sqlphile 0.1.3.4

SQL Phile

==========
SQLPhile
==========

.. contents:: Table of Contents

Introduce
=============

SQLPhile is a SQL template engine and Python style SQL generator. It looks like Django ORM but it hasn't any relationship with Django or ORM.

But it is inspired by Django ORM and iBATIS SQL Maps.

SQLPhile might be useful for keeping clean look of your app script. It can make hide SQL statements for your script by using Python functions or/and writing SQL templates to seperated files.

For Example,

.. code:: python

conn = psycopg2.connect (...)
cursor = conn.cursor ()

cursor.execute ("""
SELECT type, org, count(*) cnt FROM rc_file
WHERE org = {} AND filename LIKE '%{}'
GROUP BY {}
ORDER BY {}
LIMIT {}
OFFSET {}
""".format (1, 'OCD', 'type', 'org, cnt DESC', 10, 10))

This codes can be written with SQLPhile:

.. code:: python

sp = SQLPhile ()

conn = psycopg2.connect (...)
cursor = conn.cursor ()

q = sp.ops.select ("rc_file", "type", "count(*) cnt")
q.filter (org = 1, name__endswith = 'OCD')
q.group_by ("type").order_by ("org", "-cnt")[10:20]
cursor.execute (q.as_sql ())

Or you can use SQL template file: sqlmaps/file.sql:

.. code:: html

<sql name="get_stat">
SELECT type, org, count(*) cnt FROM rc_file
WHERE {_filters}
GROUP BY type
ORDER BY org, cnt DESC
{_limit} {_offset}
</sql>

Your app code is,

.. code:: python

sp = SQLPhile ("sqlmaps")

conn = psycopg2.connect (...)
cursor = conn.cursor ()

q = sp.file.get_stat.filter (org = 1, name__endswith = 'OCD')[10:20]
cursor.execute (q.as_sql ())


SQLPhile
===========

SQLPhile is main class of this package.

.. code:: python

from sqlphile import SQLPhile

sp = SQLPhile (dir = None, auto_reload = False, engine = "postgresql")

Once SQLPhile is created, you can reuse it through entire your app.


Simple Query
--------------

SQLPhile provide *ops* object for generic SQL operation.

.. code:: python

q = sp.ops.insert (table = "rc_file")
q.data (_id = 1, score = 1.3242, name = "file-A", moddate = datetime.date.today ())
cursor.execute (q.as_sql ())

q = sp.ops.update ("rc_file")
q.data (name = "Jenny", modified = datetime.date.today ())
q.filter (...)

q = sp.ops.select ("rc_file")
q.columns ("id", "name", "create", "modified")
q.filter (...)

q = sp.ops.delete ("rc_file")
q.filter (...)

Also shortcuts are avaliable,

.. code:: python

q = sp.ops.insert ("rc_file", _id = 1, score = 1.3242, name = "file-A", moddate = datetime.date.today ())
cursor.execute (q.as_sql ())

q = sp.ops.update ("rc_file", name = "Jenny", modified = datetime.date.today ())
q.filter (...)

q = sp.ops.select ("rc_file", "id", "name", "create", "modified")
q.filter (...)

q = sp.ops.delete ("rc_file")
q.filter (...)


Templating For Complex and Highly Customized Query
------------------------------------------------------

If you create SQL templates in specific directory,

.. code:: python

from sqlphile import SQLPhile

sp = SQLPhile (dir = "./sqlmaps", auto_reload = True)

SQLPhile will load all of your templates in ./sqlmaps.

If you are under developing phase, set auto_reload True.

Assume there is a template file named 'file.sql':

.. code:: html

<sqlmap version="1.0">

<sql name="get_stat">
SELECT type, org, count(*) cnt FROM rc_file
WHERE {_filters}
GROUP BY type
ORDER BY org, cnt DESC
{_limit} {_offset}
</sql>

It looks like XML file, BUT IT'S NOT. All tags - <sqlmap>, <sql></sql> should be started at first of line. But SQL of inside is at your own mind but I recommend give some indentation.

Now you can access each sql temnplate via filename without extension and query name attribute:

.. code:: python

# filename.query name
q = sp.file.get_stat
q.filter (...).order_by (...)

# or
q = sp.file.get_stat.filter (...).order_by (...)

Note: filename is *default.sql*, you can ommit filename.

.. code:: python

q = sp.get_stat
q.filter (...).order_by (...)

Note 2: SHOULD NOt use "ops.*" as filename.


Filtering & Excluding
======================

filter function is very simailar with Djnago ORM.

.. code:: python

q = sp.get_stat

q.filter (__all = True)
>> 1 = 1

q.filter (id__all = True)
>> 1 = 1

q.filter (id = 1)
>> id = 1

q.filter (id__exact = 1)
>> id = 1

q.filter (id__eq = 1)
>> id = 1

q.exclude (id = 1)
>> NOT (id = 1)

q.filter (id__neq = 1)
>> id <> 1

q.filter (id__gte = 1)
>> id >= 1

q.filter (id__lt = 1)
>> id < 1

q.filter (id__between = (10, 20))
>> id BETWEEN 10 AND 20

q.filter (name__contains = "fire")
>> name LIKE '%fire%'

q.exclude (name__contains = "fire")
>> NOT name LIKE '%fire%'

q.filter (name__startswith = "fire")
>> name LIKE 'fire%'

# escaping %
q.filter (name__startswith = "fire%20ice")
>> name LIKE 'fire\%20ice%'

q.filter (name__endswith = "fire")
>> name LIKE '%fire'

q.filter (name = None)
>> name IS NULL

q.exclude (name = None)
>> NOT name IS NULL

q.filter (name__isnull = True)
>> name IS NULL

q.filter (name__isnull = False)
>> name IS NOT NULL

Also you can add multiple filters:

.. code:: python

q.filter (name__isnull = False, id = 4)
>> name IS NOT NULL AND id = 4

All filters will be joined with "AND" operator.

Q Object
----------

How can add OR operator?

.. code:: python

from sqlphile import Q

q.filter (Q (id = 4) | Q (email__contains = "org"), name__isnull = False)
>> name IS NOT NULL AND (id = 4 OR email LIKE '%org%')

Note that Q objects are first, keywords arguments late. Also you can add seperatly.

.. code:: python

q.filter (name__isnull = False)
q.filter (Q (id = 4) | Q (email__contains = "org"))
>> (id = 4 OR email LIKE '%org%') AND name IS NOT NULL

If making excluding filter with Q use tilde(*~*),

.. code:: python

q.filter (Q (id = 4) | ~Q (email__contains = "org"))
>> (id = 4 OR NOT email LIKE '%org%')


F Object
----------

All value will be escaped or automatically add single quotes, but for comparing with other fileds use *F*.

.. code:: python

from sqlphile import F

Q (email = F ("b.email"))
>> email = b.email

Q (email__contains = F ("org"))
>> email LIKE '%' || org || '%'

F can be be used for ops.

.. code:: python

q = sp.ops.update (tbl, n_view = F ("n_view + 1"))
q.filter (...)
cursor.execute (q.as_sql ())

Ordering & Grouping
====================

For ordering,

.. code:: python

q = sp.ops.select (tbl, "id", "name", "create", "modified")
q.filter (...)
q.order_by ("id", "-modified")
>> ORDER BY id, modified DESC

For grouping,

.. code:: python

q = sp.ops.select (tbl, "name", "count(*) cnt")
q.filter (...)
q.group_by ("name")
>> GROUP BY name

q.having ("count(*) > 10")
>> GROUP BY name HAVING count(*) > 10

Offset & Limit
================

For limiting record set,

.. code:: python

q = sp.ops.select (tbl, "id", "name", "create", "modified")
q [:100]
>> LIMIT 100

q [10:30]
>> LIMIT 20 OFFSET 10

Be careful for slicing and limit count.

Returning
============

For Returning columns after insertinig or updating data,

.. code:: python

q = sp.ops.insert (tbl, name = "Hans", created = datetime.date.today ())
q.returning ("id", "name")
>> RETURNING id, name


Using Template
=================

Template is like this,

.. code:: html

<sqlmap version="1.0">

<sql name="get_stat">
SELECT type, org, count(*) cnt FROM rc_file
WHERE {_filters}
GROUP BY type
ORDER BY org, cnt DESC
{_limit} {offset}
</sql>

<sql name="get_file">
SELECT * cnt FROM rc_file
WHERE {_filters}
{_order_by}
{_limit}
{_offset}
</sql>

You just fill variables your query reqiures,

.. code:: python

q = sp.file.get_file.filter (id__gte = 1000)[:20]
q.order_by ("-id")

Current reserved variables are,

- _filters
- _group_by
- _order_by
- _limit
- _offset
- _having
- _returning
- _columns: comma joined column list fed by data ()
- _values: comma joined value list fed by data ()
- _pairs: comma joined column=value list fed by data ()

Variablize Your Query
-----------------------

You can add variable on your sql.

.. code:: html

<sql name="get_file">
SELECT {cols} cnt FROM {tbl}
WHERE {_filters}
</sql>

Now feed keywords args with feed ():

.. code:: python

q = sp.file.get_file.filter (id__gte = 1000)
q.feed (cols = "id, name, created", tbl = "rc_file")


Also you can feed values with similar way,

.. code:: html

<sql name="get_file">
INSERT INTO {tbl} (name, create, birth_year)
VALUES ({name}, {created}, {birth_year})
</sql>

.. code:: python

q = sp.file.get_file
q.feed (tbl = "rc_file")
q.data (name = "Hans Roh", created = datetime.date.today (), birth_year = 2000)

What differences with feed? data () will escape values for fitting SQL. You needn't care about sing quotes, escaping or type casting on date time field.

Actually, feed () can be omitable,

.. code:: python

# like instance constructor
q = sp.file.get_file (tbl = "rc_file")
q.data (name = "Hans Roh", created = datetime.date.today (), birth_year = 2000)

What differences with feed? data () will escape values for fitting SQL. You needn't care about sing quotes, escaping or type casting on date time field.

D Object
---------

Sometimes, data() method is insufficient. D object convert dictionary into SQL column and value format and can feed them into SQL template.

.. code:: python

from sqlphile import D

d = D (name = "Hans", id = 1, email = None)
d.values
>> 'Hans', 1, NULL

d.columns
>> name, id, email

d.pairs
>> name = 'Hans', id = 1, email = NULL

And you can feed to template.

.. code:: html

<sql name="get_file">
INSERT ({_columns}, {additional_columns})
VALUES ({_valuess}, {additional_values})
{_returning};
</sql>

In app,

.. code:: python

q = sp.file.get_file.data (area = "730", additional = D (name = 'Hans', id = 1))
q.returning ("id")
cursor.execute (q.as_sql ())