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.

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 pyfirebirdsql exposes to Python (see below).

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

firebirdsql.create_database()

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.
Connection.drop_database()

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 firebirdsql

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

Advanced Transaction Control

For the sake of simplicity, pyfirebirdsql 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 `rollback`ed when the connection is garbage collected.

Remember that because of ACID, every data manipulation operation in the 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 pyfirebirdsql establishes a transaction implicitly by using any SQL execution method, such as execute_immediate(), Cursor.execute(), or Cursor.callproc().

Although pyfirebirdsql 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.

Explicit transaction start

In addition to the implicit transaction initiation required by Python Database API, pyfirebirdsql allows the programmer to start transactions explicitly via the Connection.begin method.

Connection.begin(tpb)

Starts a transaction explicitly. This is never required; a transaction will be started implicitly if necessary.

Tpb:Optional transaction parameter buffer (TPB) populated with firebirdsql.isc_tpb_* constants. See the Firebird API guide for these constants’ meanings.

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.

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 begin() method and pass a TPB for that single transaction.

For details about TPB construction, see the Firebird API documentation. In particular, the ibase.h supplied with Firebird contains all possible TPB elements – single bytes that the C API defines as constants whose names begin with isc_tpb_. pyfirebirdsql 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; pyfirebirdsql requires that TPBs be constructed as Python strings. Since the constants in the firebirdsql.isc_tpb_* family are single-character Python strings, they can simply be concatenated to create a TPB.

Warning

This method requires good knowledge of tpc_block structure and proper order of various parameters, as Firebird engine will raise an error when badly structured block would be used. Also definition of table reservation parameters is uncomfortable as you’ll need to mix binary codes with table names passed as Pascal strings (characters preceded by string length).

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

import firebirdsql

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

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

For convenient and safe construction of custom tpb_block, pyfirebirdsql provides special utility class TPB.

class firebirdsql.TPB
access_mode

Required access mode. Default isc_tpb_write.

isolation_level

Required Transaction Isolation Level. Default isc_tpb_concurrency.

lock_resolution

Required lock resolution method. Default isc_tpb_wait.

lock_timeout

Required lock timeout. Default None.

table_reservation

Table reservation specification. Default None. Instead of changing the value of the table_reservation object itself, you must change its elements by manipulating it as though it were a dictionary that mapped “TABLE_NAME”: (sharingMode, accessMode) For example:

tpbBuilder.table_reservation["MY_TABLE"] =
  (firebirdsql.isc_tpb_protected, firebirdsql.isc_tpb_lock_write)
render()

Returns valid transaction parameter block according to current values of member attributes.

import firebirdsql

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

# Use TPB to construct valid transaction parameter block
# from the firebirdsql.isc_tpb_* family.
customTPB = TPB()
customTPB.access_mode = firebirdsql.isc_tpb_read
customTPB.isolation_level = firebirdsql.isc_tpb_read_committed
    + firebirdsql.isc_tpb_rec_version

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

# 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.render()
con.commit()

If you want to build only table reservation part of tpb (for example to add to various custom built parameter blocks), you can use class TableReservation instead TPB.

class firebirdsql.TableReservation

This is a dictionary-like class, where keys are table names and values must be tuples of access parameters, i.e. “TABLE_NAME”: (sharingMode, accessMode)

render()

Returns propely formatted table reservation part of transaction parameter block according to current values.

Conenction object also exposes two methods that return infromation about current transaction:

class firebirdsql.Connection
trans_info(request)

Pythonic wrapper around transaction_info() call.

Request:One or more information request codes (see transaction_info for details). Multiple codes must be passed as tuple.

Returns decoded response(s) for specified request code(s). When multiple requests are passed, returns a dictionary where key is the request code and value is the response from server.

transaction_info(request, result_type)

Thin wrapper around Firebird API isc_transaction_info call. This function returns information about active transaction. Raises ProgrammingError exception when transaction is not active.

Request:

One from the next constants:

  • isc_info_tra_id
  • isc_info_tra_oldest_interesting
  • isc_info_tra_oldest_snapshot
  • isc_info_tra_oldest_active
  • isc_info_tra_isolation
  • isc_info_tra_access
  • isc_info_tra_lock_timeout

See Firebird API Guide for details.

Result_type:

String code for result type:

  • ‘i’ for Integer
  • ‘s’ fro String

Retaining Operations

The commit and rollback methods of firebirdsql.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. However, retaining transactions must be used cautiously because they can interfere with the server’s ability to garbage collect old record versions. For details about this issue, read the “Garbage” section of this document by Ann Harrison.

For more information about retaining transactions, see Firebird documentation.

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, pyfirebirdsql also exposes savepoints at the Python API level for the sake of convenience.

Connection.savepoint(name)

Establishes a savepoint with the specified name. To roll back to a specific savepoint, call the rollback() method and provide a value (the name of the savepoint) for the optional savepoint parameter. If the savepoint parameter of rollback() is not specified, the active transaction is cancelled in its entirety, as required by the Python Database API Specification.

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

import firebirdsql

con = firebirdsql.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:
  []

Using multiple transactions with the same connection

Python Database API 2.0 was created with assumption that connection can support only one transactions per single connection. However, Firebird can support multiple independent transactions that can run simultaneously within single connection / attachment to the database. This feature is very important, as applications may require multiple transaction openned simultaneously to perform various tasks, which would require to open multiple connections and thus consume more resources than necessary.

pyfirebirdsql surfaces this Firebird feature through new class Transaction and extensions to Connection and Cursor classes.

class firebirdsql.Connection
trans(tpb=None)

Creates a new Transaction that operates within the context of this connection. Cursors can be created within that Transaction via its .cursor() method.

transactions

read-only property

List of non-close()d Transaction objects associated with this Connection. An element of this list may represent a resolved or unresolved physical transaction. Once a Transaction object has been created, it is only removed from the Connection’s tracker if the Transaction’s close() method is called (Transaction.__del__ triggers an implicit close() call if necessary), or (obviously) if the Connection itself is close()d. The initial implementation will not make any guarantees about the order of the Transactions in this list.

main_transaction

read-only property

Transaction object that represents the DB-API implicit transaction. The implementation guarantees that the same Transaction object will be reused across all DB-API transactions during the lifetime of the Connection.

prepare()

Manually triggers the first phase of a two-phase commit (2PC). Use of this method is optional; if preparation is not triggered manually, it will be performed implicitly by commit() in a 2PC. See also the `Distributed Transactions`_ section for details.

class firebirdsql.Cursor
transaction

read-only property

Transaction with which this Cursor is associated. None if the Transaction has been close()d, or if the Cursor has been close()d.

class firebirdsql.Transaction
__init__(connection, tpb=None)

Constructor requires open Connection object and optional tpb specification.

connection

read-only property

Connection object on which this Transaction is based. When the Connection’s close() method is called, all Transactions that depend on the connection will also be implicitly close()d. If a Transaction has been close()d, its connection property will be None.

closed

read-only property

True if Transaction has been closed (explicitly or implicitly).

n_physical

read-only property (int)

Number of physical transactions that have been executed via this Transaction object during its lifetime.

resolution

read-only property (int)

Zero if this Transaction object is currently managing an open physical transaction. One if the physical transaction has been resolved normally. Note that this is an int property rather than a bool, and is named resolution rather than resolved, so that the non-zero values other than one can be assigned to convey specific information about the state of the transaction, in a future implementation (consider distributed transaction prepared state, limbo state, etc.).

cursors

List of non-close()d Cursor objects associated with this Transaction. When Transaction’s close() method is called, whether explicitly or implicitly, it will implicitly close() each of its Cursors. Current implementation do not make any guarantees about the order of the Cursors in this list.

begin(tpb)

See Connection.begin() for details.

commit(retaining=False)

See firebirdsql.Connection.commit() for details.

close()

Permanently closes the Transaction object and severs its associations with other objects. If the physical transaction is unresolved when this method is called, a rollback() will be performed first.

prepare()

See Connection.prepare() for details.

rollback(retaining=False)

See firebirdsql.Connection.rollback() for details.

savepoint()

See Connection.savepoint() for details.

trans_info()

See Connection.trans_info() for details.

transaction_info()

See Connection.transaction_info() for details.

cursor()

Creates a new Cursor that will operate in the context of this Transaction. The association between a Cursor and its Transaction is set when the Cursor is created, and cannot be changed during the lifetime of that Cursor. See Connection.cursor() for more details.

If you don’t want multiple transactions, you can use implicit transaction object associated with Connection and control it via transaction-management and cursor methods of the Connection.

Alternatively, you can directly access the implicit transaction exposed as main_transaction and control it via its transaction-management methods.

To use additional transactions, create new Transaction object calling Connection.trans() method.

Prepared Statements

When you define a Python function, the interpreter initially parses the textual representation of the function and generates a binary equivalent called bytecode. The bytecode representation can then be executed directly by the Python interpreter any number of times and with a variety of parameters, but the human-oriented textual definition of the function never need be parsed again.

Database engines perform a similar series of steps when executing a SQL statement. Consider the following series of statements:

cur.execute("insert into the_table (a,b,c) values ('aardvark', 1, 0.1)")
...
cur.execute("insert into the_table (a,b,c) values ('zymurgy', 2147483647, 99999.999)")

If there are many statements in that series, wouldn’t it make sense to “define a function” to insert the provided “parameters” into the predetermined fields of the predetermined table, instead of forcing the database engine to parse each statement anew and figure out what database entities the elements of the statement refer to? In other words, why not take advantage of the fact that the form of the statement (“the function”) stays the same throughout, and only the values (“the parameters”) vary? Prepared statements deliver that performance benefit and other advantages as well.

The following code is semantically equivalent to the series of insert operations discussed previously, except that it uses a single SQL statement that contains Firebird’s parameter marker ( ?) in the slots where values are expected, then supplies those values as Python tuples instead of constructing a textual representation of each value and passing it to the database engine for parsing:

insertStatement = "insert into the_table (a,b,c) values (?,?,?)"
cur.execute(insertStatement, ('aardvark', 1, 0.1))
...
cur.execute(insertStatement, ('zymurgy', 2147483647, 99999.999))

Only the values change as each row is inserted; the statement remains the same. For many years, pyfirebirdsql has recognized situations similar to this one and automatically reused the same prepared statement in each Cursor.execute() call. In pyfirebirdsql 3.2, the scheme for automatically reusing prepared statements has become more sophisticated, and the API has been extended to offer the client programmer manual control over prepared statement creation and use.

The entry point for manual statement preparation is the Cursor.prep method.

Cursor.prep(sql)
Sql:string parameter that contains the SQL statement to be prepared. Returns a PreparedStatement instance.
class firebirdsql.PreparedStatement

PreparedStatement has no public methods, but does have the following public read-only properties:

sql

A reference to the string that was passed to prep() to create this PreparedStatement.

statement_type

An integer code that can be matched against the statement type constants in the firebirdsql.isc_info_sql_stmt_* series. The following statement type codes are currently available:

  • isc_info_sql_stmt_commit
  • isc_info_sql_stmt_ddl
  • isc_info_sql_stmt_delete
  • isc_info_sql_stmt_exec_procedure
  • isc_info_sql_stmt_get_segment
  • isc_info_sql_stmt_insert
  • isc_info_sql_stmt_put_segment
  • isc_info_sql_stmt_rollback
  • isc_info_sql_stmt_savepoint
  • isc_info_sql_stmt_select
  • isc_info_sql_stmt_select_for_upd
  • isc_info_sql_stmt_set_generator
  • isc_info_sql_stmt_start_trans
  • isc_info_sql_stmt_update
n_input_params

The number of input parameters the statement requires.

n_output_params

The number of output fields the statement produces.

plan

A string representation of the execution plan generated for this statement by the database engine’s optimizer. This property can be used, for example, to verify that a statement is using the expected index.

description

A Python DB API 2.0 description sequence (of the same format as Cursor.description) that describes the statement’s output parameters. Statements without output parameters have a description of None.

In addition to programmatically examining the characteristics of a SQL statement via the properties of PreparedStatement, the client programmer can submit a PreparedStatement to Cursor.execute() or Cursor.executemany() for execution. The code snippet below is semantically equivalent to both of the previous snippets in this section, but it explicitly prepares the INSERT statement in advance, then submits it to Cursor.executemany() for execution:

insertStatement = cur.prep("insert into the_table (a,b,c) values (?,?,?)")
inputRows = [
    ('aardvark', 1, 0.1),
    ...
    ('zymurgy', 2147483647, 99999.999)
  ]
cur.executemany(insertStatement, inputRows)

Example Program

The following program demonstrates the explicit use of PreparedStatements. It also benchmarks explicit PreparedStatement reuse against pyfirebirdsql’s automatic PreparedStatement reuse, and against an input strategy that prevents PreparedStatement reuse.

import time
import firebirdsql

con = firebirdsql.connect(dsn=r'localhost:D:\temp\test-20.firebird',
    user='sysdba', password='masterkey'
  )

cur = con.cursor()

# Create supporting database entities:
cur.execute("recreate table t (a int, b varchar(50))")
con.commit()
cur.execute("create unique index unique_t_a on t(a)")
con.commit()

# Explicitly prepare the insert statement:
psIns = cur.prep("insert into t (a,b) values (?,?)")
print 'psIns.sql: "%s"' % psIns.sql
print 'psIns.statement_type == firebirdsql.isc_info_sql_stmt_insert:', (
    psIns.statement_type == firebirdsql.isc_info_sql_stmt_insert
  )
print 'psIns.n_input_params: %d' % psIns.n_input_params
print 'psIns.n_output_params: %d' % psIns.n_output_params
print 'psIns.plan: %s' % psIns.plan

print

N = 10000
iStart = 0

# The client programmer uses a PreparedStatement explicitly:
startTime = time.time()
for i in xrange(iStart, iStart + N):
    cur.execute(psIns, (i, str(i)))
print (
    'With explicit prepared statement, performed'
    '\n  %0.2f insertions per second.' % (N / (time.time() - startTime))
  )
con.commit()

iStart += N

# pyfirebirdsql automatically uses a PreparedStatement "under the hood":
startTime = time.time()
for i in xrange(iStart, iStart + N):
    cur.execute("insert into t (a,b) values (?,?)", (i, str(i)))
print (
    'With implicit prepared statement, performed'
    '\n  %0.2f insertions per second.' % (N / (time.time() - startTime))
  )
con.commit()

iStart += N

# A new SQL string containing the inputs is submitted every time, so
# pyfirebirdsql is not able to implicitly reuse a PreparedStatement.  Also, in a
# more complicated scenario where the end user supplied the string input
# values, the program would risk SQL injection attacks:
startTime = time.time()
for i in xrange(iStart, iStart + N):
    cur.execute("insert into t (a,b) values (%d,'%s')" % (i, str(i)))
print (
    'When unable to reuse prepared statement, performed'
    '\n  %0.2f insertions per second.' % (N / (time.time() - startTime))
  )
con.commit()

# Prepare a SELECT statement and examine its properties.  The optimizer's plan
# should use the unique index that we created at the beginning of this program.
print
psSel = cur.prep("select * from t where a = ?")
print 'psSel.sql: "%s"' % psSel.sql
print 'psSel.statement_type == firebirdsql.isc_info_sql_stmt_select:', (
    psSel.statement_type == firebirdsql.isc_info_sql_stmt_select
  )
print 'psSel.n_input_params: %d' % psSel.n_input_params
print 'psSel.n_output_params: %d' % psSel.n_output_params
print 'psSel.plan: %s' % psSel.plan

# The current implementation does not allow PreparedStatements to be prepared
# on one Cursor and executed on another:
print
print 'Note that PreparedStatements are not transferrable from one cursor to another:'
cur2 = con.cursor()
cur2.execute(psSel)

Output:

psIns.sql: "insert into t (a,b) values (?,?)"
psIns.statement_type == firebirdsql.isc_info_sql_stmt_insert: True
psIns.n_input_params: 2
psIns.n_output_params: 0
psIns.plan: None

With explicit prepared statement, performed
  9551.10 insertions per second.
With implicit prepared statement, performed
  9407.34 insertions per second.
When unable to reuse prepared statement, performed
  1882.53 insertions per second.

psSel.sql: "select * from t where a = ?"
psSel.statement_type == firebirdsql.isc_info_sql_stmt_select: True
psSel.n_input_params: 1
psSel.n_output_params: 2
psSel.plan: PLAN (T INDEX (UNIQUE_T_A))

Note that PreparedStatements are not transferrable from one cursor to another:
Traceback (most recent call last):
  File "adv_prepared_statements__overall_example.py", line 86, in ?
    cur2.execute(psSel)
firebirdsql.ProgrammingError: (0, 'A PreparedStatement can only be used with the
 Cursor that originally prepared it.')

As you can see, the version that prevents the reuse of prepared statements is about five times slower – for a trivial statement. In a real application, SQL statements are likely to be far more complicated, so the speed advantage of using prepared statements would only increase.

As the timings indicate, pyfirebirdsql does a good job of reusing prepared statements even if the client program is written in a style strictly compatible with the Python DB API 2.0 (which accepts only strings – not PreparedStatement objects – to the Cursor.execute() method). The performance loss in this case is less than one percent.

Named Cursors

To allow the Python programmer to perform scrolling UPDATE or DELETE via the “SELECT ... FOR UPDATE” syntax, pyfirebirdsql provides the read/write property Cursor.name.

Cursor.name

Name for the SQL cursor. This property can be ignored entirely if you don’t need to use it.

Example Program

import firebirdsql

con = firebirdsql.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()

Parameter Conversion

pyfirebirdsql 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 pyfirebirdsql 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 certain string values such as ‘yesterday’ and ‘now’ as having special meaning in a date/time context. If pyfirebirdsql did not accept strings as the values of parameters destined for storage in date/time fields, the resulting code would be awkward. Consider the difference between the two Python 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 pyfirebirdsql 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 pyfirebirdsql 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 pyfirebirdsql 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 pyfirebirdsql will manage them behind the scenes. Otherwise, the client programmer can use any of several standard type translators included with pyfirebirdsql, register custom translators, or set the translators to None to deal directly with the pyfirebirdsql-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 managed with next methods of Connection and Cursor.

Connection.get_type_trans_in()

Retrieves the inbound type translation map.

Connection.set_type_trans_in(trans_dict)

Changes the inbound type translation map.

Cursor.get_type_trans_in()

Retrieves the inbound type translation map.

Cursor.set_type_trans_in(trans_dict)

Changes the inbound type translation map.

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.

Cursor`s 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. By default, pyfirebirdsql uses dynamic type translation to implement the conversion of DATE, TIME, TIMESTAMP, NUMERIC, and DECIMAL values. For the source code locations of pyfirebirdsql’s reference translators, see the table in the next section.

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 pyfirebirdsql-specific 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’ for fields with charsets NONE, OCTETS, or ASCII

‘TEXT_UNICODE’ for all other charsets

BLOB ‘BLOB’
SMALLINT/INTEGER/BIGINT ‘INTEGER’
FLOAT/ DOUBLE PRECISION ‘FLOATING’
NUMERIC / DECIMAL ‘FIXED’
DATE ‘DATE’
TIME ‘TIME’
TIMESTAMP ‘TIMESTAMP’

Database Arrays

pyfirebirdsql 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 Firebird implementation of database arrays, like that of most relational database engines that support this data type, is fraught with limitations.

Database arrays are of fixed size, with a predeclared number of dimensions (max. 16) 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 preferable to avoid using database arrays unless you have a compelling reason.

Example Program

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

import firebirdsql

con = firebirdsql.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]]

Blobs

pyfirebirdsql supports the insertion and retrieval of blobs either wholly in memory (“materialized mode”) or in chunks (“streaming mode”) to reduce memory usage when handling large blobs. The default handling mode is “materialized”; the “streaming” method is selectable via a special case of Dynamic Type Translation.

In materialized mode, input and output blobs are represented as Python str objects, with the result that the entirety of each blob’s contents is loaded into memory. Unfortunately, flaws in the database engine’s C API prevent automatic Unicode conversion from applying to textual blobs in the way it applies to Unicode CHAR and VARCHAR fields in any Firebird version prior to version 2.1.

Note

pyfirebirdsql 3.3 introduces new type_conv mode 300 that enables automatic type conversion for textual blobs when you’re working with Firebird 2.1 and newer.

In streaming mode, any Python “file-like” object is acceptable as input for a blob parameter. Obvious examples of such objects are instances of file or StringIO. Each output blob is represented by a firebirdsql.BlobReader object.

class firebirdsql.BlobReader

BlobReader is a “file-like” class, so it acts much like a file instance opened in rb mode.

BlobReader adds one method not found in the “file-like” interface:

chunks()

Takes a single integer parameter that specifies the number of bytes to retrieve in each chunk (the final chunk may be smaller).

For example, if the size of the blob is 50000000 bytes, BlobReader.chunks(2**20) will return 47 one-megabyte chunks, and a smaller final chunk of 716928 bytes.

Due to the combination of CPython’s deterministic finalization with careful programming in pyfirebirdsql’s internals, it is not strictly necessary to close BlobReader instances explicitly. A BlobReader object will be automatically closed by its __del__ method when it goes out of scope, or when its Connection closes, whichever comes first. However, it is always a better idea to close resources explicitly (via try...finally) than to rely on artifacts of the CPython implementation. (For the sake of clarity, the example program does not follow this practice.)

Example Program

The following program demonstrates blob storage and retrieval in both materialized and streaming modes.

import os.path
from cStringIO import StringIO

import firebirdsql

con = firebirdsql.connect(dsn=r'localhost:D:\temp\test-20.firebird',
    user='sysdba', password='masterkey'
  )

cur = con.cursor()

cur.execute("recreate table blob_test (a blob)")
con.commit()

# --- Materialized mode (str objects for both input and output) ---
# Insertion:
cur.execute("insert into blob_test values (?)", ('abcdef',))
cur.execute("insert into blob_test values (?)", ('ghijklmnop',))
# Retrieval:
cur.execute("select * from blob_test")
print 'Materialized retrieval (as str):'
print cur.fetchall()

cur.execute("delete from blob_test")

# --- Streaming mode (file-like objects for input; firebirdsql.BlobReader
#     objects for output) ---
cur.set_type_trans_in ({'BLOB': {'mode': 'stream'}})
cur.set_type_trans_out({'BLOB': {'mode': 'stream'}})

# Insertion:
cur.execute("insert into blob_test values (?)", (StringIO('abcdef'),))
cur.execute("insert into blob_test values (?)", (StringIO('ghijklmnop'),))

f = file(os.path.abspath(__file__), 'rb')
cur.execute("insert into blob_test values (?)", (f,))
f.close()

# Retrieval using the "file-like" methods of BlobReader:
cur.execute("select * from blob_test")

readerA = cur.fetchone()[0]

print '\nStreaming retrieval (via firebirdsql.BlobReader):'

# Python "file-like" interface:
print 'readerA.mode:    "%s"' % readerA.mode
print 'readerA.closed:   %s'  % readerA.closed
print 'readerA.tell():   %d'  % readerA.tell()
print 'readerA.read(2): "%s"' % readerA.read(2)
print 'readerA.tell():   %d'  % readerA.tell()
print 'readerA.read():  "%s"' % readerA.read()
print 'readerA.tell():   %d'  % readerA.tell()
print 'readerA.read():  "%s"' % readerA.read()
readerA.close()
print 'readerA.closed:   %s'  % readerA.closed

# The chunks method (not part of the Python "file-like" interface, but handy):
print '\nFor a blob with contents "ghijklmnop", iterating over'
print 'BlobReader.chunks(3) produces:'
readerB = cur.fetchone()[0]
for chunkNo, chunk in enumerate(readerB.chunks(3)):
    print 'Chunk %d is: "%s"' % (chunkNo, chunk)

Output:

Materialized retrieval (as str):
[('abcdef',), ('ghijklmnop',)]

Streaming retrieval (via firebirdsql.BlobReader):
readerA.mode:    "rb"
readerA.closed:   False
readerA.tell():   0
readerA.read(2): "ab"
readerA.tell():   2
readerA.read():  "cdef"
readerA.tell():   6
readerA.read():  ""
readerA.closed:   True

For a blob with contents "ghijklmnop", iterating over
BlobReader.chunks(3) produces:
Chunk 0 is: "ghi"
Chunk 1 is: "jkl"
Chunk 2 is: "mno"
Chunk 3 is: "p"

Connection Timeouts

Connection timeouts allow the programmer to request that a connection be automatically closed after a specified period of inactivity. The simplest uses of connection timeouts are trivial, as demonstrated by the following snippet:

import firebirdsql

con = firebirdsql.connect(dsn=r'localhost:D:\temp\test.db',
    user='sysdba', password='masterkey',
    timeout={'period': 120.0} # time out after 120.0 seconds of inactivity
  )

...

The connection created in the example above is eligible to be automatically closed by pyfirebirdsql if it remains idle for at least 120.0 consecutive seconds. pyfirebirdsql does not guarantee that the connection will be closed immediately when the specified period has elapsed. On a busy system, there might be a considerable delay between the moment a connection becomes eligible for timeout and the moment pyfirebirdsql actually closes it. However, the thread that performs connection timeouts is programmed in such a way that on a lightly loaded system, it acts almost instantaneously to take advantage of a connection’s eligibility for timeout.

After a connection has timed out, pyfirebirdsql reacts to attempts to reactivate the severed connection in a manner dependent on the state of the connection when it timed out. Consider the following example program:

import time
import firebirdsql

con = firebirdsql.connect(dsn=r'localhost:D:\temp\test.db',
    user='sysdba', password='masterkey',
    timeout={'period': 3.0}
  )
cur = con.cursor()

cur.execute("recreate table test (a int, b char(1))")
con.commit()

cur.executemany("insert into test (a, b) values (?, ?)",
    [(1, 'A'), (2, 'B'), (3, 'C')]
  )
con.commit()

cur.execute("select * from test")
print 'BEFORE:', cur.fetchall()

cur.execute("update test set b = 'X' where a = 2")

time.sleep(6.0)

cur.execute("select * from test")
print 'AFTER: ', cur.fetchall()

So, should the example program print

BEFORE: [(1, 'A'), (2, 'B'), (3, 'C')]
AFTER:  [(1, 'A'), (2, 'X'), (3, 'C')]

or

BEFORE: [(1, 'A'), (2, 'B'), (3, 'C')]
AFTER:  [(1, 'A'), (2, 'B'), (3, 'C')]

or should it raise an exception? The answer is more complex than one might think.

First of all, we cannot guarantee much about the example program’s behavior because there is a race condition between the obvious thread that’s executing the example code (which we’ll call “UserThread” for the rest of this section) and the pyfirebirdsql-internal background thread that actually closes connections that have timed out (“TimeoutThread”). If the operating system were to suspend UserThread just after the firebirdsql.connect() call for more than the specified timeout period of 3.0 seconds, the TimeoutThread might close the connection before UserThread had performed any preparatory operations on the database. Although such a scenario is extremely unlikely when more “realistic” timeout periods such as 1800.0 seconds (30 minutes) are used, it is important to consider. We’ll explore solutions to this race condition later.

The likely (but not guaranteed) behavior of the example program is that UserThread will complete all preparatory database operations including the cur. execute ( “update test set b = ‘X’ where a = 2” ) statement in the example program, then go to sleep for not less than 6.0 seconds. Not less than 3.0 seconds after UserThread executes the cur. execute ( “update test set b = ‘X’ where a = 2” ) statement, TimeoutThread is likely to close the connection because it has become eligible for timeout.

The crucial issue is how TimeoutThread should resolve the transaction that UserThread left open on con, and what should happen when UserThread reawakens and tries to execute the cur. execute ( “select * from test” ) statement, since the transaction that UserThread left open will no longer be active.

User-Supplied Connection Timeout Callbacks

In the context of a particular client program, it is not possible for pyfirebirdsql to know the best way for TimeoutThread to react when it encounters a connection that is eligible for timeout, but has an unresolved transaction. For this reason, pyfirebirdsql’s connection timeout system offers callbacks that the client programmer can use to guide the TimeoutThread’s actions, or to log information about connection timeout patterns.

The “Before Timeout” Callback

The client programmer can supply a “before timeout” callback that accepts a single dictionary parameter and returns an integer code to indicate how the TimeoutThread should proceed when it finds a connection eligible for timeout. Within the dictionary, pyfirebirdsql provides the following entries:

dsn:The dsn parameter that was passed to firebirdsql.connect when the connection was created.
has_transaction:
 A boolean that indicates whether the connection has an unresolved transaction.
active_secs:A float that indicates how many seconds elapsed between the point when the connection attached to the server and the last client program activity on the connection.
idle_secs:A float that indicates how many seconds have elapsed since the last client program activity on the connection. This value will not be less than the specified timeout period, and is likely to only a fraction of a second longer.

Based on those data, the user-supplied callback should return one of the following codes:

firebirdsql.CT_VETO

Directs the TimeoutThread not to close the connection at the current time, and not to reconsider timing the connection out until at least another timeout period has passed. For example, if a connection was created with a timeout period of 120.0 seconds, and the user-supplied “before callback” returns CT_VETO, the TimeoutThread will not reconsider timing out that particular connection until at least another 120.0 seconds have elapsed.

firebirdsql.CT_NONTRANSPARENT

(“Nontransparent rollback”)

Directs the TimeoutThread to roll back the connection’s unresolved transaction (if any), then close the connection. Any future attempt to use the connection will raise a firebirdsql.ConnectionTimedOut exception.

firebirdsql.CT_ROLLBACK

(“Transparent rollback”)

Directs the TimeoutThread to roll back the connection’s unresolved transaction (if any), then close the connection. Upon any future attempt to use the connection, pyfirebirdsql will attempt to transparently reconnect to the database and “resume where it left off” insofar as possible. Of course, network problems and the like could prevent pyfirebirdsql’s attempt at transparent resumption from succeeding. Also, highly state-dependent objects such as open result sets, BlobReader, and PreparedStatement cannot be used transparently across a connection timeout.

firebirdsql.CT_COMMIT

(“Transparent commit”)

Directs the TimeoutThread to commit the connection’s unresolved transaction (if any), then close the connection. Upon any future attempt to use the connection, pyfirebirdsql will attempt to transparently reconnect to the database and “resume where it left off” insofar as possible.

If the user does not supply a “before timeout” callback, pyfirebirdsql considers the timeout transparent only if the connection does not have an unresolved transaction.

If the user-supplied “before timeout” callback returns anything other than one of the codes listed above, or if it raises an exception, the TimeoutThread will act as though CT_NONTRANSPARENT had been returned.

You might have noticed that the input dictionary to the “before timeout” callback does not include a reference to the Connection object itself. This is a deliberate design decision intended to steer the client programmer away from writing callbacks that take a long time to complete, or that manipulate the Connection instance directly. See the caveats section for more information.

The “After Timeout” Callback

The client programmer can supply an “after timeout” callback that accepts a single dictionary parameter. Within that dictionary, pyfirebirdsql currently provides the following entries:

dsn:The dsn parameter that was passed to firebirdsql.connect() when the connection was created.
active_secs:A float that indicates how many seconds elapsed between the point when the connection attached to the server and the last client program activity on the connection.
idle_secs:A float that indicates how many seconds elapsed between the last client program activity on the connection and the moment the TimeoutThread closed the connection.

pyfirebirdsql only calls the “after timeout” callback after the connection has actually been closed by the TimeoutThread. If the “before timeout” callback returns CT_VETO to cancel the timeout attempt, the “after timeout” callback will not be called.

pyfirebirdsql discards the return value of the “after timeout” callback, and ignores any exceptions.

The same caveats that apply to the “before timeout” callback also apply to the “after timeout” callback.

User-Supplied Connection Timeout Callback Caveats

  • The user-supplied callbacks are executed by the TimeoutThread. They should be designed to avoid blocking the TimeoutThread any longer than absolutely necessary.
  • Manipulating the Connection object that is being timed out (or any of that connection’s subordinate objects such as Cursor, BlobReader, or PreparedStatement) from the timeout callbacks is strictly forbidden.

Examples

Example: `CT_VETO`

The following program registers a “before timeout” callback that unconditionally returns CT_VETO, which means that the TimeoutThread never times the connection out. Although an “after timeout” callback is also registered, it will never be called.

import time
import firebirdsql

def callback_before(info):
    print
    print 'callback_before called; input parameter contained:'
    for key, value in info.items():
        print '  %s: %s' % (repr(key).ljust(20), repr(value))
    print
    # Unconditionally veto any timeout attempts:
    return firebirdsql.CT_VETO

def callback_after(info):
    assert False, 'This will never be called.'

con = firebirdsql.connect(dsn=r'localhost:D:\temp\test.db',
    user='sysdba', password='masterkey',
    timeout={
        'period': 3.0,
        'callback_before': callback_before,
        'callback_after':  callback_after,
      }
  )
cur = con.cursor()

cur.execute("recreate table test (a int, b char(1))")
con.commit()

cur.executemany("insert into test (a, b) values (?, ?)",
    [(1, 'A'), (2, 'B'), (3, 'C')]
  )
con.commit()

cur.execute("select * from test")
print 'BEFORE:', cur.fetchall()

cur.execute("update test set b = 'X' where a = 2")

time.sleep(6.0)

cur.execute("select * from test")
rows = cur.fetchall()
# The value of the second column of the second row of the table is still 'X',
# because the transaction that changed it from 'B' to 'X' remains active.
assert rows[1][1] == 'X'
print 'AFTER: ', rows

Sample output:

BEFORE: [(1, 'A'), (2, 'B'), (3, 'C')]

callback_before called; input parameter contained:
  'dsn'               : 'localhost:D:\\temp\\test.db'
  'idle_secs'         : 3.0
  'has_transaction'   : True

AFTER:  [(1, 'A'), (2, 'X'), (3, 'C')]

Example: Supporting Module `timeout_authorizer`

The example programs for CT_NONTRANSPARENT, CT_ROLLBACK, and CT_COMMIT rely on the TimeoutAuthorizer class from the module below to guarantee that the TimeoutThread will not time the connection out before the preparatory code has executed.

import threading
import firebirdsql

class TimeoutAuthorizer(object):
    def __init__(self, opCodeWhenAuthorized):
        self.currentOpCode = firebirdsql.CT_VETO
        self.opCodeWhenAuthorized = opCodeWhenAuthorized

        self.lock = threading.Lock()

    def authorize(self):
        self.lock.acquire()
        try:
            self.currentOpCode = self.opCodeWhenAuthorized
        finally:
            self.lock.release()

    def __call__(self, info):
        self.lock.acquire()
        try:
            return self.currentOpCode
        finally:
            self.lock.release()

Example: `CT_NONTRANSPARENT`

import threading, time
import firebirdsql
import timeout_authorizer

authorizer = timeout_authorizer.TimeoutAuthorizer(firebirdsql.CT_NONTRANSPARENT)
connectionTimedOut = threading.Event()

def callback_after(info):
    print
    print 'The connection was closed nontransparently.'
    print
    connectionTimedOut.set()

con = firebirdsql.connect(dsn=r'localhost:D:\temp\test.db',
    user='sysdba', password='masterkey',
    timeout={
        'period': 3.0,
        'callback_before': authorizer,
        'callback_after':  callback_after,
      }
  )
cur = con.cursor()

cur.execute("recreate table test (a int, b char(1))")
con.commit()

cur.executemany("insert into test (a, b) values (?, ?)",
    [(1, 'A'), (2, 'B'), (3, 'C')]
  )
con.commit()

cur.execute("select * from test")
print 'BEFORE:', cur.fetchall()

cur.execute("update test set b = 'X' where a = 2")

authorizer.authorize()
connectionTimedOut.wait()

# This will raise a firebirdsql.ConnectionTimedOut exception because the
# before callback returned firebirdsql.CT_NONTRANSPARENT:
cur.execute("select * from test")

Sample output:

BEFORE: [(1, 'A'), (2, 'B'), (3, 'C')]

The connection was closed nontransparently.

Traceback (most recent call last):
  File "connection_timeouts_ct_nontransparent.py", line 42, in ?
    cur.execute("select * from test")
firebirdsql.ConnectionTimedOut: (0, 'A transaction was still unresolved when
this connection timed out, so it cannot be transparently reactivated.')

Example: `CT_ROLLBACK`

import threading, time
import firebirdsql
import timeout_authorizer

authorizer = timeout_authorizer.TimeoutAuthorizer(firebirdsql.CT_ROLLBACK)
connectionTimedOut = threading.Event()

def callback_after(info):
    print
    print 'The unresolved transaction was rolled back; the connection has been'
    print ' closed transparently.'
    print
    connectionTimedOut.set()

con = firebirdsql.connect(dsn=r'localhost:D:\temp\test.db',
    user='sysdba', password='masterkey',
    timeout={
        'period': 3.0,
        'callback_before': authorizer,
        'callback_after':  callback_after,
      }
  )
cur = con.cursor()

cur.execute("recreate table test (a int, b char(1))")
con.commit()

cur.executemany("insert into test (a, b) values (?, ?)",
    [(1, 'A'), (2, 'B'), (3, 'C')]
  )
con.commit()

cur.execute("select * from test")
print 'BEFORE:', cur.fetchall()

cur.execute("update test set b = 'X' where a = 2")

authorizer.authorize()
connectionTimedOut.wait()

# The value of the second column of the second row of the table will have
# reverted to 'B' when the transaction that changed it to 'X' was rolled back.
# The cur.execute call on the next line will transparently reactivate the
# connection, which was timed out transparently.
cur.execute("select * from test")
rows = cur.fetchall()
assert rows[1][1] == 'B'
print 'AFTER: ', rows

Sample output:

BEFORE: [(1, 'A'), (2, 'B'), (3, 'C')]

The unresolved transaction was rolled back; the connection has been
 closed transparently.

AFTER:  [(1, 'A'), (2, 'B'), (3, 'C')]

Example: `CT_COMMIT`

import threading, time
import firebirdsql
import timeout_authorizer

authorizer = timeout_authorizer.TimeoutAuthorizer(firebirdsql.CT_COMMIT)
connectionTimedOut = threading.Event()

def callback_after(info):
    print
    print 'The unresolved transaction was committed; the connection has been'
    print ' closed transparently.'
    print
    connectionTimedOut.set()

con = firebirdsql.connect(dsn=r'localhost:D:\temp\test.db',
    user='sysdba', password='masterkey',
    timeout={
        'period': 3.0,
        'callback_before': authorizer,
        'callback_after':  callback_after,
      }
  )
cur = con.cursor()

cur.execute("recreate table test (a int, b char(1))")
con.commit()

cur.executemany("insert into test (a, b) values (?, ?)",
    [(1, 'A'), (2, 'B'), (3, 'C')]
  )
con.commit()

cur.execute("select * from test")
print 'BEFORE:', cur.fetchall()

cur.execute("update test set b = 'X' where a = 2")

authorizer.authorize()
connectionTimedOut.wait()

# The modification of the value of the second column of the second row of the
# table from 'B' to 'X' will have persisted, because the TimeoutThread
# committed the transaction before it timed the connection out.
# The cur.execute call on the next line will transparently reactivate the
# connection, which was timed out transparently.
cur.execute("select * from test")
rows = cur.fetchall()
assert rows[1][1] == 'X'
print 'AFTER: ', rows

Sample output:

BEFORE: [(1, 'A'), (2, 'B'), (3, 'C')]

The unresolved transaction was committed; the connection has been
 closed transparently.

AFTER:  [(1, 'A'), (2, 'X'), (3, 'C')]

Database Event Notification

What are database events?

The database engine features a distributed, interprocess communication mechanism based on messages called database events. 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 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 typical 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 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 might 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 moment 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 there are none available.

How events are exposed to the server and the client process?

  1. Server Process (“An event just occurred!”)

    To notify any interested listeners that a specific event has occurred, issue the POST_EVENT statement from Stored Procedure or Trigger. 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

    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

    If you don’t care about the gory details of event notification, skip to the section that describes pyfirebirdsql’s Python-level event handling API.

    The Firebird C client library offers two forms of event notification. The first form is synchronous notification, by way of the function :cfunc:`isc_wait_for_event()`. This form is admirably simple for a C programmer to use, but is inappropriate as a basis for pyfirebirdsql’s event support, chiefly because it’s not sophisticated enough to serve as the basis for a comfortable Python-level API. The other form of event notification offered by the database client library is asynchronous, by way of the functions :cfunc:`isc_que_events()` (note that the name of that function is misspelled), :cfunc:`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 :cfunc:`isc_event_block()` to create a formatted binary buffer that will tell the server which events the client wants to listen for.
    2. Call :cfunc:`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 :cfunc:`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 :cfunc:`isc_event_counts()` function to determine how many times each of the registered events has occurred since the last call to :cfunc:`isc_event_counts()` (if any).
    5. [The callback thread should now “do its thing”, which may include communicating with the thread that called :cfunc:`isc_que_events()`.]
    6. When the callback thread is finished handling an event notification, it must call :cfunc:`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.

How events are exposed to the Python programmer?

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

Connection.event_conduit()

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. pyfirebirdsql’s own event-related code is capable of operating with up to 2147483647 events per conduit. However, it has been observed that the Firebird client library experiences catastrophic problems (including memory corruption) on some platforms with anything beyond about 100 events per conduit. These limitations are dependent on both the Firebird version and the platform.
class firebirdsql.EventConduit
__init__()

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(timeout=None)

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()

Cancels the standing request for this conduit to be notified of events.

After this method has been called, this EventConduit object is useless, and should be discarded. (The boolean property closed is True after an EventConduit has been closed.)

This method has no arguments.

flush()

This method allows the Python programmer to manually clear any event notifications that have accumulated in the conduit’s internal queue.

From the moment the conduit is created by the Connection.event_conduit() method, notifications of any events that occur will accumulate asynchronously within the conduit’s internal queue until the conduit is closed either explicitly (via the close method) 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 pyfirebirdsql-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 firebirdsql

RELEVANT_EVENTS = ['test_event_a', 'test_event_b']

con = firebirdsql.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 firebirdsql

con = firebirdsql.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

  • Remember that if an EventConduit is left active (not yet closed or garbage collected), notifications for any registered events that actually occur will continue to accumulate in the EventConduit’s internal queue even if the Python programmer doesn’t call EventConduit.wait() to receive the notifications or EventConduit.flush() to clear the queue. The ill-informed may misinterpret this behavior as a memory leak in pyfirebirdsql; it is not.
  • NEVER use LOCAL-protocol connections in a multithreaded program that also uses event handling! The database client library implements the local protocol on some platforms in such a way that deadlocks may arise in bizarre places if you do this. This no-LOCAL prohibition is not limited to connections that are used as the basis for event conduits; it applies to all connections throughout the process. So why doesn’t pyfirebirdsql protect the Python programmer from this mistake? Because the event handling thread is started by the database client library, and it operates beyond the synchronization domain of pyfirebirdsql at times.

Note

The restrictions on the number of active EventConduit`s in a process, and on the number of event names that a single `EventConduit can listen for, have been removed in pyfirebirdsql 3.2.

The database_info API

Firebird provides various informations about server and connected database via database_info API call. pyfirebirdsql surfaces this API through next methods on Connection object:

Connection.database_info(request, result_type)

This method is a very thin wrapper around function :cfunc:`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 :cdata:`isc_info_user_names` with the call

con.database_info(firebirdsql.isc_info_user_names, 's')

will return a binary string containing a raw succession of length- name pairs. A more convenient way to access the same functionality is via the db_info() method.

Arguments:

Request:One of the firebirdsql.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 firebirdsql

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

# Retrieving an integer info item is quite simple.
bytesInUse = con.database_info(firebirdsql.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 firebirdsql.raw_byte_to_int is provided to convert a raw
# byte to a Python integer (see examples below).
buf = con.database_info(firebirdsql.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 = firebirdsql.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 = firebirdsql.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 WEASEL.

As you can see, extracting data with the database_info function is rather clumsy. In pyfirebirdsql 3.2, a higher-level means of accessing the same information is available: the db_info() method. Also, the Services API (accessible to Python programmers via the firebirdsql.services module) provides high-level support for querying database statistics and performing maintenance.

Connection.db_info(request)

High-level convenience wrapper around the database_info() method that parses the output of database_info into Python-friendly objects instead of returning raw binary uffers in the case of complex result types. If an unrecognized isc_info_* code is requested, this method raises ValueError.

For example, requesting :cdata:`isc_info_user_names` with the call

con.db_info(firebirdsql.isc_info_user_names)

returns a dictionary that maps (username -> number of open connections). If SYSDBA has one open connection to the database to which con is connected, and TEST_USER_1 has three open connections to that same database, the return value would be {‘SYSDBA’: 1, ‘TEST_USER_1’: 3}

Arguments:

Request:

must be either:

  • A single firebirdsql.isc_info_* info request code. In this case, a single result is returned.
  • A sequence of such codes. In this case, a mapping of (info request code -> result) is returned.

Example Program

import os.path

import firebirdsql

DB_FILENAME = r'D:\temp\test-20.firebird'
DSN = 'localhost:' + DB_FILENAME

###############################################################################
# Querying an isc_info_* item that has a complex result:
###############################################################################
# Establish three connections to the test database as TEST_USER_1, and one
# connection as SYSDBA.  Then use the Connection.db_info method to query the
# number of attachments by each user to the test database.
testUserCons = []
for i in range(3):
    tCon = firebirdsql.connect(dsn=DSN, user='test_user_1', password='pass')
    testUserCons.append(tCon)

con = firebirdsql.connect(dsn=DSN, user='sysdba', password='masterkey')

print 'Open connections to this database:'
print con.db_info(firebirdsql.isc_info_user_names)

###############################################################################
# Querying multiple isc_info_* items at once:
###############################################################################
# Request multiple db_info items at once, specifically the page size of the
# database and the number of pages currently allocated.  Compare the size
# computed by that method with the size reported by the file system.
# The advantages of using db_info instead of the file system to compute
# database size are:
#   - db_info works seamlessly on connections to remote databases that reside
#     in file systems to which the client program lacks access.
#   - If the database is split across multiple files, db_info includes all of
#     them.
res = con.db_info(
    [firebirdsql.isc_info_page_size, firebirdsql.isc_info_allocation]
  )
pagesAllocated = res[firebirdsql.isc_info_allocation]
pageSize = res[firebirdsql.isc_info_page_size]
print '\ndb_info indicates database size is', pageSize * pagesAllocated, 'bytes'
print   'os.path.getsize indicates size is ', os.path.getsize(DB_FILENAME), 'bytes'

Sample output:

  Open connections to this database:
  {'SYSDBA': 1, 'TEST_USER_1': 3}

db_info indicates database size is 20684800 bytes
os.path.getsize indicates size is  20684800 bytes

Using Firebird Services API

Database server maintenance tasks such as user management, load monitoring, and database backup have traditionally been automated by scripting the command-line tools gbak, gfix, gsec, and gstat.

The API presented to the client programmer by these utilities is inelegant because they are, after all, command-line tools rather than native components of the client language. To address this problem, Firebird has a facility called the Services API, which exposes a uniform interface to the administrative functionality of the traditional command-line tools.

The native Services API, though consistent, is much lower-level than a Pythonic API. If the native version were exposed directly, accomplishing a given task would probably require more Python code than scripting the traditional command-line tools. For this reason, pyfirebirdsql presents its own abstraction over the native API via the firebirdsql.services module.

Establishing Services API Connections

All Services API operations are performed in the context of a connection to a specific database server, represented by the firebirdsql.services.Connection class.

firebirdsql.services.connect(host='service_mgr', user='sysdba', password=None)

Establish a connection to database server Services and returns firebirdsql.services.Connection object.

Host:The network name of the computer on which the database server is running.
User:The name of the database user under whose authority the maintenance tasks are to be performed.
Password:User’s password.

Since maintenance operations are most often initiated by an administrative user on the same computer as the database server, host defaults to the local computer, and user defaults to SYSDBA.

The three calls to firebirdsql.services.connect() in the following program are equivalent:

from firebirdsql import services

con = services.connect(password='masterkey')
con = services.connect(user='sysdba', password='masterkey')
con = services.connect(host='localhost', user='sysdba', password='masterkey')
class firebirdsql.services.Connection
close()

Explicitly terminates a Connection; if this is not invoked, the underlying connection will be closed implicitly when the Connection object is garbage collected.

Server Configuration and Activity Levels

Connection.getServiceManagerVersion()

To help client programs adapt to version changes, the service manager exposes its version number as an integer.

from firebirdsql import services
con = services.connect(host='localhost', user='sysdba', password='masterkey')

print con.getServiceManagerVersion()

Output (on Firebird 1.5.0):

2

firebirdsql.services is a thick wrapper of the Services API that can shield its users from changes in the underlying C API, so this method is unlikely to be useful to the typical Python client programmer.

Connection.getServerVersion()

Returns the server’s version string:

from firebirdsql import services
con = services.connect(host='localhost', user='sysdba', password='masterkey')

print con.getServerVersion()

Output (on Firebird 1.5.0/Win32):

WI-V1.5.0.4290 Firebird 1.5

At first glance, thhis method appears to duplicate the functionality of the firebirdsql.Connection.server_version property, but when working with Firebird, there is a difference. firebirdsql.Connection.server_version is based on a C API call (:cfunc:`isc_database_info()`) that existed long before the introduction of the Services API. Some programs written before the advent of Firebird test the version number in the return value of :cfunc:`isc_database_info()`, and refuse to work if it indicates that the server is too old. Since the first stable version of Firebird was labeled 1.0, this pre-Firebird version testing scheme incorrectly concludes that (e.g.) Firebird 1.0 is older than Interbase 5.0.

Firebird addresses this problem by making :cfunc:`isc_database_info()` return a “pseudo-InterBase” version number, whereas the Services API returns the true Firebird version, as shown:

import firebirdsql
con = firebirdsql.connect(dsn='localhost:C:/temp/test.db', user='sysdba', password='masterkey')
print 'Interbase-compatible version string:', con.server_version

import firebirdsql.services
svcCon = firebirdsql.services.connect(host='localhost', user='sysdba', password='masterkey')
print 'Actual Firebird version string:     ', svcCon.getServerVersion()

Output (on Firebird 1.5.0/Win32):

Interbase-compatible version string: WI-V6.3.0.4290 Firebird 1.5
Actual Firebird version string:      WI-V1.5.0.4290 Firebird 1.5
Connection.getArchitecture()

Returns platform information for the server, including hardware architecture and operating system family.

from firebirdsql import services
con = services.connect(host='localhost', user='sysdba', password='masterkey')

print con.getArchitecture()

Output (on Firebird 1.5.0/Windows 2000):

Firebird/x86/Windows NT

Unfortunately, the architecture string is almost useless because its format is irregular and sometimes outright idiotic, as with Firebird 1.5.0 running on x86 Linux:

Firebird/linux Intel

Magically, Linux becomes a hardware architecture, the ASCII store decides to hold a 31.92% off sale, and Intel grabs an unfilled niche in the operating system market.

Connection.getHomeDir()

Returns the equivalent of the RootDirectory setting from firebird.conf:

from firebirdsql import services
con = services.connect(host='localhost', user='sysdba', password='masterkey')

print con.getHomeDir()

Output (on a particular Firebird 1.5.0/Windows 2000 installation):

C:\dev\db\firebird150\

Output (on a particular Firebird 1.5.0/Linux installation):

/opt/firebird/
Connection.getSecurityDatabasePath()

Returns the location of the server’s core security database, which contains user definitions and such. Interbase® and Firebird 1.0 named this database isc4.gdb, while in Firebird 1.5 it’s renamed to security.fdb and to security2.fdb in Firebird 2.0 and later.

from firebirdsql import services
con = services.connect(host='localhost', user='sysdba', password='masterkey')

print con.getSecurityDatabasePath()

Output (on a particular Firebird 1.5.0/Windows 2000 installation):

C:\dev\db\firebird150\security.fdb

Output (on a particular Firebird 1.5.0/Linux installation):

/opt/firebird/security.fdb
Connection.getLockFileDir()

The database engine uses a lock file to coordinate interprocess communication; getLockFileDir() returns the directory in which that file resides:

from firebirdsql import services
con = services.connect(host='localhost', user='sysdba', password='masterkey')

print con.getLockFileDir()

Output (on a particular Firebird 1.5.0/Windows 2000 installation):

C:\dev\db\firebird150\

Output (on a particular Firebird 1.5.0/Linux installation):

/opt/firebird/
Connection.getCapabilityMask()

The Services API offers “a bitmask representing the capabilities currently enabled on the server”, but the only availabledocumentation for this bitmask suggests that it is “reserved for future implementation”. firebirdsql exposes this bitmask as a Python int returned from the getCapabilityMask() method.

Connection.getMessageFileDir()

To support internationalized error messages/prompts, the database engine stores its messages in a file named interbase.msg (Interbase® and Firebird 1.0) or firebird.msg (Firebird 1.5 and later). The directory in which this file resides can be determined with the getMessageFileDir() method.

from firebirdsql import services
con = services.connect(host='localhost', user='sysdba', password='masterkey')

print con.getMessageFileDir()

Output (on a particular Firebird 1.5.0/Windows 2000 installation):

C:\dev\db\firebird150\

Output (on a particular Firebird 1.5.0/Linux installation):

/opt/firebird/
Connection.getConnectionCount()

Returns the number of active connections to databases managed by the server. This count only includes database connections (such as open instances of firebirdsql.Connection), not services manager connections (such as open instances of firebirdsql.services.Connection).

import firebirdsql, firebirdsql.services
svcCon = firebirdsql.services.connect(host='localhost', user='sysdba', password='masterkey')

print 'A:', svcCon.getConnectionCount()

con1 = firebirdsql.connect(dsn='localhost:C:/temp/test.db', user='sysdba', password='masterkey')
print 'B:', svcCon.getConnectionCount()

con2 = firebirdsql.connect(dsn='localhost:C:/temp/test.db', user='sysdba', password='masterkey')
print 'C:', svcCon.getConnectionCount()

con1.close()
print 'D:', svcCon.getConnectionCount()

con2.close()
print 'E:', svcCon.getConnectionCount()

On an otherwise inactive server, the example program generates the following output:

A: 0
B: 1
C: 2
D: 1
E: 0
Connection.getAttachedDatabaseNames()

Returns a list of the names of all databases to which the server is maintaining at least one connection. The database names are not guaranteed to be in any particular order.

import firebirdsql, firebirdsql.services
svcCon = firebirdsql.services.connect(host='localhost', user='sysdba', password='masterkey')

print 'A:', svcCon.getAttachedDatabaseNames()

con1 = firebirdsql.connect(dsn='localhost:C:/temp/test.db', user='sysdba', password='masterkey')
print 'B:', svcCon.getAttachedDatabaseNames()

con2 = firebirdsql.connect(dsn='localhost:C:/temp/test2.db', user='sysdba', password='masterkey')
print 'C:', svcCon.getAttachedDatabaseNames()

con3 = firebirdsql.connect(dsn='localhost:C:/temp/test2.db', user='sysdba', password='masterkey')
print 'D:', svcCon.getAttachedDatabaseNames()

con1.close()
print 'E:', svcCon.getAttachedDatabaseNames()

con2.close()
print 'F:', svcCon.getAttachedDatabaseNames()

con3.close()
print 'G:', svcCon.getAttachedDatabaseNames()

On an otherwise inactive server, the example program generates the following output:

A: []
B: ['C:\\TEMP\\TEST.DB']
C: ['C:\\TEMP\\TEST2.DB', 'C:\\TEMP\\TEST.DB']
D: ['C:\\TEMP\\TEST2.DB', 'C:\\TEMP\\TEST.DB']
E: ['C:\\TEMP\\TEST2.DB']
F: ['C:\\TEMP\\TEST2.DB']
G: []
Connection.getLog()

Returns the contents of the server’s log file (named interbase.log by Interbase® and Firebird 1.0; firebird.log by Firebird 1.5 and later):

from firebirdsql import services
con = services.connect(host='localhost', user='sysdba', password='masterkey')

print con.getLog()

Output (on a particular Firebird 1.5.0/Windows 2000 installation):

WEASEL (Client) Thu Jun 03 12:01:35 2004
  INET/inet_error: send errno = 10054

WEASEL (Client) Sun Jun 06 19:21:17 2004
  INET/inet_error: connect errno = 10061

Database Statistics

Connection.getStatistics(database, showOnlyDatabaseLogPages=0...)

Returns a string containing a printout in the same format as the output of the gstat command-line utility. This method has one required parameter, the location of the database on which to compute statistics, and five optional boolean parameters for controlling the domain of the statistics.

Map of gstat paremeters to getStatistics options

gstat command-line option getStatistics boolean parameter
-header showOnlyDatabaseHeaderPages
-log showOnlyDatabaseLogPages
-data showUserDataPages
-index showUserIndexPages
-system showSystemTablesAndIndexes

The following program presents several getStatistics calls and their gstat-command-line equivalents. In this context, output is considered “equivalent” even if their are some whitespace differences. When collecting textual output from the Services API, firebirdsql terminates lines with n regardless of the platform’s convention; gstat is platform-sensitive.

from firebirdsql import services
con = services.connect(user='sysdba', password='masterkey')

# Equivalent to 'gstat -u sysdba -p masterkey C:/temp/test.db':
print con.getStatistics('C:/temp/test.db')

# Equivalent to 'gstat -u sysdba -p masterkey -header C:/temp/test.db':
print con.getStatistics('C:/temp/test.db', showOnlyDatabaseHeaderPages=True)

# Equivalent to 'gstat -u sysdba -p masterkey -log C:/temp/test.db':
print con.getStatistics('C:/temp/test.db', showOnlyDatabaseLogPages=True)

# Equivalent to 'gstat -u sysdba -p masterkey -data -index -system C:/temp/test.db':
print con.getStatistics('C:/temp/test.db',
    showUserDataPages=True,
    showUserIndexPages=True,
    showSystemTablesAndIndexes=True
  )

The output of the example program is not shown here because it is quite long.

Backup and Restoration

pyfirebirdsql offers convenient programmatic control over database backup and restoration via the backup and restore methods.

At the time of this writing, released versions of Firebird/Interbase® do not implement incremental backup, so we can simplistically define backup as the process of generating and storing an archived replica of a live database, and restoration as the inverse. The backup/restoration process exposes numerous parameters, which are properly documented in Firebird Documentation to gbak. The pyfirebirdsql API to these parameters is presented with minimal documentation in the sample code below.

Connection.backup(sourceDatabase, destFilenames, destFileSizes=(), <options>)

Creates a backup file from database content.

Simple Form

The simplest form of backup creates a single backup file that contains everything in the database. Although the extension ‘.fbk’ is conventional, it is not required.

from firebirdsql import services
con = services.connect(user='sysdba', password='masterkey')

backupLog = con.backup('C:/temp/test.db', 'C:/temp/test_backup.fbk')
print backupLog

In the example, backupLog is a string containing a gbak-style log of the backup process. It is too long to reproduce here.

Although the return value of the backup method is a freeform log string, backup will raise an exception if there is an error. For example:

from firebirdsql import services
con = services.connect(user='sysdba', password='masterkey')

# Pass an invalid backup path to the engine:
backupLog = con.backup('C:/temp/test.db', 'BOGUS/PATH/test_backup.fbk')
print backupLog
Traceback (most recent call last):
  File "adv_services_backup_simplest_witherror.py", line 5, in ?
    backupLog = con.backup('C:/temp/test.db', 'BOGUS/PATH/test_backup.fbk')
  File "C:\code\projects\firebirdsql\Kinterbasdb-3.0\build\lib.win32-2.3\firebirdsql\services.py", line 269, in backup
    return self._actAndReturnTextualResults(request)
  File "C:\code\projects\firebirdsql\Kinterbasdb-3.0\build\lib.win32-2.3\firebirdsql\services.py", line 613, in _actAndReturnTextualResults
    self._act(requestBuffer)
  File "C:\code\projects\firebirdsql\Kinterbasdb-3.0\build\lib.win32-2.3\firebirdsql\services.py", line 610, in _act
    return _ksrv.action_thin(self._C_conn, requestBuffer.render())
firebirdsql.OperationalError: (-902, '_kiservices could not perform the action: cannot open backup file BOGUS/PATH/test_backup.fbk. ')

Multifile Form

The database engine has built-in support for splitting the backup into multiple files, which is useful for circumventing operating system file size limits or spreading the backup across multiple discs.

pyfirebirdsql exposes this facility via the Connection.backup parameters destFilenames and destFileSizes. destFilenames (the second positional parameter of Connection.backup) can be either a string (as in the example above, when creating the backup as a single file) or a sequence of strings naming each constituent file of the backup. If destFilenames is a string-sequence with length N, destFileSizes must be a sequence of integer file sizes (in bytes) with length N-1. The database engine will constrain the size of each backup constituent file named in destFilenames[:-1] to the corresponding size specified in destFileSizes; any remaining backup data will be placed in the file name by destFilenames[-1].

Unfortunately, the database engine does not appear to expose any convenient means of calculating the total size of a database backup before its creation. The page size of the database and the number of pages in the database are available via database_info() calls: database_info(firebirdsql.isc_info_page_size, ‘i’) and database_info(firebirdsql.isc_info_db_size_in_pages, ‘i’), respectively, but the size of the backup file is usually smaller than the size of the database.

There should be no harm in submitting too many constituent specifications; the engine will write an empty header record into the excess constituents. However, at the time of this writing, released versions of the database engine hang the backup task if more than 11 constituents are specified (that is, if len(destFilenames) > 11). pyfirebirdsql does not prevent the programmer from submitting more than 11 constituents, but it does issue a warning.

The following program directs the engine to split the backup of the database at C:/temp/test.db into C:/temp/back01.fbk, a file 4096 bytes in size, C:/temp/back02.fbk, a file 16384 bytes in size, and C:/temp/back03.fbk, a file containing the remainder of the backup data.

from firebirdsql import services
con = services.connect(user='sysdba', password='masterkey')

con.backup('C:/temp/test.db',
   ('C:/temp/back01.fbk', 'C:/temp/back02.fbk', 'C:/temp/back03.fbk'),
    destFileSizes=(4096, 16384)
  )

Extended Options

In addition to the three parameters documented previously (positional sourceDatabase, positional destFilenames, and keyword destFileSizes), the Connection.backup method accepts six boolean parameters that control aspects of the backup process and the backup file output format. These options are well documented so in this document we present only a table of equivalence between gbak options and names of the boolean keyword parameters:

gbak option Parameter Name Default Value
-T transportable True
-M metadataOnly False
-G garbageCollect True
-L ignoreLimboTransactions False
-IG ignoreChecksums False
-CO convertExternalTablesToInternalTables True
Connection.restore(sourceFilenames, destFilenames, destFilePages=(), <options>)

Restores database from backup file.

Simplest Form

The simplest form of restore creates a single-file database, regardless of whether the backup data were split across multiple files.

from firebirdsql import services
con = services.connect(user='sysdba', password='masterkey')

restoreLog = con.restore('C:/temp/test_backup.fbk', 'C:/temp/test_restored.db')
print restoreLog

In the example, restoreLog is a string containing a gbak-style log of the restoration process. It is too long to reproduce here.

Multifile Form

The database engine has built-in support for splitting the restored database into multiple files, which is useful for circumventing operating system file size limits or spreading the database across multiple discs.

pyfirebirdsql exposes this facility via the Connection.restore parameters destFilenames and destFilePages. destFilenames (the second positional argument of Connection.restore) can be either a string (as in the example above, when restoring to a single database file) or a sequence of strings naming each constituent file of the restored database. If destFilenames is a string-sequence with length N, destFilePages must be a sequence of integers with length N-1. The database engine will constrain the size of each database constituent file named in destFilenames[:-1] to the corresponding page count specified in destFilePages; any remaining database pages will be placed in the file name by destFilenames[-1].

The following program directs the engine to restore the backup file at C:/temp/test_backup.fbk into a database with three constituent files: C:/temp/test_restored01.db, C:/temp/test_restored02.db, and C:/temp/test_restored03.db. The engine is instructed to place fifty user data pages in the first file, seventy in the second, and the remainder in the third file. In practice, the first database constituent file will be larger than pageSize*destFilePages[0], because metadata pages must also be stored in the first constituent of a multifile database.

from firebirdsql import services
con = services.connect(user='sysdba', password='masterkey')

con.restore('C:/temp/test_backup.fbk',
    ('C:/temp/test_restored01.db', 'C:/temp/test_restored02.db', 'C:/temp/test_restored03.db'),
    destFilePages=(50, 70),
    pageSize=1024,
    replace=True
  )

Extended Options

These options are well documented so in this document we present only a table of equivalence between the gbak options and the names of the keyword parameters to Connection.restore:

gbak option Parameter Name Default Value
-P pageSize [use server default]
-REP replace False
-O commitAfterEachTable False
-K doNotRestoreShadows False
-I deactivateIndexes False
-N doNotEnforceConstraints False
-USE useAllPageSpace False
-MO accessModeReadOnly False
-BU cacheBuffers [use server default]

Database Operating Modes, Sweeps, and Repair

Connection.sweep(database, markOutdatedRecordsAsFreeSpace=1)

Not yet documented.

Connection.setSweepInterval(database, n)

Not yet documented.

Conenction.setDefaultPageBuffers(database, n)

Not yet documented.

Conenction.setShouldReservePageSpace(database, shouldReserve)

Not yet documented.

Conenction.setWriteMode(database, mode)

Not yet documented.

Conenction.setAccessMode(database, mode)

Not yet documented.

Conenction.activateShadowFile(database)

Not yet documented.

Conenction.shutdown(database, shutdownMethod, timeout)

Not yet documented.

Conenction.bringOnline(database)

Not yet documented.

Conenction.getLimboTransactionIDs(database)

Not yet documented.

Conenction.commitLimboTransaction(database, transactionID)

Not yet documented.

Conenction.rollbackLimboTransaction(database, transactionID)

Not yet documented.

Conenction.repair(database, <options>)

Not yet documented.

User Maintenance

Conenction.getUsers(username=None)

By default, lists all users.

Conenction.addUser(user)
User:An instance of User with at least its username and password attributes specified as non-empty values.
Conenction.modifyUser(user)

Changes user data.

User:An instance of User with at least its username and password attributes specified as non-empty values.
Conenction.removeUser(user)

Accepts either an instance of services.User or a string username, and deletes the specified user.

Conenction.userExists(user)

Returns a boolean that indicates whether the specified user exists.

class firebirdsql.services.User

Not yet documented.