def _run_worker(options, skip_serve_logs):
sub_proc = _serve_logs(skip_serve_logs)
try:
celery_app.worker_main(options)
finally:
if sub_proc:
sub_proc.terminate()
def worker(args):
"""Starts Airflow Celery worker"""
if not settings.validate_session():
raise SystemExit(
"Worker exiting, database connection precheck failed.")
autoscale = args.autoscale
skip_serve_logs = args.skip_serve_logs
if autoscale is None and conf.has_option("celery", "worker_autoscale"):
autoscale = conf.get("celery", "worker_autoscale")
# Setup locations
pid_file_path, stdout, stderr, log_file = setup_locations(
process=WORKER_PROCESS_NAME,
pid=args.pid,
stdout=args.stdout,
stderr=args.stderr,
log=args.log_file,
)
if hasattr(celery_app.backend, 'ResultSession'):
# Pre-create the database tables now, otherwise SQLA via Celery has a
# race condition where one of the subprocesses can die with "Table
# already exists" error, because SQLA checks for which tables exist,
# then issues a CREATE TABLE, rather than doing CREATE TABLE IF NOT
# EXISTS
try:
session = celery_app.backend.ResultSession()
session.close()
except sqlalchemy.exc.IntegrityError:
# At least on postgres, trying to create a table that already exist
# gives a unique constraint violation or the
# "pg_type_typname_nsp_index" table. If this happens we can ignore
# it, we raced to create the tables and lost.
pass
# Setup Celery worker
options = [
'worker',
'-O',
'fair',
'--queues',
args.queues,
'--concurrency',
args.concurrency,
'--hostname',
args.celery_hostname,
'--loglevel',
conf.get('logging', 'LOGGING_LEVEL'),
'--pidfile',
pid_file_path,
]
if autoscale:
options.extend(['--autoscale', autoscale])
if args.without_mingle:
options.append('--without-mingle')
if args.without_gossip:
options.append('--without-gossip')
if conf.has_option("celery", "pool"):
pool = conf.get("celery", "pool")
options.extend(["--pool", pool])
# Celery pools of type eventlet and gevent use greenlets, which
# requires monkey patching the app:
# https://eventlet.net/doc/patching.html#monkey-patch
# Otherwise task instances hang on the workers and are never
# executed.
maybe_patch_concurrency(['-P', pool])
if args.daemon:
# Run Celery worker as daemon
handle = setup_logging(log_file)
with open(stdout, 'w+') as stdout_handle, open(stderr,
'w+') as stderr_handle:
if args.umask:
umask = args.umask
ctx = daemon.DaemonContext(
files_preserve=[handle],
umask=int(umask, 8),
stdout=stdout_handle,
stderr=stderr_handle,
)
with ctx:
sub_proc = _serve_logs(skip_serve_logs)
celery_app.worker_main(options)
else:
# Run Celery worker in the same process
sub_proc = _serve_logs(skip_serve_logs)
celery_app.worker_main(options)
if sub_proc:
sub_proc.terminate()