def run_periodic_tasks(self, context, raise_on_error=False):
"""Tasks to be run at a periodic interval."""
idle_for = DEFAULT_INTERVAL
for task_name, task in self._periodic_tasks:
full_task_name = '.'.join([self.__class__.__name__, task_name])
now = timeutils.utcnow()
spacing = self._periodic_spacing[task_name]
last_run = self._periodic_last_run[task_name]
# If a periodic task is _nearly_ due, then we'll run it early
if spacing is not None and last_run is not None:
due = last_run + datetime.timedelta(seconds=spacing)
if not timeutils.is_soon(due, 0.2):
idle_for = min(idle_for, timeutils.delta_seconds(now, due))
continue
if spacing is not None:
idle_for = min(idle_for, spacing)
LOG.debug(_("Running periodic task %(full_task_name)s"),
{"full_task_name": full_task_name})
self._periodic_last_run[task_name] = timeutils.utcnow()
try:
task(self, context)
except Exception as e:
if raise_on_error:
raise
LOG.exception(_("Error during %(full_task_name)s: %(e)s"),
{"full_task_name": full_task_name, "e": e})
time.sleep(0)
return idle_for
def soft_delete(self, synchronize_session='evaluate'):
return self.update(
{
'deleted': literal_column('id'),
'updated_at': literal_column('updated_at'),
'deleted_at': timeutils.utcnow()
},
synchronize_session=synchronize_session)
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 = 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):
# 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 = select([table.c.id]).where(delete_condition)
for row in migrate_engine.execute(rows_to_delete_select).fetchall():
LOG.info(
_("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 = 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):
# 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 = select([table.c.id]).where(delete_condition)
for row in migrate_engine.execute(rows_to_delete_select).fetchall():
LOG.info(
_("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 run_periodic_tasks(self, context, raise_on_error=False):
"""Tasks to be run at a periodic interval."""
idle_for = DEFAULT_INTERVAL
for task_name, task in self._periodic_tasks:
full_task_name = '.'.join([self.__class__.__name__, task_name])
now = timeutils.utcnow()
spacing = self._periodic_spacing[task_name]
last_run = self._periodic_last_run[task_name]
# If a periodic task is _nearly_ due, then we'll run it early
if spacing is not None and last_run is not None:
due = last_run + datetime.timedelta(seconds=spacing)
if not timeutils.is_soon(due, 0.2):
idle_for = min(idle_for, timeutils.delta_seconds(now, due))
continue
if spacing is not None:
idle_for = min(idle_for, spacing)
LOG.debug(_("Running periodic task %(full_task_name)s"),
{"full_task_name": full_task_name})
self._periodic_last_run[task_name] = timeutils.utcnow()
try:
task(self, context)
except Exception as e:
if raise_on_error:
raise
LOG.exception(_("Error during %(full_task_name)s: %(e)s"), {
"full_task_name": full_task_name,
"e": e
})
time.sleep(0)
return idle_for
def decorator(f):
# Test for old style invocation
if 'ticks_between_runs' in kwargs:
raise InvalidPeriodicTaskArg(arg='ticks_between_runs')
# Control if run at all
f._periodic_task = True
f._periodic_external_ok = kwargs.pop('external_process_ok', False)
if f._periodic_external_ok and not CONF.run_external_periodic_tasks:
f._periodic_enabled = False
else:
f._periodic_enabled = kwargs.pop('enabled', True)
# Control frequency
f._periodic_spacing = kwargs.pop('spacing', 0)
f._periodic_immediate = kwargs.pop('run_immediately', False)
if f._periodic_immediate:
f._periodic_last_run = None
else:
f._periodic_last_run = timeutils.utcnow()
return f
def decorator(f):
# Test for old style invocation
if 'ticks_between_runs' in kwargs:
raise InvalidPeriodicTaskArg(arg='ticks_between_runs')
# Control if run at all
f._periodic_task = True
f._periodic_external_ok = kwargs.pop('external_process_ok', False)
if f._periodic_external_ok and not CONF.run_external_periodic_tasks:
f._periodic_enabled = False
else:
f._periodic_enabled = kwargs.pop('enabled', True)
# Control frequency
f._periodic_spacing = kwargs.pop('spacing', 0)
f._periodic_immediate = kwargs.pop('run_immediately', False)
if f._periodic_immediate:
f._periodic_last_run = None
else:
f._periodic_last_run = timeutils.utcnow()
return f
def soft_delete(self, synchronize_session='evaluate'):
return self.update({'deleted': literal_column('id'),
'updated_at': literal_column('updated_at'),
'deleted_at': timeutils.utcnow()},
synchronize_session=synchronize_session)
def soft_delete(self, session=None):
"""Mark this object as deleted."""
self.deleted = self.id
self.deleted_at = timeutils.utcnow()
self.save(session=session)
def soft_delete(self, session=None):
"""Mark this object as deleted."""
self.deleted = self.id
self.deleted_at = timeutils.utcnow()
self.save(session=session)
