Fix/1571 Incremental: Optionally raise, load, or ignore raise records…

… with cursor_path missing or None value (#1576)

* allows specification of what happens on cursor_path missing or cursor_path having the value None: raise differentiated exceptions, exclude row, or include row.

* Documents handling None values at the incremental cursor

* fixes incremental extract crashing if one record has cursor_path = None

* test that add_map can be used to transform items before the incremental function is called

* Unifies treating of None values for python Objects (including pydantic), pandas, and arrow

---------

Co-authored-by: Marcin Rudolf <[email protected]>

dlt/extract/incremental/__init__.py

@@ -35,7 +35,12 @@
     IncrementalCursorPathMissing,
     IncrementalPrimaryKeyMissing,
 )
-from dlt.extract.incremental.typing import IncrementalColumnState, TCursorValue, LastValueFunc
+from dlt.extract.incremental.typing import (
+    IncrementalColumnState,
+    TCursorValue,
+    LastValueFunc,
+    OnCursorValueMissing,
+)
 from dlt.extract.pipe import Pipe
 from dlt.extract.items import SupportsPipe, TTableHintTemplate, ItemTransform
 from dlt.extract.incremental.transform import (
@@ -81,7 +86,7 @@ class Incremental(ItemTransform[TDataItem], BaseConfiguration, Generic[TCursorVa
     >>> info = p.run(r, destination="duckdb")
 
     Args:
-        cursor_path: The name or a JSON path to an cursor field. Uses the same names of fields as in your JSON document, before they are normalized to store in the database.
+        cursor_path: The name or a JSON path to a cursor field. Uses the same names of fields as in your JSON document, before they are normalized to store in the database.
         initial_value: Optional value used for `last_value` when no state is available, e.g. on the first run of the pipeline. If not provided `last_value` will be `None` on the first run.
         last_value_func: Callable used to determine which cursor value to save in state. It is called with a list of the stored state value and all cursor vals from currently processing items. Default is `max`
         primary_key: Optional primary key used to deduplicate data. If not provided, a primary key defined by the resource will be used. Pass a tuple to define a compound key. Pass empty tuple to disable unique checks
@@ -95,6 +100,7 @@ class Incremental(ItemTransform[TDataItem], BaseConfiguration, Generic[TCursorVa
             specified range of data. Currently Airflow scheduler is detected: "data_interval_start" and "data_interval_end" are taken from the context and passed Incremental class.
             The values passed explicitly to Incremental will be ignored.
             Note that if logical "end date" is present then also "end_value" will be set which means that resource state is not used and exactly this range of date will be loaded
+        on_cursor_value_missing: Specify what happens when the cursor_path does not exist in a record or a record has `None` at the cursor_path: raise, include, exclude
     """
 
     # this is config/dataclass so declare members
@@ -104,6 +110,7 @@ class Incremental(ItemTransform[TDataItem], BaseConfiguration, Generic[TCursorVa
     end_value: Optional[Any] = None
     row_order: Optional[TSortOrder] = None
     allow_external_schedulers: bool = False
+    on_cursor_value_missing: OnCursorValueMissing = "raise"
 
     # incremental acting as empty
     EMPTY: ClassVar["Incremental[Any]"] = None
@@ -118,6 +125,7 @@ def __init__(
         end_value: Optional[TCursorValue] = None,
         row_order: Optional[TSortOrder] = None,
         allow_external_schedulers: bool = False,
+        on_cursor_value_missing: OnCursorValueMissing = "raise",
     ) -> None:
         # make sure that path is valid
         if cursor_path:
@@ -133,6 +141,11 @@ def __init__(
         self._primary_key: Optional[TTableHintTemplate[TColumnNames]] = primary_key
         self.row_order = row_order
         self.allow_external_schedulers = allow_external_schedulers
+        if on_cursor_value_missing not in ["raise", "include", "exclude"]:
+            raise ValueError(
+                f"Unexpected argument for on_cursor_value_missing. Got {on_cursor_value_missing}"
+            )
+        self.on_cursor_value_missing = on_cursor_value_missing
 
         self._cached_state: IncrementalColumnState = None
         """State dictionary cached on first access"""
@@ -171,6 +184,7 @@ def _make_transforms(self) -> None:
                 self.last_value_func,
                 self._primary_key,
                 set(self._cached_state["unique_hashes"]),
+                self.on_cursor_value_missing,
             )
 
     @classmethod

dlt/extract/incremental/exceptions.py

@@ -5,12 +5,27 @@
 
 
 class IncrementalCursorPathMissing(PipeException):
-    def __init__(self, pipe_name: str, json_path: str, item: TDataItem, msg: str = None) -> None:
+    def __init__(
+        self, pipe_name: str, json_path: str, item: TDataItem = None, msg: str = None
+    ) -> None:
+        self.json_path = json_path
+        self.item = item
+        msg = (
+            msg
+            or f"Cursor element with JSON path `{json_path}` was not found in extracted data item. All data items must contain this path. Use the same names of fields as in your JSON document because they can be different from the names you see in database."
+        )
+        super().__init__(pipe_name, msg)
+
+
+class IncrementalCursorPathHasValueNone(PipeException):
+    def __init__(
+        self, pipe_name: str, json_path: str, item: TDataItem = None, msg: str = None
+    ) -> None:
         self.json_path = json_path
         self.item = item
         msg = (
             msg
-            or f"Cursor element with JSON path {json_path} was not found in extracted data item. All data items must contain this path. Use the same names of fields as in your JSON document - if those are different from the names you see in database."
+            or f"Cursor element with JSON path `{json_path}` has the value `None` in extracted data item. All data items must contain a value != None. Construct the incremental with on_cursor_value_none='include' if you want to include such rows"
         )
         super().__init__(pipe_name, msg)
 

dlt/extract/incremental/transform.py

@@ -1,5 +1,5 @@
-from datetime import datetime, date  # noqa: I251
-from typing import Any, Optional, Set, Tuple, List
+from datetime import datetime  # noqa: I251
+from typing import Any, Optional, Set, Tuple, List, Type
 
 from dlt.common.exceptions import MissingDependencyException
 from dlt.common.utils import digest128
@@ -11,8 +11,9 @@
     IncrementalCursorInvalidCoercion,
     IncrementalCursorPathMissing,
     IncrementalPrimaryKeyMissing,
+    IncrementalCursorPathHasValueNone,
 )
-from dlt.extract.incremental.typing import TCursorValue, LastValueFunc
+from dlt.extract.incremental.typing import TCursorValue, LastValueFunc, OnCursorValueMissing
 from dlt.extract.utils import resolve_column_value
 from dlt.extract.items import TTableHintTemplate
 from dlt.common.schema.typing import TColumnNames
@@ -55,6 +56,7 @@ def __init__(
         last_value_func: LastValueFunc[TCursorValue],
         primary_key: Optional[TTableHintTemplate[TColumnNames]],
         unique_hashes: Set[str],
+        on_cursor_value_missing: OnCursorValueMissing = "raise",
     ) -> None:
         self.resource_name = resource_name
         self.cursor_path = cursor_path
@@ -67,6 +69,7 @@ def __init__(
         self.primary_key = primary_key
         self.unique_hashes = unique_hashes
         self.start_unique_hashes = set(unique_hashes)
+        self.on_cursor_value_missing = on_cursor_value_missing
 
         # compile jsonpath
         self._compiled_cursor_path = compile_path(cursor_path)
@@ -116,21 +119,39 @@ class JsonIncremental(IncrementalTransform):
     def find_cursor_value(self, row: TDataItem) -> Any:
         """Finds value in row at cursor defined by self.cursor_path.
 
-        Will use compiled JSONPath if present, otherwise it reverts to column search if row is dict
+        Will use compiled JSONPath if present.
+        Otherwise, reverts to field access if row is dict, Pydantic model, or of other class.
         """
-        row_value: Any = None
+        key_exc: Type[Exception] = IncrementalCursorPathHasValueNone
         if self._compiled_cursor_path:
-            row_values = find_values(self._compiled_cursor_path, row)
-            if row_values:
-                row_value = row_values[0]
+            # ignores the other found values, e.g. when the path is $data.items[*].created_at
+            try:
+                row_value = find_values(self._compiled_cursor_path, row)[0]
+            except IndexError:
+                # empty list so raise a proper exception
+                row_value = None
+                key_exc = IncrementalCursorPathMissing
         else:
             try:
-                row_value = row[self.cursor_path]
-            except Exception:
-                pass
-        if row_value is None:
-            raise IncrementalCursorPathMissing(self.resource_name, self.cursor_path, row)
-        return row_value
+                try:
+                    row_value = row[self.cursor_path]
+                except TypeError:
+                    # supports Pydantic models and other classes
+                    row_value = getattr(row, self.cursor_path)
+            except (KeyError, AttributeError):
+                # attr not found so raise a proper exception
+                row_value = None
+                key_exc = IncrementalCursorPathMissing
+
+        # if we have a value - return it
+        if row_value is not None:
+            return row_value
+
+        if self.on_cursor_value_missing == "raise":
+            # raise missing path or None value exception
+            raise key_exc(self.resource_name, self.cursor_path, row)
+        elif self.on_cursor_value_missing == "exclude":
+            return None
 
     def __call__(
         self,
@@ -144,6 +165,12 @@ def __call__(
             return row, False, False
 
         row_value = self.find_cursor_value(row)
+        if row_value is None:
+            if self.on_cursor_value_missing == "exclude":
+                return None, False, False
+            else:
+                return row, False, False
+
         last_value = self.last_value
         last_value_func = self.last_value_func
 
@@ -299,6 +326,7 @@ def __call__(
 
         # TODO: Json path support. For now assume the cursor_path is a column name
         cursor_path = self.cursor_path
+
         # The new max/min value
         try:
             # NOTE: datetimes are always pendulum in UTC
@@ -310,11 +338,16 @@ def __call__(
                 self.resource_name,
                 cursor_path,
                 tbl,
-                f"Column name {cursor_path} was not found in the arrow table. Not nested JSON paths"
+                f"Column name `{cursor_path}` was not found in the arrow table. Nested JSON paths"
                 " are not supported for arrow tables and dataframes, the incremental cursor_path"
                 " must be a column name.",
             ) from e
 
+        if tbl.schema.field(cursor_path).nullable:
+            tbl_without_null, tbl_with_null = self._process_null_at_cursor_path(tbl)
+
+        tbl = tbl_without_null
+
         # If end_value is provided, filter to include table rows that are "less" than end_value
         if self.end_value is not None:
             try:
@@ -396,12 +429,28 @@ def __call__(
                 )
             )
 
+        # drop the temp unique index before concat and returning
+        if "_dlt_index" in tbl.schema.names:
+            tbl = pyarrow.remove_columns(tbl, ["_dlt_index"])
+
+        if self.on_cursor_value_missing == "include":
+            if isinstance(tbl, pa.RecordBatch):
+                assert isinstance(tbl_with_null, pa.RecordBatch)
+                tbl = pa.Table.from_batches([tbl, tbl_with_null])
+            else:
+                tbl = pa.concat_tables([tbl, tbl_with_null])
+
         if len(tbl) == 0:
             return None, start_out_of_range, end_out_of_range
-        try:
-            tbl = pyarrow.remove_columns(tbl, ["_dlt_index"])
-        except KeyError:
-            pass
         if is_pandas:
-            return tbl.to_pandas(), start_out_of_range, end_out_of_range
+            tbl = tbl.to_pandas()
         return tbl, start_out_of_range, end_out_of_range
+
+    def _process_null_at_cursor_path(self, tbl: "pa.Table") -> Tuple["pa.Table", "pa.Table"]:
+        mask = pa.compute.is_valid(tbl[self.cursor_path])
+        rows_without_null = tbl.filter(mask)
+        rows_with_null = tbl.filter(pa.compute.invert(mask))
+        if self.on_cursor_value_missing == "raise":
+            if rows_with_null.num_rows > 0:
+                raise IncrementalCursorPathHasValueNone(self.resource_name, self.cursor_path)
+        return rows_without_null, rows_with_null

dlt/extract/incremental/typing.py

@@ -1,8 +1,9 @@
-from typing import TypedDict, Optional, Any, List, TypeVar, Callable, Sequence
+from typing import TypedDict, Optional, Any, List, Literal, TypeVar, Callable, Sequence
 
 
 TCursorValue = TypeVar("TCursorValue", bound=Any)
 LastValueFunc = Callable[[Sequence[TCursorValue]], Any]
+OnCursorValueMissing = Literal["raise", "include", "exclude"]
 
 
 class IncrementalColumnState(TypedDict):

docs/website/docs/general-usage/incremental-loading.md

@@ -689,7 +689,7 @@ than `end_value`.
 
 :::caution
 In rare cases when you use Incremental with a transformer, `dlt` will not be able to automatically close
-generator associated with a row that is out of range. You can still use still call `can_close()` method on
+generator associated with a row that is out of range. You can still call the `can_close()` method on
 incremental and exit yield loop when true.
 :::
 
@@ -907,22 +907,75 @@ Consider the example below for reading incremental loading parameters from "conf
    ```
    `id_after` incrementally stores the latest `cursor_path` value for future pipeline runs.
 
-### Loading NULL values in the incremental cursor field
+### Loading when incremental cursor path is missing or value is None/NULL
 
-When loading incrementally with a cursor field, each row is expected to contain a value at the cursor field that is not `None`.
-For example, the following source data will raise an error:
+You can customize the incremental processing of dlt by setting the parameter `on_cursor_value_missing`.
+
+When loading incrementally with the default settings, there are two assumptions:
+1. each row contains the cursor path
+2. each row is expected to contain a value at the cursor path that is not `None`.
+
+For example, the two following source data will raise an error:
 ```py
 @dlt.resource
-def some_data(updated_at=dlt.sources.incremental("updated_at")):
+def some_data_without_cursor_path(updated_at=dlt.sources.incremental("updated_at")):
     yield [
         {"id": 1, "created_at": 1, "updated_at": 1},
-        {"id": 2, "created_at": 2, "updated_at": 2},
+        {"id": 2, "created_at": 2},  # cursor field is missing
+    ]
+
+list(some_data_without_cursor_path())
+
+@dlt.resource
+def some_data_without_cursor_value(updated_at=dlt.sources.incremental("updated_at")):
+    yield [
+        {"id": 1, "created_at": 1, "updated_at": 1},
+        {"id": 3, "created_at": 4, "updated_at": None},  # value at cursor field is None
+    ]
+
+list(some_data_without_cursor_value())
+```
+
+
+To process a data set where some records do not include the incremental cursor path or where the values at the cursor path are `None,` there are the following four options:
+
+1. Configure the incremental load to raise an exception in case there is a row where the cursor path is missing or has the value `None` using `incremental(..., on_cursor_value_missing="raise")`. This is the default behavior.
+2. Configure the incremental load to tolerate the missing cursor path and `None` values using `incremental(..., on_cursor_value_missing="include")`.
+3. Configure the incremental load to exclude the missing cursor path and `None` values using `incremental(..., on_cursor_value_missing="exclude")`.
+4. Before the incremental processing begins: Ensure that the incremental field is present and transform the values at the incremental cursor to a value different from `None`. [See docs below](#transform-records-before-incremental-processing)
+
+Here is an example of including rows where the incremental cursor value is missing or `None`:
+```py
+@dlt.resource
+def some_data(updated_at=dlt.sources.incremental("updated_at", on_cursor_value_missing="include")):
+    yield [
+        {"id": 1, "created_at": 1, "updated_at": 1},
+        {"id": 2, "created_at": 2},
+        {"id": 3, "created_at": 4, "updated_at": None},
+    ]
+
+result = list(some_data())
+assert len(result) == 3
+assert result[1] == {"id": 2, "created_at": 2}
+assert result[2] == {"id": 3, "created_at": 4, "updated_at": None}
+```
+
+If you do not want to import records without the cursor path or where the value at the cursor path is `None` use the following incremental configuration:
+
+```py
+@dlt.resource
+def some_data(updated_at=dlt.sources.incremental("updated_at", on_cursor_value_missing="exclude")):
+    yield [
+        {"id": 1, "created_at": 1, "updated_at": 1},
+        {"id": 2, "created_at": 2},
         {"id": 3, "created_at": 4, "updated_at": None},
     ]
 
-list(some_data())
+result = list(some_data())
+assert len(result) == 1
 ```
 
+### Transform records before incremental processing
 If you want to load data that includes `None` values you can transform the records before the incremental processing.
 You can add steps to the pipeline that [filter, transform, or pivot your data](../general-usage/resource.md#filter-transform-and-pivot-data).
 
@@ -1162,4 +1215,4 @@ sources:
 }
 ```
 
-Verify that the `last_value` is updated between pipeline runs.
+Verify that the `last_value` is updated between pipeline runs.