kafka 从入门到精通

kafka

安装

zookeeper模式

创建软件目录

mkdir /opt/soft
cd /opt/soft

下载

wget https://downloads.apache.org/kafka/3.4.0/kafka_2.13-3.4.0.tgz

解压

tar -zxvf kafka_2.13-3.4.0.tgz 

修改目录名称

mv kafka_2.13-3.4.0 kafka

配置环境变量

vim /etc/profile

export KAFKA_HOME=/opt/soft/kafka
export PATH=$PATH:$KAFKA_HOME/bin

修改配置文件

配置文件存放在 kafka/config目录

# 在每个节点创建目录
mkdir -p /opt/soft/kafka-logs

vim /opt/soft/kafka/config/server.properties

主要修改以下三个参数：

• broker.id=1 注意不同的节点id号不同

• log.dirs=/tmp/kafka-logs 修改为 log.dirs=/opt/soft/kafka-logs

• zookeeper.connect=localhost:2181 修改为

  zookeeper.connect=spark01:2181,spark02:2181,spark03:2181/kafka

# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.#
# This configuration file is intended for use in ZK-based mode, where Apache ZooKeeper is required.
# See kafka.server.KafkaConfig for additional details and defaults
############################## Server Basics ############################## The id of the broker. This must be set to a unique integer for each broker.
broker.id=1############################# Socket Server Settings ############################## The address the socket server listens on. If not configured, the host name will be equal to the value of
# java.net.InetAddress.getCanonicalHostName(), with PLAINTEXT listener name, and port 9092.
#   FORMAT:
#     listeners = listener_name://host_name:port
#   EXAMPLE:
#     listeners = PLAINTEXT://your.host.name:9092
#listeners=PLAINTEXT://:9092# Listener name, hostname and port the broker will advertise to clients.
# If not set, it uses the value for "listeners".
#advertised.listeners=PLAINTEXT://your.host.name:9092# Maps listener names to security protocols, the default is for them to be the same. See the config documentation for more details
#listener.security.protocol.map=PLAINTEXT:PLAINTEXT,SSL:SSL,SASL_PLAINTEXT:SASL_PLAINTEXT,SASL_SSL:SASL_SSL# The number of threads that the server uses for receiving requests from the network and sending responses to the network
num.network.threads=3# The number of threads that the server uses for processing requests, which may include disk I/O
num.io.threads=8# The send buffer (SO_SNDBUF) used by the socket server
socket.send.buffer.bytes=102400# The receive buffer (SO_RCVBUF) used by the socket server
socket.receive.buffer.bytes=102400# The maximum size of a request that the socket server will accept (protection against OOM)
socket.request.max.bytes=104857600############################# Log Basics ############################## A comma separated list of directories under which to store log files
log.dirs=/opt/soft/kafka-logs# The default number of log partitions per topic. More partitions allow greater
# parallelism for consumption, but this will also result in more files across
# the brokers.
num.partitions=1# The number of threads per data directory to be used for log recovery at startup and flushing at shutdown.
# This value is recommended to be increased for installations with data dirs located in RAID array.
num.recovery.threads.per.data.dir=1############################# Internal Topic Settings  #############################
# The replication factor for the group metadata internal topics "__consumer_offsets" and "__transaction_state"
# For anything other than development testing, a value greater than 1 is recommended to ensure availability such as 3.
offsets.topic.replication.factor=1
transaction.state.log.replication.factor=1
transaction.state.log.min.isr=1############################# Log Flush Policy ############################## Messages are immediately written to the filesystem but by default we only fsync() to sync
# the OS cache lazily. The following configurations control the flush of data to disk.
# There are a few important trade-offs here:
#    1. Durability: Unflushed data may be lost if you are not using replication.
#    2. Latency: Very large flush intervals may lead to latency spikes when the flush does occur as there will be a lot of data to flush.
#    3. Throughput: The flush is generally the most expensive operation, and a small flush interval may lead to excessive seeks.
# The settings below allow one to configure the flush policy to flush data after a period of time or
# every N messages (or both). This can be done globally and overridden on a per-topic basis.# The number of messages to accept before forcing a flush of data to disk
#log.flush.interval.messages=10000# The maximum amount of time a message can sit in a log before we force a flush
#log.flush.interval.ms=1000############################# Log Retention Policy ############################## The following configurations control the disposal of log segments. The policy can
# be set to delete segments after a period of time, or after a given size has accumulated.
# A segment will be deleted whenever *either* of these criteria are met. Deletion always happens
# from the end of the log.# The minimum age of a log file to be eligible for deletion due to age
log.retention.hours=168# A size-based retention policy for logs. Segments are pruned from the log unless the remaining
# segments drop below log.retention.bytes. Functions independently of log.retention.hours.
#log.retention.bytes=1073741824# The maximum size of a log segment file. When this size is reached a new log segment will be created.
#log.segment.bytes=1073741824# The interval at which log segments are checked to see if they can be deleted according
# to the retention policies
log.retention.check.interval.ms=300000############################# Zookeeper ############################## 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=spark01:2181,spark02:2181,spark03:2181/kafka# Timeout in ms for connecting to zookeeper
zookeeper.connection.timeout.ms=18000############################# Group Coordinator Settings ############################## The following configuration specifies the time, in milliseconds, that the GroupCoordinator will delay the initial consumer rebalance.
# The rebalance will be further delayed by the value of group.initial.rebalance.delay.ms as new members join the group, up to a maximum of max.poll.interval.ms.
# The default value for this is 3 seconds.
# We override this to 0 here as it makes for a better out-of-the-box experience for development and testing.
# However, in production environments the default value of 3 seconds is more suitable as this will help to avoid unnecessary, and potentially expensive, rebalances during application startup.
group.initial.rebalance.delay.ms=0

分发配置到其它节点

scp -r /opt/soft/kafka root@spark02:/opt/soft
scp -r /opt/soft/kafka root@spark03:/opt/soft

scp /etc/profile root@spark02:/etc
scp /etc/profile root@spark03:/etc

在所有节点刷新环境变量

source /etc/profile

启动停止

在每个节点分别启动

kafka-server-start.sh -daemon /opt/soft/kafka/config/server.properties

kafka-server-stop.sh

启动脚本

vim kafka-service.sh

#!/bin/bashcase $1 in
"start"){for i in spark01 spark02 spark03doecho  ------------- kafka $i 启动 ------------ssh $i "/opt/soft/kafka/bin/kafka-server-start.sh -daemon /opt/soft/kafka/config/server.properties"done
}
;;
"stop"){for i in spark01 spark02 spark03doecho  ------------- kafka $i 停止 ------------ssh $i "/opt/soft/kafka/bin/kafka-server-stop.sh"done
}
esac

kraft模式

创建软件目录

mkdir /opt/soft
cd /opt/soft

下载

wget https://downloads.apache.org/kafka/3.4.0/kafka_2.13-3.4.0.tgz

解压

tar -zxvf kafka_2.13-3.4.0.tgz 

修改目录名称

mv kafka_2.13-3.4.0 kafka

配置环境变量

vim /etc/profile

export KAFKA_HOME=/opt/soft/kafka
export PATH=$PATH:$KAFKA_HOME/bin

修改配置文件

配置文件存放在 kafka/config/kraft目录

# 在每个节点创建目录
mkdir -p /opt/soft/kraft-combined-logs

vim /opt/soft/kafka/config/kraft/server.properties

主要修改以下三个参数：

• process.roles=broker,controller
• node.id=1 注意不同的节点id号不同
• controller.quorum.voters=controller.quorum.voters=1@localhost:9093 修改为 controller.quorum.voters=controller.quorum.voters=1@spark01:9093,2@spark02:9093,3@spark03:9093
• advertised.listeners=PLAINTEXT://localhost:9092 修改为 advertised.listeners=PLAINTEXT://spark01:9092
• log.dirs=/tmp/kraft-combined-logs 修改为 log.dirs=/opt/soft/kraft-combined-logs

# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.#
# This configuration file is intended for use in KRaft mode, where
# Apache ZooKeeper is not present.  See config/kraft/README.md for details.
############################## Server Basics ############################## The role of this server. Setting this puts us in KRaft mode
process.roles=broker,controller# The node id associated with this instance's roles
node.id=1# The connect string for the controller quorum
controller.quorum.voters=1@spark01:9093,2@spark02:9093,3@spark03:9093############################# Socket Server Settings ############################## The address the socket server listens on.
# Combined nodes (i.e. those with `process.roles=broker,controller`) must list the controller listener here at a minimum.
# If the broker listener is not defined, the default listener will use a host name that is equal to the value of java.net.InetAddress.getCanonicalHostName(),
# with PLAINTEXT listener name, and port 9092.
#   FORMAT:
#     listeners = listener_name://host_name:port
#   EXAMPLE:
#     listeners = PLAINTEXT://your.host.name:9092
listeners=PLAINTEXT://:9092,CONTROLLER://:9093# Name of listener used for communication between brokers.
inter.broker.listener.name=PLAINTEXT# Listener name, hostname and port the broker will advertise to clients.
# If not set, it uses the value for "listeners".
advertised.listeners=PLAINTEXT://spark01:9092# A comma-separated list of the names of the listeners used by the controller.
# If no explicit mapping set in `listener.security.protocol.map`, default will be using PLAINTEXT protocol
# This is required if running in KRaft mode.
controller.listener.names=CONTROLLER# Maps listener names to security protocols, the default is for them to be the same. See the config documentation for more details
listener.security.protocol.map=CONTROLLER:PLAINTEXT,PLAINTEXT:PLAINTEXT,SSL:SSL,SASL_PLAINTEXT:SASL_PLAINTEXT,SASL_SSL:SASL_SSL# The number of threads that the server uses for receiving requests from the network and sending responses to the network
num.network.threads=3# The number of threads that the server uses for processing requests, which may include disk I/O
num.io.threads=8# The send buffer (SO_SNDBUF) used by the socket server
socket.send.buffer.bytes=102400# The receive buffer (SO_RCVBUF) used by the socket server
socket.receive.buffer.bytes=102400# The maximum size of a request that the socket server will accept (protection against OOM)
socket.request.max.bytes=104857600############################# Log Basics ############################## A comma separated list of directories under which to store log files
log.dirs=/opt/soft/kraft-combined-logs# The default number of log partitions per topic. More partitions allow greater
# parallelism for consumption, but this will also result in more files across
# the brokers.
num.partitions=1# The number of threads per data directory to be used for log recovery at startup and flushing at shutdown.
# This value is recommended to be increased for installations with data dirs located in RAID array.
num.recovery.threads.per.data.dir=1############################# Internal Topic Settings  #############################
# The replication factor for the group metadata internal topics "__consumer_offsets" and "__transaction_state"
# For anything other than development testing, a value greater than 1 is recommended to ensure availability such as 3.
offsets.topic.replication.factor=1
transaction.state.log.replication.factor=1
transaction.state.log.min.isr=1############################# Log Flush Policy ############################## Messages are immediately written to the filesystem but by default we only fsync() to sync
# the OS cache lazily. The following configurations control the flush of data to disk.
# There are a few important trade-offs here:
#    1. Durability: Unflushed data may be lost if you are not using replication.
#    2. Latency: Very large flush intervals may lead to latency spikes when the flush does occur as there will be a lot of data to flush.
#    3. Throughput: The flush is generally the most expensive operation, and a small flush interval may lead to excessive seeks.
# The settings below allow one to configure the flush policy to flush data after a period of time or
# every N messages (or both). This can be done globally and overridden on a per-topic basis.# The number of messages to accept before forcing a flush of data to disk
#log.flush.interval.messages=10000# The maximum amount of time a message can sit in a log before we force a flush
#log.flush.interval.ms=1000############################# Log Retention Policy ############################## The following configurations control the disposal of log segments. The policy can
# be set to delete segments after a period of time, or after a given size has accumulated.
# A segment will be deleted whenever *either* of these criteria are met. Deletion always happens
# from the end of the log.# The minimum age of a log file to be eligible for deletion due to age
log.retention.hours=168# A size-based retention policy for logs. Segments are pruned from the log unless the remaining
# segments drop below log.retention.bytes. Functions independently of log.retention.hours.
#log.retention.bytes=1073741824# The maximum size of a log segment file. When this size is reached a new log segment will be created.
log.segment.bytes=1073741824# The interval at which log segments are checked to see if they can be deleted according
# to the retention policies
log.retention.check.interval.ms=300000

分发配置到其它节点

scp -r /opt/soft/kafka root@spark02:/opt/soft
scp -r /opt/soft/kafka root@spark03:/opt/soft

scp /etc/profile root@spark02:/etc
scp /etc/profile root@spark03:/etc

在所有节点刷新环境变量

source /etc/profile

初始化集群数据目录

生成存储目录唯一 ID

kafka-storage.sh random-uuid

生成结果：

JfRaZDSORA2xK8pMSCa9AQ

用该 ID 格式化 kafka 存储目录

注意：在每个节点都要执行一次

kafka-storage.sh format -t JfRaZDSORA2xK8pMSCa9AQ \
-c /opt/soft/kafka/config/kraft/server.properties

执行结果：

Formatting /opt/soft/kraft-combined-logs with metadata.version 3.4-IV0.

启动停止

在每个节点分别启动

kafka-server-start.sh -daemon /opt/soft/kafka/config/kraft/server.properties

kafka-server-stop.sh

启动脚本

vim kafka-service.sh

#!/bin/bashcase $1 in
"start"){for i in spark01 spark02 spark03doecho  ------------- kafka $i 启动 ------------ssh $i "/opt/soft/kafka/bin/kafka-server-start.sh -daemon /opt/soft/kafka/config/kraft/server.properties"done
}
;;
"stop"){for i in spark01 spark02 spark03doecho  ------------- kafka $i 停止 ------------ssh $i "/opt/soft/kafka/bin/kafka-server-stop.sh"done
}
esac

命令行操作

主题命令行

查看操作主题命令参数

kafka-topics.sh

参数	描述
–bootstrap-server <String: server toconnect to>	连接的 Kafka Broker 主机名称和端口号
–topic <String: topic>	操作的 topic 名称
–create	创建主题
–delete	删除主题
–alter	修改主题
–list	查看所有主题
–describe	查看主题详细描述
–partitions <Integer: # of partitions>	设置分区数
–replication-factor<Integer: replication factor>	设置分区副本
–config <String: name=value>	更新系统默认的配置

查看当前服务器中的所有 topic

kafka-topics.sh --bootstrap-server spark01:9092,spark02:9092,spark03:9092 --list

创建 lihaozhe topic

选项说明：

–topic 定义 topic 名

–partitions 定义分区数

–replication-factor 定义副本数

kafka-topics.sh --bootstrap-server spark01:9092,spark02:9092,spark03:9092 \
--topic lihaozhe --create --partitions 1 --replication-factor 3 

查看主题详情

kafka-topics.sh --bootstrap-server spark01:9092,spark02:9092,spark03:9092 \
--describe --topic lihaozhe

执行结果：

Topic: lihaozhe	TopicId: kJWVrG0xQQSaFcrWGMYEGg	PartitionCount: 1	ReplicationFactor: 3	Configs: Topic: lihaozhe	Partition: 0	Leader: 1	Replicas: 1,2,3	Isr: 1,2,3

修改分区数

注意：

​ 分区数只能增加，不能减少

​ 不能通过命令行的方式修改副本

kafka-topics.sh --bootstrap-server spark01:9092,spark02:9092,spark03:9092 \
--alter --topic lihaozhe --partitions 3

执行成功后再次查看主题详细信息结果如下：

Topic: lihaozhe	TopicId: kJWVrG0xQQSaFcrWGMYEGg	PartitionCount: 3	ReplicationFactor: 3	Configs: Topic: lihaozhe	Partition: 0	Leader: 1	Replicas: 1,2,3	Isr: 1,2,3Topic: lihaozhe	Partition: 1	Leader: 2	Replicas: 2,3,1	Isr: 2,3,1Topic: lihaozhe	Partition: 2	Leader: 3	Replicas: 3,1,2	Isr: 3,1,2

生产者命令行

查看操作生产者命令参数

kafka-console-producer.sh

参数	描述
–bootstrap-server <String: server toconnect to>	连接的 Kafka Broker 主机名称和端口号
–topic <String: topic>	操作的 topic 名称
–key.serializer	指定发送消息的 key 的序列化类 一定要写全类名
–value.serializer	指定发送消息的 value 的序列化类 一定要写全类名
–buffer.memory	RecordAccumulator 缓冲区总大小，默认 32Mb
–batch.size	缓冲区一批数据最大值，默认 16Kb。 适当增加该值，可以提高吞吐量， 但是如果该值设置太大，会导致数据传输延迟增加
–linger.ms	如果数据迟迟未达到 batch.size，sender 等待 linger.time之后就会发送数据。 单位 ms，默认值是 0ms，表示没有延迟。 生产环境建议该值大小为 5-100ms 之间。
–acks	0：生产者发送过来的数据，不需要等数据落盘应答 1：生产者发送过来的数据，Leader 收到数据后应答 -1（all）：生产者发送过来的数据，Leader+和 isr 队列里面的所有节点收齐数据后应答 默认值是-1，-1 和all 是等价的
–max.in.flight.requests.per.connection	允许最多没有返回 ack 的次数，默认为 5， 开启幂等性要保证该值是 1-5 的数字
–retries	当消息发送出现错误的时候，系统会重发消息 retries表示重试次数。默认是 int 最大值，2147483647 如果设置了重试，还想保证消息的有序性，需要设置 MAX_IN_FLIGHT_REQUESTS_PER_CONNECTION=1 否则在重试此失败消息的时候，其他的消息可能发送成功了
–retry.backoff.ms	两次重试之间的时间间隔，默认是 100ms
–enable.idempotence	是否开启幂等性，默认 true，开启幂等性。
–compression.type	生产者发送的所有数据的压缩方式。 默认是 none，也就是不压缩 支持压缩类型：none、gzip、snappy、lz4 和 zstd

发送消息

kafka-console-producer.sh --bootstrap-server spark01:9092,spark02:9092,spark03:9092 --topic lihaozhe

消费者命令行

查看操作消费者命令参数

kafka-console-consumer.sh

参数	描述
–bootstrap-server <String: server toconnect to>	连接的 Kafka Broker 主机名称和端口号
–topic <String: topic>	操作的 topic 名称
–from-beginning	从头开始消费
–group <String: consumer group id>	指定消费者组名称

消费 lihaozhe 主题中的数据

kafka-console-consumer.sh --bootstrap-server spark01:9092,spark02:9092,spark03:9092 \
--topic lihaozhe

把主题中所有的数据都读取出来

包括历史数据

kafka-console-consumer.sh --bootstrap-server spark01:9092,spark02:9092,spark03:9092 \
--topic lihaozhe --from-beginning

生产者

生产者发送数据流程

1. RecordAccumulator：每一个是生产上都会维护一个固定大小的内存空间，主要用于合并单条消息，进行批量发送，提高吞吐量，减少带宽消耗。

2. RecordAccumulator的大小是可配置的，可以配置buffer.memory来修改缓冲区大小，默认值为：33554432（32M）

3. RecordAccumulator内存结构分为两部分

   • 第一部分为已经使用的内存，这一部分主要存放了很多的队列。

     每一个主题的每一个分区都会创建一个队列，来存放当前分区下待发送的消息集合。

   • 第二部分为未使用的内存，这一部分分为已经池化后的内存和未池化的整个剩余内存（nonPooledAvailableMemory）。

     池化的内存的会根据batch.size（默认值为16K）的配置进行池化多个ByteBuffer，

     放入一个队列中。所有的剩余空间会形成一个未池化的剩余空间。

java api

pom.xml

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"><modelVersion>4.0.0</modelVersion><groupId>com.lihaozhe</groupId><artifactId>kafka-code</artifactId><version>1.0.0</version><packaging>jar</packaging><name>kafka</name><url>http://maven.apache.org</url><properties><jdk.version>1.8</jdk.version><maven.compiler.source>1.8</maven.compiler.source><maven.compiler.target>1.8</maven.compiler.target><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding><maven.test.failure.ignore>true</maven.test.failure.ignore><maven.test.skip>true</maven.test.skip></properties><dependencies><!-- junit-jupiter-api --><dependency><groupId>org.junit.jupiter</groupId><artifactId>junit-jupiter-api</artifactId><version>5.9.3</version><scope>test</scope></dependency><!-- junit-jupiter-engine --><dependency><groupId>org.junit.jupiter</groupId><artifactId>junit-jupiter-engine</artifactId><version>5.9.3</version><scope>test</scope></dependency><dependency><groupId>org.projectlombok</groupId><artifactId>lombok</artifactId><version>1.18.26</version></dependency><dependency><groupId>org.apache.logging.log4j</groupId><artifactId>log4j-slf4j-impl</artifactId><version>2.20.0</version></dependency><dependency><groupId>com.alibaba.fastjson2</groupId><artifactId>fastjson2</artifactId><version>2.0.31</version></dependency><dependency><groupId>com.github.binarywang</groupId><artifactId>java-testdata-generator</artifactId><version>1.1.2</version></dependency><dependency><groupId>mysql</groupId><artifactId>mysql-connector-java</artifactId><version>8.0.33</version></dependency><dependency><groupId>org.apache.kafka</groupId><artifactId>kafka-clients</artifactId><version>3.4.0</version></dependency></dependencies><build><finalName>${project.name}</finalName><!--<outputDirectory>../package</outputDirectory>--><plugins><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-compiler-plugin</artifactId><version>3.11.0</version><configuration><!-- 设置编译字符编码 --><encoding>UTF-8</encoding><!-- 设置编译jdk版本 --><source>${jdk.version}</source><target>${jdk.version}</target></configuration></plugin><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-clean-plugin</artifactId><version>3.2.0</version></plugin><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-resources-plugin</artifactId><version>3.3.1</version></plugin><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-war-plugin</artifactId><version>3.3.2</version></plugin><!-- 编译级别 --><!-- 打包的时候跳过测试junit begin --><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-surefire-plugin</artifactId><version>2.22.2</version><configuration><skip>true</skip></configuration></plugin></plugins></build>
</project>

生产者

producer 异步发送数据到 topic 不带回调函数

com.lihaozhe.producer.AsyncProducer

package com.lihaozhe.producer;import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.common.serialization.StringSerializer;import java.util.Properties;/*** producer 异步发送数据到 topic 不带回调函数* 提前在控制台 打开消费者监听 命令如下* kafka-console-consumer.sh --bootstrap-server spark01:9092,spark02:9092,spark03:9092 --topic lihaozhe** @author 李昊哲* @version 1.0.0  2023/5/15 下午1:45*/
public class AsyncProducer {public static void main(String[] args) {// 1、基础配置Properties properties = new Properties();// 连接集群 bootstrap.serversproperties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "spark01:9092,spark02:9092,spark03:9092");// 指定对应的key和value的序列化类型 key.serializerproperties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());// 2、创建kafka生产者对象KafkaProducer<String, String> producer = new KafkaProducer<>(properties);// 3、发送数据for (int i = 0; i < 5; i++) {producer.send(new ProducerRecord<>("lihaozhe", "李昊哲" + i));}// 4、释放资源producer.close();System.out.println("success");}
}

producer 同步发送数据到 topic 不带回调函数

com.lihaozhe.producer.SyncProducer

package com.lihaozhe.producer;import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.common.serialization.StringSerializer;import java.util.Properties;
import java.util.concurrent.ExecutionException;/*** producer 同步发送数据到 topic 不带回调函数* 提前在控制台 打开消费者监听 命令如下* kafka-console-consumer.sh --bootstrap-server spark01:9092,spark02:9092,spark03:9092 --topic lihaozhe** @author 李昊哲* @version 1.0.0  2023/5/15 下午1:45*/
public class syncProducer {public static void main(String[] args) throws ExecutionException, InterruptedException {// 1、基础配置Properties properties = new Properties();// 连接集群 bootstrap.serversproperties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "spark01:9092,spark02:9092,spark03:9092");// 指定对应的key和value的序列化类型 key.serializerproperties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());// 2、创建kafka生产者对象KafkaProducer<String, String> producer = new KafkaProducer<>(properties);// 3、发送数据for (int i = 0; i < 5; i++) {producer.send(new ProducerRecord<>("lihaozhe", "李昊哲" + i)).get();}// 4、释放资源producer.close();System.out.println("success");}
}

producer 异步发送数据到 topic 带回调函数

com.lihaozhe.producer.AsyncProducerCallback

package com.lihaozhe.producer;import org.apache.kafka.clients.producer.*;
import org.apache.kafka.common.serialization.StringSerializer;import java.util.Properties;/*** producer 异步发送数据到 topic 回调函数* 提前在控制台 打开消费者监听 命令如下* kafka-console-consumer.sh --bootstrap-server spark01:9092,spark02:9092,spark03:9092 --topic lihaozhe** @author 李昊哲* @version 1.0.0  2023/5/15 下午1:45*/
public class AsyncProducerCallback {public static void main(String[] args) throws InterruptedException {// 1、基础配置Properties properties = new Properties();// 连接集群 bootstrap.serversproperties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "spark01:9092,spark02:9092,spark03:9092");// 指定对应的key和value的序列化类型 key.serializerproperties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());// 2、创建kafka生产者对象KafkaProducer<String, String> producer = new KafkaProducer<>(properties);// 3、发送数据for (int i = 0; i < 500; i++) {producer.send(new ProducerRecord<>("lihaozhe", "李昊哲" + i), (metadata, exception) -> {if (exception == null){System.out.println("topic: " + metadata.topic() + "\tpartition: " + metadata.partition());}});Thread.sleep(2);}// 4、释放资源producer.close();System.out.println("success");}
}

producer 异步发送数据到 topic 指定分区号

com.lihaozhe.producer.AsyncProducerCallbackPartitions01

package com.lihaozhe.producer;import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.common.serialization.StringSerializer;import java.util.Properties;/*** producer 异步发送数据到 topic 带回调函数* 指定分区号* 提前在控制台 打开消费者监听 命令如下* kafka-console-consumer.sh --bootstrap-server spark01:9092,spark02:9092,spark03:9092 --topic lihaozhe** @author 李昊哲* @version 1.0.0  2023/5/15 下午1:45*/
public class AsyncProducerCallbackPartitions01 {public static void main(String[] args) throws InterruptedException {// 1、基础配置Properties properties = new Properties();// 连接集群 bootstrap.serversproperties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "spark01:9092,spark02:9092,spark03:9092");// 指定对应的key和value的序列化类型 key.serializerproperties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());// 2、创建kafka生产者对象KafkaProducer<String, String> producer = new KafkaProducer<>(properties);// 3、发送数据for (int i = 0; i < 500; i++) {// topic partion key valueproducer.send(new ProducerRecord<>("lihaozhe", 0, null, "李昊哲" + i), (metadata, exception) -> {if (exception == null) {System.out.println("topic: " + metadata.topic() + "\tpartition: " + metadata.partition());}});Thread.sleep(2);}// 4、释放资源producer.close();System.out.println("success");}
}

producer 异步发送数据到 topic 根据指定的 key 的 hash 值 对分区数取模

com.lihaozhe.producer.AsyncProducerCallbackPartitions02

package com.lihaozhe.producer;import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.common.serialization.StringSerializer;import java.util.Properties;/*** producer 异步发送数据到 topic 带回调函数* 根据指定的 key 的 hash 值 对分区数取模* 提前在控制台 打开消费者监听 命令如下* kafka-console-consumer.sh --bootstrap-server spark01:9092,spark02:9092,spark03:9092 --topic lihaozhe** @author 李昊哲* @version 1.0.0  2023/5/15 下午1:45*/
public class AsyncProducerCallbackPartitions02 {public static void main(String[] args) throws InterruptedException {// 1、基础配置Properties properties = new Properties();// 连接集群 bootstrap.serversproperties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "spark01:9092,spark02:9092,spark03:9092");// 指定对应的key和value的序列化类型 key.serializerproperties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());// 2、创建kafka生产者对象KafkaProducer<String, String> producer = new KafkaProducer<>(properties);// 3、发送数据for (int i = 0; i < 500; i++) {// 字符 a 的 hash 值为 97producer.send(new ProducerRecord<>("lihaozhe", "a", "李昊哲" + i), (metadata, exception) -> {if (exception == null) {System.out.println("topic: " + metadata.topic() + "\tpartition: " + metadata.partition());}});Thread.sleep(2);}// 4、释放资源producer.close();System.out.println("success");}
}

producer 异步发送数据到 topic 关联自定义分区器

自定义分区类

com.lihaozhe.producer.MyPartitioner

package com.lihaozhe.producer;import org.apache.kafka.clients.producer.Partitioner;
import org.apache.kafka.common.Cluster;import java.util.Map;/*** 自定义分区器** @author 李昊哲* @version 1.0.0  2023/5/15 下午4:01*/
public class MyPartitioner implements Partitioner {/*** @param topic      The topic name* @param key        The key to partition on (or null if no key)* @param keyBytes   The serialized key to partition on( or null if no key)* @param value      The value to partition on or null* @param valueBytes The serialized value to partition on or null* @param cluster    The current cluster metadata* @return partition*/@Overridepublic int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {String msg = value.toString();if (msg.contains("李哲")) {return 0;} else if (msg.contains("李昊哲")) {return 1;} else {return 2;}}@Overridepublic void close() {}@Overridepublic void configure(Map<String, ?> configs) {}
}

com.lihaozhe.producer.AsyncProducerCallbackPartitions03

package com.lihaozhe.producer;import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.common.serialization.StringSerializer;import java.util.Arrays;
import java.util.List;
import java.util.Properties;/*** producer 异步发送数据到 topic 带回调函数* 关联自定义分区器* 提前在控制台 打开消费者监听 命令如下* kafka-console-consumer.sh --bootstrap-server spark01:9092,spark02:9092,spark03:9092 --topic lihaozhe** @author 李昊哲* @version 1.0.0  2023/5/15 下午1:45*/
public class AsyncProducerCallbackPartitions03 {public static void main(String[] args) throws InterruptedException {// 1、基础配置Properties properties = new Properties();// 连接集群 bootstrap.serversproperties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "spark01:9092,spark02:9092,spark03:9092");// 指定对应的key和value的序列化类型 key.serializerproperties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());// 关联自定义分区器 注意必须些完整类名字properties.put(ProducerConfig.PARTITIONER_CLASS_CONFIG, MyPartitioner.class.getName());// 2、创建kafka生产者对象KafkaProducer<String, String> producer = new KafkaProducer<>(properties);// 3、发送数据List<String> names = Arrays.asList("李昊哲", "李哲", "李大宝");for (int i = 0; i < 500; i++) {// topic partion key valueproducer.send(new ProducerRecord<>("lihaozhe", names.get(i % names.size())), (metadata, exception) -> {if (exception == null) {System.out.println("topic: " + metadata.topic() + "\tpartition: " + metadata.partition());}});Thread.sleep(2);}// 4、释放资源producer.close();System.out.println("success");}
}

调整生产者发送参数

com.lihaozhe.producer.AsyncProducerParameters

package com.lihaozhe.producer;import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.common.serialization.StringSerializer;import java.util.Properties;/*** 调整生产者发送参数* 提前在控制台 打开消费者监听 命令如下* kafka-console-consumer.sh --bootstrap-server spark01:9092,spark02:9092,spark03:9092 --topic lihaozhe** @author 李昊哲* @version 1.0.0  2023/5/15 下午1:45*/
public class AsyncProducerParameters {public static void main(String[] args) {// 1、基础配置Properties properties = new Properties();// 连接集群 bootstrap.serversproperties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "spark01:9092,spark02:9092,spark03:9092");// 指定对应的key和value的序列化类型 key.serializerproperties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());// 缓冲区大小properties.put(ProducerConfig.BUFFER_MEMORY_CONFIG,33554432);// 批次大小properties.put(ProducerConfig.BATCH_SIZE_CONFIG,16384);// linger.msproperties.put(ProducerConfig.LINGER_MS_CONFIG, 1);// 压缩 none, gzip, snappy, lz4, zstdproperties.put(ProducerConfig.COMPRESSION_TYPE_CONFIG,"snappy");// 2、创建kafka生产者对象KafkaProducer<String, String> producer = new KafkaProducer<>(properties);// 3、发送数据for (int i = 0; i < 5; i++) {producer.send(new ProducerRecord<>("lihaozhe", "李昊哲" + i));}// 4、释放资源producer.close();System.out.println("success");}
}

调整生产者发送参数 ack retries

com.lihaozhe.producer.AsyncProducerAck

package com.lihaozhe.producer;import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.common.serialization.StringSerializer;import java.util.Properties;/*** producer 异步发送数据到 topic 带回调函数* 修改 ack retries* 提前在控制台 打开消费者监听 命令如下* kafka-console-consumer.sh --bootstrap-server spark01:9092,spark02:9092,spark03:9092 --topic lihaozhe** @author 李昊哲* @version 1.0.0  2023/5/15 下午1:45*/
public class AsyncProducerAck {public static void main(String[] args) throws InterruptedException {// 1、基础配置Properties properties = new Properties();// 连接集群 bootstrap.serversproperties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "spark01:9092,spark02:9092,spark03:9092");// 指定对应的key和value的序列化类型 key.serializerproperties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());// acksproperties.put(ProducerConfig.ACKS_CONFIG, "1");// retries 重试次数properties.put(ProducerConfig.RETRIES_CONFIG, 3);// 2、创建kafka生产者对象KafkaProducer<String, String> producer = new KafkaProducer<>(properties);// 3、发送数据for (int i = 0; i < 500; i++) {producer.send(new ProducerRecord<>("lihaozhe", "李昊哲" + i), (metadata, exception) -> {if (exception == null) {System.out.println("topic: " + metadata.topic() + "\tpartition: " + metadata.partition());}});Thread.sleep(2);}// 4、释放资源producer.close();System.out.println("success");}
}

事务

com.lihaozhe.producer.AsyncProducerTransactions

package com.lihaozhe.producer;import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.common.serialization.StringSerializer;import java.util.Properties;/*** producer 异步发送数据到 topic 不带回调函数* 提前在控制台 打开消费者监听 命令如下* kafka-console-consumer.sh --bootstrap-server spark01:9092,spark02:9092,spark03:9092 --topic lihaozhe** @author 李昊哲* @version 1.0.0  2023/5/15 下午1:45*/
public class AsyncProducerTransactions {public static void main(String[] args) {// 1、基础配置Properties properties = new Properties();// 连接集群 bootstrap.serversproperties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "spark01:9092,spark02:9092,spark03:9092");// 指定对应的key和value的序列化类型 key.serializerproperties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());// 指定事务idproperties.put(ProducerConfig.TRANSACTIONAL_ID_CONFIG, "transactional_id_01");// 2、创建kafka生产者对象KafkaProducer<String, String> producer = new KafkaProducer<>(properties);producer.initTransactions();producer.beginTransaction();try {// 3、发送数据for (int i = 0; i < 5; i++) {producer.send(new ProducerRecord<>("lihaozhe", "李昊哲" + i));}// int i = 1 / 0;producer.commitTransaction();System.out.println("success");} catch (Exception e) {System.out.println("failed");producer.abortTransaction();} finally {// 4、释放资源producer.close();}}
}