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

Class GenerateArithmeticSequence

java.lang.Object
com.pervasive.datarush.operators.AbstractLogicalOperator
com.pervasive.datarush.operators.StreamingOperator
com.pervasive.datarush.operators.ExecutableOperator
com.pervasive.datarush.operators.source.GenerateArithmeticSequence
All Implemented Interfaces:
LogicalOperator, RecordSourceOperator, SourceOperator<RecordPort>
public final class GenerateArithmeticSequence extends ExecutableOperator implements RecordSourceOperator
Generates a sequence of numerical values, with a constant difference between consecutive values.

NOTE: this operator is non-parallel

  • Constructor Details

    • GenerateArithmeticSequence

      public GenerateArithmeticSequence()
      Default constructor. Prior to graph compilation the following property should be set:

    • GenerateArithmeticSequence

      public GenerateArithmeticSequence(long rowCount)
      Generate an arithmetic sequence of the given number of rows. By default we will generate a sequence of type long.
      Parameters:
      rowCount - the number of rows

    • GenerateArithmeticSequence

      public GenerateArithmeticSequence(long rowCount, RecordTokenType outputType)
      Generate an arithmetic sequence of the given number of rows and type.
      Parameters:
      rowCount - the number of rows.
      outputType - the output type. Generally will be a record type of size 1.

  • Method Details

    • setStartValue

      public void setStartValue(BigDecimal startValue)
      Sets the value of the first token in the sequence.
      Parameters:
      startValue - Value of the first token to generate

    • getStartValue

      public BigDecimal getStartValue()
      Gets the first value to generate.
      Returns:
      the first value to generate

    • setStepSize

      public void setStepSize(BigDecimal stepSize)
      Sets the difference between consecutive tokens in the generated sequence.
      Parameters:
      stepSize - Difference between consecutive generated tokens

    • getStepSize

      public BigDecimal getStepSize()
      Gets the difference between consecutive generated values.
      Returns:
      the difference between consecutive generated values

    • setRowCount

      public void setRowCount(long rowCount)
      Sets the number of rows to output.
      Parameters:
      rowCount - the number of rows to output

    • getRowCount

      public long getRowCount()
      Gets the number of rows to output
      Returns:
      the number of rows to output.

    • getOutputType

      public RecordTokenType getOutputType()
      Returns the output type. By default this is a record of a single field named "field" whose type is long.
      Returns:
      the output type

    • setOutputType

      public void setOutputType(RecordTokenType outputType)
      Sets the output type. By default this is a record of a single field named "field" whose type is long.
      Parameters:
      outputType - the output type

    • getOutput

      public RecordPort getOutput()
      Returns the output port whose values are equal to the sequence of values.
      Specified by:
      getOutput in interface RecordSourceOperator
      Specified by:
      getOutput in interface SourceOperator<RecordPort>
      Returns:
      the output port for the source

    • 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