# 8. Executing Batch Statements and Bulk Loading

Inserting or updating multiple rows can be performed efficiently with `Cursor.executemany()`, making it easy to work with large data sets with python-oracledb. This method can significantly outperform repeated calls to `Cursor.execute()` by reducing network transfer costs and database overheads. The `executemany()` method can also be used to execute PL/SQL statements multiple times at once.

There are examples in the GitHub examples directory.

The following tables will be used in the samples that follow:

    create table ParentTable (
        ParentId              number(9) not null,
        Description           varchar2(60) not null,
        constraint ParentTable_pk primary key (ParentId)
    );

    create table ChildTable (
        ChildId               number(9) not null,
        ParentId              number(9) not null,
        Description           varchar2(60) not null,
        constraint ChildTable_pk primary key (ChildId),
        constraint ChildTable_fk foreign key (ParentId)
                references ParentTable
    );

## 8.1. Batch Execution of SQL

The following example inserts five rows into the table `ParentTable`:

    data = [
        (10, 'Parent 10'),
        (20, 'Parent 20'),
        (30, 'Parent 30'),
        (40, 'Parent 40'),
        (50, 'Parent 50')
    ]
    cursor.executemany("insert into ParentTable values (:1, :2)", data)

This code requires only one round-trip from the client to the database instead of the five round-trips that would be required for repeated calls to `execute()`. For very large data sets, there may be an external buffer or network limits to how many rows can be processed, so repeated calls to `executemany()` may be required. The limits are based on both the number of rows being processed as well as the “size” of each row that is being processed. Repeated calls to `executemany()` are still better than repeated calls to `execute()`.

### 8.1.1. Predefining Memory Areas

When multiple rows of data are being processed there is the possibility that the data is not uniform in type and size. In such cases, python-oracledb makes some effort to accommodate such differences. Type determination for each column is deferred until a value that is not `None` is found in the column’s data. If all values in a particular column are `None`, then python-oracledb assumes the type is a string and has a length of 1. Python-oracledb will also adjust the size of the buffers used to store strings and bytes when a longer value is encountered in the data. These sorts of operations incur overhead as memory has to be reallocated and data copied. To eliminate this overhead, using `setinputsizes()` tells python-oracledb about the type and size of the data that is going to be used.

Consider the following code:

    data = [
        (110, "Parent 110"),
        (2000, "Parent 2000"),
        (30000, "Parent 30000"),
        (400000, "Parent 400000"),
        (5000000, "Parent 5000000")
    ]
    cursor.setinputsizes(None, 20)
    cursor.executemany("""
            insert into ParentTable (ParentId, Description)
            values (:1, :2)""", data)

If this example did not call `setinputsizes()`, then python-oracledb performs five allocations of increasing size and perform data copies as it discovers each new, longer string. However, `cursor.setinputsizes(None, 20)` tells python-oracledb that the maximum size of the strings that will be processed is 20 characters. The first parameter of `None` tells python-oracledb that its default processing will be sufficient since numeric data is already stored efficiently. Since python-oracledb allocates memory for each row based on the supplied values, do not oversize them.

## 8.2. Batch Execution of PL/SQL

PL/SQL functions and procedures and anonymous PL/SQL blocks can also be called using `executemany()` in order to improve performance. For example:

    data = [
        (10, 'Parent 10'),
        (20, 'Parent 20'),
        (30, 'Parent 30'),
        (40, 'Parent 40'),
        (50, 'Parent 50')
    ]
    cursor.executemany("begin mypkg.create_parent(:1, :2); end;", data)

If `executemany()` is used for PL/SQL code that returns OUT binds it will have the same performance characteristics as repeated calls to `execute()`.

Note that the `batcherrors` parameter (discussed below) cannot be used with PL/SQL block execution.

## 8.3. Handling Data Errors

Large datasets may contain some invalid data. When using batch execution as discussed above, the entire batch will be discarded if a single error is detected, potentially eliminating the performance benefits of batch execution and increasing the complexity of the code required to handle those errors. If the parameter `batchErrors` is set to the value `True` when calling `executemany()`, however, processing will continue even if there are data errors in some rows, and the rows containing errors can be examined afterwards to determine what course the application should take. Note that if any errors are detected, a transaction will be started but not committed, even if `Connection.autocommit` is set to `True`. After examining the errors and deciding what to do with them, the application needs to explicitly commit or roll back the transaction with `Connection.commit()` or `Connection.rollback()`, as needed.

This example shows how data errors can be identified:

    data = [
        (60, 'Parent 60'),
        (70, 'Parent 70'),
        (70, 'Parent 70 (duplicate)'),
        (80, 'Parent 80'),
        (80, 'Parent 80 (duplicate)'),
        (90, 'Parent 90')
    ]
    cursor.executemany("insert into ParentTable values (:1, :2)", data,
                       batcherrors=True)
    for error in cursor.getbatcherrors():
        print("Error", error.message, "at row offset", error.offset)

The output is:

    Error ORA-00001: unique constraint (PYTHONDEMO.PARENTTABLE_PK) violated at row offset 2
    Error ORA-00001: unique constraint (PYTHONDEMO.PARENTTABLE_PK) violated at row offset 4

The row offset is the index into the array of the data that could not be inserted due to errors. The application could choose to commit or rollback the other rows that were successfully inserted. Alternatively, it could correct the data for the two invalid rows and attempt to insert them again before committing.

## 8.4. Identifying Affected Rows

When executing a DML statement using `execute()`, the number of rows affected can be examined by looking at the attribute `rowcount`. When performing batch execution with `Cursor.executemany()`, the row count will return the total number of rows that were affected. If you want to know the total number of rows affected by each row of data that is bound you must set the parameter `arraydmlrowcounts` to `True`, as shown:

    parent_ids_to_delete = [20, 30, 50]
    cursor.executemany("delete from ChildTable where ParentId = :1",
                       [(i,) for i in parent_ids_to_delete],
                       arraydmlrowcounts=True)
    row_counts = cursor.getarraydmlrowcounts()
    for parent_id, count in zip(parent_ids_to_delete, row_counts):
        print("Parent ID:", parent_id, "deleted", count, "rows.")

Using the data found in the GitHub samples the output is as follows:

    Parent ID: 20 deleted 3 rows.
    Parent ID: 30 deleted 2 rows.
    Parent ID: 50 deleted 4 rows.

## 8.5. DML RETURNING

DML statements like INSERT, UPDATE, DELETE, and MERGE can return values by using the DML RETURNING syntax. A bind variable can be created to accept this data. See Using Bind Variables for more information.

If, instead of merely deleting the rows as shown in the previous example, you also wanted to know some information about each of the rows that were deleted, you can use the following code:

    parent_ids_to_delete = [20, 30, 50]
    child_id_var = cursor.var(int, arraysize=len(parent_ids_to_delete))
    cursor.setinputsizes(None, child_id_var)
    cursor.executemany("""
            delete from ChildTable
            where ParentId = :1
            returning ChildId into :2""",
            [(i,) for i in parent_ids_to_delete])
    for ix, parent_id in enumerate(parent_ids_to_delete):
        print("Child IDs deleted for parent ID", parent_id, "are",
              child_id_var.getvalue(ix))

The output will be:

    Child IDs deleted for parent ID 20 are [1002, 1003, 1004]
    Child IDs deleted for parent ID 30 are [1005, 1006]
    Child IDs deleted for parent ID 50 are [1012, 1013, 1014, 1015]

Note that the bind variable created to accept the returned data must have an arraysize large enough to hold data for each row that is processed. Also, the call to `Cursor.setinputsizes()` binds this variable immediately so that it does not have to be passed in each row of data.

## 8.6. Loading CSV Files into Oracle Database

The `Cursor.executemany()` method and Python’s csv module can be used to efficiently insert CSV (Comma Separated Values) data. For example, consider the file `data.csv`:

    101,Abel
    154,Baker
    132,Charlie
    199,Delta
    . . .

And the schema:

    create table test (id number, name varchar2(25));

Data loading can be done in batches of records since the number of records may prevent all data being inserted at once:

    import oracledb
    import csv

    # CSV file
    FILE_NAME = 'data.csv'

    # Adjust the number of rows to be inserted in each iteration
    # to meet your memory and performance requirements
    BATCH_SIZE = 10000

    connection = oracledb.connect(user="hr", password=userpwd,
                                  dsn="dbhost.example.com/orclpdb")

    with connection.cursor() as cursor:

        # Predefine the memory areas to match the table definition.
        # This can improve performance by avoiding memory reallocations.
        # Here, one parameter is passed for each of the columns.
        # "None" is used for the ID column, since the size of NUMBER isn't
        # variable.  The "25" matches the maximum expected data size for the
        # NAME column
        cursor.setinputsizes(None, 25)

        with open(FILE_NAME, 'r') as csv_file:
            csv_reader = csv.reader(csv_file, delimiter=',')
            sql = "insert into test (id, name) values (:1, :2)"
            data = []
            for line in csv_reader:
                data.append((line[0], line[1]))
                if len(data) % BATCH_SIZE == 0:
                    cursor.executemany(sql, data)
                    data = []
            if data:
                cursor.executemany(sql, data)
            connection.commit()

Depending on data sizes and business requirements, database changes such as temporarily disabling redo logging on the table, or disabling indexes may also be beneficial.

See load_csv.py for a runnable example.