kafka 集群环境搭建 java

Posted 2022-10-06 eian

简单记录下kafka集群环境搭建过程， 用来做备忘录

安装

第一步： 点击官网下载地址 http://kafka.apache.org/downloads.html 下载最新安装包

第二步： 解压

tar xvf  kafka_2.12-2.2.0.tgz

第三步： 检查服务器有没有安装zookeeper集群， 没有的话，自行百度补充
第四步：修改config/server.properties 文件

# 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.

# 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=0  // 说明，这个跟zookeeper的myid一样配置 0，1，2

############################# Socket Server Settings #############################

# The address the socket server listens on. It will get the value returned from 
# java.net.InetAddress.getCanonicalHostName() if not configured.
#   FORMAT:
#     listeners = listener_name://host_name:port
#   EXAMPLE:
#     listeners = PLAINTEXT://your.host.name:9092
#listeners=PLAINTEXT://:9092
host.name=192.168.175.130  // 当前服务器ip
# Hostname and port the broker will advertise to producers and consumers. If not set, 
# it uses the value for "listeners" if configured.  Otherwise, it will use the value
# returned from java.net.InetAddress.getCanonicalHostName().
#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/local/data/install/kafka2.2.0/log/kafka  // 目录要存在

# 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 for 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=192.168.175.130:2181,192.168.175.131:2181,192.168.175.132:2181  // 三台服务器配置

# Timeout in ms for connecting to zookeeper
zookeeper.connection.timeout.ms=6000


############################# 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

• 注：
  server.properties配置文件的修改主要在开头和结尾，中间保持默认配置即可；需要注意的点是broker.id的值三个节点要配 置不同的值，分别配置为0，1，2；log.dirs必须保证目录存在，不会根据配置文件自动生成；

最后，启动三台机器的zookeeper ， 然后启动三台机器的kafka

bin/kafka-server-start.sh config/server.properties  & 

三个节点均要启动；启动无报错，即搭建成功，可以生产和消费消息，来检测是否搭建成功。

### 至此，kafka集群环境搭建完毕，下面写一些java中如何使用

java环境kafka测试生产者和消费者

导入包

<dependencies>
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka_2.12</artifactId>
            <version>2.2.0</version>
        </dependency>

        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>2.2.0</version>
        </dependency>

        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-streams</artifactId>
            <version>2.2.0</version>
        </dependency>


    </dependencies>

消费者

https://kafka.apache.org/10/javadoc/index.html?org/apache/kafka/clients/consumer/KafkaConsumer.html

import java.util.Arrays;
import java.util.Properties;

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;

/**
 * 消费者
 * 
 * 测试体验， kafka的消费能力真的很快
 * 
 * @author LELE
 *
 */
public class KafkaConsu extends KafkaConsumer 

    public KafkaConsu(Properties properties) 
        super(properties);
        // TODO Auto-generated constructor stub
    

    public static void main(String[] args) 
        Properties props = new Properties();
        // kafka servers
        props.put("bootstrap.servers", "192.168.175.130:9092,192.168.175.131:9092,192.168.175.132:9092");
        // group
        props.put("group.id", "DemoConsumer");
        props.put("enable.auto.commit", "true");
        props.put("auto.commit.interval.ms", "1000");
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
        // 订阅的topic
        consumer.subscribe(Arrays.asList("my-topic"));
        while (true) 
            // 超时时间 ms
            ConsumerRecords<String, String> records = consumer.poll(100);
            for (ConsumerRecord<String, String> record : records)
                System.out.printf("测试 offset = %d, key = %s, value = %s%n", record.offset(), record.key(),
                        record.value());
        
    

kafka生产者

https://kafka.apache.org/10/javadoc/index.html?org/apache/kafka/clients/producer/KafkaProducer.html

import java.util.Properties;

import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;

/**
 * 消息生产者
 * @author LELE
 *
 */
public class KafkaProdu extends KafkaProducer 

    public KafkaProdu(Properties properties) 
        super(properties);
    

    public static void main(String[] args) throws Exception 
        Properties props = new Properties();
        // kafka servers
        props.put("bootstrap.servers", "192.168.175.130:9092,192.168.175.131:9092,192.168.175.132:9092");
        props.put("acks", "all");
        props.put("retries", 0);
        props.put("batch.size", 16384);
        props.put("linger.ms", 1);
        // topic 分组
        props.put("client.id", "DemoProducer");
        props.put("buffer.memory", 33554432);
        // 序列化工具
        props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        // 序列化工具
        props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

        Producer<String, String> producer = new KafkaProducer<>(props);
        for (int i = 0; i < 1000000; i++)
            producer.send(new ProducerRecord<String, String>("my-topic", Integer.toString(i), Integer.toString(i)));

        producer.close();