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It’s like a WHERE clause, except that it’s used with group by to narrow down the results: SELECT dept_id, Since we’re only interested in duplicates, we can filter out everything else using the HAVING clause. To see which names are duplicated in the employees table, use a summary query that displays the non-unique values along with the counts: SELECT dept_id, These queries help you characterize the extent of duplicates, but don’t show you which values are duplicated. Here’s a query to count duplicated names for each department: SELECT dept_id,ĬOUNT(*) - COUNT(DISTINCT name) AS 'duplicate names' You can also group counts of duplicates per group using a bit of math in conjunction with the group by clause. The following query counts the number of distinct rows for which neither the name nor salary is NULL: SELECT Count (DISTINCT name, salary) In this case, COUNT() returns the number of distinct combinations of values that contain no NULL values. It’s also possible to give a list of expressions separated by commas. The expression can be a column name to count the number of distinct non-NULL values in the column.Īpplying the Count distinct function on the name field produces six unique names: SELECT Count(DISTINCT name) COUNT( distinct expression) counts the number of distinct (unique) non-NULL values of the given expression. Hence: SELECT name,ĭistinct can be used with the COUNT() function to count how many distinct values a column contains. Similarly, the following statement produces the same results as our SELECT distinct did on the employees table: SELECT name,Ī difference between distinct and group by is that group by causes row sorting. For example, the following query produces the same set of rows as our first SELECT distinct did: SELECT name However, if you display and group by multiple columns, the query produces the distinct combinations of values in each column. When displaying and grouping by a single column, the query produces the distinct values in that column. For such a query, group by just produces a list of distinct grouping values. Using distinct is logically equivalent to using group by on all selected columns with no aggregate function. The moral of the story is this: When using the distinct keyword, be sure that you aren’t inadvertently removing valid data! Comparing Distinct to Group By Here’s the data in question showing the unique emp_id_numbers: Including the unique emp_id_number to the field list confirms that there are indeed two Kristen Rueggs: SELECT name, This produces the desired results, except for one small detail: There are two employees with the same name! Adding the distinct keyword created incorrect results by removing a valid row. The client responds that they don’t want duplicates, so the developer adds the trusty distinct keyword to the SELECT statement. Strangely, this produces duplicate rows for “Kristen Ruegg”: The client wants to generate a list of their employees to generate some statistics.
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One problem that the distinct keyword does nothing to solve is that sometimes removing duplicates creates misleading results. When are Duplicate Rows Not Duplicate Rows If only it were always that easy! A quick Internet search on the phrase “sql eliminating duplicates” shows that there’s more to removing duplicate values than inserting the distinct keyword into your SELECT statements. Now let’s try the query again with the distinct keyword: SELECT DISTINCT nameĪs expected, we now have only one instance of each fruit type:
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Without the color information, we have multiples of each fruit type: The following query will retrieve all the fruit names from the table and list them in alphabetical order: SELECT name
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To illustrate how it works, let’s select some data from the following table, which contains a list of fruits and their colors: Distinctrow is an alias for distinct and produces the exact same results: SELECT The distinct keyword comes directly after the SELECT in the query statement and replaces the optional all keyword, which is the default. Did you know that the group by clause can also be used to remove duplicates? If not, read on to find out what the main differences are between them and which to use to produce a desired result. It tells the query engine to remove duplicates to produce a result set in which every row is unique. The go to solution for removing duplicate rows from your result sets is to include the distinct keyword in your select statement.