Python之——网站访问流量统计
2024-05-23 06:40:04
转载请注明出处：http://blog.csdn.net/l1028386804/article/details/79056976
一、场景描述
数据源准备工作详见博文《Python之——自动上传本地log文件到HDFS(基于Hadoop 2.5.2)》。
网站访问流量作为衡量一个站点的价值、热度的重要标准，另外，在CDN服务中心流量会涉及计费，如何快速准确分析当前站点的流量数据至关重要。本实例精确到分钟统计网站访问流量，原理是在mapper操作时将Web日志中小时的每分钟作为key，将对应的发送字节数作为value, 在reducer操作时对相同key做累加(sum)统计。
二、实现MapReduce
【/usr/local/python/source/httpflow.py】
# -*- coding:UTF-8 -*-
'''
Created on 2018年1月14日@author: liuyazhuang
'''from mrjob.job import MRJob
import reclass MRCounter(MRJob):def mapper(self, key, line):i = 0;for flow in line.split():#获取时间字段,位于日志的第4列，内容如[14/Jan/2018:08:41:24if i == 3:timerow = flow.split(":")#获取“小时:分钟”作为keyhm = timerow[1] + ":" + timerow[2]#获取日志第10列 - 发送的字节数，作为valueif i == 9 and re.match(r"\d{1,}", flow):#初始化key-valueyield hm, int(flow)i += 1def reducer(self, key, occurrences):#相同key "小时:分钟"的value作累加操作yield key, sum(occurrences)if __name__ == '__main__':MRCounter.run()三、生成MapReduce任务
运行如下命令：
python httpflow.py -r hadoop --jobconf mapreduce.job.priority=VERY_HIGH --jobconf mapreduce.map.tasks=2 --jobconf mapduce.reduce.tasks=1 -o hdfs://liuyazhuang121:9000/output/httpflow hdfs://liuyazhuang121:9000/user/root/website.com/20180114此时打印的日志如下：
[root@liuyazhuang121 source]# python httpflow.py -r hadoop --jobconf mapreduce.job.priority=VERY_HIGH --jobconf mapreduce.map.tasks=2 --jobconf mapduce.reduce.tasks=1 -o hdfs://liuyazhuang121:9000/output/httpflow hdfs://liuyazhuang121:9000/user/root/website.com/20180114
No configs found; falling back on auto-configuration
No configs specified for hadoop runner
Looking for hadoop binary in $PATH...
Found hadoop binary: /usr/local/hadoop-2.5.2/bin/hadoop
Using Hadoop version 2.5.2
Looking for Hadoop streaming jar in /usr/local/hadoop-2.5.2...
Found Hadoop streaming jar: /usr/local/hadoop-2.5.2/share/hadoop/tools/lib/hadoop-streaming-2.5.2.jar
Creating temp directory /tmp/httpflow.root.20180114.073946.471689
Copying local files to hdfs:///user/root/tmp/mrjob/httpflow.root.20180114.073946.471689/files/...
Running step 1 of 1...packageJobJar: [/usr/local/hadoop-2.5.2/tmp/hadoop-unjar4177700266549256253/] [] /tmp/streamjob6159676598743062299.jar tmpDir=nullConnecting to ResourceManager at liuyazhuang121/192.168.209.121:8032Connecting to ResourceManager at liuyazhuang121/192.168.209.121:8032Total input paths to process : 1number of splits:2Submitting tokens for job: job_1515893542122_0006Submitted application application_1515893542122_0006The url to track the job: http://liuyazhuang121:8088/proxy/application_1515893542122_0006/Running job: job_1515893542122_0006Job job_1515893542122_0006 running in uber mode : falsemap 0% reduce 0%map 100% reduce 0%map 100% reduce 100%Job job_1515893542122_0006 completed successfullyOutput directory: hdfs://liuyazhuang121:9000/output/httpflow
Counters: 49File Input Format Counters Bytes Read=2355499File Output Format Counters Bytes Written=5445File System CountersFILE: Number of bytes read=55559FILE: Number of bytes written=415983FILE: Number of large read operations=0FILE: Number of read operations=0FILE: Number of write operations=0HDFS: Number of bytes read=2355749HDFS: Number of bytes written=5445HDFS: Number of large read operations=0HDFS: Number of read operations=9HDFS: Number of write operations=2Job Counters Data-local map tasks=2Launched map tasks=2Launched reduce tasks=1Total megabyte-seconds taken by all map tasks=6390784Total megabyte-seconds taken by all reduce tasks=3116032Total time spent by all map tasks (ms)=6241Total time spent by all maps in occupied slots (ms)=6241Total time spent by all reduce tasks (ms)=3043Total time spent by all reduces in occupied slots (ms)=3043Total vcore-seconds taken by all map tasks=6241Total vcore-seconds taken by all reduce tasks=3043Map-Reduce FrameworkCPU time spent (ms)=2760Combine input records=0Combine output records=0Failed Shuffles=0GC time elapsed (ms)=58Input split bytes=250Map input records=7555Map output bytes=47795Map output materialized bytes=55565Map output records=3879Merged Map outputs=2Physical memory (bytes) snapshot=652185600Reduce input groups=430Reduce input records=3879Reduce output records=430Reduce shuffle bytes=55565Shuffled Maps =2Spilled Records=7758Total committed heap usage (bytes)=468189184Virtual memory (bytes) snapshot=2668351488Shuffle ErrorsBAD_ID=0CONNECTION=0IO_ERROR=0WRONG_LENGTH=0WRONG_MAP=0WRONG_REDUCE=0
Streaming final output from hdfs://liuyazhuang121:9000/output/httpflow...
"00:00" 572
"00:18" 572
"00:35" 287
"00:55" 573
"01:10" 285
"01:38" 574
"01:53" 574
"02:16" 570
"02:31" 287
"02:51" 572
"03:06" 286
"03:07" 287
"03:26" 573
"03:37" 569
"03:58" 571
"04:11" 570
"04:42" 571
"04:49" 574
"04:58" 569
"05:17" 572
"05:23" 572
"05:47" 285
"06:07" 285
"06:18" 288
"06:38" 572
"06:55" 285
"07:09" 574
"07:29" 573
"07:39" 572
"08:07" 570
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"09:33" 574
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"09:39" 2013280
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"09:42" 859
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"09:44" 287
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"10:11" 573
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"10:17" 570
"10:18" 148452
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"10:20" 287
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"10:39" 570
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"10:47" 148724
"10:48" 1856
"10:49" 574
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"14:49" 7094
"14:50" 861
"14:51" 767325
"14:52" 1051
"14:53" 148723
"14:54" 860
"14:55" 148743
"14:56" 149333
"14:57" 857
"14:58" 5771
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"15:00" 59869
"15:01" 10255
"15:02" 859
"15:03" 2892
"15:04" 858
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"15:06" 1547763
"15:07" 1530
"15:08" 2296079
"15:09" 7799
"15:10" 3482555
Removing HDFS temp directory hdfs:///user/root/tmp/mrjob/httpflow.root.20180114.073946.471689...
Removing temp directory /tmp/httpflow.root.20180114.073946.471689...可以看出，打印出了结果，此时我们通过命令：
hadoop fs -ls /output/httpflow查看生成的结果文件：
[root@liuyazhuang121 source]# hadoop fs -ls /output/httpflow
Found 2 items
-rw-r--r-- 1 root supergroup 0 2018-01-14 15:40 /output/httpflow/_SUCCESS
-rw-r--r-- 1 root supergroup 5445 2018-01-14 15:40 /output/httpflow/part-00000然后我们通过命令
hadoop fs -cat /output/httpflow/part-00000查看输出的结果如下：
[root@liuyazhuang121 source]# hadoop fs -cat /output/httpflow/part-00000
"00:00" 572
"00:18" 572
"00:35" 287
"00:55" 573
"01:10" 285
"01:38" 574
"01:53" 574
"02:16" 570
"02:31" 287
"02:51" 572
"03:06" 286
"03:07" 287
"03:26" 573
"03:37" 569
"03:58" 571
"04:11" 570
"04:42" 571
"04:49" 574
"04:58" 569
"05:17" 572
"05:23" 572
"05:47" 285
"06:07" 285
"06:18" 288
"06:38" 572
"06:55" 285
"07:09" 574
"07:29" 573
"07:39" 572
"08:07" 570
"08:13" 573
"08:32" 573
"08:33" 571
"08:34" 574
"08:35" 575
"08:36" 572
"08:37" 575
"08:38" 576
"08:39" 570
"08:40" 575
"08:41" 570
"08:42" 571
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"08:44" 574
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"08:50" 769
"08:51" 568
"08:52" 574
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"08:59" 570
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"09:02" 570
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"09:04" 151052
"09:05" 153894
"09:06" 571
"09:07" 148374
"09:08" 857
"09:09" 857
"09:10" 148446
"09:11" 860
"09:12" 1148
"09:13" 6901
"09:14" 8062
"09:15" 11603
"09:16" 2297730
"09:17" 3352029
"09:18" 1670086
"09:19" 859
"09:20" 1042
"09:21" 574
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"09:23" 572
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"09:25" 2734
"09:26" 1174
"09:27" 1646607
"09:28" 486805
"09:29" 271606
"09:30" 55121
"09:31" 2593
"09:32" 4079807
"09:33" 574
"09:34" 288
"09:35" 287
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"09:37" 574
"09:38" 284
"09:39" 2013280
"09:40" 3875259
"09:41" 147800
"09:42" 859
"09:43" 296035
"09:44" 287
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"09:46" 32419
"09:47" 186591
"09:48" 576
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"10:02" 858
"10:03" 571
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"10:06" 287
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"10:08" 571
"10:09" 939
"10:10" 1532
"10:11" 573
"10:12" 857
"10:13" 573
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"10:15" 575
"10:16" 571
"10:17" 570
"10:18" 148452
"10:19" 964
"10:20" 287
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"10:22" 568
"10:23" 285
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"10:27" 571
"10:28" 286
"10:29" 3983
"10:30" 2812
"10:31" 922
"10:32" 1860
"10:33" 613
"10:34" 3173
"10:35" 359727
"10:36" 2298366
"10:37" 857
"10:38" 1529
"10:39" 570
"10:40" 572
"10:41" 574
"10:42" 859
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"10:45" 1221
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"10:47" 148724
"10:48" 1856
"10:49" 574
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"12:32" 857
"12:33" 573
"12:34" 571
"12:35" 571
"12:36" 571
"12:37" 854
"12:38" 571
"12:39" 571
"12:40" 572
"12:41" 577
"12:42" 858
"12:43" 573
"12:44" 571
"12:45" 573
"12:46" 573
"12:47" 856
"12:48" 574
"12:49" 571
"12:50" 571
"12:51" 572
"12:52" 1054
"12:53" 572
"12:54" 570
"12:55" 571
"12:56" 573
"12:57" 858
"12:58" 573
"12:59" 573
"13:00" 574
"13:01" 574
"13:02" 854
"13:03" 572
"13:04" 574
"13:05" 575
"13:06" 576
"13:07" 858
"13:08" 572
"13:09" 572
"13:10" 573
"13:11" 574
"13:12" 858
"13:13" 573
"13:14" 572
"13:15" 571
"13:16" 575
"13:17" 857
"13:18" 572
"13:19" 573
"13:20" 574
"13:21" 572
"13:22" 1054
"13:23" 573
"13:24" 575
"13:25" 569
"13:26" 572
"13:27" 856
"13:28" 572
"13:29" 574
"13:30" 572
"13:31" 573
"13:32" 857
"13:33" 571
"13:34" 573
"13:35" 570
"13:36" 574
"13:37" 857
"13:38" 747352
"13:39" 1548813
"13:40" 1548
"13:41" 574
"13:42" 3865293
"13:43" 6170
"13:44" 3331
"13:45" 1545861
"13:46" 901
"13:47" 1722453
"13:48" 3839352
"13:49" 1672340
"13:50" 2280
"13:51" 1818880
"13:52" 2548977
"13:53" 3401
"13:54" 862
"13:55" 858
"13:56" 572
"13:57" 1329
"13:58" 575
"13:59" 574
"14:00" 1526
"14:01" 1530
"14:02" 12994
"14:03" 2391
"14:04" 1149
"14:05" 149010
"14:06" 5492
"14:07" 857
"14:08" 857
"14:09" 1148
"14:10" 851
"14:11" 854
"14:12" 3894
"14:13" 149041
"14:14" 145109
"14:15" 754
"14:16" 1330
"14:17" 861
"14:18" 1223
"14:19" 127167
"14:20" 571
"14:21" 285
"14:22" 287
"14:23" 572
"14:24" 35292
"14:25" 569
"14:26" 570
"14:27" 867
"14:28" 2534
"14:29" 856
"14:30" 570
"14:31" 573
"14:32" 573
"14:33" 574
"14:34" 1595
"14:35" 574
"14:36" 571
"14:37" 148726
"14:38" 148452
"14:39" 148727
"14:40" 861
"14:41" 148441
"14:42" 859
"14:43" 889605
"14:44" 1144
"14:45" 858
"14:46" 857
"14:47" 862
"14:48" 1522546
"14:49" 7094
"14:50" 861
"14:51" 767325
"14:52" 1051
"14:53" 148723
"14:54" 860
"14:55" 148743
"14:56" 149333
"14:57" 857
"14:58" 5771
"14:59" 5961
"15:00" 59869
"15:01" 10255
"15:02" 859
"15:03" 2892
"15:04" 858
"15:05" 2523173
"15:06" 1547763
"15:07" 1530
"15:08" 2296079
"15:09" 7799
"15:10" 3482555可见输出了结果。
最后建议将分析结果数据定期入库MySQL，生成相应的数据报表。
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