日志分析_使用shell完整日志分析案例
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一、需求分析
1. 日志文件每天生成一份(需要将日志文件定时上传至hdfs) 2. 分析日志文件中包含的字段:访问IP,访问时间,访问URL,访问状态,访问流量
3. 现在有"昨日"的日志文件即logclean.jar 3. 需求指标 a. 统计PV值 b. 统计注册人数 c. 统计IP数 d. 统计跳出率 f. 统计二跳率
二、数据分析
1. 数据采集 使用shell脚本定时上传 2. 数据清洗 过滤字段 格式化时间等字段 3. 数据分析 使用一级分区(date) 4. 数据导出 sqoop 5. 使用到的框架有: shell脚本 hdfs mapreduce hive sqoop mysql 期望结果 pv register ip jumpprob two_jumpprob
三、实施
1. 自动上传到hdfs
$HADOOP_HOME/bin/hdfs dfs -rm -r $HDFS_INPUT_PATH > /dev/null 2>&1
$HADOOP_HOME/bin/hdfs dfs -mkdir -p $HDFS_INPUT_PATH/$yesterday > /dev/null 2>&1
$HADOOP_HOME/bin/hdfs dfs -put $LOG_PATH $HDFS_INPUT_PATH/$yesterday > /dev/null 2>&1
2. 数据清洗(使用mapreduce过滤脏数据与不需要的静态数据及去双引号,转换date)
$HADOOP_HOME/bin/hdfs dfs -rm -r $HDFS_OUTPUT_PATH > /dev/null 2>&1
$HADOOP_HOME/bin/yarn jar $JAR_PATH $ENTRANCE $HDFS_INPUT_PATH/$yesterday $HDFS_OUTPUT_PATH/date=$yesterday
3. 在Hive中创建日志数据库和分区表并将清洗后的文件加入分区
$HIVE_HOME/bin/hive -e "create database if not exists $HIVE_DATABASE" > /dev/null 2>&1
$HIVE_HOME/bin/hive --database $HIVE_DATABASE -e "create external table if not exists $HIVE_TABLE(
ip string,day string,url string) partitioned by (date string)
row format delimited fields terminated by ‘\t‘ location ‘$HDFS_OUTPUT_PATH‘ "
$HIVE_HOME/bin/hive --database $HIVE_DATABASE -e "alter table $HIVE_TABLE add partition (date=‘$yesterday‘)"
4. 分析数据并使用sqoop导出至mysql
pv:
create table if not exists pv_tb(pv string) row format delimited fields terminated by ‘\t‘;
insert overwrite table pv_tb select count(1) from weblog_clean where date=‘2016_11_13‘;
register:
create table if not exists register_tb(register string) row format delimited fields terminated by ‘\t‘;
insert overwrite table register_tb select count(1) from weblog_clean where date=‘2016_11_13‘ and instr(url,‘member.php?mod=register‘) > 0;
ip:
create table if not exists ip_tb(ip string) row format delimited fields terminated by ‘\t‘;
insert overwrite table ip_tb select count(distinct ip) from weblog_clean where date=‘2016_11_13‘;
jumpprob:
create table if not exists jumpprob_tb(jump double) row format delimited fields terminated by ‘\t‘;
insert overwrite table jumpprob_tb
select ghip.singleip/aip.ips from (select count(1) singleip from(select count(ip) ips from weblog_clean where date=‘2016_11_13‘ group by ip having ips <2) gip) ghip,
(select count(ip) ips from weblog_clean where date=‘2016_11_13‘) aip;
two_jumpprob:
create table if not exists two_jumpprob_tb(jump double) row format delimited fields terminated by ‘\t‘;
insert overwrite table two_jumpprob_tb
select ghip.singleip/aip.ips from (select count(1) singleip from(select count(ip) ips from weblog_clean where date=‘2016_11_13‘ group by ip having ips >=2) gip) ghip,
(select count(ip) ips from weblog_clean where date=‘2016_11_13‘) aip;
merge table # 注意上面几个表是分开创建,效率比下面高,但存储消耗上面较高
create table if not exists log_result(pv string,register string,ip string,jumpprob double,two_jumpprob double ) row format delimited fields terminated by ‘\t‘;
insert overwrite table log_result
select log_pv.pv,log_register.register,log_ip.ip,log_jumpprob.jumpprob,log_two_jumpprob.two_jumpprob from (select count(1) pv from weblog_clean where date=‘2016_11_13‘) log_pv,
(select count(1) register from weblog_clean where date=‘2016_11_13‘ and instr(url,‘member.php?mod=register‘) > 0) log_register,
(select count(distinct ip) ip from weblog_clean where date=‘2016_11_13‘) log_ip,
(select ghip.singleip/aip.ips jumpprob from (select count(1) singleip from(select count(ip) ips from weblog_clean where date=‘2016_11_13‘ group by ip having ips <2) gip) ghip,
(select count(ip) ips from weblog_clean where date=‘2016_11_13‘) aip) log_jumpprob,
(select ghip.singleip/aip.ips two_jumpprob from (select count(1) singleip from(select count(ip) ips from weblog_clean where date=‘2016_11_13‘ group by ip having ips >=2) gip) ghip,
(select count(ip) ips from weblog_clean where date=‘2016_11_13‘) aip) log_two_jumpprob;
四、结果展示
mysql> select * from weblog_result;
+--------+----------+-------+----------+--------------+
| pv | register | ip | jumpprob | two_jumpprob |
+--------+----------+-------+----------+--------------+
| 169857 | 28 | 10411 | 0.02 | 0.04 |
+--------+----------+-------+----------+--------------+
1 row in set (0.00 sec)
五、logclean.jar(过滤日志字段:日期转换,去除双引号,过去根url)
package org.apache.hadoop.log.project;
import java.net.URI;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.Locale;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
public class LogClean extends Configured implements Tool {
public static void main(String[] args) {
Configuration conf = new Configuration();
try {
int res = ToolRunner.run(conf, new LogClean(), args);
System.exit(res);
} catch (Exception e) {
e.printStackTrace();
}
}
public int run(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "logclean");
// 设置为可以打包运行
job.setJarByClass(LogClean.class);
FileInputFormat.setInputPaths(job, args[0]);
job.setMapperClass(MyMapper.class);
job.setMapOutputKeyClass(LongWritable.class);
job.setMapOutputValueClass(Text.class);
job.setReducerClass(MyReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(NullWritable.class);
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// 清理已存在的输出文件
FileSystem fs = FileSystem.get(new URI(args[0]), getConf());
Path outPath = new Path(args[1]);
if (fs.exists(outPath)) {
fs.delete(outPath, true);
}
boolean success = job.waitForCompletion(true);
if(success){
System.out.println("Clean process success!");
}
else{
System.out.println("Clean process failed!");
}
return 0;
}
static class MyMapper extends
Mapper<LongWritable, Text, LongWritable, Text> {
LogParser logParser = new LogParser();
Text outputValue = new Text();
protected void map(
LongWritable key,
Text value,
org.apache.hadoop.mapreduce.Mapper<LongWritable, Text, LongWritable, Text>.Context context)
throws java.io.IOException, InterruptedException {
final String[] parsed = logParser.parse(value.toString());
// step1.过滤掉静态资源访问请求
if (parsed[2].startsWith("GET /static/")
|| parsed[2].startsWith("GET /uc_server")) {
return;
}
// step2.过滤掉开头的指定字符串
if (parsed[2].startsWith("GET /")) {
parsed[2] = parsed[2].substring("GET /".length());
} else if (parsed[2].startsWith("POST /")) {
parsed[2] = parsed[2].substring("POST /".length());
}
// step3.过滤掉结尾的特定字符串
if (parsed[2].endsWith(" HTTP/1.1")) {
parsed[2] = parsed[2].substring(0, parsed[2].length()
- " HTTP/1.1".length());
}
// step4.只写入前三个记录类型项
outputValue.set(parsed[0] + "\t" + parsed[1] + "\t" + parsed[2]);
context.write(key, outputValue);
}
}
static class MyReducer extends
Reducer<LongWritable, Text, Text, NullWritable> {
protected void reduce(
LongWritable k2,
java.lang.Iterable<Text> v2s,
org.apache.hadoop.mapreduce.Reducer<LongWritable, Text, Text, NullWritable>.Context context)
throws java.io.IOException, InterruptedException {
for (Text v2 : v2s) {
context.write(v2, NullWritable.get());
}
};
}
/*
* 日志解析类
*/
static class LogParser {
public static final SimpleDateFormat FORMAT = new SimpleDateFormat(
"d/MMM/yyyy:HH:mm:ss", Locale.ENGLISH);
public static final SimpleDateFormat dateformat1 = new SimpleDateFormat(
"yyyyMMddHHmmss");
public static void main(String[] args) throws ParseException {
final String S1 = "27.19.74.143 - - [30/May/2013:17:38:20 +0800] \"GET /static/image/common/faq.gif HTTP/1.1\" 200 1127";
LogParser parser = new LogParser();
final String[] array = parser.parse(S1);
System.out.println("样例数据： " + S1);
System.out.format(
"解析结果： ip=%s, time=%s, url=%s, status=%s, traffic=%s",
array[0], array[1], array[2], array[3], array[4]);
}
/**
* 解析英文时间字符串
*
* @param string
* @return
* @throws ParseException
*/
private Date parseDateFormat(String string) {
Date parse = null;
try {
parse = FORMAT.parse(string);
} catch (ParseException e) {
e.printStackTrace();
}
return parse;
}
/**
* 解析日志的行记录
*
* @param line
* @return 数组含有5个元素，分别是ip、时间、url、状态、流量
*/
public String[] parse(String line) {
String ip = parseIP(line);
String time = parseTime(line);
String url = parseURL(line);
String status = parseStatus(line);
String traffic = parseTraffic(line);
return new String[] { ip, time, url, status, traffic };
}
private String parseTraffic(String line) {
final String trim = line.substring(line.lastIndexOf("\"") + 1)
.trim();
String traffic = trim.split(" ")[1];
return traffic;
}
private String parseStatus(String line) {
final String trim = line.substring(line.lastIndexOf("\"") + 1)
.trim();
String status = trim.split(" ")[0];
return status;
}
private String parseURL(String line) {
final int first = line.indexOf("\"");
final int last = line.lastIndexOf("\"");
String url = line.substring(first + 1, last);
return url;
}
private String parseTime(String line) {
final int first = line.indexOf("[");
final int last = line.indexOf("+0800]");
String time = line.substring(first + 1, last).trim();
Date date = parseDateFormat(time);
return dateformat1.format(date);
}
private String parseIP(String line) {
String ip = line.split("- -")[0].trim();
return ip;
}
}
}
六、完整shell,注意准备logclean.jar(用于日志过滤MR程序),与"昨日"的日志文件和文件位置
#!/bin/bash
echo -ne | cat <<eot
#############################################################################
########################## 普 度 众 生 ###########################
_oo0oo_
088888880
88" . "88
(| -_- |)
0\ = /0
___/‘---‘\___
.‘ \\\\| |// ‘.
/ \\\\||| : |||// \\
/_ ||||| -:- |||||- \\
| | \\\\\\ - /// | |
| \_| ‘‘\---/‘‘ |_/ |
\ .-\__ ‘-‘ __/-. /
___‘. .‘ /--.--\ ‘. .‘___
."" ‘< ‘.___\_<|>_/___.‘ >‘ "".
| | : ‘- \‘.;‘\ _ /‘;.‘/ - ‘ : | |
\ \ ‘_. \_ __\ /__ _/ .-‘ / /
=====‘-.____‘.___ \_____/___.-‘____.-‘=====
‘=---=‘
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
佛祖保佑 iii 永不出错
eot
##get yesterday date
yesterday=`date -d ‘-1 day‘ +‘%Y_%m_%d‘`
echo $yesterday
############
## define ##
############
HADOOP_HOME=/opt/cdh-5.6.3/hadoop-2.5.0-cdh5.3.6
HIVE_HOME=/opt/cdh-5.6.3/hive-0.13.1-cdh5.3.6
SQOOP_HOME=/opt/cdh-5.6.3/sqoop-1.4.5-cdh5.2.6
HIVE_DATABASE=weblog
HIVE_TABLE=weblog_clean
HIVE_RSTABLE=weblog_result
MYSQL_USERNAME=root
MYSQL_PASSWORD=root
EXPORT_DIR=/user/hive/warehouse/weblog.db/weblog_result
NUM_MAPPERS=1
#########################
## get logfile path ##
#########################
LOG_PATH=/home/liuwl/opt/datas/weblog/access_$yesterday.log
JAR_PATH=/home/liuwl/opt/datas/logclean.jar
ENTRANCE=org.apache.hadoop.log.project.LogClean
HDFS_INPUT_PATH=/weblog/source
HDFS_OUTPUT_PATH=/weblog/clean
SQOOP_JDBC=jdbc:mysql://hadoop09-linux-01.ibeifeng.com:3306/$HIVE_DATABASE
############################
## upload logfile to hdfs ##
############################
echo "start to upload logfile"
#$HADOOP_HOME/bin/hdfs dfs -rm -r $HDFS_INPUT_PATH > /dev/null 2>&1
HSFiles=`$HADOOP_HOME/bin/hdfs dfs -ls $HDFS_INPUT_PATH/$yesterday`
if [ -z "$HSFiles" ]; then
$HADOOP_HOME/bin/hdfs dfs -mkdir -p $HDFS_INPUT_PATH/$yesterday > /dev/null 2>&1
$HADOOP_HOME/bin/hdfs dfs -put $LOG_PATH $HDFS_INPUT_PATH/$yesterday > /dev/null 2>&1
echo "upload ok"
else
echo "exists"
fi
###########################
## clean the source file ##
###########################
echo "start to clean logfile"
HCFiles=`$HADOOP_HOME/bin/hdfs dfs -ls $HDFS_OUTPUT_PATH`
if [ -z "$HCFiles" ]; then
$HADOOP_HOME/bin/yarn jar $JAR_PATH $ENTRANCE $HDFS_INPUT_PATH/$yesterday $HDFS_OUTPUT_PATH/date=$yesterday
echo "clean ok"
fi
###########################
## create the hive table ##
###########################
echo "start to create the hive table"
$HIVE_HOME/bin/hive -e "create database if not exists $HIVE_DATABASE" > /dev/null 2>&1
$HIVE_HOME/bin/hive --database $HIVE_DATABASE -e "create external table if not exists $HIVE_TABLE(ip string,day string,url string) partitioned by (date string) row format delimited fields terminated by ‘\t‘ location ‘$HDFS_OUTPUT_PATH‘ "
echo "add patition to hive table"
$HIVE_HOME/bin/hive --database $HIVE_DATABASE -e "alter table $HIVE_TABLE add partition (date=‘$yesterday‘)"
##################################
## create the hive reslut table ##
##################################
echo "start to create the hive reslut table"
$HIVE_HOME/bin/hive --database $HIVE_DATABASE -e "create table if not exists $HIVE_RSTABLE(pv string,register string,ip string,jumpprob double,two_jumpprob double ) row format delimited fields terminated by ‘\t‘;"
#################
## insert data ##
#################
echo "start to insert data"
HTFiles=`$HADOOP_HOME/bin/hdfs dfs -ls $EXPORT_DIR`
if [ -z "$HTFiles" ]; then
$HIVE_HOME/bin/hive --database $HIVE_DATABASE -e "insert overwrite table $HIVE_RSTABLE select log_pv.pv,log_register.register,log_ip.ip,log_jumpprob.jumpprob,log_two_jumpprob.two_jumpprob from (select count(1) pv from $HIVE_TABLE where date=‘$yesterday‘) log_pv,(select count(1) register from $HIVE_TABLE where date=‘$yesterday‘ and instr(url,‘member.php?mod=register‘) > 0) log_register,(select count(distinct ip) ip from $HIVE_TABLE where date=‘$yesterday‘) log_ip,(select ghip.singleip/aip.ips jumpprob from (select count(1) singleip from(select count(ip) ips from $HIVE_TABLE where date=‘$yesterday‘ group by ip having ips <2) gip) ghip,(select count(ip) ips from $HIVE_TABLE where date=‘$yesterday‘) aip) log_jumpprob,(select ghip.singleip/aip.ips two_jumpprob from (select count(1) singleip from(select count(ip) ips from $HIVE_TABLE where date=‘$yesterday‘ group by ip having ips >=2) gip) ghip,(select count(ip) ips from $HIVE_TABLE where date=‘$yesterday‘) aip) log_two_jumpprob"
fi
###################################
## create the mysql reslut table ##
###################################
mysql -u$MYSQL_USERNAME -p$MYSQL_PASSWORD -e "
create database if not exists $HIVE_DATABASE DEFAULT CHARACTER SET utf8 COLLATE utf8_general_ci;
use $HIVE_DATABASE;
create table if not exists $HIVE_RSTABLE(pv varchar(20) not null,register varchar(20) not null,ip varchar(20) not null,jumpprob double(6,2) not null,two_jumpprob double(6,2) not null) DEFAULT CHARACTER SET utf8 COLLATE utf8_general_ci;
truncate table if exists $HIVE_RSTABLE;
quit"
#######################################
## export hive result table to mysql ##
#######################################
echo "start to export hive result table to mysql"
$SQOOP_HOME/bin/sqoop export --connect $SQOOP_JDBC --username $MYSQL_USERNAME --password $MYSQL_PASSWORD --table $HIVE_RSTABLE --export-dir $EXPORT_DIR --num-mappers $NUM_MAPPERS --input-fields-terminated-by ‘\t‘
echo "shell finished"
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