There are also specific tips in each documentation section, and many of the classes, functions, and attributes.
SQLite is different
Using APSW best practice is recommended to get best performance and avoid common mistakes.
SQLite has 5 storage types.
Text (limit 1GB when encoded as bytes)
Integer (Signed 64 bit)
Float (IEEE754 64 bit)
BLOB (binary data, limit 1GB)
Dates and times do not have a dedicated storage type, but do have a variety of functions for creating, manipulating, and storing them. JSON does not have a dedicated storage type, but does have a variety of functions for creating, manipulating, and storing JSON.
APSW provides optional type conversion, but the underlying storage will always be one of the 5 storage types.
If a column declaration gives a type then SQLite attempts conversion.
connection.execute(""" create table types1(a, b, c, d, e); create table types2(a INTEGER, b REAL, c TEXT, d, e BLOB); """) data = ("12", 3, 4, 5.5, b"\x03\x72\xf4\x00\x9e") connection.execute("insert into types1 values(?,?,?,?,?)", data) connection.execute("insert into types2 values(?,?,?,?,?)", data) for row in connection.execute("select * from types1"): print("types1", repr(row)) for row in connection.execute("select * from types2"): print("types2", repr(row))
types1 ('12', 3, 4, 5.5, b'\x03r\xf4\x00\x9e') types2 (12, 3.0, '4', 5.5, b'\x03r\xf4\x00\x9e')
Transactions are the changes applied to a database file as a whole. They either happen completely, or not at all. SQLite notes all the changes made during a transaction, and at the end when you commit will cause them to permanently end up in the database. If you do not commit, or just exit, then other/new connections will not see the changes and SQLite handles tidying up the work in progress automatically.
Committing a transaction can be quite time consuming. SQLite uses a robust multi-step process that has to handle errors that can occur at any point, and asks the operating system to ensure that data is on storage and would survive a power cycle. This will limit the rate at which you can do transactions.
If you do nothing, then each statement is a single transaction:
# this will be 3 separate transactions db.execute("INSERT ...") db.execute("INSERT ...") db.execute("INSERT ...")
You can use BEGIN/COMMIT to set the transaction boundary:
# this will be one transaction db.execute("BEGIN") db.execute("INSERT ...") db.execute("INSERT ...") db.execute("INSERT ...") db.execute("COMMIT")
However that is extra effort, and also requires error handling. For example if the second INSERT failed then you likely want to ROLLBACK the incomplete transaction, so that additional work on the same connection doesn’t see the partial data.
If you use
with Connection then the transaction
will be automatically started, and committed on success or rolled back if
# this will be one transaction with automatic commit and rollback with db: db.execute("INSERT ...") db.execute("INSERT ...") db.execute("INSERT ...")
SQLite only calculates each result row as you request it. For example
if your query returns 10 million rows, SQLite will not calculate all 10
million up front. Instead the next row will be calculated as you ask
for it. You can use
Cursor.fetchall() to get all the results.
Cursors on the same Connection
are not isolated from each other. Anything done on one cursor is
immediately visible to all other cursors on the same connection. This
still applies if you start transactions. Connections are isolated
from each other.
automatically obtain cursors from
are very cheap. It is best practise to not re-use them, and instead
get a new one each time. If you don’t, code refactoring and nested
loops can unintentionally use the same cursor object which will not
crash but will cause hard to diagnose behaviour in your program.
When issuing a query, always use bindings. String interpolation may seem more convenient but you will encounter difficulties. You may feel that you have complete control over all data accessed but if your code is at all useful then you will find it being used more and more widely. The computer will always be better than you at parsing SQL and the bad guys have years of experience finding and using SQL injection attacks in ways you never even thought possible.
The tour shows why you use bindings, and the different ways you can supply them.
Both SQLite and APSW provide detailed diagnostic information. Errors will be signalled via an exception.
APSW ensures you have detailed information both in the stack trace as well as what data APSW/SQLite was operating on.
Managing and updating your schema
If your program uses SQLite for data then you’ll need to manage and update your schema. The hard way of doing this is to test for the existence of tables and their columns, and doing that maintenance programmatically. The easy way is to use pragma user_version as in this example:
def ensure_schema(db): # a new database starts at user_version 0 if db.pragma("user_version") == 0: with db: db.execute(""" CREATE TABLE foo(x,y,z); CREATE TABLE bar(x,y,z); PRAGMA user_version = 1;""") if db.pragma("user_version") == 1: with db: db.execute(""" CREATE TABLE baz(x,y,z); CREATE INDEX .... PRAGMA user_version = 2;""") if db.pragma("user_version") == 2: with db: db.execute(""" ALTER TABLE ..... PRAGMA user_version = 3;""")
This approach will automatically upgrade the schema as you expect. You can also use pragma application_id to mark the database as made by your application.
Sometimes you want to know what a particular SQL statement does. Use
apsw.ext.query_info() which will provide as much detail as you
SQLite uses locks to coordinate access to the database by multiple connections (within the same process or in a different process). The general goal is to have the locks be as lax as possible (allowing concurrency) and when using more restrictive locks to keep them for as short a time as possible. See the SQLite documentation for more details.
By default you will get an immediate
BusyError if a lock cannot
be acquired. Use best practice which sets a
short waiting period, as well as enabling WAL which reduces contention between
readers and writers.
When starting a new database, it can be quite difficult to decide what tables and column to have and how to link them. The technique used to design SQL schemas is called normalization. The page also shows common pitfalls if you do not normalize your schema.
Write Ahead Logging
Note that if wal mode can’t be set (eg the database is in memory or
temporary) then the attempt to set wal mode will be ignored. It is
also harmless to call functions like
Connection.wal_autocheckpoint() on connections that are not in
If you write your own VFS, then inheriting from an existing VFS that supports WAL will make your VFS support the extra WAL methods too.
For example if you wanted to add an executescript method to
Connections that is like
Connection.execute() but ignores all
def executescript(self, sql, bindings=None): for _ in self.execute(sql, bindings): pass def my_hook(connection): connection.executescript = executescript apsw.connection_hooks.append(my_hook)
You can customize the behaviour of cursors. An example would be wanting a rowcount or batching returned rows. (These don’t make any sense with SQLite but the desire may be to make the code source compatible with other database drivers).
Connection.cursor_factory to any callable, which will be
called with the connection as the only parameter, and return the
object to use as a cursor.