Prathamesh9284 commented on code in PR #1376:
URL:
https://github.com/apache/datafusion-python/pull/1376#discussion_r2797728018
##########
python/tests/test_substrait.py:
##########
@@ -74,3 +74,48 @@ def test_substrait_file_serialization(ctx, tmp_path,
path_to_str):
expected_actual_plan = ss.Consumer.from_substrait_plan(ctx, actual_plan)
assert str(expected_logical_plan) == str(expected_actual_plan)
+
+def test_substrait_plan_to_from_json(ctx):
+ # Create a simple plan
+ sql = "SELECT * FROM t"
+ batch = pa.RecordBatch.from_arrays(
+ [pa.array([1, 2, 3]), pa.array([4, 5, 6])],
+ names=["a", "b"],
+ )
+ ctx.register_record_batches("t", [[batch]])
+
+ # Serialize to plan
+ plan = ss.Serde.serialize_to_plan(sql, ctx)
+
+ # Convert plan to JSON
+ json_str = plan.to_json()
+ assert isinstance(json_str, str)
+ assert "relations" in json_str # basic sanity check
Review Comment:
Hi @timsaucer,
I was able to generate a JSON-formatted Substrait plan using my DataFusion
implementation. Comparing it with Isthmus:
- Both include relations, project expressions, virtualTable values, and
correct column mappings.
- Minor differences exist: DataFusion uses i64 instead of i32, column names
differ, and optional metadata (extensions, typeAliases) are not included —
these do not affect compatibility.
- DuckDB currently does not provide a working JSON inspection method due to
extension availability issues, so direct JSON comparison via DuckDB isn’t
possible.
Overall, the DataFusion output is compatible and can be used for
reproducibility and validation.
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