Kafka如何做到全局有序

我会带着你远行 2024-02-19 15:57 47阅读 0赞

### 全局有序？ ###

业务: 1 2 3 5  
消费: 1 5 2 3

**1.一个topic 一个分区 3个 虽然保证全局有序，但是性能下降 生产**（很多公司也在使用，或则没有吧太在意）  
**2.单分区有序，那么我们想方法把同一个特征数据写到一个分区**

p0 p1 p2

id money  
业务系统：  
insert into t values(1,1)  
update t set age= 200 where id =1  
update t set age= 400 where id =1  
update t set age= 1000000 where id =1  
delete from t where id =1  
最终的结果的是0条  
**如何把特征的数据写一个分区里面**  
producer send api (key,value)  
key: erp.t.1 null  
value：SQL数据  
业务系统： 特征值  
insert into t values(1,1) erp.t.1 hash 5 %3=1…2 -->p2  
update t set age= 200 where id =1 erp.t.1  
update t set age= 400 where id =1 erp.t.1  
update t set age= 1000000 where id =1 erp.t.1  
delete from t where id =1 erp.t.1  
**源码读取**：[https://github.com/apache/kafka/blob/3cdc78e6bb1f83973a14ce1550fe3874f7348b05/clients/src/main/java/org/apache/kafka/clients/producer/internals/DefaultPartitioner.java][https_github.com_apache_kafka_blob_3cdc78e6bb1f83973a14ce1550fe3874f7348b05_clients_src_main_java_org_apache_kafka_clients_producer_internals_DefaultPartitioner.java]  
这5条记录都发送到一个分区 有序的发送 ，那么消费时也不会出现紊乱

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    package org.apache.kafka.clients.producer.internals;
    
    import java.util.List;
    import java.util.Map;
    import java.util.concurrent.ConcurrentHashMap;
    import java.util.concurrent.ConcurrentMap;
    import java.util.concurrent.ThreadLocalRandom;
    import java.util.concurrent.atomic.AtomicInteger;
    
    import org.apache.kafka.clients.producer.Partitioner;
    import org.apache.kafka.common.Cluster;
    import org.apache.kafka.common.PartitionInfo;
    import org.apache.kafka.common.utils.Utils;
    
    /**
     * The default partitioning strategy:
     * <ul>
     * <li>If a partition is specified in the record, use it
     * <li>If no partition is specified but a key is present choose a partition based on a hash of the key
     * <li>If no partition or key is present choose a partition in a round-robin fashion
     */
    public class DefaultPartitioner implements Partitioner {
    
        private final ConcurrentMap<String, AtomicInteger> topicCounterMap = new ConcurrentHashMap<>();
    
        public void configure(Map<String, ?> configs) {}
    
        /**
         * Compute the partition for the given record.
         *
         * @param topic The topic name
         * @param key The key to partition on (or null if no key)
         * @param keyBytes serialized key to partition on (or null if no key)
         * @param value The value to partition on or null
         * @param valueBytes serialized value to partition on or null
         * @param cluster The current cluster metadata
         */
        public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {
            List<PartitionInfo> partitions = cluster.partitionsForTopic(topic);
            int numPartitions = partitions.size();
            if (keyBytes == null) {
                int nextValue = nextValue(topic);
                List<PartitionInfo> availablePartitions = cluster.availablePartitionsForTopic(topic);
                if (availablePartitions.size() > 0) {
                    int part = Utils.toPositive(nextValue) % availablePartitions.size();
                    return availablePartitions.get(part).partition();
                } else {
                    // no partitions are available, give a non-available partition
                    return Utils.toPositive(nextValue) % numPartitions;
                }
            } else {
                // hash the keyBytes to choose a partition
                return Utils.toPositive(Utils.murmur2(keyBytes)) % numPartitions;
            }
        }
    
        private int nextValue(String topic) {
            AtomicInteger counter = topicCounterMap.get(topic);
            if (null == counter) {
                counter = new AtomicInteger(ThreadLocalRandom.current().nextInt());
                AtomicInteger currentCounter = topicCounterMap.putIfAbsent(topic, counter);
                if (currentCounter != null) {
                    counter = currentCounter;
                }
            }
            return counter.getAndIncrement();
        }
    
        public void close() {}
    
    }

### 重点： ###

只要把同一个表的同一个主键的数据发到同一个分区即可（如果多数据库得加入数据库名）

分区定义如下：

private int partitionDefine(String keyToPartition) {
    	if (keyToPartition == null) {
    		return new Random().nextInt(numPartitions);
    	} else {
    		return Math.abs(keyToPartition.hashCode()) % numPartitions;
    	}
    }

传入的参数 tableName+主键  
这样，消费到的数据就是有序的。不同的场景灵活运用即可。

[https_github.com_apache_kafka_blob_3cdc78e6bb1f83973a14ce1550fe3874f7348b05_clients_src_main_java_org_apache_kafka_clients_producer_internals_DefaultPartitioner.java]: https://github.com/apache/kafka/blob/3cdc78e6bb1f83973a14ce1550fe3874f7348b05/clients/src/main/java/org/apache/kafka/clients/producer/internals/DefaultPartitioner.java