Boost Your Query Accuracy with PostgreSQL Collation Expressions.

Collation is a feature in Postgresql that set rules which defines how data characters are stored, compared and sorted out in database. This feature allow you specify the sort order and character classification behavior of data per-column, or even per-operation.
A collation expression in PostgreSQL is a way to explicitly specify the collation to be used for sorting and comparing text data.

Collation can be set at different levels which reflects on different aspect of the database. These levels are:

• Database-level
• Column-level
• Expression-level

Database-Level

Database level collation determines how the system sorts and compares string data within a database, Seting the collation at the database level establishes a consistent foundation for text handling, ensuring all text data follows the same rules unless explicitly overridden. This approach simplifies configuration and maintains consistency across your database.
When creating a new database, you can specify the collation settings using the CREATE DATABASE clause with LC_COLLATE and LC_CTYPE which defines the sort order and character classification respectively.

CREATE DATABASE zion
LC_COLLATE = 'en_US.UTF-8'
LC_CTYPE = 'en_US.UTF-8';

In the above query:

• CREATE DATABASE zion clause specifies the creation of a new database named zion.
• LC_COLLATE = 'en_US.UTF-8' defines the database collation as "en_US.UTF-8", indicating US English rules for character comparison with UTF-8 encoding.
• LC_CTYPE = 'en_US.UTF-8'; clause sets the character set for the database to "en_US.UTF-8", ensuring support for a wide range of characters encoded in UTF-8;.

Column-Level

Column-level collation, unlike database-level collation we mentioned earlier, allows you to define specific sorting and comparison rules for individual columns within a table. This provides more internal control over how text data is handled.
When you define a column-level collation, it actively overrides the database-level collation for that specific column. This allows you to tailor the sorting rules for each column based on its content and needs.

Say we create a table novels where the name column might contain product names with accented characters and we want these names to be sorted correctly, considering the accents. below is a query on how to define a column-level collation for this scenario:

CREATE TABLE novels (
  product_id INT PRIMARY KEY,
  name VARCHAR(50) COLLATE "fr_FR.ci",
  price DECIMAL,
  description TEXT
);

This query creates a table named novels with four columns:

• product_id INT PRIMARY KEY: The product column with an integer data type that serves as the primary key, ensuring each value is unique and not null.
• name VARCHAR(50): The name column with character data type which can hold up to 50 characters, uses the French case-insensitive collation "fr_FR.ci" for sorting and comparison.
• price DECIMAL,: The price column with decimal data type.
• description TEXT: The description column stores variable-length text, suitable for long descriptions.

This query establishes a structured way to manage a collection of novels with consistent text handling rules.

In column level collation, the ALTER statement plays a crucial role in modifying existing table structures, which includes changing the collation of a column. This statement forms the foundation for modifying an existing table.

ALTER TABLE novels
MODIFY COLUMN name VARCHAR(50) COLLATE "de_DE";

From the query above:

• ALTER TABLE novels initiates the modification of the novel table.
• MODIFY COLUMN name specifies that we're modifying the name column.
• VARCHAR(50) COLLATE "de_DE" defines the new data type for name as varchar(50) alongside the German collation "de_DE".

Expression-Level

Expression-level collation allows you to specify the collation for individual string expressions within your queries, overriding the default collation of the column or database.
You can use the COLLATE clause within a string expression to define its collation. For example:

SELECT name COLLATE "en_US" FROM customers;

Expression-level collation allows temporary overrides without affecting the underlying column definition.

Let's consider a table named novels with columns for book_title and publication_year where the book_title column might have titles with mixed cases. We want to sort them alphabetically, ignoring case differences, using US English rules.

Below is an expression-level collation query:

SELECT book_title COLLATE "en_US.ci"
FROM books
ORDER BY book_title;

From the above query:

• book_title COLLATE "en_US.ci" applies the COLLATE clause to the book_title column, specifying the collation "en_US.ci" which defines US English sorting rules (en_US) while ignoring case.
• ci for case-insensitive.
• ORDER BY book_title clause uses the already collated title expression for sorting, ensuring case-insensitive alphabetical order.

Collation options

Collation options dictate the settings or attributes you can choose to apply at the different levels of collation mentioned above. These options define the rules for comparing and sorting strings. They act like a set of rules that determine the order of characters, considering factors like case sensitivity and accent.

For example, if you have a database with different regional content, you might use different collation options for different columns or tables to ensure that text is sorted and compared correctly based on language-specific rules.

Below is a table of commonly used options:

Option	Syntax	Description
Case sensitive	_CS	Uppercase and lowercase letters are treated differently. "Castle" would come before "castle" during sorting.
Case insensitive	_CI	Lower case and upper case are treated as identical, "Ban" and "ban" will be considered equal and might appear in order of other sorting rules.
Accent sensitive	_AS	used to consider accented and non accented letter separately. for example ‘a’ and ’ ấ’are not treated identical.
Accent insensitive	_AI	used to consider accented and non accented letter identical. for example ‘a’ and ’ ấ’are treated identical.
Width sensitive	_WS	Differentiates full width and half width characters present, if _WS wasn't mentioned then it is width-insensitive and hence full and half width characters will be considered as identical.

By understanding the available collation options and choosing the appropriate ones for your data, you can ensure your database delivers accurate and meaningful results that meet your specific requirements.