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bulk data processing with bulk collect and forall in pl/sql: a comprehensive guide

bulk data processing is a technique used in pl/sql to improve performance when working with large volumes of data. instead of processing data one row at a time, it allows you to fetch or manipulate data in batches, significantly reducing the overhead of context switching between the pl/sql engine and the sql engine. this tutorial will cover the two primary bulk processing constructs in pl/sql: `bulk collect` and `forall`.

*i. understanding the problem: row-by-row processing vs. bulk processing*

before diving into the specifics, let's illustrate why bulk processing is beneficial. consider the following scenario: we need to update the salaries of employees based on their department.

*1. row-by-row processing (inefficient):*



in this code, for each employee in department 10, we:

1. fetch the `employee_id` and `salary`.
2. execute an `update` statement to modify the salary.
3. commit the transaction.

this approach is highly inefficient for several reasons:

*context switching:* each `update` statement requires a context switch between the pl/sql engine and the sql engine, adding significant overhead.
*network round trips:* if the database is on a separate server, each `update` requires a network round trip.
*commit overhead:* committing after each row is generally a performance killer. commits are expensive operations.

*2. bulk processing (efficient):*

with `bulk collect` and `forall`, we can significantly improve performance by:

fetching multiple rows into a pl/sql collection (e.g., an array).
using `forall` to execute a single sql statement for all elements in the collection.
committing the entire operation at once.

*ii. bulk collect: fetching data in batches*

`bulk collect` is used to fetch multiple rows from a sql query into a pl/sql collection (array). this reduces the number of round trips to the database and avoids the overhead of row-by-row ...

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