topic Re: Clarification Needed: Ensuring Correct Pagination with Offset and Limit in PySpark in Data Engineering

Clarification Needed: Ensuring Correct Pagination with Offset and Limit in PySpark

himanshu_k — Thu, 04 Apr 2024 16:42:03 GMT

Hi community,

I hope you're all doing well. I'm currently engaged in a PySpark project where I'm implementing pagination-like functionality using the offset and limit functions. My aim is to retrieve data between a specified starting_index and ending_index without computing the entire dataset in memory.

Here's how I'm currently using these functions:

sliced_df = df.offset(starting_index).limit(ending_index - starting_index)

However, I'm uncertain whether this approach provides reliable results, especially considering partitioned DataFrames. The documentation doesn't offer clear guidance on how these functions behave under such circumstances.

Could someone kindly address the following questions:

Can I trust that the offset and limit functions will consistently return data between the specified starting_index and ending_index?
How do these functions behave when applied to partitioned DataFrames?
Are there any best practices or considerations to ensure correct pagination when using offset and limit, particularly with partitioned DataFrames?
Is there a recommended approach that balances speed and efficiency without computing the complete dataset in memory?
Additionally, I'd like to mention that I am using db-connect Spark session for this project.

Re: Clarification Needed: Ensuring Correct Pagination with Offset and Limit in PySpark

wise_owl — Mon, 16 Sep 2024 11:08:08 GMT

You can leverage this code base. It works as expected using "next_page_token" parameter-

Don't forget to mark this solution as correct if this helped you 🙃

import requests token = 'your token' url = 'your URL' params = {'expand_tasks': 'true'} header = {'Authorization': f'Bearer {token}'} while True: response = requests.get(url, headers=header, params=params) response_data = response.json() jobs = response_data.get("jobs", []) for job in jobs: settings = job.get('settings') task = settings.get('tasks') if task and task[0].get('existing_cluster_id'): job_name = settings.get('name') job_creator = job.get('creator_user_name') print(f'job creator name= {job_creator} & job name= {job_name}') else: print(f"{settings.get('name')} not running on ACL") next_page_token = response_data.get('next_page_token') if not next_page_token: break params['page_token'] = next_page_token

Re: Clarification Needed: Ensuring Correct Pagination with Offset and Limit in PySpark

Mathias_Peters — Tue, 17 Jun 2025 13:46:01 GMT

This code snippet seems to have no relationship with the question whatsoever. Is this generated?

Re: Clarification Needed: Ensuring Correct Pagination with Offset and Limit in PySpark

Mathias_Peters — Tue, 17 Jun 2025 13:46:58 GMT

Hi,

did you find answer to this question?
I am having similar problems and a slow solution, which I need to improve upon.

Thanks in advance