sqlite3 — Interface DB-API 2.0 pour bases de données SQLite

Code source : Lib/sqlite3/

SQLite est une bibliothèque C qui fournit une base de données légère sur disque ne nécessitant pas de processus serveur et qui utilise une variante (non standard) du langage de requête SQL pour accéder aux données. Certaines applications peuvent utiliser SQLite pour le stockage de données internes. Il est également possible de créer une application prototype utilisant SQLite, puis de modifier le code pour utiliser une base de données plus robuste telle que PostgreSQL ou Oracle.

The sqlite3 module was written by Gerhard Häring. It provides an SQL interface compliant with the DB-API 2.0 specification described by PEP 249, and requires SQLite 3.7.15 or newer.

Ce document inclus 4 sections principales :

Voir aussi

https://www.sqlite.org

Dans la page Web de SQLite, la documentation décrit la syntaxe et les types de données disponibles qui sont pris en charge par cette variante SQL.

https://www.w3schools.com/sql/

Tutoriel, référence et exemples pour apprendre la syntaxe SQL.

PEP 249 — Spécifications de l'API 2.0 pour la base de données

PEP écrite par Marc-André Lemburg.

Tutoriel

Dans ce tutoriel, vous allez créer une base de données des films des Monty Python en utilisant les fonctionnalités de base de sqlite3. Cela nécessite une compréhension élémentaire des concepts des bases de données, notamment les curseurs et les transactions.

First, we need to create a new database and open a database connection to allow sqlite3 to work with it. Call sqlite3.connect() to create a connection to the database tutorial.db in the current working directory, implicitly creating it if it does not exist:

import sqlite3
con = sqlite3.connect("tutorial.db")

L’objet Connection renvoyé — con — représente la connexion à la base de données sur disque.

Afin d’exécuter les instructions SQL et de récupérer les résultats des requêtes SQL, nous devrons utiliser un curseur de base de données. Appelez con.cursor() pour créer la Cursor :

cur = con.cursor()

Maintenant que nous avons une connexion à la base de données et un curseur, nous pouvons créer une table movie avec des colonnes pour le titre, l’année de sortie et la note de la critique. Pour plus de simplicité, nous pouvons simplement utiliser les noms des colonnes dans la déclaration de la table — grâce à la fonctionnalité de typage flexible de SQLite, spécifier les types de données est facultatif. Exécutez l’instruction CREATE TABLE en appelant cur.execute(…) :

cur.execute("CREATE TABLE movie(title, year, score)")

Nous pouvons vérifier que la nouvelle table a été créée en interrogeant la table sqlite_master intégrée à SQLite, qui devrait maintenant contenir une entrée pour la définition de la table movie (voir le schéma Table pour plus de détails). Exécutez cette requête en appelant cur.execute(…), affectez le résultat à res, et appelez res.fetchone() pour récupérer la ligne résultante :

>>> res = cur.execute("SELECT name FROM sqlite_master")
>>> res.fetchone()
('movie',)

Nous pouvons voir que la table a été créée, puisque la requête retourne un tuple contenant le nom de la table. Si nous interrogeons sqlite_master pour une table spam inexistante, res.fetchone()`() retournera None :

>>> res = cur.execute("SELECT name FROM sqlite_master WHERE name='spam'")
>>> res.fetchone() is None
True

Maintenant, ajoutez deux lignes de données en tant que littéraux SQL en exécutant une instruction INSERT, une fois encore en appelant cur.execute(…) :

cur.execute("""
    INSERT INTO movie VALUES
        ('Monty Python and the Holy Grail', 1975, 8.2),
        ('And Now for Something Completely Different', 1971, 7.5)
""")

L’instruction INSERT ouvre implicitement une transaction, qui doit être validée avant que les modifications ne soient enregistrées dans la base de données (voir contrôle des transactions SQL pour plus de détails). Appelez con.commit() sur l’objet de connexion pour valider la transaction :

con.commit()

Nous pouvons vérifier que les données ont été insérées correctement en exécutant une requête SELECT. Utilisez la désormais familière cur.execute(…) pour affecter le résultat à res, et appelez res.fetchall() pour retourner toutes les lignes résultantes :

>>> res = cur.execute("SELECT score FROM movie")
>>> res.fetchall()
[(8.2,), (7.5,)]

Le résultat est une liste de deux tuples, une par ligne, chacun contenant la valeur score de cette ligne.

Maintenant, insérez trois lignes supplémentaires en appelant cur.executemany(…) :

data = [
    ("Monty Python Live at the Hollywood Bowl", 1982, 7.9),
    ("Monty Python's The Meaning of Life", 1983, 7.5),
    ("Monty Python's Life of Brian", 1979, 8.0),
]
cur.executemany("INSERT INTO movie VALUES(?, ?, ?)", data)
con.commit()  # Remember to commit the transaction after executing INSERT.

Remarquez que les marqueurs ? sont utilisés pour lier les data à la requête. Utilisez toujours les marqueurs au lieu d’expressions formatées pour lier les valeurs Python aux instructions SQL, afin d’éviter les injections SQL (voir placeholder SQL pour plus de détails).

Nous pouvons vérifier que les nouvelles lignes ont été insérées en exécutant une requête SELECT, cette fois-ci en itérant sur les résultats de la requête :

>>> for row in cur.execute("SELECT year, title FROM movie ORDER BY year"):
...     print(row)
(1971, 'And Now for Something Completely Different')
(1975, 'Monty Python and the Holy Grail')
(1979, "Monty Python's Life of Brian")
(1982, 'Monty Python Live at the Hollywood Bowl')
(1983, "Monty Python's The Meaning of Life")

Chaque ligne est un tuple de deux éléments (année, titre), correspondant aux colonnes sélectionnées dans la requête.

Enfin, vérifiez que la base de données a été écrite sur le disque en appelant con.close() pour fermer la connexion existante, en ouvrir une nouvelle, créer un nouveau curseur, puis interroger la base de données :

>>> con.close()
>>> new_con = sqlite3.connect("tutorial.db")
>>> new_cur = new_con.cursor()
>>> res = new_cur.execute("SELECT title, year FROM movie ORDER BY score DESC")
>>> title, year = res.fetchone()
>>> print(f'The highest scoring Monty Python movie is {title!r}, released in {year}')
The highest scoring Monty Python movie is 'Monty Python and the Holy Grail', released in 1975

Vous avez maintenant créé une base de données SQLite à l’aide du module sqlite3, inséré des données et récupéré des valeurs de plusieurs façons.

Voir aussi

Référence

Fonctions du module

sqlite3.connect(database, timeout=5.0, detect_types=0, isolation_level='DEFERRED', check_same_thread=True, factory=sqlite3.Connection, cached_statements=100, uri=False)

Ouvrez une connexion à une base de données SQLite.

Paramètres

  • database (path-like object) -- Le chemin d’accès au fichier de la base de données à ouvrir. Passez ":memory:" pour ouvrir une connexion à une base de données qui est dans la RAM plutôt que sur le disque.

  • timeout (float) -- How many seconds the connection should wait before raising an OperationalError when a table is locked. If another connection opens a transaction to modify a table, that table will be locked until the transaction is committed. Default five seconds.

  • detect_types (int) -- Contrôle si et comment les types de données non nativement pris en charge par SQLite sont recherchés pour être convertis en types Python, en utilisant les convertisseurs enregistrés avec register_converter(). Définissez-le à n’importe quelle combinaison (en utilisant |, opérateurs bit-à-bit OR) de PARSE_DECLTYPES et PARSE_COLNAMES pour activer ceci. Les noms de colonnes ont la priorité sur les types déclarés si les deux drapeaux sont activés. Les types ne peuvent pas être détectés pour les champs générés (par exemple max(data)), même si le paramètre detect_types est activé ; str sera retourné à la place. Par défaut (0), la détection des types est désactivée.

  • isolation_level (str | None) -- L’attribut isolation_level de la connexion, contrôlant si et comment les transactions sont ouvertes implicitement. Peut être "DEFERRED" (par défaut), "EXCLUSIVE" ou "IMMEDIATE" ; ou None pour désactiver l’ouverture implicite des transactions. Voir contrôle des transactions sqlite3 pour en savoir plus.

  • check_same_thread (bool) -- Si True (par défaut), ProgrammingError sera levée si la connexion à la base de données est utilisée par un thread autre que celui qui l’a créée. Si False, la connexion peut être utilisée par plusieurs threads ; les opérations d’écriture devront peut-être être sérialisées par l’utilisateur pour éviter la corruption des données. Voir sécurité des threads pour plus d’informations.

  • factory (Connection) -- Une sous-classe personnalisée de Connection pour créer la connexion, si ce n’est pas la classe par défaut Connection.

  • cached_statements (int) -- The number of statements that sqlite3 should internally cache for this connection, to avoid parsing overhead. By default, 100 statements.

  • uri (bool) -- Si elle a pour valeur True, la base de données est interprétée comme un URI avec un chemin d’accès au fichier et une chaîne de requête facultative. La partie schéma doit être "file:", et le chemin peut être relatif ou absolu. La chaîne d’interrogation permet de passer des paramètres à SQLite, ce qui permet d’activer diverses astuces d’URI sqlite3.

Type renvoyé

Connection

Raises an auditing event sqlite3.connect with argument database.

Raises an auditing event sqlite3.connect/handle with argument connection_handle.

Nouveau dans la version 3.4: The uri parameter.

Modifié dans la version 3.7: database peut maintenant aussi être un objet de type chemin, et pas seulement une chaîne de caractères.

Nouveau dans la version 3.10: The sqlite3.connect/handle auditing event.

sqlite3.complete_statement(statement)

Renvoie True si la déclaration de la chaîne semble contenir une ou plusieurs déclarations SQL complètes. Aucune vérification syntaxique ou analyse syntaxique d’aucune sorte n’est effectuée, si ce n’est la vérification qu’il n’y a pas de chaîne littérale non fermée et que l’instruction se termine par un point-virgule.

Par exemple :

>>> sqlite3.complete_statement("SELECT foo FROM bar;")
True
>>> sqlite3.complete_statement("SELECT foo")
False

Cette fonction peut être utile pendant la saisie en ligne de commande pour déterminer si le texte saisi semble former une instruction SQL complète, ou si une saisie supplémentaire est nécessaire avant d’appeler execute().

sqlite3.enable_callback_tracebacks(flag, /)

Enable or disable callback tracebacks. 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.

sqlite3.register_adapter(type, adapter, /)

Enregistre un adaptateur appelable pour adapter le type Python type en un type SQLite. L’adaptateur est appelé avec un objet Python de type type comme unique argument, et doit retourner une valeur d’un type que SQLite comprend nativement.

sqlite3.register_converter(typename, converter, /)

Enregistre le convertisseur appelable pour convertir les objets SQLite de type typename en un objet Python d’un type spécifique. Le convertisseur est invoqué pour toutes les valeurs SQLite de type typename ; on lui passe un objet bytes et il doit retourner un objet du type Python désiré. Consultez le paramètre detect_types de connect() pour des informations sur le fonctionnement de la détection des types.

Remarque : typename et le nom du type dans votre requête sont comparés sans tenir compte de la casse.

Module constants

sqlite3.PARSE_COLNAMES

Pass this flag value to the detect_types parameter of connect() to look up a converter function by using the type name, parsed from the query column name, as the converter dictionary key. The type name must be wrapped in square brackets ([]).

SELECT p as "p [point]" FROM test;  ! will look up converter "point"

This flag may be combined with PARSE_DECLTYPES using the | (bitwise or) operator.

sqlite3.PARSE_DECLTYPES

Pass this flag value to the detect_types parameter of connect() to look up a converter function using the declared types for each column. The types are declared when the database table is created. sqlite3 will look up a converter function using the first word of the declared type as the converter dictionary key. For example:

CREATE TABLE test(
   i integer primary key,  ! will look up a converter named "integer"
   p point,                ! will look up a converter named "point"
   n number(10)            ! will look up a converter named "number"
 )

This flag may be combined with PARSE_COLNAMES using the | (bitwise or) operator.

sqlite3.SQLITE_OK
sqlite3.SQLITE_DENY
sqlite3.SQLITE_IGNORE

Flags that should be returned by the authorizer_callback callable passed to Connection.set_authorizer(), to indicate whether:

  • Access is allowed (SQLITE_OK),

  • The SQL statement should be aborted with an error (SQLITE_DENY)

  • The column should be treated as a NULL value (SQLITE_IGNORE)

sqlite3.apilevel

String constant stating the supported DB-API level. Required by the DB-API. Hard-coded to "2.0".

sqlite3.paramstyle

String constant stating the type of parameter marker formatting expected by the sqlite3 module. Required by the DB-API. Hard-coded to "qmark".

Note

The named DB-API parameter style is also supported.

sqlite3.sqlite_version

Version number of the runtime SQLite library as a string.

sqlite3.sqlite_version_info

Version number of the runtime SQLite library as a tuple of integers.

sqlite3.threadsafety

Integer constant required by the DB-API, stating the level of thread safety the sqlite3 module supports. Currently hard-coded to 1, meaning "Threads may share the module, but not connections." However, this may not always be true. You can check the underlying SQLite library's compile-time threaded mode using the following query:

import sqlite3
con = sqlite3.connect(":memory:")
con.execute("""
    select * from pragma_compile_options
    where compile_options like 'THREADSAFE=%'
""").fetchall()

Note that the SQLITE_THREADSAFE levels do not match the DB-API 2.0 threadsafety levels.

sqlite3.version

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

sqlite3.version_info

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

Connection objects

class sqlite3.Connection

Each open SQLite database is represented by a Connection object, which is created using sqlite3.connect(). Their main purpose is creating Cursor objects, and Transaction control.

Voir aussi

An SQLite database connection has the following attributes and methods:

cursor(factory=Cursor)

Create and return a Cursor object. The cursor method accepts a single optional parameter factory. If supplied, this must be a callable returning an instance of Cursor or its subclasses.

commit()

Commit any pending transaction to the database. If there is no open transaction, this method is a no-op.

rollback()

Roll back to the start of any pending transaction. If there is no open transaction, this method is a no-op.

close()

Close the database connection. Any pending transaction is not committed implicitly; make sure to commit() before closing to avoid losing pending changes.

execute(sql, parameters=(), /)

Create a new Cursor object and call execute() on it with the given sql and parameters. Return the new cursor object.

executemany(sql, parameters, /)

Create a new Cursor object and call executemany() on it with the given sql and parameters. Return the new cursor object.

executescript(sql_script, /)

Create a new Cursor object and call executescript() on it with the given sql_script. Return the new cursor object.

create_function(name, narg, func, *, deterministic=False)

Create or remove a user-defined SQL function.

Paramètres

  • name (str) -- The name of the SQL function.

  • narg (int) -- The number of arguments the SQL function can accept. If -1, it may take any number of arguments.

  • func (callback | None) -- A callable that is called when the SQL function is invoked. The callable must return a type natively supported by SQLite. Set to None to remove an existing SQL function.

  • deterministic (bool) -- If True, the created SQL function is marked as deterministic, which allows SQLite to perform additional optimizations.

Lève

NotSupportedError -- If deterministic is used with SQLite versions older than 3.8.3.

Nouveau dans la version 3.8: The deterministic parameter.

Exemple :

>>> import hashlib
>>> def md5sum(t):
...     return hashlib.md5(t).hexdigest()
>>> con = sqlite3.connect(":memory:")
>>> con.create_function("md5", 1, md5sum)
>>> for row in con.execute("SELECT md5(?)", (b"foo",)):
...     print(row)
('acbd18db4cc2f85cedef654fccc4a4d8',)
create_aggregate(name, /, n_arg, aggregate_class)

Create or remove a user-defined SQL aggregate function.

Paramètres

  • name (str) -- The name of the SQL aggregate function.

  • n_arg (int) -- The number of arguments the SQL aggregate function can accept. If -1, it may take any number of arguments.

  • aggregate_class (class | None) --

    A class must implement the following methods:

    The number of arguments that the step() method must accept is controlled by n_arg.

    Set to None to remove an existing SQL aggregate function.

Exemple :

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.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])

con.close()
create_collation(name, callable)

Create a collation named name using the collating function callable. callable is passed two string arguments, and it should return an integer:

  • 1 if the first is ordered higher than the second

  • -1 if the first is ordered lower than the second

  • 0 if they are ordered equal

The following example shows a reverse sorting collation:

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.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()

Remove a collation function by setting callable to None.

interrupt()

Call this method from a different thread to abort any queries that might be executing on the connection. Aborted queries will raise an OperationalError.

set_authorizer(authorizer_callback)

Register callable authorizer_callback to be invoked for each attempt to access a column of a table in the database. The callback should return one of SQLITE_OK, SQLITE_DENY, or SQLITE_IGNORE to signal how access to the column should be handled by the underlying SQLite library.

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(progress_handler, n)

Register callable progress_handler to be 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 progress_handler.

Returning a non-zero value from the handler function will terminate the currently executing query and cause it to raise an OperationalError exception.

set_trace_callback(trace_callback)

Register callable trace_callback to be invoked for each SQL statement that is actually executed by the SQLite backend.

The only argument passed to the callback is the statement (as str) 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 sqlite3 module and the execution of triggers defined in the current database.

Passing None as trace_callback will disable the trace callback.

Note

Exceptions raised in the trace callback are not propagated. As a development and debugging aid, use enable_callback_tracebacks() to enable printing tracebacks from exceptions raised in the trace callback.

Nouveau dans la version 3.3.

enable_load_extension(enabled, /)

Enable the SQLite engine to load SQLite extensions from shared libraries if enabled is True; else, disallow loading SQLite extensions. 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.

Note

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

Raises an auditing event sqlite3.enable_load_extension with arguments connection, enabled.

Nouveau dans la version 3.2.

Modifié dans la version 3.10: Added the sqlite3.enable_load_extension auditing event.

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 loading 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)

con.close()
load_extension(path, /)

Load an SQLite extension from a shared library located at path. Enable extension loading with enable_load_extension() before calling this method.

Raises an auditing event sqlite3.load_extension with arguments connection, path.

Nouveau dans la version 3.2.

Modifié dans la version 3.10: Added the sqlite3.load_extension auditing event.

iterdump()

Return an iterator to dump the database as SQL source code. Useful when saving an in-memory database for later restoration. Similar to the .dump command in the sqlite3 shell.

Exemple :

# Convert file example.db to SQL dump file dump.sql
con = sqlite3.connect('example.db')
with open('dump.sql', 'w') as f:
    for line in con.iterdump():
        f.write('%s\n' % line)
con.close()
backup(target, *, pages=- 1, progress=None, name='main', sleep=0.250)

Create a backup of an SQLite database.

Works even if the database is being accessed by other clients or concurrently by the same connection.

Paramètres

  • target (Connection) -- The database connection to save the backup to.

  • pages (int) -- The number of pages to copy at a time. If equal to or less than 0, the entire database is copied in a single step. Defaults to -1.

  • progress (callback | None) -- If set to a callable, it is invoked with three integer arguments for every backup iteration: the status of the last iteration, the remaining number of pages still to be copied, and the total number of pages. Defaults to None.

  • name (str) -- The name of the database to back up. Either "main" (the default) for the main database, "temp" for the temporary database, or the name of a custom database as attached using the ATTACH DATABASE SQL statement.

  • sleep (float) -- The number of seconds to sleep between successive attempts to back up remaining pages.

Example 1, copy an existing database into another:

def progress(status, remaining, total):
    print(f'Copied {total-remaining} of {total} pages...')

src = sqlite3.connect('example.db')
dst = sqlite3.connect('backup.db')
with dst:
    src.backup(dst, pages=1, progress=progress)
dst.close()
src.close()

Example 2, copy an existing database into a transient copy:

src = sqlite3.connect('example.db')
dst = sqlite3.connect(':memory:')
src.backup(dst)

Nouveau dans la version 3.7.

in_transaction

This read-only attribute corresponds to the low-level SQLite autocommit mode.

True if a transaction is active (there are uncommitted changes), False otherwise.

Nouveau dans la version 3.2.

isolation_level

This attribute controls the transaction handling performed by sqlite3. If set to None, transactions are never implicitly opened. If set to one of "DEFERRED", "IMMEDIATE", or "EXCLUSIVE", corresponding to the underlying SQLite transaction behaviour, implicit transaction management is performed.

If not overridden by the isolation_level parameter of connect(), the default is "", which is an alias for "DEFERRED".

row_factory

The initial row_factory for Cursor objects created from this connection. Assigning to this attribute does not affect the row_factory of existing cursors belonging to this connection, only new ones. Is None by default, meaning each row is returned as a tuple.

See How to create and use row factories for more details.

text_factory

A callable that accepts a bytes parameter and returns a text representation of it. The callable is invoked for SQLite values with the TEXT data type. By default, this attribute is set to str. If you want to return bytes instead, set text_factory to bytes.

Exemple :

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

AUSTRIA = "Österreich"

# by default, rows are returned as str
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"

con.close()
total_changes

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

Cursor objects

A Cursor object represents a database cursor which is used to execute SQL statements, and manage the context of a fetch operation. Cursors are created using Connection.cursor(), or by using any of the connection shortcut methods.

Cursor objects are iterators, meaning that if you execute() a SELECT query, you can simply iterate over the cursor to fetch the resulting rows:

for row in cur.execute("SELECT t FROM data"):
    print(row)
class sqlite3.Cursor

A Cursor instance has the following attributes and methods.

execute(sql, parameters=(), /)

Execute SQL a single SQL statement, optionally binding Python values using placeholders.

Paramètres
Lève

Warning -- If sql contains more than one SQL statement.

If isolation_level is not None, sql is an INSERT, UPDATE, DELETE, or REPLACE statement, and there is no open transaction, a transaction is implicitly opened before executing sql.

Use executescript() to execute multiple SQL statements.

executemany(sql, parameters, /)

For every item in parameters, repeatedly execute the parameterized SQL statement sql.

Uses the same implicit transaction handling as execute().

Paramètres
Lève

Exemple :

rows = [
    ("row1",),
    ("row2",),
]
# cur is an sqlite3.Cursor object
cur.executemany("INSERT INTO data VALUES(?)", rows)
executescript(sql_script, /)

Execute the SQL statements in sql_script. If there is a pending transaction, an implicit COMMIT statement is executed first. No other implicit transaction control is performed; any transaction control must be added to sql_script.

sql_script must be a string.

Exemple :

# cur is an sqlite3.Cursor object
cur.executescript("""
    BEGIN;
    CREATE TABLE person(firstname, lastname, age);
    CREATE TABLE book(title, author, published);
    CREATE TABLE publisher(name, address);
    COMMIT;
""")
fetchone()

If row_factory is None, return the next row query result set as a tuple. Else, pass it to the row factory and return its result. Return None if no more data is available.

fetchmany(size=cursor.arraysize)

Return the next set of rows of a query result as a list. Return an empty list if no more rows are available.

The number of rows to fetch per call is specified by the size parameter. If size is not given, arraysize determines the number of rows to be fetched. If fewer than size rows are available, as many rows as are available are 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()

Return all (remaining) rows of a query result as a list. Return an empty list if no rows are available. Note that the arraysize attribute can affect the performance of this operation.

close()

Close the cursor now (rather than whenever __del__ is called).

The cursor will be unusable from this point forward; a ProgrammingError exception will be raised if any operation is attempted with the cursor.

setinputsizes(sizes, /)

Required by the DB-API. Does nothing in sqlite3.

setoutputsize(size, column=None, /)

Required by the DB-API. Does nothing in sqlite3.

arraysize

Read/write attribute that controls the number of rows returned by fetchmany(). The default value is 1 which means a single row would be fetched per call.

connection

Read-only attribute that provides the SQLite database Connection belonging to the cursor. A Cursor object created by calling con.cursor() will have a connection attribute that refers to con:

>>> con = sqlite3.connect(":memory:")
>>> cur = con.cursor()
>>> cur.connection == con
True
description

Read-only attribute that 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.

lastrowid

Read-only attribute that provides the row id of the last inserted row. It is only updated after successful INSERT or REPLACE statements using the execute() method. For other statements, after executemany() or executescript(), or if the insertion failed, the value of lastrowid is left unchanged. The initial value of lastrowid is None.

Note

Inserts into WITHOUT ROWID tables are not recorded.

Modifié dans la version 3.6: Added support for the REPLACE statement.

rowcount

Read-only attribute that provides the number of modified rows for INSERT, UPDATE, DELETE, and REPLACE statements; is -1 for other statements, including CTE queries. It is only updated by the execute() and executemany() methods.

row_factory

Control how a row fetched from this Cursor is represented. If None, a row is represented as a tuple. Can be set to the included sqlite3.Row; or a callable that accepts two arguments, a Cursor object and the tuple of row values, and returns a custom object representing an SQLite row.

Defaults to what Connection.row_factory was set to when the Cursor was created. Assigning to this attribute does not affect Connection.row_factory of the parent connection.

See How to create and use row factories for more details.

Row objects

class sqlite3.Row

A Row instance serves as a highly optimized row_factory for Connection objects. It supports iteration, equality testing, len(), and mapping access by column name and index.

Two Row objects compare equal if they have identical column names and values.

See How to create and use row factories for more details.

keys()

Return a list of column names as strings. Immediately after a query, it is the first member of each tuple in Cursor.description.

Modifié dans la version 3.5: Added support of slicing.

PrepareProtocol objects

class sqlite3.PrepareProtocol

The PrepareProtocol type's single purpose is to act as a PEP 246 style adaption protocol for objects that can adapt themselves to native SQLite types.

Exceptions

The exception hierarchy is defined by the DB-API 2.0 (PEP 249).

exception sqlite3.Warning

This exception is raised by sqlite3 if an SQL query is not a string, or if multiple statements are passed to execute() or executemany(). Warning is a subclass of Exception.

exception sqlite3.Error

The base class of the other exceptions in this module. Use this to catch all errors with one single except statement. Error is a subclass of Exception.

exception sqlite3.InterfaceError

This exception is raised by sqlite3 for fetch across rollback, or if sqlite3 is unable to bind parameters. InterfaceError is a subclass of Error.

exception sqlite3.DatabaseError

Exception raised for errors that are related to the database. This serves as the base exception for several types of database errors. It is only raised implicitly through the specialised subclasses. DatabaseError is a subclass of Error.

exception sqlite3.DataError

Exception raised for errors caused by problems with the processed data, like numeric values out of range, and strings which are too long. DataError is a subclass of DatabaseError.

exception sqlite3.OperationalError

Exception raised for errors that are related to the database's operation, and not necessarily under the control of the programmer. For example, the database path is not found, or a transaction could not be processed. OperationalError is a subclass of DatabaseError.

exception sqlite3.IntegrityError

Exception raised when the relational integrity of the database is affected, e.g. a foreign key check fails. It is a subclass of DatabaseError.

exception sqlite3.InternalError

Exception raised when SQLite encounters an internal error. If this is raised, it may indicate that there is a problem with the runtime SQLite library. InternalError is a subclass of DatabaseError.

exception sqlite3.ProgrammingError

Exception raised for sqlite3 API programming errors, for example trying to operate on a closed Connection, or trying to execute non-DML statements with executemany(). ProgrammingError is a subclass of DatabaseError.

exception sqlite3.NotSupportedError

Exception raised in case a method or database API is not supported by the underlying SQLite library. For example, setting deterministic to True in create_function(), if the underlying SQLite library does not support deterministic functions. NotSupportedError is a subclass of DatabaseError.

SQLite and Python types

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

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

Type Python

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

Type Python

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 an SQLite database via object adapters, and you can let the sqlite3 module convert SQLite types to Python types via converters.

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]))

con.close()

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.

Note

The default "timestamp" converter ignores UTC offsets in the database and always returns a naive datetime.datetime object. To preserve UTC offsets in timestamps, either leave converters disabled, or register an offset-aware converter with register_converter().

How-to guides

How to use placeholders to bind values in SQL queries

SQL operations usually need to use values from Python variables. However, beware of using Python's string operations to assemble queries, as they are vulnerable to SQL injection attacks. For example, an attacker can simply close the single quote and inject OR TRUE to select all rows:

>>> # Never do this -- insecure!
>>> symbol = input()
' OR TRUE; --
>>> sql = "SELECT * FROM stocks WHERE symbol = '%s'" % symbol
>>> print(sql)
SELECT * FROM stocks WHERE symbol = '' OR TRUE; --'
>>> cur.execute(sql)

Instead, use the DB-API's parameter substitution. To insert a variable into a query string, use a placeholder in the string, and substitute the actual values into the query by providing them as a tuple of values to the second argument of the cursor's execute() method.

An SQL statement may use one of two kinds of placeholders: question marks (qmark style) or named placeholders (named style). For the qmark style, parameters must be a sequence whose length must match the number of placeholders, or a ProgrammingError is raised. For the named style, parameters should be an instance of a dict (or a subclass), which must contain keys for all named parameters; any extra items are ignored. Here's an example of both styles:

con = sqlite3.connect(":memory:")
cur = con.execute("CREATE TABLE lang(name, first_appeared)")

# This is the named style used with executemany():
data = (
    {"name": "C", "year": 1972},
    {"name": "Fortran", "year": 1957},
    {"name": "Python", "year": 1991},
    {"name": "Go", "year": 2009},
)
cur.executemany("INSERT INTO lang VALUES(:name, :year)", data)

# This is the qmark style used in a SELECT query:
params = (1972,)
cur.execute("SELECT * FROM lang WHERE first_appeared = ?", params)
print(cur.fetchall())

Note

PEP 249 numeric placeholders are not supported. If used, they will be interpreted as named placeholders.

How to adapt custom Python types to SQLite values

SQLite supports only a limited set of data types natively. To store custom Python types in SQLite databases, adapt them to one of the Python types SQLite natively understands.

There are two ways to adapt Python objects to SQLite types: letting your object adapt itself, or using an adapter callable. The latter will take precedence above the former. For a library that exports a custom type, it may make sense to enable that type to adapt itself. As an application developer, it may make more sense to take direct control by registering custom adapter functions.

How to write adaptable objects

Suppose we have a Point class that represents a pair of coordinates, x and y, in a Cartesian coordinate system. The coordinate pair will be stored as a text string in the database, using a semicolon to separate the coordinates. This can be implemented by adding a __conform__(self, protocol) method which returns the adapted value. The object passed to protocol will be of type PrepareProtocol.

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

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

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

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

How to register adapter callables

The other possibility is to create a function that converts the Python object to an SQLite-compatible type. This function can then be registered using register_adapter().

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

def adapt_point(point):
    return f"{point.x};{point.y}"

sqlite3.register_adapter(Point, adapt_point)

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

cur.execute("SELECT ?", (Point(1.0, 2.5),))
print(cur.fetchone()[0])

How to convert SQLite values to custom Python types

Writing an adapter lets you convert from custom Python types to SQLite values. To be able to convert from SQLite values to custom Python types, we use 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 are always passed a bytes object, no matter the underlying SQLite data type.

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

We now need to tell sqlite3 when it should convert a given SQLite value. This is done when connecting to a database, using the detect_types parameter of connect(). There are three options:

  • Implicit: set detect_types to PARSE_DECLTYPES

  • Explicit: set detect_types to PARSE_COLNAMES

  • Both: set detect_types to sqlite3.PARSE_DECLTYPES | sqlite3.PARSE_COLNAMES. Column names take precedence over declared types.

The following example illustrates the implicit and explicit approaches:

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

    def __repr__(self):
        return f"Point({self.x}, {self.y})"

def adapt_point(point):
    return f"{point.x};{point.y}"

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

# Register the adapter and converter
sqlite3.register_adapter(Point, adapt_point)
sqlite3.register_converter("point", convert_point)

# 1) Parse using declared types
p = Point(4.0, -3.2)
con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_DECLTYPES)
cur = con.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()

# 2) Parse using column names
con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_COLNAMES)
cur = con.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])

Adapter and converter recipes

This section shows recipes for common adapters and converters.

import datetime
import sqlite3

def adapt_date_iso(val):
    """Adapt datetime.date to ISO 8601 date."""
    return val.isoformat()

def adapt_datetime_iso(val):
    """Adapt datetime.datetime to timezone-naive ISO 8601 date."""
    return val.isoformat()

def adapt_datetime_epoch(val):
    """Adapt datetime.datetime to Unix timestamp."""
    return int(val.timestamp())

sqlite3.register_adapter(datetime.date, adapt_date_iso)
sqlite3.register_adapter(datetime.datetime, adapt_datetime_iso)
sqlite3.register_adapter(datetime.datetime, adapt_datetime_epoch)

def convert_date(val):
    """Convert ISO 8601 date to datetime.date object."""
    return datetime.date.fromisoformat(val.decode())

def convert_datetime(val):
    """Convert ISO 8601 datetime to datetime.datetime object."""
    return datetime.datetime.fromisoformat(val.decode())

def convert_timestamp(val):
    """Convert Unix epoch timestamp to datetime.datetime object."""
    return datetime.datetime.fromtimestamp(int(val))

sqlite3.register_converter("date", convert_date)
sqlite3.register_converter("datetime", convert_datetime)
sqlite3.register_converter("timestamp", convert_timestamp)

How to use connection shortcut methods

Using the execute(), executemany(), and executescript() methods of the Connection class, 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.

# Create and fill the table.
con = sqlite3.connect(":memory:")
con.execute("CREATE TABLE lang(name, first_appeared)")
data = [
    ("C++", 1985),
    ("Objective-C", 1984),
]
con.executemany("INSERT INTO lang(name, first_appeared) VALUES(?, ?)", data)

# Print the table contents
for row in con.execute("SELECT name, first_appeared FROM lang"):
    print(row)

print("I just deleted", con.execute("DELETE FROM lang").rowcount, "rows")

# close() is not a shortcut method and it's not called automatically;
# the connection object should be closed manually
con.close()

How to use the connection context manager

A Connection object can be used as a context manager that automatically commits or rolls back open transactions when leaving the body of the context manager. If the body of the with statement finishes without exceptions, the transaction is committed. If this commit fails, or if the body of the with statement raises an uncaught exception, the transaction is rolled back.

If there is no open transaction upon leaving the body of the with statement, the context manager is a no-op.

Note

The context manager neither implicitly opens a new transaction nor closes the connection.

con = sqlite3.connect(":memory:")
con.execute("CREATE TABLE lang(id INTEGER PRIMARY KEY, name VARCHAR UNIQUE)")

# Successful, con.commit() is called automatically afterwards
with con:
    con.execute("INSERT INTO lang(name) VALUES(?)", ("Python",))

# 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 lang(name) VALUES(?)", ("Python",))
except sqlite3.IntegrityError:
    print("couldn't add Python twice")

# Connection object used as context manager only commits or rollbacks transactions,
# so the connection object should be closed manually
con.close()

How to work with SQLite URIs

Some useful URI tricks include:

  • Open a database in read-only mode:

>>> con = sqlite3.connect("file:tutorial.db?mode=ro", uri=True)
>>> con.execute("CREATE TABLE readonly(data)")
Traceback (most recent call last):
OperationalError: attempt to write a readonly database

  • Do not implicitly create a new database file if it does not already exist; will raise OperationalError if unable to create a new file:

>>> con = sqlite3.connect("file:nosuchdb.db?mode=rw", uri=True)
Traceback (most recent call last):
OperationalError: unable to open database file

  • Create a shared named in-memory database:

db = "file:mem1?mode=memory&cache=shared"
con1 = sqlite3.connect(db, uri=True)
con2 = sqlite3.connect(db, uri=True)
with con1:
    con1.execute("CREATE TABLE shared(data)")
    con1.execute("INSERT INTO shared VALUES(28)")
res = con2.execute("SELECT data FROM shared")
assert res.fetchone() == (28,)

More information about this feature, including a list of parameters, can be found in the SQLite URI documentation.

How to create and use row factories

By default, sqlite3 represents each row as a tuple. If a tuple does not suit your needs, you can use the sqlite3.Row class or a custom row_factory.

While row_factory exists as an attribute both on the Cursor and the Connection, it is recommended to set Connection.row_factory, so all cursors created from the connection will use the same row factory.

Row provides indexed and case-insensitive named access to columns, with minimal memory overhead and performance impact over a tuple. To use Row as a row factory, assign it to the row_factory attribute:

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

Queries now return Row objects:

>>> res = con.execute("SELECT 'Earth' AS name, 6378 AS radius")
>>> row = res.fetchone()
>>> row.keys()
['name', 'radius']
>>> row[0]         # Access by index.
'Earth'
>>> row["name"]    # Access by name.
'Earth'
>>> row["RADIUS"]  # Column names are case-insensitive.
6378

You can create a custom row_factory that returns each row as a dict, with column names mapped to values:

def dict_factory(cursor, row):
    fields = [column[0] for column in cursor.description]
    return {key: value for key, value in zip(fields, row)}

Using it, queries now return a dict instead of a tuple:

>>> con = sqlite3.connect(":memory:")
>>> con.row_factory = dict_factory
>>> for row in con.execute("SELECT 1 AS a, 2 AS b"):
...     print(row)
{'a': 1, 'b': 2}

The following row factory returns a named tuple:

from collections import namedtuple

def namedtuple_factory(cursor, row):
    fields = [column[0] for column in cursor.description]
    cls = namedtuple("Row", fields)
    return cls._make(row)

namedtuple_factory() can be used as follows:

>>> con = sqlite3.connect(":memory:")
>>> con.row_factory = namedtuple_factory
>>> cur = con.execute("SELECT 1 AS a, 2 AS b")
>>> row = cur.fetchone()
>>> row
Row(a=1, b=2)
>>> row[0]  # Indexed access.
1
>>> row.b   # Attribute access.
2

With some adjustments, the above recipe can be adapted to use a dataclass, or any other custom class, instead of a namedtuple.

Explication

Transaction control

The sqlite3 module does not adhere to the transaction handling recommended by PEP 249.

If the connection attribute isolation_level is not None, new transactions are implicitly opened before execute() and executemany() executes INSERT, UPDATE, DELETE, or REPLACE statements; for other statements, no implicit transaction handling is performed. Use the commit() and rollback() methods to respectively commit and roll back pending transactions. You can choose the underlying SQLite transaction behaviour — that is, whether and what type of BEGIN statements sqlite3 implicitly executes – via the isolation_level attribute.

If isolation_level is set to None, no transactions are implicitly opened at all. This leaves the underlying SQLite library in autocommit mode, but also allows the user to perform their own transaction handling using explicit SQL statements. The underlying SQLite library autocommit mode can be queried using the in_transaction attribute.

The executescript() method implicitly commits any pending transaction before execution of the given SQL script, regardless of the value of isolation_level.

Modifié dans la version 3.6: sqlite3 used to implicitly commit an open transaction before DDL statements. This is no longer the case.