kafa

本文链接： https://blog.csdn.net/lucasmaluping/article/details/102548556

1.kafka介绍：

Kafka是最初由Linkedin公司开发，是一个分布式、分区的、多副本的、多订阅者，基于zookeeper协调的分布式日志系统（也可以当做MQ系统），常见可以用于web/nginx日志、访问日志，消息服务等等，Linkedin于2010年贡献给了Apache基金会并成为顶级开源项目。

主要应用场景是：日志收集系统和消息系统。

Kafka主要设计目标如下：

以时间复杂度为O(1)的方式提供消息持久化能力，即使对TB级以上数据也能保证常数时间的访问性能。
高吞吐率。即使在非常廉价的商用机器上也能做到单机支持每秒100K条消息的传输。
支持Kafka Server间的消息分区，及分布式消费，同时保证每个partition内的消息顺序传输。
同时支持离线数据处理和实时数据处理。
Scale out:支持在线水平扩展

2.kafka安装： a. 解压 b.修改配置文件 server.properties

1).将hdp-1、hdp-2和hdp-3的broker.id分别修改为1、2、3（只要保证不冲突就可以）

2).修改log配置文件 /root/kafkadata/kafka-logs

3).修改zookeeper的连接地址，多个地址使用逗号分开。

4).在socket server setting中修改listeners=PLAINTEXT://hdp-1:9092

# 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=3

############################# 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://hdp-3:9092

# 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=/root/kafkadata/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 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=hdp-1:2181,hdp-2:2181,hdp-3:2181

# 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

3.生产者producer、消费者consumer

产生数据 (消费）接受数据

4、启动kafka

a、启动zookeeper b. 启动kafka

a、启动zookeeper

b. 启动kafka

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

5、一台centos启动producer发送数据

如果没有创建该topic则新建topic

bin/kafka-topics.sh --create --zookeeper hdp-1:2181 --replication-factor 1 --partitions 1 --topic animal

参数说明：--create 创建

--zookeeper zookeeper的机器

--replication-factor:副本数量

--partitions: 分区

--topic：类型

bin/kafka-console-producer.sh --broker-list hdp-2:9092 --topic animal

一台centos启动consumer接受数据

./kafka-console-consumer.sh --zookeeper hdp-1:2181 --topic kafkatest --from beginning

6、flume采集数据下沉到kafka --------配置文件tail-kafka

a1.sources.source1.type = exec
a1.sources.source1.command = tail -F /root/log/access.log
# Describe the sink
a1.sinks.k1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.topic = animal
a1.sinks.k1.brokerList = hdp-2:9092, hdp-3:9092
a1.sinks.k1.requiredAcks = 1
a1.sinks.k1.batchSize = 20
a1.sinks.k1.channel = c1
# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

# Bind the source and sink to the channel
a1.sources.source1.channels = c1
a1.sinks.k1.channel = c1

7、java代码cousumer消费者

porm.xml

<?xml version="1.0" encoding="UTF-8"?>
<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.zpark.kafkatest</groupId>
    <artifactId>kafkatest</artifactId>
    <version>1.0-SNAPSHOT</version>
    <dependencies>
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka_2.12</artifactId>
        </dependency>
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>2.2.0</version>
        </dependency>
    </dependencies>

</project>

ConsumerDemo

import java.util.Collections;
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;

public class ConsumerDemo {
    private static KafkaConsumer<String, String> consumer;
    private static Properties props;

    static {
        props = new Properties();
        //消费者kafkka地址
        props.put("bootstrap.servers", "hdp-3:9092");
        //key反序列化
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        //组
        props.put("group.id", "yangk");
    }

    /**
     * 从kafka中获取数据（SpringBoot也集成了kafka）
     */
    private static void ConsumerMessage() {
        //允许自动提交位移
        props.put("enable.auto.commit", true);
        consumer = new KafkaConsumer<String, String>(props);
        consumer.subscribe(Collections.singleton("animal"));

        //使用轮询拉取数据--消费完成之后会根据设置时长来清除消息，被消费过的消息，如果想再次被消费，可以根据偏移量(offset)来获取
        try {
            while (true) {
                //从kafka中读到了数据放在records中
                ConsumerRecords<String, String> records = consumer.poll(100);
                for (ConsumerRecord<String, String> r : records) {
                    System.out.printf("topic = %s, offset = %s, key = %s, value = %s", r.topic(), r.offset(),
                            r.key(), r.value());

                }
            }
        } finally {
            consumer.close();
        }
    }

    public static void main(String[] args) {
        ConsumerMessage();
    }
}

猜你喜欢