Parallel Computing in SolrCloud

As we countdown to the annual Lucene/Solr Revolution conference in Boston this October, we’re highlighting talks and sessions from past…

As we countdown to the annual Lucene/Solr Revolution conference in Boston this October, we’re highlighting talks and sessions from past conferences. Today, we’re highlighting Joel Bernstein’s session about Parallel Computing in SolrCloud.

This presentation provides a deep dive into SolrCloud’s parallel computing capabilities – breaking down the framework into four main areas: shuffling, worker collections, the Streaming API, and Streaming Expressions. The talk describes how each of these technologies work individually and how they interact with each other to provide a general purpose parallel computing framework.

Also included is a discussion of some of the core use cases for the parallel computing framework. Use cases involving real-time map reduce, parallel relational algebra, and streaming analytics will be covered.

Joel Bernstein is a Solr committer and search engineer for the open source ECM company Alfresco.

http://www.slideshare.net/lucidworks/parallel-computing-with-solrcloud-presented-by-joel-bernstein-alfresco

lucenerevolution-avatarJoin us at Lucene/Solr Revolution 2016, the biggest open source conference dedicated to Apache Lucene/Solr on October 11-14, 2016 in Boston, Massachusetts. Come meet and network with the thought leaders building and deploying Lucene/Solr open source search technology. Full details and registration…

You Might Also Like

From search company to practical AI pioneer: Our vision for 2025 and beyond

CEO Mike Sinoway shares insights on AI's future, introducing Commerce Studio™ and...

Read More

When AI Goes Wrong: Real-World Fails and How to Prevent Them

Don’t let your AI chatbot sell a $50,000 Tahoe for $1! This...

Read More

Lucidworks Core Packages: Industry-Optimized AI Search & Personalization Solutions

Discover our comprehensive Core Packages that combine Analytics Studio, Commerce Studio, and...

Read More

Quick Links