By Jayant Kumar
Leverage the ability of Apache Solr to strength up your small business via navigating your clients to their facts speedy and efficiently
About This Book
study 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 is often an easy-to-follow booklet with a step by step method of assist you get the easiest out of Solr seek patterns
Who This booklet Is For
This ebook is for builders who already know the way to exploit Solr and are taking a look at buying complex concepts for bettering their seek utilizing Solr. This ebook can be for those who paintings with analytics to generate graphs and studies utilizing Solr. furthermore, when you are a seek architect who's looking ahead to scale your seek utilizing Solr, it is a should have publication for you.
It will be worthwhile while you're acquainted with the Java programming language.
Apache Solr is an open resource seek platform outfitted on a Java library known as Lucene. It serves as a seek platform for lots of web pages, because it has the potential of indexing and looking a number of web content to fetch wanted results.
We start with a quick creation of analyzers and tokenizers to appreciate the demanding situations linked to enforcing large-scale indexing and multilingual seek performance. We then circulate 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. 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 recommendations 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.
Read Online or Download Apache Solr Search Patterns PDF
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Additional resources for Apache Solr Search Patterns
Once a document is boosted during index time, the boost is stored with the document. 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.
Finally, the lowercase filter converts all tokens to lowercase. An important point to note over here is that the lowercase filter appears as the last filter. This is to prevent any mismatch between the text in the index and that in the search due to either of them having a capital letter in the token. The Solr analysis feature can be used to analyze and check whether the analyzer we have created gives output in the desired format during indexing and search. It can also be used to debug if we find any cases where the results are not as expected.
Leader: One of the cores within a shard will act as a leader. The leader is responsible for making sure that all the replicas within a shard are up to date. SolrCloud concepts – collection, shard, leader, replicas, core SolrCloud has a central configuration that can be replicated automatically across all the nodes that are part of the SolrCloud cluster. The central configuration is maintained using a configuration management and coordination system known as Zookeeper. Zookeeper provides reliable coordination across a huge cluster of distributed systems.