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Kafka-Install-And-Settings.md

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Kafka 安装和配置

消息系统的好处

  • 解耦(各个业务系统各自为政,有各自新需求,各自系统自行修改,只通过消息来通信)
  • 大系统层面的扩展性(不用改旧业务系统代码,增加新系统,接收新消息)
  • 异步通信(一个消息,多个业务系统来消费。某些场景可以堆积到一定程度再去消费)
  • 缓冲(解耦某些需要长时间处理业务)

Kafka 介绍

A distributed streaming platform

  • 官网:https://kafka.apache.org/
  • Github:https://github.com/apache/kafka
    • 主要是由 Java 和 Scala 开发
  • 官网下载:https://kafka.apache.org/downloads
  • 当前最新稳定版本(201803):1.0.1
  • 官网 quickstart:https://kafka.apache.org/quickstart
  • 运行的机子不要小于 2G 内存
  • Kafka 流行的主要原因:
    • 支持常见的发布订阅功能
    • 分布式
    • 高吞吐量(听说:普通单机也支持每秒 100000 条消息的传输)
    • 磁盘数据持久化,消费者 down 后,重新 up 的时候可以继续接收前面未接收到的消息
    • 支持流数据处理,常见于大数据
  • 核心概念:
    • Producer:生产者(业务系统),负责发布消息到 broker
    • Consumer:消费者(业务系统),向 broker 读取消息的客户端
    • Broker:可以理解为:存放消息的管道(kafka 软件节点本身)
    • Topic:可以理解为:消息主题、消息标签、消息通道、消息队列(物理上不同 Topic 的消息分开存储,根据 Partition 参数决定一个 Topic 的消息保存于一个或多个 broker 上。作为使用者,不用关心 Topic 实际物理存储地方。)
    • Partition:是物理上的概念,每个 Topic 包含一个或多个 Partition。一般有几个 Broker,填写分区最好是等于大于节点值。分区目的主要是数据分片,解决水平扩展、高吞吐量。当 Producer 生产消息的时候,消息会被算法计算后分配到对应的分区,Consumer 读取的时候算法也会帮我们找到消息所在分区,这是内部实现的,应用层面不用管。
    • Replication-factor:副本。假设有 3 个 Broker 的情况下,当副本为 3 的时候每个 Partition 会在每个 Broker 都会存有一份,目的主要是容错。
      • 其中有一个 Leader。
    • Consumer Group:每个 Consumer 属于一个特定的 Consumer Group(可为每个 Consumer 指定 group name,若不指定 group name 则属于默认的 group)一般一个业务系统集群指定同一个一个 group id,然后一个业务系统集群只能一个节点来消费同一个消息。
      • Consumer Group 信息存储在 zookeeper 中,需要通过 zookeeper 的客户端来查看和设置
      • 如果某 Consumer Group 中 consumer 数量少于 partition 数量,则至少有一个 consumer 会消费多个 partition 的数据
      • 如果 consumer 的数量与 partition 数量相同,则正好一个 consumer 消费一个 partition 的数据
      • 如果 consumer 的数量多于 partition 的数量时,会有部分 consumer 无法消费该 topic 下任何一条消息。
      • 具体实验可以看这篇文章:Kafka深度解析
    • Record:消息数据本身,由一个 key、value、timestamp 组成
  • 业界常用的 docker 镜像:
  • 官网 quickstart 指导:https://kafka.apache.org/quickstart
  • 常用命令:
    • wurstmeister/kafka-docker 容器中 kafka home:cd /opt/kafka
    • 假设我的 zookeeper 地址:10.135.157.34:2181,如果你有多个节点用逗号隔开
    • 列出所有 topic:bin/kafka-topics.sh --list --zookeeper 10.135.157.34:2181
    • 创建 topic:bin/kafka-topics.sh --create --topic kafka-test-topic-1 --partitions 3 --replication-factor 1 --zookeeper 10.135.157.34:2181
      • 创建名为 kafka-test-topic-1 的 topic,3个分区分别存放数据,数据备份总共 2 份
    • 查看特定 topic 的详情:bin/kafka-topics.sh --describe --topic kafka-test-topic-1 --zookeeper 10.135.157.34:2181
    • 删除 topic:bin/kafka-topics.sh --delete --topic kafka-test-topic-1 --zookeeper 10.135.157.34:2181
    • 更多命令可以看:http://orchome.com/454
  • 假设 topic 详情的返回信息如下:
    • PartitionCount:6:分区为 6 个
    • ReplicationFactor:3:副本为 3 个
    • Partition: 0 Leader: 3:Partition 下标为 0 的主节点是 broker.id=3
      • 当 Leader down 掉之后,其他节点会选举中一个新 Leader
    • Replicas: 3,1,2:在 Partition: 0 下共有 3 个副本,broker.id 分别为 3,1,2
    • Isr: 3,1,2:在 Partition: 0 下目前存活的 broker.id 分别为 3,1,2
Topic:kafka-all    PartitionCount:6    ReplicationFactor:3    Configs:
    Topic: kafka-all    Partition: 0    Leader: 3    Replicas: 3,1,2    Isr: 3,1,2
    Topic: kafka-all    Partition: 1    Leader: 1    Replicas: 1,2,3    Isr: 1,2,3
    Topic: kafka-all    Partition: 2    Leader: 2    Replicas: 2,3,1    Isr: 2,3,1
    Topic: kafka-all    Partition: 3    Leader: 3    Replicas: 3,2,1    Isr: 3,2,1
    Topic: kafka-all    Partition: 4    Leader: 1    Replicas: 1,3,2    Isr: 1,3,2
    Topic: kafka-all    Partition: 5    Leader: 2    Replicas: 2,1,3    Isr: 2,1,3

Docker 单个实例部署(1.0.1)

  • 目前 latest 用的时候 kafka 1.0.1,要指定版本可以去作者 github 看下 tag 目录,切换不同 tag,然后看下 Dockerfile 里面的 kafka 版本号
  • 我的服务器外网 ip:182.61.19.177,hostname 为:instance-3v0pbt5d
  • 在我的开发机上上配置 host:
182.61.19.177 instance-3v0pbt5d
  • 部署 kafka:
  • 目前 latest 用的时候 kafka 1.0.1,要指定版本可以去作者 github 看下 tag 目录,切换不同 tag,然后看下 Dockerfile 里面的 kafka 版本号
  • 新建文件:vim docker-compose.yml
  • 这里的 kafka 对外网暴露端口是 9094,内网端口是 9092
version: '3.2'
services:
  zookeeper:
    image: wurstmeister/zookeeper
    ports:
      - "2181:2181"
  kafka:
    image: wurstmeister/kafka:latest
    ports:
      - target: 9094
        published: 9094
        protocol: tcp
        mode: host
    environment:
      HOSTNAME_COMMAND: "docker info | grep ^Name: | cut -d' ' -f 2"
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: INSIDE:PLAINTEXT,OUTSIDE:PLAINTEXT
      KAFKA_ADVERTISED_PROTOCOL_NAME: OUTSIDE
      KAFKA_ADVERTISED_PORT: 9094
      KAFKA_PROTOCOL_NAME: INSIDE
      KAFKA_PORT: 9092
      KAFKA_LOG_DIRS: /data/docker/kafka/logs
      KAFKA_AUTO_CREATE_TOPICS_ENABLE: 'true'
      KAFKA_LOG_RETENTION_HOURS: 168
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
      - /data/docker/kafka/logs:/data/docker/kafka/logs
  • 测试:
    • 进入 kafka 容器:docker exec -it kafkadocker_kafka_1 /bin/bash
    • 根据官网 Dockerfile 说明,kafka home 应该是:cd /opt/kafka
    • 创建 topic 命令:bin/kafka-topics.sh --create --zookeeper zookeeper:2181 --replication-factor 1 --partitions 1 --topic my-topic-test
    • 查看 topic 命令:bin/kafka-topics.sh --list --zookeeper zookeeper:2181
    • 删除 topic:bin/kafka-topics.sh --delete --topic my-topic-test --zookeeper zookeeper:2181
    • 给 topic 发送消息命令:bin/kafka-console-producer.sh --broker-list localhost:9092 --topic my-topic-test,然后在出现交互输入框的时候输入你要发送的内容
    • 再开一个终端,进入 kafka 容器,接受消息:bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic my-topic-test --from-beginning
      • 其中 --from-beginning 参数表示在启动该客户端的时候接受前面 kafka 的所有记录。不加这个参数,则旧数据不会收到,生产者新生产的消息才会接收到。
    • 此时发送的终端输入一个内容回车,接受消息的终端就可以收到。

Docker 多机多实例部署(外网无法访问)

  • 三台机子:
    • 内网 ip:172.24.165.129,外网 ip:47.91.22.116
    • 内网 ip:172.24.165.130,外网 ip:47.91.22.124
    • 内网 ip:172.24.165.131,外网 ip:47.74.6.138
  • 修改三台机子 hostname:
    • 节点 1:hostnamectl --static set-hostname youmeekhost1
    • 节点 2:hostnamectl --static set-hostname youmeekhost2
    • 节点 3:hostnamectl --static set-hostname youmeekhost3
  • 三台机子的 hosts 都修改为如下内容:vim /etc/hosts
172.24.165.129 youmeekhost1
172.24.165.130 youmeekhost2
172.24.165.131 youmeekhost3
  • 开发机设置 hosts:
47.91.22.116 youmeekhost1
47.91.22.124 youmeekhost2
47.74.6.138 youmeekhost3

Zookeeper 集群

  • 节点 1:
docker run -d --name=zookeeper1 --net=host --restart=always \
-v /data/docker/zookeeper/data:/data \
-v /data/docker/zookeeper/log:/datalog \
-v /etc/hosts:/etc/hosts \
-e ZOO_MY_ID=1 \
-e "ZOO_SERVERS=server.1=youmeekhost1:2888:3888 server.2=youmeekhost2:2888:3888 server.3=youmeekhost3:2888:3888" \
zookeeper:latest
  • 节点 2:
docker run -d --name=zookeeper2 --net=host --restart=always \
-v /data/docker/zookeeper/data:/data \
-v /data/docker/zookeeper/log:/datalog \
-v /etc/hosts:/etc/hosts \
-e ZOO_MY_ID=2 \
-e "ZOO_SERVERS=server.1=youmeekhost1:2888:3888 server.2=youmeekhost2:2888:3888 server.3=youmeekhost3:2888:3888" \
zookeeper:latest
  • 节点 3:
docker run -d --name=zookeeper3 --net=host --restart=always \
-v /data/docker/zookeeper/data:/data \
-v /data/docker/zookeeper/log:/datalog \
-v /etc/hosts:/etc/hosts \
-e ZOO_MY_ID=3 \
-e "ZOO_SERVERS=server.1=youmeekhost1:2888:3888 server.2=youmeekhost2:2888:3888 server.3=youmeekhost3:2888:3888" \
zookeeper:latest

先安装 nc 再来校验 zookeeper 集群情况

  • 环境:CentOS 7.4
  • 官网下载:https://nmap.org/download.html,找到 rpm 包
  • 当前时间(201803)最新版本下载:wget https://nmap.org/dist/ncat-7.60-1.x86_64.rpm
  • 安装并 ln:sudo rpm -i ncat-7.60-1.x86_64.rpm && ln -s /usr/bin/ncat /usr/bin/nc
  • 检验:nc --version

zookeeper 集群测试

  • 节点 1 执行命令:echo stat | nc youmeekhost1 2181,能得到如下信息:
Zookeeper version: 3.4.11-37e277162d567b55a07d1755f0b31c32e93c01a0, built on 11/01/2017 18:06 GMT
Clients:
 /172.31.154.16:35336[0](queued=0,recved=1,sent=0)

Latency min/avg/max: 0/0/0
Received: 1
Sent: 0
Connections: 1
Outstanding: 0
Zxid: 0x0
Mode: follower
Node count: 4
  • 节点 2 执行命令:echo stat | nc youmeekhost2 2181,能得到如下信息:
Zookeeper version: 3.4.11-37e277162d567b55a07d1755f0b31c32e93c01a0, built on 11/01/2017 18:06 GMT
Clients:
 /172.31.154.17:55236[0](queued=0,recved=1,sent=0)

Latency min/avg/max: 0/0/0
Received: 1
Sent: 0
Connections: 1
Outstanding: 0
Zxid: 0x100000000
Mode: leader
Node count: 4
  • 节点 3 执行命令:echo stat | nc youmeekhost3 2181,能得到如下信息:
Zookeeper version: 3.4.11-37e277162d567b55a07d1755f0b31c32e93c01a0, built on 11/01/2017 18:06 GMT
Clients:
 /172.31.65.88:41840[0](queued=0,recved=1,sent=0)

Latency min/avg/max: 0/0/0
Received: 1
Sent: 0
Connections: 1
Outstanding: 0
Zxid: 0x100000000
Mode: follower
Node count: 4
Kafka 集群
  • 节点 1 执行:
docker run -d --net=host --name=kafka1 \
--restart=always \
--env KAFKA_BROKER_ID=1 \
--env KAFKA_ZOOKEEPER_CONNECT=youmeekhost1:2181,youmeekhost2:2181,youmeekhost3:2181 \
--env KAFKA_LOG_DIRS=/data/docker/kafka/logs \
--env HOSTNAME_COMMAND="docker info | grep ^Name: | cut -d' ' -f 2" \
--env KAFKA_LISTENER_SECURITY_PROTOCOL_MAP=INSIDE:PLAINTEXT,OUTSIDE:PLAINTEXT \
--env KAFKA_ADVERTISED_PROTOCOL_NAME=OUTSIDE \
--env KAFKA_ADVERTISED_PORT=9094 \
--env KAFKA_PROTOCOL_NAME=INSIDE \
--env KAFKA_PORT=9092 \
--env KAFKA_AUTO_CREATE_TOPICS_ENABLE=true \
--env KAFKA_LOG_RETENTION_HOURS=168 \
--env KAFKA_HEAP_OPTS="-Xmx1G -Xms1G" \
-v /var/run/docker.sock:/var/run/docker.sock \
-v /etc/localtime:/etc/localtime \
-v /data/docker/kafka/logs:/data/docker/kafka/logs \
-v /etc/hosts:/etc/hosts \
wurstmeister/kafka:latest
  • 节点 2 执行:
docker run -d --net=host --name=kafka2 \
--restart=always \
--env KAFKA_BROKER_ID=2 \
--env KAFKA_ZOOKEEPER_CONNECT=youmeekhost1:2181,youmeekhost2:2181,youmeekhost3:2181 \
--env KAFKA_LOG_DIRS=/data/docker/kafka/logs \
--env HOSTNAME_COMMAND="docker info | grep ^Name: | cut -d' ' -f 2" \
--env KAFKA_LISTENER_SECURITY_PROTOCOL_MAP=INSIDE:PLAINTEXT,OUTSIDE:PLAINTEXT \
--env KAFKA_ADVERTISED_PROTOCOL_NAME=OUTSIDE \
--env KAFKA_ADVERTISED_PORT=9094 \
--env KAFKA_PROTOCOL_NAME=INSIDE \
--env KAFKA_PORT=9092 \
--env KAFKA_AUTO_CREATE_TOPICS_ENABLE=true \
--env KAFKA_LOG_RETENTION_HOURS=168 \
--env KAFKA_HEAP_OPTS="-Xmx1G -Xms1G" \
-v /var/run/docker.sock:/var/run/docker.sock \
-v /etc/localtime:/etc/localtime \
-v /data/docker/kafka/logs:/data/docker/kafka/logs \
-v /etc/hosts:/etc/hosts \
wurstmeister/kafka:latest
  • 节点 3 执行:
docker run -d --net=host --name=kafka3 \
--restart=always \
--env KAFKA_BROKER_ID=3 \
--env KAFKA_ZOOKEEPER_CONNECT=youmeekhost1:2181,youmeekhost2:2181,youmeekhost3:2181 \
--env KAFKA_LOG_DIRS=/data/docker/kafka/logs \
--env HOSTNAME_COMMAND="docker info | grep ^Name: | cut -d' ' -f 2" \
--env KAFKA_LISTENER_SECURITY_PROTOCOL_MAP=INSIDE:PLAINTEXT,OUTSIDE:PLAINTEXT \
--env KAFKA_ADVERTISED_PROTOCOL_NAME=OUTSIDE \
--env KAFKA_ADVERTISED_PORT=9094 \
--env KAFKA_PROTOCOL_NAME=INSIDE \
--env KAFKA_PORT=9092 \
--env KAFKA_AUTO_CREATE_TOPICS_ENABLE=true \
--env KAFKA_LOG_RETENTION_HOURS=168 \
--env KAFKA_HEAP_OPTS="-Xmx1G -Xms1G" \
-v /var/run/docker.sock:/var/run/docker.sock \
-v /etc/localtime:/etc/localtime \
-v /data/docker/kafka/logs:/data/docker/kafka/logs \
-v /etc/hosts:/etc/hosts \
wurstmeister/kafka:latest

Kafka 集群测试

  • 在 kafka1 上测试:
    • 进入 kafka1 容器:docker exec -it kafka1 /bin/bash
    • 根据官网 Dockerfile 说明,kafka home 应该是:cd /opt/kafka
    • 创建 topic 命令:bin/kafka-topics.sh --create --zookeeper youmeekhost1:2181,youmeekhost2:2181,youmeekhost3:2181 --replication-factor 3 --partitions 3 --topic my-topic-test
    • 查看 topic 命令:bin/kafka-topics.sh --list --zookeeper youmeekhost1:2181,youmeekhost2:2181,youmeekhost3:2181
    • 给 topic 发送消息命令:bin/kafka-console-producer.sh --broker-list youmeekhost1:9092 --topic my-topic-test,然后在出现交互输入框的时候输入你要发送的内容
  • 在 kafka2 上测试:
    • 进入 kafka2 容器:docker exec -it kafka2 /bin/bash
    • 接受消息:cd /opt/kafka && bin/kafka-console-consumer.sh --bootstrap-server youmeekhost2:9092 --topic my-topic-test --from-beginning
  • 在 kafka3 上测试:
    • 进入 kafka3 容器:docker exec -it kafka3 /bin/bash
    • 接受消息:cd /opt/kafka && bin/kafka-console-consumer.sh --bootstrap-server youmeekhost3:9092 --topic my-topic-test --from-beginning
  • 如果 kafka1 输入的消息,kafka2 和 kafka3 能收到,则表示已经成功。

部署 kafka-manager(未能访问成功)

  • 节点 1:docker run -d --name=kafka-manager1 --restart=always -p 9000:9000 -e ZK_HOSTS="youmeekhost1:2181,youmeekhost2:2181,youmeekhost3:2181" sheepkiller/kafka-manager:latest

Kafka 1.0.1 源码安装

  • 一台机子:CentOS 7.4,根据文章最开头,已经修改了 hosts
  • 确保本机安装有 JDK8(JDK 版本不能随便挑选)
  • 先用上面的 docker 方式部署一个 zookeeper,我这里的 zookeeper IP 地址为:172.16.0.2
    • 如果该 zookeeper 前面已经用过了,最好重新删除,重新 run,因为 zookeeper 上保留的旧的 topic 配置
  • 官网下载:https://kafka.apache.org/downloads
  • 当前(201803)最新版本为:1.0.1,同时推荐 Scala 版本为 2.11,这里要特别注意:kafka_2.11-1.0.1.tgz 中的 2.11 指的是 Scala 版本
    • 找到:Binary downloads 下面的链接
    • 下载:wget http://mirrors.shu.edu.cn/apache/kafka/1.0.1/kafka_2.11-1.0.1.tgz
  • 解压:tar zxvf kafka_2.11-1.0.1.tgz,假设当前目录为:/usr/local/kafka_2.11-1.0.1
  • 为了方便,修改目录名字:mv /usr/local/kafka_2.11-1.0.1 /usr/local/kafka
  • 创建 log 输出目录:mkdir -p /data/kafka/logs
  • 修改 kafka-server 的配置文件:vim /usr/local/kafka/config/server.properties
  • 找到下面两个参数内容,修改成如下:
# 唯一ID(kafka 集群环境下,该值必须唯一,默认从 0 开始),和 zookeeper 的配置文件中的 myid 类似道理(单节点多 broker 的情况下该参数必改)
broker.id=1
# 监听地址(单节点多 broker 的情况下该参数必改)
listeners=PLAINTEXT://0.0.0.0:9092
# 向 Zookeeper 注册的地址。这里可以直接填写外网IP地址,但是不建议这样做,而是通过配置 hosts 的方式来设置。不然填写外网 IP 地址会导致所有流量都走外网(单节点多 broker 的情况下该参数必改)
advertised.listeners=PLAINTEXT://youmeekhost:9092
# 日志数据目录,可以通过逗号来指定多个目录(单节点多 broker 的情况下该参数必改)
log.dirs=/data/kafka/logs
# 创建新 topic 的时候默认 1 个分区。需要特别注意的是:已经创建好的 topic 的 partition 的个数只可以被增加,不能被减少。
# 如果对消息有高吞吐量的要求,可以增加分区数来分摊压力
num.partitions=1
# 允许删除topic
delete.topic.enable=false
# 允许自动创建topic(默认是 true)
auto.create.topics.enable=false
# 磁盘IO不足的时候,可以适当调大该值 ( 当内存足够时 )
#log.flush.interval.messages=10000
#log.flush.interval.ms=1000
# kafka 数据保留时间 默认 168 小时 == 7 天
log.retention.hours=168
# zookeeper,存储了 broker 的元信息
zookeeper.connect=youmeekhost:2181

# 其余都使用默认配置,但是顺便解释下:
# borker 进行网络处理的线程数
num.network.threads=3

# borker 进行 I/O 处理的线程数
num.io.threads=8

# 发送缓冲区 buffer 大小,数据不是一下子就发送的,先回存储到缓冲区了到达一定的大小后在发送,能提高性能
socket.send.buffer.bytes=102400

# 接收缓冲区大小,当数据到达一定大小后在序列化到磁盘
socket.receive.buffer.bytes=102400

# 这个参数是向 kafka 请求消息或者向 kafka 发送消息的请请求的最大数,这个值不能超过 java 的堆栈大小
socket.request.max.bytes=104857600
  • 启动 kafka 服务(必须制定配置文件):cd /usr/local/kafka && bin/kafka-server-start.sh config/server.properties
    • 后台方式运行 kafka 服务:cd /usr/local/kafka && bin/kafka-server-start.sh -daemon config/server.properties
    • 停止 kafka 服务:cd /usr/local/kafka && bin/kafka-server-stop.sh
  • 再开一个终端测试:
    • 进入目录:cd /usr/local/kafka
    • 创建 topic 命令:bin/kafka-topics.sh --create --zookeeper youmeekhost:2181 --replication-factor 1 --partitions 1 --topic my-topic-test
    • 查看 topic 命令:bin/kafka-topics.sh --list --zookeeper youmeekhost:2181
    • 删除 topic:bin/kafka-topics.sh --delete --topic my-topic-test --zookeeper youmeekhost:2181
    • 给 topic 发送消息命令:bin/kafka-console-producer.sh --broker-list youmeekhost:9092 --topic my-topic-test,然后在出现交互输入框的时候输入你要发送的内容
    • 再开一个终端,进入 kafka 容器,接受消息:bin/kafka-console-consumer.sh --bootstrap-server youmeekhost:9092 --topic my-topic-test --from-beginning
    • 此时发送的终端输入一个内容回车,接受消息的终端就可以收到。
  • Spring Boot 依赖:
<dependency>
	<groupId>org.springframework.kafka</groupId>
	<artifactId>spring-kafka</artifactId>
	<version>1.3.3.RELEASE</version>
</dependency>

<dependency>
	<groupId>org.apache.kafka</groupId>
	<artifactId>kafka-clients</artifactId>
	<version>1.0.1</version>
</dependency>

<dependency>
	<groupId>org.apache.kafka</groupId>
	<artifactId>kafka-streams</artifactId>
	<version>1.0.1</version>
</dependency>

kafka 1.0.1 默认配置文件内容

# 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

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

# 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 seperated list of directories under which to store log files
log.dirs=/tmp/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 exceessive 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=localhost: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

其他资料