Add open/closed range arguments for incremental

dlt/common/incremental/typing.py

@@ -8,6 +8,8 @@
 LastValueFunc = Callable[[Sequence[TCursorValue]], Any]
 OnCursorValueMissing = Literal["raise", "include", "exclude"]
 
+TIncrementalRange = Literal["open", "closed"]
+
 
 class IncrementalColumnState(TypedDict):
     initial_value: Optional[Any]
@@ -26,3 +28,5 @@ class IncrementalArgs(TypedDict, total=False):
     allow_external_schedulers: Optional[bool]
     lag: Optional[Union[float, int]]
     on_cursor_value_missing: Optional[OnCursorValueMissing]
+    range_start: Optional[TIncrementalRange]
+    range_end: Optional[TIncrementalRange]

dlt/extract/incremental/__init__.py

@@ -42,6 +42,7 @@
     LastValueFunc,
     OnCursorValueMissing,
     IncrementalArgs,
+    TIncrementalRange,
 )
 from dlt.extract.items import SupportsPipe, TTableHintTemplate, ItemTransform
 from dlt.extract.incremental.transform import (
@@ -104,6 +105,11 @@ class Incremental(ItemTransform[TDataItem], BaseConfiguration, Generic[TCursorVa
             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
         lag: Optional value used to define a lag or attribution window. For datetime cursors, this is interpreted as seconds. For other types, it uses the + or - operator depending on the last_value_func.
+        range_start: Decide whether the incremental filtering range is `open` or `closed` on the start value side. Default is `closed`.
+            Setting this to `open` means that items with the same cursor value as the last value from the previous run (or `initial_value`) are excluded from the result.
+            The `open` range disables deduplication logic so it can serve as an optimization when you know cursors don't overlap between pipeline runs.
+        range_end: Decide whether the incremental filtering range is `open` or `closed` on the end value side. Default is `open` (exact `end_value` is excluded).
+            Setting this to `closed` means that items with the exact same cursor value as the `end_value` are included in the result.
     """
 
     # this is config/dataclass so declare members
@@ -116,6 +122,8 @@ class Incremental(ItemTransform[TDataItem], BaseConfiguration, Generic[TCursorVa
     on_cursor_value_missing: OnCursorValueMissing = "raise"
     lag: Optional[float] = None
     duplicate_cursor_warning_threshold: ClassVar[int] = 200
+    range_start: TIncrementalRange = "closed"
+    range_end: TIncrementalRange = "open"
 
     # incremental acting as empty
     EMPTY: ClassVar["Incremental[Any]"] = None
@@ -132,6 +140,8 @@ def __init__(
         allow_external_schedulers: bool = False,
         on_cursor_value_missing: OnCursorValueMissing = "raise",
         lag: Optional[float] = None,
+        range_start: TIncrementalRange = "closed",
+        range_end: TIncrementalRange = "open",
     ) -> None:
         # make sure that path is valid
         if cursor_path:
@@ -174,9 +184,11 @@ def __init__(
         self.start_out_of_range: bool = False
         """Becomes true on the first item that is out of range of `start_value`. I.e. when using `max` this is a value that is lower than `start_value`"""
 
-        self._transformers: Dict[str, IncrementalTransform] = {}
+        self._transformers: Dict[Type[IncrementalTransform], IncrementalTransform] = {}
         self._bound_pipe: SupportsPipe = None
         """Bound pipe"""
+        self.range_start = range_start
+        self.range_end = range_end
 
     @property
     def primary_key(self) -> Optional[TTableHintTemplate[TColumnNames]]:
@@ -190,22 +202,6 @@ def primary_key(self, value: str) -> None:
             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:
-            self._transformers[dt] = kls(
-                self.resource_name,
-                self.cursor_path,
-                self.initial_value,
-                self.start_value,
-                self.end_value,
-                self.last_value_func,
-                self._primary_key,
-                set(self._cached_state["unique_hashes"]),
-                self.on_cursor_value_missing,
-                self.lag,
-            )
-
     @classmethod
     def from_existing_state(
         cls, resource_name: str, cursor_path: str
@@ -489,7 +485,8 @@ def bind(self, pipe: SupportsPipe) -> "Incremental[TCursorValue]":
         )
         # cache state
         self._cached_state = self.get_state()
-        self._make_transforms()
+        # Clear transforms so we get new instances
+        self._transformers.clear()
         return self
 
     def can_close(self) -> bool:
@@ -520,15 +517,34 @@ def __str__(self) -> str:
             f" {self.last_value_func}"
         )
 
+    def _make_or_get_transformer(self, cls: Type[IncrementalTransform]) -> IncrementalTransform:
+        if transformer := self._transformers.get(cls):
+            return transformer
+        transformer = self._transformers[cls] = cls(
+            self.resource_name,
+            self.cursor_path,
+            self.initial_value,
+            self.start_value,
+            self.end_value,
+            self.last_value_func,
+            self._primary_key,
+            set(self._cached_state["unique_hashes"]),
+            self.on_cursor_value_missing,
+            self.lag,
+            self.range_start,
+            self.range_end,
+        )
+        return transformer
+
     def _get_transformer(self, items: TDataItems) -> IncrementalTransform:
         # Assume list is all of the same type
         for item in items if isinstance(items, list) else [items]:
             if is_arrow_item(item):
-                return self._transformers["arrow"]
+                return self._make_or_get_transformer(ArrowIncremental)
             elif pandas is not None and isinstance(item, pandas.DataFrame):
-                return self._transformers["arrow"]
-            return self._transformers["json"]
-        return self._transformers["json"]
+                return self._make_or_get_transformer(ArrowIncremental)
+            return self._make_or_get_transformer(JsonIncremental)
+        return self._make_or_get_transformer(JsonIncremental)
 
     def __call__(self, rows: TDataItems, meta: Any = None) -> Optional[TDataItems]:
         if rows is None:

dlt/extract/incremental/transform.py

@@ -13,7 +13,12 @@
     IncrementalPrimaryKeyMissing,
     IncrementalCursorPathHasValueNone,
 )
-from dlt.common.incremental.typing import TCursorValue, LastValueFunc, OnCursorValueMissing
+from dlt.common.incremental.typing import (
+    TCursorValue,
+    LastValueFunc,
+    OnCursorValueMissing,
+    TIncrementalRange,
+)
 from dlt.extract.utils import resolve_column_value
 from dlt.extract.items import TTableHintTemplate
 
@@ -57,6 +62,8 @@ def __init__(
         unique_hashes: Set[str],
         on_cursor_value_missing: OnCursorValueMissing = "raise",
         lag: Optional[float] = None,
+        range_start: TIncrementalRange = "closed",
+        range_end: TIncrementalRange = "open",
     ) -> None:
         self.resource_name = resource_name
         self.cursor_path = cursor_path
@@ -71,6 +78,9 @@ def __init__(
         self.start_unique_hashes = set(unique_hashes)
         self.on_cursor_value_missing = on_cursor_value_missing
         self.lag = lag
+        self.range_start = range_start
+        self.range_end = range_end
+
         # compile jsonpath
         self._compiled_cursor_path = compile_path(cursor_path)
         # for simple column name we'll fallback to search in dict
@@ -107,6 +117,8 @@ def __call__(
     def deduplication_disabled(self) -> bool:
         """Skip deduplication when length of the key is 0 or if lag is applied."""
         # disable deduplication if end value is set - state is not saved
+        if self.range_start == "open":
+            return True
         if self.end_value is not None:
             return True
         # disable deduplication if lag is applied - destination must deduplicate ranges
@@ -191,10 +203,10 @@ def __call__(
         # Filter end value ranges exclusively, so in case of "max" function we remove values >= end_value
         if self.end_value is not None:
             try:
-                if (
-                    last_value_func((row_value, self.end_value)) != self.end_value
-                    or last_value_func((row_value,)) == self.end_value
-                ):
+                if last_value_func((row_value, self.end_value)) != self.end_value:
+                    return None, False, True
+
+                if self.range_end == "open" and last_value_func((row_value,)) == self.end_value:
                     return None, False, True
             except Exception as ex:
                 raise IncrementalCursorInvalidCoercion(
@@ -221,6 +233,9 @@ def __call__(
             ) from ex
         # new_value is "less" or equal to last_value (the actual max)
         if last_value == new_value:
+            if self.range_start == "open":
+                # We only want greater than last_value
+                return None, False, False
             # use func to compute row_value into last_value compatible
             processed_row_value = last_value_func((row_value,))
             # skip the record that is not a start_value or new_value: that record was already processed
@@ -258,6 +273,31 @@ def __call__(
 class ArrowIncremental(IncrementalTransform):
     _dlt_index = "_dlt_index"
 
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+        if self.last_value_func is max:
+            self.compute = pa.compute.max
+            self.end_compare = (
+                pa.compute.less if self.range_end == "open" else pa.compute.less_equal
+            )
+            self.last_value_compare = (
+                pa.compute.greater_equal if self.range_start == "closed" else pa.compute.greater
+            )
+            self.new_value_compare = pa.compute.greater
+        elif self.last_value_func is min:
+            self.compute = pa.compute.min
+            self.end_compare = (
+                pa.compute.greater if self.range_end == "open" else pa.compute.greater_equal
+            )
+            self.last_value_compare = (
+                pa.compute.less_equal if self.range_start == "closed" else pa.compute.less
+            )
+            self.new_value_compare = pa.compute.less
+        else:
+            raise NotImplementedError(
+                "Only min or max last_value_func is supported for arrow tables"
+            )
+
     def compute_unique_values(self, item: "TAnyArrowItem", unique_columns: List[str]) -> List[str]:
         if not unique_columns:
             return []
@@ -312,28 +352,13 @@ def __call__(
         if not tbl:  # row is None or empty arrow table
             return tbl, start_out_of_range, end_out_of_range
 
-        if self.last_value_func is max:
-            compute = pa.compute.max
-            end_compare = pa.compute.less
-            last_value_compare = pa.compute.greater_equal
-            new_value_compare = pa.compute.greater
-        elif self.last_value_func is min:
-            compute = pa.compute.min
-            end_compare = pa.compute.greater
-            last_value_compare = pa.compute.less_equal
-            new_value_compare = pa.compute.less
-        else:
-            raise NotImplementedError(
-                "Only min or max last_value_func is supported for arrow tables"
-            )
-
         # 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
-            row_value = from_arrow_scalar(compute(tbl[cursor_path]))
+            row_value = from_arrow_scalar(self.compute(tbl[cursor_path]))
             cursor_data_type = tbl.schema.field(cursor_path).type
             row_value_scalar = to_arrow_scalar(row_value, cursor_data_type)
         except KeyError as e:
@@ -364,10 +389,10 @@ def __call__(
                     cursor_data_type,
                     str(ex),
                 ) from ex
-            tbl = tbl.filter(end_compare(tbl[cursor_path], end_value_scalar))
+            tbl = tbl.filter(self.end_compare(tbl[cursor_path], end_value_scalar))
             # Is max row value higher than end value?
             # NOTE: pyarrow bool *always* evaluates to python True. `as_py()` is necessary
-            end_out_of_range = not end_compare(row_value_scalar, end_value_scalar).as_py()
+            end_out_of_range = not self.end_compare(row_value_scalar, end_value_scalar).as_py()
 
         if self.start_value is not None:
             try:
@@ -383,7 +408,7 @@ def __call__(
                     str(ex),
                 ) from ex
             # Remove rows lower or equal than the last start value
-            keep_filter = last_value_compare(tbl[cursor_path], start_value_scalar)
+            keep_filter = self.last_value_compare(tbl[cursor_path], start_value_scalar)
             start_out_of_range = bool(pa.compute.any(pa.compute.invert(keep_filter)).as_py())
             tbl = tbl.filter(keep_filter)
             if not self.deduplication_disabled:
@@ -407,7 +432,7 @@ def __call__(
 
         if (
             self.last_value is None
-            or new_value_compare(
+            or self.new_value_compare(
                 row_value_scalar, to_arrow_scalar(self.last_value, cursor_data_type)
             ).as_py()
         ):  # Last value has changed

dlt/sources/sql_database/helpers.py

@@ -94,12 +94,16 @@ def __init__(
             self.end_value = incremental.end_value
             self.row_order: TSortOrder = self.incremental.row_order
             self.on_cursor_value_missing = self.incremental.on_cursor_value_missing
+            self.range_start = self.incremental.range_start
+            self.range_end = self.incremental.range_end
         else:
             self.cursor_column = None
             self.last_value = None
             self.end_value = None
             self.row_order = None
             self.on_cursor_value_missing = None
+            self.range_start = None
+            self.range_end = None
 
     def _make_query(self) -> SelectAny:
         table = self.table
@@ -110,11 +114,11 @@ def _make_query(self) -> SelectAny:
 
         # generate where
         if last_value_func is max:  # Query ordered and filtered according to last_value function
-            filter_op = operator.ge
-            filter_op_end = operator.lt
+            filter_op = operator.ge if self.range_start == "closed" else operator.gt
+            filter_op_end = operator.lt if self.range_end == "open" else operator.le
         elif last_value_func is min:
-            filter_op = operator.le
-            filter_op_end = operator.gt
+            filter_op = operator.le if self.range_start == "closed" else operator.lt
+            filter_op_end = operator.gt if self.range_end == "open" else operator.ge
         else:  # Custom last_value, load everything and let incremental handle filtering
             return query  # type: ignore[no-any-return]