Source code is currently divided into next submodules:
All important data, functions, classes and constants are available directly in fdb namespace, so there is not need to import or use fbcore and ibase submodules directly. Exception is the fdb.services submodule that contains functions and classes for work with Firebird Services. Because Services are optional, not so frequently used Firebird facility, all service-related code was isolated in separate submodule rather than exposed directly through main module namespace. Because services submodule contains names also used by main driver (connect(), Connection), it’s advised to use fully qualified names when refering to them instead importing them via from fdb.services import ....
Access to the database is made available through Connection objects. FDB provides two constructors for these:
This constructor has number of keyword parameters that could be divided into several groups:
To establish a connection to database, you always must specify the database, either as connection string parameter dsn, or as required combination of parameters host, database and port.
Important
Current driver version ignores the value of port parameter. If you need to specify the port number, you have to use dsn parameter instead.
Although specification of user and password parameters is optional (if environment variables ISC_USER and ISC_PASSWORD are set, their values are used if these parameters are ommited), it’s recommended practice to use them. Parameter role is needed only when you use Firebird roles.
Connection options are optional (see Firebird Documentation for details). However you may often want to specify charset, as it directs automatic conversions of string data between client and server, and automatic conversions from/to unicode performed by FDB driver (see Data handling and conversions for details).
Examples:
# Connecting via 'dsn'
#
# Local database (local protocol, if supported)
con = fdb.connect(dsn='/path/database.fdb', user='sysdba', password='pass')
# Local database (TCP/IP)
con = fdb.connect(dsn='localhost:/path/database.fdb', user='sysdba', password='pass')
# Local database (TCP/IP with port specification)
con = fdb.connect(dsn='localhost/3050:/path/database.fdb', user='sysdba', password='pass')
# Remote database
con = fdb.connect(dsn='host:/path/database.db', user='sysdba', password='pass')
# Remote database with port specification
con = fdb.connect(dsn='host/3050:/path/database.db', user='sysdba', password='pass')
#
# Connecting via 'database', 'host' and 'port'
#
# Local database (local protocol, if supported)
con = fdb.connect(database='/path/database.db', user='sysdba', password='pass')
# Local database (TCP/IP)
con = fdb.connect(host='localhost', database='/path/database.db', user='sysdba', password='pass')
# Local database (TCP/IP with port specification)
con = fdb.connect(host='localhost', port=3050, database='/path/database.db', user='sysdba', password='pass')
# Remote database
con = fdb.connect(host='myhost', database='/path/database.db', user='sysdba', password='pass')
The Firebird engine supports dynamic database creation via the SQL statement CREATE DATABASE. FDB wraps it into create_database(), that returns Connection instance attached to newly created database.
Example:
con = fdb.create_database("create database 'host:/temp/db.db' user 'sysdba' password 'pass'")
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.
Examples:
import fdb
con = fdb.create_database("create database '/temp/db.db' user 'sysdba' password 'pass'")
con.drop_database()
con = fdb.connect(dsn='/path/database.fdb', user='sysdba', password='pass')
con.drop_database()
Connection object represents a direct link to database, and works as gateway for next operations with it:
Firebird provides various informations about server and connected database via database_info API call. FDB surfaces this API through methods db_info() and database_info() on Connection object.
Connection.database_info() is a very thin wrapper around function isc_database_info(). This method does not attempt to interpret its results except with regard to whether they are a string or an integer. For example, requesting isc_info_user_names with the call:
con.database_info(kinterbasdb.isc_info_user_names, 's')
will return a binary string containing a raw succession of length-name pairs.
Example program:
import fdb
con = fdb.connect(dsn='localhost:/temp/test.db', user='sysdba', password='pass')
# Retrieving an integer info item is quite simple.
bytesInUse = con.database_info(fdb.isc_info_current_memory, 'i')
print 'The server is currently using %d bytes of memory.' % bytesInUse
# Retrieving a string info item is somewhat more involved, because the
# information is returned in a raw binary buffer that must be parsed
# according to the rules defined in the Interbase® 6 API Guide section
# entitled "Requesting buffer items and result buffer values" (page 51).
#
# Often, the buffer contains a succession of length-string pairs
# (one byte telling the length of s, followed by s itself).
# Function fdb.ibase.ord2 is provided to convert a raw
# byte to a Python integer (see examples below).
buf = con.database_info(fdb.isc_info_db_id, 's')
# Parse the filename from the buffer.
beginningOfFilename = 2
# The second byte in the buffer contains the size of the database filename
# in bytes.
lengthOfFilename = fdb.ibase.ord2(buf[1])
filename = buf[beginningOfFilename:beginningOfFilename + lengthOfFilename]
# Parse the host name from the buffer.
beginningOfHostName = (beginningOfFilename + lengthOfFilename) + 1
# The first byte after the end of the database filename contains the size
# of the host name in bytes.
lengthOfHostName = fdb.ibase.ord2(buf[beginningOfHostName - 1])
host = buf[beginningOfHostName:beginningOfHostName + lengthOfHostName]
print 'We are connected to the database at %s on host %s.' % (filename, host)
Sample output:
The server is currently using 8931328 bytes of memory.
We are connected to the database at C:\TEMP\TEST.DB on host WEASEL.
A more convenient way to access the same functionality is via the db_info() method, which is high-level convenience wrapper around the database_info() method that parses the output of database_info into Python-friendly objects instead of returning raw binary buffers in the case of complex result types. For example, requesting isc_info_user_names with the call:
con.db_info(kinterbasdb.isc_info_user_names)
returns a dictionary that maps (username -> number of open connections). If SYSDBA has one open connection to the database to which con is connected, and TEST_USER_1 has three open connections to that same database, the return value would be:
{‘SYSDBA’: 1, ‘TEST_USER_1’: 3}
Example program:
import fdb
import os.path
###############################################################################
# Querying an isc_info_* item that has a complex result:
###############################################################################
# Establish three connections to the test database as TEST_USER_1, and one
# connection as SYSDBA. Then use the Connection.db_info method to query the
# number of attachments by each user to the test database.
testUserCons = []
for i in range(3):
tcon = fdb.connect(dsn='localhost:/temp/test.db', user='TEST_USER_1', password='pass')
testUserCons.append(tcon)
con = fdb.connect(dsn='localhost:/temp/test.db', user='sysdba', password='pass')
print 'Open connections to this database:'
print con.db_info(fdb.isc_info_user_names)
###############################################################################
# Querying multiple isc_info_* items at once:
###############################################################################
# Request multiple db_info items at once, specifically the page size of the
# database and the number of pages currently allocated. Compare the size
# computed by that method with the size reported by the file system.
# The advantages of using db_info instead of the file system to compute
# database size are:
# - db_info works seamlessly on connections to remote databases that reside
# in file systems to which the client program lacks access.
# - If the database is split across multiple files, db_info includes all of
# them.
res = con.db_info([fdb.isc_info_page_size, fdb.isc_info_allocation])
pagesAllocated = res[fdb.isc_info_allocation]
pageSize = res[fdb.isc_info_page_size]
print '\ndb_info indicates database size is', pageSize * pagesAllocated, 'bytes'
print 'os.path.getsize indicates size is ', os.path.getsize(DB_FILENAME), 'bytes'
Sample output:
Open connections to this database:
{'SYSDBA': 1, 'TEST_USER_1': 3}
db_info indicates database size is 20684800 bytes
os.path.getsize indicates size is 20684800 bytes
FDB implements two ways for execution of SQL commands against connected database:
Because Cursor objects always operate in context of single Connection (and Transaction), Cursor instances are not created directly, but by constructor method. Python DB API 2.0 assume 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, FDB uses concept of internal main_transaction and secondary transactions. Cursor constructor is primarily defined by Transaction, and Cursor constructor on Connection is therefore a shortcut for main_transaction.cursor().
Cursor objects are used for next operations:
There are three methods how to execute SQL commands:
Connection.execute_immediate() or Transaction.execute_immediate() for SQL commands that don’t return any result, and are not executed frequently. This method also doesn’t support either parametrized statements or prepared statements.
Tip
This method is efficient for administrative and DDL SQL commands, like DROP, CREATE or ALTER commands, SET STATISTICS etc.
Cursor.execute() or Cursor.executemany() for commands that return result sets, i.e. sequence of rows of the same structure, and sequence has unknown number of rows (including zero).
Tip
This method is preferred for all SELECT and other DML statements, or any statement that is executed frequently, either as is or in parametrized form.
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.
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("select * from the_table where col == '%s'" % 'aardvark')
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:
Parametrized statemets also have some limitations. Currently:
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(), Cursor’s built-in support for iterator protocol or call to iter() instead this method.
fetchonemap() - Returns the next row like fetchone(), but returns a mapping of field name to field value, rather than a tuple.
fetchmanymap() - Returns the next set of rows of a query result like fetchmany(), but returns a list of mapping of field name to field value, rather than a tuple.
fetchallmap() - Returns all (remaining) rows of a query result like fetchall(), returns a list of mappings of field name to field value, rather than a tuple.
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 with mapping support, use itermap() instead this method.
iter() - Equivalent to the fetchall(), except that it returns iterator rather than materialized list.
itermap() - Equivalent to the fetchallmap(), except that it returns iterator rather than materialized list.
Call to execute() returns self (Cursor instance) that itself supports the iterator protocol, yielding tuples of values like fetchone().
Important
FDB makes absolutely no guarantees about the return value of the fetchone / fetchmany / fetchall methods except that it is a sequence indexed by field position. FDB makes absolutely no guarantees about the return value of the fetchonemap / fetchmanymap / fetchallmap methods except that it is a mapping of field name to field value. Therefore, client programmers should not rely on the return value being an instance of a particular class or type.
Examples:
import fdb
con = fdb.connect(dsn='/temp/test.db', user='sysdba', password='masterkey')
cur = con.cursor()
SELECT = "select name, year_released from languages order by year_released"
# 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 (name, year_released):
cur.execute(SELECT)
for (name, year_released) in cur:
print '%s has been publicly available since %d.' % (name, year_released)
# 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 '%s has been publicly available since %d.' % (row[0], row[1])
# 3. Using mapping-iteration rather than sequence-iteration:
cur.execute(SELECT)
for row in cur.itermap():
print '%(name)s has been publicly available since %(year_released)d.' % row
Execution of any SQL statement has three phases:
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 better overall performance.
FDB builds on this by encapsulating all statement-related code into separate PreparedStatement class, and implementing Cursor class as a wrapper around it. Once executed statement has to be disposed in favour of new one, the PreparedStatement instance is stored into Cursor’s internal cache. Whenever SQL statement execution is requested, Cursor checks whether appropriate PreparedStatement isn’t in this cache, and if it is, uses it instead creating new one. Because identity of SQL statement is defined by its command string, this mechanism works best for SQL commands that either are static or parametrized.
Example:
insertStatement = "insert into the_table (a,b,c) values (?,?,?)"
# First execution initializes the PreparedStatement
cur.execute(insertStatement, ('aardvark', 1, 0.1))
# This execution reuses PreparedStatement from cache
cur.execute(insertStatement, ('zymurgy', 2147483647, 99999.999))
# And this one too
cur.execute("insert into the_table (a,b,c) values (?,?,?)", ('foobar', 2000, 9.9))
Additionally to automatic, implicit reuse of prepared statements, Cursor also allows to aquire and use PreparedStatement instances explicitly. PreparedStatement aquired by calling prep() method could be then passed to execute() or executemany() instead command string.
Example:
insertStatement = cur.prep("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)
While both implicit and explicit prepared statements could be used to the same effect, there are significant differences between both methods:
Implicit prepared statements remain in the cache until Cursor instance is disposed, preventing server-side resources allocated for the statement to be fully released. If it may become a problem, it’s necessary to release the Cursor instance, or call its clear_cache() method.
Note
Because Cursor is just a wrapper around PreparedStatement instance(s), calling close() doesn’t render the Cursor instance unusable (unlike other objects like Connection or Transaction). It just closes the current prepared statement, but all prepared statements (current and all in internal cache) are still available for use, and Cursor itself is still bound to Connection and Transaction, and could be still used to execute SQL statements.
Warning
FDB’s implementation of Cursor 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.
If you’ll take advantage of this anomaly, your code would be less portable to other Python DB API 2.0 compliant drivers.
Explicit prepared statements are never stored in Cursor’s cache, but they are still bound to Cursor instance that created them, and can’t be used with any other Cursor instance. Beside repeated execution they are also useful to get information about statement (like its output description, execution plan or statement_type) before its execution.
Example Program:
The following program demonstrates the explicit use of PreparedStatements. It also benchmarks explicit PreparedStatement reuse against FDB’s automatic PreparedStatement reuse, and against an input strategy that prevents PreparedStatement reuse.
import time
import fdb
con = fdb.connect(dsn='localhost:employee',
user='sysdba', password='masterkey'
)
cur = con.cursor()
# Create supporting database entities:
cur.execute("recreate table t (a int, b varchar(50))")
con.commit()
cur.execute("create unique index unique_t_a on t(a)")
con.commit()
# Explicitly prepare the insert statement:
psIns = cur.prep("insert into t (a,b) values (?,?)")
print 'psIns.sql: "%s"' % psIns.sql
print 'psIns.statement_type == fdb.isc_info_sql_stmt_insert:', (
psIns.statement_type == fdb.isc_info_sql_stmt_insert
)
print 'psIns.n_input_params: %d' % psIns.n_input_params
print 'psIns.n_output_params: %d' % psIns.n_output_params
print 'psIns.plan: %s' % psIns.plan
print
N = 50000
iStart = 0
# The client programmer uses a PreparedStatement explicitly:
startTime = time.time()
for i in xrange(iStart, iStart + N):
cur.execute(psIns, (i, str(i)))
print (
'With explicit prepared statement, performed'
'\n %0.2f insertions per second.' % (N / (time.time() - startTime))
)
con.commit()
iStart += N
# FDB automatically uses a PreparedStatement "under the hood":
startTime = time.time()
for i in xrange(iStart, iStart + N):
cur.execute("insert into t (a,b) values (?,?)", (i, str(i)))
print (
'With implicit prepared statement, performed'
'\n %0.2f insertions per second.' % (N / (time.time() - startTime))
)
con.commit()
iStart += N
# A new SQL string containing the inputs is submitted every time, so
# FDB is not able to implicitly reuse a PreparedStatement. Also, in a
# more complicated scenario where the end user supplied the string input
# values, the program would risk SQL injection attacks:
startTime = time.time()
for i in xrange(iStart, iStart + N):
cur.execute("insert into t (a,b) values (%d,'%s')" % (i, str(i)))
print (
'When unable to reuse prepared statement, performed'
'\n %0.2f insertions per second.' % (N / (time.time() - startTime))
)
con.commit()
# Prepare a SELECT statement and examine its properties. The optimizer's plan
# should use the unique index that we created at the beginning of this program.
print
psSel = cur.prep("select * from t where a = ?")
print 'psSel.sql: "%s"' % psSel.sql
print 'psSel.statement_type == fdb.isc_info_sql_stmt_select:', (
psSel.statement_type == fdb.isc_info_sql_stmt_select
)
print 'psSel.n_input_params: %d' % psSel.n_input_params
print 'psSel.n_output_params: %d' % psSel.n_output_params
print 'psSel.plan: %s' % psSel.plan
# The current implementation does not allow PreparedStatements to be prepared
# on one Cursor and executed on another:
print
print 'Note that PreparedStatements are not transferrable from one cursor to another:'
cur2 = con.cursor()
cur2.execute(psSel)
Sample output:
psIns.sql: "insert into t (a,b) values (?,?)"
psIns.statement_type == fdb.isc_info_sql_stmt_insert: True
psIns.n_input_params: 2
psIns.n_output_params: 0
psIns.plan: None
With explicit prepared statement, performed
4276.00 insertions per second.
With implicit prepared statement, performed
4185.09 insertions per second.
When unable to reuse prepared statement, performed
2037.70 insertions per second.
psSel.sql: "select * from t where a = ?"
psSel.statement_type == fdb.isc_info_sql_stmt_select: True
psSel.n_input_params: 1
psSel.n_output_params: 2
psSel.plan: PLAN (T INDEX (UNIQUE_T_A))
Note that PreparedStatements are not transferrable from one cursor to another:
Traceback (most recent call last):
File "pstest.py", line 85, in <module>
cur2.execute(psSel)
File "/home/job/python/envs/pyfirebird/fdb/fdb/fbcore.py", line 2623, in execute
raise ValueError("PreparedStatement was created by different Cursor.")
ValueError: PreparedStatement was created by different Cursor.
As you can see, the version that prevents the reuse of prepared statements is about two times slower – for a trivial statement. In a real application, SQL statements are likely to be far more complicated, so the speed advantage of using prepared statements would only increase.
As the timings indicate, FDB does a good job of reusing prepared statements even if the client program is written in a style strictly compatible with the Python DB API 2.0 (which accepts only strings – not PreparedStatement objects – to the Cursor.execute() method). The performance loss in this case is about one percent.
To allow the Python programmer to perform scrolling UPDATE or DELETE via the “SELECT ... FOR UPDATE” syntax, FDB provides the read/write property Cursor.name.
Example Program:
import fdb
con = fdb.connect(dsn='localhost:/temp/test.db', user='sysdba', password='pass')
curScroll = con.cursor()
curUpdate = con.cursor()
curScroll.execute("select city from addresses for update")
curScroll.name = 'city_scroller'
update = "update addresses set city=? where current of " + curScroll.name
for (city,) in curScroll:
city = ... # make some changes to city
curUpdate.execute( update, (city,) )
con.commit()
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 FDB, 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, use code such as this:
cur.callproc("the_proc", (input1, input2))
# If there are output parameters, retrieve them as though they were the
# first row of a result set. For example:
outputParams = cur.fetchone()
con.commit() # If the procedure had any side effects, commit them.
This latter is not very elegant; it would be preferable to access the procedure’s output parameters as the return value of Cursor.callproc(). The Python DB API specification requires the current behavior, however.
The database engine treats most SQL data types in a weakly typed fashion: the engine may attempt to convert the raw value to a different type, as appropriate for the current context. For instance, the SQL expressions 123 (integer) and ‘123’ (string) are treated equivalently when the value is to be inserted into an integer field; the same applies when ‘123’ and 123 are to be inserted into a varchar field.
This weak typing model is quite unlike Python’s dynamic yet strong typing. Although weak typing is regarded with suspicion by most experienced Python programmers, the database engine is in certain situations so aggressive about its typing model that KInterbasDB must compromise in order to remain an elegant means of programming the database engine.
An example is the handling of “magic values” for date and time fields. The database engine interprets certain string values such as ‘yesterday’ and ‘now’ as having special meaning in a date/time context. If FDB 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 = "insert into test_table (i, t) values (?, '%s')" % t
cur.execute( sqlWithMagicValues, (i,) )
i = 1
t = 'now'
cur.execute( "insert into test_table (i, t) values (?, ?)", (i, t) )
If FDB did not support weak parameter typing, string parameters that the database engine is to interpret as “magic values” would have to be rolled into the SQL statement in a separate operation from the binding of the rest of the parameters, as in the first Python snippet above. Implicit conversion of parameter values from strings allows the consistency evident in the second snippet, which is both more readable and more general.
It should be noted that FDB 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 FDB 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.
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 manupulation, 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 parameter in connection 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.
FDB helps with client side of this character set bargain by automatically converting unicode strings into bytes/strings encoded in connection character set, and vice versa. However, developers are still responsible that non-unicode strings passed to server are in correct encoding (because FDB makes no assuption about encoding of non-unicode 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, FDB 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 regarding Python version you use. Python 2.x native strings are bytes, suitable for such binary streams, but Python 3 native strings are unicode, and you would probably want to use bytes type instead. However, FDB 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 unicode to OCTETS column (unless you’ll pass more bytes than column may hold, or you intend to store unicode that way).
Rules for automatic conversion depend on Python version you use:
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.
FDB uses two types of BLOB values:
Materialized BLOBs are easy to work with, but are not suitable for:
These drawbacks are addressed by stream BLOBs. Using BLOBs in stream mode is easy:
The BlobReader instance is bound to particular BLOB value returned by server, so its life time is limited. The actual BLOB value is not opened initially, so no additonal API calls to server are made if you’ll decide to ignore the value completely. You also don’t need to open the BLOB value explicitly, as BLOB is opened automatically on first call to next(), read(), readline(), readlines() or seek(). However, it’s good practice to close() the reader once you’re finished reading, as it’s likely that Python’s garbage collector would call the __del__ method too late, when fetch context is already gone, and closing the reader would cause an error.
Warning
If BLOB was NOT CREATED as stream BLOB, calling BlobReader.seek() method will raise DatabaseError exception. This constraint is set by Firebird.
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:
import os.path
from cStringIO import StringIO
import fdb
con = fdb.connect(dsn='localhost:employee',user='sysdba', password='masterkey')
cur = con.cursor()
cur.execute("recreate table blob_test (a blob)")
con.commit()
# --- Materialized mode (str objects for both input and output) ---
# Insertion:
cur.execute("insert into blob_test values (?)", ('abcdef',))
cur.execute("insert into blob_test values (?)", ('ghijklmnop',))
# Retrieval:
cur.execute("select * from blob_test")
print 'Materialized retrieval (as str):'
print cur.fetchall()
cur.execute("delete from blob_test")
# --- Streaming mode (file-like objects for input; fdb.BlobReader objects for output) ---
# Insertion:
cur.execute("insert into blob_test values (?)", (StringIO('abcdef'),))
cur.execute("insert into blob_test values (?)", (StringIO('ghijklmnop'),))
f = file(os.path.abspath(__file__), 'rb')
cur.execute("insert into blob_test values (?)", (f,))
f.close()
# Retrieval using the "file-like" methods of BlobReader:
cur.execute("select * from blob_test")
cur.set_stream_blob('A') # Note the capital letter
readerA = cur.fetchone()[0]
print '\nStreaming retrieval (via fdb.BlobReader):'
# Python "file-like" interface:
print 'readerA.mode: "%s"' % readerA.mode
print 'readerA.closed: %s' % readerA.closed
print 'readerA.tell(): %d' % readerA.tell()
print 'readerA.read(2): "%s"' % readerA.read(2)
print 'readerA.tell(): %d' % readerA.tell()
print 'readerA.read(): "%s"' % readerA.read()
print 'readerA.tell(): %d' % readerA.tell()
print 'readerA.read(): "%s"' % readerA.read()
readerA.close()
print 'readerA.closed: %s' % readerA.closed
Output:
Materialized retrieval (as str):
[('abcdef',), ('ghijklmnop',)]
Streaming retrieval (via fdb.BlobReader):
readerA.mode: "rb"
readerA.closed: False
readerA.tell(): 0
readerA.read(2): "ab"
readerA.tell(): 2
readerA.read(): "cdef"
readerA.tell(): 6
readerA.read(): ""
readerA.closed: True
FDB 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:
>>> import fdb
>>> con = fdb.connect(dsn='localhost: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:
import fdb
con = fdb.connect(dsn='localhost:/temp/test.db', user='sysdba', password='pass')
con.execute_immediate("recreate table array_table (a int[3,4])")
con.commit()
cur = con.cursor()
arrayIn = [
[1, 2, 3, 4],
[5, 6, 7, 8],
[9,10,11,12]
]
print 'arrayIn: %s' % arrayIn
cur.execute("insert into array_table values (?)", (arrayIn,))
cur.execute("select a from array_table")
arrayOut = cur.fetchone()[0]
print 'arrayOut: %s' % arrayOut
con.commit()
Output:
arrayIn: [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
arrayOut: [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
For the sake of simplicity, FDB 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 FDB establishes a transaction implicitly by using any SQL execution method, such as execute_immediate(), Cursor.execute(), or Cursor.callproc().
Although FDB 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.
When it comes to transactions, Python Database API 2.0 specify that Connection object has to respond to the following methods:
Commit 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.
(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, FDB 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 openned simultaneously to perform various tasks, which would require to open multiple connections and thus consume more resources than necessary.
FDB surfaces this Firebird feature by separating transaction management out from Connection into separate Transaction objects. To comply with Python DB API 2.0 requirements, Connection object uses one Transaction instance as main transaction, and delegates begin(), savepoint(), commit(), rollback(), trans_info() and transaction_info() calls to it.
See also
More about using multiple transactions with the same connection in separate section.
Example:
import fdb
con = fdb.connect(dsn='localhost:employee',user='sysdba', password='masterkey')
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')
# This will roll back the transaction
# because Python DB API 2.0 requires that closing connection
# with pending transaction must cause an implicit rollback
con.close()
FDB doesn’t support auto-commit feature directly, but developers may achieve the similar result using explicit transaction start, taking advantage of default_action and its default value (commit).
Example:
import fdb
con = fdb.connect(dsn='localhost:employee',user='sysdba', password='masterkey')
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()
con.close()
The database engine offers the client programmer an optional facility called transaction parameter buffers (TPBs) for tweaking the operating characteristics of the transactions he initiates. These include characteristics such as whether the transaction has read and write access to tables, or read-only access, and whether or not other simultaneously active transactions can share table access with the transaction.
Connections have a default_tpb attribute that can be changed to set the default TPB for all transactions subsequently started on the connection. Alternatively, if the programmer only wants to set the TPB for a single transaction, he can start a transaction explicitly via the begin() method and pass a TPB for that single transaction.
For details about TPB construction, see the Firebird API documentation. In particular, the ibase.h supplied with Firebird contains all possible TPB elements – single bytes that the C API defines as constants whose names begin with isc_tpb_. FDB makes all of those TPB constants available (under the same names) as module-level constants. A transaction parameter buffer is handled in C as a character array; FDB requires that TPBs be constructed as Python strings (or bytes for Python 3). Since the constants in the fdb.isc_tpb_* family are numbers, they can’t be simply concatenated to create a TPB, but you may use utility function fdb.bs(byte_array) that accepts sequence of numbers and returns string (P2) or bytes (P3).
For example next call returns TPB for typical READ COMMITED transaction:
from fdb import *
TPB = bs([isc_tpb_version3,
isc_tpb_write,
isc_tpb_wait,
isc_tpb_read_committed,
isc_tpb_rec_version])
Warning
This method requires good knowledge of tpc_block structure and proper order of various parameters, as Firebird engine will raise an error when badly structured block would be used. Also definition of table reservation parameters is uncomfortable as you’ll need to mix binary codes with table names passed as Pascal strings (characters preceded by string length).
FDB provides several predefined TPB’s for convenience:
Read/Write READ COMMITED with record version and WAIT option. Isolation level with greatest concurrent throughput. This is Default TPB.
Tip
This isolation level is optimal for transactions that write data and doesn’t require stable snapshot of database for their operations (i.e. most operations are limited to individual rows).
Read/Write READ COMMITED with NO record version and WAIT option.
Warning
This isolation level emulates RDBMS that use locks instead multiversion control (MVC). It’s not recommended to use it at all, except for legacy applications lazily ported from such RDBMS to Firebird.
Like ISOLATION_LEVEL_READ_COMMITED, but Read Only.
Tip
Firebird threats these transactions as pre-committed, so they are best option for long running transactions that only read data.
Read/Write SNAPSHOT (concurrency) with WAIT option.
Tip
This isolation level is necessary for transactions that process data in bulk, like reporting, recalculations etc.
Read/Write SNAPSHOT TABLE STABILITY (consistency) with WAIT option. Like REPEATABLE_READ/SNAPSHOT, but locks whole tables for writes from other transactions. Isolation level with lowest concurrent throughput.
Warning
Because tables are locked for protected write (i.e. no other transaction can write until lock is released) at time of first access, there is a great risk of deadlock between transactions.
Tip
To prevent deadlocks and increase concurrent throughput it’s recommended to use custom TPB’s with fine-grained table access reservation.
Example:
import fdb
con = fdb.connect(dsn='localhost:employee',user='sysdba', password='masterkey')
cur = con.cursor()
# Start transaction with default_tpb (ISOLATION_LEVEL_READ_COMMITED)
con.begin()
cur.execute('select * from JOB')
com.commit()
# Start using transactions in REPEATABLE READ (SNAPSHOT) isolation
con.default_tpb = fdb.ISOLATION_LEVEL_REPEATABLE_READ
con.begin()
cur.execute('select * from JOB')
com.commit()
# Start THIS transaction as R/O READ COMMITTED
con.begin(fdb.ISOLATION_LEVEL_READ_COMMITED_RO)
cur.execute('select * from JOB')
com.commit()
For cases when predefined transaction parameter blocks are not suitable for your needs, FDB offers utility class TPB for convenient and safe construction of custom tpb blocks. Simply create instance of this class, set member attributes to required values and use either rendered binary tpb block or TPB instance itself to set default_tpb or as paraneter to begin().
Example:
import fdb
con = fdb.connect(dsn='localhost:employee',user='sysdba', password='masterkey')
# Use TPB to construct valid transaction parameter block
# from the fdb.isc_tpb_* family.
customTPB = fdb.TPB()
customTPB.isolation_level = fdb.isc_tpb_consistency # SERIALIZABLE
customTPB.table_reservation["MY_TABLE"] = (fdb.isc_tpb_protected, fdb.isc_tpb_lock_write)
# Explicitly start a transaction with the custom TPB:
con.begin(tpb=customTPB)
# For frequent use, it's better to use already assembled version of TPB
customTPB = fdb.TPB()
customTPB.access_mode = fdb.isc_tpb_read # read only
customTPB.isolation_level = fdb.isc_tpb_concurrency # SNAPSHOT
customTPB = customTPB.render() # Create valid block according to current values of member attributes.
for x in range(1000):
con.begin(tpb=customTPB)
If you want to build only table reservation part of tpb (for example to add to various custom built parameter blocks), you can use class TableReservation instead TPB.
Transaction object exposes two methods that return infromation about currently managed active transaction (the same methods are exposed also by Connection object for main_transaction):
transaction_info() is a very thin wrapper around function isc_transaction_info(). This method does not attempt to interpret its results except with regard to whether they are a string or an integer.
A more convenient way to access the same functionality is via the trans_info() method, which is high-level convenience wrapper around the transaction_info method that parses the output of transaction_info into Python-friendly objects instead of returning raw binary buffers in the case of complex result types.
Example program:
import fdb
con = fdb.connect(dsn='localhost:employee',user='sysdba', password='masterkey')
# Start transaction, so we can get information about it
con.begin()
info = con.trans_info([fdb.isc_info_tra_id, fdb.isc_info_tra_oldest_interesting,
fdb.isc_info_tra_oldest_snapshot, fdb.isc_info_tra_oldest_active,
fdb.isc_info_tra_isolation, fdb.isc_info_tra_access,
fdb.isc_info_tra_lock_timeout])
print info
print "TransactionID:", info[fdb.isc_info_tra_id]
print "Oldest Interesting (OIT):",info[fdb.isc_info_tra_oldest_interesting]
print "Oldest Snapshot:",info[fdb.isc_info_tra_oldest_snapshot]
print "Oldest Active (OAT):",info[fdb.isc_info_tra_oldest_active]
print "Isolation Level:",info[fdb.isc_info_tra_isolation]
print "Access Mode:",info[fdb.isc_info_tra_access]
print "Lock Timeout:",info[fdb.isc_info_tra_lock_timeout]
Output:
{4: 459, 5: 430, 6: 459, 7: 459, 8: (3, 1), 9: 1, 10: -1}
TransactionID: 459
Oldest Interesting (OIT): 430
Oldest Snapshot: 459
Oldest Active (OAT): 459
Isolation Level: (3, 1)
Access Mode: 1
Lock Timeout: -1
Note
Isolation level info values are available as FDB constants isc_info_tra_consistency, isc_info_tra_concurrency and isc_info_tra_read_committed. For read committed, a tuple of two values is returned instead single value, where the second value is record version flag isc_info_tra_no_rec_version or isc_info_tra_rec_version.
Access mode values are available as FDB constants isc_info_tra_readonly and isc_info_tra_readwrite.
The commit() and rollback() methods accept an optional boolean parameter retaining (default False) to indicate whether to recycle the transactional context of the transaction being resolved by the method call.
If retaining is True, the infrastructural support for the transaction active at the time of the method call will be “retained” (efficiently and transparently recycled) after the database server has committed or rolled back the conceptual transaction.
Important
In code that commits or rolls back frequently, “retaining” the transaction yields considerably better performance. However, retaining transactions must be used cautiously because they can interfere with the server’s ability to garbage collect old record versions. For details about this issue, read the “Garbage” section of this document by Ann Harrison.
For more information about retaining transactions, see Firebird documentation.
Savepoints 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, FDB also exposes savepoints at the Python API level for the sake of convenience.
Call to method savepoint() establishes a savepoint with the specified name. To roll back to a specific savepoint, call the rollback() method and provide a value (the name of the savepoint) for the optional savepoint parameter. If the savepoint parameter of rollback() is not specified, the active transaction is cancelled in its entirety, as required by the Python Database API Specification.
The following program demonstrates savepoint manipulation via the FDB API, rather than raw SQL.
import fdb
con = fdb.connect(dsn='employee', user='sysdba', password='pass')
cur = con.cursor()
cur.execute("recreate table test_savepoints (a integer)")
con.commit()
print 'Before the first savepoint, the contents of the table are:'
cur.execute("select * from test_savepoints")
print ' ', cur.fetchall()
cur.execute("insert into test_savepoints values (?)", [1])
con.savepoint('A')
print 'After savepoint A, the contents of the table are:'
cur.execute("select * from test_savepoints")
print ' ', cur.fetchall()
cur.execute("insert into test_savepoints values (?)", [2])
con.savepoint('B')
print 'After savepoint B, the contents of the table are:'
cur.execute("select * from test_savepoints")
print ' ', cur.fetchall()
cur.execute("insert into test_savepoints values (?)", [3])
con.savepoint('C')
print 'After savepoint C, the contents of the table are:'
cur.execute("select * from test_savepoints")
print ' ', cur.fetchall()
con.rollback(savepoint='A')
print 'After rolling back to savepoint A, the contents of the table are:'
cur.execute("select * from test_savepoints")
print ' ', cur.fetchall()
con.rollback()
print 'After rolling back entirely, the contents of the table are:'
cur.execute("select * from test_savepoints")
print ' ', cur.fetchall()
The output of the example program is shown below:
Before the first savepoint, the contents of the table are:
[]
After savepoint A, the contents of the table are:
[(1,)]
After savepoint B, the contents of the table are:
[(1,), (2,)]
After savepoint C, the contents of the table are:
[(1,), (2,), (3,)]
After rolling back to savepoint A, the contents of the table are:
[(1,)]
After rolling back entirely, the contents of the table are:
[]
To use additional transactions that could run simultaneously with main transaction managed by Connection, create new Transaction object calling Connection.trans() method. If you don’t specify the optional default_tpb parameter, this new Transaction inherits the default_tpb from Connection. Physical transaction is not started when Transaction instance is created, but implicitly when first SQL statement is executed, or explicitly via Transaction.begin() call.
To execute statements in context of this additional transaction you have to use cursors obtained directly from this Transaction instance calling its cursor() method, or call Transaction.execute_immediate() method.
Example:
import fdb
con = fdb.connect(dsn='employee', user='sysdba', password='pass')
# Cursor for main_transaction context
cur = con.cursor()
# Create new READ ONLY READ COMMITTED transaction
ro_transaction = con.trans(fdb.ISOLATION_LEVEL_READ_COMMITED_RO)
# 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 are transactions that span multiple databases. FDB provides this Firebird feature through ConnectionGroup class. Instances of this class act as managers for Transaction object that is bound to multiple connections, and to cursors bound to it and connections participated in group. That’s it, distributed transaction is fully independent from all other transactions, main or secondary, of member connections.
To assemble a group of connections, you can either pass the sequence of Connection instances to ConnectionGroup constructor, or add connections latter calling ConnectionGroup.add() method.
Any Connection could be a member of only one group, and attempt to add it to another one would raise an exception. Also, Connection participating in group cannot be closed before it’s removed or whole group is disbanded.
Warning
Never add more than one connection to the same database to the same ConnectionGroup!
Similarly to Transaction, distributed transactions are managed through ConnectionGroup.begin(), ConnectionGroup.savepoint(). ConnectionGroup.commit() and ConnectionGroup.rollback() methods. Additionally, ConnectionGroup exposes method prepare() that explicitly initiates the first phase of Two-Phase Commit Protocol. Transaction parameters are defined similarly to Transaction using ConnectionGroup.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 ConnectionGroup.cursor() method, or calling ConnectionGroup.execute_immediate() method.
Note
Because Cursor instances can belong to only one Connection, the cursor() method has manadory parameter connection, to specify to which member connection cursor should belong.
Example program:
import fdb
# First database
con1 = fdb.create_database("CREATE DATABASE 'testdb-1.fdb' USER 'SYSDBA' PASSWORD 'masterkey'")
con1.execute_immediate("recreate table T (PK integer, C1 integer)")
con1.commit()
# Second database
con2 = fdb.create_database("CREATE DATABASE 'testdb-2,fdb' USER 'SYSDBA' PASSWORD 'masterkey'")
con2.execute_immediate("recreate table T (PK integer, C1 integer)")
con2.commit()
# Create connection group
cg = fdb.ConnectionGroup((con1,con2))
# Prepare Group cursors for each connection
gc1 = cg.cursor(con1)
gc2 = cg.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"
# ===============================
gc1.execute('insert into t (pk) values (1)')
gc2.execute('insert into t (pk) values (1)')
cg.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"
# =========================================
gc1.execute('insert into t (pk) values (2)')
gc2.execute('insert into t (pk) values (2)')
cg.prepare()
cg.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"
# ===================================================
gc1.execute('insert into t (pk) values (3)')
cg.savepoint('CG_SAVEPOINT')
gc2.execute('insert into t (pk) values (3)')
cg.rollback(savepoint='CG_SAVEPOINT')
# check it - via group cursors, as transaction is still active
gc1.execute(q)
print 'db1:',gc1.fetchall()
gc2.execute(q)
print 'db2:',gc2.fetchall()
print "Distributed transaction: ROLLBACK"
# =================================
cg.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"
# ==========================================
cg.execute_immediate('insert into t (pk) values (3)')
cg.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)]
The Firebird engine features a distributed, interprocess communication mechanism based on messages called database events. A database event is a message passed from a trigger or stored procedure to an application to announce the occurrence of a specified condition or action, usually a database change such as an insertion, modification, or deletion of a record. The Firebird event mechanism enables applications to respond to actions and database changes made by other, concurrently running applications without the need for those applications to communicate directly with one another, and without incurring the expense of CPU time required for periodic polling to determine if an event has occurred.
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.
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.
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:
- Call isc_event_block() to create a formatted binary buffer that will tell the server which events the client wants to listen for.
- 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.
- [The thread that called isc_que_events() to initiate event listening must now do something else.]
- 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).
- [The callback thread should now “do its thing”, which may include communicating with the thread that called isc_que_events().]
- 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.
The FDB database event API is comprised of the following: the method Connection.event_conduit() and the class EventConduit.
The EventConduit class serve as “conduit” through which database event notifications will flow into the Python program. It’s not designed to be instantiated directly by the Python programmer. Instead, use the Connection.event_conduit() method to create EventConduit instances. event_conduit is a method of Connection rather than a module-level function or a class constructor because the database engine deals with events in the context of a particular database (after all, POST_EVENT must be issued by a stored procedure or a trigger).
Connection.event_conduit() takes a sequence of string event names as parameter, and returns EventConduit instance that immediately starts to accumulate notifications of any events that occur within the conduit’s internal queue until the conduit is closed either explicitly (via the close() method) or implicitly (via garbage collection).
Notifications about events are aquired through call to EventConduit.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.
Example:
>>> conduit = connection.event_conduit( ('event_a', 'event_b') )
>>> conduit.wait()
{
'event_a': 1,
'event_b': 0
}
If you want to drop notifications accumulated so far by conduit, call EventConduit.flush() method.
Example program:
import fdb
import threading
import time
# Prepare database
con = fdb.create_database("CREATE DATABASE 'event_test.fdb' USER 'SYSDBA' PASSWORD 'masterkey'")
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""")
con.commit()
cur = con.cursor()
# Utility function
def send_events(command_list):
for cmd in command_list:
cur.execute(cmd)
con.commit()
print "One event"
# =========
timed_event = threading.Timer(3.0,send_events,args=[["insert into T (PK,C1) values (1,1)",]])
events = con.event_conduit(['insert_1'])
timed_event.start()
e = events.wait()
events.close()
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])
events = self.con.event_conduit(['insert_1','insert_3'])
timed_event.start()
e = events.wait()
events.close()
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])
events = con.event_conduit(['insert_1','A','B','C','D',
'E','F','G','H','I','J','K','L','M',
'N','O','P','Q','R','insert_3'])
timed_event.start()
time.sleep(3)
e = events.wait()
events.close()
print e
print "Flush events"
# ============
timed_event = threading.Timer(3.0,send_events,args=[["insert into T (PK,C1) values (1,1)",]])
events = con.event_conduit(['insert_1'])
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()
events.close()
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}
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, FDB presents its own abstraction over the native API via the fdb.services module.
All Services API operations are performed in the context of a connection to a specific database server, represented by the fdb.services.Connection class. Similarly to database connections, FDB provides connect() constructor function to create such connections.
This constructor has three keyword parameters:
host: The network name of the computer on which the database server is running. user: The name of the database user under whose authority the maintenance tasks are to be performed. password: User’s password.
Since maintenance operations are most often initiated by an administrative user on the same computer as the database server, host defaults to the local computer, and user defaults to SYSDBA.
The three calls to fdb.services.connect() in the following program are equivalent:
from fdb import services
con = services.connect(password='masterkey')
con = services.connect(user='sysdba', password='masterkey')
con = services.connect(host='localhost', user='sysdba', password='masterkey')
Note
Like database connections, it’s good practice to close() them when you don’t need them anymore.
Connection object provides number of methods that could be divided into several groups:
To help client programs adapt to version changes, the service manager exposes its version number as an integer.
# 64-bit Linux Firebird 2.5.1 SuperServer >>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> print con.get_service_manager_version() 2fdb.services is a thick wrapper of the Services API that can shield its users from changes in the underlying C API, so this method is unlikely to be useful to the typical Python client programmer.
Returns the server’s version string
# 64-bit Linux Firebird 2.5.1 SuperServer >>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> print con.get_server_version() LI-V2.5.2.26536 Firebird 2.5At first glance, this method appears to duplicate the functionality of the fdb.Connection.server_version property, but when working with Firebird, there is a difference. fdb.Connection.server_version is based on a C API call (isc_database_info()) that existed long before the introduction of the Services API. Some programs written before the advent of Firebird test the version number in the return value of isc_database_info(), and refuse to work if it indicates that the server is too old. Since the first stable version of Firebird was labeled 1.0, this pre-Firebird version testing scheme incorrectly concludes that (e.g.) Firebird 1.0 is older than Interbase 5.0.
Firebird addresses this problem by making isc_database_info() return a “pseudo-InterBase” version number, whereas the Services API returns the true Firebird version, as shown:
# 64-bit Linux Firebird 2.5.1 SuperServer import fdb con = fdb.connect(dsn='employee', user='sysdba', password='masterkey') print 'Interbase-compatible version string:', con.server_version svcCon = fdb.services.connect(password='masterkey') print 'Actual Firebird version string: ', svcCon.get_server_version()Output (on Firebird 2.5.1/Linux64):
Interbase-compatible version string: LI-V6.3.1.26351 Firebird 2.5 Actual Firebird version string: LI-V2.5.1.26351 Firebird 2.5
Returns platform information for the server, including hardware architecture and operating system family.
# 64-bit Linux Firebird 2.5.1 SuperServer >>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> print con.get_architecture() Firebird/linux AMD64
Returns the equivalent of the RootDirectory setting from firebird.conf.
# 64-bit Linux Firebird 2.5.1 SuperServer >>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> print con.get_home_directory() /opt/firebird/
Returns the location of the server’s core security database, which contains user definitions and such. Name of this database is security2.fdb (Firebird 2.0 and later) or security.fdb (Firebird 1.5).
# 64-bit Linux Firebird 2.5.1 SuperServer >>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> print con.get_security_database_path() /opt/firebird/security2.fdb
Returns the directory location for Firebird lock files.
# 64-bit Linux Firebird 2.5.1 SuperServer >>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> print con.get_lock_file_directory() /tmp/firebird/
Returns tuple of capability info codes for each capability reported by Firebird server. Following constants are defined in fdb.services for convenience:
# 64-bit Linux Firebird 2.5.1 SuperServer >>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> print con.get_server_capabilities() (2L, 4L, 512L, 256L) >>> fdb.services.CAPABILITY_MULTI_CLIENT in con.get_server_capabilities() True >>> fdb.services.CAPABILITY_QUOTED_FILENAME in con.get_server_capabilities() False
To support internationalized error messages/prompts, the database engine stores its messages in a file named firebird.msg. The directory in which this file resides can be determined with this method.
# 64-bit Linux Firebird 2.5.1 SuperServer >>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> print con.get_message_file_directory() /opt/firebird/
Returns the number of active connections to databases managed by the server. This count only includes database connections (such as open instances of fdb.Connection), not services manager connections (such as open instances of fdb.services.Connection).
# 64-bit Linux Firebird 2.5.1 SuperServer >>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> db1 = fdb.connect(dsn='employee',user='sysdba',password='masterkey') >>> db2 = fdb.connect(dsn='employee',user='sysdba',password='masterkey') >>> print con.get_connection_count() 2
Returns a list of the names of all databases to which the server is maintaining at least one connection. The database names are not guaranteed to be in any particular order.
# 64-bit Linux Firebird 2.5.1 SuperServer >>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> db1 = fdb.connect(dsn='employee',user='sysdba',password='masterkey') >>> db2 = fdb.connect(dsn='employee',user='sysdba',password='masterkey') >>> print con.get_attached_database_names() ['/opt/firebird/examples/empbuild/employee.fdb']
Request the contents of the server’s log file (firebird.log).
This method is so-called Async method that only initiates log transfer. Actual log content could be read by one from many methods for text ouput from Services that Connection provides .
# 64-bit Linux Firebird 2.5.1 SuperServer >>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> con.get_log() >>> log = con.readlines()
Sets individual page cache size for Database.
>>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> con.set_default_page_buffers('employee',100)
Sets treshold for automatic sweep.
>>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> con.set_sweep_interval('employee',100000)
Sets data page space reservation policy.
>>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') # Use all space >>> con.set_reserve_page_space('employee',False)
Sets Disk Write Mode: Sync (forced writes) or Async (buffered). Following constants are defined in fdb.services for convenience:
>>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') # Disable Forced Writes >>> con.set_write_mode('employee',services.WRITE_BUFFERED)
Sets Database Access mode: Read Only or Read/Write. Following constants are defined in fdb.services for convenience:
>>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') # Set database to R/O mode >>> con.set_access_mode('employee',services.ACCESS_READ_ONLY)
Sets SQL Dialect for Database.
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 fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') # Use SQL dialect 1 >>> con.set_sql_dialect('employee',1)
Returns list of transactions in limbo.
Resolves limbo transaction with commit.
Resolves limbo transaction with rollback.
Request database statisctics. Report is in the same format as the output of the gstat command-line utility. This method has one required parameter, the location of the database on which to compute statistics, and six optional boolean parameters for controlling the domain of the statistics.
This method is so-called Async method that only initiates report processing. Actual report could be read by one from many methods for text ouput from Services that Connection provides .
Note
Until statistical report is not fully fetched from service (or ignored via wait()), any attempt to start another asynchronous service will fail with exception.
>>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> con.get_statistics('employee') >>> stat_report = con.readlines()
Request logical (GBAK) database backup. Produces report about backup process.
This method is so-called Async method that only initiates backup process. Actual report could be read by one from many methods for text ouput from Services that Connection provides .
Note
Until backup report is not fully fetched from service (or ignored via wait()), any attempt to start another asynchronous service will fail with exception.
# 64-bit Linux Firebird 2.5.1 SuperServer >>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> con.backup('employee', '/home/data/employee.fbk', metadata_only=True, collect_garbage=False) >>> backup_report = con.readlines()
Request database restore from logical (GBAK) backup. Produces report about restore process.
This method is so-called Async method that only initiates restore process. Actual report could be read by one from many methods for text ouput from Services that Connection provides .
Note
Until restore report is not fully fetched from service (or ignored via wait()), any attempt to start another asynchronous service will fail with exception.
# 64-bit Linux Firebird 2.5.1 SuperServer >>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> con.restore('/home/data/employee.fbk', '/home/data/empcopy.fdb') >>> restore_report = con.readlines()
Perform physical (NBACKUP) database backup.
Note
Method call will not return until sweep is finished.
Perform restore from physical (NBACKUP) database backup.
Note
Method call will not return until sweep is finished.
Database shutdown. Following constants are defined in fdb.services for convenience:
For shutdow mode:
For shutdown method:
# 64-bit Linux Firebird 2.5.1 SuperServer >>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') # Shutdown database to single-user maintenance mode >>> con.shutdown('empoyee', services.SHUT_SINGLE, services.SHUT_FORCE, 0) # Go to full shutdown mode, disabling new attachments during 5 seconds >>> con.shutdown('empoyee', services.SHUT_FULL, services.SHUT_DENY_NEW_ATTACHMENTS, 5)
Bring previously shut down database back online. Following constants are defined in fdb.services for convenience:
For on-line mode:
# 64-bit Linux Firebird 2.5.1 SuperServer >>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') # Enable multi-user maintenance >>> con.bring_online('employee', services.SHUT_MULTI) # Enable single-user maintenance >>> con.bring_online('employee', services.SHUT_SINGLE) # Return to normal state >>> con.bring_online('employee')
Activates Database Shadow(s).
Performs Database Sweep.
Note
Method call will not return until sweep is finished.
>>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> con.sweep('empoyee')
Database Validation and Repair.
Note
Method call will not return until action is finished.
>>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') # Just validate >>> con.repair('empoyee', ignore_checksums=True, read_only_validation=True) # Mend the database >>> con.repair('empoyee', ignore_checksums=True, mend_database=True)
Returns information about specified user or all users as a list of User instances.
>>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> users = con.get_users() >>> for user in users: ... print user.name ... print user.first_name, user.middle_name, user.last_name ... print user.user_id, user.group_id SYSDBA Sql Server Administrator 0 0
Adds new user. Requires instance of User with at least its name and password attributes specified as non-empty values. All other attributes are optional.
>>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> user = services.User('NewUser') >>> user.password = 'secret' >>> user.first_name = 'John' >>> user.last_name = 'Doe' >>> con.add_users(User)
Modifycation of user information. Requires instance of User with at least its name attribute specified as non-empty value.
Note
Sets first_name, middle_name and last_name to their actual values, and ignores the user_id and group_id attributes regardless of their values. password is set only when it has value.
>>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> user = services.User('SYSDBA') # Change pasword >>> user.password = 'Pa$$w0rd' >>> con.modify_user(User)
Starts new trace session. Requires trace configuration and returns Session ID.
Trace session output could be retrieved through readline(), readlines(), iteration over Connection or ignored via call to wait().
Note
Until session output is not fully fetched from service (or ignored via wait()), any attempt to start another asynchronous service including call to any trace_ method will fail with exception.
import fdb svc = fdb.services.connect(password='masterkey') # Because trace session blocks the connection, we need another one to stop trace session! svc_aux = fdb.services.connect(password='masterkey') trace_config = """<database 'employee'> enabled true log_statement_finish true print_plan true include_filter %%SELECT%% exclude_filter %%RDB$%% time_threshold 0 max_sql_length 2048 </database> """ trace_id = svc.trace_start(trace_config,'test_trace_1') trace_log = [] # Get first 10 lines of trace output for i in range(10): trace_log.append(svc.readline()) # Stop trace session svc_aux.stop_trace(trace_id)
Stops trace session specified by ID.
>>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> con.trace_stop(15) Trace session ID 15 stopped
Suspends trace session specified by ID.
>>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> con.trace_suspend(15) Trace session ID 15 paused
resumes trace session specified by ID.
>>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> con.trace_resume(15) Trace session ID 15 resumed
Returns information about existing trace sessions as dictionary mapping SESSION_ID -> SESSION_PARAMS. Session parameters is another dictionary with next keys:
name: (string) (optional) Session name if specified. date: (datetime.datetime) Session start date and time. user: (string) Trace user name. flags: (list of strings) Session flags. >>> from fdb import services >>> con = services.connect(host='localhost', user='sysdba', password='masterkey') >>> con.trace_list() {53: {'date': datetime.datetime(2012, 10, 5, 10, 45, 4), 'flags': ['active', ' admin', ' trace'], 'user': 'SYSDBA'}}
Some services like backup or trace may return significant amount of text. Rather than return the whole text as single string value by methods that provide access to these services, FDB isolated the transfer process to separate methods:
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 Connection.fetching attribute or Connection.isrunning() method.
In cases when you’re not interested in output produced by Service, call wait() to wait for service to complete.
Examples:
import fdb
svc = fdb.services.connect(password='masterkey')
print "Fetch materialized"
print "=================="
print "Start backup"
svc.backup('employee', 'employee.fbk')
print "svc.fetching is", svc.fetching
print "svc.running is", svc.isrunning()
report = svc.readlines()
print "%i lines returned" % len(report)
print "First 5 lines from output:"
for i in xrange(5):
print i,report[i]
print "svc.fetching is", svc.fetching
print "svc.running is", svc.isrunning()
print
print "Iterate over result"
print "==================="
svc.backup('employee', 'employee.fbk')
output = []
for line in svc:
output.append(line)
print "%i lines returned" % len(output)
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)
svc.backup('employee', 'employee.fbk', callback=fetchline)
print "%i lines returned" % len(output)
print "Last 5 lines from output:"
for line in output[-5:]:
print line
Output:
Fetch materialized
==================
Start backup
svc.fetching is True
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.fetching is False
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