标签：record scribe super evel env direct Stub retention 体验
简单记录下kafka集群环境搭建过程， 用来做备忘录
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。
<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>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());
}
}
}
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();
}
}
标签：record scribe super evel env direct Stub retention 体验
原文地址：https://www.cnblogs.com/eian/p/11478467.html
