fix(parsing): normalize nested column names

src/datachain/lib/arrow.py

@@ -1,4 +1,3 @@
-import re
 from collections.abc import Sequence
 from tempfile import NamedTemporaryFile
 from typing import TYPE_CHECKING, Any, Optional
@@ -13,6 +12,7 @@
 from datachain.lib.model_store import ModelStore
 from datachain.lib.signal_schema import SignalSchema
 from datachain.lib.udf import Generator
+from datachain.lib.utils import normalize_col_names
 
 if TYPE_CHECKING:
     from datasets.features.features import Features
@@ -128,7 +128,7 @@ def schema_to_output(schema: pa.Schema, col_names: Optional[Sequence[str]] = Non
     signal_schema = _get_datachain_schema(schema)
     if signal_schema:
         return signal_schema.values
-    columns = _convert_col_names(col_names)  # type: ignore[arg-type]
+    columns = list(normalize_col_names(col_names).keys())  # type: ignore[arg-type]
     hf_schema = _get_hf_schema(schema)
     if hf_schema:
         return {
@@ -143,19 +143,6 @@ def schema_to_output(schema: pa.Schema, col_names: Optional[Sequence[str]] = Non
     return output
 
 
-def _convert_col_names(col_names: Sequence[str]) -> list[str]:
-    default_column = 0
-    converted_col_names = []
-    for column in col_names:
-        column = column.lower()
-        column = re.sub("[^0-9a-z_]+", "", column)
-        if not column:
-            column = f"c{default_column}"
-            default_column += 1
-        converted_col_names.append(column)
-    return converted_col_names
-
-
 def arrow_type_mapper(col_type: pa.DataType, column: str = "") -> type:  # noqa: PLR0911
     """Convert pyarrow types to basic types."""
     from datetime import datetime

src/datachain/lib/data_model.py

@@ -2,9 +2,10 @@
 from datetime import datetime
 from typing import ClassVar, Union, get_args, get_origin
 
-from pydantic import BaseModel, create_model
+from pydantic import BaseModel, Field, create_model
 
 from datachain.lib.model_store import ModelStore
+from datachain.lib.utils import normalize_col_names
 
 StandardType = Union[
     type[int],
@@ -60,7 +61,14 @@ def is_chain_type(t: type) -> bool:
 
 
 def dict_to_data_model(name: str, data_dict: dict[str, DataType]) -> type[BaseModel]:
-    fields = {name: (anno, ...) for name, anno in data_dict.items()}
+    # Gets a map of a normalized_name -> original_name
+    columns = normalize_col_names(list(data_dict.keys()))
+    # We reverse if for convenience to original_name -> normalized_name
+    columns = {v: k for k, v in columns.items()}
+
+    fields = {
+        columns[name]: (anno, Field(alias=name)) for name, anno in data_dict.items()
+    }
     return create_model(
         name,
         __base__=(DataModel,),  # type: ignore[call-overload]

src/datachain/lib/utils.py

@@ -1,4 +1,6 @@
+import re
 from abc import ABC, abstractmethod
+from collections.abc import Sequence
 
 
 class AbstractUDF(ABC):
@@ -28,3 +30,31 @@ def __init__(self, message):
 class DataChainColumnError(DataChainParamsError):
     def __init__(self, col_name, msg):
         super().__init__(f"Error for column {col_name}: {msg}")
+
+
+def normalize_col_names(col_names: Sequence[str]) -> dict[str, str]:
+    gen_col_counter = 0
+    new_col_names = {}
+    org_col_names = set(col_names)
+
+    for org_column in col_names:
+        new_column = org_column.lower()
+        new_column = re.sub("[^0-9a-z]+", "_", new_column)
+        new_column = new_column.strip("_")
+
+        generated_column = new_column
+
+        while (
+            not generated_column.isidentifier()
+            or generated_column in new_col_names
+            or (generated_column != org_column and generated_column in org_col_names)
+        ):
+            if new_column:
+                generated_column = f"c{gen_col_counter}_{new_column}"
+            else:
+                generated_column = f"c{gen_col_counter}"
+            gen_col_counter += 1
+
+        new_col_names[generated_column] = org_column
+
+    return new_col_names

tests/unit/lib/test_arrow.py

@@ -168,13 +168,21 @@ def test_parquet_convert_column_names():
             ("dot.notation.col", pa.int32()),
             ("with-dashes", pa.int32()),
             ("with spaces", pa.int32()),
+            ("with-multiple--dashes", pa.int32()),
+            ("with__underscores", pa.int32()),
+            ("__leading__underscores", pa.int32()),
+            ("trailing__underscores__", pa.int32()),
         ]
     )
     assert list(schema_to_output(schema)) == [
         "uppercasecol",
-        "dotnotationcol",
-        "withdashes",
-        "withspaces",
+        "dot_notation_col",
+        "with_dashes",
+        "with_spaces",
+        "with_multiple_dashes",
+        "with_underscores",
+        "leading_underscores",
+        "trailing_underscores",
     ]
 
 

tests/unit/lib/test_datachain.py

@@ -36,6 +36,18 @@
     "city": ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"],
 }
 
+DF_DATA_NESTED_NOT_NORMALIZED = {
+    "nAmE": [
+        {"first-SELECT": "Alice", "l--as@t": "Smith"},
+        {"l--as@t": "Jones", "first-SELECT": "Bob"},
+        {"first-SELECT": "Charlie", "l--as@t": "Brown"},
+        {"first-SELECT": "David", "l--as@t": "White"},
+        {"first-SELECT": "Eva", "l--as@t": "Black"},
+    ],
+    "AgE": [25, 30, 35, 40, 45],
+    "citY": ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"],
+}
+
 DF_OTHER_DATA = {
     "last_name": ["Smith", "Jones"],
     "country": ["USA", "Russia"],
@@ -272,7 +284,9 @@ def test_listings(test_session, tmp_dir):
     assert listing.expires
     assert listing.version == 1
     assert listing.num_objects == 1
-    assert listing.size == 2912
+    # Exact number if unreliable here since it depends on the PyArrow version
+    assert listing.size > 1000
+    assert listing.size < 5000
     assert listing.status == 4
 
 
@@ -988,6 +1002,25 @@ def test_parse_tabular_format(tmp_dir, test_session):
     assert df1.equals(df)
 
 
+def test_parse_nested_json(tmp_dir, test_session):
+    df = pd.DataFrame(DF_DATA_NESTED_NOT_NORMALIZED)
+    path = tmp_dir / "test.jsonl"
+    path.write_text(df.to_json(orient="records", lines=True))
+    dc = DataChain.from_storage(path.as_uri(), session=test_session).parse_tabular(
+        format="json"
+    )
+    # Field names are normalized, values are preserved
+    # E.g. nAmE -> name, l--as@t -> l_as_t, etc
+    df1 = dc.select("name", "age", "city").to_pandas()
+
+    assert df1["name"]["first_select"].to_list() == [
+        d["first-SELECT"] for d in df["nAmE"].to_list()
+    ]
+    assert df1["name"]["l_as_t"].to_list() == [
+        d["l--as@t"] for d in df["nAmE"].to_list()
+    ]
+
+
 def test_parse_tabular_partitions(tmp_dir, test_session):
     df = pd.DataFrame(DF_DATA)
     path = tmp_dir / "test.parquet"

tests/unit/lib/test_utils.py

@@ -5,6 +5,7 @@
 from pydantic import BaseModel
 
 from datachain.lib.convert.python_to_sql import python_to_sql
+from datachain.lib.utils import normalize_col_names
 from datachain.sql.types import JSON, Array, String
 
 
@@ -56,3 +57,72 @@ def test_convert_type_to_datachain_array(typ, expected):
 def test_convert_type_to_datachain_error(typ):
     with pytest.raises(TypeError):
         python_to_sql(typ)
+
+
+def test_normalize_column_names():
+    res = normalize_col_names(
+        [
+            "UpperCase",
+            "_underscore_start",
+            "double__underscore",
+            "1start_with_number",
+            "не_ascii_start",
+            "  space_start",
+            "space_end  ",
+            "dash-end-",
+            "-dash-start",
+            "--multiple--dash--",
+            "-_ mix_  -dash_ -",
+            "__2digit_after_uderscore",
+            "",
+            "_-_-  _---_ _",
+            "_-_-  _---_ _1",
+        ]
+    )
+    assert list(res.keys()) == [
+        "uppercase",
+        "underscore_start",
+        "double_underscore",
+        "c0_1start_with_number",
+        "ascii_start",
+        "space_start",
+        "space_end",
+        "dash_end",
+        "dash_start",
+        "multiple_dash",
+        "mix_dash",
+        "c1_2digit_after_uderscore",
+        "c2",
+        "c3",
+        "c4_1",
+    ]
+
+
+def test_normalize_column_names_case_repeat():
+    res = normalize_col_names(["UpperCase", "UpPerCase"])
+
+    assert list(res.keys()) == ["uppercase", "c0_uppercase"]
+
+
+def test_normalize_column_names_exists_after_normalize():
+    res = normalize_col_names(["1digit", "c0_1digit"])
+
+    assert list(res.keys()) == ["c1_1digit", "c0_1digit"]
+
+
+def test_normalize_column_names_normalized_repeat():
+    res = normalize_col_names(["column", "_column"])
+
+    assert list(res.keys()) == ["column", "c0_column"]
+
+
+def test_normalize_column_names_normalized_case_repeat():
+    res = normalize_col_names(["CoLuMn", "_column"])
+
+    assert res == {"column": "CoLuMn", "c0_column": "_column"}
+
+
+def test_normalize_column_names_repeat_generated_after_normalize():
+    res = normalize_col_names(["c0_CoLuMn", "_column", "column"])
+
+    assert res == {"c0_column": "c0_CoLuMn", "c1_column": "_column", "column": "column"}