# 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

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 `numRows` 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(num_rows)
        if not rows:
            break
        for row in rows:
            print(row)

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

A cursor may be used to execute multiple statements. Once it is no longer needed, it should be closed by calling `close()` in order to reclaim resources in the database. It will be closed automatically when the variable referencing it goes out of scope (and no further references are retained). One other way to control the lifetime of a cursor is to use a “with” block, which ensures that a cursor is closed once the block is completed. 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 resources have been reclaimed by the database. In addition, any attempt to use the variable `cursor` outside of the block will simply fail.

### 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])