SQLite is a C library that provides a lightweight disk-based database that doesn’t require a separate server process and allows accessing the database using a nonstandard variant of the SQL query language. Some applications can use SQLite for internal data storage. It’s also possible to prototype an application using SQLite and then port the code to a larger database such as PostgreSQL or Oracle.
sqlite3 was written by Gerhard Häring and provides a SQL interface compliant with the DB-API 2.0 specification described by PEP 249.
To use the module, you must first create a Connection object that represents the database. Here the data will be stored in the /tmp/example file:
conn = sqlite3.connect('/tmp/example')
You can also supply the special name :memory: to create a database in RAM.
Once you have a Connection, you can create a Cursor object and call its execute() method to perform SQL commands:
c = conn.cursor()
# Create table
c.execute('''create table stocks
(date text, trans text, symbol text,
qty real, price real)''')
# Insert a row of data
c.execute("""insert into stocks
values ('2006-01-05','BUY','RHAT',100,35.14)""")
# Save (commit) the changes
conn.commit()
# We can also close the cursor if we are done with it
c.close()
Usually your SQL operations will need to use values from Python variables. You shouldn’t assemble your query using Python’s string operations because doing so is insecure; it makes your program vulnerable to an SQL injection attack.
Instead, use the DB-API’s parameter substitution. Put ? as a placeholder wherever you want to use a value, and then provide a tuple of values as the second argument to the cursor’s execute() method. (Other database modules may use a different placeholder, such as %s or :1.) For example:
# Never do this -- insecure!
symbol = 'IBM'
c.execute("... where symbol = '%s'" % symbol)
# Do this instead
t = (symbol,)
c.execute('select * from stocks where symbol=?', t)
# Larger example
for t in [('2006-03-28', 'BUY', 'IBM', 1000, 45.00),
('2006-04-05', 'BUY', 'MSOFT', 1000, 72.00),
('2006-04-06', 'SELL', 'IBM', 500, 53.00),
]:
c.execute('insert into stocks values (?,?,?,?,?)', t)
To retrieve data after executing a SELECT statement, you can either treat the cursor as an iterator, call the cursor’s fetchone() method to retrieve a single matching row, or call fetchall() to get a list of the matching rows.
This example uses the iterator form:
>>> c = conn.cursor()
>>> c.execute('select * from stocks order by price')
>>> for row in c:
... print(row)
...
(u'2006-01-05', u'BUY', u'RHAT', 100, 35.140000000000001)
(u'2006-03-28', u'BUY', u'IBM', 1000, 45.0)
(u'2006-04-06', u'SELL', u'IBM', 500, 53.0)
(u'2006-04-05', u'BUY', u'MSOFT', 1000, 72.0)
>>>
See also
This constant is meant to be used with the detect_types parameter of the connect() function.
Setting it makes the sqlite3 module parse the declared type for each column it returns. It will parse out the first word of the declared type, i. e. for “integer primary key”, it will parse out “integer”, or for “number(10)” it will parse out “number”. Then for that column, it will look into the converters dictionary and use the converter function registered for that type there.
This constant is meant to be used with the detect_types parameter of the connect() function.
Setting this makes the SQLite interface parse the column name for each column it returns. It will look for a string formed [mytype] in there, and then decide that ‘mytype’ is the type of the column. It will try to find an entry of ‘mytype’ in the converters dictionary and then use the converter function found there to return the value. The column name found in Cursor.description is only the first word of the column name, i. e. if you use something like 'as "x [datetime]"' in your SQL, then we will parse out everything until the first blank for the column name: the column name would simply be “x”.
Opens a connection to the SQLite database file database. You can use ":memory:" to open a database connection to a database that resides in RAM instead of on disk.
When a database is accessed by multiple connections, and one of the processes modifies the database, the SQLite database is locked until that transaction is committed. The timeout parameter specifies how long the connection should wait for the lock to go away until raising an exception. The default for the timeout parameter is 5.0 (five seconds).
For the isolation_level parameter, please see the Connection.isolation_level property of Connection objects.
SQLite natively supports only the types TEXT, INTEGER, FLOAT, BLOB and NULL. If you want to use other types you must add support for them yourself. The detect_types parameter and the using custom converters registered with the module-level register_converter() function allow you to easily do that.
detect_types defaults to 0 (i. e. off, no type detection), you can set it to any combination of PARSE_DECLTYPES and PARSE_COLNAMES to turn type detection on.
By default, the sqlite3 module uses its Connection class for the connect call. You can, however, subclass the Connection class and make connect() use your class instead by providing your class for the factory parameter.
Consult the section SQLite and Python types of this manual for details.
The sqlite3 module internally uses a statement cache to avoid SQL parsing overhead. If you want to explicitly set the number of statements that are cached for the connection, you can set the cached_statements parameter. The currently implemented default is to cache 100 statements.
Returns True if the string sql contains one or more complete SQL statements terminated by semicolons. It does not verify that the SQL is syntactically correct, only that there are no unclosed string literals and the statement is terminated by a semicolon.
This can be used to build a shell for SQLite, as in the following example:
# A minimal SQLite shell for experiments
import sqlite3
con = sqlite3.connect(":memory:")
con.isolation_level = None
cur = con.cursor()
buffer = ""
print("Enter your SQL commands to execute in sqlite3.")
print("Enter a blank line to exit.")
while True:
line = input()
if line == "":
break
buffer += line
if sqlite3.complete_statement(buffer):
try:
buffer = buffer.strip()
cur.execute(buffer)
if buffer.lstrip().upper().startswith("SELECT"):
print(cur.fetchall())
except sqlite3.Error as e:
print("An error occurred:", e.args[0])
buffer = ""
con.close()
Creates a user-defined function that you can later use from within SQL statements under the function name name. num_params is the number of parameters the function accepts, and func is a Python callable that is called as the SQL function.
The function can return any of the types supported by SQLite: bytes, str, int, float, buffer and None.
Example:
import sqlite3
import hashlib
def md5sum(t):
return hashlib.md5(t).hexdigest()
con = sqlite3.connect(":memory:")
con.create_function("md5", 1, md5sum)
cur = con.cursor()
cur.execute("select md5(?)", ("foo",))
print(cur.fetchone()[0])
Creates a user-defined aggregate function.
The aggregate class must implement a step method, which accepts the number of parameters num_params, and a finalize method which will return the final result of the aggregate.
The finalize method can return any of the types supported by SQLite: bytes, str, int, float, buffer and None.
Example:
import sqlite3
class MySum:
def __init__(self):
self.count = 0
def step(self, value):
self.count += value
def finalize(self):
return self.count
con = sqlite3.connect(":memory:")
con.create_aggregate("mysum", 1, MySum)
cur = con.cursor()
cur.execute("create table test(i)")
cur.execute("insert into test(i) values (1)")
cur.execute("insert into test(i) values (2)")
cur.execute("select mysum(i) from test")
print(cur.fetchone()[0])
Creates a collation with the specified name and callable. The callable will be passed two string arguments. It should return -1 if the first is ordered lower than the second, 0 if they are ordered equal and 1 if the first is ordered higher than the second. Note that this controls sorting (ORDER BY in SQL) so your comparisons don’t affect other SQL operations.
Note that the callable will get its parameters as Python bytestrings, which will normally be encoded in UTF-8.
The following example shows a custom collation that sorts “the wrong way”:
import sqlite3
def collate_reverse(string1, string2):
if string1 == string2:
return 0
elif string1 < string2:
return 1
else:
return -1
con = sqlite3.connect(":memory:")
con.create_collation("reverse", collate_reverse)
cur = con.cursor()
cur.execute("create table test(x)")
cur.executemany("insert into test(x) values (?)", [("a",), ("b",)])
cur.execute("select x from test order by x collate reverse")
for row in cur:
print(row)
con.close()
To remove a collation, call create_collation with None as callable:
con.create_collation("reverse", None)
This routine registers a callback. The callback is invoked for each attempt to access a column of a table in the database. The callback should return SQLITE_OK if access is allowed, SQLITE_DENY if the entire SQL statement should be aborted with an error and SQLITE_IGNORE if the column should be treated as a NULL value. These constants are available in the sqlite3 module.
The first argument to the callback signifies what kind of operation is to be authorized. The second and third argument will be arguments or None depending on the first argument. The 4th argument is the name of the database (“main”, “temp”, etc.) if applicable. The 5th argument is the name of the inner-most trigger or view that is responsible for the access attempt or None if this access attempt is directly from input SQL code.
Please consult the SQLite documentation about the possible values for the first argument and the meaning of the second and third argument depending on the first one. All necessary constants are available in the sqlite3 module.
This routine registers a callback. The callback is invoked for every n instructions of the SQLite virtual machine. This is useful if you want to get called from SQLite during long-running operations, for example to update a GUI.
If you want to clear any previously installed progress handler, call the method with None for handler.
You can change this attribute to a callable that accepts the cursor and the original row as a tuple and will return the real result row. This way, you can implement more advanced ways of returning results, such as returning an object that can also access columns by name.
Example:
import sqlite3
def dict_factory(cursor, row):
d = {}
for idx, col in enumerate(cursor.description):
d[col[0]] = row[idx]
return d
con = sqlite3.connect(":memory:")
con.row_factory = dict_factory
cur = con.cursor()
cur.execute("select 1 as a")
print(cur.fetchone()["a"])
If returning a tuple doesn’t suffice and you want name-based access to columns, you should consider setting row_factory to the highly-optimized sqlite3.Row type. Row provides both index-based and case-insensitive name-based access to columns with almost no memory overhead. It will probably be better than your own custom dictionary-based approach or even a db_row based solution.
Using this attribute you can control what objects are returned for the TEXT data type. By default, this attribute is set to str and the sqlite3 module will return Unicode objects for TEXT. If you want to return bytestrings instead, you can set it to bytes.
For efficiency reasons, there’s also a way to return str objects only for non-ASCII data, and bytes otherwise. To activate it, set this attribute to sqlite3.OptimizedUnicode.
You can also set it to any other callable that accepts a single bytestring parameter and returns the resulting object.
See the following example code for illustration:
import sqlite3
con = sqlite3.connect(":memory:")
cur = con.cursor()
# Create the table
con.execute("create table person(lastname, firstname)")
AUSTRIA = "\xd6sterreich"
# by default, rows are returned as Unicode
cur.execute("select ?", (AUSTRIA,))
row = cur.fetchone()
assert row[0] == AUSTRIA
# but we can make sqlite3 always return bytestrings ...
con.text_factory = str
cur.execute("select ?", (AUSTRIA,))
row = cur.fetchone()
assert type(row[0]) == str
# the bytestrings will be encoded in UTF-8, unless you stored garbage in the
# database ...
assert row[0] == AUSTRIA.encode("utf-8")
# we can also implement a custom text_factory ...
# here we implement one that will ignore Unicode characters that cannot be
# decoded from UTF-8
con.text_factory = lambda x: str(x, "utf-8", "ignore")
cur.execute("select ?", ("this is latin1 and would normally create errors" +
"\xe4\xf6\xfc".encode("latin1"),))
row = cur.fetchone()
assert type(row[0]) == str
# sqlite3 offers a builtin optimized text_factory that will return bytestring
# objects, if the data is in ASCII only, and otherwise return unicode objects
con.text_factory = sqlite3.OptimizedUnicode
cur.execute("select ?", (AUSTRIA,))
row = cur.fetchone()
assert type(row[0]) == str
cur.execute("select ?", ("Germany",))
row = cur.fetchone()
assert type(row[0]) == str
Returns an iterator to dump the database in an SQL text format. Useful when saving an in-memory database for later restoration. This function provides the same capabilities as the .dump command in the sqlite3 shell.
Example:
# Convert file existing_db.db to SQL dump file dump.sql
import sqlite3, os
con = sqlite3.connect('existing_db.db')
with open('dump.sql', 'w') as f:
for line in con.iterdump():
f.write('%s\n' % line)
Executes an SQL statement. The SQL statement may be parametrized (i. e. placeholders instead of SQL literals). The sqlite3 module supports two kinds of placeholders: question marks (qmark style) and named placeholders (named style).
This example shows how to use parameters with qmark style:
import sqlite3
con = sqlite3.connect("mydb")
cur = con.cursor()
who = "Yeltsin"
age = 72
cur.execute("select name_last, age from people where name_last=? and age=?", (who, age))
print(cur.fetchone())
This example shows how to use the named style:
import sqlite3
con = sqlite3.connect("mydb")
cur = con.cursor()
who = "Yeltsin"
age = 72
cur.execute("select name_last, age from people where name_last=:who and age=:age",
{"who": who, "age": age})
print(cur.fetchone())
execute() will only execute a single SQL statement. If you try to execute more than one statement with it, it will raise a Warning. Use executescript() if you want to execute multiple SQL statements with one call.
Executes an SQL command against all parameter sequences or mappings found in the sequence sql. The sqlite3 module also allows using an iterator yielding parameters instead of a sequence.
import sqlite3
class IterChars:
def __init__(self):
self.count = ord('a')
def __iter__(self):
return self
def __next__(self):
if self.count > ord('z'):
raise StopIteration
self.count += 1
return (chr(self.count - 1),) # this is a 1-tuple
con = sqlite3.connect(":memory:")
cur = con.cursor()
cur.execute("create table characters(c)")
theIter = IterChars()
cur.executemany("insert into characters(c) values (?)", theIter)
cur.execute("select c from characters")
print(cur.fetchall())
Here’s a shorter example using a generator:
import sqlite3
def char_generator():
import string
for c in string.letters[:26]:
yield (c,)
con = sqlite3.connect(":memory:")
cur = con.cursor()
cur.execute("create table characters(c)")
cur.executemany("insert into characters(c) values (?)", char_generator())
cur.execute("select c from characters")
print(cur.fetchall())
This is a nonstandard convenience method for executing multiple SQL statements at once. It issues a COMMIT statement first, then executes the SQL script it gets as a parameter.
sql_script can be an instance of str or bytes.
Example:
import sqlite3
con = sqlite3.connect(":memory:")
cur = con.cursor()
cur.executescript("""
create table person(
firstname,
lastname,
age
);
create table book(
title,
author,
published
);
insert into book(title, author, published)
values (
'Dirk Gently''s Holistic Detective Agency',
'Douglas Adams',
1987
);
""")
Fetches the next set of rows of a query result, returning a list. An empty list is returned when no more rows are available.
The number of rows to fetch per call is specified by the size parameter. If it is not given, the cursor’s arraysize determines the number of rows to be fetched. The method should try to fetch as many rows as indicated by the size parameter. If this is not possible due to the specified number of rows not being available, fewer rows may be returned.
Note there are performance considerations involved with the size parameter. For optimal performance, it is usually best to use the arraysize attribute. If the size parameter is used, then it is best for it to retain the same value from one fetchmany() call to the next.
Although the Cursor class of the sqlite3 module implements this attribute, the database engine’s own support for the determination of “rows affected”/”rows selected” is quirky.
For DELETE statements, SQLite reports rowcount as 0 if you make a DELETE FROM table without any condition.
For executemany() statements, the number of modifications are summed up into rowcount.
As required by the Python DB API Spec, the rowcount attribute “is -1 in case no executeXX() has been performed on the cursor or the rowcount of the last operation is not determinable by the interface”.
This includes SELECT statements because we cannot determine the number of rows a query produced until all rows were fetched.
A Row instance serves as a highly optimized row_factory for Connection objects. It tries to mimic a tuple in most of its features.
It supports mapping access by column name and index, iteration, representation, equality testing and len().
If two Row objects have exactly the same columns and their members are equal, they compare equal.
Let’s assume we initialize a table as in the example given above:
conn = sqlite3.connect(":memory:")
c = conn.cursor()
c.execute('''create table stocks
(date text, trans text, symbol text,
qty real, price real)''')
c.execute("""insert into stocks
values ('2006-01-05','BUY','RHAT',100,35.14)""")
conn.commit()
c.close()
Now we plug Row in:
>>> conn.row_factory = sqlite3.Row
>>> c = conn.cursor()
>>> c.execute('select * from stocks')
<sqlite3.Cursor object at 0x7f4e7dd8fa80>
>>> r = c.fetchone()
>>> type(r)
<type 'sqlite3.Row'>
>>> r
(u'2006-01-05', u'BUY', u'RHAT', 100.0, 35.140000000000001)
>>> len(r)
5
>>> r[2]
u'RHAT'
>>> r.keys()
['date', 'trans', 'symbol', 'qty', 'price']
>>> r['qty']
100.0
>>> for member in r: print member
...
2006-01-05
BUY
RHAT
100.0
35.14
SQLite 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 |
| bytes (UTF8-encoded) | TEXT |
| str | TEXT |
| buffer | BLOB |
This is how SQLite types are converted to Python types by default:
| SQLite type | Python type |
|---|---|
| NULL | None |
| INTEGER | :class`int` |
| REAL | float |
| TEXT | depends on text_factory, str by default |
| BLOB | buffer |
The type system of the sqlite3 module is extensible in two ways: you can store additional Python types in a SQLite database via object adaptation, and you can let the sqlite3 module convert SQLite types to different Python types via converters.
As described before, SQLite supports only a limited set of types natively. To use other Python types with SQLite, you must adapt them to one of the sqlite3 module’s supported types for SQLite: one of NoneType, int, float, str, bytes, buffer.
The sqlite3 module uses Python object adaptation, as described in PEP 246 for this. The protocol to use is PrepareProtocol.
There are two ways to enable the sqlite3 module to adapt a custom Python type to one of the supported ones.
This is a good approach if you write the class yourself. Let’s suppose you have a class like this:
class Point(object):
def __init__(self, x, y):
self.x, self.y = x, y
Now you want to store the point in a single SQLite column. First you’ll have to choose one of the supported types first to be used for representing the point. Let’s just use str and separate the coordinates using a semicolon. Then you need to give your class a method __conform__(self, protocol) which must return the converted value. The parameter protocol will be PrepareProtocol.
import sqlite3
class Point(object):
def __init__(self, x, y):
self.x, self.y = x, y
def __conform__(self, protocol):
if protocol is sqlite3.PrepareProtocol:
return "%f;%f" % (self.x, self.y)
con = sqlite3.connect(":memory:")
cur = con.cursor()
p = Point(4.0, -3.2)
cur.execute("select ?", (p,))
print(cur.fetchone()[0])
The other possibility is to create a function that converts the type to the string representation and register the function with register_adapter().
import sqlite3
class Point(object):
def __init__(self, x, y):
self.x, self.y = x, y
def adapt_point(point):
return "%f;%f" % (point.x, point.y)
sqlite3.register_adapter(Point, adapt_point)
con = sqlite3.connect(":memory:")
cur = con.cursor()
p = Point(4.0, -3.2)
cur.execute("select ?", (p,))
print(cur.fetchone()[0])
The sqlite3 module has two default adapters for Python’s built-in datetime.date and datetime.datetime types. Now let’s suppose we want to store datetime.datetime objects not in ISO representation, but as a Unix timestamp.
import sqlite3
import datetime, time
def adapt_datetime(ts):
return time.mktime(ts.timetuple())
sqlite3.register_adapter(datetime.datetime, adapt_datetime)
con = sqlite3.connect(":memory:")
cur = con.cursor()
now = datetime.datetime.now()
cur.execute("select ?", (now,))
print(cur.fetchone()[0])
Writing an adapter lets you send custom Python types to SQLite. But to make it really useful we need to make the Python to SQLite to Python roundtrip work.
Enter converters.
Let’s go back to the Point class. We stored the x and y coordinates separated via semicolons as strings in SQLite.
First, we’ll define a converter function that accepts the string as a parameter and constructs a Point object from it.
Note
Converter functions always get called with a string, no matter under which data type you sent the value to SQLite.
def convert_point(s):
x, y = map(float, s.split(";"))
return Point(x, y)
Now you need to make the sqlite3 module know that what you select from the database is actually a point. There are two ways of doing this:
Both ways are described in section Module functions and constants, in the entries for the constants PARSE_DECLTYPES and PARSE_COLNAMES.
The following example illustrates both approaches.
import sqlite3
class Point(object):
def __init__(self, x, y):
self.x, self.y = x, y
def __repr__(self):
return "(%f;%f)" % (self.x, self.y)
def adapt_point(point):
return "%f;%f" % (point.x, point.y)
def convert_point(s):
x, y = list(map(float, s.split(";")))
return Point(x, y)
# Register the adapter
sqlite3.register_adapter(Point, adapt_point)
# Register the converter
sqlite3.register_converter("point", convert_point)
p = Point(4.0, -3.2)
#########################
# 1) Using declared types
con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_DECLTYPES)
cur = con.cursor()
cur.execute("create table test(p point)")
cur.execute("insert into test(p) values (?)", (p,))
cur.execute("select p from test")
print("with declared types:", cur.fetchone()[0])
cur.close()
con.close()
#######################
# 1) Using column names
con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_COLNAMES)
cur = con.cursor()
cur.execute("create table test(p)")
cur.execute("insert into test(p) values (?)", (p,))
cur.execute('select p as "p [point]" from test')
print("with column names:", cur.fetchone()[0])
cur.close()
con.close()
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]))
By default, the sqlite3 module opens transactions implicitly before a Data Modification Language (DML) statement (i.e. INSERT/UPDATE/DELETE/REPLACE), and commits transactions implicitly before a non-DML, non-query statement (i. e. anything other than SELECT or the aforementioned).
So if you are within a transaction and issue a command like CREATE TABLE ..., VACUUM, PRAGMA, the sqlite3 module will commit implicitly before executing that command. There are two reasons for doing that. The first is that some of these commands don’t work within transactions. The other reason is that sqlite3 needs to keep track of the transaction state (if a transaction is active or not).
You can control which kind of BEGIN statements sqlite3 implicitly executes (or none at all) via the isolation_level parameter to the connect() call, or via the isolation_level property of connections.
If you want autocommit mode, then set isolation_level to None.
Otherwise leave it at its default, which will result in a plain “BEGIN” statement, or set it to one of SQLite’s supported isolation levels: “DEFERRED”, “IMMEDIATE” or “EXCLUSIVE”.
Using the nonstandard execute(), executemany() and executescript() methods of the Connection object, your code can be written more concisely because you don’t have to create the (often superfluous) Cursor objects explicitly. Instead, the Cursor objects are created implicitly and these shortcut methods return the cursor objects. This way, you can execute a SELECT statement and iterate over it directly using only a single call on the Connection object.
import sqlite3
persons = [
("Hugo", "Boss"),
("Calvin", "Klein")
]
con = sqlite3.connect(":memory:")
# Create the table
con.execute("create table person(firstname, lastname)")
# Fill the table
con.executemany("insert into person(firstname, lastname) values (?, ?)", persons)
# Print the table contents
for row in con.execute("select firstname, lastname from person"):
print(row)
# Using a dummy WHERE clause to not let SQLite take the shortcut table deletes.
print("I just deleted", con.execute("delete from person where 1=1").rowcount, "rows")
One useful feature of the sqlite3 module is the builtin sqlite3.Row class designed to be used as a row factory.
Rows wrapped with this class can be accessed both by index (like tuples) and case-insensitively by name:
import sqlite3
con = sqlite3.connect("mydb")
con.row_factory = sqlite3.Row
cur = con.cursor()
cur.execute("select name_last, age from people")
for row in cur:
assert row[0] == row["name_last"]
assert row["name_last"] == row["nAmE_lAsT"]
assert row[1] == row["age"]
assert row[1] == row["AgE"]
Connection objects can be used as context managers that automatically commit or rollback transactions. In the event of an exception, the transaction is rolled back; otherwise, the transaction is committed:
import sqlite3
con = sqlite3.connect(":memory:")
con.execute("create table person (id integer primary key, firstname varchar unique)")
# Successful, con.commit() is called automatically afterwards
with con:
con.execute("insert into person(firstname) values (?)", ("Joe",))
# con.rollback() is called after the with block finishes with an exception, the
# exception is still raised and must be catched
try:
with con:
con.execute("insert into person(firstname) values (?)", ("Joe",))
except sqlite3.IntegrityError:
print("couldn't add Joe twice")