Overall Table of Contents

KInterbasDB Usage Guide

Contents


Introduction

The Firebird relational database engine has a large feature set, conforms closely to SQL standards, and is flexible enough to operate either as a standalone server or as an embedded library on diverse platforms. In spite of this versatility, the database is exceptionally easy to use--almost self-managing.

The Python programming language supports numerous paradigms, is suitable for the construction of very large programs, and integrates well with native C and C++ libraries. Despite the power of the language, well written Python code achieves an almost astonishing lucidity that has led some to call the language "executable pseudocode". Noted author and teacher Bruce Eckel has praised Python as

"the most efficient language I've ever used. It's 10 times better than any of the other tools I have used. It's free, it's object-oriented, it adapts to everything, it runs on everything. There is almost an indescribable, 'quality without a name' attraction on my part."

These two top-flight software tools intersect in a library named KInterbasDB. KInterbasDB implements Python's standard Database API 2.0, but also extends far beyond, to cover Firebird's entire native client API. KInterbasDB strives to deliver the power of Firebird into the hands of the Python programmer without compromising the qualities of either tool.

This Usage Guide is not a tutorial on Python, SQL, or Firebird; rather, it is a topical presentation of KInterbasDB's feature set, with example code to demonstrate basic usage patterns. This guide is meant to be consumed in conjunction with the Python Database API Specification and the Firebird documentation, especially the professional, seven-volume manual for Firebird's commercial ancestor, Interbase.

The table of contents presents a structural overview of this document.




Python Database API 2.0 Compliance

Incompatibilities

Unsupported Optional Features

Nominally Supported Optional Features

Extensions and Caveats

KInterbasDB offers a large feature set beyond the minimal requirements of the Python DB API. Most of these extensions are documented in the section of this document entitled Native Database Engine Features and Extensions Beyond the Python DB API.

This section attempts to document only those features that overlap with the DB API, or are too insignificant to warrant their own subsection elsewhere.


Tutorial

This brief tutorial aims to get the reader started by demonstrating elementary usage of KInterbasDB. It is not a comprehensive Python Database API tutorial, nor is it comprehensive in its coverage of anything else.

The numerous advanced features of KInterbasDB are covered in another section of this document, which is not in a tutorial format, though it is replete with examples.

Connecting to a Database

Example 1

A database connection is typically established with code such as this:

import kinterbasdb

# The server is named 'bison'; the database file is at '/temp/test.db'.
con = kinterbasdb.connect(dsn='bison:/temp/test.db', user='sysdba', password='pass')

# Or, equivalently:
con = kinterbasdb.connect(
    host='bison', database='/temp/test.db',
    user='sysdba', password='pass'
  )

Example 2

Suppose we want to connect to an Interbase 5.5 server, specifying UNICODE_FSS as the character set of the connection:

import kinterbasdb

con = kinterbasdb.connect(
    dsn='bison:/temp/test.db',
    user='sysdba', password='pass',
    dialect=1, # necessary for Interbase < 6.0
    charset='UNICODE_FSS' # specify a character set for the connection
  )


Executing SQL Statements

For this section, suppose we have a table defined and populated by the following SQL code:

create table people
(
  name_last      varchar(20),
  age            integer
);

insert into people (name_last, age) values ('Yeltsin',   72);
insert into people (name_last, age) values ('Putin',     51);

Example 1

This example shows the simplest way to print the entire contents of the people table:

import kinterbasdb

con = kinterbasdb.connect(
    dsn='bison:/temp/test.db',
    user='sysdba', password='pass'
  )

# Get a Cursor object that operates in the context of Connection con:
cur = con.cursor()

# Execute the SELECT statement:
cur.execute("select * from people order by age")

# Retrieve all rows as a sequence and print that sequence:
print cur.fetchall()

Sample output:

[('Putin', 51), ('Yeltsin', 72)]

Example 2

Here's another trivial example that demonstrates various ways of fetching a single row at a time from a SELECT-cursor:

import kinterbasdb

con = kinterbasdb.connect(
    dsn='bison:/temp/test.db',
    user='sysdba', password='pass'
  )

cur = con.cursor()
SELECT = "select name_last, age from people order by age, name_last"

# 1. Iterate over the rows available from the cursor, unpacking the
# resulting sequences to yield their elements (name_last, age):
cur.execute(SELECT)
for (name_last, age) in cur:
    print '%s is %d years old.' % (name_last, age)

# 2. Equivalently:
cur.execute(SELECT)
for row in cur:
    print '%s is %d years old.' % (row[0], row[1])

# 3. Using mapping-iteration rather than sequence-iteration:
cur.execute(SELECT)
for row in cur.itermap():
    print '%(name_last)s is %(age)d years old.' % row

# 4. Here's the ugly pre-iterator (i.e., Python 2.1) approach:
cur.execute(SELECT)
while 1:
    row = cur.fetchonemap()
    if not row:
        break
    print '%(name_last)s is %(age)d years old.' % row

Sample output:

Putin is 51 years old.
Yeltsin is 72 years old.
Putin is 51 years old.
Yeltsin is 72 years old.
Putin is 51 years old.
Yeltsin is 72 years old.
Putin is 51 years old.
Yeltsin is 72 years old.

Example 3

The following program is a simplistic table printer (applied in this example to people):

import kinterbasdb as k

TABLE_NAME = 'people'
SELECT = 'select * from %s order by age, name_last' % TABLE_NAME

con = k.connect(dsn='bison:/temp/test.db', user='sysdba', password='pass')

cur = con.cursor()
cur.execute(SELECT)

# Print a header.
for fieldDesc in cur.description:
    print fieldDesc[k.DESCRIPTION_NAME].ljust(fieldDesc[k.DESCRIPTION_DISPLAY_SIZE]) ,
print # Finish the header with a newline.
print '-' * 78

# For each row, print the value of each field left-justified within
# the maximum possible width of that field.
fieldIndices = range(len(cur.description))
for row in cur:
    for fieldIndex in fieldIndices:
        fieldValue = str(row[fieldIndex])
        fieldMaxWidth = cur.description[fieldIndex][k.DESCRIPTION_DISPLAY_SIZE]

        print fieldValue.ljust(fieldMaxWidth) ,

    print # Finish the row with a newline.

Sample output:

NAME_LAST            AGE
------------------------------------------------------------------------------
Putin                51
Yeltsin              72

Example 4

Let's insert more people into the people table:

import kinterbasdb

con = kinterbasdb.connect(
    dsn='bison:/temp/test.db',
    user='sysdba', password='pass'
  )

cur = con.cursor()

newPeople = (
    ('Lebed'       , 53),
    ('Zhirinovsky' , 57),
  )

for person in newPeople:
    cur.execute("insert into people (name_last, age) values (?, ?)", person)

# The changes will not be saved unless the transaction is committed explicitly:
con.commit()

Note the use of a parameterized SQL statement above. When dealing with repetitive statements, this is much faster and less error-prone than assembling each SQL statement manually.

It's also worth noting that in the example above, the code:

for person in newPeople:
    cur.execute("insert into people (name_last, age) values (?, ?)", person)
could be rewritten as:
cur.executemany("insert into people (name_last, age) values (?, ?)", newPeople)

After running Example 4, the table printer from Example 3 would print:

NAME_LAST            AGE
------------------------------------------------------------------------------
Putin                51
Lebed                53
Zhirinovsky          57
Yeltsin              72

Calling Stored Procedures

Interbase and Firebird support stored procedures written in a proprietary procedural SQL language. IB/FB stored procedures can have input parameters and/or output parameters. Some databases support input/output parameters, where the same parameter is used for both input and output; IB/FB does not support this.

It is important to distinguish between procedures that return a result set and procedures that populate and return their output parameters exactly once. Conceptually, the latter "return their output parameters" like a Python function, whereas the former "yield result rows" like a Python generator.

IB/FB's server-side procedural SQL syntax makes no such distinction, but client-side SQL code (and C API code) must. A result set is retrieved from a stored procedure by SELECTing from the procedure, whereas output parameters are retrieved with an EXECUTE PROCEDURE statement.

To retrieve a result set from a stored procedure with KInterbasDB, use code such as this:

cur.execute("select output1, output2 from the_proc(?, ?)", (input1, input2))

# Ordinary fetch code here, such as:
for row in cur:
    ... # process row

con.commit() # If the procedure had any side effects, commit them.

To execute a stored procedure and access its output parameters with KInterbasDB, use code such as this:

cur.callproc("the_proc", (input1, input2))

# If there are output parameters, retrieve them as though they were the
# first row of a result set.  For example:
outputParams = cur.fetchone()

con.commit() # If the procedure had any side effects, commit them.

This latter is not very elegant; it would be preferable to access the procedure's output parameters as the return value of Cursor.callproc. The Python DB API specification requires the current behavior, however.





Native Database Engine Features and Extensions Beyond the Python DB API


Programmatic Database Creation and Deletion

The Firebird engine stores a database in a fairly straightforward manner: as a single file or, if desired, as a segmented group of files.

The engine supports dynamic database creation via the SQL statement CREATE DATABASE, which is documented on page 49 of the Interbase 6 Language Reference.

The engine also supports dropping (deleting) databases dynamically, but dropping is a more complicated operation than creating, for several reasons: an existing database may be in use by users other than the one who requests the deletion, it may have supporting objects such as temporary sort files, and it may even have dependent shadow databases. Although the database engine recognizes a DROP DATABASE SQL statement, support for that statement is limited to the isql command-line administration utility. However, the engine supports the deletion of databases via an API call, which KInterbasDB exposes to Python (see below).

KInterbasDB supports dynamic database creation and deletion via the module-level function create_database and the method Connection.drop_database. These are documented below, then demonstrated by a brief example.

create_database  (function; member of kinterbasdb)

Creates a database according to the supplied CREATE DATABASE SQL statement. Returns an open connection to the newly created database.

Arguments:

  • sql - string containing the CREATE DATABASE statement.

    Note that this statement may need to include a username and password (see the IB 6 Language Reference for syntax).

  • dialect (optional) - the SQL dialect under which to execute the statement (defaults to 3).
drop_database  (method; member of kinterbasdb.Connection)

Deletes the database to which the connection is attached.

This method performs the database deletion in a responsible fashion. Specifically, it:

  • raises an OperationalError instead of deleting the database if there are other active connections to the database
  • deletes supporting files and logs in addition to the primary database file(s)

This method has no arguments.

Example program:

import kinterbasdb

con = kinterbasdb.create_database(
    "create database '/temp/db.db' user 'sysdba' password 'pass'"
  )
con.drop_database()

Database Event Notification

What are database events?

The database engine features a distributed, interprocess communication mechanism based on messages called database events. Chapter 11 of the Interbase 6 API Guide describes database events this way:

[A database event is] a message passed from a trigger or stored procedure to an application to announce the occurrence of a specified condition or action, usually a database change such as an insertion, modification, or deletion of a record.
The InterBase [and Firebird] event mechanism enables applications to respond to actions and database changes made by other, concurrently running applications without the need for those applications to communicate directly with one another, and without incurring the expense of CPU time required for periodic polling to determine if an event has occurred.

Why use database events?

Anything that can be accomplished with database events can also be implemented using other techniques, so why bother with events? Since you've chosen to write database-centric programs in Python rather than assembly language, you probably already know the answer to this question, but let's illustrate.

A classic application for database events is the handling of administrative messages. Suppose you have an administrative message database with a messages table, into which various applications insert timestamped status reports. It may be desirable to react to these messages in diverse ways, depending on the status they indicate: to ignore them, to initiate the update of dependent databases upon their arrival, to forward them by e-mail to a remote administrator, or even to set off an audible alarm so that on-site administrators will know a problem has occurred.

It is undesirable to tightly couple the program whose status is being reported (the message producer) to the program that handles the status reports (the message handler). There are obvious losses of flexibility in doing so. For example, the message producer may run on a separate machine from the administrative message database and may lack access rights to the downstream reporting facilities (e.g., network access to the SMTP server, in the case of forwarded e-mail notifications). Additionally, the actions required to handle status reports may themselves be time-consuming and error-prone, as in accessing a remote network to transmit e-mail.

In the absence of database event support, the message handler would probably be implemented via polling. Polling is simply the repetition of a check for a condition at a specified interval. In this case, the message handler would check in an infinite loop to see whether the most recent record in the messages table was more recent than the last message it had handled. If so, it would handle the fresh message(s); if not, it would go to sleep for a specified interval, then loop.

The polling-based implementation of the message handler is fundamentally flawed. Polling is a form of busy-wait; the check for new messages is performed at the specified interval, regardless of the actual activity level of the message producers. If the polling interval is lengthy, messages may not be handled within a reasonable time period after their arrival; if the polling interval is brief, the message handler program (and there may be many such programs) will waste a large amount of CPU time on unnecessary checks.

The database server is necessarily aware of the exact moment when a new message arrives. Why not let the message handler program request that the database server send it a notification when a new message arrives? The message handler can then efficiently sleep until the exact moment when its services are needed. Under this event-based scheme, the message handler becomes aware of new messages at the instant they arrive, yet it does not waste CPU time checking in vain for new messages when none is available.

How does the database engine expose events to SQL (in the server process) and C (in the client process)?

  1. Server Process ("An event just occurred!")

    Recall from Chapter 11 of the Interbase 6 API Guide that

    [A database event is] a message passed from a trigger or stored procedure to an application to announce the occurrence of a specified condition or action, usually a database change such as an insertion, modification, or deletion of a record.

    To notify any interested listeners that a specific event has occurred, issue the POST_EVENT statement (see page 176 of the Interbase 6 Language Reference). The POST_EVENT statement has one parameter: the name of the event to post.

    In the preceding example of the administrative message database, POST_EVENT might be used from an after insert trigger on the messages table, like this:

    create trigger trig_messages_handle_insert
      for messages
        after insert
    as
    begin
      POST_EVENT 'new_message';
    end
    

    Note that the physical notification of the client process does not occur until the transaction in which the POST_EVENT took place is actually committed. Therefore, multiple events may conceptually occur before the client process is physically informed of even one occurrence.

    Furthermore, the database engine makes no guarantee that clients will be informed of events in the same groupings in which they conceptually occurred. If, within a single transaction, an event named event_a is posted once and an event named event_b is posted once, the client may receive those posts in separate "batches", despite the fact that they occurred in the same conceptual unit (a single transaction). This also applies to multiple occurrences of the same event within a single conceptual unit: the physical notifications may arrive at the client separately.

  2. Client Process ("Send me a message when an event occurs.")

    Note: This section is intended mainly as an implementation hint to the authors of future libraries based on the Firebird C client library, and as a reminder to the author David Rushby himself, stored away in plain sight as a vaccination against the recurrence of the headaches that ensued from his attempt to use the most abysmally documented area of the Firebird C API. If you're a Python programmer who doesn't care about the gory details and isn't anxious to read a whole series of sentences as long as the previous one, skip to the section that describes KInterbasDB's Python-level event handling API.

    The Interbase/Firebird C client library offers two forms of event notification.

    The first form is synchronous notification, by way of the function isc_wait_for_event. This form is admirably simple for a C programmer to use, but completely inappropriate as a basis for KInterbasDB's event support, for two reasons. The database's C client library implements isc_wait_for_event via process suspension, which puts the entire process to sleep until an event notification arrives. This behavior clashes with multithreaded programs, which may need to have one thread enter a blocking wait for event notifications while other threads remain active. Secondly, process suspension is not available on the Windows platform, and the database client library does not implement isc_wait_for_event on Linux. Although the implementation of isc_wait_for_event makes it unsuitable for use by the internals of KInterbasDB, the ease with which it exposes database event notification to the client programmer is quite Pythonic, a fact that was not lost on the bleary-eyed Mr. Rushby.

    The other form of event notification offered by the database client library is asynchronous, by way of the functions isc_que_events (apparently the letters u and e were in short supply that day), isc_cancel_events, and others.

    The details are as nasty as they are numerous, but the essence of using asynchronous notification from C is as follows:

    1. Call isc_event_block to create a formatted binary buffer that will tell the server which events the client wants to listen for.
    2. Call isc_que_events (passing the buffer created in the previous step) to inform the server that the client is ready to receive event notifications, and provide a callback that will be asynchronously invoked when one or more of the registered events occurs.
    3. [The thread that called isc_que_events to initiate event listening must now do something else.]
    4. When the callback is invoked (the database client library starts a thread dedicated to this purpose), it can use the isc_event_counts function to determine how many times each of the registered events has occurred since the last call to isc_event_counts (if any).
    5. [The callback thread should now "do its thing", which may include communicating with the thread that called isc_que_events.]
    6. When the callback thread is finished handling an event notification, it must call isc_que_events again in order to receive future notifications. Future notifications will invoke the callback again, effectively "looping" the callback thread back to Step 4.

    As implemented by the Interbase/Firebird C client library, asynchronous event notification suffers from a significant limitation: only one thread per process can listen for events at any given time.

    The rest of this section describes the C-level internals of KInterbasDB's event support; the exposed Python API is documented in the next section.

    KInterbasDB's event-related internals conform loosely to the outline above, although the Python interpreter's own threading limitations complicate matters greatly. Let's fill in the blanks of Steps 3 and 5 from the outline with specific descriptions.

    3. [The thread that called isc_que_events to initiate event listening must now do something else.]

    In KInterbasDB, "the thread that called isc_que_events to initiate event listening" is a native thread started by Python (either explicitly by the Python programmer, or implicitly by the Python interpreter to run the main program); let's call it Thread-Py.

    Thread-Py, running in KInterbasDB's C layer, executes Steps 1 and 2, then waits on a native event object (on Win32, an Event; on POSIX, a pair of pthread_cond_t and pthread_mutex_t).

    5. [The callback thread should now "do its thing", which may include communicating with the thread that called isc_que_events.]

    In KInterbasDB, the "callback thread" is a native thread started by the database's C client library; let's call it Thread-Ev.

    The client library actually starts Thread-Ev as soon as Thread-Py calls isc_que_events, without waiting for any events to occur. This initial "dummy run" gives Thread-Ev a chance to perform any necessary initialization. In KInterbasDB, this consists merely of clearing the buffer used to tally the occurrence counts of the registered events (and of re-queueing Thread-Ev to receive future event notifications--Step 6 from the outline).

    Thread-Ev, having been started by the database client library rather than the Python interpreter, is a "naked" native thread. Although any thread started via Python's thread or threading modules is a full-fledged native thread, a "naked" thread must have Python threadstate bootstrapped onto it before the Python interpreter can execute Python code on that thread. Thread-Ev does not need any Python threadstate, however, because KInterbasDB's C-level event callback function is designed to avoid Python code and operate solely in native C.

    Step 5 consists of the callback thread "doing its thing"; in KInterbasDB, the mission of Thread-Ev during a given iteration of the event callback is threefold: first, to supply Thread-Py with enough information to generate the return value required by the interface of the EventConduit.wait method; secondly, to notify Thread-Py that an event has occurred; and finally (Step 6 from the outline) for Thread-Ev to re-queue itself via isc_que_events so that the callback will be invoked upon the occurrence of future events.

    To accomplish the first element of the mission, Thread-Ev inserts a node into a C linked list associated with the EventConduit upon which Thread-Py is waiting. Every EventConduit holds a C linked list of type EventQueue. An EventQueue is comprised of EventQueueItems; each EventQueueItem contains an array of C longs. This array is designed to hold the occurrence counts of the registered events, as reported by isc_event_counts during the ongoing iteration of the event callback by Thread-Ev.

    Secondly, Thread-Ev issues a native event notification (SetEvent on Win32; pthread_cond_signal on POSIX) to release Thread-Py from its blocking call to EventConduit.wait.

    Finally, Thread-Ev re-queues itself via isc_que_events, and control of the thread passes out of the callback and back into the database client library. The database client library does not start and destroy a new thread per event notification; rather, it starts the thread we've nicknamed Thread-Ev upon the first call to isc_que_events, then reuses that same thread for all future event notifications within the same process. This thread is recycled even for notifications that concern a different set of event names (in KInterbasDB terminology, "even for a different EventConduit object").

    When Thread-Py is awakened by Thread-Ev, it retrieves the head EventQueueItem from the EventQueue, extracts the long values from that node's count array into a Python dictionary that maps event name -> event occurence count, and returns the dictionary to the Python programmer as the return value of EventConduit.wait.

    Throughout this process, responsible thread synchronization is observed (with respect to the Python interpreter, the database client library, and a specific EventQueue). There is one avoidable scenario in which deadlocks are possible; it is documented in the Pitfalls and Limitations subsection.

    The high-level event handling API that KInterbasDB exposes to the Python programmer is documented in the next section.

How does KInterbasDB expose database events to the Python programmer?

The KInterbasDB database event API is comprised of the following: the method Connection.event_conduit and the class EventConduit.

event_conduit  (method; member of kinterbasdb.Connection)

Creates a conduit (an instance of EventConduit) through which database event notifications will flow into the Python program.

event_conduit is a method of Connection rather than a module-level function or a class constructor because the database engine deals with events in the context of a particular database (after all, POST_EVENT must be issued by a stored procedure or a trigger).

Arguments:

  • event_names - a sequence of string event names

    The EventConduit.wait method will block until the occurrence of at least one of the events named by the strings in event_names.

EventConduit:

__init__  (method; member of kinterbasdb.EventConduit)

The EventConduit class is not designed to be instantiated directly by the Python programmer. Instead, use the Connection.event_conduit method to create EventConduit instances.

wait  (method; member of kinterbasdb.EventConduit)

Blocks the calling thread until at least one of the events occurs, or the specified timeout (if any) expires.

If one or more event notifications has arrived since the last call to wait, this method will retrieve a notification from the head of the EventConduit's internal queue and return immediately.

The names of the relevant events were supplied to the Connection.event_conduit method during the creation of this EventConduit. In the code snippet below, the relevant events are named event_a and event_b:

conduit = connection.event_conduit( ('event_a', 'event_b') )
conduit.wait()

Arguments:

  • timeout (optional) - number of seconds (use a float to indicate fractions of seconds)

    If not even one of the relevant events has occurred after timeout seconds, this method will unblock and return None. The default timeout is infinite.

Returns:

None if the wait timed out, otherwise a dictionary that maps event_name -> event_occurrence_count.

In the code snippet above, if event_a occurred once and event_b did not occur at all, the return value from conduit.wait() would be the following dictionary:

{
  'event_a': 1,
  'event_b': 0
}
close  (method; member of kinterbasdb.EventConduit)

Cancels the standing request for this conduit to be notified of events, clearing the way for the creation of another EventConduit (via the Connection.event_conduit method).

After this method has been called, this EventConduit object is useless, and should be discarded.

This method has no arguments.

flush  (method; member of kinterbasdb.EventConduit)

This method allows the Python programmer to manually clear any event notifications that have queued up since the last wait call.

After the first wait call on a given EventConduit, notifications of any events that occur will accumulate asynchronously within the conduit's internal queue until the conduit is closed either explicitly or implicitly (via garbage collection). There are two ways to dispose of the accumulated notifications: call wait to receive them one at a time (wait will block when the conduit's internal queue is empty), or call this method to get rid of all accumulated notifications.

This method has no arguments.

Returns:

The number of event notifications that were flushed from the queue. The "number of event notifications" is not necessarily the same as the "number of event occurrences", since a single notification can indicate multiple occurrences of a given event (see the return value of the wait method).



Example Program

The following code (a SQL table definition, a SQL trigger definition, and two Python programs) demonstrates KInterbasDB-based event notification.

The example is based on a database at 'localhost:/temp/test.db', which contains a simple table named test_tabletest_table has an after insert trigger that posts several events. Note that the trigger posts test_event_a twice, test_event_b once, and test_event_c once.

The Python event handler program connects to the database and establishes an EventConduit in the context of that connection. As specified by the list of RELEVANT_EVENTS passed to event_conduit, the event conduit will concern itself only with events named test_event_a and test_event_b. Next, the program calls the conduit's wait method without a timeout; it will wait infinitely until at least one of the relevant events is posted in a transaction that is subsequently committed.

The Python event producer program simply connects to the database, inserts a row into test_table, and commits the transaction. Notice that except for the printed comment, no code in the producer makes any mention of events--the events are posted as an implicit consequence of the row's insertion into test_table.

The insertion into test_table causes the trigger to conceptually post events, but those events are not physically sent to interested listeners until the transaction is committed. When the commit occurs, the handler program returns from the wait call and prints the notification that it received.

SQL table definition:

create table test_table (a integer)

SQL trigger definition:

create trigger trig_test_insert_event
  for test_table
    after insert
as
begin
  POST_EVENT 'test_event_a';
  POST_EVENT 'test_event_b';
  POST_EVENT 'test_event_c';

  POST_EVENT 'test_event_a';
end

Python event handler program:

import kinterbasdb

RELEVANT_EVENTS = ['test_event_a', 'test_event_b']

con = kinterbasdb.connect(dsn='localhost:/temp/test.db', user='sysdba', password='pass')
conduit = con.event_conduit(RELEVANT_EVENTS)

print 'HANDLER: About to wait for the occurrence of one of %s...\n' % RELEVANT_EVENTS
result = conduit.wait()
print 'HANDLER: An event notification has arrived:'
print result
conduit.close()

Python event producer program:

import kinterbasdb

con = kinterbasdb.connect(dsn='localhost:/temp/test.db', user='sysdba', password='pass')
cur = con.cursor()

cur.execute("insert into test_table values (1)")
print 'PRODUCER: Committing transaction that will cause event notification to be sent.'
con.commit()

Event producer output:

PRODUCER: Committing transaction that will cause event notification to be sent.

Event handler output (assuming that the handler was already started and waiting when the event producer program was executed):

HANDLER: About to wait for the occurrence of one of ['test_event_a', 'test_event_b']...

HANDLER: An event notification has arrived:
{'test_event_a': 2, 'test_event_b': 1}

Notice that there is no mention of test_event_c in the result dictionary received by the event handler program. Although test_event_c was posted by the after insert trigger, the event conduit in the handler program was created to listen only for test_event_a and test_event_b events.


Pitfalls and Limitations




Advanced Transaction Control

For the sake of simplicity, KInterbasDB lets the Python programmer ignore transaction management to the greatest extent allowed by the Python Database API Specification 2.0. The specification says, "if the database supports an auto-commit feature, this must be initially off". At a minimum, therefore, it is necessary to call the commit method of the connection in order to persist any changes made to the database. Transactions left unresolved by the programmer will be rollbacked when the connection is garbage collected.

Remember that because of ACID, every data manipulation operation in the Interbase/Firebird database engine takes place in the context of a transaction, including operations that are conceptually "read-only", such as a typical SELECT. The client programmer of KInterbasDB establishes a transaction implicitly by using any SQL execution method, such as Connection.execute_immediate, Cursor.execute, or Cursor.callproc.

Although KInterbasDB allows the programmer to pay little attention to transactions, it also exposes the full complement of the database engine's advanced transaction control features: transaction parameters, retaining transactions, savepoints, and distributed transactions.


Transaction Parameters

The database engine offers the client programmer an optional facility called transaction parameter buffers (TPBs) for tweaking the operating characteristics of the transactions he initiates. These include characteristics such as "whether the transaction has read and write access to tables, or read-only access, and whether or not other simultaneously active transactions can share table access with the transaction" (IB 6 API Guide, page 62).

In addition to the implicit transaction initiation mentioned in the introduction of this section, KInterbasDB allows the programmer to start transactions explicitly via the Connection.begin method. Connections have a default_tpb attribute that can be changed to set the default TPB for all transactions subsequently started on the connection. Alternatively, if the programmer only wants to set the TPB for a single transaction, he can start a transaction explicitly via the Connection.begin method and pass a TPB for that single transaction.

For details about TPB construction, see Chapter 5 of the Interbase 6 API Guide. In particular, page 63 of that document presents a table of possible TPB elements--single bytes that the C API defines as constants whose names begin with isc_tpb_. KInterbasDB makes all of those TPB constants available (under the same names) as module-level constants in the form of single-character strings. A transaction parameter buffer is handled in C as a character array; KInterbasDB requires that TPBs be constructed as Python strings. Since the constants in the kinterbasdb.isc_tpb_* family are single-character Python strings, they can simply be concatenated to create a TPB.

The following example program uses explicit transaction initiation and TPB construction to establish an unobtrusive transaction for read-only access to the database:

import kinterbasdb

con = kinterbasdb.connect(dsn='localhost:/temp/test.db', user='sysdba', password='pass')

# Construct a TPB by concatenating single-character strings (bytes)
# from the kinterbasdb.isc_tpb_* family.
customTPB = (
      kinterbasdb.isc_tpb_read
    + kinterbasdb.isc_tpb_read_committed
    + kinterbasdb.isc_tpb_rec_version
  )

# Explicitly start a transaction with the custom TPB:
con.begin(tpb=customTPB)

# Now read some data using cursors:
...

# Commit the transaction with the custom TPB.  Future transactions
# opened on con will not use a custom TPB unless it is explicitly
# passed to con.begin every time, as it was above, or
# con.default_tpb is changed to the custom TPB, as in:
#   con.default_tpb = customTPB
con.commit()

Retaining Operations

The commit and rollback methods of kinterbasdb.Connection accept an optional boolean parameter retaining (default False) to indicate whether to recycle the transactional context of the transaction being resolved by the method call.

If retaining is True, the infrastructural support for the transaction active at the time of the method call will be "retained" (efficiently and transparently recycled) after the database server has committed or rolled back the conceptual transaction. In code that commits or rolls back frequently, "retaining" the transaction yields considerably better performance.

For more information about retaining transactions, see page 291 of the Interbase 6 API Guide.


Savepoints

Firebird 1.5 introduced support for transaction savepoints. Savepoints are named, intermediate control points within an open transaction that can later be rolled back to, without affecting the preceding work. Multiple savepoints can exist within a single unresolved transaction, providing "multi-level undo" functionality.

Although Firebird savepoints are fully supported from SQL alone via the SAVEPOINT 'name' and ROLLBACK TO 'name' statements, KInterbasDB also exposes savepoints at the Python API level for the sake of convenience. The method Connection.savepoint(name) establishes a savepoint with the specified name. To roll back to a specific savepoint, call the Connection.rollback method and provide a value (the name of the savepoint) for the optional savepoint parameter. If the savepoint parameter of Connection.rollback is not specified, the active transaction is cancelled in its entirety, as required by the Python Database API Specification.

The following example program demonstrates savepoint manipulation via the KInterbasDB API, rather than raw SQL.

import kinterbasdb

con = kinterbasdb.connect(dsn='localhost:/temp/test.db', user='sysdba', password='pass')
cur = con.cursor()

cur.execute("recreate table test_savepoints (a integer)")
con.commit()

print 'Before the first savepoint, the contents of the table are:'
cur.execute("select * from test_savepoints")
print ' ', cur.fetchall()

cur.execute("insert into test_savepoints values (?)", [1])
con.savepoint('A')
print 'After savepoint A, the contents of the table are:'
cur.execute("select * from test_savepoints")
print ' ', cur.fetchall()

cur.execute("insert into test_savepoints values (?)", [2])
con.savepoint('B')
print 'After savepoint B, the contents of the table are:'
cur.execute("select * from test_savepoints")
print ' ', cur.fetchall()

cur.execute("insert into test_savepoints values (?)", [3])
con.savepoint('C')
print 'After savepoint C, the contents of the table are:'
cur.execute("select * from test_savepoints")
print ' ', cur.fetchall()

con.rollback(savepoint='A')
print 'After rolling back to savepoint A, the contents of the table are:'
cur.execute("select * from test_savepoints")
print ' ', cur.fetchall()

con.rollback()
print 'After rolling back entirely, the contents of the table are:'
cur.execute("select * from test_savepoints")
print ' ', cur.fetchall()

The output of the example program is shown below.

Before the first savepoint, the contents of the table are:
  []
After savepoint A, the contents of the table are:
  [(1,)]
After savepoint B, the contents of the table are:
  [(1,), (2,)]
After savepoint C, the contents of the table are:
  [(1,), (2,), (3,)]
After rolling back to savepoint A, the contents of the table are:
  [(1,)]
After rolling back entirely, the contents of the table are:
  []

Distributed Transactions

XXX: KInterbasDB's support for distributed transactions has not yet been thoroughly documented. In the meantime, read the source code for the kinterbasdb.ConnectionGroup class and examine the brief example program below.
import kinterbasdb

# Establish multiple connections the usual way:
con1 = kinterbasdb.connect(dsn='stalin:/temp/test.db', user='sysdba', password='pass')
con2 = kinterbasdb.connect(dsn='bison:/temp/test.db', user='sysdba', password='pass')

# Create a ConnectionGroup to associate multiple connections in such a
# way that they can participate in a distributed transaction.
# !!!
# NO TWO MEMBERS OF A SINGLE CONNECTIONGROUP SHOULD BE ATTACHED TO THE SAME DATABASE!
# !!!
group = kinterbasdb.ConnectionGroup( connections=(con1,con2) )

# Start a distributed transaction involving all of the members of the group
# (con1 and con2 in this case) with one of the following approaches:
#   - Call  group.begin()
#   - Call  con1.begin(); the operation will "bubble upward" and apply to the group.
#   - Call  con2.begin(); the operation will "bubble upward" and apply to the group.
#   - Just start executing some SQL statements on either con1 or con2.
#     A transaction will be started implicitly; it will be a distributed
#     transaction because con1 and con2 are members of a ConnectionGroup.
group.begin()

# Perform some database changes the usual way (via cursors on con1 and con2):
...

# Commit or roll back the distributed transaction by calling the commit
# or rollback method of the ConnectionGroup itself, or the commit or
# rollback method of any member connection (con1 or con2 in this case).
group.commit()

# Unless you want to perform another distributed transaction, disband the
# group so that member connections can operate independently again.
group.clear()

Notes:

While a Connection belongs to a ConnectionGroup, any calls to the connection's transactional methods (begin, commit, rollback) will "bubble upward" to apply to the distributed transaction shared by the group as a whole.

Connections can be dynamically added and removed from a ConnectionGroup provided that neither the group nor the connection itself has an unresolved transaction at the time of the addition/removal.

Never add two connections to the same database to the same ConnectionGroup!





Parameter Conversion

KInterbasDB converts bound parameters marked with a ? in SQL code in a standard way. However, the module also offers several extensions to standard parameter binding, intended to make client code more readable and more convenient to write.

Implicit Conversion of Input Parameters from Strings

The database engine treats most SQL data types in a weakly typed fashion: the engine may attempt to convert the raw value to a different type, as appropriate for the current context. For instance, the SQL expressions 123 (integer) and '123' (string) are treated equivalently when the value is to be inserted into an integer field; the same applies when '123' and 123 are to be inserted into a varchar field.

This weak typing model is quite unlike Python's dynamic yet strong typing. Although weak typing is regarded with suspicion by most experienced Python programmers, the database engine is in certain situations so aggressive about its typing model that KInterbasDB must compromise in order to remain an elegant means of programming the database engine.

An example is the handling of "magic values" for date and time fields. The database engine interprets string values such as 'yesterday', 'now', and 'current_timestamp' as having special meanings in a date/time context. If KInterbasDB did not accept strings as the values of parameters destined for storage in date/time fields, the resulting programs would be awkward. For example, consider the difference between the two Python code snippets below, which insert a row containing an integer and a timestamp into a table defined with the following DDL statement:

create table test_table (i int, t timestamp)
i = 1
t = 'now'
sqlWithMagicValues = "insert into test_table (i, t) values (?, '%s')" % t
cur.execute( sqlWithMagicValues, (i,) )
i = 1
t = 'now'
cur.execute( "insert into test_table (i, t) values (?, ?)", (i, t) )

If KInterbasDB did not support weak parameter typing, string parameters that the database engine is to interpret as "magic values" would have to be rolled into the SQL statement in a separate operation from the binding of the rest of the parameters, as in the first Python snippet above. Implicit conversion of parameter values from strings allows the consistency evident in the second snippet, which is both more readable and more general.

It should be noted that KInterbasDB does not perform the conversion from string itself. Instead, it passes that responsibility to the database engine by changing the parameter metadata structure dynamically at the last moment, then restoring the original state of the metadata structure after the database engine has performed the conversion.

A secondary benefit is that when one uses KInterbasDB to import large amounts of data from flat files into the database, the incoming values need not necessarily be converted to their proper Python types before being passed to the database engine. Eliminating this intermediate step may accelerate the import process considerably, although other factors such as the chosen connection protocol and the deactivation of indexes during the import are more consequential. For bulk import tasks, the database engine's external tables also deserve consideration. External tables can be used to suck semi-structured data from flat files directly into the relational database without the intervention of an ad hoc conversion program.

Dynamic Type Translation

Dynamic type translators are conversion functions registered by the Python programmer to transparently convert database field values to and from their internal representation.

The client programmer can choose to ignore translators altogether, in which case KInterbasDB will manage them behind the scenes. Otherwise, the client programmer can use any of several standard type translators included with KInterbasDB, register custom translators, or set the translators to None to deal directly with the KInterbasDB-internal representation of the data type. When translators have been registered for a specific SQL data type, Python objects on their way into a database field of that type will be passed through the input translator before they are presented to the database engine; values on their way out of the database into Python will be passed through the corresponding output translator. Output and input translation for a given type is usually implemented by two different functions.

Specifics of the Dynamic Type Translation API

Translators are registered with the [set|get]_type_trans_[in|out] methods of Connection and Cursor. The set_type_trans_[in|out] methods accept a single argument: a mapping of type name to translator. The get_type_trans[in|out] methods return a copy of the translation table. Cursors inherit their Connection's translation settings, but can override them without affecting the connection or other cursors (much as subclasses can override the methods of their base classes).

The following code snippet installs an input translator for fixed point types (NUMERIC/DECIMAL SQL types) into a connection:

con.set_type_trans_in( {'FIXED': fixed_input_translator_function} )

The following method call retrieves the type translation table for con:

con.get_type_trans_in()

The method call above would return a translation table (dictionary) such as this:

{
  'DATE': <function date_conv_in at 0x00920648>,
  'TIMESTAMP': <function timestamp_conv_in at 0x0093E090>,
  'FIXED': <function <lambda> at 0x00962DB0>,
  'TIME': <function time_conv_in at 0x009201B0>
}

Notice that although the sample code registered only one type translator, there are four listed in the mapping returned by the get_type_trans_in method. KInterbasDB itself uses dynamic type translation to implement mx.DateTime-based date/time I/O, and to implement the deprecated Connection.precision_mode API. For the source code locations of KInterbasDB's reference translators, see the table in the next section. The Connection.precision_mode API is deprecated because using it in combination with dynamic type translation is error-prone. KInterbasDB itself installs new dynamic type translators when the value of Connection.precision_mode is changed; if the programmer has previously registered input or output translators for 'FIXED' types, those translators will be overwritten.

In the sample above, a translator is registered under the key 'FIXED', but Firebird has no SQL data type named FIXED. The following table lists the names of the database engine's SQL data types in the left column, and the corresponding key under which client programmers can register translators in the right column.

Mapping of SQL Data Type Names to Translator Keys
SQL Type(s) Translator Key
CHAR/VARCHAR 'TEXT'
BLOB 'BLOB'
SMALLINT/INTEGER/BIGINT 'INTEGER'
FLOAT/DOUBLE PRECISION 'FLOATING'
NUMERIC/DECIMAL 'FIXED'
DATE 'DATE'
TIME 'TIME'
TIMESTAMP 'TIMESTAMP'

Consequences of the Availability of Dynamic Type Translation in KInterbasDB

Dynamic type translation has eliminated KInterbasDB's compile-time dependency on mx.DateTime. Although KInterbasDB will continue to use mx.DateTime as its default date/time representation for the sake of backward compatibility, dynamic type translation allows Python 2.3 users to conveniently deal with database date/time values in terms of the new standard library module datetime, if they choose to.

Dynamic type translation also allows NUMERIC/DECIMAL values to be transparently represented as fixedpoint.FixedPoint objects rather than scaled integers, which is much more convenient. For backward compatibility, NUMERIC/DECIMAL values are still represented by default as Python floats, and the older API based on Connection.precision_mode is still present. However, all of these representations are now implemented "under the hood" via dynamic type translation.

Reference implementations of all of the translators discussed above are provided with KInterbasDB 3.1_pre4 and later, in these modules:

Reference Translators Included with KInterbasDB
SQL Type(s) Python Type(s) Reference Implementation In Module
NUMERIC/DECIMAL float (imprecise) (default) kinterbasdb.typeconv_fixed_stdlib
scaled int (precise) kinterbasdb.typeconv_fixed_stdlib
fixedpoint.FixedPoint (precise) kinterbasdb.typeconv_fixed_fixedpoint
DATE/TIME/TIMESTAMP mx.DateTime (default) kinterbasdb.typeconv_datetime_mx
Python 2.3+ datetime kinterbasdb.typeconv_datetime_stdlib

Writing Custom Translators

Below is a table that specifies the required argument and return value signatures of input and output converters for the various translator keys. Python's native types map perfectly to 'TEXT', 'BLOB', 'INTEGER', and 'FLOATING' types, so in those cases the translator signatures are very simple. The signatures for 'FIXED', 'DATE', 'TIME', and 'TIMESTAMP' are not as simple because Python (before 2.3) lacks native types to represent these values with both precision and convenience. KInterbasDB handles 'FIXED' values internally as scaled integers; the date and time types as tuples.

KInterbasDB itself uses translators implemented according to the rules in the table below; the code for these reference translators can be found in the Python modules named kinterbasdb.typeconv_* (see the table in the previous section for details).

Signature Specifications for Input and Output Translators
Translator Key Input Translator Argument/Return Value Signature Output Translator Signature
'TEXT' Args: a single Python string argument (or None)

Returns: a single Python string
Same signature as input translator, except that return value is not constrained.
'BLOB' Same signature as above. Same signature as input translator, except that return value is not constrained.
'INTEGER' Args: a single Python int argument (or None)

Returns: a single Python int
Same signature as input translator, except that return value is not constrained.
'FLOATING' Args: a single Python float argument (or None)
Returns: a single Python float
Same signature as input translator, except that return value is not constrained.
'FIXED' Args: a single Python 2-tuple argument containing a scaled Python integer in the first element and the scale factor in the second element (the tuple is of the form (val, scale)). val will be None if the database field was NULL.

Returns: a single Python integer, scaled appropriately
Same signature as input translator, except that return value is not constrained.
'DATE' Args: an instance of the chosen date type (such as Python 2.3's datetime.date) or None

Returns: a single Python 3-tuple of the form (year, month, day)
Args: a single Python 3-tuple of the form (year, month, day) (or None if the database field was NULL)

Return value is not constrained.
'TIME' Args: an instance of the chosen time type (such as Python 2.3's datetime.time) or None

Returns: a single Python 3-tuple of the form (hour, minute, second)
Accept a single Python 3-tuple of the form (hour, minute, second) (or None if the database field was NULL).

Return value is not constrained.
'TIMESTAMP' Args: an instance of the chosen time type (such as Python 2.3's datetime.datetime) or None

Returns: a single Python 6-tuple of the form (year, month, day, hour, minute, second)
Args: a single Python 3-tuple of the form (year, month, day, hour, minute, second). (or None if the database field was NULL).

Return value is not constrained.


Example Program

import datetime # Python 2.3 standard library module

import kinterbasdb
import kinterbasdb.typeconv_datetime_stdlib as tc_dt


def connect(*args, **kwargs):
    """
      This wrapper around kinterbasdb.connect creates connections that use
    the datetime module (which entered the standard library in Python 2.3)
    for both input and output of DATE, TIME, and TIMESTAMP database fields.
      This wrapper simply registers kinterbasdb's official date/time
    translators for the datetime module, which reside in the
    kinterbasdb.typeconv_datetime_stdlib module.
      An equivalent set of translators for mx.DateTime (which kinterbasdb
    uses by default for backward compatibility) resides in the
    kinterbasdb.typeconv_datetime_mx module.
      Note that because cursors inherit their connection's dynamic type
    translation settings, cursors created upon connections returned by this
    function will also use the datetime module.
    """
    con = kinterbasdb.connect(*args, **kwargs)

    con.set_type_trans_in({
        'DATE':             tc_dt.date_conv_in,
        'TIME':             tc_dt.time_conv_in,
        'TIMESTAMP':        tc_dt.timestamp_conv_in,
      })

    con.set_type_trans_out({
        'DATE':             tc_dt.date_conv_out,
        'TIME':             tc_dt.time_conv_out,
        'TIMESTAMP':        tc_dt.timestamp_conv_out,
      })

    return con


def _test():
    con = connect(dsn='localhost:/temp/test.db', user='sysdba', password='pass')
    cur = con.cursor()

    # Retrieve the current timestamp of the database server.
    cur.execute("select current_timestamp from rdb$database")
    curStamp = cur.fetchone()[0]
    print 'The type of curStamp is', type(curStamp)
    print 'curStamp is', curStamp

    # Create a test table with a single TIMESTAMP column.
    con.execute_immediate("recreate table test_stamp (a timestamp)")
    con.commit()

    # Insert a timestamp into the database, then retrieve it.
    py23StandardLibTimestamp = datetime.datetime.now()
    cur.execute("insert into test_stamp values (?)", (py23StandardLibTimestamp,))
    cur.execute("select * from test_stamp")
    curStamp = cur.fetchone()[0]
    print 'The type of curStamp is', type(curStamp)
    print 'curStamp is', curStamp

    con.rollback()


if __name__ == '__main__':
    _test()

Sample output:

The type of curStamp is <type 'datetime.datetime'>
curStamp is 2003-05-20 03:55:42
The type of stamp is <type 'datetime.datetime'>
stamp is 2003-05-20 03:55:42

Deferred Loading of Dynamic Type Translators

In versions of KInterbasDB prior to 3.1_pre5, there was a difficulty due to backward compatibility constraints: KInterbasDB would unconditionally import the mx.DateTime module initially, even if the client programmer did not intend to use it. Although the advent of dynamic type translation technically obviated KInterbasDB's dependency on the mx package, KInterbasDB still required that the mx package be available due to the aforementioned unconditional import.

KInterbasDB 3.1_pre5 introduces a workaround: it defers the loading of the dynamic type translators so that the client programmer can forestall an attempt to import third-party modules he has no intention of using. The new kinterbasdb.init function takes a keyword argument type_conv, which controls KInterbasDB's initial choice of type translators. type_conv can be either an integer or an object that has all of the attributes named in kinterbasdb.BASELINE_TYPE_TRANSLATION_FACILITIES (an example of such an object is the module kinterbasdb.typeconv_backcompat). If type_conv is an integer, it will cause KInterbasDB to use one of the following predefined type translator configurations:

type_conv integer "convenience code" Resulting translator configuration
0

Minimal type translators that represent date/time values as tuples and fixed point values as either floats or scaled integers, depending on the connection's precision_mode.

Implemented by the kinterbasdb.typeconv_naked module.

1
(default)

Backward-compatible type translators that represent date/time values via the mx.DateTime module and fixed point values as either floats or scaled integers, depending on the connection's precision_mode.

Implemented by the kinterbasdb.typeconv_backcompat module.

This translator configuration, which is the default, perfectly mimics the behavior of KInterbasDB 3.0.

100

This translator configuration, which is intended for use with Python 2.3 and later, represents date/time values via the new standard library module datetime and fixed point values via the third-party fixedpoint module.

Implemented by the kinterbasdb.typeconv_23plus module.

These integer type conversion codes are defined solely for convenience. The same functionality is available via the object variant of type_conv, but setting it up is more laborious for typical translator configurations.

It is anticipated that Python 2.4 or 2.5 will introduce a fixed point module into the standard library. For convenience, a set of type translators will be added to the official KInterbasDB distribution to support it. At that time, the combination of date/time handling via the standard library datetime module and fixed point handling via the standard library fixed point module will become the "ideal" translator configuration.

Deferred Loading: Backward Compatibility Issues

The deferred type translator loading scheme introduced in KInterbasDB 3.1_pre5 goes to great lengths to maintain backward compatibility. If the client programmer does not call kinterbasdb.init, KInterbasDB will implicitly initialize itself in a backward-compatible manner (type_conv=1) the first time one of its public functions is called or one of its public classes is instantiated.

The only known backward incompatiblity is this: the DB API type comparison singleton DATETIME will not compare equal to any type until the kinterbasdb.init function has been called (whether explicitly or implicitly). After kinterbasdb.init has been called, DATETIME will compare equal to the date, time, and timestamp types that were loaded.

This issue should affect hardly any existing KInterbasDB-based programs.



Database Arrays

KInterbasDB converts database arrays from Python sequences (except strings) on input; to Python lists on output. On input, the Python sequence must be nested appropriately if the array field is multi-dimensional, and the incoming sequence must not fall short of its maximum possible length (it will not be "padded" implicitly--see below). On output, the lists will be nested if the database array has multiple dimensions.

Database arrays have no place in a purely relational data model, which requires that data values be atomized (that is, every value stored in the database must be reduced to elementary, non-decomposable parts). The Interbase/Firebird implementation of database arrays, like that of most relational database engines that support this data type, is fraught with limitations.

First of all, the database engine claims to support up to 16 dimensions, but actually malfunctions catastrophically above 10 (this bug is fixed in Firebird 1.5-RC1 and later, thanks to Dmitry Yemanov).

Database arrays are of fixed size, with a predeclared number of dimensions and number of elements per dimension. Individual array elements cannot be set to NULL/None, so the mapping between Python lists (which have dynamic length and are therefore not normally "padded" with dummy values) and non-trivial database arrays is clumsy.

Stored procedures cannot have array parameters.

Finally, many interface libraries, GUIs, and even the isql command line utility do not support database arrays.

In general, it is preferrable to avoid using database arrays unless you have a compelling reason.

Example Program

The following example program inserts a 3-d array (nested Python list) into a single database field, then retrieves it.

import kinterbasdb

con = kinterbasdb.connect(dsn='localhost:/temp/test.db', user='sysdba', password='pass')
con.execute_immediate("recreate table array_table (a int[3,4])")
con.commit()

cur = con.cursor()

arrayIn = [
    [1, 2, 3, 4],
    [5, 6, 7, 8],
    [9,10,11,12]
  ]

print 'arrayIn:  %s' % arrayIn
cur.execute("insert into array_table values (?)", (arrayIn,))

cur.execute("select a from array_table")
arrayOut = cur.fetchone()[0]
print 'arrayOut: %s' % arrayOut

con.commit()

Output:

arrayIn:  [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
arrayOut: [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]



Named Cursors

The read/write property Cursor.name allows the Python programmer to perform scrolling UPDATEs or DELETEs via the "SELECT ... FOR UPDATE" syntax. If you don't know what this means, refer to the section of the Interbase 6 Language Reference that covers the SELECT statement (page 139). The Cursor.name property can be ignored entirely if you don't need to use it.

Example Program

import kinterbasdb

con = kinterbasdb.connect(dsn='localhost:/temp/test.db', user='sysdba', password='pass')
curScroll = con.cursor()
curUpdate = con.cursor()

curScroll.execute("select city from addresses for update")
curScroll.name = 'city_scroller'
update = "update addresses set city=? where current of " + curScroll.name

for (city,) in curScroll:
    city = ... # make some changes to city
    curUpdate.execute( update, (city,) )

con.commit()



Programmatic Server, Database, and User Maintenance

Services API

(XXX: not yet documented, but already implemented by the kinterbasdb.services module)

Establishing Services API Connections

(XXX: not yet documented)

Querying Server Configuration, Resource Usage, and Logs

(XXX: not yet documented)

Querying Database Statistics

(XXX: not yet documented)

Database Operating Modes, Sweeps, and Repair

(XXX: not yet documented)

Backup and Restoration

(XXX: not yet documented)

User Maintenance

(XXX: not yet documented)



The database_info Method

database_info  (method; member of kinterbasdb.Connection)

Wraps the Interbase C API function isc_database_info . For extensive documentation, see the Interbase 6 API Guide section entitled "Requesting information about an attachment" (page 51).

Note that this method is a very thin wrapper around function isc_database_info . This method does not attempt to interpret its results except with regard to whether they are a string or an integer.

For example, requesting isc_info_user_names with the call

con.database_info(kinterbasdb.isc_info_user_names, 's')
will return a binary string containing a raw succession of length-name pairs. A thicker wrapper might interpret those raw results and return a Python tuple, but it would need to handle a multitude of special cases in order to cover all possible isc_info_* items.

Arguments:

  • request - one of the kinterbasdb.isc_info_* constants.
  • result_type - must be either 's' if you expect a string result, or 'i' if you expect an integer result.

Example Program

import kinterbasdb

con = kinterbasdb.connect(dsn='localhost:/temp/test.db', user='sysdba', password='pass')

# Retrieving an integer info item is quite simple.
bytesInUse = con.database_info(kinterbasdb.isc_info_current_memory, 'i')

print 'The server is currently using %d bytes of memory.' % bytesInUse

# Retrieving a string info item is somewhat more involved, because the
# information is returned in a raw binary buffer that must be parsed
# according to the rules defined in the Interbase 6 API Guide section
# entitled "Requesting buffer items and result buffer values" (page 51).
#
# Often, the buffer contains a succession of length-string pairs
# (one byte telling the length of s, followed by s itself).
# Function kinterbasdb.raw_byte_to_int is provided to convert a raw
# byte to a Python integer (see examples below).
buf = con.database_info(kinterbasdb.isc_info_db_id, 's')

# Parse the filename from the buffer.
beginningOfFilename = 2
# The second byte in the buffer contains the size of the database filename
# in bytes.
lengthOfFilename = kinterbasdb.raw_byte_to_int(buf[1])
filename = buf[beginningOfFilename:beginningOfFilename + lengthOfFilename]

# Parse the host name from the buffer.
beginningOfHostName = (beginningOfFilename + lengthOfFilename) + 1
# The first byte after the end of the database filename contains the size
# of the host name in bytes.
lengthOfHostName = kinterbasdb.raw_byte_to_int(buf[beginningOfHostName - 1])
host = buf[beginningOfHostName:beginningOfHostName + lengthOfHostName]

print 'We are connected to the database at %s on host %s.' % (filename, host)

Sample output:

The server is currently using 8931328 bytes of memory.
We are connected to the database at C:\TEMP\TEST.DB on host STALIN.

As you can see, extracting data with the database_info function is rather clumsy. The Services API (accessible to Python programmers via the new kinterbasdb.services module) provides much higher-level support for querying many database statistics, as well as numerous other maintenance tasks.




Frequently Asked Questions and Frequently Encountered Pitfalls

Refer to Result Row Fields by Name Rather than Index

Use the Cursor.fetch*map series of methods for traditional fetches, or the Cursor.itermap method to iterate over mappings rather than sequences. Example code appears in the Tutorial section entitled "Executing SQL Statements".



Precise Fixed Point (NUMERIC/DECIMAL) Handling

KInterbasDB's dynamic type translation allows database fixed point types to be handled both precisely and conveniently, when combined with a full-featured fixed point data type such as that implemented by the fixedpoint module.

An official implementation of dynamic type translators for the fixedpoint module is distributed with KInterbasDB in the kinterbasdb.typeconv_fixed_fixedpoint module; it can be loaded conveniently using the features discussed in this section.



Dates and Times:  egenix mx.DateTime vs. Python 2.3+ standard library datetime

KInterbasDB's dynamic type translation allows either of these date/time modules to be used with equal convenience. For example, see this program.

Additionally, see this section for a discussion of how to conveniently load alternatives to mx.DateTime.



Using KInterbasDB with Embedded Firebird (Windows Only)

The Firebird 1.5 Release Notes (included with RC5 and later) describe Embedded Firebird as "a dll that merges a single client attachment with a Firebird Superserver for building very quick and efficient stand-alone and briefcase applications."

The KInterbasDB distribution linked against the Firebird 1.5 client library fbclient.dll (kinterbasdb-V.V.win32-FB1.5-pyV.V.exe) works fine with Embedded Firebird, though only local-protocol connections are supported.

Setup instructions for Embedded Firebird may be found in the "Installation Notes" section of the Firebird 1.5 Release Notes. The instructions indicate that one should "copy fbembed.dll into the directory with your application", then rename it to fbclient.dll. For Python programs based on KInterbasDB, "the directory with your application" refers to the KInterbasDB installation directory (where _kinterbasdb.pyd resides).



Services API with the Classic and Embedded Server Architectures

Firebird's Classic architecture provides only crippled support for the Services API; the Embedded architecture provides none at all. Of course KInterbasDB's Services API support is subject to the constraints of these architectures when used in conjunction with them.



Using KInterbasDB with Zope

There exist at least two Zope adapters based on KInterbasDB; see the links page.



Unicode Fields and KInterbasDB

XXX: not yet documented




References (External Links)




Feedback

Send feedback about this documentation or the KInterbasDB code to the author of the current versions of both, David Rushby.




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