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Post 5: The Segmented Filter Cache and Block Join Query Parser in Solr

Search Aug 16, 2016 Grid Dynamics

by Mikhail Khludnev

Mikhail Khludnev

The “law of unintended consequences” applies to using the block join query parser in Solr, just as it does to many other things in life (and software). Leave out certain query strings in Solr, and It seems to make no difference. But this action can actually have positive effects, especially when working with Solr in a Near Real Time (NRT) environment. There are a number of other steps you can take to make Solr more NRT-capable, too.

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Post 4: How to Use Block Join to Improve Search Efficiency with Nested Documents in Solr

Search Aug 10, 2016 Grid Dynamics

by Mikhail Khludnev

Mikhail Khludnev

Faster responses make customers happy. Lower hardware requirements make budget people happy. Block Join can help accomplish both these goals, which is why we strongly suggest using it for nested document searches in Solr. But that’s enough about why we advocate using Block Join for nested and faceted searches in Solr. Now we’ll talk about how to do it.

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Post 3:  How to Implement Block Join Faceting in Solr/Lucene

Search Aug 9, 2016 Grid Dynamics

by Oleg Savrasov

Oleg Savrasov

In a previous post, we talked about business motivations behind the support of structured documents in a Solr/Lucene index and the unique requirements for a  faceting engine which is created by this approach to modeling data. We have introduced SOLR-5743. Now it is time to take a deep dive into the implementation details of Block Join Faceting in Solr search.

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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 2: Why Isn’t Continuous Performance Testing More Common?

Test Automation Jul 19, 2016 Grid Dynamics

by Mikhail Klokov, Victor Samoylov

The main reason website and application performance testing is not already continuous in many companies is clear: it’s hard to implement. Why? Let’s look at a few CPT implementation issues:

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Post 1. Making a Case for Continuous Performance Testing

Test Automation Jul 18, 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 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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