def _ensure_backend_available(cls, url):
url = sa_url.make_url(str(url))
try:
eng = sqlalchemy.create_engine(url)
except ImportError as i_e:
# SQLAlchemy performs an "import" of the DBAPI module
# within create_engine(). So if ibm_db_sa, cx_oracle etc.
# isn't installed, we get an ImportError here.
LOG.info(
_LI("The %(dbapi)s backend is unavailable: %(err)s"),
dict(dbapi=url.drivername, err=i_e))
raise exception.BackendNotAvailable(
"Backend '%s' is unavailable: No DBAPI installed" %
url.drivername)
else:
try:
conn = eng.connect()
except sqlalchemy.exc.DBAPIError as d_e:
# upon connect, SQLAlchemy calls dbapi.connect(). This
# usually raises OperationalError and should always at
# least raise a SQLAlchemy-wrapped DBAPI Error.
LOG.info(
_LI("The %(dbapi)s backend is unavailable: %(err)s"),
dict(dbapi=url.drivername, err=d_e)
)
raise exception.BackendNotAvailable(
"Backend '%s' is unavailable: Could not connect" %
url.drivername)
else:
conn.close()
return eng
def _ensure_backend_available(cls, url):
url = sa_url.make_url(str(url))
try:
eng = sqlalchemy.create_engine(url)
except ImportError as i_e:
# SQLAlchemy performs an "import" of the DBAPI module
# within create_engine(). So if ibm_db_sa, cx_oracle etc.
# isn't installed, we get an ImportError here.
LOG.info(
_LI("The %(dbapi)s backend is unavailable: %(err)s"),
dict(dbapi=url.drivername, err=i_e))
raise exception.BackendNotAvailable("No DBAPI installed")
else:
try:
conn = eng.connect()
except sqlalchemy.exc.DBAPIError as d_e:
# upon connect, SQLAlchemy calls dbapi.connect(). This
# usually raises OperationalError and should always at
# least raise a SQLAlchemy-wrapped DBAPI Error.
LOG.info(
_LI("The %(dbapi)s backend is unavailable: %(err)s"),
dict(dbapi=url.drivername, err=d_e)
)
raise exception.BackendNotAvailable("Could not connect")
else:
conn.close()
return eng
def drop_old_duplicate_entries_from_table(migrate_engine, table_name, use_soft_delete, *uc_column_names):
"""Drop all old rows having the same values for columns in uc_columns.
This method drop (or mark ad `deleted` if use_soft_delete is True) old
duplicate rows form table with name `table_name`.
:param migrate_engine: Sqlalchemy engine
:param table_name: Table with duplicates
:param use_soft_delete: If True - values will be marked as `deleted`,
if False - values will be removed from table
:param uc_column_names: Unique constraint columns
"""
meta = MetaData()
meta.bind = migrate_engine
table = Table(table_name, meta, autoload=True)
columns_for_group_by = [table.c[name] for name in uc_column_names]
columns_for_select = [func.max(table.c.id)]
columns_for_select.extend(columns_for_group_by)
duplicated_rows_select = sqlalchemy.sql.select(
columns_for_select, group_by=columns_for_group_by, having=func.count(table.c.id) > 1
)
for row in migrate_engine.execute(duplicated_rows_select).fetchall():
# NOTE(boris-42): Do not remove row that has the biggest ID.
delete_condition = table.c.id != row[0]
is_none = None # workaround for pyflakes
delete_condition &= table.c.deleted_at == is_none
for name in uc_column_names:
delete_condition &= table.c[name] == row[name]
rows_to_delete_select = sqlalchemy.sql.select([table.c.id]).where(delete_condition)
for row in migrate_engine.execute(rows_to_delete_select).fetchall():
LOG.info(
_LI("Deleting duplicated row with id: %(id)s from table: " "%(table)s"),
dict(id=row[0], table=table_name),
)
if use_soft_delete:
delete_statement = (
table.update()
.where(delete_condition)
.values(
{
"deleted": literal_column("id"),
"updated_at": literal_column("updated_at"),
"deleted_at": timeutils.utcnow(),
}
)
)
else:
delete_statement = table.delete().where(delete_condition)
migrate_engine.execute(delete_statement)
def drop_old_duplicate_entries_from_table(migrate_engine, table_name,
use_soft_delete, *uc_column_names):
"""Drop all old rows having the same values for columns in uc_columns.
This method drop (or mark ad `deleted` if use_soft_delete is True) old
duplicate rows form table with name `table_name`.
:param migrate_engine: Sqlalchemy engine
:param table_name: Table with duplicates
:param use_soft_delete: If True - values will be marked as `deleted`,
if False - values will be removed from table
:param uc_column_names: Unique constraint columns
"""
meta = MetaData()
meta.bind = migrate_engine
table = Table(table_name, meta, autoload=True)
columns_for_group_by = [table.c[name] for name in uc_column_names]
columns_for_select = [func.max(table.c.id)]
columns_for_select.extend(columns_for_group_by)
duplicated_rows_select = sqlalchemy.sql.select(
columns_for_select,
group_by=columns_for_group_by,
having=func.count(table.c.id) > 1)
for row in migrate_engine.execute(duplicated_rows_select).fetchall():
# NOTE(boris-42): Do not remove row that has the biggest ID.
delete_condition = table.c.id != row[0]
is_none = None # workaround for pyflakes
delete_condition &= table.c.deleted_at == is_none
for name in uc_column_names:
delete_condition &= table.c[name] == row[name]
rows_to_delete_select = sqlalchemy.sql.select(
[table.c.id]).where(delete_condition)
for row in migrate_engine.execute(rows_to_delete_select).fetchall():
LOG.info(
_LI("Deleting duplicated row with id: %(id)s from table: "
"%(table)s"), dict(id=row[0], table=table_name))
if use_soft_delete:
delete_statement = table.update().\
where(delete_condition).\
values({
'deleted': literal_column('id'),
'updated_at': literal_column('updated_at'),
'deleted_at': timeutils.utcnow()
})
else:
delete_statement = table.delete().where(delete_condition)
migrate_engine.execute(delete_statement)