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Lucene 4 Cookbook - Sample Chapter - Free download as PDF File .pdf), Text File .txt) or read online for free. Chapter No. 1 Introducing Lucene Over Committer and PMC member of Apache Lucene and Solr - main focus is on development of Lucene Every Lucene 4 release got new features! – API glitches!!!. Chapter 4 delves deep into the heart of Lucene's indexing magic, the anal- .. local file systems, simple text files, Microsoft Word documents, HTML or PDF We can translate this recipe to the following mixture of pseudocode and the.

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Go to Subscription. You don't have anything in your cart right now. Lucene 4 Cookbook is a practical guide that shows you how to build a scalable search engine for your application, from an internal documentation search to a wide-scale web implementation with millions of records.

Starting with helping you to successfully install Apache Lucene, it will guide you through creating your first search application. Furthermore, the book walks you through analyzing your text and indexing your data to leverage the performance of your search application.

As you progress through the chapters, you will learn to effectively search your indexes and successfully employ real-time searching.

The chapters start off with simple concepts and build up to complex solutions that should help you on your way to becoming a search engine expert. Edwood Ng is a technologist with over a decade of experience in building scalable solutions from proprietary implementations to client-facing web-based applications. His background in search engine began at Endeca Technologies in , where he was a technical consultant helping numerous clients to architect and implement faceted search solutions.

After Endeca, he drew on his knowledge and began designing and building Lucene-based solutions. His first Lucene implementation that went to production was the search engine behind http: From there on, he continued to create search applications using Lucene extensively to deliver robust and scalable systems for his clients. Edwood is a supporter of an open source software. Vineeth Mohan is an architect and developer.

He loves to spend time studying emerging technologies and applications related to data analytics, data visualizations, machine learning, natural language processing, and developments in search analytics.

He began coding during his high school days, which later ignited his interest in computer science, and he pursued engineering at Model Engineering College, Cochin. He was recruited by the search giant Yahoo!

After 2 years of work at Yahoo! Finally, he started his own big data consulting company, Factweavers. Under his leadership and technical expertise, Factweavers is one of the early adopters of Elasticsearch and has been engaged with projects related to end-to-end big data solutions and analytics for the last few years.

There, he got the opportunity to learn various big-data-based technologies, such as Hadoop, and high-performance data ingress systems and storage. Later, he moved to a start-up in his hometown, where he chose Elasticsearch as the primary search and analytic engine for the project assigned to him. Later in , he founded Factweavers Technologies along with Jalaluddeen; it is consultancy that aims at providing Elasticsearch-based solutions.

He is also an Elasticsearch-certified corporate trainer who conducts trainings in India. Till date, he has worked on numerous projects that are mostly based on Elasticsearch and has trained numerous multinationals on Elasticsearch. Sign up to our emails for regular updates, bespoke offers, exclusive discounts and great free content.

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Features Free Trial. Search for eBooks and Videos. Lucene 4 Cookbook. Over 70 hands-on recipes to quickly and effectively integrate Lucene into your search application. Are you sure you want to claim this product using a token? Edwood Ng, Vineeth Mohan June Quick links: What do I get with a Packt subscription? What do I get with an eBook? What do I get with a Video? Frequently bought together. Learn more Add to cart. Instant Lucene. Paperback pages. Zend Search: Lucene implementation in Perl http: This is a new Lucene implementation in Perl http: Installing Lucene This section will show you what you need, in order to get started with Lucene.

How to do it First, let's download Lucene. Apache Lucene can be downloaded from its official download page. As of now, the latest version of Lucene is 4.

Here is the link to the official page of Lucene: How it works Lucene is written entirely in Java. The prerequisite for running Lucene is Java Runtime Environment. Lucene runs on Java 6 or higher. If you use Java 7, make sure you install update 1 as well. Once your download is complete, you can extract the contents to a directory and you are good to go.

In case you get some errors, the links to the FAQ and mailing list of Lucene users is as follows: Mailing List: Setting up a simple Java Lucene project Having Lucene downloaded, we can get started working with Lucene. Let's take a look at how a Lucene project is set up. Getting ready Java Runtime is required. If you have not installed Java yet, visit Oracle's website to download Java.

Here is a link to the Java download page: You may also want to use an IDE to work on a Lucene project. If you want to give Eclipse IDE a try, you can refer to the following link: Chapter 1 Having set up a development environment, let's proceed to create our first Lucene project.

The core library provides the basic functionality to start a Lucene project. By adding it to your Java classpath, you can begin to build a powerful search engine.

Solution 1

We will show you a couple ways to do this in Eclipse. First, we will set up a normal Java project in Eclipse. Then, we will add Lucene libraries to the project.

To do so, follow these steps:. Maven is a project management and build tool that provides facilities to manage project development lifecycles.

A detailed explanation of Maven is beyond the scope of this book. If you want to know more about Maven, you can check out the following link: To set up Lucene in Maven, you need to insert its dependency information into your project's pom. You can visit a Maven repository http: After you have updated pom.

How it works Once the JAR files are made available to the classpath, you can go ahead and start writing code. Both methods we described here would provide access to the Lucene library.

The first method adds JARs directly. With Maven, when you add dependency to pom. The class can be found in Lucene-core. It handles basic operations where you can add, delete, and update documents. It also handles more complex use cases that we will cover during the course of this book. An IndexWriter constructor takes two arguments: Directory, org. IndexWriterConfig IndexWriter https: Construct a new IndexWriter as per the settings given in the conf file.

The first argument is a Directory object. Directory is a location where the Lucene index is stored. The second argument is an IndexWriterConfig object where it holds the configuration information.

Lucene provides a number of directory implementations. For performance or quick prototyping, we can use RAMDirectory to store the index entirely in memory. Otherwise, the index is typically stored in FSDirectory on a file system.

Lucene has several FSDirectory implementations that have different strengths and weaknesses depending on your hardware and environment. In most cases, we should let Lucene decide which implementation to use by calling FSDirectory. How to do it We need to first define an analyzer to initialize IndexWriterConfig.

Then, a Directory should be created to tell Lucene where to store the index. With these two objects defined, we are ready to instantiate an IndexWriter. The following is a code snippet that shows you how to obtain an IndexWriter: How it works First, we instantiate a WhitespaceAnalyzer to parse input text, and tokenize text into word tokens.

The IndexWriter is now ready to update the index. An IndexWriter consists of two major components, directory and analyzer. These are necessary so that Lucene knows where to persist indexing information and what treatment to apply to the documents before they are indexed. Analyzer's treatment is especially important because it maintains data consistency. If an index already exists in the specified directory, Lucene will update the existing index. Otherwise, a new index is created.

Creating an analyzer Analyzer's job is to analyse text. It enforces configured policies IndexWriterConfig on how index terms are extracted and tokenized from a raw text input. The output from Analyzer is a set of indexable tokens ready to be processed by the indexer. This step is necessary to ensure consistency in both the data store and search functionality. Also, note that Lucene only accepts plain text. Imagine you have this piece of text: Lucene is an information retrieval library written in Java.

An analyzer will tokenize this text, manipulate the data to conform to a certain data formatting policy for example, turn to lowercase, remove stop words, and so on , and eventually output as a set of tokens. Token is a basic element in Lucene's indexing process. Let's take a look at the tokens generated by an analyzer for the above text: Each individual unit enclosed in braces is referred to as a token.

In this example, we are leveraging WhitespaceAnalyzer to analyze text. This specific analyzer uses whitespace as a delimiter to separate the text into individual words. Note that the separated words are unaltered and stop words is, an, in are included. Essentially, every single word is extracted as a token. Getting ready The Lucene-analyzers-common module contains all the major components we discussed in this section. Most commonly-used analyzers can be found in the org.

For language-specific analysis, you can refer to the org. Many analyzers in Lucene-analyzers-common require little or no configuration, so instantiating them is almost effortless.

For our current exercise, we will instantiate the WhitespaceAnalyzer by simply using new object: The analysis phase includes pre- and post-tokenization functions, and this is where the character filter and token filter come into play. The character filter preprocesses text before tokenization to clean up data such as striping out HTML markups, removing user-defined patterns, and converting a special character or specific text.

The token filter executes the post tokenization filtering; its operations involve various kinds of manipulations. For instance, stemming, stop word filtering, text normalization, and synonym expansion are all part of token filter. As described earlier, the tokenizer splits up text into tokens. The output of these analysis processes is TokenStream where the indexing process can consume and produce an index. Lucene provides a number of standard analyzer implementations that should fit most of the search applications.

Here are some additional analyzers, which we haven't talked about yet:. As the names suggest, this analyzer lowercases text, tokenizes non-letter characters and removes stop words. This is built with a LowerCaseTokenizer so that it simply splits text at non-letter characters, and lowercases the tokens.

This is slightly more complex than SimpleAnalyzer. StandardTokenizer uses a grammar-based tokenization technique that's applicable for most European languages. StandardFilter normalizes tokens extracted with StandardTokenizer. This is the most featured of the bunch. SnowballFilter stems words, so this analyzer is essentially StandardAnalyzer plus stemming.

In simple terms, stemming is a technique to reduce words to their word stem or root form. By reducing words, we can easily find matches of words with the same meaning, but in different forms such as plural and singular forms.

Creating fields We have learned that indexing information in Lucene requires the creation of document objects. A Lucene document contains one or more field where each one represents a single data point about the document.

A field can be a title, description, article ID, and so on. In this section, we will show you the basic structure and how to create a field. A Lucene field has three attributes:. Name and value are self-explanatory.

You can think of a name as a column name in a table, and value as a value in one of the records where record itself is a document. Type determines how the field is treated.

You can set FieldType to control whether to store value, to index it or even tokenize text. A Lucene field can hold the following:. This code snippet shows you how to create a simple TextField: YES ;. How It Works In this scenario, we create a document object, initialize a text, and add a field by creating a TextField object. We also configure the field to store a value so it can be retrieved during a search. A Lucene document is a collection of field objects. A field is the name of the value pairs, which you may add to the document.

A field is created by simply instantiating one of the Field classes. Field can be inserted into a document via the add method. Creating and writing documents to an index This recipe shows you how to index a document. In fact, here we are putting together all that we learned so far from the previous recipes.

Let's see how it is done. The following code sample shows you an example of adding a simple document to an index: YES ; indexWriter.

Downloading the example code You can download the example code files from your account at http: If you downloadd this book elsewhere, you can visit http: How it works Note that the preceding code snippet combined all the sample codes we learned so far. The Document is then added to IndexWriter. Also, note that we call indexWriter. The IndexWriter class exposes an addDocument doc method that allows you to add documents to an index. IndexWriter will write to an index specified by directory.

Deleting documents We have learned how documents are added to an index. Now, we will see how to delete Documents. Suppose you want to keep your index up to date by deleting documents that are a week old.

All of a sudden, the ability to remove documents becomes a very important feature. Let's see how can we do that. IndexWriter provides the interface to delete documents from an index. It takes either term or query as argument, and will delete all the documents matching these arguments:. Here is a code snippet on how deleteDocuments is called: How it works Assuming IndexWriter is already instantiated, this code will trigger IndexWriter to delete all the documents that contain the term id where the value equals 1.

Then, we call close to commit changes and close the IndexWriting.

Note that this is a match to a Field called id; it's not the same as DocId. In fact, deletions do not happen at once. They are kept in the memory buffer and later flushed to the directory. The documents are initially marked as deleted on disk so subsequent searches will simply skip the deleted documents; however, to free the memory, you still need to wait. We will see the underlying process in detail in due course. Obtaining an IndexSearcher Having reviewed the indexing cycle in Lucene, let's now turn our attention towards search.

Keep in mind that indexing is a necessary evil you have to go through to make your text searchable. We take all the pain to customize a search engine now, so we can obtain good search experiences for the users. This will be well worth the effort when users can find information quickly and seamlessly. A well-tuned search engine is the key to every search application. Consider a simple search scenario where we have an index built already.

User is doing research on Lucene and wants to find all Lucene-related documents. Naturally, the term Lucene will be used in a search query. Note that Lucene leverages an inverted index see the preceding image. Lucene can now locate documents quickly by stepping into the term Lucene in the index, and returning all the related documents by their DocIds.

A term in Lucene contains two elementsthe value and field in which the term occurs. How do we specifically perform a search?

Apache Solr

We create a Query object. In simple terms, a query can be thought of as the communication with an index. This action is also referred to as querying an index. We issue a query to an index and get matched documents back. The IndexSearcher class is the gateway to search an index as far as Lucene is concerned.

An IndexSearcher takes an IndexReader object and performs a search via the reader. IndexReader talks to the index physically and returns the results. IndexSearcher executes a search by accepting a query object. Next, we will learn how to perform a search and create a Query object with a QueryParser. For now, let's take a look at how we can obtain an IndexSearcher. Here is a code snippet that shows you how to obtain an IndexSearcher: How it works The first line assumes we can gain access to a Directory object by calling getDirectory.

Then, we obtain an IndexReader by calling DirectoryReader.

Lucene 4 Cookbook

The open method in DirectoryReader is a static method that opens an index to read, which is analogous to IndexWriter opening a directory to write. With an IndexReader initialized, we can instantiate an IndexSearcher with the reader. Creating queries with the Lucene QueryParser Now, we understand that we need to create Query objects to perform a search. We will look at QueryParser and show you how it's done.

Lucene supports a powerful query engine that allows for a wide range of query types. You can use search modifier or operator to tell Lucene how matches are done. You can also use fuzzy search and wild card matching. Internally, Lucene processes Query objects to execute a search. QueryParser is an interpreter that parses a query string into Query objects.

It provides the utility to convert textual input into objects. The key method in QueryParser is parse String.

If you want more control over how a search is performed, you can create Query objects directly without using QueryParser, but this would be a much more complicated process. The query string syntax Lucene uses has a few rules.

Here is an excerpt from Lucene's Javadoc: The syntax for query strings is as follows: A clause can be prefixed by:.

Alternatively, a term followed by a colon, indicating the field to be searched. This enables us to construct queries that search multiple fields. Alternatively, a nested query, enclosed in parentheses. Thus, in BNF, the query grammar is: Note that you need to import lucene-queryparser package to use QueryParser. It is not a part of the lucene-core package. The Backus Normal Form BNF is a notation technique to specify syntax for a language, and is often used in computer science.

Here is a code snippet: How it works Assuming an analyzer is already declared and available as a variable, we pass it into QueryParser to initialize the parser. The second parameter is the name of the field where we will perform a search.

In this case, we are searching a field called Content. Then, we call parse String to interpret the search string Lucene into Query object. Note that, at this point, we only return a Query object. We have not actually executed a search yet. Performing a search Now that we have a Query object, we are ready to execute a search.

We will leverage IndexSearcher from two recipes ago to perform a search. Note that, by default, Lucene sorts results based on relevance. It has a scoring mechanism assigning a score to every matching document. This score is responsible for the sort order in search results.Document id 3: We will show you how text analyzing works and how to customize it to suit your needs. Wassim Akel. It is not a part of the lucene-core package.

For now, we will focus on the essentials of creating a search engine: A detailed explanation of Maven is beyond the scope of this book. From there on, he continued to create search applications using Lucene extensively to deliver robust and scalable systems for his clients.

And plotting math expressions in Apache Zeppelin is now possible. Lucene's speed and low RAM requirement is indicative of its efficiency.

Later, he moved to a start-up in his hometown, where he chose Elasticsearch as the primary search and analytic engine for the project assigned to him.

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