大資料處理框架之:Storm + Kafka + zookeeper 叢集
Storm kafka zookeeper 叢集
我們知道storm的作用主要是進行流式計算，對於源源不斷的均勻資料流流入處理是非常有效的，而現實生活中大部分場景並不是均勻的資料流，而是時而多時而少的資料流入，這種情況下顯然用批量處理是不合適的，如果使用storm做實時計算的話可能因為資料擁堵而導致伺服器掛掉，應對這種情況，使用kafka作為訊息佇列是非常合適的選擇，kafka可以將不均勻的資料轉換成均勻的訊息流，從而和storm比較完善的結合，這樣才可以實現穩定的流式計算。
storm和kafka結合，實質上無非是之前我們說過的計算模式結合起來，就是資料先進入kafka生產者，然後storm作為消費者進行消費，最後將消費後的資料輸出或者儲存到檔案、資料庫、分散式儲存等等，具體框圖如下：
這張圖片摘自部落格地址：http://www.cnblogs.com/tovin/p/3974417.html 在此感謝作者的奉獻
一、環境安裝前準備：
（1）準備三臺機器：作業系統centos7
（2）JDK: jdk-8u191-linux-x64.tar.gz 可以到官網下載: 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）進行解壓 配置環境變數 vi /ect/profile
# JAVA_HOME
export JAVA_HOME=/usr/local/java/jdk1.8.0_191
export CLASSPATH 環境變數需要重啟生效 source /ect/profile
二、zookeeper叢集安裝(三臺機器上都需要安裝)
（1）tar -zxvf zookeeper-3.4.13.tar.gz
（2）cd /usr/local/java/zookeeper-3.4.13/conf 進入解壓後zk conf目錄
（3）mv zoo_sample.cfg zoo.cfg 拷貝檔案 為 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）建立 mkdir dataDir=/usr/local/java/zookeeper-3.4.13/dateDir
（6）建立 mkdir dataLogDir=/usr/local/java/zookeeper-3.4.13/logs
（7）建立 echo “1” >/usr/local/java/zookeeper-3.4.13/dateDir/myid
（8）需要把zookeeper-3.4.13 這個目錄拷貝到其他兩臺機器上 scp -r zookeeper-3.4.13 [email protected]:/usr/local/java/ 等待輸入密碼即可
（9）server.2 和 server.3 相對應機器 /usr/local/java/zookeeper-3.4.13/dateDir/myid 改成 2 和 3
虛擬機器 互相拷貝，新增IP ，輸入密碼
ssh -o StrictHostKeyChecking=no [email protected]
（10）啟動 ./bin/zkServer.sh start 三臺機器都需要啟動 啟動過程會報錯，等待三臺都啟動成功後
./zkServer.sh status
注意：檢視zookeeper叢集的狀態，出現Mode:follower或是Mode:leader則代表成功
[[email protected] bin]# ./zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /usr/local/java/zookeeper-3.4.13/bin/../conf/zoo.cfg
Mode: follower
[[email protected] bin]#
[[email protected] bin]# ./zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /usr/local/java/zookeeper-3.4.13/bin/../conf/zoo.cfg
Mode: leader
[[email protected] bin]#
[[email protected] bin]# ./zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /usr/local/java/zookeeper-3.4.13/bin/../conf/zoo.cfg
Mode: follower
[[email protected] bin]#
三、kafka叢集安裝（三臺機器上都需要安裝）
（1）tar -zxvf kafka_2.11-2.0.0.tgz
（2）cd /usr/local/java/kafka_2.11-2.0.0/config 進入解壓後 config 目錄
（3）vi server.properties 進行配置
（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）建立 mkdir log.dirs=/usr/local/java/kafka_2.11-2.0.0/logs
（6）需要把kafka_2.11-2.0.0 這個目錄拷貝到其他兩臺機器上 scp -r kafka_2.11-2.0.0 [email protected]:/usr/local/java/ 等待輸入密碼即可
（7）要修改其他兩臺機器 server.properties broker.id=2 和 broker.id=3
ssh -o StrictHostKeyChecking=no [email protected]
（8）啟動
[[email protected] java]# cd kafka_2.11-2.0.0
[[email protected] kafka_2.11-2.0.0]# cd bin/
[[email protected] bin]# ./bin/kafka-server-start.sh -daemon ./config/server.properties
四、storm叢集安裝（三臺機器上都需要安裝）
（1）tar -zxvf apache-storm-1.2.2.tar.gz
（2）cd /usr/local/java/apache-storm-1.2.2/conf 進入解壓後conf 目錄
（3）vi storm.yaml 進行配置
（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）建立 mkdir /usr/local/java/apache-storm-1.2.2/logs
（6）需要把apache-storm-1.2.2 這個目錄拷貝到其他兩臺機器上 scp -r kafka_2.11-2.0.0 [email protected]:/usr/local/java/ 等待輸入密碼即可
（7）啟動 storm
#在192.168.164.133 啟動
[[email protected] apache-storm-1.2.2]# cd bin/
[[email protected] bin]# ./storm nimbus >/dev/null 2>&1 &
[[email protected] apache-storm-1.2.2]# cd bin/
[[email protected] bin]# ./storm ui &
在其他兩臺機器啟動
#在192.168.164.134, 192.168.164.135 啟動
[[email protected] apache-storm-1.2.2]# cd bin/
[[email protected] bin]# ./storm supervisor >/dev/null 2>&1 &
（8）訪問 http://192.168.164.133:8080/
五、虛擬機器 centos7 一些注意
（1）修改了hosts 需要重啟 service network restart
127.0.0.1 hadoop1
192.168.164.134 hadoop2
192.168.164.135 hadoop3
（2）防火牆配置
1、通過systemctl status firewalld檢視firewalld狀態，發現當前是dead狀態，即防火牆未開啟
2、通過systemctl start firewalld開啟防火牆，沒有任何提示即開啟成功。
3、再次通過systemctl status firewalld檢視firewalld狀態，顯示running即已開啟了
4、systemctl stop firewalld 關閉防火牆
5、開啟以下埠
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=
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進入.ssh資料夾裡面，用命令ls檢視公鑰和祕鑰
將公鑰儲存在authorized_keys檔案中
修改authorized
大資料環境基礎之Centos安裝Haoop叢集（1）CentOS系統配置
首先開啟兩個節點
接下來就是開啟終端了
方式一：在桌面上右擊，找到Open in Terminal,點選
方式二：
這樣終端就打開了，如下圖
了
接下來就正式開始配置系統環境
1.配置網路
我們能看到右端的電腦有個紅叉，說明網路是斷開的，其實我們可以點進去，然後點選S
Storm之——Storm+Kafka+Flume+Zookeeper+MySQL實現資料實時分析(環境搭建篇)
Storm之——Storm+Kafka+Flume+Zookeeper+MySQL實現資料實時分析(環境搭建篇)
2018年03月04日 23:05:29 冰 河 閱讀數：1602更多
所屬專欄： Hadoop生態
版權宣告：本文為博主原創文章，未經博主允許不得轉載。 https:/
流式大資料處理 （實時）的三種框架：Storm，Spark和Samza
摘要：許多分散式計算系統都可以實時或接近實時地處理大資料流。本文將對Storm、Spark和Samza等三種Apache框架分別進行簡單介紹，然後嘗試快速、高度概述其異同。
許多分散式計算系統都可以實時或接近實時地處理大資料流。本文將對三種Apache框架分別進行簡單介紹，
流式大資料處理的三種框架：Storm，Spark和Samza
許多分散式計算系統都可以實時或接近實時地處理大資料流。本文將對三種Apache框架分別進行簡單介紹，然後嘗試快速、高度概述其異同。Apache Storm在Storm中，先要設計一個用於實時計算的圖狀結構，我們稱之為拓撲（topology）。這個拓撲將會被提交給叢集，由叢集中
[BigData]流式大資料處理的三種框架：Storm，Spark和Samza
許多分散式計算系統都可以實時或接近實時地處理大資料流。本文將對三種Apache框架分別進行簡單介紹，然後嘗試快速、高度概述其異同。
Apache Storm
在Storm中，先要設計一個用於實時計算的圖狀結構，我們稱之為拓撲（topology）。這個拓撲將會被提交給叢集，由叢集中的主控節點（maste
流式大資料處理的三種框架：Storm，Spark和Flink
storm、spark streaming、flink都是開源的分散式系統，具有低延遲、可擴充套件和容錯性諸多優點，允許你在執行資料流程式碼時，將任務分配到一系列具有容錯能力的計算機上並行執行,都提供
Storm之——Storm+Kafka+Flume+Zookeeper+MySQL實現資料實時分析(程式案例篇)
一、前言二、簡單介紹為了方便，這裡我們只是簡單的向/home/flume/log.log中追加單詞，每行一個單詞，利用Storm接收每個單詞，將單詞計數更新到資料庫，具體的邏輯為，如果資料庫中沒有相關單詞，則將資料插入資料庫，如果存在相關單詞，則更新資料庫中的計數。具體SQL