szehon-ho commented on code in PR #56045:
URL: https://github.com/apache/spark/pull/56045#discussion_r3290552940
##########
python/pyspark/pipelines/api.py:
##########
@@ -525,3 +527,136 @@ 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,
+ apply_as_truncates: 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,
+ ignore_null_updates_column_list: Optional[Union[List[str], List[Column]]]
= None,
+ ignore_null_updates_except_column_list: Optional[Union[List[str],
List[Column]]] = 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.
Review Comment:
Doc says mutual exclusion and non-empty `keys`, but nothing enforces it.
Validate after normalization (like other SDP APIs) so users get a clear client
error.
##########
python/pyspark/pipelines/spark_connect_graph_element_registry.py:
##########
@@ -133,6 +134,47 @@ def register_flow(self, flow: Flow) -> None:
command.pipeline_command.define_flow.CopyFrom(inner_command)
self._client.execute_command(command)
+ def register_auto_cdc_flow(self, flow: AutoCdcFlow) -> None:
Review Comment:
After server implements `AUTO_CDC_FLOW_DETAILS`, add a Connect registry
test; note in PR that Connect still throws today.
##########
python/pyspark/pipelines/api.py:
##########
@@ -525,3 +527,136 @@ 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,
+ apply_as_truncates: 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,
+ ignore_null_updates_column_list: Optional[Union[List[str], List[Column]]]
= None,
+ ignore_null_updates_except_column_list: Optional[Union[List[str],
List[Column]]] = 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",
+ ignore_null_updates_column_list = ["value"],
+ column_list = ["key", "value"],
+ )
+
+ Note that for keys, sequence_by, column_list, except_column_list,
+ ignore_null_updates_column_list, and
ignore_null_updates_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.
Review Comment:
Nit: `Pyspark` → `PySpark` in this docstring (573, 575, 581–585).
##########
python/pyspark/pipelines/api.py:
##########
@@ -525,3 +527,136 @@ 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,
+ apply_as_truncates: 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,
+ ignore_null_updates_column_list: Optional[Union[List[str], List[Column]]]
= None,
+ ignore_null_updates_except_column_list: Optional[Union[List[str],
List[Column]]] = 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",
+ ignore_null_updates_column_list = ["value"],
+ column_list = ["key", "value"],
+ )
+
+ Note that for keys, sequence_by, column_list, except_column_list,
+ ignore_null_updates_column_list, and
ignore_null_updates_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 apply_as_truncates: Truncate condition for the merged operation.
This should be a string \
+ expression e.g. "operation = 'TRUNCATE'"
+ :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.
+ :param ignore_null_updates_column_list: Subset of columns to ignore null
values in during \
+ updates. When a source row has a null for one of these columns, the
existing value in the \
+ target is preserved. Only one of ignore_null_updates_column_list and \
+ ignore_null_updates_except_column_list can be specified.
+ :param ignore_null_updates_except_column_list: Columns excluded from
null-update ignoring. \
+ All other columns will have null values ignored during updates. Only
one of \
+ ignore_null_updates_column_list and
ignore_null_updates_except_column_list can be specified.
+ """
+ keys = _normalize_column_list(keys)
+
+ column_list = _normalize_optional_column_list(column_list)
+ except_column_list = _normalize_optional_column_list(except_column_list)
+ ignore_null_updates_column_list = _normalize_optional_column_list(
+ ignore_null_updates_column_list
+ )
+ ignore_null_updates_except_column_list = _normalize_optional_column_list(
+ ignore_null_updates_except_column_list
+ )
+
+ if isinstance(sequence_by, str):
+ sequence_by = F.expr(sequence_by)
+
+ if isinstance(apply_as_deletes, str):
+ apply_as_deletes = F.expr(apply_as_deletes)
+
+ if isinstance(apply_as_truncates, str):
+ apply_as_truncates = F.expr(apply_as_truncates)
+
+ if stored_as_scd_type is not None and str(stored_as_scd_type) != "1":
+ raise PySparkTypeError(
+ errorClass="NOT_EXPECTED_TYPE",
+ messageParameters={
+ "arg_name": "stored_as_scd_type",
+ "expected_type": "Literal[1, '1']",
+ "arg_type": type(stored_as_scd_type).__name__,
+ },
+ )
+
+ source_code_location = get_caller_source_code_location(stacklevel=1)
+
+ flow = AutoCdcFlow(
+ name=name,
+ target=target,
+ source=source,
+ keys=keys,
+ sequence_by=sequence_by,
+ apply_as_deletes=apply_as_deletes,
+ apply_as_truncates=apply_as_truncates,
+ column_list=column_list,
+ except_column_list=except_column_list,
+ stored_as_scd_type=stored_as_scd_type,
+ ignore_null_updates_column_list=ignore_null_updates_column_list,
+
ignore_null_updates_except_column_list=ignore_null_updates_except_column_list,
+ source_code_location=source_code_location,
+ )
+
+ get_active_graph_element_registry().register_auto_cdc_flow(flow)
+
+
+def _normalize_optional_column_list(
+ column_list: Optional[Union[List[str], List[Column]]],
+) -> Optional[List[Column]]:
+ if column_list is None:
+ return None
+ return _normalize_column_list(column_list)
+
+
+def _normalize_column_list(
+ column_list: Union[List[str], List[Column]],
+) -> List[Column]:
+ return [F.col(c) if isinstance(c, str) else c for c in column_list]
Review Comment:
Add tests for string args (`keys=["id"]`, `sequence_by="ts"`, etc.), not
only Connect `col`/`expr`.
##########
python/pyspark/pipelines/spark_connect_graph_element_registry.py:
##########
@@ -17,8 +17,13 @@
from pathlib import Path
Review Comment:
Nit: import shuffle only — consider keeping prior order to shrink diff.
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