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Download Apache Solr Search Patterns by Jayant Kumar PDF

By Jayant Kumar

Leverage the facility of Apache Solr to energy up your corporation via navigating your clients to their information speedy and efficiently

About This Book

study the easiest use circumstances 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 ebook with a step by step method of assist you get the easiest out of Solr seek patterns

Who This booklet Is For

This e-book is for builders who already understand how to exploit Solr and are taking a look at paying for complex ideas for making improvements to their seek utilizing Solr. This e-book can also be for those that paintings with analytics to generate graphs and reviews utilizing Solr. furthermore, while you are a seek architect who's waiting for scale your seek utilizing Solr, it is a should have ebook for you.

It will be necessary while you're acquainted with the Java programming language.
In Detail

Apache Solr is an open resource seek platform equipped on a Java library known as Lucene. It serves as a seek platform for plenty of web content, because it has the aptitude of indexing and looking a number of web content to fetch wanted results.

We commence with a short creation of analyzers and tokenizers to appreciate the demanding situations linked to enforcing large-scale indexing and multilingual seek performance. We then flow 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 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 beneficial properties comparable to indexing recommendations 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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Let us see how. Suppose an index has 2 shards, sh1 and sh2. 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.

In addition to this, we will look at tweaking the algorithm and create our own algorithm for scoring results. Then, we will add it as a plugin to Solr and see how the search results are ranked. We will discuss the problems with the default algorithm used in Solr and define a new algorithm known called the information gain model. This chapter will incorporate the following topics: • The relevance calculation algorithm • Building a custom scorer • Drawback of the TF-IDF model • The information gain model • Implementing the information gain model • Options to TF-IDF similarity • BM25 similarity • DFR similarity [ 29 ] Customizing the Solr Scoring Algorithm Relevance calculation Now that we are aware of how Solr works in the creation of an inverted index and how a search returns results for a query from an index, the question that comes to our mind is how Solr or Lucene (the underlying API) decides which documents should be at the top and how the results are sorted.

We saw some tips on improving the speed of indexing. Finally, we discussed distributed indexing and search and how SolrCloud provides a solution for implementing the same. [ 28 ] Customizing the Solr Scoring Algorithm In this chapter, we will go through the relevance calculation algorithm used by Solr for ranking results and understand how relevance calculation works with reference to the parameters in the algorithm. In addition to this, we will look at tweaking the algorithm and create our own algorithm for scoring results.

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