Solr With Kubernetes And How Lucene More Like This Works

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Solr with Kubernetes and How Lucene More Like This Works

16 May 2019

London

Added 02-Dec-2019

We're back after a long hiatus with two great talks on running Solr with Kubernetes and how Lucene/Solr's More Like This feature works (details below). Join us with our kind hosts Loveholidays for the talks, an update on the world of Lucene and Solr and the usual Q&A. Looking forward to meeting members old and new! Doors open at 6.30 pm with talks starting at 7 sharp (don't be late, you'll miss the pizza!).

** Please note you will need to give us your full name (not just a nickname) and email address for security when you register **

Our first talk is from Dmitri Lerko, Head of DevOps at Loveholidays on "Evolving production Solr - from on-prem to Kubernetes":

Loveholidays has been running Solr in production for nearly 8 years. We will briefly explain the evolution of our on-prem Solr clusters towards its current form - Solr running on Kubernetes using Google Cloud. We will look at how problem domains change as business scales and solutions increase complexity of your Solr Cluster setup. Those of you curious about running Solr using public cloud and especially Kubernetes will benefit from learning what works well and what doesn’t in the world of ephemeral infrastructure.

Our second speaker is Alessandro Benedetti of Sease Ltd., co-creator of the Rated Ranking Evaluator search testing tool, on "How the Lucene More Like This Works":

The More Like This search functionality is a key feature in Apache Lucene that allows to find similar documents to an input one (text or document). Being widely used but rarely explored, this presentation will start introducing how the MLT works internally. The focus of the talk is to improve the general understanding of MLT and the way you could benefit from it. Building on the introduction the focus will be on the BM25 text similarity function and how this has been (tentatively) included in the MLT through a conspicious refactor and testing process, to improve the identification of the most interesting terms from the input that can drive the similarity search. The presentation will include real world usage examples, proposed patches, pending contributions and future developments such as improved query building through positional phrase queries.

 

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