By Jayant Kumar
Leverage the facility of Apache Solr to energy up your corporation by way of navigating your clients to their information quick and efficiently
About This Book
research the easiest use instances 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 easiest out of Solr seek patterns
Who This ebook Is For
This publication is for builders who already understand how to exploit Solr and are taking a look at purchasing complicated thoughts for making improvements to their seek utilizing Solr. This e-book can be for those that paintings with analytics to generate graphs and studies utilizing Solr. additionally, while you are a seek architect who's looking ahead to scale your seek utilizing Solr, it is a should have ebook for you.
It will be invaluable while you are acquainted with 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 pages, because it has the aptitude of indexing and looking out a number of web content to fetch wanted results.
We commence with a quick 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 knowing how filters paintings internally. whereas doing so, we additionally create our personal question language or Solr plugin that does proximity searches. moreover, we talk 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 factors akin 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 unravel those problems.
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Extra info for Apache Solr Search Patterns
37 ] Customizing the Solr Scoring Algorithm Some of these similarities, such as SweetSpotSimilarity, have an option of specifying additional parameters for different fieldTypes. xml file while defining the similarity class implementation for fieldType by adding additional parameters during definition. 5 We will discuss some of these similarity algorithms later in this chapter. Drawbacks of the TF-IDF model Suppose, on an e-commerce website, a customer is searching for a jacket and intends to purchase a jacket with a unique design.
The default scoring mechanism is a mix of the Boolean model and the Vector Space Model (VSM) of information retrieval. The binary model is used to figure out documents that match the query set, and then the VSM is used to calculate the score of each and every document in the result set. In addition to the VSM, the Lucene scoring mechanism supports a number of pluggable models, such as probabilistic models and language models. However, we will focus on the VSM as it is a default scoring algorithm, and it works pretty well for most of the cases.
First, a standard tokenizer is applied that breaks the input text into tokens. Note that here Half-Blood was broken into Half and Blood. Next, we saw the stop filter removing the stop words we mentioned previously. The words And and The are discarded from the token stream. Finally, the lowercase filter converts all tokens to lowercase. During the search, suppose the query entered is Half-Blood and King. To check how it is analyzed, enter the value in Field Value (Query), select the text value in the FieldName / FieldType, and click on Analyze values.