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Lucene 4 Cookbook

Lucene 4 Cookbook

By : Edwood Ng, Vineeth Mohan
3.2 (5)
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Lucene 4 Cookbook

Lucene 4 Cookbook

3.2 (5)
By: Edwood Ng, Vineeth Mohan

Overview of this book

This book is for software developers who are new to Lucene and who want to explore the more advanced topics to build a search engine. Knowledge of Java is necessary to follow the code samples. You will learn core concepts, best practices, and also advanced features, in order to build an effective search application.
Table of Contents (11 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Sections
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Introducing Lucene
1. Introducing Lucene
Introduction
Installing Lucene
Setting up a simple Java Lucene project
Obtaining an IndexWriter
Creating an analyzer
Creating fields
Creating and writing documents to an index
Deleting documents
Obtaining an IndexSearcher
Creating queries with the Lucene QueryParser
Performing a search
Enumerating results
2
2. Analyzing Your Text
2. Analyzing Your Text
Introduction
Obtaining a common analyzer
Obtaining a TokenStream
Obtaining TokenAttribute values
Using PositionIncrementAttribute
Using PerFieldAnalyzerWrapper
Defining custom TokenFilters
Defining custom analyzers
Defining custom tokenizers
Defining custom attributes
3
3. Indexing Your Data
3. Indexing Your Data
Introduction
Obtaining an IndexWriter
Creating a StringField
Creating a TextField
Creating a numeric field
Creating a DocValue Field
Transactional commits and index versioning
Reusing field and document objects per thread
Delving into field norms
Changing similarity implementation used during indexing
4
4. Searching Your Indexes
4. Searching Your Indexes
Introduction
Obtaining IndexReaders
Un-inverting single-valued fields in memory with FieldCache
TermVectors
IndexSearcher
Constructing queries
Specifying sort logic
Forming a search result
Pagination
Using Collectors
Sorting with custom FieldComparator
5
5. Near Real-time Searching
5. Near Real-time Searching
Introduction
Using the DirectoryReader to open index in Near Real-Time
Using the SearcherManager to refresh IndexSearcher
Generational indexing with TrackingIndexWriter
Maintaining search sessions with SearcherLifetimeManager
Performance tuning: latency and throughput
6
6. Querying and Filtering Data
6. Querying and Filtering Data
Introduction
Performing advanced filtering
Creating a custom filter
Searching with QueryParser
TermQuery and TermRangeQuery
BooleanQuery
PrefixQuery and WildcardQuery
PhraseQuery and MultiPhraseQuery
FuzzyQuery
NumericRangeQuery
DisjunctionMaxQuery
RegexpQuery
SpanQuery
CustomScoreQuery
7
7. Flexible Scoring
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7. Flexible Scoring
Introduction
Overriding similarity
Implementing the BM25 model
Implementing the language model
Implementing the divergence from randomness model
Implementing the information-based model
8
8. Introducing Elasticsearch
8. Introducing Elasticsearch
Introduction
Getting Elasticsearch
Creating a new index
Predefine field mappings
Adding a document
Deleting a document
Updating a document
Performing bulk indexing
Searching the index
Scaling Elasticsearch
9
9. Extending Lucene with Modules
9. Extending Lucene with Modules
Introduction
Exploring spatial search
Implementing joins
Performing faceting
Implementing grouping
Employing autosuggest
Implementing highlighting
10
Index
Index

Implementing the BM25 model

Let's take a look at how we use the BM25 model in Lucene. Lucene implements this model as BM25Similarity. We can start using this model as simply as instantiating it with default parameters. The constructor accepts two parameters for tuning. The first parameter controls nonlinear term frequency normalization. Its default value is 1.2. The second parameter controls to what degree a document length normalizes the tf values.

How to do It…

Here we have our sample code to demonstrate how to use BM25Similarity;

StandardAnalyzer analyzer = new StandardAnalyzer();
Directory directory = new RAMDirectory();
IndexWriterConfig config = new IndexWriterConfig(Version.LATEST, analyzer);
BM25Similarity similarity = new BM25Similarity(1.2f, 0.75f);
config.setSimilarity(similarity);
IndexWriter indexWriter = new IndexWriter(directory, config);
Document doc = new Document();
TextField textField = new TextField("content", "", Field.Store.YES);
String[] contents = {"Humpty Dumpty sat...
End of Section 5
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