/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.flink.runtime.executiongraph;
import org.apache.flink.annotation.VisibleForTesting;
import org.apache.flink.api.common.Archiveable;
import org.apache.flink.api.common.JobID;
import org.apache.flink.api.common.time.Time;
import org.apache.flink.runtime.JobException;
import org.apache.flink.runtime.concurrent.Future;
import org.apache.flink.runtime.deployment.InputChannelDeploymentDescriptor;
import org.apache.flink.runtime.deployment.InputGateDeploymentDescriptor;
import org.apache.flink.runtime.deployment.PartialInputChannelDeploymentDescriptor;
import org.apache.flink.runtime.deployment.ResultPartitionDeploymentDescriptor;
import org.apache.flink.runtime.deployment.TaskDeploymentDescriptor;
import org.apache.flink.runtime.execution.ExecutionState;
import org.apache.flink.runtime.instance.SimpleSlot;
import org.apache.flink.runtime.instance.SlotProvider;
import org.apache.flink.runtime.io.network.partition.ResultPartitionID;
import org.apache.flink.runtime.io.network.partition.ResultPartitionType;
import org.apache.flink.runtime.jobgraph.DistributionPattern;
import org.apache.flink.runtime.jobgraph.IntermediateDataSetID;
import org.apache.flink.runtime.jobgraph.IntermediateResultPartitionID;
import org.apache.flink.runtime.jobgraph.JobEdge;
import org.apache.flink.runtime.jobgraph.JobVertexID;
import org.apache.flink.configuration.JobManagerOptions;
import org.apache.flink.runtime.jobmanager.scheduler.CoLocationConstraint;
import org.apache.flink.runtime.jobmanager.scheduler.CoLocationGroup;
import org.apache.flink.runtime.state.KeyGroupRangeAssignment;
import org.apache.flink.runtime.state.TaskStateHandles;
import org.apache.flink.runtime.taskmanager.TaskManagerLocation;
import org.apache.flink.runtime.util.EvictingBoundedList;
import org.apache.flink.util.ExceptionUtils;
import org.apache.flink.util.Preconditions;
import org.apache.flink.util.SerializedValue;
import org.slf4j.Logger;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashSet;
import java.util.LinkedHashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.Set;
import static org.apache.flink.runtime.execution.ExecutionState.FINISHED;
/**
* The ExecutionVertex is a parallel subtask of the execution. It may be executed once, or several times, each of
* which time it spawns an {@link Execution}.
*/
public class ExecutionVertex implements AccessExecutionVertex, Archiveable<ArchivedExecutionVertex> {
private static final Logger LOG = ExecutionGraph.LOG;
private static final int MAX_DISTINCT_LOCATIONS_TO_CONSIDER = 8;
// --------------------------------------------------------------------------------------------
private final ExecutionJobVertex jobVertex;
private final Map<IntermediateResultPartitionID, IntermediateResultPartition> resultPartitions;
private final ExecutionEdge[][] inputEdges;
private final int subTaskIndex;
private final EvictingBoundedList<Execution> priorExecutions;
private final Time timeout;
/** The name in the format "myTask (2/7)", cached to avoid frequent string concatenations */
private final String taskNameWithSubtask;
private volatile CoLocationConstraint locationConstraint;
/** The current or latest execution attempt of this vertex's task */
private volatile Execution currentExecution; // this field must never be null
// --------------------------------------------------------------------------------------------
/**
* Convenience constructor for tests. Sets various fields to default values.
*/
@VisibleForTesting
ExecutionVertex(
ExecutionJobVertex jobVertex,
int subTaskIndex,
IntermediateResult[] producedDataSets,
Time timeout) {
this(
jobVertex,
subTaskIndex,
producedDataSets,
timeout,
1L,
System.currentTimeMillis(),
JobManagerOptions.MAX_ATTEMPTS_HISTORY_SIZE.defaultValue());
}
/**
*
* @param timeout
* The RPC timeout to use for deploy / cancel calls
* @param initialGlobalModVersion
* The global modification version to initialize the first Execution with.
* @param createTimestamp
* The timestamp for the vertex creation, used to initialize the first Execution with.
* @param maxPriorExecutionHistoryLength
* The number of prior Executions (= execution attempts) to keep.
*/
public ExecutionVertex(
ExecutionJobVertex jobVertex,
int subTaskIndex,
IntermediateResult[] producedDataSets,
Time timeout,
long initialGlobalModVersion,
long createTimestamp,
int maxPriorExecutionHistoryLength) {
this.jobVertex = jobVertex;
this.subTaskIndex = subTaskIndex;
this.taskNameWithSubtask = String.format("%s (%d/%d)",
jobVertex.getJobVertex().getName(), subTaskIndex + 1, jobVertex.getParallelism());
this.resultPartitions = new LinkedHashMap<>(producedDataSets.length, 1);
for (IntermediateResult result : producedDataSets) {
IntermediateResultPartition irp = new IntermediateResultPartition(result, this, subTaskIndex);
result.setPartition(subTaskIndex, irp);
resultPartitions.put(irp.getPartitionId(), irp);
}
this.inputEdges = new ExecutionEdge[jobVertex.getJobVertex().getInputs().size()][];
this.priorExecutions = new EvictingBoundedList<>(maxPriorExecutionHistoryLength);
this.currentExecution = new Execution(
getExecutionGraph().getFutureExecutor(),
this,
0,
initialGlobalModVersion,
createTimestamp,
timeout);
// create a co-location scheduling hint, if necessary
CoLocationGroup clg = jobVertex.getCoLocationGroup();
if (clg != null) {
this.locationConstraint = clg.getLocationConstraint(subTaskIndex);
}
else {
this.locationConstraint = null;
}
getExecutionGraph().registerExecution(currentExecution);
this.timeout = timeout;
}
// --------------------------------------------------------------------------------------------
// Properties
// --------------------------------------------------------------------------------------------
public JobID getJobId() {
return this.jobVertex.getJobId();
}
public ExecutionJobVertex getJobVertex() {
return jobVertex;
}
public JobVertexID getJobvertexId() {
return this.jobVertex.getJobVertexId();
}
public String getTaskName() {
return this.jobVertex.getJobVertex().getName();
}
/**
* Creates a simple name representation in the style 'taskname (x/y)', where
* 'taskname' is the name as returned by {@link #getTaskName()}, 'x' is the parallel
* subtask index as returned by {@link #getParallelSubtaskIndex()}{@code + 1}, and 'y' is the total
* number of tasks, as returned by {@link #getTotalNumberOfParallelSubtasks()}.
*
* @return A simple name representation in the form 'myTask (2/7)'
*/
@Override
public String getTaskNameWithSubtaskIndex() {
return this.taskNameWithSubtask;
}
public int getTotalNumberOfParallelSubtasks() {
return this.jobVertex.getParallelism();
}
public int getMaxParallelism() {
return this.jobVertex.getMaxParallelism();
}
@Override
public int getParallelSubtaskIndex() {
return this.subTaskIndex;
}
public int getNumberOfInputs() {
return this.inputEdges.length;
}
public ExecutionEdge[] getInputEdges(int input) {
if (input < 0 || input >= this.inputEdges.length) {
throw new IllegalArgumentException(String.format("Input %d is out of range [0..%d)", input, this.inputEdges.length));
}
return inputEdges[input];
}
public CoLocationConstraint getLocationConstraint() {
return locationConstraint;
}
@Override
public Execution getCurrentExecutionAttempt() {
return currentExecution;
}
@Override
public ExecutionState getExecutionState() {
return currentExecution.getState();
}
@Override
public long getStateTimestamp(ExecutionState state) {
return currentExecution.getStateTimestamp(state);
}
@Override
public String getFailureCauseAsString() {
return ExceptionUtils.stringifyException(getFailureCause());
}
public Throwable getFailureCause() {
return currentExecution.getFailureCause();
}
public SimpleSlot getCurrentAssignedResource() {
return currentExecution.getAssignedResource();
}
@Override
public TaskManagerLocation getCurrentAssignedResourceLocation() {
return currentExecution.getAssignedResourceLocation();
}
@Override
public Execution getPriorExecutionAttempt(int attemptNumber) {
synchronized (priorExecutions) {
if (attemptNumber >= 0 && attemptNumber < priorExecutions.size()) {
return priorExecutions.get(attemptNumber);
} else {
throw new IllegalArgumentException("attempt does not exist");
}
}
}
/**
* Gets the location where the latest completed/canceled/failed execution of the vertex's
* task happened.
*
* @return The latest prior execution location, or null, if there is none, yet.
*/
public TaskManagerLocation getLatestPriorLocation() {
synchronized (priorExecutions) {
final int size = priorExecutions.size();
if (size > 0) {
return priorExecutions.get(size - 1).getAssignedResourceLocation();
}
else {
return null;
}
}
}
EvictingBoundedList<Execution> getCopyOfPriorExecutionsList() {
synchronized (priorExecutions) {
return new EvictingBoundedList<>(priorExecutions);
}
}
public ExecutionGraph getExecutionGraph() {
return this.jobVertex.getGraph();
}
public Map<IntermediateResultPartitionID, IntermediateResultPartition> getProducedPartitions() {
return resultPartitions;
}
// --------------------------------------------------------------------------------------------
// Graph building
// --------------------------------------------------------------------------------------------
public void connectSource(int inputNumber, IntermediateResult source, JobEdge edge, int consumerNumber) {
final DistributionPattern pattern = edge.getDistributionPattern();
final IntermediateResultPartition[] sourcePartitions = source.getPartitions();
ExecutionEdge[] edges;
switch (pattern) {
case POINTWISE:
edges = connectPointwise(sourcePartitions, inputNumber);
break;
case ALL_TO_ALL:
edges = connectAllToAll(sourcePartitions, inputNumber);
break;
default:
throw new RuntimeException("Unrecognized distribution pattern.");
}
this.inputEdges[inputNumber] = edges;
// add the consumers to the source
// for now (until the receiver initiated handshake is in place), we need to register the
// edges as the execution graph
for (ExecutionEdge ee : edges) {
ee.getSource().addConsumer(ee, consumerNumber);
}
}
private ExecutionEdge[] connectAllToAll(IntermediateResultPartition[] sourcePartitions, int inputNumber) {
ExecutionEdge[] edges = new ExecutionEdge[sourcePartitions.length];
for (int i = 0; i < sourcePartitions.length; i++) {
IntermediateResultPartition irp = sourcePartitions[i];
edges[i] = new ExecutionEdge(irp, this, inputNumber);
}
return edges;
}
private ExecutionEdge[] connectPointwise(IntermediateResultPartition[] sourcePartitions, int inputNumber) {
final int numSources = sourcePartitions.length;
final int parallelism = getTotalNumberOfParallelSubtasks();
// simple case same number of sources as targets
if (numSources == parallelism) {
return new ExecutionEdge[] { new ExecutionEdge(sourcePartitions[subTaskIndex], this, inputNumber) };
}
else if (numSources < parallelism) {
int sourcePartition;
// check if the pattern is regular or irregular
// we use int arithmetics for regular, and floating point with rounding for irregular
if (parallelism % numSources == 0) {
// same number of targets per source
int factor = parallelism / numSources;
sourcePartition = subTaskIndex / factor;
}
else {
// different number of targets per source
float factor = ((float) parallelism) / numSources;
sourcePartition = (int) (subTaskIndex / factor);
}
return new ExecutionEdge[] { new ExecutionEdge(sourcePartitions[sourcePartition], this, inputNumber) };
}
else {
if (numSources % parallelism == 0) {
// same number of targets per source
int factor = numSources / parallelism;
int startIndex = subTaskIndex * factor;
ExecutionEdge[] edges = new ExecutionEdge[factor];
for (int i = 0; i < factor; i++) {
edges[i] = new ExecutionEdge(sourcePartitions[startIndex + i], this, inputNumber);
}
return edges;
}
else {
float factor = ((float) numSources) / parallelism;
int start = (int) (subTaskIndex * factor);
int end = (subTaskIndex == getTotalNumberOfParallelSubtasks() - 1) ?
sourcePartitions.length :
(int) ((subTaskIndex + 1) * factor);
ExecutionEdge[] edges = new ExecutionEdge[end - start];
for (int i = 0; i < edges.length; i++) {
edges[i] = new ExecutionEdge(sourcePartitions[start + i], this, inputNumber);
}
return edges;
}
}
}
/**
* Gets the overall preferred execution location for this vertex's current execution.
* The preference is determined as follows:
*
* <ol>
* <li>If the task execution has state to load (from a checkpoint), then the location preference
* is the location of the previous execution (if there is a previous execution attempt).
* <li>If the task execution has no state or no previous location, then the location preference
* is based on the task's inputs.
* </ol>
*
* These rules should result in the following behavior:
*
* <ul>
* <li>Stateless tasks are always scheduled based on co-location with inputs.
* <li>Stateful tasks are on their initial attempt executed based on co-location with inputs.
* <li>Repeated executions of stateful tasks try to co-locate the execution with its state.
* </ul>
*
* @return The preferred excution locations for the execution attempt.
*
* @see #getPreferredLocationsBasedOnState()
* @see #getPreferredLocationsBasedOnInputs()
*/
public Iterable<TaskManagerLocation> getPreferredLocations() {
Iterable<TaskManagerLocation> basedOnState = getPreferredLocationsBasedOnState();
return basedOnState != null ? basedOnState : getPreferredLocationsBasedOnInputs();
}
/**
* Gets the preferred location to execute the current task execution attempt, based on the state
* that the execution attempt will resume.
*
* @return A size-one iterable with the location preference, or null, if there is no
* location preference based on the state.
*/
public Iterable<TaskManagerLocation> getPreferredLocationsBasedOnState() {
TaskManagerLocation priorLocation;
if (currentExecution.getTaskStateHandles() != null && (priorLocation = getLatestPriorLocation()) != null) {
return Collections.singleton(priorLocation);
}
else {
return null;
}
}
/**
* Gets the location preferences of the vertex's current task execution, as determined by the locations
* of the predecessors from which it receives input data.
* If there are more than MAX_DISTINCT_LOCATIONS_TO_CONSIDER different locations of source data, this
* method returns {@code null} to indicate no location preference.
*
* @return The preferred locations based in input streams, or an empty iterable,
* if there is no input-based preference.
*/
public Iterable<TaskManagerLocation> getPreferredLocationsBasedOnInputs() {
// otherwise, base the preferred locations on the input connections
if (inputEdges == null) {
return Collections.emptySet();
}
else {
Set<TaskManagerLocation> locations = new HashSet<>();
Set<TaskManagerLocation> inputLocations = new HashSet<>();
// go over all inputs
for (int i = 0; i < inputEdges.length; i++) {
inputLocations.clear();
ExecutionEdge[] sources = inputEdges[i];
if (sources != null) {
// go over all input sources
for (int k = 0; k < sources.length; k++) {
// look-up assigned slot of input source
SimpleSlot sourceSlot = sources[k].getSource().getProducer().getCurrentAssignedResource();
if (sourceSlot != null) {
// add input location
inputLocations.add(sourceSlot.getTaskManagerLocation());
// inputs which have too many distinct sources are not considered
if (inputLocations.size() > MAX_DISTINCT_LOCATIONS_TO_CONSIDER) {
inputLocations.clear();
break;
}
}
}
}
// keep the locations of the input with the least preferred locations
if (locations.isEmpty() || // nothing assigned yet
(!inputLocations.isEmpty() && inputLocations.size() < locations.size())) {
// current input has fewer preferred locations
locations.clear();
locations.addAll(inputLocations);
}
}
return locations.isEmpty() ? Collections.<TaskManagerLocation>emptyList() : locations;
}
}
// --------------------------------------------------------------------------------------------
// Actions
// --------------------------------------------------------------------------------------------
/**
* Archives the current Execution and creates a new Execution for this vertex.
*
* <p>This method atomically checks if the ExecutionGraph is still of an expected
* global mod. version and replaces the execution if that is the case. If the ExecutionGraph
* has increased its global mod. version in the meantime, this operation fails.
*
* <p>This mechanism can be used to prevent conflicts between various concurrent recovery and
* reconfiguration actions in a similar way as "optimistic concurrency control".
*
* @param timestamp
* The creation timestamp for the new Execution
* @param originatingGlobalModVersion
* The
*
* @return Returns the new created Execution.
*
* @throws GlobalModVersionMismatch Thrown, if the execution graph has a new global mod
* version than the one passed to this message.
*/
public Execution resetForNewExecution(final long timestamp, final long originatingGlobalModVersion)
throws GlobalModVersionMismatch
{
LOG.debug("Resetting execution vertex {} for new execution.", getTaskNameWithSubtaskIndex());
synchronized (priorExecutions) {
// check if another global modification has been triggered since the
// action that originally caused this reset/restart happened
final long actualModVersion = getExecutionGraph().getGlobalModVersion();
if (actualModVersion > originatingGlobalModVersion) {
// global change happened since, reject this action
throw new GlobalModVersionMismatch(originatingGlobalModVersion, actualModVersion);
}
final Execution oldExecution = currentExecution;
final ExecutionState oldState = oldExecution.getState();
if (oldState.isTerminal()) {
priorExecutions.add(oldExecution);
final Execution newExecution = new Execution(
getExecutionGraph().getFutureExecutor(),
this,
oldExecution.getAttemptNumber() + 1,
originatingGlobalModVersion,
timestamp,
timeout);
this.currentExecution = newExecution;
CoLocationGroup grp = jobVertex.getCoLocationGroup();
if (grp != null) {
this.locationConstraint = grp.getLocationConstraint(subTaskIndex);
}
// register this execution at the execution graph, to receive call backs
getExecutionGraph().registerExecution(newExecution);
// if the execution was 'FINISHED' before, tell the ExecutionGraph that
// we take one step back on the road to reaching global FINISHED
if (oldState == FINISHED) {
getExecutionGraph().vertexUnFinished();
}
return newExecution;
}
else {
throw new IllegalStateException("Cannot reset a vertex that is in non-terminal state " + oldState);
}
}
}
public boolean scheduleForExecution(SlotProvider slotProvider, boolean queued) {
return this.currentExecution.scheduleForExecution(slotProvider, queued);
}
public void deployToSlot(SimpleSlot slot) throws JobException {
this.currentExecution.deployToSlot(slot);
}
/**
*
* @return A future that completes once the execution has reached its final state.
*/
public Future<ExecutionState> cancel() {
// to avoid any case of mixup in the presence of concurrent calls,
// we copy a reference to the stack to make sure both calls go to the same Execution
final Execution exec = this.currentExecution;
exec.cancel();
return exec.getTerminationFuture();
}
public void stop() {
this.currentExecution.stop();
}
public void fail(Throwable t) {
this.currentExecution.fail(t);
}
/**
* Schedules or updates the consumer tasks of the result partition with the given ID.
*/
void scheduleOrUpdateConsumers(ResultPartitionID partitionId) {
final Execution execution = currentExecution;
// Abort this request if there was a concurrent reset
if (!partitionId.getProducerId().equals(execution.getAttemptId())) {
return;
}
final IntermediateResultPartition partition = resultPartitions.get(partitionId.getPartitionId());
if (partition == null) {
throw new IllegalStateException("Unknown partition " + partitionId + ".");
}
if (partition.getIntermediateResult().getResultType().isPipelined()) {
// Schedule or update receivers of this partition
execution.scheduleOrUpdateConsumers(partition.getConsumers());
}
else {
throw new IllegalArgumentException("ScheduleOrUpdateConsumers msg is only valid for" +
"pipelined partitions.");
}
}
public void cachePartitionInfo(PartialInputChannelDeploymentDescriptor partitionInfo){
getCurrentExecutionAttempt().cachePartitionInfo(partitionInfo);
}
void sendPartitionInfos() {
currentExecution.sendPartitionInfos();
}
/**
* Returns all blocking result partitions whose receivers can be scheduled/updated.
*/
List<IntermediateResultPartition> finishAllBlockingPartitions() {
List<IntermediateResultPartition> finishedBlockingPartitions = null;
for (IntermediateResultPartition partition : resultPartitions.values()) {
if (partition.getResultType().isBlocking() && partition.markFinished()) {
if (finishedBlockingPartitions == null) {
finishedBlockingPartitions = new LinkedList<IntermediateResultPartition>();
}
finishedBlockingPartitions.add(partition);
}
}
if (finishedBlockingPartitions == null) {
return Collections.emptyList();
}
else {
return finishedBlockingPartitions;
}
}
// --------------------------------------------------------------------------------------------
// Notifications from the Execution Attempt
// --------------------------------------------------------------------------------------------
void executionFinished(Execution execution) {
getExecutionGraph().vertexFinished();
}
void executionCanceled(Execution execution) {
// nothing to do
}
void executionFailed(Execution execution, Throwable cause) {
// nothing to do
}
// --------------------------------------------------------------------------------------------
// Miscellaneous
// --------------------------------------------------------------------------------------------
/**
* Simply forward this notification
*/
void notifyStateTransition(Execution execution, ExecutionState newState, Throwable error) {
// only forward this notification if the execution is still the current execution
// otherwise we have an outdated execution
if (currentExecution == execution) {
getExecutionGraph().notifyExecutionChange(execution, newState, error);
}
}
/**
* Creates a task deployment descriptor to deploy a subtask to the given target slot.
*
* TODO: This should actually be in the EXECUTION
*/
TaskDeploymentDescriptor createDeploymentDescriptor(
ExecutionAttemptID executionId,
SimpleSlot targetSlot,
TaskStateHandles taskStateHandles,
int attemptNumber) throws ExecutionGraphException {
// Produced intermediate results
List<ResultPartitionDeploymentDescriptor> producedPartitions = new ArrayList<>(resultPartitions.size());
// Consumed intermediate results
List<InputGateDeploymentDescriptor> consumedPartitions = new ArrayList<>(inputEdges.length);
boolean lazyScheduling = getExecutionGraph().getScheduleMode().allowLazyDeployment();
for (IntermediateResultPartition partition : resultPartitions.values()) {
List<List<ExecutionEdge>> consumers = partition.getConsumers();
if (consumers.isEmpty()) {
//TODO this case only exists for test, currently there has to be exactly one consumer in real jobs!
producedPartitions.add(ResultPartitionDeploymentDescriptor.from(
partition,
KeyGroupRangeAssignment.UPPER_BOUND_MAX_PARALLELISM,
lazyScheduling));
} else {
Preconditions.checkState(1 == consumers.size(),
"Only one consumer supported in the current implementation! Found: " + consumers.size());
List<ExecutionEdge> consumer = consumers.get(0);
ExecutionJobVertex vertex = consumer.get(0).getTarget().getJobVertex();
int maxParallelism = vertex.getMaxParallelism();
producedPartitions.add(ResultPartitionDeploymentDescriptor.from(partition, maxParallelism, lazyScheduling));
}
}
for (ExecutionEdge[] edges : inputEdges) {
InputChannelDeploymentDescriptor[] partitions = InputChannelDeploymentDescriptor
.fromEdges(edges, targetSlot, lazyScheduling);
// If the produced partition has multiple consumers registered, we
// need to request the one matching our sub task index.
// TODO Refactor after removing the consumers from the intermediate result partitions
int numConsumerEdges = edges[0].getSource().getConsumers().get(0).size();
int queueToRequest = subTaskIndex % numConsumerEdges;
IntermediateResult consumedIntermediateResult = edges[0].getSource().getIntermediateResult();
final IntermediateDataSetID resultId = consumedIntermediateResult.getId();
final ResultPartitionType partitionType = consumedIntermediateResult.getResultType();
consumedPartitions.add(new InputGateDeploymentDescriptor(resultId, partitionType, queueToRequest, partitions));
}
SerializedValue<JobInformation> serializedJobInformation = getExecutionGraph().getSerializedJobInformation();
SerializedValue<TaskInformation> serializedJobVertexInformation = null;
try {
serializedJobVertexInformation = jobVertex.getSerializedTaskInformation();
} catch (IOException e) {
throw new ExecutionGraphException(
"Could not create a serialized JobVertexInformation for " + jobVertex.getJobVertexId(), e);
}
return new TaskDeploymentDescriptor(
serializedJobInformation,
serializedJobVertexInformation,
executionId,
targetSlot.getAllocatedSlot().getSlotAllocationId(),
subTaskIndex,
attemptNumber,
targetSlot.getRoot().getSlotNumber(),
taskStateHandles,
producedPartitions,
consumedPartitions);
}
// --------------------------------------------------------------------------------------------
// Utilities
// --------------------------------------------------------------------------------------------
@Override
public String toString() {
return getTaskNameWithSubtaskIndex();
}
@Override
public ArchivedExecutionVertex archive() {
return new ArchivedExecutionVertex(this);
}
}