Hive Streaming

本是古典何须时尚 2022-08-19 14:21 17阅读 0赞

# 1.Hive Streaming介绍 #

在前面我们看到了UDF、UDTF、UDAF的实现并不是很简单，而且还要求对Java比较熟悉，而Hive设计的初衷是方便那些非Java人员使用。因此，Hive提供了另一种数据处理方式——Streaming，这样就可以不需要编写Java代码了，其实Streaming处理方式可以支持很多语言。但是，Streaming的执行效率通常比对应编写的UDF或改写InputFormat对象的方式要低。管道中序列化然后反序列化数据通常时低效的。而且以通常的方式很难调试整个程序。

Hive中提供了多种语法来使用Streaming，包括：

*  MAP()
 *  REDUCE()
 *  TRANSFORM()

但是，注意MAP()实际上并非在Mapper阶段执行Streaming，正如REDUCE()实际上并非在Reducer阶段执行Streaming。因此，相同的功能，通常建议使用TRANSFORM()语句，这样可以避免产生疑惑。

# 2.Streaming的编写和使用 #

Streaming的实现需要TRANSFORM()函数和USING关键字，TRANSFORM()的参数是表的列名，USING关键字用于指定脚本。本节的数据仍然使用[**Hive UDF教程（一）**][Hive UDF]中所使用的employee表。

**例一：Streaming使用Linux命令**

先看Streaming直接使用Linux系统中的命令cat来查询表，cat.q是HiveQL文件，内容如下：

SELECT TRANSFORM(e.name, e.salary)
    USING '/bin/cat' AS name, salary
    FROM employee e;

执行结果：

hive (mydb)> SOURCE cat.q;
    OK
    Time taken: 0.044 seconds
    Query ID = root_20160120000909_2de2d4f9-b50c-4ed1-a876-768c0127f067
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks is set to 0 since there's no reduce operator
    Starting Job = job_1453275977382_0001, Tracking URL = http://master:8088/proxy/application_1453275977382_0001/
    Kill Command = /root/install/hadoop-2.4.1/bin/hadoop job  -kill job_1453275977382_0001
    Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
    2016-01-20 00:10:16,258 Stage-1 map = 0%,  reduce = 0%
    2016-01-20 00:10:22,942 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.12 sec
    MapReduce Total cumulative CPU time: 1 seconds 120 msec
    Ended Job = job_1453275977382_0001
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 1   Cumulative CPU: 1.12 sec   HDFS Read: 1040 HDFS Write: 139 SUCCESS
    Total MapReduce CPU Time Spent: 1 seconds 120 msec
    OK
    John Doe	100000.0
    Mary Smith	80000.0
    Todd Jones	70000.0
    Bill King	60000.0
    Boss Man	200000.0
    Fred Finance	150000.0
    Stacy Accountant	60000.0
    Time taken: 24.758 seconds, Fetched: 7 row(s)

**例二：Streaming使用Python脚本**

下面，在对比下Hive的sum()函数，和使用sum.py的Python脚本执行情况，先看Hive的sum()函数执行：

hive (mydb)> SELECT sum(salary) FROM employee;
    Query ID = root_20160120012525_1abf156b-d44b-4f1c-b2c2-3604e4c1bba0
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks determined at compile time: 1
    In order to change the average load for a reducer (in bytes):
      set hive.exec.reducers.bytes.per.reducer=<number>
    In order to limit the maximum number of reducers:
      set hive.exec.reducers.max=<number>
    In order to set a constant number of reducers:
      set mapreduce.job.reduces=<number>
    Starting Job = job_1453281391968_0002, Tracking URL = http://master:8088/proxy/application_1453281391968_0002/
    Kill Command = /root/install/hadoop-2.4.1/bin/hadoop job  -kill job_1453281391968_0002
    Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
    2016-01-20 01:25:20,364 Stage-1 map = 0%,  reduce = 0%
    2016-01-20 01:25:31,620 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.55 sec
    2016-01-20 01:25:42,394 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 2.73 sec
    MapReduce Total cumulative CPU time: 2 seconds 730 msec
    Ended Job = job_1453281391968_0002
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 2.73 sec   HDFS Read: 1040 HDFS Write: 9 SUCCESS
    Total MapReduce CPU Time Spent: 2 seconds 730 msec
    OK
    720000.0
    Time taken: 33.891 seconds, Fetched: 1 row(s)

然后，在看Streaming的方式执行，sum.py脚本：

#!/usr/bin/env python
    
    import sys
    
    def sum(arg):
        global total
        total += arg
    
    if __name__ == "__main__":
        total  = 0.0
        for arg in sys.stdin:
            sum(float(arg))
        print total;

HiveQL脚本sum.q：

SELECT TRANSFORM(salary)                     
    USING 'python /root/experiment/hive/sum.py' AS total
    FROM employee;

最后是执行结果（用虚拟机搭建的完全分布式，且数据量很小，所以和sum()函数相比，执行时间仅供参考）：

hive> source sum.q;
    OK
    Time taken: 0.022 seconds
    Query ID = root_20160120002626_0ced0b93-e4e8-4f3a-91d0-f2aaa06b5f11
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks is set to 0 since there's no reduce operator
    Starting Job = job_1453278047512_0002, Tracking URL = http://master:8088/proxy/application_1453278047512_0002/
    Kill Command = /root/install/hadoop-2.4.1/bin/hadoop job  -kill job_1453278047512_0002
    Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
    2016-01-20 00:26:28,341 Stage-1 map = 0%,  reduce = 0%
    2016-01-20 00:26:36,185 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.4 sec
    MapReduce Total cumulative CPU time: 1 seconds 400 msec
    Ended Job = job_1453278047512_0002
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 1   Cumulative CPU: 1.4 sec   HDFS Read: 1040 HDFS Write: 9 SUCCESS
    Total MapReduce CPU Time Spent: 1 seconds 400 msec
    OK
    720000.0
    Time taken: 17.048 seconds, Fetched: 1 row(s)

**例三：Streaming的WordCount**

本节最后，在给一个用Hive Streaming的方式运行WordCount的例子。先看docs数据表：

hive (mydb)> SELECT * FROM docs;                                      
    OK
    hello world
    hello hadoop
    hello spark
    Time taken: 0.044 seconds, Fetched: 3 row(s)

wc\_mapper.py进行Mapper阶段的处理，即取出所有单词，并且计数为1：

#!/sur/bin/env python
    
    import sys
    
    def splitWord(rows):
        words = rows.strip().split(" ")
        for word in words:
            print "%s\t1" % (word)
    
    if __name__ == "__main__":
        for line in sys.stdin:
            splitWord(line)

wc\_reducer.py进行Reducer阶段的处理，即对单词进行统计：

#!/usr/bin/env python
    
    import sys
    
    (lastKey, lastCount) = (None, 0)
    #f = open("test")
    for line in sys.stdin:
        (key, count) = line.strip().split("\t")
        if (lastKey) and (lastKey != key):
            print "%s\t%d" % (lastKey, lastCount)
            (lastKey, lastCount) = (key, int(count))
        else:
            lastKey = key
            lastCount += int(count)
    
    if lastKey:
        print "%s\t%d" % (lastKey, lastCount)

HiveQL脚本wc.q写的是要执行的HQL语句，我使用了中间表wordcount存储结果，当然也可以直接查询输出：

CREATE TABLE IF NOT EXISTS wordcount(
        word STRING,
        count INT
    )
    ROW FORMAT DELIMITED
    FIELDS TERMINATED BY '\t';
    
    FROM(
        FROM docs
        SELECT TRANSFORM(line) USING 'python /root/experiment/hive/wc_mapper.py'
        AS word, count
        CLUSTER BY word) wc
    INSERT OVERWRITE TABLE wordcount
    SELECT TRANSFORM(wc.word, wc.count) USING 'python /root/experiment/hive/wc_reducer.py'
    AS words, counts;

最后是执行结果，因为我使用了中间表wordcount，所以执行完后，还需要从wordcount表中查询出结果：

hive (mydb)> SOURCE wc.q;
    OK
    Time taken: 0.022 seconds
    OK
    Time taken: 0.066 seconds
    Query ID = root_20160120013535_c6e957a9-1981-475a-b21a-e73576df6a99
    Total jobs = 1
    Launching Job 1 out of 1
    Number of reduce tasks not specified. Estimated from input data size: 1
    In order to change the average load for a reducer (in bytes):
      set hive.exec.reducers.bytes.per.reducer=<number>
    In order to limit the maximum number of reducers:
      set hive.exec.reducers.max=<number>
    In order to set a constant number of reducers:
      set mapreduce.job.reduces=<number>
    Starting Job = job_1453281391968_0003, Tracking URL = http://master:8088/proxy/application_1453281391968_0003/
    Kill Command = /root/install/hadoop-2.4.1/bin/hadoop job  -kill job_1453281391968_0003
    Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
    2016-01-20 01:35:53,691 Stage-1 map = 0%,  reduce = 0%
    2016-01-20 01:36:00,339 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.15 sec
    2016-01-20 01:36:08,961 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 2.98 sec
    MapReduce Total cumulative CPU time: 2 seconds 980 msec
    Ended Job = job_1453281391968_0003
    Loading data to table mydb.wordcount
    Table mydb.wordcount stats: [numFiles=1, numRows=4, totalSize=33, rawDataSize=29]
    MapReduce Jobs Launched: 
    Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 2.98 sec   HDFS Read: 260 HDFS Write: 103 SUCCESS
    Total MapReduce CPU Time Spent: 2 seconds 980 msec
    OK
    Time taken: 25.652 seconds
    
    hive (mydb)> SELECT * FROM wordcount;
    OK
    hadoop	1
    hello	3
    spark	1
    world	1
    Time taken: 0.047 seconds, Fetched: 4 row(s)

[Hive UDF]: http://blog.csdn.net/u010376788/article/details/50532166