Merge pull request #959 from dlt-hub/devel

dlt 0.4.4 release master merge

dlt/common/libs/pydantic.py

@@ -140,7 +140,7 @@ def pydantic_to_table_schema_columns(
             # This case is for a single field schema/model
             # we need to generate snake_case field names
             # and return flattened field schemas
-            schema_hints = pydantic_to_table_schema_columns(field.annotation)
+            schema_hints = pydantic_to_table_schema_columns(inner_type)
 
             for field_name, hints in schema_hints.items():
                 schema_key = snake_case_naming_convention.make_path(name, field_name)

dlt/common/typing.py

@@ -25,7 +25,26 @@
     runtime_checkable,
     IO,
 )
-from typing_extensions import TypeAlias, ParamSpec, Concatenate, Annotated, get_args, get_origin
+
+from typing_extensions import (
+    Annotated,
+    Never,
+    ParamSpec,
+    TypeAlias,
+    Concatenate,
+    get_args,
+    get_origin,
+)
+
+try:
+    from types import UnionType  # type: ignore[attr-defined]
+except ImportError:
+    # Since new Union syntax was introduced in Python 3.10
+    # we need to substitute it here for older versions.
+    # it is defined as type(int | str) but for us having it
+    # as shown here should suffice because it is valid only
+    # in versions of Python>=3.10.
+    UnionType = Never
 
 from dlt.common.pendulum import timedelta, pendulum
 
@@ -103,18 +122,35 @@ def extract_type_if_modifier(t: Type[Any]) -> Type[Any]:
 
 
 def is_union_type(hint: Type[Any]) -> bool:
-    if get_origin(hint) is Union:
+    # We need to handle UnionType because with Python>=3.10
+    # new Optional syntax was introduced which treats Optionals
+    # as unions and probably internally there is no additional
+    # type hints to handle this edge case, see the examples below
+    # >>> type(str | int)
+    # <class 'types.UnionType'>
+    # >>> type(str | None)
+    # <class 'types.UnionType'>
+    # type(Union[int, str])
+    # <class 'typing._GenericAlias'>
+    origin = get_origin(hint)
+    if origin is Union or origin is UnionType:
         return True
+
     if hint := extract_type_if_modifier(hint):
         return is_union_type(hint)
+
     return False
 
 
 def is_optional_type(t: Type[Any]) -> bool:
-    if get_origin(t) is Union:
-        return type(None) in get_args(t)
+    origin = get_origin(t)
+    is_union = origin is Union or origin is UnionType
+    if is_union and type(None) in get_args(t):
+        return True
+
     if t := extract_type_if_modifier(t):
         return is_optional_type(t)
+
     return False
 
 
@@ -232,7 +268,7 @@ def get_generic_type_argument_from_instance(
 
 
 def copy_sig(
-    wrapper: Callable[TInputArgs, Any]
+    wrapper: Callable[TInputArgs, Any],
 ) -> Callable[[Callable[..., TReturnVal]], Callable[TInputArgs, TReturnVal]]:
     """Copies docstring and signature from wrapper to func but keeps the func return value type"""
 

dlt/destinations/impl/athena/athena.py

@@ -351,7 +351,9 @@ def _from_db_type(
         return self.type_mapper.from_db_type(hive_t, precision, scale)
 
     def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = None) -> str:
-        return f"{self.sql_client.escape_ddl_identifier(c['name'])} {self.type_mapper.to_db_type(c, table_format)}"
+        return (
+            f"{self.sql_client.escape_ddl_identifier(c['name'])} {self.type_mapper.to_db_type(c, table_format)}"
+        )
 
     def _get_table_update_sql(
         self, table_name: str, new_columns: Sequence[TColumnSchema], generate_alter: bool
@@ -376,19 +378,15 @@ def _get_table_update_sql(
         # use qualified table names
         qualified_table_name = self.sql_client.make_qualified_ddl_table_name(table_name)
         if is_iceberg and not generate_alter:
-            sql.append(
-                f"""CREATE TABLE {qualified_table_name}
+            sql.append(f"""CREATE TABLE {qualified_table_name}
                     ({columns})
                     LOCATION '{location}'
-                    TBLPROPERTIES ('table_type'='ICEBERG', 'format'='parquet');"""
-            )
+                    TBLPROPERTIES ('table_type'='ICEBERG', 'format'='parquet');""")
         elif not generate_alter:
-            sql.append(
-                f"""CREATE EXTERNAL TABLE {qualified_table_name}
+            sql.append(f"""CREATE EXTERNAL TABLE {qualified_table_name}
                     ({columns})
                     STORED AS PARQUET
-                    LOCATION '{location}';"""
-            )
+                    LOCATION '{location}';""")
         # alter table to add new columns at the end
         else:
             sql.append(f"""ALTER TABLE {qualified_table_name} ADD COLUMNS ({columns});""")

dlt/destinations/impl/bigquery/bigquery.py

@@ -252,9 +252,9 @@ def _get_table_update_sql(
             elif (c := partition_list[0])["data_type"] == "date":
                 sql[0] = f"{sql[0]}\nPARTITION BY {self.capabilities.escape_identifier(c['name'])}"
             elif (c := partition_list[0])["data_type"] == "timestamp":
-                sql[
-                    0
-                ] = f"{sql[0]}\nPARTITION BY DATE({self.capabilities.escape_identifier(c['name'])})"
+                sql[0] = (
+                    f"{sql[0]}\nPARTITION BY DATE({self.capabilities.escape_identifier(c['name'])})"
+                )
             # Automatic partitioning of an INT64 type requires us to be prescriptive - we treat the column as a UNIX timestamp.
             # This is due to the bounds requirement of GENERATE_ARRAY function for partitioning.
             # The 10,000 partitions limit makes it infeasible to cover the entire `bigint` range.
@@ -272,7 +272,9 @@ def _get_table_update_sql(
 
     def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = None) -> str:
         name = self.capabilities.escape_identifier(c["name"])
-        return f"{name} {self.type_mapper.to_db_type(c, table_format)} {self._gen_not_null(c.get('nullable', True))}"
+        return (
+            f"{name} {self.type_mapper.to_db_type(c, table_format)} {self._gen_not_null(c.get('nullable', True))}"
+        )
 
     def get_storage_table(self, table_name: str) -> Tuple[bool, TTableSchemaColumns]:
         schema_table: TTableSchemaColumns = {}

dlt/destinations/impl/databricks/databricks.py

@@ -166,12 +166,14 @@ def __init__(
             else:
                 raise LoadJobTerminalException(
                     file_path,
-                    f"Databricks cannot load data from staging bucket {bucket_path}. Only s3 and azure buckets are supported",
+                    f"Databricks cannot load data from staging bucket {bucket_path}. Only s3 and"
+                    " azure buckets are supported",
                 )
         else:
             raise LoadJobTerminalException(
                 file_path,
-                "Cannot load from local file. Databricks does not support loading from local files. Configure staging with an s3 or azure storage bucket.",
+                "Cannot load from local file. Databricks does not support loading from local files."
+                " Configure staging with an s3 or azure storage bucket.",
             )
 
         # decide on source format, stage_file_path will either be a local file or a bucket path
@@ -181,27 +183,33 @@ def __init__(
             if not config.get("data_writer.disable_compression"):
                 raise LoadJobTerminalException(
                     file_path,
-                    "Databricks loader does not support gzip compressed JSON files. Please disable compression in the data writer configuration: https://dlthub.com/docs/reference/performance#disabling-and-enabling-file-compression",
+                    "Databricks loader does not support gzip compressed JSON files. Please disable"
+                    " compression in the data writer configuration:"
+                    " https://dlthub.com/docs/reference/performance#disabling-and-enabling-file-compression",
                 )
             if table_schema_has_type(table, "decimal"):
                 raise LoadJobTerminalException(
                     file_path,
-                    "Databricks loader cannot load DECIMAL type columns from json files. Switch to parquet format to load decimals.",
+                    "Databricks loader cannot load DECIMAL type columns from json files. Switch to"
+                    " parquet format to load decimals.",
                 )
             if table_schema_has_type(table, "binary"):
                 raise LoadJobTerminalException(
                     file_path,
-                    "Databricks loader cannot load BINARY type columns from json files. Switch to parquet format to load byte values.",
+                    "Databricks loader cannot load BINARY type columns from json files. Switch to"
+                    " parquet format to load byte values.",
                 )
             if table_schema_has_type(table, "complex"):
                 raise LoadJobTerminalException(
                     file_path,
-                    "Databricks loader cannot load complex columns (lists and dicts) from json files. Switch to parquet format to load complex types.",
+                    "Databricks loader cannot load complex columns (lists and dicts) from json"
+                    " files. Switch to parquet format to load complex types.",
                 )
             if table_schema_has_type(table, "date"):
                 raise LoadJobTerminalException(
                     file_path,
-                    "Databricks loader cannot load DATE type columns from json files. Switch to parquet format to load dates.",
+                    "Databricks loader cannot load DATE type columns from json files. Switch to"
+                    " parquet format to load dates.",
                 )
 
             source_format = "JSON"
@@ -311,7 +319,7 @@ def _get_column_def_sql(self, c: TColumnSchema, table_format: TTableFormat = Non
 
     def _get_storage_table_query_columns(self) -> List[str]:
         fields = super()._get_storage_table_query_columns()
-        fields[
-            1
-        ] = "full_data_type"  # Override because this is the only way to get data type with precision
+        fields[1] = (  # Override because this is the only way to get data type with precision
+            "full_data_type"
+        )
         return fields

dlt/destinations/impl/snowflake/snowflake.py

@@ -175,15 +175,13 @@ def __init__(
                     f'PUT file://{file_path} @{stage_name}/"{load_id}" OVERWRITE = TRUE,'
                     " AUTO_COMPRESS = FALSE"
                 )
-            client.execute_sql(
-                f"""COPY INTO {qualified_table_name}
+            client.execute_sql(f"""COPY INTO {qualified_table_name}
                 {from_clause}
                 {files_clause}
                 {credentials_clause}
                 FILE_FORMAT = {source_format}
                 MATCH_BY_COLUMN_NAME='CASE_INSENSITIVE'
-                """
-            )
+                """)
             if stage_file_path and not keep_staged_files:
                 client.execute_sql(f"REMOVE {stage_file_path}")
 

dlt/extract/incremental/__init__.py

@@ -119,7 +119,7 @@ def __init__(
         self.start_value: Any = initial_value
         """Value of last_value at the beginning of current pipeline run"""
         self.resource_name: Optional[str] = None
-        self.primary_key: Optional[TTableHintTemplate[TColumnNames]] = primary_key
+        self._primary_key: Optional[TTableHintTemplate[TColumnNames]] = primary_key
         self.allow_external_schedulers = allow_external_schedulers
 
         self._cached_state: IncrementalColumnState = None
@@ -133,6 +133,18 @@ def __init__(
 
         self._transformers: Dict[str, IncrementalTransform] = {}
 
+    @property
+    def primary_key(self) -> Optional[TTableHintTemplate[TColumnNames]]:
+        return self._primary_key
+
+    @primary_key.setter
+    def primary_key(self, value: str) -> None:
+        # set key in incremental and data type transformers
+        self._primary_key = value
+        if self._transformers:
+            for transform in self._transformers.values():
+                transform.primary_key = value
+
     def _make_transforms(self) -> None:
         types = [("arrow", ArrowIncremental), ("json", JsonIncremental)]
         for dt, kls in types:
@@ -143,7 +155,7 @@ def _make_transforms(self) -> None:
                 self.end_value,
                 self._cached_state,
                 self.last_value_func,
-                self.primary_key,
+                self._primary_key,
             )
 
     @classmethod
@@ -163,7 +175,7 @@ def copy(self) -> "Incremental[TCursorValue]":
             self.cursor_path,
             initial_value=self.initial_value,
             last_value_func=self.last_value_func,
-            primary_key=self.primary_key,
+            primary_key=self._primary_key,
             end_value=self.end_value,
             allow_external_schedulers=self.allow_external_schedulers,
         )
@@ -178,7 +190,7 @@ def merge(self, other: "Incremental[TCursorValue]") -> "Incremental[TCursorValue
         >>>
         >>> my_resource(updated=incremental(initial_value='2023-01-01', end_value='2023-02-01'))
         """
-        kwargs = dict(self, last_value_func=self.last_value_func, primary_key=self.primary_key)
+        kwargs = dict(self, last_value_func=self.last_value_func, primary_key=self._primary_key)
         for key, value in dict(
             other, last_value_func=other.last_value_func, primary_key=other.primary_key
         ).items():
@@ -395,7 +407,6 @@ def __call__(self, rows: TDataItems, meta: Any = None) -> Optional[TDataItems]:
             return rows
 
         transformer = self._get_transformer(rows)
-        transformer.primary_key = self.primary_key
 
         if isinstance(rows, list):
             return [
@@ -476,7 +487,7 @@ def _wrap(*args: Any, **kwargs: Any) -> Any:
             elif isinstance(p.default, Incremental):
                 new_incremental = p.default.copy()
 
-            if not new_incremental or new_incremental.is_partial():
+            if (not new_incremental or new_incremental.is_partial()) and not self._incremental:
                 if is_optional_type(p.annotation):
                     bound_args.arguments[p.name] = None  # Remove partial spec
                     return func(*bound_args.args, **bound_args.kwargs)
@@ -486,15 +497,16 @@ def _wrap(*args: Any, **kwargs: Any) -> Any:
                 )
             # pass Generic information from annotation to new_incremental
             if (
-                not hasattr(new_incremental, "__orig_class__")
+                new_incremental
+                and not hasattr(new_incremental, "__orig_class__")
                 and p.annotation
                 and get_args(p.annotation)
             ):
                 new_incremental.__orig_class__ = p.annotation  # type: ignore
 
             # set the incremental only if not yet set or if it was passed explicitly
             # NOTE: the _incremental may be also set by applying hints to the resource see `set_template` in `DltResource`
-            if p.name in bound_args.arguments or not self._incremental:
+            if (new_incremental and p.name in bound_args.arguments) or not self._incremental:
                 self._incremental = new_incremental
             self._incremental.resolve()
             # in case of transformers the bind will be called before this wrapper is set: because transformer is called for a first time late in the pipe
@@ -531,6 +543,9 @@ def __call__(self, item: TDataItems, meta: Any = None) -> Optional[TDataItems]:
             return item
         if self._incremental.primary_key is None:
             self._incremental.primary_key = self.primary_key
+        elif self.primary_key is None:
+            # propagate from incremental
+            self.primary_key = self._incremental.primary_key
         return self._incremental(item, meta)
 
 

dlt/pipeline/pipeline.py

@@ -1163,9 +1163,9 @@ def _set_context(self, is_active: bool) -> None:
             # set destination context on activation
             if self.destination:
                 # inject capabilities context
-                self._container[
-                    DestinationCapabilitiesContext
-                ] = self._get_destination_capabilities()
+                self._container[DestinationCapabilitiesContext] = (
+                    self._get_destination_capabilities()
+                )
         else:
             # remove destination context on deactivation
             if DestinationCapabilitiesContext in self._container: