com.pervasive.datarush.operators.io.paraccel

Class LoadMatrixDataWorker

java.lang.Object

com.pervasive.datarush.operators.AbstractLogicalOperator

com.pervasive.datarush.operators.StreamingOperator

com.pervasive.datarush.operators.ExecutableOperator

com.pervasive.datarush.operators.io.paraccel.LoadMatrixDataWorker

All Implemented Interfaces:

LogicalOperator

public class LoadMatrixDataWorker
extends ExecutableOperator

Field Summary

Fields

Modifier and Type	Field and Description
protected static org.apache.commons.logging.Log	logger

Constructor Summary

Constructors

Constructor and Description
LoadMatrixDataWorker(com.pervasive.datarush.operators.io.paraccel.TableInfo tableInfo, List<String> columns, Map<String,String> columnMap, int logFrequency, int retryCount, int startPortRange, int endPortRange, JDBCConnector connector)

Method Summary

Methods

Modifier and Type	Method and Description
protected void	cancelQuery(int queryPID)
protected void	computeMetadata(StreamingMetadataContext ctx) Implementations must adhere to the following contracts
protected void	execute(ExecutionContext ctx) Executes the operator.
Map<String,String>	getColumnMap()
List<String>	getColumns()
JDBCConnector	getConnector()
RecordPort	getDataInput()
DoneSignalPort	getDoneSignalPort()
int	getLogFrequency()
int	getRetryCount()
SimpleModelPort<com.pervasive.datarush.operators.io.paraccel.SliceInfo>	getSliceInput()
com.pervasive.datarush.operators.io.paraccel.TableInfo	getTableInfo()

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

Field Detail

logger

protected static final org.apache.commons.logging.Log logger

Constructor Detail

LoadMatrixDataWorker

public LoadMatrixDataWorker(com.pervasive.datarush.operators.io.paraccel.TableInfo tableInfo,
                    List<String> columns,
                    Map<String,String> columnMap,
                    int logFrequency,
                    int retryCount,
                    int startPortRange,
                    int endPortRange,
                    JDBCConnector connector)

Method Detail

getConnector

public JDBCConnector getConnector()

cancelQuery

protected void cancelQuery(int queryPID)

getDataInput

public RecordPort getDataInput()

getSliceInput

public SimpleModelPort<com.pervasive.datarush.operators.io.paraccel.SliceInfo> getSliceInput()

getDoneSignalPort

public DoneSignalPort getDoneSignalPort()

getTableInfo

public com.pervasive.datarush.operators.io.paraccel.TableInfo getTableInfo()

getColumns

public List<String> getColumns()

getColumnMap

public Map<String,String> getColumnMap()

getLogFrequency

public int getLogFrequency()

getRetryCount

public int getRetryCount()

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