Java开源搜索引擎,Apache Lucene 4.0-alpha 发布

jopen 12年前
   <a href="/misc/goto?guid=4958185765343622769" target="_blank">Apache Lucene</a> 是一个开放源代码的全文检索引擎工具包,即它不是一个完整的全文检索引擎,而是一个全文检索引擎的架构,提供了完整的查询引擎和索引引擎,部分文本分析引擎(英文与德文两种西方语言)。Lucene的目的是为软件开发人员提供一个简单易用的工具包,以方便的在目标系统中实现全文检索的功能,或者是以此为基础建立起完整的全文检索引擎。    <br />    <a href="/misc/goto?guid=4958185765343622769"><img border="0" alt="Java开源搜索引擎,Apache Lucene 4.0-alpha 发布" src="" width="300" height="46" /></a>    <br />    <br />    <a href="/misc/goto?guid=4958185765343622769" target="_blank">Apache Lucene</a> 4.0 Alpha 发布了,宣布 Lucene 项目进入 4.0 版本阶段。该版本包含多个 bug 修复、优化和改进,详细记录如下:    <pre>3 July 2012, Apache Lucene‚ 4.0-alpha available  The Lucene PMC is pleased to announce the release of Apache Lucene 4.0-alpha    Apache Lucene is a high-performance, full-featured text search engine  library written entirely in Java. It is a technology suitable for nearly  any application that requires full-text search, especially cross-platform.    This release contains numerous bug fixes, optimizations, and  improvements, some of which are highlighted below.  The release  is available for immediate download at:    See the CHANGES.txt file included with the release for a full list of  details.    Lucene 4.0-alpha Release Highlights:     * The index formats for terms, postings lists, stored fields, term  vectors, etc     are pluggable via the Codec api. You can select from the provided     implementations or customize the index format with your own Codec  to meet your needs.     * Similarity has been decoupled from the vector space model (TF/IDF).  Additional models     such as BM25, Divergence from Randomness, Language Models, and  Information-based models     are provided (see     * Added support for per-document values (DocValues). DocValues can be  used for custom     scoring factors (accessible via Similarity), for pre-sorted Sort  values, and more.     * When indexing via multiple threads, each IndexWriter thread now  flushes its own segment     to disk concurrently, resulting in substantial performance improvements     (see     * Per-document normalization factors ("norms") are no longer limited  to a single byte.     Similarity implementations can use any DocValues type to store norms.     * Added index statistics such as the number of tokens for a term or  field, number of postings     for a field, and number of documents with a posting for a field:  these support additional     scoring models (see     * Implemented a new default term dictionary/index (BlockTree) that  indexes shared prefixes     instead of every n'th term. This is not only more time- and space-  efficient, but can     also sometimes avoid going to disk at all for terms that do not  exist. Alternative term     dictionary implementions are provided and pluggable via the Codec api.     * Indexed terms are no longer UTF-16 char sequences, instead terms  can be any binary     value encoded as byte arrays. By default, text terms are now encoded as UTF-8     bytes. Sort order of terms is now defined by their binary value,  which is identical     to UTF-8 sort order.     * Substantially faster performance when using a Filter during searching.     * File-system based directories can rate-limit the IO (MB/sec) of merge     threads, to reduce IO contention between merging and searching threads.     * Added a number of alternative Codecs and components for different  use-cases: "Appending"     works with append-only filesystems (such as Hadoop DFS), "Memory"  writes the entire     terms+postings as an FST read into RAM (see,     "Pulsing" inlines the postings for low-frequency terms into the  term dictionary (see,     "SimpleText" writes all files in plain-text for easy  debugging/transparency (see,  among others.     * Term offsets can be optionally encoded into the postings lists and  can be retrieved     per-position.     * A new AutomatonQuery returns all documents containing any term  matching a provided     finite-state automaton (see     * FuzzyQuery is 100-200 times faster than in past releases (see     * A new spell checker, DirectSpellChecker, finds possible corrections  directly against the     main search index without requiring a separate index.     * Various in-memory data structures such as the term dictionary and  FieldCache are represented     more efficiently with less object overhead (see     * All search logic is now required to work per segment, IndexReader  was therefore refactored to     differentiate between atomic and composite readers     (see     * Lucene 4.0 provides a modular API, consolidating components such as  Analyzers and Queries     that were previously scattered across Lucene core, contrib, and  Solr. These modules also     include additional functionality such as UIMA analyzer integration  and a completely reworked     spatial search implementation.    Please read CHANGES.txt and MIGRATE.txt for a full list of new  features and notes on upgrading.  Particularly, the new apis are not compatible with previous version of  Lucene, however, file  format backwards compatibility is provided for indexes from the 3.0 series.    This is an alpha release for early adopters. The guarantee for this  alpha release is that the index  format will be the 4.0 index format, supported through the 5.x series  of Apache Lucene, unless there  is a critical bug (e.g. that would cause index corruption) that would  prevent this.    Please report any feedback to the mailing lists  (    Happy searching,    Apache Lucene/Solr Developers</pre>