Notice how the number of characters before “son” does not matter with this wildcard. To achieve this, we can simply write the following query: As an example, imagine we want to find all the people whose last name ends in “son”. The percent (%) wildcard is used to substitute for multiple characters. Let’s now look at the percent (%) wildcard in detail and apply it to our person_info table. “ _om”, “ T_m”, or “ To_”, are all valid patterns. The underscore wildcard can be placed anywhere in the pattern you are looking for. By placing two underscores after “ Wa”, we explicitly specify that the LastName we are looking for is 4 characters long. Notice how Andrew Wade matches the pattern but not Tom Waters. This time, we will substitute two characters with two underscore ( _) wildcard characters.Įxecuting this query retrieves a single record from our table. Our SQL query is ignoring that letter and looking for the pattern we have specified. Notice how the second letter of the name can be anything. The second letter FirstName can be anything.The third letter of FirstName must be “m”, and.The FirstName must start with the letter “T”,.Imagine we want to retrieve, from the table person_info, the first names of the persons with the following conditions: Let’s look at the underscore (_) wildcard first and apply it to our person_info table. The underscore ( _) wildcard substitutes for exactly one character in a string.The percent ( %) wildcard substitutes for one or more characters in a string.Wildcard characters are used to substitute for one or more characters in a pattern string: In addition to examining the use of SQL LIKE and NOT LIKE, we will look at two wildcard characters: percent ( %) and underscore ( _). In comparison, LIKE compares character by character through the use of wildcards, which will be discussed in detail in this article. When comparing strings, the equals operator compares whole strings. So, what is the difference between using LIKE and equals? Equals (=) is a comparison operator that operates on numbers and strings. For simple cases like this, we could have also written: This is a simple use case for the LIKE operator. Let’s use the LIKE operator to extract the information for people with the last name “Peterson”.Įxecuting this SQL query would yield the following result set: FirstName Imagine we have a table called person_info containing information about people’s first name, last name, and age. Let’s apply this syntax to a practical example. Let’s examine how we can use the LIKE operator to filter the data returned, thereby retrieving only the desired records. If you want to further advance your SQL skills in this area, try out our interactive course SQL Practice Set, where you will practice SQL JOINs, aggregations with GROUP BY, and other advanced topics in 88 hands-on practical exercises. Becoming proficient in using the LIKE operator will allow you to parse through large databases with ease, and retrieve exactly the data you need. The LIKE operator is often used in the WHERE clause of SELECT, DELETE, and UPDATE statements to filter data based on patterns. A simple example of this is when you try to find if a name column contains any four-letter name starting with J (such as “John”). The SQL LIKE is a logical operator that checks whether or not a string contains a specified pattern. Gaining an understanding of these operators will allow you to write better queries and demonstrate to potential employers that you have the skills required to filter through expansive data sets. They are part of standard SQL and work across all database types, making it essential knowledge for all SQL users. The SQL LIKE and NOT LIKE operators are used to find matches between a string and a given pattern.
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