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Speeding up Ruby Tests

Alena Vasilenko

Test driven development is quite a popular thing that allows you to stay on the safe side and be sure that the system works correctly. However, there are some routines that make tests in Ruby really slow. In this post, you will read how to save some precious seconds when you start Ruby on Rails tests. You will learn how to avoid re-starting the tests each time when any changes to a file are made. This post also explains how to check the test coverage of your app.

To learn how to make your Ruby/Ruby on Rails tests work much faster, read the full article.

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Diving into Ember.js: Part 1

Alena Vasilenko


Good news from Nastia Shaternik, a Ruby Developer at Altoros. She prepared a tutorial on how to create an Ember.js application from scratch without any bootstrapping tools. She uploaded the source code, so you could take a look at a commit history to catch on to her idea. This is the first part of the tutorial, the updates will arrive soon!

Find the full version of the article here. Have a nice reading!

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How to Install Cloud Foundry on Vagrant

Alena Vasilenko

Sometimes, a full-scale PaaS deployment is not a very affordable option for the testing/development stage. There are two options to address this issue. One is to run Micro Cloud Foundry as a virtual machine image on your laptop. The second option is to install Cloud Foundry with the Vagrant VM toolbox. To simplify a start, our Argentine team prepared the article “Installing Cloud Foundry on Vagrant.” It describes how to install the tool, start/stop Cloud Foundry components, work with custom configuration files, etc. You will find detailed instructions and examples that will help you to deploy a simple Ruby Sinatra Web application on a local Cloud Foundry PaaS in under 30 minutes.

Read the full article, “Installing Cloud Foundry on Vagrant,” at the Cloud Foundry blog.

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Which Ruby Framework Is Faster, Sinatra, Espresso, Padrino, Goliath, or Ruby on Rails?..

Eugene Melnikov

There are a number of Ruby frameworks that allow for creating amazing feature-rich applications. However, very often you need some simple functionality and your main goal is to ensure the fastest performance possible. I decided to compare performance of the basic applications that were created with Sinatra, Espresso, Padrino, Goliath, and Ruby on Rails to find out which framework is the fastest one.

See all 4 tables with the performance tests on our Github blog.

Updated: The new version of this performance comparison was released on Feb 7, 2014.

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Hadoop + GPU: Boost Performance of Your Big Data Project by 50x-200x?

Vladimir Starostenkov

Hadoop, an open-source framework that enables distributed computing, has changed the way we deal with big data. Parallel processing with this set of tools can improve performance several times over. The question is, can we make it work even faster? What about offloading calculations from a CPU to a graphics processing unit (GPU) designed to perform complex 3D and mathematical tasks? In theory, if the process is optimized for parallel computing, a GPU could perform calculations 50-100 times faster than a CPU.

Read my article at NetworkWorld to find out what is possible and how you can try this for your large-scale system.

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Mandrill—a Free SMTP Server For Applications

Olga Belokurskaya

Transactional e-mails are important part of any project or business, no matter if it’s a startup or a big company. But if corporations can afford the huge mailouts and the costs of big mail distribution services, startups usually look for cheaper options.

Mandrill by MailChimp is one of such options. It is a transactional mail distribution service that allows for sending up to 12,000 free e-mails per month. If you need more, there are various affordable pricing options. Mandrill supports SPF and DKIM records ensuring your emails won’t be regarded as spam by the most of e-mail services. Moreover, it allows for tracking e-mail statuses, such as sent, bounced, received, clicked, marked as spam, etc. It also supports templates and special tags for A/B testing, which is an advantage.

Please, have a look at the full overview of the service by our specialist Eugene Melnikov, following this link: http://altoros.github.io/2013/mandrill-free-smtp-server-for-application

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Let’s Test It Well (And Simply, And Smartly)

Olga Belokurskaya

If you are fond of testing, just like our Ruby Developer Nastia Shaternik, you’ll probably be interested to read her post about using RSpec. There, she dwells on how some RSpec features that are not commonly used can help you simplify testing and make tests clearer.

Learn how to make your tests readable as short documentation. Find out how using mock_model can make your tests run faster, see an example of using RSpec’s built-in expectations, get two strategies of sharing the same data among different examples, and more.

You may read the full Nastia’s posting, following this link: http://altoros.github.io/2013/lets-test-it-well/

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DigitalOcean—a New Amazing Cloud VPS Hosting

Olga Belokurskaya

DigitalOcean—a new cloud VPS hosting—being pretty cheap and easy to use can become one of the solutions that may interest startups, small and fast-growing projects.

The service provides a comparatively affordable pricing, starting from 5$/month, and bills hourly. This combines with ease of use: all you need is to get a dedicated IP and root access to your server, and you can start working; the control panel is plain and simple. DigitalOcean uses SSD hard drives and fast network that provide speed to servers’ work. Moreover, the service boldly promises a 99.99% uptime around network, power and virtual server availability.

Combine this all, and you’ll get an interesting solution to think about. But are there any pitfalls? For more detail, please, read the wide DigitalOcean overview by our Ruby Developer Eugene Melnikov: http://altoros.github.io/2013/digitalocean-new-amazing-cloud-vps-hosting

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Hadoop on Windows Azure: Hive vs. JavaScript for Processing Big Data

Alena Vasilenko

For some time Microsoft didn’t offer a solution for processing big data in cloud environments. SQL Server is good for storage, but its ability to analyze terabytes of data is limited. Hadoop, which was designed for this purpose, is written in Java and was not available to .NET developers. So, Microsoft launched the Hadoop on Windows Azure service to make it possible to distribute the load and speed up big data computations.

The Altoros’s R&D engineers evaluated two out-of-the-box ways of processing big data with Hadoop on Windows Azure—Hive querying and JavaScript implementations—and compared their performance.

For the research, we created eight types of queries in both languages and measured how fast they were processed. Since we wanted to test how the system would handle big data, we downloaded information on US Air Carrier Flight Delays from Windows Azure Marketplace and generated a data set of 9.15 GB.

The article reveals how additional grouping parameters of the query and type of an arithmetic operation affect the throughput. It also shows the dependency between the number of MapReduce tasks and the speed of calculations. In addition, the paper contains conclusions on how the HDFS block size (8 MB, 64 MB, and 256 MB) influences performance. You’ll find two tables and three graphs with the findings.

Find out the results of the evaluation in NetworkWorld.

Read the full version of the research in the White Paper.

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A vendor-independent comparison of NoSQL databases: Cassandra, HBase, MongoDB, Riak

Olga Belokurskaya

In 2010, when the world became enchanted by the capabilities of cloud systems and new databases designed to serve them, a group of researchers from Yahoo decided to look into NoSQL. The results were published in the paper, “Benchmarking Cloud Serving Systems with YCSB.” The Yahoo guys did a great job, but like any paper, it could not include everything.

In 2012, the number of NoSQL products reached 120-plus and the figure is still growing. That variety makes it difficult to select the best tool for a particular case. Database vendors usually measure productivity of their products with custom hardware and software settings designed to demonstrate the advantages of their solutions.

As R&D engineers at Altoros, a big data specialist, we were inspired by Yahoo’s endeavors and decided to add some effort of our own. This article is our vendor-independent analysis of NoSQL databases, based on performance measured under different system workloads. It’s an unbiased research to complement the work done by the folks at Yahoo.

Using Amazon virtual machines to ensure verifiable results and research transparency, we have analyzed and evaluated the following NoSQL solutions:

We also tested MySQL Cluster and sharded MySQL, taking them as benchmarks.

The aim of this investigation is to determine the best use cases for different NoSQL products.

Read about the results of the investigation in the article on NetworkWorld.

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