Flume学习 & Kafka & Storm 等 & Log4J 配置

正在学习这篇文章：

http://blog.****.net/ymh198816/article/details/51998085

和工作中接触的电商、订单、分析，可以结合起来。

开宗明义，这幅图片：

Strom是一个非常快的实时计算框架，至于快到什么程度呢？

官网首页给出的数据是每一个Storm集群上的节点每一秒能处理一百万条数据。
相比Hadoop的“Mapreduce”计算框架，Storm使用的是"Topology"；
Mapreduce程序在计算完成后最终会停下来，而Topology则是会永远运行下去除非你显式地使用“kill -9 XXX”命令停掉它。

准备实际写一个实时分析系统。不然纸上得来终觉浅。

首先需要让Java程序在Linux环境上运行。用leetcode的java程序来做实验，把leetcode工程的output目录拷贝到安装了java的机器（m42n05.gzns）。

$ pwd
/home/work/data/code/out/production/leetcode

$ java com.company.Main
Hello!
ret:3

注意，只能在这个目录，运行完成的package。如果换到子目录，就不行：

$ pwd
/home/work/data/code/out/production/leetcode/com

$ java company.Main
Error: Could not find or load main class company.Main

用Idea创建了一个Maven项目“LogGenerator”，项目的主要代码如下：

package com.comany.log.generator;

/**
 * Created by baidu on 16/11/7.
 */

import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.Random;

// Import log4j classes.
import org.apache.log4j.LogManager;
import org.apache.log4j.Logger;

public class LogGenerator {

    public enum paymentWays {
        Wechat,Alipay,Paypal
    }
    public enum merchantNames {
        优衣库,天猫,淘宝,咕噜大大,快乐宝贝,守望先峰,哈毒妇,Storm,Oracle,Java,****,跑男,路易斯威登,
        暴雪公司,Apple,Sumsam,Nissan,Benz,BMW,Maserati
    }

    public enum productNames {
        黑色连衣裙, 灰色连衣裙, 棕色衬衫, 性感牛仔裤, 圆脚牛仔裤,塑身牛仔裤, 朋克卫衣,高腰阔腿休闲裤,人字拖鞋,
        沙滩拖鞋
    }

    float[] skuPriceGroup = {299,399,699,899,1000,2000};
    float[] discountGroup = {10,20,50,100};
    float totalPrice = 0;
    float discount = 0;
    float paymentPrice = 0;

    private static final Logger logger = LogManager.getLogger(LogGenerator.class);
    private int logsNumber = 10;

    public void generate() {

        for(int i = 0; i <= logsNumber; i++) {
            logger.info(randomOrderInfo());
        }
    }

    public String randomOrderInfo() {

        SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
        Date date = new Date();

        String orderNumber = randomNumbers(5) + date.getTime();

        String orderDate = sdf.format(date);

        String paymentNumber = randomPaymentWays() + "-" + randomNumbers(8);

        String paymentDate = sdf.format(date);

        String merchantName = randomMerchantNames();

        String skuInfo = randomSkus();

        String priceInfo = calculateOrderPrice();

        return "orderNumber: " + orderNumber + " | orderDate: " + orderDate + " | paymentNumber: " +
                paymentNumber + " | paymentDate: " + paymentDate + " | merchantName: " + merchantName +
                " | sku: " + skuInfo + " | price: " + priceInfo;
    }

    private String randomPaymentWays() {

        paymentWays[] paymentWayGroup = paymentWays.values();
        Random random = new Random();
        return paymentWayGroup[random.nextInt(paymentWayGroup.length)].name();
    }

    private String randomMerchantNames() {

        merchantNames[] merchantNameGroup = merchantNames.values();
        Random random = new Random();
        return merchantNameGroup[random.nextInt(merchantNameGroup.length)].name();
    }

    private String randomProductNames() {

        productNames[] productNameGroup = productNames.values();
        Random random = new Random();
        return productNameGroup[random.nextInt(productNameGroup.length)].name();
    }

    private String randomSkus() {

        Random random = new Random();
        int skuCategoryNum = random.nextInt(3);

        String skuInfo ="[";

        totalPrice = 0;
        for(int i = 1; i <= 3; i++) {

            int skuNum = random.nextInt(3)+1;
            float skuPrice = skuPriceGroup[random.nextInt(skuPriceGroup.length)];
            float totalSkuPrice = skuPrice * skuNum;
            String skuName = randomProductNames();
            String skuCode = randomCharactersAndNumbers(10);
            skuInfo += " skuName: " + skuName + " skuNum: " + skuNum + " skuCode: " + skuCode
                    + " skuPrice: " + skuPrice + " totalSkuPrice: " + totalSkuPrice + ";";
            totalPrice += totalSkuPrice;
        }

        skuInfo += " ]";

        return skuInfo;
    }

    private String calculateOrderPrice() {

        Random random = new Random();
        discount = discountGroup[random.nextInt(discountGroup.length)];
        paymentPrice = totalPrice - discount;

        String priceInfo = "[ totalPrice: " + totalPrice + " discount: " + discount + " paymentPrice: " + paymentPrice +" ]";

        return priceInfo;
    }

    private String randomCharactersAndNumbers(int length) {

        String characters = "abcdefghijklmnopqrstuvwxyz0123456789";
        String randomCharacters = "";
        Random random = new Random();
        for (int i = 0; i < length; i++) {
            randomCharacters += characters.charAt(random.nextInt(characters.length()));
        }
        return randomCharacters;
    }

    private String randomNumbers(int length) {

        String characters = "0123456789";
        String randomNumbers = "";
        Random random = new Random();
        for (int i = 0; i < length; i++) {
            randomNumbers += characters.charAt(random.nextInt(characters.length()));
        }
        return randomNumbers;
    }

    public static void main(String[] args) {

        LogGenerator generator = new LogGenerator();
        generator.generate();
    }
}

运行的时候报错：

log4j:WARN No appenders could be found for logger (com.comany.log.generator.LogGenerator).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.

所以增加一个 log4j.properties

log4j.rootLogger=INFO,Console,File

#控制台日志
log4j.appender.Console=org.apache.log4j.ConsoleAppender
log4j.appender.Console.Target=System.out
log4j.appender.Console.layout=org.apache.log4j.PatternLayout
log4j.appender.Console.layout.ConversionPattern=[%p][%t][%d{yyyy-MM-dd HH\:mm\:ss}][%C] - %m%n

#普通文件日志
log4j.appender.File=org.apache.log4j.RollingFileAppender
log4j.appender.File.File=logs/generator.log
log4j.appender.File.MaxFileSize=10MB
#输出日志，如果换成DEBUG表示输出DEBUG以上级别日志
log4j.appender.File.Threshold=ALL
log4j.appender.File.layout=org.apache.log4j.PatternLayout
log4j.appender.File.layout.ConversionPattern=[%p][%t][%d{yyyy-MM-dd HH\:mm\:ss}][%C] - %m%n

然后在外层目录能够看到有Log目录生成：

直接把target目录拷贝到Linux机器上，发现找不到依赖包 log4j。

需要用 Intellij 进行打包。在File -> Project Structure里面。

然后应该会自动生成Jar包（也可以Build->Build Artifacts） LogGenerator.jar ，拷贝到Linux机器上。

但是开始不能运行。提示找不到Manifest.mf。搜索之后，发现，要选第二个选项“copy to ...”，而且必须把Manifest的目录从java改到Resources才行。

这样选项之后，生成的目录里面有两个jar。目录拷贝到Linux，然后运行：

$ java -jar LogGenerator.jar 

能够看到logs目录下有新的日志生成：
$ ll logs/*
-rw-rw-r--  1 work work 12328 Nov  7 20:12 logs/generator.log

但是Linux的log文件都是乱码。试了改SecureCRT配置什么的，都没有用。

查看到文件编码是 ASCII TEXT

只好用  (printf "\357\273\277";cat generator.log) > File2 来修改文件编码格式。

还是乱码。在log4j的配置文件里面加上

log4j.appender.file.encoding=UTF-8

还是乱码。加上上面哪个 BOM符号，还是乱码。

这时候把mac上面的日志拷贝到Linux上，发现是正常的。那么还是log4j打印的地方出了问题。

再仔细检查log4j的地方，发现上面log4j配置里面的file应该大写才行，要与上下文一致：

log4j.appender.File.encoding=UTF-8

修改之后，重新生成artifact，拷贝，运行。能看到中文啦：

[INFO][main][2016-11-07 23:50:51][com.comany.log.generator.LogGenerator] - orderNumber: 494341478533851064 | orderDate: 2016-11-07 23:50:51 | paymentNumber: Alipay-46983228 | paymentDate: 2016-11-07 23:50:51 | merchantName: 守望先峰 | sku: [ skuName: 黑色连衣裙 skuNum: 3 skuCode: 06vteu0ewx skuPrice: 899.0 totalSkuPrice: 2697.0; skuName: 塑身牛仔裤 skuNum: 3 skuCode: oz13bdht0w skuPrice: 2000.0 totalSkuPrice: 6000.0; skuName: 圆脚牛仔裤 skuNum: 3 skuCode: geuum757jk skuPrice: 399.0 totalSkuPrice: 1197.0; ] | price: [ totalPrice: 9894.0 discount: 100.0 paymentPrice: 9794.0 ]
[INFO][main][2016-11-07 23:50:51][com.comany.log.generator.LogGenerator] - orderNumber: 623011478533851071 | orderDate: 2016-11-07 23:50:51 | paymentNumber: Alipay-58335677 | paymentDate: 2016-11-07 23:50:51 | merchantName: 暴雪公司 | sku: [ skuName: 黑色连衣裙 skuNum: 1 skuCode: mp9fbajaj9 skuPrice: 699.0 totalSkuPrice: 699.0; skuName: 黑色连衣裙 skuNum: 1 skuCode: oaiwi0xj3z skuPrice: 2000.0 totalSkuPrice: 2000.0; skuName: 黑色连衣裙 skuNum: 1 skuCode: tkbd5a91iq skuPrice: 399.0 totalSkuPrice: 399.0; ] | price: [ totalPrice: 3098.0 discount: 100.0 paymentPrice: 2998.0 ]

安装Flume

参考 http://www.aboutyun.com/thread-8917-1-1.html

http://www.cnblogs.com/smartloli/p/4468708.html

下载了apache-flume-1.7.0-bin.tar.gz ， 拷贝到 m42n06:/home/work/data/installed

解压后在conf目录 cp flume-env.sh.template flume-env.sh

把JAVA_HOME改对：

export JAVA_HOME=/home/work/.jumbo/opt/sun-java8/

运行命令，报错：

$ bin/flume-ng version
bin/flume-ng: line 82: syntax error in conditional expression: unexpected token `('
bin/flume-ng: line 82: syntax error near `^java\.library\.path=(.'
bin/flume-ng: line 82: `      if [[ $line =~ ^java\.library\.path=(.*)$ ]]; then'

怀疑又是shell脚本的原因。。

用jumbo 装了一个 zsh，但是执行的时候貌似还是有点问题。再用jumbo装一个coreutils试试吧。

装好coreutils之后 bash版本没有变化，试了一下还是不行，只好还是用zsh。

把flume-ng脚本的第一行改成 #!/home/work/.jumbo/bin/zsh-5.0.2

运行报错：

$ bin/flume-ng version
bin/flume-ng version
bin/flume-ng:cd:409: too many arguments
run_flume:13: no such file or directory: /home/work/data/installed/apache-flume-1.7.0-bin/bin/java

发现原来是要指定配置文件，而且不能指定文件名，只能指定目录，如下：

$ bin/flume-ng version -c conf
bin/flume-ng version -c conf
Flume 1.7.0
Source code repository: https://git-wip-us.apache.org/repos/asf/flume.git
Revision: 511d868555dd4d16e6ce4fedc72c2d1454546707
Compiled by bessbd on Wed Oct 12 20:51:10 CEST 2016
From source with checksum 0d21b3ffdc55a07e1d08875872c00523

准备配一个flume，sink输出到hdfs上面，注意我们能够通过两种命令方式访问hdfs上面的文件：

$ ../hadoop-2.7.3/bin/hdfs dfs -cat /output/part-00000
../hadoop-2.7.3/bin/hdfs dfs -cat /output/part-00000
      5       5      15

$ ../hadoop-2.7.3/bin/hadoop fs -cat /output/part-00000
../hadoop-2.7.3/bin/hadoop fs -cat /output/part-00000
      5       5      15

配置成hdfs的方式：

agent1.sources = origin
agent1.channels = memoryChannel
agent1.sinks = hsink

# For each one of the sources, the type is defined
agent1.sources.origin.type = exec
agent1.sources.origin.command = tail -f /home/work/data/LogGenerator_jar/logs/generator.log
# The channel can be defined as follows.
agent1.sources.origin.channels = memoryChannel

# Each sink's type must be defined
agent1.sinks.hsink.type = hdfs
agent1.sinks.hsink.hdfs.path = /output/logOut
agent1.sinks.hsink.hdfs.fileType = DataStream
agent1.sinks.hsink.hdfs.writeFormat=TEXT
agent1.sinks.hsink.hdfs.rollInterval=1
agent1.sinks.hsink.hdfs.filePrefix=%Y-%m-%d
#Specify the channel the sink should use
agent1.sinks.hsink.channel = memoryChannel

# Each channel's type is defined.
agent1.channels.memoryChannel.type = memory
# Other config values specific to each type of channel(sink or source)
# can be defined as well
# In this case, it specifies the capacity of the memory channel
agent1.channels.memoryChannel.capacity = 100

然后运行命令，还是报错：

$ bin/flume-ng agent -n agent -f conf/flume-conf.properties  -c conf
Info: Sourcing environment configuration script /home/work/data/installed/apache-flume-1.7.0-bin/conf/flume-env.sh
Info: Including Hive libraries found via () for Hive access
+run_flume:13> /home/work/.jumbo/opt/sun-java8//bin/java -Xmx20m -cp '/home/work/data/installed/apache-flume-1.7.0-bin/conf:/home/work/data/installed/apache-flume-1.7.0-bin/lib/*:/lib/*' '-Djava.library.path=' org.apache.flume.node.Application ' -n agent1 -f flume-conf.properties'

日志 logs/flums.log显示：

08 Nov 2016 17:17:01,783 ERROR [main] (org.apache.flume.node.Application.main:348)  - A fatal error occurred while running. Exception follows.
org.apache.commons.cli.MissingOptionException: Missing required option: n
        at org.apache.commons.cli.Parser.checkRequiredOptions(Parser.java:299)
        at org.apache.commons.cli.Parser.parse(Parser.java:231)
        at org.apache.commons.cli.Parser.parse(Parser.java:85)
        at org.apache.flume.node.Application.main(Application.java:263)

无语。。

又安装了flume 1.5试了下，还是不行。

又仔细检查了一下，很可能是java版本太高导致的。只能再装下java1.7试试看。发现还是不行。

最后发现是运行的java命令里面貌似有点问题：

/home/work/.jumbo/opt/sun-java8//bin/java -Xmx20m -cp '/home/work/data/installed/apache-flume-1.7.0-bin/conf:/home/work/data/installed/apache-flume-1.5.0-bin/lib/*' '-Djava.library.path=' org.apache.flume.node.Application ' -n agent -f flume-conf.properties'

需要把最后的引号去掉。

然后改成向日志输出。配置文件flume-conf.properties如下：

agent.sources = origin
agent.channels = memoryChannel
agent.sinks = loggerSink

# For each one of the sources, the type is defined
agent.sources.origin.type =  exec
agent.sources.origin.command = tail -f /home/work/data/LogGenerator_jar/logs/generator.log

# The channel can be defined as follows.
agent.sources.origin.channels = memoryChannel

# Each sink's type must be defined
agent.sinks.loggerSink.type = logger

#Specify the channel the sink should use
agent.sinks.loggerSink.channel = memoryChannel

# Each channel's type is defined.
agent.channels.memoryChannel.type = memory

# Other config values specific to each type of channel(sink or source)
# can be defined as well
# In this case, it specifies the capacity of the memory channel
agent.channels.memoryChannel.capacity = 100

Java命令如下：

/home/work/.jumbo/opt/sun-java8//bin/java -Xmx20m -cp '/home/work/data/installed/apache-flume-1.7.0-bin/conf:/home/work/data/installed/apache-flume-1.5.0-bin/lib/*' '-Dflume.root.logger=INFO,console' org.apache.flume.node.Application   -n agent -f conf/flume-conf.properties 

然后貌似能够监听到日志了。。。

其他的地方，还要再处理。。。

不知道为什么。登出重新登录之后，居然就好了。。。

用了HDFS作为sink的新的配置文件内容:

agent.sources = origin
agent.channels = memoryChannel
agent.sinks = hdfsSink

# For each one of the sources, the type is defined
agent.sources.origin.type =  exec
agent.sources.origin.command = tail -f /home/work/data/LogGenerator_jar/logs/generator.log
# The channel can be defined as follows.
agent.sources.origin.channels = memoryChannel

# Each sink's type must be defined
agent.sinks.hdfsSink.type = hdfs
agent.sinks.hdfsSink.hdfs.path = /output/Logger
agent.sinks.hdfsSink.hdfs.fileType = DataStream
agent.sinks.hdfsSink.hdfs.writeFormati = TEXT
agent.sinks.hdfsSink.hdfs.rollInterval = 1
agent.sinks.hdfsSink.hdfs.filePrefix=%Y-%m-%d

# 不加下面这一行会一直报错：Expected timestamp in the Flume event headers
agent.sinks.hdfsSink.hdfs.useLocalTimeStamp = true
#Specify the channel the sink should use
agent.sinks.hdfsSink.channel = memoryChannel

# Each channel's type is defined.
agent.channels.memoryChannel.type = memory

# Other config values specific to each type of channel(sink or source)
# can be defined as well
# In this case, it specifies the capacity of the memory channel
agent.channels.memoryChannel.capacity = 100

然后运行命令：

bin/flume-ng agent -n agent -f conf/flume-conf.properties  -c conf

然后查看日志 logs/flume.log，貌似成功了（因为直接tail -f 是有内容的）

08 Nov 2016 23:19:03,709 INFO  [SinkRunner-PollingRunner-DefaultSinkProcessor] (org.apache.flume.sink.hdfs.BucketWriter.open:231)  - Creating /output/Logger/2016-11-08.1478618341857.tmp
08 Nov 2016 23:19:04,818 INFO  [hdfs-hdfsSink-roll-timer-0] (org.apache.flume.sink.hdfs.BucketWriter.close:357)  - Closing /output/Logger/2016-11-08.1478618341857.tmp
08 Nov 2016 23:19:04,828 INFO  [hdfs-hdfsSink-call-runner-9] (org.apache.flume.sink.hdfs.BucketWriter$8.call:618)  - Renaming /output/Logger/2016-11-08.1478618341857.tmp to /output/Logger/2016-11-08.1478618341857
08 Nov 2016 23:19:04,835 INFO  [hdfs-hdfsSink-roll-timer-0] (org.apache.flume.sink.hdfs.HDFSEventSink$1.run:382)  - Writer callback called.

在另一台机器05上面看下HDFS是否生成。

$ ~/data/installed/hadoop-2.7.3/bin/hadoop fs -ls /output/Logger
Found 5 items
-rw-r--r--   3 work supergroup       1187 2016-11-08 23:19 /output/Logger/2016-11-08.1478618341853
-rw-r--r--   3 work supergroup       1182 2016-11-08 23:19 /output/Logger/2016-11-08.1478618341854
-rw-r--r--   3 work supergroup       1188 2016-11-08 23:19 /output/Logger/2016-11-08.1478618341855
-rw-r--r--   3 work supergroup       1196 2016-11-08 23:19 /output/Logger/2016-11-08.1478618341856
-rw-r--r--   3 work supergroup       1176 2016-11-08 23:19 /output/Logger/2016-11-08.1478618341857

发现生成的是一个目录，然后里面有按照时间戳生成的文件

然后打开一个文件看看：

$ ~/data/installed/hadoop-2.7.3/bin/hadoop fs -cat /output/Logger/2016-11-08.1478618810929
[INFO][main][2016-11-08 23:26:50][com.comany.log.generator.LogGenerator] - orderNumber: 966811478618810487 | orderDate: 2016-11-08 23:26:50 | paymentNumber: Wechat-92539661 | paymentDate: 2016-11-08 23:26:50 | merchantName: Sumsam | sku: [ skuName: 人字拖鞋 skuNum: 1 skuCode: 1q9vaq3484 skuPrice: 2000.0 totalSkuPrice: 2000.0; skuName: 朋克卫衣 skuNum: 3 skuCode: 7vwna4abw2 skuPrice: 2000.0 totalSkuPrice: 6000.0; skuName: 塑身牛仔裤 skuNum: 3 skuCode: 50qem6vlid skuPrice: 699.0 totalSkuPrice: 2097.0; ] | price: [ totalPrice: 10097.0 discount: 100.0 paymentPrice: 9997.0 ]

采用HDFS输出成功。

然后安装 Kafka。

Kafka安装新起一篇：http://www.cnblogs.com/charlesblc/p/6046023.html