Package: pyg-sql
pyg-sqlIntroductionpip install fromhttps://pypi.org/project/pyg-sql/conda install: I no longer maintain a conda environment so an old version is available here:https://anaconda.org/yoavgit/pyg-sql. The project itself will happily conda-build.pyg-sql creates sql_cursor (and its constructor, sql_table), a thin wrapper on sql-alchemy (sa.Table), providing three different functionailities:simplified create/filter/sort/access/join of a sql tablecreation of a full no-sql like document-storefull "in-the-background" maintainance of indexed unique records are per specified primary keys while we auto-archive old datasupports both a transactional and an ad-hoc approachpyg-sql supports very light joins but makes playing with a single table MUCH easier than traditional sqlalchemy.access simplificationsqlalchemy use-pattern makes Table create the "statement" and then let the engine session/connection execute. Conversely, the sql_cursor keeps tabs internally of:- the table
- the engine
- the "select", the "order by" and the "where" expressionsThis allows us to
- "query and execute" in one go
- build statements interactively, each time adding select/where/sort to previous where/select:Example: table creation
------------------------
>>> from pyg_base import * 
>>> from pyg_sql import * 
>>> import datetime

>>> t = sql_table(db = 'test_db', table = 'students', non_null = ['name', 'surname'], server = 'DESKTOP-LU5C5QF',
                      _id = dict(_id = int, created = datetime.datetime), 
                      nullable =  dict(doc = str, details = str, dob = datetime.date, age = int, grade = float))
>>> t = t.delete()


:Example: table insertion
-------------------------
>>> t = t.insert(name = 'yoav', surname = 'git', age = 48)
>>> t = t.insert(name = 'anna', surname = 'git', age = 37)
>>> assert len(t) == 2
>>> t = t.insert(name = ['ayala', 'itamar', 'opher'], surname = 'gate', age = [17, 11, 16])
>>> assert len(t) == 5

:Example: simple access
-----------------------
You can access rows either as dicts or as pd.Series

>>> t[0]

{'_id': 1,
 'created': datetime.datetime(2023, 1, 23, 17, 0, 6, 553000),
 'name': 'yoav',
 'surname': 'git',
 'doc': None,
 'details': None,
 'dob': None,
 'age': 48,
 'grade': None}

>>> t.df[0]
_id                                 1
created    2023-01-23 17:00:06.553000
name                             yoav
surname                           git
doc                              None
details                          None
dob                              None
age                                48
grade                            None
dtype: object

>>> assert t.sort('age')[0].name == 'itamar'                                                     # youngest
>>> assert t.sort('age')[-1].name == 'yoav'                                                      # access of last record
>>> assert t.sort(dict(age=-1))[0].name == 'yoav'                                                # sort in descending order
>>> assert t.sort('name')[::].name == ['anna', 'ayala', 'itamar', 'opher', 'yoav']
>>> assert t.sort('name')[['name', 'surname']][::].shape == (5, 2)                              ## access of specific column(s)
>>> assert t.surname == ['gate', 'git']
>>> assert t['surname'] == ['gate', 'git']
>>> assert t[dict(name = 'yoav')] == t.inc(name = 'yoav')[0]

>>> names = [doc.name for doc in t.sort(dict(age=-1))]
>>> assert names == ['yoav', 'anna', 'ayala', 'opher', 'itamar']

:Example: DataFrame access:
---------------------------
>>> t.df() ## get all the data as a dataframe
>>> t.sort('age')[['age', 'name']].df[2:4]

   age   name
0   17  ayala
1   37   anna    

:Example: simple filtering
--------------------------
>>> assert len(t.inc(surname = 'gate')) == 3
>>> assert len(t.inc(surname = 'gate').inc(name = 'ayala')) == 1    # you can build filter in stages
>>> assert len(t.inc(surname = 'gate', name = 'ayala')) == 1        # or build in one step
>>> assert len(t.inc(surname = 'gate').exc(name = 'ayala')) == 2

>>> assert len(t > dict(age = 30)) == 2
>>> assert len(t <= dict(age = 37)) == 4
>>> assert len(t.inc(t.c.age > 30)) == 2  # can filter using the standard sql-alchemy .c.column objects
>>> assert len(t.where(t.c.age > 30)) == 2  # can filter using the standard sql-alchemy "where" statementinsertion of "documents" into string columns...It is important to realise that we already have much flexibility behind the scene in using "documents" inside string columns:>>> t = t.delete()
>>> assert len(t) == 0; assert t.count() == 0
>>> import numpy as np
>>> t.insert(name = 'yoav', surname = 'git', details = dict(kids = {'ayala' : dict(age = 17, gender = 'f'), 'opher' : dict(age = 16, gender = 'f'), 'itamar': dict(age = 11, gender = 'm')}, salary = np.array([100,200,300]), ))

>>> t[0] # we can grab the full data back!

{'_id': 81,
 'created': datetime.datetime(2022, 6, 30, 0, 10, 33, 900000),
 'name': 'yoav',
 'surname': 'git',
 'doc': None,
 'details': {'kids': {'ayala':  {'age': 17, 'gender': 'f'},
                      'opher':  {'age': 16, 'gender': 'f'},
                      'itamar': {'age': 11, 'gender': 'm'}},
             'salary': array([100, 200, 300])},
 'dob': None,
 'age': None,
 'grade': None}

>>> class Temp():
        pass
        
>>> t.insert(name = 'anna', surname = 'git', details = dict(temp = Temp())) ## yep, we can store actual objects...
>>> t[1]  # and get them back as proper objects on loading

{'_id': 83,
 'created': datetime.datetime(2022, 6, 30, 0, 16, 10, 340000),
 'name': 'anna',
 'surname': 'git',
 'doc': None,
 'details': {'temp': <__main__.Temp at 0x1a91d9fd3a0>},
 'dob': None,
 'age': None,
 'grade': None}primary keys and auto-archivePrimary Keys are applied if the primary keys (pk) are specified.
Now, when we insert into a table, if another record with same pk exists, the record will be replaced.
Rather than simply delete old records, we create automatically a parallel database.archived_schema.table to auto-archive these replaced records.
This ensure a full audit and roll-back of records is possible.:Example: primary keys and deleted records
------------------------------------------
The table as set up can have multiple items so:

>>> from pyg import * 
>>> t = t.delete()
>>> t = t.insert(name = 'yoav', surname = 'git', age = 46)
>>> t = t.insert(name = 'yoav', surname = 'git', age = 47)
>>> t = t.insert(name = 'yoav', surname = 'git', age = 48)
>>> assert len(t) == 3

>>> t = t.delete() 
>>> t = sql_table(db = 'test', table = 'students', non_null = ['name', 'surname'], 
                      _id = dict(_id = int, created = datetime.datetime), 
                      nullable =  dict(doc = str, details = str, dob = datetime.date, age = int, grade = float), 
                      pk = ['name', 'surname'])         ## <<<------- We set primary keys

>>> t = t.delete()
>>> t = t.insert(name = 'yoav', surname = 'git', age = 46)
>>> t = t.insert(name = 'yoav', surname = 'git', age = 47)
>>> t = t.insert(name = 'yoav', surname = 'git', age = 48)
>>> assert len(t) == 1 
>>> assert t[0].age == 48

Where did the data go to? We automatically archive the deleted old records for dict(name = 'yoav', surname = 'git') here:

>>> t.archived()

t.archived() is a table by same name,

- exists on same database, schema name changed prefixed with 'archived_'
- same table structure with added 'deleted' column into the primary keys

>>> assert len(t.archived().inc(name = 'yoav', age = 46)) > 0
>>> t.archived().delete()sql_cursor as a document storeIf we set doc = True, the table will be viewed internally as a no-sql-like document store. 

- the nullable columns supplied are the columns on which querying will be possible
- the primary keys are still used to ensure we have one document per unique pk
- the document is jsonified (handling non-json stuff like dates, np.array and pd.DataFrames) and put into the 'doc' column in the table, but this is invisible to the user.

:Example: doc management
------------------------

We now suppose that we are not sure what records we want to keep for each student

>>> from pyg import *
>>> import datetime
>>> t = sql_table(db = 'test', table = 'unstructured_students', non_null = ['name', 'surname'], 
                      _id = dict(_id = int, created = datetime.datetime), 
                      nullable =  dict(doc = str, details = str, dob = datetime.date, age = int, grade = float), 
                      pk = ['name', 'surname'],
                      doc = True)   ##<---- The table will actually be a document store

We are now able to keep varied structure per each record. 

>>> t = t.delete()

>>> doc = dict(name = 'yoav', surname = 'git', age = 30, profession = 'coder', children = ['ayala', 'opher', 'itamar'])
>>> inserted_doc = t.insert_one(doc)
>>> assert t.inc(name = 'yoav', surname = 'git')[0].children == ['ayala', 'opher', 'itamar']

>>> doc2 = dict(name = 'anna', surname = 'git', age = 28, employer = 'Cambridge University', hobbies = ['chess', 'music', 'swimming'])
>>> _ = t.insert_one(doc2)
>>> assert t[dict(age = 28)].hobbies == ['chess', 'music', 'swimming']  # Note that we can filter or search easily using the column 'age' that was specified in table. We cannot do this on 'employer'

:Example: document store containing pd.DataFrames.
----------

>>> from pyg import *
>>> doc = dict(name = 'yoav', surname = 'git', age = 35, 
               salary = pd.Series([100,200,300], drange(2)),
               costs = dict(fixed_cost     = 100, 
                            variable_costs = pd.DataFrame(dict(transport = [0,1,2], food = [4,5,6], education = [10,20,30]), drange(2))))  # pandas object is in doc.costs.variable_costs

>>> t = sql_table(db = 'test', table = 'unstructured_students', non_null = ['name', 'surname'], 
                      _id = dict(_id = int, created = datetime.datetime), 
                      nullable =  dict(doc = str, details = str, dob = datetime.date, age = int, grade = float), 
                      pk = ['name', 'surname'],
                      writer = 'c:/temp/%name/%surname.parquet', ##<---- The location where pd.DataFrame/Series are to be stored
                      doc = True)   

>>> inserted = t.insert_one(doc)
>>> import os
>>> assert 'salary.parquet' in os.listdir('c:/temp/yoav/git')
>>> assert 'variable_costs.parquet' in os.listdir('c:/temp/yoav/git/costs') ## yes, dicts of dicts, or dicts of lists are all fine...

We can now access the data seemlessly:

>>> read_from_db = t.inc(name = 'yoav')[0]     
>>> read_from_file = pd_read_parquet('c:/temp/yoav/git/salary.parquet')
>>> assert list(read_from_db.salary.values) == [100, 200, 300]
>>> assert list(read_from_file.values) == [100, 200, 300]