Kafka Replication High-level Design

The purpose of adding replication in Kafka is for stronger durability and higher availability. We want to guarantee that any successfully published message will not be lost and can be consumed, even when there are server failures. Such failures can be caused by machine error, program error, or more commonly, software upgrades. We have the following high-level goals:

Configurable durability guarantees: For example, an application with critical data can choose stronger durability, with increased write latency, and another application generating a large volume of soft-state data can choose weaker durability but better write response time.
Automated replica management: We want to simplify the assignment of replicas to broker servers and be able to grow the cluster incrementally.

There are mainly two problems that we need to solve here:

How to assign replicas of a partition to broker servers evenly?
For a given partition, how to propagate every message to all replicas?

Replica placements

Initial placement

(only create topic, make decision based on current live brokers (manual create topic command); rebalance command (recomputed assignment based on new cluster topology, every partition is moved) and make the move)
We first use an administrative api to create the initial set of brokers:

create cluster with brokers broker-0, broker-1, broker2

We then use another administrative api to create a new topic:

create topic topicX with 100 partitions

After that, the following information will be registered in zookeeper:

a list of brokers;
a list of topics and for each topic, a list of partitions.

For better load balancing, we want to over partition a topic. Typically, there will be many more partitions than servers. For each topic, we want to divide the partitions evenly among all the brokers. We sort the list of brokers and the list of partitions. If there are n brokers, we assign the ith partition to the (i mod n)th broker. The first replica of this partition will reside on this assigned broker and is referred to as the preferred replica of this partition. We want to place the other replicas in such a way that if a broker is down, its load is spread evenly to all surviving brokers, instead of to a single one. In order to achieve that, suppose there are m partitions assigned to a broker i. The jth replica of partition k will be assigned to broker (i + j + k) mod n. The following figure illustrates the replica assignments for partitions p0 to p14 on brokers broker-0 to broker-4. In this example, if broker-0 goes down, partitions p0, p1, and p2 can be served from all remaining 4 brokers. We store the information about the replica assignment for ach partition in Zookeeper.

Incrementally add brokers online

We’d like to be able to incrementally grow the set of brokers using an administrative command like the following.

alter cluster add brokers broker-3, broker-4

When a new broker is added, we will automatically move some partitions from existing brokers to the new one. Out goal is to minimize the amount of data movement while maintaining a balanced load on each broker. We use a standalone coordinator process to do the rebalance and the algorithm is given below.

Data replication

We’d like to allow a client to choose either asynchronous or synchronous replication. In the former case, a message to be published is acknowledged as soon as it reaches 1 replica. In the latter case, we will make our best effort to make sure that a message is only acknowledged after it reaches multiple replicas. When a client tries to publish a message to a partition of a topic, we need to propagate the message to all replicas. We have to decide:

how to propagate a message;
how many replicas receive the message before we acknowledge to the client;
what happens when a replica goes down;
what happens when a failed replica comes back again.

We introduce existing replication strategies in Section 2.1. We then describe our synchronous and asynchronous replication in Section 2.2 and Section 2.3, respectively.

Related work

There are two common strategies for keeping replicas in sync, primary-backup replication and quorum-based replication. In both cases, one replica is designated as the leader and the rest of the replicas are called followers. All write requests go through the leader and the leader propagates the writes to the follower.
In primary-backup replication, the leader waits until the write completes on every replica in the group before acknowledging the client. If one of the replicas is down, the leader drops it from the current group and continues to write to the remaining replicas. A failed replica is allowed to rejoin the group if it comes back and catches up with the leader. With f replicas, primary-backup replication can tolerate f-1 failures.
In the quorum-based approach, the leader waits until a write completes on a majority of the replicas. The size of the replica group doesn’t change even when some replicas are down. If there are 2f+1 replicas, quorum-based replication can tolerate f replica failures. If the leader fails, it needs at least f+1 replicas to elect a new leader.

There are tradeoffs between the 2 approaches:

The quorum-based approach has better write latency than the primary-backup one. A delay (e.g., long GC) in any replica increases the write latency in the latter, but not the former.
Given the same number of replicas, the primary-backup approach tolerates more concurrent failures.
A replication factor of 2 works well with the primary-backup approach. In quorum-based replication, both replicas have to be up for the system to be available.

We choose the primary-backup replication in Kafka since it tolerates more failures and works well with 2 replicas. A hiccup can happen when a replica is down or becomes slow. However, those are relatively rare events and the hiccup time can be reduced by tuning various timeout parameters.

Synchronous replication

Our synchronous replication follows the typical primary-backup approach. Each partition has n replicas and can tolerate n-1 replica failures. One of the replicas is elected as the leader and the rest of the replicas are followers. The leader maintains a set of in-sync replicas (ISR): the set of replicas that have fully caught up with the leader. For each partition, we store in Zookeeper the current leader and the current ISR.

Each replica stores messages in a local log and maintains a few important offset positions in the log (depicted in Figure 1). The log end offset (LEO) represents the tail of the log. The high watermark (HW) is the offset of the last committed message. Each log is periodically synced to disks. Data before the flushed offset is guaranteed to be persisted on disks. As we will see, the flush offset can be before or after HW.

Writes

To publish a message to a partition, the client first finds the leader of the partition from Zookeeper and sends the message to the leader. The leader writes the message to its local log. Each follower constantly pulls new messages from the leader using a single socket channel. That way, the follower receives all messages in the same order as written in the leader. The follower writes each received message to its own log and sends an acknowledgment back to the leader. Once the leader receives the acknowledgment from all replicas in ISR, the message is committed. The leader advances the HW and sends an acknowledgment to the client. For better performance, each follower sends an acknowledgment after the message is written to memory. So, for each committed message, we guarantee that the message is stored in multiple replicas in memory. However, there is no guarantee that any replica has persisted the commit message to disks though. Given that correlated failures are relatively rare, this approach gives us a good balance between response time and durability. In the future, we may consider adding options that provide even stronger guarantees. The leader also periodically broadcasts the HW to all followers. The broadcasting can be piggybacked on the return value of the fetch requests from the followers. From time to time, each replica checkpoints its HW to its disk.

Reads

For simplicity, reads are always served from the leader. Only messages up to the HW are exposed to the reader.

Failure scenarios

Follower failure

After a configured timeout period, the leader will drop the failed follower from its ISR and writes will continue on the remaining replicas in ISR. If the failed follower comes back, it first truncates its log to the last checkpointed HW. It then starts to catch up all messages after its HW from the leader. When the follower fully catches up, the leader will add it back to the current ISR.

Leader failure

There are 3 cases of leader failure which should be considered -

The leader crashes before writing the messages to its local log. In this case, the client will timeout and resend the message to the new leader.
The leader crashes after writing the messages to its local log, but before sending the response back to the client
1. Atomicity has to be guaranteed: Either all the replicas wrote the messages or none of them
2. The client will retry sending the message. In this scenario, the system should ideally ensure that the messages are not written twice. Maybe, one of the replicas had written the message to its local log, committed it, and it gets elected as the new leader.
The leader crashes after sending the response. In this case, a new leader will be elected and start receiving requests.

When this happens, we need to perform the following steps to elect a new leader.

Each surviving replica in ISR registers itself in Zookeeper.
The replica that registers first becomes the new leader. The new leader chooses its LEO as the new HW.
Each replica registers a listener in Zookeeper so that it will be informed of any leader change. Everytime a replica is notified about a new leader:
1. If the replica is not the new leader (it must be a follower), it truncates its log to its HW and then starts to catch up from the new leader.
The leader waits until all surviving replicas in ISR have caught up or a configured time has passed. The leader writes the current ISR to Zookeeper and opens itself up for both reads and writes.

(Note, during the initial startup when ISR is empty, any replica can become the leader.)

Asynchronous replication

To support asynchronous replication, the leader can acknowledge the client as soon it finishes writing the message to its local log. The only caveat is that during the catchup phase, the follower may have to truncate the data before its HW. Since the replication is asynchronous, there is no guarantee that a commit message can survive any broker failure.

Open Issues

How can atomicity be guaranteed in the 2nd type of leader failure
If the brokers are in multiple racks, how to guarantee that at least one replica goes to a different rack?

Kafka Replication Detailed Design

The document describes key data structures and algorithms in Kafka replication.

Paths stored in Zookeeper

Notation: When an element in a path is denoted xyz, that means that the value of xyz is not fixed and there is in fact a znode for each possible value of xyz. For example /topics/topic would be a directory named /topics containing a directory for each topic name. An arrow -> is used to indicate the contents of a znode. For example /hello -> world would indicate a znode /hello containing the value "world". A path is persistent unless it’s marked as ephemeral.

We store the following paths in Zookeeper:

Stores the information of all live brokers.
```
 /brokers/ids/[broker_id] --> host:port 
```
(ephemeral; created by admin)
Stores for each partition, a list of the currently assigned replicas. For each replica, we store the id of the broker to which the replica is assigned. The first replica is the preferred replica. Note that for a given partition, there is at most 1 replica on a broker. Therefore, the broker id can be used as the replica id
```
/brokers/topics/[topic]/[partition_id]/replicas --> {broker_id …}
```
(created by admin)
Stores the id of the replica that’s the current leader of this partition
```
 /brokers/topics/[topic]/[partition_id]/leader --> broker_id (ephemeral) 
```
(created by leader)

Stores the id of the set of replicas that are in-sync with the leader

 /brokers/topics/[topic]/[partition_id]/ISR -->{broker_id, …}(created by leader)

This path is used when we want to reassign some partitions to a different set of brokers. For each partition to be reassigned, it stores a list of new replicas and their corresponding assigned brokers. This path is created by an administrative process and is automatically removed once the partition has been moved successfully
```
 /brokers/partitions_reassigned/[topic]/[partition_id] --> {broker_id …} (created by admin) 
```

Key data structures

Every broker stores a list of partitions and replicas assigned to it. The current leader of a partition further maintains 3 sets: AR, ISR, CUR and RAR, which correspond to the set of replicas that are assigned to the partition, in-sync with the leader, catching up with the leader, and being reassigned to other brokers. Normally, ISR  AR and AR = ISR + CUR. The leader of a partition maintains a commitQ and uses it to buffer all produce requests to be committed. For each replica assigned to a broker, the broker periodically stores its HW in a checkpoint file.

Replica { // a replica of a partition
  broker_id : int
  partition   : Partition
  isLocal     : Boolean  // is this replica local to this broker
  log           : Log          // local log associated with this replica
  hw           : long         // offset of the last committed message
  leo           : long         // log end offset
}

Partition { //a partition in a topic
  topic           : string
  partition_id : int
  leader         : Replica        // the leader replica of this partition
  commitQ     : Queue         // produce requests pending commit at the leader
  AR              : Set[Replica] // replicas assigned to this partition
  ISR             : Set[Replica] // In-sync replica set, maintained at the leader
  CUR           : Set[Replica] // Catch-up replica set, maintained at the leader
  RAR           : Set[Replica] // Reassigned replica set, maintained at the leader
}

Key algorithms

Zookeeper listeners ONLY on the leader

Replica-change listener:
1. child change on /brokers/topics (new topic registered)
2. child change on /brokers/topics/topic (new partition registered)
3. value change on /brokers/topics/topic/partition_id/replicas (new replica assigned)
Partition-reassigned listener:
1. child change on /brokers/partitions_reassigned
2. child change on /brokers/partitions_reassigned/topic

Zookeeper listeners on all brokers

Leader-change listener: value change on /brokers/topics/topic/partition_id/leader
State-change listener: child change on /brokers/topics/topic/partition_id/state/broker_id

Configuration parameters

LeaderElectionWaitTime: controls the maximum amount of time that we wait during leader election.
KeepInSyncTime: controls the maximum amount of time that a leader waits before dropping a follower from the in-sync replica set.

Broker startup

Each time a broker starts up, it calls brokerStartup() and the algorithms are described below

brokerStartup()
{
  register its broker_id in /brokers/ids/[broker_id] in ZK
  get replica info from ZK and compute AR, a list of replicas assigned to this broker
  for each r in AR
  {
      register its broker_id in /brokers/topics/[topic]/[partition_id]/state/[broker_id] in ZK
      register stateChangeListener() on /brokers/topics/[topic]/[partition_id]/state/[broker_id]
  }
}

State change listener

Each broker has a ZK path that it listens to for state change requests from the leader

stateChangeListener(r) {  // listens to state change requests issued by the leader and acts on those
    read next state change request
    if(becomeFollowerRequest)   becomeFollower(r)
    if(replicaUnassignedRequest)  closeReplica(r)
    if(deleteReplicaRequest)  deleteReplica(r)
    if(replicaAssignedRequest)  replicaStateChange(r)
}

State change operations

Replica state change

This state change is requested by the leader for a new replica assignment

replicaStateChange(r: Replica) {
  if( r's log is not already started) {
     do local recovery of r's log
     r.hw = min(last checkpointed HW for r, r.leo)
      register a leader-change listener on partition r.partition.partition_id
  }
   if( a leader does not exist for partition r.partition.partition_id in ZK)
      leaderElection(r)
}

Close replica

This state change is requested by the leader when a topic is deleted

closeReplica(r: Replica)
{
   stop the fetcher associated with r, if one exists
   close and delete r
}

Become follower

This state change is requested by the leader when the leader for a replica changes

becomeFollower(r: Replica)
{
  stop the current ReplicaFetcherThread, if any
  truncate the log to r.hw
  start a new ReplicaFetcherThread to the current leader of r, from offset r.leo
  start HW checkpoint thread for r
}

Become leader

This state change is done by the new leader

becomeLeader(r: Replica, ISR: Set[Replica], AR: Set[Replica])
   stop HW checkpoint thread for r
   r.hw = r.leo // TODO: check if this should actually be r.hw = last checkpointed HW for r
   wait until every live replica in AR catches up (i.e. its leo == r.hw) or a KeepInSyncTime has passed
   r.partition.ISR = the current set of replicas in sync with r
   r.partition.CUR = AR - ISR
   write r.partition.ISR & r.partition.CUR in ZK
   r.partition.RAR = replicas in /brokers/partitions_reassigned/[topic]/[partition_id] in ZK
   r.partition.leader = r                  // this enables reads/writes to this partition on this broker
   start a commit thread on r.partition
   start HW checkpoint thread for r
    get replica info from ZK and compute CUR, a list of replicas assigned to this broker
    for each r in CUR
       send become follower request to r

Leader election

leaderElection(r: Replica)
  read the current ISR and AR for r.partition.partition_id from ZK
  if( (r in AR) && (ISR is empty || r in ISR) ) {
  get r.hw for all r in ISR
  if(r.hw < max(hw) for r in ISR)
   wait for PreferredReplicaTime if r is not the preferred replica
   if(successfully write r as the current leader of r.partition in ZK)
     becomeLeader(r, ISR, CUR)
} else {
   // some other replica will become leader
}

Produce requests

Produce request handler on the leader

produceRequestHandler(pr: ProduceRequest)
{
   if( the request partition pr.partition doesn't have leader replica on this broker)
      throw NotLeaderException
   log = r.partition.leader.log
   append pr.messages to log
   pr.offset = log.LEO
   add pr to pr.partition.commitQ
}

HW checkpoint thread for partition p

while(true) {
       r = p.leader
       flush r.hw & r.log to disk
}

Committer thread for partition p

while(true) {
  pr = commitQ.dequeue
  canCommit = false
  while(!canCommit) {
    canCommit = true
    for each r in ISR
       if(!offsetReached(r, pr.offset)) {
	canCommit = false
	break
       }
   if(!canCommit) {
      p.CUR.add(r)
      p.ISR.delete(r)
      write p.ISR to ZK
   }
  }
  for each c in CUR
      if(c.leo >= pr.offset) {
	p.ISR.add(c); p.CUR.delete(c); write p.ISR to ZK
      }
      checkReassignedReplicas(pr, p.RAR, p.ISR)
      checkLoadBalancing()
       r.hw = pr.offset         // increment the HW to indicate that pr is committed
      send ACK to the client that pr is committed
}

offsetReached(r: Replica, offset: Long) {
   if(r.leo becomes equal or larger than offset within KeepInSyncTime)  return true
   return false
}

checkLoadBalancing() { // see if we need to switch the leader to the preferred replica
   if(leader replica is not the preferred one & the preferred replica is in ISR) {
       delete /brokers/topics/[topic]/[partition_id]/leader  in ZK
       stop this commit thread
       stop the HW checkpoint thread
   }
 }

checkReassignedReplicas(pr: ProduceRequest, RAR: Set[Replica], ISR: Set[Replica])

{
    // see if all reassigned replicas have fully caught up, if so, switch to those replicas

    // optimization, do the check periodically

    If (every replica in RAR has its leo >= pr.offset) {

    //newly assigned replicas are in-sync, switch over to the new replicas

       write (RAR + ISR) as the new ISR in ZK //need (RAR + ISR) in case we fail right after here

       update /brokers/topics/[topic]/[partition_id]/replicas in ZK with the new replicas in RAR

       delete /brokers/topics/[topic]/[partition_id]/leader in ZK //triggers leader election

       delete /brokers/partitions_reassigned/[topic]/[partition_id] in ZK

       stop this commit thread

    }
}

Follower fetching from leader

A follower keeps sending ReplicaFetcherRequests to the leader. The process at the leader and the follower are described below -

ReplicaFetchReqeust {
   topic: String
   partition_id: Int
   replica_id: Int
   offset: Long
}
ReplicaFetchResponse {
   hw: Long			// the offset of the last message committed at the leader
   messages: MessageSet	// fetched messages
}

At the leader

replicaFetch (f: ReplicaFetchRequest) {	// handler for ReplicaFetchRequest at leader
  leader = getLeaderReplica(f.topic, f.partition_id)

  if(leader == null) throw NotLeaderException
   response = new ReplicaFetcherResponse
   getReplica(f.topic, f.partition_id, f.replica_id).leo = f.offset
    response.messages = fetch messages starting from f.offset from leader.log
    response.hw = leader.hw
    send response back
}

At the follower

ReplicaFetcherThread for Replica r:
while(true) {
   send ReplicaFetchRequest to leader and get response:ReplicaFetcherResponse back
   append response.messages to r's log
   r.hw = response.hw
   advance offset in ReplicaFetchRequest
}

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