Feature/ndit 1146 improve contract casting statements

src/dve/core_engine/backends/implementations/duckdb/contract.py

@@ -31,6 +31,7 @@
     duckdb_read_parquet,
     duckdb_record_index,
     duckdb_write_parquet,
+    get_duckdb_cast_statement_from_annotation,
     get_duckdb_type_from_annotation,
     relation_is_empty,
 )
@@ -101,18 +102,7 @@ def create_entity_from_py_iterator(  # pylint: disable=unused-argument
         _lazy_df = pl.LazyFrame(records, polars_schema)  # type: ignore # pylint: disable=unused-variable
         return self._connection.sql("select * from _lazy_df")
 
-    @staticmethod
-    def generate_ddb_cast_statement(
-        column_name: str, dtype: DuckDBPyType, null_flag: bool = False
-    ) -> str:
-        """Helper method to generate sql statements for casting datatypes (permissively).
-        Current duckdb python API doesn't play well with this currently.
-        """
-        if not null_flag:
-            return f'try_cast("{column_name}" AS {dtype}) AS "{column_name}"'
-        return f'cast(NULL AS {dtype}) AS "{column_name}"'
-
-    # pylint: disable=R0914
+    # pylint: disable=R0914,R0915
     def apply_data_contract(
         self,
         working_dir: URI,
@@ -180,12 +170,16 @@ def apply_data_contract(
 
                 casting_statements = [
                     (
-                        self.generate_ddb_cast_statement(column, dtype)
+                        get_duckdb_cast_statement_from_annotation(column, mdl_fld.annotation)
+                        + f""" AS "{column}" """
                         if column in relation.columns
-                        else self.generate_ddb_cast_statement(column, dtype, null_flag=True)
+                        else f"CAST(NULL AS {ddb_schema[column]}) AS {column}"
                     )
-                    for column, dtype in ddb_schema.items()
+                    for column, mdl_fld in entity_fields.items()
                 ]
+                casting_statements.append(
+                    f"CAST({RECORD_INDEX_COLUMN_NAME} AS {get_duckdb_type_from_annotation(int)}) AS {RECORD_INDEX_COLUMN_NAME}"  # pylint: disable=C0301
+                )
                 try:
                     relation = relation.project(", ".join(casting_statements))
                 except Exception as err:  # pylint: disable=broad-except

src/dve/core_engine/backends/implementations/duckdb/duckdb_helpers.py

@@ -313,3 +313,108 @@ def duckdb_record_index(cls):
     setattr(cls, "add_record_index", _add_duckdb_record_index)
     setattr(cls, "drop_record_index", _drop_duckdb_record_index)
     return cls
+
+
+def _cast_as_ddb_type(field_expr: str, type_annotation: Any) -> str:
+    """Cast to Duck DB type"""
+    return f"""try_cast({field_expr} as {get_duckdb_type_from_annotation(type_annotation)})"""
+
+
+def _ddb_safely_quote_name(field_name: str) -> str:
+    """Quote field names in case reserved"""
+    try:
+        sep_idx = field_name.index(".")
+        return f'"{field_name[: sep_idx]}"' + field_name[sep_idx:]
+    except ValueError:
+        return f'"{field_name}"'
+
+
+# pylint: disable=R0801,R0911,R0912
+def get_duckdb_cast_statement_from_annotation(
+    element_name: str,
+    type_annotation: Any,
+    parent_element: bool = True,
+    date_regex: str = r"^[0-9]{4}-[0-9]{2}-[0-9]{2}$",
+    timestamp_regex: str = r"^[0-9]{4}-[0-9]{2}-[0-9]{2}T[0-9]{2}:[0-9]{2}:[0-9]{2}((\+|\-)[0-9]{2}:[0-9]{2})?$",  # pylint: disable=C0301
+    time_regex: str = r"^[0-9]{2}:[0-9]{2}:[0-9]{2}$",
+) -> str:
+    """Generate casting statements for duckdb relations from type annotations"""
+    type_origin = get_origin(type_annotation)
+
+    quoted_name = _ddb_safely_quote_name(element_name)
+
+    # An `Optional` or `Union` type, check to ensure non-heterogenity.
+    if type_origin is Union:
+        python_type = _get_non_heterogenous_type(get_args(type_annotation))
+        return get_duckdb_cast_statement_from_annotation(
+            element_name, python_type, parent_element, date_regex, timestamp_regex
+        )
+
+    # Type hint is e.g. `List[str]`, check to ensure non-heterogenity.
+    if type_origin is list or (isinstance(type_origin, type) and issubclass(type_origin, list)):
+        element_type = _get_non_heterogenous_type(get_args(type_annotation))
+        stmt = f"list_transform({quoted_name}, x -> {get_duckdb_cast_statement_from_annotation('x',element_type, False, date_regex, timestamp_regex)})"  # pylint: disable=C0301
+        return stmt if not parent_element else _cast_as_ddb_type(stmt, type_annotation)
+
+    if type_origin is Annotated:
+        python_type, *other_args = get_args(type_annotation)  # pylint: disable=unused-variable
+        return get_duckdb_cast_statement_from_annotation(
+            element_name, python_type, parent_element, date_regex, timestamp_regex
+        )  # add other expected params here
+    # Ensure that we have a concrete type at this point.
+    if not isinstance(type_annotation, type):
+        raise ValueError(f"Unsupported type annotation {type_annotation!r}")
+
+    if (
+        # Type hint is a dict subclass, but not dict. Possibly a `TypedDict`.
+        (issubclass(type_annotation, dict) and type_annotation is not dict)
+        # Type hint is a dataclass.
+        or is_dataclass(type_annotation)
+        # Type hint is a `pydantic` model.
+        or (type_origin is None and issubclass(type_annotation, BaseModel))
+    ):
+        fields: dict[str, str] = {}
+        for field_name, field_annotation in get_type_hints(type_annotation).items():
+            # Technically non-string keys are disallowed, but people are bad.
+            if not isinstance(field_name, str):
+                raise ValueError(
+                    f"Dictionary/Dataclass keys must be strings, got {type_annotation!r}"
+                )  # pragma: no cover
+            if get_origin(field_annotation) is ClassVar:
+                continue
+
+            fields[field_name] = get_duckdb_cast_statement_from_annotation(
+                f"{element_name}.{field_name}", field_annotation, False, date_regex, timestamp_regex
+            )
+
+        if not fields:
+            raise ValueError(
+                f"No type annotations in dict/dataclass type (got {type_annotation!r})"
+            )
+        cast_exprs = ",".join([f'"{nme}":= {stmt}' for nme, stmt in fields.items()])
+        stmt = f"struct_pack({cast_exprs})"
+        return stmt if not parent_element else _cast_as_ddb_type(stmt, type_annotation)
+
+    if type_annotation is list:
+        raise ValueError(
+            f"List must have type annotation (e.g. `List[str]`), got {type_annotation!r}"
+        )
+    if type_annotation is dict or type_origin is dict:
+        raise ValueError(f"dict must be `typing.TypedDict` subclass, got {type_annotation!r}")
+
+    for type_ in type_annotation.mro():
+        # datetime is subclass of date, so needs to be handled first
+        if issubclass(type_, datetime):
+            stmt = rf"CASE WHEN REGEXP_MATCHES(TRIM({quoted_name}), '{timestamp_regex}') THEN TRY_CAST(TRIM({quoted_name}) as TIMESTAMP) ELSE NULL END"  # pylint: disable=C0301
+            return stmt
+        if issubclass(type_, date):
+            stmt = rf"CASE WHEN REGEXP_MATCHES(TRIM({quoted_name}), '{date_regex}') THEN TRY_CAST(TRIM({quoted_name}) as DATE) ELSE NULL END"  # pylint: disable=C0301
+            return stmt
+        if issubclass(type_, time):
+            stmt = rf"CASE WHEN REGEXP_MATCHES(TRIM({quoted_name}), '{time_regex}') THEN TRY_CAST(TRIM({quoted_name}) as TIME) ELSE NULL END" # pylint: disable=C0301
+            return stmt
+        duck_type = get_duckdb_type_from_annotation(type_)
+        if duck_type:
+            stmt = f"trim({quoted_name})"
+            return _cast_as_ddb_type(stmt, type_) if parent_element else stmt
+    raise ValueError(f"No equivalent DuckDB type for {type_annotation!r}")

src/dve/core_engine/backends/implementations/spark/spark_helpers.py

@@ -439,3 +439,103 @@ def spark_record_index(cls):
     setattr(cls, "add_record_index", _add_spark_record_index)
     setattr(cls, "drop_record_index", _drop_spark_record_index)
     return cls
+
+
+def _cast_as_spark_type(field_expr: str, field_type: Any) -> Column:
+    """Cast to spark type"""
+    return sf.expr(field_expr).cast(get_type_from_annotation(field_type))
+
+
+def _spark_safely_quote_name(field_name: str) -> str:
+    """Quote field names in case reserved"""
+    try:
+        sep_idx = field_name.index(".")
+        return f"`{field_name[: sep_idx]}`" + field_name[sep_idx:]
+    except ValueError:
+        return f"`{field_name}`"
+
+
+# pylint: disable=R0801
+def get_spark_cast_statement_from_annotation(
+    element_name: str,
+    type_annotation: Any,
+    parent_element: bool = True,
+    date_regex: str = r"^[0-9]{4}-[0-9]{2}-[0-9]{2}$",
+    timestamp_regex: str = r"^[0-9]{4}-[0-9]{2}-[0-9]{2}T[0-9]{2}:[0-9]{2}:[0-9]{2}((\\+|\\-)[0-9]{2}:[0-9]{2})?$",  # pylint: disable=C0301
+):
+    """Generate casting statements for spark dataframes based on type annotations"""
+    type_origin = get_origin(type_annotation)
+
+    quoted_name = _spark_safely_quote_name(element_name)
+
+    # An `Optional` or `Union` type, check to ensure non-heterogenity.
+    if type_origin is Union:
+        python_type = _get_non_heterogenous_type(get_args(type_annotation))
+        return get_spark_cast_statement_from_annotation(
+            element_name, python_type, parent_element, date_regex, timestamp_regex
+        )
+
+    # Type hint is e.g. `List[str]`, check to ensure non-heterogenity.
+    if type_origin is list or (isinstance(type_origin, type) and issubclass(type_origin, list)):
+        element_type = _get_non_heterogenous_type(get_args(type_annotation))
+        stmt = f"transform({quoted_name}, x -> {get_spark_cast_statement_from_annotation('x',element_type, False, date_regex, timestamp_regex)})"  # pylint: disable=C0301
+        return stmt if not parent_element else _cast_as_spark_type(stmt, type_annotation)
+
+    if type_origin is Annotated:
+        python_type, *_ = get_args(type_annotation)  # pylint: disable=unused-variable
+        return get_spark_cast_statement_from_annotation(
+            element_name, python_type, parent_element, date_regex, timestamp_regex
+        )  # add other expected params here
+    # Ensure that we have a concrete type at this point.
+    if not isinstance(type_annotation, type):
+        raise ValueError(f"Unsupported type annotation {type_annotation!r}")
+
+    if (
+        # Type hint is a dict subclass, but not dict. Possibly a `TypedDict`.
+        (issubclass(type_annotation, dict) and type_annotation is not dict)
+        # Type hint is a dataclass.
+        or is_dataclass(type_annotation)
+        # Type hint is a `pydantic` model.
+        or (type_origin is None and issubclass(type_annotation, BaseModel))
+    ):
+        fields: dict[str, str] = {}
+        for field_name, field_annotation in get_type_hints(type_annotation).items():
+            # Technically non-string keys are disallowed, but people are bad.
+            if not isinstance(field_name, str):
+                raise ValueError(
+                    f"Dictionary/Dataclass keys must be strings, got {type_annotation!r}"
+                )  # pragma: no cover
+            if get_origin(field_annotation) is ClassVar:
+                continue
+
+            fields[field_name] = get_spark_cast_statement_from_annotation(
+                f"{element_name}.{field_name}", field_annotation, False, date_regex, timestamp_regex
+            )
+
+        if not fields:
+            raise ValueError(
+                f"No type annotations in dict/dataclass type (got {type_annotation!r})"
+            )
+        cast_exprs = ",".join([f"{stmt} AS `{nme}`" for nme, stmt in fields.items()])
+        stmt = f"struct({cast_exprs})"
+        return stmt if not parent_element else _cast_as_spark_type(stmt, type_annotation)
+    if type_annotation is list:
+        raise ValueError(
+            f"List must have type annotation (e.g. `List[str]`), got {type_annotation!r}"
+        )
+    if type_annotation is dict or type_origin is dict:
+        raise ValueError(f"dict must be `typing.TypedDict` subclass, got {type_annotation!r}")
+
+    for type_ in type_annotation.mro():
+        # datetime is subclass of date, so needs to be handled first
+        if issubclass(type_, dt.datetime):
+            stmt = rf"CASE WHEN REGEXP(TRIM({quoted_name}), '{timestamp_regex}') THEN TRIM({quoted_name}) ELSE NULL END"  # pylint: disable=C0301
+            return _cast_as_spark_type(stmt, type_) if parent_element else stmt
+        if issubclass(type_, dt.date):
+            stmt = rf"CASE WHEN REGEXP(TRIM({quoted_name}), '{date_regex}') THEN TRIM({quoted_name}) ELSE NULL END"  # pylint: disable=C0301
+            return _cast_as_spark_type(stmt, type_) if parent_element else stmt
+        spark_type = get_type_from_annotation(type_)
+        if spark_type:
+            stmt = f"trim({quoted_name})"
+            return _cast_as_spark_type(stmt, type_) if parent_element else stmt
+    raise ValueError(f"No equivalent Spark type for {type_annotation!r}")

src/dve/pipeline/foundry_ddb_pipeline.py

@@ -42,13 +42,13 @@ def persist_audit_records(self, submission_info: SubmissionInfo) -> URI:
             write_to.parent.mkdir(parents=True, exist_ok=True)
             write_to = write_to.as_posix()
         self.write_parquet(  # type: ignore # pylint: disable=E1101
-            self._audit_tables._processing_status.get_relation().filter( # pylint: disable=W0212
+            self._audit_tables._processing_status.get_relation().filter(  # pylint: disable=W0212
                 f"submission_id = '{submission_info.submission_id}'"
             ),
             fh.joinuri(write_to, "processing_status.parquet"),
         )
         self.write_parquet(  # type: ignore # pylint: disable=E1101
-            self._audit_tables._submission_statistics.get_relation().filter( # pylint: disable=W0212
+            self._audit_tables._submission_statistics.get_relation().filter(  # pylint: disable=W0212
                 f"submission_id = '{submission_info.submission_id}'"
             ),
             fh.joinuri(write_to, "submission_statistics.parquet"),

tests/test_core_engine/test_backends/test_implementations/test_duckdb/test_duckdb_helpers.py

@@ -3,17 +3,73 @@
 import datetime
 import tempfile
 from pathlib import Path
-from typing import Any
+from typing import Any, List
 
 import pytest
 import pyspark.sql.types as pst
 from duckdb import DuckDBPyRelation, DuckDBPyConnection
+from pydantic import BaseModel
 from pyspark.sql import Row, SparkSession
 
 from dve.core_engine.backends.implementations.duckdb.duckdb_helpers import (
     _ddb_read_parquet,
-    duckdb_rel_to_dictionaries)
+    duckdb_rel_to_dictionaries,
+    get_duckdb_cast_statement_from_annotation,
+    get_duckdb_type_from_annotation)
 
+@pytest.fixture
+def casting_test_table(temp_ddb_conn):
+    _, conn = temp_ddb_conn
+    conn.sql("""CREATE TABLE test_casting (
+    str_test VARCHAR,
+    int_test VARCHAR,
+    date_test VARCHAR,
+    timestamp_test VARCHAR,
+    list_int_field VARCHAR[],
+    basic_model STRUCT(str_field VARCHAR, date_field VARCHAR),
+    another_model STRUCT(unique_id VARCHAR, basic_models STRUCT(str_field VARCHAR, date_field VARCHAR)[]))""")
+
+    conn.sql("""INSERT INTO test_casting
+              VALUES(
+              'good_one',
+              '1',
+              '2024-11-13',
+              '2024-04-15 12:25:36',
+              ['1', '2', '3'],
+              {'str_field': 'test', 'date_field': '2024-12-11'},
+              {'unique_id': '1', "basic_models": [{'str_field': 'test_nest', 'date_field': '2020-01-04'}, {'str_field': 'test_nest2', 'date_field': '2020-01-05'}]}),
+              (
+              'dodgy_dates',
+              '2',
+              '24-11-13',
+              '2024-4-15 12:25:36',
+              ['4', '5', '6'],
+              {'str_field': 'test', 'date_field': '202-1-11'},
+              {'unique_id': '2', "basic_models": [{'str_field': 'test_dd', 'date_field': '20-01-04'}, {'str_field': 'test_dd2', 'date_field': '2020-1-5'}]})""")
+
+
+    yield temp_ddb_conn
+
+    conn.sql("DROP TABLE IF EXISTS test_casting")
+
+
+
+class BasicModel(BaseModel):
+    str_field: str
+    date_field: datetime.date
+
+class AnotherModel(BaseModel):
+    unique_id: int
+    basic_models: List[BasicModel]
+
+class CastingRecord(BaseModel):
+    str_test: str
+    int_test: int
+    date_test: datetime.date
+    timestamp_test: datetime.datetime
+    list_int_field: list[int]
+    basic_model: BasicModel
+    another_model: AnotherModel
 
 class TempConnection:
     """
@@ -25,6 +81,7 @@ def __init__(self, connection: DuckDBPyConnection) -> None:
         self._connection = connection
 
 
+
 @pytest.mark.parametrize(
     "outpath",
     [
@@ -94,4 +151,29 @@ def test_duckdb_rel_to_dictionaries(temp_ddb_conn: DuckDBPyConnection,
         res.append(chunk)
 
     assert res == data
+
+# add decimal check
+@pytest.mark.parametrize("field_name,field_type,cast_statement",
+                         [("str_test", str, "try_cast(trim(\"str_test\") as VARCHAR)"),
+                          ("int_test", int, "try_cast(trim(\"int_test\") as BIGINT)"),
+                          ("date_test", datetime.date,"CASE WHEN REGEXP_MATCHES(TRIM(\"date_test\"), '^[0-9]{4}-[0-9]{2}-[0-9]{2}$') THEN TRY_CAST(TRIM(\"date_test\") as DATE) ELSE NULL END"),
+                          ("timestamp_test", datetime.datetime,"CASE WHEN REGEXP_MATCHES(TRIM(\"timestamp_test\"), '^[0-9]{4}-[0-9]{2}-[0-9]{2}T[0-9]{2}:[0-9]{2}:[0-9]{2}((\+|\-)[0-9]{2}:[0-9]{2})?$') THEN TRY_CAST(TRIM(\"timestamp_test\") as TIMESTAMP) ELSE NULL END"),
+                          ("list_int_field", list[int], "try_cast(list_transform(\"list_int_field\", x -> trim(\"x\")) as BIGINT[])"),
+                          ("basic_model", BasicModel, "try_cast(struct_pack(\"str_field\":= trim(\"basic_model\".str_field),\"date_field\":= CASE WHEN REGEXP_MATCHES(TRIM(\"basic_model\".date_field), '^[0-9]{4}-[0-9]{2}-[0-9]{2}$') THEN TRY_CAST(TRIM(\"basic_model\".date_field) as DATE) ELSE NULL END) as STRUCT(str_field VARCHAR, date_field DATE))"),
+                          ("another_model", AnotherModel, "try_cast(struct_pack(\"unique_id\":= trim(\"another_model\".unique_id),\"basic_models\":= list_transform(\"another_model\".basic_models, x -> struct_pack(\"str_field\":= trim(\"x\".str_field),\"date_field\":= CASE WHEN REGEXP_MATCHES(TRIM(\"x\".date_field), '^[0-9]{4}-[0-9]{2}-[0-9]{2}$') THEN TRY_CAST(TRIM(\"x\".date_field) as DATE) ELSE NULL END))) as STRUCT(unique_id BIGINT, basic_models STRUCT(str_field VARCHAR, date_field DATE)[]))")])
+def test_get_duckdb_cast_statement_from_annotation(field_name, field_type, cast_statement):
+    assert get_duckdb_cast_statement_from_annotation(field_name, field_type) == cast_statement
+
+
+def test_use_cast_statements(casting_test_table):
+    _, conn = casting_test_table
+    test_rel = conn.sql("SELECT * from test_casting")
+    casting_statements = [ f"{get_duckdb_cast_statement_from_annotation(fld.name, fld.annotation)} as {fld.name}" for fld in CastingRecord.__fields__.values()]
+    test_rel = test_rel.project(",".join(casting_statements))
+    assert dict(zip(test_rel.columns, test_rel.dtypes)) == {fld.name: get_duckdb_type_from_annotation(fld.annotation) for fld in CastingRecord.__fields__.values()}
+    dodgy_date_rec = test_rel.pl()[1].to_dicts()[0]
+    assert (not dodgy_date_rec.get("date_test") and 
+            not dodgy_date_rec.get("basic_model",{}).get("date_field")
+            and all(not val.get("date_field") for val in dodgy_date_rec.get("another_model",{}).get("basic_models",[]))
+    )