Examples with WorkerLoad org.apache.kafka.connect.runtime.distributed.WorkerCoordinator.WorkerLoad used on opensource projects

Example 1 with WorkerLoad

use of org.apache.kafka.connect.runtime.distributed.WorkerCoordinator.WorkerLoad in project kafka by apache.

the class IncrementalCooperativeAssignor method performTaskAssignment.

/**
 * Performs task assignment based on the incremental cooperative connect protocol.
 * Read more on the design and implementation in:
 * {@see https://cwiki.apache.org/confluence/display/KAFKA/KIP-415%3A+Incremental+Cooperative+Rebalancing+in+Kafka+Connect}
 *
 * @param leaderId the ID of the group leader
 * @param maxOffset the latest known offset of the configuration topic
 * @param memberConfigs the metadata of all the members of the group as gather in the current
 * round of rebalancing
 * @param coordinator the worker coordinator instance that provide the configuration snapshot
 * and get assigned the leader state during this assignment
 * @param protocolVersion the Connect subprotocol version
 * @return the serialized assignment of tasks to the whole group, including assigned or
 * revoked tasks
 */
protected Map<String, ByteBuffer> performTaskAssignment(String leaderId, long maxOffset, Map<String, ExtendedWorkerState> memberConfigs, WorkerCoordinator coordinator, short protocolVersion) {
    log.debug("Performing task assignment during generation: {} with memberId: {}", coordinator.generationId(), coordinator.memberId());
    // Base set: The previous assignment of connectors-and-tasks is a standalone snapshot that
    // can be used to calculate derived sets
    log.debug("Previous assignments: {}", previousAssignment);
    int lastCompletedGenerationId = coordinator.lastCompletedGenerationId();
    if (previousGenerationId != lastCompletedGenerationId) {
        log.debug("Clearing the view of previous assignments due to generation mismatch between " + "previous generation ID {} and last completed generation ID {}. This can " + "happen if the leader fails to sync the assignment within a rebalancing round. " + "The following view of previous assignments might be outdated and will be " + "ignored by the leader in the current computation of new assignments. " + "Possibly outdated previous assignments: {}", previousGenerationId, lastCompletedGenerationId, previousAssignment);
        this.previousAssignment = ConnectorsAndTasks.EMPTY;
    }
    ClusterConfigState snapshot = coordinator.configSnapshot();
    Set<String> configuredConnectors = new TreeSet<>(snapshot.connectors());
    Set<ConnectorTaskId> configuredTasks = configuredConnectors.stream().flatMap(c -> snapshot.tasks(c).stream()).collect(Collectors.toSet());
    // Base set: The set of configured connectors-and-tasks is a standalone snapshot that can
    // be used to calculate derived sets
    ConnectorsAndTasks configured = new ConnectorsAndTasks.Builder().with(configuredConnectors, configuredTasks).build();
    log.debug("Configured assignments: {}", configured);
    // Base set: The set of active connectors-and-tasks is a standalone snapshot that can be
    // used to calculate derived sets
    ConnectorsAndTasks activeAssignments = assignment(memberConfigs);
    log.debug("Active assignments: {}", activeAssignments);
    // appropriately and be ready to re-apply revocation of tasks
    if (!previousRevocation.isEmpty()) {
        if (previousRevocation.connectors().stream().anyMatch(c -> activeAssignments.connectors().contains(c)) || previousRevocation.tasks().stream().anyMatch(t -> activeAssignments.tasks().contains(t))) {
            previousAssignment = activeAssignments;
            canRevoke = true;
        }
        previousRevocation.connectors().clear();
        previousRevocation.tasks().clear();
    }
    // Derived set: The set of deleted connectors-and-tasks is a derived set from the set
    // difference of previous - configured
    ConnectorsAndTasks deleted = diff(previousAssignment, configured);
    log.debug("Deleted assignments: {}", deleted);
    // Derived set: The set of remaining active connectors-and-tasks is a derived set from the
    // set difference of active - deleted
    ConnectorsAndTasks remainingActive = diff(activeAssignments, deleted);
    log.debug("Remaining (excluding deleted) active assignments: {}", remainingActive);
    // Derived set: The set of lost or unaccounted connectors-and-tasks is a derived set from
    // the set difference of previous - active - deleted
    ConnectorsAndTasks lostAssignments = diff(previousAssignment, activeAssignments, deleted);
    log.debug("Lost assignments: {}", lostAssignments);
    // Derived set: The set of new connectors-and-tasks is a derived set from the set
    // difference of configured - previous - active
    ConnectorsAndTasks newSubmissions = diff(configured, previousAssignment, activeAssignments);
    log.debug("New assignments: {}", newSubmissions);
    // A collection of the complete assignment
    List<WorkerLoad> completeWorkerAssignment = workerAssignment(memberConfigs, ConnectorsAndTasks.EMPTY);
    log.debug("Complete (ignoring deletions) worker assignments: {}", completeWorkerAssignment);
    // Per worker connector assignments without removing deleted connectors yet
    Map<String, Collection<String>> connectorAssignments = completeWorkerAssignment.stream().collect(Collectors.toMap(WorkerLoad::worker, WorkerLoad::connectors));
    log.debug("Complete (ignoring deletions) connector assignments: {}", connectorAssignments);
    // Per worker task assignments without removing deleted connectors yet
    Map<String, Collection<ConnectorTaskId>> taskAssignments = completeWorkerAssignment.stream().collect(Collectors.toMap(WorkerLoad::worker, WorkerLoad::tasks));
    log.debug("Complete (ignoring deletions) task assignments: {}", taskAssignments);
    // A collection of the current assignment excluding the connectors-and-tasks to be deleted
    List<WorkerLoad> currentWorkerAssignment = workerAssignment(memberConfigs, deleted);
    Map<String, ConnectorsAndTasks> toRevoke = computeDeleted(deleted, connectorAssignments, taskAssignments);
    log.debug("Connector and task to delete assignments: {}", toRevoke);
    // Revoking redundant connectors/tasks if the workers have duplicate assignments
    toRevoke.putAll(computeDuplicatedAssignments(memberConfigs, connectorAssignments, taskAssignments));
    log.debug("Connector and task to revoke assignments (include duplicated assignments): {}", toRevoke);
    // Recompute the complete assignment excluding the deleted connectors-and-tasks
    completeWorkerAssignment = workerAssignment(memberConfigs, deleted);
    connectorAssignments = completeWorkerAssignment.stream().collect(Collectors.toMap(WorkerLoad::worker, WorkerLoad::connectors));
    taskAssignments = completeWorkerAssignment.stream().collect(Collectors.toMap(WorkerLoad::worker, WorkerLoad::tasks));
    handleLostAssignments(lostAssignments, newSubmissions, completeWorkerAssignment, memberConfigs);
    // Do not revoke resources for re-assignment while a delayed rebalance is active
    // Also we do not revoke in two consecutive rebalances by the same leader
    canRevoke = delay == 0 && canRevoke;
    // Compute the connectors-and-tasks to be revoked for load balancing without taking into
    // account the deleted ones.
    log.debug("Can leader revoke tasks in this assignment? {} (delay: {})", canRevoke, delay);
    if (canRevoke) {
        Map<String, ConnectorsAndTasks> toExplicitlyRevoke = performTaskRevocation(activeAssignments, currentWorkerAssignment);
        log.debug("Connector and task to revoke assignments: {}", toRevoke);
        toExplicitlyRevoke.forEach((worker, assignment) -> {
            ConnectorsAndTasks existing = toRevoke.computeIfAbsent(worker, v -> new ConnectorsAndTasks.Builder().build());
            existing.connectors().addAll(assignment.connectors());
            existing.tasks().addAll(assignment.tasks());
        });
        canRevoke = toExplicitlyRevoke.size() == 0;
    } else {
        canRevoke = delay == 0;
    }
    assignConnectors(completeWorkerAssignment, newSubmissions.connectors());
    assignTasks(completeWorkerAssignment, newSubmissions.tasks());
    log.debug("Current complete assignments: {}", currentWorkerAssignment);
    log.debug("New complete assignments: {}", completeWorkerAssignment);
    Map<String, Collection<String>> currentConnectorAssignments = currentWorkerAssignment.stream().collect(Collectors.toMap(WorkerLoad::worker, WorkerLoad::connectors));
    Map<String, Collection<ConnectorTaskId>> currentTaskAssignments = currentWorkerAssignment.stream().collect(Collectors.toMap(WorkerLoad::worker, WorkerLoad::tasks));
    Map<String, Collection<String>> incrementalConnectorAssignments = diff(connectorAssignments, currentConnectorAssignments);
    Map<String, Collection<ConnectorTaskId>> incrementalTaskAssignments = diff(taskAssignments, currentTaskAssignments);
    log.debug("Incremental connector assignments: {}", incrementalConnectorAssignments);
    log.debug("Incremental task assignments: {}", incrementalTaskAssignments);
    coordinator.leaderState(new LeaderState(memberConfigs, connectorAssignments, taskAssignments));
    Map<String, ExtendedAssignment> assignments = fillAssignments(memberConfigs.keySet(), Assignment.NO_ERROR, leaderId, memberConfigs.get(leaderId).url(), maxOffset, incrementalConnectorAssignments, incrementalTaskAssignments, toRevoke, delay, protocolVersion);
    previousAssignment = computePreviousAssignment(toRevoke, connectorAssignments, taskAssignments, lostAssignments);
    previousGenerationId = coordinator.generationId();
    previousMembers = memberConfigs.keySet();
    log.debug("Actual assignments: {}", assignments);
    return serializeAssignments(assignments);
}

Example 2 with WorkerLoad

use of org.apache.kafka.connect.runtime.distributed.WorkerCoordinator.WorkerLoad in project kafka by apache.

the class IncrementalCooperativeAssignor method performTaskRevocation.

/**
 * Task revocation is based on an rough estimation of the lower average number of tasks before
 * and after new workers join the group. If no new workers join, no revocation takes place.
 * Based on this estimation, tasks are revoked until the new floor average is reached for
 * each existing worker. The revoked tasks, once assigned to the new workers will maintain
 * a balanced load among the group.
 *
 * @param activeAssignments
 * @param completeWorkerAssignment
 * @return
 */
private Map<String, ConnectorsAndTasks> performTaskRevocation(ConnectorsAndTasks activeAssignments, Collection<WorkerLoad> completeWorkerAssignment) {
    int totalActiveConnectorsNum = activeAssignments.connectors().size();
    int totalActiveTasksNum = activeAssignments.tasks().size();
    Collection<WorkerLoad> existingWorkers = completeWorkerAssignment.stream().filter(wl -> wl.size() > 0).collect(Collectors.toList());
    int existingWorkersNum = existingWorkers.size();
    int totalWorkersNum = completeWorkerAssignment.size();
    int newWorkersNum = totalWorkersNum - existingWorkersNum;
    if (log.isDebugEnabled()) {
        completeWorkerAssignment.forEach(wl -> log.debug("Per worker current load size; worker: {} connectors: {} tasks: {}", wl.worker(), wl.connectorsSize(), wl.tasksSize()));
    }
    Map<String, ConnectorsAndTasks> revoking = new HashMap<>();
    // after logging the status
    if (!(newWorkersNum > 0 && existingWorkersNum > 0)) {
        log.debug("No task revocation required; workers with existing load: {} workers with " + "no load {} total workers {}", existingWorkersNum, newWorkersNum, totalWorkersNum);
        // connectors and tasks as well
        return revoking;
    }
    log.debug("Task revocation is required; workers with existing load: {} workers with " + "no load {} total workers {}", existingWorkersNum, newWorkersNum, totalWorkersNum);
    // We have at least one worker assignment (the leader itself) so totalWorkersNum can't be 0
    log.debug("Previous rounded down (floor) average number of connectors per worker {}", totalActiveConnectorsNum / existingWorkersNum);
    int floorConnectors = totalActiveConnectorsNum / totalWorkersNum;
    int ceilConnectors = floorConnectors + ((totalActiveConnectorsNum % totalWorkersNum == 0) ? 0 : 1);
    log.debug("New average number of connectors per worker rounded down (floor) {} and rounded up (ceil) {}", floorConnectors, ceilConnectors);
    log.debug("Previous rounded down (floor) average number of tasks per worker {}", totalActiveTasksNum / existingWorkersNum);
    int floorTasks = totalActiveTasksNum / totalWorkersNum;
    int ceilTasks = floorTasks + ((totalActiveTasksNum % totalWorkersNum == 0) ? 0 : 1);
    log.debug("New average number of tasks per worker rounded down (floor) {} and rounded up (ceil) {}", floorTasks, ceilTasks);
    int numToRevoke;
    for (WorkerLoad existing : existingWorkers) {
        Iterator<String> connectors = existing.connectors().iterator();
        numToRevoke = existing.connectorsSize() - ceilConnectors;
        for (int i = existing.connectorsSize(); i > floorConnectors && numToRevoke > 0; --i, --numToRevoke) {
            ConnectorsAndTasks resources = revoking.computeIfAbsent(existing.worker(), w -> new ConnectorsAndTasks.Builder().build());
            resources.connectors().add(connectors.next());
        }
    }
    for (WorkerLoad existing : existingWorkers) {
        Iterator<ConnectorTaskId> tasks = existing.tasks().iterator();
        numToRevoke = existing.tasksSize() - ceilTasks;
        log.debug("Tasks on worker {} is higher than ceiling, so revoking {} tasks", existing, numToRevoke);
        for (int i = existing.tasksSize(); i > floorTasks && numToRevoke > 0; --i, --numToRevoke) {
            ConnectorsAndTasks resources = revoking.computeIfAbsent(existing.worker(), w -> new ConnectorsAndTasks.Builder().build());
            resources.tasks().add(tasks.next());
        }
    }
    return revoking;
}

Example 3 with WorkerLoad

use of org.apache.kafka.connect.runtime.distributed.WorkerCoordinator.WorkerLoad in project kafka by apache.

the class IncrementalCooperativeAssignor method assignConnectors.

/**
 * Perform a round-robin assignment of connectors to workers with existing worker load. This
 * assignment tries to balance the load between workers, by assigning connectors to workers
 * that have equal load, starting with the least loaded workers.
 *
 * @param workerAssignment the current worker assignment; assigned connectors are added to this list
 * @param connectors the connectors to be assigned
 */
protected void assignConnectors(List<WorkerLoad> workerAssignment, Collection<String> connectors) {
    workerAssignment.sort(WorkerLoad.connectorComparator());
    WorkerLoad first = workerAssignment.get(0);
    Iterator<String> load = connectors.iterator();
    while (load.hasNext()) {
        int firstLoad = first.connectorsSize();
        int upTo = IntStream.range(0, workerAssignment.size()).filter(i -> workerAssignment.get(i).connectorsSize() > firstLoad).findFirst().orElse(workerAssignment.size());
        for (WorkerLoad worker : workerAssignment.subList(0, upTo)) {
            String connector = load.next();
            log.debug("Assigning connector {} to {}", connector, worker.worker());
            worker.assign(connector);
            if (!load.hasNext()) {
                break;
            }
        }
    }
}

Example 4 with WorkerLoad

use of org.apache.kafka.connect.runtime.distributed.WorkerCoordinator.WorkerLoad in project kafka by apache.

the class IncrementalCooperativeAssignor method assignTasks.

/**
 * Perform a round-robin assignment of tasks to workers with existing worker load. This
 * assignment tries to balance the load between workers, by assigning tasks to workers that
 * have equal load, starting with the least loaded workers.
 *
 * @param workerAssignment the current worker assignment; assigned tasks are added to this list
 * @param tasks the tasks to be assigned
 */
protected void assignTasks(List<WorkerLoad> workerAssignment, Collection<ConnectorTaskId> tasks) {
    workerAssignment.sort(WorkerLoad.taskComparator());
    WorkerLoad first = workerAssignment.get(0);
    Iterator<ConnectorTaskId> load = tasks.iterator();
    while (load.hasNext()) {
        int firstLoad = first.tasksSize();
        int upTo = IntStream.range(0, workerAssignment.size()).filter(i -> workerAssignment.get(i).tasksSize() > firstLoad).findFirst().orElse(workerAssignment.size());
        for (WorkerLoad worker : workerAssignment.subList(0, upTo)) {
            ConnectorTaskId task = load.next();
            log.debug("Assigning task {} to {}", task, worker.worker());
            worker.assign(task);
            if (!load.hasNext()) {
                break;
            }
        }
    }
}

Example 5 with WorkerLoad

use of org.apache.kafka.connect.runtime.distributed.WorkerCoordinator.WorkerLoad in project kafka by apache.

the class IncrementalCooperativeAssignor method handleLostAssignments.

// visible for testing
protected void handleLostAssignments(ConnectorsAndTasks lostAssignments, ConnectorsAndTasks newSubmissions, List<WorkerLoad> completeWorkerAssignment, Map<String, ExtendedWorkerState> memberConfigs) {
    if (lostAssignments.isEmpty()) {
        resetDelay();
        return;
    }
    final long now = time.milliseconds();
    log.debug("Found the following connectors and tasks missing from previous assignments: " + lostAssignments);
    if (scheduledRebalance <= 0 && memberConfigs.keySet().containsAll(previousMembers)) {
        log.debug("No worker seems to have departed the group during the rebalance. The " + "missing assignments that the leader is detecting are probably due to some " + "workers failing to receive the new assignments in the previous rebalance. " + "Will reassign missing tasks as new tasks");
        newSubmissions.connectors().addAll(lostAssignments.connectors());
        newSubmissions.tasks().addAll(lostAssignments.tasks());
        return;
    }
    if (scheduledRebalance > 0 && now >= scheduledRebalance) {
        // delayed rebalance expired and it's time to assign resources
        log.debug("Delayed rebalance expired. Reassigning lost tasks");
        List<WorkerLoad> candidateWorkerLoad = Collections.emptyList();
        if (!candidateWorkersForReassignment.isEmpty()) {
            candidateWorkerLoad = pickCandidateWorkerForReassignment(completeWorkerAssignment);
        }
        if (!candidateWorkerLoad.isEmpty()) {
            log.debug("Assigning lost tasks to {} candidate workers: {}", candidateWorkerLoad.size(), candidateWorkerLoad.stream().map(WorkerLoad::worker).collect(Collectors.joining(",")));
            Iterator<WorkerLoad> candidateWorkerIterator = candidateWorkerLoad.iterator();
            for (String connector : lostAssignments.connectors()) {
                // Loop over the candidate workers as many times as it takes
                if (!candidateWorkerIterator.hasNext()) {
                    candidateWorkerIterator = candidateWorkerLoad.iterator();
                }
                WorkerLoad worker = candidateWorkerIterator.next();
                log.debug("Assigning connector id {} to member {}", connector, worker.worker());
                worker.assign(connector);
            }
            candidateWorkerIterator = candidateWorkerLoad.iterator();
            for (ConnectorTaskId task : lostAssignments.tasks()) {
                if (!candidateWorkerIterator.hasNext()) {
                    candidateWorkerIterator = candidateWorkerLoad.iterator();
                }
                WorkerLoad worker = candidateWorkerIterator.next();
                log.debug("Assigning task id {} to member {}", task, worker.worker());
                worker.assign(task);
            }
        } else {
            log.debug("No single candidate worker was found to assign lost tasks. Treating lost tasks as new tasks");
            newSubmissions.connectors().addAll(lostAssignments.connectors());
            newSubmissions.tasks().addAll(lostAssignments.tasks());
        }
        resetDelay();
    } else {
        candidateWorkersForReassignment.addAll(candidateWorkersForReassignment(completeWorkerAssignment));
        if (now < scheduledRebalance) {
            // a delayed rebalance is in progress, but it's not yet time to reassign
            // unaccounted resources
            delay = calculateDelay(now);
            log.debug("Delayed rebalance in progress. Task reassignment is postponed. New computed rebalance delay: {}", delay);
        } else {
            // This means scheduledRebalance == 0
            // We could also also extract the current minimum delay from the group, to make
            // independent of consecutive leader failures, but this optimization is skipped
            // at the moment
            delay = maxDelay;
            log.debug("Resetting rebalance delay to the max: {}. scheduledRebalance: {} now: {} diff scheduledRebalance - now: {}", delay, scheduledRebalance, now, scheduledRebalance - now);
        }
        scheduledRebalance = now + delay;
    }
}