RoundRobinPartitioner/HashPartitioner:

import java.util
import java.util.concurrent.atomic.AtomicLong import org.apache.kafka.clients.producer.Partitioner
import org.apache.kafka.common.Cluster class SelfRoundRobinPartitioner extends Partitioner { val next = new AtomicLong(); override def partition(topic: String, key: scala.Any, keyBytes: Array[Byte], value: scala.Any, valueBytes: Array[Byte], cluster: Cluster) = {
val partitionInfo = cluster.partitionsForTopic(topic)
val numPartitions = partitionInfo.size()
val nextIndex = next.incrementAndGet()
val partionNum: Long = nextIndex % numPartitions
partionNum.toInt
} override def close() = { } override def configure(configs: util.Map[String, _]) = { }
}
import java.util

import scala.math._
import kafka.utils.VerifiableProperties
import org.apache.kafka.clients.producer.Partitioner
import org.apache.kafka.common.Cluster class SelfHashPartitioner extends Partitioner { override def partition(topic: String, key: scala.Any, keyBytes: Array[Byte], value: scala.Any, valueBytes: Array[Byte], cluster: Cluster) = {
val partitionInfo = cluster.partitionsForTopic(topic)
val numPartitions = partitionInfo.size() if (key.isInstanceOf[Int]) {
abs(key.toString().toInt) % numPartitions
} key.hashCode() % numPartitions } override def close() = { } override def configure(configs: util.Map[String, _]) = { }
}
import java.util.Properties
import org.apache.kafka.clients.producer.{KafkaProducer, ProducerRecord} object KafkaProducer {
def main(args: Array[String]): Unit = { val brokers = "192.168.1.151:9092,192.168.1.152:9092,192.168.1.153:9092"
// val brokers = "192.168.1.151:9092"
val topic = "ScalaTopic"; val props = new Properties()
props.put("bootstrap.servers", brokers)
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer")
// props.put("partitioner.class", classOf[SelfHashPartitioner].getName)
props.put("partitioner.class", classOf[SelfRoundRobinPartitioner].getName)
props.put("producer.type", "sync")
props.put("batch.size", "1")
props.put("acks", "all") val producer = new KafkaProducer[String, String](props); val sleepFlag = false;
val message1 = new ProducerRecord[String, String](topic, "1", "test 1aa");
producer.send(message1);
if (sleepFlag) Thread.sleep(5000);
val message2 = new ProducerRecord[String, String](topic, "1", "test 1bb");
producer.send(message2);
if (sleepFlag) Thread.sleep(5000);
val message3 = new ProducerRecord[String, String](topic, "1", "test 1cc");
producer.send(message3);
if (sleepFlag) Thread.sleep(5000);
val message4 = new ProducerRecord[String, String](topic, "4", "test 4dd");
producer.send(message4);
if (sleepFlag) Thread.sleep(5000);
val message5 = new ProducerRecord[String, String](topic, "4", "test 4aa");
producer.send(message5);
if (sleepFlag) Thread.sleep(5000);
val message6 = new ProducerRecord[String, String](topic, "3", "test 3bb");
producer.send(message6);
if (sleepFlag) Thread.sleep(5000);
val message7 = new ProducerRecord[String, String](topic, "2", "test 2bb");
producer.send(message7);
if (sleepFlag) Thread.sleep(5000);
producer.close()
}
}
import java.lang
import java.util.Properties import org.apache.kafka.clients.consumer.{ConsumerRecord, ConsumerRecords, KafkaConsumer}
import scala.collection.JavaConversions._ object KafkaTConsumer {
def main(args: Array[String]): Unit = {
var groupid = "ScalaGroup"
var consumerid = "ScalaConsumer"
var topic = "ScalaTopic" //args match {
// case Array(arg1, arg2, arg3) => topic = arg1; groupid = arg2; consumerid = arg3
//} val props = new Properties()
props.put("bootstrap.servers", "192.168.1.151:9092,192.168.1.152:9092,192.168.1.153:9092")
props.put("group.id", groupid)
props.put("client.id", "test")
props.put("consumer.id", consumerid)
// props.put("auto.offset.reset", "smallest")
props.put("enable.auto.commit", "true")
props.put("auto.commit.interval.ms", "100")
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer") val consumer = new KafkaConsumer[String, String](props)
consumer.subscribe(java.util.Arrays.asList(topic)) while (true) {
val records: ConsumerRecords[String, String] = consumer.poll(100)
for (record <- records) {
println(s"Topic = ${record.topic()}, partition = ${record.partition()}, key = ${record.key()}, value = ${record.value()}")
}
} }
}

Roud robin运行结果:

Topic = ScalaTopic, partition = 0, key = 1, value = test 1cc
Topic = ScalaTopic, partition = 0, key = 3, value = test 3bb
Topic = ScalaTopic, partition = 1, key = 1, value = test 1aa
Topic = ScalaTopic, partition = 1, key = 4, value = test 4dd
Topic = ScalaTopic, partition = 2, key = 1, value = test 1bb
Topic = ScalaTopic, partition = 2, key = 4, value = test 4aa
Topic = ScalaTopic, partition = 1, key = 2, value = test 2bb

Hash 运行结果:

Topic = ScalaTopic, partition = 1, key = 1, value = test 1aa
Topic = ScalaTopic, partition = 1, key = 1, value = test 1bb
Topic = ScalaTopic, partition = 0, key = 3, value = test 3bb
Topic = ScalaTopic, partition = 2, key = 2, value = test 2bb
Topic = ScalaTopic, partition = 1, key = 1, value = test 1cc
Topic = ScalaTopic, partition = 1, key = 4, value = test 4dd
Topic = ScalaTopic, partition = 1, key = 4, value = test 4aa

Kafka0.11之RoundRobinPartitioner/HashPartitioner(Scala):的更多相关文章

  1. Kafka 学习笔记之 Kafka0.11之producer/consumer(Scala)

    Kafka0.11之producer/consumer(Scala): KafkaConsumer: import java.util.Properties import org.apache.kaf ...

  2. Kafka 学习笔记之 Kafka0.11之console-producer/console-consumer

    Kafka 学习笔记之 Kafka0.11之console-producer/console-consumer: 启动Zookeeper 启动Kafka0.11 创建一个新的Topic: ./kafk ...

  3. kafka 幂等生产者及事务(kafka0.11之后版本新特性)

    1. 幂等性设计1.1 引入目的生产者重复生产消息.生产者进行retry会产生重试时,会重复产生消息.有了幂等性之后,在进行retry重试时,只会生成一个消息. 1.2 幂等性实现1.2.1 PID ...

  4. kafka0.8--0.11各个版本特性预览介绍

    kafka-0.8.2 新特性 producer不再区分同步(sync)和异步方式(async),所有的请求以异步方式发送,这样提升了客户端效率.producer请求会返回一个应答对象,包括偏移量或者 ...

  5. kafka 0.11.0.3 源码编译

    首先下载 kafka 0.11.0.3 版本 源码: http://mirrors.hust.edu.cn/apache/kafka/0.11.0.3/ 下载源码 首先安装 gradle,不再说明 1 ...

  6. intellij 调试spark scala 程序 报错

    spark用的是cdh spark-2.0.1 package main.scala import org.apache.spark.rdd.RDD import org.apache.spark.{ ...

  7. geotrellis使用(六)Scala并发(并行)编程

    本文主要讲解Scala的并发(并行)编程,那么为什么题目概称geotrellis使用(六)呢,主要因为本系列讲解如何使用Geotrellis,具体前几篇博文已经介绍过了.我觉得干任何一件事情基础很重要 ...

  8. Eclipse+maven+scala2.11.8+spark2.0.0的环境部署

    主要在maven-for-scalaIDE纠结了,因为在eclipse版本是luna4.x 里面有自己带有的maven. 根据网上面无脑的下一步下一步,出现了错误,在此讲解各个插件的用途,以此新人看见 ...

  9. 如何在Ubuntu上配置scala教程

    Scala是一门多范式的编程语言,一种类似java的编程语言,设计初衷是实现可伸缩的语言 .并集成面向对象和函数式变成的各种特性.这里为了学习spark特地配置了scala. 1.下载scala安装包 ...

随机推荐

  1. POJ - 3164-Command Network 最小树形图——朱刘算法

    POJ - 3164 题意: 一个有向图,存在从某个点为根的,可以到达所有点的一个最小生成树,则它就是最小树形图. 题目就是求这个最小的树形图. 参考资料:https://blog.csdn.net/ ...

  2. 快速幂 HDU 1061 Rightmost Digit *

    Rightmost Digit Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 65536/32768 K (Java/Others)T ...

  3. mybatis foreach批量处理

    ---恢复内容开始--- http://blog.csdn.net/jiesa/article/details/52185617 foreach属性 属性 描述 item 循环体中的具体对象.支持属性 ...

  4. Python操作MongoDB文档数据库

    1.Pymongo 安装 安装pymongo: pip install pymongo PyMongo是驱动程序,使python程序能够使用Mongodb数据库,使用python编写而成: 2.Pym ...

  5. Day005_Linux基础之文件权限

    test.sh  举例: [oldboy@luffy001 ~]$ ls -l test.sh -rw-r--r-- 1 oldboy ops 0 Nov 14 10:42 test.sh  该文件权 ...

  6. 联邦学习开源框架FATE助力腾讯神盾沙箱,携手打造数据安全合作生态

    近日,微众银行联邦学习FATE开源社区迎来了两位新贡献者——来自腾讯的刘洋及秦姝琦,作为云计算安全领域的专家,两位为FATE构造了新的功能点,并在Github上提交修复了相关漏洞.(Github项目地 ...

  7. Django之模型层(2)

    Django之模型层(2) 一.创建模型 实例:我们来假定下面这些概念,字段和关系. 作者模型:一个作者由姓名和年龄. 作者详细模型:把作者的详情放到详情表,包含生日,手机号,家庭住址等信息.作者详情 ...

  8. Google 官方 侧滑 drawerlayout

    一.概述 目前侧滑框架已经很多了,但是我常用的也就那么2个 ,slidingmenu 和sidemenu-android, 但是项目要求使用官方的,所以就看了一下drawerlayout 二.代码 官 ...

  9. java.io.IOException: 设备上没有空间

    解决: 逐层目录查找最大文件夹du -h --max-depth=1 确定最大目录为log目录,删除log目录下的所有日志文件rm -f *

  10. Elastic-Job:动态添加任务,支持动态分片

    多情只有春庭月,犹为离人照落花. 概述 因项目中使用到定时任务,且服务部署多实例,因此需要解决定时任务重复执行的问题.即在同一时间点,每一个定时任务只在一个节点上执行.常见的开源方案,如 elasti ...