kevinjqliu commented on PR #1427:
URL: https://github.com/apache/iceberg-python/pull/1427#issuecomment-2557356375
Thanks everyone for the great discussion here! To summarize the thread
above, I think the main concern here is around exposing this functionality as
part of PyIceberg's `DataScan` public API.
Currently, PyIceberg's read path assumes to be run on a single node machine.
This assumption is embedded in the way we plan and execute the read path. For
example, we use multi-threading and not (yet) multi-processing.
As an Iceberg library for the python ecosystem, I do believe there's value
for PyIceberg to provide the helper methods for distributed processing.
I'd like to propose a path forward for this PR. Instead of integrating the
feature directly into `DataScan`, what if we create a new class (or subclass
`DataScan`) specifically for distributed processing? We can encapsulate the
planning and execution logic inside this new class. The goal is to provide
primitives to allow work to be distributed.
I imagine something like this if I want to integrate with Ray.
```
table = catalog.load_table("blah")
tasks = table.distributed_scan()
futures = [process_task_remote.remote(task) for task in tasks] # Submit
tasks to Ray for parallel processing
results = ray.get(futures)
```
The `distributed_scan` is a helper function in the `Table` class.
Alternatively, we can not expose this at all and have users call the new class
directly.
```
tasks = DistributedTableScan(table).plan_files()
```
Looking forward to hear what people think!
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