Usage Guide

Driver structure

Source code is currently divided into next submodules:

  • types - Data types used by driver.

  • interfaces - Interface wrappers for Firebird new API

  • core - Main driver source code.

  • fbapi - Python ctypes interface to Firebird client library.

  • config - Driver configuration.

  • hooks - Drivers hooks.

All important data, functions, classes and constants are available directly in firebird.driver name space. In normal circumstances is not necessary to import sub-modules directly. However, you may need them to access some not so frequently needed driver functionality like driver hooks, or to implement your own callback interfaces.

Configuration

The driver uses configuration built on top of configuration system provided by firebird-base package. In addition to global settings, the configuration also includes the definition of connection parameters to Firebird servers and databases.

The default configuration connects to embedded server using direct/local connection method. To access remote servers and databases (or local ones through remote protocols), it’s necessary to adjust default configuration, or register them in configuration manager.

You can manipulate the configuration objects directly, or load configuration from files or strings (in ini-style configparser format).

The ‘driver_config’ object

The global driver_config object holds all configurable driver parameters, and access configuration parameters for registered Firebird servers and databases.

In initial state, all parameters have default values and there are no registered servers and databases. You can set individual parameter values directly, or you can set multiple parameters (including registered servers and databases) at once by loading them from configuration string, dict or file(s).

Important

If you want to use specific Firebird client library, you must set the value of DriverConfig.fb_client_library configuration option before your application calls any from following functions: connect(), create_database(), connect_server(), load_api() or get_api().

See also

DriverConfig for list of available methods and parameters.

Server and database configuration

Firebird provides ever-increasing list of parameter options for database and server connections. To keep the Python API clean and manageable, the firebird-driver uses server and database configuration objects instead function parameters to specify values for almost all such options. Connection functions then provide a name parameter that can refer to particular server / database or configuration, and few keyword parameters to specify / override selected options.

Important

The configuration objects does not allow specification of next options:

  • set database encryption callback (for technical reasons)

  • set db_key scope (for security reasons)

  • disable garbage collection (for security reasons)

  • disable database triggers (for security reasons)

  • allow overwrite of existing database with newly created database (for security reasons)

These options could be specified only as keyword arguments in appropriate functions.

See also

ServerConfig and DatabaseConfig for list of available methods and parameters.

Databases

Access to the database is made available through Connection objects. Firebird-driver provides two constructors for these:

Using connect()

This constructor has one positional and several keyword parameters.

The value of database positional parameter must be one of:

  • name of registered database configuration

  • database name / alias

Important

This value cannot be DSN / fully qualified Firebird connection string!

Keyword parameters are intended to override selected configuration options, or to specify options that are not configurable.

Note

If database value is not recognized as name of registered database configuration, the driver uses db_defaults and server_defaults configuration objects.

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

from firebird.driver import connect

# Attach to 'employee' database/alias using embedded server connection
con = connect('employee', user='sysdba', password='masterkey')

# Attach to 'employee' database/alias using local server connection
from firebird.driver import driver_config
driver_config.server_defaults.host.value = 'localhost'
con = connect('employee', user='sysdba', password='masterkey')

# Set 'user' and 'password' via configuration
driver_config.server_defaults.user.value = 'SYSDBA'
driver_config.server_defaults.password.value = 'masterkey'
con = connect('employee')

However, it’s recommended to use specific configuration for servers and databases. It’s possible to register servers and databases directly in code like this:

from firebird.driver import connect, driver_config

# Register Firebird server
srv_cfg = """[local]
host = localhost
user = SYSDBA
password = masterkey
"""
driver_config.register_server('local', srv_cfg)

# Register database
db_cfg = """[employee]
server = local
database = employee.fdb
protocol = inet
charset = utf8
"""
driver_config.register_database('employee', db_cfg)

# Attach to 'employee' database
con = connect('employee')

But more convenient approach is using single configuration file:

# file: myapp.cfg

[firebird.driver]
servers = local
databases = employee

[local]
host = localhost
user = SYSDBA
password = masterkey

[employee]
server = local
database = employee.fdb
protocol = inet
charset = utf8
from firebird.driver import connect, driver_config

driver_config.read('myapp.cfg')

# Attach to 'employee' database
con = connect('employee')

See also

connect() for details.

Using create_database()

This constructor returns connection to newly created database. It works in the same way as connect(), but utilizes additional database configuration options.

It’s possible to specify these options in code like this:

from firebird.driver import connect, driver_config

# Register Firebird server
srv_cfg = """[local]
host = localhost
user = SYSDBA
password = masterkey
"""
driver_config.register_server('local', srv_cfg)

# Register database
db_cfg = """[mydb]
server = local
database = mydb.fdb
protocol = inet
charset = utf8
# create options
page_size = 16384
db_charset = utf8
sweep_interval = 80000
reserve_space = no
"""
driver_config.register_database('mydb', db_cfg)

# create 'mydb' database
con = create_database('mydb')

But more convenient approach is using single configuration file:

# file: myapp.cfg

[firebird.driver]
servers = local
databases = mydb

[local]
host = localhost
user = SYSDBA
password = masterkey

[mydb]
server = local
database = mydb.fdb
protocol = inet
charset = utf8
# create options
page_size = 16384
db_charset = utf8
sweep_interval = 80000
reserve_space = no
from firebird.driver import create_database, driver_config

driver_config.read('myapp.cfg')

# create 'mydb' database
con = create_database('mydb')

See also

create_database() for details.

Deleting databases

The Firebird 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 FDB exposes as drop_database method in Connection class. So, to drop a database you need to connect to it first.

Example:

from firebird.driver import connect, driver_config

driver_config.read('myapp.cfg')

# Attach to 'myapp' database
con = connect('myapp')
con.drop_database()

See also

Connection.drop_database() for details.

Connection object

Connection object represents a direct link to database, and works as gateway for next operations with it:

Closing the connection

There are many local and server resources used by firebird-driver that must be properly managed, and disposed when they are no longer necessary. All objects that require proper finalization provide close() method that must be called when object is no longer needed. The Connection objects are the most important ones, as other most frequently used objects like cursors, prepared statements and transactions are typically associated with connections.

You may call the close() method directly, or use the with statement and context manager support provided by all these objects.

Example:

from firebird.driver import connect, driver_config

driver_config.read('myapp.cfg')

with connect('employee') as con:
    cur = con.cursor()
    cur.execute('select 1 from rdb$database')
    print(cur.fetchone()[0])

Note

Objects that require proper finalization are: Connection, TransactionManager and DistributedTransactionManager, Statement, BlobReader, Cursor and Server.

Although only Connection and Server objects must be closed directly because all other objects are associated with them and thus closed when connection is closed, it’s recommended to directly close any resource object obtained by your code when it’s no longer needed (either directly by calling close() or using with statement).

Important

All managed objects have __del__ method, which ensures that the object in the active state is properly closed before it is destroyed by the Python memory manager. However, the close operation may fail as the state of your application could be arbitrary and the sequence in which objects are disposed by memory manager is not deterministic.

The __del__ methods should be thus considered as safe guard of last resort that your code should not rely upon. To indicate that your code is not managing resources properly, the ResourceWarning is raises when active object is disposed by memory manager.

Note

Such warnings may not reach your attention if warnings are disabled or filtered on your system. You should always develop and test your applications with enabled delivery of resource warnings.

See also

Connection.close() for details.

Getting information about connection

Only (most useful) part of information associated with Connection object is directly available:

  • It’s possible to check whether Connection object is closed or not with is_closed() method.

  • It’s possible to check whether connection to the Firebird server is not broken with ping() method.

  • The DSN (fully qualified Firebird database connection string) is surfaced as dsn read-only property.

  • The character set used by Connection is surfaced as charset read-only property.

  • The SQL dialect used by Connection is surfaced as sql_dialect read-only property.

Tip

Additional connection-specific information is currently held as bytes in protected Connection._dpb attribute that could be processed using DPB object.

Getting information about database

Because the scope and type of database information depends on the version of the Firebird server and database ODS, this information is made available through a separate class DatabaseInfoProvider. The Connection.info property provides access to DatabaseInfoProvider object appropriate for attached database.

Although you may query the information directly from server using DatabaseInfoProvider.get_info() method (that wraps the Firebird IAttachment.getInfo() API call), the DatabaseInfoProvider object provides more convenient methods and properties for obtaining specific information directly.

Note

Some information provided by DatabaseInfoProvider properties (like cache_hit_ratio) could not be obtained via get_info() method.

Example:

from firebird.driver import connect

with connect('employee', user='SYSDBA', password='masterkey') as con:
    print(f"Database character set: {con.info.charset}")
    print(f"Page size (in bytes): {con.info.page_size}")
    print(f"Attachment ID: {con.info.attachment_id}")
    print(f"SQL dialect used by connected database: {con.info.sql_dialect}")
    print(f"Database name (filename or alias): {con.info.name}")
    print(f"Database site name: {con.info.site}")
    print(f"Implementation (old format): {con.info.implementation!s}")
    print(f"Database Provider: {con.info.provider!s}")
    print(f"Database Class: {con.info.db_class!s}")
    print(f"Date when database was created: {con.info.creation_date}")
    print(f"Size of page cache used by connection: {con.info.page_cache_size}")
    print(f"Number of pages allocated for database: {con.info.pages_allocated}")
    print(f"Number of database pages in active use: {con.info.pages_used}")
    print(f"Number of free allocated pages in database: {con.info.pages_free}")
    print(f"Sweep interval: {con.info.sweep_interval}")
    print(f"Data page space usage (USE_FULL or RESERVE): {con.info.space_reservation!s}")
    print(f"Database write mode (SYNC or ASYNC): {con.info.write_mode!s}")
    print(f"Database access mode (READ_ONLY or READ_WRITE): {con.info.access_mode!s}")
    print(f"Current I/O statistics - Reads from disk to page cache: {con.info.reads}")
    print(f"Current I/O statistics - Fetches from page cache: {con.info.fetches}")
    print(f"Cache hit ratio = 1 - (reads / fetches): {con.info.cache_hit_ratio}")
    print(f"Current I/O statistics - Writes from page cache to disk: {con.info.writes}")
    print(f"Current I/O statistics - Writes to page in cache: {con.info.marks}")
    print(f"Total amount of memory curretly used by database engine: {con.info.current_memory}")
    print(f"Max. total amount of memory so far used by database engine: {con.info.max_memory}")
    print(f"ID of Oldest Interesting Transaction: {con.info.oit}")
    print(f"ID of Oldest Active Transaction: {con.info.oat}")
    print(f"ID of Oldest Snapshot Transaction: {con.info.ost}")
    print(f"ID for next transaction: {con.info.next_transaction}")

Sample output:

Database character set: NONE
Page size (in bytes): 8192
Attachment ID: 378
SQL dialect used by connected database: 3
Database name (filename or alias): /opt/firebird/examples/empbuild/employee.fdb
Database site name: NewAmarisk
Implementation (old format): Implementation.RDB_VMS
Database Provider: DbProvider.FIREBIRD
Database Class: DbClass.SERVER_ACCESS
Date when database was created: 2020-05-13 10:13:57.005010
Size of page cache used by connection: 2048
Number of pages allocated for database: 346
Number of database pages in active use: 311
Number of free allocated pages in database: 35
Sweep interval: 20000
Data page space usage (USE_FULL or RESERVE): DbSpaceReservation.RESERVE
Database write mode (SYNC or ASYNC): DbWriteMode.SYNC
Database access mode (READ_ONLY or READ_WRITE): DbAccessMode.READ_WRITE
Current I/O statistics - Reads from disk to page cache: 87
Current I/O statistics - Fetches from page cache: 1525
Cache hit ratio = 1 - (reads / fetches): 0.9429508196721311
Current I/O statistics - Writes from page cache to disk: 2
Current I/O statistics - Writes to page in cache: 5
Total amount of memory curretly used by database engine: 21925248
Max. total amount of memory so far used by database engine: 22033760
ID of Oldest Interesting Transaction: 307
ID of Oldest Active Transaction: 308
ID of Oldest Snapshot Transaction: 308
ID for next transaction: 308

See also

DatabaseInfoProvider for details.

Getting information about Firebird version

Because functionality and some features depends on actual Firebird version, it could be important for driver users to check it. This (otherwise) simple task could be confusing for new Firebird users, because Firebird uses two different version lineages. This abomination was introduced to Firebird thanks to its InterBase legacy (Firebird 1.0 is a fork of InterBase 6.0), as applications designed to work with InterBase can often work with Firebird without problems (and vice versa).

DatabaseInfoProvider provides these version strings as two properties:

However, this version string contains more information than version number. For example for Linux Firebird 4.0.0 it’s ‘LI-T4.0.0.1963 Firebird 4.0 Beta 2’. So DatabaseInfoProvider provides two more properties for your convenience:

  • version - Only Firebird version number. It’s a string with format: major.minor.subrelease.build

  • engine_version - Engine (major.minor) version as (float) number.

Example:

from firebird.driver import connect

with connect('employee', user='SYSDBA', password='masterkey') as con:
    print(f"server_version: '{con.info.server_version}'")
    print(f"firebird_version: '{con.info.firebird_version}'")
    print(f"version: '{con.info.version}'")
    print(f"engine_version: {con.info.engine_version}")

Sample output:

server_version: 'LI-T6.3.0.1963 Firebird 4.0 Beta 2'
firebird_version: 'LI-T4.0.0.1963 Firebird 4.0 Beta 2'
version: '4.0.0.1963'
engine_version: 4.0

Database On-Disk Structure

Particular Firebird features may also depend on specific support in database (for example number and structure of monitoring tables). These required structures are present automatically when database is created by particular engine verison that needs them, but Firebird engine may work with databases created by older versions and thus with older structure, so it could be necessary to consult also On-Disk Structure (ODS for short) version. DatabaseInfoProvider provides this number as ods (float) property.

Example:

from firebird.driver import connect

with connect('employee', user='SYSDBA', password='masterkey') as con:
    print(f"ods: {con.info.ods}")
    print(f"ods_version: {con.info.ods_version}")
    print(f"ods_minor_version: {con.info.ods_minor_version}")

Sample output:

ods: 13.0
ods_version: 13
ods_minor_version: 0

Executing SQL Statements

Firebird-driver implements two ways for execution of SQL commands against connected database:

  • execute_immediate - for execution of SQL commands that don’t return any result.

  • Cursor objects that offer rich interface for execution of SQL commands and fetching their results.

Cursor object

Because Cursor objects always operate in context of single Connection (and TransactionManager), Cursor instances are not created directly, but by constructor method. Python DB API 2.0 assumes that if database engine supports transactions, it supports only one transaction per connection, hence it defines constructor method cursor (and other transaction-related methods) as part of Connection interface. However, Firebird supports multiple independent transactions per connection. To conform to Python DB API, firebird-driver uses concept of internal main_transaction and secondary transactions. Cursor constructor is primarily defined by TransactionManager, and Cursor constructor on Connection is therefore a shortcut for main_transaction.cursor().

Cursor objects are used for next operations:

SQL Execution Basics

There are five methods how to execute SQL commands:

  1. Connection.execute_immediate() or TransactionManager.execute_immediate() for SQL commands that don’t return any result, and are not executed frequently. This method also doesn’t support either parameterized statements or prepared statements.

    Tip

    This method is efficient for administrative and DDL SQL commands, like DROP, CREATE or ALTER commands, SET STATISTICS etc.

  2. Cursor.execute() for SQL commands that return result sets, i.e. sequence of rows of the same structure, and sequence has unknown number of rows (including zero). Each row of the sequence can be read only once, and is returned in the order it is read from the server.

    Tip

    This method is preferred for all SELECT and other DML statements, or any statement that is executed frequently, either as is or in parameterized form.

  3. Cursor.executemany() for execution of single parameterized SQL command with various set of parameters.

    Important

    Because executemany() is basically a simple loop that calls execute() with different parameters, it’s possible to execute any statement acceptable by execute(). However, it’s possible to access the result set only from last executed command, so this method should not be used for SQL commands that return results.

  4. Cursor.open() for SQL command that return result sets, i.e. sequence of rows of the same structure, and sequence has unknown number of rows (including zero). Instead of just fetching rows sequentially in a forward direction like execute(), this method allows flexible navigation through an open cursor set both backwards and forwards. Rows next to, prior to and relative to the current cursor row can be targeted.

    See also

    Scrollable cursors for details.

  5. Cursor.callproc() for execution of Stored procedures that always return exactly one set of values.

    Note

    This method of SP invocation is equivalent to "EXECUTE PROCEDURE ..." SQL statement.

Fetching data from server

Result of SQL statement execution consists from sequence of zero to unknown number of rows, where each row is a set of exactly the same number of values. Cursor object offer number of different methods for fetching these rows, that should satisfy all your specific needs:

  • fetchone() - Returns the next row of a query result set, or None when no more data is available.

    Tip

    Cursor supports the iterator protocol, yielding tuples of values like fetchone().

  • fetchmany() - Returns the next set of rows of a query result, returning a sequence of sequences (e.g. a list of tuples). An empty sequence is returned when no more rows are available.

    The number of rows to fetch per call is specified by the parameter. If it is not given, the cursor’s arraysize determines the number of rows to be fetched. The method does try to fetch as many rows as indicated by the size parameter. If this is not possible due to the specified number of rows not being available, fewer rows may be returned.

    Note

    The default value of arraysize is 1, so without paremeter it’s equivalent to fetchone(), but returns list of rows, instead actual row directly.

  • fetchall() - Returns all (remaining) rows of a query result as list of tuples, where each tuple is one row of returned values.

    Tip

    This method can potentially return huge amount of data, that may exhaust available memory. If you need just iteration over potentially big result set, use loops with fetchone() or Cursor’s built-in support for iterator protocol instead this method.

  • Call to execute() returns self (Cursor instance) that itself supports the iterator protocol, yielding tuples of values like fetchone().

Important

Firebird-driver makes absolutely no guarantees about the row return value of the fetch*() methods except that it is a sequence indexed by field position. Therefore, client programmers should not rely on the return value being an instance of a particular class or type.

Examples:

from firebird.driver import connect

with connect('employee', user='SYSDBA', password='masterkey') as con:
    cur = con.cursor()
    SELECT = "select country, currency from country"

    # 1. Using built-in support for iteration protocol to iterate over the rows available
    # from the cursor, unpacking the resulting sequences to yield their elements (country, currency):
    cur.execute(SELECT)
    for (country, currency) in cur:
        print(f"{country} uses {currency} as currency.")
    # or alternatively you can take an advantage of cur.execute() returning self.
    for (country, currency) in cur.execute(SELECT):
        print(f"{country} uses {currency} as currency.")

    # 2. Equivalently using fetchall():
    # This is potentially dangerous if result set is huge, as the whole result set is
    # first materialized as list and then used for iteration.
    cur.execute(SELECT)
    for row in cur.fetchall():
        print(f"{row[0]} uses {row[1]} as currency.")

Important

Method Cursor.executemany() is not intended for operations that return results, so it does NOT returns self like Cursor.execute(), and you can’t use calls to this method as iterator.

Scrollable cursors

SQL statements executed by Cursor.open() have scrollable result set that could be navigated using next methods:

  • fetch_next() - Moves the cursor’s current position to the next row and returns it. Returns None if the cursor is empty or already positioned at the last row.

  • fetch_prior() - Moves the cursor’s current position to the prior row and returns it. Returns None if the cursor is empty or already positioned at the first row.

  • fetch_first() - Moves the cursor’s current position to the first row and returns it. Returns None if the cursor is empty.

  • fetch_last() - Moves the cursor’s current position to the last row and returns it. Returns None if the cursor is empty.

  • fetch_absolute() - Moves the cursor’s current position to the specified <position> and returns the located row. Returns None if <position> is beyond the cursor’s boundaries.

  • fetch_relative() - Moves the cursor’s current position backward or forward by the specified <offset> and returns the located row. Returns None if the calculated position is beyond the cursor’s boundaries.

Important

Please note that scrollable cursors:

  1. are not supported by all versions of Firebird server.

  2. are internally materialized as a temporary record set, thus consuming memory/disk resources, so this feature should be used only when really necessary.

Example:

from firebird.driver import connect

def print_row(row):
    if row:
        print(f"{row[0]}, {row[1]}, {row[2]}")
    else:
        print('NO DATA')

with connect('employee', user='SYSDBA', password='masterkey') as con:
    cur = con.cursor()
    cur.open('select row_number() over (order by country), country, currency from country order by country')

    # You can iterate over scrollable cursors
    for row in cur:
        print_row(row)

    print('-' * 10)
    # or fetch particular rows directly
    print_row(cur.fetch_first())
    print_row(cur.fetch_last())
    print_row(cur.fetch_absolute(10))
    print_row(cur.fetch_next())
    print_row(cur.fetch_prior())
    print_row(cur.fetch_relative(-5))
    print_row(cur.fetch_relative(10))

    print('-' * 10)

    cur.fetch_last()
    print_row(cur.fetch_next())

Sample output:

1, Australia, ADollar
2, Austria, Euro
3, Belgium, Euro
4, Canada, CdnDlr
5, England, Pound
6, Fiji, FDollar
7, France, Euro
8, Germany, Euro
9, Hong Kong, HKDollar
10, Italy, Euro
11, Japan, Yen
12, Netherlands, Euro
13, Romania, RLeu
14, Russia, Ruble
15, Switzerland, SFranc
16, USA, Dollar
----------
1, Australia, ADollar
16, USA, Dollar
10, Italy, Euro
11, Japan, Yen
10, Italy, Euro
5, England, Pound
15, Switzerland, SFranc
----------
NO DATA

Parameterized statements

When SQL command you want to execute contains data values, you can either:

  • Embed them directly or via string formatting into command string, e.g.:

    cur.execute("insert into the_table (a,b,c) values ('aardvark', 1, 0.1)")
    # or
    cur.execute("select * from the_table where col == 'aardvark'")
    # or
    cur.execute("insert into the_table (a,b,c) values ('%s', %i, %f)" % ('aardvark',1,0.1))
    # or
    cur.execute(f"select * from the_table where col == '{value}'")
    
  • Use parameter marker (?) in command string in the slots where values are expected, then supply those values as Python list or tuple:

    cur.execute("insert into the_table (a,b,c) values (?,?,?)", ('aardvark', 1, 0.1))
    # or
    cur.execute("select * from the_table where col == ?",('aardvark',))
    

While both methods have the same results, the second one (called parametrized) has several important advantages:

  • You don’t need to handle conversions from Python data types to strings.

  • Firebird-driver will handle all data type conversions (if necessary) from Python data types to Firebird ones, including None/NULL conversion and conversion from str to bytes in encoding expected by server.

  • You may pass BLOB values as open file-like objects, and firebird-driver will handle the transfer of BLOB value.

Parametrized statemets also have some limitations. Currently:

  • DATE, TIME and DATETIME values must be relevant datetime objects.

  • NUMERIC and DECIMAL values must be decimal objects.

Prepared Statements

Execution of any SQL statement has three phases:

  • Preparation: command is analyzed, validated, execution plan is determined by optimizer and all necessary data structures (for example for input and output parameters) are initialized.

  • Execution: input parameters (if any) are passed to server and previously prepared statement is actually executed by database engine.

  • Fetching: result of execution and data (if any) are transferred from server to client, and allocated resources are then released (by closing the statement).

The preparation phase consumes some amount of server resources (memory and CPU). Although preparation and release of resources typically takes only small amount of CPU time, it builds up as number of executed statements grows. Firebird (like most database engines) allows to spare this time for subsequent execution if particular statement should be executed repeatedly - by reusing once prepared statement for repeated execution. This may save significant amount of server processing time, and result in better overall performance.

Firebird-driver builds on this by encapsulating the Firebird SQL statement data and related code into separate Statement class, and implementing the Cursor class around it. The Cursor uses either an internally managed Statement instance to execute SQL commands provided as string, or uses Statement instance provided by your code as SQL command.

To get the (prepared) Statement instance for later (repeated) execution, use prepare() method. You can then pass this instance to execute(), executemany() or open() instead command string.

Statement instances are bound to Connection instance, and can’t be used with any other Connection. Beside repeated execution they are also useful to get information about statement (like its execution plan or type) before its execution.

Note

The internally managed Statement instance is released when Cursor is closed, or before any new statement is executed. It means that if your code executes the same SQL command (passed as string) repeatedly without closing the cursor between calls, the same Statement instance is (re)used.

Important

Implementation of Cursor in firebird-driver somewhat violates the Python DB API 2.0, which requires that cursor will be unusable after call to close(); and an Error (or subclass) exception should be raised if any operation is attempted with the cursor. In firebird-driver, the Cursor.close() call only releases resources associated with executed statement like the result set, and you can’t fetch data or query information about the SQL statement. However, you can use the cursor instance to execute new SQL commands.

Warning

If you’ll take advantage of this anomaly, your code would be less portable to other Python DB API 2.0 compliant drivers.

Example:

insertStatement = cur.prepare("insert into the_table (a,b,c) values (?,?,?)")

inputRows = [
    ('aardvark', 1, 0.1),
    ('zymurgy', 2147483647, 99999.999),
    ('foobar', 2000, 9.9)
  ]

for row in inputRows:
   cur.execute(insertStatement,row)
#
# or you can use executemany
#
cur.executemany(insertStatement, inputRows)

See also

Statement for details.

Named Cursors

To allow the Python programmer to perform scrolling UPDATE or DELETE via the “SELECT … FOR UPDATE” syntax, the firebird-driver provides the read-only property Cursor.name and method Cursor.set_cursor_name().

Example Program:

from firebird.driver import connect

with connect('employee', user='sysdba', password='masterkey') as con:
    curScroll = con.cursor()
    curUpdate = con.cursor()

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

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

    con.commit()

Working with stored procedures

Firebird stored procedures can have input parameters and/or output parameters. Some databases support input/output parameters, where the same parameter is used for both input and output; Firebird does not support this.

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

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

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

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

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

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

To execute a stored procedure and access its output parameters, you can choose from two options:

  1. Method Cursor.callproc() that conforms to Python DB API 2.0. This method does not returns the output parameters directly, and you must call Cursor.fetchone() exactly once to retrieve them.

  2. Method Cursor.call_procedure() that returns output parameters directly (or None if procedure does not have output parameters).

Examples:

# Python DB API 2.0 compliant method:
cur.callproc("the_proc", (input1, input2))
# If there are output parameters, retrieve them as though they were the
# first row of a result set...
outputParams = cur.fetchone()

# alternative method:
outputParams = cur.call_procedure("the_proc", (input1, input2))

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

Data handling and conversions

Implicit Conversion of Input Parameters from Strings

The Firebird 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 firebird-driver 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 firebird-driver 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 integer, t timestamp)
i = 1
t = 'now'
sqlWithMagicValues = f"insert into test_table (i, t) values (?, '{t}')"
cur.execute(sqlWithMagicValues, (i,))
i = 1
t = 'now'
cur.execute("insert into test_table (i, t) values (?, ?)", (i, t))

If firebird-driver 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.

Note

It should be noted that firebird-driver 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 firebird-driver 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.

Automatic conversion from/to Unicode

In Firebird, every CHAR, VARCHAR or textual BLOB field can (or, better: must) have a character set assigned. While it’s possible to define single character set for whole database, it’s also possible to define different character set for each textual field. This information is used to correctly store the bytes that make up the character string, and together with collation information (that defines the sort ordering and uppercase conversions for a string) is vital for correct data manipulation, including automatic transliteration between character sets when necessary.

Important

Because data also flow between server and client application, it’s vital that client will send data encoded only in character set(s) that server expects. While it’s possible to leave this responsibility completely on client application, it’s better when client and server settle on single character set they would use for communication, especially when database operates with multiple character sets, or uses character set that is not native for client application.

Character set for communication is specified using charset configuration option, or parameter in connect() or create_database() call.

When connection charset is defined, all textual data returned from server are encoded in this charset, and client application must ensure that all textual data sent to server are encoded only in this charset as well.

Firebird-driver helps with client side of this character set bargain by automatically converting Python str values into bytes encoded in connection character set, and vice versa. However, developers are still responsible that bytes strings passed to server are in correct encoding (because firebird-driver makes no assumption about encoding of bytes strings, so it can’t recode them to connection charset).

Important

In case that connection charset is NOT defined at all, or NONE charset is specified, firebird-driver uses locale.getpreferredencoding to determine encoding for conversions from/to unicode.

Important

There is one exception to automatic conversion: when character set OCTETS is defined for data column. Values assigned to OCTETS columns are always passed as is, because they’re basically binary streams. This has specific implications. Python 3 native strings are unicode, and you would probably want to use bytes type instead. However, firebird-driver in this case doesn’t check the value type at all, so you’ll not be warned if you’ll make a mistake and pass str to OCTETS column (unless you’ll pass more bytes than column may hold, or you intend to store unicode that way).

Conversion is fully automatic in both directions for all textual data, i.e. including for string values returned by Firebird Service and info calls etc. When connection charset is not specified, firebird-driver uses locale.getpreferredencoding to determine encoding for conversions from/to unicode.

Tip

Except for legacy databases that doesn’t have character set defined, always define character set for your databases and specify connection charset. It will make your life much easier.

Working with BLOBs

Firebird-driver uses two types of BLOB values:

  • Materialized BLOB values are Python str or bytes values. This is the default type.

  • Streamed BLOB values are file-like objects.

Materialized BLOBs are easy to work with, but are not suitable for:

  • deferred loading of BLOBs. They’re called materialized because they’re always fetched from server as part of row fetch. Fetching BLOB value means separate API calls (and network roundtrips), which may slow down you application considerably.

  • large values, as they are always stored in memory in full size.

These drawbacks are addressed by stream BLOBs. Using BLOBs in stream mode is easy:

  • For input values, simply use parameterized statement and pass any file-like object in place of BLOB parameter. The file-like object must implement only the read method, as no other method is used.

  • For output values, add column name(s) that should be returned as file-like objects to Cursor.stream_blobs list attribute. Firebird-driver then returns BlobReader instance instead string in place of returned BLOB value for these column(s).

Important

The firebird-driver provides Cursor.stream_blob_threshold attribute that controls the maximum size of materialized blobs (as memory exhaustion safeguard). When particular blob value exceeds this threshold, an instance of BlobReader is returned instead string/bytes value.

Zero threshold value effectively forces all blobs to be returned as stream blobs. Negative value means no size limit for materialized blobs (use at your own risk). Please note that positive threshold value means that your application has to be prepared to handle BLOBs in both incarnations.

The default threshold is 64K and could be changed using DriverConfig.stream_blob_threshold configuration option.

Blob size threshold has effect only on materialized blob columns, i.e. columns not explicitly requested to be returned as streamed ones using Cursor.stream_blobs attribute, that are always returned as stream blobs.

The BlobReader instance is bound to Cursor instance, and it’s automatically closed with cursor. However, it’s good practice to use with statement or call BlobReader.close() once you’re finished reading to release system resources associated with BLOB value.

Important

When working with BLOB values, always have memory efficiency in mind, especially when you’re processing huge quantity of rows with BLOB values at once. Materialized BLOB values may exhaust your memory quickly, but using stream BLOBs may have inpact on performance too, as new BlobReader instance is created for each value fetched.

Example program:

from firebird.driver import connect

with connect('employee', user='SYSDBA', password='masterkey') as con:
    cur = con.cursor()
    print("Materialized retrieval (as str):")
    cur.execute('select proj_id, proj_desc from project')
    proj_id, proj_desc = cur.fetchone()
    print(f"{proj_id=}, {proj_desc=}")

    print("\nStreaming retrieval (via BlobReader):")
    cur.stream_blobs.append('PROJ_DESC')
    cur.execute('select proj_id, proj_desc from project')
    proj_id, proj_desc = cur.fetchone()
    with proj_desc:
        print(f"{proj_id=}, {proj_desc=}")
        print(proj_desc.read())

Output:

Materialized retrieval (as str):
proj_id='VBASE', proj_desc='Design a video data base management system for\ncontrolling on-demand video distribution.\n'

Streaming retrieval (via BlobReader):
proj_id='VBASE', proj_desc=BlobReader[size=89]
Design a video data base management system for
controlling on-demand video distribution.

Firebird ARRAY type

Firebird-driver supports Firebird ARRAY data type. ARRAY values are represented as Python lists. On input, the Python sequence (list or tuple) 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.

Note

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:

>>> from firebird.driver import connect
>>> con = connect('employee',user='sysdba', password='masterkey')
>>> cur = con.cursor()
>>> cur.execute("select LANGUAGE_REQ from job where job_code='Eng' and job_grade=3 and job_country='Japan'")
>>> cur.fetchone()
(['Japanese\n', 'Mandarin\n', 'English\n', '\n', '\n'],)

Example program:

from firebird.driver import connect

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

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

    cur = con.cursor()

    print(f"{arrayIn=}")
    cur.execute("insert into array_table values (?)", (arrayIn,))
    con.commit()

    cur.execute("select a from array_table")
    arrayOut = cur.fetchone()[0]
    print(f"{arrayOut=})

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]]

Transanction management

For the sake of simplicity, firebird-driver 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.

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 firebird-driver establishes a transaction implicitly by using any SQL execution method, such as Connection.execute_immediate(), Cursor.execute(), or Cursor.callproc().

Although firebird-driver 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.

Basics

When it comes to transactions, Python Database API 2.0 specify that Connection object has to respond to the following methods:

Connection.commit()

Commits any pending transaction to the database. Note that if the database supports an auto-commit feature, this must be initially off. An interface method may be provided to turn it back on. Database modules that do not support transactions should implement this method with void functionality.

Connection.rollback()

(optional) In case a database does provide transactions this method causes the the database to roll back to the start of any pending transaction. Closing a connection without committing the changes first will cause an implicit rollback to be performed.

In addition to the implicit transaction initiation required by Python Database API, firebird-driver allows the programmer to start transactions explicitly via the Connection.begin() method. Also Connection.savepoint() method was added to provide support for Firebird SAVEPOINTs.

But 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 opened simultaneously to perform various tasks, which would require to open multiple connections and thus consume more resources than necessary.

Firebird-driver surfaces this Firebird feature by separating transaction management out from Connection into separate TransactionManager objects. To comply with Python DB API 2.0 requirements, Connection object uses one TransactionManager instance as main transaction, and delegates begin(), savepoint(), commit(), rollback() and execute_immediate() calls to it.

See also

More about using multiple transactions with the same connection in separate section.

Example:

from firebird.driver import connect

with connect('employee', user='SYSDBA', password='masterkey') as con:
    cur = con.cursor()

    # Most minimalistic transaction management -> implicit start, only commit() and rollback()
    # ========================================================================================
    #
    # Transaction is started implicitly
    cur.execute('insert into country values ('Oz','Crowns')
    con.commit() # commits active transaction
    # Again, transaction is started implicitly
    cur.execute('insert into country values ('Barsoom','XXX')
    con.rollback() # rolls back active transaction
    cur.execute('insert into country values ('Pellucidar','Shells')

# Commit was not performed before connection context was closed
# This will roll back the transaction because Python DB API 2.0
# requires that closing connection with pending transaction must
# cause an implicit rollback

See also

TransactionManager for details.

Auto-commit

Firebird-driver doesn’t support auto-commit feature directly, but developers may achieve the similar result using explicit transaction start, taking advantage of TransactionManager.default_action and its default value (COMMIT).

Example:

from firebird.driver import connect

with connect('employee', user='SYSDBA', password='masterkey') as con:
    cur = con.cursor()

    con.begin()
    cur.execute('insert into country values ('Oz','Crowns')
    con.begin() # commits active transaction and starts new one
    cur.execute('insert into country values ('Barsoom','XXX')
    con.begin() # commits active transaction and starts new one
    cur.execute('insert into country values ('Pellucidar','Shells')

    # However, commit is required before connection is closed,
    # because Python DB API 2.0 requires that closing connection
    # with pending transaction must cause an implicit rollback
    con.commit()

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.

Transaction manager has default_tpb attribute that can be changed to set the default TPB to be used for all subsequent transactions started by this manager. Also Connection have a default_tpb attribute, but it’s used to set the default TPB for all transactions managers subsequently created for the connection (see Connection.transaction_manager()).

Alternatively, if the programmer only wants to set the TPB for a single transaction, he can start a transaction explicitly via the Connection.begin() or TransactionManager.begin() method and pass a TPB for that single transaction.

The TPB is a bytes value constructed from various tags and binary values, as defined by API. While you can construct the TPB manually, the firebird-driver provides several convenient ways for TPB construction:

  1. The tpb() function for simple TPBs.

  2. The TPB class for complex TPBs (including table reservation etc.).

Examples:

from firebird.driver import tpb, TPB, Isolation, TraAccessMode, TableShareMode, TableAccessMode

# Use tpb() if isolation, timeout and access_mode parameters are enough for you
simple_tpb = tpb(Isolation.READ_COMMITTED_READ_CONSISTENCY,100,TraAccessMode.WRITE)

# Use TPB if you want additional parameters than isolation, timeout and access_mode
my_tpb = TPB(isolation=Isolation.SNAPSHOT,
             access_mode=TraAccessMode.WRITE,
             lock_timeout=100,
             no_auto_undo=True,
             auto_commit=True,
             ignore_limbo=True)
my_tpb.reserve_table('MY_TABLE', TableShareMode.PROTECTED, TableAccessMode.LOCK_WRITE)
complex_tpb = my_tpb.get_buffer()

Getting information about transaction

The TransactionManager.info property provides access to TransactionInfoProvider object that could be used to obtain information about active transaction.

Although you may query the information directly from server using TransactionInfoProvider.get_info() method (that wraps the Firebird ITransaction.getInfo() API call), the TransactionInfoProvider object provides more convenient methods and properties for obtaining specific information directly.

Example:

from firebird.driver import connect, driver_config

driver_config.server_defaults.host.value = 'localhost'
with connect('employee', user='SYSDBA', password='masterkey') as con:
    con.begin()
    info = con.main_transaction.info
    print(f"Transaction ID: {info.id}")
    print(f"Database: {info.database}")
    print(f"Isolation level: {info.isolation!s}")
    print(f"Lock timeout: {info.lock_timeout}")
    print(f"Is Read-Only: {info.is_read_only()}")
    print(f"ID of Oldest Interesting Transaction: {info.oit}")
    print(f"ID of Oldest Active Transaction: {info.oat}")
    print(f"ID of Oldest Snapshot Transaction: {info.ost}")

Output:

Transaction ID: 352
Database: localhost:employee
Isolation level: Isolation.SNAPSHOT
Lock timeout: -1
Is Read-Only: False
ID of Oldest Interesting Transaction: 350
ID of Oldest Active Transaction: 352
ID of Oldest Snapshot Transaction: 352

Retaining transactions

The commit() and rollback() methods accept an optional boolean keyword 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.

Important

In code that commits or rolls back frequently in short amount of time (like in loop), “retaining” the transaction may yield 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.

It’s definitely no recommended to use retaining for all transactions, or retain the transaction context indefinitely (you should eventually commit/rollback the transaction in normal way).

For more information about retaining transactions, see Firebird documentation.

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, firebird-driver also exposes savepoints at the Python API level for the sake of convenience.

Call to method TransactionManager.savepoint() establishes a savepoint with the specified name. To roll back to a specific savepoint, call the rollback() method and provide the name of the savepoint for the savepoint keyword 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 firebird-driver API, rather than raw SQL.

from firebird.driver import connect

with connect('employee', user='SYSDBA', password='masterkey') as con:
    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='B')
    print('After rolling back to savepoint B, 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 B, the contents of the table are:
  [(1,), (2,)]
After rolling back entirely, the contents of the table are:
  []

Using multiple transactions with the same connection

To use additional transactions that could run simultaneously with main transaction managed by Connection, create new TransactionManager object calling Connection.transaction_manager() method. If you don’t specify the optional default_tpb parameter, this new TransactionManager inherits the default_tpb from Connection. Physical transaction is not started when TransactionManager instance is created, but implicitly when first SQL statement for cursor created from this manager is executed, or explicitly via TransactionManager.begin() call.

To execute statements in context of this additional transaction you have to use cursors obtained directly from this TransactionManager instance calling its cursor() method, or call TransactionManager.execute_immediate() method.

Example:

from firebird.driver import connect, tpb, Isolation, TraAccessMode

with connect('employee', user='SYSDBA', password='masterkey') as con:
    # Cursor for main_transaction context
    cur = con.cursor()

    # Create new READ ONLY READ COMMITTED transaction
    ro_transaction = con.transaction_manager(tpb(Isolation.READ_COMMITTED_RECORD_VERSION,
                                                 access=TraAccessMode.READ))
    # and cursor
    ro_cur = ro_transaction.cursor()

    cur.execute('insert into country values ('Oz','Crowns')
    con.commit() # commits main transaction

    # Read data created by main transaction from second one
    ro_cur.execute("select * from COUNTRY where COUNTRY = `Oz`")
    print(ro_cur.fetchall())

    # Insert more data, but don't commit
    cur.execute('insert into country values ('Barsoom','XXX')

    # Read data created by main transaction from second one
    ro_cur.execute("select * from COUNTRY where COUNTRY = `Barsoom`")
    print(ro_cur.fetchall())

Distributed Transactions

Distributed transactions are transactions that span multiple databases. Firebird-driver provides this Firebird feature through DistributedTransactionManager class. Instances of this class must be created manually, and managed transactions are fully independent from all other transactions, main or secondary, of member connections.

Similarly to TransactionManager, distributed transactions are managed through begin(), savepoint(), commit() and rollback() methods. Additionally, DistributedTransactionManager exposes method prepare() that explicitly initiates the first phase of Two-Phase Commit Protocol. Transaction parameters are defined similarly to TransactionManager using default_tpb or as optional parameter to begin() call.

SQL statements that should belong to context of distributed transaction are executed via Cursor instances aquired through DistributedTransactionManager.cursor() method, or calling DistributedTransactionManager.execute_immediate() method.

Note

Because Cursor instances can belong to only one Connection, the cursor() method has mandatory parameter connection, to specify to which member connection cursor should belong.

The execute_immediate() method operates on all databases in group.

Example program:

from firebird.driver import create_database, DistributedTransactionManager

# First database
con1 = create_database('db1.fdb', user='SYSDBA', password='masterkey')
con1.execute_immediate("recreate table T (PK integer, C1 integer)")
con1.commit()

# Second database
con2 = create_database('db2.fdb', user='SYSDBA', password='masterkey')
con2.execute_immediate("recreate table T (PK integer, C1 integer)")
con2.commit()

# Create distributed transaction manager
dt = DistributedTransactionManager((con1,con2))

# Prepare cursors for each connection
dc1 = dt.cursor(con1)
dc2 = dt.cursor(con2)

# Connection cursors to check content of databases
q = 'select * from T order by pk'

cc1 = con1.cursor()
p1 = cc1.prep(q)

cc2 = con2.cursor()
p2 = cc2.prep(q)

print("Distributed transaction: COMMIT")
#      ===============================
dc1.execute('insert into t (pk) values (1)')
dc2.execute('insert into t (pk) values (1)')
dt.commit()

# check it
con1.commit()
cc1.execute(p1)
print('db1:', cc1.fetchall())
con2.commit()
cc2.execute(p2)
print('db2:', cc2.fetchall())

print("Distributed transaction: PREPARE + COMMIT")
#      =========================================
dc1.execute('insert into t (pk) values (2)')
dc2.execute('insert into t (pk) values (2)')
dt.prepare()
dt.commit()

# check it
con1.commit()
cc1.execute(p1)
print('db1:', cc1.fetchall())
con2.commit()
cc2.execute(p2)
print('db2:', cc2.fetchall())

print("Distributed transaction: SAVEPOINT + ROLLBACK to it")
#      ===================================================
dc1.execute('insert into t (pk) values (3)')
dt.savepoint('CG_SAVEPOINT')
dc2.execute('insert into t (pk) values (3)')
dt.rollback(savepoint='CG_SAVEPOINT')

# check it - via group cursors, as transaction is still active
dc1.execute(q)
print('db1:', dc1.fetchall())
dc2.execute(q)
print('db2:', dc2.fetchall())

print("Distributed transaction: ROLLBACK")
#      =================================
dt.rollback()

# check it
con1.commit()
cc1.execute(p1)
print('db1:', cc1.fetchall())
con2.commit()
cc2.execute(p2)
print('db2:', cc2.fetchall())

print("Distributed transaction: EXECUTE_IMMEDIATE")
#      ==========================================
dt.execute_immediate('insert into t (pk) values (3)')
dt.commit()

# check it
con1.commit()
cc1.execute(p1)
print('db1:', cc1.fetchall())
con2.commit()
cc2.execute(p2)
print('db2:', cc2.fetchall())

# Finalize
con1.drop_database()
con1.close()
con2.drop_database()
con2.close()

Output:

Distributed transaction: COMMIT
db1: [(1, None)]
db2: [(1, None)]
Distributed transaction: PREPARE + COMMIT
db1: [(1, None), (2, None)]
db2: [(1, None), (2, None)]
Distributed transaction: SAVEPOINT + ROLLBACK to it
db1: [(1, None), (2, None), (3, None)]
db2: [(1, None), (2, None)]
Distributed transaction: ROLLBACK
db1: [(1, None), (2, None)]
db2: [(1, None), (2, None)]
Distributed transaction: EXECUTE_IMMEDIATE
db1: [(1, None), (2, None), (3, None)]
db2: [(1, None), (2, None), (3, None)]

Transaction Context Manager

Firebird-driver provides context manager transaction that allows automatic transaction management using WITH statement. It can work with any object that supports begin(), commit() and rollback() methods, i.e. Connection, TransactionManager or DistributedTransactionManager.

It starts transaction when WITH block is entered and commits it if no exception occurs within it, or calls rollback() otherwise. Exceptions raised in WITH block are never suppressed.

Examples:

from firebird.driver import connect, transaction, DistributedTransactionManager

with connect('employee', user='SYSDBA', password='masterkey') as con:

    # Uses default main transaction
    with transaction(con):
        cur = con.cursor()
        cur.execute("insert into T (PK,C1) values (1,'TXT')")

    # Uses separate transaction
    with transaction(con.transaction_manager()) as tr:
        cur = tr.cursor()
        cur.execute("insert into T (PK,C1) values (2,'AAA')")

    # Uses distributed transaction
    with connect('employee2', user='SYSDBA', password='masterkey') as con2,
         DistributedTransactionManager(con, con2) as dtm:
        with transaction(dtm):
            cur1 = cg.cursor(con)
            cur2 = cg.cursor(con2)
            cur1.execute("insert into T (PK,C1) values (3,'Local')")
            cur2.execute("insert into T (PK,C1) values (3,'Remote')")

Database Events

What they are

The Firebird engine features a distributed, inter-process 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 them

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 message's 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

  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 FDB’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 isc_wait_for_event(). This form is admirably simple for a C programmer to use, but is inappropriate as a basis for FDB’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 isc_que_events() (note that the name of that function is misspelled), isc_cancel_events(), and others. The details are as nasty as they are numerous, but the essence of using asynchronous notification from C is as follows:

    1. Call isc_event_block() to create a formatted binary buffer that will tell the server which events the client wants to listen for.

    2. Call isc_que_events() (passing the buffer created in the previous step) to inform the server that the client is ready to receive event notifications, and provide a callback that will be asynchronously invoked when one or more of the registered events occurs.

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

    4. When the callback is invoked (the database client library starts a thread dedicated to this purpose), it can use the isc_event_counts() function to determine how many times each of the registered events has occurred since the last call to isc_event_counts() (if any).

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

    6. When the callback thread is finished handling an event notification, it must call isc_que_events() again in order to receive future notifications. Future notifications will invoke the callback again, effectively “looping” the callback thread back to Step 4.

API for Python developers

The Firebird-driver database event API is comprised of the following: the method Connection.event_collector() and the class EventCollector.

Use the Connection.event_collector() method (takes a sequence of string event names as parameter) to create EventCollector instance, that collects database event notifications sent from the server for given database.

Important

To start listening for events it’s necessary to call EventCollector.begin() method or use EventCollector’s context manager interface.

Immediately when begin() method is called, EventCollector starts to accumulate notifications of any event that occur within the collector’s internal queue until the collector is closed either explicitly (via the close() method) or implicitly (via garbage collection).

Notifications about events are aquired through call to wait() method, that blocks the calling thread until at least one of the events occurs, or the specified timeout (if any) expires, and returns None if the wait timed out, or a dictionary that maps event_name -> event_occurrence_count.

Important

EventCollector can act as context manager that ensures execution of begin() and close() methods. It’s strongly advised to use the EventCollector with the with statement.

Example:

with connection.event_collector(['event_a', 'event_b']) as collector:
    events = collector.wait()
    process_events(events)

If you want to drop notifications accumulated so far by conduit, call EventCollector.flush() method.

Example program:

from firebird.driver import create_database, transaction
import threading
import time

# Prepare database
con = create_database('event-test', user='SYSDBA', password='masterkey')
with transaction(con):
    con.execute_immediate("CREATE TABLE T (PK Integer, C1 Integer)")
    con.execute_immediate("""CREATE TRIGGER EVENTS_AU FOR T ACTIVE
    BEFORE UPDATE POSITION 0
    AS
    BEGIN
       if (old.C1 <> new.C1) then
          post_event 'c1_updated' ;
    END""")
    con.execute_immediate("""CREATE TRIGGER EVENTS_AI FOR T ACTIVE
    AFTER INSERT POSITION 0
    AS
    BEGIN
       if (new.c1 = 1) then
         post_event 'insert_1' ;
       else if (new.c1 = 2) then
         post_event 'insert_2' ;
       else if (new.c1 = 3) then
         post_event 'insert_3' ;
       else
         post_event 'insert_other' ;
    END""")
cur = con.cursor()

# Utility function
def send_events(command_list):
    with transaction(con):
        for cmd in command_list:
            cur.execute(cmd)

print("One event")
#      =========
timed_event = threading.Timer(3.0,send_events,args=[["insert into T (PK,C1) values (1,1)",]])
with con.event_collector(['insert_1']) as events:
    timed_event.start()
    e = events.wait()
print(e)

print("Multiple events")
#      ===============
cmds = ["insert into T (PK,C1) values (1,1)",
        "insert into T (PK,C1) values (1,2)",
        "insert into T (PK,C1) values (1,3)",
        "insert into T (PK,C1) values (1,1)",
        "insert into T (PK,C1) values (1,2)",]
timed_event = threading.Timer(3.0,send_events,args=[cmds])
with con.event_collector(['insert_1','insert_3']) as events:
    timed_event.start()
    e = events.wait()
print(e)

print("20 events")
#      =========
cmds = ["insert into T (PK,C1) values (1,1)",
        "insert into T (PK,C1) values (1,2)",
        "insert into T (PK,C1) values (1,3)",
        "insert into T (PK,C1) values (1,1)",
        "insert into T (PK,C1) values (1,2)",]
timed_event = threading.Timer(1.0,send_events,args=[cmds])
with con.event_collector(['insert_1','A','B','C','D',
                          'E','F','G','H','I','J','K','L','M',
                          'N','O','P','Q','R','insert_3']) as events:
    timed_event.start()
    time.sleep(3)
    e = events.wait()
print(e)

print("Flush events")
#      ============
timed_event = threading.Timer(3.0,send_events,args=[["insert into T (PK,C1) values (1,1)",]])
with con.event_collector(['insert_1']) as events:
    send_events(["insert into T (PK,C1) values (1,1)",
                 "insert into T (PK,C1) values (1,1)"])
    time.sleep(2)
    events.flush()
    timed_event.start()
    e = events.wait()
print(e)

# Finalize
con.drop_database()
con.close()

Output:

One event
{'insert_1': 1}
Multiple events
{'insert_3': 1, 'insert_1': 2}
20 events
{'A': 0, 'C': 0, 'B': 0, 'E': 0, 'D': 0, 'G': 0, 'insert_1': 2, 'I': 0, 'H': 0, 'K': 0, 'J': 0, 'M': 0,
 'L': 0, 'O': 0, 'N': 0, 'Q': 0, 'P': 0, 'R': 0, 'insert_3': 1, 'F': 0}
Flush events
{'insert_1': 1}

Working with Services

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, the firebird-driver presents its own abstraction over the native API.

Services API Connections

All Services API operations are performed in the context of a connection to a specific database server, represented by the Server class. Similarly to database connections, firebird-driver provides connect_server() constructor function to create such connections.

This constructor has one positional and several keyword parameters.

The value of server positional parameter must be one of:

  • name of registered server configuration

  • server host name or address

Keyword parameters are intended to override selected configuration options, or to specify options that are not configurable.

A simple server connection is typically established with code such as this:

from firebird.driver import connect_server

# Attach to 'embedded' server
srv = connect_server('', user='SYSDBA', password='masterkey')

# Attach to 'local' server
srv = connect_server('localhost', user='SYSDBA', password='masterkey')

# Set 'user' and 'password' via configuration
from firebird.driver import driver_config
driver_config.server_defaults.user.value = 'SYSDBA'
driver_config.server_defaults.password.value = 'masterkey'
srv = connect_server('localhost')

However, it’s recommended to use specific configuration for servers. It’s possible to register servers directly in code like this:

from firebird.driver import connect_server, driver_config

# Register Firebird server
srv_cfg = """[main_server]
host = 192.168.0.15
user = SYSDBA
password = Xyzzy
"""
driver_config.register_server('main_server', srv_cfg)

# Attach to 'main' server
con = connect('main_server')

But more convenient approach is using single configuration file:

# file: myapp.cfg

[firebird.driver]
servers = main_server
databases = employee

[main_server]
host = 192.168.0.15
user = SYSDBA
password = Xyzzy
from firebird.driver import connect_server, driver_config

driver_config.read('myapp.cfg')

# Attach to 'main' server
con = connect('main_server')

Note

Like database connections, it’s important to properly close() them when you don’t need them anymore.

Server object provides main infrastructure for communication with Firebird server services, and manages number of objects that provide actual server services:

See also

connect_server() and Server for details.

Text output from Services

Some services like ServerDbServices.backup() may return significant amount of text. Rather than return the whole text as single string value by methods that provide access to these services, firebird-driver isolated the transfer process to separate methods:

  • readline() - Similar to file.readline, returns next line of output from Service.

  • readlines() - Like file.readlines, returns list of output lines.

  • Iteration over Server object, because Server has built-in support for iterator protocol.

  • Using callback method provided by developer. Each Server method that returns its result asynchronously accepts an optional parameter callback, which must be a function that accepts one string parameter. This method is then called with each output line coming from service.

  • wait() - Waits for Sevice to finish, ignoring rest of the output it may produce.

Warning

Until output is not fully fetched from service, any attempt to start another asynchronous service will fail with exception! This constraint is set by Firebird Service API.

You may check the status of asynchronous Services using Server.is_running() method.

In cases when you’re not interested in output produced by Service, call wait() to wait for service to complete.

Examples:

from firebird.driver import connect_server
with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
    print("Fetch materialized")
    print("==================")
    print("Start backup")
    srv.database.backup(database='employee', backup='employee.fbk')
    print("srv.running is", srv.isrunning())
    report = srv.readlines()
    print(f"{len(report)} lines returned")
    print("First 5 lines from output:")
    for i in xrange(5):
        print(i,report[i])
    print("srv.running is", srv.isrunning())
    print()
    print("Iterate over result")
    print("===================")
    srv.database.backup(database='employee', backup='employee.fbk')
    output = []
    for line in srv:
        output.append(line)
    print(f"{len(output)} lines returned")
    print("Last 5 lines from output:")
    for line in output[-5:]:
        print(line)
    print()
    print("Callback")
    print("========")

    output = []

    # Callback function
    def fetchline(line):
        output.append(line)

    srv.database.backup(database='employee', backup='employee.fbk', callback=fetchline)
    print(f"{len(output)} lines returned")
    print("Last 5 lines from output:")
    for line in output[-5:]:
        print(line)

Output:

Fetch materialized
==================
Start backup
svc.running is True
558 lines returned
First 5 lines from output:
0 gbak:readied database employee for backup
1 gbak:creating file employee.fbk
2 gbak:starting transaction
3 gbak:database employee has a page size of 4096 bytes.
4 gbak:writing domains
svc.running is False

Iterate over result
===================
558 lines returned
Last 5 lines from output:
gbak:writing referential constraints
gbak:writing check constraints
gbak:writing SQL roles
gbak:writing names mapping
gbak:closing file, committing, and finishing. 74752 bytes written

Callback
========
558 lines returned
Last 5 lines from output:
gbak:writing referential constraints
gbak:writing check constraints
gbak:writing SQL roles
gbak:writing names mapping
gbak:closing file, committing, and finishing. 74752 bytes written

Server Configuration and State information

Server configuration and other information is provided through ServerInfoProvider instance accessible via Server.info property.

ServerInfoProvider methods and properties:

Example:

from firebird.driver import driver_config, connect, connect_server

srv_cfg = """[local]
host = localhost
user = SYSDBA
password = masterkey
"""
driver_config.register_server('local', srv_cfg)
db_cfg = """[employee]
server = local
database = employee.fdb
protocol = inet
"""
driver_config.register_database('employee', db_cfg)
with connect('employee', user='SYSDBA', password='masterkey'), connect_server('local', user='SYSDBA', password='masterkey') as srv:
    print(f'{srv.info.version=}')
    print(f'{srv.info.engine_version=}')
    print(f'{srv.info.manager_version=}')
    print(f'{srv.info.architecture=}')
    print(f'{srv.info.home_directory=}')
    print(f'{srv.info.security_database=}')
    print(f'{srv.info.lock_directory=}')
    print(f'{srv.info.message_directory=}')
    print(f'{srv.info.capabilities=!s}')
    print(f'{srv.info.connection_count=}')
    print(f'{srv.info.attached_databases=}')

Sample output for 64-bit Linux Firebird 4.0 Beta 2:

srv.info.version='4.0.0.1963'
srv.info.engine_version=4.0
srv.info.manager_version=2
srv.info.architecture='Firebird/Linux/AMD/Intel/x64'
srv.info.home_directory='/opt/firebird/'
srv.info.security_database='/opt/firebird/security4.fdb'
srv.info.lock_directory='/tmp/firebird/'
srv.info.message_directory='/opt/firebird/'
srv.info.capabilities=ServerCapability.REMOTE_HOP|MULTI_CLIENT
srv.info.connection_count=1
srv.info.attached_databases=['/opt/firebird/examples/empbuild/employee.fdb']

Database options

Database options could be set using ServerDbServices instance accessible via Server.database property.

set_default_cache_size() - Sets individual page cache size for database.

>>> from firebird.driver import connect_server
>>> with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
>>>     srv.database.set_default_cache_size(database='employee.fdb', size=5000)

set_sweep_interval() - Sets database sweep interval.

>>> from firebird.driver import connect_server
>>> with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
>>>     srv.database.set_sweep_interval(database='employee.fdb', interval=100000)

set_space_reservation() - Sets space reservation option for database.

>>> from firebird.driver import connect_server, DbSpaceReservation
>>> with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
>>>     srv.database.set_space_reservation(database='employee.fdb', mode=DbSpaceReservation.USE_FULL)

set_write_mode() - Sets database write mode (SYNC/ASYNC).

>>> from firebird.driver import connect_server, DbWriteMode
>>> with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
>>>     srv.database.set_write_mode(database='employee.fdb', mode=DbWriteMode.ASYNC)

set_access_mode() - Sets database access mode (R/W or R/O).

>>> from firebird.driver import connect_server, DbAccessMode
>>> with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
>>>     srv.database.set_access_mode(database='employee.fdb', mode=DbAccessMode.READ_ONLY)

set_sql_dialect() - Sets database SQL dialect.

Warning

Changing SQL dialect on existing database is not recommended. Only newly created database objects would respect new dialect setting, while objects created with previous dialect remain unchanged. That may have dire consequences.

>>> from firebird.driver import connect_server
>>> with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
>>>     srv.database.set_sql_dialect(database='employee.fdb', dialect=1)

no_linger() - Sets one-off override for database linger.

>>> from firebird.driver import connect_server
>>> with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
>>>     srv.database.no_linger(database='employee.fdb')

Database maintenance

Database maintenance and other database-related actions could be performed using methods of ServerDbServices instance accessible via Server.database property.

get_statistics() - Returns database statistics produced by gstat utility.

This method is so-called Async service that only initiates report processing. Actual report content could be read by one from many methods for text output from Services that Server provides .

from firebird.driver import connect_server, SrvStatFlag
with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
    srv.database.get_statistics(database='employee.fdb',
                                flags=SrvStatFlag.DATA_PAGES | SrvStatFlag.RECORD_VERSIONS),
                                tables=['EMPLOYEE','PROJECT'])
    stats = srv.readlines()

backup() - Performs logical (GBAK) database backup.

This method is so-called Async service that only initiates the backup process. Output from gbak could be read by one from many methods for text output from Services that Server provides .

from firebird.driver import connect_server, SrvBackupFlag
with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
    srv.database.backup(database='employee.fdb',
                        backup='/backup/employee.fbk',
                        flags=SrvBackupFlag.IGNORE_CHECKSUMS | SrvBackupFlag.NO_GARBAGE_COLLECT,
                        stats='TD', verbose=True)
    report = srv.readlines()

local_backup() - Performs logical (GBAK) database backup into local byte stream.

from firebird.driver import connect_server, SrvBackupFlag
f = open('/backup/employee.fbk',mode='wb')
with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
    srv.database.local_backup(database='employee.fdb', backup_stream=f,
                              flags=SrvBackupFlag.IGNORE_CHECKSUMS | SrvBackupFlag.NO_GARBAGE_COLLECT)
f.close()

restore() - Performs database restore from logical (GBAK) backup.

This method is so-called Async service that only initiates the restore process. Output from gbak could be read by one from many methods for text output from Services that Server provides .

from firebird.driver import connect_server, SrvRestoreFlag
with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
    srv.database.restore(backup='/backup/employee.fbk',
                         database='/data/employee.fdb',
                         flags=SrvRestoreFlag.REPLACE,
                         stats='TD', verbose=True, page_size=8192)
    report = srv.readlines()

local_restore() - Performs database restore from logical (GBAK) backup stored in local byte stream.

from firebird.driver import connect_server, SrvRestoreFlag
f = open('/backup/employee.fbk',mode='rb')
with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
    srv.database.local_restore(backup_stream=f, database='/data/employee.fdb',
                               flags=SrvRestoreFlag.REPLACE, page_size=8192)
f.close()

nbackup() - Performs physical (NBACKUP) database backup.

from firebird.driver import connect_server
with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
    srv.database.nbackup(database='employee.fdb', backup='/backup/employee.bkp1', level=1)

nrestore() - Performs restore from physical (NBACKUP) database backup.

from firebird.driver import connect_server
with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
    srv.database.nrestore(backups=['/backup/employee.bkp0', '/backup/employee.bkp1'],
                          database='/data/employee.fdb')

shutdown() - Database shutdown.

from firebird.driver import connect_server, ShutdownMode, ShutdownMethod
with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
    srv.database.shutdown(database='employee.fdb', mode=ShutdownMode.SINGLE,
                          method=ShutdownMethod.FORCED, timeout=10)

bring_online() - Bring previously shut down database back online.

from firebird.driver import connect_server, OnlineMode
with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
    srv.database.bring_online(database='employee.fdb', mode=OnlineMode.MULTI)

sweep() - Performs database sweep operation.

from firebird.driver import connect_server
with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
    srv.database.sweep(database='employee.fdb')

validate() - Performs database validation.

This method is so-called Async service that only initiates the validation process. Output from validation could be read by one from many methods for text output from Services that Server provides .

from firebird.driver import connect_server
with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
    srv.database.validate(database='employee.fdb')
    report = srv.readlines()

repair() - Performs database repair operation.

from firebird.driver import connect_server, SrvRepairFlag
with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
    srv.database.repair(database='employee.fdb',
                        flags=SrvRepairFlag.REPAIR | SrvRepairFlag.KILL_SHADOWS)

get_limbo_transaction_ids() - Returns list of transactions in limbo.

commit_limbo_transaction() - Resolves limbo transaction with commit.

rollback_limbo_transaction() - Resolves limbo transaction with rollback.

User maintenance

User maintenance could be performed using methods of ServerUserServices instance accessible via Server.user property.

Note

Since Firebird 2.5 you can use SQL commands (CREATE/ALTER/DROP USER) to manage users in a security database from a regular database attachment.

The SQL set of DDL commands for managing user accounts has been further enhanced in Firebird 3, thus improving the DDL capabilities in a way that exceeds capabilities of user management services.

get_all() - Returns information about all users.

get() - Returns information about specified users.

add() - Add new user.

update() - Update user information.

delete() - Delete user.

exists() - Returns True if user exists.

Trace sessions

Trace sessions could be managed using methods of ServerTraceServices instance accessible via Server.trace property.

ServerTraceServices.start() - Start new trace session. Requires trace configuration and returns Session ID.

This method is so-called Async service that only starts the trace session. Output from trace session could be read by one from many methods for text output from Services that Server provides .

from firebird.driver import connect_server

trace_config = """database = %[\\/]employee.fdb
{
    enabled = true
    log_statement_finish = true
    print_plan = true
    include_filter = %%SELECT%%
    exclude_filter = %%RDB$%%
    time_threshold = 0
    max_sql_length = 2048
}
"""

with connect_server('localhost', user='SYSDBA', password='masterkey') as srv:
    trace_id = srv.trace.start(trace_config,'test_trace_1')
    trace_log = []
    # Get first 10 lines of trace output
    for i in range(10):
        trace_log.append(srv.readline())
    # Stop trace session
    # Because trace session blocks the connection, we need another one to stop trace session!
    with connect_server('localhost', user='SYSDBA', password='masterkey') as srv_aux:
        srv_aux.trace.stop(trace_id)

ServerTraceServices.stop() - Stop trace session.

ServerTraceServices.suspend() - Suspend trace session.

ServerTraceServices.resume() - Resume trace session.

ServerTraceServices.sessions - Dictionary with active trace sessions. The key is session ID, value is TraceSession instance.

Logging driver activities

The firebird-driver supports context-based logging system provided by firebird-base package through use of LoggingIdMixin.

Classes that use LoggingIdMixin:

Example:

import logging
from firebird.base.logging import get_logger, LogLevel
from firebird.driver import connect, driver_config

# Helper function
def execute(cur: Cursor, cmd: str) -> None:
    get_logger(cur).debug(f"Execute [{cmd=}]")
    cur.execute(cmd)

# Basic Python logging configuration
sh = logging.StreamHandler()
sh.setFormatter(logging.Formatter('%(levelname)-10s: [%(agent)s][%(context)s] %(message)s'))
logger = logging.getLogger()
logger.addHandler(sh)
logger.setLevel(LogLevel.DEBUG)

# Firebird driver configuration
srv_cfg = """[local]
host = localhost
user = SYSDBA
password = masterkey
"""
driver_config.register_server('local', srv_cfg)
db_cfg = """[employee]
server = local
database = employee.fdb
protocol = inet
"""
driver_config.register_database('employee', db_cfg)

print("Default logging ids")
with connect('employee') as con:
    con_log = get_logger(con)
    con_log.info('Start')
    cur1 = con.cursor()
    cur2 = con.cursor()
    execute(cur1, 'select * from country')
    execute(cur2, 'select * from project')
    con_log.info('Stop')

print("Custom logging ids")
with connect('employee') as con:
    con._logging_id_ = 'employee-1'
    con_log = get_logger(con)
    con_log.info('Start')
    cur1 = con.cursor()
    cur1._logging_id_ = 'cursor-1'
    cur2 = con.cursor()
    cur2._logging_id_ = 'cursor-2'
    execute(cur1, 'select * from country')
    execute(cur2, 'select * from project')
    con_log.info('Stop')

Output:

Default logging ids
INFO      : [Connection][UNDEFINED] Start
DEBUG     : [Cursor][Connection] Execute [cmd='select * from country']
DEBUG     : [Cursor][Connection] Execute [cmd='select * from project']
INFO      : [Connection][UNDEFINED] Stop
Custom logging ids
INFO      : [employee-1][UNDEFINED] Start
DEBUG     : [cursor-1][employee-1] Execute [cmd='select * from country']
DEBUG     : [cursor-2][employee-1] Execute [cmd='select * from project']
INFO      : [employee-1][UNDEFINED] Stop

You can also trace the driver activities using trace/audit for class instances provided by firebird-base package.

Example:

import logging
from firebird.base.logging import get_logger, LogLevel
from firebird.base.trace import trace_manager, add_trace, trace_object, traced, TraceFlag
from firebird.driver import connect, driver_config
from firebird.driver.core import TransactionManager

# Helper function
def execute(cur: Cursor, cmd: str) -> None:
    cur.execute(cmd)

# Basic Python logging configuration
sh = logging.StreamHandler()
sh.setFormatter(logging.Formatter('%(levelname)-10s: [%(agent)s][%(context)s] %(message)s'))
logger = logging.getLogger()
logger.addHandler(sh)
logger.setLevel(LogLevel.DEBUG)

# Firebird driver configuration
srv_cfg = """[local]
host = localhost
user = SYSDBA
password = masterkey
"""
driver_config.register_server('local', srv_cfg)
db_cfg = """[employee]
server = local
database = employee.fdb
protocol = inet
"""
driver_config.register_database('employee', db_cfg)

# Trace configuration
# First: register classes and methods that could be traced
trace_manager.register(Connection)
add_trace(Connection, 'close', traced)
trace_manager.register(TransactionManager)
add_trace(TransactionManager, 'begin', traced)
add_trace(TransactionManager, 'commit', traced)
add_trace(TransactionManager, 'rollback', traced)
trace_manager.register(Cursor)
add_trace(Cursor, 'execute', traced)
add_trace(Cursor, 'close', traced)
# Activate trace/audit
trace_manager.trace |= (TraceFlag.ACTIVE | TraceFlag.BEFORE | TraceFlag.AFTER | TraceFlag.FAIL)

# Traced code
with connect('employee') as con:
    trace_object(con)
    trace_object(con.main_transaction)
    con._logging_id_ = 'employee-1'
    cur1 = con.cursor()
    trace_object(cur1)
    cur1._logging_id_ = 'cursor-1'
    cur2 = con.cursor()
    trace_object(cur2)
    cur2._logging_id_ = 'cursor-2'
    execute(cur1, 'select * from country')
    execute(cur2, 'select * from project')
    con.commit()

Sample output:

DEBUG     : [cursor-1][employee-1] >>> execute(operation='select * from country', parameters=None)
DEBUG     : [Transaction.Main][employee-1] >>> begin(tpb=None)
DEBUG     : [Transaction.Main][employee-1] <<< begin[0.00672]
DEBUG     : [cursor-1][employee-1] >>> close()
DEBUG     : [cursor-1][employee-1] <<< close[0.00004]
DEBUG     : [cursor-1][employee-1] <<< execute[0.01119] Result: cursor-1
DEBUG     : [cursor-2][employee-1] >>> execute(operation='select * from project', parameters=None)
DEBUG     : [cursor-2][employee-1] >>> close()
DEBUG     : [cursor-2][employee-1] <<< close[0.00002]
DEBUG     : [cursor-2][employee-1] <<< execute[0.00352] Result: cursor-2
DEBUG     : [Transaction.Main][employee-1] >>> commit(retaining=False)
DEBUG     : [cursor-1][employee-1] >>> close()
DEBUG     : [cursor-1][employee-1] <<< close[0.00017]
DEBUG     : [cursor-2][employee-1] >>> close()
DEBUG     : [cursor-2][employee-1] <<< close[0.00012]
DEBUG     : [Transaction.Main][employee-1] <<< commit[0.00225]
DEBUG     : [employee-1][UNDEFINED] >>> close()
DEBUG     : [employee-1][UNDEFINED] <<< close[0.00764]

If you will remove the con.commit() from the example above, the output will change to:

DEBUG     : [cursor-1][employee-1] >>> execute(operation='select * from country', parameters=None)
DEBUG     : [Transaction.Main][employee-1] >>> begin(tpb=None)
DEBUG     : [Transaction.Main][employee-1] <<< begin[0.00338]
DEBUG     : [cursor-1][employee-1] >>> close()
DEBUG     : [cursor-1][employee-1] <<< close[0.00003]
DEBUG     : [cursor-1][employee-1] <<< execute[0.00730] Result: cursor-1
DEBUG     : [cursor-2][employee-1] >>> execute(operation='select * from project', parameters=None)
DEBUG     : [cursor-2][employee-1] >>> close()
DEBUG     : [cursor-2][employee-1] <<< close[0.00003]
DEBUG     : [cursor-2][employee-1] <<< execute[0.00345] Result: cursor-2
DEBUG     : [employee-1][UNDEFINED] >>> close()
DEBUG     : [Transaction.Main][employee-1] >>> rollback(retaining=False, savepoint=None)
DEBUG     : [cursor-1][employee-1] >>> close()
DEBUG     : [cursor-1][employee-1] <<< close[0.00016]
DEBUG     : [cursor-2][employee-1] >>> close()
DEBUG     : [cursor-2][employee-1] <<< close[0.00014]
DEBUG     : [Transaction.Main][employee-1] <<< rollback[0.00171]
DEBUG     : [employee-1][UNDEFINED] <<< close[0.01001]

Driver hooks

The firebird-driver uses hook manager from firebird-base package to provide internal notification mechanism that allows installation of custom hooks into certain driver tasks.

Driver hooks are divided into several types exposed as enums in firebird.driver.hooks module.

APIHook

APIHook.LOADED - This hook is invoked once when instance of FirebirdAPI is created. It could be used for additional initialization tasks that require Firebird API, or to manipulate the FirebirdAPI instance itself before its use.

Hook routine must have signature: hook_func(api: FirebirdAPI) -> None. Any value returned by hook is ignored.

ConnectionHook

ConnectionHook.ATTACH_REQUEST

This hook is invoked after all parameters are preprocessed and before Connection is created.

Hook routine must have signature: hook_func(dsn: str, dpb: bytes) -> Optional[Connection] where dpb is Database Parameter Buffer that would be used to create the attachment to the database defined by dsn. It may return Connection (or subclass) instance or None.

First instance returned by any hook of this type will become the return value of caller function and other hooks of the same type are not invoked.

ConnectionHook.ATTACHED

This hook is invoked just before Connection (or subclass) instance is returned to the client application.

Hook routine must have signature: hook_func(con: Connection) -> None.

ConnectionHook.DETACH_REQUEST

This hook is invoked before connection is closed.

Hook must have signature: hook_func(con: Connection) -> None.

If any hook function returns True, connection is not closed.

ConnectionHook.CLOSED

This hook is invoked after connection is closed.

Hook routine must have signature: hook_func(con: Connection) -> None.

ConnectionHook.DROPPED

This hook is invoked after database is dropped (and connection is closed).

Hook routine must have signature: hook_func(con: Connection) -> None.

ServerHook

ServerHook.ATTACHED

This hook is invoked before Server instance is returned.

Hook routine must have signature: hook_func(srv: Server) -> None.