By Jayant Kumar
Leverage the facility of Apache Solr to energy up your enterprise by way of navigating your clients to their info fast and efficiently
About This Book
research the simplest use situations for utilizing Solr in e-commerce, advertisements, real-estate, and different sites
discover Solr internals and customise the scoring set of rules in Solr
this can be an easy-to-follow publication with a step by step method of assist you get the simplest out of Solr seek patterns
Who This booklet Is For
This publication is for builders who already understand how to exploit Solr and are purchasing complicated techniques for bettering their seek utilizing Solr. This publication is additionally for those who paintings with analytics to generate graphs and experiences utilizing Solr. in addition, when you are a seek architect who's expecting scale your seek utilizing Solr, it is a should have publication for you.
It will be necessary while you're acquainted with the Java programming language.
Apache Solr is an open resource seek platform equipped on a Java library referred to as Lucene. It serves as a seek platform for plenty of web content, because it has the potential of indexing and looking out a number of web content to fetch wanted results.
We start with a short advent of analyzers and tokenizers to appreciate the demanding situations linked to imposing large-scale indexing and multilingual seek performance. We then stream directly to operating with customized queries and realizing how filters paintings internally. whereas doing so, we additionally create our personal question language or Solr plugin that does proximity searches. additionally, we speak about how Solr can be utilized for real-time analytics and take on difficulties confronted in the course of its implementation in e-commerce seek. We then dive deep into the spatial positive aspects comparable to indexing suggestions and search/filtering thoughts for a spatial seek. We additionally do an in-depth research of difficulties confronted in an advert serving platform and the way Solr can be utilized to resolve those problems.
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Extra resources for Apache Solr Search Patterns
The Japanese analyzer available in our Solr schema configuration is text_ja. This analyzer identifies the words in the sentence and creates tokens. A few tokens identified are as follows: Japanese tokens It also identifies some of the stop words and removes them from the sentence. As in English, there are other languages where a word is modified by adding a suffix or prefix to change the tense, grammatical mood, voice, aspect, person, number, or gender of the word. This concept is called inflection and is handled by stemmers during indexing.
Therefore, after the search completes and during relevancy calculation, the stored boost is taken into consideration. Search time or query time boosting is dynamic. Certain fields can be boosted in the query that can result in certain documents getting a higher relevancy score than others. For example, we can boost the score of books by adding the parameter cat:book^4 in the query. This boosting will make the score of books relatively higher than the score of other items in the index. html. [ 31 ] Customizing the Solr Scoring Algorithm Building a custom scorer Now that we know how the default relevance calculation works, let us look at how to create a custom scorer.
The total number of documents in the index including both shards is say n out of which sh1 contains k documents and sh2 contains n-k documents. The formula for the IDF of term t, which we saw earlier was: idf(t) = 1+log(numDocs / (docFreq+1)) Suppose we search for the term jacket across both the shards. Let us see what happens to the IDF of jacket on shards sh1 and sh2 and the entire index. Shard sh1 has k documents each having different frequencies for the term jacket. The IDF of the term jacket on sh1 would be calculated separately as follows: IDF for jacket on shard sh1 [ 40 ] Chapter 2 Shard sh2 had the remaining (n-k) documents.