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 Summary

Constructors

Constructor and Description
GenerateArithmeticSequence() Default constructor.
GenerateArithmeticSequence(long rowCount) Generate an arithmetic sequence of the given number of rows.
GenerateArithmeticSequence(long rowCount, RecordTokenType outputType) Generate an arithmetic sequence of the given number of rows and type.

Method Summary

Methods

Modifier and Type	Method and Description
protected void	computeMetadata(StreamingMetadataContext ctx) Implementations must adhere to the following contracts
protected void	execute(ExecutionContext ctx) Executes the operator.
RecordPort	getOutput() Returns the output port whose values are equal to the sequence of values.
RecordTokenType	getOutputType() Returns the output type.
long	getRowCount() Gets the number of rows to output
BigDecimal	getStartValue() Gets the first value to generate.
BigDecimal	getStepSize() Gets the difference between consecutive generated values.
void	setOutputType(RecordTokenType outputType) Sets the output type.
void	setRowCount(long rowCount) Sets the number of rows to output.
void	setStartValue(BigDecimal startValue) Sets the value of the first token in the sequence.
void	setStepSize(BigDecimal stepSize) Sets the difference between consecutive tokens in the generated sequence.

Methods inherited from class com.pervasive.datarush.operators.ExecutableOperator

cloneForExecution, getNumInputCopies, getPortSettings, handleInactiveOutput

Methods inherited from class com.pervasive.datarush.operators.AbstractLogicalOperator

disableParallelism, getInputPorts, getOutputPorts, newInput, newInput, newOutput, newRecordInput, newRecordInput, newRecordOutput, notifyError

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface com.pervasive.datarush.operators.LogicalOperator

disableParallelism, getInputPorts, getOutputPorts

Constructor Detail

GenerateArithmeticSequence

public GenerateArithmeticSequence()

Default constructor. Prior to graph compilation the following property should be set:

• setRowCount(long)

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 Detail

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:
Implementations that have data ordering requirements must declare them by calling RecordPort.setRequiredDataOrdering(com.pervasive.datarush.operators.MetadataCalculationContext, com.pervasive.datarush.ports.record.DataOrdering), otherwise data may arrive in any order.
2. Required data distribution (only applies to parallelizable operators):
Implementations that have data distribution requirements must declare them by calling RecordPort.setRequiredDataDistribution(com.pervasive.datarush.operators.MetadataCalculationContext, com.pervasive.datarush.ports.record.DataDistribution), 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(com.pervasive.datarush.operators.MetadataContext) and RecordPort.getSourceDataOrdering(com.pervasive.datarush.operators.MetadataContext). 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(com.pervasive.datarush.operators.MetadataCalculationContext, com.pervasive.datarush.types.RecordTokenType).

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(com.pervasive.datarush.operators.MetadataCalculationContext, com.pervasive.datarush.ports.record.DataOrdering)
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(com.pervasive.datarush.operators.MetadataCalculationContext, com.pervasive.datarush.ports.record.DataDistribution)

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(com.pervasive.datarush.operators.MetadataCalculationContext, com.pervasive.datarush.ports.model.ModelMergeHandler<T>).

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