564 lines
21 KiB
Python
564 lines
21 KiB
Python
# ruff: noqa: BLE001, S110, B008
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import logging
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from pathlib import Path
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import typer
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from tht.adapters.factory import build_dwh
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from tht.cli.config_cmd import CONFIG_OPT
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from tht.config import ConfigError, load_config
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from tht.db.sampling import is_text_type
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from tht.mschema.eligibility import classify_all
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schema_app = typer.Typer(help="Gestione mschema (rappresentazione canonica dello schema)")
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logger = logging.getLogger(__name__)
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def _add_examples(dwh, phys, examples) -> None:
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for table_name, table in phys.tables.items():
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for column_name, column in table.columns.items():
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if not is_text_type(column.type):
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continue
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try:
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sampled = dwh.sample_column(
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table_name, column_name, limit=examples.max_per_column
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)
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except Exception as exc:
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logger.warning("Campionamento saltato per %s.%s: %s",
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table_name, column_name, exc)
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continue
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column.examples = [str(value) for value in sampled if value not in (None, "")]
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def _load_config_or_exit(config: Path):
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try:
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return load_config(config)
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except ConfigError as e:
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typer.secho(f"ERRORE: {e}", fg=typer.colors.RED, err=True)
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raise typer.Exit(code=1)
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def physical_path(cfg) -> Path:
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from tht.jobs.dwh_pipeline import resolve_dwh_snapshot
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if not (cfg.paths.artifacts.parent / ".tht-dwh").exists():
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return cfg.paths.artifacts / "mschema" / "physical.yaml"
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return resolve_dwh_snapshot(cfg).physical
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def annotations_path(cfg) -> Path:
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return cfg.paths.artifacts / "mschema" / "annotations.yaml"
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def refresh_catalog(cfg, *, dwh=None, output_path: Path | None = None):
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"""Run the existing catalog algorithm and persist its canonical output."""
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target = dwh if dwh is not None else build_dwh(cfg)
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physical = target.introspect()
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_add_examples(target, physical, cfg.examples)
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classify_all(physical, cfg.eligibility)
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physical.to_yaml(output_path or (cfg.paths.artifacts / "mschema" / "physical.yaml"))
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return physical
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@schema_app.command("introspect")
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def introspect_cmd(
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config: Path = CONFIG_OPT,
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refresh: bool = typer.Option(
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False,
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"--refresh",
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help="Forza la re-introspezione del DWH anche se physical.yaml esiste già.",
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),
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) -> None:
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"""Introspeziona lo schema target e genera artifacts/mschema/physical.yaml.
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Se physical.yaml esiste già, esce subito (cache); usa --refresh per rigenerarlo.
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"""
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cfg = _load_config_or_exit(config)
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dwh_root = cfg.paths.artifacts.parent / ".tht-dwh"
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out = cfg.paths.artifacts / "mschema" / "physical.yaml"
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if dwh_root.exists() or dwh_root.is_symlink():
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out = physical_path(cfg)
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if (dwh_root.exists() or dwh_root.is_symlink()) and out.exists() and not refresh:
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from datetime import UTC, datetime
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from tht.mschema.models import PhysicalSchema
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try:
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cached = PhysicalSchema.from_yaml(out)
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except Exception:
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pass # catalogo illeggibile: procedi con la re-introspezione
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else:
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ts = cached.introspected_at
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if ts.tzinfo is None:
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ts = ts.replace(tzinfo=UTC)
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age_days = (datetime.now(UTC) - ts).days
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typer.secho(
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f"OK (cache): {out} esistente ({len(cached.tables)} tabelle, "
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f"età {age_days}g). Re-introspezione solo con --refresh (manutenzione).",
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fg=typer.colors.GREEN,
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)
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return
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try:
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from tht.cli.preprocess_cmd import run_dwh_from_config
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from tht.mschema.models import PhysicalSchema
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report = run_dwh_from_config(config, steps=("introspect",))
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if report.status != "succeeded":
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raise RuntimeError("DWH preprocessing failed")
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out = physical_path(cfg)
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phys = PhysicalSchema.from_yaml(out)
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except Exception as e:
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typer.secho(f"ERRORE: {e}", fg=typer.colors.RED, err=True)
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raise typer.Exit(code=1)
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n_cols = sum(len(t.columns) for t in phys.tables.values())
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n_ignored = sum(
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1 for t in phys.tables.values() for c in t.columns.values() if not c.eligible
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)
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typer.secho(
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f"OK: {len(phys.tables)} tabelle, {n_cols} colonne "
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f"({n_ignored} ignorate: testo ampio) -> {out}",
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fg=typer.colors.GREEN,
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)
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class _MachineSchemaError(Exception):
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"""An expected schema CLI failure with a stable public code."""
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def __init__(self, code: str):
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self.code = code
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super().__init__(code)
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_MAX_STAGED_SQL_BYTES = 1 << 20
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_MAX_STAGED_SQL_TOTAL = 16 << 20
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def _schema_json(payload: dict) -> None:
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import json
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typer.echo(json.dumps(payload, ensure_ascii=False, sort_keys=True, separators=(",", ":")))
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def _machine_config(config: Path):
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try:
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return load_config(config)
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except ConfigError:
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raise _MachineSchemaError("invalid_configuration") from None
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def _physical_or_error(cfg):
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path = physical_path(cfg)
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if not path.exists():
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raise _MachineSchemaError("physical_schema_missing")
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try:
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from tht.mschema.models import PhysicalSchema
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return PhysicalSchema.from_yaml(path)
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except _MachineSchemaError:
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raise
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except Exception:
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raise _MachineSchemaError("physical_schema_invalid") from None
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def _staged_sql_files(inputs: list[Path] | None) -> list[Path]:
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files: list[Path] = []
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for item in inputs or []:
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if item.is_file():
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files.append(item)
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elif item.is_dir():
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files.extend(path for path in item.rglob("*.sql") if path.is_file())
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else:
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raise _MachineSchemaError("staged_sql_invalid")
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# Resolve only for ordering; content remains read from the caller's staged path.
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return sorted(set(files), key=lambda path: path.resolve().as_posix())
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def _read_staged_sql(inputs: list[Path] | None) -> tuple[list[Path], list[str]]:
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paths = _staged_sql_files(inputs)
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contents: list[str] = []
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total = 0
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for path in paths:
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try:
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raw = path.read_bytes()
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except (OSError, UnicodeError):
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raise _MachineSchemaError("staged_sql_invalid") from None
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if len(raw) > _MAX_STAGED_SQL_BYTES or total + len(raw) > _MAX_STAGED_SQL_TOTAL:
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raise _MachineSchemaError("staged_sql_too_large")
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total += len(raw)
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try:
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contents.append(raw.decode("utf-8"))
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except UnicodeDecodeError:
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raise _MachineSchemaError("staged_sql_invalid") from None
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return paths, contents
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def _candidate_key(fk) -> tuple:
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return (tuple(fk.columns), fk.ref_table, tuple(fk.ref_columns))
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def suggest_fks_data(config: Path, *, from_sql: list[Path] | None = None,
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assume: list[str] | None = None) -> dict:
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"""Return deterministic FK candidates without reviewing or mutating annotations."""
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import hashlib
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import json
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from tht.mschema.fkmine import mine_join_pairs
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from tht.mschema.merge import find_orphans
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from tht.mschema.models import Annotations, ForeignKey
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cfg = _machine_config(config)
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physical = _physical_or_error(cfg)
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_, sql_contents = _read_staged_sql(from_sql)
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annotations = Annotations.from_yaml(annotations_path(cfg))
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assumed: dict[str, str] = {}
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for value in assume or []:
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col, sep, ref = value.partition("=")
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if not sep or not col or ref not in physical.tables:
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raise _MachineSchemaError("assumption_invalid")
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assumed[col] = ref
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def single_pk(table) -> str | None:
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pks = [name for name, column in table.columns.items() if column.pk]
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return pks[0] if len(pks) == 1 else None
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pk_owners: dict[str, list[str]] = {}
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for table_name in sorted(physical.tables):
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pk = single_pk(physical.tables[table_name])
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if pk:
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pk_owners.setdefault(pk, []).append(table_name)
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dim_time_pk = single_pk(physical.tables["dim_time"]) if "dim_time" in physical.tables else None
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known: dict[str, set] = {}
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for table_name in physical.tables:
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keys = {
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_candidate_key(fk)
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for fk in physical.tables[table_name].foreign_keys
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}
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annotation = annotations.tables.get(table_name)
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if annotation:
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keys.update(_candidate_key(fk) for fk in annotation.foreign_keys)
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known[table_name] = keys
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suggested: dict[str, list[ForeignKey]] = {}
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def add(table_name: str, column: str, ref_table: str, ref_column: str) -> None:
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key = ((column,), ref_table, (ref_column,))
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if key in known[table_name]:
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return
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known[table_name].add(key)
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suggested.setdefault(table_name, []).append(
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ForeignKey(columns=[column], ref_table=ref_table, ref_columns=[ref_column])
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)
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mined_total = 0
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for sql_text in sql_contents:
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pairs = mine_join_pairs(sql_text, physical)
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mined_total += sum(pairs.values())
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for src_table, src_column, ref_table, ref_column in sorted(pairs):
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add(src_table, src_column, ref_table, ref_column)
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ambiguous_columns: set[str] = set()
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for table_name in sorted(physical.tables):
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table = physical.tables[table_name]
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for column_name in sorted(table.columns):
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if dim_time_pk and column_name.endswith("time_key") and table_name != "dim_time":
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add(table_name, column_name, "dim_time", dim_time_pk)
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continue
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if column_name in assumed:
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if assumed[column_name] != table_name:
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add(table_name, column_name, assumed[column_name], column_name)
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continue
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owners = [owner for owner in pk_owners.get(column_name, []) if owner != table_name]
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if len(pk_owners.get(column_name, [])) > 1:
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ambiguous_columns.add(column_name)
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continue
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if not owners or column_name in _GENERIC_PK_NAMES:
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continue
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add(table_name, column_name, owners[0], column_name)
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candidates = [
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{
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"table": table_name,
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"foreign_keys": [
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fk.model_dump(exclude_defaults=True)
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for fk in sorted(fks, key=_candidate_key)
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],
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}
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for table_name, fks in sorted(suggested.items())
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]
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canonical = json.dumps(candidates, ensure_ascii=False, sort_keys=True, separators=(",", ":"))
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digest = "sha256:" + hashlib.sha256(canonical.encode("utf-8")).hexdigest()
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orphan_count = len(find_orphans(physical, annotations))
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return {
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"status": "succeeded",
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"code": "ok",
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"candidates": candidates,
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"candidate_count": sum(len(item["foreign_keys"]) for item in candidates),
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"candidate_digest": digest,
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"orphan_count": orphan_count,
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"staged_sql_count": len(sql_contents),
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"mined_join_count": mined_total,
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"ambiguous_columns": sorted(ambiguous_columns),
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}
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def check_schema_data(config: Path) -> dict:
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"""Validate the physical catalog and imported annotations without writing."""
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from tht.mschema.merge import find_orphans
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from tht.mschema.models import Annotations
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cfg = _machine_config(config)
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physical = _physical_or_error(cfg)
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try:
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annotations = Annotations.from_yaml(annotations_path(cfg))
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except Exception:
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raise _MachineSchemaError("annotations_invalid") from None
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orphans = find_orphans(physical, annotations)
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return {
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"status": "succeeded" if not orphans else "failed",
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"code": "ok" if not orphans else "annotation_orphans",
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"orphan_count": len(orphans),
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"orphans": orphans,
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}
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@schema_app.command("check")
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def check_cmd(
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config: Path = CONFIG_OPT,
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json_output: bool = typer.Option(False, "--json", help="Emetti JSON puro su stdout."),
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) -> None:
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"""Confronta physical.yaml e annotations.yaml; segnala annotazioni orfane."""
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try:
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payload = check_schema_data(config)
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except _MachineSchemaError as error:
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if json_output:
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_schema_json({"status": "failed", "code": error.code})
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raise typer.Exit(code=1) from None
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_load_config_or_exit(config)
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raise typer.Exit(code=1) from None
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except Exception:
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if json_output:
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_schema_json({"status": "failed", "code": "schema_check_failed"})
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raise typer.Exit(code=1) from None
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typer.secho("ERRORE: impossibile verificare le annotazioni.", fg=typer.colors.RED, err=True)
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raise typer.Exit(code=1) from None
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if json_output:
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# Keep the machine response to one object, including expected validation failures.
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_schema_json(payload)
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if payload["status"] != "succeeded":
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raise typer.Exit(code=3)
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return
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if payload["orphan_count"]:
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typer.secho(f"ATTENZIONE: {payload['orphan_count']} annotazioni orfane:", fg=typer.colors.YELLOW)
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for orphan in payload["orphans"]:
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typer.echo(f" - {orphan}")
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raise typer.Exit(code=3)
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typer.secho("OK: nessuna annotazione orfana.", fg=typer.colors.GREEN)
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@schema_app.command("suggest-fks")
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def suggest_fks_cmd(
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config: Path = CONFIG_OPT,
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from_sql: list[Path] = typer.Option(None, "--from-sql", help="Directory/file SQL approvati (ripetibile)."),
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assume: list[str] = typer.Option(None, "--assume", help="Disambigua una PK: col=tabella."),
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write: bool = typer.Option(False, "--write", help="Fonde i suggerimenti in annotations.yaml."),
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json_output: bool = typer.Option(False, "--json", help="Emetti JSON puro su stdout."),
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) -> None:
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"""Suggerisce FK logiche per la curazione umana in annotations.yaml."""
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import yaml as _yaml
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from tht.mschema.models import Annotations, TableAnnotation
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if json_output and write:
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_schema_json({"status": "failed", "code": "write_not_allowed"})
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raise typer.Exit(code=2)
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try:
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payload = suggest_fks_data(config, from_sql=from_sql, assume=assume)
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except _MachineSchemaError as error:
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if json_output:
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_schema_json({"status": "failed", "code": error.code})
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raise typer.Exit(code=1) from None
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if error.code == "assumption_invalid":
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typer.secho("ERRORE: --assume non valido (atteso col=tabella nel catalogo).", fg=typer.colors.RED, err=True)
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else:
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_load_config_or_exit(config)
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typer.secho("ERRORE: impossibile elaborare il catalogo.", fg=typer.colors.RED, err=True)
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raise typer.Exit(code=1) from None
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except Exception:
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if json_output:
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_schema_json({"status": "failed", "code": "schema_suggestion_failed"})
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raise typer.Exit(code=1) from None
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typer.secho("ERRORE: impossibile elaborare il catalogo.", fg=typer.colors.RED, err=True)
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raise typer.Exit(code=1) from None
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if json_output:
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_schema_json(payload)
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return
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# Human mode remains the original renderer over the pure result.
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if payload["mined_join_count"]:
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typer.secho(
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f"Minati {payload['mined_join_count']} equi-join da {payload['staged_sql_count']} file SQL.",
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fg=typer.colors.BLUE, err=True,
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)
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if payload["ambiguous_columns"]:
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typer.secho(
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"PK ambigue saltate dalla regola same-name: " + ", ".join(payload["ambiguous_columns"]),
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fg=typer.colors.YELLOW, err=True,
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)
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if not payload["candidates"]:
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typer.secho("OK: nessuna FK da suggerire.", fg=typer.colors.GREEN)
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return
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candidate_tables = {
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item["table"]: {"foreign_keys": item["foreign_keys"]}
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for item in payload["candidates"]
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}
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if write:
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cfg = _load_config_or_exit(config)
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annotations = Annotations.from_yaml(annotations_path(cfg))
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for table_name, table_payload in candidate_tables.items():
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ann = annotations.tables.setdefault(table_name, TableAnnotation())
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from tht.mschema.models import ForeignKey
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ann.foreign_keys.extend(ForeignKey(**fk) for fk in table_payload["foreign_keys"])
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annotations.to_yaml(annotations_path(cfg))
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typer.secho(
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f"OK: {payload['candidate_count']} FK suggerite aggiunte a {annotations_path(cfg)}.",
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fg=typer.colors.GREEN,
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)
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return
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typer.echo(_yaml.safe_dump({"tables": candidate_tables}, sort_keys=False, allow_unicode=True))
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typer.secho(
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f"{payload['candidate_count']} FK candidate ({len(candidate_tables)} tabelle). "
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"Usa --write per fonderle in annotations.yaml, poi curale a mano.",
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fg=typer.colors.YELLOW,
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)
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# PK con questi nomi sono identificatori generici: la regola same-name non si applica
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# (nel DWH reale `id` e' la PK di ~50 tabelle e produrrebbe migliaia di falsi positivi).
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_GENERIC_PK_NAMES = {"id", "key", "code"}
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@schema_app.command("render")
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def render_cmd(
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config: Path = CONFIG_OPT,
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format: str = typer.Option(
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"markdown", "--format", "-f", help="Formato: markdown | mschema-text | schema-dict"
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),
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tables: list[str] = typer.Option(
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None, "--table", "-t", help="Limita alle tabelle indicate (ripetibile)."
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),
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output: Path = typer.Option(None, "--output", "-o", help="File di output (default stdout)."),
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) -> None:
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"""Serializza mschema (physical + annotations) nel formato richiesto."""
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import json
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from tht.mschema.models import Annotations, PhysicalSchema
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from tht.mschema.render import to_markdown, to_mschema_text, to_schema_dict
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cfg = _load_config_or_exit(config)
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phys_file = physical_path(cfg)
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if not phys_file.exists():
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typer.secho(
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f"ERRORE: {phys_file} non trovato. Esegui prima `tht schema introspect`.",
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fg=typer.colors.RED, err=True,
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)
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raise typer.Exit(code=1)
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physical = PhysicalSchema.from_yaml(phys_file)
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annotations = Annotations.from_yaml(annotations_path(cfg))
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table_filter = list(tables) if tables else None
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if format == "markdown":
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out = to_markdown(physical, annotations)
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elif format == "mschema-text":
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out = to_mschema_text(physical, annotations, tables=table_filter)
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elif format == "schema-dict":
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out = json.dumps(to_schema_dict(physical, annotations), ensure_ascii=False, indent=2)
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else:
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typer.secho(f"ERRORE: formato sconosciuto: {format}", fg=typer.colors.RED, err=True)
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raise typer.Exit(code=1)
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if output:
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output.parent.mkdir(parents=True, exist_ok=True)
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output.write_text(out)
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typer.secho(f"OK: scritto {output}", fg=typer.colors.GREEN)
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else:
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typer.echo(out)
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@schema_app.command("columns")
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def columns_cmd(
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table: str = typer.Argument(..., help="Nome tabella (chiave in physical.yaml)."),
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json_out: bool = typer.Option(False, "--json", help="Emetti JSON puro su stdout."),
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config: Path = CONFIG_OPT,
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) -> None:
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"""Elenca nome/descrizione/tipo/pk delle colonne di una tabella dal catalogo."""
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import json as _json
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from tht.mschema.models import PhysicalSchema
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cfg = _load_config_or_exit(config)
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phys_file = physical_path(cfg)
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if not phys_file.exists():
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typer.secho(
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f"ERRORE: {phys_file} non trovato. Esegui prima `tht schema introspect`.",
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fg=typer.colors.RED, err=True,
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)
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raise typer.Exit(code=1)
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physical = PhysicalSchema.from_yaml(phys_file)
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def _payload(name, tbl):
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return {
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"table": name,
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"description": tbl.comment,
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"columns": [
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{"name": n, "description": col.comment, "type": col.type, "pk": col.pk}
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for n, col in tbl.columns.items()
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],
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}
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def _emit_human(p):
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typer.echo(f"{p['table']}: {p['description']}")
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for c in p["columns"]:
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typer.echo(f" {'*' if c['pk'] else ' '} {c['name']} ({c['type']}) — {c['description']}")
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# Glob-friendly: a pattern (containing * ? [) resolves to every matching catalog
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# table, so a model can ask for a whole family (e.g. fact_sost_impianto_*) in one
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# call instead of stalling on an unknown wildcard. Exact names keep the original
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# single-object contract; JSON for a pattern is an array of per-table objects.
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if any(ch in table for ch in "*?["):
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from fnmatch import fnmatch
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matches = sorted(n for n in physical.tables if fnmatch(n, table))
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if not matches:
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typer.secho(
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f"ERRORE: nessuna tabella corrisponde al pattern: {table}",
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fg=typer.colors.RED, err=True,
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)
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raise typer.Exit(code=1)
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payloads = [_payload(n, physical.tables[n]) for n in matches]
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if json_out:
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typer.echo(_json.dumps(payloads, ensure_ascii=False))
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return
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for i, p in enumerate(payloads):
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if i:
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typer.echo("")
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_emit_human(p)
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return
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tbl = physical.tables.get(table)
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if tbl is None:
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# Aid recovery: suggest catalog tables that share the leading segment.
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prefix = table.rsplit("_", 1)[0] + "_" if "_" in table else table
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hints = sorted(n for n in physical.tables if n.startswith(prefix))[:12]
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msg = f"ERRORE: tabella non nel catalogo: {table}"
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if hints:
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msg += f" (forse: {', '.join(hints)})"
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typer.secho(msg, fg=typer.colors.RED, err=True)
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raise typer.Exit(code=1)
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payload = _payload(table, tbl)
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if json_out:
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typer.echo(_json.dumps(payload, ensure_ascii=False))
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return
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_emit_human(payload)
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