Basic module usage — Psycopg v2.0.14 documentation

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# Basic module usage

The basic Psycopg usage is common to all the database adapters implementing the DB API 2.0 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 cursor 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 `connect()` creates a new database session and returns a new `connection` instance.
- The class `connection` encapsulates a database session. It allows to:
  - create new `cursor`s using the `cursor()` method to execute database commands and queries,
  - terminate the session using the methods `commit()` or `rollback()`.
- The class `cursor` allows interaction with the database:
  - send commands to the database using methods such as `execute()` and `executemany()`,
  - retrieve data from the database by iteration or using methods such as `fetchone()`, `fetchmany()`, `fetchall()`.

## 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.

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 `%(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 `execute()` method accepts a tuple or dictionary of values as second parameter. Never use `%` or `+` to merge values into queries.

- 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. And remember that Python requires a comma to create a single element tuple:

      >>> cur.execute("INSERT INTO foo VALUES (%s)", "bar")    # WRONG
      >>> 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 `execute()`.

### 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.

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:

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 `execute()` method:

    >>> SQL = "INSERT INTO authors (name) VALUES (%s);" # Notice: no quotes
    >>> data = ("O'Reilly", )
    >>> cur.execute(SQL, data) # Notice: no % operator

## 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 Adapting new Python types to SQL syntax and Type casting of SQL types into Python objects. You can also find a few other specialized adapters in the `psycopg2.extras` module.

In the following examples the method `mogrify()` is used to show the SQL string that would be sent to the database.

- 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;'

- Numeric objects: `int`, `long`, `float`, `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;'

- String types: `str`, `unicode` are converted in SQL string syntax. `buffer` is converted in PostgreSQL binary string syntax, suitable for `bytea` fields. When reading textual fields, either `str` or `unicode` can be received: see Unicode handling.

- Date and time objects: builtin `datetime`, `date`, `time`. `timedelta` are converted into PostgreSQL’s `timestamp`, `date`, `time`, `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';"

- Python lists are converted into PostgreSQL `ARRAY`s:

      >>> cur.mogrify("SELECT %s;", ([10, 20, 30], ))
      'SELECT ARRAY[10, 20, 30];'

- Python tuples are converted in a syntax suitable for the 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.

  New in version 2.0.6: the tuple `IN` adaptation.

  Changed in version 2.0.14: the tuple `IN` adapter is always active. In previous releases it was necessary to import the `extensions` module to have it registered.

### Unicode handling

Psycopg can exchange Unicode data with a PostgreSQL database. Python `unicode` objects are automatically encoded in the client encoding defined on the database connection (the PostgreSQL encoding, available in `connection.encoding`, is translated into a Python codec using the `encodings` mapping):

    >>> print u, type(u)
    àèìòù€ <type 'unicode'>

    >>> cur.execute("INSERT INTO test (num, data) VALUES (%s,%s);", (74, u))

When reading data from the database, the strings returned are usually 8 bit `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€ <type 'str'> '\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)
    <type 'str'> '\xe0\xe8\xec\xf2\xf9\xa4'

In order to obtain `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€ <type 'unicode'> u'\xe0\xe8\xec\xf2\xf9\u20ac'

In the above example, the `UNICODE` typecaster is registered only on the cursor. It is also possible to register typecasters on the connection or globally: see the function `register_type()` and Type casting of SQL types into Python objects 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.

## Transactions control

In Psycopg transactions are handled by the `connection` class. By default, the first time a command is sent to the database (using one of the `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 `connection.rollback()` method.

The connection is responsible to terminate its transaction, calling either the `commit()` or `rollback()` method. Committed changes are immediately made persistent into the database. Closing the connection using the `close()` method or destroying the connection object (calling `__del__()` or letting it fall out of scope) will result in an implicit `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. `CREATE DATABASE`, `VACUUM`...) 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 `connection.set_isolation_level()` to know how to change the commit mode.

## Server side cursors

When a database query is executed, the Psycopg `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 `MOVE`, `FETCH` and `CLOSE` commands.

Psycopg wraps the database server side cursor in named cursors. A named cursor is created using the `cursor()` method specifying the `name` parameter. Such cursor will behave mostly like a regular cursor, allowing the user to move in the dataset using the `scroll()` method and to read the data using `fetchone()` and `fetchmany()` methods.

## Thread safety

The Psycopg module is thread-safe: threads can access the same database using separate sessions (by creating a `connection` per thread) or using the same session (accessing to the same connection and creating separate `cursor`s). In DB API 2.0 parlance, Psycopg is level 2 thread safe.

## Using COPY TO and COPY FROM

Psycopg `cursor` objects provide an interface to the efficient PostgreSQL `COPY` command to move data from files to tables and back. The methods exposed are:

`copy_from()`
  Reads data from a file-like object appending them to a database table (`COPY table FROM file` syntax). The source file must have both `read()` and `readline()` method.
`copy_to()`
  Writes the content of a table to a file-like object (`COPY table TO file` syntax). The target file must have a `write()` method.
`copy_expert()`
  Allows to handle more specific cases and to use all the `COPY` features available in PostgreSQL.

Please refer to the documentation of the single methods for details and examples.

## Access to PostgreSQL large objects

PostgreSQL offers support to large objects, which provide stream-style access to user data that is stored in a special large-object structure. They are useful with data values too large to be manipulated conveniently as a whole.

Psycopg allows access to the large object using the `lobject` class. Objects are generated using the `connection.lobject()` factory method.

Psycopg large object support efficient import/export with file system files using the `lo_import()` and `lo_export()` libpq functions.

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