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

[himanshu_k]

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:

1. Can I trust that the offset and limit functions will consistently return data between the specified starting_index and ending_index?
2. How do these functions behave when applied to partitioned DataFrames?
3. Are there any best practices or considerations to ensure correct pagination when using offset and limit, particularly with partitioned DataFrames?
4. 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.

[Kaniz_Fatma]

Hi @himanshu_k, Let’s delve into your questions regarding pagination using the offset and limit functions in PySpark, especially when dealing with partitioned data frames.

1. Consistency of offset and limit Functions:

   • The offset and limit functions are commonly used for pagination. However, their behavior depends on the underlying data distribution and partitioning.
   • When you apply offset and limit, you can trust that they will consistently return data between the specified starting_index and ending_index. However, there are caveats related to partitioning, which I’ll address next.

2. Behavior with Partitioned DataFrames:

   • Partitioned DataFrames are divided into smaller chunks (partitions) to enable parallel processing. Each partition contains a subset of the data.
   • When you use offset and limit, they operate within individual partitions. Here’s how it works:
     • The offset function skips rows within a partition.
     • The limit function restricts the number of rows within a partition.
   • If your starting_index and ending_index span multiple partitions, you may encounter unexpected results. For example:
     • If your starting_index is in partition 1 and your ending_index is in partition 2, the limit function won’t automatically adjust to include data from both partitions.
     • You might end up with fewer rows than expected if the ending_index is close to the partition boundary.
   • To address this, consider repartitioning your DataFrame based on a suitable column (e.g., an evenly distributed key) before applying offset and limit.

3. Best Practices and Considerations:

   • Here are some best practices to ensure correct pagination:
     • Repartitioning: Repartition your DataFrame to evenly distribute data across partitions. Use a column that evenly distributes keys (e.g., timestamp, category, or user ID).
     • Ordering: Ensure that your data is ordered consistently (e.g., by timestamp or unique identifier) before applying offset and limit.
     • Avoiding Skewed Partitions: Be cautious of skewed partitions (where one partition has significantly more data than others). Skewed partitions can affect pagination accuracy.
     • Use Row Numbers: Instead of relying solely on offset and limit, consider using row numbers (e.g., via row_number() window function) to paginate data. This approach is more robust across partitions.

4. Balancing Speed and Efficiency:

   • To balance speed and efficiency without computing the complete dataset in memory:
     • Use an appropriate partitioning strategy (e.g., hash-based or range-based) based on your data characteristics.
     • Experiment with different partition sizes to find the right balance.
     • Leverage Spark’s lazy evaluation to minimize unnecessary computations.

5. Using db-connect Spark Session:

   • If you’re using a db-connect Spark session, the same principles apply. Ensure that your DataFrame is properly partitioned and ordered before applying offset and limit.

Remember that while offset and limit are useful, understanding partitioning and ordering is crucial for accurate pagination.

Feel free to adapt these guidelines to your specific use case, and happy Spark coding! 🚀🔥

[wise_owl]

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