
"sqlite3" --- DB-API 2.0 interface for SQLite databases
*******************************************************

SQLite is a C library that provides a lightweight disk-based database
that doesn't require a separate server process and allows accessing
the database using a nonstandard variant of the SQL query language.
Some applications can use SQLite for internal data storage.  It's also
possible to prototype an application using SQLite and then port the
code to a larger database such as PostgreSQL or Oracle.

pysqlite was written by Gerhard Häring and provides a SQL interface
compliant with the DB-API 2.0 specification described by **PEP 249**.

To use the module, you must first create a "Connection" object that
represents the database.  Here the data will be stored in the
"/tmp/example" file:

   conn = sqlite3.connect('/tmp/example')

You can also supply the special name ":memory:" to create a database
in RAM.

Once you have a "Connection", you can create a "Cursor"  object and
call its "execute()" method to perform SQL commands:

   c = conn.cursor()

   # Create table
   c.execute('''create table stocks
   (date text, trans text, symbol text,
    qty real, price real)''')

   # Insert a row of data
   c.execute("""insert into stocks
             values ('2006-01-05','BUY','RHAT',100,35.14)""")

   # Save (commit) the changes
   conn.commit()

   # We can also close the cursor if we are done with it
   c.close()

Usually your SQL operations will need to use values from Python
variables.  You shouldn't assemble your query using Python's string
operations because doing so is insecure; it makes your program
vulnerable to an SQL injection attack.

Instead, use the DB-API's parameter substitution.  Put "?" as a
placeholder wherever you want to use a value, and then provide a tuple
of values as the second argument to the cursor's "execute()" method.
(Other database modules may use a different placeholder, such as "%s"
or ":1".) For example:

   # Never do this -- insecure!
   symbol = 'IBM'
   c.execute("... where symbol = '%s'" % symbol)

   # Do this instead
   t = (symbol,)
   c.execute('select * from stocks where symbol=?', t)

   # Larger example
   for t in [('2006-03-28', 'BUY', 'IBM', 1000, 45.00),
             ('2006-04-05', 'BUY', 'MSOFT', 1000, 72.00),
             ('2006-04-06', 'SELL', 'IBM', 500, 53.00),
            ]:
       c.execute('insert into stocks values (?,?,?,?,?)', t)

To retrieve data after executing a SELECT statement, you can either
treat the cursor as an *iterator*, call the cursor's "fetchone()"
method to retrieve a single matching row, or call "fetchall()" to get
a list of the matching rows.

This example uses the iterator form:

   >>> c = conn.cursor()
   >>> c.execute('select * from stocks order by price')
   >>> for row in c:
   ...    print row
   ...
   (u'2006-01-05', u'BUY', u'RHAT', 100, 35.14)
   (u'2006-03-28', u'BUY', u'IBM', 1000, 45.0)
   (u'2006-04-06', u'SELL', u'IBM', 500, 53.0)
   (u'2006-04-05', u'BUY', u'MSOFT', 1000, 72.0)
   >>>

See also:

   http://code.google.com/p/pysqlite/
      The pysqlite web page -- sqlite3 is developed externally under
      the name "pysqlite".

   http://www.sqlite.org
      The SQLite web page; the documentation describes the syntax and
      the available data types for the supported SQL dialect.

   **PEP 249** - Database API Specification 2.0
      PEP written by Marc-André Lemburg.


Module functions and constants
==============================

sqlite3.PARSE_DECLTYPES

   This constant is meant to be used with the *detect_types* parameter
   of the "connect()" function.

   Setting it makes the "sqlite3" module parse the declared type for
   each column it returns.  It will parse out the first word of the
   declared type, i. e.  for "integer primary key", it will parse out
   "integer", or for "number(10)" it will parse out "number". Then for
   that column, it will look into the converters dictionary and use
   the converter function registered for that type there.

sqlite3.PARSE_COLNAMES

   This constant is meant to be used with the *detect_types* parameter
   of the "connect()" function.

   Setting this makes the SQLite interface parse the column name for
   each column it returns.  It will look for a string formed [mytype]
   in there, and then decide that 'mytype' is the type of the column.
   It will try to find an entry of 'mytype' in the converters
   dictionary and then use the converter function found there to
   return the value. The column name found in "Cursor.description" is
   only the first word of the column name, i.  e. if you use something
   like "'as "x [datetime]"'" in your SQL, then we will parse out
   everything until the first blank for the column name: the column
   name would simply be "x".

sqlite3.connect(database[, timeout, isolation_level, detect_types, factory])

   Opens a connection to the SQLite database file *database*. You can
   use "":memory:"" to open a database connection to a database that
   resides in RAM instead of on disk.

   When a database is accessed by multiple connections, and one of the
   processes modifies the database, the SQLite database is locked
   until that transaction is committed. The *timeout* parameter
   specifies how long the connection should wait for the lock to go
   away until raising an exception. The default for the timeout
   parameter is 5.0 (five seconds).

   For the *isolation_level* parameter, please see the
   "Connection.isolation_level" property of "Connection" objects.

   SQLite natively supports only the types TEXT, INTEGER, FLOAT, BLOB
   and NULL. If you want to use other types you must add support for
   them yourself. The *detect_types* parameter and the using custom
   **converters** registered with the module-level
   "register_converter()" function allow you to easily do that.

   *detect_types* defaults to 0 (i. e. off, no type detection), you
   can set it to any combination of "PARSE_DECLTYPES" and
   "PARSE_COLNAMES" to turn type detection on.

   By default, the "sqlite3" module uses its "Connection" class for
   the connect call.  You can, however, subclass the "Connection"
   class and make "connect()" use your class instead by providing your
   class for the *factory* parameter.

   Consult the section *SQLite and Python types* of this manual for
   details.

   The "sqlite3" module internally uses a statement cache to avoid SQL
   parsing overhead. If you want to explicitly set the number of
   statements that are cached for the connection, you can set the
   *cached_statements* parameter. The currently implemented default is
   to cache 100 statements.

sqlite3.register_converter(typename, callable)

   Registers a callable to convert a bytestring from the database into
   a custom Python type. The callable will be invoked for all database
   values that are of the type *typename*. Confer the parameter
   *detect_types* of the "connect()" function for how the type
   detection works. Note that the case of *typename* and the name of
   the type in your query must match!

sqlite3.register_adapter(type, callable)

   Registers a callable to convert the custom Python type *type* into
   one of SQLite's supported types. The callable *callable* accepts as
   single parameter the Python value, and must return a value of the
   following types: int, long, float, str (UTF-8 encoded), unicode or
   buffer.

sqlite3.complete_statement(sql)

   Returns "True" if the string *sql* contains one or more complete
   SQL statements terminated by semicolons. It does not verify that
   the SQL is syntactically correct, only that there are no unclosed
   string literals and the statement is terminated by a semicolon.

   This can be used to build a shell for SQLite, as in the following
   example:

      # A minimal SQLite shell for experiments

      from pysqlite2 import dbapi2 as sqlite3

      con = sqlite3.connect(":memory:")
      con.isolation_level = None
      cur = con.cursor()

      buffer = ""

      print "Enter your SQL commands to execute in SQLite."
      print "Enter a blank line to exit."

      while True:
          line = raw_input()
          if line == "":
              break
          buffer += line
          if sqlite3.complete_statement(buffer):
              try:
                  buffer = buffer.strip()
                  cur.execute(buffer)

                  if buffer.lstrip().upper().startswith("SELECT"):
                      print cur.fetchall()
              except sqlite3.Error, e:
                  print "An error occurred:", e.args[0]
              buffer = ""

      con.close()

sqlite3.enable_callback_tracebacks(flag)

   By default you will not get any tracebacks in user-defined
   functions, aggregates, converters, authorizer callbacks etc. If you
   want to debug them, you can call this function with *flag* as True.
   Afterwards, you will get tracebacks from callbacks on "sys.stderr".
   Use "False" to disable the feature again.


Connection Objects
==================

class class sqlite3.Connection

   A SQLite database connection has the following attributes and
   methods:

Connection.isolation_level

   Get or set the current isolation level. "None" for autocommit mode
   or one of "DEFERRED", "IMMEDIATE" or "EXCLUSIVE". See section
   *Controlling Transactions* for a more detailed explanation.

Connection.cursor([cursorClass])

   The cursor method accepts a single optional parameter
   *cursorClass*. If supplied, this must be a custom cursor class that
   extends "sqlite3.Cursor".

Connection.commit()

   This method commits the current transaction. If you don't call this
   method, anything you did since the last call to "commit()" is not
   visible from from other database connections. If you wonder why you
   don't see the data you've written to the database, please check you
   didn't forget to call this method.

Connection.rollback()

   This method rolls back any changes to the database since the last
   call to "commit()".

Connection.close()

   This closes the database connection. Note that this does not
   automatically call "commit()". If you just close your database
   connection without calling "commit()" first, your changes will be
   lost!

Connection.execute(sql[, parameters])

   This is a nonstandard shortcut that creates an intermediate cursor
   object by calling the cursor method, then calls the cursor's
   "execute" method with the parameters given.

Connection.executemany(sql[, parameters])

   This is a nonstandard shortcut that creates an intermediate cursor
   object by calling the cursor method, then calls the cursor's
   "executemany" method with the parameters given.

Connection.executescript(sql_script)

   This is a nonstandard shortcut that creates an intermediate cursor
   object by calling the cursor method, then calls the cursor's
   "executescript" method with the parameters given.

Connection.create_function(name, num_params, func)

   Creates a user-defined function that you can later use from within
   SQL statements under the function name *name*. *num_params* is the
   number of parameters the function accepts, and *func* is a Python
   callable that is called as the SQL function.

   The function can return any of the types supported by SQLite:
   unicode, str, int, long, float, buffer and None.

   Example:

      from pysqlite2 import dbapi2 as sqlite3
      import md5

      def md5sum(t):
          return md5.md5(t).hexdigest()

      con = sqlite3.connect(":memory:")
      con.create_function("md5", 1, md5sum)
      cur = con.cursor()
      cur.execute("select md5(?)", ("foo",))
      print cur.fetchone()[0]

Connection.create_aggregate(name, num_params, aggregate_class)

   Creates a user-defined aggregate function.

   The aggregate class must implement a "step" method, which accepts
   the number of parameters *num_params*, and a "finalize" method
   which will return the final result of the aggregate.

   The "finalize" method can return any of the types supported by
   SQLite: unicode, str, int, long, float, buffer and None.

   Example:

      from pysqlite2 import dbapi2 as sqlite3

      class MySum:
          def __init__(self):
              self.count = 0

          def step(self, value):
              self.count += value

          def finalize(self):
              return self.count

      con = sqlite3.connect(":memory:")
      con.create_aggregate("mysum", 1, MySum)
      cur = con.cursor()
      cur.execute("create table test(i)")
      cur.execute("insert into test(i) values (1)")
      cur.execute("insert into test(i) values (2)")
      cur.execute("select mysum(i) from test")
      print cur.fetchone()[0]

Connection.create_collation(name, callable)

   Creates a collation with the specified *name* and *callable*. The
   callable will be passed two string arguments. It should return -1
   if the first is ordered lower than the second, 0 if they are
   ordered equal and 1 if the first is ordered higher than the second.
   Note that this controls sorting (ORDER BY in SQL) so your
   comparisons don't affect other SQL operations.

   Note that the callable will get its parameters as Python
   bytestrings, which will normally be encoded in UTF-8.

   The following example shows a custom collation that sorts "the
   wrong way":

      from pysqlite2 import dbapi2 as sqlite3

      def collate_reverse(string1, string2):
          return -cmp(string1, string2)

      con = sqlite3.connect(":memory:")
      con.create_collation("reverse", collate_reverse)

      cur = con.cursor()
      cur.execute("create table test(x)")
      cur.executemany("insert into test(x) values (?)", [("a",), ("b",)])
      cur.execute("select x from test order by x collate reverse")
      for row in cur:
          print row
      con.close()

   To remove a collation, call "create_collation" with None as
   callable:

      con.create_collation("reverse", None)

Connection.interrupt()

   You can call this method from a different thread to abort any
   queries that might be executing on the connection. The query will
   then abort and the caller will get an exception.

Connection.set_authorizer(authorizer_callback)

   This routine registers a callback. The callback is invoked for each
   attempt to access a column of a table in the database. The callback
   should return "SQLITE_OK" if access is allowed, "SQLITE_DENY" if
   the entire SQL statement should be aborted with an error and
   "SQLITE_IGNORE" if the column should be treated as a NULL value.
   These constants are available in the "sqlite3" module.

   The first argument to the callback signifies what kind of operation
   is to be authorized. The second and third argument will be
   arguments or "None" depending on the first argument. The 4th
   argument is the name of the database ("main", "temp", etc.) if
   applicable. The 5th argument is the name of the inner-most trigger
   or view that is responsible for the access attempt or "None" if
   this access attempt is directly from input SQL code.

   Please consult the SQLite documentation about the possible values
   for the first argument and the meaning of the second and third
   argument depending on the first one. All necessary constants are
   available in the "sqlite3" module.

Connection.set_progress_handler(handler, n)

   This routine registers a callback. The callback is invoked for
   every *n* instructions of the SQLite virtual machine. This is
   useful if you want to get called from SQLite during long-running
   operations, for example to update a GUI.

   If you want to clear any previously installed progress handler,
   call the method with "None" for *handler*.

Connection.enable_load_extension(enabled)

   This routine allows/disallows the SQLite engine to load SQLite
   extensions from shared libraries.  SQLite extensions can define new
   functions, aggregates or whole new virtual table implementations.
   One well-known extension is the fulltext-search extension
   distributed with SQLite.

      from pysqlite2 import dbapi2 as sqlite3

      con = sqlite3.connect(":memory:")

      # enable extension loading
      con.enable_load_extension(True)

      # Load the fulltext search extension
      con.execute("select load_extension('./fts3.so')")

      # alternatively you can load the extension using an API call:
      # con.load_extension("./fts3.so")

      # disable extension laoding again
      con.enable_load_extension(False)

      # example from SQLite wiki
      con.execute("create virtual table recipe using fts3(name, ingredients)")
      con.executescript("""
          insert into recipe (name, ingredients) values ('broccoli stew', 'broccoli peppers cheese tomatoes');
          insert into recipe (name, ingredients) values ('pumpkin stew', 'pumpkin onions garlic celery');
          insert into recipe (name, ingredients) values ('broccoli pie', 'broccoli cheese onions flour');
          insert into recipe (name, ingredients) values ('pumpkin pie', 'pumpkin sugar flour butter');
          """)
      for row in con.execute("select rowid, name, ingredients from recipe where name match 'pie'"):
          print row



Connection.load_extension(path)

   This routine loads a SQLite extension from a shared library. You
   have to enable extension loading with "enable_load_extension"
   before you can use this routine.

Connection.row_factory

   You can change this attribute to a callable that accepts the cursor
   and the original row as a tuple and will return the real result
   row.  This way, you can implement more advanced ways of returning
   results, such  as returning an object that can also access columns
   by name.

   Example:

      from pysqlite2 import dbapi2 as sqlite3

      def dict_factory(cursor, row):
          d = {}
          for idx, col in enumerate(cursor.description):
              d[col[0]] = row[idx]
          return d

      con = sqlite3.connect(":memory:")
      con.row_factory = dict_factory
      cur = con.cursor()
      cur.execute("select 1 as a")
      print cur.fetchone()["a"]

   If returning a tuple doesn't suffice and you want name-based access
   to columns, you should consider setting "row_factory" to the
   highly-optimized "sqlite3.Row" type. "Row" provides both index-
   based and case-insensitive name-based access to columns with almost
   no memory overhead. It will probably be better than your own custom
   dictionary-based approach or even a db_row based solution.

Connection.text_factory

   Using this attribute you can control what objects are returned for
   the "TEXT" data type. By default, this attribute is set to
   "unicode" and the "sqlite3" module will return Unicode objects for
   "TEXT". If you want to return bytestrings instead, you can set it
   to "str".

   For efficiency reasons, there's also a way to return Unicode
   objects only for non-ASCII data, and bytestrings otherwise. To
   activate it, set this attribute to "sqlite3.OptimizedUnicode".

   You can also set it to any other callable that accepts a single
   bytestring parameter and returns the resulting object.

   See the following example code for illustration:

      from pysqlite2 import dbapi2 as sqlite3

      con = sqlite3.connect(":memory:")
      cur = con.cursor()

      # Create the table
      con.execute("create table person(lastname, firstname)")

      AUSTRIA = u"\xd6sterreich"

      # by default, rows are returned as Unicode
      cur.execute("select ?", (AUSTRIA,))
      row = cur.fetchone()
      assert row[0] == AUSTRIA

      # but we can make pysqlite always return bytestrings ...
      con.text_factory = str
      cur.execute("select ?", (AUSTRIA,))
      row = cur.fetchone()
      assert type(row[0]) == str
      # the bytestrings will be encoded in UTF-8, unless you stored garbage in the
      # database ...
      assert row[0] == AUSTRIA.encode("utf-8")

      # we can also implement a custom text_factory ...
      # here we implement one that will ignore Unicode characters that cannot be
      # decoded from UTF-8
      con.text_factory = lambda x: unicode(x, "utf-8", "ignore")
      cur.execute("select ?", ("this is latin1 and would normally create errors" + u"\xe4\xf6\xfc".encode("latin1"),))
      row = cur.fetchone()
      assert type(row[0]) == unicode

      # pysqlite offers a builtin optimized text_factory that will return bytestring
      # objects, if the data is in ASCII only, and otherwise return unicode objects
      con.text_factory = sqlite3.OptimizedUnicode
      cur.execute("select ?", (AUSTRIA,))
      row = cur.fetchone()
      assert type(row[0]) == unicode

      cur.execute("select ?", ("Germany",))
      row = cur.fetchone()
      assert type(row[0]) == str

Connection.total_changes

   Returns the total number of database rows that have been modified,
   inserted, or deleted since the database connection was opened.

Connection.iterdump

   Returns an iterator to dump the database in an SQL text format.
   Useful when saving an in-memory database for later restoration.
   This function provides the same capabilities as the ".dump" command
   in the **sqlite3** shell.

   Example:

      # Convert file existing_db.db to SQL dump file dump.sql
      import sqlite3, os

      con = sqlite3.connect('existing_db.db')
      full_dump = os.linesep.join([line for line in con.iterdump()])
      f = open('dump.sql', 'w')
      f.writelines(full_dump)
      f.close()


Cursor Objects
==============

A "Cursor" instance has the following attributes and methods:

   A SQLite database cursor has the following attributes and methods:

Cursor.execute(sql[, parameters])

   Executes an SQL statement. The SQL statement may be parametrized
   (i. e. placeholders instead of SQL literals). The "sqlite3" module
   supports two kinds of placeholders: question marks (qmark style)
   and named placeholders (named style).

   This example shows how to use parameters with qmark style:

      from pysqlite2 import dbapi2 as sqlite3

      con = sqlite3.connect("mydb")

      cur = con.cursor()

      who = "Yeltsin"
      age = 72

      cur.execute("select name_last, age from people where name_last=? and age=?", (who, age))
      print cur.fetchone()

   This example shows how to use the named style:

      from pysqlite2 import dbapi2 as sqlite3

      con = sqlite3.connect("mydb")

      cur = con.cursor()

      who = "Yeltsin"
      age = 72

      cur.execute("select name_last, age from people where name_last=:who and age=:age",
          {"who": who, "age": age})
      print cur.fetchone()

   "execute()" will only execute a single SQL statement. If you try to
   execute more than one statement with it, it will raise a Warning.
   Use "executescript()" if you want to execute multiple SQL
   statements with one call.

Cursor.executemany(sql, seq_of_parameters)

   Executes an SQL command against all parameter sequences or mappings
   found in the sequence *sql*.  The "sqlite3" module also allows
   using an *iterator* yielding parameters instead of a sequence.

      from pysqlite2 import dbapi2 as sqlite3

      class IterChars:
          def __init__(self):
              self.count = ord('a')

          def __iter__(self):
              return self

          def next(self):
              if self.count > ord('z'):
                  raise StopIteration
              self.count += 1
              return (chr(self.count - 1),) # this is a 1-tuple

      con = sqlite3.connect(":memory:")
      cur = con.cursor()
      cur.execute("create table characters(c)")

      theIter = IterChars()
      cur.executemany("insert into characters(c) values (?)", theIter)

      cur.execute("select c from characters")
      print cur.fetchall()

   Here's a shorter example using a *generator*:

      from pysqlite2 import dbapi2 as sqlite3

      def char_generator():
          import string
          for c in string.letters[:26]:
              yield (c,)

      con = sqlite3.connect(":memory:")
      cur = con.cursor()
      cur.execute("create table characters(c)")

      cur.executemany("insert into characters(c) values (?)", char_generator())

      cur.execute("select c from characters")
      print cur.fetchall()

Cursor.executescript(sql_script)

   This is a nonstandard convenience method for executing multiple SQL
   statements at once. It issues a "COMMIT" statement first, then
   executes the SQL script it gets as a parameter.

   *sql_script* can be a bytestring or a Unicode string.

   Example:

      from pysqlite2 import dbapi2 as sqlite3

      con = sqlite3.connect(":memory:")
      cur = con.cursor()
      cur.executescript("""
          create table person(
              firstname,
              lastname,
              age
          );

          create table book(
              title,
              author,
              published
          );

          insert into book(title, author, published)
          values (
              'Dirk Gently''s Holistic Detective Agency',
              'Douglas Adams',
              1987
          );
          """)

Cursor.fetchone()

   Fetches the next row of a query result set, returning a single
   sequence, or "None" when no more data is available.

Cursor.fetchmany([size=cursor.arraysize])

   Fetches the next set of rows of a query result, returning a list.
   An empty list is returned when no more rows are available.

   The number of rows to fetch per call is specified by the *size*
   parameter. If it is not given, the cursor's arraysize determines
   the number of rows to be fetched. The method should try to fetch as
   many rows as indicated by the size parameter. If this is not
   possible due to the specified number of rows not being available,
   fewer rows may be returned.

   Note there are performance considerations involved with the *size*
   parameter. For optimal performance, it is usually best to use the
   arraysize attribute. If the *size* parameter is used, then it is
   best for it to retain the same value from one "fetchmany()" call to
   the next.

Cursor.fetchall()

   Fetches all (remaining) rows of a query result, returning a list.
   Note that the cursor's arraysize attribute can affect the
   performance of this operation. An empty list is returned when no
   rows are available.

Cursor.rowcount

   Although the "Cursor" class of the "sqlite3" module implements this
   attribute, the database engine's own support for the determination
   of "rows affected"/"rows selected" is quirky.

   For "DELETE" statements, SQLite reports "rowcount" as 0 if you make
   a "DELETE FROM table" without any condition.

   For "executemany()" statements, the number of modifications are
   summed up into "rowcount".

   As required by the Python DB API Spec, the "rowcount" attribute "is
   -1 in case no "executeXX()" has been performed on the cursor or the
   rowcount of the last operation is not determinable by the
   interface".

   This includes "SELECT" statements because we cannot determine the
   number of rows a query produced until all rows were fetched.

Cursor.lastrowid

   This read-only attribute provides the rowid of the last modified
   row. It is only set if you issued a "INSERT" statement using the
   "execute()" method. For operations other than "INSERT" or when
   "executemany()" is called, "lastrowid" is set to "None".

Cursor.description

   This read-only attribute provides the column names of the last
   query. To remain compatible with the Python DB API, it returns a
   7-tuple for each column where the last six items of each tuple are
   "None".

   It is set for "SELECT" statements without any matching rows as
   well.


Row Objects
===========

class class sqlite3.Row

   A "Row" instance serves as a highly optimized "row_factory" for
   "Connection" objects. It tries to mimic a tuple in most of its
   features.

   It supports mapping access by column name and index, iteration,
   representation, equality testing and "len()".

   If two "Row" objects have exactly the same columns and their
   members are equal, they compare equal.

   Changed in version 2.6: Added iteration and equality (hashability).

   keys()

      This method returns a tuple of column names. Immediately after a
      query, it is the first member of each tuple in
      "Cursor.description".

      New in version 2.6.

Let's assume we initialize a table as in the example given above:

   conn = sqlite3.connect(":memory:")
   c = conn.cursor()
   c.execute('''create table stocks
   (date text, trans text, symbol text,
    qty real, price real)''')
   c.execute("""insert into stocks
             values ('2006-01-05','BUY','RHAT',100,35.14)""")
   conn.commit()
   c.close()

Now we plug "Row" in:

   >>> conn.row_factory = sqlite3.Row
   >>> c = conn.cursor()
   >>> c.execute('select * from stocks')
   <sqlite3.Cursor object at 0x7f4e7dd8fa80>
   >>> r = c.fetchone()
   >>> type(r)
   <type 'sqlite3.Row'>
   >>> r
   (u'2006-01-05', u'BUY', u'RHAT', 100.0, 35.14)
   >>> len(r)
   5
   >>> r[2]
   u'RHAT'
   >>> r.keys()
   ['date', 'trans', 'symbol', 'qty', 'price']
   >>> r['qty']
   100.0
   >>> for member in r: print member
   ...
   2006-01-05
   BUY
   RHAT
   100.0
   35.14


SQLite and Python types
=======================


Introduction
------------

SQLite natively supports the following types: "NULL", "INTEGER",
"REAL", "TEXT", "BLOB".

The following Python types can thus be sent to SQLite without any
problem:

+-------------------------------+---------------+
| Python type                   | SQLite type   |
+===============================+===============+
| "None"                        | "NULL"        |
+-------------------------------+---------------+
| "int"                         | "INTEGER"     |
+-------------------------------+---------------+
| "long"                        | "INTEGER"     |
+-------------------------------+---------------+
| "float"                       | "REAL"        |
+-------------------------------+---------------+
| "str" (UTF8-encoded)          | "TEXT"        |
+-------------------------------+---------------+
| "unicode"                     | "TEXT"        |
+-------------------------------+---------------+
| "buffer"                      | "BLOB"        |
+-------------------------------+---------------+

This is how SQLite types are converted to Python types by default:

+---------------+------------------------------------------------+
| SQLite type   | Python type                                    |
+===============+================================================+
| "NULL"        | "None"                                         |
+---------------+------------------------------------------------+
| "INTEGER"     | "int" or "long", depending on size             |
+---------------+------------------------------------------------+
| "REAL"        | "float"                                        |
+---------------+------------------------------------------------+
| "TEXT"        | depends on "text_factory", "unicode" by        |
+---------------+------------------------------------------------+
| "BLOB"        | "buffer"                                       |
+---------------+------------------------------------------------+

The type system of the "sqlite3" module is extensible in two ways: you
can store additional Python types in a SQLite database via object
adaptation, and you can let the "sqlite3" module convert SQLite types
to different Python types via converters.


Using adapters to store additional Python types in SQLite databases
-------------------------------------------------------------------

As described before, SQLite supports only a limited set of types
natively. To use other Python types with SQLite, you must **adapt**
them to one of the sqlite3 module's supported types for SQLite: one of
NoneType, int, long, float, str, unicode, buffer.

The "sqlite3" module uses Python object adaptation, as described in
**PEP 246** for this.  The protocol to use is "PrepareProtocol".

There are two ways to enable the "sqlite3" module to adapt a custom
Python type to one of the supported ones.


Letting your object adapt itself
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

This is a good approach if you write the class yourself. Let's suppose
you have a class like this:

   class Point(object):
       def __init__(self, x, y):
           self.x, self.y = x, y

Now you want to store the point in a single SQLite column.  First
you'll have to choose one of the supported types first to be used for
representing the point. Let's just use str and separate the
coordinates using a semicolon. Then you need to give your class a
method "__conform__(self, protocol)" which must return the converted
value. The parameter *protocol* will be "PrepareProtocol".

   from pysqlite2 import dbapi2 as sqlite3

   class Point(object):
       def __init__(self, x, y):
           self.x, self.y = x, y

       def __conform__(self, protocol):
           if protocol is sqlite3.PrepareProtocol:
               return "%f;%f" % (self.x, self.y)

   con = sqlite3.connect(":memory:")
   cur = con.cursor()

   p = Point(4.0, -3.2)
   cur.execute("select ?", (p,))
   print cur.fetchone()[0]


Registering an adapter callable
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The other possibility is to create a function that converts the type
to the string representation and register the function with
"register_adapter()".

Note: The type/class to adapt must be a *new-style class*, i. e. it must
  have "object" as one of its bases.

   from pysqlite2 import dbapi2 as sqlite3

   class Point(object):
       def __init__(self, x, y):
           self.x, self.y = x, y

   def adapt_point(point):
       return "%f;%f" % (point.x, point.y)

   sqlite3.register_adapter(Point, adapt_point)

   con = sqlite3.connect(":memory:")
   cur = con.cursor()

   p = Point(4.0, -3.2)
   cur.execute("select ?", (p,))
   print cur.fetchone()[0]

The "sqlite3" module has two default adapters for Python's built-in
"datetime.date" and "datetime.datetime" types.  Now let's suppose we
want to store "datetime.datetime" objects not in ISO representation,
but as a Unix timestamp.

   from pysqlite2 import dbapi2 as sqlite3
   import datetime, time

   def adapt_datetime(ts):
       return time.mktime(ts.timetuple())

   sqlite3.register_adapter(datetime.datetime, adapt_datetime)

   con = sqlite3.connect(":memory:")
   cur = con.cursor()

   now = datetime.datetime.now()
   cur.execute("select ?", (now,))
   print cur.fetchone()[0]


Converting SQLite values to custom Python types
-----------------------------------------------

Writing an adapter lets you send custom Python types to SQLite. But to
make it really useful we need to make the Python to SQLite to Python
roundtrip work.

Enter converters.

Let's go back to the "Point" class. We stored the x and y coordinates
separated via semicolons as strings in SQLite.

First, we'll define a converter function that accepts the string as a
parameter and constructs a "Point" object from it.

Note: Converter functions **always** get called with a string, no matter
  under which data type you sent the value to SQLite.

   def convert_point(s):
       x, y = map(float, s.split(";"))
       return Point(x, y)

Now you need to make the "sqlite3" module know that what you select
from the database is actually a point. There are two ways of doing
this:

* Implicitly via the declared type

* Explicitly via the column name

Both ways are described in section *Module functions and constants*,
in the entries for the constants "PARSE_DECLTYPES" and
"PARSE_COLNAMES".

The following example illustrates both approaches.

   from pysqlite2 import dbapi2 as sqlite3

   class Point(object):
       def __init__(self, x, y):
           self.x, self.y = x, y

       def __repr__(self):
           return "(%f;%f)" % (self.x, self.y)

   def adapt_point(point):
       return "%f;%f" % (point.x, point.y)

   def convert_point(s):
       x, y = map(float, s.split(";"))
       return Point(x, y)

   # Register the adapter
   sqlite3.register_adapter(Point, adapt_point)

   # Register the converter
   sqlite3.register_converter("point", convert_point)

   p = Point(4.0, -3.2)

   #########################
   # 1) Using declared types
   con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_DECLTYPES)
   cur = con.cursor()
   cur.execute("create table test(p point)")

   cur.execute("insert into test(p) values (?)", (p,))
   cur.execute("select p from test")
   print "with declared types:", cur.fetchone()[0]
   cur.close()
   con.close()

   #######################
   # 1) Using column names
   con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_COLNAMES)
   cur = con.cursor()
   cur.execute("create table test(p)")

   cur.execute("insert into test(p) values (?)", (p,))
   cur.execute('select p as "p [point]" from test')
   print "with column names:", cur.fetchone()[0]
   cur.close()
   con.close()


Default adapters and converters
-------------------------------

There are default adapters for the date and datetime types in the
datetime module. They will be sent as ISO dates/ISO timestamps to
SQLite.

The default converters are registered under the name "date" for
"datetime.date" and under the name "timestamp" for
"datetime.datetime".

This way, you can use date/timestamps from Python without any
additional fiddling in most cases. The format of the adapters is also
compatible with the experimental SQLite date/time functions.

The following example demonstrates this.

   from pysqlite2 import dbapi2 as sqlite3
   import datetime

   con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_DECLTYPES|sqlite3.PARSE_COLNAMES)
   cur = con.cursor()
   cur.execute("create table test(d date, ts timestamp)")

   today = datetime.date.today()
   now = datetime.datetime.now()

   cur.execute("insert into test(d, ts) values (?, ?)", (today, now))
   cur.execute("select d, ts from test")
   row = cur.fetchone()
   print today, "=>", row[0], type(row[0])
   print now, "=>", row[1], type(row[1])

   cur.execute('select current_date as "d [date]", current_timestamp as "ts [timestamp]"')
   row = cur.fetchone()
   print "current_date", row[0], type(row[0])
   print "current_timestamp", row[1], type(row[1])


Controlling Transactions
========================

By default, the "sqlite3" module opens transactions implicitly before
a Data Modification Language (DML)  statement (i.e.
"INSERT"/"UPDATE"/"DELETE"/"REPLACE"), and commits transactions
implicitly before a non-DML, non-query statement (i. e. anything other
than "SELECT" or the aforementioned).

So if you are within a transaction and issue a command like "CREATE
TABLE ...", "VACUUM", "PRAGMA", the "sqlite3" module will commit
implicitly before executing that command. There are two reasons for
doing that. The first is that some of these commands don't work within
transactions. The other reason is that pysqlite needs to keep track of
the transaction state (if a transaction is active or not).

You can control which kind of "BEGIN" statements sqlite3 implicitly
executes (or none at all) via the *isolation_level* parameter to the
"connect()" call, or via the "isolation_level" property of
connections.

If you want **autocommit mode**, then set "isolation_level" to None.

Otherwise leave it at its default, which will result in a plain
"BEGIN" statement, or set it to one of SQLite's supported isolation
levels: "DEFERRED", "IMMEDIATE" or "EXCLUSIVE".


Using "sqlite3" efficiently
===========================


Using shortcut methods
----------------------

Using the nonstandard "execute()", "executemany()" and
"executescript()" methods of the "Connection" object, your code can be
written more concisely because you don't have to create the (often
superfluous) "Cursor" objects explicitly. Instead, the "Cursor"
objects are created implicitly and these shortcut methods return the
cursor objects. This way, you can execute a "SELECT" statement and
iterate over it directly using only a single call on the "Connection"
object.

   from pysqlite2 import dbapi2 as sqlite3

   persons = [
       ("Hugo", "Boss"),
       ("Calvin", "Klein")
       ]

   con = sqlite3.connect(":memory:")

   # Create the table
   con.execute("create table person(firstname, lastname)")

   # Fill the table
   con.executemany("insert into person(firstname, lastname) values (?, ?)", persons)

   # Print the table contents
   for row in con.execute("select firstname, lastname from person"):
       print row

   # Using a dummy WHERE clause to not let SQLite take the shortcut table deletes.
   print "I just deleted", con.execute("delete from person where 1=1").rowcount, "rows"


Accessing columns by name instead of by index
---------------------------------------------

One useful feature of the "sqlite3" module is the built-in
"sqlite3.Row" class designed to be used as a row factory.

Rows wrapped with this class can be accessed both by index (like
tuples) and case-insensitively by name:

   from pysqlite2 import dbapi2 as sqlite3

   con = sqlite3.connect("mydb")
   con.row_factory = sqlite3.Row

   cur = con.cursor()
   cur.execute("select name_last, age from people")
   for row in cur:
       assert row[0] == row["name_last"]
       assert row["name_last"] == row["nAmE_lAsT"]
       assert row[1] == row["age"]
       assert row[1] == row["AgE"]


Using the connection as a context manager
-----------------------------------------

With Python 2.5 or higher, connection objects can be used as context
managers that automatically commit or rollback transactions.  In the
event of an exception, the transaction is rolled back; otherwise, the
transaction is committed:

   from __future__ import with_statement
   from pysqlite2 import dbapi2 as sqlite3

   con = sqlite3.connect(":memory:")
   con.execute("create table person (id integer primary key, firstname varchar unique)")

   # Successful, con.commit() is called automatically afterwards
   with con:
       con.execute("insert into person(firstname) values (?)", ("Joe",))

   # con.rollback() is called after the with block finishes with an exception, the
   # exception is still raised and must be catched
   try:
       with con:
           con.execute("insert into person(firstname) values (?)", ("Joe",))
   except sqlite3.IntegrityError:
       print "couldn't add Joe twice"




Combining APSW and pysqlite
---------------------------

APSW is "Another Python SQLite Wrapper". Its goal is to directly wrap
the SQLite API for Python. If there's SQLite functionality that is
only wrapped via APSW, but not (yet) via pysqlite, then you can still
use the APSW functionality in pysqlite.

Just use the APSW Connection as a parameter to the connect function
and reuse an existing APSW connection like this.

   from pysqlite2 import dbapi2 as sqlite3
   import apsw

   apsw_con = apsw.Connection(":memory:")
   apsw_con.createscalarfunction("times_two", lambda x: 2*x, 1)

   # Create pysqlite connection from APSW connection
   con = sqlite3.connect(apsw_con)
   result = con.execute("select times_two(15)").fetchone()[0]
   assert result == 30
   con.close()


This feature only works if both APSW and pysqlite are dynamically
linked against the same SQLite shared library. I. e. it will *not*
work on Windows without a custom built pysqlite and APSW.
