List of Tables

Chapter 1. Meet Lucene

Table 1.1. Lucene’s release history

Chapter 2. Building a search index

Table 2.1. A summary of various field characteristics, showing you how fields are created, along with common usage examples

Table 2.2. Lucene’s several core Directory implementations

Table 2.3. Issues related to accessing a Lucene index across remote file systems

Table 2.4. Locking implementations provided by Lucene

Table 2.5. Parameters that control merge selection with the default MergePolicy, LogByteSizeMergePolicy

Chapter 3. Adding search to your application

Table 3.1. Lucene’s primary searching API

Table 3.2. Expression examples that QueryParser handles

Table 3.3. Primary IndexSearcher search methods

Table 3.4. TopDocs methods for efficiently accessing search results03_Ch03.fm

Table 3.5. Factors in the scoring formula

Table 3.6. Boolean query operator shortcuts

Chapter 4. Lucene’s analysis process

Table 4.1. Analyzer building blocks provided in Lucene’s core API

Table 4.2. Lucene’s built-in token attributes

Table 4.3. Primary analyzers available in Lucene

Chapter 5. Advanced search techniques

Table 5.1. SpanQuery family

Table 5.2. Methods that a custom TermVectorMapper must implement

Table 5.3. Built-in implementations of TermVectorMapper

Table 5.4. FieldSelectorResult options when loading a stored field

Table 5.5. Core FieldSelector implementations

Chapter 6. Extending search

Table 6.1. Methods to implement for a custom Collector

Table 6.2. QueryParser’s extensibility points

Table 6.3. TokenFilter in contrib/analyzers that encode certain TokenAttributes as payloads

Chapter 7. Extracting text with Tika

Table 7.1. Supported document formats and the library used to parse them

Table 7.2. Metadata keys that Tika extracts

Table 7.3. Useful methods exposed by the Tika utility class

Chapter 8. Essential Lucene extensions

Table 8.1. Contrib analyzers

Table 8.2. The ngrams for the word lettuce

Table 8.3. The ngrams for the misspelled word letuce

Chapter 9. Further Lucene extensions

Table 9.1. Searching and filtering time with varying result counts

Chapter 10. Using Lucene from other programming languages

Table 10.1. Types of Lucene ports

Table 10.2. CLucene summary

Table 10.3. Lucene.Net summary

Table 10.4. KinoSearch summary

Table 10.5. Lucy summary

Table 10.6. Ferret summary

Table 10.7. Zend Framework summary

Table 10.8. PyLucene summary

Table 10.9. Solr summary

Table 10.10. The many Solr clients currently available

Chapter 12. Case study 1: Krugle

Table 12.1. Combined terms, which improves performance for common single terms

Table 12.2. Most common single and combined terms in the source code index

Table 12.3. Index file growth after combining high-frequency individual terms

Table 12.4. A case-aware token filter breaks a single token into multiple tokens when the case changes, making it possible to search on subtokens quickly without resorting to more expensive wildcards queries.

Chapter 13. Case study 2: SIREn

Table 13.1. Representation of an entity tuple table

Table 13.2. The Lucene document schema used for the Sindice use case

Table 13.3. Comparison of size (in kb) of the main index files (synthetic data set with 128 fields)

Table 13.4. Query time (in ms) for conjunction of two keywords across all fields (wildcard for field names)

Table 13.5. Query time (in ms) for disjunction of two keywords across all fields (wildcard for field names)

Table 13.6. Query time (in ms) for keyword search in one, two, or three randomly selected fields

Chapter 14. Case study 3: LinkedIn

Table 14.1. Bobo Browse allows you to configure facets declaratively through a Spring file.

Table 14.2. State of Zoie’s indexes over time

Appendix C. Lucene/contrib benchmark

Table C.1. General settings

Table C.2. Settings that affect logging

Table C.3. Settings that affect IndexWriter

Table C.4. Built-in ContentSources

Table C.5. Built-in query makers

Table C.6. Administration tasks

Table C.7. Built-in tasks for indexing and searching

Table C.8. Reporting tasks