# sqlite3 module differences

The `sqlite3` standard module and APSW approached the problem of providing access to SQLite from Python from fundamentally different directions.

APSW provides access in whatever way is normal for SQLite. It makes no effort to hide how SQLite is different from other databases. It also uses that extra detailed integration to provide a significantly enhanced developer experience with better debugging, tooling, tracing, and making use of all of SQLite’s features.

sqlite3 tries to provide a DBAPI (PEP 249) compliant wrapper for SQLite and in doing so needs to make it have the same behaviour as other databases. Consequently it does hide some of SQLite’s nuances.

Note

I suggest using APSW when you want to directly use SQLite and its functionality or are using your own code to deal with database independence rather than DBAPI. Use `sqlite3` and DBAPI if your needs are simple, and you don’t want to use SQLite specific features, and want to easily switch to other databases.

## What APSW does better

APSW has the following enhancements/differences over the sqlite3 module:

- APSW stays up to date with SQLite. As features are added and functionality changed in SQLite, APSW tracks them.

- APSW stays up to date with Python, including releases under development as well as older still supported releases. The current APSW release brings the most recent SQLite to Python 3.9 all the way through Python 3.14.

- APSW gives all functionality of SQLite including full text search (FTS5), Session extension, :ref:`virtual tables <virtualtables>, Virtual File System (VFS), BLOB I/O, backups, `logging`, `file control`, and `tracing`.

- APSW includes `apsw.bestpractice` which configures SQLite for best performance, and catches common mistakes.

- You can use the same `Connection` across threads with APSW without needing any additional level of locking. sqlite3 requires that the `Connection` and any `cursors` are used in the same thread.

- APSW on PyPI includes SQLite statically inside which does not interfere with any system SQLite, so you have the latest SQLite without disruptions.

- If you don’t use PyPI, APSW build instructions include extensive customisation options for SQLite.

- Nothing happens behind your back. By default sqlite3 tries to manage transactions (for DBAPI compliance) by processing your SQL for you, but you can turn it off. This can result in very unexpected behaviour with sqlite3.

- When using a `Connection` as a `context manager` APSW uses SQLite’s ability to have nested transactions. sqlite3 has a context manager, but does not implement nesting.

- You can use semi-colons at the end of commands and you can have multiple commands in the execute string in APSW. There are no restrictions on the type of commands used. For example this will work fine in APSW but is not allowed in sqlite3:

      import apsw
      con=apsw.Connection(":memory:")
      cur=con.cursor()
      for row in cur.execute("create table foo(x,y,z);insert into foo values (?,?,?);"
                             "insert into foo values(?,?,?);select * from foo;drop table foo;"
                             "create table bar(x,y);insert into bar values(?,?);"
                             "insert into bar values(?,?);select * from bar;",
                             (1,2,3,4,5,6,7,8,9,10)):
                                 print (row)

  And the output as you would expect:

      (1, 2, 3)
      (4, 5, 6)
      (7, 8)
      (9, 10)

- `Cursor.executemany()` also works with statements that return data such as selects, and you can have multiple statements. sqlite3’s executescript method doesn’t allow any form of data being returned (it silently ignores any returned data).

- APSW has better execution and row tracing. Both APSW and sqlite3 wrap sqlite3_trace_v2. sqlite3 only lets you see `the text of executed statements`. APSW provides `a lot more information`, and allows for multiple callbacks. And provides a `helpful context block tracer`.

- Various interesting and useful bits of functionality includes:

  - Pretty printing query results

  - Forwarding SQLite log messages to the Python logger

  - Helper for implementing the complex xBestIndex method on your own virtual tables

  - A wrapper to turn Python functions into virtual tables, including taking positional and keyword arguments

  - Converting data types going into and out of SQlite

  - Detailed query information

  - Tracing individual SQL statements in a block, or overall summary for the block

- sqlite3 swallows exceptions in your callbacks making it far harder to debug problems. That also prevents you from raising exceptions in your callbacks to be handled in your code that called SQLite. sqlite3 does let you turn on `printing of tracebacks` but that is a poor substitute.

  APSW does the right thing as demonstrated by this example. APSW converts Python errors into SQLite errors, so SQLite is aware errors happened.

  Source:

      def badfunc(t):
          return 1/0

      # sqlite3
      import sqlite3

      con = sqlite3.connect(":memory:")
      con.create_function("badfunc", 1, badfunc)
      cur = con.cursor()
      cur.execute("select badfunc(3)")

      # apsw
      import apsw
      con = apsw.Connection(":memory:")
      con.create_scalar_function("badfunc", badfunc, 1)
      cur = con.cursor()
      cur.execute("select badfunc(3)")

  Exceptions:

      # sqlite3

      Traceback (most recent call last):
        File "func.py", line 8, in ?
          cur.execute("select badfunc(3)")
      sqlite3.OperationalError: user-defined function raised exception

      # apsw

      Traceback (most recent call last):
        File "t.py", line 8, in ?
          cur.execute("select badfunc(3)")
        File "apsw.c", line 3660, in resetcursor
        File "apsw.c", line 1871, in user-defined-scalar-badfunc
        File "t.py", line 3, in badfunc
          return 1/0

- APSW has significantly enhanced debuggability. More details are available than just what is printed out when exceptions happen like above. See augmented stack traces

- APSW has an trace utility script that traces execution and results in your code without having to modify it in any way. It also outputs summary reports making it easy to see what your most time consuming queries are, which are most popular etc.

- APSW has an exception corresponding to each SQLite error code and provides the extended error code. sqlite3 combines several SQLite error codes into corresponding DBAPI exceptions. This is a good example of the difference in approach of the two wrappers.

- The APSW test suite is larger and tests more functionality. Virtually every failure condition is tested including running out of memory, error returns etc. Code coverage by the test suite is 99.6%. sqlite3 is good at 81% for C code although there are several places that coverage can be improved. I haven’t measured code coverage for sqlite3’s Python code. The consequences of this are that APSW catches issues earlier and gives far better diagnostics. As an example try returning an unsupported type from a registered scalar function.

- APSW includes a full featured shell. sqlite3 has a simple one available since Python 3.12.

- APSW is faster than sqlite3 in my testing. Try the speedtest benchmark. All C code in APSW called by Python implements the fastcall mechanism, as does most of sqlite3. All code called from C in APSW also uses fastcall, while sqlite3 uses the older mechanism that is significantly slower.

## What sqlite3 does better

- sqlite3 is part of the standard library, and is widely supported by libraries that abstract away the database layer.