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Demystifying language_handler in PostgreSQL: When and Why It's Used


What are Pseudo-Types in PostgreSQL?

PostgreSQL's data type system includes a category of special types called pseudo-types. These types differ from standard data types in that:

  • They are specifically employed to declare the argument or return type of a function.
  • They cannot be used to define the data type of a column in a table.

What is the language_handler Pseudo-Type?

The language_handler pseudo-type signifies that a function:

  • Returns a data type that's internal to the language itself. These internal types are not directly representable within the SQL data type system.
  • Is written in a procedural language (like PL/pgSQL, PL/vtable, etc.) that PostgreSQL can execute.

In simpler terms

  • The language_handler pseudo-type tells PostgreSQL that a function is written in a procedural language and returns a value that's specific to that language's internal workings. PostgreSQL itself cannot directly understand or manipulate this returned value.

When is language_handler Used?

This pseudo-type is typically used in functions that:

  • Return results that require further processing within the procedural language itself.
  • Perform complex operations that involve language-specific constructs.
  • Interact with procedural language data structures that don't have direct SQL equivalents.

Example

Imagine a PL/pgSQL function that calculates complex statistics on a dataset. The function might employ data structures like records or arrays to store intermediate results internally. Since these structures aren't standard SQL data types, the function would declare a language_handler return type. The function would then process the internal results further and return the final outcome in a suitable SQL data type (like numeric or JSON).

  • It's used for functions that require language-specific processing or data structures.
  • It signifies an internal language data type that SQL cannot directly handle.
  • language_handler indicates a function written in a procedural language.

CREATE OR REPLACE FUNCTION calculate_average(numbers INTEGER[])
RETURNS language_handler AS $$
DECLARE
  -- Internal PL/pgSQL record to store sum and count
  total_record RECORD;
BEGIN
  -- Initialize variables
  total_record.sum := 0;
  total_record.count := 0;

  -- Loop through the integer array and calculate sum/count
  FOR i IN 1 .. array_upper(numbers, 1) LOOP
    total_record.sum := total_record.sum + numbers[i];
    total_record.count := total_record.count + 1;
  END LOOP;

  -- Return the internal record (which SQL doesn't understand directly)
  RETURN total_record;
END;
$$ LANGUAGE plpgsql;
  1. Function Definition
    • CREATE OR REPLACE FUNCTION calculate_average(numbers INTEGER[]) defines a function named calculate_average that takes an integer array numbers as input.
  2. Return Type
    • RETURNS language_handler indicates that the function returns a value specific to PL/pgSQL's internal workings.
  3. PL/pgSQL Code Block
    • $$ ... $$ LANGUAGE plpgsql; defines the function body using dollar-quoted string literals.
  4. Internal Record
    • DECLARE total_record RECORD; declares a record variable total_record to hold the calculated sum and count within PL/pgSQL.
  5. Calculations
    • The function iterates through the numbers array, summing the elements and keeping track of the count.
  6. Returning Internal Data
    • RETURN total_record; returns the total_record containing both sum and count. However, this record is not directly usable in SQL queries because it's an internal PL/pgSQL data structure.
  • You could then calculate the average (sum / count) and return a value in a standard SQL data type (e.g., numeric).
  • In a separate PL/pgSQL block or another function, you could access the returned total_record and extract the sum and count values.

    • Concept
      Output functions provide a mechanism to convert internal procedural language data types into SQL-compatible representations.
    • Implementation
      • Create an output function for the internal data type.
      • Register the output function with PostgreSQL.
      • Use the output function within the procedural language function to convert the internal data into a SQL-compatible format before returning it.

  2. Employing Serialization Techniques

    • Concept
      Serialization involves converting complex data structures into a serialized format, like JSON or a binary representation.
    • Implementation
      • Serialize the internal data structure within the procedural language function.
      • Return the serialized data as a string or bytea type.
      • In a separate query or function, deserialize the serialized data back into the original data structure.

  3. Leveraging Temporary Tables

    • Concept
      Temporary tables allow for storing and manipulating intermediate results within a procedural language function.
    • Implementation
      • Create a temporary table with a structure matching the internal data type.
      • Populate the temporary table with data from the procedural language function.
      • Access and process the data in the temporary table using SQL queries within the function.
      • Drop the temporary table when no longer needed.

Choosing the Right Approach

  • Temporary Tables
    Useful for intermediate results that need SQL-based processing.
  • Serialization
    Ideal for complex data structures requiring external representation.
  • Output Functions
    Suitable for simple data conversions.