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elasticsearch插件bug fix

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–    public void reset(Reader input) throws IOException {
+    public void reset() throws IOException {








  • TokenStream enumerates the sequence of tokens, either from Fields of a Document or from query text.This is an abstract class; concrete subclasses are:

    • Tokenizer, a TokenStream whose input is a Reader; and
    • TokenFilter, a TokenStream whose input is another TokenStream.

    A new TokenStream API has been introduced with Lucene 2.9. This API has moved from being Token-based to Attribute-based. While Token still exists in 2.9 as a convenience class, the preferred way to store the information of a Token is to use AttributeImpls.TokenStream now extends AttributeSource, which provides access to all of the token Attributes for the TokenStream. Note that only one instance per AttributeImpl is created and reused for every token. This approach reduces object creation and allows local caching of references to the AttributeImpls. See incrementToken() for further details.

    The workflow of the new TokenStream API is as follows:

    1. Instantiation of TokenStream/TokenFilters which add/get attributes to/from the AttributeSource.
    2. The consumer calls reset().
    3. The consumer retrieves attributes from the stream and stores local references to all attributes it wants to access.
    4. The consumer calls incrementToken() until it returns false consuming the attributes after each call.
    5. The consumer calls end() so that any end-of-stream operations can be performed.
    6. The consumer calls close() to release any resource when finished using the TokenStream.

    To make sure that filters and consumers know which attributes are available, the attributes must be added during instantiation. Filters and consumers are not required to check for availability of attributes in incrementToken().You can find some example code for the new API in the analysis package level Javadoc.

    Sometimes it is desirable to capture a current state of a TokenStream, e.g., for buffering purposes (see CachingTokenFilter, TeeSinkTokenFilter). For this usecase AttributeSource.captureState() andAttributeSource.restoreState(org.apache.lucene.util.AttributeSource.State) can be used.

    The TokenStream-API in Lucene is based on the decorator pattern. Therefore all non-abstract subclasses must be final or have at least a final implementation of incrementToken()! This is checked when Java assertions are enabled.


An Analyzer builds TokenStreams, which analyze text. It thus represents a policy for extracting index terms from text.

In order to define what analysis is done, subclasses must define their TokenStreamComponents in createComponents(String, Reader). The components are then reused in each call to tokenStream(String, Reader).

Simple example:

For more examples, see the Analysis package documentation.

For some concrete implementations bundled with Lucene, look in the analysis modules:

  • Common: Analyzers for indexing content in different languages and domains.
  • ICU: Exposes functionality from ICU to Apache Lucene.
  • Kuromoji: Morphological analyzer for Japanese text.
  • Morfologik: Dictionary-driven lemmatization for the Polish language.
  • Phonetic: Analysis for indexing phonetic signatures (for sounds-alike search).
  • Smart Chinese: Analyzer for Simplified Chinese, which indexes words.
  • Stempel: Algorithmic Stemmer for the Polish Language.
  • UIMA: Analysis integration with Apache UIMA.


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