# Execution and tracing

## Execution model

SQLite does execution in two steps. First a statement is prepared, which verifies the syntax, tables and fields and converts the statement into an internal representation. The prepared statement is then run. Execution stops when a row is available, there is an error or the statement is complete.

The `Cursor.execute()` method automatically does the preparing and starts execution. If none of the statements return rows then execution will go to the end. If a row is returned then you use the cursor as an iterator. Execution will resume as necessary to return the next result row.

However this means that if you don’t read the rows returned then the rest of your statements won’t be executed. APSW will detect unexecuted previous statements and generate an exception. For example:

    >>> cursor.execute("select * from foo ; create table bar(x,y,z)")
    >>> cursor.execute("create table bam(x,y,z)")
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    apsw.IncompleteExecutionError: Error: there are still remaining sql statements to execute

Because I didn’t read the results of `select * from foo` then the following create table command didn’t have a chance to get executed. On the next execute that condition is detected and an exception raised.

## Multi-threading

ASPW lets you use SQLite in multi-threaded programs and will let other threads execute while SQLite is working. It checks at start that SQLite was compiled in threadsafe mode which is the default.

SQLite can only be executing in one thread at a time, and uses a mutex to ensure this. You can issue further queries in the same thread, for example in callback functions and virtual tables.

Attempts to do SQLite operations in one thread while another is busy will result in a `ThreadingViolationError`. However calling `Cursor.execute()`, `Cursor.executemany()`, or `iteration` will wait up to a third of second for the mutex before raising the exception.

You will not get any concurrency by trying to use multiple threads on the same `Connection`. You will however get concurrency by using multiple connections.

Python has a global lock to ensure only one thread at a time is running Python code (maintaining the integrity of Python data structures) named the GIL. APSW releases the GIL around SQLite calls that could take time like preparing statements for execution, and executing them.

## Re-entrancy

You cannot use the same cursor object in multiple threads concurrently to execute statements. APSW will detect this and raise an `ThreadingViolationError`. It is safe to use the cursor serially such as calling `Cursor.execute()` in one thread and iterator next in another. You also can’t do things like try to `close()` a Connection concurrently in two threads.

A cursor object can only be executing one query at a time. You can issue a new query from inside a trace function or from a user defined function or collation provided you use a new cursor.

## 64 bit

APSW is tested and works correctly on 32 and 64 bit hosts. SQLite is limited to 32 bit quantities for strings, blobs, number of columns etc even when compiled for 64 bit. You will get a `TooBigError` if trying to use strings and blobs larger than 1 gigabyte.

## Statement Cache

Each `Connection` maintains a cache mapping SQL queries to a prepared statement to avoid the overhead of repreparing queries that are executed multiple times. This is a classic trade off using more memory to reduce CPU consumption.

By default there are up to 100 entries in the cache. Once the cache is full, the least recently used item is discarded to make space for new items.

You should pick a larger cache size if you have more than 100 unique queries that you run. For example if you have 101 different queries you run in order then the cache will not help.

If you are using `authorizers` then be aware authorizer callback is only called while statements are being prepared. You can `specify zero` which will disable the statement cache completely, use use can_cache = False flag to execute/executemany.

## Tracing

You may find `apsw.ext.Trace()` as a quick convenient overview of what get executed and its effects. If you need more detailed information than that, then keep reading.

You can install tracers on `cursors` or `connections` as an easy way of seeing exactly what gets executed and what is returned. The tracers can also abort execution and cause different values to be returned. This is very useful for diagnostics and testing without having to modify your main code.

Note

You cannot issue new execute statements against the cursor your tracer was called from. If you would like to make more queries in the tracer then do them from a new cursor object. For example:

    def exec_tracer(cursor, sql, bindings):
      cursor.connection.execute("insert into log values(?,?)", (sql,str(bindings)))
      return True

### Execution Tracer

The execution tracer is called after an SQL statement has been prepared. (ie syntax errors will have caused an exception during preparation so you won’t see them with a tracer). It is called with three arguments.

> cursor

>   The cursor executing the statement

> sql

>   The SQL text being executed

> bindings

>   The bindings being used. This may be None, a dictionary or a tuple.

If the tracer return value is False then execution is aborted with an `ExecTraceAbort` exception. See the example.

Execution tracers can be installed on a specific cursor by setting `Cursor.exec_trace` or for all cursors by setting `Connection.exec_trace`, with the cursor tracer taking priority.

### Row Tracer

The row tracer is called before each row is returned. It is called with two arguments.

> cursor

>   The cursor returning the row

> row

>   A tuple of the values about to be returned

Whatever you return from the tracer is what is actually returned to the caller of `execute()`. If you return None then the whole row is skipped. See the example. They are useful for logging and testing.

Row tracers can be installed on a specific cursor by setting `Cursor.row_trace` or for all cursors by setting `Connection.row_trace`, with the cursor tracer taking priority.

If you want to convert types then see Converting types into and out of SQLite.

## APSW Trace

APSW includes a tracer that lets you easily trace SQL execution as well as providing a summary report without modifying your code.

    $ python3 -m apsw.trace [apswtrace options] yourscript.py [your options]

The following options are available:

    $ python3 -m apsw.trace --help
    Usage: apswtrace.py [options] pythonscript.py [pythonscriptoptions]

    This script runs a Python program that uses APSW and reports on SQL queries
    without modifying the program.  This is done by using connection_hooks and
    registering row and execution tracers.  See APSW documentation for more
    details on the output.

    Options:
      -h, --help            show this help message and exit
      -o OUTPUT, --output=OUTPUT
                            Where to send the output.  Use a filename, a single
                            dash for stdout, or the words stdout and stderr.
                            [stdout]
      -s, --sql             Log SQL statements as they are executed. [False]
      -r, --rows            Log returned rows as they are returned (turns on sql).
                            [False]
      -t, --timestamps      Include timestamps in logging
      -i, --thread          Include thread id in logging
      -l LENGTH, --length=LENGTH
                            Max amount of a string to print [30]
      --no-report           A summary report is normally generated at program
                            exit.  This turns off the report and saves memory.
      --report-items=N      How many items to report in top lists [15]
      --reports=REPORTS     Which reports to show
                            [summary,popular,aggregate,individual]

This is sample output with the following options: –sql, –rows, –timestamps, –thread

    1e0e5a0 0.152 7fccea8456e0 OPEN: ":memory:" unix READWRITE|CREATE
    1f72ac0 0.161 7fccea8456e0 OPEN: "testdb" unix READWRITE|CREATE
    1f6b8d0 0.162 7fccea8456e0 CURSORFROM: 1f72ac0 DB: "testdb"
    1f6b8d0 0.162 7fccea8456e0 SQL: create table foo(x,y,z)
    1f6b8d0 0.239 7fccea8456e0 CURSORFROM: 1f72ac0 DB: "testdb"
    1f6b8d0 0.239 7fccea8456e0 SQL: insert into foo values(?,?,?) BINDINGS: ("kjfhgk", "gkjlfdhgjkhsdfkjg", "gklsdfjgkldfjhnbnvc,mnxb,mnxcv..")
    1f6b8d0 0.242 7fccea8456e0 CURSORFROM: 1f72ac0 DB: "testdb"
    1f6b8d0 0.242 7fccea8456e0 SQL: insert into foo values(?,?,?) BINDINGS: ("gdfklhj", ":gjkhgfdsgfd", "gjkfhgjkhdfkjh")
    1f6b8d0 0.244 7fccea8456e0 CURSORFROM: 1f72ac0 DB: "testdb"
    1f6b8d0 0.245 7fccea8456e0 SQL: insert into foo values(?,?,?) BINDINGS: ("gdfjkhg", "gkjlfd", "")
    1f6b8d0 0.247 7fccea8456e0 CURSORFROM: 1f72ac0 DB: "testdb"
    1f6b8d0 0.247 7fccea8456e0 SQL: insert into foo values(?,?,?) BINDINGS: (1, 2, 30)
    1f6b8d0 0.257 7fccea8456e0 CURSORFROM: 1f72ac0 DB: "testdb"
    1f6b8d0 0.257 7fccea8456e0 SQL: select longest(x,y,z) from foo
    1f6b8d0 0.257 7fccea8456e0 ROW: ("gklsdfjgkldfjhnbnvc,mnxb,mnxcv..")

Each row starts with the following fields:

> id

>   This is the `id()` of the `Cursor` or `Connection`. You can easily filter the log if you just want to find out what happened on a specific cursor or connection.

> timestamp

>   This is time since the program started in seconds

> threadid

>   The unique `thread identifier`

The remainder of the line has one of the following forms:

> OPEN: “dbname” vfs open_flags

>   A `Connection` has been opened. The dbname is the filename exactly as given in the call to `Connection`. vfs is the name of the VFS used to open the database. open_flags is the set of `flags` supplied with the leading SQLITE_OPEN prefix omitted.

> CURSORFROM: connectionid DB: “dbname”

>   A cursor has been allocated. The id at the beginning of this row is of the new cursor. connectionid is the id of the Connection it was created from. The dbname is provided for convenience. This message is logged the first time a cursor issues a query.

> SQL: query BINDINGS: bindings

>   A query was issued on a cursor.

> ROW: row

>   A result row was returned by a cursor.

A report is also generated by default. This is example output from running the test suite. When calculating time for queries, your code execution time is included as well. For example if your query returned 10 rows and you slept for 1 second on reading each row then the time for the query will be recorded as 10 seconds. Because you can have multiple queries active at the same time, as well as across multiple threads, the total processing time can be larger than the program run time. The processing time is only recorded for queries that have no results or where you read all the result rows. Processing time also includes waiting time on busy connections.

>     APSW TRACE SUMMARY REPORT

>     Program run time                    83.073 seconds
>     Total connections                   1308
>     Total cursors                       3082
>     Number of threads used for queries  21
>     Total queries                       127973
>     Number of distinct queries          578
>     Number of rows returned             2369
>     Time spent processing queries       120.530 seconds

This shows how many times each query was run.

>     MOST POPULAR QUERIES

>      121451 insert into foo values(?)
>        1220 insert into abc values(1,2,?)
>        1118 select x from foo
>         909 select timesten(x) from foo where x=? order by x
>         654 select * from foo
>         426 update t1 set b=b||a||b
>         146 begin
>          88 create table foo(x,y)
>          79 insert into foo values(1,2)
>          76 rollback
>          71 pragma locking_mode=exclusive
>          71 insert into t1 values(2, 'abcdefghijklmnopqrstuvwxyz')
>          71 insert into t1 values(1, 'abcdefghijklmnopqrstuvwxyz')
>          71 insert into t1 select 4-a, b from t2
>          71 insert into foo values(date('now'), date('now'))

This shows how many times a query was run and the sum of the processing times in seconds. The `begin immediate` query illustrates how time spent busy waiting is included.

>     LONGEST RUNNING - AGGREGATE

>         413   94.305 select timesten(x) from foo where x=? order by x
>      120637   12.941 select * from foo
>          12    4.115 begin immediate
>      121449    2.179 insert into foo values(?)
>        1220    1.509 insert into abc values(1,2,?)
>           3    1.380 create index foo_x on foo(x)
>         426    0.715 update t1 set b=b||a||b
>          38    0.420 insert into foo values(?,?)
>          71    0.241 create table t1(a unique, b)
>          88    0.206 create table foo(x,y)
>          61    0.170 create table abc(a,b,c)
>          27    0.165 insert into foo values(?,?,?)
>           1    0.158 select row,x,snap(x) from foo
>          80    0.150 insert into foo values(1,2)
>          71    0.127 insert into foo values(date('now'), date('now'))

This shows the longest running queries with time in seconds.

>     LONGEST RUNNING - INDIVIDUAL

>       3.001 begin immediate
>       1.377 create index foo_x on foo(x)
>       1.102 begin immediate
>       0.944 select timesten(x) from foo where x=? order by x
>       0.893 select timesten(x) from foo where x=? order by x
>       0.817 select timesten(x) from foo where x=? order by x
>       0.816 select timesten(x) from foo where x=? order by x
>       0.786 select timesten(x) from foo where x=? order by x
>       0.783 select timesten(x) from foo where x=? order by x
>       0.713 select timesten(x) from foo where x=? order by x
>       0.701 select timesten(x) from foo where x=? order by x
>       0.651 select timesten(x) from foo where x=? order by x
>       0.646 select timesten(x) from foo where x=? order by x
>       0.631 select timesten(x) from foo where x=? order by x
>       0.620 select timesten(x) from foo where x=? order by x