- Create runtime - Create function - Invoke function(i.e. create execution)changes/17/465917/1
Lingxian Kong
6 years ago
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============ |
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Qinling Installation Guide |
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========================== |
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Prerequisites |
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------------- |
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It is necessary to install some specific system libs for installing Qinling. |
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They can be installed on most popular operating system using their package |
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manager (for Ubuntu - *apt*, for Fedora - *dnf*, CentOS - *yum*, for Mac OS - |
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*brew* or *macports*). |
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The list of needed packages is shown below: |
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1. **python-dev** |
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2. **python-setuptools** |
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3. **python-pip** |
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4. **libffi-dev** |
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5. **libxslt1-dev (or libxslt-dev)** |
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6. **libxml2-dev** |
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7. **libyaml-dev** |
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8. **libssl-dev** |
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In case of Ubuntu, just run:: |
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$ apt-get install -y python-dev python-setuptools python-pip libffi-dev libxslt1-dev \ |
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libxml2-dev libyaml-dev libssl-dev |
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**NOTE:** **Qinling can be used without authentication at all or it can work |
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with OpenStack.** In case of OpenStack, it works **only on Keystone v3**, make |
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sure **Keystone v3** is installed. |
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Installation |
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============ |
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------------ |
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First of all, clone the repo and go to the repo directory:: |
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$ git clone https://github.com/openstack/qinling.git |
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$ cd qinling |
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Generate config:: |
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$ tox -egenconfig |
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Configure Qinling as needed. The configuration file is located in |
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``etc/qinling.conf.sample``. You will need to modify the configuration options |
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and then copy it into ``/etc/qinling/qinling.conf``. |
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For details see :doc:`Qinling Configuration Guide </guides/configuration_guide>` |
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**Virtualenv installation**:: |
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$ tox |
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This will install necessary virtual environments and run all the project tests. |
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Installing virtual environments may take significant time (~10-15 mins). |
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**Local installation**:: |
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$ pip install -e . |
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or:: |
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$ pip install -r requirements.txt |
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$ python setup.py install |
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**NOTE**: Differences *pip install -e* and *setup.py install*. **pip install -e** |
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works very similarly to **setup.py install** or the EasyInstall tool, except |
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that it doesn’t actually install anything. Instead, it creates a special |
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.egg-link file in the deployment directory, that links to your project’s |
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source code. |
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Before the first run |
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-------------------- |
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After installation you will see **qinling-server** and **qinling-db-manage** commands |
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in your environment, either in system or virtual environment. |
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**NOTE**: In case of using **virtualenv**, all Qinling related commands available via |
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**tox -evenv --**. For example, *qinling-server* is available via |
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*tox -evenv -- qinling-server*. |
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**qinling-db-manage** command can be used for migrations. |
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For updating the database to the latest revision type:: |
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$ qinling-db-manage --config-file <path-to-qinling.conf> upgrade head |
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Before starting Qinling server, run *qinling-db-manage populate* command. |
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It prepares the DB, creates in it with all standard actions and standard |
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workflows which Qinling provides for all Qinling users.:: |
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$ qinling-db-manage --config-file <path-to-qinling.conf> populate |
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For more detailed information about *qinling-db-manage* script please see :doc:`Qinling Upgrade Guide </guides/upgrade_guide>`. |
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**NOTE**: For users who want a dry run with **SQLite** database backend(not |
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used in production), *qinling-db-manage* is not recommended for database |
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initialization because of `SQLite limitations <http://www.sqlite.org/omitted.html>`_. |
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Please use sync_db script described below instead for database initialization. |
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**If you use virtualenv**:: |
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$ tools/sync_db.sh --config-file <path-to-qinling.conf> |
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**Or run sync_db directly**:: |
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$ python tools/sync_db.py --config-file <path-to-qinling.conf> |
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Running Qinling API server |
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-------------------------- |
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To run Qinling API server perform the following command in a shell:: |
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$ qinling-server --server api --config-file <path-to-qinling.conf> |
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Running Qinling Engines |
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----------------------- |
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To run Qinling Engine perform the following command in a shell:: |
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$ qinling-server --server engine --config-file <path-to-qinling.conf> |
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Running Qinling Task Executors |
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------------------------------ |
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To run Qinling Task Executor instance perform the following command in a shell:: |
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$ qinling-server --server executor --config-file <path-to-qinling.conf> |
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Note that at least one Engine instance and one Executor instance should be |
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running so that workflow tasks are processed by Qinling. |
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Running Multiple Qinling Servers Under the Same Process |
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------------------------------------------------------- |
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To run more than one server (API, Engine, or Task Executor) on the same process, |
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perform the following command in a shell:: |
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$ qinling-server --server api,engine --config-file <path-to-qinling.conf> |
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The --server command line option can be a comma delimited list. The valid |
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options are "all" (by default if not specified) or any combination of "api", |
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"engine", and "executor". It's important to note that the "fake" transport for |
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the rpc_backend defined in the config file should only be used if "all" the |
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Qinling servers are launched on the same process. Otherwise, messages do not |
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get delivered if the Qinling servers are launched on different processes |
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because the "fake" transport is using an in process queue. |
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Qinling And Docker |
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------------------ |
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Please first refer `installation steps for docker <https://docs.docker.com/installation/>`_. |
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To build the image from the qinling source, change directory to the root |
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directory of the Qinling git repository and run:: |
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$ docker build -t <Name of image> . |
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In case you want pre-built image, you can download it from `openstack tarballs source <https://tarballs.openstack.org/qinling/images/qinling-docker.tar.gz>`_. |
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To load this image to docker registry, please run following command:: |
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$ docker load -i '<path of qinling-docker.tar.gz>' |
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The Qinling Docker image is configured to store the database in the user's home |
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directory. For persistence of these data, you may want to keep this directory |
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outside of the container. This may be done by the following steps:: |
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$ sudo mkdir '<user-defined-directory>' |
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$ docker run -it -v '<user-defined-directory>':/home/qinling <Name of image> |
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At the command line:: |
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More about docker: https://www.docker.com/ |
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$ pip install qinling |
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**NOTE:** This docker image uses **SQLite** database. So, it cannot be used for |
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production environment. If you want to use this for production environment, |
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then put customized qinling.conf to '<user-defined-directory>'. |
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Or, if you have virtualenvwrapper installed:: |
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Qinling Client Installation |
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--------------------------- |
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$ mkvirtualenv qinling |
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$ pip install qinling |
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Please refer to :doc:`Qinling Client / CLI Guide </guides/mistralclient_guide>` |
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@ -0,0 +1,106 @@ |
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# Copyright 2017 Catalyst IT Limited |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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from oslo_log import log as logging |
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from pecan import rest |
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import requests |
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import wsmeext.pecan as wsme_pecan |
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from qinling.api.controllers.v1 import resources |
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from qinling.api.controllers.v1 import types |
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from qinling.db import api as db_api |
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from qinling import exceptions as exc |
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from qinling import rpc |
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from qinling.utils import rest_utils |
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LOG = logging.getLogger(__name__) |
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class ExecutionsController(rest.RestController): |
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def __init__(self, *args, **kwargs): |
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self.engine_client = rpc.get_engine_client() |
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super(ExecutionsController, self).__init__(*args, **kwargs) |
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@rest_utils.wrap_wsme_controller_exception |
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@wsme_pecan.wsexpose( |
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resources.Execution, |
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body=resources.Execution, |
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status_code=201 |
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) |
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def post(self, execution): |
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params = execution.to_dict() |
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LOG.info("Creating execution. [execution=%s]", params) |
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function_id = params['function_id'] |
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# Check if the service url is existing. |
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try: |
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mapping = db_api.get_function_service_mapping(function_id) |
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LOG.debug('Found Service url for function: %s', function_id) |
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func_url = '%s/execute' % mapping.service_url |
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LOG.info('Invoke function %s, url: %s', function_id, func_url) |
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r = requests.post(func_url, data=params.get('input')) |
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params.update( |
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{'status': 'success', 'output': {'result': r.json()}} |
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) |
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db_model = db_api.create_execution(params) |
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return resources.Execution.from_dict(db_model.to_dict()) |
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except exc.DBEntityNotFoundError: |
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pass |
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func = db_api.get_function(function_id) |
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runtime_id = func.runtime_id |
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params.update({'status': 'running'}) |
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db_model = db_api.create_execution(params) |
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self.engine_client.create_execution( |
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db_model.id, function_id, runtime_id, input=params.get('input') |
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) |
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updated_db = db_api.get_execution(db_model.id) |
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return resources.Execution.from_dict(updated_db.to_dict()) |
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@rest_utils.wrap_wsme_controller_exception |
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@wsme_pecan.wsexpose(resources.Executions) |
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def get_all(self): |
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LOG.info("Get all executions.") |
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executions = [resources.Execution.from_dict(db_model.to_dict()) |
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for db_model in db_api.get_executions()] |
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return resources.Executions(executions=executions) |
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@rest_utils.wrap_wsme_controller_exception |
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@wsme_pecan.wsexpose(resources.Execution, types.uuid) |
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def get(self, id): |
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LOG.info("Fetch execution [id=%s]", id) |
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execution_db = db_api.get_execution(id) |
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return resources.Execution.from_dict(execution_db.to_dict()) |
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@rest_utils.wrap_wsme_controller_exception |
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@wsme_pecan.wsexpose(None, types.uuid, status_code=204) |
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def delete(self, id): |
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"""Delete the specified Execution.""" |
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LOG.info("Delete execution [id=%s]", id) |
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return db_api.delete_execution(id) |
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