c21 commented on code in PR #7148:
URL: https://github.com/apache/iceberg/pull/7148#discussion_r1145289777
##########
python/pyiceberg/table/__init__.py:
##########
@@ -415,3 +416,8 @@ def to_duckdb(self, table_name: str, connection:
Optional[DuckDBPyConnection] =
con.register(table_name, self.to_arrow())
return con
+
+ def to_ray(self) -> ray.data.dataset.Dataset:
+ import ray
+
+ return ray.data.from_arrow(self.to_arrow())
Review Comment:
It looks like `self.to_arrow()` will eventually call [`pq.read_table()` to
read the table into memory (with optionally row filtering and columns
pruning)](https://github.com/apache/iceberg/blob/d6e258120d86de791fd7363391c0301ba5c39ae3/python/pyiceberg/io/pyarrow.py#L520-L527)
(`to_arrow` -> `project_table` -> `_file_to_table` -> `pq.read_table`).
Ray also leverages PyArrow dataset to read a variety of format (Parquet,
CSV, etc) ([API
list](https://docs.ray.io/en/releases-2.3.0/data/api/input_output.html)). And
internally Ray uses PyArrow to read data in batches and in parallel with many
tasks.
This PR is a good starting point. We can further collaborate to add a
`read_iceberg()` API in Ray, to implement a [custom data
source](https://docs.ray.io/en/latest/data/custom-datasource.html) for Iceberg,
so it can do the parallel read and be scalable.
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