add support for text item type and text to sql job writer

dlt/common/data_writers/writers.py

@@ -44,7 +44,7 @@
     from dlt.common.libs.pyarrow import pyarrow as pa
 
 
-TDataItemFormat = Literal["arrow", "object", "file"]
+TDataItemFormat = Literal["arrow", "object", "file", "text"]
 TWriter = TypeVar("TWriter", bound="DataWriter")
 
 
@@ -115,6 +115,8 @@ def item_format_from_file_extension(cls, extension: str) -> TDataItemFormat:
             return "object"
         elif extension == "parquet":
             return "arrow"
+        elif extension == "sql":
+            return "text"
         # those files may be imported by normalizer as is
         elif extension in LOADER_FILE_FORMATS:
             return "file"
@@ -175,6 +177,31 @@ def writer_spec(cls) -> FileWriterSpec:
         )
 
 
+class TextWriter(DataWriter):
+    """Writes incoming items row by row into a text file"""
+
+    def write_header(self, columns_schema: TTableSchemaColumns) -> None:
+        pass
+
+    def write_data(self, items: Sequence[TDataItem]) -> None:
+        super().write_data(items)
+        self.items_count += len(items)
+
+        for item in items:
+            self._f.write(item + "\n")
+
+    @classmethod
+    def writer_spec(cls) -> FileWriterSpec:
+        return FileWriterSpec(
+            "sql",
+            "text",
+            file_extension="sql",
+            is_binary_format=False,
+            supports_schema_changes="True",
+            supports_compression=False,
+        )
+
+
 class TypedJsonlListWriter(JsonlWriter):
     def write_data(self, items: Sequence[TDataItem]) -> None:
         # skip JsonlWriter when calling super
@@ -670,6 +697,7 @@ def is_native_writer(writer_type: Type[DataWriter]) -> bool:
     ArrowToJsonlWriter,
     ArrowToTypedJsonlListWriter,
     ArrowToCsvWriter,
+    TextWriter,
 ]
 
 WRITER_SPECS: Dict[FileWriterSpec, Type[DataWriter]] = {
@@ -689,6 +717,11 @@ def is_native_writer(writer_type: Type[DataWriter]) -> bool:
         for writer in ALL_WRITERS
         if writer.writer_spec().data_item_format == "arrow" and is_native_writer(writer)
     ),
+    "text": tuple(
+        writer
+        for writer in ALL_WRITERS
+        if writer.writer_spec().data_item_format == "text" and is_native_writer(writer)
+    ),
 }
 
 

dlt/common/destination/utils.py

@@ -152,7 +152,7 @@ def verify_supported_data_types(
     for parsed_file in new_jobs:
         formats = table_file_formats.setdefault(parsed_file.table_name, set())
         if parsed_file.file_format in LOADER_FILE_FORMATS:
-            formats.add(parsed_file.file_format)  # type: ignore[arg-type]
+            formats.add(parsed_file.file_format)
     # all file formats
     all_file_formats = set(capabilities.supported_loader_file_formats or []) | set(
         capabilities.supported_staging_file_formats or []

dlt/common/schema/typing.py

@@ -126,7 +126,7 @@ class TColumnPropInfo(NamedTuple):
     if prop in COLUMN_HINTS:
         ColumnPropInfos[prop] = ColumnPropInfos[prop]._replace(is_hint=True)
 
-TTableFormat = Literal["iceberg", "delta", "hive", "native"]
+TTableFormat = Literal["iceberg", "delta", "hive", "native", "view"]
 TFileFormat = Literal[Literal["preferred"], TLoaderFileFormat]
 TTypeDetections = Literal[
     "timestamp", "iso_timestamp", "iso_date", "large_integer", "hexbytes_to_text", "wei_to_double"

dlt/common/typing.py

@@ -126,7 +126,9 @@ class SecretSentinel:
 VARIANT_FIELD_FORMAT = "v_%s"
 TFileOrPath = Union[str, PathLike, IO[Any]]
 TSortOrder = Literal["asc", "desc"]
-TLoaderFileFormat = Literal["jsonl", "typed-jsonl", "insert_values", "parquet", "csv", "reference"]
+TLoaderFileFormat = Literal[
+    "jsonl", "typed-jsonl", "insert_values", "parquet", "csv", "reference", "sql"
+]
 """known loader file formats"""
 
 TDynHintType = TypeVar("TDynHintType")

dlt/destinations/impl/duckdb/factory.py

@@ -129,7 +129,7 @@ class duckdb(Destination[DuckDbClientConfiguration, "DuckDbClient"]):
     def _raw_capabilities(self) -> DestinationCapabilitiesContext:
         caps = DestinationCapabilitiesContext()
         caps.preferred_loader_file_format = "insert_values"
-        caps.supported_loader_file_formats = ["insert_values", "parquet", "jsonl"]
+        caps.supported_loader_file_formats = ["insert_values", "parquet", "jsonl", "sql"]
         caps.preferred_staging_file_format = None
         caps.supported_staging_file_formats = []
         caps.type_mapper = DuckDbTypeMapper

dlt/extract/extract.py

@@ -45,7 +45,7 @@
 from dlt.extract.reference import SourceReference
 from dlt.extract.resource import DltResource
 from dlt.extract.storage import ExtractStorage
-from dlt.extract.extractors import ObjectExtractor, ArrowExtractor, Extractor
+from dlt.extract.extractors import ObjectExtractor, ArrowExtractor, Extractor, TextExtractor
 from dlt.extract.utils import get_data_item_format
 
 
@@ -343,6 +343,9 @@ def _extract_single_source(
             "arrow": ArrowExtractor(
                 load_id, self.extract_storage.item_storages["arrow"], schema, collector=collector
             ),
+            "text": TextExtractor(
+                load_id, self.extract_storage.item_storages["text"], schema, collector=collector
+            ),
         }
         # make sure we close storage on exception
         with collector(f"Extract {source.name}"):
@@ -363,7 +366,7 @@ def _extract_single_source(
                             collector.update("Resources", delta)
                         signals.raise_if_signalled()
                         resource = source.resources[pipe_item.pipe.name]
-                        item_format = get_data_item_format(pipe_item.item)
+                        item_format = get_data_item_format(pipe_item.item, resource.file_format)
                         extractors[item_format].write_items(
                             resource, pipe_item.item, pipe_item.meta
                         )

dlt/extract/extractors.py

@@ -292,6 +292,12 @@ class ObjectExtractor(Extractor):
     pass
 
 
+class TextExtractor(Extractor):
+    """Extracts text items and writes them row by row into a text file"""
+
+    pass
+
+
 class ArrowExtractor(Extractor):
     """Extracts arrow data items into parquet. Normalizes arrow items column names.
     Compares the arrow schema to actual dlt table schema to reorder the columns and to

dlt/extract/hints.py

@@ -178,6 +178,10 @@ def schema_contract(self) -> TTableHintTemplate[TSchemaContract]:
     def table_format(self) -> TTableHintTemplate[TTableFormat]:
         return None if self._hints is None else self._hints.get("table_format")
 
+    @property
+    def file_format(self) -> TTableHintTemplate[TFileFormat]:
+        return None if self._hints is None else self._hints.get("file_format")
+
     @property
     def parent_table_name(self) -> TTableHintTemplate[str]:
         return None if self._hints is None else self._hints.get("parent")

dlt/extract/storage.py

@@ -50,6 +50,9 @@ def __init__(self, config: NormalizeStorageConfiguration) -> None:
             "arrow": ExtractorItemStorage(
                 self.new_packages, DataWriter.writer_spec_from_file_format("parquet", "arrow")
             ),
+            "text": ExtractorItemStorage(
+                self.new_packages, DataWriter.writer_spec_from_file_format("sql", "text")
+            ),
         }
 
     def create_load_package(self, schema: Schema, reuse_exiting_package: bool = True) -> str:

dlt/extract/utils.py

@@ -44,6 +44,8 @@
     SupportsPipe,
 )
 
+from dlt.common.schema.typing import TFileFormat
+
 try:
     from dlt.common.libs import pydantic
 except MissingDependencyException:
@@ -61,14 +63,22 @@
     pandas = None
 
 
-def get_data_item_format(items: TDataItems) -> TDataItemFormat:
+def get_data_item_format(
+    items: TDataItems, file_format: TTableHintTemplate[TFileFormat] = None
+) -> TDataItemFormat:
     """Detect the format of the data item from `items`.
 
     Reverts to `object` for empty lists
 
     Returns:
         The data file format.
     """
+
+    # if file format is specified as sql, we expect pure text from the resource
+    file_format = file_format(items) if callable(file_format) else file_format
+    if file_format == "sql":
+        return "text"
+
     if not pyarrow and not pandas:
         return "object"
 

dlt/normalize/worker.py

@@ -111,7 +111,7 @@ def _get_items_normalizer(
             if item_format == "file":
                 # if we want to import file, create a spec that may be used only for importing
                 best_writer_spec = create_import_spec(
-                    parsed_file_name.file_format, items_supported_file_formats  # type: ignore[arg-type]
+                    parsed_file_name.file_format, items_supported_file_formats
                 )
 
             config_loader_file_format = config.loader_file_format

tests/load/test_sql_resource.py

@@ -0,0 +1,41 @@
+# test the sql insert job loader, works only on duckdb for now
+
+from typing import Any
+
+import dlt
+
+from dlt.common.destination.dataset import SupportsReadableDataset
+
+from tests.pipeline.utils import load_table_counts
+
+
+def test_sql_job() -> None:
+    # populate a table with 10 items and retrieve dataset
+    pipeline = dlt.pipeline(
+        pipeline_name="example_pipeline", destination="duckdb", dataset_name="example_dataset"
+    )
+    pipeline.run([{"a": i} for i in range(10)], table_name="example_table")
+    dataset = pipeline.dataset()
+
+    # create a resource that generates sql statements to create 2 new tables
+    @dlt.resource(file_format="sql")
+    def copied_table() -> Any:
+        query = dataset["example_table"].limit(5).query()
+        yield f"CREATE OR REPLACE TABLE copied_table AS {query};"
+        query = dataset["example_table"].limit(7).query()
+        yield f"CREATE OR REPLACE TABLE copied_table2 AS {query};"
+
+    # run sql jobs
+    pipeline.run(copied_table())
+
+    # the two tables where created
+    assert load_table_counts(pipeline, "example_table", "copied_table", "copied_table2") == {
+        "example_table": 10,
+        "copied_table": 5,
+        "copied_table2": 7,
+    }
+
+    # we have a table entry for the main table "copied_table"
+    assert "copied_table" in pipeline.default_schema.tables
+    # but no columns, it's up to the user to provide a schema
+    assert len(pipeline.default_schema.tables["copied_table"]["columns"]) == 0