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A load-balancer package for Python

Project description


pyLoadBalancer has been developped by IBIS team (INRIA Grenoble Rhône-Alpes, France) and is distributed under the GPL licence.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see


To install the pyLoadBalancer package, simply run :

pip install pyLoadBalancer

Getting Started

The pyLoadBalancer Package contains 5 main objects: 1. The Load Balancer, that distribute tasks 2. The Client, that send tasks 3. The Worker, that receive tasks 4. The Health Check, that check the status of the Load Balancer (optional) 5. The Monitor, a small web server that displays information on the Load Balancer (optional)

The following scheme explains the relationship:

                       |              |     info     +----------+
                       | Health Check | <----------> |          |
                       |              |              | Worker 1 |
+----------+  tasks    +------^-------+        +---> |          |
| Client 1 +----+             |                |     +----------+
+----------+    |             |info          task
                |    +--------v----------+     |     +----------+
+----------+    |    |                   |     |     |          |
| Client 2 +-------->+   Load Balancer   | <-------> | Worker 2 |
+----------+    |    |                   |     |     |          |
                |    +--------^----------+     |     +----------+
+----------+    |             |                |
| Client 3 +----+                              |     +----------+
+----------+           +------v-------+        +---> |          |
                       |    Monitor   |              | Worker 3 |
    ...                +--------------+              |          |

All the messages between objects are exchanged in json format.


pyLoadBalancer has to be configured in order to allow the communication between the different objects (and also between different computers).

The default parameters are stored in parameters.json file.

Load Balancer parameters

  • "SOCKET_TIMEOUT" : 500 Timeout for communication in ms (increase this values if computers are not inside the same local network)
  • "LB_IP": "" IP of the LoadBalancer computer as visible for other objects of the Load Balancer, means local computer
  • "LB_WKPULLPORT": 5699 Port for communication with the workers
  • "LB_HCREPPORT" : 5999 Port for communication with the healt check
  • "LB_CLIENTPULLPORT" : 5799 Port for communication with clients
  • "LB_QUEING_MAXPERUSER" : 1000 Maximum number of queuing task for a given user

Workers parameters

  • "WKHUB_IP" : "" IP of the Worker computer as visible for the Load Balancer, means local computer
  • "WKHUB_LBPORT" : 8300 Port for communication with the Load Balancer
  • "WKHUB_HCPORT" : 8301 Port for communication with the Health Check
  • "WKHUB_WORKERS" : [] List of workers

The list of workers is defined with the following template:

{"nWorkers" : Number of workers, "minP" : minimum prority, "maxP": minimum prority, "processP" : process prority}

To start 12 workers, with 3 different load balancer priorities, and one low process priority, use:

[ {"nWorkers" : 4, "minP" : 0, "maxP": 9, "processP" : 10},
  {"nWorkers" : 4, "minP" : 10, "maxP": 99, "processP" : 0},
  {"nWorkers" : 4, "minP" : 100, "maxP": 1000, "processP" : 0}

Monitor parameters

  • "MONITOR_IP": "" IP of the Monitor computer as visible for the user that wants to monitor the load balancer, means local computer
  • "MONITOR_PORT" : 9000, Port used to access the monitor from a web browser

Starting the Load Balancer core

To start the LoadBalancer on one computer, run the following command :

from pyLoadBalancer import startAll
# A parameter file other than the default one can be given
# by specifying startAll('parameters.json')

Where parameters.json is a parameter file as defined in the previous section.

The startAll() function starts the core of the Load Balancer: - The Load Balancer, that distribute tasks - The Helth Check, that check the status of the Load Balancer (optional) - The Monitor, a small web server that displays information on the Load Balancer (optional)

There is still no client to ask for a job and no workers to do it, the HealthCheck should therefore print the following warning :


If you get another error message, please check check your firewall and antivirus settings that may block the communication between the Load Balancer objects.

The LoadBalancer can be monitored by typing the monitor address (http://localhost:9000 by default) in a web browser.

Note that it is possible to execute the core objects on separate computers. See advanced uses.

Starting Workers

Now the LoadBalancer core is running, workers have to be run. Workers are configured to connect automatically to the Load Balancer (with the help of the parameter file).

Workers can be created using the following syntax :

from pyLoadBalancer import WorkerHub
WKHub = WorkerHub()
# A parameter file other than the default one can be given
# by specifying WorkerHub('parameters.json')

The workers have to learn what tasks they will perform and how to perform them. This can be done by adding a task like:

WKHub.addTask('HELLO', helloFunction)

In this exemple, when the worker receive a task named HELLO, it will call the helloFunction.

A task function should be in the following form:

def helloFunction(**kwargs):
    print('WORKER GOT TASK', kwargs)
    result = 'Hello %s %s !' % (kwargs.get('task').get('firstName'), kwargs.get('task').get('lastName'))
    #do something with task
    return result

kwargs arguments will contains the task sent by the Client. It can be accessed by the worker task function using `kwargs.get(‘task’)``

Then, when all tasks are defined, start the worker using :


When the workers are started, you should see the Load Balancer console displaying massages like LB - ADDING WORKER (worker_c0b28c1f)

Starting Client

Now we have a pretty consistent Load Balancer with active workers. Let’s execute the Client side that will send task to the Load Balancer. Use the following syntax:

from pyLoadBalancer import Client
CL = Client()
# A parameter file other than the default one can be given
# by specifying Client('parameters.json')

Then create the task you want to send. It simply is a python dictionary that corresponds the parameters of the task function to be done :

task = {'firstName': 'John', 'lastName': 'Doe'}
The dictionnary must be JSON serializable, because sockets are using JSON format to communicate between each-others.
When the task is to be sent, send it using:
taskid = CL.sendTask('HELLO', task, userid='username').get('taskid')

The Load Balancer will return a task unique id, that can be used to asynchronously retrieved the status of the task:

taskinfo = CL.getTask(taskid)
progress = taskinfo.get('progress')
result = taskinfo.get('result')

The returned progress can take the following values:

  • None taskid is not correct or task result in an error
  • 0 task is queing
  • 100 task is done

When the task is done, the result is return by the Load Balancer

Please note that the Load Balancer automatically remove tasks whose result has been collected by the Client within 60 seconds.

A waiting (or processing) task can also be canceled by using the following command:



A monitoring interface is available to easilly monitor the Load Balancer status (queing task, statistics …)

It can be access by a web browser at the monitor address (http://localhost:9000 by default).

Advanced usages

User priority

The username sent by clients have an influence on the priority of the queuing tasks.

When a worker is available, the first task from the user that have the lowest number of occupied worker will be processed.

Task priority

A task is sent by default with a zero priority. This priority can be changed by setting the

taskid = CL.sendTask('HELLO', task, userid='username', priority=100)

This task will only be processed by a worker that follows minP ≥ 100 and a maxP ≤ 100.

This behavior allows to keep available workers for high priority tasks.

Run on clusters of computers

pyLoadBalancer is designed to be run in clusters of computers.

Every objects of pyLoadBalancer can be run by a different computer (one computer can run the Load Balancer core, and few other computers can run each one a hub of workers).

Configure a parameters.json file in each computers in order to assign the correct IPs and ports, and be sure to open corresponding ports.

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