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https://github.com/amdmi3/aesqlapius
Manage SQL queries as a Python API
https://github.com/amdmi3/aesqlapius
python sql
Last synced: about 4 hours ago
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Manage SQL queries as a Python API
- Host: GitHub
- URL: https://github.com/amdmi3/aesqlapius
- Owner: AMDmi3
- License: mit
- Created: 2020-11-22T23:21:01.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2022-11-07T11:21:39.000Z (about 2 years ago)
- Last Synced: 2024-04-23T13:09:41.556Z (7 months ago)
- Topics: python, sql
- Language: Python
- Homepage:
- Size: 152 KB
- Stars: 3
- Watchers: 3
- Forks: 1
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# aeSQLAPIus
[](https://codecov.io/gh/AMDmi3/aesqlapius)
[](https://pypi.org/project/aesqlapius/)
[](https://github.com/AMDmi3/aesqlapius)
## 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 `fetchrow`s 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:
```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:
```python
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:
```python
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:
```python
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:
```python
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`):
```python
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:
```sql
-- 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:
```sql
-- def myfunc(foo, bar: str, baz=123) -> None: ...`
...some SQL code...
```
```pyton
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:
```sql
-- 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](https://pypi.org/project/psycopg2/) connections:
```python
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:
```python
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](https://dev.mysql.com/doc/connector-python/en/) connections:
```python
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](https://pypi.org/project/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).
```python
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](https://pypi.org/project/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).
```python
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 [email protected].