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Performance Comparison of Ruby Frameworks, App Servers, Template Engines, and ORMs (2016)

Eugene Melnikov

performance-ruby-rails-sinatra-rack

The Ruby ecosystem is constantly evolving. There have been many changes in the engineering world since our comparison of Ruby frameworks in 2014. During the two years, we received a few requests from fellow engineers asking for an updated benchmark. So, here is the 2016 edition with more things tested.

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NoSQL Tech Comparison 2014: Cassandra (DataStax), MongoDB, and Couchbase

Alex Khizhniak

Introducing a NoSQL scoring framework

Even if you have years of experience with data-intensive apps, selecting a NoSQL data store for a particular case out of dozens of options may be a daunting task. The variety of databases goes way beyond sheer numbers, so you have to carefully compare and benchmark several options before you can choose the most appropriate solution.

To help companies select the best database based on particular use cases, workloads, or requirements, we decided to come up with a handy template for evaluating NoSQL solutions. While many other comparisons focus only on one or two dimensions, we compiled a scoring framework that approaches the databases from 20+ angles (including performance, scalability, availability, ease of installation, maintenance, data consistency, fault tolerance, replication, recovery, etc.).

As a real-life example of such an evaluation benchmark, today we present “The NoSQL Technical Comparison Report,” which provides an in-depth analysis of the leading NoSQL systems: Cassandra (DataStax), MongoDB, and Couchbase Server. Each of the databases was scored on a scale from 1 to 10 across 21 criteria.

With 29 charts and 30 tables, this paper features a scoring template for evaluating and comparing NoSQL data stores for your particular use case—depending on the weight of each criterion. We also give recommendations on the best ways to configure, install, and use NoSQL databases depending on their specific features.

 

Want details? Watch a webinar!

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Performance Comparison of Ruby Frameworks: Sinatra, Padrino, Goliath, and Ruby on Rails

Eugene Melnikov

ruby-frameworks-1

The main goal of this article was to find the best framework for a very basic but highly loaded Ruby application. This is the updated version of the comparison that was first posted in Jun 2013. Now we ran all the tests again, using the latest versions of Sinatra, Padrino, Goliath, and RoR. Unfortunately, the Espresso framework that we had tested last time disappeared from all the repositories, so it is no longer included.

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Hadoop Benchmark: Cloudera vs. Hortonworks vs. MapR

Alex Khizhniak

Evaluating Hadoop distributions across 7 workloads

Cloudera, Hortonworks, and MapR are the most popular Hadoop distributions available today. However, even with this short list, there are few unbiased comparisons of their cluster performance. So, today we’re introducing a 65-page research paper that contains a vendor-independent overview of Cloudera, Hortonworks, and MapR distributions.

cloudera_hortonworks_mapr

Vladimir Starostenkov of Altoros compared throughput of 8-, 12-, and 16-node clusters against performance of a 4-node cluster. (The speed of data processing of 8-, 12-, and 16-node clusters was divided by the throughput of a 4-node cluster.) The results were quite unexpected.

 

Hadoop cluster performance: bigger doesn’t mean faster

In a recent interview to TechTarget, our R&D Engineer Dmitriy Kalyada explained why adding nodes to a Hadoop cluster not always results in better performance. The new benchmark of Hadoop distributions confirms this behavior under several workloads.

For instance, when sorting unstructured text data (the Sort workload), the performance of a MapR cluster was growing linearly (as we were increasing its size from 4 to 8 nodes). After that, when new machines were added, the throughput of each separate node was degrading.

mapr_performance

As you can see on the diagram, an 8-node cluster turned out to be faster than clusters of 12 and 16 nodes. The same situation was observed in the DFSIO write test. Other Hadoop distributions had similar results under some of the workloads, too.

Download the benchmark to see all the performance results (83 diagrams, 7 types of workloads), including:

  • detailed performance results for 4-, 8-, 12-, and 16-node clusters
  • how the size of a cluster affects data processing speed
  • how different clusters behave under CPU and disk-bound workloads (including Bayes, DFSIO, Hive aggregation, PageRank, Sort, TeraSort, and WordCount)
  • what issues slow down deployment and how to maximize Hadoop processing speed

Get your copy of “Hadoop Distributions: Cloudera vs. Hortonworks vs. MapR” and let us know what you think about these results.

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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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Benchmarking Couchbase Server vs. Cassandra vs. MongoDB for Interactive Apps (2013)

Alex Khizhniak

Looking for a new database for your data-intensive application? Don’t miss our new research paper revealing performance test results for Cassandra, MongoDB, and Couchbase Server.

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A Vendor-Independent Comparison of NoSQL Databases: Cassandra, HBase, MongoDB, and 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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Cloud Platform Comparison: CloudStack, Eucalyptus, vCloud Director, and OpenStack

Vadim Truksha

Cloud computing remains one of the hottest topics in IT today given the promise of greatly improved efficiencies, significant cost savings, scalable infrastructure and high performance and secured data storage.

Choosing the appropriate cloud platform, however, can be difficult. They all have pros and cons. So, when a customer asked me and my colleagues what would be the best cloud platform for his project and why, we decided to take a deep look at the most notable systems available, compare their capabilities, and summarize the findings in a product-by-product table. We tested CloudStack, Eucalyptus, vCloud Director, and OpenStack.

cloup platforms compared

The goal of this independent comparison is to help you align your business requirements with the capabilities of a particular cloud system and—finally—select the best-fit product. Read the full text of the article in NetworkWorld. Feel free to send me your feedback.

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Benchmarks and Research

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