Скачать или смотреть Efficiently Handling Python Memory Errors When Saving Large CSV Files

Discover how to handle `Python memory errors` while saving large CSV files by splitting them into smaller chunks efficiently in your Python script.
---
This video is based on the question https://stackoverflow.com/q/65839009/ asked by the user 'Fanny Zhingre' ( https://stackoverflow.com/u/6653079/ ) and on the answer https://stackoverflow.com/a/65847726/ provided by the user 'David Reed' ( https://stackoverflow.com/u/1159783/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Python memory error Save split csv python

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Efficiently Handling Python Memory Errors When Saving Large CSV Files

When working with large datasets, particularly in environments like Salesforce with Python, you may encounter a frustrating Python memory error. This often happens when you're attempting to load an extensive dataset entirely into memory, such as when querying the Salesforce Lead object. In this blog, we’ll explore the solution to this problem by splitting large CSV files into manageable chunks.

Understanding the Problem

In your scenario, you’re trying to extract data from Salesforce using the simple_salesforce library and save it as a CSV file. However, if the dataset exceeds a thousand rows, you might experience a memory overload. This happens because:

You are loading all queried data into memory at once.

Python’s memory management might not be able to handle large datasets efficiently.

As a result, you receive a TypeError when trying to open the CSV file with an invalid keyword argument while attempting to manage chunks.

Solution Overview

To effectively manage memory usage while exporting large datasets to CSV, you can modify your code to use an iterator. Instead of loading all queried data at once, utilize the query_all_iter method instead of query_all. This way, the data is retrieved in smaller chunks, significantly reducing memory load. Below is a step-by-step breakdown:

Step 1: Modify Your Import Statements

Ensure that you have all the necessary imports in your code:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Query Data Efficiently

Replace your original data query line with the following:

[[See Video to Reveal this Text or Code Snippet]]

This command retrieves data as an iterator, avoiding large memory usage.

Step 3: Write to CSV Incrementally

Instead of writing to a single CSV file all at once, you’ll want to write each chunk into separate files (e.g., output1.csv, output2.csv, etc.). Here’s an example of how to implement this:

[[See Video to Reveal this Text or Code Snippet]]

Note: The writer.writerows_done is a pseudo-code logic. You need to implement your row counting based on your requirements, as the actual csv module does not have this built in.

Conclusion

By making these adjustments to your code, you can effectively manage memory usage while exporting large datasets to CSV without encountering Python memory errors. Using iterators and splitting your output into manageable files can enhance your application's efficiency and reliability. This method allows you to handle extensive datasets seamlessly.

Whether you’re working with Salesforce or any other APIs, applying these principles can help you avoid costly memory issues in your scripts. If you have further questions or need clarification, feel free to ask!