【Kafka】核心API

矫情吗；* 2022-12-15 03:24 333阅读 0赞

### 文章目录 ###

*  资料
 *  导学
 *   *  思维导图
     *  问卷
     *  前置基础
     *  适合人群
 *  开发环境准备
 *   *  虚拟机网络
 *  Kafka配置
 *   *  上传文件并解压
     *  配置环境变量java
     *  zookeeper配置
     *  kafka配置
     *  kafka基础概念
 *  Kafka客户端操作
 *   *  区别
     *  Java客户端
 *  Kafka核心API之Producer
 *   *  API
     *  源码分析流程
     *  生产者理解
     *  Producer自定义Partition负载均衡
     *  消息传递保障
 *  【实战】Kafka Producer实战——调查问卷
 *  Kafka核心API之Consumer
 *   *  helloworld(傻瓜式 不推荐)
     *  存在问题
     *  手动提交
     *  理解Consumer Group手动控制一到多个分区
     *  单Partition提交Offset
     *  手动提交offset(订阅topic下的某个或某些partition分区)
     *  多线程创建一个consumer
     *  offset手动控制
     *  面试题：consumer限流
     *  面试题Consumer Rebalance负载均衡
 *  Kafka核心API之Stream
 *  Kafka核心API之Connect
 *   *  介绍
     *  本地数据库环境
     *  配置文件
     *  启动命令
 *  通过Confluent connect 将mysql传入kafka
 *   *  修改本地数据源和服务器数据连通
     *  解决本地数据库拒绝连接问题
     *  创建连接+验证连接
     *  传入数据验证读取
     *  通过kafka取出数据
     *  connect关键字
 *  Connect 之 Debezium MySQL
 *  Connect 之 Debezium MongoDB
 *  Kafka集群
 *   *  集群部署并启动
     *  副本集
     *  代码体验副本集
     *  kafka节点故障
     *  kafka节点故障:数据基本不会丢失
     *  面试题 kafkaLeader选举
     *  配置建议
 *  Linux命令
 *   *  插件上传文件
     *  解压zip文件
     *  解压tar
 *  面试亮点

# 资料 #

官方文档kafka.apache.org/

# 导学 #

## 思维导图 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 1]

## 问卷 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 2]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 3]

## 前置基础 ##

java基础+boot

## 适合人群 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 4]

# 开发环境准备 #

主机  
连接工具  
域名

虚拟化软件推荐  
VM  
[https://www.cnblogs.com/PrayzzZ/p/11330937.html][https_www.cnblogs.com_PrayzzZ_p_11330937.html]  
VirtualBOX

Linux-CentOS  
[http://mirror.bit.edu.cn/centos/7.8.2003/isos/x86\_64/CentOS-7-x86\_64-Minimal-2003.iso][http_mirror.bit.edu.cn_centos_7.8.2003_isos_x86_64_CentOS-7-x86_64-Minimal-2003.iso]

## 虚拟机网络 ##

参考  
[https://blog.csdn.net/wujiele/article/details/92803655][https_blog.csdn.net_wujiele_article_details_92803655]

先v8改ip4信息，然后再lan共享v8，vm编辑选择nat模式进行修改网关及网段区间。设置CentOS对应网段

互相ping验证  
![在这里插入图片描述][2020101515080955.png_pic_center]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 5]勾选

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 6]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 7]

# Kafka配置 #

分布式流平台  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 8]  
kafka由scala写出来的因此先配置jdk环境  
zookeeper也是java开发的也需要jdk

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 9]

## 上传文件并解压 ##

（新建两个文件夹software、install）  
解压到指定文件夹

tar -zxf ./software/apache-zookeeper-3.5.8-bin.tar.gz -C ./install/

## 配置环境变量java ##

[root@hadoop01 jdk1.8.0_261]# vi /etc/profile
    [root@hadoop01 jdk1.8.0_261]# source /etc/profile
    [root@hadoop01 jdk1.8.0_261]# java -version
    java version "1.8.0_261"
    
    
    # PATH
    export JAVA_HOME=/opt/install/jdk1.8.0_261
    export PATH=$PATH:$JAVA_HOME

## zookeeper配置 ##

1.修改配置文件  
conf目录修改默认样例文件。cp复制，修改zookeeper的默认生成位置（vm未分存储磁盘不用划分，和端口号）  
默认就行不用改，但是参数注意

/opt/install/apache-zookeeper-3.5.8-bin/conf
    
    [root@hadoop01 conf]# cp zoo_sample.cfg zoo.cfg 
    
    dataDir=/tmp/zookeeper
    # the port at which the clients will connect
    clientPort=2181

2  
启动

[root@hadoop01 bin]# ./zkServer.sh start
    // 法1：检验链接成功
    [root@hadoop01 bin]# ./zkCli.sh
    Connecting to localhost:2181
    [zk: localhost:2181(CONNECTED) 0] 
    // 法2：监控关键字
    [root@hadoop01 bin]# ps -ef | grep zookeeper
    // 法3：自动生成log日志在zookeeper下

## kafka配置 ##

1修改conf目录下配置  
server里修改listener  
advertised.listener  
kafka的log（先默认，现在没有划分磁盘）  
zookeeper位置（暂时默认在一台机器不用改）  
![在这里插入图片描述][20201015163133565.png_pic_center]

[root@hadoop01 config]# pwd
    /opt/install/kafka_2.13-2.6.0/config
    [root@hadoop01 config]# vi server.properties 
    
    
    # listeners = PLAINTEXT://your.host.name:9092
    listeners=PLAINTEXT://192.168.137.121:9092
    
    advertised.listeners=PLAINTEXT://192.168.137.121:9092
    // 日志位置，暂时默认
    log.dirs=/tmp/kafka-logs
    
    //链接zookeeper相关，可能为集群
    # Zookeeper connection string (see zookeeper docs for details).
    # This is a comma separated host:port pairs, each corresponding to a zk
    # server. e.g. "127.0.0.1:3000,127.0.0.1:3001,127.0.0.1:3002".
    # You can also append an optional chroot string to the urls to specify the
    # root directory for all kafka znodes.
    zookeeper.connect=localhost:2181
    
    # Timeout in ms for connecting to zookeeper
    zookeeper.connection.timeout.ms=18000

基本启停命令  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 10]

启动：kafka文件夹下 后台启动 生成日志

[root@hadoop01 kafka_2.13-2.6.0]# bin/kafka-server-start.sh config/server.properties &
    [1] 9134
    //检验1 日志输出
    Connecting to zookeeper on localhost:2181 
    // 检验2 捕获进程日志
    [root@hadoop01 kafka_2.13-2.6.0]# ps -ef|grep kafka
    // 3 直接看日志
    [root@hadoop01 kafka_2.13-2.6.0]# cd logs/
    [root@hadoop01 logs]# ll

停止

[root@hadoop01 kafka_2.13-2.6.0]# bin/kafka-server-stop.sh
    // 进程日志就没了
    [root@hadoop01 kafka_2.13-2.6.0]# ps -ef|grep kafka
    root       9558   1287  0 17:06 pts/0    00:00:00 grep --color=auto kafka

创建topic

[root@hadoop01 kafka_2.13-2.6.0]# bin/kafka-topics.sh --create --zookeeper 192.168.137.121:2181 --replication-factor 1 --partitions 1 --topic rxguo-topic
    Created topic rxguo-topic.

查看已创建的topic信息

[root@hadoop01 kafka_2.13-2.6.0]# bin/kafka-topics.sh --list --zookeeper 192.168.137.121:2181
    rxguo-topic

topic为自定义的名称  
生产消息到topic

[root@hadoop01 kafka_2.13-2.6.0]# bin/kafka-console-producer.sh --broker-list 192.168.137.121:9092 --topic rxguo-topic
    >hello world
    >rxguo

消费消息从topic

[root@hadoop01 kafka_2.13-2.6.0]# bin/kafka-console-consumer.sh --bootstrap-server 192.168.137.121:9092 --topic rxguo-topic --from-beginning
    hello world
    rxguo
    welcome on

## kafka基础概念 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 11]

# Kafka客户端操作 #

admin管理级别的操作，管理类工作，单独作为一个API  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 12]

## 区别 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 13]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 14]

## Java客户端 ##

**1 创建Java的SpringBoot文件**  
删除test测试的包和mvn的依赖，以及碍眼的自动生成文件夹

准备mvn的kafka依赖  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 15]

<dependency>
                <groupId>org.apache.kafka</groupId>
                <artifactId>kafka-clients</artifactId>
                <version>2.4.0</version>
            </dependency>

或者kafka官网查看依赖包  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 16]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 17]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 18]

**2 收集整理开发文档**  
先用kafka-client2.4,官网API文档  
[javadoc][]

通过kafka官网找到adminAPI->JavaDoc  
[http://kafka.apache.org/26/documentation.html\#api][http_kafka.apache.org_26_documentation.html_api]  
通过JavaDoc可以找到API的kafka  
Admin-Client-API  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 19]自己可以整理一下配置文档

**3 写java**

package com.bennyrhys.kafka_study1.admin;
    
    import org.apache.kafka.clients.admin.*;
    import org.apache.kafka.common.config.ConfigResource;
    
    import java.util.*;
    
    public class AdminSample { 
    
        private final static String TOPIC_NAME = "rxguo-topic";
    
        private final static AdminClient adminClient = AdminSample.adminClient();
    
        public static void main(String[] args)throws Exception { 
    // 创建连接
            System.out.println("adminClient:==================================" + adminClient);
    // 创建Topic实例
    // createTopic();
    // 删除Topic实例
    // delTopic();
    // 获取Topic列表
    // topicList();
    // 描述Topic
    // desccribeTopic();
    // 修改Config 旧版Api->新版API(对单体支持不太友好，集群可以)
    // alterConfig();
    // alterConfig2();
    // 查询Config
    // describeConfig();
    // 增加partition数量
            incrPartitions(2);
    
            while(true) { 
    
            }
        }
    
        /** * 增加partition数量 * 之所以是incr，是因为在kafka中partitions默认只能增加，不能减少 * * 检验： * 法1. 调用函数 描述Topic * name:rxguo-topic, desc:(name=rxguo-topic, internal=false, partitions=( * partition=0, leader=192.168.137.121:9092 (id: 0 rack: null), replicas=192.168.137.121:9092 (id: 0 rack: null), isr=192.168.137.121:9092 (id: 0 rack: null)),( * partition=1, leader=192.168.137.121:9092 (id: 0 rack: null), replicas=192.168.137.121:9092 (id: 0 rack: null), isr=192.168.137.121:9092 (id: 0 rack: null)), authorizedOperations=null) * 法2. 命令行查看日志 * drwxr-xr-x. 2 root root 178 10月 20 09:19 rxguo-topic-0 * drwxr-xr-x. 2 root root 141 10月 20 10:23 rxguo-topic-1 * [root@hadoop01 kafka-logs]# pwd * /tmp/kafka-logs */
        public static void incrPartitions(int partition_num) throws Exception{ 
            Map<String, NewPartitions> partitionsMap = new HashMap<>();
            NewPartitions newPartitions = NewPartitions.increaseTo(partition_num);
            partitionsMap.put(TOPIC_NAME, newPartitions);
    
            CreatePartitionsResult createPartitionsResult = adminClient.createPartitions(partitionsMap);
            createPartitionsResult.all().get();
        }
    
        /** * 修改Topic (版2 新版API incrementalAlterConfigs()) * ConfigEntry(name=preallocate, value=false * @throws Exception */
        public static void alterConfig2() throws Exception { 
    // 组织两个参数
    // alterConfigs过期了，但是暂时比incrementalAlterConfigs()好用。用法一样
    // adminClient.incrementalAlterConfigs()
            HashMap<ConfigResource, Collection<AlterConfigOp>> configMaps = new HashMap<>();
            ConfigResource configResource = new ConfigResource(ConfigResource.Type.TOPIC, TOPIC_NAME);
    // 运算费文件配置 preallocate 默认是false，此处改为true做检验修改
            AlterConfigOp alterConfigOp = new AlterConfigOp(new ConfigEntry("preallocate", "false"), AlterConfigOp.OpType.SET);
            configMaps.put(configResource, Arrays.asList(alterConfigOp));
    
            AlterConfigsResult alterConfigsResult = adminClient.incrementalAlterConfigs(configMaps);
            alterConfigsResult.all().get();
        }
        /** * 修改 配置信息（版1 alterConfigs过期了） * ConfigEntry(name=preallocate, value=true, source=DYNAMIC_TOPIC_CONFIG * @throws Exception */
        public static void alterConfig() throws Exception { 
    // 组织两个参数
    // alterConfigs过期了，但是暂时比incrementalAlterConfigs()好用。用法一样
    // adminClient.incrementalAlterConfigs()
            HashMap<ConfigResource, Config> configMaps = new HashMap<>();
            ConfigResource configResource = new ConfigResource(ConfigResource.Type.TOPIC, TOPIC_NAME);
    // 运算费 preallocate 默认是false，此处改为true做检验修改
            Config config = new Config(Arrays.asList(new ConfigEntry("preallocate", "true")));
            configMaps.put(configResource, config);
    
            AlterConfigsResult alterConfigsResult = adminClient.alterConfigs(configMaps);
            alterConfigsResult.all().get();
        }
    
    
        /** * 查询Config * configResource:ConfigResource(type=TOPIC, name='rxguo-topic'), * Config:Config(entries=[ConfigEntry(name=compression.type, * value=producer, * source=DEFAULT_CONFIG, * isSensitive=false, * isReadOnly=false, synonyms=[]), * ConfigEntry(name=leader.replication.throttled.replicas, * value=, source=DEFAULT_CONFIG, * isSensitive=false, isReadOnly=false, synonyms=[]), * ConfigEntry(name=message.downconversion.enable, value=true, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), * ConfigEntry(name=min.insync.replicas, value=1, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), * ConfigEntry(name=segment.jitter.ms, value=0, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), * ConfigEntry(name=cleanup.policy, value=delete, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), * ConfigEntry(name=flush.ms, value=9223372036854775807, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=follower.replication.throttled.replicas, value=, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=segment.bytes, value=1073741824, source=STATIC_BROKER_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=retention.ms, value=604800000, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=flush.messages, value=9223372036854775807, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=message.format.version, value=2.6-IV0, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=max.compaction.lag.ms, value=9223372036854775807, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=file.delete.delay.ms, value=60000, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=max.message.bytes, value=1048588, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=min.compaction.lag.ms, value=0, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=message.timestamp.type, value=CreateTime, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), * ConfigEntry(name=preallocate, value=false, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=min.cleanable.dirty.ratio, value=0.5, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=index.interval.bytes, value=4096, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=unclean.leader.election.enable, value=false, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=retention.bytes, value=-1, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=delete.retention.ms, value=86400000, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=segment.ms, value=604800000, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=message.timestamp.difference.max.ms, value=9223372036854775807, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[]), ConfigEntry(name=segment.index.bytes, value=10485760, source=DEFAULT_CONFIG, isSensitive=false, isReadOnly=false, synonyms=[])]) */
        public static void describeConfig() throws Exception { 
            // TODO 这里做预留，集群会处理
            ConfigResource configResource = new ConfigResource(ConfigResource.Type.TOPIC, TOPIC_NAME);
            DescribeConfigsResult describeConfigsResult = adminClient.describeConfigs(Arrays.asList(configResource));
            Map<ConfigResource, Config> configResourceConfigMap = describeConfigsResult.all().get();
            configResourceConfigMap.entrySet().stream().forEach((entry)->{ 
                System.out.println("configResource:" + entry.getKey() + ",Config:" + entry.getValue());
            });
        }
    
    
        /** * 描述topic * name:rxguo-topic, * desc:(name=rxguo-topic, * internal=false, * partitions=(partition=0, * leader=192.168.137.121:9092 (id: 0 rack: null), * replicas=192.168.137.121:9092 (id: 0 rack: null), * isr=192.168.137.121:9092 (id: 0 rack: null)), * authorizedOperations=null) */
        public static void desccribeTopic() throws Exception { 
            DescribeTopicsResult describeTopicsResult = adminClient.describeTopics(Arrays.asList(TOPIC_NAME));
            Map<String, TopicDescription> stringTopicDescriptionMap = describeTopicsResult.all().get();
            Set<Map.Entry<String, TopicDescription>> entries = stringTopicDescriptionMap.entrySet();
            entries.stream().forEach((entry)->{ 
                System.out.println("name:" + entry.getKey() + ", desc:" + entry.getValue());
            });
        }
    
        /** * 删除topic */
        public static void delTopic() throws Exception { 
            DeleteTopicsResult deleteTopicsResult = adminClient().deleteTopics(Arrays.asList(TOPIC_NAME));
            deleteTopicsResult.all().get();
        }
    
        /** * 获取topic列表 * @throws Exception */
        public static void topicList() throws Exception { 
            // 开启 可打印内部使用，__consumer_offsets(查看是否选择internal)
            ListTopicsOptions options = new ListTopicsOptions();
            options.listInternal(true);
    
            ListTopicsResult listTopicsResult = adminClient.listTopics(options);
    // ListTopicsResult listTopicsResult = adminClient.listTopics();
            Set<String> names = listTopicsResult.names().get();
            Collection<TopicListing> topicListings = listTopicsResult.listings().get();
            Map<String, TopicListing> stringTopicListingMap = listTopicsResult.namesToListings().get();
    
            // 打印names
            names.stream().forEach(System.out::println);
            // 打印topicListings
            topicListings.stream().forEach((listing)->{ 
                System.out.println(listing);
            });
    
        }
    
        /** * 创建Topic实例 */
        public static void createTopic() { 
            // 副本因子,暂时测试的单节点
            Short rs = 1;
            NewTopic newTopic = new NewTopic(TOPIC_NAME, 1, rs);
            CreateTopicsResult topics = adminClient.createTopics(Arrays.asList(newTopic));
            System.out.println("==CreateTopicsResult==:" + topics);
        }
    
        /** * 设置AdminClient *注意AdminClient为kafka包下的 */
        public static AdminClient adminClient() { 
            Properties properties = new Properties();
            properties.setProperty(AdminClientConfig.BOOTSTRAP_SERVERS_CONFIG, "192.168.137.121:9092");
    
            return AdminClient.create(properties);
        }
    }

输出
    - [AdminClient clientId=adminclient-4219] Kafka admin client initialized

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 20]

**解决：【kafka】Java连接出现Connection refused: no further information的解决方法**  
![在这里插入图片描述][20201019152439207.png_pic_center]

kafka的server配置文件需要具体指定机器ip，不能localhost  
1去conf修改  
localhost 192.168.137.121

localhost变成具体ip，zookeeper.connect=192.168.137.121:2181

2、重启kafka服务

注意，使用kafka自带的生产者和消费者客户端测试时，不能使用localhost，须明确指定本机域名

# Kafka核心API之Producer #

connect连接配置  
负载均衡，同步异步  
反推，如何达到高吞吐量。

## API ##

**注意：offset唯一是针对topic的 不是针对partition的**

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 22]

package com.bennyrhys.kafka_study1.producer;
    
    import org.apache.kafka.clients.producer.*;
    
    import java.util.Properties;
    import java.util.concurrent.ExecutionException;
    import java.util.concurrent.Future;
    
    public class ProducerSample { 
    
        private final static String TOPIC_NAME = "rxguo-topic";
    
        public static void main(String[] args) throws ExecutionException, InterruptedException { 
    // producer异步发送
    // producerSend();
    // producer异步阻塞发送（同步发送）
    // producerSyncSend();
    // producer异步发送带回调函数
            producerSendWithCallBack();
    // Producer异步发送带回调函数和负载均衡Partition
    // producerSendWithCallBackAndPartition();
        }
    
        /** * Producer异步发送 * * 验证：命令行打印输出 * key-0values-0 */
        public static void producerSend() { 
            Properties properties = new Properties();
            properties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "192.168.137.121:9092");
            properties.put(ProducerConfig.ACKS_CONFIG, "all");
            properties.put(ProducerConfig.RETRIES_CONFIG, "0"); // 重试，流程图说了
            properties.put(ProducerConfig.BATCH_SIZE_CONFIG, "16384");// 空间计量，批量写入
            properties.put(ProducerConfig.LINGER_MS_CONFIG, "1"); // 1s为记时，批量写入
            properties.put(ProducerConfig.BUFFER_MEMORY_CONFIG, "33554432");// 缓存最大
    
            // 序列化k，v（因为要放到通道里传输）扩展类，kafka->org包->comm->serialization
            properties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
            properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
    
            // Producer的主对象
            Producer<String, String> producer = new KafkaProducer<>(properties);
    
            // 消息对象
            for (int i = 0; i < 10; i++) { 
                ProducerRecord<String, String> record = new ProducerRecord<>(TOPIC_NAME, "key-"+ i + "values-" + i);
                producer.send(record);
            }
    
            // 所以的通道打开需要关闭
            producer.close();
        }
    
        /** * Producer异步阻塞发送（同步发送） * 验证：命令行打印输出 * partition:0， offset:27 * partition:1， offset:15 * partition 0， 1个数基本一致，offset不太一致 */
        public static void producerSyncSend() throws ExecutionException, InterruptedException { 
            Properties properties = new Properties();
            properties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "192.168.137.121:9092");
            properties.put(ProducerConfig.ACKS_CONFIG, "all");
            properties.put(ProducerConfig.RETRIES_CONFIG, "0");
            properties.put(ProducerConfig.BATCH_SIZE_CONFIG, "16384");
            properties.put(ProducerConfig.LINGER_MS_CONFIG, "1");
            properties.put(ProducerConfig.BUFFER_MEMORY_CONFIG, "33554432");
    
            // 序列化k，v（因为要放到通道里传输）扩展类，kafka->org包->comm->serialization
            properties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
            properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
    
            // Producer的主对象
            Producer<String, String> producer = new KafkaProducer<>(properties);
    
            // 消息对象
            for (int i = 0; i < 10; i++) { 
                ProducerRecord<String, String> record = new ProducerRecord<>(TOPIC_NAME, "key-"+ i + "values-" + i);
                Future<RecordMetadata> send = producer.send(record);
                // future是发送出去就不管了，但是每次get()就相当于阻塞在这里
                RecordMetadata recordMetadata = send.get();
                System.out.println("partition:" + recordMetadata.partition() + "， offset:" + recordMetadata.offset());
            }
    
            // 所以的通道打开需要关闭
            producer.close();
        }
    
        /** * Producer异步发送带回调函数 * send方法，添加参数CallBack * */
        public static void producerSendWithCallBack() { 
            Properties properties = new Properties();
            properties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "192.168.137.121:9092");
            properties.put(ProducerConfig.ACKS_CONFIG, "all");
            properties.put(ProducerConfig.RETRIES_CONFIG, "0");
            properties.put(ProducerConfig.BATCH_SIZE_CONFIG, "16384");
            properties.put(ProducerConfig.LINGER_MS_CONFIG, "1");
            properties.put(ProducerConfig.BUFFER_MEMORY_CONFIG, "33554432");
    
            // 序列化k，v（因为要放到通道里传输）扩展类，kafka->org包->comm->serialization
            properties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
            properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
    
            // Producer的主对象
            Producer<String, String> producer = new KafkaProducer<>(properties);
    
            // 消息对象
            for (int i = 0; i < 10; i++) { 
                ProducerRecord<String, String> record = new ProducerRecord<>(TOPIC_NAME, "key-"+ i , "values-" + i);
                producer.send(record, new Callback() { 
                    @Override
                    public void onCompletion(RecordMetadata recordMetadata, Exception e) { 
                        System.out.println("partition:" + recordMetadata.partition() + "， offset:" + recordMetadata.offset());
                    }
                });
            }
    
            // 所以的通道打开需要关闭
            producer.close();
        }
    
        /** * Producer异步发送带回调函数和负载均衡Partition * 添加定制化Partition的类，注意类中的截取要和字符格式一致 * partition:0， offset:52 * partition:0， offset:53 * partition:0， offset:54 * partition:0， offset:55 * partition:1， offset:60 * partition:1， offset:61 * partition:1， offset:62 * partition:1， offset:63 * partition:1， offset:64 * partition:1， offset:65 */
        public static void producerSendWithCallBackAndPartition() { 
            Properties properties = new Properties();
            properties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "192.168.137.121:9092");
            properties.put(ProducerConfig.ACKS_CONFIG, "all");
            properties.put(ProducerConfig.RETRIES_CONFIG, "0");
            properties.put(ProducerConfig.BATCH_SIZE_CONFIG, "16384");
            properties.put(ProducerConfig.LINGER_MS_CONFIG, "1");
            properties.put(ProducerConfig.BUFFER_MEMORY_CONFIG, "33554432");
    
            // 序列化k，v（因为要放到通道里传输）扩展类，kafka->org包->comm->serialization
            properties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
            properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
            // 制定partition的类
            properties.put(ProducerConfig.PARTITIONER_CLASS_CONFIG, "com.bennyrhys.kafka_study1.producer.SamplePartition");
    
            // Producer的主对象
            Producer<String, String> producer = new KafkaProducer<>(properties);
    
            // 消息对象
            for (int i = 0; i < 10; i++) { 
                ProducerRecord<String, String> record = new ProducerRecord<>(TOPIC_NAME, "key-"+ i ,"values-" + i);
                producer.send(record, new Callback() { 
                    @Override
                    public void onCompletion(RecordMetadata recordMetadata, Exception e) { 
                        System.out.println("partition:" + recordMetadata.partition() + "， offset:" + recordMetadata.offset());
                    }
                });
            }
    
            // 所以的通道打开需要关闭
            producer.close();
        }
    }

## 源码分析流程 ##

TODO  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 23]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 24][]

## 生产者理解 ##

*  直接发送  
    不论单节点，多借点，先发送到集群中的leader节点
 *  负载均衡  
    加载负载均衡器，计算分区（默认类似随机分配）
 *  异步发送  
    fature返回值，通过批量发送，减少io次数

**能干预的地方，分区器**  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 25]

## Producer自定义Partition负载均衡 ##

ProducerSample.java

/** * Producer异步发送带回调函数和负载均衡Partition * 添加定制化Partition的类，注意类中的截取要和字符格式一致 * * 问题：负载均衡，自定义的partition接受不到key的数据 */
        public static void producerSendWithCallBackAndPartition() { 
            Properties properties = new Properties();
            properties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "192.168.137.121:9092");
            properties.put(ProducerConfig.ACKS_CONFIG, "all");
            properties.put(ProducerConfig.RETRIES_CONFIG, "0");
            properties.put(ProducerConfig.BATCH_SIZE_CONFIG, "16384");
            properties.put(ProducerConfig.LINGER_MS_CONFIG, "1");
            properties.put(ProducerConfig.BUFFER_MEMORY_CONFIG, "33554432");
    
            // 序列化k，v（因为要放到通道里传输）扩展类，kafka->org包->comm->serialization
            properties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
            properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
            // 制定partition的类
            properties.put(ProducerConfig.PARTITIONER_CLASS_CONFIG, "com.bennyrhys.kafka_study1.producer.SamplePartition");
    
            // Producer的主对象
            Producer<String, String> producer = new KafkaProducer<>(properties);
    
            // 消息对象
            for (int i = 0; i < 10; i++) { 
                ProducerRecord<String, String> record = new ProducerRecord<>(TOPIC_NAME, "key-"+ i ,"values-" + i);
                producer.send(record, new Callback() { 
                    @Override
                    public void onCompletion(RecordMetadata recordMetadata, Exception e) { 
                        System.out.println("partition:" + recordMetadata.partition() + "， offset:" + recordMetadata.offset());
                    }
                });
            }
    
            // 所以的通道打开需要关闭
            producer.close();
        }

SamplePartition.java

package com.bennyrhys.kafka_study1.producer;
    
    import org.apache.kafka.clients.producer.Partitioner;
    import org.apache.kafka.common.Cluster;
    
    import java.util.Map;
    
    /** * 自定义Producer的Partition */
    public class SamplePartition implements Partitioner { 
        @Override
        public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) { 
            /** * key-1 * key-2 */
            System.out.println(key);
            System.out.println((String) key);
    
            String keyStr = key + "";
            String keyInt = keyStr.substring(4);
            System.out.println("keyStr:" + keyStr + "keyInt:" + keyInt);
    
            int i = Integer.parseInt(keyInt);
            return i%2;
        }
    
        @Override
        public void close() { 
    
        }
    
        @Override
        public void configure(Map<String, ?> map) { 
    
        }
    }

## 消息传递保障 ##

producer和consumer共同实现。  
主要是靠producer  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 26]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 27]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 28]

transId标识生产者，消费者拿到id进行去重。  
all级别是正好一次  
最多一次是速度最快的，因为发出去了就不校验了。

# 【实战】Kafka Producer实战——调查问卷 #

[https://bennyrhys.blog.csdn.net/article/details/109184665][https_bennyrhys.blog.csdn.net_article_details_109184665]

# Kafka核心API之Consumer #

官方javadoc之consumer  
[http://kafka.apache.org/26/javadoc/index.html?org/apache/kafka/clients/consumer/KafkaConsumer.html][http_kafka.apache.org_26_javadoc_index.html_org_apache_kafka_clients_consumer_KafkaConsumer.html]

## helloworld(傻瓜式 不推荐) ##

自动提交offset，经典配置直接拿来用  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 29]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 30]

## 存在问题 ##

offset为偏移量，记录kafka上消费的问题，上次中断的偏移量位置，继续处理

kafka的consumer自动定时批量提交，假如情况特殊(数据没消费完，会造成数据丢失)  
变自动为手动提交

## 手动提交 ##

/** * 手动提交 */
        private static void commitedOffset() { 
            Properties props = new Properties();
            props.setProperty("bootstrap.servers", "192.168.137.121:9092");
            props.setProperty("group.id", "test");
            props.setProperty("enable.auto.commit", "false");
            props.setProperty("auto.commit.interval.ms", "1000");
            props.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
            props.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
    
            KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
    
            // 消费订阅哪一个topic或者哪几个topic
            consumer.subscribe(Arrays.asList(TOPIC_NAME));
            while (true) { 
                ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(10000));
                for (ConsumerRecord<String, String> record : records) { 
                    // 想把数据保存到数据库，成功就成功，不成功则在当前时间戳回滚
                    // TODO record 2 db
                    System.out.printf("partition = %d, offset = %d, key = %s, value = %s%n", record.partition(), record.offset(), record.key(), record.value());
                    // 如果失败，则回滚，不要提交offset
                }
                // 手动提交
                consumer.commitAsync();
            }
        }

## 理解Consumer Group手动控制一到多个分区 ##

多个独立的consumer组成的一组group  
consumer->partition，可以一对一，可以一对多

一对一  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 31]一对多  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 32]  
一个partition不能被多个consumer消费  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 33]

注意  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 34]

## 单Partition提交Offset ##

/** * 手动提交（并且手动控制partition） * 场景：适用于多线程时，一个topic下的partition，失败的partition单独处理，而不需要所有的重新来 * 缺点：全部拉取下来，只是节省之后处理逻辑。应该尝试单个partition的拉取 */
        private static void commitedOffsetWithPartition() { 
            Properties props = new Properties();
            props.setProperty("bootstrap.servers", "192.168.137.121:9092");
            props.setProperty("group.id", "test");
            props.setProperty("enable.auto.commit", "false");
            props.setProperty("auto.commit.interval.ms", "1000");
            props.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
            props.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
    
            KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
    
            // 消费订阅哪一个topic或者哪几个topic
            consumer.subscribe(Arrays.asList(TOPIC_NAME));
            while (true) { 
                ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(10000));
                for (TopicPartition partition : records.partitions()) { 
                    List<ConsumerRecord<String, String>> pRecord = records.records(partition);
                    for (ConsumerRecord<String, String> record : pRecord) { 
                        System.out.printf("partition = %d, offset = %d, key = %s, value = %s%n", record.partition(), record.offset(), record.key(), record.value());
                    }
                    long lastOffset = pRecord.get(pRecord.size() - 1).offset();
                    // 单个partition中的offset，并且提交
                    Map<TopicPartition, OffsetAndMetadata> offset = new HashMap<>();
                    // offset的记录的取出起始点，必须+1
                    offset.put(partition, new OffsetAndMetadata(lastOffset + 1));
                    // 提交offset
                    consumer.commitSync(offset);
                }
            }
        }

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 35]

## 手动提交offset(订阅topic下的某个或某些partition分区) ##

创建多个concumer  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 36]

// commitedOffsetWithPartition2();

/** * 手动提交offset（并且手动控制partition） * */
        private static void commitedOffsetWithPartition2() { 
            Properties props = new Properties();
            props.setProperty("bootstrap.servers", "192.168.137.121:9092");
            props.setProperty("group.id", "test");
            props.setProperty("enable.auto.commit", "false");
            props.setProperty("auto.commit.interval.ms", "1000");
            props.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
            props.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
    
            KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
    
            // rxguo-topic 0 1 有两个partition
            TopicPartition p0 = new TopicPartition(TOPIC_NAME, 0);
            TopicPartition p1 = new TopicPartition(TOPIC_NAME, 1);
    
    
            // 消费订阅哪一个topic或者哪几个topic
    // consumer.subscribe(Arrays.asList(TOPIC_NAME));
    
            // 消费订阅某个topic的某个分区
            consumer.assign(Arrays.asList(p0));
    
            while (true) { 
                ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(10000));
                for (TopicPartition partition : records.partitions()) { 
                    List<ConsumerRecord<String, String>> pRecord = records.records(partition);
                    for (ConsumerRecord<String, String> record : pRecord) { 
                        System.out.printf("partition = %d, offset = %d, key = %s, value = %s%n", record.partition(), record.offset(), record.key(), record.value());
                    }
                    long lastOffset = pRecord.get(pRecord.size() - 1).offset();
                    // 单个partition中的offset，并且提交
                    Map<TopicPartition, OffsetAndMetadata> offset = new HashMap<>();
                    // offset的记录的取出起始点，必须+1
                    offset.put(partition, new OffsetAndMetadata(lastOffset + 1));
                    // 提交offset
                    consumer.commitSync(offset);
                }
            }
        }

## 多线程创建一个consumer ##

场景：非业务、流式系统。推过来成功就成功，减少consumer的资源消耗  
GPS数据，业务峰值数据监控

kafka的consumer和producer不同，consumer是线程不安全的  
官方建议，自己用需要自己解决线程安全的问题

两种方式

*  partition和分区对应
 *  使用一个consumer
 *  ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 37]经典线程

package com.bennyrhys.kafka_study1.consumer;
    
    import org.apache.kafka.clients.consumer.ConsumerRecord;
    import org.apache.kafka.clients.consumer.ConsumerRecords;
    import org.apache.kafka.clients.consumer.KafkaConsumer;
    import org.apache.kafka.clients.consumer.OffsetAndMetadata;
    import org.apache.kafka.common.TopicPartition;
    import org.apache.kafka.common.errors.WakeupException;
    
    import java.time.Duration;
    import java.util.Arrays;
    import java.util.Collections;
    import java.util.List;
    import java.util.Properties;
    import java.util.concurrent.atomic.AtomicBoolean;
    
    
    public class ConsumerThreadSample { 
        private final static String TOPIC_NAME="rxguo-topic";
    
        /** * 这种类型是经典模式、每次创建一个consumer。 * 每个consumer消费一个partition的分区 */
        public static void main(String[] args) throws InterruptedException { 
            KafkaConsumerRunner r1 = new KafkaConsumerRunner();
            Thread t1 = new Thread(r1);
    
            t1.start();
    
            Thread.sleep(15000);
    
            r1.shutdown();
        }
    
        public static class KafkaConsumerRunner implements Runnable{ 
            private final AtomicBoolean closed = new AtomicBoolean(false);
            private final KafkaConsumer consumer;
    
            public KafkaConsumerRunner() { 
                Properties props = new Properties();
                props.put("bootstrap.servers", "192.168.137.121:9092");
                props.put("group.id", "test");
                props.put("enable.auto.commit", "false");
                props.put("auto.commit.interval.ms", "1000");
                props.put("session.timeout.ms", "30000");
                props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
                props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
    
                consumer = new KafkaConsumer<>(props);
    
                TopicPartition p0 = new TopicPartition(TOPIC_NAME, 0);
                TopicPartition p1 = new TopicPartition(TOPIC_NAME, 1);
    
                consumer.assign(Arrays.asList(p0,p1));
            }
    
    
            public void run() { 
                try { 
                    while(!closed.get()) { 
                        //处理消息
                        ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(10000));
    
                        for (TopicPartition partition : records.partitions()) { 
                            List<ConsumerRecord<String, String>> pRecord = records.records(partition);
                            // 处理每个分区的消息
                            for (ConsumerRecord<String, String> record : pRecord) { 
                                System.out.printf("patition = %d , offset = %d, key = %s, value = %s%n",
                                        record.partition(),record.offset(), record.key(), record.value());
                            }
    
                            // 返回去告诉kafka新的offset
                            long lastOffset = pRecord.get(pRecord.size() - 1).offset();
                            // 注意加1
                            consumer.commitSync(Collections.singletonMap(partition, new OffsetAndMetadata(lastOffset + 1)));
                        }
    
                    }
                }catch(WakeupException e) { 
                    if(!closed.get()) { 
                        throw e;
                    }
                }finally { 
                    consumer.close();
                }
            }
    
            public void shutdown() { 
                closed.set(true);
                consumer.wakeup();
            }
        }
    
    }

只创建一个consumer

package com.bennyrhys.kafka_study1.consumer;
    
    import org.apache.kafka.clients.consumer.ConsumerRecord;
    import org.apache.kafka.clients.consumer.ConsumerRecords;
    import org.apache.kafka.clients.consumer.KafkaConsumer;
    
    import java.util.Arrays;
    import java.util.Properties;
    import java.util.concurrent.ArrayBlockingQueue;
    import java.util.concurrent.ExecutorService;
    import java.util.concurrent.ThreadPoolExecutor;
    import java.util.concurrent.TimeUnit;
    
    /** * 类似生产环境（只创建一个consumer） * 创建一个线程池，只有一个kafka的consumer * 每个consumer一个单独的线程来处理 */
    public class ConsumerRecordThreadSample { 
        private final static String TOPIC_NAME = "rxguo-topic";
    
        public static void main(String[] args) throws InterruptedException { 
            String brokerList = "192.168.137.121:9092";
            String groupId = "test";
            int workerNum = 5;
    
            CunsumerExecutor consumers = new CunsumerExecutor(brokerList, groupId, TOPIC_NAME);
            consumers.execute(workerNum);
    
            Thread.sleep(1000000);
    
            consumers.shutdown();
    
        }
    
        // Consumer处理
        public static class CunsumerExecutor{ 
            private final KafkaConsumer<String, String> consumer;
            private ExecutorService executors;
    
            public CunsumerExecutor(String brokerList, String groupId, String topic) { 
                Properties props = new Properties();
                props.put("bootstrap.servers", brokerList);
                props.put("group.id", groupId);
                props.put("enable.auto.commit", "true");
                props.put("auto.commit.interval.ms", "1000");
                props.put("session.timeout.ms", "30000");
                props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
                props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
                consumer = new KafkaConsumer<>(props);
                consumer.subscribe(Arrays.asList(topic));
            }
    
            public void execute(int workerNum) { 
                executors = new ThreadPoolExecutor(workerNum, workerNum, 0L, TimeUnit.MILLISECONDS,
                        new ArrayBlockingQueue<>(1000), new ThreadPoolExecutor.CallerRunsPolicy());
    
                while (true) { 
                    ConsumerRecords<String, String> records = consumer.poll(200);
                    for (final ConsumerRecord record : records) { 
                        executors.submit(new ConsumerRecordWorker(record));
                    }
                }
            }
    
            public void shutdown() { 
                if (consumer != null) { 
                    consumer.close();
                }
                if (executors != null) { 
                    executors.shutdown();
                }
                try { 
                    if (!executors.awaitTermination(10, TimeUnit.SECONDS)) { 
                        System.out.println("Timeout.... Ignore for this case");
                    }
                } catch (InterruptedException ignored) { 
                    System.out.println("Other thread interrupted this shutdown, ignore for this case.");
                    Thread.currentThread().interrupt();
                }
            }
    
    
        }
    
        // 记录处理
        public static class ConsumerRecordWorker implements Runnable { 
    
            private ConsumerRecord<String, String> record;
    
            public ConsumerRecordWorker(ConsumerRecord record) { 
                this.record = record;
            }
    
            @Override
            public void run() { 
                System.out.println("Thread - "+ Thread.currentThread().getName());
                System.err.printf("patition = %d , offset = %d, key = %s, value = %s%n",
                        record.partition(), record.offset(), record.key(), record.value());
            }
    
        }
    }

## offset手动控制 ##

已经有自动提交为啥还要手动？  
重复消费offset定位  
多线程的只创建一个consumer的流式无法管控  
老版本把客户端消费的offset存在zookeeper里  
新版只从1左右的版本合并老版的l h，将offset消费情况转移到了kafka内部变成了一个内部的topic，存储在consumer——obejct。

存在问题：  
消费失败，却不知道，无法重复消费  
存储容量受限于kafka，单独抽取存放offset的topic 放到redis等存放更新起始位置  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 38]

/** * 手动指定offset起始位置，手动提交offset */
        private static void controlOffset() { 
            Properties props = new Properties();
            props.setProperty("bootstrap.servers", "192.168.137.121:9092");
            props.setProperty("group.id", "test");
            props.setProperty("enable.auto.commit", "false");
            props.setProperty("auto.commit.interval.ms", "1000");
            props.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
            props.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
    
            KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
    
            // rxguo-topic 0 1 有两个partition
            TopicPartition p0 = new TopicPartition(TOPIC_NAME, 0);
    
    
    
    
            // 消费订阅某个topic的某个分区
            consumer.assign(Arrays.asList(p0));
    
            while (true) { 
                // 手动指定offset起始位置
                consumer.seek(p0, 500);
    
                ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(10000));
                for (TopicPartition partition : records.partitions()) { 
                    List<ConsumerRecord<String, String>> pRecord = records.records(partition);
                    for (ConsumerRecord<String, String> record : pRecord) { 
                        System.out.printf("partition = %d, offset = %d, key = %s, value = %s%n", record.partition(), record.offset(), record.key(), record.value());
                    }
                    long lastOffset = pRecord.get(pRecord.size() - 1).offset();
                    // 单个partition中的offset，并且提交
                    Map<TopicPartition, OffsetAndMetadata> offset = new HashMap<>();
                    // offset的记录的取出起始点，必须+1
                    offset.put(partition, new OffsetAndMetadata(lastOffset + 1));
                    // 提交offset
                    consumer.commitSync(offset);
                }
            }
        }

## 面试题：consumer限流 ##

场景 令牌桶  
限流主要应用在单个topic上  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 39]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 40]

/** * 流量控制-限流 */
        private static void controlPause() { 
            Properties props = new Properties();
            props.setProperty("bootstrap.servers", "192.168.137.121:9092");
            props.setProperty("group.id", "test");
            props.setProperty("enable.auto.commit", "false");
            props.setProperty("auto.commit.interval.ms", "1000");
            props.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
            props.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
    
            KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
    
            // rxguo-topic 0 1 有两个partition
            TopicPartition p0 = new TopicPartition(TOPIC_NAME, 0);
            TopicPartition p1 = new TopicPartition(TOPIC_NAME, 1);
    
            // 消费订阅某个topic的某个分区
            consumer.assign(Arrays.asList(p0));
    
            long totalNum = 40; // 阈值
            while (true) { 
                ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(10000));
                for (TopicPartition partition : records.partitions()) { 
                    List<ConsumerRecord<String, String>> pRecord = records.records(partition);
                    long num = 0;
                    for (ConsumerRecord<String, String> record : pRecord) { 
                        System.out.printf("partition = %d, offset = %d, key = %s, value = %s%n", record.partition(), record.offset(), record.key(), record.value());
                        num++;
                        if (record.partition() == 0) { 
                            if (num >= totalNum) { 
                                consumer.pause(Arrays.asList(p0)); // 停掉
                            }
                        }
                        if (record.partition() == 1) { 
                            if (num == 40) { 
                                consumer.resume(Arrays.asList(p0)); // 启动
                            }
                        }
    
                    }
                    long lastOffset = pRecord.get(pRecord.size() - 1).offset();
                    // 单个partition中的offset，并且提交
                    Map<TopicPartition, OffsetAndMetadata> offset = new HashMap<>();
                    // offset的记录的取出起始点，必须+1
                    offset.put(partition, new OffsetAndMetadata(lastOffset + 1));
                    // 提交offset
                    consumer.commitSync(offset);
                }
            }
        }

## 面试题Consumer Rebalance负载均衡 ##

颁发generation作为携带标识（感觉像乐观锁）  
心跳请求  
新入组请求，则rebalance重新均衡  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 41]崩溃和主动离组差不多。被动和主动  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 42]  
提交位移，类似jvm里面的stop-world。新组员加入，老组员重新分配  
rebanlance和位移请求间应该成功。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 43]

# Kafka核心API之Stream #

# Kafka核心API之Connect #

## 介绍 ##

背景、架构、使用

stream只是处理kafka里面的数据

加载数据到kafka，或把kafka里的数据转出去  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 44]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 45]connect好处是方便链接第三方数据源，但是需要定制化。  
下载第三方链接工具[https://www.confluent.io][https_www.confluent.io]  
怎么链接，怎么读数据

## 本地数据库环境 ##

[https://www.cnblogs.com/cnbp/p/12620825.html][https_www.cnblogs.com_cnbp_p_12620825.html]

其他迁移方案  
elk的logsteach  
数据库到es，es到其他

[mysql数据库+数据库工具navicat][mysql_navicat]

create table users_bak(
    `uuid` INT PRIMARY KEY AUTO_INCREMENT,
    `name` VARCHAR(20),
    `age` INT
    )

建立connect  
依赖连接包地址[https://www.confluent.io/connector/kafka-connect-jdbc/][https_www.confluent.io_connector_kafka-connect-jdbc]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 46]

**配服务器**

// 解压
    [root@hadoop01 plugins]# pwd
    /opt/plugins
    [root@hadoop01 plugins]# rz
    
    [root@hadoop01 plugins]# ll
    总用量 11592
    -rw-r--r--. 1 root root 8531741 6月   2 23:09 confluentinc-kafka-connect-jdbc-5.4.1.zip
    -rw-r--r--. 1 root root 1006956 6月   2 23:09 mysql-connector-java-5.1.48.jar
    -rw-r--r--. 1 root root 2330539 6月   2 23:09 mysql-connector-java-8.0.18.jar
    // 下载unzip解压工具
    [root@hadoop01 plugins]# yum install -y unzip
    // 解压
    unzip 文件名
    
    // 拷贝到解压包的lib目录下
    [root@hadoop01 lib]# pwd
    /opt/plugins/confluentinc-kafka-connect-jdbc-5.4.1/lib
    
    [root@hadoop01 lib]# cp /opt/plugins/mysql-connector-java-*.jar ./
    [root@hadoop01 lib]# ll
    总用量 11668
    -rw-r--r--. 1 root root   17561 3月   5 2020 common-utils-5.4.1.jar
    -rw-r--r--. 1 root root  317816 3月   5 2020 jtds-1.3.1.jar
    -rw-r--r--. 1 root root  228877 3月   5 2020 kafka-connect-jdbc-5.4.1.jar
    -rw-r--r--. 1 root root 1006956 10月 23 03:00 mysql-connector-java-5.1.48.jar
    -rw-r--r--. 1 root root 2330539 10月 23 03:00 mysql-connector-java-8.0.18.jar
    -rw-r--r--. 1 root root  927447 3月   5 2020 postgresql-42.2.10.jar
    -rw-r--r--. 1 root root   41139 3月   5 2020 slf4j-api-1.7.26.jar
    -rw-r--r--. 1 root root 7064881 3月   5 2020 sqlite-jdbc-3.25.2.jar

修改kafka配置  
source是上传，sink是输出  
单机版的配置standalone，集群版的distributed  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 47]

## 配置文件 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 48]  
json转换  
![在这里插入图片描述][20201027171803755.png_pic_center]  
rest端口打开8083  
![在这里插入图片描述][20201027171841572.png_pic_center]  
引入当期文件存储的外部依赖目录  
![在这里插入图片描述][20201027172408339.png_pic_center]

## 启动命令 ##

## connect启动命令
    ## 后台启动在 kafka目录下
    bin/connect-distributed.sh -daemon config/connect-distributed.properties
    ## 前台启动
    bin/connect-distributed.sh config/connect-distributed.properties
    
    
    ## 验证启动成功
    http://192.168.137.121:8083/connector-plugins
    
    http://192.168.137.121:8083/connectors
    
    ## 数据源里传数据（windows gitbash执行）
    curl -X POST -H 'Content-Type: application/json' -i 'http://192.168.137.121:8083/connectors' \
    --data \
    '{"name":"imooc-upload-mysql","config":{ "connector.class":"io.confluent.connect.jdbc.JdbcSourceConnector", "connection.url":"jdbc:mysql://10.7.107.11:3306/kafka_study?user=root&password=", "table.whitelist":"users", "incrementing.column.name": "uuid", "mode":"incrementing", "topic.prefix": "imooc-mysql-"}}'
    
    ## 打印存入数据
    bin/kafka-console-consumer.sh --bootstrap-server 192.168.137.121:9092 --topic imooc-mysql-users --from-beginning
    
    ## 从kafka转出数据
    curl -X POST -H 'Content-Type: application/json' -i 'http://192.168.137.121:8083/connectors' \
    --data \
    '{"name":"imooc-download-mysql","config":{ "connector.class":"io.confluent.connect.jdbc.JdbcSinkConnector", "connection.url":"jdbc:mysql://10.7.107.11:3306/kafka_study?user=root&password=", "topics":"imooc-mysql-users", "auto.create":"false", "insert.mode": "upsert", "pk.mode":"record_value", "pk.fields":"uuid", "table.name.format": "users_bak"}}'
    
    
    bin/kafka-console-consumer.sh --bootstrap-server 192.168.220.128:9092 --topic test-mysql-jdbc-users --from-beginning
    
    
    curl -X DELETE -i 'http://192.168.220.128:8083/connectors/load-mysql-data'

验证，虽然是404但是已经成功了  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 49]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 50]

# 通过Confluent connect 将mysql传入kafka #

## 修改本地数据源和服务器数据连通 ##

先在本地ifconfig，再通过  
10.7.107.11

git bash执行

curl -X POST -H 'Content-Type: application/json' -i 'http://192.168.137.121:8083/connectors' \
    --data \
    '{"name":"imooc-upload-mysql","config":{ "connector.class":"io.confluent.connect.jdbc.JdbcSourceConnector", "connection.url":"jdbc:mysql://192.168.152.1:3306/kafka_study?user=root&password=", "table.whitelist":"users", "incrementing.column.name": "uuid", "mode":"incrementing", "topic.prefix": "imooc-mysql-"}}'

## 解决本地数据库拒绝连接问题 ##

[https://blog.csdn.net/m0\_37460012/article/details/90290148][https_blog.csdn.net_m0_37460012_article_details_90290148]

//虚拟机安装测试网络连接
    yum install telnet

数据库字段  
[https://www.cnblogs.com/ziroro/p/9479869.html][https_www.cnblogs.com_ziroro_p_9479869.html]  
接下来就是查看 my.cnf 了，发现还真的有这个参数

bind-address = 127.0.0.1

把他注释了之后，重启MySQL。连接正常  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 51]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 52]

## 创建连接+验证连接 ##

## 传入数据验证读取 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 54]

kafkaconsumer  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 55]

## 通过kafka取出数据 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 56]  
查看任务  
![在这里插入图片描述][20201028164436907.png_pic_center]  
验证数据库数据同步  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 57]  
!\[在这里插入图片描述\](https://img-blog.csdnimg.cn/20201029105002991.png?x-oss-process=image/watermark,type\_ZmFuZ3poZW5naGVpdGk,shadow\_10,text\_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA==,size\_16,color\_FFFFFF,t\_70\#pic\_center

## connect关键字 ##

建议一直选用分布式dis  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 58]

connect项目管理者  
task任务执行  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 59]

任务重平衡  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 60]

数据迁移  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 61]  
如果elk，loagstach做一些数据牵引

# Connect 之 Debezium MySQL #

debezium文档  
  
docker官网  
[https://docs.docker.com/][https_docs.docker.com]  
镜像：参考docker菜鸟教程  
[https://www.runoob.com/docker/centos-docker-install.html][https_www.runoob.com_docker_centos-docker-install.html]

下载mysql插件

tar -zxf debezium-connector-mysql-0.9.5.Final-plugin.tar.gz -C ../plugins/
    tar -zxf debezium-connector-mongodb-0.9.5.Final-plugin.tar.gz -C ../plugins/

配置启动connect 同上

执行连接器json  
注意白名单命名规则  
表的白名单，数据库.表名

{ 
      "name": "rxguo-upload-mysql4",  
      "config": { 
        "connector.class": "io.debezium.connector.mysql.MySqlConnector", 
        "database.hostname": "10.7.107.11", 
        "database.port": "3306", 
        "database.user": "root", 
        "database.password": "", 
        "database.server.id": "1008611", 
        "database.server.name": "mysql-connect", 
        "table.whitelist": "kafka_study.users", 
        "database.history.kafka.bootstrap.servers": "192.168.137.121:9092", 
        "database.history.kafka.topic": "dbhistory.mysql-connect", 
        "database.serverTimezone":"Asia/Shanghai" ,
        "include.schema.changes": "true" 
      }
    }

postman验证get(config\\status)  
url:http://192.168.137.121:8083/connectors/rxguo-upload-mysql3

centos控制台 kafka中输出  
注意验证规则**servername**  
fulfillment.inventory.products == servername.数据库名.表名

// 查看创建的topic信息
    bin/kafka-topics.sh --list --zookeeper 192.168.137.121:2181 rxguo-topic
    // 输出kafka对应的连接器内容
    bin/kafka-console-consumer.sh --bootstrap-server 192.168.137.121:9092 --topic mysql-connect.kafka_study.users  --from-beginning

将kafka的数据同步到本地  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 62]

curl -X DELETE -i 'http://192.168.220.128:8083/connectors/load-mysql-data'

# Connect 之 Debezium MongoDB #

Debezium Mongo保证在集群基础之上  
[【ManggoDB】安装配置（伪分布式集群搭建）Replia Set][ManggoDB_Replia Set]

# Kafka集群 #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 63]

## 集群部署并启动 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 64]一台机器部署集群

复制三台kafka的文件  
`cp -r ka/ ka1/`

**修改配置文件**

注意修改端口号，日志生成目录，连接zookeeper信息  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 65]  
启动  
依次修改完三个文件再启动。三个不同的kafka文件

验证  
依次查询zookeeper、kafka\_1\\kafka\_\\2\\kafka\_3  
`ps -ef | grep kafka_1`

## 副本集 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 66]

kafka的进程称为broker。本次模拟broker1、2、3  
同一个topic的partition一般分布在不同的节点机器上。每个partition的副本集也分布在其他机器上  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 67]

## 代码体验副本集 ##

代码：先创建topic，再打印topic描述  
三个partition，三个副本集  
可能会先报错，因为强一致性，然后再读取描述

/** * 创建Topic实例 */
        public static void createTopic() { 
            // 副本因子,暂时测试的单节点
            Short rs = 3;
            NewTopic newTopic = new NewTopic(TOPIC_NAME, 3, rs);
            CreateTopicsResult topics = adminClient.createTopics(Arrays.asList(newTopic));
            System.out.println("==CreateTopicsResult==:" + topics);
        }
    
    
    name:rxguo-topic3, 
    desc:(name=rxguo-topic3, internal=false, 
    partitions=
    (partition=0, leader=192.168.137.121:9292 
    	(id: 2 rack: null), replicas=192.168.137.121:9292 
    	(id: 2 rack: null), 192.168.137.121:9192 
    	(id: 1 rack: null), 192.168.137.121:9392 
    	(id: 3 rack: null), isr=192.168.137.121:9292 
    	(id: 2 rack: null), 192.168.137.121:9192 
    	(id: 1 rack: null), 192.168.137.121:9392 
    	(id: 3 rack: null)),
    (partition=1, leader=192.168.137.121:9392 
    	(id: 3 rack: null), replicas=192.168.137.121:9392 
    	(id: 3 rack: null), 192.168.137.121:9292 
    	(id: 2 rack: null), 192.168.137.121:9192 
    	(id: 1 rack: null), isr=192.168.137.121:9392 
    	(id: 3 rack: null), 192.168.137.121:9292 
    	(id: 2 rack: null), 192.168.137.121:9192 
    	(id: 1 rack: null)),
    (partition=2, leader=192.168.137.121:9192 
    	(id: 1 rack: null), replicas=192.168.137.121:9192 
    	(id: 1 rack: null), 192.168.137.121:9392 
    	(id: 3 rack: null), 192.168.137.121:9292 
    	(id: 2 rack: null), isr=192.168.137.121:9192 
    	(id: 1 rack: null), 192.168.137.121:9392
    	(id: 3 rack: null), 192.168.137.121:9292 
    	(id: 2 rack: null)), authorizedOperations=null)

关键字  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 68]理解副本  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 69]

## kafka节点故障 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 70]

## kafka节点故障:数据基本不会丢失 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 71]

1副本集

2语义担保 isr和leader同步数据  
![在这里插入图片描述][2020102914350957.png_pic_center]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 72]

## 面试题 kafkaLeader选举 ##

kafka没有采用多数投票来选举leader

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 73]选出新的leader，通过controller找速度比较快根据ISR  
Controller比较快，是因为broker节点机器抢占zookeeper，谁先抢占入住，谁就最快  
管理broker是否损坏，损坏的broker里面有多少leader和fllower

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 74]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 75]

**极端情况：leader和ISR备份信息同时丢失**  
脏选举：  
1死等ISR,数据不丢失牺牲时间  
2允许数据部分丢失保证时效，选用fllower信息  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 76]

## 配置建议 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 77]

# Linux命令 #

## 插件上传文件 ##

插件：方便，本地拷贝到公网命令  
效果，弹窗选择  
`yum install -y lrzsz`

`rz`

## 解压zip文件 ##

// 下载unzip解压工具
    [root@hadoop01 plugins]# yum install -y unzip
    // 解压
    unzip 文件名

## 解压tar ##

\-C 指定目录

tar -zxf debezium-connector-mysql-0.9.5.Final-plugin.tar.gz -C ../plugins/
    tar -zxf debezium-connector-mongodb-0.9.5.Final-plugin.tar.gz -C ../plugins/

# 面试亮点 #

生产者key冲突，自定义负载均衡partition解决  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 78]  
单例producer，防止service的频繁创建，上下文切换  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 79]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center]: /images/20221123/cd99ad89ae3846c99bc144a5f5d8a84c.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 1]: /images/20221123/3ea19a7e507e48d4afe5ff436169b3d2.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 2]: https://img-blog.csdnimg.cn/20201015100101660.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA==,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 3]: /images/20221123/191b1911cd80470f8b9dc695f7fe7836.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzQ2OTY4MA_size_16_color_FFFFFF_t_70_pic_center 4]: /images/20221123/55ad20a651274c04916b00956d2feacd.png
[https_www.cnblogs.com_PrayzzZ_p_11330937.html]: https://www.cnblogs.com/PrayzzZ/p/11330937.html
[http_mirror.bit.edu.cn_centos_7.8.2003_isos_x86_64_CentOS-7-x86_64-Minimal-2003.iso]: http://mirror.bit.edu.cn/centos/7.8.2003/isos/x86_64/CentOS-7-x86_64-Minimal-2003.iso
[https_blog.csdn.net_wujiele_article_details_92803655]: https://blog.csdn.net/wujiele/article/details/92803655
[2020101515080955.png_pic_center]: /images/20221123/dc9c4966443545fc920d00f0856766d2.png
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[20201015163133565.png_pic_center]: /images/20221123/a10cc9bd46df438a926f668734feef70.png
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[javadoc]: http://kafka.apache.org/26/javadoc/index.html?org/apache/kafka/clients/admin/Admin.html
[http_kafka.apache.org_26_documentation.html_api]: http://kafka.apache.org/26/documentation.html#api
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[https_bennyrhys.blog.csdn.net_article_details_109184665]: https://bennyrhys.blog.csdn.net/article/details/109184665
[http_kafka.apache.org_26_javadoc_index.html_org_apache_kafka_clients_consumer_KafkaConsumer.html]: http://kafka.apache.org/26/javadoc/index.html?org/apache/kafka/clients/consumer/KafkaConsumer.html
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[https_www.confluent.io]: https://www.confluent.io
[https_www.cnblogs.com_cnbp_p_12620825.html]: https://www.cnblogs.com/cnbp/p/12620825.html
[mysql_navicat]: https://bennyrhys.blog.csdn.net/article/details/109245143
[https_www.confluent.io_connector_kafka-connect-jdbc]: https://www.confluent.io/connector/kafka-connect-jdbc/
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[20201027171803755.png_pic_center]: /images/20221123/c8ec31f21cf949bf8657811aa70a99d5.png
[20201027171841572.png_pic_center]: /images/20221123/9db9f3743e854658970d69126a1c7d68.png
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[https_blog.csdn.net_m0_37460012_article_details_90290148]: https://blog.csdn.net/m0_37460012/article/details/90290148
[https_www.cnblogs.com_ziroro_p_9479869.html]: https://www.cnblogs.com/ziroro/p/9479869.html
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[https_docs.docker.com]: https://docs.docker.com/
[https_www.runoob.com_docker_centos-docker-install.html]: https://www.runoob.com/docker/centos-docker-install.html
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[ManggoDB_Replia Set]: https://bennyrhys.blog.csdn.net/article/details/108659689
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