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483 lines
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ReStructuredText
483 lines
17 KiB
ReStructuredText
Basic module usage
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==================
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.. sectionauthor:: Daniele Varrazzo <daniele.varrazzo@gmail.com>
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.. index::
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pair: Example; Usage
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The basic Psycopg usage is common to all the database adapters implementing
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the |DBAPI|_ protocol. Here is an interactive session showing some of the
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basic commands::
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>>> import psycopg2
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# Connect to an existing database
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>>> conn = psycopg2.connect("dbname=test user=postgres")
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# Open a curstor to perform database operations
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>>> cur = conn.cursor()
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# Execute a command: this creates a new table
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>>> cur.execute("CREATE TABLE test (id serial PRIMARY KEY, num integer, data varchar);")
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# Pass data to fill a query placeholders and let Psycopg perform
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# the correct conversion (no more SQL injections!)
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>>> cur.execute("INSERT INTO test (num, data) VALUES (%s, %s)",
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... (100, "abc'def"))
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# Query the database and obtain data as Python objects
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>>> cur.execute("SELECT * FROM test;")
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>>> cur.fetchone()
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(1, 100, "abc'def")
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# Make the changes to the database persistent
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>>> conn.commit()
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# Close communication with the database
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>>> cur.close()
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>>> conn.close()
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The main entry point of Psycopg are:
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- The function :func:`~psycopg2.connect` creates a new database session and
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returns a new :class:`connection` instance.
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- The class :class:`connection` encapsulates a database session. It allows to:
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- create new :class:`cursor`\s using the :meth:`~connection.cursor` method to
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execute database commands and queries,
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- terminate the session using the methods :meth:`~connection.commit` or
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:meth:`~connection.rollback`.
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- The class :class:`cursor` allows interaction with the database:
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- send commands to the database using methods such as :meth:`~cursor.execute`
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and :meth:`~cursor.executemany`,
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- retrieve data from the database using methods such as
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:meth:`~cursor.fetchone`, :meth:`~cursor.fetchmany`,
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:meth:`~cursor.fetchall`.
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.. index:: Transaction, Begin, Commit, Rollback, Autocommit
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.. _transactions-control:
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Transactions control
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--------------------
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In Psycopg transactions are handled by the :class:`connection` class. By
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default, every time a command is sent to the database (using one of the
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:class:`cursor`\ s created by the connection), a new transaction is created.
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The following database commands will be executed in the context of the same
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transaction -- not only the commands issued by the first cursor, but the ones
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issued by all the cursors created by the same connection. Should any command
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fail, the transaction will be aborted and no further command will be executed
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until a call to the :meth:`connection.rollback` method.
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The connection is responsible to terminate its transaction, calling either the
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:meth:`~connection.commit` or :meth:`~connection.rollback` method. Committed
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changes are immediately made persistent into the database. Closing the
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connection using the :meth:`~connection.close` method or destroying the
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connection object (calling :meth:`!__del__` or letting it fall out of scope)
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will result in an implicit :meth:`!rollback` call.
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It is possible to set the connection in *autocommit* mode: this way all the
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commands executed will be immediately committed and no rollback is possible. A
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few commands (e.g. :sql:`CREATE DATABASE`) require to be run outside any
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transaction: in order to be able to run these commands from Psycopg, the
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session must be in autocommit mode. Read the documentation for
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:meth:`connection.set_isolation_level` to know how to change the commit mode.
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.. index::
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pair: Query; Parameters
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.. _query-parameters:
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Passing parameters to SQL queries
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---------------------------------
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Psycopg casts Python variables to SQL literals by type. Many standard Python types
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are already `adapted to the correct SQL representation`__.
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.. __: python-types-adaptation_
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Example: the Python function call::
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>>> cur.execute(
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... """INSERT INTO some_table (an_int, a_date, a_string)
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... VALUES (%s, %s, %s);""",
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... (10, datetime.date(2005, 11, 18), "O'Reilly"))
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is converted into the SQL command::
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INSERT INTO some_table (an_int, a_date, a_string)
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VALUES (10, '2005-11-18', 'O''Reilly');
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Named arguments are supported too using :samp:`%({name})s` placeholders.
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Using named arguments the values can be passed to the query in any order and
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many placeholder can use the same values::
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>>> cur.execute(
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... """INSERT INTO some_table (an_int, a_date, another_date, a_string)
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... VALUES (%(int)s, %(date)s, %(date)s, %(str)s);""",
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... {'int': 10, 'str': "O'Reilly", 'date': datetime.date(2005, 11, 18)})
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While the mechanism resembles regular Python strings manipulation, there are a
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few subtle differences you should care about when passing parameters to a
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query:
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- The Python string operator ``%`` is not used: the :meth:`~cursor.execute`
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method accepts a tuple or dictionary of values as second parameter.
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|sql-warn|__.
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.. |sql-warn| replace:: **Never** use ``%`` or ``+`` to merge values
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into queries
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.. __: sql-injection_
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- The variables placeholder must *always be a* ``%s``, even if a different
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placeholder (such as a ``%d`` for integers or ``%f`` for floats) may look
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more appropriate::
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>>> cur.execute("INSERT INTO numbers VALUES (%d)", (42,)) # WRONG
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>>> cur.execute("INSERT INTO numbers VALUES (%s)", (42,)) # correct
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- For positional variables binding, *the second argument must always be a
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tuple*, even if it contains a single variable::
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>>> cur.execute("INSERT INTO foo VALUES (%s)", "bar") # WRONG
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>>> cur.execute("INSERT INTO foo VALUES (%s)", ("bar",)) # correct
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- Only variable values should be bound via this method: it shouldn't be used
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to set table or field names. For these elements, ordinary string formatting
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should be used before running :meth:`~cursor.execute`.
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.. index:: Security, SQL injection
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.. _sql-injection:
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The problem with the query parameters
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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The SQL representation for many data types is often not the same of the Python
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string representation. The classic example is with single quotes in
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strings: SQL uses them as string constants bounds and requires them to be
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escaped, whereas in Python single quotes can be left unescaped in strings
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bounded by double quotes. For this reason a naïve approach to the composition
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of query strings, e.g. using string concatenation, is a recipe for terrible
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problems::
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>>> SQL = "INSERT INTO authors (name) VALUES ('%s');" # NEVER DO THIS
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>>> data = ("O'Reilly", )
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>>> cur.execute(SQL % data) # THIS WILL FAIL MISERABLY
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ProgrammingError: syntax error at or near "Reilly"
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LINE 1: INSERT INTO authors (name) VALUES ('O'Reilly')
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^
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If the variable containing the data to be sent to the database comes from an
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untrusted source (e.g. a form published on a web site) an attacker could
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easily craft a malformed string, either gaining access to unauthorized data or
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performing destructive operations on the database. This form of attack is
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called `SQL injection`_ and is known to be one of the most widespread forms of
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attack to servers. Before continuing, please print `this page`__ as a memo and
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hang it onto your desk.
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.. _SQL injection: http://en.wikipedia.org/wiki/SQL_injection
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.. __: http://xkcd.com/327/
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Psycopg can `convert automatically Python objects into and from SQL
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literals`__: using this feature your code will result more robust and
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reliable. It is really the case to stress this point:
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.. __: python-types-adaptation_
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.. warning::
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Never, **never**, **NEVER** use Python string concatenation (``+``) or
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string parameters interpolation (``%``) to pass variables to a SQL query
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string. Not even at gunpoint.
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The correct way to pass variables in a SQL command is using the second
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argument of the :meth:`~cursor.execute` method::
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>>> SQL = "INSERT INTO authors (name) VALUES (%s);" # Notice: no quotes
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>>> data = ("O'Reilly", )
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>>> cur.execute(SQL, data) # Notice: no % operator
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.. index::
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pair: Objects; Adaptation
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single: Data types; Adaptation
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.. _python-types-adaptation:
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Adaptation of Python values to SQL types
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----------------------------------------
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Many standards Python types are adapted into SQL and returned as Python
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objects when a query is executed.
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If you need to convert other Python types to and from PostgreSQL data types,
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see :ref:`adapting-new-types` and :ref:`type-casting-from-sql-to-python`. You
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can also find a few other specialized adapters in the :mod:`psycopg2.extras`
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module.
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In the following examples the method :meth:`~cursor.mogrify` is used to show
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the SQL string that would be sent to the database.
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.. index::
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single: None; Adaptation
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single: NULL; Adaptation
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single: Boolean; Adaptation
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- Python ``None`` and boolean values are converted into the proper SQL
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literals::
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>>> cur.mogrify("SELECT %s, %s, %s;", (None, True, False))
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>>> 'SELECT NULL, true, false;'
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.. index::
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single: Integer; Adaptation
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single: Float; Adaptation
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single: Decimal; Adaptation
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- Numeric objects: :class:`!int`, :class:`!long`, :class:`!float`,
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:class:`!Decimal` are converted in the PostgreSQL numerical representation::
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>>> cur.mogrify("SELECT %s, %s, %s, %s;", (10, 10L, 10.0, Decimal("10.00")))
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>>> 'SELECT 10, 10, 10.0, 10.00;'
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.. index::
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single: Strings; Adaptation
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single: Unicode; Adaptation
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single: Buffer; Adaptation
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single: bytea; Adaptation
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single: Binary string
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- String types: :class:`!str`, :class:`!unicode` are converted in SQL string
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syntax. :class:`!buffer` is converted in PostgreSQL binary string syntax,
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suitable for :sql:`bytea` fields. When reading textual fields, either
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:class:`!str` or :class:`!unicode` can be received: see
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:ref:`unicode-handling`.
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.. index::
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single: Date objects; Adaptation
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single: Time objects; Adaptation
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single: Interval objects; Adaptation
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single: mx.DateTime; Adaptation
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- Date and time objects: builtin :class:`!datetime`, :class:`!date`,
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:class:`!time`. :class:`!timedelta` are converted into PostgreSQL's
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:sql:`timestamp`, :sql:`date`, :sql:`time`, :sql:`interval` data types.
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Time zones are supported too. The Egenix `mx.DateTime`_ objects are adapted
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the same way::
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>>> dt = datetime.datetime.now()
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>>> dt
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datetime.datetime(2010, 2, 8, 1, 40, 27, 425337)
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>>> cur.mogrify("SELECT %s, %s, %s;", (dt, dt.date(), dt.time()))
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"SELECT '2010-02-08T01:40:27.425337', '2010-02-08', '01:40:27.425337';"
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>>> cur.mogrify("SELECT %s;", (dt - datetime.datetime(2010,1,1),))
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"SELECT '38 days 6027.425337 seconds';"
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.. index::
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single: Array; Adaptation
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single: Lists; Adaptation
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- Python lists are converted into PostgreSQL :sql:`ARRAY`\ s::
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>>> cur.mogrify("SELECT %s;", ([10, 20, 30], ))
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'SELECT ARRAY[10, 20, 30];'
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.. index::
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single: Tuple; Adaptation
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single: IN operator
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- Python tuples are converted in a syntax suitable for the SQL :sql:`IN`
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operator::
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>>> cur.mogrify("SELECT %s IN %s;", (10, (10, 20, 30)))
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'SELECT 10 IN (10, 20, 30);'
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.. note::
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SQL doesn't allow an empty list in the IN operator, so your code should
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guard against empty tuples.
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.. note::
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The IN adapter is automatically registered when the
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:mod:`~psycopg2.extensions` module is imported. This behaviour may change
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in the future and the adapter will probably be always active.
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.. versionadded:: 2.0.6
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the tuple :sql:`IN` adaptation.
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.. index::
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single: Unicode
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.. _unicode-handling:
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Unicode handling
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^^^^^^^^^^^^^^^^
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Psycopg can exchange Unicode data with a PostgreSQL database. Python
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:class:`!unicode` objects are automatically *encoded* in the client encoding
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defined on the database connection (the `PostgreSQL encoding`__, available in
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:attr:`connection.encoding`, is translated into a `Python codec`__ using an
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:data:`~psycopg2.extensions.encodings` mapping)::
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>>> print u, type(u)
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àèìòù€ <type 'unicode'>
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>>> cur.execute("INSERT INTO test (num, data) VALUES (%s,%s);", (74, u))
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.. __: http://www.postgresql.org/docs/8.4/static/multibyte.html
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.. __: http://docs.python.org/library/codecs.html#standard-encodings
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When reading data from the database, the strings returned are usually 8 bit
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:class:`!str` objects encoded in the database client encoding::
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>>> print conn.encoding
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UTF8
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>>> cur.execute("SELECT data FROM test WHERE num = 74")
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>>> x = cur.fetchone()[0]
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>>> print x, type(x), repr(x)
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àèìòù€ <type 'str'> '\xc3\xa0\xc3\xa8\xc3\xac\xc3\xb2\xc3\xb9\xe2\x82\xac'
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>>> conn.set_client_encoding('LATIN9')
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>>> cur.execute("SELECT data FROM test WHERE num = 74")
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>>> x = cur.fetchone()[0]
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>>> print type(x), repr(x)
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<type 'str'> '\xe0\xe8\xec\xf2\xf9\xa4'
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In order to obtain :class:`!unicode` objects instead, it is possible to
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register a typecaster so that PostgreSQL textual types are automatically
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*decoded* using the current client encoding::
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>>> psycopg2.extensions.register_type(psycopg2.extensions.UNICODE, cur)
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>>> cur.execute("SELECT data FROM test WHERE num = 74")
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>>> x = cur.fetchone()[0]
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>>> print x, type(x), repr(x)
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àèìòù€ <type 'unicode'> u'\xe0\xe8\xec\xf2\xf9\u20ac'
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In the above example, the :data:`~psycopg2.extensions.UNICODE` typecaster is
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registered only on the cursor. It is also possible to register typecasters on
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the connection or globally: see the function
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:func:`~psycopg2.extensions.register_type` and
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:ref:`type-casting-from-sql-to-python` for details.
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.. note::
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If you want to receive uniformly all your database input in Unicode, you
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can register the related typecasters globally as soon as Psycopg is
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imported::
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import psycopg2
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import psycopg2.extensions
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psycopg2.extensions.register_type(psycopg2.extensions.UNICODE)
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psycopg2.extensions.register_type(psycopg2.extensions.UNICODEARRAY)
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and then forget about this story.
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.. index::
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pair: Server side; Cursor
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pair: Named; Cursor
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pair: DECLARE; SQL command
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pair: FETCH; SQL command
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pair: MOVE; SQL command
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.. _server-side-cursors:
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Server side cursors
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-------------------
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When a database query is executed, the Psycopg :class:`cursor` usually fetches
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all the records returned by the backend, transferring them to the client
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process. If the query returned an huge amount of data, a proportionally large
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amount of memory will be allocated by the client.
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If the dataset is too large to be practically handled on the client side, it is
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possible to create a *server side* cursor. Using this kind of cursor it is
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possible to transfer to the client only a controlled amount of data, so that a
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large dataset can be examined without keeping it entirely in memory.
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Server side cursor are created in PostgreSQL using the |DECLARE|_ command and
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subsequently handled using :sql:`MOVE`, :sql:`FETCH` and :sql:`CLOSE` commands.
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Psycopg wraps the database server side cursor in *named cursors*. A named
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cursor is created using the :meth:`~connection.cursor` method specifying the
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:obj:`!name` parameter. Such cursor will behave mostly like a regular cursor,
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allowing the user to move in the dataset using the :meth:`~cursor.scroll`
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methog and to read the data using :meth:`~cursor.fetchone` and
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:meth:`~cursor.fetchmany` methods.
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.. |DECLARE| replace:: :sql:`DECLARE`
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.. _DECLARE: http://www.postgresql.org/docs/8.4/static/sql-declare.html
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.. index:: Thread safety, Multithread
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.. _thread-safety:
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Thread safety
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-------------
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The Psycopg module is *thread-safe*: threads can access the same database
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using separate session (by creating a :class:`connection` per thread) or using
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the same session (accessing to the same connection and creating separate
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:class:`cursor`\ s). In |DBAPI|_ parlance, Psycopg is *level 2 thread safe*.
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.. index::
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pair: COPY; SQL command
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.. _copy:
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Using COPY TO and COPY FROM
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---------------------------
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Psycopg :class:`cursor` objects provide an interface to the efficient
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PostgreSQL |COPY|__ command to move data from files to tables and back.
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The methods exposed are:
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:meth:`~cursor.copy_from`
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Reads data *from* a file-like object appending them to a database table
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(:sql:`COPY table FROM file` syntax). The source file must have both
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:meth:`!read` and :meth:`!readline` method.
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:meth:`~cursor.copy_to`
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Writes the content of a table *to* a file-like object (:sql:`COPY table TO
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file` syntax). The target file must have a :meth:`write` method.
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:meth:`~cursor.copy_expert`
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Allows to handle more specific cases and to use all the :sql:`COPY`
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features available in PostgreSQL.
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Please refer to the documentation of the single methods for details and
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examples.
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.. |COPY| replace:: :sql:`COPY`
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.. __: http://www.postgresql.org/docs/8.4/static/sql-copy.html
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