{"id":8075,"date":"2018-01-12T06:12:33","date_gmt":"2018-01-12T11:12:33","guid":{"rendered":"https:\/\/qxf2.com\/blog\/?p=8075"},"modified":"2022-09-11T11:47:40","modified_gmt":"2022-09-11T15:47:40","slug":"getting-started-wallaroo","status":"publish","type":"post","link":"https:\/\/qxf2.com\/blog\/getting-started-wallaroo\/","title":{"rendered":"Getting started with Wallaroo"},"content":{"rendered":"

We used to think writing (near) real-time applications that process multiple data streams was for the high IQ crowd and well-funded teams. This belief was probably strengthened by the fact that we (at Qxf2<\/a>) love Python … and our favorite language lacked good complex event processors, stream processors. So we were excited to discover Wallaroo<\/a> has set out to fill that gap.<\/p>\n

Wallaroo describes itself as “a fast, elastic data processing engine that rapidly takes you from prototype to production by eliminating infrastructure complexity”. The charm for us lay in the fact that it could do stream processing and gave us an easy way to designate nodes, write business logic for them and define the structure of a distributed, stream processing application.<\/p>\n

We decided to explore Wallaroo. We used a Docker image with sample applications that Wallaroo Labs published. We were able to do the tutorials fairly easily. In this post, we will walk you through one of the examples Wallaroo Labs has provided.<\/p>\n

Wait! Are we using somebody else’s code for a post?<\/h3>\n

Yeah, we will be using Wallaroo Labs’s Docker image and their own code in this post. So no new code from our end. We know that seems lazy but we have a few good reasons:<\/p>\n

a) The excellent posts on Wallaroo Labs’ blog<\/a> are written for a far more technical audience. But we know there is a class of engineers who want to first see something work before they actually spend time learning something new. This post is meant for such engineers.<\/p>\n

b) Google is struggling to rank Wallaroo Labs’ material well. So there is no simple “30-minute guide to Wallaroo” that a reader can scan and grok without having to try things out.<\/p>\n

c) This post helped us articulate our understanding (or lack thereof!) of Wallaroo<\/p>\n

\n

Getting started with Wallaroo using Docker<\/h3>\n

In this blog, we will show you how to set up Wallaroo environment using Docker image and then run a sample “Celsius to Fahrenheit” application. We will be using a Linux box. Below are the steps involved<\/p>\n

A) Install Docker Community Edition(CE)
\nB) Get the official Wallaroo Docker image
\nC) Run a Wallaroo application in Docker
\nD) Start the Metrics UI
\nE) Run Giles Receiver
\nF) Run the “Celsius to Fahrenheit” application
\nG) Send Data to the application
\nH) Open “Celsius To Fahrenheit” application
\nI) Shut down the Cluster
\nJ) Shut down Giles Sender and Giles Receiver
\nK) Shut down the Metrics UI
\nL) Shut down the Wallaroo container<\/p>\n

\n

A) Install Docker Community Edition(CE)<\/strong><\/p>\n

Get Docker Community Edition (CE)<\/a> to set up Docker in your system so that we can install the Wallaroo Docker image.<\/p>\n

B) Get the official Wallaroo image<\/strong><\/p>\n

Once Docker is installed on your system pull the official Wallaroo image.<\/p>\n

Open a new terminal and run the following command.<\/p>\n

docker pull wallaroo-labs-docker-wallaroolabs.bintray.io\/release\/wallaroo:0.3.1<\/pre>\n
Wallaroo Labs uses a number of names\/words to describe the various components of Wallaroo. We are beginners but we will take a shot at giving you some sort of idea (even if it is inaccurate) about the different components. Following things are included in the Docker image :<\/p>\n