Initial SFERA platform baseline

This commit is contained in:
2026-05-16 19:03:49 +03:00
commit 3b845c8fce
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from __future__ import annotations
from collections import defaultdict
from pydantic import BaseModel, Field
from sir import EdgeKind, NodeKind, SemanticNode, SirSnapshot
class SemanticPattern(BaseModel):
pattern_id: str
kind: str
title: str
participants: list[SemanticNode] = Field(default_factory=list)
targets: list[SemanticNode] = Field(default_factory=list)
support: int
attributes: dict = Field(default_factory=dict)
def mine_patterns(snapshot: SirSnapshot, *, min_support: int = 2) -> list[SemanticPattern]:
nodes = {node.lineage_id: node for node in snapshot.nodes}
patterns: list[SemanticPattern] = []
patterns.extend(_table_usage_patterns(snapshot, nodes, min_support=min_support))
patterns.extend(_routine_read_write_patterns(snapshot, nodes, min_support=min_support))
return sorted(patterns, key=lambda item: (item.kind, -item.support, item.title))
def _table_usage_patterns(
snapshot: SirSnapshot,
nodes: dict[str, SemanticNode],
*,
min_support: int,
) -> list[SemanticPattern]:
query_owner: dict[str, SemanticNode] = {}
for edge in snapshot.edges:
if edge.kind == EdgeKind.OWNS_QUERY and edge.source_lineage in nodes:
query_owner[edge.target_lineage] = nodes[edge.source_lineage]
readers_by_table: dict[str, dict[str, SemanticNode]] = defaultdict(dict)
writers_by_table: dict[str, dict[str, SemanticNode]] = defaultdict(dict)
for edge in snapshot.edges:
if edge.kind == EdgeKind.READS_TABLE:
table = nodes.get(edge.target_lineage)
owner = query_owner.get(edge.source_lineage)
if table is not None and owner is not None:
readers_by_table[table.lineage_id][owner.lineage_id] = owner
elif edge.kind == EdgeKind.WRITES:
table = nodes.get(edge.target_lineage)
writer = nodes.get(edge.source_lineage)
if table is not None and writer is not None:
writers_by_table[table.lineage_id][writer.lineage_id] = writer
patterns: list[SemanticPattern] = []
for table_lineage, readers in readers_by_table.items():
table = nodes[table_lineage]
if len(readers) >= min_support:
patterns.append(
SemanticPattern(
pattern_id=f"pattern.repeated_read.{table.lineage_id}",
kind="REPEATED_TABLE_READ",
title=f"Repeated reads of {table.qualified_name}",
participants=sorted(readers.values(), key=lambda node: node.qualified_name),
targets=[table],
support=len(readers),
)
)
for table_lineage, writers in writers_by_table.items():
table = nodes[table_lineage]
if len(writers) >= min_support:
patterns.append(
SemanticPattern(
pattern_id=f"pattern.repeated_write.{table.lineage_id}",
kind="REPEATED_TABLE_WRITE",
title=f"Repeated writes to {table.qualified_name}",
participants=sorted(writers.values(), key=lambda node: node.qualified_name),
targets=[table],
support=len(writers),
)
)
return patterns
def _routine_read_write_patterns(
snapshot: SirSnapshot,
nodes: dict[str, SemanticNode],
*,
min_support: int,
) -> list[SemanticPattern]:
query_owner: dict[str, str] = {}
for edge in snapshot.edges:
if edge.kind == EdgeKind.OWNS_QUERY:
query_owner[edge.target_lineage] = edge.source_lineage
reads_by_routine: dict[str, set[str]] = defaultdict(set)
writes_by_routine: dict[str, set[str]] = defaultdict(set)
for edge in snapshot.edges:
if edge.kind == EdgeKind.READS_TABLE and edge.source_lineage in query_owner:
reads_by_routine[query_owner[edge.source_lineage]].add(edge.target_lineage)
elif edge.kind == EdgeKind.WRITES:
writes_by_routine[edge.source_lineage].add(edge.target_lineage)
grouped: dict[tuple[tuple[str, ...], tuple[str, ...]], list[SemanticNode]] = defaultdict(list)
routine_kinds = {NodeKind.PROCEDURE, NodeKind.FUNCTION}
for routine_lineage in sorted(set(reads_by_routine) | set(writes_by_routine)):
routine = nodes.get(routine_lineage)
if routine is None or routine.kind not in routine_kinds:
continue
key = (tuple(sorted(reads_by_routine[routine_lineage])), tuple(sorted(writes_by_routine[routine_lineage])))
if key == ((), ()):
continue
grouped[key].append(routine)
patterns: list[SemanticPattern] = []
for index, ((reads, writes), routines) in enumerate(sorted(grouped.items()), start=1):
if len(routines) < min_support:
continue
targets = [nodes[lineage] for lineage in [*reads, *writes] if lineage in nodes]
patterns.append(
SemanticPattern(
pattern_id=f"pattern.routine_io.{index}",
kind="REPEATED_ROUTINE_IO",
title="Repeated routine read/write shape",
participants=sorted(routines, key=lambda node: node.qualified_name),
targets=sorted(targets, key=lambda node: node.qualified_name),
support=len(routines),
attributes={"read_count": len(reads), "write_count": len(writes)},
)
)
return patterns
__all__ = ["SemanticPattern", "mine_patterns"]