Files
ss-tools/backend/src/services/dataset_review/orchestrator.py
2026-03-20 20:01:58 +03:00

1156 lines
51 KiB
Python

# [DEF:DatasetReviewOrchestrator:Module]
# @COMPLEXITY: 5
# @SEMANTICS: dataset_review, orchestration, session_lifecycle, intake, recovery
# @PURPOSE: Coordinate dataset review session startup and lifecycle-safe intake recovery for one authenticated user.
# @LAYER: Domain
# @RELATION: [DEPENDS_ON] ->[DatasetReviewSessionRepository]
# @RELATION: [DEPENDS_ON] ->[SemanticSourceResolver]
# @RELATION: [DEPENDS_ON] ->[ClarificationEngine]
# @RELATION: [DEPENDS_ON] ->[SupersetContextExtractor]
# @RELATION: [DEPENDS_ON] ->[SupersetCompilationAdapter]
# @RELATION: [DEPENDS_ON] ->[TaskManager]
# @PRE: session mutations must execute inside a persisted session boundary scoped to one authenticated user.
# @POST: state transitions are persisted atomically and emit observable progress for long-running steps.
# @SIDE_EFFECT: creates task records, updates session aggregates, triggers upstream Superset calls, persists audit artifacts.
# @DATA_CONTRACT: Input[SessionCommand] -> Output[DatasetReviewSession | CompiledPreview | DatasetRunContext]
# @INVARIANT: Launch is blocked unless a current session has no open blocking findings, all launch-sensitive mappings are approved, and a non-stale Superset-generated compiled preview matches the current input fingerprint.
from __future__ import annotations
# [DEF:DatasetReviewOrchestrator.imports:Block]
from dataclasses import dataclass, field
from datetime import datetime
import hashlib
import json
from typing import Any, Dict, List, Optional, cast
from src.core.config_manager import ConfigManager
from src.core.logger import belief_scope, logger
from src.core.task_manager import TaskManager
from src.core.utils.superset_compilation_adapter import (
PreviewCompilationPayload,
SqlLabLaunchPayload,
SupersetCompilationAdapter,
)
from src.core.utils.superset_context_extractor import (
SupersetContextExtractor,
SupersetParsedContext,
)
from src.models.auth import User
from src.models.dataset_review import (
ApprovalState,
BusinessSummarySource,
CompiledPreview,
ConfidenceState,
DatasetProfile,
DatasetReviewSession,
DatasetRunContext,
ExecutionMapping,
FilterConfidenceState,
FilterRecoveryStatus,
FilterSource,
FindingArea,
FindingSeverity,
ImportedFilter,
LaunchStatus,
MappingMethod,
MappingStatus,
PreviewStatus,
RecommendedAction,
ReadinessState,
ResolutionState,
SessionPhase,
SessionStatus,
TemplateVariable,
ValidationFinding,
VariableKind,
)
from src.services.dataset_review.repositories.session_repository import (
DatasetReviewSessionRepository,
)
from src.services.dataset_review.semantic_resolver import SemanticSourceResolver
from src.services.dataset_review.event_logger import SessionEventPayload
# [/DEF:DatasetReviewOrchestrator.imports:Block]
logger = cast(Any, logger)
# [DEF:StartSessionCommand:Class]
# @COMPLEXITY: 2
# @PURPOSE: Typed input contract for starting a dataset review session.
@dataclass
class StartSessionCommand:
user: User
environment_id: str
source_kind: str
source_input: str
# [/DEF:StartSessionCommand:Class]
# [DEF:StartSessionResult:Class]
# @COMPLEXITY: 2
# @PURPOSE: Session-start result carrying the persisted session and intake recovery metadata.
@dataclass
class StartSessionResult:
session: DatasetReviewSession
parsed_context: Optional[SupersetParsedContext] = None
findings: List[ValidationFinding] = field(default_factory=list)
# [/DEF:StartSessionResult:Class]
# [DEF:PreparePreviewCommand:Class]
# @COMPLEXITY: 2
# @PURPOSE: Typed input contract for compiling one Superset-backed session preview.
@dataclass
class PreparePreviewCommand:
user: User
session_id: str
# [/DEF:PreparePreviewCommand:Class]
# [DEF:PreparePreviewResult:Class]
# @COMPLEXITY: 2
# @PURPOSE: Result contract for one persisted compiled preview attempt.
@dataclass
class PreparePreviewResult:
session: DatasetReviewSession
preview: CompiledPreview
blocked_reasons: List[str] = field(default_factory=list)
# [/DEF:PreparePreviewResult:Class]
# [DEF:LaunchDatasetCommand:Class]
# @COMPLEXITY: 2
# @PURPOSE: Typed input contract for launching one dataset-review session into SQL Lab.
@dataclass
class LaunchDatasetCommand:
user: User
session_id: str
# [/DEF:LaunchDatasetCommand:Class]
# [DEF:LaunchDatasetResult:Class]
# @COMPLEXITY: 2
# @PURPOSE: Launch result carrying immutable run context and any gate blockers surfaced before launch.
@dataclass
class LaunchDatasetResult:
session: DatasetReviewSession
run_context: DatasetRunContext
blocked_reasons: List[str] = field(default_factory=list)
# [/DEF:LaunchDatasetResult:Class]
# [DEF:DatasetReviewOrchestrator:Class]
# @COMPLEXITY: 5
# @PURPOSE: Coordinate safe session startup while preserving cross-user isolation and explicit partial recovery.
# @RELATION: [DEPENDS_ON] ->[DatasetReviewSessionRepository]
# @RELATION: [DEPENDS_ON] ->[SupersetContextExtractor]
# @RELATION: [DEPENDS_ON] ->[TaskManager]
# @RELATION: [DEPENDS_ON] ->[SessionRepo]
# @RELATION: [DEPENDS_ON] ->[ConfigManager]
# @PRE: constructor dependencies are valid and tied to the current request/task scope.
# @POST: orchestrator instance can execute session-scoped mutations for one authenticated user.
# @SIDE_EFFECT: downstream operations may persist session/profile/finding state and enqueue background tasks.
# @DATA_CONTRACT: Input[StartSessionCommand] -> Output[StartSessionResult]
# @INVARIANT: session ownership is preserved on every mutation and recovery remains explicit when partial.
class DatasetReviewOrchestrator:
# [DEF:DatasetReviewOrchestrator.__init__:Function]
# @COMPLEXITY: 3
# @PURPOSE: Bind repository, config, and task dependencies required by the orchestration boundary.
# @RELATION: [DEPENDS_ON] ->[SessionRepo]
# @RELATION: [DEPENDS_ON] ->[ConfigManager]
def __init__(
self,
repository: DatasetReviewSessionRepository,
config_manager: ConfigManager,
task_manager: Optional[TaskManager] = None,
semantic_resolver: Optional[SemanticSourceResolver] = None,
) -> None:
self.repository = repository
self.config_manager = config_manager
self.task_manager = task_manager
self.semantic_resolver = semantic_resolver or SemanticSourceResolver()
# [/DEF:DatasetReviewOrchestrator.__init__:Function]
# [DEF:DatasetReviewOrchestrator.start_session:Function]
# @COMPLEXITY: 5
# @PURPOSE: Initialize a new session from a Superset link or dataset selection and trigger context recovery.
# @RELATION: [DEPENDS_ON] ->[SessionRepo]
# @RELATION: [CALLS] ->[SupersetContextExtractor.parse_superset_link]
# @RELATION: [CALLS] ->[create_task]
# @PRE: source input is non-empty and environment is accessible.
# @POST: session exists in persisted storage with intake/recovery state and task linkage when async work is required.
# @SIDE_EFFECT: persists session and may enqueue recovery task.
# @DATA_CONTRACT: Input[StartSessionCommand] -> Output[StartSessionResult]
# @INVARIANT: no cross-user session leakage occurs; session and follow-up task remain owned by the authenticated user.
def start_session(self, command: StartSessionCommand) -> StartSessionResult:
with belief_scope("DatasetReviewOrchestrator.start_session"):
normalized_source_kind = str(command.source_kind or "").strip()
normalized_source_input = str(command.source_input or "").strip()
normalized_environment_id = str(command.environment_id or "").strip()
if not normalized_source_input:
logger.explore(
"Blocked dataset review session start due to empty source input"
)
raise ValueError("source_input must be non-empty")
if normalized_source_kind not in {"superset_link", "dataset_selection"}:
logger.explore(
"Blocked dataset review session start due to unsupported source kind",
extra={"source_kind": normalized_source_kind},
)
raise ValueError(
"source_kind must be 'superset_link' or 'dataset_selection'"
)
environment = self.config_manager.get_environment(normalized_environment_id)
if environment is None:
logger.explore(
"Blocked dataset review session start because environment was not found",
extra={"environment_id": normalized_environment_id},
)
raise ValueError("Environment not found")
logger.reason(
"Starting dataset review session",
extra={
"user_id": command.user.id,
"environment_id": normalized_environment_id,
"source_kind": normalized_source_kind,
},
)
parsed_context: Optional[SupersetParsedContext] = None
findings: List[ValidationFinding] = []
dataset_ref = normalized_source_input
dataset_id: Optional[int] = None
dashboard_id: Optional[int] = None
readiness_state = ReadinessState.IMPORTING
recommended_action = RecommendedAction.REVIEW_DOCUMENTATION
current_phase = SessionPhase.RECOVERY
if normalized_source_kind == "superset_link":
extractor = SupersetContextExtractor(environment)
parsed_context = extractor.parse_superset_link(normalized_source_input)
dataset_ref = parsed_context.dataset_ref
dataset_id = parsed_context.dataset_id
dashboard_id = parsed_context.dashboard_id
if parsed_context.partial_recovery:
readiness_state = ReadinessState.RECOVERY_REQUIRED
recommended_action = RecommendedAction.REVIEW_DOCUMENTATION
findings.extend(
self._build_partial_recovery_findings(parsed_context)
)
else:
readiness_state = ReadinessState.REVIEW_READY
else:
dataset_ref, dataset_id = self._parse_dataset_selection(
normalized_source_input
)
readiness_state = ReadinessState.REVIEW_READY
current_phase = SessionPhase.REVIEW
session = DatasetReviewSession(
user_id=command.user.id,
environment_id=normalized_environment_id,
source_kind=normalized_source_kind,
source_input=normalized_source_input,
dataset_ref=dataset_ref,
dataset_id=dataset_id,
dashboard_id=dashboard_id,
readiness_state=readiness_state,
recommended_action=recommended_action,
status=SessionStatus.ACTIVE,
current_phase=current_phase,
)
persisted_session = cast(Any, self.repository.create_session(session))
recovered_filters: List[ImportedFilter] = []
template_variables: List[TemplateVariable] = []
execution_mappings: List[ExecutionMapping] = []
if normalized_source_kind == "superset_link" and parsed_context is not None:
recovered_filters, template_variables, execution_mappings, findings = (
self._build_recovery_bootstrap(
environment=environment,
session=persisted_session,
parsed_context=parsed_context,
findings=findings,
)
)
profile = self._build_initial_profile(
session_id=persisted_session.session_id,
parsed_context=parsed_context,
dataset_ref=dataset_ref,
)
self.repository.event_logger.log_event(
SessionEventPayload(
session_id=persisted_session.session_id,
actor_user_id=command.user.id,
event_type="session_started",
event_summary="Dataset review session shell created",
current_phase=persisted_session.current_phase.value,
readiness_state=persisted_session.readiness_state.value,
event_details={
"source_kind": persisted_session.source_kind,
"dataset_ref": persisted_session.dataset_ref,
"dataset_id": persisted_session.dataset_id,
"dashboard_id": persisted_session.dashboard_id,
"partial_recovery": bool(
parsed_context and parsed_context.partial_recovery
),
},
)
)
persisted_session = self.repository.save_profile_and_findings(
persisted_session.session_id,
command.user.id,
profile,
findings,
)
if recovered_filters or template_variables or execution_mappings:
persisted_session = self.repository.save_recovery_state(
persisted_session.session_id,
command.user.id,
recovered_filters,
template_variables,
execution_mappings,
)
active_task_id = self._enqueue_recovery_task(
command=command,
session=persisted_session,
parsed_context=parsed_context,
)
if active_task_id:
persisted_session.active_task_id = active_task_id
self.repository.db.commit()
self.repository.db.refresh(persisted_session)
self.repository.event_logger.log_event(
SessionEventPayload(
session_id=persisted_session.session_id,
actor_user_id=command.user.id,
event_type="recovery_task_linked",
event_summary="Recovery task linked to dataset review session",
current_phase=persisted_session.current_phase.value,
readiness_state=persisted_session.readiness_state.value,
event_details={"task_id": active_task_id},
)
)
logger.reason(
"Linked recovery task to started dataset review session",
extra={
"session_id": persisted_session.session_id,
"task_id": active_task_id,
},
)
logger.reflect(
"Dataset review session start completed",
extra={
"session_id": persisted_session.session_id,
"dataset_ref": persisted_session.dataset_ref,
"dataset_id": persisted_session.dataset_id,
"dashboard_id": persisted_session.dashboard_id,
"readiness_state": persisted_session.readiness_state.value,
"active_task_id": persisted_session.active_task_id,
"finding_count": len(findings),
},
)
return StartSessionResult(
session=persisted_session,
parsed_context=parsed_context,
findings=findings,
)
# [/DEF:DatasetReviewOrchestrator.start_session:Function]
# [DEF:DatasetReviewOrchestrator.prepare_launch_preview:Function]
# @COMPLEXITY: 4
# @PURPOSE: Assemble effective execution inputs and trigger Superset-side preview compilation.
# @RELATION: [CALLS] ->[SupersetCompilationAdapter.compile_preview]
# @PRE: all required variables have candidate values or explicitly accepted defaults.
# @POST: returns preview artifact in pending, ready, failed, or stale state.
# @SIDE_EFFECT: persists preview attempt and upstream compilation diagnostics.
# @DATA_CONTRACT: Input[PreparePreviewCommand] -> Output[PreparePreviewResult]
def prepare_launch_preview(
self, command: PreparePreviewCommand
) -> PreparePreviewResult:
with belief_scope("DatasetReviewOrchestrator.prepare_launch_preview"):
session = self.repository.load_session_detail(
command.session_id, command.user.id
)
if session is None or session.user_id != command.user.id:
logger.explore(
"Preview preparation rejected because owned session was not found",
extra={
"session_id": command.session_id,
"user_id": command.user.id,
},
)
raise ValueError("Session not found")
if session.dataset_id is None:
raise ValueError("Preview requires a resolved dataset_id")
environment = self.config_manager.get_environment(session.environment_id)
if environment is None:
raise ValueError("Environment not found")
execution_snapshot = self._build_execution_snapshot(session)
preview_blockers = execution_snapshot["preview_blockers"]
if preview_blockers:
logger.explore(
"Preview preparation blocked by incomplete execution context",
extra={
"session_id": session.session_id,
"blocked_reasons": preview_blockers,
},
)
raise ValueError("Preview blocked: " + "; ".join(preview_blockers))
adapter = SupersetCompilationAdapter(environment)
preview = adapter.compile_preview(
PreviewCompilationPayload(
session_id=session.session_id,
dataset_id=session.dataset_id,
preview_fingerprint=execution_snapshot["preview_fingerprint"],
template_params=execution_snapshot["template_params"],
effective_filters=execution_snapshot["effective_filters"],
)
)
persisted_preview = self.repository.save_preview(
session.session_id,
command.user.id,
preview,
)
session.current_phase = SessionPhase.PREVIEW
session.last_activity_at = datetime.utcnow()
if persisted_preview.preview_status == PreviewStatus.READY:
launch_blockers = self._build_launch_blockers(
session=session,
execution_snapshot=execution_snapshot,
preview=persisted_preview,
)
if launch_blockers:
session.readiness_state = ReadinessState.COMPILED_PREVIEW_READY
session.recommended_action = RecommendedAction.APPROVE_MAPPING
else:
session.readiness_state = ReadinessState.RUN_READY
session.recommended_action = RecommendedAction.LAUNCH_DATASET
else:
session.readiness_state = ReadinessState.PARTIALLY_READY
session.recommended_action = RecommendedAction.GENERATE_SQL_PREVIEW
self.repository.db.commit()
self.repository.db.refresh(session)
self.repository.event_logger.log_event(
SessionEventPayload(
session_id=session.session_id,
actor_user_id=command.user.id,
event_type="preview_generated",
event_summary="Superset preview generation persisted",
current_phase=session.current_phase.value,
readiness_state=session.readiness_state.value,
event_details={
"preview_id": persisted_preview.preview_id,
"preview_status": persisted_preview.preview_status.value,
"preview_fingerprint": persisted_preview.preview_fingerprint,
},
)
)
logger.reflect(
"Superset preview preparation completed",
extra={
"session_id": session.session_id,
"preview_id": persisted_preview.preview_id,
"preview_status": persisted_preview.preview_status.value,
"preview_fingerprint": persisted_preview.preview_fingerprint,
},
)
return PreparePreviewResult(
session=session,
preview=persisted_preview,
blocked_reasons=[],
)
# [/DEF:DatasetReviewOrchestrator.prepare_launch_preview:Function]
# [DEF:DatasetReviewOrchestrator.launch_dataset:Function]
# @COMPLEXITY: 5
# @PURPOSE: Start the approved dataset execution through SQL Lab and persist run context for audit/replay.
# @RELATION: [CALLS] ->[SupersetCompilationAdapter.create_sql_lab_session]
# @PRE: session is run-ready and compiled preview is current.
# @POST: returns persisted run context with SQL Lab session reference and launch outcome.
# @SIDE_EFFECT: creates SQL Lab execution session and audit snapshot.
# @DATA_CONTRACT: Input[LaunchDatasetCommand] -> Output[LaunchDatasetResult]
# @INVARIANT: launch remains blocked unless blocking findings are closed, approvals are satisfied, and the latest Superset preview fingerprint matches current execution inputs.
def launch_dataset(self, command: LaunchDatasetCommand) -> LaunchDatasetResult:
with belief_scope("DatasetReviewOrchestrator.launch_dataset"):
session = self.repository.load_session_detail(
command.session_id, command.user.id
)
if session is None or session.user_id != command.user.id:
logger.explore(
"Launch rejected because owned session was not found",
extra={
"session_id": command.session_id,
"user_id": command.user.id,
},
)
raise ValueError("Session not found")
if session.dataset_id is None:
raise ValueError("Launch requires a resolved dataset_id")
environment = self.config_manager.get_environment(session.environment_id)
if environment is None:
raise ValueError("Environment not found")
execution_snapshot = self._build_execution_snapshot(session)
current_preview = self._get_latest_preview(session)
launch_blockers = self._build_launch_blockers(
session=session,
execution_snapshot=execution_snapshot,
preview=current_preview,
)
if launch_blockers:
logger.explore(
"Launch gate blocked dataset execution",
extra={
"session_id": session.session_id,
"blocked_reasons": launch_blockers,
},
)
raise ValueError("Launch blocked: " + "; ".join(launch_blockers))
adapter = SupersetCompilationAdapter(environment)
try:
sql_lab_session_ref = adapter.create_sql_lab_session(
SqlLabLaunchPayload(
session_id=session.session_id,
dataset_id=session.dataset_id,
preview_id=current_preview.preview_id,
compiled_sql=str(current_preview.compiled_sql or ""),
template_params=execution_snapshot["template_params"],
)
)
launch_status = LaunchStatus.STARTED
launch_error = None
except Exception as exc:
logger.explore(
"SQL Lab launch failed after passing gates",
extra={"session_id": session.session_id, "error": str(exc)},
)
sql_lab_session_ref = "unavailable"
launch_status = LaunchStatus.FAILED
launch_error = str(exc)
run_context = DatasetRunContext(
session_id=session.session_id,
dataset_ref=session.dataset_ref,
environment_id=session.environment_id,
preview_id=current_preview.preview_id,
sql_lab_session_ref=sql_lab_session_ref,
effective_filters=execution_snapshot["effective_filters"],
template_params=execution_snapshot["template_params"],
approved_mapping_ids=execution_snapshot["approved_mapping_ids"],
semantic_decision_refs=execution_snapshot["semantic_decision_refs"],
open_warning_refs=execution_snapshot["open_warning_refs"],
launch_status=launch_status,
launch_error=launch_error,
)
persisted_run_context = self.repository.save_run_context(
session.session_id,
command.user.id,
run_context,
)
session.current_phase = SessionPhase.LAUNCH
session.last_activity_at = datetime.utcnow()
if launch_status == LaunchStatus.FAILED:
session.readiness_state = ReadinessState.COMPILED_PREVIEW_READY
session.recommended_action = RecommendedAction.LAUNCH_DATASET
else:
session.readiness_state = ReadinessState.RUN_IN_PROGRESS
session.recommended_action = RecommendedAction.EXPORT_OUTPUTS
self.repository.db.commit()
self.repository.db.refresh(session)
self.repository.event_logger.log_event(
SessionEventPayload(
session_id=session.session_id,
actor_user_id=command.user.id,
event_type="dataset_launch_requested",
event_summary="Dataset launch handoff persisted",
current_phase=session.current_phase.value,
readiness_state=session.readiness_state.value,
event_details={
"run_context_id": persisted_run_context.run_context_id,
"launch_status": persisted_run_context.launch_status.value,
"preview_id": persisted_run_context.preview_id,
"sql_lab_session_ref": persisted_run_context.sql_lab_session_ref,
},
)
)
logger.reflect(
"Dataset launch orchestration completed with audited run context",
extra={
"session_id": session.session_id,
"run_context_id": persisted_run_context.run_context_id,
"launch_status": persisted_run_context.launch_status.value,
"preview_id": persisted_run_context.preview_id,
},
)
return LaunchDatasetResult(
session=session,
run_context=persisted_run_context,
blocked_reasons=[],
)
# [/DEF:DatasetReviewOrchestrator.launch_dataset:Function]
# [DEF:DatasetReviewOrchestrator._parse_dataset_selection:Function]
# @COMPLEXITY: 3
# @PURPOSE: Normalize dataset-selection payload into canonical session references.
# @RELATION: [DEPENDS_ON] ->[DatasetReviewSession]
def _parse_dataset_selection(self, source_input: str) -> tuple[str, Optional[int]]:
normalized = str(source_input or "").strip()
if not normalized:
raise ValueError("dataset selection input must be non-empty")
if normalized.isdigit():
dataset_id = int(normalized)
return f"dataset:{dataset_id}", dataset_id
if normalized.startswith("dataset:"):
suffix = normalized.split(":", 1)[1].strip()
if suffix.isdigit():
return normalized, int(suffix)
return normalized, None
return normalized, None
# [/DEF:DatasetReviewOrchestrator._parse_dataset_selection:Function]
# [DEF:DatasetReviewOrchestrator._build_initial_profile:Function]
# @COMPLEXITY: 3
# @PURPOSE: Create the first profile snapshot so exports and detail views remain usable immediately after intake.
# @RELATION: [DEPENDS_ON] ->[DatasetProfile]
def _build_initial_profile(
self,
session_id: str,
parsed_context: Optional[SupersetParsedContext],
dataset_ref: str,
) -> DatasetProfile:
dataset_name = (
dataset_ref.split(".")[-1] if dataset_ref else "Unresolved dataset"
)
business_summary = (
f"Review session initialized for {dataset_ref}."
if dataset_ref
else "Review session initialized with unresolved dataset context."
)
confidence_state = (
ConfidenceState.MIXED
if parsed_context and parsed_context.partial_recovery
else ConfidenceState.MOSTLY_CONFIRMED
)
return DatasetProfile(
session_id=session_id,
dataset_name=dataset_name or "Unresolved dataset",
schema_name=dataset_ref.split(".")[0] if "." in dataset_ref else None,
business_summary=business_summary,
business_summary_source=BusinessSummarySource.IMPORTED,
description="Initial review profile created from source intake.",
dataset_type="unknown",
is_sqllab_view=False,
completeness_score=0.25,
confidence_state=confidence_state,
has_blocking_findings=False,
has_warning_findings=bool(
parsed_context and parsed_context.partial_recovery
),
manual_summary_locked=False,
)
# [/DEF:DatasetReviewOrchestrator._build_initial_profile:Function]
# [DEF:DatasetReviewOrchestrator._build_partial_recovery_findings:Function]
# @COMPLEXITY: 4
# @PURPOSE: Project partial Superset intake recovery into explicit findings without blocking session usability.
# @RELATION: [DEPENDS_ON] ->[ValidationFinding]
# @PRE: parsed_context.partial_recovery is true.
# @POST: returns warning-level findings that preserve usable but incomplete state.
# @SIDE_EFFECT: none beyond structured finding creation.
# @DATA_CONTRACT: Input[SupersetParsedContext] -> Output[List[ValidationFinding]]
def _build_partial_recovery_findings(
self,
parsed_context: SupersetParsedContext,
) -> List[ValidationFinding]:
findings: List[ValidationFinding] = []
for unresolved_ref in parsed_context.unresolved_references:
findings.append(
ValidationFinding(
area=FindingArea.SOURCE_INTAKE,
severity=FindingSeverity.WARNING,
code="PARTIAL_SUPERSET_RECOVERY",
title="Superset context recovered partially",
message=(
"Session remains usable, but some Superset context requires review: "
f"{unresolved_ref.replace('_', ' ')}."
),
resolution_state=ResolutionState.OPEN,
caused_by_ref=unresolved_ref,
)
)
return findings
# [/DEF:DatasetReviewOrchestrator._build_partial_recovery_findings:Function]
# [DEF:DatasetReviewOrchestrator._build_recovery_bootstrap:Function]
# @COMPLEXITY: 4
# @PURPOSE: Recover and materialize initial imported filters, template variables, and draft execution mappings after session creation.
# @RELATION: [CALLS] ->[SupersetContextExtractor.recover_imported_filters]
# @RELATION: [CALLS] ->[SupersetContextExtractor.discover_template_variables]
# @PRE: session belongs to the just-created review aggregate and parsed_context was produced for the same environment scope.
# @POST: Returns bootstrap imported filters, template variables, execution mappings, and updated findings without persisting them directly.
# @SIDE_EFFECT: Performs Superset reads through the extractor and may append warning findings for incomplete recovery.
# @DATA_CONTRACT: Input[Environment, DatasetReviewSession, SupersetParsedContext, List[ValidationFinding]] -> Output[Tuple[List[ImportedFilter], List[TemplateVariable], List[ExecutionMapping], List[ValidationFinding]]]
def _build_recovery_bootstrap(
self,
environment,
session: DatasetReviewSession,
parsed_context: SupersetParsedContext,
findings: List[ValidationFinding],
) -> tuple[
List[ImportedFilter],
List[TemplateVariable],
List[ExecutionMapping],
List[ValidationFinding],
]:
session_record = cast(Any, session)
extractor = SupersetContextExtractor(environment)
imported_filters_payload = extractor.recover_imported_filters(parsed_context)
if imported_filters_payload is None:
imported_filters_payload = []
imported_filters = [
ImportedFilter(
session_id=session_record.session_id,
filter_name=str(item.get("filter_name") or f"imported_filter_{index}"),
display_name=item.get("display_name"),
raw_value=item.get("raw_value"),
normalized_value=item.get("normalized_value"),
source=FilterSource(
str(item.get("source") or FilterSource.SUPERSET_URL.value)
),
confidence_state=FilterConfidenceState(
str(
item.get("confidence_state")
or FilterConfidenceState.UNRESOLVED.value
)
),
requires_confirmation=bool(item.get("requires_confirmation", False)),
recovery_status=FilterRecoveryStatus(
str(
item.get("recovery_status")
or FilterRecoveryStatus.PARTIAL.value
)
),
notes=item.get("notes"),
)
for index, item in enumerate(imported_filters_payload)
]
template_variables: List[TemplateVariable] = []
execution_mappings: List[ExecutionMapping] = []
if session.dataset_id is not None:
try:
dataset_payload = extractor.client.get_dataset_detail(
session_record.dataset_id
)
discovered_variables = extractor.discover_template_variables(
dataset_payload
)
template_variables = [
TemplateVariable(
session_id=session_record.session_id,
variable_name=str(
item.get("variable_name") or f"variable_{index}"
),
expression_source=str(item.get("expression_source") or ""),
variable_kind=VariableKind(
str(item.get("variable_kind") or VariableKind.UNKNOWN.value)
),
is_required=bool(item.get("is_required", True)),
default_value=item.get("default_value"),
mapping_status=MappingStatus(
str(
item.get("mapping_status")
or MappingStatus.UNMAPPED.value
)
),
)
for index, item in enumerate(discovered_variables)
]
except Exception as exc:
if (
"dataset_template_variable_discovery_failed"
not in parsed_context.unresolved_references
):
parsed_context.unresolved_references.append(
"dataset_template_variable_discovery_failed"
)
if not any(
finding.caused_by_ref
== "dataset_template_variable_discovery_failed"
for finding in findings
):
findings.append(
ValidationFinding(
area=FindingArea.TEMPLATE_MAPPING,
severity=FindingSeverity.WARNING,
code="TEMPLATE_VARIABLE_DISCOVERY_FAILED",
title="Template variables could not be discovered",
message="Session remains usable, but dataset template variables still need review.",
resolution_state=ResolutionState.OPEN,
caused_by_ref="dataset_template_variable_discovery_failed",
)
)
logger.explore(
"Template variable discovery failed during session bootstrap",
extra={
"session_id": session_record.session_id,
"dataset_id": session_record.dataset_id,
"error": str(exc),
},
)
filter_lookup = {
str(imported_filter.filter_name or "").strip().lower(): imported_filter
for imported_filter in imported_filters
if str(imported_filter.filter_name or "").strip()
}
for template_variable in template_variables:
matched_filter = filter_lookup.get(
str(template_variable.variable_name or "").strip().lower()
)
if matched_filter is None:
continue
requires_explicit_approval = bool(
matched_filter.requires_confirmation
or matched_filter.recovery_status != FilterRecoveryStatus.RECOVERED
)
execution_mappings.append(
ExecutionMapping(
session_id=session_record.session_id,
filter_id=matched_filter.filter_id,
variable_id=template_variable.variable_id,
mapping_method=MappingMethod.DIRECT_MATCH,
raw_input_value=matched_filter.raw_value,
effective_value=matched_filter.normalized_value
if matched_filter.normalized_value is not None
else matched_filter.raw_value,
transformation_note="Bootstrapped from Superset recovery context",
warning_level=None if not requires_explicit_approval else None,
requires_explicit_approval=requires_explicit_approval,
approval_state=ApprovalState.PENDING
if requires_explicit_approval
else ApprovalState.NOT_REQUIRED,
approved_by_user_id=None,
approved_at=None,
)
)
return imported_filters, template_variables, execution_mappings, findings
# [/DEF:DatasetReviewOrchestrator._build_recovery_bootstrap:Function]
# [DEF:DatasetReviewOrchestrator._build_execution_snapshot:Function]
# @COMPLEXITY: 4
# @PURPOSE: Build effective filters, template params, approvals, and fingerprint for preview and launch gating.
# @RELATION: [DEPENDS_ON] ->[DatasetReviewSession]
# @PRE: Session aggregate includes imported filters, template variables, and current execution mappings.
# @POST: returns deterministic execution snapshot for current session state without mutating persistence.
# @SIDE_EFFECT: none.
# @DATA_CONTRACT: Input[DatasetReviewSession] -> Output[Dict[str,Any]]
def _build_execution_snapshot(
self, session: DatasetReviewSession
) -> Dict[str, Any]:
session_record = cast(Any, session)
filter_lookup = {
item.filter_id: item for item in session_record.imported_filters
}
variable_lookup = {
item.variable_id: item for item in session_record.template_variables
}
effective_filters: List[Dict[str, Any]] = []
template_params: Dict[str, Any] = {}
approved_mapping_ids: List[str] = []
open_warning_refs: List[str] = []
preview_blockers: List[str] = []
mapped_filter_ids: set[str] = set()
for mapping in session_record.execution_mappings:
imported_filter = filter_lookup.get(mapping.filter_id)
template_variable = variable_lookup.get(mapping.variable_id)
if imported_filter is None:
preview_blockers.append(f"mapping:{mapping.mapping_id}:missing_filter")
continue
if template_variable is None:
preview_blockers.append(
f"mapping:{mapping.mapping_id}:missing_variable"
)
continue
effective_value = mapping.effective_value
if effective_value is None:
effective_value = imported_filter.normalized_value
if effective_value is None:
effective_value = imported_filter.raw_value
if effective_value is None:
effective_value = template_variable.default_value
if effective_value is None and template_variable.is_required:
preview_blockers.append(
f"variable:{template_variable.variable_name}:missing_required_value"
)
continue
mapped_filter_ids.add(imported_filter.filter_id)
if effective_value is not None:
effective_filters.append(
{
"mapping_id": mapping.mapping_id,
"filter_id": imported_filter.filter_id,
"filter_name": imported_filter.filter_name,
"display_name": imported_filter.display_name,
"variable_id": template_variable.variable_id,
"variable_name": template_variable.variable_name,
"effective_value": effective_value,
"raw_input_value": mapping.raw_input_value,
"normalized_filter_payload": imported_filter.normalized_value,
}
)
template_params[template_variable.variable_name] = effective_value
if mapping.approval_state == ApprovalState.APPROVED:
approved_mapping_ids.append(mapping.mapping_id)
if (
mapping.requires_explicit_approval
and mapping.approval_state != ApprovalState.APPROVED
):
open_warning_refs.append(mapping.mapping_id)
for imported_filter in session_record.imported_filters:
if imported_filter.filter_id in mapped_filter_ids:
continue
effective_value = imported_filter.normalized_value
if effective_value is None:
effective_value = imported_filter.raw_value
if effective_value is None:
continue
effective_filters.append(
{
"filter_id": imported_filter.filter_id,
"filter_name": imported_filter.filter_name,
"display_name": imported_filter.display_name,
"effective_value": effective_value,
"raw_input_value": imported_filter.raw_value,
"normalized_filter_payload": imported_filter.normalized_value,
}
)
mapped_variable_ids = {
mapping.variable_id for mapping in session_record.execution_mappings
}
for variable in session_record.template_variables:
if variable.variable_id in mapped_variable_ids:
continue
if variable.default_value is not None:
template_params[variable.variable_name] = variable.default_value
continue
if variable.is_required:
preview_blockers.append(f"variable:{variable.variable_name}:unmapped")
semantic_decision_refs = [
field.field_id
for field in session.semantic_fields
if field.is_locked
or not field.needs_review
or field.provenance.value != "unresolved"
]
preview_fingerprint = self._compute_preview_fingerprint(
{
"dataset_id": session_record.dataset_id,
"template_params": template_params,
"effective_filters": effective_filters,
}
)
return {
"effective_filters": effective_filters,
"template_params": template_params,
"approved_mapping_ids": sorted(approved_mapping_ids),
"semantic_decision_refs": sorted(semantic_decision_refs),
"open_warning_refs": sorted(open_warning_refs),
"preview_blockers": sorted(set(preview_blockers)),
"preview_fingerprint": preview_fingerprint,
}
# [/DEF:DatasetReviewOrchestrator._build_execution_snapshot:Function]
# [DEF:DatasetReviewOrchestrator._build_launch_blockers:Function]
# @COMPLEXITY: 4
# @PURPOSE: Enforce launch gates from findings, approvals, and current preview truth.
# @RELATION: [DEPENDS_ON] ->[CompiledPreview]
# @PRE: execution_snapshot was computed from current session state and preview is the latest persisted preview or None.
# @POST: returns explicit blocker codes for every unmet launch invariant.
# @SIDE_EFFECT: none.
# @DATA_CONTRACT: Input[DatasetReviewSession,Dict[str,Any],CompiledPreview|None] -> Output[List[str]]
def _build_launch_blockers(
self,
session: DatasetReviewSession,
execution_snapshot: Dict[str, Any],
preview: Optional[CompiledPreview],
) -> List[str]:
session_record = cast(Any, session)
blockers = list(execution_snapshot["preview_blockers"])
for finding in session_record.findings:
if (
finding.severity == FindingSeverity.BLOCKING
and finding.resolution_state
not in {ResolutionState.RESOLVED, ResolutionState.APPROVED}
):
blockers.append(f"finding:{finding.code}:blocking")
for mapping in session_record.execution_mappings:
if (
mapping.requires_explicit_approval
and mapping.approval_state != ApprovalState.APPROVED
):
blockers.append(f"mapping:{mapping.mapping_id}:approval_required")
if preview is None:
blockers.append("preview:missing")
else:
if preview.preview_status != PreviewStatus.READY:
blockers.append(f"preview:{preview.preview_status.value}")
if preview.preview_fingerprint != execution_snapshot["preview_fingerprint"]:
blockers.append("preview:fingerprint_mismatch")
return sorted(set(blockers))
# [/DEF:DatasetReviewOrchestrator._build_launch_blockers:Function]
# [DEF:DatasetReviewOrchestrator._get_latest_preview:Function]
# @COMPLEXITY: 2
# @PURPOSE: Resolve the current latest preview snapshot for one session aggregate.
def _get_latest_preview(
self, session: DatasetReviewSession
) -> Optional[CompiledPreview]:
session_record = cast(Any, session)
if not session_record.previews:
return None
if session_record.last_preview_id:
for preview in session_record.previews:
if preview.preview_id == session_record.last_preview_id:
return preview
return sorted(
session_record.previews,
key=lambda item: (item.created_at or datetime.min, item.preview_id),
reverse=True,
)[0]
# [/DEF:DatasetReviewOrchestrator._get_latest_preview:Function]
# [DEF:DatasetReviewOrchestrator._compute_preview_fingerprint:Function]
# @COMPLEXITY: 2
# @PURPOSE: Produce deterministic execution fingerprint for preview truth and staleness checks.
def _compute_preview_fingerprint(self, payload: Dict[str, Any]) -> str:
serialized = json.dumps(payload, sort_keys=True, default=str)
return hashlib.sha256(serialized.encode("utf-8")).hexdigest()
# [/DEF:DatasetReviewOrchestrator._compute_preview_fingerprint:Function]
# [DEF:DatasetReviewOrchestrator._enqueue_recovery_task:Function]
# @COMPLEXITY: 4
# @PURPOSE: Link session start to observable async recovery when task infrastructure is available.
# @RELATION: [CALLS] ->[create_task]
# @PRE: session is already persisted.
# @POST: returns task identifier when a task could be enqueued, otherwise None.
# @SIDE_EFFECT: may create one background task for progressive recovery.
# @DATA_CONTRACT: Input[StartSessionCommand,DatasetReviewSession,SupersetParsedContext|None] -> Output[task_id:str|None]
def _enqueue_recovery_task(
self,
command: StartSessionCommand,
session: DatasetReviewSession,
parsed_context: Optional[SupersetParsedContext],
) -> Optional[str]:
session_record = cast(Any, session)
if self.task_manager is None:
logger.reason(
"Dataset review session started without task manager; continuing synchronously",
extra={"session_id": session_record.session_id},
)
return None
task_params: Dict[str, Any] = {
"session_id": session_record.session_id,
"user_id": command.user.id,
"environment_id": session_record.environment_id,
"source_kind": session_record.source_kind,
"source_input": session_record.source_input,
"dataset_ref": session_record.dataset_ref,
"dataset_id": session_record.dataset_id,
"dashboard_id": session_record.dashboard_id,
"partial_recovery": bool(
parsed_context and parsed_context.partial_recovery
),
}
create_task = getattr(self.task_manager, "create_task", None)
if create_task is None:
logger.explore(
"Task manager has no create_task method; skipping recovery enqueue"
)
return None
try:
task_object = create_task(
plugin_id="dataset-review-recovery",
params=task_params,
)
except TypeError:
logger.explore(
"Recovery task enqueue skipped because task manager create_task contract is incompatible",
extra={"session_id": session_record.session_id},
)
return None
task_id = getattr(task_object, "id", None)
return str(task_id) if task_id else None
# [/DEF:DatasetReviewOrchestrator._enqueue_recovery_task:Function]
# [/DEF:DatasetReviewOrchestrator:Class]
# [/DEF:DatasetReviewOrchestrator:Module]