Chapter 1. Meet Lucene
Chapter 2. Building a search index
Table 2.2. Lucene’s several core Directory implementations
Table 2.3. Issues related to accessing a Lucene index across remote file systems
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
Chapter 4. Lucene’s analysis process
Table 4.1. Analyzer building blocks provided in Lucene’s core API
Chapter 5. Advanced search techniques
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
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
Chapter 8. Essential Lucene extensions
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 10.3. Lucene.Net summary
Table 10.4. KinoSearch summary
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
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 13.6. 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.
Appendix C. Lucene/contrib benchmark
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