Once Oracle, Python, cxOracle package and Instant Client are ready, we can connect to Oracle from Python. The following program will connect to Oracle Database using username hr and password hr. In case you are trying to use a different account or a different version of Oracle database then feel free to change the details and experiment. First of all, it just seems like doing anything with Oracle is obnoxiously painful for no good reason. It's the nature of the beast I suppose. Cxoracle is a python module that allows you to connect to an Oracle Database and issue queries, inserts, updates.usual jazz. CxOracle is a Python extension module that enables access to Oracle Database. It conforms to the Python database API 2.0 specification with a considerable number of additions and a couple of exclusions. See the homepage for a feature list. CxOracle 8.2 has been tested with Python versions 3.6 through 3.9. Python interface to Oracle Database conforming to the Python DB API 2.0 specification. oracle/python-cxOracle.

Contents

• 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

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:

  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:

  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:

• 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:

  and execute the command:

  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:

  Also change the default username and connection string in the SQL*Plusconfiguration file db_config.sql:

  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'.

  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. Documentationlink for further reading: Connecting to Oracle Database.

• 1.1 Creating a basic connection

  Review the code contained in connect.py:

  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:

  Run the Python script:

  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:

  Save the script and run it again:

  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:

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

  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:

  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:

  Running the script completes without error:

  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:

  Run the script:

  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:

  When the script is run, it will display:

  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:

  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:

  The output shows interleaved query results as each thread fetchesvalues independently. The order of interleaving may vary from run torun.

• 2.2 Connection pool experiments

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

  In a command terminal, run:

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

  Try changing getmode tocx_Oracle.SPOOL_ATTRVAL_NOWAIT. When numberOfThreadsexceeds the maximum size of the pool, the acquire() call will nowgenerate an error such as 'ORA-24459: OCISessionGet() timed out waiting for poolto create new connections'.

  Pool configurations where min is the same asmax (and increment = 0) are oftenrecommended as a best practice. This avoids connection storms on thedatabase 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.

  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:

  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.

  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:

  The script logic does not need to be changed to benefit from DRCP connection pooling.

  Run the script:

  Review drcp_query.sql and set the connection string to your database. Then open a new a terminal window and invoke SQL*Plus:

  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:

  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:

  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:

  The output is:

• 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:

  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:

  The first two rows of the table are printed.

• 3.3 Using fetchmany()

  Review the code contained in query_many.py:

  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:

  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:

• 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:

  Run the script in a terminal window:

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

  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:

  In a terminal window run the script as:

  Review the code contained in query_arraysize.py:

  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:

  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:

  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:

  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:

  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:

  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:

  Run the script as:

  Review the code contained in bind_insert.py:

  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:

  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:

  From a terminal window, run:

  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:

  Run the file:

  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:

  To force cx_Oracle to roll back, use:

• 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:

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

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

  Review the code contained in bind_sdo.py:

  This uses gettype() to get the database types of theSDO and its object attributes. The newobject() callscreate Python representations of those objects. The python objectatributes are then set. Oracle VARRAY types such asSDO_ELEM_INFO_ARRAY are set with extend().

  Run the file:

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

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

  Run the file again:

  This shows

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

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

  Before deciding to use objects, review your performance goals becauseworking 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. Manyhttps:>.

• 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:

  In a terminal window, run:

  This shows the department number represented as digits like 10.

  Add an output type handler to the bottom of the file:

  This type handler converts any number columns to strings with maxium size 9.

  Run the script again:

  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:

  Run the file:

  The output is like:

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

  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:

  Output is like:

  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:

• 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):

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

  which is then directly bound into the INSERT statement like:

  The mapping between Python and Oracle objects is handled inSDOInConverter which uses the cx_Oraclenewobject() and extend() methods to createan Oracle object from the Python object values. TheSDOInConverter method is called by the input type handlerSDOInputTypeHandler whenever an instance ofmySDO is inserted with the cursor.

  To confirm the behavior, run the file:

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:

  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:

  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:

  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:

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:

  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:

  Both access methods gives the same results.

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

  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:

  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:

  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:

  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:

  The query executes successfully.

• 9.2 Subclassing cursors

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

  When the application gets a cursor from theMyConnection class, the new cursor() methodreturns an instance of our new MyCursor class.

  The 'application' query code remains unchanged. The newexecute() and fetchone() methods of theMyCursor class get invoked. They do some logging andinvoke the parent methods to do the actual statement execution.

  To confirm this, run the file again:

10. Advanced Queuing

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

• 10.1 Message passing with Oracle Advanced Queuing

  Review aq.py:

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

  Run the file:

  The output shows messages being queued and dequeued.

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

  Try removing the commit() call inaq-dequeue.py. Now run aq-enqueue.py onceand then aq-dequeue.py several times. The same messageswill be available each time you try to dequeue them.

  Change aq-dequeue.py to commit in a separatetransaction by changing the 'visibility' setting:

  This gives the same behavior as the original code.

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

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

  If you are stuck, look in the solutions directory atthe aq-dequeue.py, aq-enqueue.py andaq-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:

  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:

  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:

  Run the script again:

  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:

  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:

• 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:

But this indentation prints 'done' in each iteration:

Strings

Python strings can be enclosed in single or double quotes:

Multi line strings use a triple-quote syntax:

Variables

Variables do not need types declared:

Comments

Comments are either single line:

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

Printing

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

Data Structures

Associative arrays are called 'dictionaries':

Ordered arrays are called 'lists':

Lists can be accessed via indexes.

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

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

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:

Cx Oracle Python Install

If the assignment is:

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

Objects

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

Cx_oracle Python Documentation

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

Flow Control

Code flow can be controlled with tests and loops. Theif/elif/else statements looklike:

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

Loops

A traditional loop is:

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:

This sets v to each element of the lista5 in turn.

Functions

A function may be defined as:

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

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 functiondescription:

Modules

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

Cx_oracle Python Insert Example

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

Resources