Not surprisingly, the answer is that it uses an index. So, you must be wondering how Lucene can perform very fast full-text searches. And Apache Lucene delivers, that’s why is used in most of the aforementioned applications. A platform that has power, effectiveness, the necessary flexibility and customization. That has led to the need to upgrade searching from a simple feature to a full platform. And that’s why big vendors don’t want to risk messing up their searching features and want to keep them as fast and yet as simple as possible. Adding to that, is the fact that searching has become such an important aspect of end user’s experience that for modern web applications, varying from simple blogs to big platforms like Twitter or Facebook and even military grade applications, is incomprehensible not to have searching facilities. One can now see why fully customized search-based applications are gaining lots of attention and traction. And the retrieval of that document has to seem as easy as breathing. It becomes easy to understand that the information each individual user needs, might reside in a little document, somewhere in a vast ocean of different information resources. But what about huge file systems with millions of files, and if that seems extraordinary to you, what about web pages, databases, emails, code repositories, to name just a few, and what about all of them combined. This might actually suffice for a small number of documents. One could argue that searching files for a word or a phrase is as simple as scanning it in a serial manner from top to bottom, just like you would using a grep command. The most well known and used tool to achieve that is, of course, searching. Having said that, gathering and storing all that data is beneficial only if you are able to extract useful information out of them, plus make them reachable by your application’s end users. It goes by the term information overload. The process of generating vast amounts of data is one of the defining characteristics of our time and a major consequence of technological advancements. 1.2 Why do we need full-text search engines They just cannot handle the amount of data that full-text searched engines can. But that are not efficient, nor fast enough or customizable. Granted, many database systems, like MySQL and PostgreSQL support full text searching, either natively or using external libraries. That kind of queries in a classic relational database would be hopeless. find certain words or phrases in its content. That’s because in a full text search, the search engine has to scan all of the words of the text document, or text stream in general, and try to match several criteria against it, e.g. Now, what if a user wants to obtain all the documents that contain a certain word or phrase in their actual content? If you try to use a traditional database and store the raw content of all the documents in a field of a tuple, searching would take unacceptably long. If you hold a table that stores (title,author, publisher, year of publishing) tuples, the above searches can be completed efficiently. The above queries can be easily handled by well know relational database. Or all of the books published in a specific year from a specific publisher. Or all of the books that have a specific word or phrase in their title. For example a library user needs to be able to find all of the books written by a specific author. It is a common need for users to want to retrieve list of documents or sources that match certain criteria. We are going to deal with the Java flavor of Lucene, but bear in mind that there are API clients for a variety of programming languages. This means that Lucene is going to help you implement a full-text search engine, tailored to your applications needs. Lucene is a rich, open source, full-text search suit. In this course we are going to dive into Apache Lucene. A simple searcher class 4.5 Download the source code 5. A Simple Search Application 4.1 Create a new Maven Project with Eclipse 4.2 Maven Dependencies 4.3. Basic components for searching 3.1 QueryBuilder and Query 3.2 IndexReader 3.3 IndexSearcher 3.4 TopDocs 3.5 ScoreDoc 4. Analyzers 2.6 Interacting with the Index 3. Basic components for Indexing 2.1 Directories 2.2 Documents 2.3. Introduction 1.1 What is full-text search 1.2 Why do we need full-text search engines 1.3 How Lucene works 1.4 Basic Lucene workflow 2.
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