New RunLLM response generated: >>>*# Question* Hi all! I have a question regarding parallel processing with Flyte. I have a generator that yields rows of data which I need to process further with Python and then insert into a database table. The amount of data is quite large (approximately 150GB), so I would like to process it in batches. Specifically, I want the generator to yield, say, a million rows that would be passed to a Flyte task (or any other suitable construct). This task would start a container to process that batch, reset the data in memory, and then the generator would yield the next million rows to be assigned to a second container, and so on. My understanding is that using a dynamic workflow or map_task requires all the data to be loaded “in memory” before any containers are started, which means the generator needs to be fully exhausted first. This ruins the idea of batch processing. Why I would like to use Flyte in such a way for batch processing is to avoids the overhead of converting traditional Python code to PySpark. However, it seems challenging to achieve this with the current Flyte capabilities. Is there something I have missed? For context, the generator lists a large number of S3 files and reads the rows to yield from them. I know one solution would be to map the file names to each task, but I would like to avoid rewriting the logic like that, as I am using a similar generator construction in many places and would prefer not to restructure everything. Is there a way to achieve this batch processing approach with Flyte, where containers start processing batches incrementally without requiring the entire dataset to be loaded upfront? Any advice or suggestions would be greatly appreciated! Thank you! # Answer Your understanding is correct that Flyte's

map_task

requires all data to be available upfront, which is not ideal for your use case of batch processing large datasets incrementally. However, there are workarounds to achieve batch processing without fully loading the dataset into memory. Approach 1: Use Flyte Dynamic Workflows with Batching You can use a dynamic workflow to generate batches of data and launch tasks for each batch. This allows you to process data incrementally without loading the entire dataset at once. Approach 2: Custom Producer-Consumer Pattern Another approach is to implement a producer-consumer pattern using Flyte tasks. The producer task can generate batches of data and enqueue them, while consumer tasks dequeue and process each batch. Here’s a simplified example using a dynamic workflow to handle batches:

import flytekit
from flytekit import task, workflow, dynamic
from typing import List

@task
def generate_batches(batch_size: int) -> List[List[int]]:
    # Simulate generating batches of data
    data = list(range(150000000))  # Example data
    return [data[i:i + batch_size] for i in range(0, len(data), batch_size)]

@task