![]() ![]() SIMILAR TO Regular Expressions # string SIMILAR TO pattern In some obscure cases it may be necessary to use the underlying operator names instead.Īlso see the starts-with operator and the corresponding starts_with() function, which are useful in cases where simply matching the beginning of a string is needed.ĩ.7.2. The phrases LIKE, ILIKE, NOT LIKE, and NOT ILIKE are generally treated as operators in PostgreSQL syntax for example they can be used in expression operator ANY ( subquery) constructs, although an ESCAPE clause cannot be included there. You may see these operator names in EXPLAIN output and similar places, since the parser actually translates LIKE et al. All of these operators are PostgreSQL-specific. There are also !~~ and !~~* operators that represent NOT LIKE and NOT ILIKE, respectively. The operator ~~ is equivalent to LIKE, and ~~* corresponds to ILIKE. This is not in the SQL standard but is a PostgreSQL extension. The key word ILIKE can be used instead of LIKE to make the match case-insensitive according to the active locale. PostgreSQL's behavior in this regard is therefore slightly nonstandard. This effectively disables the escape mechanism, which makes it impossible to turn off the special meaning of underscore and percent signs in the pattern.Īccording to the SQL standard, omitting ESCAPE means there is no escape character (rather than defaulting to a backslash), and a zero-length ESCAPE value is disallowed. It's also possible to select no escape character by writing ESCAPE ''. See Section 4.1.2.1 for more information. If you have standard_conforming_strings turned off, any backslashes you write in literal string constants will need to be doubled. To match the escape character itself, write two escape characters. The default escape character is the backslash but a different one can be selected by using the ESCAPE clause. To match a literal underscore or percent sign without matching other characters, the respective character in pattern must be preceded by the escape character. ![]() Therefore, if it's desired to match a sequence anywhere within a string, the pattern must start and end with a percent sign. LIKE pattern matching always covers the entire string. An underscore ( _) in pattern stands for (matches) any single character a percent sign ( %) matches any sequence of zero or more characters. If pattern does not contain percent signs or underscores, then the pattern only represents the string itself in that case LIKE acts like the equals operator. An equivalent expression is NOT ( string LIKE pattern).) (As expected, the NOT LIKE expression returns false if LIKE returns true, and vice versa. The LIKE expression returns true if the string matches the supplied pattern. This is not an exhaustive list of string functionality or use cases, but contains some common scenarios analytics engineers face day-to-day.9.7.1. Creating a new string column type based off a CASE WHEN statement to bucket data by.Filtering queries on certain string values.Casting a column of a different type to a string for better compatibility or usability in a BI tool.Unnesting JSON or more complex structured data objects and converting those values to explicit strings.Concatenating strings together to create more robust, uniform, or descriptive string values.Changing the casing (uppering/lowering) to create some standard for your string type columns in your data warehouse.Most often, when you’re working with strings in a dbt model or query, you’re: 'Jaffle Shop', '1234 Shire Lane', 'Plan A'). To formalize it a bit, a string type is a word, or the combination of characters that you’ll typically see encased in single quotes (ex. Strings are inherent in your data-they’re the name fields that someone inputs when they sign up for an account, they represent the item someone bought from your ecommerce store, they describe the customer address, and so on. ![]() Strings are everywhere in data-they allow folks to have descriptive text field columns, use regex in their data work, and honestly, they just make the data world go ‘round.īelow, we’ll unpack the different string formats you might see in a modern cloud data warehouse and common use cases for strings. We can almost guarantee that there is not a single dbt model or table in your database that doesn’t have at least one column of a string type. ![]()
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