Motivation

Summary of existing controller

Current Kafka controller is a multi-threaded controller that emulates a state machine. It works in the following way.

Maintained state:

1. Partitions replicas on each machine.
2. Leaders of partitions.

State change input source:

• Listeners Registered to Zookeeper.
  1. AddPartitionsListener
  2. BrokerChangeListener
  3. DeleteTopicListener
  4. PartitionReassignedListener(Admin)
  5. PreferredReplicaElectionListener(Admin)
  6. ReassignedPartitionsIsrChangeListener
  7. TopicChangeListener
• Channels to brokers (controlled shutdown)
• Internal scheduled tasks (preferred leader election)

State change execution:

• Listener threads, KafkaApi thread and internal scheduler thread makes state change concurrently.

State change propagation model:

• P2P blocking channel from controller to each broker.
• Dedicated message queue for each controller-to-broker connection.
• No synchronization on message sent to different brokers.
• No callback for sending messages except topic deletion.

Fail Over/Back model:

• Zookeeper based leader election
• Zookeeper as persistent state store for fault tolerance.

Problems of existing controller

1. State change are executed by different listeners concurrently. Hence complicated synchronization is needed which is error prone and difficult to debug.
2. State propagation is not synchronized. Brokers might be in different state for undetermined time. This leads to unnecessary extra data loss.
3. During controlled shutdown process, two connections are used for controller to broker communication. This makes state change propagation and controlled shutdown approval out of order.

New controller design

Outline

We will keep maintained state and fail over/back model unchanged.

We are going to change the state change propagation model, state change execution and output of the state change input source. More specifically:

1. Abstract the output of each state change input source to an event.
2. Have a single execution thread to serially process events one at a time.
3. The zk listeners are responsible for only context updating but not event execution.
4. Use o.a.k.clients.NetworClient + callback for state change propagation.

We would also like to

1. Modify KafkaServer to use new NetworkClient and prioritize the controller-to-broker traffic.
2. Change the reads/writes to Zookeeper to maybe use multi-operation or async operation.

Related tickets

KAFKA-2139, KAFKA-2029, KAFKA-1305 (and definitely some other tickets... Appreciate it if people can add it here.)

Architecture

• The Controller Context contains two kinds of information: cluster reachability and Topic State(Partition, Replica, Leaders, etc)
• Two types of ZK listeners:
  • Responsible of updating cluster reachability by listening to broker path in zookeeper.
  • Responsible for create events and add them to Event Queue.
• A controlled shutdown event will be generated when receive controlled shutdown from broker.
  
  • The controlled shutdown process will be changed to make state change and controlled shutdown approval occur in order. (This might involve broker side change as well)
• Scheduled tasks (e.g. preferred leader election) will
• On controller starting up or resignation, a ControllerStartUp/ControllerResignation event will be generated.
• Event Executor Thread:
  • Change Topic State in Controller Context
  • Propagate the new state to each broker using o.a.k.clients.NetworkClient in non-blocking way.
• Broker will only trigger Zookeeper data change when:
  1. Broker is down (or long GC)
  2. New topic automatically created

Event Types and Handling Process

Event Types

There will be eight types of events in the controller, which are defined as below:

object ControllerEventType extends Enumeration {
  type ControllerEventType = Value
  val AddPartition, TopicChange, DeleteTopic, BrokerChange, PreferredReplicaElection, PartitionReassigned,
      ReassignedPartitionIsrChange, ControlledShutdown = Value
}

KafkaControllerEvent

A generic controller event class will be defined:

abstract class KafkaControllerEvent(eventType: ControllerEventType) {
  // A set that tracks the responses from brokers
  val unAckedNode = new mutable.HashSet[Int]
  val eventDone = new CountDownLatch(1)
  
  def makeStatesChange(currentState: PartitionStateMachine): Map[Int, ClientRequest]

  def controllerRequestCallback(response: ClientResponse) {
    handleBrokerResponse(response)
    unAckedNode.remove(response.request().request().destination())
    if (unAckedNode.isEmpty)
      eventDone.countDown()
  }

  def handleBrokerResponse(response: ClientResponse)
}

AddPartitionEvent

class AddPartitionEvent extends KafkaControllerEvent(ControllerEventType.AddPartition) {
	override def makeStatesChange(): Map[Int, ClientRequest] = {
		// make state change and generate requests to each broker
	}
  	
	override def handleBrokerResponse(response: ClientResponse) {
		// If necessary, do something when response is received
  	}
}

TopicChangeEvent

class TopicChangeEvent extends KafkaControllerEvent(ControllerEventType.TopicChange) {
	override def makeStatesChange(): Map[Int, ClientRequest] = {
		// make state change and generate requests to each broker
	}
  	
	override def handleBrokerResponse(response: ClientResponse) {
		// If necessary, do something when response is received
  	}
}

DeleteTopicEvent

class DeleteTopicEvent extends KafkaControllerEvent(ControllerEventType.DeleteTopic) {
	override def makeStatesChange(): Map[Int, ClientRequest] = {
		// make state change and generate requests to each broker
	}
  	
	override def handleBrokerResponse(response: ClientResponse) {
		// If necessary, do something when response is received
  	}
}

BrokerChangeEvent

class BrokerChangeEvent extends KafkaControllerEvent(ControllerEventType.BrokerChange) {
	override def makeStatesChange(): Map[Int, ClientRequest] = {
		// make state change and generate requests to each broker
	}
  	
	override def handleBrokerResponse(response: ClientResponse) {
		// If necessary, do something when response is received
  	}
}

PreferredReplicaElectionEvent

class PreferredReplicaElectionEvent extends KafkaControllerEvent(ControllerEventType.PreferredReplicaElection) {
	override def makeStatesChange(): Map[Int, ClientRequest] = {
		// make state change and generate requests to each broker
	}
  	
	override def handleBrokerResponse(response: ClientResponse) {
		// If necessary, do something when response is received
  	}
}

PartitionReassignedEvent

class PartitionReassignedEvent extends KafkaControllerEvent(ControllerEventType.PartitionReassigned) {
	override def makeStatesChange(): Map[Int, ClientRequest] = {
		// make state change and generate requests to each broker
	}
  	
	override def handleBrokerResponse(response: ClientResponse) {
		// If necessary, do something when response is received
  	}
}

ReassignedPartitionIsrChangeEvent

class ReassignedPartitionIsrChangeEvent extends KafkaControllerEvent(ControllerEventType.ReassignedPartitionIsrChange) {
	override def makeStatesChange(): Map[Int, ClientRequest] = {
		// make state change and generate requests to each broker
	}
  	
	override def handleBrokerResponse(response: ClientResponse) {
		// If necessary, do something when response is received
  	}
}

ControlledShutdown

class ControlledShutdownEvent extends KafkaControllerEvent(ControllerEventType.ControlledShutdown) {
	override def makeStatesChange(): Map[Int, ClientRequest] = {
		// make state change and generate requests to each broker
	}
  	
	override def handleBrokerResponse(response: ClientResponse) {
		// If necessary, do something when response is received
  	}
}

Event Handling Process

The general event handling process would be something like this:

while(!shutdown){
	Event event = eventQueue.pollFirst()
	// Make state change
	try {
		val brokerRequests = event.makeStateChange(partitionStateMachine)
   		brokerRequests.map { case (broker, request) =>
    		networkClient.send(request)
        	event.unAckedNode.add(broker)
    	
		while (!event.unAckedNode.isEmpty) {
			try {
				networkClient.poll(timeout)
			} catch {
				case KafkaApiException =>
					// Do something
				case Exception =>
					// Error handling
			}
			checkNodeLivenessAndIgnoreDeadNode()
		}
	} catch {
		case StateChangeException =>
			// handle illegal state change
	}
}

Discussion Required

1. As stated in KAFKA-2029, current state change propagation has an issue that in an unbalanced cluster. State change on a heavily loaded broker will be much slower than a lightly loaded broker. This is because of the following two reasons:
   • Controller traffic is not prioritized on broker side. So controller messages needs to wait until some clients requests are handled which takes much longer on a heavily loaded broker.
   • Heavily loaded broker needs to take state change for more partitions while same amount state changes are distributed among several brokers as followers.
   Batching state change into a single request and prioritize the controller traffic on broker will solve the problem. But I wonder is there any specific reason we did not batch the state change in current controller?
2. Ideally, we should try to let the brokers in consistent state if possible. That indicates that we should put synchronization barriers between two events. Which means we do not execute the next event until:
   1. Callback has been fired on each broker, or
   2. A broker is down and we will just skip waiting for its callback and move on.
   Does it make sense to do so? Implementation wise, it means that if a broker is down, we will stop sending message to all brokers until we know it's down. (And this leads to question 3)
3. We actually have two ways to determine if a broker is alive or not: from Zookeeper or from NetworkClient. Currently we take zookeeper as source of truth, but when we send messages, NetworkClient connectivity is the one actually matters. Because Zookeeper timeout could take a while, so what should we do if Zookeeper says a broker is alive but NetworkClient shows it is disconnected. Should we block the current event processing? If there is a long GC, is it possible that NetworkClient says broker is connected but Zookeeper says it's dead? Back to question 2, it is about when we consider "a broker is down".