AnishMahto commented on code in PR #56069:
URL: https://github.com/apache/spark/pull/56069#discussion_r3293652510
##########
python/pyspark/pipelines/api.py:
##########
@@ -525,3 +527,109 @@ def create_sink(
comment=None,
)
get_active_graph_element_registry().register_output(sink)
+
+
+def create_auto_cdc_flow(
+ target: str,
+ source: str,
+ keys: Union[List[str], List[Column]],
+ sequence_by: Union[str, Column],
+ apply_as_deletes: Optional[Union[str, Column]] = None,
+ column_list: Optional[Union[List[str], List[Column]]] = None,
+ except_column_list: Optional[Union[List[str], List[Column]]] = None,
+ stored_as_scd_type: Optional[Literal[1, "1"]] = None,
+ name: Optional[str] = None,
+) -> None:
+ """
+ Create an Auto CDC flow into the target table from the Change Data Capture
(CDC) source.
+ Target table must have already been created using create_streaming_table
function. Only one
+ of column_list and except_column_list can be specified.
+
+ Example:
+ create_auto_cdc_flow(
+ target = "target",
+ source = "source",
+ keys = ["key"],
+ sequence_by = "sequence_expr",
+ column_list = ["key", "value"],
+ )
+
+ Note that for keys, sequence_by, column_list, and except_column_list the
arguments have to
+ be column identifiers without qualifiers, e.g. they cannot be
col("sourceTable.keyId").
+
+ :param target: The name of the target table that receives the Auto CDC
flow.
+ :param source: The name of the CDC source to stream from.
+ :param keys: The column or combination of columns that uniquely identify a
row in the source \
+ data. This is used to identify which CDC events apply to specific
records in the target \
+ table. These keys also identify records in the target table, e.g., if
there exists a record \
+ for given keys and the CDC source has an UPSERT operation for the same
keys, we will update \
+ the existing record. At least one key must be provided. This should be
a list of column \
+ identifiers without qualifiers, expressed as either Python strings or
PySpark Columns.
+ :param sequence_by: An expression that we use to order the source data.
This can be expressed \
+ as either a Python string or PySpark Expression.
+ :param apply_as_deletes: Delete condition for the merged operation. This
should be a string of \
+ expression e.g. "operation = 'DELETE'"
+ :param column_list: Columns that will be included in the output table.
This should be a list \
+ of column identifiers without qualifiers, expressed as either Python
strings or PySpark \
+ Column. Only one of column_list and except_column_list can be
specified.
+ :param except_column_list: Columns that will be excluded in the output
table. This should be a \
+ list of column identifiers without qualifiers, expressed as either
Python strings or \
+ PySpark Column. Only one of column_list and except_column_list can be
specified. When this \
+ is specified, all columns in the dataframe of the target table except
those in this list \
+ will be in the output table.
+ :param stored_as_scd_type: The SCD type for the target table. Only 1 (or
"1") is supported. \
+ When not specified the server default applies.
+ :param name: The name of the flow for this create_auto_cdc_flow command.
When unspecified this \
+ will build a "default flow" with name equal to the target name.
+ """
+ keys = _normalize_column_list(keys)
Review Comment:
In compliance with other SDP APIs, I added type checks. These actually make
sense to do at the Python API layer, since Python specifically does not provide
strong static typing.
Leaving logical validations for the Spark driver/pipelines handler though,
since these validations are client language independent (ex. if we support any
other language clients in the future, the validation should be the same).
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