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Manage SQL queries as a Python API

Project description

aeSQLAPIus

Packaging status

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Summary

So you don't want to use ORM, but want to organize your SQL queries in a convenient way. Don't mix them with your python code, don't write execute and fetchrows by hand for each query. With aesqlapius:

  • Store your SQL queries separately from the code, in a dedicated file or directory hierarchy
  • Annotate each query with python-like function definition specifying input arguments and output types and patterns

aesqlapius builds a class out of this, where you can call your queries as plain methods. It handles arguments (pass positional or keyword arguments as you like, default values are also handled) and output types and patterns (you may specify whether a method returns iterator, list, dict of rows, or a single row, where row may be represented as a tuple, list, dict, single value or a custom type such as a dataclass).

Example

queries.sql:

-- specify arguments to queries in python format, including
-- type annotations and support for default values

-- def add_city(name: str, population: int = None) -> None: ...
INSERT INTO cities VALUES (%(name)s, %(population)s);

-- specify return value format out of wide range of formats
-- (iterator, list, dict, or single instance of tuples, dicts
-- or simple values)

-- def list_cities() -> List[Value]: ...
SELECT name FROM cities ORDER BY name;

-- def get_population(city: str) -> Single[Value]: ...
SELECT population FROM cities WHERE name = %(city)s;

-- def get_populations() -> Dict[-'name', Value]: ...
SELECT name, population
FROM cities
WHERE population IS NOT NONE
ORDER BY name;

-- def iter_cities()() -> Iterator[Tuple] ...
SELECT * FROM cities ORDER BY name;

script.py:

from aesqlapius import generate_api

db = psycopg2.connect('...')
api = generate_api('queries.sql', 'psycopg2', db)

# pass arguments to queries in either positional and kw form
api.add_city('Moscow', 12500000)
api.add_city('Paris')
api.add_city(population=3800000, name='Berlin')

# get query results in the desired format
assert api.list_cities() == ['Berlin', 'Moscow', 'Paris']
assert api.get_population('Moscow') == 12500000
assert api.get_populations() == {'Berlin': 3800000, 'Moscow': 12500000}
assert next(api.iter_cities()) == ('Berlin', 3800000)

Reference

Python API

The module has a single entry point in form of a function:

def generate_api(path, driver, db=None, *, target=None, extension='.sql', namespace_mode='dirs', namespace_root='__init__')

This loads SQL queries from path (a file or directory) and returns an API class to use with specified database driver (psycopg2, sqlite3, mysql, aiopg, asyncpg).

If db is specified, all generated methods are bound to the given database connection object:

db = psycopg2.connect('...')
api = generate_api('queries.sql', 'psycopg2', db)
api.my_method('arg1', 'arg2')

otherwise caller is expected to pass database connection object to each call:

db = psycopg2.connect('...')
api = generate_api('queries.sql', 'psycopg2')
api.my_method(db, 'arg1', 'arg2')

If target is specified, methods are injected into the given object (which is also returned from generate_api):

db = psycopg2.connect('...')
generate_api('queries.sql', 'psycopg2', db, target=db)
db.my_method('arg1', 'arg2')

extension (by default .sql) specifies which files are loaded from the queries directory.

namespace_mode controls how hierarchy of files is converted into hierarchy of objects when constructing the API class. There are 3 modes supported:

  • dirs (the default), which maps each subdirectory to a nested method namespace ignoring file names:
path under query dir function definition resulting API
root.sql -- def a(): ... api.a()
subdir/foo.sql -- def b(): ... api.subdir.b()
subdir/bar.sql -- def c(): ... api.subdir.c()
  • files which uses file names (after stripping the extension) as an extra nesting level:
path under query dir function resulting API
root.sql -- def a(): ... api.root.a()
subdir/foo.sql -- def b(): ... api.subdir.foo.b()
subdir/bar.sql -- def c(): ... api.subdir.bar.c()

In this mode, namespace_root allows to specify a special file name which circumvents this behavior, allowing to mimic how Python handles module namespaces. For example, when namespace_root = "__init__" (the default):

path under query dir function resulting API
__init__.sql -- def a(): ... api.a()
foo.sql -- def b(): ... api.foo.b()
subdir/__init__.sql -- def c(): ... api.subdir.c()
subdir/bar.sql -- def d(): ... api.subdir.bar.d()
  • flat mode which ignores hierarchy:
path under query dir function resulting API
root.sql -- def a(): ... api.a()
subdir/foo.sql -- def b(): ... api.b()
subdir/bar.sql -- def c(): ... api.c()

Query annotations

Each query managed by aesqlapius must be preceded with a -- (SQL comment) followed by a Python-style function definition:

-- def function_name(parameters, ...) -> return_type: ...
...some SQL code...

Parameters

Parameters allow optional literal default values and optional type annotations (which are currently ignored) and may be specified in both positional, keyword or mixed style in the resulting API:

-- def myfunc(foo, bar: str, baz=123) -> None: ...`
...some SQL code...
api.myfunc(1, bar="sometext")  # foo=1, bar="sometext", baz=123

Return value

Return value annotation is required and may either be None (when query does not return anything) or a nested type annotation with specific structure RowsFormat[RowFormat].

Outer RowsFormat specifies how multiple rows returned by the query are handled. Allowed values are:

  • Iterator[RowFormat] - return a row iterator.
  • List[RowFormat] - return a list of rows.
  • Single[RowFormat] - return a single row.
  • Dict[KeyColumn, RowFormat] - return a dictionary of rows. The column to be used as a dictionary key is specified in the first argument, e.g. Dict[0, ...] uses first returned column as key and `Dict['colname', ...] uses column named colname. Precede column index or name with unary minus to make it removed from the row contents.

Inner RowFormat specifies how data for each row is presented:

  • Tuple - return row as a tuple of values.
  • Dict - return row as a dict, where keys are set to the column names returned by the query.
  • Value - return single value from the row. If the query returns multiple fields, the first one is returned.

Examples:

-- def example1() -> List[Tuple]: ...
SELECT 1, 'foo' UNION SELECT 2, 'bar';
-- def example2() -> Single[Value]: ...
SELECT 2*2;
-- def example3() -> Iterator[Dict]: ...
SELECT 1 AS n, 'foo' AS s UNION SELECT 2 AS n, 'bar' AS s;
-- def example4() -> Dict['key', Dict]: ...
SELECT 'foo' AS key, 1 AS a, 2 AS b;
-- def example5() -> Dict[-'key', Dict]: ...
SELECT 'foo' AS key, 1 AS a, 2 AS b;
>>> api.example1()
[(1, 'foo'), (2, 'bar')]
>>> api.example2()
4
>>> it = api.example3()
>>> next(it)
{'n': 1, 's': 'foo'}
>>> next(it)
{'n': 2, 's': 'bar'}
>>> api.example4()
{'foo': {'key': 'foo', 'a': 1, 'b': 2}}
>>> api.example5()
{'foo': {'a': 1, 'b': 2}}

Body

Function body of the annotationis required to contain a single ellipsis.

Drivers

psycopg2

Use with psycopg2 connections:

import aesqlapius, psycopg2
dbconn = psycopg2.connect('dname=... user=... password=...')
api = aesqlapius.generate_api('queries.sql', 'psycopg2', dbconn)
api.some_method(arg1=1, arg2=2)

sqlite3

Use with sqlite3 connections:

import aesqlapius, sqlite3
dbconn = sqlite3.connect('path_to_database.sqlite')
api = aesqlapius.generate_api('queries.sql', 'sqlite3', dbconn)
api.some_method(arg1=1, arg2=2)

mysql

Use with mysql.connector connections:

import aesqlapius, mysql.connector
dbconn = mysql.connector.connect(database=..., user=..., password=...)
api = aesqlapius.generate_api('queries.sql', 'mysql', dbconn)
api.some_method(arg1=1, arg2=2)

Notes:

  • The driver uses buffered=True parameter when creating cursor.

aiopg

Use with aiopg module. This driver generates asynchronous APIs, and accepts both connection and pool objects (in the latter case, connection is automatically acquired from the pool).

import aesqlapius, aiopg

async def pool_example():
    async with aiopg.create_pool('dname=... user=... password=...') as pool:
        api = aesqlapius.generate_api('queries.sql', 'aiopg', pool)
        await api.some_method(arg1=1, arg2=2)

async def connection_example():
    api = aesqlapius.generate_api('queries.sql', 'aiopg')
    async with aiopg.create_pool('dname=... user=... password=...') as pool:
        async with pool.acquire() as conn:
	    await api.some_method(conn, arg1=1, arg2=2)

asyncpg

Use with asyncpg module. This driver generates asynchronous APIs, and accepts both connection and pool objects (in the latter case, connection is automatically acquired from the pool).

import aesqlapius, asyncpg

async def pool_example():
    async with asyncpg.create_pool(database=..., user=..., password=...) as pool:
        api = aesqlapius.generate_api('queries.sql', 'asyncpg', pool)
        await api.some_method(arg1=1, arg2=2)

async def connection_example():
    conn = await asyncpg.connect(database=..., user=..., password=...)
    api = aesqlapius.generate_api('queries.sql', 'asyncpg', conn)
    await api.some_method(arg1=1, arg2=2)

async def another_connection_example():
    api = aesqlapius.generate_api('queries.sql', 'asyncpg')
    async with asyncpg.create_pool('dname=... user=... password=...') as pool:
        async with pool.acquire() as conn:
	    await api.some_method(conn, arg1=1, arg2=2)

Notes:

  • Methods with Iterator rows format use asyncpg cursors under the hood which are only available in transaction. The driver automatically wraps such methods in a transaction if they are called outside of one.

License

MIT license, copyright (c) 2020 Dmitry Marakasov amdmi3@amdmi3.ru.

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