Categories and Libraries

This chapter discusses the usage and implementation of connection categories and libraries.


First-time users are encouraged to read the Audience and Motivation section first.

Libraries are a collection of SQL statements that can be bound to a connection. Libraries are normally bound directly to the connection object as an attribute using a name specified by the library.

Libraries provide a common way for SQL statements to be managed outside of the code that uses them. When using ILFs, this increases the portability of the SQL by keeping the statements isolated from the Python code in an accessible format that can be easily used by other languages or systems — An ILF parser can be implemented within a few dozen lines using basic text tools.

SQL statements defined by a Library are identified by their Symbol. These symbols are named and annotated in order to allow the user to define how a statement is to be used. The user may state the default execution method of the statement object, or whether the symbol is to be preloaded at bind time–these properties are Symbol Annotations.

The purpose of libraries are to provide a means to manage statements on disk and at runtime. ILFs provide a means to reference a collection of statements on disk, and, when loaded, the symbol bindings provides means to reference a statement already prepared for use on a given connection.

The postgresql.lib package-module provides fundamental classes for supporting categories and libraries.

Writing Libraries

ILF files are the recommended way to build a library. These files use the naming convention “lib{NAME}.sql”. The prefix and suffix are used describe the purpose of the file and to provide a hint to editors that SQL highlighting should be used. The format of an ILF takes the form:


Where multiple symbols may be defined. The Preface that comes before the first symbol is an arbitrary block of text that should be used to describe the library. This block is free-form, and should be considered a good place for some general documentation.

Symbols are named and described using the contents of section markers: ('[' ... ']'). Section markers have three components: the symbol name, the symbol type and the symbol method. Each of these components are separated using a single colon, :. All components are optional except the Symbol name. For example:

SELECT * FROM user WHERE user_id = $1

SELECT * FROM user WHERE user_id = $1

In the above example, get_user_info and get_user_info_v2 are identical. Empty components indicate the default effect.

The second component in the section identifier is the symbol type. All Symbol types are listed in Symbol Types. This can be used to specify what the section’s contents are or when to bind the symbol:

SELECT * FROM user WHERE user_id = $1

This provides the Binding with the knowledge that the statement should be prepared when the Library is bound. Therefore, when this Symbol’s statement is used for the first time, it will have already been prepared.

Another type is the const Symbol type. This defines a data Symbol whose statement results will be resolved when the Library is bound:

SELECT user_type_id, user_type FROM user_types;

Constant Symbols cannot take parameters as they are data properties. The result of the above query is set to the Bindings’ user_type_ids attribute:

>>> db.lib.user_type_ids
<sequence of (user_type_id, user_type)>

Where lib in the above is a Binding of the Library containing the user_type_ids Symbol.

Finally, procedures can be bound as symbols using the proc type:


All procedures symbols are loaded when the Library is bound. Procedure symbols are special because the execution method is effectively specified by the procedure itself.

The third component is the symbol method. This defines the execution method of the statement and ultimately what is returned when the Symbol is called at runtime. All the execution methods are listed in Symbol Execution Methods.

The default execution method is the default execution method of postgresql.api.PreparedStatement objects; return the entire result set in a list object:

SELECT i FROM generate_series(0, 100-1) AS g(i);

When bound:

>>> db.lib.get_numbers() == [(x,) for x in range(100)]

The transformation of range in the above is necessary as statements return a sequence of row objects by default.

For large result-sets, fetching all the rows would be taxing on a system’s memory. The rows and chunks methods provide an iterator to rows produced by a statement using a stream:

SELECT i FROM generate_series(0, 1000) AS g(i);

SELECT i FROM generate_series(0, 1000) AS g(i);

rows means that the Symbol will return an iterator producing individual rows of the result, and chunks means that the Symbol will return an iterator producing sequences of rows of the result.

When bound:

>>> from itertools import chain
>>> list(db.lib.get_some_rows()) == list(chain.from_iterable(db.lib.get_some_chunks()))

Other methods include column and first. The column method provides a means to designate that the symbol should return an iterator of the values in the first column instead of an iterator to the rows:

SELECT i FROM generate_series(0, $1::int) AS g(i)

In use:

>>> list(db.lib.another_generate_series_example(100-1)) == list(range(100))
>>> list(db.lib.another_generate_series_example(10-1))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

The first method provides direct access to simple results. Specifically, the first column of the first row when there is only one column. When there are multiple columns the first row is returned:



In use:

>>> db.lib.get_one() == 1
>>> db.lib.get_one_twice() == (1,1)


first should be used with care. When the result returns no rows, None will be returned.

Using Libraries

After a library is created, it must be loaded before it can be bound using programmer interfaces. The postgresql.lib.load interface provides the primary entry point for loading libraries.

When load is given a string, it identifies if a directory separator is in the string, if there is it will treat the string as a path to the ILF to be loaded. If no separator is found, it will treat the string as the library name fragment and look for “lib{NAME}.sql” in the directories listed in postgresql.sys.libpath.

Once a postgresql.lib.Library instance has been acquired, it can then be bound to a connection for use. postgresql.lib.Binding is used to create an object that provides and manages the Bound Symbols:

>>> import postgresql.lib as pg_lib
>>> lib = pg_lib.load(...)
>>> B = pg_lib.Binding(db, lib)

The B object in the above example provides the Library’s Symbols as attributes which can be called to in order to execute the Symbol’s statement:

>>> B.symbol(param)

While it is sometimes necessary, manual creation of a Binding is discouraged. Rather, postgresql.lib.Category objects should be used to manage the set of Libraries to be bound to a connection.


Libraries provide access to a collection of symbols; Bindings provide an interface to the symbols with respect to a subject database. When a connection is established, multiple Bindings may need to be created in order to fulfill the requirements of the programmer. When a Binding is created, it exists in isolation; this can be an inconvenience when access to both the Binding and the Connection is necessary. Categories exist to provide a formal method for defining the interface extensions on a postgresql.api.Database instance(connection).

A Category is essentially a runtime-class for connections. It provides a formal initialization procedure for connection objects at runtime. However, the connection resource must be connected prior to category initialization.

Categories are sets of Libraries to be bound to a connection with optional name substitutions. In order to create one directly, pass the Library instances to postgresql.lib.Category:

>>> import postgresql.lib as pg_lib
>>> cat = pg_lib.Category(lib1, lib2, libN)

Where lib1, lib2, libN are postgresql.lib.Library instances; usually created by postgresql.lib.load. Once created, categories can then used by passing the category keyword to connection creation interfaces:

>>> import postgresql
>>> db = = cat)

The db object will now have Bindings for lib1, lib2, ..., and libN.

Categories can alter the access point(attribute name) of Bindings. This is done by instantiating the Category using keyword parameters:

>>> cat = pg_lib.Category(lib1, lib2, libname = libN)

At this point, when a connection is established as the category cat, libN will be bound to the connection object on the attribute libname instead of the name defined by the library.

And a final illustration of Category usage:

>>> db = = pg_lib.Category(pg_lib.load('name')))

Symbol Types

The symbol type determines how a symbol is going to be treated by the Binding. For instance, const symbols are resolved when the Library is bound and the statement object is immediately discarded. Here is a list of symbol types that can be used in ILF libraries:

<default> (Empty component)
The symbol’s statement will never change. This allows the Bound Symbol to hold onto the postgresql.api.PreparedStatement object. When the symbol is used again, it will refer to the existing prepared statement object.
Like the default type, the Symbol is a simple statement, but it should be loaded when the library is bound to the connection.
The statement takes no parameters and only needs to be executed once. This will cause the statement to be executed when the library is bound and the results of the statement will be set to the Binding using the symbol name so that it may be used as a property by the user.
The contents of the section is a procedure identifier. When this type is used the symbol method should not be specified as the method annotation will be automatically resolved based on the procedure’s signature.
The Symbol is a statement that should not be retained. Specifically, it is a statement object that will be discarded when the user discard the referenced Symbol. Used in cases where the statement is used once or very infrequently.

Symbol Execution Methods

The Symbol Execution Method provides a way to specify how a statement is going to be used. Specifically, which postgresql.api.PreparedStatement method should be executed when a Bound Symbol is called. The following is a list of the symbol execution methods and the effect it will have when invoked:

<default> (Empty component)
Returns the entire result set in a single list object. If the statement does not return rows, a (command, count) pair will be returned.
Returns an iterator producing each row in the result set.
Returns an iterator producing “chunks” of rows in the result set.
Returns the first column of the first row if there is one column in the result set. If there are multiple columns in the result set, the first row is returned. If query is non-RETURNING DML–insert, update, or delete, the row count is returned.
Returns an iterator to values in the first column. (Equivalent to executing a statement as map(operator.itemgetter(0), ps.rows()).)
Returns a scrollable cursor, postgresql.api.Cursor, to the result set.
Takes an iterable row-chunks to be given to the statement. Returns None. If the statement is a COPY ... FROM STDIN, the iterable must produce chunks of COPY lines.
Takes an iterable rows to be given as parameters. If the statement is a COPY ... FROM STDIN, the iterable must produce COPY lines.

Reference Symbols

Reference Symbols provide a way to construct a Bound Symbol using the Symbol’s query. When invoked, A Reference Symbol’s query is executed in order to produce an SQL statement to be used as a Bound Symbol. In ILF files, a reference is identified by its symbol name being prefixed with an ampersand:

SELECT 'SELECT 1::int4'::text

Then executed:

>>> # Runs the 'refsym' SQL, and creates a Bound Symbol using the results.
>>> sym = lib.refsym()
>>> assert sym() == 1

The Reference Symbol’s type and execution method are inherited by the created Bound Symbol. With one exception, const reference symbols are special in that they immediately resolved into the target Bound Symbol.

A Reference Symbol’s source query must produce rows of text columns. Multiple columns and multiple rows may be produced by the query, but they must be character types as the results are promptly joined together with whitespace so that the target statement may be prepared.

Reference Symbols are most likely to be used in dynamic DDL and DML situations, or, somewhat more specifically, any query whose definition depends on a generated column list.

Distributing and Usage

For applications, distribution and management can easily be a custom process. The application designates the library directory; the entry point adds the path to the postgresql.sys.libpath list; a category is built; and, a connection is made using the category.

For mere Python extensions, however, distutils has a feature that can aid in ILF distribution. The package_data setup keyword can be used to include ILF files alongside the Python modules that make up a project. See for more detailed information on the keyword parameter.

The recommended way to manage libraries for extending projects is to create a package to contain them. For instance, consider the following layout:


The project’s SQL libraries are organized into a single package directory, lib, so package_data would be configured:

package_data = {'pkg.lib': ['*.sql']}

Subsequently, the lib package initialization script can then be used to load the libraries, and create any categories(project/pkg/lib/

import os.path
import postgresql.lib as pg_lib
import postgresql.sys as pg_sys
libdir = os.path.dirname(__file__)
libthis = pg_lib.load('this')
libthat = pg_lib.load('that')
stdcat = pg_lib.Category(libthis, libthat)

However, it can be undesirable to add the package directory to the global postgresql.sys.libpath search paths. Direct path loading can be used in those cases:

import os.path
import postgresql.lib as pg_lib
libdir = os.path.dirname(__file__)
libthis = pg_lib.load(os.path.join(libdir, 'libthis.sql'))
libthat = pg_lib.load(os.path.join(libdir, 'libthat.sql'))
stdcat = pg_lib.Category(libthis, libthat)

Using the established project context, a connection would then be created as:

from pkg.lib import stdcat
import postgresql as pg
db =, category = stdcat)
# And execute some fictitious symbols.

Audience and Motivation

This chapter covers advanced material. It is not recommended that categories and libraries be used for trivial applications or introductory projects.


Libraries and categories are not likely to be of interest to ORM or DB-API users.

With exception to ORMs or other similar abstractions, the most common pattern for managing connections and statements is delegation:

class MyAppDB(object):
        def __init__(self, connection):
                self.connection = connection

        def my_operation(self, op_arg1, op_arg2):
                return self.connection.prepare(
                        "SELECT my_operation_proc($1,$2)",
                )(op_arg1, op_arg2)

The straightforward nature is likeable, but the usage does not take advantage of prepared statements. In order to do that an extra condition is necessary to see if the statement has already been prepared:


def my_operation(self, op_arg1, op_arg2):
        if self.hasattr(self, '_my_operation'):
                ps = self._my_operation
                ps = self._my_operation = self.connection.prepare(
                        "SELECT my_operation_proc($1, $2)",
        return ps(op_arg1, op_arg2)

There are many variations that can implement the above. It works and it’s simple, but it will be exhausting if repeated and error prone if the initialization condition is not factored out. Additionally, if access to statement metadata is needed, the above example is still lacking as it would require execution of the statement and further protocol expectations to be established. This is the province of libraries: direct database interface management.

Categories and Libraries are used to factor out and simplify the above functionality so re-implementation is unnecessary. For example, an ILF library containing the symbol:

SELECT my_operation_proc($1, $2)


Will provide the same functionality as the my_operation method in the latter Python implementation.


The following terms are used throughout this chapter:

The information of about a Symbol describing what it is and how it should be used.
An interface to the Symbols provided by a Library for use with a given connection.
Bound Symbol
An interface to an individual Symbol ready for execution against the subject database.
Bound Reference
An interface to an individual Reference Symbol that will produce a Bound Symbol when executed.
INI-style Library Format. “lib{NAME}.sql” files.
A collection of Symbols–mapping of names to SQL statements.
Local Symbol
A relative term used to denote a symbol that exists in the same library as the subject symbol.
The block of text that comes before the first symbol in an ILF file.
An named database operation provided by a Library. Usually, an SQL statement with Annotations.
Reference Symbol
A Symbol whose SQL statement produces the source for a Bound Symbol.
An object supporting a classification for connectors that provides database initialization facilities for produced connections. For libraries, postgresql.lib.Category objects are a set of Libraries, postgresql.lib.Library.