kafka知识体系-集群partitions/replicas默认分配解析

本系列主要讲解kafka基本设计和原理分析，分如下内容：

1. 基本概念
2. 消息模型
3. kafka副本同步机制
4. kafka文件存储机制
5. kafka数据可靠性和一致性保证
6. kafka leader选举
7. kafka消息传递语义
8. Kafka集群partitions/replicas默认分配解析

##Kafka集群partitions/replicas默认分配解析
kafka在创建topic，需要指定分区数和副本的数量，本节探讨分区、副本在broker上的分配情况。

目标

replica assignment有三个目标：

• 在brokers之间均分replicas
• partition与它的其他replicas不再同一个broker上
• 如果broker有rack信息，则partition的replicas尽量分配在不同rack上面

kafka0.10版本支持了2种replica assignment策略(对于partition来说，它也是由n个replica组成的)，一种是rack unware，一种是rack-ware，这里的rack就是机架的意思。

rack unaware

• 随机从broker list选一个开始，然后对每个partition的第一个replica进行round-robin分配
• 之后对每个partition的其余replicas进行递增1位错位开来

这种策略分配算法核心代码如下：

private def assignReplicasToBrokersRackUnaware(nPartitions: Int,
                                                 replicationFactor: Int,
                                                 brokerList: Seq[Int],
                                                 fixedStartIndex: Int,
                                                 startPartitionId: Int): Map[Int, Seq[Int]] = {
    val ret = mutable.Map[Int, Seq[Int]]()
    val brokerArray = brokerList.toArray
    val startIndex = if (fixedStartIndex >= 0) fixedStartIndex else rand.nextInt(brokerArray.length)
    var currentPartitionId = math.max(0, startPartitionId)
    var nextReplicaShift = if (fixedStartIndex >= 0) fixedStartIndex else rand.nextInt(brokerArray.length)
    for (_ <- 0 until nPartitions) {
      if (currentPartitionId > 0 && (currentPartitionId % brokerArray.length == 0))
        nextReplicaShift += 1
      val firstReplicaIndex = (currentPartitionId + startIndex) % brokerArray.length
      val replicaBuffer = mutable.ArrayBuffer(brokerArray(firstReplicaIndex))
      for (j <- 0 until replicationFactor - 1)
        replicaBuffer += brokerArray(replicaIndex(firstReplicaIndex, nextReplicaShift, j, brokerArray.length))
      ret.put(currentPartitionId, replicaBuffer)
      currentPartitionId += 1
    }
    ret
  }


  private def replicaIndex(firstReplicaIndex: Int, secondReplicaShift: Int, replicaIndex: Int, nBrokers: Int): Int = {
    val shift = 1 + (secondReplicaShift + replicaIndex) % (nBrokers - 1)
    (firstReplicaIndex + shift) % nBrokers
  }

上述代码含义大致如下先分配分区，再分配该分区的副本
假设我们现在有5个broker,对topic1设置10个分区，三个副本。即
nPartitions=10，replicationFactor=3，brokerList={0,1,2,3,4},nBrokers=5

假设从broker-0开始，有10个partition，每个partition有3个replica
则可以看到p0在broker-0，p1在broker-1，依次round下来。
到了第二个replica的时候，可以看到p0在broker-1，p1在broker-2，这样递增1位错开。

通过继承RackAwareTest类的测试代码如下：

package unit.kafka.admin

import kafka.admin.{BrokerMetadata, AdminUtils, RackAwareTest}
import kafka.utils.Logging
import org.junit.Assert._
import org.junit.Test

import scala.collection.Map

class AdminRackUnAwareTest extends RackAwareTest with Logging {
  @Test
  def testReplicaAssignment() {
    val brokerMetadatas = (0 to 4).map(new BrokerMetadata(_, None))
    val actualAssignment = AdminUtils.assignReplicasToBrokers(brokerMetadatas, 10, 3, 0)
    println(actualAssignment)
  }
}

输出结果为：

Map(8 -> ArrayBuffer(3, 0, 1), 2 -> ArrayBuffer(2, 3, 4), 5 -> ArrayBuffer(0, 2, 3), 4 -> ArrayBuffer(4, 0, 1), 7 -> ArrayBuffer(2, 4, 0), 1 -> ArrayBuffer(1, 2, 3), 9 -> ArrayBuffer(4, 1, 2), 3 -> ArrayBuffer(3, 4, 0), 6 -> ArrayBuffer(1, 3, 4), 0 -> ArrayBuffer(0, 1, 2))

为方便查看，绘图如下：

分配策略：

首分区
	broker=i%nBrokers
副本分区
shift=1+(i/nBrokers+j)%(nBrokers-1)
broker=[i+shift]%nBrokers

针对本文情况,i取值{0,1,2,3,4,5,6,7,8,9},j取值{0,1}

i=0
首分区
	broker=0
副本分区
i=0,j=0:shift=1,broker=1
i=0,j=1:shift=2,broker=2

i=3
首分区
	broker=3
副本分区
i=3,j=0:shift=1,broker=4
i=3,j=1:shift=2,broker=0


i=6
首分区
	broker=1
副本分区
i=6,j=0:shift=2,broker=3
i=6,j=1:shift=3,broker=4

rack aware

• 首先对broker list跟rack进行一次映射
• 按rack顺序round起来得到一个新的broker-list
• 使用round-robbin对parition跟broker进行映射

核心代码如下：

private def assignReplicasToBrokersRackAware(nPartitions: Int,
                                               replicationFactor: Int,
                                               brokerMetadatas: Seq[BrokerMetadata],
                                               fixedStartIndex: Int,
                                               startPartitionId: Int): Map[Int, Seq[Int]] = {
    val brokerRackMap = brokerMetadatas.collect { case BrokerMetadata(id, Some(rack)) =>
      id -> rack
    }.toMap
    val numRacks = brokerRackMap.values.toSet.size
    val arrangedBrokerList = getRackAlternatedBrokerList(brokerRackMap)
    val numBrokers = arrangedBrokerList.size
    val ret = mutable.Map[Int, Seq[Int]]()
    val startIndex = if (fixedStartIndex >= 0) fixedStartIndex else rand.nextInt(arrangedBrokerList.size)
    var currentPartitionId = math.max(0, startPartitionId)
    var nextReplicaShift = if (fixedStartIndex >= 0) fixedStartIndex else rand.nextInt(arrangedBrokerList.size)
    for (_ <- 0 until nPartitions) {
      if (currentPartitionId > 0 && (currentPartitionId % arrangedBrokerList.size == 0))
        nextReplicaShift += 1
      val firstReplicaIndex = (currentPartitionId + startIndex) % arrangedBrokerList.size
      val leader = arrangedBrokerList(firstReplicaIndex)
      val replicaBuffer = mutable.ArrayBuffer(leader)
      val racksWithReplicas = mutable.Set(brokerRackMap(leader))
      val brokersWithReplicas = mutable.Set(leader)
      var k = 0
      for (_ <- 0 until replicationFactor - 1) {
        var done = false
        while (!done) {
          val broker = arrangedBrokerList(replicaIndex(firstReplicaIndex, nextReplicaShift * numRacks, k, arrangedBrokerList.size))
          val rack = brokerRackMap(broker)
          // Skip this broker if
          // 1. there is already a broker in the same rack that has assigned a replica AND there is one or more racks
          //    that do not have any replica, or
          // 2. the broker has already assigned a replica AND there is one or more brokers that do not have replica assigned
          if ((!racksWithReplicas.contains(rack) || racksWithReplicas.size == numRacks)
              && (!brokersWithReplicas.contains(broker) || brokersWithReplicas.size == numBrokers)) {
            replicaBuffer += broker
            racksWithReplicas += rack
            brokersWithReplicas += broker
            done = true
          }
          k += 1
        }
      }
      ret.put(currentPartitionId, replicaBuffer)
      currentPartitionId += 1
    }
    ret
  }

假设有6个broker，3个rack，6个partition，每个partition有3个replica
broker和rack映射如下：
0 -> "rack1", 1 -> "rack3", 2 -> "rack3", 3 -> "rack2", 4 -> "rack2", 5 -> "rack1"

按rack顺序round起来得到一个新的broker-list,
0(rack1),3(rack2),1(rack3),5(rack1),4(rack2),2(rack3)

最后使用round-robbin对parition跟broker进行映射

0 -> 0,3,1
1 -> 3,1,5
2 -> 1,5,4
3 -> 5,4,2
4 -> 4,2,0
5 -> 2,0,3

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