At the time we speak ( Solr 7.3.0 ) SolrCloud is a reliable and stable distributed architecture for Apache Solr.But it is not perfect and failures happen.This lightning blog post will present some practical tips to follow when a specific shard of a collection is down with no leader and the situation is stuck.The following…
Quantity detection? What is a quantity? And why do we need to detect it? A quantity, as described by Martin Fowler in his “Analysis Patterns” [1] is defined as a pair which combines an amount and unit (such as 30 litres, 0.25 cl, or 140 cm). In search-based applications, there are many cases where you may want to…
This blog is a quick summary of our experience at the ECIR 2018, the European Conference on Information Retrieval.
Info about our speakers and talk at the ECIR (European Conference in Information Retrieval) in March 2018 in Grenoble (France).
What are “Invisible Queries”? This is an extract of an article [1] on Lucidworks.com, by Grant Ingersoll, talking about invisible queries: “It is often necessary in many applications to execute more than one query for any given user query. For instance, in applications that require very high precision (only good results, forgoing marginal results), the…
Distributed search is the foundation for Apache Solr Scalability : It’s possible to distributed search across different Apache Solr nodes of the same collection ( both in a legacy [1] or SolrCloud [2] architecture), but it is also possible to distribute search across different collections in a SolrCloud cluster.Aggregating results from different collections may be useful when…
Info about our speaker and talk at the Apache Lucene/Solr Meetup in 2017 in London (UK).
Info about our speaker and talk at the Open Source Summit in May 2017, Tokyo (Japan)
Info about our speakers and talk at the Apachecon in November 2016, Seville (Spain).
Last Stage Of The Journey This blog post is about the Apache Solr Learning To Rank ( LTR ) integration. We modelled our dataset, we collected the data and refined it in Part 1 .Trained the model in Part 2 .Analysed and evaluate the model and training set in Part 3 .We are ready to…