By Jayant Kumar
Leverage the ability of Apache Solr to energy up your corporation through navigating your clients to their information speedy and efficiently
About This Book
research the easiest use situations for utilizing Solr in e-commerce, ads, real-estate, and different sites
discover Solr internals and customise the scoring set of rules in Solr
this can be an easy-to-follow ebook with a step by step method of assist you get the simplest out of Solr seek patterns
Who This publication Is For
This booklet is for builders who already understand how to take advantage of Solr and are taking a look at buying complex techniques for bettering their seek utilizing Solr. This e-book can also be for those that paintings with analytics to generate graphs and studies utilizing Solr. furthermore, while you are a seek architect who's waiting for scale your seek utilizing Solr, it is a should have e-book for you.
It will be valuable while you are accustomed to the Java programming language.
Apache Solr is an open resource seek platform outfitted 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 pages 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 circulate directly to operating with customized queries and figuring out how filters paintings internally. whereas doing so, we additionally create our personal question language or Solr plugin that does proximity searches. moreover, 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 gains equivalent to indexing ideas and search/filtering suggestions 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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Additional resources for Apache Solr Search Patterns
Our distributed Hadoop cluster would do the following: • Count all occurrences of each term in the index • Count all occurrences of each co-occurring term in the index • Construct a hash table or a map of co-occurring terms • Calculate the information gain for each term and store it in a file in the Hadoop cluster In order to implement this in our scoring algorithm, we will need to build a custom scorer where the IDF calculation is overwritten by the algorithm for deriving the information gain for the term from the Hadoop cluster.
Token processing: Given a token, what processing should happen on the token to make it a part of the index? Should words be broken up or synonyms added? Should diacritics and grammars be normalized? A stop-word dictionary specific to the language needs to be applied. Token processing can be done within Solr by using an appropriate analyzer, tokenizer, or filter. However, for this, all possibilities have to be thought through and certain rules need to be formed. The default analyzers can also be used, but it may not help in improving the relevance factor of the result set.
The TF-IDF formula is the core of the relevance calculation in Lucene. getBoost( ) ⋅ norm t,d ) t in q The default implementation of the tf-idf equation for Lucene is known as default similarity (the class is DefaultSimilarity inside Lucene). Let us look at the terms in the equation before understanding how the formula works: • tf(t in d): This is the term frequency, or the number of times term t (in the query) appears in document d. Documents that have more occurrences for a given term will have a higher score.
Apache Solr Search Patterns by Jayant Kumar