pg_query

Pythonic wrapper around libpg_query and PostgreSQL prettifier

author: Lele Gaifax

contact: lele@metapensiero.it

license: GNU General Public License version 3 or later

author:	Lele Gaifax
contact:	lele@metapensiero.it
license:	GNU General Public License version 3 or later

This is a Python 3 implementation of a wrapper to libpg_query, a C library that repackages the PostgreSQL language parser as a standalone static library.

I needed a better SQL reformatter than the one implemented by sqlparse, and was annoyed by a few glitches (subselects in particular) that ruins the otherwise excellent job it does, considering that it is a generic library that tries to swallow many different SQL dialects.

When I found psqlparse I decided to try implementing a PostgreSQL focused tool: at the beginning it's been easier than I feared, but I quickly hit some shortcomings in that implementation, so I opted for writing my own solution restarting from scratch, with the following goals:

target only Python 3.4+
target PostgreSQL 10
use a more dynamic approach to represent the parse tree, with a twofold advantage:
1. it is much less boring to code, because there's no need to write one Python class for each PostgreSQL node tag
2. the representation is version agnostic, it can be adapted to newer/older Elephants in a snap
allow exploration of parse tree in both directions, because I realized that some kinds of nodes require that knowledge to determine their textual representation
avoid introducing arbitrary renames of tags and attributes, so what you read in PostgreSQL documentation/sources[*] is available without the hassle of guessing how a symbol has been mapped
use a zero copy approach, keeping the original parse tree returned from the underlying libpg_query functions and have each node just borrow a reference to its own subtree

[*]	Currently what you can find in the following headers: lockoptions.h nodes.h parsenodes.h pg_class.h primnodes.h

Introduction

At the lower level the module exposes two libpg_query functions, parse_sql() and parse_plpgsql(), that take respectively an SQL statement and a PLpgSQL statement and return a parse tree as a hierarchy of Python dictionaries, lists and scalar values. In some cases these scalars correspond to some C typedef enums, that are automatically extracted from the PostgreSQL headers mentioned above and are available as pg_query.enums.

At a higher level that tree is represented by three Python classes, a Node that represents a single node, a List that wraps a sequence of nodes and a Scalar for plain values such a strings, integers, booleans or none.

Every node is identified by a tag, a string label that characterize its content that is exposed as a set of attributes as well as with a dictionary-like interface (technically they implements both a __getattr__ method and a __getitem__ method). When asked for an attribute, the node returns an instance of the base classes, i.e. another Node, or a List or a Scalar, depending on the data type of that item. When the node does not contain the requested attribute it returns a singleton Missing marker instance.

A List wraps a plain Python list and may contains a sequence of Node instances, or in some cases other sub-lists, that can be accessed with the usual syntax, or iterated.

Finally, a Scalar carries a single value of some type, accessible through its value attribute.

On top of that, the module implements two serializations, one that transforms a Node into a raw textual representation and another that returns a prettified representation. The latter is exposed by the pgpp CLI tool, see below for an example.

Installation

As usual, the easiest way is with pip:

$ pip install pg_query

Alternatively you can clone the repository:

$ git clone https://github.com/lelit/pg_query.git --recursive

and install from there:

$ pip install ./pg_query

Development

There is a set of makefiles implementing the most common operations, a make help will show a brief table of contents. A comprehensive test suite, based on pytest, covers 98% of the source lines.

Examples of usage

Parse an SQL statement and get its AST root node:

>>> from pg_query import Node, parse_sql
>>> root = Node(parse_sql('SELECT foo FROM bar'))
>>> print(root)
None=[1*{RawStmt}]

Recursively traverse the parse tree:

>>> for node in root.traverse():
...   print(node)
...
None[0]={RawStmt}
stmt={SelectStmt}
fromClause[0]={RangeVar}
inh=<True>
location=<16>
relname=<'bar'>
relpersistence=<'p'>
op=<0>
targetList[0]={ResTarget}
location=<7>
val={ColumnRef}
fields[0]={String}
str=<'foo'>
location=<7>

As you can see, the representation of each value is mnemonic: {some_tag} means a Node with tag some_tag, [X*{some_tag}] is a List containing X nodes of that particular kind[†] and <value> is a Scalar.

Get a particular node:

>>> from_clause = root[0].stmt.fromClause
>>> print(from_clause)
fromClause=[1*{RangeVar}]

Obtain some information about a node:

>>> range_var = from_clause[0]
>>> print(range_var.node_tag)
RangeVar
>>> print(range_var.attribute_names)
dict_keys(['relname', 'inh', 'relpersistence', 'location'])
>>> print(range_var.parent_node)
stmt={SelectStmt}

Iterate over nodes:

>>> for a in from_clause:
...     print(a)
...     for b in a:
...         print(b)
...
fromClause[0]={RangeVar}
inh=<True>
location=<16>
relname=<'bar'>
relpersistence=<'p'>

Reformat a SQL statement[‡] from the command line:

$ echo "select a,b,c from sometable" | pgpp
SELECT a
     , b
     , c
FROM sometable

$ echo 'update "table" set value=123 where value is null' | pgpp
UPDATE "table"
SET value = 123
WHERE value IS NULL

$ echo "
insert into t (id, description)
values (1, 'this is short enough'),
       (2, 'this is too long, and will be splitted')" | pgpp -s 20
INSERT INTO t (id, description)
VALUES (1, 'this is short enough')
     , (2, 'this is too long, an'
           'd will be splitted')

Programmatically reformat a SQL statement:

>>> from pg_query import prettify
>>> print(prettify('delete from sometable where value is null'))
DELETE FROM sometable
WHERE value IS NULL

Documentation

Latest documentation is hosted by Read the Docs at http://pg-query.readthedocs.io/en/latest/

[†]	This is an approximation, because in principle a list could contain different kinds of nodes, or even sub-lists in some cases: the `List` representation arbitrarily shows the tag of the first object.

[‡]	Currently this covers most DML statements such as `SELECT`s, `INSERT`s, `DELETE`s and `UPDATE`s, fulfilling my needs, but I'd like to extend it to handle also DDL statements and, why not, PLpgSQL instructions too.