def _fix_artifact_tags_duplications(db: mlrun.api.db.sqldb.db.SQLDB,
db_session: sqlalchemy.orm.Session):
logger.info("Fixing artifact tags duplications")
# get all artifacts
artifacts = db._find_artifacts(db_session, None, "*")
# get all artifact tags
tags = db._query(db_session, mlrun.api.db.sqldb.models.Artifact.Tag).all()
# artifact record id -> artifact
artifact_record_id_map = {artifact.id: artifact for artifact in artifacts}
tags_to_delete = []
projects = {artifact.project for artifact in artifacts}
for project in projects:
artifact_keys = {
artifact.key
for artifact in artifacts if artifact.project == project
}
for artifact_key in artifact_keys:
artifact_key_tags = []
for tag in tags:
# sanity
if tag.obj_id not in artifact_record_id_map:
logger.warning("Found orphan tag, deleting",
tag=tag.to_dict())
if artifact_record_id_map[tag.obj_id].key == artifact_key:
artifact_key_tags.append(tag)
tag_name_tags_map = collections.defaultdict(list)
for tag in artifact_key_tags:
tag_name_tags_map[tag.name].append(tag)
for tag_name, _tags in tag_name_tags_map.items():
if len(_tags) == 1:
continue
tags_artifacts = [
artifact_record_id_map[tag.obj_id] for tag in _tags
]
last_updated_artifact = _find_last_updated_artifact(
tags_artifacts)
for tag in _tags:
if tag.obj_id != last_updated_artifact.id:
tags_to_delete.append(tag)
if tags_to_delete:
logger.info(
"Found duplicated artifact tags. Removing duplications",
tags_to_delete=[
tag_to_delete.to_dict() for tag_to_delete in tags_to_delete
],
tags=[tag.to_dict() for tag in tags],
artifacts=[artifact.to_dict() for artifact in artifacts],
)
for tag in tags_to_delete:
db_session.delete(tag)
db_session.commit()
def _fix_datasets_large_previews(
db: mlrun.api.db.sqldb.db.SQLDB,
db_session: sqlalchemy.orm.Session,
leader_session: typing.Optional[str] = None,
):
# get all artifacts
artifacts = db._find_artifacts(db_session, None, "*")
for artifact in artifacts:
try:
artifact_dict = artifact.struct
if (artifact_dict and artifact_dict.get("kind")
== mlrun.artifacts.DatasetArtifact.kind):
header = artifact_dict.get("header", [])
if header and len(
header) > mlrun.artifacts.dataset.max_preview_columns:
logger.debug(
"Found dataset artifact with more than allowed columns in preview fields. Fixing",
artifact=artifact_dict,
)
columns_to_remove = header[mlrun.artifacts.dataset.
max_preview_columns:]
# align preview
if artifact_dict.get("preview"):
new_preview = []
for preview_row in artifact_dict["preview"]:
# sanity
if (len(preview_row) <
mlrun.artifacts.dataset.max_preview_columns
):
logger.warning(
"Found artifact with more than allowed columns in header definition, "
"but preview data is valid. Leaving preview as is",
artifact=artifact_dict,
)
new_preview.append(
preview_row[:mlrun.artifacts.dataset.
max_preview_columns])
artifact_dict["preview"] = new_preview
# align stats
for column_to_remove in columns_to_remove:
if column_to_remove in artifact_dict.get("stats", {}):
del artifact_dict["stats"][column_to_remove]
# align schema
if artifact_dict.get("schema", {}).get("fields"):
new_schema_fields = []
for field in artifact_dict["schema"]["fields"]:
if field.get("name") not in columns_to_remove:
new_schema_fields.append(field)
artifact_dict["schema"]["fields"] = new_schema_fields
# lastly, align headers
artifact_dict["header"] = header[:mlrun.artifacts.dataset.
max_preview_columns]
logger.debug(
"Fixed dataset artifact preview fields. Storing",
artifact=artifact_dict,
)
db._store_artifact(
db_session,
artifact.key,
artifact_dict,
artifact.uid,
project=artifact.project,
tag_artifact=False,
ensure_project=False,
leader_session=leader_session,
)
except Exception as exc:
logger.warning(
"Failed fixing dataset artifact large preview. Continuing",
exc=exc,
)
