To use oslo.service in a project:

import oslo_service

Migrating to oslo.service

The oslo.service library no longer assumes a global configuration object is available. Instead the following functions and classes have been changed to expect the consuming application to pass in an oslo.config configuration object:

When using service from oslo-incubator

from foo.openstack.common import service

launcher = service.launch(service, workers=2)

When using oslo.service

from oslo_config import cfg
from oslo_service import service

launcher = service.launch(CONF, service, workers=2)

Using oslo.service with oslo-config-generator

The oslo.service provides several entry points to generate configuration files.

ATTENTION: The library doesn’t provide an oslo.service entry point.

$ oslo-config-generator --namespace oslo.service.service \
--namespace oslo.service.periodic_task \
--namespace oslo.service.sslutils

Launching and controlling services

The oslo_service.service module provides tools for launching OpenStack services and controlling their lifecycles.

A service is an instance of any class that subclasses oslo_service.service.ServiceBase. ServiceBase is an abstract class that defines an interface every service should implement. oslo_service.service.Service can serve as a base for constructing new services.


The oslo_service.service module provides two launchers for running services:

It is possible to initialize whatever launcher is needed and then launch a service using it.

from oslo_config import cfg
from oslo_service import service


service_launcher = service.ServiceLauncher(CONF)

process_launcher = service.ProcessLauncher(CONF, wait_interval=1.0)
process_launcher.launch_service(service.Service(), workers=2)

Or one can simply call oslo_service.service.launch() which will automatically pick an appropriate launcher based on a number of workers that are passed to it (ServiceLauncher if workers=1 or None and ProcessLauncher in other case).

from oslo_config import cfg
from oslo_service import service


launcher = service.launch(CONF, service.Service(), workers=3)


It is highly recommended to use no more than one instance of the ServiceLauncher or ProcessLauncher class per process.

Signal handling

oslo_service.service provides handlers for such signals as SIGTERM, SIGINT, and SIGHUP.

SIGTERM is used for graceful termination of services. This can allow a server to wait for all clients to close connections while rejecting new incoming requests. Config option graceful_shutdown_timeout specifies how many seconds after receiving a SIGTERM signal a server should continue to run, handling the existing connections. Setting graceful_shutdown_timeout to zero means that the server will wait indefinitely until all remaining requests have been fully served.

To force instantaneous termination the SIGINT signal must be sent.

The behavior on receiving SIGHUP varies based on how the service is configured. If the launcher uses restart_method='reload' (the default), then the service will reload its configuration and any threads will be completely restarted. If restart_method='mutate' is used, then only the configuration options marked as mutable will be reloaded and the service threads will not be restarted.

See oslo_service.service.Launcher for more details on the restart_method parameter.


SIGHUP is not supported on Windows.


Config option graceful_shutdown_timeout is not supported on Windows.

Below is an example of a service with a reset method that allows reloading logging options by sending a SIGHUP.

from oslo_config import cfg
from oslo_log import log as logging
from oslo_service import service


LOG = logging.getLogger(__name__)

class FooService(service.ServiceBase):

    def start(self):

    def wait(self):

    def stop(self):

    def reset(self):
        logging.setup(cfg.CONF, 'foo')


Processes spawned through oslo_service.service can be profiled (function calltrace) through the eventlet_backdoor module. The service must be configured with the backdoor_port option to enable its workers to listen on TCP ports. The user can then send the prof() command to capture the worker process’s function calltrace.

  1. To start profiling send the prof() command on the process’s listening port

  2. To stop profiling and capture pstat calltrace to a file, send the prof() command with a file basename as an argument (prof(basename)) to the worker process’s listening port. A stats file (in pstat format) will be generated in the temp directory with the user-provided basename with a .prof suffix .

For example, to profile a neutron server process (which is listening on port 8002 configured through the backdoor_port option):

$ echo "prof()" | nc localhost 8002
$ neutron net-create n1; neutron port-create --name p1 n1;
$ neutron port-delete p1; neutron port-delete p1
$ echo "prof('neutron')" | nc localhost 8002

This will generate a stats file in /tmp/ Stats can be printed from the trace file as follows:

import pstats

stats = pstats.Stats('/tmp/')