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https://github.com/akdor1154/python-csql


https://github.com/akdor1154/python-csql

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# csql - Composeable SQL



**csql** is a Python library to help you write more manageable SQL queries. You can write your queries as small, self-contained chunks, preview the results without pulling a whole result-set down from your database, then refer to them in future queries.



There are also useful features for handling database parameters properly.



The intended use-case is for data analysis and exploration.



[![PyPI version](https://badge.fury.io/py/csql.svg)](https://pypi.org/project/csql/)



**Full documentation** is available at https://csql.readthedocs.io/en/latest/api.html .



## Example



```py

from csql import Q, Parameters

import pandas as pd

from datetime import date



con = your_database_connection()

```



Start with a straightforward query:

```py

p = Parameters(

	created_on = date(2020,1,1)

)

q1 = Q(f"""

select

	customers.id,

	first(customers.name) as name,

	first(created_on) as created_on,

	sum(sales.value) as sales

from

	customers

	join sales on customers.id = sales.customer_id

where created_on > {p['created_on']}}

group by customers.id

""")



print(q1.preview_pd(con))

```



|  | id | name | created_on | sales |

|--|----|------|------------|-------|

|1 |111 |John Smith | 2020-02-05 | 32.0 |

|2 |112 |Amy Zhang | 2020-05-01 | 101.5 |

|3 |115 |Tran Thanh | 2020-03-02 | 100000.0 |



The preview will pull down 10 rows to a) sanity-check the result of what you've just written, and b) validate your sql.



-----



Now, try building some new queries *that build on your previous queries*:

```py

q2 = Q(f"""

select

	ntile(100) over (order by sales)

		as ntile_100,

	name,

	sales

from {q1}

""")



print(q2.preview_pd(con))

```



|  | ntile_100 | name | sales |

|--|-----------|------|-------|

| 1|29| John Smith| 32.0 |

| 2|50|Amy Zhang | 101.5 |

| 3|99|Tran Thanh | 100000.0 |



-----



```py

q3 = Q(f"""

select

	ntile_100,

	min(sales),

	max(sales)

from {q2}

group by ntile_100

order by ntile_100

""")



# this time, we'll pull the whole result instead of just previewing:

result = pd.read_sql(**q3.pd(), con=con)

print(result)

```

|  | ntile_100 | min(sales) | max(sales) |

|--|-----------|----------|--------------|

| 28| 29 | 25 | 33.3 |

| 49| 50 | 98 | 120 |

| 98| 99 | 5004 | 100000.0 |



-----



## Cool! But, how does it work?



The basic idea is to turn your queries into a CTE by keeping track of what builds on top of what. For example, for the last query shown, `q3`, what actually gets sent to the database is:



```sql

with _subQuery0 as (

	select

		customers.id,

		first(customers.name) as name,

		first(created_on) as created_on,

		sum(sales.value) as sales

	from

		customers

		join sales on customers.id = sales.customer_id

	where created_on > :1

	group by customers.id

),

_subQuery1 as (

	select

		ntile(100) over (order by sales)

			as ntile_100,

		name,

		sales

	from _subQuery0

)

select

	ntile_100,

	min(sales),

	max(sales)

from _subQuery1

group by ntile_100

order by ntile_100

```



which is exactly the sort of unmaintainable and undebuggable monstrosity that this library is designed to help you avoid.



## Design Notes



I am perhaps overly optimistic about this, but currently I think this should work with most SQL dialects. It doesn't attempt to parse your SQL, uses CTEs which are widely supported, and passes numeric style parameters.

It's also not actually tied to `pandas` at all - `.pd()` is just a convenience method to build a dict you can splat into pd.read_sql.



## Easy Parameters



Using proper SQL prepared statements is great to do, but can be annoying to maintain. Additionally, it can be incredibly

annoying when you are trying to use a list from Python:



```py

con = my_connection()

ids_i_want = [1, 2, 3]

with con.cursor() as c:

	# uh oh, you can't do this

	c.execute('select * from customers where id in :1', (ids_i_want,))



	# you need to do something like this instead

	c.execute('select * from customers where id in (:1, :2, :3), (ids_i_want[0], ids_i_want[1], ids_i_want[2],))

```



`csql` makes this much easier - you can embed your parameters naturally with string interpolation, and they will still be

sent as proper parameterized statements.



```py

p = Parameters(

	ids_i_want = [1, 2, 3],

	name = 'Jarrad'

)



get_customers = Q(f'''

	select * from customers

	where

		ids in {p['ids_i_want']}

		or name = {p['name']}

''')



with con.cursor() as c:

	c.execute(*get_customers.db)

```



That final statement is actually equivalent to:



```py

with con.cursor() as c:

	c.execute('''

		select * from customers

		where

			ids in (:1, :2, :3)

			or name = :4

	''', [1, 2, 3, 'Jarrad'])

```



## Changing Parameter Values



Parameters aren't super useful if they are set in stone, but `csql` wants you

to give values at the query definition time! How can you pass different values later?



This is achieved by passing `newParams` to {meth}`csql.Query.build`:



```py

p = Parameters(

  start=datetime.now() - timedelta(days=3),

  end=datetime.now()

)

q = Q(f'select count(*) from events where start <= date and date < end')

pd.read_sql(**q.pd, con=con)

# 42 # 3 days ago to now, as per `p`.

newParams = {'start': date(2010,1,1)}

pd.read_sql(**q.build(newParams=newParams).pd, con=con)

# 42000 # 2010 to now, with new value for `start` provided.

```



## SQL Dialects



Different dialects can be specified at render time, or as the default dialect of your Queries. Currently the only things dialects control are parameter rendering and limits, but I expect to see some scope creep around here...

Dialects are instances of {class}`csql.dialect.SQLDialect` and can be found in {mod}`csql.dialect`. The default dialect is {class}`csql.dialect.DefaultDialect`, which uses a numeric parameter renderer. You can specify your own prefered dialect per-query:



```py

q = csql.Q(

	f"select 1 from thinger",

	dialect=csql.dialect.DuckDB

)

```



If you want to set a default, use `functools.partial` like so:



```py

import functools

Q = functools.partial(csql.Q, dialect=csql.dialect.DuckDB)

q = Q(f"select 1 from thinger")

```



### Inferred Dialects



If a query `q2` references a previous query `q1`, and `q1` has a dialect specified, then `q2` will use `q1`'s dialect by default.



```py

q1 = csql.Q('select 1 from thinger', dialect=csql.dialect.Snowflake)

q2 = csql.Q('select count(*) from {q1})

assert q2.default_dialect == csql.dialect.Snowflake

```



If you reference multiple queries with conflicting dialects, you'll get an error. Normally this is because you've actually

forgotten to specify something somewhere. If you're doing this on purpose, override by setting `dialect=` to `Q` manually.



### DIY Dialects 



You can construct your own dialects:



```py

import csql.dialect

MyDialect = csql.dialect.SQLDialect(

  paramstyle=csql.dialect.ParamStyle.qmark

)

```



There are presets for some common databases (see below), and I'm very happy to accept PRs for any

others.



How to use Caching

==================



Once you have a few queries chained together, you may

start to get annoyed by how long one or two big things at the start

take, and wonder if there's a way to stop them being executed each time.



For example,



```py

q1 = Q(f'select id, date, rank() over (partition by name order by date) as rank from customers')

q2 = Q(f'select date, count(*) from {q1}')

print(q2.preview_pd(con))

# takes 2 mins becuase q1 is so slow

print(q2.preview_pd(con))

# same thing again, also takes 2 mins

q3 = Q(f'select max(date) from {q2}')

print(q3.preview_pd(con))

# also takes 2 mins because q1 is so slow

```



The solution is to use {meth}`csql.Query.persist` on the slow query you want to re-use.

Above, we could either do this on `q1` or `q2`, depending on what works best with

our database. I'll demonstrate `q2`:



```py

q1 = Q(f'select id, date, rank() over (partition by name order by date) as rank from customers')

cache = TempTableCacher(con)

q2 = Q(f'select date, count(*) from {q1}').persist(cache) # <--- !!

print(q2.preview_pd(con))

# still takes 2 mins

print(q2.preview_pd(con))

# now this is fast!

q3 = Q(f'select max(date) from {q2}')

print(q3.preview_pd(con))

# now this is fast as well!

```



The only general builtin caching method is {class}`csql.contrib.persist.TempTableCacher`, however it's straightforward

to write your own. You may want to also see {mod}`csql.contrib.persist` as there is a Snowflake-specific example in there as well.