ableegoldman commented on code in PR #16033:
URL: https://github.com/apache/kafka/pull/16033#discussion_r1618288306
##########
streams/src/main/java/org/apache/kafka/streams/processor/assignment/TaskAssignmentUtils.java:
##########
@@ -16,78 +16,408 @@
*/
package org.apache.kafka.streams.processor.assignment;
+import java.util.ArrayList;
+import java.util.Collection;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.List;
import java.util.Map;
+import java.util.Optional;
+import java.util.Set;
import java.util.SortedSet;
+import java.util.UUID;
+import java.util.stream.Collectors;
import org.apache.kafka.streams.processor.TaskId;
+import
org.apache.kafka.streams.processor.assignment.KafkaStreamsAssignment.AssignedTask;
+import org.apache.kafka.streams.processor.internals.assignment.Graph;
+import
org.apache.kafka.streams.processor.internals.assignment.MinTrafficGraphConstructor;
+import
org.apache.kafka.streams.processor.internals.assignment.RackAwareGraphConstructor;
+import
org.apache.kafka.streams.processor.internals.assignment.RackAwareGraphConstructorFactory;
+import org.apache.kafka.streams.StreamsConfig;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
/**
* A set of utilities to help implement task assignment via the {@link
TaskAssignor}
*/
public final class TaskAssignmentUtils {
+ private static final Logger LOG =
LoggerFactory.getLogger(TaskAssignmentUtils.class);
+
+ private TaskAssignmentUtils() {}
+
/**
- * Assign standby tasks to KafkaStreams clients according to the default
logic.
- * <p>
- * If rack-aware client tags are configured, the rack-aware standby task
assignor will be used
+ * Return a "no-op" assignment that just copies the previous assignment of
tasks to KafkaStreams clients
*
- * @param applicationState the metadata and other info describing
the current application state
- * @param kafkaStreamsAssignments the current assignment of tasks to
KafkaStreams clients
+ * @param applicationState the metadata and other info describing the
current application state
*
- * @return a new map containing the mappings from KafkaStreamsAssignments
updated with the default
- * standby assignment
+ * @return a new map containing an assignment that replicates exactly the
previous assignment reported
+ * in the applicationState
*/
- public static Map<ProcessId, KafkaStreamsAssignment>
defaultStandbyTaskAssignment(
- final ApplicationState applicationState,
- final Map<ProcessId, KafkaStreamsAssignment> kafkaStreamsAssignments
- ) {
- throw new UnsupportedOperationException("Not Implemented.");
+ public static Map<ProcessId, KafkaStreamsAssignment>
identityAssignment(final ApplicationState applicationState) {
+ final Map<ProcessId, KafkaStreamsAssignment> assignments = new
HashMap<>();
+ applicationState.kafkaStreamsStates(false).forEach((processId, state)
-> {
+ final Set<AssignedTask> tasks = new HashSet<>();
+ state.previousActiveTasks().forEach(taskId -> {
+ tasks.add(new AssignedTask(taskId,
+ AssignedTask.Type.ACTIVE));
+ });
+ state.previousStandbyTasks().forEach(taskId -> {
+ tasks.add(new AssignedTask(taskId,
+ AssignedTask.Type.STANDBY));
+ });
+
+ final KafkaStreamsAssignment newAssignment =
KafkaStreamsAssignment.of(processId, tasks);
+ assignments.put(processId, newAssignment);
+ });
+ return assignments;
}
/**
- * Optimize the active task assignment for rack-awareness
+ * Optimize active task assignment for rack awareness. This optimization
is based on the
+ * {@link StreamsConfig#RACK_AWARE_ASSIGNMENT_TRAFFIC_COST_CONFIG
trafficCost}
+ * and {@link StreamsConfig#RACK_AWARE_ASSIGNMENT_NON_OVERLAP_COST_CONFIG
nonOverlapCost}
+ * configs which balance cross rack traffic minimization and task movement.
+ * Setting {@code trafficCost} to a larger number reduces the overall
cross rack traffic of the resulting
+ * assignment, but can increase the number of tasks shuffled around
between clients.
+ * Setting {@code nonOverlapCost} to a larger number increases the
affinity of tasks to their intended client
+ * and reduces the amount by which the rack-aware optimization can shuffle
tasks around, at the cost of higher
+ * cross-rack traffic.
+ * In an extreme case, if we set {@code nonOverlapCost} to 0 and @{code
trafficCost} to a positive value,
+ * the resulting assignment will have an absolute minimum of cross rack
traffic. If we set {@code trafficCost} to 0,
+ * and {@code nonOverlapCost} to a positive value, the resulting
assignment will be identical to the input assignment.
+ * <p>
+ * This method optimizes cross-rack traffic for active tasks only. For
standby task optimization,
+ * use {@link #optimizeRackAwareStandbyTasks}.
*
* @param applicationState the metadata and other info describing
the current application state
* @param kafkaStreamsAssignments the current assignment of tasks to
KafkaStreams clients
- * @param tasks the set of tasks to reassign if
possible. Must already be assigned
- * to a KafkaStreams client
+ * @param tasks the set of tasks to reassign if
possible. Must already be assigned to a KafkaStreams client
*
- * @return a new map containing the mappings from KafkaStreamsAssignments
updated with the default
- * rack-aware assignment for active tasks
+ * @return a new map containing the mappings from KafkaStreamsAssignments
updated with the default rack-aware assignment for active tasks
*/
public static Map<ProcessId, KafkaStreamsAssignment>
optimizeRackAwareActiveTasks(
final ApplicationState applicationState,
final Map<ProcessId, KafkaStreamsAssignment> kafkaStreamsAssignments,
final SortedSet<TaskId> tasks
) {
- throw new UnsupportedOperationException("Not Implemented.");
+ if (tasks.isEmpty()) {
+ return kafkaStreamsAssignments;
+ }
+
+ if (!hasValidRackInformation(applicationState)) {
+ LOG.warn("Cannot optimize active tasks with invalid rack
information.");
+ return kafkaStreamsAssignments;
+ }
+
+ final int crossRackTrafficCost =
applicationState.assignmentConfigs().rackAwareTrafficCost();
+ final int nonOverlapCost =
applicationState.assignmentConfigs().rackAwareNonOverlapCost();
+ final long currentCost = computeTaskCost(
+ applicationState.allTasks().stream().filter(taskInfo ->
tasks.contains(taskInfo.id())).collect(
+ Collectors.toSet()),
+ applicationState.kafkaStreamsStates(false),
+ crossRackTrafficCost,
+ nonOverlapCost,
+ false,
+ false
+ );
+ LOG.info("Assignment before active task optimization has cost {}",
currentCost);
+
+ final List<UUID> clientIds =
kafkaStreamsAssignments.keySet().stream().map(ProcessId::id).collect(
+ Collectors.toList());
+ final Map<ProcessId, KafkaStreamsState> kafkaStreamsStates =
applicationState.kafkaStreamsStates(false);
+ final Map<UUID, Optional<String>> clientRacks =
kafkaStreamsStates.values().stream().collect(
+ Collectors.toMap(state -> state.processId().id(),
KafkaStreamsState::rackId));
+ final Map<UUID, Set<TaskId>> previousTaskIdsByProcess =
kafkaStreamsStates.values().stream().collect(Collectors.toMap(
+ state -> state.processId().id(),
+ KafkaStreamsState::previousActiveTasks
+ ));
+ final Map<TaskId, Set<TaskTopicPartition>> topicPartitionsByTaskId =
applicationState.allTasks().stream()
+ .filter(taskInfo -> tasks.contains(taskInfo.id()))
+ .collect(Collectors.toMap(TaskInfo::id,
TaskInfo::topicPartitions));
+
+ final List<TaskId> taskIds = new ArrayList<>(tasks);
+ final RackAwareGraphConstructor<UUID> graphConstructor =
RackAwareGraphConstructorFactory.create(
+
applicationState.assignmentConfigs().rackAwareAssignmentStrategy(), taskIds);
+ final AssignmentGraph assignmentGraph = buildTaskGraph(
+ clientIds,
+ clientRacks,
+ taskIds,
+ previousTaskIdsByProcess,
+ topicPartitionsByTaskId,
+ crossRackTrafficCost,
+ nonOverlapCost,
+ false,
+ false,
+ graphConstructor
+ );
+
+ assignmentGraph.graph.solveMinCostFlow();
+
+ final Map<UUID, Set<AssignedTask>> reassigned = new HashMap<>();
+ final Map<UUID, Set<TaskId>> unassigned = new HashMap<>();
+ graphConstructor.assignTaskFromMinCostFlow(
+ assignmentGraph.graph,
+ clientIds,
+ taskIds,
+ clientIds.stream().collect(Collectors.toMap(id -> id, id -> id)),
+ assignmentGraph.taskCountByClient,
+ assignmentGraph.clientByTask,
+ (processId, taskId) -> {
+ reassigned.computeIfAbsent(processId, k -> new HashSet<>());
+ reassigned.get(processId).add(new AssignedTask(taskId,
AssignedTask.Type.ACTIVE));
+ },
+ (processId, taskId) -> {
+ unassigned.computeIfAbsent(processId, k -> new HashSet<>());
+ unassigned.get(processId).add(taskId);
+ },
+ (processId, taskId) -> {
+ return
previousTaskIdsByProcess.get(processId).contains(taskId);
+ }
+ );
+
+ return processTaskMoves(kafkaStreamsAssignments.values(), reassigned,
unassigned);
}
/**
- * Optimize the standby task assignment for rack-awareness
+ * Optimize standby task assignment for rack awareness. This optimization
is based on the
+ * {@link StreamsConfig#RACK_AWARE_ASSIGNMENT_TRAFFIC_COST_CONFIG
trafficCost}
+ * and {@link StreamsConfig#RACK_AWARE_ASSIGNMENT_NON_OVERLAP_COST_CONFIG
nonOverlapCost}
+ * configs which balance cross rack traffic minimization and task movement.
+ * Setting {@code trafficCost} to a larger number reduces the overall
cross rack traffic of the resulting
+ * assignment, but can increase the number of tasks shuffled around
between clients.
+ * Setting {@code nonOverlapCost} to a larger number increases the
affinity of tasks to their intended client
+ * and reduces the amount by which the rack-aware optimization can shuffle
tasks around, at the cost of higher
+ * cross-rack traffic.
+ * In an extreme case, if we set {@code nonOverlapCost} to 0 and @{code
trafficCost} to a positive value,
+ * the resulting assignment will have an absolute minimum of cross rack
traffic. If we set {@code trafficCost} to 0,
+ * and {@code nonOverlapCost} to a positive value, the resulting
assignment will be identical to the input assignment.
+ * <p>
+ * This method optimizes cross-rack traffic for standby tasks only. For
active task optimization,
+ * use {@link #optimizeRackAwareActiveTasks}.
*
* @param kafkaStreamsAssignments the current assignment of tasks to
KafkaStreams clients
* @param applicationState the metadata and other info describing
the current application state
*
- * @return a new map containing the mappings from KafkaStreamsAssignments
updated with the default
- * rack-aware assignment for standby tasks
+ * @return a new map containing the mappings from KafkaStreamsAssignments
updated with the default rack-aware assignment for standy tasks
*/
public static Map<ProcessId, KafkaStreamsAssignment>
optimizeRackAwareStandbyTasks(
final ApplicationState applicationState,
final Map<ProcessId, KafkaStreamsAssignment> kafkaStreamsAssignments
) {
+ if (!hasValidRackInformation(applicationState)) {
+ LOG.warn("Cannot optimize standby tasks with invalid rack
information.");
+ return kafkaStreamsAssignments;
+ }
+
+ final int crossRackTrafficCost =
applicationState.assignmentConfigs().rackAwareTrafficCost();
+ final int nonOverlapCost =
applicationState.assignmentConfigs().rackAwareNonOverlapCost();
+ final long currentCost = computeTaskCost(
+ applicationState.allTasks(),
+ applicationState.kafkaStreamsStates(false),
+ crossRackTrafficCost,
+ nonOverlapCost,
+ true,
+ true
+ );
+ LOG.info("Assignment before standby task optimization has cost {}",
currentCost);
throw new UnsupportedOperationException("Not Implemented.");
}
+ private static long computeTaskCost(final Set<TaskInfo> tasks,
+ final Map<ProcessId,
KafkaStreamsState> clients,
+ final int crossRackTrafficCost,
+ final int nonOverlapCost,
+ final boolean hasReplica,
+ final boolean isStandby) {
+ if (tasks.isEmpty()) {
+ return 0;
+ }
+
+ final List<UUID> clientIds =
clients.keySet().stream().map(ProcessId::id).collect(
+ Collectors.toList());
+
+ final List<TaskId> taskIds =
tasks.stream().map(TaskInfo::id).collect(Collectors.toList());
+ final Map<TaskId, Set<TaskTopicPartition>> topicPartitionsByTaskId =
tasks.stream().collect(
+ Collectors.toMap(TaskInfo::id, TaskInfo::topicPartitions));
+
+ final Map<UUID, Optional<String>> clientRacks =
clients.values().stream().collect(
+ Collectors.toMap(state -> state.processId().id(),
KafkaStreamsState::rackId));
+
+ final Map<UUID, Set<TaskId>> taskIdsByProcess =
clients.values().stream().collect(
+ Collectors.toMap(state -> state.processId().id(), state -> {
+ if (isStandby) {
+ return state.previousStandbyTasks();
+ }
+ return state.previousActiveTasks();
+ })
+ );
+
+ final RackAwareGraphConstructor<UUID> graphConstructor = new
MinTrafficGraphConstructor<>();
+ final AssignmentGraph assignmentGraph = buildTaskGraph(clientIds,
clientRacks, taskIds, taskIdsByProcess, topicPartitionsByTaskId,
+ crossRackTrafficCost, nonOverlapCost, hasReplica, isStandby,
graphConstructor);
+ return assignmentGraph.graph.totalCost();
+ }
+
+ private static AssignmentGraph buildTaskGraph(final List<UUID> clientIds,
+ final Map<UUID,
Optional<String>> clientRacks,
+ final List<TaskId> taskIds,
+ final Map<UUID, Set<TaskId>>
previousTaskIdsByProcess,
+ final Map<TaskId,
Set<TaskTopicPartition>> topicPartitionsByTaskId,
+ final int
crossRackTrafficCost,
+ final int nonOverlapCost,
+ final boolean hasReplica,
+ final boolean isStandby,
+ final
RackAwareGraphConstructor<UUID> graphConstructor) {
+ final Map<UUID, UUID> clientsUuidByUuid =
clientIds.stream().collect(Collectors.toMap(id -> id, id -> id));
+ final Map<TaskId, UUID> clientByTask = new HashMap<>();
+ final Map<UUID, Integer> taskCountByClient = new HashMap<>();
+ final Graph<Integer> graph = graphConstructor.constructTaskGraph(
+ clientIds,
+ taskIds,
+ clientsUuidByUuid,
+ clientByTask,
+ taskCountByClient,
+ (processId, taskId) -> {
+ return
previousTaskIdsByProcess.get(processId).contains(taskId);
+ },
+ (taskId, processId, inCurrentAssignment, unused0, unused1,
unused2) -> {
+ final int assignmentChangeCost = !inCurrentAssignment ?
nonOverlapCost : 0;
+ final Optional<String> clientRack = clientRacks.get(processId);
+ final Set<TaskTopicPartition> topicPartitions =
topicPartitionsByTaskId.get(taskId).stream().filter(tp -> {
+ return isStandby ? tp.isChangelog() : true;
+ }).collect(Collectors.toSet());
+ return assignmentChangeCost +
getCrossRackTrafficCost(topicPartitions, clientRack, crossRackTrafficCost);
+ },
+ crossRackTrafficCost,
+ nonOverlapCost,
+ hasReplica,
+ isStandby
+ );
+ return new AssignmentGraph(graph, clientByTask, taskCountByClient);
+ }
+
+ /**
+ * This internal structure is used to keep track of the graph solving
outputs alongside the graph
+ * structure itself.
+ */
+ private static final class AssignmentGraph {
+ public final Graph<Integer> graph;
+ public final Map<TaskId, UUID> clientByTask;
+ public final Map<UUID, Integer> taskCountByClient;
+
+ public AssignmentGraph(final Graph<Integer> graph,
+ final Map<TaskId, UUID> clientByTask,
+ final Map<UUID, Integer> taskCountByClient) {
+ this.graph = graph;
+ this.clientByTask = clientByTask;
+ this.taskCountByClient = taskCountByClient;
+ }
+ }
+
/**
- * Return a "no-op" assignment that just copies the previous assignment of
tasks to KafkaStreams clients
*
- * @param applicationState the metadata and other info describing the
current application state
+ * @return the traffic cost of assigning this {@param task} to the client
{@param streamsState}.
+ */
+ private static int getCrossRackTrafficCost(final Set<TaskTopicPartition>
topicPartitions,
+ final Optional<String>
clientRack,
+ final int crossRackTrafficCost)
{
+ if (!clientRack.isPresent()) {
+ throw new IllegalStateException("Client doesn't have rack
configured.");
+ }
+
+ int cost = 0;
+ for (final TaskTopicPartition topicPartition : topicPartitions) {
+ final Optional<Set<String>> topicPartitionRacks =
topicPartition.rackIds();
+ if (topicPartitionRacks == null ||
!topicPartitionRacks.isPresent()) {
+ throw new IllegalStateException("TopicPartition " +
topicPartition + " has no rack information.");
+ }
+
+ if (topicPartitionRacks.get().contains(clientRack.get())) {
+ continue;
+ }
+
+ cost += crossRackTrafficCost;
+ }
+ return cost;
+ }
+
+ /**
+ * This function returns whether the current application state has the
required rack information
+ * to make assignment decisions with.
+ * This includes ensuring that every client has a known rack id, and that
every topic partition for
+ * every logical task that needs to be assigned also has known rack ids.
+ * If a logical task has no source topic partitions, it will return false.
+ * If standby tasks are configured, but a logical task has no changelog
topic partitions, it will return false.
*
- * @return a new map containing an assignment that replicates exactly the
previous assignment reported
- * in the applicationState
+ * @return whether rack-aware assignment decisions can be made for this
application.
*/
- public static Map<ProcessId, KafkaStreamsAssignment> identityAssignment(
- final ApplicationState applicationState
- ) {
- throw new UnsupportedOperationException("Not Implemented.");
+ private static boolean hasValidRackInformation(final ApplicationState
applicationState) {
+ for (final KafkaStreamsState state :
applicationState.kafkaStreamsStates(false).values()) {
+ if (!hasValidRackInformation(state)) {
+ return false;
+ }
+ }
+
+ for (final TaskInfo task : applicationState.allTasks()) {
+ if (!hasValidRackInformation(task)) {
+ return false;
+ }
+ }
+ return true;
+ }
+
+ private static boolean hasValidRackInformation(final KafkaStreamsState
state) {
+ if (!state.rackId().isPresent()) {
+ LOG.error("Client " + state.processId() + " doesn't have rack
configured.");
+ return false;
+ }
+ return true;
+ }
+
+ private static boolean hasValidRackInformation(final TaskInfo task) {
+ for (final TaskTopicPartition topicPartition : task.topicPartitions())
{
+ final Optional<Set<String>> racks = topicPartition.rackIds();
+ if (!racks.isPresent() || racks.get().isEmpty()) {
+ LOG.error("Topic partition {} for task {} does not have racks
configured.", topicPartition, task.id());
+ return false;
+ }
+ }
+ return true;
+ }
+
+ /**
+ * This function returns a copy of the old collection of {@code
KafkaStreamsAssignment} with tasks
+ * moved according to the {@param reassigned} tasks and {@param
unassigned} tasks.
+ *
+ * @param kafkaStreamsAssignments the collection to start from when moving
tasks from process to process
+ * @param reassigned the map from process id to tasks that
this client has newly been assigned
+ * @param unassigned the map from process id to tasks that
this client has newly been unassigned
+ *
+ * @return the new mapping from processId to {@code
KafkaStreamsAssignment}.
+ */
+ private static Map<ProcessId, KafkaStreamsAssignment>
processTaskMoves(final Collection<KafkaStreamsAssignment>
kafkaStreamsAssignments,
Review Comment:
Alright I went ahead and tried this out for myself, I think it helps a lot
to simplify things in this PR so it makes sense to incorporate here rather than
doing it as an entirely separate PR. Lmk your thoughts as always:
https://github.com/apourchet/kafka/pull/1
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