![]() ![]() Like the first example using a single column, this second example similarly allows us to find errors in the ordering system. It’s very similar to the one for a single column:Ībove, we can confirm that the ordering system does indeed have a bug. To find duplicates in multiple column values, we can use the following query. A glitch of this type may impact business operations negatively if the orders are being fulfilled, packaged, and shipped automatically. If multiple quantities of that product are ordered, the Quantity value would simply be increased separate (duplicate) rows should not be created. This type of duplicate likely means there is a bug in the ordering system, since each order will process each product in that order only once in the cart. We want to find entries where the OrderID and ProductID columns are identical. For this example, we will be using the OrderDetails table, a sample of which is shown below. Often, you’re interested in finding rows where a combination of a few columns match. Once you have validated that the rows are the same, you may choose to remove the duplicate(s) using the DELETE statement. Using the GROUP BY and HAVING clauses can neatly show the duplicates in your data. To find the duplicates, we can use the following query:Īs we can see, OrderID 10251 (which we saw in the table sample above) and OrderID 10276 have duplicates. ![]() For some reason, that wasn’t implemented here. ![]() Ideally, each row should have a unique value for OrderID, since each individual order is assigned its own value. In this example, there are a few duplicates in the OrderID column. For this example, we will be using the Orders table, a modified version of the table we used in my previous article on using GROUP BY in SQL. Here, we will be demonstrating how you can find duplicate values in a single column. Our SQL Basics course also covers these concepts in great detail. Using the COUNT function in the HAVING clause to check if any of the groups have more than 1 entry those would be the duplicate values.įor a quick visual refresher on GROUP BY, check out our We Learn SQL Series’ SQL GROUP BY video. ![]() the column(s) you want to check for duplicate values on.
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