955 lines
34 KiB
Python
955 lines
34 KiB
Python
from __future__ import annotations
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import hashlib
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import json
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import re
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from collections import deque
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from dataclasses import dataclass
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from typing import Any
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from govoplan_core.core.definition_graphs import (
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DefinitionEdge,
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DefinitionNode,
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validate_definition_graph,
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)
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from govoplan_dataflow.backend.node_library import (
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DATAFLOW_GRAPH_LIBRARY,
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NODE_TYPES,
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node_definition,
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)
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from govoplan_dataflow.backend.schemas import DataflowDiagnostic, GraphEdge, GraphNode, PipelineGraph
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SUPPORTED_NODE_TYPES = frozenset(NODE_TYPES)
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FILTER_OPERATORS = frozenset(
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{"eq", "ne", "gt", "gte", "lt", "lte", "contains", "is_null", "not_null"}
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)
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AGGREGATE_FUNCTIONS = frozenset({"count", "sum", "avg", "min", "max"})
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DERIVE_OPERATIONS = frozenset(
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{
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"copy",
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"upper",
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"lower",
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"trim",
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"concat",
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"coalesce",
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"add",
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"subtract",
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"multiply",
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"divide",
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}
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)
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JOIN_TYPES = frozenset({"inner", "left", "right", "full"})
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def canonical_graph_payload(graph: PipelineGraph) -> dict[str, Any]:
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return graph.model_dump(mode="json", exclude_none=True)
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def definition_hash(graph: PipelineGraph, sql_text: str | None = None) -> str:
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payload = {
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"graph": canonical_graph_payload(graph),
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"sql_text": (sql_text or "").strip() or None,
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}
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encoded = json.dumps(payload, sort_keys=True, separators=(",", ":"), ensure_ascii=True)
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return hashlib.sha256(encoded.encode("utf-8")).hexdigest()
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def preserve_compatible_graph_layout(
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reference: PipelineGraph,
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compiled: PipelineGraph,
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) -> PipelineGraph:
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"""Keep graph identity and layout when SQL preserves the same topology."""
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reference_keys = _structural_node_keys(reference)
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compiled_keys = _structural_node_keys(compiled)
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if reference_keys is None or compiled_keys is None:
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return compiled
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if (
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len(set(reference_keys.values())) != len(reference_keys)
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or len(set(compiled_keys.values())) != len(compiled_keys)
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or set(reference_keys.values()) != set(compiled_keys.values())
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):
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return compiled
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compiled_by_key = {
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key: next(node for node in compiled.nodes if node.id == node_id)
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for node_id, key in compiled_keys.items()
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}
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reference_edges = {
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_structural_edge_key(edge, reference_keys): edge
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for edge in reference.edges
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}
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compiled_edges = {
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_structural_edge_key(edge, compiled_keys): edge
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for edge in compiled.edges
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}
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if (
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len(reference_edges) != len(reference.edges)
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or len(compiled_edges) != len(compiled.edges)
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or set(reference_edges) != set(compiled_edges)
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):
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return compiled
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nodes = []
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for reference_node in reference.nodes:
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node = compiled_by_key[reference_keys[reference_node.id]]
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nodes.append(
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node.model_copy(
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update={
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"id": reference_node.id,
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"label": reference_node.label,
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"position": reference_node.position.model_copy(deep=True),
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},
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deep=True,
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)
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)
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edges = []
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for reference_edge in reference.edges:
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edge_key = _structural_edge_key(reference_edge, reference_keys)
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edge = compiled_edges[edge_key]
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edges.append(
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edge.model_copy(
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update={
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"id": reference_edge.id,
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"source": reference_edge.source,
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"target": reference_edge.target,
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},
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deep=True,
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)
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)
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return compiled.model_copy(
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update={"nodes": nodes, "edges": edges},
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deep=True,
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)
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def validate_graph(graph: PipelineGraph) -> list[DataflowDiagnostic]:
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diagnostics = [
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DataflowDiagnostic(
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severity=item.severity,
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code=item.code,
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message=item.message,
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node_id=item.node_id,
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field=item.field,
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)
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for item in validate_definition_graph(
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DATAFLOW_GRAPH_LIBRARY,
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nodes=tuple(DefinitionNode(id=node.id, type=node.type) for node in graph.nodes),
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edges=tuple(
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DefinitionEdge(
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id=edge.id,
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source=edge.source,
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target=edge.target,
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source_port=edge.source_port,
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target_port=edge.target_port,
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)
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for edge in graph.edges
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),
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)
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]
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nodes = {node.id: node for node in graph.nodes}
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incoming: dict[str, list[GraphEdge]] = {node_id: [] for node_id in nodes}
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outgoing: dict[str, list[str]] = {node_id: [] for node_id in nodes}
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for edge in graph.edges:
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if edge.source not in nodes or edge.target not in nodes or edge.source == edge.target:
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continue
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source_definition = node_definition(nodes[edge.source].type)
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target_definition = node_definition(nodes[edge.target].type)
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if (
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source_definition is None
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or target_definition is None
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or edge.source_port not in {port.id for port in source_definition.output_ports}
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or edge.target_port not in {port.id for port in target_definition.input_ports}
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):
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continue
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outgoing[edge.source].append(edge.target)
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incoming[edge.target].append(edge)
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source_nodes = [node for node in graph.nodes if node.type.startswith("source.")]
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output_nodes = [node for node in graph.nodes if node.type == "output"]
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source_names = [
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str(node.config.get("source_name", "")).strip()
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for node in source_nodes
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if _non_empty_text(node.config.get("source_name"))
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]
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duplicate_source_names = sorted(
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{
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name
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for name in source_names
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if sum(candidate.casefold() == name.casefold() for candidate in source_names) > 1
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},
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key=str.casefold,
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)
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if duplicate_source_names:
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diagnostics.append(
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_error(
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"source.duplicate_name",
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"Logical source names must be unique: "
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f"{', '.join(duplicate_source_names)}.",
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)
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)
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for node in graph.nodes:
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if node.type not in SUPPORTED_NODE_TYPES:
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continue
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diagnostics.extend(_validate_node_config(node))
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ordered, cyclic = topological_order(graph)
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if not cyclic and source_nodes and len(output_nodes) == 1:
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reachable: set[str] = set()
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for source in source_nodes:
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reachable.update(_reachable_from(source.id, outgoing))
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if len(reachable) != len(nodes):
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diagnostics.append(
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_error("graph.disconnected", "Every node must be connected to a pipeline source.")
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)
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reverse_adjacency = {
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node_id: [edge.source for edge in edges]
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for node_id, edges in incoming.items()
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}
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reaches_output = _reachable_from(output_nodes[0].id, reverse_adjacency)
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if len(reaches_output) != len(nodes):
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diagnostics.append(
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_error("graph.dead_end", "Every node must lead to the pipeline output.")
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)
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if ordered and ordered[-1] != output_nodes[0].id:
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diagnostics.append(
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_error("graph.output_not_terminal", "The output node must be the terminal transform.")
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)
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diagnostics.extend(_validate_graph_schemas(graph, ordered=ordered))
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return _dedupe_diagnostics(diagnostics)
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def _structural_node_keys(
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graph: PipelineGraph,
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) -> dict[str, tuple[object, ...]] | None:
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node_by_id = {node.id: node for node in graph.nodes}
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if (
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len(node_by_id) != len(graph.nodes)
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or any(
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edge.source not in node_by_id or edge.target not in node_by_id
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for edge in graph.edges
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)
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):
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return None
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ordered, cyclic = topological_order(graph)
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if cyclic:
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return None
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incoming: dict[str, list[GraphEdge]] = {
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node.id: []
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for node in graph.nodes
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}
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for edge in graph.edges:
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if edge.target in incoming:
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incoming[edge.target].append(edge)
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keys: dict[str, tuple[object, ...]] = {}
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for node_id in ordered:
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node = node_by_id[node_id]
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input_keys: list[tuple[object, ...]] = []
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for edge in incoming[node_id]:
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source_key = keys.get(edge.source)
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if source_key is None:
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return None
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input_keys.append(
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(
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edge.target_port,
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edge.source_port,
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source_key,
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)
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)
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if node.type != "combine.union":
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input_keys.sort(key=repr)
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source_name = (
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str(node.config.get("source_name", "")).strip().casefold()
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if node.type.startswith("source.")
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else None
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)
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keys[node_id] = (
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node.type,
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source_name,
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tuple(input_keys),
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)
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return keys
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def _structural_edge_key(
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edge: GraphEdge,
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node_keys: dict[str, tuple[object, ...]],
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) -> tuple[object, ...]:
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return (
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node_keys[edge.source],
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node_keys[edge.target],
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edge.source_port,
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edge.target_port,
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)
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def _dedupe_diagnostics(
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diagnostics: list[DataflowDiagnostic],
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) -> list[DataflowDiagnostic]:
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seen: set[tuple[str, str | None, str | None]] = set()
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result: list[DataflowDiagnostic] = []
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for item in diagnostics:
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key = (item.code, item.node_id, item.field)
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if key in seen:
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continue
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seen.add(key)
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result.append(item)
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return result
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def topological_order(graph: PipelineGraph) -> tuple[list[str], bool]:
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node_ids = [node.id for node in graph.nodes]
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incoming_count = {node_id: 0 for node_id in node_ids}
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outgoing: dict[str, list[str]] = {node_id: [] for node_id in node_ids}
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for edge in graph.edges:
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if edge.source in outgoing and edge.target in incoming_count and edge.source != edge.target:
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outgoing[edge.source].append(edge.target)
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incoming_count[edge.target] += 1
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ready = deque(node_id for node_id in node_ids if incoming_count[node_id] == 0)
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ordered: list[str] = []
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while ready:
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node_id = ready.popleft()
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ordered.append(node_id)
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for target in outgoing[node_id]:
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incoming_count[target] -= 1
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if incoming_count[target] == 0:
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ready.append(target)
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return ordered, len(ordered) != len(node_ids)
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def graph_inputs_by_port(graph: PipelineGraph) -> dict[str, dict[str, list[str]]]:
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result: dict[str, dict[str, list[str]]] = {}
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for edge in graph.edges:
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result.setdefault(edge.target, {}).setdefault(edge.target_port, []).append(edge.source)
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return result
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def _reachable_from(start: str, adjacency: dict[str, list[str]]) -> set[str]:
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seen: set[str] = set()
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pending = [start]
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while pending:
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node_id = pending.pop()
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if node_id in seen:
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continue
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seen.add(node_id)
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pending.extend(adjacency.get(node_id, ()))
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return seen
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@dataclass(frozen=True)
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class _SchemaState:
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columns: frozenset[str]
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open: bool = False
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def knows(self, column: str) -> bool:
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return self.open or column in self.columns
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def _validate_graph_schemas(
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graph: PipelineGraph,
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*,
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ordered: list[str],
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) -> list[DataflowDiagnostic]:
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diagnostics: list[DataflowDiagnostic] = []
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node_by_id = {node.id: node for node in graph.nodes}
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inputs = graph_inputs_by_port(graph)
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schemas: dict[str, _SchemaState] = {}
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for node_id in ordered:
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node = node_by_id[node_id]
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node_inputs = inputs.get(node.id, {})
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input_states = [
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schemas[source_id]
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for port_sources in node_inputs.values()
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for source_id in port_sources
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if source_id in schemas
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]
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input_state = input_states[0] if input_states else _SchemaState(frozenset(), open=True)
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if node.type == "source.inline":
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rows = node.config.get("rows")
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columns = {
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str(column)
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for row in rows if isinstance(rows, list) and isinstance(row, dict)
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for column in row
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} if isinstance(rows, list) else set()
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schemas[node.id] = _SchemaState(
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frozenset(columns),
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open=not columns,
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)
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continue
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if node.type == "source.reference":
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columns = _configured_source_columns(node.config.get("source_columns"))
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schemas[node.id] = _SchemaState(
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frozenset(columns),
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open=not columns,
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)
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continue
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if node.type == "combine.union":
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if len(input_states) > 1:
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closed_shapes = {
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state.columns
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for state in input_states
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if not state.open
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}
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if len(closed_shapes) > 1:
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diagnostics.append(
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_warning(
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"union.schema_mismatch",
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"Appended inputs use different columns; missing values will be null.",
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node_id=node.id,
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)
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)
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schemas[node.id] = _SchemaState(
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frozenset().union(*(state.columns for state in input_states)),
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open=any(state.open for state in input_states),
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)
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continue
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if node.type == "combine.join":
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left_state = _port_schema(node_inputs, schemas, "left")
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right_state = _port_schema(node_inputs, schemas, "right")
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_validate_columns(
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diagnostics,
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node=node,
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state=left_state,
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columns=node.config.get("left_keys"),
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field="left_keys",
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)
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_validate_columns(
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diagnostics,
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node=node,
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state=right_state,
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columns=node.config.get("right_keys"),
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field="right_keys",
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)
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prefix = str(node.config.get("right_prefix", "right_"))
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prefixed_right = {f"{prefix}{column}" for column in right_state.columns}
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collisions = left_state.columns & prefixed_right
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if collisions:
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diagnostics.append(
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_error(
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"join.output_collision",
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f"Join output columns collide: {', '.join(sorted(collisions))}.",
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node_id=node.id,
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field="right_prefix",
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)
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)
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schemas[node.id] = _SchemaState(
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frozenset(left_state.columns | prefixed_right),
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open=left_state.open or right_state.open,
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)
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continue
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if node.type == "filter":
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_validate_columns(
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diagnostics,
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node=node,
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state=input_state,
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columns=[node.config.get("column")],
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field="column",
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)
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schemas[node.id] = input_state
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continue
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if node.type == "distinct":
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_validate_columns(
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diagnostics,
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node=node,
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state=input_state,
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columns=node.config.get("columns"),
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field="columns",
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)
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schemas[node.id] = input_state
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continue
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if node.type == "select":
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fields = node.config.get("fields")
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selected_columns: list[str] = []
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output_columns: list[str] = []
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if isinstance(fields, list):
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for field in fields:
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if isinstance(field, str):
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selected_columns.append(field)
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output_columns.append(field)
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elif isinstance(field, dict):
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column = field.get("column")
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alias = field.get("alias") or column
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if isinstance(column, str):
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selected_columns.append(column)
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if isinstance(alias, str):
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output_columns.append(alias)
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_validate_columns(
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diagnostics,
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node=node,
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state=input_state,
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columns=selected_columns,
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field="fields",
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)
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_validate_output_names(
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diagnostics,
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node=node,
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columns=output_columns,
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field="fields",
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)
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schemas[node.id] = _SchemaState(frozenset(output_columns))
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continue
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if node.type == "derive":
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_validate_columns(
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diagnostics,
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node=node,
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state=input_state,
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columns=node.config.get("source_columns"),
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field="source_columns",
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)
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target = node.config.get("target_column")
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if isinstance(target, str) and target:
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if target in input_state.columns:
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diagnostics.append(
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_warning(
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"derive.overwrites_column",
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f"Derived column {target!r} replaces an existing value.",
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node_id=node.id,
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field="target_column",
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)
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)
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schemas[node.id] = _SchemaState(
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input_state.columns | frozenset((target,)),
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open=input_state.open,
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)
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else:
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schemas[node.id] = input_state
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continue
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if node.type == "aggregate":
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group_by = _text_items(node.config.get("group_by"))
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aggregates = node.config.get("aggregates")
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aggregate_columns = [
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str(item.get("column"))
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for item in aggregates
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if isinstance(aggregates, list)
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and isinstance(item, dict)
|
|
and item.get("column") not in (None, "", "*")
|
|
] if isinstance(aggregates, list) else []
|
|
_validate_columns(
|
|
diagnostics,
|
|
node=node,
|
|
state=input_state,
|
|
columns=[*group_by, *aggregate_columns],
|
|
field="aggregates",
|
|
)
|
|
aliases = [
|
|
str(item.get("alias"))
|
|
for item in aggregates
|
|
if isinstance(aggregates, list)
|
|
and isinstance(item, dict)
|
|
and item.get("alias")
|
|
] if isinstance(aggregates, list) else []
|
|
output_columns = [*group_by, *aliases]
|
|
_validate_output_names(
|
|
diagnostics,
|
|
node=node,
|
|
columns=output_columns,
|
|
field="aggregates",
|
|
)
|
|
schemas[node.id] = _SchemaState(frozenset(output_columns))
|
|
continue
|
|
if node.type == "sort":
|
|
fields = node.config.get("fields")
|
|
columns = [
|
|
str(item.get("column"))
|
|
for item in fields
|
|
if isinstance(fields, list)
|
|
and isinstance(item, dict)
|
|
and item.get("column")
|
|
] if isinstance(fields, list) else []
|
|
_validate_columns(
|
|
diagnostics,
|
|
node=node,
|
|
state=input_state,
|
|
columns=columns,
|
|
field="fields",
|
|
)
|
|
schemas[node.id] = input_state
|
|
continue
|
|
schemas[node.id] = input_state
|
|
return diagnostics
|
|
|
|
|
|
def _configured_source_columns(value: object) -> set[str]:
|
|
if not isinstance(value, list):
|
|
return set()
|
|
return {
|
|
item if isinstance(item, str) else str(item.get("name"))
|
|
for item in value
|
|
if (
|
|
isinstance(item, str) and item
|
|
or isinstance(item, dict) and item.get("name")
|
|
)
|
|
}
|
|
|
|
|
|
def _port_schema(
|
|
inputs: dict[str, list[str]],
|
|
schemas: dict[str, _SchemaState],
|
|
port: str,
|
|
) -> _SchemaState:
|
|
source_ids = inputs.get(port, [])
|
|
return schemas.get(source_ids[0], _SchemaState(frozenset(), open=True)) if source_ids else _SchemaState(frozenset(), open=True)
|
|
|
|
|
|
def _validate_columns(
|
|
diagnostics: list[DataflowDiagnostic],
|
|
*,
|
|
node: GraphNode,
|
|
state: _SchemaState,
|
|
columns: object,
|
|
field: str,
|
|
) -> None:
|
|
for column in _text_items(columns):
|
|
if not state.knows(column):
|
|
diagnostics.append(
|
|
_error(
|
|
"schema.unknown_column",
|
|
f"Column {column!r} is not available at this node.",
|
|
node_id=node.id,
|
|
field=field,
|
|
)
|
|
)
|
|
|
|
|
|
def _validate_output_names(
|
|
diagnostics: list[DataflowDiagnostic],
|
|
*,
|
|
node: GraphNode,
|
|
columns: list[str],
|
|
field: str,
|
|
) -> None:
|
|
duplicates = sorted({column for column in columns if columns.count(column) > 1})
|
|
if duplicates:
|
|
diagnostics.append(
|
|
_error(
|
|
"schema.duplicate_output",
|
|
f"Output column names must be unique: {', '.join(duplicates)}.",
|
|
node_id=node.id,
|
|
field=field,
|
|
)
|
|
)
|
|
|
|
|
|
def _text_items(value: object) -> list[str]:
|
|
if not isinstance(value, list):
|
|
return []
|
|
return [item for item in value if isinstance(item, str) and item]
|
|
|
|
|
|
def _validate_node_config(node: GraphNode) -> list[DataflowDiagnostic]:
|
|
config = node.config
|
|
diagnostics: list[DataflowDiagnostic] = []
|
|
if node.type in {"source.inline", "source.reference"}:
|
|
source_name = config.get("source_name")
|
|
if not isinstance(source_name, str) or not source_name.strip():
|
|
diagnostics.append(
|
|
_error(
|
|
"source.name_required",
|
|
"A source needs a logical SQL name.",
|
|
node_id=node.id,
|
|
field="source_name",
|
|
)
|
|
)
|
|
elif re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*", source_name.strip()) is None:
|
|
diagnostics.append(
|
|
_error(
|
|
"source.name_invalid",
|
|
"Logical source names must be SQL identifiers, such as monthly_cases.",
|
|
node_id=node.id,
|
|
field="source_name",
|
|
)
|
|
)
|
|
if node.type == "source.reference":
|
|
if not _non_empty_text(config.get("source_ref")):
|
|
diagnostics.append(
|
|
_error(
|
|
"source.reference_required",
|
|
"Choose a datasource.",
|
|
node_id=node.id,
|
|
field="source_ref",
|
|
)
|
|
)
|
|
expected_fingerprint = config.get("expected_fingerprint")
|
|
if expected_fingerprint is not None and not isinstance(expected_fingerprint, str):
|
|
diagnostics.append(
|
|
_error(
|
|
"source.fingerprint",
|
|
"The expected source fingerprint must be text.",
|
|
node_id=node.id,
|
|
field="expected_fingerprint",
|
|
)
|
|
)
|
|
return diagnostics
|
|
rows = config.get("rows")
|
|
if not isinstance(rows, list):
|
|
diagnostics.append(
|
|
_error("source.rows_required", "Inline source rows must be a list.", node_id=node.id, field="rows")
|
|
)
|
|
elif len(rows) > 250:
|
|
diagnostics.append(
|
|
_error("source.row_limit", "Inline sources are limited to 250 rows.", node_id=node.id, field="rows")
|
|
)
|
|
elif any(not isinstance(row, dict) for row in rows):
|
|
diagnostics.append(
|
|
_error("source.row_shape", "Every inline source row must be an object.", node_id=node.id, field="rows")
|
|
)
|
|
if len(json.dumps(config, default=str).encode("utf-8")) > 512_000:
|
|
diagnostics.append(
|
|
_error("source.byte_limit", "Inline source configuration is limited to 512 KiB.", node_id=node.id)
|
|
)
|
|
elif node.type == "filter":
|
|
if not _non_empty_text(config.get("column")):
|
|
diagnostics.append(_node_field_error(node, "filter.column", "Choose a column.", "column"))
|
|
operator = config.get("operator")
|
|
if operator not in FILTER_OPERATORS:
|
|
diagnostics.append(
|
|
_node_field_error(node, "filter.operator", "Choose a supported comparison operator.", "operator")
|
|
)
|
|
if operator not in {"is_null", "not_null"} and "value" not in config:
|
|
diagnostics.append(_node_field_error(node, "filter.value", "Enter a comparison value.", "value"))
|
|
elif node.type == "distinct":
|
|
columns = config.get("columns", [])
|
|
if not isinstance(columns, list) or any(not _non_empty_text(item) for item in columns):
|
|
diagnostics.append(
|
|
_node_field_error(
|
|
node,
|
|
"distinct.columns",
|
|
"Distinct key columns must be named.",
|
|
"columns",
|
|
)
|
|
)
|
|
elif node.type == "combine.union":
|
|
if config.get("mode", "all") not in {"all", "distinct"}:
|
|
diagnostics.append(
|
|
_node_field_error(
|
|
node,
|
|
"union.mode",
|
|
"Choose whether duplicate rows are kept or removed.",
|
|
"mode",
|
|
)
|
|
)
|
|
elif node.type == "combine.join":
|
|
if config.get("join_type", "inner") not in JOIN_TYPES:
|
|
diagnostics.append(
|
|
_node_field_error(node, "join.type", "Choose a supported join type.", "join_type")
|
|
)
|
|
left_keys = config.get("left_keys")
|
|
right_keys = config.get("right_keys")
|
|
if (
|
|
not isinstance(left_keys, list)
|
|
or not left_keys
|
|
or any(not _non_empty_text(item) for item in left_keys)
|
|
):
|
|
diagnostics.append(
|
|
_node_field_error(node, "join.left_keys", "Add at least one left key.", "left_keys")
|
|
)
|
|
if (
|
|
not isinstance(right_keys, list)
|
|
or not right_keys
|
|
or any(not _non_empty_text(item) for item in right_keys)
|
|
):
|
|
diagnostics.append(
|
|
_node_field_error(node, "join.right_keys", "Add at least one right key.", "right_keys")
|
|
)
|
|
if isinstance(left_keys, list) and isinstance(right_keys, list) and len(left_keys) != len(right_keys):
|
|
diagnostics.append(
|
|
_node_field_error(
|
|
node,
|
|
"join.key_count",
|
|
"Left and right joins need the same number of keys.",
|
|
"right_keys",
|
|
)
|
|
)
|
|
right_prefix = config.get("right_prefix", "right_")
|
|
if (
|
|
not _non_empty_text(right_prefix)
|
|
or re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*_", str(right_prefix)) is None
|
|
):
|
|
diagnostics.append(
|
|
_node_field_error(
|
|
node,
|
|
"join.right_prefix",
|
|
"Enter an identifier prefix ending in an underscore, such as right_.",
|
|
"right_prefix",
|
|
)
|
|
)
|
|
elif node.type == "select":
|
|
fields = config.get("fields")
|
|
if not isinstance(fields, list) or not fields:
|
|
diagnostics.append(
|
|
_node_field_error(node, "select.fields", "Select at least one output column.", "fields")
|
|
)
|
|
else:
|
|
for field in fields:
|
|
column = field if isinstance(field, str) else field.get("column") if isinstance(field, dict) else None
|
|
if not _non_empty_text(column):
|
|
diagnostics.append(
|
|
_node_field_error(node, "select.field", "Selected fields need a source column.", "fields")
|
|
)
|
|
break
|
|
elif node.type == "aggregate":
|
|
group_by = config.get("group_by", [])
|
|
aggregates = config.get("aggregates")
|
|
if not isinstance(group_by, list) or any(not _non_empty_text(item) for item in group_by):
|
|
diagnostics.append(
|
|
_node_field_error(node, "aggregate.group_by", "Group-by columns must be named.", "group_by")
|
|
)
|
|
if not isinstance(aggregates, list) or not aggregates:
|
|
diagnostics.append(
|
|
_node_field_error(node, "aggregate.required", "Add at least one aggregate.", "aggregates")
|
|
)
|
|
else:
|
|
for aggregate in aggregates:
|
|
if not isinstance(aggregate, dict) or aggregate.get("function") not in AGGREGATE_FUNCTIONS:
|
|
diagnostics.append(
|
|
_node_field_error(
|
|
node,
|
|
"aggregate.function",
|
|
"Choose COUNT, SUM, AVG, MIN, or MAX.",
|
|
"aggregates",
|
|
)
|
|
)
|
|
break
|
|
if aggregate.get("function") != "count" and not _non_empty_text(aggregate.get("column")):
|
|
diagnostics.append(
|
|
_node_field_error(
|
|
node,
|
|
"aggregate.column",
|
|
"This aggregate needs a source column.",
|
|
"aggregates",
|
|
)
|
|
)
|
|
break
|
|
if not _non_empty_text(aggregate.get("alias")):
|
|
diagnostics.append(
|
|
_node_field_error(node, "aggregate.alias", "Every aggregate needs an alias.", "aggregates")
|
|
)
|
|
break
|
|
elif node.type == "derive":
|
|
if not _non_empty_text(config.get("target_column")):
|
|
diagnostics.append(
|
|
_node_field_error(
|
|
node,
|
|
"derive.target_column",
|
|
"Choose an output column.",
|
|
"target_column",
|
|
)
|
|
)
|
|
operation = config.get("operation")
|
|
if operation not in DERIVE_OPERATIONS:
|
|
diagnostics.append(
|
|
_node_field_error(
|
|
node,
|
|
"derive.operation",
|
|
"Choose a supported derive operation.",
|
|
"operation",
|
|
)
|
|
)
|
|
source_columns = config.get("source_columns")
|
|
if (
|
|
not isinstance(source_columns, list)
|
|
or not source_columns
|
|
or any(not _non_empty_text(item) for item in source_columns)
|
|
):
|
|
diagnostics.append(
|
|
_node_field_error(
|
|
node,
|
|
"derive.source_columns",
|
|
"Choose at least one source column.",
|
|
"source_columns",
|
|
)
|
|
)
|
|
if operation in {"copy", "upper", "lower", "trim"} and isinstance(source_columns, list) and len(source_columns) != 1:
|
|
diagnostics.append(
|
|
_node_field_error(
|
|
node,
|
|
"derive.source_count",
|
|
"This operation requires exactly one source column.",
|
|
"source_columns",
|
|
)
|
|
)
|
|
if operation in {"add", "subtract", "multiply", "divide"} and isinstance(source_columns, list) and len(source_columns) != 2:
|
|
diagnostics.append(
|
|
_node_field_error(
|
|
node,
|
|
"derive.numeric_source_count",
|
|
"Numeric operations require exactly two source columns.",
|
|
"source_columns",
|
|
)
|
|
)
|
|
elif node.type == "sort":
|
|
fields = config.get("fields")
|
|
if not isinstance(fields, list) or not fields:
|
|
diagnostics.append(_node_field_error(node, "sort.fields", "Add at least one sort field.", "fields"))
|
|
elif any(
|
|
not isinstance(item, dict)
|
|
or not _non_empty_text(item.get("column"))
|
|
or item.get("direction", "asc") not in {"asc", "desc"}
|
|
for item in fields
|
|
):
|
|
diagnostics.append(
|
|
_node_field_error(node, "sort.field", "Sort fields need a column and direction.", "fields")
|
|
)
|
|
elif node.type == "limit":
|
|
count = config.get("count")
|
|
if not isinstance(count, int) or isinstance(count, bool) or not 1 <= count <= 100_000:
|
|
diagnostics.append(
|
|
_node_field_error(node, "limit.count", "Limit must be between 1 and 100,000.", "count")
|
|
)
|
|
return diagnostics
|
|
|
|
|
|
def _non_empty_text(value: object) -> bool:
|
|
return isinstance(value, str) and bool(value.strip())
|
|
|
|
|
|
def _node_field_error(node: GraphNode, code: str, message: str, field: str) -> DataflowDiagnostic:
|
|
return _error(code, message, node_id=node.id, field=field)
|
|
|
|
|
|
def _error(
|
|
code: str,
|
|
message: str,
|
|
*,
|
|
node_id: str | None = None,
|
|
field: str | None = None,
|
|
) -> DataflowDiagnostic:
|
|
return DataflowDiagnostic(
|
|
severity="error",
|
|
code=code,
|
|
message=message,
|
|
node_id=node_id,
|
|
field=field,
|
|
)
|
|
|
|
|
|
def _warning(
|
|
code: str,
|
|
message: str,
|
|
*,
|
|
node_id: str | None = None,
|
|
field: str | None = None,
|
|
) -> DataflowDiagnostic:
|
|
return DataflowDiagnostic(
|
|
severity="warning",
|
|
code=code,
|
|
message=message,
|
|
node_id=node_id,
|
|
field=field,
|
|
)
|
|
|
|
|
|
__all__ = [
|
|
"AGGREGATE_FUNCTIONS",
|
|
"DERIVE_OPERATIONS",
|
|
"FILTER_OPERATORS",
|
|
"JOIN_TYPES",
|
|
"SUPPORTED_NODE_TYPES",
|
|
"canonical_graph_payload",
|
|
"definition_hash",
|
|
"graph_inputs_by_port",
|
|
"topological_order",
|
|
"validate_graph",
|
|
]
|