An eBook copy of the previous edition of this book is included at no and unmatched advice, Lucene in Action, Second Edition is still the definitive guide to . Lucene is a gem in the open-source world—a highly scalable, fast search engine . It delivers performance and is disarmingly easy to use. Lucene in Action is the. Information Retrieval for Contribute to debarshri/IR development by creating an account on GitHub.
|Language:||English, Spanish, Dutch|
|Genre:||Business & Career|
|Distribution:||Free* [*Sign up for free]|
in an offer from Manning to co-author Lucene in Action with Erik Hatcher. Lucene . We've taken a unique approach to the code examples in Lucene in Action. Get this from a library! Lucene in action. [Otis Gospodnetić; Erik Hatcher]. Lucene is in my top list of open source projects. It is quality. Erik Hatcher and Otis have done a great job on the Manning book, and it was good.
Querying on multiple fields at once. Span queries: Searching across multiple Lucene indexes. Extending search 6. Using a custom sort method.
Developing a custom HitCollector.
Parsing common document formats 7. Handling rich-text documents.
Indexing a Microsoft Word document. Indexing a plain-text document. Creating a document-handling framework.
Tools and extensions 8. Analyzers, tokenizers, and TokenFilters, oh my. Java Development with Ant and Lucene. Storing an index in Berkeley DB. Lucene ports 9. Ports' relation to Lucene. Case studies Artful searching at Michaels.
Appendix A: Installing Lucene. Appendix B: Lucene index format. About the Technology Lucene powers search in surprising places—in discussion groups at Fortune companies, in commercial issue trackers, in email search from Microsoft, in the Nutch web search engine that scales to billions of pages. About the book Adding search to your application can be easy. Chaining filters.
Storing an index in Berkeley DB. XML QueryParser: Beyond "one box" search interfaces. Searching multiple indexes remotely. Using Lucene from other programming languages Ports primer. Net C and other. NET languages. Solr many programming languages.
Lucene administration and performance tuning Performance tuning. Managing resource consumption. Case study 1: Krugle Krugle: Searching source code. Case study 2: Searching entities with SIREn. Case study 3: Faceted search with Bobo Browse.
Appendix A: Installing Lucene. Appendix B: Lucene index format.
Appendix C: Appendix D: What's inside Performing hot backups Using numeric fields Tuning for indexing or searching speed Boosting matches with payloads Creating reusable analyzers Adding concurrency with threads Four new case studies Much more! About the authors Michael McCandless is a Lucene PMC member and committer with more than a decade of experience building search engines.
Lucene in Action, Second Edition combo added to cart. Your book will ship via to:. Commercial Address. If your documents have a specific structure or type of content, you can take advantage of either to improve search quality and query capability. As an example of this sort of customization, in this Lucene tutorial we will index the corpus of Project Gutenberg , which offers thousands of free e-books.
We know that many of these books are novels. Suppose we are especially interested in the dialogue within these novels. Neither Lucene, Elasticsearch, nor Solr provides out-of-the-box tools to identify content as dialogue. In fact, they will throw away punctuation at the earliest stages of text analysis, which runs counter to being able to identify portions of the text that are dialogue.
So it is therefore in these early stages where our customization must begin. Pieces of the Apache Lucene Analysis Pipeline The Lucene analysis JavaDoc provides a good overview of all the moving parts in the text analysis pipeline. The standard analysis pipeline can be visualized as such: We will see how to customize this pipeline to recognize regions of text marked by double-quotes, which I will call dialogue, and then bump up matches that occur when searching in those regions.
Reading Characters When documents are initially added to the index, the characters are read from a Java InputStream , and so they can come from files, databases, web service calls, etc. To create an index for Project Gutenberg, we download the e-books, and create a small application to read these files and write them to the index. YES ; document. YES indicates that we store the title field, which is just the filename.
The actual reading of the stream begins with addDocument. The IndexWriter pulls tokens from the end of the pipeline. This pull proceeds back through the pipe until the first stage, the Tokenizer, reads from the InputStream. Tokenizing Characters The Lucene StandardTokenizer throws away punctuation, and so our customization will begin here, as we need to preserve quotes.
The documentation for StandardTokenizer invites you to copy the source code and tailor it to your needs, but this solution would be unnecessarily complex.
It is possible to write a Tokenizer that produces separate tokens for each quote, but Tokenizer is also concerned with fiddly, easy-to-screw-up details such as buffering and scanning, so it is best to keep your Tokenizer simple and clean up the token stream further along in the pipeline. Note, incidentally, that filter is a bit of a misnomer, as a TokenFilter can add, remove, or modify tokens.
This cleanup will involve the production of an extra start quote token if the quote appears at the beginning of a word, or an end quote token if the quote appears at the end. We will put aside the handling of single quoted words for simplicity.
Creating a TokenFilter subclass involves implementing one method: incrementToken. This method must call incrementToken on the previous filter in the pipe, and then manipulate the results of that call to perform whatever work the filter is responsible for.
The results of incrementToken are available via Attribute objects, which describe the current state of token processing. After our implementation of incrementToken returns, it is expected that the attributes have been manipulated to setup the token for the next filter or the index if we are at the end of the pipe.
The attributes we are interested in at this point in the pipeline are: CharTermAttribute: Contains a char buffer holding the characters of the current token. We will need to manipulate this to remove the quote, or to produce a quote token.
Because we are adding start and end quotes to the token stream, we will introduce two new types using our filter.
OffsetAttribute: Lucene can optionally store references to the location of terms in the original document. If we change the buffer in CharTermAttribute to point to just a substring of the token, we must adjust these offsets accordingly.