5. Executing SQL

Executing SQL statements is the primary way in which a Python application communicates with Oracle Database. Statements are executed using the methods Cursor.execute() or Cursor.executemany(). Statements include queries, Data Manipulation Language (DML), and Data Definition Language (DDL). A few other specialty statements can also be executed.

PL/SQL statements are discussed in Executing PL/SQL. Other chapters contain information on specific data types and features. See Executing Batch Statements and Bulk Loading, Using CLOB and BLOB Data, Using JSON Data, and Using XMLTYPE Data.

Python-oracledb can be used to execute individual statements, one at a time. It does not read SQL*Plus “.sql” files. To read SQL files, use a technique like the one in run_sql_script() in samples/sample_env.py

SQL statements should not contain a trailing semicolon (“;”) or forward slash (“/”). This will fail:

cur.execute("select * from MyTable;")

This is correct:

cur.execute("select * from MyTable")

5.1. SQL Queries

Queries (statements beginning with SELECT or WITH) can only be executed using the method Cursor.execute(). Rows can then be iterated over, or can be fetched using one of the methods Cursor.fetchone(), Cursor.fetchmany() or Cursor.fetchall(). There is a default type mapping to Python types that can be optionally overridden.

Important

Interpolating or concatenating user data with SQL statements, for example cur.execute("SELECT * FROM mytab WHERE mycol = '" + myvar + "'"), is a security risk and impacts performance. Use bind variables instead. For example, cur.execute("SELECT * FROM mytab WHERE mycol = :mybv", mybv=myvar).

5.1.1. Fetch Methods

Rows can be fetched in various ways.

• After Cursor.execute(), the cursor is returned as a convenience. This allows code to iterate over rows like:

  cur = connection.cursor()
  for row in cur.execute("select * from MyTable"):
      print(row)
  

• Rows can also be fetched one at a time using the method Cursor.fetchone():

  cur = connection.cursor()
  cur.execute("select * from MyTable")
  while True:
      row = cur.fetchone()
      if row is None:
          break
      print(row)
  

• If rows need to be processed in batches, the method Cursor.fetchmany() can be used. The size of the batch is controlled by the size parameter, which defaults to the value of Cursor.arraysize.

  cur = connection.cursor()
  cur.execute("select * from MyTable")
  num_rows = 10
  while True:
      rows = cur.fetchmany(size=num_rows)
      if not rows:
          break
      for row in rows:
          print(row)
  

  Note the size parameter only affects the number of rows returned to the application, not to the internal buffer size used for tuning fetch performance. That internal buffer size is controlled only by changing Cursor.arraysize, see Tuning Fetch Performance.

• If all of the rows need to be fetched and can be contained in memory, the method Cursor.fetchall() can be used.

  cur = connection.cursor()
  cur.execute("select * from MyTable")
  rows = cur.fetchall()
  for row in rows:
      print(row)
  

  The fetch methods return data as tuples. To return results as dictionaries, see Changing Query Results with Rowfactories.

5.1.2. Closing Cursors

Once cursors are no longer needed, they should be closed in order to reclaim resources in the database. Note cursors may be used to execute multiple statements.

Cursors can be closed in various ways:

• A cursor will be closed automatically when the variable referencing it goes out of scope (and no further references are retained). A with block is a convenient way to ensure this. For example:

  with connection.cursor() as cursor:
      for row in cursor.execute("select * from MyTable"):
          print(row)
  

  This code ensures that once the block is completed, the cursor is closed and database resources can be reclaimed. In addition, any attempt to use the variable cursor outside of the block will fail.

• Cursors can be explicitly closed by calling close()

  cursor = connection.cursor()
  
  ...
  
  cursor.close()
  

5.1.3. Query Column Metadata

After executing a query, the column metadata such as column names and data types can be obtained using Cursor.description:

cur = connection.cursor()
cur.execute("select * from MyTable")
for column in cur.description:
    print(column)

This could result in metadata like:

('ID', <class 'oracledb.DB_TYPE_NUMBER'>, 39, None, 38, 0, 0)
('NAME', <class 'oracledb.DB_TYPE_VARCHAR'>, 20, 20, None, None, 1)

5.1.4. Fetch Data Types

The following table provides a list of all of the data types that python-oracledb knows how to fetch. The middle column gives the type that is returned in the query metadata. The last column gives the type of Python object that is returned by default. Python types can be changed with Output Type Handlers.

Oracle Database Type	oracledb Database Type	Default Python type
BFILE	oracledb.DB_TYPE_BFILE	oracledb.LOB
BINARY_DOUBLE	oracledb.DB_TYPE_BINARY_DOUBLE	float
BINARY_FLOAT	oracledb.DB_TYPE_BINARY_FLOAT	float
BLOB	oracledb.DB_TYPE_BLOB	oracledb.LOB
CHAR	oracledb.DB_TYPE_CHAR	str
CLOB	oracledb.DB_TYPE_CLOB	oracledb.LOB
CURSOR	oracledb.DB_TYPE_CURSOR	oracledb.Cursor
DATE	oracledb.DB_TYPE_DATE	datetime.datetime
INTERVAL DAY TO SECOND	oracledb.DB_TYPE_INTERVAL_DS	datetime.timedelta
JSON	oracledb.DB_TYPE_JSON	dict, list or a scalar value [4]
LONG	oracledb.DB_TYPE_LONG	str
LONG RAW	oracledb.DB_TYPE_LONG_RAW	bytes
NCHAR	oracledb.DB_TYPE_NCHAR	str
NCLOB	oracledb.DB_TYPE_NCLOB	oracledb.LOB
NUMBER	oracledb.DB_TYPE_NUMBER	float or int [1]
NVARCHAR2	oracledb.DB_TYPE_NVARCHAR	str
OBJECT [3]	oracledb.DB_TYPE_OBJECT	oracledb.Object
RAW	oracledb.DB_TYPE_RAW	bytes
ROWID	oracledb.DB_TYPE_ROWID	str
TIMESTAMP	oracledb.DB_TYPE_TIMESTAMP	datetime.datetime
TIMESTAMP WITH LOCAL TIME ZONE	oracledb.DB_TYPE_TIMESTAMP_LTZ	datetime.datetime [2]
TIMESTAMP WITH TIME ZONE	oracledb.DB_TYPE_TIMESTAMP_TZ	datetime.datetime [2]
UROWID	oracledb.DB_TYPE_ROWID, oracledb.DB_TYPE_UROWID	str
VARCHAR2	oracledb.DB_TYPE_VARCHAR	str

[1]

If the precision and scale obtained from query column metadata indicate that the value can be expressed as an integer, the value will be returned as an int. If the column is unconstrained (no precision and scale specified), the value will be returned as a float or an int depending on whether the value itself is an integer. In all other cases the value is returned as a float.

[2] (1,2)

The timestamps returned are naive timestamps without any time zone information present.

[3]

These include all user-defined types such as VARRAY, NESTED TABLE, etc.

[4]

If the JSON is an object, then a dict is returned. If it is an array, then a list is returned. If it is a scalar value, then that particular scalar value is returned.

5.1.5. Changing Fetched Data Types with Output Type Handlers

Sometimes the default conversion from an Oracle Database type to a Python type must be changed in order to prevent data loss or to fit the purposes of the Python application. In such cases, an output type handler can be specified for queries. Output type handlers do not affect values returned from Cursor.callfunc() or Cursor.callproc().

Output type handlers can be specified on the connection or on the cursor. If specified on the cursor, fetch type handling is only changed on that particular cursor. If specified on the connection, all cursors created by that connection will have their fetch type handling changed.

The output type handler is expected to be a function with the following signature:

handler(cursor, name, defaultType, size, precision, scale)

The parameters are the same information as the query column metadata found in Cursor.description. The function is called once for each column that is going to be fetched. The function is expected to return a variable object (generally by a call to Cursor.var()) or the value None. The value None indicates that the default type should be used.

Examples of output handlers are shown in Fetched Number Precision, Fetching LOBs as Strings and Bytes and Fetching Raw Data. Also see samples such as samples/type_handlers.py

5.1.6. Fetched Number Precision

One reason for using an output type handler is to ensure that numeric precision is not lost when fetching certain numbers. Oracle Database uses decimal numbers and these cannot be converted seamlessly to binary number representations like Python floats. In addition, the range of Oracle numbers exceeds that of floating point numbers. Python has decimal objects which do not have these limitations and python-oracledb knows how to perform the conversion between Oracle numbers and Python decimal values if directed to do so.

The following code sample demonstrates the issue:

cur = connection.cursor()
cur.execute("create table test_float (X number(5, 3))")
cur.execute("insert into test_float values (7.1)")
connection.commit()
cur.execute("select * from test_float")
val, = cur.fetchone()
print(val, "* 3 =", val * 3)

This displays 7.1 * 3 = 21.299999999999997

Using Python decimal objects, however, there is no loss of precision:

import decimal

def number_to_decimal(cursor, name, default_type, size, precision, scale):
    if default_type == oracledb.DB_TYPE_NUMBER:
        return cursor.var(decimal.Decimal, arraysize=cursor.arraysize)

cur = connection.cursor()
cur.outputtypehandler = number_to_decimal
cur.execute("select * from test_float")
val, = cur.fetchone()
print(val, "* 3 =", val * 3)

This displays 7.1 * 3 = 21.3

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.

See samples/return_numbers_as_decimals.py

5.1.7. Changing Query Results with Outconverters

Python-oracledb “outconverters” can be used with output type handlers to change returned data.

For example, to make queries return empty strings instead of NULLs:

def out_converter(value):
    if value is None:
        return ''
    return value

def output_type_handler(cursor, name, default_type, size, precision, scale):
    if default_type in (oracledb.DB_TYPE_VARCHAR, oracledb.DB_TYPE_CHAR):
        return cursor.var(str, size, arraysize=cur.arraysize,
                          outconverter=out_converter)

connection.outputtypehandler = output_type_handler

5.1.8. Changing Query Results with Rowfactories

Python-oracledb “rowfactories” are methods called for each row that is retrieved from the database. The Cursor.rowfactory() method is called with the tuple that would normally be returned from the database. The method can convert the tuple to a different value and return it to the application in place of the tuple.

For example, to fetch each row of a query as a dictionary:

cursor.execute("select * from locations where location_id = 1000")
columns = [col[0] for col in cursor.description]
cursor.rowfactory = lambda *args: dict(zip(columns, args))
data = cursor.fetchone()
print(data)

The output is:

{'LOCATION_ID': 1000, 'STREET_ADDRESS': '1297 Via Cola di Rie', 'POSTAL_CODE': '00989', 'CITY': 'Roma', 'STATE_PROVINCE': None, 'COUNTRY_ID': 'IT'}

If you join tables where the same column name occurs in both tables with different meanings or values, then use a column alias in the query. Otherwise, only one of the similarly named columns will be included in the dictionary:

select
    cat_name,
    cats.color as cat_color,
    dog_name,
    dogs.color
from cats, dogs

5.1.9. Scrollable Cursors

Scrollable cursors enable applications to move backwards, forwards, to skip rows, and to move to a particular row in a query result set. The result set is cached on the database server until the cursor is closed. In contrast, regular cursors are restricted to moving forward.

Note

Scrollable cursors are only supported in the python-oracledb Thick mode. See Enabling python-oracledb Thick mode.

A scrollable cursor is created by setting the parameter scrollable=True when creating the cursor. The method Cursor.scroll() is used to move to different locations in the result set.

Examples are:

cursor = connection.cursor(scrollable=True)
cursor.execute("select * from ChildTable order by ChildId")

cursor.scroll(mode="last")
print("LAST ROW:", cursor.fetchone())

cursor.scroll(mode="first")
print("FIRST ROW:", cursor.fetchone())

cursor.scroll(8, mode="absolute")
print("ROW 8:", cursor.fetchone())

cursor.scroll(6)
print("SKIP 6 ROWS:", cursor.fetchone())

cursor.scroll(-4)
print("SKIP BACK 4 ROWS:", cursor.fetchone())

5.1.10. Fetching Oracle Database Objects and Collections

Oracle Database named object types and user-defined types can be fetched directly in queries. Each item is represented as a Python object corresponding to the Oracle Database object. This Python object can be traversed to access its elements. Attributes including DbObjectType.name and DbObjectType.iscollection, and methods including DbObject.aslist() and DbObject.asdict() are available.

For example, if a table mygeometrytab contains a column geometry of Oracle’s predefined Spatial object type SDO_GEOMETRY, then it can be queried and printed:

cur.execute("select geometry from mygeometrytab")
for obj, in cur:
    dumpobject(obj)

Where dumpobject() is defined as:

def dumpobject(obj, prefix = ""):
    if obj.type.iscollection:
        print(prefix, "[")
        for value in obj.aslist():
            if isinstance(value, oracledb.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, oracledb.Object):
                print(prefix + "   " + attr.name + ":")
                dumpobject(value, prefix + "  ")
            else:
                print(prefix + "   " + attr.name + ":", repr(value))
        print(prefix, "}")

This might produce output like:

{
  SDO_GTYPE: 2003
  SDO_SRID: None
  SDO_POINT:
  {
    X: 1
    Y: 2
    Z: 3
  }
  SDO_ELEM_INFO:
  [
    1
    1003
    3
  ]
  SDO_ORDINATES:
  [
    1
    1
    5
    7
  ]
}

Other information on using Oracle objects is in Using Bind Variables.

Performance-sensitive applications should consider using scalar types instead of objects. If you do use objects, avoid calling Connection.gettype() unnecessarily, and avoid objects with large numbers of attributes.

5.1.11. Limiting Rows

Query data is commonly broken into one or more sets:

• To give an upper bound on the number of rows that a query has to process, which can help improve database scalability.

• To perform ‘Web pagination’ that allows moving from one set of rows to a next, or previous, set on demand.

• For fetching of all data in consecutive small sets for batch processing. This happens because the number of records is too large for Python to handle at one time.

The latter can be handled by calling Cursor.fetchmany() with one execution of the SQL query.

‘Web pagination’ and limiting the maximum number of rows are detailed in this section. For each ‘page’ of results, a SQL query is executed to get the appropriate set of rows from a table. Since the query may be executed more than once, ensure to use bind variables for row numbers and row limits.

Oracle Database 12c SQL introduced an OFFSET / FETCH clause which is similar to the LIMIT keyword of MySQL. In Python, you can fetch a set of rows using:

myoffset = 0       // do not skip any rows (start at row 1)
mymaxnumrows = 20  // get 20 rows

sql =
  """SELECT last_name
     FROM employees
     ORDER BY last_name
     OFFSET :offset ROWS FETCH NEXT :maxnumrows ROWS ONLY"""

cur = connection.cursor()
for row in cur.execute(sql, offset=myoffset, maxnumrows=mymaxnumrows):
    print(row)

In applications where the SQL query is not known in advance, this method sometimes involves appending the OFFSET clause to the ‘real’ user query. Be very careful to avoid SQL injection security issues.

For Oracle Database 11g and earlier there are several alternative ways to limit the number of rows returned. The old, canonical paging query is:

SELECT *
FROM (SELECT a.*, ROWNUM AS rnum
      FROM (YOUR_QUERY_GOES_HERE -- including the order by) a
      WHERE ROWNUM <= MAX_ROW)
WHERE rnum >= MIN_ROW

Here, MIN_ROW is the row number of first row and MAX_ROW is the row number of the last row to return. For example:

SELECT *
FROM (SELECT a.*, ROWNUM AS rnum
      FROM (SELECT last_name FROM employees ORDER BY last_name) a
      WHERE ROWNUM <= 20)
WHERE rnum >= 1

This always has an ‘extra’ column, here called RNUM.

An alternative and preferred query syntax for Oracle Database 11g uses the analytic ROW_NUMBER() function. For example, to get the 1st to 20th names the query is:

SELECT last_name FROM
(SELECT last_name,
        ROW_NUMBER() OVER (ORDER BY last_name) AS myr
        FROM employees)
WHERE myr BETWEEN 1 and 20

Ensure to use bind variables for the upper and lower limit values.

5.1.12. Client Result Cache

Python-oracledb applications can use Oracle Database’s Client Result Cache The CRC enables client-side caching of SQL query (SELECT statement) results in client memory for immediate use when the same query is re-executed. This is useful for reducing the cost of queries for small, mostly static, lookup tables, such as for postal codes. CRC reduces network round-trips, and also reduces database server CPU usage.

Note

Client Result Caching is only supported in the python-oracledb Thick mode. See Enabling python-oracledb Thick mode.

The cache is at the application process level. Access and invalidation is managed by the Oracle Client libraries. This removes the need for extra application logic, or external utilities, to implement a cache.

CRC can be enabled by setting the database parameters CLIENT_RESULT_CACHE_SIZE and CLIENT_RESULT_CACHE_LAG, and then restarting the database. For example, to set the parameters:

SQL> ALTER SYSTEM SET CLIENT_RESULT_CACHE_LAG = 3000 SCOPE=SPFILE;
SQL> ALTER SYSTEM SET CLIENT_RESULT_CACHE_SIZE = 64K SCOPE=SPFILE;

CRC can alternatively be configured in an oraaccess.xml or sqlnet.ora file on the Python host, see Client Configuration Parameters.

Tables can then be created, or altered, so repeated queries use CRC. This allows existing applications to use CRC without needing modification. For example:

SQL> CREATE TABLE cities (id number, name varchar2(40)) RESULT_CACHE (MODE FORCE);
SQL> ALTER TABLE locations RESULT_CACHE (MODE FORCE);

Alternatively, hints can be used in SQL statements. For example:

SELECT /*+ result_cache */ postal_code FROM locations

5.1.13. Fetching Raw Data

Sometimes python-oracledb may have problems converting data stored in the database to Python strings. This can occur if the data stored in the database does not match the character set defined by the database. The encoding_errors parameter to Cursor.var() permits the data to be returned with some invalid data replaced, but for additional control the parameter bypass_decode can be set to True and python-oracledb will bypass the decode step and return bytes instead of str for data stored in the database as strings. The data can then be examined and corrected as required. This approach should only be used for troubleshooting and correcting invalid data, not for general use!

The following sample demonstrates how to use this feature:

# define output type handler
def return_strings_as_bytes(cursor, name, default_type, size,
                            precision, scale):
    if default_type == oracledb.DB_TYPE_VARCHAR:
        return cursor.var(str, arraysize=cursor.arraysize,
                          bypass_decode=True)

# set output type handler on cursor before fetching data
with connection.cursor() as cursor:
    cursor.outputtypehandler = return_strings_as_bytes
    cursor.execute("select content, charset from SomeTable")
    data = cursor.fetchall()

This will produce output as:

[(b'Fianc\xc3\xa9', b'UTF-8')]

Note that last \xc3\xa9 is é in UTF-8. Since this is valid UTF-8 you can then perform a decode on the data (the part that was bypassed):

value = data[0][0].decode("UTF-8")

This will return the value “Fiancé”.

If you want to save b'Fianc\xc3\xa9' into the database directly without using a Python string, you will need to create a variable using Cursor.var() that specifies the type as DB_TYPE_VARCHAR (otherwise the value will be treated as DB_TYPE_RAW). The following sample demonstrates this:

with oracledb.connect(user="hr", password=userpwd,
                       dsn="dbhost.example.com/orclpdb") as conn:
    with conn.cursor() cursor:
        var = cursor.var(oracledb.DB_TYPE_VARCHAR)
        var.setvalue(0, b"Fianc\xc4\x9b")
        cursor.execute("""
            update SomeTable set
                SomeColumn = :param
            where id = 1""",
            param=var)

Warning

The database will assume that the bytes provided are in the character set expected by the database so only use this for troubleshooting or as directed.

5.1.14. Querying Corrupt Data

If queries fail with the error “codec can’t decode byte” when you select data, then:

• Check if your character set is correct. Review the database character sets. Check with Fetching Raw Data. Note that the encoding used for all character data in python-oracledb is “UTF-8”.

• Check for corrupt data in the database.

If data really is corrupt, you can pass options to the internal decode() used by python-oracledb to allow it to be selected and prevent the whole query failing. Do this by creating an outputtypehandler and setting encoding_errors. For example to replace corrupt characters in character columns:

def output_type_handler(cursor, name, default_type, size, precision, scale):
    if default_type == oracledb.DB_TYPE_VARCHAR:
        return cursor.var(default_type, size, arraysize=cursor.arraysize,
                          encoding_errors="replace")

cursor.outputtypehandler = output_type_handler

cursor.execute("select column1, column2 from SomeTableWithBadData")

Other codec behaviors can be chosen for encoding_errors, see Error Handlers.

5.2. INSERT and UPDATE Statements

SQL Data Manipulation Language statements (DML) such as INSERT and UPDATE can easily be executed with python-oracledb. For example:

cur = connection.cursor()
cur.execute("insert into MyTable values (:idbv, :nmbv)", [1, "Fredico"])

Do not concatenate or interpolate user data into SQL statements. See Using Bind Variables instead.

See Managing Transactions for best practices on committing and rolling back data changes.

When handling multiple data values, use executemany() for performance. See Executing Batch Statements and Bulk Loading

5.2.1. Inserting NULLs

Oracle requires a type, even for null values. When you pass the value None, then python-oracledb assumes the type is STRING. If this is not the desired type, you can explicitly set it. For example, to insert a null Oracle Spatial SDO_GEOMETRY object:

type_obj = connection.gettype("SDO_GEOMETRY")
cur = connection.cursor()
cur.setinputsizes(type_obj)
cur.execute("insert into sometable values (:1)", [None])