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

Class DeriveFields

  • All Implemented Interfaces:
    LogicalOperator, PipelineOperator<RecordPort>, RecordPipelineOperator
    public final class DeriveFields
extends AbstractExecutableRecordPipeline
    Applies one or more functions to the input record data. One output field is generated per function. The result is an output record flow that contains the input data plus the function results. It is possible to overwrite existing fields with derived values. It is also possible to omit input fields in the result, effectively applying a complete transform to the record.

    Applying multiple functions to an input record flow within a single dataflow process can be more efficient than applying each function in its own process. This is due to many factors, but mainly: preventing processor cache thrashing, saving data copies and lowering thread context switching.

    • Constructor Detail

      • DeriveFields

        public DeriveFields()
        Applies no functions to the input records. This effectively copies records from the input to the output. Use setDerivedFields(FieldDerivation...) to set the functions to apply.

      • DeriveFields

        public DeriveFields​(String derivationExpression)
        Applies the specified derivations to all input records. All input fields are present in the output, containing the same value unless explicitly replaced by a derivation. If multiple derivations apply to an output field, the last one defined is used.
        Parameters:
        derivations - the expression containing field derivations to apply

      • DeriveFields

        public DeriveFields​(List<FieldDerivation> derivations)
        Applies the specified derivations to all input records. All input fields are present in the output, containing the same value unless explicitly replaced by a derivation. If multiple derivations apply to an output field, the last one defined is used.
        Parameters:
        derivations - the field derivations to apply

      • DeriveFields

        public DeriveFields​(FieldDerivation... derivations)
        Applies the specified derivations to all input records. All input fields are present in the output, containing the same value unless explicitly replaced by a derivation. If multiple derivations apply to an output field, the last one defined is used.
        Parameters:
        derivations - the field derivations to apply

      • DeriveFields

        public DeriveFields​(String derivationExpression,
                    boolean dropUnderived)
        Applies the specified derivations to all input records. If requested, input fields will not be automatically copied to the output. If multiple derivations apply to an output field, the last one defined is used.
        Parameters:
        derivationExpression - the expression containing field derivations to apply
        dropUnderived - true if input fields should be dropped; false otherwise

      • DeriveFields

        public DeriveFields​(List<FieldDerivation> derivations,
                    boolean dropUnderived)
        Applies the specified derivations to all input records. If requested, input fields will not be automatically copied to the output. If multiple derivations apply to an output field, the last one defined is used.
        Parameters:
        derivations - the field derivations to apply
        dropUnderived - true if input fields should be dropped; false otherwise

    • Method Detail

      • setDerivedFields

        public void setDerivedFields​(String derivationExpression)
        Set the list of field derivations to apply, using a field derivation expression. If multiple derivations apply to an output field, the last one defined is used.
        Parameters:
        derivations - the field derivations to apply

      • setDerivedFields

        public void setDerivedFields​(List<FieldDerivation> derivations)
        Set the list of field derivations to apply. Derivations can be easily constructed using the convenience method FieldDerivation.derive(String, ScalarValuedFunction). If multiple derivations apply to an output field, the last one defined is used.
        Parameters:
        derivations - the field derivations to apply

      • setDerivedFields

        public void setDerivedFields​(FieldDerivation... derivations)
        Set the list of field derivations to apply. Derivations can be easily constructed using the convenience method FieldDerivation.derive(String, ScalarValuedFunction). If multiple derivations apply to an output field, the last one defined is used.
        Parameters:
        derivations - the field derivations to apply

      • getDerivedFields

        public List<FieldDerivation> getDerivedFields()
        Get the list of derivations that will be applied.
        Returns:
        the field derivations to apply

      • getDropUnderivedFields

        public boolean getDropUnderivedFields()
        Indicates whether input fields are dropped from the output. That is, whether the output consists solely of derived fields.
        Returns:
        true if non-derived fields are dropped; false otherwise

      • setDropUnderivedFields

        public void setDropUnderivedFields​(boolean dropUnderived)
        Set whether input fields are dropped from the output. If set to true only derived fields are included in the output.

        This value is false by default.

        Parameters:
        dropUnderived - indicates whether to drop input fields from the output

      • 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