicate data is a common task, especially when dealing with large amounts of data. Repeated data queries often require consideration of many details and factors, including data type, index usage, performance, etc.

This article will introduce the method of querying duplicate data in Oracle, and provide some optimization techniques to help readers handle query tasks more efficiently.

1. Use the GROUP BY statement

The GROUP BY statement is the basic method for Oracle to query duplicate data. Users can use this statement to group data according to specified fields and count the total number of data in each group. Finding duplicates is usually done on the basis of this statistical total. For example, the following SQL statement will find people whose names appear more than 1 time:

SELECT name, COUNT(*) 
FROM person 
GROUP BY name 
HAVING COUNT(*) > 1;

This query will return all names of people whose names appear more than 1 time and their number of occurrences. The key to this query statement is the use of the GROUP BY clause, which groups the data by name. Another key is the HAVING clause, which filters out records with occurrences greater than 1. This method is suitable for finding duplicate non-unique index data, such as people's names, birthdays, etc.

2. Use inner joins

Inner joins are another way to handle complex queries in Oracle. After merging two tables through an inner join, you can use the WHERE clause to find duplicate data. For example, the following SQL statement will find duplicate names in the person table:

SELECT DISTINCT p1.name 
FROM person p1, person p2 
WHERE p1.name = p2.name AND p1.id  p2.id;

In this query, the person table is self-joined twice and uses the WHERE clause to find records with the same name but different IDs. Due to the use of the DISTINCT clause, the query results will only contain distinct names. This method is suitable for finding duplicate unique index data, such as ID number, mobile phone number, etc.

3. Use the ROW_NUMBER() OVER statement

ROW_NUMBER() OVER statement is an advanced query method of Oracle that can be used to find duplicate data and other common queries. The ROW_NUMBER() OVER statement uses a window function to assign a row number to each row of the query results. Then, the user can use the WHERE clause to find records with row numbers greater than 1 and get duplicate data. The following SQL statement uses the ROW_NUMBER() OVER statement to find duplicate names in the person table:

SELECT name 
FROM (SELECT name, ROW_NUMBER() OVER (PARTITION BY name ORDER BY id) rn 
      FROM person) 
WHERE rn > 1;

In this query, a subquery is used to sort the names by ID, and the ROW_NUMBER() OVER statement is used to assign row numbers. Then, use the WHERE clause in the main query to find records with row numbers greater than 1 and output all duplicate names. This method is suitable for finding data with multiple non-unique fields, such as multiple columns of duplicate data.

4. Optimize query performance

The performance of querying duplicate data is usually the main bottleneck of query tasks. In order to optimize performance, we can use the following techniques:

1. Use indexes to optimize queries. When querying duplicate data, using indexes can speed up queries. If the query object is a non-unique index, you can use a covering index to avoid accessing the data table. And if the query object is a unique index, you need to use an inner join for best performance.
2. Use subqueries to optimize performance. When querying repeated data, you can use subqueries to preprocess the data, and use GROUP BY statements in the subqueries to optimize query performance.
3. Narrow the query scope. When querying duplicate data, you can use the WHERE clause to add some conditions to narrow the query scope and speed up the query.
4. Process data in batches. For query tasks involving a large amount of data, you can use the batch processing method to split the big data into multiple small data sets for query, thereby avoiding performance problems caused by processing a large amount of data at one time.

Summary:

Querying duplicate data is not only a common and important task in Oracle query tasks, but also involves many optimization techniques and adjustment methods. When processing query tasks, you need to consider multiple factors such as data type, index usage, performance, etc., and adopt appropriate optimization strategies to obtain faster and more accurate results. At the same time, we also hope that the methods and techniques introduced in this article can help readers handle query tasks more efficiently in actual work.

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