Load Balance multiple RTMP Servers to Horizontally Scale Streaming (Node Media Server and HAProxy)

In this video I will show case the new Node Media Server , a Node JS implementation of an RTMP server used for streaming setup. I will show how to install and spinup an RTMP server and stream to it from OBS. Then I will spin up multiple ones, then I’ll show how to relay the stream to multiple RTMP servers and finally I will use HAProxy to load balance the stream consumption to achieve high availability.

Configurations
https://github.com/hnasr/javascript_playground/tree/master/rtmp

Node Media Server
https://www.npmjs.com/package/node-media-server

  • 0:00 Intro
  • 2:00 Single RTMP Server
  • 6:00 Multiple RTMP Servers
  • 7:00 Relay Server
  • 12:00 Load Balancing RTMP Servers

#developer #programming

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Load Balance multiple RTMP Servers to Horizontally Scale Streaming (Node Media Server and HAProxy)
Marlon  Boyle

Marlon Boyle

1589634120

Hands on with Node.Js Streams | Examples & Approach

Never heard of Node.js? Node.js is an accessible asynchronous environment based on Javascript which contains several core modules helpful for performing various tasks. Node.js is famous worldwide due to its efficiency and being open-source, it brings a lot to the table. Node.js allows the developers to handle multiple requests on a single thread and thereby allowing them more breathing space.

Node.js handles data using two approaches – Buffered and Streamed. In the buffered approach, you have to write the entire data before the receiver may read it. Such an approach doesn’t support its asynchronous paradigm. When it comes to the Streamed approach, the information starts the interpreting process as soon as you enter it.

Before you read further, we would like to inform you that this article is about streams. Streams are an essential part of the Node.js environment. What it stream, and what do they do? What are the different types of streams? We have tried to cover several important questions that may help you in understanding Node.js Streams. Let’s get started.

#nodejs #streams in node.js #using streams in node js #node.js streams #node.js tutorial #data streams

Load Balance multiple RTMP Servers to Horizontally Scale Streaming (Node Media Server and HAProxy)

In this video I will show case the new Node Media Server , a Node JS implementation of an RTMP server used for streaming setup. I will show how to install and spinup an RTMP server and stream to it from OBS. Then I will spin up multiple ones, then I’ll show how to relay the stream to multiple RTMP servers and finally I will use HAProxy to load balance the stream consumption to achieve high availability.

Configurations
https://github.com/hnasr/javascript_playground/tree/master/rtmp

Node Media Server
https://www.npmjs.com/package/node-media-server

  • 0:00 Intro
  • 2:00 Single RTMP Server
  • 6:00 Multiple RTMP Servers
  • 7:00 Relay Server
  • 12:00 Load Balancing RTMP Servers

#developer #programming

Hal  Sauer

Hal Sauer

1593444960

Sample Load balancing solution with Docker and Nginx

Most of today’s business applications use load balancing to distribute traffic among different resources and avoid overload of a single resource.

One of the obvious advantages of load balancing architecture is to increase the availability and reliability of applications, so if a certain number of clients request some number of resources to backends, Load balancer stays between them and route the traffic to the backend that fills most the routing criteria (less busy, most healthy, located in a given region … etc).

There are a lot of routing criteria, but we will focus on this article on fixed round-robin criteria — meaning each backend receives a fixed amount of traffic — which I think rarely documented :).

To simplify we will create two backends “applications” based on flask Python files. We will use NGINX as a load balancer to distribute 60% of traffic to application1 and 40% of traffic to application2.

Let’s start the coding, hereafter the complete architecture of our project:

app1/app1.py

from flask import request, Flask
import json

app1 = Flask(__name__)
@app1.route('/')
def hello_world():
return 'Salam alikom, this is App1 :) '
if __name__ == '__main__':
app1.run(debug=True, host='0.0.0.0')

app2/app2.py

from flask import request, Flask
import json

app1 = Flask(__name__)
@app1.route('/')
def hello_world():
return 'Salam alikom, this is App2 :) '
if __name__ == '__main__':
app1.run(debug=True, host='0.0.0.0')

Then we have to dockerize both applications by adding the requirements.txt file. It will contain only the flask library since we are using the python3 image.

#load-balancing #python-flask #docker-load-balancing #nginx #flask-load-balancing

Ray  Patel

Ray Patel

1625843760

Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services

Introduction

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

  • revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
  • microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
  • Anaconda 4.2 – Anaconda is an opensource Python package

#machine learning #sql server #executing python in sql server #machine learning using python #machine learning with sql server #ml in sql server using python #python in sql server ml #python packages #python packages for machine learning services #sql server machine learning services

Gerhard  Brink

Gerhard Brink

1622108520

Stateful stream processing with Apache Flink(part 1): An introduction

Apache Flink, a 4th generation Big Data processing framework provides robust **stateful stream processing capabilitie**s. So, in a few parts of the blogs, we will learn what is Stateful stream processing. And how we can use Flink to write a stateful streaming application.

What is stateful stream processing?

In general, stateful stream processing is an application design pattern for processing an unbounded stream of events. Stateful stream processing means a** “State”** is shared between events(stream entities). And therefore past events can influence the way the current events are processed.

Let’s try to understand it with a real-world scenario. Suppose we have a system that is responsible for generating a report. It comprising the total number of vehicles passed from a toll Plaza per hour/day. To achieve it, we will save the count of the vehicles passed from the toll plaza within one hour. That count will be used to accumulate it with the further next hour’s count to find the total number of vehicles passed from toll Plaza within 24 hours. Here we are saving or storing a count and it is nothing but the “State” of the application.

Might be it seems very simple, but in a distributed system it is very hard to achieve stateful stream processing. Stateful stream processing is much more difficult to scale up because we need different workers to share the state. Flink does provide ease of use, high efficiency, and high reliability for the**_ state management_** in a distributed environment.

#apache flink #big data and fast data #flink #streaming #streaming solutions ##apache flink #big data analytics #fast data analytics #flink streaming #stateful streaming #streaming analytics