原创

wordcount之Spark与Hadoop比较

WordCount

1. hadoop版

首先,我们在/root/hadoop-test/文件夹下,vi一个wordcount.txt,内容如下:

之后,在HDFS下创建一个/test文件夹,用于存放我们的wordcount文本文件,将/root/hadoop-test/wordcount.txt上传到HDFS的/test文件夹下:

运行MapReduce的,提交job:

[root@hadoop ~]# hadoop jar /root/Hadoop/hadoop-2.9.1/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.1.jar wordcount /test /output

注意:

*wordcount是主程序名字;/test是主程序所在的文件夹,/output是输出的结果目录。*

/root/Hadoop/hadoop-2.9.1/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.1.jar是hadoop自带的测试jar包。

运行过程:

[root@hadoop ~]# hadoop jar /root/Hadoop/hadoop-2.9.1/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.1.jar wordcount /test /output
18/12/18 16:47:20 INFO client.RMProxy: Connecting to ResourceManager at /192.168.200.110:8032
18/12/18 16:47:22 INFO input.FileInputFormat: Total input files to process : 1
18/12/18 16:47:23 INFO mapreduce.JobSubmitter: number of splits:1
18/12/18 16:47:23 INFO Configuration.deprecation: yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead, use yarn.system-metrics-publisher.enabled
18/12/18 16:47:24 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1545122331329_0002
18/12/18 16:47:25 INFO impl.YarnClientImpl: Submitted application application_1545122331329_0002
18/12/18 16:47:25 INFO mapreduce.Job: The url to track the job: http://hadoop:8088/proxy/application_1545122331329_0002/
18/12/18 16:47:25 INFO mapreduce.Job: Running job: job_1545122331329_0002
18/12/18 16:47:39 INFO mapreduce.Job: Job job_1545122331329_0002 running in uber mode : false
18/12/18 16:47:39 INFO mapreduce.Job:  map 0% reduce 0%
18/12/18 16:47:49 INFO mapreduce.Job:  map 100% reduce 0%
18/12/18 16:47:59 INFO mapreduce.Job:  map 100% reduce 100%
18/12/18 16:48:00 INFO mapreduce.Job: Job job_1545122331329_0002 completed successfully
18/12/18 16:48:00 INFO mapreduce.Job: Counters: 49
    File System Counters
        FILE: Number of bytes read=61
        FILE: Number of bytes written=395161
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=225
        HDFS: Number of bytes written=43
        HDFS: Number of read operations=6
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=2
    Job Counters 
        Launched map tasks=1
        Launched reduce tasks=1
        Data-local map tasks=1
        Total time spent by all maps in occupied slots (ms)=7658
        Total time spent by all reduces in occupied slots (ms)=5576
        Total time spent by all map tasks (ms)=7658
        Total time spent by all reduce tasks (ms)=5576
        Total vcore-milliseconds taken by all map tasks=7658
        Total vcore-milliseconds taken by all reduce tasks=5576
        Total megabyte-milliseconds taken by all map tasks=7841792
        Total megabyte-milliseconds taken by all reduce tasks=5709824
    Map-Reduce Framework
        Map input records=12
        Map output records=9
        Map output bytes=147
        Map output materialized bytes=61
        Input split bytes=111
        Combine input records=9
        Combine output records=3
        Reduce input groups=3
        Reduce shuffle bytes=61
        Reduce input records=3
        Reduce output records=3
        Spilled Records=6
        Shuffled Maps =1
        Failed Shuffles=0
        Merged Map outputs=1
        GC time elapsed (ms)=314
        CPU time spent (ms)=2430
        Physical memory (bytes) snapshot=410759168
        Virtual memory (bytes) snapshot=4207472640
        Total committed heap usage (bytes)=302120960
    Shuffle Errors
        BAD_ID=0
        CONNECTION=0
        IO_ERROR=0
        WRONG_LENGTH=0
        WRONG_MAP=0
        WRONG_REDUCE=0
    File Input Format Counters 
        Bytes Read=114
    File Output Format Counters 
        Bytes Written=43

此时,MapReduce运行完毕之后,在HDFS上有运行结果/output/part-r-00000

查看运行结果:

[root@hadoop ~]# hdfs dfs -ls /output
Found 2 items
-rw-r--r--   1 root supergroup          0 2018-12-18 16:47 /output/_SUCCESS
-rw-r--r--   1 root supergroup         43 2018-12-18 16:47 /output/part-r-00000
[root@hadoop ~]# hdfs dfs -cat /output/part-r-00000
lewis.org.cn    3
lzhpo    3
www.liuzhaopo.top    3

时间计算:大概花费了4秒钟左右的样子。

2.spark版

和hadoop版一样,创建测试文本文件:

运行spark程序,一行代码即可:

计算时间:大概一秒不到,一按确定,结果就出来了。

来web页面查看一下,果然是快如闪电:

总结

hadoop与spark,面对百万数据的时候,spark能力就发挥的淋漓尽致了,这个wordcount只是测试。

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