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The built-in analyzers pre-package these buildingblocks into analyzers suitable for different languages and types of text.Elasticsearch also exposes the individual building blocks so that they can becombined to define new custom analyzers.

The ESA609 Electrical Safety Analyzer is a rugged, portable and easy-to-use analyzer designed for general electrical safety testing. Engineered for on-the-go technicians, the ESA609 requires no prior training, has a rubberized case that allows it to sustain the rigor of transportation, and helps prevent damage when accidentally dropped.

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Its functional strap and featherweight design make it one of the most portable medical electrical safety analyzers in its class. Heavy-duty switches allow you to effortlessly change polarity and configuration of the neutral connection between open and closed, while push-button operation ensures fast transition between tests for complete basic testing in minutes. The ESA609 integrates all functions needed to test medical devices when patient lead testing is not required, including: line (mains) voltage, ground wire (protective earth) resistance, equipment current, leakage current and point-to point tests. Versatile to global electrical safety standards of choice, the ESA609 tests to ANSI/AAMI ES1, NFPA-99, and parts of IEC62353 and IEC60601-1.

Additionally, the analyzer identifies the future change in flood risk driven specifically by climate change and socio-economic development, which helps decision makers identify the drivers of future change and prioritize development focuses accordingly for strategic planning.

In order to define what analysis is done, subclasses must define their TokenStreamComponents in createComponents(String). The components are then reused in each call to tokenStream(String, Reader). Simple example: Analyzer analyzer = new Analyzer() { @Override protected TokenStreamComponents createComponents(String fieldName) { Tokenizer source = new FooTokenizer(reader); TokenStream filter = new FooFilter(source); filter = new BarFilter(filter); return new TokenStreamComponents(source, filter); } @Override protected TokenStream normalize(TokenStream in) { // Assuming FooFilter is about normalization and BarFilter is about // stemming, only FooFilter should be applied return new FooFilter(in); } }; 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. Nested Class SummaryNested Classes Modifier and TypeClass and Descriptionstatic class Analyzer.ReuseStrategyStrategy defining how TokenStreamComponents are reused per call to tokenStream(String, java.io.Reader).static class Analyzer.TokenStreamComponentsThis class encapsulates the outer components of a token stream.Field SummaryFields Modifier and TypeField and Descriptionstatic Analyzer.ReuseStrategyGLOBAL_REUSE_STRATEGYA predefined Analyzer.ReuseStrategy that reuses the same components for every field.static Analyzer.ReuseStrategyPER_FIELD_REUSE_STRATEGYA predefined Analyzer.ReuseStrategy that reuses components per-field by maintaining a Map of TokenStreamComponent per field name.Constructor SummaryConstructors Constructor and DescriptionAnalyzer()Create a new Analyzer, reusing the same set of components per-thread across calls to tokenStream(String, Reader).Analyzer(Analyzer.ReuseStrategy reuseStrategy)Expert: create a new Analyzer with a custom Analyzer.ReuseStrategy.Method SummaryAll Methods Instance Methods Abstract Methods Concrete Methods Modifier and TypeMethod and Descriptionprotected AttributeFactoryattributeFactory(String fieldName)Return the AttributeFactory to be used for analysis and normalization on the given FieldName.voidclose()Frees persistent resources used by this Analyzerprotected abstract Analyzer.TokenStreamComponentscreateComponents(String fieldName)Creates a new Analyzer.TokenStreamComponents instance for this analyzer.intgetOffsetGap(String fieldName)Just like getPositionIncrementGap(java.lang.String), except for Token offsets instead.intgetPositionIncrementGap(String fieldName)Invoked before indexing a IndexableField instance if terms have already been added to that field.Analyzer.ReuseStrategygetReuseStrategy()Returns the used Analyzer.ReuseStrategy.VersiongetVersion()Return the version of Lucene this analyzer will mimic the behavior of for analysis.protected ReaderinitReader(String fieldName, Reader reader)Override this if you want to add a CharFilter chain.protected ReaderinitReaderForNormalization(String fieldName, Reader reader)Wrap the given Reader with CharFilters that make sense for normalization.BytesRefnormalize(String fieldName, String text)Normalize a string down to the representation that it would have in the index.protected TokenStreamnormalize(String fieldName, TokenStream in)Wrap the given TokenStream in order to apply normalization filters.voidsetVersion(Version v)Set the version of Lucene this analyzer should mimic the behavior for for analysis.TokenStreamtokenStream(String fieldName, Reader reader)Returns a TokenStream suitable for fieldName, tokenizing the contents of reader.TokenStreamtokenStream(String fieldName, String text)Returns a TokenStream suitable for fieldName, tokenizing the contents of text.Methods inherited from class java.lang.Objectclone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitField DetailGLOBAL_REUSE_STRATEGYpublic static final Analyzer.ReuseStrategy GLOBAL_REUSE_STRATEGYA predefined Analyzer.ReuseStrategy that reuses the same components for every field.PER_FIELD_REUSE_STRATEGYpublic static final Analyzer.ReuseStrategy PER_FIELD_REUSE_STRATEGYA predefined Analyzer.ReuseStrategy that reuses components per-field by maintaining a Map of TokenStreamComponent per field name.Constructor DetailAnalyzerpublic Analyzer()Create a new Analyzer, reusing the same set of components per-thread across calls to tokenStream(String, Reader).Analyzerpublic Analyzer(Analyzer.ReuseStrategy reuseStrategy)Expert: create a new Analyzer with a custom Analyzer.ReuseStrategy. NOTE: if you just want to reuse on a per-field basis, it's easier to use a subclass of AnalyzerWrapper such as PerFieldAnalyerWrapper instead.Method DetailcreateComponentsprotected abstract Analyzer.TokenStreamComponents createComponents(String fieldName)Creates a new Analyzer.TokenStreamComponents instance for this analyzer.Parameters:fieldName - the name of the fields content passed to the Analyzer.TokenStreamComponents sink as a readerReturns:the Analyzer.TokenStreamComponents for this analyzer.normalizeprotected TokenStream normalize(String fieldName, TokenStream in)Wrap the given TokenStream in order to apply normalization filters. The default implementation returns the TokenStream as-is. This is used by normalize(String, String).tokenStreampublic final TokenStream tokenStream(String fieldName, Reader reader)Returns a TokenStream suitable for fieldName, tokenizing the contents of reader. This method uses createComponents(String) to obtain an instance of Analyzer.TokenStreamComponents. It returns the sink of the components and stores the components internally. Subsequent calls to this method will reuse the previously stored components after resetting them through Analyzer.TokenStreamComponents.setReader(Reader). NOTE: After calling this method, the consumer must follow the workflow described in TokenStream to properly consume its contents. See the Analysis package documentation for some examples demonstrating this. NOTE: If your data is available as a String, use tokenStream(String, String) which reuses a StringReader-like instance internally.Parameters:fieldName - the name of the field the created TokenStream is used forreader - the reader the streams source reads fromReturns:TokenStream for iterating the analyzed content of readerThrows:AlreadyClosedException - if the Analyzer is closed.See Also:tokenStream(String, String)tokenStreampublic final TokenStream tokenStream(String fieldName, String text)Returns a TokenStream suitable for fieldName, tokenizing the contents of text. This method uses createComponents(String) to obtain an instance of Analyzer.TokenStreamComponents. It returns the sink of the components and stores the components internally. Subsequent calls to this method will reuse the previously stored components after resetting them through Analyzer.TokenStreamComponents.setReader(Reader). NOTE: After calling this method, the consumer must follow the workflow described in TokenStream to properly consume its contents. See the Analysis package documentation for some examples demonstrating this.Parameters:fieldName - the name of the field the created TokenStream is used fortext - the String the streams source reads fromReturns:TokenStream for iterating the analyzed content of readerThrows:AlreadyClosedException - if the Analyzer is closed.See Also:tokenStream(String, Reader)normalizepublic final BytesRef normalize(String fieldName, String text)Normalize a string down to the representation that it would have in the index. This is typically used by query parsers in order to generate a query on a given term, without tokenizing or stemming, which are undesirable if the string to analyze is a partial word (eg. in case of a wildcard or fuzzy query). This method uses initReaderForNormalization(String, Reader) in order to apply necessary character-level normalization and then normalize(String, TokenStream) in order to apply the normalizing token filters.initReaderprotected Reader initReader(String fieldName, Reader reader)Override this if you want to add a CharFilter chain. The default implementation returns reader unchanged.Parameters:fieldName - IndexableField name being indexedreader - original ReaderReturns:reader, optionally decorated with CharFilter(s)initReaderForNormalizationprotected Reader initReaderForNormalization(String fieldName, Reader reader)Wrap the given Reader with CharFilters that make sense for normalization. This is typically a subset of the CharFilters that are applied in initReader(String, Reader). This is used by normalize(String, String).attributeFactoryprotected AttributeFactory attributeFactory(String fieldName)Return the AttributeFactory to be used for analysis and normalization on the given FieldName. The default implementation returns TokenStream.DEFAULT_TOKEN_ATTRIBUTE_FACTORY.getPositionIncrementGappublic int getPositionIncrementGap(String fieldName)Invoked before indexing a IndexableField instance if terms have already been added to that field. This allows custom analyzers to place an automatic position increment gap between IndexbleField instances using the same field name. The default value position increment gap is 0. With a 0 position increment gap and the typical default token position increment of 1, all terms in a field, including across IndexableField instances, are in successive positions, allowing exact PhraseQuery matches, for instance, across IndexableField instance boundaries.Parameters:fieldName - IndexableField name being indexed.Returns:position increment gap, added to the next token emitted from tokenStream(String,Reader). This value must be >= 0.getOffsetGappublic int getOffsetGap(String fieldName)Just like getPositionIncrementGap(java.lang.String), except for Token offsets instead. By default this returns 1. This method is only called if the field produced at least one token for indexing.Parameters:fieldName - the field just indexedReturns:offset gap, added to the next token emitted from tokenStream(String,Reader). This value must be >= 0.getReuseStrategypublic final Analyzer.ReuseStrategy getReuseStrategy()Returns the used Analyzer.ReuseStrategy.setVersionpublic void setVersion(Version v)Set the version of Lucene this analyzer should mimic the behavior for for analysis.getVersionpublic Version getVersion()Return the version of Lucene this analyzer will mimic the behavior of for analysis.closepublic void close()Frees persistent resources used by this AnalyzerSpecified by:close in interface CloseableSpecified by:close in interface AutoCloseableSkip navigation linksOverviewPackageClassUseTreeDeprecatedHelpPrev ClassNext ClassFramesNo FramesAll ClassesSummary: Nested | Field | Constr | MethodDetail: Field | Constr | Method Copyright 2000-2017 Apache Software Foundation. All Rights Reserved. e24fc04721