audit: validate-first routing, read-only activation lookup, delta-log barrier patches
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@@ -1,7 +1,7 @@
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any, Literal
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from typing import Any, Literal, overload
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from wf_core.errors import WorkflowExecutionError
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from wf_core.run_state import ExecutionFrame, RunState
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@@ -303,9 +303,13 @@ class ForeachBarrierState:
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def _activation_entry(
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frame: ExecutionFrame, table: dict[str, Any], foreach_node_id: str
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frame: ExecutionFrame,
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table: dict[str, Any] | None,
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foreach_node_id: str,
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) -> dict[str, Any] | None:
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"""Return the mutable activation entry or fail fast on corrupt state."""
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if table is None:
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return None
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entry = table.get(foreach_node_id)
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if entry is None:
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return None
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@@ -368,7 +372,7 @@ def save_foreach_activation(
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frame: ExecutionFrame, activation: ForeachActivationState
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) -> None:
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"""Persist barrier progress for the named active activation."""
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table = _activation_table(frame)
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table = _activation_table(frame, create=False)
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entry = _activation_entry(frame, table, activation.foreach_node_id)
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if entry is None:
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raise WorkflowExecutionError(
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@@ -391,7 +395,7 @@ def close_foreach_activation(
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The barrier is removed so a later visit starts fresh; the sequence keeps
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increasing so child and lineage ids cannot collide across visits.
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"""
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table = _activation_table(frame)
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table = _activation_table(frame, create=False)
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entry = _activation_entry(frame, table, activation.foreach_node_id)
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if entry is None:
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raise WorkflowExecutionError(
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@@ -413,8 +417,11 @@ def load_foreach_activation(
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A child result naming a closed or different activation must fail closed in
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the caller rather than buffering into the wrong barrier.
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This is a read-only lookup: a missing table or entry raises without
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mutating frame metadata.
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"""
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table = _activation_table(frame)
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table = _activation_table(frame, create=False)
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entry = _activation_entry(frame, table, foreach_node_id)
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if entry is None:
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raise WorkflowExecutionError(
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@@ -469,9 +476,30 @@ def item_frame_owner(frame: ExecutionFrame) -> ForeachItemOwner | None:
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)
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def _activation_table(frame: ExecutionFrame) -> dict[str, Any]:
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@overload
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def _activation_table(
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frame: ExecutionFrame, *, create: Literal[True] = True
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) -> dict[str, Any]: ...
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@overload
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def _activation_table(
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frame: ExecutionFrame, *, create: Literal[False]
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) -> dict[str, Any] | None: ...
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def _activation_table(
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frame: ExecutionFrame, *, create: bool = True
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) -> dict[str, Any] | None:
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"""Return the activation table, optionally creating it.
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Read-only lookups pass ``create=False`` so a failed lookup leaves
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frame metadata untouched. Only ``load_or_begin`` creates the table.
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"""
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raw = frame.metadata.get(_ACTIVATION_METADATA_KEY)
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if raw is None:
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if not create:
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return None
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table: dict[str, Any] = {}
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frame.metadata[_ACTIVATION_METADATA_KEY] = table
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return table
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@@ -249,6 +249,15 @@ def build_barrier_patch(
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committed aggregate values. A barrier trace is the single visible state
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commit for all buffered item patches, so showing raw per-item incoming
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values would hide what actually landed in `RunState.state`.
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The emitted `writes` log keeps every constituent item write in order
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instead of one merged write per path. A combined patch buffered in a
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lineage can itself be re-merged by an outer barrier, and replaying merged
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cumulative values would duplicate whatever was already committed when the
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constituents were built. Replaying the original per-item deltas stays
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correct at any nesting depth. Each kept write still carries the merged
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aggregate as its `visible_value`, so overlay reads and `visible_values`
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keep showing the final value.
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"""
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state_fields = workflow.state_schema.field_index()
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validate_barrier_writes(item_patches, state_fields, reducers=reducers)
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@@ -269,22 +278,31 @@ def build_barrier_patch(
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safe_set_nested_value(staged_state, key_path, merged_value)
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prepared_patch[destination_path] = (key_path, merged_value)
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committed_changes[str(destination_path)] = merged_value
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merged_visible = {
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destination_path: merged_value
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for destination_path, (_key_path, merged_value) in prepared_patch.items()
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}
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writes = [
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StateWrite(
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path=destination_path,
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incoming_value=merged_value,
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visible_value=merged_value,
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reducer=reducer_for_state_path(destination_path, state_fields),
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path=write.path,
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incoming_value=write.incoming_value,
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visible_value=merged_visible[write.path],
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reducer=write.reducer,
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)
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for destination_path, (_key_path, merged_value) in prepared_patch.items()
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for item_patch in item_patches
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for write in item_patch.writes
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]
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validate_staged_state_patch(staged_state, prepared_patch, state_fields)
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return StatePatch(
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changes=committed_changes,
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combined = StatePatch(
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writes=writes,
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_prepared_writes=prepared_patch,
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_staged_state=staged_state,
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)
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# The trace-facing view reports the aggregate, while the replay log above
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# intentionally carries per-item deltas (see docstring). Assign it after
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# construction: passing both to the constructor requires them to agree.
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combined.changes = committed_changes
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return combined
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def validate_barrier_writes(
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