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Merge "Add documentation for multi-engine"

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doc/source/index.rst
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@ -55,8 +55,10 @@ Developers Documentation
Operations Documentation
========================
.. toctree::
:maxdepth: 1
scale_deployment
scale_deployment
Code Documentation
==================

105
doc/source/scale_deployment.rst
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@ -11,18 +11,20 @@
License for the specific language governing permissions and limitations
under the License.
================
Scaling Out APIs
================
====================
Scaling a Deployment
====================
Working in an architecture where a large number of incoming requests need to be
handled, the API services can be overloaded. In those scenarios, in order to
increase the system performance, it can be helpful to run multiple APIs and use
a load balancing mechanism.
When deploying in an environment where a large number of incoming requests need
to be handled, the API and engine services can be overloaded. In those
scenarios, in order to increase the system performance, it can be helpful to run
multiple load-balanced APIs and engines.
This guide details how to scale out the ReST and CFN APIs, also known as the
*heat-api* and *heat-api-cfn* services, respectively.
This guide details how to scale out the ReST API, the CFN API, and the engine,
also known as the *heat-api*, *heat-api-cfn*, and *heat-engine* services,
respectively.
.. _scale_deployment_assumptions:
Assumptions
===========
@ -41,7 +43,7 @@ Architecture
============
This section shows the basic Heat architecture, the load balancing mechanism
used and the target scaling out architecture.
used and the target scaled out architecture.
Basic Architecture
------------------
@ -52,9 +54,9 @@ diagram below, where we have a CLI that sends HTTP requests to the ReST and CFN
APIs, which in turn make calls using AMQP to the Heat engine.
::
|- [ REST API ] -|
[ CLI ] -- < HTTP > -- -- < AMQP > -- [ ENGINE ]
|- [ CFN API ] -|
|- [REST API] -|
[CLI] -- <HTTP> -- -- <AMQP> -- [ENGINE]
|- [CFN API] -|
Load Balancing
--------------
@ -70,19 +72,23 @@ This way, the proxy will take the CLIs requests to the APIs and act on their
behalf. Once the proxy receives a response, it will be redirected to the caller
CLI.
A round-robin distribution of messages from the AMQP queue will act as the load
balancer for multiple engines. Check that your AMQP service is configured to
distribute messages round-robin (RabbitMQ does this by default).
Target Architecture
-------------------
A scaling out Heat architecture is represented in the diagram below:
A scaled out Heat architecture is represented in the diagram below:
::
|- [ REST-API ] -|
|- ... -|
|- [ REST-API ] -|
[ CLI ] -- < HTTP > -- [ PROXY ] -- -- < AMQP > -- [ ENGINE ]
|- [ API-CFN ] -|
|- ... -|
|- [ API-CFN ] -|
|- [REST-API] -|
|- ... -|
|- [REST-API] -| |- [ENGINE] -|
[CLI] -- <HTTP> -- [PROXY] -- -- <AMQP> -- |- ... -|
|- [API-CFN] -| |- [ENGINE] -|
|- ... -|
|- [API-CFN] -|
Thus, a request sent from the CLI looks like:
@ -90,8 +96,8 @@ Thus, a request sent from the CLI looks like:
1. CLI contacts the proxy;
2. The HAProxy server, acting as a load balancer, redirects the call to an
API instance;
3. The API server sends messages to RabbitMQ, and the engine server picks up
messages from the RabbitMQ queue.
3. The API server sends messages to the AMQP queue, and the engines pick up
messages in round-robin fashion.
Deploying Multiple APIs
=======================
@ -107,9 +113,6 @@ new ReST and CFN API services, you must run:
python bin/heat-api --config-file=/etc/heat/heat.conf
python bin/heat-api-cfn --config-file=/etc/heat/heat.conf
Running multiple APIs require that you create as many copies of this file as the
number of instances you want to deploy.
Each API service must have a unique address to listen. This address have to be
defined in the configuration file. For ReST and CFN APIs, modify the
*[heat_api]* and *[heat_api_cfn]* blocks, respectively.
@ -125,11 +128,35 @@ defined in the configuration file. For ReST and CFN APIs, modify the
bind_port = {API_CFN_PORT}
bind_host = {API_CFN_HOST}
If you wish to run multiple API processes on the same machine, you must create
multiple copies of the heat.conf file, each containing a unique port number.
In addition, if you want to run some API services in different machines than
the devstack server, you have to update the loopback addresses found at the
*sql_connection* and *rabbit_host* properties to the devstack server's IP,
which must be reachable from the remote machine.
Deploying Multiple Engines
==========================
All engines must be configured to use the same AMQP service. Ensure that all of
the *rabbit_*\* and *kombu_*\* configuration options in the *[DEFAULT]* section
of */etc/heat/heat.conf* match across each machine that will be running an
engine. By using the same AMQP configuration, each engine will subscribe to the
same AMQP *engine* queue and pick up work in round-robin fashion with the other
engines.
One or more engines can be deployed per host. Depending on the host's CPU
architecture, it may be beneficial to deploy several engines on a single
machine.
To start multiple engines on the same machine, simply start multiple
*heat-engine* processes:
::
python bin/heat-engine --config-file=/etc/heat/heat.conf &
python bin/heat-engine --config-file=/etc/heat/heat.conf &
Deploying the Proxy
===================
@ -201,8 +228,8 @@ as to show more details of the configuration by describing a concrete sample.
Architecture
------------
This subsection shows a basic OpenStack architecture and the target one that
will be meet.
This section shows a basic OpenStack architecture and the target one
that will be used for testing of the scaled-out heat services.
Basic Architecture
^^^^^^^^^^^^^^^^^^
@ -212,7 +239,8 @@ For this sample, consider that:
1. We have an architecture composed by 3 machines configured in a LAN, with
the addresses A: 10.0.0.1; B: 10.0.0.2; and C: 10.0.0.3;
2. The OpenStack devstack installation, including the Heat module, has been
done in the machine A, as shown in the Assumptions section.
done in the machine A, as shown in the
:ref:`scale_deployment_assumptions` section.
Target Architecture
^^^^^^^^^^^^^^^^^^^
@ -223,8 +251,8 @@ subsections show how to deploy a scaling out Heat architecture by:
1. Running one ReST and one CFN API on the machines B and C;
2. Setting up the HAProxy server on the machine A.
Running API Services
--------------------
Running the API and Engine Services
-----------------------------------
For each machine, B and C, you must do the following steps:
@ -234,10 +262,19 @@ For each machine, B and C, you must do the following steps:
3. Make required changes on the configuration file;
4. Enter the Heat local repository and run:
::
::
python bin/heat-api --config-file=/etc/heat/heat.conf
python bin/heat-api-cfn --config-file=/etc/heat/heat.conf
python bin/heat-api --config-file=/etc/heat/heat.conf
python bin/heat-api-cfn --config-file=/etc/heat/heat.conf
5. Start as many *heat-engine* processes as you want running on that
machine:
::
python bin/heat-engine --config-file=/etc/heat/heat.conf &
python bin/heat-engine --config-file=/etc/heat/heat.conf &
...
Changes On Configuration
^^^^^^^^^^^^^^^^^^^^^^^^