pypgwrap - efficient PostgreSQL database wrapper
-------------------------------------------

The 'efficient' module provides a efficient wrapper over psycopg2 supporting a
Python API for common sql functions, explicit and implicit transactions mechanism and
connection pooling.

This is not intended to provide ORM-like functionality, just to make it
easier to interact with PostgreSQL from python code for simple use-cases
and allow direct SQL access for more complex operations.

The module wraps the excellent 'psycopg2' library and most of the
functionality is provided by this behind the scenes, except for pooling.

The module provides:

    * Simplified handling of connections/cursor
        * Connection pool, single or multithreaded (inherited by psycopg2.pool)
        * Cursor context handler
        * Context Manager for explicit transactions
    * Python API to wrap basic SQL functionality
        * Simple select,update,delete,join methods extending the cursor
          context handler (also available as stand-alone methods which
          create an implicit cursor for simple queries) (from pgwrap)
    * Query results as dict (using psycopg2.extras.DictCursor)
    * Callable prepared statements
    * Logging support

Basic usage
-----------

    >>> import pypgwrap
    >>> pypgwrap.config_pool(max_pool=10, pool_expiration=1, url='postgres://localhost/', pool_manager=SimpleConnectionPool)
    >>> db = pypgwrap.connection()
    >>> with db.cursor() as c:
    ...     c.query('select version()')
    [['PostgreSQL...']]
    >>> v = db.query_one('select version()')
    >>> v
    ['PostgreSQL...']
    >>> v.items()
    [('version', 'PostgreSQL...')]
    >>> v['version']
    'PostgreSQL...'


Basic usage, with transaction
-----------
    Init pool at application start:

        >>> import pypgwrap
        >>> pypgwrap.config_pool(max_pool=10, pool_expiration=1, url='postgres://localhost/', pool_manager=SimpleConnectionPool)

    Explicit transactions:

        >>> db = pypgwrap.connection()
        >>> db.create_table('t1', '''id SERIAL PRIMARY KEY,
                                   name TEXT NOT NULL,
                                   count INTEGER NOT NULL DEFAULT 0,
                                   active BOOLEAN NOT NULL DEFAULT true''')
        >>> id0 = db.insert('doctest_t1', {'name': 'name_one'}, returning='id')['id']
        >>> id1 = db.insert('doctest_t1', {'name': 'name_two'}, returning='id')['id']
        >>> db.commit()

    Implicity transactions:

        >>> with pypgwrap.connection() as db:
        >>>     db.create_table('t1', '''id SERIAL PRIMARY KEY,
                                      name TEXT NOT NULL,
                                      count INTEGER NOT NULL DEFAULT 0,
                                      active BOOLEAN NOT NULL DEFAULT true''')
        >>>     id0 = db.insert('doctest_t1', {'name': 'name_one'}, returning='id')['id']
        >>>     id1 = db.insert('doctest_t1', {'name': 'name_two'}, returning='id')['id']

    Distributed transactions:

        >>> import uuid
        >>> key = uuid.uuid4()

        >>> with pypgwrap.connection(key=key) as db:
        >>>     db.create_table('t1', '''id SERIAL PRIMARY KEY,
                                      name TEXT NOT NULL,
                                      count INTEGER NOT NULL DEFAULT 0,
                                      active BOOLEAN NOT NULL DEFAULT true''')
        >>>     id0 = db.insert('doctest_t1', {'name': 'name_one'}, returning='id')['id']
        >>>     id1 = db.insert('doctest_t1', {'name': 'name_two'}, returning='id')['id']

        >>> db2 = pypgwrap.connection(key=key)
        >>> id3 = db.insert('doctest_t1', {'name': 'name_three'}, returning='id')['id']
        >>> id4 = db.insert('doctest_t1', {'name': 'name_four'}, returning='id')['id']

        >>> db3 = pypgwrap.connection(key=key)
        >>> db3.commit()

    Distributed transactions, with ContextManager:

        >>> with pypgwrap.ContextManager() as context:

        >>>     with pypgwrap.connection(key=context.key) as db:
        >>>         db.create_table('t1', '''id SERIAL PRIMARY KEY,
                                          name TEXT NOT NULL,
                                          count INTEGER NOT NULL DEFAULT 0,
                                          active BOOLEAN NOT NULL DEFAULT true''')
        >>>         id0 = db.insert('doctest_t1', {'name': 'name_one'}, returning='id')['id']
        >>>         id1 = db.insert('doctest_t1', {'name': 'name_two'}, returning='id')['id']

        >>>     db2 = pypgwrap.connection(key)
        >>>     id3 = db.insert('doctest_t1', {'name': 'name_three'}, returning='id')['id']
        >>>     id4 = db.insert('doctest_t1', {'name': 'name_four'}, returning='id')['id']


Connection
----------

The config_pool need some parameters:
    - max_pool: Maximum of connections created and mainteined in memory
    - pool_expiration: Idle time (in minutes) for close and destroy memory connection
    - url: Url with connection parameters

The intention of this method is to call at application start up, only!

The connection class provides methods to return a cursor object or execute SQL queries
directly (using an implicit cursor).

The connection context provides the following basic methods:

    cursor          - create a new instance of cursor class
    commit          - Commit transaction (called implicitly on exiting context handler)
    rollback        - Rollback transaction

Cursor
------

The module provides a cursor context handler wrapping the psycopg2 cursor.

The cursor object uses the psycopg2 'DictCursor' by default (which
returns rows as a pseudo python dictionary) however this can be overridden
by providing a 'cursor_factory' parameter to the constructor.

>>> db = pypgwrap.connection()
>>> with db.cursor() as c:
...     c.query('select version()')
[['PostgreSQL...']]

The cursor context provides the following basic methods:

    execute         - execute SQL query and return rowcount
    query           - execute SQL query and fetch results
    query_one       - execute SQL query and fetch first result
    query_dict      - execute SQL query and return results as dict
                      keyed on specified key (which should be unique)

In addition the cursor can use the SQL API methods described below or
access the underlying psycopg2 cursor (via the self.cursor attribute).

The cursor methods are also available as standalone functions which
run inside an implicit cursor object.

SQL API
-------

The cursor class also provides a simple Python API for common SQL
operations.  The basic methods provides are:

    select          - single table select (with corresponding select_one, select_dict methods)
    join            - two table join (with corresponding join_one, join_dict methods)
    insert          - SQL insert
    update          - SQL update
    delete          - SQL delete

The methods can be parameterised to customise the associated query
(see db module for detail):

    where           - 'where' clause as dict (column operators can be
                      specified using the colunm__operator format)

                      where = {'name':'abc','status__in':(1,2,3)}

    columns         - list of columns to be returned - these can
                      be real columns or expressions. If spefified
                      as a tuple the column is explicitly named
                      using the AS operator

                      columns = ('name',('status > 1','updated'))

    order           - sort order as list (use 'column__desc' to
                      reverse order)

                      order = ('name__desc',)

    limit           - row limit (int)

    offset          - offset (int)

    on              - join columns (as tuple)

    values          - insert data as dict

    returning       - columns to return (string)

The methods are also available as standalone functions which create an
implicit cursor object.

Basic usage:

    >>> db.create_table('t1','id serial,name text,count int')
    >>> db.create_table('t2','id serial,t1_id int,value text')
    >>> db.log = sys.stdout
    >>> db.insert('t1',{'name':'abc','count':0},returning='id,name')
    INSERT INTO t1 (name) VALUES ('abc') RETURNING id,name
    [1, 'abc']
    >>> db.insert('t2',{'t1_id':1,'value':'t2'})
    INSERT INTO t2 (t1_id,value) VALUES (1,'t2')
    1
    >>> db.select('t1')
    SELECT * FROM t1
    [[1, 'abc', 0]]
    >>> db.select_one('t1',where={'name':'abc'},columns=('name','count'))
    SELECT name, count FROM t1 WHERE name = 'abc'
    ['abc', 0]
    >>> db.join(('t1','t2'),columns=('t1.id','t2.value'))
    SELECT t1.id, t2.value FROM t1 JOIN t2 ON t1.id = t2.t1_id
    [[1, 't2']]
    >>> db.insert('t1',{'name':'abc'},returning='id')
    INSERT INTO t1 (name) VALUES ('abc') RETURNING id
    [2]
    >>> db.update('t1',{'name':'xyz'},where={'name':'abc'})
    UPDATE t1 SET name = 'xyz' WHERE name = 'abc'
    2
    >>> db.update('t1',{'count__func':'count + 1'},where={'count__lt':10},returning="id,count")
    UPDATE t1 SET count = count + 1 WHERE count < 10 RETURNING id,count
    [[1, 1]]

Prepared Statements
-------------------

    Prepared statements can be created using the

        connection.prepare(stmt,params,name,call_type)

        stmt      : prepared statement (with parameters identified
                    in the statement using the psql $1,$2... notation)
        params    : list of optional parameter types (usually not
                    needed - infered by psql)
        name      : name for the prepared statement (usually
                    autogenerated)
        call_type : method used when instance called as method
                    (defaults to 'query')

    The constructor returns a PreparedStatement object which can be used
    instead of an sql statement in the connection.execute and
    connection.query_xxx methods.

    >>> p = db.prepare('UPDATE t1 SET name = $2 WHERE id = $1')
    PREPARE _pstmt_001  AS UPDATE t1 SET name = $2 WHERE id = $1
    >>> with db.cursor() as c:
    ...     c.execute(p,(1,'xxx'))
    EXECUTE _pstmt_001 (1,'xxx')

    The PreparedStatement object can also be called directly using the
    execute/query/query_one/query_dict methods. The instance is also
    directly callable using the method type identified in 'call_type'

    >>> p = db.prepare('UPDATE t1 SET name = $2 WHERE id = $1')
    PREPARE _pstmt_001  AS UPDATE t1 SET name = $2 WHERE id = $1
    >>> p.execute(1,'xxx')
    EXECUTE _pstmt_001 (1,'xxx')
    >>> p(1,'xxx')
    EXECUTE _pstmt_001 (1,'xxx')

Logging
-------

    To enable logging the connection.log attribute can be set to either an
    instance of logging.Logger or a file-like object (supporting the write
    method).

    The log message is generated using the self.logf function (called with
    the cursor object as a parameter). By default this just returns the
    query string however can be customised as needed. A cursor.timestamp
    attribute is available to allow execution time to be tracked.

    >>> db.log = sys.stdout
    >>> db.logf = lambda c : '[%f] %s' % (time.time() - c.timestamp,c.query)
    >>> db.query('SELECT * FROM t1')
    [0.000536] SELECT * FROM t1

Changelog
---------

    *   0.1.0     03-06-2013  Initial import
    *   0.1.1     10-06-2013  Transaction context issues
    *   0.1.2     11-06-2013  ContextManager commit issues
    *   0.1.3     07-08-2013  ContextManager __exit__ fail on TypeError exception
    *   0.1.4     07-08-2013  ContextManager __exit__ fail on TypeError exception
    *   0.1.5     08-10-2013  - ThreadedConnectionPool fix when pool is exausted or max_con of Postgres is reached.
                              - Created a param [pool_manager] in config_pool method. Params: SimpleConnectionPool,
                              ThreadedConnectionPool. In Multthread enviroments must use ThreadedConnectionPool.
    *   0.1.6     14-10-2014  - Bugfix. Fix import of OperationalError. Avoid use protected member of psycopg2.
                              - Change "from psycopg2._psycopg import OperationalError" to
                                "from psycopg2 import OperationalError"
    *   0.1.11    04-09-2015  Non Threaded AutoCloseConnectionPool to use with pgpool
    *   0.1.12    04-09-2015  Deleted class AutoCloseConnectionPool.
                              - Create configuration in env PYPGWRAP_CLOSE_CONNECTION_ON_EXIT to control
                                when pypgwrap disable pool. When this env variable is True, all connections
                                is discarded when execution is finished. No pooling is persisted. Util to use with
                                PgPool or external pooling tools.
    *   0.1.13    17-09-2015  Fix use of ast.literal_eval to read Environment variable
    *   0.1.14    27-10-2015  Change setup to remove install requirement to Psycopg2
    *   0.1.15    18-03-2016  Create configuration in env PYPGWRAP_AUTOCOMMIT to avoid problems with PgBouncer

Author
------

    *   Erick Phillipe R. de Almeida (ephillipe@gmail.com)

Master Repository/Issues
------------------------

    *   https://github.com/ephillipe/pypgwrap

Credits
------------------------
    pypgwrap is inherited from pgwrap, an excelent wraper for Postgres but with lacks
    *   https://github.com/paulchakravarti/pgwrap

    Pooling is iherited from Psycopg2
    * https://github.com/psycopg/psycopg2/

About me
------------------------
    *   http://about.me/erick.almeida
    *   http://erickalmeida.brandyourself.com