Oracle 12c Tutorial Pdf Free Download

Python and Oracle Database Tutorial: Scripting for the Future

Overview
Setup
Connecting to Oracle
- 1.1 Creating a basic connection
- 1.2 Indentation indicates code structure
- 1.3 Executing a query
- 1.4 Closing connections
- 1.5 Checking versions
Connection Pooling
- 2.1 Connection pooling
- 2.2 Connection pool experiments
- 2.3 Creating a DRCP Connection
- 2.4 Connection pooling and DRCP
- 2.5 More DRCP investigation
Fetching Data
- 3.1 A simple query
- 3.2 Using fetchone()
- 3.3 Using fetchmany()
- 3.4 Scrollable cursors
- 3.5 Tuning with arraysize and prefetchrows
Binding Data
- 4.1 Binding in queries
- 4.2 Binding in inserts
- 4.3 Batcherrors
- 4.4 Binding named objects
PL/SQL
- 5.1 PL/SQL functions
- 5.2 PL/SQL procedures
Type Handlers
- 6.1 Basic output type handler
- 6.2 Output type handlers and variable converters
- 6.3 Input type handlers
LOBs
- 7.1 Fetching a CLOB using a locator
- 7.2 Fetching a CLOB as a string
Rowfactory functions
- 8.1 Rowfactory for mapping column names
Subclassing connections and cursors
- 9.1 Subclassing connections
- 9.2 Subclassing cursors
Advanced Queuing
- 10.1 Message passing with Oracle Advanced Queuing
Simple Oracle Document Access (SODA)
- 11.1 Inserting JSON Documents
- 11.2 Searching SODA Documents
Summary
Appendix: Python Primer
Resources

Overview

This tutorial is an introduction to using Python with Oracle Database. It contains beginner and advanced material. Sections can be done in any order. Choose the content that interests you and your skill level. The tutorial has scripts to run and modify, and has suggested solutions.

Python is a popular general purpose dynamic scripting language. The cx_Oracle interface provides the Python API to access Oracle Database.

If you are new to Python review the Appendix: Python Primer to gain an understanding of the language.

When you have finished this tutorial, we recommend reviewing the cx_Oracle documention.

The original copy of these instructions that you are reading is here.

cx_Oracle Architecture

Python programs call cx_Oracle functions. Internally cx_Oracle dynamically loads Oracle Client libraries to access Oracle Database. The database can be on the same machine as Python, or it can be remote. If the database is local, the client libraries from the Oracle Database software installation can be used.

Setup

Install software

To get going, follow either of the quick start instructions:
- Quick Start: Developing Python Applications for Oracle Database (On-Premises)
- Quick Start: Developing Python Applications for Oracle Autonomous Database
For this tutorial, you will need Python 3.6 (or later), cx_Oracle 7.3 (or later), and access to Oracle Database.

The Advanced Queuing section requires Python cx_Oracle to be using Oracle client libraries 12.2 or later. The SODA section requires Oracle Database 18 or later, and Python cx_Oracle must be using Oracle libraries from 18.5, or later.
Download the tutorial scripts

The Python scripts used in this example are in the cx_Oracle GitHub repository.

Download a zip file of the repository from here and unzip it. Alternatively you can use 'git' to clone the repository with git clone https://github.com/oracle/python-cx_Oracle.git

The samples/tutorial directory has scripts to run and modify. The samples/tutorial/solutions directory has scripts with suggested code changes.
Create a database user

If you have an existing user, you may be able to use it for most examples (some examples may require extra permissions).

If you need to create a new user, review the grants created in samples/tutorial/sql/create_user.sql. Then open a terminal window, change to the samples/tutorial/sql directory, and run the create_user.sql script as the SYSTEM user, for example:
```
cd samples/tutorial/sql sqlplus -l system/systempassword@localhost/orclpdb1 @create_user          
```
The example above connects as the SYSTEM user. The connection string is "localhost/orclpdb1", meaning use the database service "orclpdb1" running on localhost (the computer you are running SQL*Plus on). Substitute values for your environment. If you are using Oracle Autonomous Database, use the ADMIN user instead of SYSTEM.

When the tutorial is finished, the drop_user.sql script in the same directory can be used to remove the tutorial user.
Install the sample tables

Once you have a database user, then you can create the tutorial tables by running a command like this, using your values for the tutorial username, password and connection string:
```
sqlplus -l pythonhol/welcome@localhost/orclpdb1 @setup_tables          
```
Start the Database Resident Connection Pool (DRCP)

If you want to try the DRCP examples in section 2, start the DRCP pool. (The pool is already started in Oracle Autonomous Database).

Run SQL*Plus with SYSDBA privileges, for example:
```
sqlplus -l sys/syspassword@localhost/orclcdb as sysdba          
```
and execute the command:
```
execute dbms_connection_pool.start_pool()          
```
Note you may need to do this in the container database, not a pluggable database.
Review the connection credentials used by the tutorial scripts

Review db_config.py and db_config.sql in the tutorial directory. These are included in other Python and SQL files.

Edit db_config.py and change the default values to match the connection information for your environment. Alternatively you can set the given envionment variables in your terminal window. For example, the default username is "pythonhol" unless the envionment variable "PYTHON_USER" contains a different username. The default connection string is for the 'orclpdb1' database service on the same machine as Python. (In Python Database API terminology, the connection string parameter is called the "data source name", or "dsn".) Using envionment variables is convenient because you will not be asked to re-enter the password when you run scripts:
```
user = os.environ.get("PYTHON_USER", "pythonhol")  dsn = os.environ.get("PYTHON_CONNECT_STRING", "localhost/orclpdb1")  pw = os.environ.get("PYTHON_PASSWORD") if pw is None:     pw = getpass.getpass("Enter password for %s: " % user)          
```
Also change the default username and connection string in the SQL*Plus configuration file db_config.sql:
```
-- Default database username def user = "pythonhol"  -- Default database connection string def connect_string = "localhost/orclpdb1"  -- Prompt for the password accept pw char prompt 'Enter database password for &user: ' hide          
```
The tutorial instructions may need adjusting, depending on how you have set up your environment.
Review the Instant Client library path

Review the Oracle Client library path settings in db_config.py. If cx_Oracle cannot locate Oracle Client libraries, then your applications will fail with an error like "DPI-1047: Cannot locate a 64-bit Oracle Client library".
```
# On Linux this must be None. # Instead, the Oracle environment must be set before Python starts. instant_client_dir = None  # On Windows, if your database is on the same machine, comment these lines out # and let instant_client_dir be None.  Otherwise, set this to your Instant # Client directory.  Note the use of the raw string r"..."  so backslashes can # be used as directory separators. if sys.platform.startswith("win"):     instant_client_dir = r"c:\oracle\instantclient_19_10"  # On macOS (Intel x86) set the directory to your Instant Client directory if sys.platform.startswith("darwin"):     instant_client_dir = os.environ.get("HOME")+"/Downloads/instantclient_19_8"  # This can be called at most once per process. if instant_client_dir is not None:     cx_Oracle.init_oracle_client(lib_dir=instant_client_dir)          
```
Set instant_client_dir to None or to a valid path according to the following notes:
- If you are on macOS or Windows, and you have installed Oracle Instant Client libraries because your database is on a remote machine, then set instant_client_dir to the path of the Instant Client libraries.
- If you are on Windows and have a local database installed, then comment out the two Windows lines, so that instant_client_dir remains None.
- In all other cases (including Linux with Oracle Instant Client), make sure that instant_client_dir is set to None. In these cases you must make sure that the Oracle libraries from Instant Client or your ORACLE_HOME are in your system library search path before you start Python. On Linux, the path can be configured with ldconfig or with the LD_LIBRARY_PATH environment variables.

1. Connecting to Oracle

You can connect from Python to a local, remote or cloud database. Documentation link for further reading: Connecting to Oracle Database.

1.1 Creating a basic connection

Review the code contained in connect.py:
```
import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) print("Database version:", con.version)          
```
The cx_Oracle module is imported to provide the API for accessing the Oracle database. Many inbuilt and third party modules can be included in Python scripts this way.

The connect() method is passed the username, the password and the connection string that you configured in the db_config.py module. In this case, Oracle's Easy Connect connection string syntax is used. It consists of the hostname of your machine, localhost, and the database service name orclpdb1. (In Python Database API terminology, the connection string parameter is called the "data source name", or "dsn".)

Open a command terminal and change to the tutorial directory:
```
            cd samples/tutorial          
```
Run the Python script:
```
            python connect.py          
```
The version number of the database should be displayed. An exception is raised if the connection fails. Adjust the username, password or connection string parameters to invalid values to see the exception.

cx_Oracle also supports "external authentication", which allows connections without needing usernames and passwords to be embedded in the code. Authentication would then instead be performed by, for example, LDAP or Oracle Wallets.
1.2 Indentation indicates code structure

There are no statement terminators or begin/end keywords or braces to indicate blocks of code.

Open connect.py in an editor. Indent the print statement with some spaces:
```
import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn)   print("Database version:", con.version)          
```
Save the script and run it again:
```
            python connect.py          
```
This raises an exception about the indentation. The number of spaces or tabs must be consistent in each block; otherwise, the Python interpreter will either raise an exception or execute code unexpectedly.

Python may not always be able to identify accidental from deliberate indentation. Check your indentation is correct before running each example. Make sure to indent all statement blocks equally. Note the sample files use spaces, not tabs.

1.3 Executing a query

Open query.py in an editor. It looks like:

import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn)

Edit the file and add the code shown in bold below:

import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn)            cur = con.cursor() cur.execute("select * from dept order by deptno") res = cur.fetchall() for row in res:     print(row)

Make sure the print(row) line is indented. This lab uses spaces, not tabs.

The code executes a query and fetches all data.

Save the file and run it:

            python query.py

In each loop iteration a new row is stored in row as a Python "tuple" and is displayed.

Fetching data is described further in section 3.

1.4 Closing connections

Connections and other resources used by cx_Oracle will automatically be closed at the end of scope. This is a common programming style that takes care of the correct order of resource closure.

Resources can also be explicitly closed to free up database resources if they are no longer needed. This is strongly recommended in blocks of code that remain active for some time.

Open query.py in an editor and add calls to close the cursor and connection like:
```
import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn)  cur = con.cursor() cur.execute("select * from dept order by deptno") res = cur.fetchall() for row in res:     print(row)            cur.close()            con.close()          
```
Running the script completes without error:
```
            python query.py          
```
If you swap the order of the two close() calls you will see an error.
1.5 Checking versions

Review the code contained in versions.py:
```
import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn)  print(cx_Oracle.version)
```
Run the script:
```
            python versions.py          
```
This gives the version of the cx_Oracle interface.

Edit the file to print the version of the database, and of the Oracle client libraries used by cx_Oracle:
```
import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn)  print(cx_Oracle.version)            print("Database version:", con.version) print("Client version:", cx_Oracle.clientversion())          
```
When the script is run, it will display:
```
7.1.0 Database version: 19.3.0.0.0 Client version: (19, 8, 0, 0, 0)          
```
Note the client version is a tuple.

Any cx_Oracle installation can connect to older and newer Oracle Database versions. By checking the Oracle Database and client versions numbers, the application can make use of the best Oracle features available.

2. Connection Pooling

Connection pooling is important for performance in when multi-threaded applications frequently connect and disconnect from the database. Pooling also gives the best support for Oracle high availability features. Documentation link for further reading: Connection Pooling.

2.1 Connection pooling

Review the code contained in connect_pool.py:
```
import cx_Oracle import threading import db_config  pool = cx_Oracle.SessionPool(db_config.user, db_config.pw, db_config.dsn,                              min = 2, max = 5, increment = 1, threaded = True,                              getmode = cx_Oracle.SPOOL_ATTRVAL_WAIT)  def Query():     con = pool.acquire()     cur = con.cursor()     for i in range(4):         cur.execute("select myseq.nextval from dual")         seqval, = cur.fetchone()         print("Thread", threading.current_thread().name, "fetched sequence =", seqval)  thread1 = threading.Thread(name='#1', target=Query) thread1.start()  thread2 = threading.Thread(name='#2', target=Query) thread2.start()  thread1.join() thread2.join()  print("All done!")          
```
The SessionPool() function creates a pool of Oracle connections for the user. Connections in the pool can be used by cx_Oracle by calling pool.acquire(). The initial pool size is 2 connections. The maximum size is 5 connections. When the pool needs to grow, then 1 new connection will be created at a time. The pool can shrink back to the minimum size of 2 when connections are no longer in use.

The def Query(): line creates a method that is called by each thread.

In the method, the pool.acquire() call gets one connection from the pool (as long as less than 5 are already in use). This connection is used in a loop of 4 iterations to query the sequence myseq. At the end of the method, cx_Oracle will automatically close the cursor and release the connection back to the pool for reuse.

The seqval, = cur.fetchone() line fetches a row and puts the single value contained in the result tuple into the variable seqval. Without the comma, the value in seqval would be a tuple like "(1,)".

Two threads are created, each invoking the Query() method.

In a command terminal, run:
```
            python connect_pool.py          
```
The output shows interleaved query results as each thread fetches values independently. The order of interleaving may vary from run to run.

2.2 Connection pool experiments

Review connect_pool2.py, which has a loop for the number of threads, each iteration invoking the Query() method:

import cx_Oracle import threading import db_config  pool = cx_Oracle.SessionPool(db_config.user, db_config.pw, db_config.dsn,                              min = 2, max = 5, increment = 1, threaded = True,                              getmode = cx_Oracle.SPOOL_ATTRVAL_WAIT)  def Query():     con = pool.acquire()     cur = con.cursor()     for i in range(4):         cur.execute("select myseq.nextval from dual")         seqval, = cur.fetchone()         print("Thread", threading.current_thread().name, "fetched sequence =", seqval)            numberOfThreads = 2 threadArray = []  for i in range(numberOfThreads):     thread = threading.Thread(name = '#' + str(i), target = Query)     threadArray.append(thread)     thread.start()  for t in threadArray:     t.join()            print("All done!")

In a command terminal, run:

            python connect_pool2.py

Experiment with different values of the pool parameters and numberOfThreads. Larger initial pool sizes will make the pool creation slower, but the connections will be available immediately when needed.

Try changing getmode to cx_Oracle.SPOOL_ATTRVAL_NOWAIT. When numberOfThreads exceeds the maximum size of the pool, the acquire() call will now generate an error such as "ORA-24459: OCISessionGet() timed out waiting for pool to create new connections".

Pool configurations where min is the same as max (and increment = 0) are often recommended as a best practice. This avoids connection storms on the database server.

2.3 Creating a DRCP Connection

Database Resident Connection Pooling allows multiple Python processes on multiple machines to share a small pool of database server processes.

Below left is a diagram without DRCP. Every application standalone connection (or cx_Oracle connection-pool connection) has its own database server process. Standalone application connect() and close calls require the expensive create and destroy of those database server processes. cx_Oracle connection pools reduce these costs by keeping database server processes open, but every cx_Oracle connection pool will requires its own set of database server processes, even if they are not doing database work: these idle server processes consumes database host resources. Below right is a diagram with DRCP. Scripts and Python processes can share database servers from a precreated pool of servers and return them when they are not in use.

Without DRCP

With DRCP

DRCP is useful when the database host machine does not have enough memory to handle the number of database server processes required. If DRCP is enabled, it is best used in conjunction with cx_Oracle's connection pooling. However, if the database host memory is large enough, then the default, 'dedicated' server process model is generally recommended. This can be with or without a cx_Oracle connection pool, depending on the connection rate.

Batch scripts doing long running jobs should generally use dedicated connections. Both dedicated and DRCP servers can be used together in the same application or database.

Review the code contained in connect_drcp.py:

import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn + ":pooled",                         cclass="PYTHONHOL", purity=cx_Oracle.ATTR_PURITY_SELF) print("Database version:", con.version)

This is similar to connect.py but ":pooled" is appended to the connection string, telling the database to use a pooled server. A Connection Class "PYTHONHOL" is also passed into the connect() method to allow grouping of database servers to applications. Note with Autonomous Database, the connection string has a different form, see the ADB documentation.

The "purity" of the connection is defined as the ATTR_PURITY_SELF constant, meaning the session state (such as the default date format) might be retained between connection calls, giving performance benefits. Session information will be discarded if a pooled server is later reused by an application with a different connection class name.

Applications that should never share session information should use a different connection class and/or use ATTR_PURITY_NEW to force creation of a new session. This reduces overall scalability but prevents applications mis-using session information.

Run connect_drcp.py in a terminal window.

            python connect_drcp.py

The output is simply the version of the database.

2.4 Connection pooling and DRCP

DRCP works well with cx_Oracle's connection pooling.

Edit connect_pool2.py, reset any changed pool options, and modify it to use DRCP:
```
import cx_Oracle import threading  pool = cx_Oracle.SessionPool(db_config.user, db_config.pw, db_config.dsn            + ":pooled",                              min = 2, max = 5, increment = 1, threaded = True,                              getmode = cx_Oracle.SPOOL_ATTRVAL_WAIT)  def Query():     con = pool.acquire(cclass = "PYTHONHOL", purity = cx_Oracle.ATTR_PURITY_SELF)     cur = conn.cursor()     for i in range(4):         cur.execute("select myseq.nextval from dual")         seqval, = cur.fetchone()         print("Thread", threading.current_thread().name, "fetched sequence =", seqval)  numberOfThreads = 2 threadArray = []  for i in range(numberOfThreads):     thread = threading.Thread(name = '#' + str(i), target = Query)     threadArray.append(thread)     thread.start()  for t in threadArray:     t.join()  print("All done!")          
```
The script logic does not need to be changed to benefit from DRCP connection pooling.

Run the script:
```
            python connect_pool2.py          
```
Review drcp_query.sql and set the connection string to your database. Then open a new a terminal window and invoke SQL*Plus:
```
            sqlplus /nolog @drcp_query.sql          
```
This will prompt for the SYSTEM password and the database connection string. With Pluggable databases, you will need to connect to the container database. Note that with ADB, this view does not contain rows, so running this script is not useful.

For other databases, the script shows the number of connection requests made to the pool since the database was started ("NUM_REQUESTS"), how many of those reused a pooled server's session ("NUM_HITS"), and how many had to create new sessions ("NUM_MISSES"). Typically the goal is a low number of misses.

To see the pool configuration you can query DBA_CPOOL_INFO.
2.5 More DRCP investigation

To explore the behaviors of cx_Oracle connection pooling and DRCP pooling futher, you could try changing the purity to cx_Oracle.ATTR_PURITY_NEW to see the effect on the DRCP NUM_MISSES statistic.

Another experiement is to include the time module at the file top:
```
import time
```
and add calls to time.sleep(1) in the code, for example in the query loop. Then look at the way the threads execute. Use drcp_query.sql to monitor the pool's behavior.

3. Fetching Data

Executing SELECT queries is the primary way to get data from Oracle Database. Documentation link for further reading: SQL Queries.

3.1 A simple query

There are a number of functions you can use to query an Oracle database, but the basics of querying are always the same:

1. Execute the statement.
2. Bind data values (optional).
3. Fetch the results from the database.

Review the code contained in query2.py:

import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn)  cur = con.cursor() cur.execute("select * from dept order by deptno") for deptno, dname, loc in cur:     print("Department number: ", deptno)     print("Department name: ", dname)     print("Department location:", loc)

The cursor() method opens a cursor for statements to use.

The execute() method parses and executes the statement.

The loop fetches each row from the cursor and unpacks the returned tuple into the variables deptno, dname, loc, which are then printed.

Run the script in a terminal window:

            python query2.py

The output is:

Department number:  10 Department name:  ACCOUNTING Department location: NEW YORK Department number:  20 Department name:  RESEARCH Department location: DALLAS Department number:  30 Department name:  SALES Department location: CHICAGO Department number:  40 Department name:  OPERATIONS Department location: BOSTON

3.2 Using fetchone()

When the number of rows is large, the fetchall() call may use too much memory.

Review the code contained in query_one.py:
```
import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor()  cur.execute("select * from dept order by deptno") row = cur.fetchone() print(row)  row = cur.fetchone() print(row)          
```
This uses the fetchone() method to return just a single row as a tuple. When called multiple time, consecutive rows are returned:

Run the script in a terminal window:
```
            python query_one.py          
```
The first two rows of the table are printed.
3.3 Using fetchmany()

Review the code contained in query_many.py:
```
import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor()  cur.execute("select * from dept order by deptno") res = cur.fetchmany(numRows = 3) print(res)          
```
The fetchmany() method returns a list of tuples. By default the number of rows returned is specified by the cursor attribute arraysize (which defaults to 100). Here the numRows parameter specifies that three rows should be returned.

Run the script in a terminal window:
```
            python query_many.py          
```
The first three rows of the table are returned as a list (Python's name for an array) of tuples.

You can access elements of the lists by position indexes. To see this, edit the file and add:
```
            print(res[0])            # first row            print(res[0][1])            # second element of first row          
```

3.4 Scrollable cursors

Scrollable cursors enable the application to move backwards as well as forwards in query results. They can be used to skip rows as well as move to a particular row.

Review the code contained in query_scroll.py:

import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor(scrollable = True)  cur.execute("select * from dept order by deptno")  cur.scroll(2, mode = "absolute")  # go to second row print(cur.fetchone())  cur.scroll(-1)                    # go back one row print(cur.fetchone())

Run the script in a terminal window:

            python query_scroll.py

Edit query_scroll.py and experiment with different scroll options and orders, such as:

cur.scroll(1)  # go to next row print(cur.fetchone())  cur.scroll(mode = "first")  # go to first row print(cur.fetchone())

Try some scroll options that go beyond the number of rows in the resultset.

3.5 Tuning with arraysize and prefetchrows

This section demonstrates a way to improve query performance by increasing the number of rows returned in each batch from Oracle to the Python program.

Row prefetching and array fetching are both internal buffering techniques to reduce round-trips to the database. The difference is the code layer that is doing the buffering, and when the buffering occurs.

First, create a table with a large number of rows. Review query_arraysize.sql:
```
create table bigtab (mycol varchar2(20)); begin   for i in 1..20000   loop    insert into bigtab (mycol) values (dbms_random.string('A',20));   end loop; end; / show errors  commit;          
```
In a terminal window run the script as:
```
            sqlplus /nolog @query_arraysize.sql          
```
Review the code contained in query_arraysize.py:
```
import cx_Oracle import time import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn)  start = time.time()  cur = con.cursor() cur.prefetchrows = 100 cur.arraysize = 100 cur.execute("select * from bigtab") res = cur.fetchall() # print(res)  # uncomment to display the query results  elapsed = (time.time() - start) print(elapsed, "seconds")          
```
This uses the 'time' module to measure elapsed time of the query. The prefetchrows and arraysize values are 100. This causes batches of 100 records at a time to be returned from the database to a cache in Python. These values can be tuned to reduce the number of "round-trips" made to the database, often reducing network load and reducing the number of context switches on the database server. The fetchone(), fetchmany() and fetchall() methods will read from the cache before requesting more data from the database.

In a terminal window, run:
```
            python query_arraysize.py          
```
Rerun a few times to see the average times.

Experiment with different prefetchrows and arraysize values. For example, edit query_arraysize.py and change the arraysize to:
```
cur.arraysize =            2000          
```
Rerun the script to compare the performance of different arraysize settings.

In general, larger array sizes improve performance. Depending on how fast your system is, you may need to use different values than those given here to see a meaningful time difference.

There is a time/space tradeoff for increasing the values. Larger values will require more memory in Python for buffering the records.

If you know the query returns a fixed number of rows, for example 20 rows, then set arraysize to 20 and prefetchrows to 21. The addition of one for prefetchrows prevents a round-trip to check for end-of-fetch. The statement execution and fetch will take a total of one round-trip. This minimizes load on the database.

If you know a query only returns a few records, decrease the arraysize from the default to reduce memory usage.

4. Binding Data

Bind variables enable you to re-execute statements with new data values without the overhead of re-parsing the statement. Binding improves code reusability, improves application scalability, and can reduce the risk of SQL injection attacks. Using bind variables is strongly recommended. Documentation link for further reading: Using Bind Variables.

4.1 Binding in queries

Review the code contained in bind_query.py:
```
import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor()  sql = "select * from dept where deptno = :id order by deptno"  cur.execute(sql, id = 20) res = cur.fetchall() print(res)  cur.execute(sql, id = 10) res = cur.fetchall() print(res)          
```
The statement contains a bind variable ":id" placeholder. The statement is executed twice with different values for the WHERE clause.

From a terminal window, run:
```
            python bind_query.py          
```
The output shows the details for the two departments.

An arbitrary number of named arguments can be used in an execute() call. Each argument name must match a bind variable name. Alternatively, instead of passing multiple arguments you could pass a second argument to execute() that is a sequence or a dictionary. Later examples show these syntaxes.

To bind a database NULL, use the Python value None

cx_Oracle uses Oracle Database's Statement Cache. As long as the statement you pass to execute() is in that cache, you can use different bind values and still avoid a full statement parse. The statement cache size is configurable for each connection. To see the default statement cache size, edit bind_query.py and add a line at the end:
```
print(con.stmtcachesize)          
```
Re-run the file.

In your applications you would set the statement cache size to the number of unique statements commonly executed.

4.2 Binding in inserts

Review the code in bind_insert.sql creating a table for inserting data:

create table mytab (id number, data varchar2(20), constraint my_pk primary key (id));

Run the script as:

            sqlplus /nolog @bind_insert.sql

Review the code contained in bind_insert.py:

import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor()  rows = [ (1, "First" ), (2, "Second" ),          (3, "Third" ), (4, "Fourth" ),          (5, "Fifth" ), (6, "Sixth" ),          (7, "Seventh" ) ]  cur.executemany("insert into mytab(id, data) values (:1, :2)", rows)  # Now query the results back  cur2 = con.cursor() cur2.execute('select * from mytab') res = cur2.fetchall() print(res)

The 'rows' array contains the data to be inserted.

The executemany() call inserts all rows. This call uses "array binding", which is an efficient way to insert multiple records.

The final part of the script queries the results back and displays them as a list of tuples.

From a terminal window, run:

            python bind_insert.py

The new results are automatically rolled back at the end of the script so re-running it will always show the same number of rows in the table.

4.3 Batcherrors

The Batcherrors features allows invalid data to be identified while allowing valid data to be inserted.

Edit the data values in bind_insert.py and create a row with a duplicate key:

rows = [ (1, "First" ), (2, "Second" ),          (3, "Third" ), (4, "Fourth" ),          (5, "Fifth" ), (6, "Sixth" ),            (6, "Duplicate" ),            (7, "Seventh" ) ]

From a terminal window, run:

            python bind_insert.py

The duplicate generates the error "ORA-00001: unique constraint (PYTHONHOL.MY_PK) violated". The data is rolled back and the query returns no rows.

Edit the file again and enable batcherrors like:

import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor()  rows = [ (1, "First" ), (2, "Second" ),          (3, "Third" ), (4, "Fourth" ),          (5, "Fifth" ), (6, "Sixth" ),            (6, "Duplicate" ),            (7, "Seventh" ) ]  cur.executemany("insert into mytab(id, data) values (:1, :2)", rows, batcherrors = True)            for error in cur.getbatcherrors():     print("Error", error.message.rstrip(), "at row offset", error.offset)            # Now query the results back  cur2 = con.cursor() cur2.execute('select * from mytab') res = cur2.fetchall() print(res)

Run the file:

            python bind_insert.py

The new code shows the offending duplicate row: "ORA-00001: unique constraint (PYTHONHOL.MY_PK) violated at row offset 6". This indicates the 6th data value (counting from 0) had a problem.

The other data gets inserted and is queried back.

At the end of the script, cx_Oracle will roll back an uncommitted transaction. If you want to commit results, you can use:

con.commit()

To force cx_Oracle to roll back, use:

con.rollback()

4.4 Binding named objects

cx_Oracle can fetch and bind named object types such as Oracle's Spatial Data Objects (SDO).

In a terminal window, start SQL*Plus using the lab credentials and connection string, such as:

sqlplus -l pythonhol/welcome@localhost/orclpdb1

Use the SQL*Plus DESCRIBE command to look at the SDO definition:

desc MDSYS.SDO_GEOMETRY

It contains various attributes and methods. The top level description is:

            Name                                      Null?    Type  ----------------------------------------- -------- ----------------------------  SDO_GTYPE                                          NUMBER  SDO_SRID                                           NUMBER  SDO_POINT                                          MDSYS.SDO_POINT_TYPE  SDO_ELEM_INFO                                      MDSYS.SDO_ELEM_INFO_ARRAY  SDO_ORDINATES                                      MDSYS.SDO_ORDINATE_ARRAY

Review the code contained in bind_sdo.py:

import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor()  # Create table cur.execute("""begin                  execute immediate 'drop table testgeometry';                  exception when others then                    if sqlcode <> -942 then                      raise;                    end if;                end;""") cur.execute("""create table testgeometry (                id number(9) not null,                geometry MDSYS.SDO_GEOMETRY not null)""")  # Create and populate Oracle objects typeObj = con.gettype("MDSYS.SDO_GEOMETRY") elementInfoTypeObj = con.gettype("MDSYS.SDO_ELEM_INFO_ARRAY") ordinateTypeObj = con.gettype("MDSYS.SDO_ORDINATE_ARRAY") obj = typeObj.newobject()            obj.SDO_GTYPE = 2003 obj.SDO_ELEM_INFO = elementInfoTypeObj.newobject()            obj.SDO_ELEM_INFO.extend([1, 1003, 3]) obj.SDO_ORDINATES = ordinateTypeObj.newobject()            obj.SDO_ORDINATES.extend([1, 1, 5, 7]) print("Created object", obj)  # Add a new row print("Adding row to table...") cur.execute("insert into testgeometry values (1, :objbv)", objbv = obj) print("Row added!")  # Query the row print("Querying row just inserted...") cur.execute("select id, geometry from testgeometry"); for row in cur:     print(row)

This uses gettype() to get the database types of the SDO and its object attributes. The newobject() calls create Python representations of those objects. The python object atributes are then set. Oracle VARRAY types such as SDO_ELEM_INFO_ARRAY are set with extend().

Run the file:

            python bind_sdo.py

The new SDO is shown as an object, similar to:

(1, <cx_Oracle.Object MDSYS.SDO_GEOMETRY at 0x104a76230>)

To show the attribute values, edit the the query code section at the end of the file. Add a new method that traverses the object. The file below the existing comment "# (Change below here)") should look like:

# (Change below here)  # Define a function to dump the contents of an Oracle object def dumpobject(obj, prefix = "  "):     if obj.type.iscollection:         print(prefix, "[")         for value in obj.aslist():             if isinstance(value, cx_Oracle.Object):                 dumpobject(value, prefix + "  ")             else:                 print(prefix + "  ", repr(value))         print(prefix, "]")     else:         print(prefix, "{")         for attr in obj.type.attributes:             value = getattr(obj, attr.name)             if isinstance(value, cx_Oracle.Object):                 print(prefix + "  " + attr.name + " :")                 dumpobject(value, prefix + "    ")             else:                 print(prefix + "  " + attr.name + " :", repr(value))         print(prefix, "}")  # Query the row print("Querying row just inserted...") cur.execute("select id, geometry from testgeometry") for id, obj in cur:     print("Id: ", id)     dumpobject(obj)

Run the file again:

            python bind_sdo.py

This shows

Querying row just inserted... Id:  1    {     SDO_GTYPE : 2003     SDO_SRID : None     SDO_POINT : None     SDO_ELEM_INFO :        [          1          1003          3        ]     SDO_ORDINATES :        [          1          1          5          7        ]    }

To explore further, try setting the SDO attribute SDO_POINT, which is of type SDO_POINT_TYPE.

The gettype() and newobject() methods can also be used to bind PL/SQL Records and Collections.

Before deciding to use objects, review your performance goals because working with scalar values can be faster.

5. PL/SQL

PL/SQL is Oracle's procedural language extension to SQL. PL/SQL procedures and functions are stored and run in the database. Using PL/SQL lets all database applications reuse logic, no matter how the application accesses the database. Many data-related operations can be performed in PL/SQL faster than extracting the data into a program (for example, Python) and then processing it. Documentation link for further reading: PL/SQL Execution.

5.1 PL/SQL functions

Review plsql_func.sql which creates a PL/SQL stored function myfunc() to insert a row into a new table named ptab and return double the inserted value:
```
create table ptab (mydata varchar(20), myid number);  create or replace function myfunc(d_p in varchar2, i_p in number) return number as   begin     insert into ptab (mydata, myid) values (d_p, i_p);     return (i_p * 2);   end; /          
```
Run the script using:
```
            sqlplus /nolog @plsql_func.sql          
```
Review the code contained in plsql_func.py:
```
import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor()  res = cur.callfunc('myfunc', int, ('abc', 2)) print(res)          
```
This uses callfunc() to execute the function. The second parameter is the type of the returned value. It should be one of the types supported by cx_Oracle or one of the type constants defined by cx_Oracle (such as cx_Oracle.NUMBER). The two PL/SQL function parameters are passed as a tuple, binding them to the function parameter arguments.

From a terminal window, run:
```
            python plsql_func.py          
```
The output is a result of the PL/SQL function calculation.
5.2 PL/SQL procedures

Review plsql_proc.sql which creates a PL/SQL procedure myproc() to accept two parameters. The second parameter contains an OUT return value.
```
create or replace procedure myproc(v1_p in number, v2_p out number) as begin   v2_p := v1_p * 2; end; /          
```
Run the script with:
```
            sqlplus /nolog @plsql_proc.sql          
```
Review the code contained in plsql_proc.py:
```
import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor()  myvar = cur.var(int) cur.callproc('myproc', (123, myvar)) print(myvar.getvalue())          
```
This creates an integer variable myvar to hold the value returned by the PL/SQL OUT parameter. The input number 123 and the output variable name are bound to the procedure call parameters using a tuple.

To call the PL/SQL procedure, the callproc() method is used.

In a terminal window, run:
```
            python plsql_proc.py          
```
The getvalue() method displays the returned value.

6. Type Handlers

Type handlers enable applications to alter data that is fetched from, or sent to, the database. Documentation links for further reading: Changing Fetched Data Types with Output Type Handlers and Changing Bind Data Types using an Input Type Handler.

6.1 Basic output type handler

Output type handlers enable applications to change how data is fetched from the database. For example, numbers can be returned as strings or decimal objects. LOBs can be returned as string or bytes.

A type handler is enabled by setting the outputtypehandler attribute on either a cursor or the connection. If set on a cursor it only affects queries executed by that cursor. If set on a connection it affects all queries executed on cursors created by that connection.

Review the code contained in type_output.py:
```
import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor()  print("Standard output...") for row in cur.execute("select * from dept"):     print(row)          
```
In a terminal window, run:
```
            python type_output.py          
```
This shows the department number represented as digits like 10.

Add an output type handler to the bottom of the file:
```
            def ReturnNumbersAsStrings(cursor, name, defaultType, size, precision, scale):     if defaultType == cx_Oracle.NUMBER:         return cursor.var(str, 9, cursor.arraysize)  print("Output type handler output...") cur = con.cursor() cur.outputtypehandler = ReturnNumbersAsStrings for row in cur.execute("select * from dept"):     print(row)          
```
This type handler converts any number columns to strings with maxium size 9.

Run the script again:
```
            python type_output.py          
```
The new output shows the department numbers are now strings within quotes like '10'.

6.2 Output type handlers and variable converters

When numbers are fetched from the database, the conversion from Oracle's decimal representation to Python's binary format may need careful handling. To avoid unexpected issues, the general recommendation is to do number operations in SQL or PL/SQL, or to use the decimal module in Python.

Output type handlers can be combined with variable converters to change how data is fetched.

Review type_converter.py:

import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor()  for value, in cur.execute("select 0.1 from dual"):     print("Value:", value, "* 3 =", value * 3)

Run the file:

            python type_converter.py

The output is like:

Value: 0.1 * 3 = 0.30000000000000004

Edit the file and add a type handler that uses a Python decimal converter:

import cx_Oracle            import decimal            import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor()            def ReturnNumbersAsDecimal(cursor, name, defaultType, size, precision, scale):     if defaultType == cx_Oracle.NUMBER:         return cursor.var(str, 9, cursor.arraysize, outconverter = decimal.Decimal)  cur.outputtypehandler = ReturnNumbersAsDecimal            for value, in cur.execute("select 0.1 from dual"):     print("Value:", value, "* 3 =", value * 3)

The Python decimal.Decimal converter gets called with the string representation of the Oracle number. The output from decimal.Decimal is returned in the output tuple.

Run the file again:

            python type_converter.py

Output is like:

Value: 0.1 * 3 = 0.3

Although the code demonstrates the use of outconverter, in this particular case, the variable can be created simply by using the following code to replace the outputtypehandler function defined above:

def ReturnNumbersAsDecimal(cursor, name, defaultType, size, precision, scale):     if defaultType == cx_Oracle.NUMBER:         return cursor.var(decimal.Decimal, arraysize = cursor.arraysize)

6.3 Input type handlers

Input type handlers enable applications to change how data is bound to statements, or to enable new types to be bound directly without having to be converted individually.

Review type_input.py, which is similar to the final bind_sdo.py from section 4.4, with the addition of a new class and converter (shown in bold):

import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor()  # Create table cur.execute("""begin                  execute immediate 'drop table testgeometry';                  exception when others then                    if sqlcode <> -942 then                      raise;                    end if;                end;""") cur.execute("""create table testgeometry (                id number(9) not null,                geometry MDSYS.SDO_GEOMETRY not null)""")            # Create a Python class for an SDO class mySDO(object):      def __init__(self, gtype, elemInfo, ordinates):         self.gtype = gtype         self.elemInfo = elemInfo         self.ordinates = ordinates  # Get Oracle type information objType = con.gettype("MDSYS.SDO_GEOMETRY") elementInfoTypeObj = con.gettype("MDSYS.SDO_ELEM_INFO_ARRAY") ordinateTypeObj = con.gettype("MDSYS.SDO_ORDINATE_ARRAY")  # Convert a Python object to MDSYS.SDO_GEOMETRY def SDOInConverter(value):     obj = objType.newobject()     obj.SDO_GTYPE = value.gtype     obj.SDO_ELEM_INFO = elementInfoTypeObj.newobject()     obj.SDO_ELEM_INFO.extend(value.elemInfo)     obj.SDO_ORDINATES = ordinateTypeObj.newobject()     obj.SDO_ORDINATES.extend(value.ordinates)     return obj  def SDOInputTypeHandler(cursor, value, numElements):     if isinstance(value, mySDO):         return cursor.var(cx_Oracle.OBJECT, arraysize = numElements,                 inconverter = SDOInConverter, typename = objType.name)  sdo = mySDO(2003, [1, 1003, 3], [1, 1, 5, 7])  # Python object cur.inputtypehandler = SDOInputTypeHandler cur.execute("insert into testgeometry values (:1, :2)", (1, sdo))            # Define a function to dump the contents of an Oracle object def dumpobject(obj, prefix = "  "):     if obj.type.iscollection:         print(prefix, "[")         for value in obj.aslist():             if isinstance(value, cx_Oracle.Object):                 dumpobject(value, prefix + "  ")             else:                 print(prefix + "  ", repr(value))         print(prefix, "]")     else:         print(prefix, "{")         for attr in obj.type.attributes:             value = getattr(obj, attr.name)             if isinstance(value, cx_Oracle.Object):                 print(prefix + "  " + attr.name + " :")                 dumpobject(value, prefix + "    ")             else:                 print(prefix + "  " + attr.name + " :", repr(value))         print(prefix, "}")  # Query the row print("Querying row just inserted...") cur.execute("select id, geometry from testgeometry") for (id, obj) in cur:     print("Id: ", id)     dumpobject(obj)

In the new file, a Python class mySDO is defined, which has attributes corresponding to each Oracle MDSYS.SDO_GEOMETRY attribute. The mySDO class is used lower in the code to create a Python instance:

sdo = mySDO(2003, [1, 1003, 3], [1, 1, 5, 7])

which is then directly bound into the INSERT statement like:

cur.execute("insert into testgeometry values (:1, :2)", (1, sdo))

The mapping between Python and Oracle objects is handled in SDOInConverter which uses the cx_Oracle newobject() and extend() methods to create an Oracle object from the Python object values. The SDOInConverter method is called by the input type handler SDOInputTypeHandler whenever an instance of mySDO is inserted with the cursor.

To confirm the behavior, run the file:

            python type_input.py

7. LOBs

Oracle Database "LOB" long objects can be streamed using a LOB locator, or worked with directly as strings or bytes. Documentation link for further reading: Using CLOB and BLOB Data.

7.1 Fetching a CLOB using a locator

Review the code contained in clob.py:

import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor()  print("Inserting data...") cur.execute("truncate table testclobs") longString = "" for i in range(5):     char = chr(ord('A') + i)     longString += char * 250     cur.execute("insert into testclobs values (:1, :2)",                    (i + 1, "String data " + longString + ' End of string')) con.commit()  print("Querying data...") cur.execute("select * from testclobs where id = :id", {'id': 1}) (id, clob) = cur.fetchone() print("CLOB length:", clob.size()) clobdata = clob.read() print("CLOB data:", clobdata)

This inserts some test string data and then fetches one record into clob, which is a cx_Oracle character LOB Object. Methods on LOB include size() and read().

To see the output, run the file:

            python clob.py

Edit the file and experiment reading chunks of data by giving start character position and length, such as clob.read(1,10)

7.2 Fetching a CLOB as a string

For CLOBs small enough to fit in the application memory, it is much faster to fetch them directly as strings.

Review the code contained in clob_string.py. The differences from clob.py are shown in bold:

import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor()  print("Inserting data...") cur.execute("truncate table testclobs") longString = "" for i in range(5):     char = chr(ord('A') + i)     longString += char * 250     cur.execute("insert into testclobs values (:1, :2)",                 (i + 1, "String data " + longString + ' End of string')) con.commit()            def OutputTypeHandler(cursor, name, defaultType, size, precision, scale):     if defaultType == cx_Oracle.CLOB:         return cursor.var(cx_Oracle.LONG_STRING, arraysize = cursor.arraysize)  con.outputtypehandler = OutputTypeHandler            print("Querying data...") cur.execute("select * from testclobs where id = :id", {'id': 1})            (id, clobdata) = cur.fetchone() print("CLOB length:", len(clobdata)) print("CLOB data:", clobdata)

The OutputTypeHandler causes cx_Oracle to fetch the CLOB as a string. Standard Python string functions such as len() can be used on the result.

The output is the same as for clob.py. To check, run the file:

            python clob_string.py

8. Rowfactory functions

Rowfactory functions enable queries to return objects other than tuples. They can be used to provide names for the various columns or to return custom objects.

8.1 Rowfactory for mapping column names

Review the code contained in rowfactory.py:

import collections import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor()  cur.execute("select deptno, dname from dept") rows = cur.fetchall()  print('Array indexes:') for row in rows:     print(row[0], "->", row[1])  print('Loop target variables:') for c1, c2 in rows:     print(c1, "->", c2)

This shows two methods of accessing result set items from a data row. The first uses array indexes like row[0]. The second uses loop target variables which take the values of each row tuple.

Run the file:

            python rowfactory.py

Both access methods gives the same results.

To use a rowfactory function, edit rowfactory.py and add this code at the bottom:

            print('Rowfactory:') cur.execute("select deptno, dname from dept") cur.rowfactory = collections.namedtuple("MyClass", ["DeptNumber", "DeptName"])  rows = cur.fetchall() for row in rows:     print(row.DeptNumber, "->", row.DeptName)

This uses the Python factory function namedtuple() to create a subclass of tuple that allows access to the elements via indexes or the given field names.

The print() function shows the use of the new named tuple fields. This coding style can help reduce coding errors.

Run the script again:

            python rowfactory.py

The output results are the same.

9. Subclassing connections and cursors

Subclassing enables application to "hook" connection and cursor creation. This can be used to alter or log connection and execution parameters, and to extend cx_Oracle functionality. Documentation link for further reading: Tracing SQL and PL/SQL Statements.

9.1 Subclassing connections

Review the code contained in subclass.py:
```
import cx_Oracle import db_config  class MyConnection(cx_Oracle.Connection):      def __init__(self):         print("Connecting to database")         return super(MyConnection, self).__init__(db_config.user, db_config.pw, db_config.dsn)  con = MyConnection() cur = con.cursor()  cur.execute("select count(*) from emp where deptno = :bv", (10,)) count, = cur.fetchone() print("Number of rows:", count)          
```
This creates a new class "MyConnection" that inherits from the cx_Oracle Connection class. The __init__ method is invoked when an instance of the new class is created. It prints a message and calls the base class, passing the connection credentials.

In the "normal" application, the application code:
```
con = MyConnection()
```
does not need to supply any credentials, as they are embedded in the custom subclass. All the cx_Oracle methods such as cursor() are available, as shown by the query.

Run the file:
```
            python subclass.py          
```
The query executes successfully.

9.2 Subclassing cursors

Edit subclass.py and extend the cursor() method with a new MyCursor class:

import cx_Oracle import db_config  class MyConnection(cx_Oracle.Connection):      def __init__(self):         print("Connecting to database")         return super(MyConnection, self).__init__(db_config.user, db_config.pw, db_config.dsn)                          def cursor(self):         return MyCursor(self)  class MyCursor(cx_Oracle.Cursor):     def execute(self, statement, args):        print("Executing:", statement)        print("Arguments:")        for argIndex, arg in enumerate(args):            print("  Bind", argIndex + 1, "has value", repr(arg))            return super(MyCursor, self).execute(statement, args)     def fetchone(self):        print("Fetchone()")        return super(MyCursor, self).fetchone()            con = MyConnection() cur = con.cursor()  cur.execute("select count(*) from emp where deptno = :bv", (10,)) count, = cur.fetchone() print("Number of rows:", count)

When the application gets a cursor from the MyConnection class, the new cursor() method returns an instance of our new MyCursor class.

The "application" query code remains unchanged. The new execute() and fetchone() methods of the MyCursor class get invoked. They do some logging and invoke the parent methods to do the actual statement execution.

To confirm this, run the file again:

            python subclass.py

10. Advanced Queuing

Oracle Advanced Queuing (AQ) allows messages to be passed between applications. Documentation link for further reading: Oracle Advanced Queuing (AQ).

10.1 Message passing with Oracle Advanced Queuing

Review aq.py:

import cx_Oracle import decimal import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn) cur = con.cursor()  BOOK_TYPE_NAME = "UDT_BOOK" QUEUE_NAME = "BOOKS" QUEUE_TABLE_NAME = "BOOK_QUEUE_TABLE"  # Cleanup cur.execute(     """begin          dbms_aqadm.stop_queue('""" + QUEUE_NAME + """');          dbms_aqadm.drop_queue('""" + QUEUE_NAME + """');          dbms_aqadm.drop_queue_table('""" + QUEUE_TABLE_NAME + """');          execute immediate 'drop type """ + BOOK_TYPE_NAME + """';          exception when others then            if sqlcode <> -24010 then              raise;            end if;        end;""")  # Create a type print("Creating books type UDT_BOOK...") cur.execute("""         create type %s as object (             title varchar2(100),             authors varchar2(100),             price number(5,2)         );""" % BOOK_TYPE_NAME)  # Create queue table and queue and start the queue print("Creating queue table...") cur.callproc("dbms_aqadm.create_queue_table",         (QUEUE_TABLE_NAME, BOOK_TYPE_NAME)) cur.callproc("dbms_aqadm.create_queue", (QUEUE_NAME, QUEUE_TABLE_NAME)) cur.callproc("dbms_aqadm.start_queue", (QUEUE_NAME,))  booksType = con.gettype(BOOK_TYPE_NAME) queue = con.queue(QUEUE_NAME, booksType)  # Enqueue a few messages print("Enqueuing messages...")  BOOK_DATA = [     ("The Fellowship of the Ring", "Tolkien, J.R.R.", decimal.Decimal("10.99")),     ("Harry Potter and the Philosopher's Stone", "Rowling, J.K.",             decimal.Decimal("7.99")) ]  for title, authors, price in BOOK_DATA:     book = booksType.newobject()     book.TITLE = title     book.AUTHORS = authors     book.PRICE = price     print(title)     queue.enqOne(con.msgproperties(payload=book))     con.commit()  # Dequeue the messages print("\nDequeuing messages...") queue.deqOptions.wait = cx_Oracle.DEQ_NO_WAIT while True:     props = queue.deqOne()     if not props:         break     print(props.payload.TITLE)     con.commit()  print("\nDone.")

This file sets up Advanced Queuing using Oracle's DBMS_AQADM package. The queue is used for passing Oracle UDT_BOOK objects. The file uses AQ interface features enhanced in cx_Oracle 7.2.

Run the file:

            python aq.py

The output shows messages being queued and dequeued.

To experiment, split the code into three files: one to create and start the queue, and two other files to queue and dequeue messages. Experiment running the queue and dequeue files concurrently in separate terminal windows.

Try removing the commit() call in aq-dequeue.py. Now run aq-enqueue.py once and then aq-dequeue.py several times. The same messages will be available each time you try to dequeue them.

Change aq-dequeue.py to commit in a separate transaction by changing the "visibility" setting:

queue.deqOptions.visibility = cx_Oracle.DEQ_IMMEDIATE

This gives the same behavior as the original code.

Now change the options of enqueued messages so that they expire from the queue if they have not been dequeued after four seconds:

queue.enqOne(con.msgproperties(payload=book, expiration=4))

Now run aq-enqueue.py and wait four seconds before you run aq-dequeue.py. There should be no messages to dequeue.

If you are stuck, look in the solutions directory at the aq-dequeue.py, aq-enqueue.py and aq-queuestart.py files.

11. Simple Oracle Document Access (SODA)

Simple Oracle Document Access (SODA) is a set of NoSQL-style APIs. Documents can be inserted, queried, and retrieved from Oracle Database. By default, documents are JSON strings. SODA APIs exist in many languages. Documentation link for further reading: Simple Oracle Document Access (SODA).

11.1 Inserting JSON Documents

Review soda.py:

import cx_Oracle import db_config  con = cx_Oracle.connect(db_config.user, db_config.pw, db_config.dsn)  soda = con.getSodaDatabase()  # Explicit metadata is used for maximum version portability metadata = {                "keyColumn": {                    "name":"ID"                },                "contentColumn": {                    "name": "JSON_DOCUMENT",                    "sqlType": "BLOB"                },                "versionColumn": {                    "name": "VERSION",                    "method": "UUID"                },                "lastModifiedColumn": {                    "name": "LAST_MODIFIED"                },                "creationTimeColumn": {                    "name": "CREATED_ON"                }            }  collection = soda.createCollection("friends", metadata)  content = {'name': 'Jared', 'age': 35, 'address': {'city': 'Melbourne'}}  doc = collection.insertOneAndGet(content) key = doc.key  doc = collection.find().key(key).getOne() content = doc.getContent() print('Retrieved SODA document dictionary is:') print(content)

soda.createCollection() will create a new collection, or open an existing collection, if the name is already in use. (Due to a change in the default "sqlType" storage for Oracle Database 21c, the metadata is explicitly stated to use a BLOB column. This lets the example run with different client and database versions).

insertOneAndGet() inserts the content of a document into the database and returns a SODA Document Object. This allows access to meta data such as the document key. By default, document keys are automatically generated.

The find() method is used to begin an operation that will act upon documents in the collection.

content is a dictionary. You can also get a JSON string by calling doc.getContentAsString().

Run the file:

            python soda.py

The output shows the content of the new document.

11.2 Searching SODA Documents

Extend soda.py to insert some more documents and perform a find filter operation:

myDocs = [     {'name': 'Gerald', 'age': 21, 'address': {'city': 'London'}},     {'name': 'David', 'age': 28, 'address': {'city': 'Melbourne'}},     {'name': 'Shawn', 'age': 20, 'address': {'city': 'San Francisco'}} ] collection.insertMany(myDocs)  filterSpec = { "address.city": "Melbourne" } myDocuments = collection.find().filter(filterSpec).getDocuments()  print('Melbourne people:') for doc in myDocuments:     print(doc.getContent()["name"])

Run the script again:

            python soda.py

The find operation filters the collection and returns documents where the city is Melbourne. Note the insertMany() method is currently in preview.

SODA supports query by example (QBE) with an extensive set of operators. Extend soda.py with a QBE to find documents where the age is less than 25:

filterSpec = {'age': {'$lt': 25}} myDocuments = collection.find().filter(filterSpec).getDocuments()  print('Young people:') for doc in myDocuments:     print(doc.getContent()["name"])

Running the script displays the names.

Summary

In this tutorial, you have learned how to:

Create connections
Use cx_Oracle connection pooling and Database Resident Connection Pooling
Execute queries and fetch data
Use bind variables
Use PL/SQL stored functions and procedures
Extend cx_Oracle classes
Use Oracle Advanced Queuing
Use the "SODA" document store API

For further reading see the cx_Oracle documentation.

Appendix: Python Primer

Python is a dynamically typed scripting language. It is most often used to run command-line scripts but is also used for web applications and web services.

Running Python

You can either:

Create a file of Python commands, such as myfile.py. This can be run with:
```
            python myfile.py          
```
Alternatively run the Python interpreter by executing the python command in a terminal, and then interactively enter commands. Use Ctrl-D to exit back to the operating system prompt.

When you run scripts, Python automatically creates bytecode versions of them in a folder called __pycache__. These improve performance of scripts that are run multiple times. They are automatically recreated if the source file changes.

Indentation

Whitespace indentation is significant in Python. When copying examples, use the same column alignment as shown. The samples in this lab use spaces, not tabs.

The following indentation prints 'done' once after the loop has completed:

for i in range(5):     print(i) print('done')

But this indentation prints 'done' in each iteration:

for i in range(5):     print(i)     print('done')

Strings

Python strings can be enclosed in single or double quotes:

'A string constant' "another constant"

Multi line strings use a triple-quote syntax:

""" SELECT * FROM EMP """

Variables

Variables do not need types declared:

count = 1 ename = 'Arnie'

Comments

Comments are either single line:

# a short comment

They can be multi-line using the triple-quote token to create a string that does nothing:

""" a longer comment """

Printing

Strings and variables can be displayed with a print() function:

print('Hello, World!') print('Value:', count)

Data Structures

Associative arrays are called 'dictionaries':

a2 = {'PI':3.1415, 'E':2.7182}

Ordered arrays are called 'lists':

a3 = [101, 4, 67]

Lists can be accessed via indexes.

print(a3[0]) print(a3[-1]) print(a3[1:3])

Tuples are like lists but cannot be changed once they are created. They are created with parentheses:

a4 = (3, 7, 10)

Individual values in a tuple can be assigned to variables like:

v1, v2, v3 = a4

Now the variable v1 contains 3, the variable v2 contains 7 and the variable v3 contains 10.

The value in a single entry tuple like "(13,)"can be assigned to a variable by putting a comma after the variable name like:

v1, = (13,)

If the assignment is:

v1 = (13,)

then v1 will contain the whole tuple "(13,)"

Objects

Everything in Python is an object. As an example, given the of the list a3 above, the append() method can be used to add a value to the list.

a3.append(23)

Now a3 contains [101, 4, 67, 23]

Flow Control

Code flow can be controlled with tests and loops. The if/elif/else statements look like:

if v == 2 or v == 4:     print('Even') elif v == 1 or v == 3:     print('Odd') else:     print('Unknown number')

This also shows how the clauses are delimited with colons, and each sub block of code is indented.

Loops

A traditional loop is:

for i in range(10):     print(i)

This prints the numbers from 0 to 9. The value of i is incremented in each iteration.

The 'for' command can also be used to iterate over lists and tuples:

a5 = ['Aa', 'Bb', 'Cc'] for v in a5:     print(v)

This sets v to each element of the list a5 in turn.

Functions

A function may be defined as:

def myfunc(p1, p2):     "Function documentation: add two numbers"     print(p1, p2)     return p1 + p2

Functions may or may not return values. This function could be called using:

v3 = myfunc(1, 3)

Function calls must appear after their function definition.

Functions are also objects and have attributes. The inbuilt __doc__ attribute can be used to find the function description:

print(myfunc.__doc__)

Modules

Sub-files can be included in Python scripts with an import statement.

import os import sys

Many predefined modules exist, such as the os and the sys modules.

Resources

Python Documentation
Python cx_Oracle Documentation
Python cx_Oracle Source Code Repository

Copyright © 2017, 2021, Oracle and/or its affiliates. All rights reserved

Posted by: sharynsharynrummagee0267951.blogspot.com

Source: https://oracle.github.io/python-cx_Oracle/samples/tutorial/Python-and-Oracle-Database-Scripting-for-the-Future.html

Oracle 12c Tutorial Pdf Free Download

Python and Oracle Database Tutorial: Scripting for the Future

Contents

Overview

cx_Oracle Architecture

Setup

Install software

Download the tutorial scripts

Create a database user

Install the sample tables

Start the Database Resident Connection Pool (DRCP)

Review the connection credentials used by the tutorial scripts

Review the Instant Client library path

1. Connecting to Oracle

1.1 Creating a basic connection

1.2 Indentation indicates code structure

1.3 Executing a query

1.4 Closing connections

1.5 Checking versions

2. Connection Pooling

2.1 Connection pooling

2.2 Connection pool experiments

2.3 Creating a DRCP Connection

2.4 Connection pooling and DRCP

2.5 More DRCP investigation

3. Fetching Data

3.1 A simple query

3.2 Using fetchone()

3.3 Using fetchmany()

3.4 Scrollable cursors

3.5 Tuning with arraysize and prefetchrows

4. Binding Data

4.1 Binding in queries

4.2 Binding in inserts

4.3 Batcherrors

4.4 Binding named objects

5. PL/SQL

5.1 PL/SQL functions

5.2 PL/SQL procedures

6. Type Handlers

6.1 Basic output type handler

6.2 Output type handlers and variable converters

6.3 Input type handlers

7. LOBs

7.1 Fetching a CLOB using a locator

7.2 Fetching a CLOB as a string

8. Rowfactory functions

8.1 Rowfactory for mapping column names

9. Subclassing connections and cursors

9.1 Subclassing connections

9.2 Subclassing cursors

10. Advanced Queuing

10.1 Message passing with Oracle Advanced Queuing

11. Simple Oracle Document Access (SODA)

11.1 Inserting JSON Documents

11.2 Searching SODA Documents

Summary

Appendix: Python Primer

Running Python

Indentation

Strings

Variables

Comments

Printing

Data Structures

Objects

Flow Control

Loops

Functions

Modules

Resources

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