MySQL应用架构优化-实时数据处理

1.1. 场景

在和开发人员做优化的时候，讨论最多的应该是结合应用场景编写出合适的SQL。并培训开发应该如何编写SQL让MySQL的性能尽量好。但是有一些的场景对于SQL的优化是行不通的。

打个比方，产品有这样的需求：针对每个商品实时显示销售者的销售量，并且按销售量做排序，还要实现分页。这个需求看上去很简单，实现起来也很容易。无非就是对三张表（seller、goods、orders）进行查询、聚合、排序。但是对于上亿订单量来说，这样查询简直就是一个噩梦。

分析：这边主要是需要按销售量来进行排序，这样的操作在这里就比较可怕。如果没有这个的话，我们的按下面步骤来写SQL，MySQL跑起来就很爽：

按分页需求，先查出销售者的个数（如：100个销售着，去10个）。
通过1取出的销售者到orders中查询订单并GROUP BY。
通过2中的orders中的goods id 取出商品信息。

可惜的是我们不是产品，不能按照我们的意愿来。当然，最好的办法就是说服产品，干掉类似这样的需求。或改变一种方式来实现这中需求，如：不要实时的展示。可是说服产品和顶在头上的人谈何容易啊，也许是关注的东西是不一样的吧。

1.2. 解决方案

首先要说明一些，像这样统计的计算能不实时就不实时。我们应该把OLAP的操作和OLTP的操作尽量解耦。来做。如白天我们关注的是OLTP（就是大家普通在用的应用程序），晚上我们关注的OLAP（Hadoop实现）。

这是我们的奇葩需求需要实时，所以Hadoop都是用不上的了。这时我们就需要用气实时计算的系统。现在业界应该有两个实时计算系统比较流行：Storm、Spark。这边我选用了使用Storm来作为我们的方案。这边选择Storm的原因主要也就是之前接触过，比较熟悉上手比较快。

当然这边我们要需要用到其他的主键：

Logstash：主要也就是用来收集日志。这边我们将交易一笔订单记录都会化成json格式输出到日志文件中。之前也考虑过使用Flume，但是Flume没有Logstash来的实时。
Kafka：主要是为了接受Logstash传过来的信息，并将它持久化和提供给Storm来使用。说明一下：有些系统会将Kafka放入程序就使用，就是说订单完成时将订单json信息直接发送给Kafka。我们这边多一层Logstash的目的是为了让应用系统和Kafka进行解耦。如果Kafka挂掉或者网络出现问题。不会影响到应用系统的正常运行。
Storm：接收Kafka中的信息进行分析计算出想要的数据。
MongoDB：主要是为了存储从Storm中分析完的数据。
Zookeeper：用来管理Kafka和Storm用的。

在使用这样的架构之前希望大家能理解一下“实时”这个概念：其实无论在什么应用程序中基本没有实时能完成的。只是电脑运行的快，让你误认为是实时的。所以，这边我们还是准实时。

实时理解的图：

mysql

1.3. 实时计算架构部署图

mysql

1.4. 架构部署

1.4.1. 环境说明

部署演示使用的是VirtualBox虚拟机，总共启用了5台虚拟机(可以根据自己的机器配置而定，我的配置：i3、12G内存、固态硬盘250G)。

系统应用部署信息

操作系统	IP	部署软件	端口
Centos7.2	10.10.10.11	Logstash
Centos7.2	10.10.10.11	Kafka	9092
~	10.10.10.12	MongoDB	27017、27018、27019
~	10.10.10.21	Zookeeper	2181、3887
~	10.10.10.21	Storm
~	10.10.10.22	Zookeeper	2181、3887
~	10.10.10.22	Storm
~	10.10.10.23	Zookeeper	2181、3887
~	10.10.10.23	Storm

这边我就不部署Jetty了，到时候我使用变相的方式来向日志文件中输入json格式数据。

22.4.2. 统一配置

这边5台机子的有统一的hosts文件，并且都需要配置好JDK:

[root@storm_1 conf]# cat /etc/hosts

127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4

10.10.10.21 storm_1

10.10.10.22 storm_2

10.10.10.23 storm_3

10.10.10.11 normal_11

10.10.10.12 normal_12

[root@normal_11 local]# java -version

java version "1.7.0_79"

Java(TM) SE Runtime Environment (build 1.7.0_79-b15)

Java HotSpot(TM) 64-Bit Server VM (build 24.79-b02, mixed mode)

22.4.3. 部署Zookeeper

因为这边我们部署的是Zookeeper集群因此在3台机子上的操作步骤都是一样的：这边我以操作其中一台（10.10.10.21 storm_1）机子为例。

到官网下载软件（zookeeper-3.4.6.tar.gz）
将软件解压到 /usr/local/ 目录下

[root@storm_1 local]# tar zookeeper-3.4.6.tar.gz -C /usr/local

[root@storm_1 local]# pwd

/usr/local

[root@storm_1 local]# ls -la

drwxr-xr-x. 10 1000 1000 4096 Feb 20 2014 zookeeper-3.4.6

Zookeeper配置文件

如果没有/usr/local/zookeeper-3.4.6/conf/zoo.cfg配置文件则新建一个。

[root@storm_1 conf]# pwd

/usr/local/zookeeper-3.4.6/conf

[root@storm_1 conf]# cat 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

dataDir=/u01/zookeeper/data

dataLogDir=/u01/zookeeper/data_log

# the directory where the snapshot is stored.

# do not use /tmp for storage, /tmp here is just

# example sakes.

# 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=7

# Purge task interval in hours

# Set to "0" to disable auto purge feature

#autopurge.purgeInterval=1

server.1 = storm_1:2887:3887

server.2 = storm_2:2887:3887

server.3 = storm_3:2887:3887

[root@storm_1 zookeeper]# mkdir -p /u01/zookeeper/data

[root@storm_1 zookeeper]# mkdir -p /u01/zookeeper/data_log

[root@storm_1 zookeeper]# ll /u01/zookeeper/

total 0

drwxr-xr-x 3 root root 33 Apr 19 00:23 data

drwxr-xr-x 3 root root 22 Apr 2 12:36 data_log

创建myid文件

下面的命令应该在不同的机器上执行

# 在 10.10.10.21 storm_1 机器上执行

[root@storm_1 zookeeper]# echo 1 > /u01/zookeeper/data/myid

# 在 10.10.10.21 storm_2 机器上执行

[root@storm_2 zookeeper]# echo 2 > /u01/zookeeper/data/myid

# 在 10.10.10.21 storm_3 机器上执行

[root@storm_3 zookeeper]# echo 3 > /u01/zookeeper/data/myid

启动Zookeeper

分表在三台机子上都要执行下面命令

1	[root@sotrm_1 ~]# /usr/local/zookeeper-3.4.6/bin/zkServer.sh start

查看Zookeeper状态

[root@storm_1 zookeeper]# /usr/local/zookeeper-3.4.6/bin/zkServer.sh status

JMX enabled by default

Using config: /usr/local/zookeeper-3.4.6/bin/../conf/zoo.cfg

Mode: follower

客户端链接Zookeeper进行验证

[root@storm_1 zookeeper]# /usr/local/zookeeper-3.4.6/bin/zkCli.sh -timeout 5000 -r -server storm_1:2181

Connecting to storm_1:2181

... omit ...

WatchedEvent state:SyncConnected type:None path:null

[zk: storm_1:2181(CONNECTED) 0] ls /

[consumers, config, isr_change_notification, admin, brokers, zookeeper, controller_epoch]

以上就算是部署好了Zookeeper集群了。

22.4.4. 部署Kafka

按照前面规划的我们应该将Kafka部署在（10.10.10.12 normal_12）机子上。

到官网下载Kafka（10-0.9.0.0.tgz）
将Kafka解压到/usr/local目录下，并从命令为kafka

[root@normal_11 local]# tar -zxf kafka_2.10-0.9.0.0.tgz -C /usr/local

[root@normal_11 local]# mv /usr/local/kafka_2.10-0.9.0.0 /usr/local/kafka

[root@normal_11 local]# cd /usr/local

[root@normal_11 local]# ls -la

drwxr-xr-x 7 root root 4096 Apr 18 22:41 kafka

设置Kafka配置文件

[root@normal_11 config]# pwd

/usr/local/kafka/config

[root@normal_11 config]# cat server.properties

broker.id=0

listeners=PLAINTEXT://:9092

num.network.threads=3

num.io.threads=8

socket.send.buffer.bytes=102400

socket.receive.buffer.bytes=102400

socket.request.max.bytes=104857600

log.dirs=/u01/kafka-logs

num.partitions=1

num.recovery.threads.per.data.dir=1

log.retention.hours=168

log.segment.bytes=1073741824

log.retention.check.interval.ms=300000

log.cleaner.enable=false

zookeeper.connect=storm_1:2181,storm_2:2181,storm_3:2181

zookeeper.connection.timeout.ms=6000

1	[root@normal_11 config]# mkdir -p /u01/kafka-logs

启动Kafka

[root@normal_11 config]# /usr/local/kafka/bin/kafka-server-start.sh \

/usr/local/kafka/config/server.properties > /u01/kafka-logs/kafka.out 2>&1 &

[1] 3656

创建test topic

[root@normal_11 config]# /usr/local/kafka/bin/kafka-topics.sh \

--create \

--zookeeper storm_1:2181 \

--replication-factor 1 \

--partitions 1 \

--topic test

查看创建的topic

[root@normal_11 config]# /usr/local/kafka/bin/kafka-topics.sh \

--list \

--zookeeper storm_1:2181

test

新建一个session，开启Kafka消费者客户端

[root@normal_11 config]# /usr/local/kafka/bin/kafka-console-consumer.sh \

--zookeeper storm_1:2181 \

--topic test \

--from-beginning

新建一个session，开启Kafka生产者客户端，并生产一个消息。

/usr/local/kafka/bin/kafka-console-producer.sh \

--broker-list localhost:9092 \

--topic test

this is message 1

在消费者的session中能看到同时也出现了 “this is message 1”这个消息

22.4.5. 部署Logstash

按照前面规划的我们应该将Kafka部署在（10.10.10.12 normal_12）机子上。这边我们使用Logstash监听/tmp/orders.log文件，并且将Kafka作为它的输出。

到官网下载Logstash（logstash-2.3.1.tar.gz）
将Logstash解压到/usr/local/目录下

[root@normal_11 local]# tar -zxf logstash-2.3.1.tar.gz

[root@normal_11 local]# mv logstash-2.3.1 /usr/local/logstash

[root@normal_11 local]# cd /usr/local

[root@normal_11 local]# ll

drwxr-xr-x 5 root root 4096 May 8 21:35 logstash

设置Logstash配置文件

[root@normal_11 logstash]# mkdir -p /usr/local/logstash/conf

[root@normal_11 logstash]# cat /usr/local/logstash/conf/logstash-kafka.conf

input {

file {

path => "/tmp/orders.log"

}

output {

kafka {

bootstrap_servers => "normal_11:9092"

topic_id => "test"

}

启动Logstash agent

[root@normal_11 config]# /usr/local/logstash/bin/logstash agent \

--verbose \

-f /usr/local/logstash/conf/logstash-kafka.conf \

--log /tmp/logstash.log &

向/tmp/orders.log中输入一个json字符串

1	[root@normal_11 tmp]# echo '{"order_price":20, "good_name":"good_1", "seller_id":1}' >> /tmp/orders.log

再之前启动的Kafka消费者客户端会出现如下消息

1	{"message":"{\"order_price\":20, \"good_name\":\"good_1\", \"seller_id\":1}","@version":"1","@timestamp":"2016-05-08T13:59:45.080Z","path":"/tmp/orders.log","host":"normal_11"}

22.4.6. 部署MongoDB

这边我们MongoDB部署在（10.10.10.12 normal_12）机子上。并且采用的是复制的形式。

官网下载MongoDB（mongodb-linux-x86_64-rhel70-3.2.5.tgz）
解压MongoDB到/usr/local/目录下

1 2	[root@normal_12 soft]# tar -zxf mongodb-linux-x86_64-rhel70-3.2.5.tgz [root@normal_12 soft]# mv mongodb-linux-x86_64-rhel70-3.2.5 /usr/local/mongodb

[root@normal_12 soft]# mkdir -p /u01/mongodb_2701{7,8,9}/{data,logs,tmp}

[root@normal_12 soft]# cd /u01

[root@normal_12 soft]# ll *

mongodb_27017:

total 6396

drwxr-xr-x 4 root root 4096 May 8 02:07 data

drwxr-xr-x 2 root root 41 Apr 30 22:17 logs

-rw-r--r-- 1 root root 658 May 7 15:21 mongodb.yaml

drwxr-xr-x 2 root root 24 Apr 30 22:17 tmp

mongodb_27018:

total 20

drwxr-xr-x 2 root root 6 May 7 10:04 data

drwxr-xr-x 2 root root 24 May 1 20:42 logs

-rw-r--r-- 1 root root 662 May 7 11:14 mongodb.yaml

drwxr-xr-x 2 root root 24 May 1 20:42 tmp

mongodb_27019:

total 20

drwxr-xr-x 2 root root 6 May 7 10:04 data

drwxr-xr-x 2 root root 24 May 1 20:55 logs

-rw-r--r-- 1 root root 662 May 7 11:15 mongodb.yaml

drwxr-xr-x 2 root root 24 May 1 20:55 tmp

分别在/u01/mongodb_27017、/u01/mongodb_27018、/u01/mongodb_27019中创建配置文件和启动脚本
/u01/mongodb_27017中的配置文件和启动脚本

[root@normal_12 u01]# cat /u01/mongodb_27017/mongodb.yaml

# http://docs.mongoing.com/manual-zh/reference/configuration-options.html

systemLog:

destination: file

path: /u01/mongodb_27017/logs/mongodb.log

logAppend: true

logRotate: reopen

storage:

dbPath: /u01/mongodb_27017/data

engine: wiredTiger

journal:

enabled: true

processManagement:

fork: true

pidFilePath: /u01/mongodb_27017/tmp/mongodb.pid

net:

bindIp: 10.10.10.12

port: 27017

unixDomainSocket:

enabled: false

operationProfiling:

mode: slowOp

slowOpThresholdMs: 100

replication:

replSetName: rs_12

enableMajorityReadConcern: true

[root@normal_12 u01]# cat /u01/mongodb_27017/start_mongodb.sh

#!/usr/bin/env bash

/usr/local/mongodb/bin/mongod \

--config=/u01/mongodb_27017/mongodb.yaml

[root@normal_12 u01]# cat /u01/mongodb_27017/stop_mongodb.sh

#!/usr/bin/env bash

/usr/local/mongodb/bin/mongod \

--config /u01/mongodb_27017/mongodb.yaml \

--shutdown

[root@normal_12 u01]# cat /u01/mongodb_27017/client_mongodb.sh

#!/usr/bin/env bash

/usr/local/mongodb/bin/mongo 10.10.10.12:27017

/u01/mongodb_27018中的配置文件和启动脚本

[root@normal_12 u01]# cat /u01/mongodb_27018/mongodb.yaml

# http://docs.mongoing.com/manual-zh/reference/configuration-options.html

systemLog:

destination: file

path: /u01/mongodb_27018/logs/mongodb.log

logAppend: true

logRotate: reopen

storage:

dbPath: /u01/mongodb_27018/data

engine: wiredTiger

journal:

enabled: true

processManagement:

fork: true

pidFilePath: /u01/mongodb_27018/tmp/mongodb.pid

net:

bindIp: 10.10.10.12

port: 27018

unixDomainSocket:

enabled: false

operationProfiling:

mode: slowOp

slowOpThresholdMs: 100

replication:

replSetName: rs_12

enableMajorityReadConcern: true

[root@normal_12 u01]# cat /u01/mongodb_27018/start_mongodb.sh

#!/usr/bin/env bash

/usr/local/mongodb/bin/mongod \

--config=/u01/mongodb_27018/mongodb.yaml

[root@normal_12 u01]# cat /u01/mongodb_27018/stop_mongodb.sh

#!/usr/bin/env bash

/usr/local/mongodb/bin/mongod \

--config /u01/mongodb_27018/mongodb.yaml \

--shutdown

[root@normal_12 u01]# cat /u01/mongodb_27018/client_mongodb.sh

#!/usr/bin/env bash

/usr/local/mongodb/bin/mongo 10.10.10.12:27018

/u01/mongodb_27019中的配置文件和启动脚本

[root@normal_12 u01]# cat /u01/mongodb_27019/mongodb.yaml

# http://docs.mongoing.com/manual-zh/reference/configuration-options.html

systemLog:

destination: file

path: /u01/mongodb_27019/logs/mongodb.log

logAppend: true

logRotate: reopen

storage:

dbPath: /u01/mongodb_27019/data

engine: wiredTiger

journal:

enabled: true

processManagement:

fork: true

pidFilePath: /u01/mongodb_27019/tmp/mongodb.pid

net:

bindIp: 10.10.10.12

port: 27019

unixDomainSocket:

enabled: false

operationProfiling:

mode: slowOp

slowOpThresholdMs: 100

replication:

replSetName: rs_12

enableMajorityReadConcern: true

[root@normal_12 u01]# cat /u01/mongodb_27019/start_mongodb.sh

#!/usr/bin/env bash

/usr/local/mongodb/bin/mongod \

--config=/u01/mongodb_27019/mongodb.yaml

[root@normal_12 u01]# cat /u01/mongodb_27019/stop_mongodb.sh

#!/usr/bin/env bash

/usr/local/mongodb/bin/mongod \

--config /u01/mongodb_27019/mongodb.yaml \

--shutdown

[root@normal_12 u01]# cat /u01/mongodb_27019/client_mongodb.sh

#!/usr/bin/env bash

/usr/local/mongodb/bin/mongo 10.10.10.12:27019

启动MongoDB

[root@normal_12 u01]# /u01/mongodb_27017/start_mongodb.sh

about to fork child process, waiting until server is ready for connections.

forked process: 3817

child process started successfully, parent exiting

[root@normal_12 u01]# /u01/mongodb_27018/start_mongodb.sh

about to fork child process, waiting until server is ready for connections.

forked process: 3841

child process started successfully, parent exiting

[root@normal_12 u01]# /u01/mongodb_27019/start_mongodb.sh

about to fork child process, waiting until server is ready for connections.

forked process: 3865

child process started successfully, parent exiting

初始化复制

100

101

102

103

104

105

[root@normal_12 u01]# /u01/mongodb_27017/client_mongodb.sh

MongoDB shell version: 3.2.5

connecting to: 10.10.10.12:27017/test

(test) 22:48:43>

(test) 22:48:45>rs.initiate()

{

"info2" : "no configuration specified. Using a default configuration for the set",

"me" : "10.10.10.12:27017",

"ok" : 1

}

(test) 22:48:43>

(test) 22:48:56>rs.add("10.10.10.12:27018")

{ "ok" : 1 }

(test) 22:48:43>

(test) 22:49:30>rs.add("10.10.10.12:27019")

{ "ok" : 1 }

(test) 22:48:43>

(test) 22:49:33>rs.conf()

{

"_id" : "rs_12",

"version" : 3,

"protocolVersion" : NumberLong(1),

"members" : [

{

"_id" : 0,

"host" : "10.10.10.12:27017",

"arbiterOnly" : false,

"buildIndexes" : true,

"hidden" : false,

"priority" : 1,

"tags" : {

"slaveDelay" : NumberLong(0),

"votes" : 1

{

"_id" : 1,

"host" : "10.10.10.12:27018",

"arbiterOnly" : false,

"buildIndexes" : true,

"hidden" : false,

"priority" : 1,

"tags" : {

"slaveDelay" : NumberLong(0),

"votes" : 1

{

"_id" : 2,

"host" : "10.10.10.12:27019",

"arbiterOnly" : false,

"buildIndexes" : true,

"hidden" : false,

"priority" : 1,

"tags" : {

"slaveDelay" : NumberLong(0),

"votes" : 1

}

"settings" : {

"chainingAllowed" : true,

"heartbeatIntervalMillis" : 2000,

"heartbeatTimeoutSecs" : 10,

"electionTimeoutMillis" : 10000,

"getLastErrorModes" : {

"getLastErrorDefaults" : {

"w" : 1,

"wtimeout" : 0

"replicaSetId" : ObjectId("572f51d8d37af8a0cf4cabf8")

}

(test) 22:49:42>

(test) 22:49:48>

(test) 22:49:48>rs.isMaster()

{

"hosts" : [

"10.10.10.12:27017",

"10.10.10.12:27018",

"10.10.10.12:27019"

"setName" : "rs_12",

"setVersion" : 3,

"ismaster" : true,

"secondary" : false,

"primary" : "10.10.10.12:27017",

"me" : "10.10.10.12:27017",

"electionId" : ObjectId("7fffffff0000000000000001"),

"maxBsonObjectSize" : 16777216,

"maxMessageSizeBytes" : 48000000,

"maxWriteBatchSize" : 1000,

"localTime" : ISODate("2016-05-08T14:49:54.119Z"),

"maxWireVersion" : 4,

"minWireVersion" : 0,

"ok" : 1

}

22.4.7. 初始化Storm

这边我们在（10.10.10.21 storm_1、10.10.10.22 storm_2、10.10.10.23 storm_3）这三台部署storm。

到官网下载Stormtorm（apache-storm-0.9.6.zip）
解压到/usr/local/目录下，三台机子都执行同样的命令

1 2	[root@storm_1 wordcount]# unzip apache-storm-0.9.6.zip [root@storm_1 wordcount]# mv apache-storm-0.9.6 /usr/local/

设置yaml配置文件

[root@storm_2 wordcount]# cat /usr/local/apache-storm-0.9.6/conf/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:

# - "server1"

# - "server2"

# nimbus.host: "nimbus"

# ##### 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

# topology.metrics.consumer.register:

# - class: "backtype.storm.metric.LoggingMetricsConsumer"

# parallelism.hint: 1

# - class: "org.mycompany.MyMetricsConsumer"

# parallelism.hint: 1

# argument:

# - endpoint: "metrics-collector.mycompany.org"

storm.zookeeper.servers:

- "storm_1"

- "storm_2"

- "storm_3"

nimbus.host: "storm_1"

storm.local.dir: "/u01/storm/status"

supervisor.slots.ports:

- 6700

- 6701

- 6702

- 6703

1	[root@storm_1 wordcount]# mkdir -p /u01/storm/status

启动Storm

# Node1：启动 storm UI界面

[root@storm_1 wordcount]# /usr/local/apache-storm-0.9.6/bin/storm ui > /dev/null 2>&1 &

# Node1：启动 storm nimbus

[root@storm_1 wordcount]# /usr/local/apache-storm-0.9.6/bin/storm nimbus > /dev/null 2>&1 &

# Node2：启动 supervisor

[root@storm_1 wordcount]# /usr/local/apache-storm-0.9.6/bin/storm supervisor > /dev/null 2>&1 &

# Node3：启动 supervisor

[root@storm_1 wordcount]# /usr/local/apache-storm-0.9.6/bin/storm supervisor > /dev/null 2>&1 &

# 在各个节点上运行 jps 查看服务状态

[root@storm_1 wordcount]# lps

2151 core

2097 QuorumPeerMain

3969 Jps

2191 nimbus

开启web界面访问Storm UI

mysql

看到上的界面就说明我们的Storm已经部署完毕了。

22.4.1. 构建streamparse（Python Storm框架）

streamparse 是Python Storm的一个框架，他可以将python代码打包为一个jar包运行在Storm中。

官网：http://streamparse.readthedocs.io/en/master/quickstart.html。

（PS:streamparse 3 以上的拓扑已经改变。和作者沟通过他是为了让streamparse能够更好的独立运行，从而脱离storm环境。）

创建3机信任，分别在3台机子上都生成ssh的公钥，分别执行以下命令

[root@storm_1 ~]# ssh-keygen -t rsa

Generating public/private rsa key pair.

Enter file in which to save the key (/root/.ssh/id_rsa): Enter passphrase (empty for no passphrase):

Enter same passphrase again:

Your identification has been saved in /root/.ssh/id_rsa.

Your public key has been saved in /root/.ssh/id_rsa.pub.

The key fingerprint is:

1e:20:62:da:f5:fb:69:32:da:ac:09:ef:7c:35:a5:01 root@storm_3

The key's randomart image is:

+--[ RSA 2048]----+

| |

| E |

| o o .. |

| + o o .. . |

|. . . S+ |

| o+. |

| . .... |

| + ++... |

| .B+o+o |

+-----------------+

执行完上面命令后会在各个主机的 ~/.ssh/ 目录下会生成 id_rsa.pub 文件。将3台机子中的公钥都拷贝到一个文件中并且让3台机子的这个文件内容都一样

# storm_1 节点

[root@storm_1 ~]# cat ~/.ssh/id_rsa.pub

ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQD0z8u8K0wGWLhhzxcztokzVWHqKf1O5PScjkIvXFh2AnEqZz+d5/LqyT6qDi1T8p+k4UHCkgmRqWZbG+LEtzQEjE3/Guya4uEP5g8MGvnLUSQQWS5oQN6EAq2fQ7G806fipQCEKWETF7axk6We1NNUjO27c071OMQ2JXM7PLVQACaUcaI8sJg3uHOs7Bcb871tyiuXtVygpyjJEBxvzpuxcjEJI/C/Yu+q28KXRfSWblJ7hIN7c8eIGYumTi4vSKo3Rwwm5UGvBIopK8Xc4SmvrZ6jpHInR2YLQsEznrcR9MprhHXPeFgnoJ3vCLeXbOesmH+h6Cb4UJChUR7owWKr root@storm_1

# storm_2 节点

[root@storm_2 ~]# cat ~/.ssh/id_rsa.pub

ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQC/n9bY6jD8Z2mkgZLO9meXAMNvDt/YJRpcTM57ko2p9Cmm4c+CgQzstBExOAciQR9ckranLj8k/GYDBL5jBIBjquvVEtA06sCp6vGsvUOOOg07VgrmpbEvGNovNa8bfVOXR5cSbqwoesPu33wG43WXDdpD7vKU9YrqUyNXj1xPi+xTQwWkUMz9zEH8zwYuhD7pglP7iJsvzl/GpJRA5kwlPj0PWOLocq8D26pNSMiP034Ah9bojpM6jnbFT4lXeV85PdCABhcqyLZVNiKqU/Yozx1Ui9UsXfPLcHl1SnvIOBFRIaih5WzZ0CMAENXzjrfSxvrFGCYLrwORO/uSJc0t root@storm_2

# storm_3 节点

[root@storm_3 ~]# cat ~/.ssh/id_rsa.pub

ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQCzwB3Qq0ElUY6EDRYQF5NupPtQ6hILzzDrVp9GGdavdsjxlO1kD5LroeP2s94A38u0jbXiEYJZhNprfA+a+UuT6DtVVIIl9/gPrNlRUFLy+8vbzhN9G8hsqcB0nb3VNtnMJGsS9QyOmOieqp4fW15HZn0jQIS+TgmgaMeaMlK8LV5cO0S4sCjPTbtXMDKZ/oNWFenZ143Ul4ViAPudLm9o6ik4UkFaP847cxyKy/jgpDdEQBibRucrTiQWoJ/uhiHH020MqEv6H2ZbmjOXbEQLFo8b6feSJSp0RaFZuook0CNs88QXxSKRw+kKEDlEZGCUuvFHLfIzV7C4PExEViml root@storm_3

# 每个节点中的 authorized_keys 文件内容

[root@storm_1 ~]# cat ~/.ssh/authorized_keys

在3台机子上创建config文件（3台机子都要执行）

1	[root@storm_1 wordcount]# touch /root/.ssh/config

下载 lein 文件到 /usr/local/bin 目录中，授予可执行权限（3台机子都要执行）

[root@storm_1 wordcount]# wget https://raw.githubusercontent.com/technomancy/leiningen/stable/bin/lein

[root@storm_1 wordcount]# mv lein /usr/local/bin/

[root@storm_1 wordcount]# chmod 755 /usr/local/bin/lein

安装streamparse（3台机子都要执行）

1	[root@storm_1 wordcount]# pip install streamparse

创建storm_project 目录，并且开始一个简单的Storm项目（在storm_2上操作），这边不要再Storm启动的Nimbus节点上创建，因为到时候运行Storm项目会有端口上的冲突。

[root@storm_2 ~]# mkdir -p /u01/storm_project

[root@storm_2 ~]# cd /u01/storm_project/

[root@storm_2 storm_project]# pwd

/u01/storm_project

[root@storm_2 ~]# sparse quickstart wordcount

Creating your wordcount streamparse project...

create wordcount

create wordcount/.gitignore

create wordcount/config.json

create wordcount/fabfile.py

create wordcount/project.clj

create wordcount/README.md

create wordcount/src

create wordcount/src/bolts/

create wordcount/src/bolts/__init__.py

create wordcount/src/bolts/wordcount.py

create wordcount/src/spouts/

create wordcount/src/spouts/__init__.py

create wordcount/src/spouts/words.py

create wordcount/topologies

create wordcount/topologies/wordcount.py

create wordcount/virtualenvs

create wordcount/virtualenvs/wordcount.txt

Done.

Try running your topology locally with:

cd wordcount

sparse run

设置json配置文件（在storm_2上操作）

[root@storm_2 wordcount]# cat /u01/storm_project/wordcount/config.json

{

"library": "",

"topology_specs": "topologies/",

"virtualenv_specs": "virtualenvs/",

"envs": {

"prod": {

"user": "root",

"nimbus": "storm_1",

"workers": [

"storm_1",

"storm_2",

"storm_3"

"log": {

"path": "/tmp/storm/stream/log",

"file": "pystorm_{topolopy_name}_{component_name}_{task_id}_{pid}.log",

"max_bytes": 1000000,

"backup_count": 10,

"level": "info"

"use_ssh_for_nimbus": true,

"virtualenv_root": "/tmp/storm/stream/virtualenvs"

}

创建相关目录（3个机器上都需要执行）

1 2	[root@storm_1 wordcount]# mkdir -p /tmp/storm/stream/log [root@storm_1 wordcount]# mkdir -p /tmp/storm/stream/virtualenvs

将wordcount程序提交到Storm集群上（在storm_2上操作）

[root@storm_2 wordcount]# pwd

/u01/storm_project/wordcount

[root@storm_2 wordcount]# sparse submit

[storm_1] Executing task '_create_or_update_virtualenv'

[storm_2] Executing task '_create_or_update_virtualenv'

... omit ...

[storm_1] run: rm /tmp/streamparse_requirements-oD8qdm4We.txt

[storm_3] out:

[storm_3] run: rm /tmp/streamparse_requirements-5greXfqjW.txt

Cleaning from prior builds...

# 需要敲回车键

Creating topology Uber-JAR...

# 需要敲回车键

Uber-JAR created: /u01/storm_project/wordcount/_build/wordcount-0.0.1-SNAPSHOT-standalone.jar

Deploying "wordcount" topology...

ssh tunnel to Nimbus storm_1:6627 established.

Routing Python logging to /tmp/storm/stream/log.

Running lein command to submit topology to nimbus:

lein run -m streamparse.commands.submit_topology/-main topologies/wordcount.clj --option 'topology.workers=2' --option 'topology.acker.executors=2' --option 'topology.python.path="/tmp/storm/stream/virtualenvs/wordcount/bin/python"' --option 'streamparse.log.path="/tmp/storm/stream/log"' --option 'streamparse.log.max_bytes=1000000' --option 'streamparse.log.backup_count=10' --option 'streamparse.log.level="info"'

WARNING: You're currently running as root; probably by accident.

Press control-C to abort or Enter to continue as root.

Set LEIN_ROOT to disable this warning.

# 需要敲回车键

{:option {streamparse.log.level info, streamparse.log.backup_count 10, streamparse.log.max_bytes 1000000, streamparse.log.path /tmp/storm/stream/log, topology.python.path /tmp/storm/stream/virtualenvs/wordcount/bin/python, topology.acker.executors 2, topology.workers 2}, :debug false, :port 6627, :host localhost, :help false}

1604 [main] INFO backtype.storm.StormSubmitter - Jar not uploaded to master yet. Submitting jar...

1620 [main] INFO backtype.storm.StormSubmitter - Uploading topology jar /u01/storm_project/wordcount/_build/wordcount-0.0.1-SNAPSHOT-standalone.jar to assigned location: /u01/storm/status/nimbus/inbox/stormjar-03200d7a-dec1-44a6-b0f7-e775d0227864.jar

3853 [main] INFO backtype.storm.StormSubmitter - Successfully uploaded topology jar to assigned location: /u01/storm/status/nimbus/inbox/stormjar-03200d7a-dec1-44a6-b0f7-e775d0227864.jar

3854 [main] INFO backtype.storm.StormSubmitter - Submitting topology wordcount in distributed mode with conf {"streamparse.log.backup_count":10,"streamparse.log.path":"\/tmp\/storm\/stream\/log","topology.python.path":"\/tmp\/storm\/stream\/virtualenvs\/wordcount\/bin\/python","topology.debug":false,"nimbus.thrift.port":6627,"topology.max.spout.pending":5000,"nimbus.host":"localhost","topology.workers":2,"topology.acker.executors":2,"streamparse.log.max_bytes":1000000,"streamparse.log.level":"info","topology.message.timeout.secs":60}

4487 [main] INFO backtype.storm.StormSubmitter - Finished submitting topology: wordcount

如果输出类似上面的信息就算是部署完成了。

确认wordcount程序已经部署到了 Storm中

mysql

停止Storm中的wordcount程序

[root@storm_2 wordcount]# pwd

/u01/storm_project/wordcount

[root@storm_2 wordcount]# sparse kill -n wordcount

WARNING: You're currently running as root; probably by accident.

Press control-C to abort or Enter to continue as root.

Set LEIN_ROOT to disable this warning.

5180 [main] INFO backtype.storm.thrift - Connecting to Nimbus at localhost:6627

Killed topology: wordcount

出现上面信息就说明wordcount程序已经从Storm集群中停止并移除了。

mysql

22.4.9. streamparse代码编写

由于这是示例程序，我们就在之前创建好的wordcount项目中修改代码。在这里我们只需要修改spout和bolt的代码就好。

这边我们需要安装Python Kafka和Python MongoDB的相关模块，执行如下命令：

# 在操作系统自带的Python中安装，主要是为了使用sparse run时会调用

pip install pykafka

pip install pymongo

# 在streamparse Storm Python虚拟环境中安装（sparse submit）

/tmp/storm/stream/virtualenvs/wordcount/bin/pip install pykafka

/tmp/storm/stream/virtualenvs/wordcount/bin/pip install pymongo

words.py代码（spout）

words.py的功能就是不断消费kafka产生的消息，并且发送（emit）下面一个接收者（spout|bolt）。

[root@storm_2 spouts]# pwd

/u01/storm_project/wordcount/src/spouts

[root@storm_2 spouts]#

[root@storm_2 spouts]# cat words.py

# -*- coding:utf-8 -*-

from __future__ import absolute_import, print_function, unicode_literals

import itertools

from streamparse.spout import Spout

from pykafka import KafkaClient

import simplejson as json

import sys

reload(sys)

sys.setdefaultencoding('utf-8')

class WordSpout(Spout):

def initialize(self, stormconf, context):

# self.words = itertools.cycle(['dog', 'cat',

# 'zebra', 'elephant'])

client = KafkaClient(hosts="10.10.10.11:9092")

topic = client.topics[b"test"]

self.balanced_consumer = topic.get_balanced_consumer(

consumer_group=b"test_group",

auto_commit_enable=True,

zookeeper_connect="storm_1:2181,storm_2:2181,storm_3:2181"

)

def next_tuple(self):

# word = next(self.words)

# self.emit([word])

message = self.balanced_consumer.consume()

# Logstash字符串转化为dict

log_info = json.loads(message.value)

word = log_info["message"]

with open("/tmp/storm.log", "a") as f:

f.write(word)

self.emit([word])

py代码

wordcount.py主要是实现了，接收从words.py发送的信息（json字符串），并将接收的到信息解析成转化成python的字典类型，分析数据存放到MongoDB（10.10.10.12）中。

[root@storm_2 bolts]# pwd

/u01/storm_project/wordcount/src/bolts

[root@storm_2 bolts]#

[root@storm_2 bolts]# cat wordcount.py

# -*- coding:utf-8 -*-

from __future__ import absolute_import, print_function, unicode_literals

from collections import Counter

from streamparse.bolt import Bolt

import simplejson as json

from pymongo import MongoClient

import sys

reload(sys)

sys.setdefaultencoding('utf-8')

class WordCounter(Bolt):

def initialize(self, conf, ctx):

# self.counts = Counter()

client = MongoClient(b"10.10.10.12:27017,10.10.10.12:27018,10.10.10.12:27019",

replicaset="rs_12")

# 获得 order_stat 数据库

self.db = client.shop

def process(self, tup):

# 获得从spout传过来的字符串

word = tup.values[0]

# self.counts[word] += 1

# self.emit([word, self.counts[word]])

# self.log('%s: %d' % (word, self.counts[word]))

# 将spout传来的字符串解析成dict

order_info = json.loads(word)

# 通过 kafka 传入的 user_name 查找相关用户统计信息

condition = {"user_name": order_info["user_name"]}

order_stat_info = self.db.order_stat.find_one(condition)

## 如果order_stat_info无值则插入, 有值则更新

# 1、无值情况

if not order_stat_info:

order_stat_info_new = {

"user_name": order_info.get("user_name", "Unknow"),

"order_num": 1,

"total_price": order_info.get("price", 0.00),

"min_order_price": order_info.get("price", 0.00),

"max_order_price": order_info.get("price", 0.00),

"min_order": order_info.get("order_id", 0),

"max_order": order_info.get("order_id", 0),

}

self.db.order_stat.insert_one(order_stat_info_new)

# 2、有值情况

else:

min_order_price = min(order_stat_info["min_order_price"],

order_info.get("price", 0.00))

max_order_price = max(order_stat_info["max_order_price"],

order_info.get("price", 0.00))

min_order = order_stat_info["min_order"]

max_order = order_stat_info["max_order"]

# 设置最小order id

if min_order_price == order_info.get("price", 0.00):

min_order = order_info.get("order_id", min_order)

# 设置最大order id

if max_order_price == order_info.get("price", 0.00):

max_order = order_info.get("order_id", max_order)

# 构造更新的信息

order_stat_info_new = {

"order_num": order_stat_info["order_num"] + 1,

"total_price": order_stat_info["total_price"] +

order_info.get("price", 0.00),

"min_order_price": min_order_price,

"max_order_price": max_order_price,

"min_order": min_order,

"max_order": max_order

}

# 跟新信息

self.db.order_stat.update_one({"_id": order_stat_info["_id"]},

{"$set": order_stat_info_new})

编写好上面代码之后就需要测试运行情况了。

运行streamparse进行测试

由于我们还不知道我们写的代码正确性，因此需要使用sparse run来记性调试，而非使用sparse submit直接提交到Storm环境中。

[root@storm_2 wordcount]# pwd

/u01/storm_project/wordcount

[root@storm_2 wordcount]#

[root@storm_2 wordcount]# sparse run

... Omit ...

8653 [Thread-15-count-bolt] INFO backtype.storm.task.ShellBolt - Launched subprocess with pid 3719

8703 [Thread-16-word-spout] INFO backtype.storm.spout.ShellSpout - Launched subprocess with pid 3717

8706 [Thread-13-count-bolt] INFO backtype.storm.task.ShellBolt - Start checking heartbeat...

8706 [Thread-13-count-bolt] INFO backtype.storm.daemon.executor - Prepared bolt count-bolt:(3)

8708 [Thread-15-count-bolt] INFO backtype.storm.task.ShellBolt - Start checking heartbeat...

8708 [Thread-15-count-bolt] INFO backtype.storm.daemon.executor - Prepared bolt count-bolt:(4)

8708 [Thread-16-word-spout] INFO backtype.storm.daemon.executor - Opened spout word-spout:(5)

8715 [Thread-16-word-spout] INFO backtype.storm.daemon.executor - Activating spout word-spout:(5)

8715 [Thread-16-word-spout] INFO backtype.storm.spout.ShellSpout - Start checking heartbeat...

向Logstash（10.10.11）监听的文件中输入相关的订单信息

echo '{"order_id":1, "price":20, "user_name":"Bob", "goods_name":"good_name2"}' > /tmp/orders.log

echo '{"order_id":2, "price":120, "user_name":"Bob", "goods_name":"good_name1"}' >> /tmp/orders.log

echo '{"order_id":3, "price":1120, "user_name":"Bob", "goods_name":"good_name4"}' >> /tmp/orders.log

echo '{"order_id":4, "price":11120, "user_name":"Bob", "goods_name":"good_name3"}' >> /tmp/orders.log

echo '{"order_id":1, "price":10, "user_name":"Tom", "goods_name":"good_name2"}' >> /tmp/orders.log

echo '{"order_id":2, "price":110, "user_name":"Tom", "goods_name":"good_name1"}' >> /tmp/orders.log

echo '{"order_id":3, "price":1110, "user_name":"Tom", "goods_name":"good_name4"}' >> /tmp/orders.log

echo '{"order_id":4, "price":11110, "user_name":"Tom", "goods_name":"good_name3"}' >> /tmp/orders.log

查看MongoDB（10.10.12）中的订单统计信息

[root@normal_12 ~]# /u01/mongodb_27018/client_mongodb.sh

MongoDB shell version: 3.2.5

connecting to: 10.10.10.12:27018/test

(test) 01:01:10>

(test) 01:01:11> use shop

switched to db shop

(shop) 01:01:16>

(shop) 01:22:32>db.order_stat.find()

{

"_id" : ObjectId("5734bba0172d290f86e2d2e4"),

"total_price" : 12380,

"min_order_price" : 20,

"min_order" : 1,

"order_num" : 4,

"max_order_price" : 11120,

"user_name" : "Bob",

"max_order" : 4

}

{

"_id" : ObjectId("5734bbf1172d290f844d2fdc"),

"total_price" : 12230,

"min_order_price" : 10,

"min_order" : 1,

"order_num" : 3,

"max_order_price" : 11110,

"user_name" : "Tom",

"max_order" : 4

}

最后只要将我们的项目提交到Storm上面去就好了

[root@storm_2 wordcount]# pwd

/u01/storm_project/wordcount

[root@storm_2 wordcount]#

[root@storm_2 wordcount]# sparse submit

到这里我们就使用Python完成了Storm环境的搭建和开发。

22.4. 总结

其实许多的系统中都不纯属于的OLTP或者OLAP，基本上是他们两个的结合体。当OLTP中掺杂OLAP的时候有时候如果单靠数据库查询来解决问题，这样就会造成OLTP系统变的慢（因为查询变大而复杂）。因此，遇到这类的情况就需要在架构层面上去解决了。现在，Storm和Spark都是用于实时计算。因此，有碰到类似以上场景的朋友，可以考虑给系统换上“新装”了。

昵称: HH

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