
"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.

sqlite3 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
"example.db" file:

   import sqlite3
   conn = sqlite3.connect('example.db')

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("select * from stocks where symbol = '%s'" % symbol)

   # Do this instead
   t = ('IBM',)
   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', 'MSFT', 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)
   ...
   ('2006-01-05', 'BUY', 'RHAT', 100, 35.14)
   ('2006-03-28', 'BUY', 'IBM', 1000, 45.0)
   ('2006-04-06', 'SELL', 'IBM', 500, 53.0)
   ('2006-04-05', 'BUY', 'MSOFT', 1000, 72.0)
   >>>

See also: https://github.com/ghaering/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.version

   The version number of this module, as a string. This is not the
   version of the SQLite library.

sqlite3.version_info

   The version number of this module, as a tuple of integers. This is
   not the version of the SQLite library.

sqlite3.sqlite_version

   The version number of the run-time SQLite library, as a string.

sqlite3.sqlite_version_info

   The version number of the run-time SQLite library, as a tuple of
   integers.

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, detect_types, isolation_level, check_same_thread, factory, cached_statements])

   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, REAL, 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, float, str or bytes.

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

      import sqlite3

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

      buffer = ""

      print("Enter your SQL commands to execute in sqlite3.")
      print("Enter a blank line to exit.")

      while True:
          line = 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 as 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* set to
   "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:

   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.

   in_transaction

      "True" if a transaction is active (there are uncommitted
      changes), "False" otherwise.  Read-only attribute.

      New in version 3.2.

   cursor([cursorClass])

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

   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 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.

   rollback()

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

   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!

   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.

   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.

   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.

   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:
      bytes, str, int, float and None.

      Example:

         import sqlite3
         import hashlib

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

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

   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: bytes, str, int, float and None.

      Example:

         import 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])

   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":

         import sqlite3

         def collate_reverse(string1, string2):
             if string1 == string2:
                 return 0
             elif string1 < string2:
                 return 1
             else:
                 return -1

         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)

   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.

   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.

   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*.

   set_trace_callback(trace_callback)

      Registers *trace_callback* to be called for each SQL statement
      that is actually executed by the SQLite backend.

      The only argument passed to the callback is the statement (as
      string) that is being executed. The return value of the callback
      is ignored. Note that the backend does not only run statements
      passed to the "Cursor.execute()" methods.  Other sources include
      the transaction management of the Python module and the
      execution of triggers defined in the current database.

      Passing "None" as *trace_callback* will disable the trace
      callback.

      New in version 3.3.

   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.

      Loadable extensions are disabled by default. See [1].

      New in version 3.2.

         import 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)

   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.

      Loadable extensions are disabled by default. See [1].

      New in version 3.2.

   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:

         import 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.

   text_factory

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

      For efficiency reasons, there's also a way to return "str"
      objects only for non-ASCII data, and "bytes" 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:

         import sqlite3

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

         AUSTRIA = "\xd6sterreich"

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

         # but we can make sqlite3 always return bytestrings ...
         con.text_factory = bytes
         cur.execute("select ?", (AUSTRIA,))
         row = cur.fetchone()
         assert type(row[0]) is bytes
         # 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 appends "foo" to all strings
         con.text_factory = lambda x: x.decode("utf-8") + "foo"
         cur.execute("select ?", ("bar",))
         row = cur.fetchone()
         assert row[0] == "barfoo"

   total_changes

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

   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')
         with open('dump.sql', 'w') as f:
             for line in con.iterdump():
                 f.write('%s\n' % line)


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

class class sqlite3.Cursor

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

   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).

      Here's an example of both styles:

         import sqlite3

         con = sqlite3.connect(":memory:")
         cur = con.cursor()
         cur.execute("create table people (name_last, age)")

         who = "Yeltsin"
         age = 72

         # This is the qmark style:
         cur.execute("insert into people values (?, ?)", (who, age))

         # And this is the named style:
         cur.execute("select * 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.

   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.

         import 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*:

         import sqlite3
         import string

         def char_generator():
             for c in string.ascii_lowercase:
                 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())

   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 an instance of "str" or "bytes".

      Example:

         import 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
             );
             """)

   fetchone()

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

   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.

   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.

   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 "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.

      With SQLite versions before 3.6.5, "rowcount" is set to 0 if you
      make a "DELETE FROM table" without any condition.

   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".

   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.

   keys()

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

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)
   <class 'sqlite3.Row'>
   >>> tuple(r)
   ('2006-01-05', 'BUY', 'RHAT', 100.0, 35.14)
   >>> len(r)
   5
   >>> r[2]
   '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"     |
+---------------------------------+---------------+
| "float"                         | "REAL"        |
+---------------------------------+---------------+
| "str"                           | "TEXT"        |
+---------------------------------+---------------+
| "bytes"                         | "BLOB"        |
+---------------------------------+---------------+

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

+---------------+-----------------------------------------------+
| SQLite type   | Python type                                   |
+===============+===============================================+
| "NULL"        | "None"                                        |
+---------------+-----------------------------------------------+
| "INTEGER"     | "int"                                         |
+---------------+-----------------------------------------------+
| "REAL"        | "float"                                       |
+---------------+-----------------------------------------------+
| "TEXT"        | depends on text_factory, str by default       |
+---------------+-----------------------------------------------+
| "BLOB"        | "bytes"                                       |
+---------------+-----------------------------------------------+

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, float, str, bytes.

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:
       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".

   import sqlite3

   class Point:
       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()".

   import sqlite3

   class Point:
       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.

   import sqlite3
   import datetime
   import 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(b";"))
       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.

   import sqlite3

   class Point:
       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)).encode('ascii')

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

   import 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]))

If a timestamp stored in SQLite has a fractional part longer than 6
numbers, its value will be truncated to microsecond precision by the
timestamp converter.


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 sqlite3 needs to keep track of
the transaction state (if a transaction is active or not).  The
current transaction state is exposed through the
"Connection.in_transaction" attribute of the connection object.

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.

   import 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)

   print("I just deleted", con.execute("delete from person").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:

   import sqlite3

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

   cur = con.cursor()
   cur.execute("select 'John' as name, 42 as age")
   for row in cur:
       assert row[0] == row["name"]
       assert row["name"] == row["nAmE"]
       assert row[1] == row["age"]
       assert row[1] == row["AgE"]


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

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:

   import 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 caught
   try:
       with con:
           con.execute("insert into person(firstname) values (?)", ("Joe",))
   except sqlite3.IntegrityError:
       print("couldn't add Joe twice")


Common issues
=============


Multithreading
--------------

Older SQLite versions had issues with sharing connections between
threads. That's why the Python module disallows sharing connections
and cursors between threads. If you still try to do so, you will get
an exception at runtime.

The only exception is calling the "interrupt()" method, which only
makes sense to call from a different thread.

-[ Footnotes ]-

[1] The sqlite3 module is not built with loadable extension
    support by default, because some platforms (notably Mac OS X) have
    SQLite libraries which are compiled without this feature. To get
    loadable extension support, you must pass --enable-loadable-
    sqlite- extensions to configure.
