Flink包含8中分区策略，这8中分区策略(分区器)分别如下面所示，本文将从源码的角度一一解读每个分区器的实现方式。

• GlobalPartitioner
• ShufflePartitioner
• RebalancePartitioner
• RescalePartitioner
• BroadcastPartitioner
• ForwardPartitioner
• KeyGroupStreamPartitioner
• CustomPartitionerWrapper

继承关系图

接口

名称

ChannelSelector

实现

public interface ChannelSelector<T extends IOReadableWritable> {

    /**
     * 初始化channels数量，channel可以理解为下游Operator的某个实例(并行算子的某个subtask).
     */
    void setup(int numberOfChannels);

    /**
     *根据当前的record以及Channel总数，
     *决定应将record发送到下游哪个Channel。
     *不同的分区策略会实现不同的该方法。
     */
    int selectChannel(T record);

    /**
    *是否以广播的形式发送到下游所有的算子实例
     */
    boolean isBroadcast();
}

抽象类

名称

StreamPartitioner

实现

public abstract class StreamPartitioner<T> implements
        ChannelSelector<SerializationDelegate<StreamRecord<T>>>, Serializable {
    private static final long serialVersionUID = 1L;

    protected int numberOfChannels;

    @Override
    public void setup(int numberOfChannels) {
        this.numberOfChannels = numberOfChannels;
    }

    @Override
    public boolean isBroadcast() {
        return false;
    }

    public abstract StreamPartitioner<T> copy();
}

继承关系图

GlobalPartitioner

简介

该分区器会将所有的数据都发送到下游的某个算子实例(subtask id = 0)

源码解读

/**
 * 发送所有的数据到下游算子的第一个task(ID = 0)
 * @param <T>
 */
@Internal
public class GlobalPartitioner<T> extends StreamPartitioner<T> {
    private static final long serialVersionUID = 1L;

    @Override
    public int selectChannel(SerializationDelegate<StreamRecord<T>> record) {
        //只返回0，即只发送给下游算子的第一个task
        return 0;
    }

    @Override
    public StreamPartitioner<T> copy() {
        return this;
    }

    @Override
    public String toString() {
        return "GLOBAL";
    }
}

图解

ShufflePartitioner

简介

随机选择一个下游算子实例进行发送

源码解读

/**
 * 随机的选择一个channel进行发送
 * @param <T>
 */
@Internal
public class ShufflePartitioner<T> extends StreamPartitioner<T> {
    private static final long serialVersionUID = 1L;

    private Random random = new Random();

    @Override
    public int selectChannel(SerializationDelegate<StreamRecord<T>> record) {
        //产生[0,numberOfChannels)伪随机数，随机发送到下游的某个task
        return random.nextInt(numberOfChannels);
    }

    @Override
    public StreamPartitioner<T> copy() {
        return new ShufflePartitioner<T>();
    }

    @Override
    public String toString() {
        return "SHUFFLE";
    }
}

图解

BroadcastPartitioner

简介

发送到下游所有的算子实例

源码解读

/**
 * 发送到所有的channel
 */
@Internal
public class BroadcastPartitioner<T> extends StreamPartitioner<T> {
    private static final long serialVersionUID = 1L;
    /**
     * Broadcast模式是直接发送到下游的所有task，所以不需要通过下面的方法选择发送的通道
     */
    @Override
    public int selectChannel(SerializationDelegate<StreamRecord<T>> record) {
        throw new UnsupportedOperationException("Broadcast partitioner does not support select channels.");
    }

    @Override
    public boolean isBroadcast() {
        return true;
    }

    @Override
    public StreamPartitioner<T> copy() {
        return this;
    }

    @Override
    public String toString() {
        return "BROADCAST";
    }
}

图解

RebalancePartitioner

简介

通过循环的方式依次发送到下游的task

源码解读

/**
 *通过循环的方式依次发送到下游的task
 * @param <T>
 */
@Internal
public class RebalancePartitioner<T> extends StreamPartitioner<T> {
    private static final long serialVersionUID = 1L;

    private int nextChannelToSendTo;

    @Override
    public void setup(int numberOfChannels) {
        super.setup(numberOfChannels);
        //初始化channel的id，返回[0,numberOfChannels)的伪随机数
        nextChannelToSendTo = ThreadLocalRandom.current().nextInt(numberOfChannels);
    }

    @Override
    public int selectChannel(SerializationDelegate<StreamRecord<T>> record) {
        //循环依次发送到下游的task，比如：nextChannelToSendTo初始值为0，numberOfChannels(下游算子的实例个数，并行度)值为2
        //则第一次发送到ID = 1的task，第二次发送到ID = 0的task，第三次发送到ID = 1的task上...依次类推
        nextChannelToSendTo = (nextChannelToSendTo + 1) % numberOfChannels;
        return nextChannelToSendTo;
    }

    public StreamPartitioner<T> copy() {
        return this;
    }

    @Override
    public String toString() {
        return "REBALANCE";
    }
}

图解

RescalePartitioner

简介

基于上下游Operator的并行度，将记录以循环的方式输出到下游Operator的每个实例。
举例: 上游并行度是2，下游是4，则上游一个并行度以循环的方式将记录输出到下游的两个并行度上;上游另一个并行度以循环的方式将记录输出到下游另两个并行度上。 若上游并行度是4，下游并行度是2，则上游两个并行度将记录输出到下游一个并行度上；上游另两个并行度将记录输出到下游另一个并行度上。

源码解读

@Internal
public class RescalePartitioner<T> extends StreamPartitioner<T> {
    private static final long serialVersionUID = 1L;

    private int nextChannelToSendTo = -1;

    @Override
    public int selectChannel(SerializationDelegate<StreamRecord<T>> record) {
        if (++nextChannelToSendTo >= numberOfChannels) {
            nextChannelToSendTo = 0;
        }
        return nextChannelToSendTo;
    }

    public StreamPartitioner<T> copy() {
        return this;
    }

    @Override
    public String toString() {
        return "RESCALE";
    }
}

图解

尖叫提示

Flink 中的执行图可以分成四层：StreamGraph -> JobGraph -> ExecutionGraph -> 物理执行图。

StreamGraph：是根据用户通过 Stream API 编写的代码生成的最初的图。用来表示程序的拓扑结构。

JobGraph：StreamGraph经过优化后生成了 JobGraph，提交给 JobManager 的数据结构。主要的优化为，将多个符合条件的节点 chain 在一起作为一个节点，这样可以减少数据在节点之间流动所需要的序列化/反序列化/传输消耗。

ExecutionGraph：JobManager 根据 JobGraph 生成ExecutionGraph。ExecutionGraph是JobGraph的并行化版本，是调度层最核心的数据结构。

物理执行图：JobManager 根据 ExecutionGraph 对 Job 进行调度后，在各个TaskManager 上部署 Task 后形成的“图”，并不是一个具体的数据结构。

而StreamingJobGraphGenerator就是StreamGraph转换为JobGraph。在这个类中，把ForwardPartitioner和RescalePartitioner列为POINTWISE分配模式，其他的为ALL_TO_ALL分配模式。代码如下：

if (partitioner instanceof ForwardPartitioner || partitioner instanceof RescalePartitioner) {
            jobEdge = downStreamVertex.connectNewDataSetAsInput(
                headVertex,

               // 上游算子(生产端)的实例(subtask)连接下游算子(消费端)的一个或者多个实例(subtask)
                DistributionPattern.POINTWISE,
                resultPartitionType);
        } else {
            jobEdge = downStreamVertex.connectNewDataSetAsInput(
                headVertex,
                // 上游算子(生产端)的实例(subtask)连接下游算子(消费端)的所有实例(subtask)
                DistributionPattern.ALL_TO_ALL,
                resultPartitionType);
        }

ForwardPartitioner

简介

发送到下游对应的第一个task，保证上下游算子并行度一致，即上有算子与下游算子是1:1的关系

源码解读

/**
 * 发送到下游对应的第一个task
 * @param <T>
 */
@Internal
public class ForwardPartitioner<T> extends StreamPartitioner<T> {
    private static final long serialVersionUID = 1L;

    @Override
    public int selectChannel(SerializationDelegate<StreamRecord<T>> record) {
        return 0;
    }

    public StreamPartitioner<T> copy() {
        return this;
    }

    @Override
    public String toString() {
        return "FORWARD";
    }
}

图解

尖叫提示

在上下游的算子没有指定分区器的情况下，如果上下游的算子并行度一致，则使用ForwardPartitioner，否则使用RebalancePartitioner，对于ForwardPartitioner，必须保证上下游算子并行度一致，否则会抛出异常

//在上下游的算子没有指定分区器的情况下，如果上下游的算子并行度一致，则使用ForwardPartitioner，否则使用RebalancePartitioner
            if (partitioner == null && upstreamNode.getParallelism() == downstreamNode.getParallelism()) {
                partitioner = new ForwardPartitioner<Object>();
            } else if (partitioner == null) {
                partitioner = new RebalancePartitioner<Object>();
            }

            if (partitioner instanceof ForwardPartitioner) {
                //如果上下游的并行度不一致，会抛出异常
                if (upstreamNode.getParallelism() != downstreamNode.getParallelism()) {
                    throw new UnsupportedOperationException("Forward partitioning does not allow " +
                        "change of parallelism. Upstream operation: " + upstreamNode + " parallelism: " + upstreamNode.getParallelism() +
                        ", downstream operation: " + downstreamNode + " parallelism: " + downstreamNode.getParallelism() +
                        " You must use another partitioning strategy, such as broadcast, rebalance, shuffle or global.");
                }
            }

KeyGroupStreamPartitioner

简介

根据key的分组索引选择发送到相对应的下游subtask

源码解读

• org.apache.flink.streaming.runtime.partitioner.KeyGroupStreamPartitioner

/**
 * 根据key的分组索引选择发送到相对应的下游subtask
 * @param <T>
 * @param <K>
 */
@Internal
public class KeyGroupStreamPartitioner<T, K> extends StreamPartitioner<T> implements ConfigurableStreamPartitioner {
...

    @Override
    public int selectChannel(SerializationDelegate<StreamRecord<T>> record) {
        K key;
        try {
            key = keySelector.getKey(record.getInstance().getValue());
        } catch (Exception e) {
            throw new RuntimeException("Could not extract key from " + record.getInstance().getValue(), e);
        }
        //调用KeyGroupRangeAssignment类的assignKeyToParallelOperator方法,代码如下所示
        return KeyGroupRangeAssignment.assignKeyToParallelOperator(key, maxParallelism, numberOfChannels);
    }
...
}

• org.apache.flink.runtime.state.KeyGroupRangeAssignment

public final class KeyGroupRangeAssignment {
...

    /**
     * 根据key分配一个并行算子实例的索引，该索引即为该key要发送的下游算子实例的路由信息，
     * 即该key发送到哪一个task
     */
    public static int assignKeyToParallelOperator(Object key, int maxParallelism, int parallelism) {
        Preconditions.checkNotNull(key, "Assigned key must not be null!");
        return computeOperatorIndexForKeyGroup(maxParallelism, parallelism, assignToKeyGroup(key, maxParallelism));
    }

    /**
     *根据key分配一个分组id(keyGroupId)
     */
    public static int assignToKeyGroup(Object key, int maxParallelism) {
        Preconditions.checkNotNull(key, "Assigned key must not be null!");
        //获取key的hashcode
        return computeKeyGroupForKeyHash(key.hashCode(), maxParallelism);
    }

    /**
     * 根据key分配一个分组id(keyGroupId),
     */
    public static int computeKeyGroupForKeyHash(int keyHash, int maxParallelism) {

        //与maxParallelism取余，获取keyGroupId
        return MathUtils.murmurHash(keyHash) % maxParallelism;
    }

    //计算分区index，即该key group应该发送到下游的哪一个算子实例
    public static int computeOperatorIndexForKeyGroup(int maxParallelism, int parallelism, int keyGroupId) {
        return keyGroupId * parallelism / maxParallelism;
    }
...

图解

CustomPartitionerWrapper

简介

通过Partitioner实例的partition方法(自定义的)将记录输出到下游。

public class CustomPartitionerWrapper<K, T> extends StreamPartitioner<T> {
    private static final long serialVersionUID = 1L;

    Partitioner<K> partitioner;
    KeySelector<T, K> keySelector;

    public CustomPartitionerWrapper(Partitioner<K> partitioner, KeySelector<T, K> keySelector) {
        this.partitioner = partitioner;
        this.keySelector = keySelector;
    }

    @Override
    public int selectChannel(SerializationDelegate<StreamRecord<T>> record) {
        K key;
        try {
            key = keySelector.getKey(record.getInstance().getValue());
        } catch (Exception e) {
            throw new RuntimeException("Could not extract key from " + record.getInstance(), e);
        }
//实现Partitioner接口，重写partition方法
        return partitioner.partition(key, numberOfChannels);
    }

    @Override
    public StreamPartitioner<T> copy() {
        return this;
    }

    @Override
    public String toString() {
        return "CUSTOM";
    }
}

比如：

public class CustomPartitioner implements Partitioner<String> {
      // key: 根据key的值来分区
      // numPartitions: 下游算子并行度
      @Override
      public int partition(String key, int numPartitions) {
         return key.length() % numPartitions;//在此处定义分区策略
      }
  }