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