Cx Oracle Python Executemany Example

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Cx Oracle Python Executemany Example Free

The following are code examples for showing how to use cxOracle.DatabaseError.They are extracted from open source Python projects. You can vote up the examples you like or vote down the ones you don't like. Welcome to cxOracle’s documentation!¶ cxOracle is a module that enables access to Oracle Database and conforms to the Python database API specification. This module is currently tested against Oracle Client 19, 18, 12, and 11.2, and Python 2.7, 3.5, 3.6 and 3.7. The Database API (in this case the Oracle API) is one example. Using the cxOracle Python module from Computronix, you can take command over the Oracle query model while maintaining compatibility with Python Database API Specification v2.0. Here are the examples of the python api cxOracle.Cursor.executemany taken from open source projects. By voting up you can indicate which examples are most useful and appropriate. By voting up you can indicate which examples are most useful and appropriate. Does this happen with 'bare' cxOracle? In other words, without pandas? Can you provide a script that demonstrates the problem? Since the amount of data is significant the only meaningful way would be by generating the data. Using Python with Oracle. This page discusses using Python with Oracle. The page is based on the cxoracle Python extension module. It was developed on a VM running Oracle Enterprise Linux 6U4 runnng Oracle 11.2.0.4 and Python 2.6.6.

When I pretend to use 3.5Million records x 225 columns with np.nan in some columns, the cx_Oracle library ends the executemany + commit statements without errors. It gives the impression that it works but it does not insert any data in the database. Very dangerous behavior to have it in production!.

Nevertheless, I did some further investigation:

Python mysqldb executemany

  1. Before trying to save the pandas dataframe (df), I replaced all nan with None:
    df = df.where(pd.notnull(df), None)

  2. Given that the performance of the executemany seems to be exponentially affected by the number of records to insert (I can provide the stats if required), and that there was an error saying 'cx_Oracle.DatabaseError: DPI-1015: array size of 3500000 is too large' (btw, it would be nice to document the limit somewhere), I decided to split the df into 100k chunks, decision that speed up the process.. but:

  3. I have 1 prepare statement
    cursor.prepare(sql)

    /microsoft-visual-studio-free-download.html. .After Visual Studio installation is complete, choose the Launch button to get started developing with Visual Studio.On the start window, choose Create a new project.In the search box, enter the type of app you want to create to see a list of available templates. To see different templates, choose different workloads.You can also filter your search for a specific programming language by using the Language drop-down list. The list of templates depends on the workload(s) that you chose during installation.

  4. I setinputsizes before each executemany
    cursor.setinputsizes(*cx_types)

  5. But, when I am in the X number of executemany, the following error appears:
    'TypeError: expecting string or bytes object'

Cx Oracle Python Executemany Example For Students

Reading the documentation from Cursor.execute, it seems that I have a columns with None that comes up with a number:
'For maximum efficiency when reusing an statement, it is best to use the setinputsizes() method to specify the parameter types and sizes ahead of time; in particular, None is assumed to be a string of length 1 so any values that are later bound as numbers or dates will raise a TypeError exception.'

But, I do have a cursor.setinputsizes that says that the expected column is a cx_Oracle.NATIVE_FLOAT

Questions:

Executemany

Cx Oracle Python Executemany Example 1

  • I cannot see in the documentation that cursor.setinputsizes is taken into account for executemany. Is this a missing entry in the documentation? (http://cx-oracle.readthedocs.io/en/latest/cursor.html?highlight=executemany) or is this a fact and therefore, when I am slicing the df I will have different datatypes based on the 'None's that the executemany is finding in the first column?

Thanks
DT