org.knime.core.node.workflow.execresult
Class WorkflowExecutionResult
java.lang.Object
org.knime.core.node.workflow.execresult.NodeContainerExecutionResult
org.knime.core.node.workflow.execresult.WorkflowExecutionResult
- All Implemented Interfaces:
- NodeContainerExecutionStatus
public class WorkflowExecutionResult
- extends NodeContainerExecutionResult
Specialized execution result for WorkflowManager
. Offers access
to all contained node's execution result.
- Author:
- Bernd Wiswedel, University of Konstanz
WorkflowExecutionResult
public WorkflowExecutionResult(NodeID baseID)
- Creates new workflow execution result with no particular settings.
- Parameters:
baseID
- The node id of the workflow (the loading procedure in
the target workflow will correct the prefix).
- Throws:
NullPointerException
- If the argument is null.
getBaseID
public NodeID getBaseID()
- Returns:
- The base id of the workflow. Used to amend the node ids in
getExecutionResultMap()
.
getExecutionResultMap
public Map<NodeID,NodeContainerExecutionResult> getExecutionResultMap()
- Returns:
- The map containing node id to their execution result,
never null.
addNodeExecutionResult
public boolean addNodeExecutionResult(NodeID id,
NodeContainerExecutionResult execResult)
- Adds the execution result for a child node.
- Parameters:
id
- The node id of the child, it must have the "correct" prefix.execResult
- The execution result for the child
- Returns:
true
if this map did not contain an entry for
this child before.
- Throws:
IllegalArgumentException
- If the id prefix is invalid
NullPointerException
- If either argument is null
getChildStatus
public NodeContainerExecutionStatus getChildStatus(int idSuffix)
- Query the execution status for a child given its
node id suffix. If the child is unknown,
the implementation should return
NodeContainerExecutionStatus.FAILURE
.
- Parameters:
idSuffix
- The child id suffix
- Returns:
- The child execution status.
Copyright, 2003 - 2010. All rights reserved.
University of Konstanz, Germany.
Chair for Bioinformatics and Information Mining, Prof. Dr. Michael R. Berthold.
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