sqlite3 — DB-API 2.0 interface for SQLite databases

xinzheng新政2024-07-11 11:13

sqlite3 --- DB-API 2.0 interface for SQLite databases --- Python 3.12.4 documentation

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

sqlite3 --- SQLite数据库的DB-API 2.0接口

Source code: Lib/sqlite3/ 源代码位置：Lib/sqlite3/

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.

SQLite是一个C语言库，它提供了一个轻量级的基于磁盘的数据库，这个数据库不需要单独的服务器进程，并且允许使用SQL查询语言的一个非标准变体来访问数据库。一些应用程序可以使用SQLite进行内部数据存储。此外，还可以使用SQLite来原型化一个应用程序，然后再将代码迁移到如PostgreSQL或Oracle等大型数据库上。

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.
sqlite3模块由Gerhard Häring编写。它提供了一个符合PEP 249描述的DB-API 2.0规范的SQL接口，并且要求SQLite版本为3.7.15或更高。

This document includes four main sections: 本文档主要包括以下四个主要部分

  • Tutorial teaches how to use the sqlite3 module. 教程部分教授如何使用sqlite3模块。

  • Reference describes the classes and functions this module defines.
    参考部分描述了该模块定义的类和函数。

  • How-to guides details how to handle specific tasks.
    操作指南部分详细说明了如何处理特定任务。

  • Explanation provides in-depth background on transaction control.
    解释部分深入介绍了事务控制的背景知识。

See also 另请参阅：

https://www.sqlite.org

The SQLite web page; the documentation describes the syntax and the available data types for the supported SQL dialect.
SQLite的网页；其文档描述了支持的SQL方言的语法和可用的数据类型。

SQL Tutorial SQL教程

Tutorial, reference and examples for learning SQL syntax.
学习SQL语法的教程、参考和示例。

PEP 249 - Database API Specification 2.0 PEP 249 - 数据库API规范2.0

PEP written by Marc-André Lemburg. 由Marc-André Lemburg编写的PEP。

Tutorial 教程

In this tutorial, you will create a database of Monty Python movies using basic sqlite3 functionality. It assumes a fundamental understanding of database concepts, including cursors and transactions.

在本教程中，您将使用基本的sqlite3功能来创建一个关于Monty Python电影的数据库。本教程假设您已经具备数据库概念的基础知识，包括游标（cursors）和事务（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:

首先，我们需要创建一个新的数据库并打开一个数据库连接，以便sqlite3能够与其进行交互。调用sqlite3.connect()来创建到当前工作目录中tutorial.db数据库的连接，如果该数据库不存在，则隐式创建它：

python 复制代码 
import sqlite3
con = sqlite3.connect("tutorial.db")

The returned Connection object con represents the connection to the on-disk database.

返回的Connection对象（在此例中命名为con）表示到磁盘上数据库的连接。

In order to execute SQL statements and fetch results from SQL queries, we will need to use a database cursor. Call con.cursor() to create the Cursor:

为了执行SQL语句并从SQL查询中获取结果，我们需要使用数据库游标。调用con.cursor()来创建Cursor：

python 复制代码 
cur = con.cursor()

Now that we've got a database connection and a cursor, we can create a database table movie with columns for title, release year, and review score. For simplicity, we can just use column names in the table declaration -- thanks to the flexible typing feature of SQLite, specifying the data types is optional. Execute the CREATE TABLE statement by calling cur.execute(...):

现在我们已经有了数据库连接和游标，我们可以创建一个名为movie的数据库表，其中包含标题、发行年份和评论分数等列。为了简化操作，我们可以在表声明中直接使用列名------由于SQLite的灵活类型特性，指定数据类型是可选的。通过调用cur.execute(...)来执行CREATE TABLE语句：

python 复制代码 
cur.execute("CREATE TABLE movie(title, year, score)")

We can verify that the new table has been created by querying the sqlite_master table built-in to SQLite, which should now contain an entry for the movie table definition (see The Schema Table for details). Execute that query by calling cur.execute(...), assign the result to res, and call res.fetchone() to fetch the resulting row:

我们可以通过查询SQLite内置的sqlite_master表来验证新表是否已创建，该表现在应该包含movie表定义的条目（请参阅模式表以获取详细信息）。通过调用cur.execute(...)来执行该查询，将结果分配给res，并调用res.fetchone()来获取结果行：

>>>

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

We can see that the table has been created, as the query returns a tuple containing the table's name. If we query sqlite_master for a non-existent table spam, res.fetchone() will return None:

我们可以看到表已经创建成功，因为查询返回了一个包含表名的元组。如果我们查询一个不存在的表（比如spam）的sqlite_master，那么res.fetchone()将返回None。

>>>

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

Now, add two rows of data supplied as SQL literals by executing an INSERT statement, once again by calling cur.execute(...):

现在，通过执行INSERT语句（再次调用cur.execute(...)）来添加两行数据，这些数据作为SQL字面量提供：

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

The INSERT statement implicitly opens a transaction, which needs to be committed before changes are saved in the database (see Transaction control for details). Call con.commit() on the connection object to commit the transaction:

INSERT语句隐式地开启了一个事务，该事务在更改保存到数据库之前需要被提交（请参阅事务控制以获取详细信息）。在连接对象上调用con.commit()来提交事务：

python 复制代码 
con.commit()

We can verify that the data was inserted correctly by executing a SELECT query. Use the now-familiar cur.execute(...) to assign the result to res, and call res.fetchall() to return all resulting rows:

我们可以通过执行一个SELECT查询来验证数据是否正确插入。使用现在熟悉的cur.execute(...)将结果分配给res，并调用res.fetchall()来返回所有结果行：

>>>

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

The result is a list of two tuples, one per row, each containing that row's score value.

结果是一个包含两个元组的列表，每个元组对应一行，每个元组包含该行的score值。

Now, insert three more rows by calling cur.executemany(...):

现在，通过调用cur.executemany(...)来插入另外三行数据：

python 复制代码 
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.

Notice that ? placeholders are used to bind data to the query. Always use placeholders instead of string formatting to bind Python values to SQL statements, to avoid SQL injection attacks (see How to use placeholders to bind values in SQL queries for more details).

请注意，这里使用了?占位符来将数据绑定到查询中。始终使用占位符而不是字符串格式化来将Python值绑定到SQL语句中，以避免SQL注入攻击（请参阅"如何在SQL查询中使用占位符绑定值"以获取更多详细信息）。

We can verify that the new rows were inserted by executing a SELECT query, this time iterating over the results of the query:

我们可以通过执行SELECT查询来验证新行是否已插入，这次我们将遍历查询结果：

>>>

python 复制代码 
>>> 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")

Each row is a two-item tuple of (year, title), matching the columns selected in the query.

每个行都是一个包含两个元素的元组（year, title），这与查询中选择的列相匹配。

Finally, verify that the database has been written to disk by calling con.close() to close the existing connection, opening a new one, creating a new cursor, then querying the database:

最后，通过调用con.close()来关闭现有的数据库连接，确保数据库已经被写入磁盘。然后，打开一个新的连接，创建一个新的游标，并查询数据库以验证数据是否已经成功写入。

>>>

python 复制代码 
>>> 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
>>> new_con.close()

You've now created an SQLite database using the sqlite3 module, inserted data and retrieved values from it in multiple ways.

你现在已经使用sqlite3模块创建了一个SQLite数据库，并以多种方式插入了数据和从中检索了值。

See also 另请参阅

Reference 参数说明：

Module functions

python 复制代码 
sqlite3.connect(database, timeout=5.0, detect_types=0, isolation_level='DEFERRED', check_same_thread=True, factory=sqlite3.Connection, cached_statements=128, uri=False, *, autocommit=sqlite3.LEGACY_TRANSACTION_CONTROL)

Open a connection to an SQLite database. 这个函数用于打开一个到SQLite数据库的连接。

Parameters:

  • database (path-like object) -- The path to the database file to be opened. You can pass ":memory:" to create an SQLite database existing only in memory, and open a connection to it.
    database : 必需的参数，表示要连接的数据库文件的名称。如果文件不存在，将会创建一个新的数据库文件。如果参数以特殊的前缀（如sqlite://）开始，并且uri参数为True，则它会被当作一个URI来处理。

  • 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.
    timeout: 可选参数，默认为5.0秒。这个参数设置了数据库操作的超时时间（秒）。如果操作在这个时间内没有完成，将会抛出一个异常。

  • detect_types (int) -- Control whether and how data types not natively supported by SQLite are looked up to be converted to Python types, using the converters registered with register_converter(). Set it to any combination (using |, bitwise or) of PARSE_DECLTYPES and PARSE_COLNAMES to enable this. Column names takes precedence over declared types if both flags are set. Types cannot be detected for generated fields (for example max(data)), even when the detect_types parameter is set; str will be returned instead. By default (0), type detection is disabled.
    detect_types : 可选参数，默认为0。这个参数控制SQLite和Python之间的类型检测。它可以被设置为sqlite3.PARSE_DECLTYPES（用于解析列声明类型）、sqlite3.PARSE_COLNAMES（用于解析列名中的类型提示）或它们的组合（使用按位或操作符|）。

  • isolation_level (str| None ) -- Control legacy transaction handling behaviour. See Connection.isolation_level and Transaction control via the isolation_level attribute for more information. Can be "DEFERRED" (default), "EXCLUSIVE" or "IMMEDIATE"; or None to disable opening transactions implicitly. Has no effect unless Connection.autocommit is set to LEGACY_TRANSACTION_CONTROL (the default).
    isolation_level : 可选参数，默认为'DEFERRED'。这个参数设置数据库连接的事务隔离级别。有效的值包括'DEFERRED'、'IMMEDIATE'、'EXCLUSIVE'和'SERIALIZABLE'。

  • check_same_thread (bool) -- If True (default), ProgrammingError will be raised if the database connection is used by a thread other than the one that created it. If False, the connection may be accessed in multiple threads; write operations may need to be serialized by the user to avoid data corruption. See threadsafety for more information.
    check_same_thread : 可选参数，默认为True。如果为True，则确保连接对象不会在创建它的线程之外的线程中使用。这有助于避免多线程环境下潜在的竞争条件。

  • factory (Connection) -- A custom subclass of Connection to create the connection with, if not the default Connection class.
    factory : 可选参数，默认为sqlite3.Connection。这个参数允许你指定一个工厂函数，该函数将用于创建连接对象。这可以用于创建连接对象的子类实例。

  • cached_statements (int) -- The number of statements that sqlite3 should internally cache for this connection, to avoid parsing overhead. By default, 128 statements.
    cached_statements: 可选参数，默认为128。这个参数控制SQLite预编译语句（prepared statements）的缓存大小。增加这个值可以提高性能，尤其是在执行大量相同语句时，但也会增加内存使用。

  • uri (bool) -- If set to True, database is interpreted as a URI with a file path and an optional query string. The scheme part must be "file:", and the path can be relative or absolute. The query string allows passing parameters to SQLite, enabling various How to work with SQLite URIs.
    uri : 可选参数，默认为False。如果为True，则允许database参数以URI格式指定，这允许更复杂的数据库名称和选项。

  • autocommit (bool) -- Control PEP 249 transaction handling behaviour. See Connection.autocommit and Transaction control via the autocommit attribute for more information. autocommit currently defaults to LEGACY_TRANSACTION_CONTROL. The default will change to False in a future Python release.
    autocommit : 可选参数，默认为sqlite3.LEGACY_TRANSACTION_CONTROL。这个参数控制自动提交的行为。在Python 3.7及更高版本中，它允许你更精细地控制事务的自动提交行为。设置为True会使每个单独的SQL语句都在其自己的事务中执行，而无需显式调用commit()。但是，请注意，这个参数的位置是关键字参数（使用*作为分隔符），意味着它必须显式地以关键字形式传递。

请注意，*参数在函数定义中用作位置参数和关键字参数之间的分隔符，表示autocommit及之后的所有参数都必须是关键字参数。

Return type: 返回类型：

Connection

Raises an auditing event sqlite3.connect with argument database.
sqlite3.connect：当使用database参数连接数据库时抛出。

Raises an auditing event sqlite3.connect/handle with argument connection_handle.
sqlite3.connect/handle：当连接句柄（connection_handle）被创建时抛出。

Changed in version 3.4: Added the uri parameter.
在3.4版本中：添加了uri参数，允许使用URI格式指定数据库文件。

Changed in version 3.7: database can now also be a path-like object, not only a string.
在3.7版本中：database参数现在可以是一个类路径对象（path-like object），而不仅仅是字符串。

Changed in version 3.10: Added the sqlite3.connect/handle auditing event.
在3.10版本中：添加了sqlite3.connect/handle审计事件，该事件在连接句柄被创建时触发。

Changed in version 3.12: Added the autocommit parameter.
在3.12版本中：添加了autocommit参数，允许更精细地控制事务的自动提交行为。

sqlite3.complete_statement(statement )

Return True if the string statement appears to contain one or more complete SQL statements. No syntactic verification or parsing of any kind is performed, other than checking that there are no unclosed string literals and the statement is terminated by a semicolon.

如果字符串statement看起来包含一个或多个完整的SQL语句，则返回True。除了检查没有未闭合的字符串字面量和语句以分号结束外，不进行任何语法验证或解析。

For example: 示例

>>>

python 复制代码 
>>> sqlite3.complete_statement("SELECT foo FROM bar;")
True
>>> sqlite3.complete_statement("SELECT foo")
False

This function may be useful during command-line input to determine if the entered text seems to form a complete SQL statement, or if additional input is needed before calling execute().

这个函数可能在命令行输入时非常有用，用于确定输入的文本是否构成了一个完整的SQL语句，或者在调用execute()之前是否需要额外的输入。这在处理用户输入的SQL语句时特别有用，可以确保在执行之前语句是完整和有效的。

See runsource() in Lib/sqlite3/main.py for real-world use.

在实际应用中，可以参考Lib/sqlite3/__main__.py中的runsource()函数来了解其用法。

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.

启用或禁用回调跟踪。默认情况下，在用户定义的函数、聚合函数、转换器、授权回调等中，你不会获得任何跟踪信息（traceback）。如果你想要调试它们，可以调用这个函数并将flag设置为True。之后，你将能够在sys.stderr上获得来自回调的跟踪信息。使用False来再次禁用此功能。

Note

Errors in user-defined function callbacks are logged as unraisable exceptions. Use an unraisable hook handler for introspection of the failed callback.

用户定义的函数回调中的错误会被记录为无法捕获的异常。使用**无法捕获的钩子处理器（unraisable hook handler）**来对失败的回调进行内省（introspection）。

这意味着，当在SQLite的用户定义函数（如聚合函数、标量函数等）的回调中发生错误时，这些错误不会像普通的Python异常那样被抛出并可以由try-except块捕获。相反，它们会被SQLite或Python的sqlite3模块捕获，并以某种方式记录（通常是写入日志或标准错误输出），但不会中断程序的执行（除非错误非常严重）。

为了检查和调试这些无法捕获的异常，你可以设置一个"无法捕获的钩子处理器"。这个处理器是一个函数，当发生无法捕获的异常时，Python会调用这个函数，并将异常信息作为参数传递给它。这样，你就可以在处理器函数中编写代码来记录或检查这些异常，从而帮助诊断问题。

请注意，具体的实现方式可能会根据Python的版本和sqlite3模块的实现细节而有所不同。因此，建议查阅最新的Python文档或sqlite3模块的文档来了解如何设置和使用无法捕获的钩子处理器。

sqlite3.register_adapter(type , adapter , / )

Register an adapter callable to adapt the Python type type into an SQLite type. The adapter is called with a Python object of type type as its sole argument, and must return a value of a type that SQLite natively understands.

注册一个**适配器（adapter）**可调用对象，用于将Python的type类型适配为SQLite能够原生理解的类型。这个适配器函数以Python中的一个type类型的对象作为唯一参数被调用，并且必须返回一个SQLite原生支持的类型的值。

需要注意的是，这里的"type类型"可能有些误导，因为通常我们不会将Python的内置type对象（即类型本身）直接存储到数据库中。更常见的是，我们想要将Python中的对象（这些对象可能是自定义类型的实例）适配为SQLite可以存储的格式。然而，按照这句话的字面意思，如果你确实需要处理type对象本身（尽管这在实践中很少见），你需要编写一个适配器来将其转换为SQLite可以存储的某种形式，比如一个字符串，该字符串表示了类型的名称。
但是，更常见的用例是为自定义的Python类型或内置类型（如datetime、decimal.Decimal等）编写适配器，以便它们可以被SQLite数据库正确地存储和检索。

例如，如果你有一个自定义的Python类MyClass，并且想要将其实例存储在SQLite数据库中，你可以编写一个适配器来将这个类的实例转换为一个可以被SQLite存储的字符串（或者其他类型），然后编写一个转换器（converter）来将这个字符串转换回MyClass的实例。

然而，对于这个问题，如果你只是想要处理Python的type对象（即类型的元数据），你可能会编写一个适配器来返回类型的名称（作为字符串），但这通常不是将对象存储到数据库中的常见做法。

sqlite3.register_converter(typename , converter , / )

Register the converter callable to convert SQLite objects of type typename into a Python object of a specific type. The converter is invoked for all SQLite values of type typename ; it is passed a bytes object and should return an object of the desired Python type. Consult the parameter detect_types of connect() for information regarding how type detection works.

注册一个**转换器（converter）**可调用对象，用于将SQLite中类型为typename的对象转换为特定类型的Python对象。对于所有类型为typename的SQLite值，都会调用这个转换器；它接收一个bytes对象作为参数，并应该返回一个所需Python类型的对象。关于类型检测的工作原理，请参考connect()函数的detect_types参数。

Note: typename and the name of the type in your query are matched case-insensitively.
注意 ：typename和查询中类型的名称在进行匹配时是不区分大小写的。

Module constants

sqlite3.LEGACY_TRANSACTION_CONTROL

Set autocommit to this constant to select old style (pre-Python 3.12) transaction control behaviour. See Transaction control via the isolation_level attribute for more information.

将autocommit设置为这个常量以选择旧式（Python 3.12之前）的事务控制行为。更多信息请参考"通过isolation_level属性控制事务"。

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 ([]).

将此标志值传递给connect()函数的detect_types参数，以便通过查询列名中解析出的类型名称（作为转换器字典的键）来查找转换器函数。类型名称必须用方括号([])括起来。

python 复制代码 
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.

这个标志可以使用|（按位或）运算符与PARSE_DECLTYPES结合使用。

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:

python 复制代码 
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 2.0, stating the level of thread safety the sqlite3 module supports. This attribute is set based on the default threading mode the underlying SQLite library is compiled with. The SQLite threading modes are:

  1. Single-thread: In this mode, all mutexes are disabled and SQLite is unsafe to use in more than a single thread at once.

  2. Multi-thread: In this mode, SQLite can be safely used by multiple threads provided that no single database connection is used simultaneously in two or more threads.

  3. Serialized: In serialized mode, SQLite can be safely used by multiple threads with no restriction.

The mappings from SQLite threading modes to DB-API 2.0 threadsafety levels are as follows:

SQLite threading mode threadsafety SQLITE_THREADSAFE DB-API 2.0 meaning
single-thread 0 0 Threads may not share the module
multi-thread 1 2 Threads may share the module, but not connections
serialized 3 1 Threads may share the module, connections and cursors

Changed in version 3.11: Set threadsafety dynamically instead of hard-coding it to 1.

sqlite3.version

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

Deprecated since version 3.12, will be removed in version 3.14: This constant used to reflect the version number of the pysqlite package, a third-party library which used to upstream changes to sqlite3. Today, it carries no meaning or practical value.

sqlite3.version_info

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

Deprecated since version 3.12, will be removed in version 3.14: This constant used to reflect the version number of the pysqlite package, a third-party library which used to upstream changes to sqlite3. Today, it carries no meaning or practical value.

sqlite3.SQLITE_DBCONFIG_DEFENSIVE

sqlite3.SQLITE_DBCONFIG_DQS_DDL

sqlite3.SQLITE_DBCONFIG_DQS_DML

sqlite3.SQLITE_DBCONFIG_ENABLE_FKEY

sqlite3.SQLITE_DBCONFIG_ENABLE_FTS3_TOKENIZER

sqlite3.SQLITE_DBCONFIG_ENABLE_LOAD_EXTENSION

sqlite3.SQLITE_DBCONFIG_ENABLE_QPSG

sqlite3.SQLITE_DBCONFIG_ENABLE_TRIGGER

sqlite3.SQLITE_DBCONFIG_ENABLE_VIEW

sqlite3.SQLITE_DBCONFIG_LEGACY_ALTER_TABLE

sqlite3.SQLITE_DBCONFIG_LEGACY_FILE_FORMAT

sqlite3.SQLITE_DBCONFIG_NO_CKPT_ON_CLOSE

sqlite3.SQLITE_DBCONFIG_RESET_DATABASE

sqlite3.SQLITE_DBCONFIG_TRIGGER_EQP

sqlite3.SQLITE_DBCONFIG_TRUSTED_SCHEMA

sqlite3.SQLITE_DBCONFIG_WRITABLE_SCHEMA

These constants are used for the Connection.setconfig() and getconfig() methods.

The availability of these constants varies depending on the version of SQLite Python was compiled with.

Added in version 3.12.

See also

Database Connection Configuration Options

SQLite docs: Database Connection Configuration Options

Connection objects

classsqlite3.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.

See also

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.

blobopen(table , column , row , / , * , readonly=False , name='main')

Open a Blob handle to an existing BLOB.

Parameters:

  • table (str) -- The name of the table where the blob is located.

  • column (str) -- The name of the column where the blob is located.

  • row (str) -- The name of the row where the blob is located.

  • readonly (bool) -- Set to True if the blob should be opened without write permissions. Defaults to False.

  • name (str) -- The name of the database where the blob is located. Defaults to "main".

Raises:

OperationalError -- When trying to open a blob in a WITHOUT ROWID table.

Return type:

Blob

Note

The blob size cannot be changed using the Blob class. Use the SQL function zeroblob to create a blob with a fixed size.

Added in version 3.11.

commit()

Commit any pending transaction to the database. If autocommit is True, or there is no open transaction, this method does nothing. If autocommit is False, a new transaction is implicitly opened if a pending transaction was committed by this method.

rollback()

Roll back to the start of any pending transaction. If autocommit is True, or there is no open transaction, this method does nothing. If autocommit is False, a new transaction is implicitly opened if a pending transaction was rolled back by this method.

close()

Close the database connection. If autocommit is False, any pending transaction is implicitly rolled back. If autocommit is True or LEGACY_TRANSACTION_CONTROL, no implicit transaction control is executed. 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.

Parameters:

  • 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.

Raises:

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

Changed in version 3.8: Added the deterministic parameter.

Example:

>>>

复制代码 
>>> 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',)
>>> con.close()

create_aggregate(name , n_arg , aggregate_class)

Create or remove a user-defined SQL aggregate function.

Parameters:

  • 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.

Example:

复制代码 
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_window_function(name , num_params , aggregate_class , /)

Create or remove a user-defined aggregate window function.

Parameters:

  • name (str) -- The name of the SQL aggregate window function to create or remove.

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

  • aggregate_class (class | None) --

    A class that must implement the following methods:

    • step(): Add a row to the current window.

    • value(): Return the current value of the aggregate.

    • inverse(): Remove a row from the current window.

    • finalize(): Return the final result of the aggregate as a type natively supported by SQLite.

    The number of arguments that the step() and value() methods must accept is controlled by num_params.

    Set to None to remove an existing SQL aggregate window function.

Raises:

NotSupportedError -- If used with a version of SQLite older than 3.25.0, which does not support aggregate window functions.

Added in version 3.11.

Example:

复制代码 
# Example taken from https://www.sqlite.org/windowfunctions.html#udfwinfunc
class WindowSumInt:
    def __init__(self):
        self.count = 0

    def step(self, value):
        """Add a row to the current window."""
        self.count += value

    def value(self):
        """Return the current value of the aggregate."""
        return self.count

    def inverse(self, value):
        """Remove a row from the current window."""
        self.count -= value

    def finalize(self):
        """Return the final value of the aggregate.

        Any clean-up actions should be placed here.
        """
        return self.count


con = sqlite3.connect(":memory:")
cur = con.execute("CREATE TABLE test(x, y)")
values = [
    ("a", 4),
    ("b", 5),
    ("c", 3),
    ("d", 8),
    ("e", 1),
]
cur.executemany("INSERT INTO test VALUES(?, ?)", values)
con.create_window_function("sumint", 1, WindowSumInt)
cur.execute("""
    SELECT x, sumint(y) OVER (
        ORDER BY x ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING
    ) AS sum_y
    FROM test ORDER BY x
""")
print(cur.fetchall())
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:

复制代码 
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.

Changed in version 3.11: The collation name can contain any Unicode character. Earlier, only ASCII characters were allowed.

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.

Passing None as authorizer_callback will disable the authorizer.

Changed in version 3.11: Added support for disabling the authorizer using None.

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 a DatabaseError 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.

Added in 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.

Added in version 3.2.

Changed in version 3.10: Added the sqlite3.enable_load_extension auditing event.

复制代码 
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)

load_extension(path , / , * , entrypoint=None)

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

Parameters:

  • path (str) -- The path to the SQLite extension.

  • entrypoint (str| None ) -- Entry point name. If None (the default), SQLite will come up with an entry point name of its own; see the SQLite docs Loading an Extension for details.

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

Added in version 3.2.

Changed in version 3.10: Added the sqlite3.load_extension auditing event.

Changed in version 3.12: Added the entrypoint parameter.

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.

Example:

复制代码 
# 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()

See also

How to handle non-UTF-8 text encodings

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.

Parameters:

  • 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:

复制代码 
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:

复制代码 
src = sqlite3.connect('example.db')
dst = sqlite3.connect(':memory:')
src.backup(dst)
dst.close()
src.close()

Added in version 3.7.

See also

How to handle non-UTF-8 text encodings

getlimit(category , /)

Get a connection runtime limit.

Parameters:

category (int) -- The SQLite limit category to be queried.

Return type:

int

Raises:

ProgrammingError -- If category is not recognised by the underlying SQLite library.

Example, query the maximum length of an SQL statement for Connection con (the default is 1000000000):

>>>

复制代码 
>>> con.getlimit(sqlite3.SQLITE_LIMIT_SQL_LENGTH)
1000000000

Added in version 3.11.

setlimit(category , limit , /)

Set a connection runtime limit. Attempts to increase a limit above its hard upper bound are silently truncated to the hard upper bound. Regardless of whether or not the limit was changed, the prior value of the limit is returned.

Parameters:

  • category (int) -- The SQLite limit category to be set.

  • limit (int) -- The value of the new limit. If negative, the current limit is unchanged.

Return type:

int

Raises:

ProgrammingError -- If category is not recognised by the underlying SQLite library.

Example, limit the number of attached databases to 1 for Connection con (the default limit is 10):

>>>

复制代码 
>>> con.setlimit(sqlite3.SQLITE_LIMIT_ATTACHED, 1)
10
>>> con.getlimit(sqlite3.SQLITE_LIMIT_ATTACHED)
1

Added in version 3.11.

getconfig(op , /)

Query a boolean connection configuration option.

Parameters:

op (int) -- A SQLITE_DBCONFIG code.

Return type:

bool

Added in version 3.12.

setconfig(op , enable=True , /)

Set a boolean connection configuration option.

Parameters:

  • op (int) -- A SQLITE_DBCONFIG code.

  • enable (bool) -- True if the configuration option should be enabled (default); False if it should be disabled.

Added in version 3.12.

serialize(* , name='main')

Serialize a database into a bytes object. For an ordinary on-disk database file, the serialization is just a copy of the disk file. For an in-memory database or a "temp" database, the serialization is the same sequence of bytes which would be written to disk if that database were backed up to disk.

Parameters:

name (str) -- The database name to be serialized. Defaults to "main".

Return type:

bytes

Note

This method is only available if the underlying SQLite library has the serialize API.

Added in version 3.11.

deserialize(data , / , * , name='main')

Deserialize a serialized database into a Connection. This method causes the database connection to disconnect from database name , and reopen name as an in-memory database based on the serialization contained in data.

Parameters:

  • data (bytes) -- A serialized database.

  • name (str) -- The database name to deserialize into. Defaults to "main".

Raises:

Note

This method is only available if the underlying SQLite library has the deserialize API.

Added in version 3.11.

autocommit

This attribute controls PEP 249-compliant transaction behaviour. autocommit has three allowed values:

Changing autocommit to False will open a new transaction, and changing it to True will commit any pending transaction.

See Transaction control via the autocommit attribute for more details.

Note

The isolation_level attribute has no effect unless autocommit is LEGACY_TRANSACTION_CONTROL.

Added in version 3.12.

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.

Added in version 3.2.

isolation_level

Controls the legacy transaction handling mode of 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".

Note

Using autocommit to control transaction handling is recommended over using isolation_level. isolation_level has no effect unless autocommit is set to LEGACY_TRANSACTION_CONTROL (the default).

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.

See How to handle non-UTF-8 text encodings for more details.

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:

复制代码 
for row in cur.execute("SELECT t FROM data"):
    print(row)

classsqlite3.Cursor

A Cursor instance has the following attributes and methods.

execute(sql , parameters=() , /)

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

Parameters:

Raises:

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

If autocommit is LEGACY_TRANSACTION_CONTROL, 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.

Deprecated since version 3.12, will be removed in version 3.14: DeprecationWarning is emitted if named placeholders are used and parameters is a sequence instead of a dict. Starting with Python 3.14, ProgrammingError will be raised instead.

Use executescript() to execute multiple SQL statements.

executemany(sql , parameters , /)

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

Uses the same implicit transaction handling as execute().

Parameters:

Raises:

ProgrammingError -- If sql contains more than one SQL statement, or is not a DML statement.

Example:

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

Note

Any resulting rows are discarded, including DML statements with RETURNING clauses.

Deprecated since version 3.12, will be removed in version 3.14: DeprecationWarning is emitted if named placeholders are used and the items in parameters are sequences instead of dicts. Starting with Python 3.14, ProgrammingError will be raised instead.

executescript(sql_script , /)

Execute the SQL statements in sql_script . If the autocommit is LEGACY_TRANSACTION_CONTROL and 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.

Example:

复制代码 
# 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:

>>>

复制代码 
>>> con = sqlite3.connect(":memory:")
>>> cur = con.cursor()
>>> cur.connection == con
True
>>> con.close()

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.

Changed in 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, after the statement has run to completion. This means that any resulting rows must be fetched in order for rowcount to be updated.

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

classsqlite3.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.

Changed in version 3.5: Added support of slicing.

Blob objects

classsqlite3.Blob

Added in version 3.11.

A Blob instance is a file-like object that can read and write data in an SQLite BLOB. Call len(blob) to get the size (number of bytes) of the blob. Use indices and slices for direct access to the blob data.

Use the Blob as a context manager to ensure that the blob handle is closed after use.

复制代码 
con = sqlite3.connect(":memory:")
con.execute("CREATE TABLE test(blob_col blob)")
con.execute("INSERT INTO test(blob_col) VALUES(zeroblob(13))")

# Write to our blob, using two write operations:
with con.blobopen("test", "blob_col", 1) as blob:
    blob.write(b"hello, ")
    blob.write(b"world.")
    # Modify the first and last bytes of our blob
    blob[0] = ord("H")
    blob[-1] = ord("!")

# Read the contents of our blob
with con.blobopen("test", "blob_col", 1) as blob:
    greeting = blob.read()

print(greeting)  # outputs "b'Hello, world!'"
con.close()

close()

Close the blob.

The blob will be unusable from this point onward. An Error (or subclass) exception will be raised if any further operation is attempted with the blob.

read(length=-1 , /)

Read length bytes of data from the blob at the current offset position. If the end of the blob is reached, the data up to EOF will be returned. When length is not specified, or is negative, read() will read until the end of the blob.

write(data , /)

Write data to the blob at the current offset. This function cannot change the blob length. Writing beyond the end of the blob will raise ValueError.

tell()

Return the current access position of the blob.

seek(offset , origin=os.SEEK_SET , /)

Set the current access position of the blob to offset . The origin argument defaults to os.SEEK_SET (absolute blob positioning). Other values for origin are os.SEEK_CUR (seek relative to the current position) and os.SEEK_END (seek relative to the blob's end).

PrepareProtocol objects

classsqlite3.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).

exceptionsqlite3.Warning

This exception is not currently raised by the sqlite3 module, but may be raised by applications using sqlite3, for example if a user-defined function truncates data while inserting. Warning is a subclass of Exception.

exceptionsqlite3.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.

If the exception originated from within the SQLite library, the following two attributes are added to the exception:

sqlite_errorcode

The numeric error code from the SQLite API

Added in version 3.11.

sqlite_errorname

The symbolic name of the numeric error code from the SQLite API

Added in version 3.11.

exceptionsqlite3.InterfaceError

Exception raised for misuse of the low-level SQLite C API. In other words, if this exception is raised, it probably indicates a bug in the sqlite3 module. InterfaceError is a subclass of Error.

exceptionsqlite3.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.

exceptionsqlite3.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.

exceptionsqlite3.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.

exceptionsqlite3.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.

exceptionsqlite3.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.

exceptionsqlite3.ProgrammingError

Exception raised for sqlite3 API programming errors, for example supplying the wrong number of bindings to a query, or trying to operate on a closed Connection. ProgrammingError is a subclass of DatabaseError.

exceptionsqlite3.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:

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 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 (deprecated)

Note

The default adapters and converters are deprecated as of Python 3.12. Instead, use the Adapter and converter recipes and tailor them to your needs.

The deprecated default adapters and converters consist of:

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().

Deprecated since version 3.12.

Command-line interface

The sqlite3 module can be invoked as a script, using the interpreter's -m switch, in order to provide a simple SQLite shell. The argument signature is as follows:

复制代码 
python -m sqlite3 [-h] [-v] [filename] [sql]

Type .quit or CTRL-D to exit the shell.

-h, --help

Print CLI help.

-v, --version

Print underlying SQLite library version.

Added in version 3.12.

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:

>>>

复制代码 
>>> # 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 must 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:

复制代码 
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())
con.close()

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.

复制代码 
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])
con.close()
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().

复制代码 
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])
con.close()

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.

复制代码 
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:

复制代码 
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])
cur.close()
con.close()

Adapter and converter recipes

This section shows recipes for common adapters and converters.

复制代码 
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.

复制代码 
# 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 autocommit is False, a new transaction is implicitly opened after committing or rolling back.

If there is no open transaction upon leaving the body of the with statement, or if autocommit is True, the context manager does nothing.

Note

The context manager neither implicitly opens a new transaction nor closes the connection. If you need a closing context manager, consider using contextlib.closing().

复制代码 
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:

>>>

复制代码 
>>> 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:

>>>

复制代码 
>>> 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:
复制代码 
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,)

con1.close()
con2.close()

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:

>>>

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

Queries now return Row objects:

>>>

复制代码 
>>> 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
>>> con.close()

Note

The FROM clause can be omitted in the SELECT statement, as in the above example. In such cases, SQLite returns a single row with columns defined by expressions, e.g. literals, with the given aliases expr AS alias.

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

复制代码 
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:

>>>

复制代码 
>>> 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}
>>> con.close()

The following row factory returns a named tuple:

复制代码 
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:

>>>

复制代码 
>>> 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
>>> con.close()

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

How to handle non-UTF-8 text encodings

By default, sqlite3 uses str to adapt SQLite values with the TEXT data type. This works well for UTF-8 encoded text, but it might fail for other encodings and invalid UTF-8. You can use a custom text_factory to handle such cases.

Because of SQLite's flexible typing, it is not uncommon to encounter table columns with the TEXT data type containing non-UTF-8 encodings, or even arbitrary data. To demonstrate, let's assume we have a database with ISO-8859-2 (Latin-2) encoded text, for example a table of Czech-English dictionary entries. Assuming we now have a Connection instance con connected to this database, we can decode the Latin-2 encoded text using this text_factory:

复制代码 
con.text_factory = lambda data: str(data, encoding="latin2")

For invalid UTF-8 or arbitrary data in stored in TEXT table columns, you can use the following technique, borrowed from the Unicode HOWTO:

复制代码 
con.text_factory = lambda data: str(data, errors="surrogateescape")

Note

The sqlite3 module API does not support strings containing surrogates.

See also

Unicode HOWTO

Explanation

Transaction control

sqlite3 offers multiple methods of controlling whether, when and how database transactions are opened and closed. Transaction control via the autocommit attribute is recommended, while Transaction control via the isolation_level attribute retains the pre-Python 3.12 behaviour.

Transaction control via the autocommit attribute

The recommended way of controlling transaction behaviour is through the Connection.autocommit attribute, which should preferably be set using the autocommit parameter of connect().

It is suggested to set autocommit to False, which implies PEP 249-compliant transaction control. This means:

  • sqlite3 ensures that a transaction is always open, so connect(), Connection.commit(), and Connection.rollback() will implicitly open a new transaction (immediately after closing the pending one, for the latter two). sqlite3 uses BEGIN DEFERRED statements when opening transactions.

  • Transactions should be committed explicitly using commit().

  • Transactions should be rolled back explicitly using rollback().

  • An implicit rollback is performed if the database is close()-ed with pending changes.

Set autocommit to True to enable SQLite's autocommit mode. In this mode, Connection.commit() and Connection.rollback() have no effect. Note that SQLite's autocommit mode is distinct from the PEP 249-compliant Connection.autocommit attribute; use Connection.in_transaction to query the low-level SQLite autocommit mode.

Set autocommit to LEGACY_TRANSACTION_CONTROL to leave transaction control behaviour to the Connection.isolation_level attribute. See Transaction control via the isolation_level attribute for more information.

Transaction control via the isolation_level attribute

Note

The recommended way of controlling transactions is via the autocommit attribute. See Transaction control via the autocommit attribute.

If Connection.autocommit is set to LEGACY_TRANSACTION_CONTROL (the default), transaction behaviour is controlled using the Connection.isolation_level attribute. Otherwise, isolation_level has no effect.

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.

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

Changed in version 3.12: The recommended way of controlling transactions is now via the autocommit attribute.

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