Initial SFERA platform baseline
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from __future__ import annotations
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from collections import defaultdict
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from pydantic import BaseModel, Field
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from sir import EdgeKind, NodeKind, SemanticNode, SirSnapshot
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class SemanticPattern(BaseModel):
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pattern_id: str
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kind: str
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title: str
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participants: list[SemanticNode] = Field(default_factory=list)
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targets: list[SemanticNode] = Field(default_factory=list)
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support: int
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attributes: dict = Field(default_factory=dict)
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def mine_patterns(snapshot: SirSnapshot, *, min_support: int = 2) -> list[SemanticPattern]:
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nodes = {node.lineage_id: node for node in snapshot.nodes}
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patterns: list[SemanticPattern] = []
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patterns.extend(_table_usage_patterns(snapshot, nodes, min_support=min_support))
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patterns.extend(_routine_read_write_patterns(snapshot, nodes, min_support=min_support))
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return sorted(patterns, key=lambda item: (item.kind, -item.support, item.title))
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def _table_usage_patterns(
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snapshot: SirSnapshot,
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nodes: dict[str, SemanticNode],
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*,
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min_support: int,
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) -> list[SemanticPattern]:
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query_owner: dict[str, SemanticNode] = {}
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for edge in snapshot.edges:
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if edge.kind == EdgeKind.OWNS_QUERY and edge.source_lineage in nodes:
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query_owner[edge.target_lineage] = nodes[edge.source_lineage]
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readers_by_table: dict[str, dict[str, SemanticNode]] = defaultdict(dict)
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writers_by_table: dict[str, dict[str, SemanticNode]] = defaultdict(dict)
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for edge in snapshot.edges:
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if edge.kind == EdgeKind.READS_TABLE:
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table = nodes.get(edge.target_lineage)
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owner = query_owner.get(edge.source_lineage)
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if table is not None and owner is not None:
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readers_by_table[table.lineage_id][owner.lineage_id] = owner
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elif edge.kind == EdgeKind.WRITES:
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table = nodes.get(edge.target_lineage)
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writer = nodes.get(edge.source_lineage)
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if table is not None and writer is not None:
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writers_by_table[table.lineage_id][writer.lineage_id] = writer
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patterns: list[SemanticPattern] = []
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for table_lineage, readers in readers_by_table.items():
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table = nodes[table_lineage]
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if len(readers) >= min_support:
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patterns.append(
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SemanticPattern(
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pattern_id=f"pattern.repeated_read.{table.lineage_id}",
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kind="REPEATED_TABLE_READ",
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title=f"Repeated reads of {table.qualified_name}",
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participants=sorted(readers.values(), key=lambda node: node.qualified_name),
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targets=[table],
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support=len(readers),
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)
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)
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for table_lineage, writers in writers_by_table.items():
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table = nodes[table_lineage]
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if len(writers) >= min_support:
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patterns.append(
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SemanticPattern(
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pattern_id=f"pattern.repeated_write.{table.lineage_id}",
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kind="REPEATED_TABLE_WRITE",
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title=f"Repeated writes to {table.qualified_name}",
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participants=sorted(writers.values(), key=lambda node: node.qualified_name),
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targets=[table],
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support=len(writers),
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)
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)
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return patterns
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def _routine_read_write_patterns(
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snapshot: SirSnapshot,
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nodes: dict[str, SemanticNode],
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*,
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min_support: int,
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) -> list[SemanticPattern]:
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query_owner: dict[str, str] = {}
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for edge in snapshot.edges:
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if edge.kind == EdgeKind.OWNS_QUERY:
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query_owner[edge.target_lineage] = edge.source_lineage
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reads_by_routine: dict[str, set[str]] = defaultdict(set)
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writes_by_routine: dict[str, set[str]] = defaultdict(set)
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for edge in snapshot.edges:
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if edge.kind == EdgeKind.READS_TABLE and edge.source_lineage in query_owner:
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reads_by_routine[query_owner[edge.source_lineage]].add(edge.target_lineage)
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elif edge.kind == EdgeKind.WRITES:
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writes_by_routine[edge.source_lineage].add(edge.target_lineage)
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grouped: dict[tuple[tuple[str, ...], tuple[str, ...]], list[SemanticNode]] = defaultdict(list)
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routine_kinds = {NodeKind.PROCEDURE, NodeKind.FUNCTION}
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for routine_lineage in sorted(set(reads_by_routine) | set(writes_by_routine)):
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routine = nodes.get(routine_lineage)
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if routine is None or routine.kind not in routine_kinds:
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continue
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key = (tuple(sorted(reads_by_routine[routine_lineage])), tuple(sorted(writes_by_routine[routine_lineage])))
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if key == ((), ()):
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continue
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grouped[key].append(routine)
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patterns: list[SemanticPattern] = []
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for index, ((reads, writes), routines) in enumerate(sorted(grouped.items()), start=1):
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if len(routines) < min_support:
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continue
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targets = [nodes[lineage] for lineage in [*reads, *writes] if lineage in nodes]
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patterns.append(
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SemanticPattern(
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pattern_id=f"pattern.routine_io.{index}",
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kind="REPEATED_ROUTINE_IO",
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title="Repeated routine read/write shape",
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participants=sorted(routines, key=lambda node: node.qualified_name),
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targets=sorted(targets, key=lambda node: node.qualified_name),
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support=len(routines),
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attributes={"read_count": len(reads), "write_count": len(writes)},
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)
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)
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return patterns
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__all__ = ["SemanticPattern", "mine_patterns"]
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