大数据处理框架之:Storm + Kafka + zookeeper 集群

Storm kafka zookeeper 集群

我们知道storm的作用主要是进行流式计算，对于源源不断的均匀数据流流入处理是非常有效的，而现实生活中大部分场景并不是均匀的数据流，而是时而多时而少的数据流入，这种情况下显然用批量处理是不合适的，如果使用storm做实时计算的话可能因为数据拥堵而导致服务器挂掉，应对这种情况，使用kafka作为消息队列是非常合适的选择，kafka可以将不均匀的数据转换成均匀的消息流，从而和storm比较完善的结合，这样才可以实现稳定的流式计算。
　　storm和kafka结合，实质上无非是之前我们说过的计算模式结合起来，就是数据先进入kafka生产者，然后storm作为消费者进行消费，最后将消费后的数据输出或者保存到文件、数据库、分布式存储等等，具体框图如下：
　　这张图片摘自博客地址：http://www.cnblogs.com/tovin/p/3974417.html 在此感谢作者的奉献

一、环境安装前准备：

（1）准备三台机器：操作系统centos7
（2）JDK: jdk-8u191-linux-x64.tar.gz 可以到官网下载: wget https://download.oracle.com/otn-pub/java/jdk/8u191-b12/2787e4a523244c269598db4e85c51e0c/jdk-8u191-linux-x64.tar.gz
（3）zookeeper：zookeeper-3.4.13 wget http://archive.apache.org/dist/zookeeper/zookeeper-3.4.13/zookeeper-3.4.13.tar.gz
（4）kafka: kafka_2.11-2.0.0 wget http://mirrors.hust.edu.cn/apache/kafka/2.0.0/kafka_2.11-2.0.0.tgz
（5）storm:apache-storm-1.2.2.tar.gz wget http://www.apache.org/dist/storm/apache-storm-1.2.2/apache-storm-1.2.2.tar.gz
（6）进行解压 配置环境变量 vi /ect/profile

# JAVA_HOME
export JAVA_HOME=/usr/local/java/jdk1.8.0_191
export CLASSPATH=.:$JAVA_HOME/jre/lib/rt.jar:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar
export ZOOKEEPER_HOME=/usr/local/java/zookeeper-3.4.13
export PATH=$PATH:$ZOOKEEPER_HOME/bin/:$JAVA_HOME/bin
#KAFKA_HOME
export KAFKA_HOME=/usr/local/java/kafka_2.11-2.0.0
export PATH=$PATH:$KAFKA_HOME/bin
# STORM_HOME
export STORM_HOME=/usr/local/java/apache-storm-1.2.2
export PATH=.:${JAVA_HOME}/bin:${ZK_HOME}/bin:${STORM_HOME}/bin:$PATH

环境变量需要重启生效 source /ect/profile

二、zookeeper集群安装(三台机器上都需要安装)

（1）tar -zxvf zookeeper-3.4.13.tar.gz
（2）cd /usr/local/java/zookeeper-3.4.13/conf 进入解压后zk conf目录
（3）mv zoo_sample.cfg zoo.cfg 拷贝文件 为 zoo.cfg
（4）配置zoo.cfg

# The number of milliseconds of each tick
tickTime=2000
# The number of ticks that the initial
# synchronization phase can take
initLimit=10
# The number of ticks that can pass between
# sending a request and getting an acknowledgement
syncLimit=5
# the directory where the snapshot is stored.
# do not use /tmp for storage, /tmp here is just
# example sakes.
dataDir=/usr/local/java/zookeeper-3.4.13/dateDir
dataLogDir=/usr/local/java/zookeeper-3.4.13/logs
# the port at which the clients will connect
clientPort=2181
# the maximum number of client connections.
# increase this if you need to handle more clients
#maxClientCnxns=60
#
# Be sure to read the maintenance section of the
# administrator guide before turning on autopurge.
#
# http://zookeeper.apache.org/doc/current/zookeeperAdmin.html#sc_maintenance
#
# The number of snapshots to retain in dataDir
#autopurge.snapRetainCount=3
# Purge task interval in hours
# Set to "0" to disable auto purge feature
#autopurge.purgeInterval=1
server.1 = 0.0.0.0:2888:3888
server.2 = 192.168.164.134:2888:3888
server.3 = 192.168.164.135:2888:3888

（5）创建 mkdir dataDir=/usr/local/java/zookeeper-3.4.13/dateDir
（6）创建 mkdir dataLogDir=/usr/local/java/zookeeper-3.4.13/logs
（7）创建 echo “1” >/usr/local/java/zookeeper-3.4.13/dateDir/myid
（8）需要把zookeeper-3.4.13 这个目录拷贝到其他两台机器上 scp -r zookeeper-3.4.13 [email protected]:/usr/local/java/ 等待输入密码即可
（9）server.2 和 server.3 相对应机器 /usr/local/java/zookeeper-3.4.13/dateDir/myid 改成 2 和 3
虚拟机 互相拷贝，新增IP ，输入密码
ssh -o StrictHostKeyChecking=no [email protected]
（10）启动 ./bin/zkServer.sh start 三台机器都需要启动 启动过程会报错，等待三台都启动成功后
./zkServer.sh status
注意：查看zookeeper集群的状态，出现Mode:follower或是Mode:leader则代表成功

[root@hadoop bin]# ./zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /usr/local/java/zookeeper-3.4.13/bin/../conf/zoo.cfg
Mode: follower
[root@hadoop bin]# 

[root@hadoop bin]# ./zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /usr/local/java/zookeeper-3.4.13/bin/../conf/zoo.cfg
Mode: leader
[root@hadoop bin]# 

[root@hadoop bin]# ./zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /usr/local/java/zookeeper-3.4.13/bin/../conf/zoo.cfg
Mode: follower
[root@hadoop bin]# 

三、kafka集群安装（三台机器上都需要安装）

（1）tar -zxvf kafka_2.11-2.0.0.tgz
（2）cd /usr/local/java/kafka_2.11-2.0.0/config 进入解压后 config 目录
（3）vi server.properties 进行配置
（4）server.properties

# 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=1

############################# 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 separated list of directories under which to store log files
log.dirs=/usr/local/java/kafka_2.11-2.0.0/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=hadoop1:2181,hadoop2:2181,hadoop3:2181/kafka

# 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

（5）创建 mkdir log.dirs=/usr/local/java/kafka_2.11-2.0.0/logs
（6）需要把kafka_2.11-2.0.0 这个目录拷贝到其他两台机器上 scp -r kafka_2.11-2.0.0 [email protected]:/usr/local/java/ 等待输入密码即可
（7）要修改其他两台机器 server.properties broker.id=2 和 broker.id=3
ssh -o StrictHostKeyChecking=no [email protected]
（8）启动

[root@hadoop java]# cd kafka_2.11-2.0.0
[root@hadoop kafka_2.11-2.0.0]# cd bin/
[root@hadoop bin]#  ./bin/kafka-server-start.sh -daemon ./config/server.properties

四、storm集群安装（三台机器上都需要安装）

（1）tar -zxvf apache-storm-1.2.2.tar.gz
（2）cd /usr/local/java/apache-storm-1.2.2/conf 进入解压后conf 目录
（3）vi storm.yaml 进行配置
（4）storm.yaml

# 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.

########### These MUST be filled in for a storm configuration
 storm.zookeeper.servers:
     - "hadoop1"
     - "hadoop2"
     - "hadoop3"
 storm.zookeeper.port: 2181
 nimbus.seeds: ["hadoop1"]
 storm.local.dir: "/usr/local/java/apache-storm-1.2.2/logs"
 supervisor.slots.ports:
     - 6700
     - 6701
     - 6702
     - 6703
# nimbus.seeds: ["host1", "host2", "host3"]
# 
# 
# ##### These may optionally be filled in:
#    
## List of custom serializations
# topology.kryo.register:
#     - org.mycompany.MyType
#     - org.mycompany.MyType2: org.mycompany.MyType2Serializer
#
## List of custom kryo decorators
# topology.kryo.decorators:
#     - org.mycompany.MyDecorator
#
## Locations of the drpc servers
# drpc.servers:
#     - "server1"
#     - "server2"

## Metrics Consumers
## max.retain.metric.tuples
## - task queue will be unbounded when max.retain.metric.tuples is equal or less than 0.
## whitelist / blacklist
## - when none of configuration for metric filter are specified, it'll be treated as 'pass all'.
## - you need to specify either whitelist or blacklist, or none of them. You can't specify both of them.
## - you can specify multiple whitelist / blacklist with regular expression
## expandMapType: expand metric with map type as value to multiple metrics
## - set to true when you would like to apply filter to expanded metrics
## - default value is false which is backward compatible value
## metricNameSeparator: separator between origin metric name and key of entry from map
## - only effective when expandMapType is set to true
# topology.metrics.consumer.register:
#   - class: "org.apache.storm.metric.LoggingMetricsConsumer"
#     max.retain.metric.tuples: 100
#     parallelism.hint: 1
#   - class: "org.mycompany.MyMetricsConsumer"
#     max.retain.metric.tuples: 100
#     whitelist:
#       - "execute.*"
#       - "^__complete-latency$"
#     parallelism.hint: 1
#     argument:
#       - endpoint: "metrics-collector.mycompany.org"
#     expandMapType: true
#     metricNameSeparator: "."

## Cluster Metrics Consumers
# storm.cluster.metrics.consumer.register:
#   - class: "org.apache.storm.metric.LoggingClusterMetricsConsumer"
#   - class: "org.mycompany.MyMetricsConsumer"
#     argument:
#       - endpoint: "metrics-collector.mycompany.org"
#
# storm.cluster.metrics.consumer.publish.interval.secs: 60

# Event Logger
# topology.event.logger.register:
#   - class: "org.apache.storm.metric.FileBasedEventLogger"
#   - class: "org.mycompany.MyEventLogger"
#     arguments:
#       endpoint: "event-logger.mycompany.org"

# Metrics v2 configuration (optional)
#storm.metrics.reporters:
#  # Graphite Reporter
#  - class: "org.apache.storm.metrics2.reporters.GraphiteStormReporter"
#    daemons:
#        - "supervisor"
#        - "nimbus"
#        - "worker"
#    report.period: 60
#    report.period.units: "SECONDS"
#    graphite.host: "localhost"
#    graphite.port: 2003
#
#  # Console Reporter
#  - class: "org.apache.storm.metrics2.reporters.ConsoleStormReporter"
#    daemons:
#        - "worker"
#    report.period: 10
#    report.period.units: "SECONDS"
#    filter:
#        class: "org.apache.storm.metrics2.filters.RegexFilter"
#        expression: ".*my_component.*emitted.*"

（5）创建 mkdir /usr/local/java/apache-storm-1.2.2/logs
（6）需要把apache-storm-1.2.2 这个目录拷贝到其他两台机器上 scp -r kafka_2.11-2.0.0 [email protected]:/usr/local/java/ 等待输入密码即可
（7）启动 storm

#在192.168.164.133  启动 
[root@hadoop apache-storm-1.2.2]# cd bin/
[root@hadoop bin]# ./storm nimbus >/dev/null 2>&1 &

[root@hadoop apache-storm-1.2.2]# cd bin/
[root@hadoop bin]# ./storm ui &

在其他两台机器启动

#在192.168.164.134, 192.168.164.135 启动 
[root@hadoop apache-storm-1.2.2]# cd bin/
[root@hadoop bin]# ./storm supervisor >/dev/null 2>&1 &

（8）访问 http://192.168.164.133:8080/

五、虚拟机 centos7 一些注意

（1）修改了hosts 需要重启 service network restart

127.0.0.1   hadoop1
192.168.164.134 hadoop2
192.168.164.135 hadoop3

（2）防火墙配置

1、通过systemctl status firewalld查看firewalld状态，发现当前是dead状态，即防火墙未开启
2、通过systemctl start firewalld开启防火墙，没有任何提示即开启成功。
3、再次通过systemctl status firewalld查看firewalld状态，显示running即已开启了
4、systemctl stop firewalld 关闭防火墙
5、开启以下端口
			 firewall-cmd --zone=public --add-port=2888/tcp --permanent
			 firewall-cmd --zone=public --add-port=3888/tcp --permanent
 		 	 firewall-cmd --zone=public --add-port=2181/tcp --permanent
 		 	 firewall-cmd --zone=public --add-port=8080/tcp --permanent
 		 	  firewall-cmd --zone=public --add-port=9092/tcp --permanent
6、firewall-cmd --reload 重新启动防火墙
7、firewall-cmd --list-all  查询开放端口
8、sudo iptables -F  把虚拟机中的防火墙给清了一下

（3）安装 telnet centos、ubuntu安装telnet命令的方法

yum list telnet*                   列出telnet相关的安装包
yum install telnet-server          安装telnet服务
yum install telnet.*               安装telnet客户端

github 源码下载地址：https://github.com/liruizi/storm_Kafka_demo