Python cx_Oracle 操作Oracle 数据库

bj2008_0201

[color="#666666"]Developer: Open Source
        
      
      
      
      
Mastering Oracle+Python, Part 1: Querying Best Practices
      
      by Przemyslaw Piotrowski
      
      
As a first step, get familiar with the basic concepts of Oracle-Python connectivity
      
Published September 2007
      
Among
the core principles of Python's way of doing things there is a rule
about having high-level interfaces to APIs. The Database API (in this
case the Oracle API) is one example. Using the cx_Oracle Python module
from Computronix, you can take command over the Oracle query model
while maintaining compatibility with Python Database API Specification
v2.0.
The model of querying databases using DB API 2.0 remains
consistent for all client libraries conforming to the specification. On
top of this, Anthony Tuininga, the principal developer of cx_Oracle,
has added a wide set of properties and methods that expose
Oracle-specific features to developers. It is absolutely possible to
use only the standard methods and forget about the "extra" ones, but in
this installment you won't be doing that. The concept of universal
database wrappers might work in some cases but at the same time, you
lose all the optimizations that the RDBMS offers.
          
      Introducing DB API 2.0 and cx_Oracle
      
The
Python Database API Specification v2.0 is a community effort to unify
the model of accessing different database systems. Having a relatively
small set of methods and properties, it is easy to learn and remains
consistent when switching database vendors. It doesn't map database
objects to Python structures in any way. Users are still required to
write SQL by hand. After changing to another database, this SQL would
probably need to be rewritten. Nevertheless it solves Python-database
connectivity issues in an elegant and clean manner.
      
The specification defines parts of the API such as the
module interface, connection objects, cursor objects, type objects and
constructors, optional extensions to the DB API and optional error
handling mechanisms.
          
The gateway between the database and
Python language is the Connection object. It contains all the
ingredients for cooking database-driven applications, not only adhering
to the DB API 2.0 but being a superset of the specification methods and
attributes. In multi-threaded programs, modules as well as connections
can be shared across threads; sharing cursors is not supported. This
limitation is usually acceptable because shareable cursors can carry
the risk of deadlocks.
          
          
      
Python makes extensive use of the exception
model and the DB API defines several standard exceptions that could be
very helpful in debugging problems in the application. Below are the
standard exceptions with a short description of the types of causes:

          
          

Warning—Data was truncated during inserts, etc.

Error—Base class for all of the exceptions mentioned here except for Warning

InterfaceError—The database interface failed rather than the database itself (a cx_Oracle problem in this case)

DatabaseError—Strictly a database problem

DataError—Problems with the result data: division by zero, value out of range, etc.

OperationalError—Database error independent of the programmer: connection loss, memory allocation error, transaction processing error, etc.

IntegrityError—Database relational integrity has been affected, e.g. foreign key constraint fails

InternalError—Database has run into an internal error, e.g. invalid cursor, transaction out of synchronization

ProgrammingError—Table not found, syntax error in SQL statement, wrong number of parameters specified etc.

NotSupportedError—A non-existent part of API has been called          
          
The connect process begins with the Connection object, which is the
base for creating Cursor objects. Beside cursor operations, the
Connection object also manages transactions with the commit() and
rollback() methods. The process of executing SQL queries, issuing
DML/DCL statements and fetching results are all controlled by cursors.
cx_Oracle extends the standard DB API 2.0 specification in its
implementation of the Cursor and Connection classes at most. All such
extensions will be clearly marked in the text if needed.

          
Getting Started  
          
Before working with queries and cursors, a connection to the
database needs to be established. The credentials and data source names
can be supplied in one of several ways, with similar results. In the
extract from the Python interactive session below, connection objects
db, db1 and db2 are all equivalent. The makedsn() function creates a
TNS entry based on the given parameter values. Here it is being
assigned to the variable dsn_tns. When environment settings are
properly set then you can use the shorter form
cx_Oracle.connect('hr/hrpwd'), skipping even the Easy Connect string
used for db and db1.           
>>> import cx_Oracle
>>> db = cx_Oracle.connect('hr', 'hrpwd', 'localhost:1521/XE')
>>> db1 = cx_Oracle.connect('hr/hrpwd@localhost:1521/XE')
>>> dsn_tns = cx_Oracle.makedsn('localhost', 1521, 'XE')
>>> print dsn_tns
(DESCRIPTION=(ADDRESS_LIST=(ADDRESS=(PROTOCOL=TCP)(HOST=localhost)(PORT=1521)))
  (CONNECT_DATA=(SID=XE)))
>>> db2 = cx_Oracle.connect('hr', 'hrpwd', dsn_tns)          
Within the scope of a Connection object (such as assigned to the db
variable above) you can get the database version by querying the
version attribute (an extension to DB API 2.0). This can be used to
make Python programs Oracle-version dependent. Likewise, you can get
the connect string for the connection by querying the dsn attribute.
>>> print db.version
10.2.0.1.0
>>> versioning = db.version.split('.')
>>> print versioning
['10', '2', '0', '1', '0']
>>> if versioning[0]=='10':
...     print "Running 10g"
... elif versioning[0]=='9':
...    print "Running 9i"
...
Running 10g
>>> print db.dsn
localhost:1521/XE
          
Cursor Objects  
          
          
You can define an arbitrary
number of cursors using the cursor() method of the Connection object.
Simple programs will do fine with just a single cursor, which can be
used over and over again. Larger projects might however require several
distinct cursors.
          
>>> cursor = db.cursor()
Application logic often requires clearly distinguishing the stages of
processing a statement issued against the database. This will help
understand performance bottlenecks better and allow writing faster,
optimized code. The three stages of processing a statement are:

Parse (optional)

cx_Oracle.Cursor.parse([statement])
Not really required to be called because SQL statements are
automatically parsed at the Execute stage. It can be used to validate
statements before executing them. When an error is detected in such a
statement, a DatabaseError exception is raised with a corresponding
error message, most likely "ORA-00900: invalid SQL statement,
ORA-01031: insufficient privileges or ORA-00921: unexpected end of SQL
command."

Execute

cx_Oracle.Cursor.execute(statement, [parameters], **keyword_parameters)
This method can accept a single argument - a SQL statement - to be run
directly against the database. Bind variables assigned through the
parameters or keyword_parameters arguments can be specified as a
dictionary, sequence, or a set of keyword arguments. If dictionary or
keyword arguments are supplied then the values will be name-bound. If a
sequence is given, the values will be resolved by their position. This
method returns a list of variable objects if it is a query, and None
when it's not.

cx_Oracle.Cursor.executemany(statement, parameters)
Especially useful for bulk inserts because it can limit the number of
required Oracle execute operations to just a single one. For more
information about how to use it please see the "Many at once" section
below.

Fetch (optional)—Only used for queries (because
DDL and DCL statements don't return results). On a cursor that didn't
execute a query, these methods will raise an InterfaceError exception.

cx_Oracle.Cursor.fetchall()
Fetches all remaining rows
of the result set as a list of tuples. If no more rows are available,
it returns an empty list. Fetch actions can be fine-tuned by setting
the arraysize attribute of the cursor which sets the number of rows to
return from the database in each underlying request. The higher setting
of arraysize, the fewer number of network round trips required. The
default value for arraysize is 1.

cx_Oracle.Cursor.fetchmany([rows_no])
Fetches the next rows_no rows from the database. If the parameter isn't
specified it fetches the arraysize number of rows. In situations where
rows_no is greater than the number of fetched rows, it simply gets the
remaining number of rows.

cx_Oracle.Cursor.fetchone()
Fetches a single tuple from the database or none if no more rows are available.
      
Before going forward with cursor examples please
welcome the pprint function from the pprint module. It outputs Python
data structures in a clean, readable form.
          
>>> from pprint import pprint
>>> cursor.execute('SELECT * FROM jobs')
[, ,
, ]
>>> pprint(cursor.fetchall())
[('AD_PRES', 'President', 20000, 40000),
('AD_VP', 'Administration Vice President', 15000, 30000),
('AD_ASST', 'Administration Assistant', 3000, 6000),
('FI_MGR', 'Finance Manager', 8200, 16000),
('FI_ACCOUNT', 'Accountant', 4200, 9000),
  |
('PR_REP', 'Public Relations Representative', 4500, 10500)]       
cx_Oracle cursors are iterators. These powerful Python
structures let you iterate over sequences in a natural way that fetches
subsequent items on demand only. Costly database select operations
naturally fit into this idea because the data only gets fetched when
needed. Instead of creating or fetching the whole result set, you
iterate until the desired value is found or another condition
fulfilled. >>> cursor = db.cursor()
>>> cursor.execute('SELECT * FROM jobs')
[, ,
, ]
>>> for row in cursor:    ## notice that this is plain English!
...     print row
...
('AD_VP', 'Administration Vice President', 15000, 30000)
('AD_ASST', 'Administration Assistant', 3000, 6000)
('FI_MGR', 'Finance Manager', 8200, 16000)
('FI_ACCOUNT', 'Accountant', 4200, 9000)
('AC_MGR', 'Accounting Manager', 8200, 16000)
|
('PR_REP', 'Public Relations Representative', 4500, 10500)       
Just after an execute list(cursor) does the same job as
cursor.fetchall(). This is because the built-in list() function
iterates until the end of the given iterator. Datatypes
          
      
During the fetch stage, basic
Oracle data types get mapped into their Python equivalents. cx_Oracle
maintains a separate set of data types that helps in this transition.
The Oracle - cx_Oracle - Python mappings are:
          
          
  
  
  
  
    Oracle
    cx_Oracle
    Python
  
  
    VARCHAR2
NVARCHAR2
LONG
   
cx_Oracle.STRING
   
str
  
  
   
CHAR
   
cx_Oracle.FIXED_CHAR
  
  
   
NUMBER
   
cx_Oracle.NUMBER
   
int
  
  
   
FLOAT
   
float
  
  
   
DATE
   
cx_Oracle.DATETIME
   
datetime.datetime
  
  
   
TIMESTAMP
   
cx_Oracle.TIMESTAMP
  
  
   
CLOB
   
cx_Oracle.CLOB
   
cx_Oracle.LOB
  
  
   
BLOB
   
cx_Oracle.BLOB
  
      
The above data types are usually
transparent to the user except for cases involving Large Objects. As of
version 4.3, cx_Oracle still handles them itself and not wrapped with
the built-in file type.
          
         
          
      
Other
data types that are not yet handled by cx_Oracle include XMLTYPE and
all complex types. All queries involving columns of unsupported types
will currently fail with a NotSupportedError exception. You need to
remove them from queries or cast to a supported data type.
          
          
For example, consider the following table for storing aggregated RSS feeds:
  
          
CREATE TABLE rss_feeds (
  feed_id NUMBER PRIMARY KEY,
  feed_url VARCHAR2(250) NOT NULL,
  feed_xml XMLTYPE
);          
When trying to query this table with Python, some additional
steps need to be performed. In the example below XMLType.GetClobVal()
is used to return XML from the table as CLOB values.
          
>>> cursor.execute('SELECT * FROM rss_feeds')
Traceback (most recent call last):
  File "", line 1, in
    cursor.execute('SELECT * FROM rss_feeds')
NotSupportedError: Variable_TypeByOracleDataType: unhandled data type 108
>>> cursor.execute('SELECT feed_id, feed_url, XMLType.GetClobVal(feed_xml) FROM rss_feeds')
[, ,
]
You might have already noticed the cx_Oracle.Cursor.execute*
family of methods returns column data types for queries. These are
lists of Variable objects (an extension to DB API 2.0), which get the
value None before the fetch phase and proper data values after the
fetch. Detailed information about data types is available through the
description attribute of cursor objects. The description is a list of
7-item tuples where each tuple consists of a column name, column type,
display size, internal size, precision, scale and whether null is
possible. Note that column information is only accessible for SQL
statements that are queries.
>>> column_data_types = cursor.execute('SELECT * FROM employees')
>>> print column_data_types
[, ,
, ,
, ,
, ,
, ,
]
>>> pprint(cursor.description)
[('EMPLOYEE_ID', , 7, 22, 6, 0, 0),
('FIRST_NAME', , 20, 20, 0, 0, 1),
('LAST_NAME', , 25, 25, 0, 0, 0),
('EMAIL', , 25, 25, 0, 0, 0),
('PHONE_NUMBER', , 20, 20, 0, 0, 1),
('HIRE_DATE', , 23, 7, 0, 0, 0),
('JOB_ID', , 10, 10, 0, 0, 0),
('SALARY', , 12, 22, 8, 2, 1),
('COMMISSION_PCT', , 6, 22, 2, 2, 1),
('MANAGER_ID', , 7, 22, 6, 0, 1),
('DEPARTMENT_ID', , 5, 22, 4, 0, 1)]
Bind Variable Patterns
As advertised by Oracle guru Tom Kyte, bind variables are core
principles of database development. They do not only make programs run
faster but also protect against SQL injection attacks. Consider the
following queries:
SELECT * FROM emp_details_view WHERE department_id=50
SELECT * FROM emp_details_view WHERE department_id=60
SELECT * FROM emp_details_view WHERE department_id=90
SELECT * FROM emp_details_view WHERE department_id=110
When run one-by-one, each need to be parsed separately which adds
extra overhead to your application. By using bind variables you can
tell Oracle to parse a query only once. cx_Oracle supports binding
variables by name or by position.
Passing bind variables by name requires the parameters argument
of the execute method to be a dictionary or a set of keyword arguments.
query1 and query2 below are equivalent:
          
>>> named_params = {'dept_id':50, 'sal':1000}
>>> query1 = cursor.execute('SELECT * FROM employees
  WHERE department_id=:dept_id AND salary>:sal', named_params)
>>> query2 = cursor.execute('SELECT * FROM employees
  WHERE department_id=:dept_id AND salary>:sal', dept_id=50, sal=1000)          
          
        When using named bind variables you can check the currently assigned ones using the bindnames() method of the cursor:
          
>>> print cursor.bindnames()
['DEPT_ID', 'SAL']         

Passing by position is similar but you need to be careful about naming.
Variable names are arbitrary so it's easy to mess up queries this way.
In the example below, all three queries r1, r2, and r3 are equivalent.
The parameters variable must be given as a sequence.
>>> r1 = cursor.execute('SELECT * FROM locations
WHERE country_id=:1 AND city=:2', ('US', 'Seattle'))
>>> r2 = cursor.execute('SELECT * FROM locations
WHERE country_id=:9 AND city=:4', ('US', 'Seattle'))
>>> r3 = cursor.execute('SELECT * FROM locations
WHERE country_id=:m AND city=:0', ('US', 'Seattle'))
When binding, you can first prepare the statement and then
execute None with changed parameters. Oracle will handle it as in the
above case, governed by the rule that one prepare is enough when
variables are bound. Any number of executions can be involved for
prepared statements. >>> cursor.prepare('SELECT * FROM jobs WHERE min_salary>:min')
>>> r = cursor.execute(None, {'min':1000})
>>> print len(cursor.fetchall())
19
You have already limited the number of parses. In the next
paragraph we'll be eliminating unnecessary executions, especially
expensive bulk inserts. Many at Once
Large insert operations don't require many separate inserts because
Python fully supports inserting many rows at once with the
cx_Oracle.Cursor.executemany method. Limiting the number of execute
operations improves program performance a lot and should be the first
thing to think about when writing applications heavy on INSERTs.
          
      
Let's create a table for a Python module list, this time directly from Python. You will drop it later.
          
>>> create_table = """
CREATE TABLE python_modules (
  module_name VARCHAR2(50) NOT NULL,
  file_path VARCHAR2(300) NOT NULL
)
"""
>>> from sys import modules
>>> cursor.execute(create_table)
>>> M = []
>>> for m_name, m_info in modules.items():
...   try:
...     M.append((m_name, m_info.__file__))
...   except AttributeError:
...     pass
...
>>> len(M)
76
>>> cursor.prepare("INSERT INTO python_modules(module_name, file_path) VALUES (:1, :2)")
>>> cursor.executemany(None, M)
>>> db.commit()
>>> r = cursor.execute("SELECT COUNT(*) FROM python_modules")
>>> print cursor.fetchone()
(76,)
>>> cursor.execute("DROP TABLE python_modules PURGE")
Only one execute has been issued to the database to insert all
76 module names. This is huge performance boost for large insert
operations. Notice two small quirks here: cursor.execute(create_tab)
doesn't produce any output since it is a DDL statement and (76,) is a
tuple with a single element. (76) without a comma would simply be
equivalent to an integer 76.
Conclusion
After familiarizing yourself with basic concepts of Oracle-Python
connectivity you are ready to start writing your own database-driven
applications. I highly recommend playing with the Python interactive
shell for some time as it really brings down the learning curve.
You have learned about three stages that SQL statements go through
and how to minimize the number of steps the Oracle Database needs to
perform. Bind variables are an inevitable part of database application
development and Python enables binding them by name or by position.
You have also been introduced to the smooth transition between
Oracle and Python datatypes and the natural way of handling database
data in the context of handling cursors as iterators. All these
features boost productivity and enable focusing on the data, which is
what it's all about.
       
Mastering Oracle+Python, Part 2: Working with Times and Dates
Mastering Oracle+Python, Part 3: Data Parsing
               
               
               

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