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Post 1. Making a Case for Continuous Performance Testing

Test Automation Jul 26, 2016 Grid Dynamics

by Mikhail Klokov, Victor Samoylov

Nobody wants situations where e-commerce performance issues lead to lost revenue. Nothing upsets engineering teams more than frantic application troubleshooting and patching in the middle of the night. And absolutely nothing upsets business people and destroys careers more than watching customers switch to your competition during the year’s heaviest sales days because their website or mobile application is visibly faster. Continuous Performance Testing can help prevent these problems by catching them during the development process instead of after an application is in production.
Here are four studies that show how software performance problems can affect busy e-commerce properties:

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Post 2: High-Performance Join in Solr with BlockJoinQuery

Search Jul 21, 2016 Grid Dynamics

by Mikhail Khludnev

Mikhail Khludnev

Join support is a highly-requested Solr feature, especially in e-commerce. So I repeated Erick Erickson’s benchmark test with block join support for Solr, and I want to share my observations on how BlockJoinQuery can maximize Solr/Lucene performance. 

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Post 1. Introduction to Block Join Faceting in Solr

Search Jul 20, 2016 Grid Dynamics

by Oleg Savrasov

Oleg Savrasov

A straightforward look at how Block Join Faceting works, how it can save your customers from frustrating search experiences, and why Grid Dynamics created SOLR-5743 to bring Block Join Faceting to Solr

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Advanced Solr/Lucene Topics: High-Performance Nested Search for E-commerce Applications

Search Jul 19, 2016 Grid Dynamics

by Victoria Livschitz, Eugene Steinberg

Solr/Lucene has emerged over the last few years as a leading open source search platform for large-scale e-commerce search engines. Systems based on Solr power major sites including Macy’s, Kohl’s, Walmart, Etsy, and many others. An increasing number of tier-1 digital retailers are building their next-generation search and catalog navigation platforms using the Solr technology stack, often replacing commercial engines such as Oracle Endeca, FAST or Mercado.

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Post 4. In-Stream Processing Service Blueprint

Big Data Jul 10, 2016 Grid Dynamics

by Sergey Tryuber, Anton Ovchinnikov, Victoria Livschitz

This article introduces the Grid Dynamics Blueprint for In-Stream Processing. It is based on our experience and the lessons we have learned from multiple large-scale client implementations.

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Post 3. Overview of In-Stream Processing Solutions On the Market

Big Data May 6, 2016 Grid Dynamics

by Sergey Tryuber, Anton Ovchinnikov, Victoria Livschitz

This post contains a brief survey of better-known products related to In-Stream Processing that are available on the market at the time of this writing. In this survey, we focus specifically on critical architectural differentiations, rather than functional differences, that affect why customers may choose one approach over the others.

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Post 2. How In-Stream Processing Works

Big Data May 5, 2016 Grid Dynamics

by Sergey Tryuber, Anton Ovchinnikov, Victoria Livschitz

Now that we have introduced the high-level concepts behind In-Stream Processing and how it fits into the Big Data and Fast Data landscapes, it is time to dive deeper and explain how In-Stream Processing works.

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Post 1. What is In-Stream Processing?

Big Data May 4, 2016 Grid Dynamics

by Sergey Tryuber, Anton Ovchinnikov, Victoria Livschitz

In-Stream Processing is a powerful technology that can scan huge volumes of data coming from sensors, credit card swipes, clickstreams and other inputs, and find actionable insights nearly instantaneously. For example, In-Stream Processing can detect a single fraudulent transaction in a stream containing millions of legitimate purchases, act as a recommendation engine to determine what ad or promotion to display for a particular customer while he or she is actually shopping or compute the optimal price for a car service ride in only a few seconds.

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