Module datarush.analytics
Package com.pervasive.datarush.analytics.text

Class TextTokenizer

java.lang.Object
com.pervasive.datarush.operators.AbstractLogicalOperator
com.pervasive.datarush.operators.StreamingOperator
com.pervasive.datarush.operators.ExecutableOperator
com.pervasive.datarush.analytics.text.TextTokenizer
All Implemented Interfaces:
LogicalOperator, PipelineOperator<RecordPort>, RecordPipelineOperator
public class TextTokenizer extends ExecutableOperator implements RecordPipelineOperator
Tokenizes a string field as a TokenizedText object. This operator can be used to tokenize a String field as an object that can then be used for a variety of other text processing tasks. The TextTokenizer operator has two properties: input field and output field. The input field must be of type string while the output field will be of type object. The contents of the string field will be parsed and tokenized creating a TokenizedText object that will be encoded into the output field. This object can then be used for various other processing tasks.

  • Constructor Details

    • TextTokenizer

      public TextTokenizer()
      Default constructor. Use setInputField(String) and setOutputField(String) to set the name of the text field to tokenize and its output field.

    • TextTokenizer

      public TextTokenizer(String textField)
      Constructor specifying the string field to tokenize.
      Parameters:
      textField - The name of the field to tokenize

  • Method Details

    • addWordPattern

      public void addWordPattern(String wordPattern)
      Add a pattern that should be recognized as a single word.
      Parameters:
      wordPattern - A regular expression

    • setWordPatterns

      public void setWordPatterns(List<String> wordPatterns)
      Set the list of custom word patterns that will be recognized.
      Parameters:
      wordPatterns - The list of word patterns

    • getWordPatterns

      public List<String> getWordPatterns()
      Get the list of custom word patterns that will be recognized.
      Returns:
      list of word patterns

    • setInputField

      public void setInputField(String textField)
      Set the string field to tokenize.

      If this field does not exist in the input, or is not of type String, an exception will be thrown at composition time.

      Parameters:
      textField - The name of the field to tokenize

    • getInputField

      public String getInputField()
      Get the string field to tokenize.
      Returns:
      The name of the field to tokenize

    • setOutputField

      public void setOutputField(String tokenField)
      Set the tokenized output field.
      Parameters:
      tokenField - The name of the token output field

    • getOutputField

      public String getOutputField()
      Get the tokenized output field.
      Returns:
      The name of the token output field

    • getInput

      public RecordPort getInput()
      Get the input port of this operator.
      Specified by:
      getInput in interface PipelineOperator<RecordPort>
      Returns:
      input port

    • getOutput

      public RecordPort getOutput()
      Get the output port of this operator.
      Specified by:
      getOutput in interface PipelineOperator<RecordPort>
      Returns:
      output port

    • computeMetadata

      protected void computeMetadata(StreamingMetadataContext ctx)
      Description copied from class: StreamingOperator
      Implementations must adhere to the following contracts

      General

      Regardless of input ports/output port types, all implementations must do the following:

      1. Validation. Validation of configuration should always be performed first.
      2. Declare parallelizability.. Implementations must declare parallelizability by calling StreamingMetadataContext.parallelize(ParallelismStrategy).

      Input record ports

      Implementations with input record ports must declare the following:
      1. Required data ordering:
        2. Implementations that have data ordering requirements must declare them by calling RecordPort#setRequiredDataOrdering, otherwise data may arrive in any order.
      2. Required data distribution (only applies to parallelizable operators):
        3. Implementations that have data distribution requirements must declare them by calling RecordPort#setRequiredDataDistribution, otherwise data will arrive in an unspecified partial distribution.
      Note that if the upstream operator's output distribution/ordering is compatible with those required, we avoid a re-sort/re-distribution which is generally a very large savings from a performance standpoint. In addition, some operators may chose to query the upstream output distribution/ordering by calling RecordPort#getSourceDataDistribution and RecordPort#getSourceDataOrdering. These should be viewed as a hints to help chose a more efficient algorithm. In such cases, though, operators must still declare data ordering and data distribution requirements; otherwise there is no guarantee that data will arrive sorted/distributed as required.

      Output record ports

      Implementations with output record ports must declare the following:
      1. Type: Implementations must declare their output type by calling RecordPort#setType.
      Implementations with output record ports may declare the following:
      1. Output data ordering: Implementations that can make guarantees as to their output ordering may do so by calling RecordPort#setOutputDataOrdering
      2. Output data distribution (only applies to parallelizable operators): Implementations that can make guarantees as to their output distribution may do so by calling RecordPort#setOutputDataDistribution
      Note that both of these properties are optional; if unspecified, performance may suffer since the framework may unnecessarily re-sort/re-distributed the data.

      Input model ports

      In general, there is nothing special to declare for input model ports. Models are implicitly duplicated to all partitions when going from non-parallel to parallel operators. The case of a model going from a parallel to a non-parallel node is a special case of a "model reducer" operator. In the case of a model reducer, the downstream operator, must declare the following:
      1. Merge handler: Model reducers must declare a merge handler by calling AbstractModelPort#setMergeHandler.
      Note that MergeModel is a convenient, re-usable model reducer, parameterized with a merge-handler.

      Output model ports

      SimpleModelPort's have no associated metadata and therefore there is never any output metadata to declare. PMMLPort's, on the other hand, do have associated metadata. For all PMMLPorts, implementations must declare the following:
      1. pmmlModelSpec: Implementations must declare the PMML model spec by calling PMMLPort.setPMMLModelSpec.
      Specified by:
      computeMetadata in class StreamingOperator
      Parameters:
      ctx - the context

    • execute

      protected void execute(ExecutionContext ctx)
      Description copied from class: ExecutableOperator
      Executes the operator. Implementations should adhere to the following contracts:
      1. Following execution, all input ports must be at end-of-data.
      2. Following execution, all output ports must be at end-of-data.
      Specified by:
      execute in class ExecutableOperator
      Parameters:
      ctx - context in which to lookup physical ports bound to logical ports