Module datarush.library
Package com.pervasive.datarush.operators.string

Class SplitField

  • All Implemented Interfaces:
    LogicalOperator, PipelineOperator<RecordPort>, RecordPipelineOperator
    public class SplitField
extends AbstractExecutableRecordPipeline
    Splits a string field into multiple fields, based on a specified pattern.

    The SplitField operator has three properties:

    • Split Field - The name of the field to split. The field must be of type String.
    • Split Pattern - A regular expression describing the delimiter used for splitting.
    • Result Mapping - A map of integers to strings, detailed below.

    The contents of the split field will be split using the defined split pattern, resulting in an array of substrings. The key of the result mapping corresponds to an index within this array, and the associated value defines the output field in which to place the substring.

    For example, if you had a record with a field named time containing times in the format of 18:30:00, you could use the following SplitField operator to split the time into hour, minute, and second fields.

    HashMap<Integer,String> map = new HashMap<Integer,String>();
    map.put(0,"hour");
    map.put(1,"minute");
    map.put(2,"second");
    SplitField splitter = new SplitField("time",":",map);

    • Constructor Detail

      • SplitField

        public SplitField()
        Construct the operator with no properties set.

        The split pattern defaults to whitespace. The other properties (split field and result mapping) must be set manually.

    • Method Detail

      • setSplitField

        public void setSplitField​(String splitField)
        Set the string field to be split.

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

        Parameters:
        splitField - The name of the field to be split.

      • getSplitField

        public String getSplitField()
        Get the string field to be split.
        Returns:
        The name of the field to be split.

      • setSplitPattern

        public void setSplitPattern​(String splitPattern)
        Set the splitting pattern.

        The pattern should be expressed as a regular expression. The default value matches any whitespace.

        Parameters:
        splitPattern - The splitting pattern.
        Throws:
        com.pervasive.datarush.graphs.physical.InvalidPropertyValueException - If the given pattern is not a valid regular expression.
        See Also:
        String.split(String)

      • getSplitPattern

        public String getSplitPattern()
        Get the splitting pattern.
        Returns:
        The splitting pattern.

      • setResultMapping

        public void setResultMapping​(Map<Integer,​String> resultMapping)
        Set the mapping of split indices to output field names.

        The key of each entry represents an index in the array resulting from splitting the input string, and the value represents the name of the output field in which to store that substring.

        It is not necessary for every array index to be mapped, or for every mapped index to exist in each split. If a value does not exist at a mapped index for a particular split, an empty string will be placed in the specified output field.

        If an output field already exists in the input, or if a single output field is mapped to multiple indices, an exception will be thrown at composition time.

        Parameters:
        resultMapping - The mapping of indices to field names.

      • getResultMapping

        public Map<Integer,​String> getResultMapping()
        Get the mapping of split indices to output field names.
        Returns:
        The mapping of indices to field names.
        See Also:
        setResultMapping(Map)

      • computeMetadata

        public 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