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https://github.com/baverman/sqlbind

Lightweight text-based SQL parameter binds
https://github.com/baverman/sqlbind

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Lightweight text-based SQL parameter binds

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README 

        
# sqlbind



**sqlbind** allows to bind parameters in text based raw SQL queries.



```python

>>> q = sqlbind.Dialect.default()

>>> email = '[email protected]'

>>> sql = f'SELECT * FROM users WHERE email = {q/email}'

>>> sql

'SELECT * FROM users WHERE email = ?'

>>> q

['[email protected]']

>>> data = connection.execute(sql, q)



```



Supports all [DBAPI parameter styles][dbapi]. Isn't limited by DBAPI compatible drivers and

could be used with anything accepting raw SQL query and parameters in some way. For example

**sqlbind** could be used with [SQLAlchemy textual queries][sqa-text]. Or with [clickhouse-driver][ch]'s

non-DBAPI interface.



[dbapi]: https://peps.python.org/pep-0249/#paramstyle

[sqa-text]: https://docs.sqlalchemy.org/en/20/core/sqlelement.html#sqlalchemy.sql.expression.text

[ch]: https://clickhouse-driver.readthedocs.io/en/latest/quickstart.html#selecting-data



## Installation



```

pip install sqlbind

```



## Motivation



ORMs are great and could be used effectively for a huge number of tasks. But

after many years with SQLAlchemy I've noticed some repeating patterns:



* It's really not an easy task to decipher complex SQLAlchemy expression back into SQL.

  Especially when CTEs, sub-queries, nested queries or self-referential queries

  are involved. It composes quite well but it takes too much effort to write

  and read SQLAlchemy queries. For novices it could be a hard time to deal

  with it.



* Most of reporting queries are big enough already not to be bothered with ORMs and

  use raw SQL anyway. This kind of SQL often requires dynamic constructs and becomes

  string fiddling contraption.



* For a few tasks ORMs bring too much overhead and the only solution is to get

  down to raw DBAPI connection and raw SQL.



* (*Minor personal grudge, please ignore it*) For some ORMs (like Django ORM) your

  SQL intuition could be useless and requires deep ORM understanding. To the

  side: sqlalchemy hybrid properties, cough.



It boils down to one thing: from time to time you have to write raw

SQL queries. I could highlight 3 types of queries:



1. Fixed queries. They don't contain any parameters. For example

   `SELECT id, name FROM users ORDER BY registered DESC LIMIT 10`.

   In general fixed queries or fixed query parts compose well and don't require any

   special treatment. Python's f-strings are enough.



2. Static queries. They contain parameters but structure is fully known beforehand.

   For example `SELECT id, name FROM users WHERE email = :email LIMIT 1`. They

   are also could be composed without large issues, especially for connection

   drivers supporting named parameters (`:param`, `%(param)s`) and accepting dicts as parameters.

   Although for positional connection drivers (`%s`, `?`) composition requires careful

   parameter tracking and queries could be fragile to change.



3. Dynamic queries. Query part presence could depend on parameter value or

   external condition. For example to provide result on input filter you have

   to add CTE and corresponding JOIN to a query. Or add filters only for non

   `None` input values. ORMs are effective for composing such queries. Using

   raw SQL are almost impossible for abstraction and leads to a complex

   boilerplate heavy code.



Note: here and in following sections I deliberately use simple examples. In real life

there is no need to use **sqlbind** for such kind of queries.



Note: by composing I mean ability to assemble a final query from parts which could be

abstracted and reused.



**sqlbind** tries to address issues with static and dynamic query types. It tracks

parameter binds and could help with dynamic query parts.



## Quick start



Some things to consider:



* **sqlbind** tries to provide an API for a simple composition of raw SQL. Most

  operations return string-like objects ready to be inserted in the final query.

  **sqlbind** does trivial things and is easy to reason about.



* There is a large set of functions/methods to address dynamic queries but you

  haven't use it inline in a single query string. You could use variables to

  keep query parts and stitch resulted SQL from these parts.



* This README misses large portions of API. Feel free to explore doc strings

  with examples of fully strictly type-hinted **sqlbind**'s source code!



General use case looks like:



```python

# a global alias to a dialect used by connection backend, see `sqlbind.Dialect`

QParams = sqlbind.Dialect.some_dialect



def get_my_data(value1, value2):

    # Construct empty fresh sqlbind.QueryParams

    q = QParams()



    # Use `q` to bind parameter values in SQL string.

    sql = f'SELECT * FROM table WHERE field1 = {q/value1} AND field2 > {q/value2}'



    # Pass query and parameters into connection's execute.

    return get_connection().execute(sql, q).fetchall()

```



## Static queries



For queries or query parts with a known structure the most simple way to bind a parameter is to

use bind operator `/`:



```python

>>> date = "2023-01-01"

>>> q = sqlbind.Dialect.default()

>>> f'SELECT * FROM users WHERE registered > {q/date}'

'SELECT * FROM users WHERE registered > ?'

>>> q

['2023-01-01']



```



Or for named style parameters:



```python

>>> date = "2023-01-01"

>>> q = sqlbind.Dialect.default_named()

>>> f'SELECT * FROM users WHERE registered > {q/date}'

'SELECT * FROM users WHERE registered > :p0'

>>> q

{'p0': '2023-01-01'}



```



There is no any magic. Bind operator returns a string with a placeholder for a

corresponding dialect and adds parameter's value to `q` object. That's all.

`q` object is inherited from a `dict` or a `list` depending from a used

dialect.



```python

>>> value = 10

>>> q = sqlbind.Dialect.default()

>>> q/value

'?'

>>> q

[10]



```



Note: there is no much value in **sqlbind** if you have only static

queries and use connection backends accepting named parameters.



## Dynamic queries



Here begins a fun part. We can't use simple binds for dynamic queries.

For example we have a function returning recently registered users:



```python

def get_fresh_users(registered_since: datetime):

    q = QParams()  # an alias to some dialect to construct sqlbind.QueryParams instance

    sql = f'''\

        SELECT * FROM users

        WHERE registered > {q/registered_since}

        ORDER BY registered

    '''

    return connection.execute(sql, q)

```



And later there is a new requirement for the function. It should return only

enabled or only disabled users if corresponding argument is passed.



```python

def get_fresh_users(registered_since: datetime, enabled: Optional[bool] = None):

    q = QParams()



    if enabled is not None:

        enabled_filter = f' AND enabled = {q/enabled}'

    else:

        enabled_filter = ''



    sql = f'''\

        SELECT * FROM users

        WHERE registered > {q/registered_since} {enabled_filter}

        ORDER BY registered

    '''

    return connection.execute(sql, q)

```



It looks almost pretty. See how `q/enabled` helped to track additional parameter.

But you can predict where we are going. Another one or two

additional filters and it would be a complete mess. Take note how `WHERE` lost `AND`

between two filters.



### q-templates



In reality bind operator `/` is a sugar on top of generic **sqlbind**'s API to

bind parameters via q-templates.



```python

>>> q = sqlbind.Dialect.default()

>>> q('field BETWEEN {} AND {}', 10, 20)

'field BETWEEN ? AND ?'

>>> q

[10, 20]



```



`QueryParams` `q` object is also a callable accepting a template with `{}`

placeholders and following parameters to substitute. `q/value` is same as calling

`q('{}', value)`



```python

>>> q/10

'?'

>>> q('{}', 10)

'?'



```



You could use q-templates to bind parameters in complex SQL expressions.



### Conditionals



`q.cond` could render a q-template as an empty string based on some condition.



```python

>>> enabled = True

>>> q.cond(enabled is not None, ' AND enabled = {}', enabled)

' AND enabled = ?'

>>> enabled = None

>>> q.cond(enabled is not None, ' AND enabled = {}', enabled)

''



```



`q.cond` is a generic form. To remove a repetition (`enabled is not

None`/`enabled`) when value is used both in a condition and as a parameter

value there are two helpers for most common cases:



* `q.not_none`: to check value is not None.

* `q.truthy`: to check value's trueness (`bool(value) is True`). `not_empty`

  could be used as an alias to `truthy`.



```python

>>> enabled = True

>>> q.not_none(' AND enabled = {}', enabled)

' AND enabled = ?'

>>> enabled = None

>>> q.not_none(' AND enabled = {}', enabled)

''



```



Let's try it in the function:



```python

def get_fresh_users(registered_since: datetime, enabled: Optional[bool] = None):

    q = QParams()



    enabled_filter = q.not_none(' AND enabled = {}', enabled)



    sql = f'''\

        SELECT * FROM users

        WHERE registered > {q/registered_since} {enabled_filter}

        ORDER BY registered

    '''

    return connection.execute(sql, q)

```



Hmm. But really nothing was changed. You could write previous code with ternary

if/else and it would look the same from semantic standpoint. May be use it

inline?



```python

def get_fresh_users(registered_since: datetime, enabled: Optional[bool] = None):

    q = QParams()



    sql = f'''\

        SELECT * FROM users

        WHERE registered > {q/registered_since}

              {q.not_none(' AND enabled = {}', enabled)}

        ORDER BY registered

    '''

    return connection.execute(sql, q)

```



Ugh. Abomination, to say at least.



* `AND` in the middle of a cryptic expression.

* `q.not_none` and `enabled` are far away and it's not obvious they are connected

* expression is too long and noisy



Let's tackle issues bit by bit.



### `AND_`/`OR_` prependers



Prependers could render non-empty inputs with corresponding prefixes and empty

string otherwise.



```python

>>> AND_('field1 > 1', 'field2 < 1')

'AND field1 > 1 AND field2 < 1'

>>> OR_('field1 > 1', 'field2 < 1')

'OR field1 > 1 OR field2 < 1'

>>> AND_(q.not_none('enabled = {}', True))

'AND enabled = ?'

>>> AND_(q.not_none('enabled = {}', None))

''



```



Our function with prependers:



```python

from sqlbind import AND_



def get_fresh_users(registered_since: datetime, enabled: Optional[bool] = None):

    q = QParams()



    sql = f'''\

        SELECT * FROM users

        WHERE registered > {q/registered_since}

              {AND_(q.not_none('enabled = {}', enabled))}

        ORDER BY registered

    '''

    return connection.execute(sql, q)

```



At least AND is almost on it's place in SQL structure.



### Conditional markers



Conditional markers `sqlbind.not_none`/`sqlbind.truthy`/`sqlbind.cond` allows to tie conditionals

with a value via `/` operator:



```python

>>> q('enabled = {}', sqlbind.not_none/10)

'enabled = ?'

>>> q('enabled = {}', sqlbind.not_none/None)

''



```



Conditional markers return value itself or special UNDEFINED object.

UNDEFINED parameters force expressions to be rendered as empty strings.



**`sqlbind.not_none`** returns `UNDEFINED` if value is `None`:



```python

>>> sqlbind.not_none/10

10

>>> sqlbind.not_none/None is sqlbind.UNDEFINED

True



```



**`sqlbind.truthy`** or `sqlbind.not_empty` returns `UNDEFINED` if `bool(value) != True`:



```python

>>> sqlbind.truthy/10

10

>>> sqlbind.not_empty/10

10

>>> sqlbind.truthy/0 is sqlbind.UNDEFINED

True



```



**`sqlbind.cond`** returns `UNDEFINED` if condition is False:



```python

>>> sqlbind.cond(True)/10

10

>>> sqlbind.cond(False)/10 is sqlbind.UNDEFINED

True



```



Note: `sqlbind.cond` is almost always awkward to use inline in real life and exists largely for symmetry with `q.cond`.



Rewritten function:



```python

from sqlbind import AND_, not_none



def get_fresh_users(registered_since: datetime, enabled: Optional[bool] = None):

    q = QParams()



    sql = f'''\

        SELECT * FROM users

        WHERE registered > {q/registered_since}

              {AND_(q('enabled = {}', not_none/enabled))}

        ORDER BY registered

    '''

    return connection.execute(sql, q)

```



Almost there. May be there is a way to reduce number of quotes inside `AND_`?



### q-expressions



q-expressions allow to generate templated results with infix operators.



Any unknown attribute access to `q` object returns `QExpr` which has str

conversion as an attribute name:



```python

>>> str(q.field)

'field'

>>> str(q.table.field)

'table.field'



```



`q` has a number of attributes itself those names could conflict with existing

DB tables/columns. To resolve conflicts you could use `q._.` (stare) expression:



```python

>>> str(q._.cond)

'cond'



```



Real DB tables/columns could use quite peculiar names. You could youse `q._`

(pirate) expression to construct `QExpr` from any string:



```python

>>> str(q._('"weird table"."weird column"'))

'"weird table"."weird column"'



```



`QExpr` object knows about parent `q` object and defines a set of infix operators

allowing to bind a right value:



```python

>>> q.field > 10

'field > ?'

>>> q.table.field == 20

'table.field = ?'

>>> q._.table.field == None

'table.field IS NULL'

>>> q._('"my column"') != None

'"my column" IS NOT NULL'

>>> q.field <= not_none/None  # conditional marks also work!

''

>>> q.field.IN(not_none/[10]) # BTW sqlbind has workaround for SQLite to deal with arrays in IN

'field IN ?'



```



It could look like a hack and feel ORM-ish but there is no any

expression trees and tree compilation passes. q-expressions

are immediately rendered as strings and simple to reason about.



Also set of operations is really small it includes only comparisons and `QExpr.IN`.



Let's use q-expressions with the function:



```python

from sqlbind import AND_, not_none



def get_fresh_users(registered_since: datetime, enabled: Optional[bool] = None):

    q = QParams()



    sql = f'''\

        SELECT * FROM users

        WHERE registered > {q/registered_since}

              {AND_(q.enabled == not_none/enabled)}

        ORDER BY registered

    '''

    return connection.execute(sql, q)

```



I have no any other tricks. It's the final inline version. I can't make it

more pretty or readable. It's true, inline expressions looks a bit noisy and to

make it manageable try to extract as much logic and use only `not_none` conditional marker.



IMHO instead of



```python

>>> now = None

>>> show_only_enabled = True

>>> f'SELECT * FROM users WHERE registered > {q/((now or datetime.utcnow()) - timedelta(days=30))} {AND_(q.enabled == cond(show_only_enabled)/1)}'

'SELECT * FROM users WHERE registered > ? AND enabled = ?'



```



please consider to use:



```python

>>> now = None

>>> show_only_enabled = True

>>> registered_since = (now or datetime.utcnow()) - timedelta(days=30)

>>> enabled = 1 if show_only_enabled else None

>>> f'SELECT * FROM users WHERE registered > {q/registered_since} {AND_(q.enabled == not_none/enabled)}'

'SELECT * FROM users WHERE registered > ? AND enabled = ?'



```



Also there is a possibility to construct filters out of line via `WHERE`

prepender.



### WHERE prepender



It could be useful to extract filters outside of f-strings and use `sqlbind.WHERE`

prepender. It can help with readability of long complex filters.



```python

from sqlbind import not_none, WHERE



def get_fresh_users(registered_since: datetime, enabled: Optional[bool] = None):

    q = QParams()



    filters = [

        q.registered > registered_since,

        q.enabled == not_none/enabled,

    ]



    sql = f'SELECT * FROM users {WHERE(*filters)} ORDER BY registered'

    return connection.execute(sql, q)

```



There are also other prependers: `WITH`, `LIMIT`, `OFFSET`, `GROUP_BY`,

`ORDER_BY`, `SET`. They all omit empty parts or are rendered as

empty string if all parts are empty.



Also you could use `&` operator to join filters to assemble condition expression without a list:



```python

>>> filters = (q.registered > '2023-01-01') & (q.enabled == not_none/True)

>>> WHERE(filters)

'WHERE (registered > ? AND enabled = ?)'



```



— "Wait a minute. How does it work? You said there is no expression trees and compilation! And

all operations return strings!"



### Expressions



Well, technically they are strings. Almost all methods and functions return `sqlbind.Expr`. It's a very shallow

descendant of `str` with only `__or__`, `__and__` and `__invert__` overrides.



```python

>>> q('enabled') & q('registered')

'(enabled AND registered)'

>>> type(q('enabled'))



>>> type(q.enabled == True)



```



All Expr instances could be composed with `&`, `|` and `~` (negate) operations.

Sadly due to python's' precedence rules you have to wrap expressions into

additional parens to make it work.



### Outro



It's a matter of preference and team code agreements. Personally I don't see anything

criminal in inline expressions. But it could be a huge red flag for other

person and it's ok. **sqlbind** gives a choice to use inline or out of line

approach.



But take a note. For positional dialects (like qmark style) out of line

rendering has a major drawback. You should take care on part ordering. Binding

and part usage should be synchronised. For example:



```python

>>> q = sqlbind.Dialect.default()

>>> filter1 = q.registered > '2023-01-01'

>>> filter2 = q.enabled == 1

>>> f'SELECT * FROM users WHERE {filter2} AND {filter1}'

'SELECT * FROM users WHERE enabled = ? AND registered > ?'

>>> q  # parameter ordering mismatches placeholders

['2023-01-01', 1]



```



It's a largely artificial example but for complex queries composed from

multiple parts it could be an issue. To reduce chance you could abstract composition

parts in a way to contain bindings and SQL construction in one go to be

fully synchronised.



BTW, you could already noticed but out of line variants of `get_fresh_users`

from [Dynamic queries](#dynamic-queries) and [Conditionals](#conditionals) have

the same ordering bug: inline and out of line approaches mix quite bad. Always

use named style Dialect if your connection backend allows it.