Question

今天我开始研究rhdfs和rmr2包。

1D向量上的

mapreduce（）函数如预期的那样运行良好。 1D矢量上的代码

a1 <- to.dfs(1:20)
a2 <- mapreduce(input=a1, map=function(k,v) keyval(v, v^2))
a3 <- as.data.frame(from.dfs(a2())

返回以下数据框

    Key  Val
1     1    1
2    10  100
3    11  121
4    12  144
5    13  169
6    14  196
7    15  225
8    16  256
9    17  289
10   18  324
11   19  361
12    2    4
13   20  400
14    3    9
15    4   16
16    5   25
17    6   36
18    7   49
19    8   64
20    9   81

直到现在，情况还不错。

但是，在mtcars数据集上处理mapreduce函数时，我收到以下错误消息。无法进一步调试。请提供一些线索继续前进。

我的代码：

rs1 <- mapreduce(input=mtcars, 
                  map=function(k, v) {
                      if (mtcars$hp > 150) keyval("Bigger", 1) },
                  reduce=function(k, v)  keyval(k, sum(v))
                  )

带有上述代码的

错误消息。

13/09/21 07:24:49 ERROR streaming.StreamJob: Missing required option: input
Usage: $HADOOP_HOME/bin/hadoop jar \
          $HADOOP_HOME/hadoop-streaming.jar [options]
Options:
  -input    <path>     DFS input file(s) for the Map step
  -output   <path>     DFS output directory for the Reduce step
  -mapper   <cmd|JavaClassName>      The streaming command to run
  -combiner <cmd|JavaClassName> The streaming command to run
  -reducer  <cmd|JavaClassName>      The streaming command to run
  -file     <file>     File/dir to be shipped in the Job jar file
  -inputformat TextInputFormat(default)|SequenceFileAsTextInputFormat|JavaClassName Optional.
  -outputformat TextOutputFormat(default)|JavaClassName  Optional.
  -partitioner JavaClassName  Optional.
  -numReduceTasks <num>  Optional.
  -inputreader <spec>  Optional.
  -cmdenv   <n>=<v>    Optional. Pass env.var to streaming commands
  -mapdebug <path>  Optional. To run this script when a map task fails 
  -reducedebug <path>  Optional. To run this script when a reduce task fails 
  -io <identifier>  Optional.
  -verbose

Generic options supported are
-conf <configuration file>     specify an application configuration file
-D <property=value>            use value for given property
-fs <local|namenode:port>      specify a namenode
-jt <local|jobtracker:port>    specify a job tracker
-files <comma separated list of files>    specify comma separated files to be copied to the map reduce cluster
-libjars <comma separated list of jars>    specify comma separated jar files to include in the classpath.
-archives <comma separated list of archives>    specify comma separated archives to be unarchived on the compute machines.

The general command line syntax is
bin/hadoop command [genericOptions] [commandOptions]


For more details about these options:
Use $HADOOP_HOME/bin/hadoop jar build/hadoop-streaming.jar -info

Streaming Command Failed!
Error in mr(map = map, reduce = reduce, combine = combine, vectorized.reduce,  : 
  hadoop streaming failed with error code 1

我们非常感谢快速而详细的回复...

Answer 1

您为Keyval传递的数据将其视为向量，而不是单个实体。尝试从下面的代码解释。

在本地尝试

加载数据
```
data(mtcars)
```

查看几条数据线

head(mtcars)
hpTest=mtcars$hp # taking required data
print(hpTest)

最终款项
```
sum(hpTest[which(hpTest>150)])  # 2804
```

在Hadoop-MapReduce上运行

导出env变量

# requied
Sys.setenv(HADOOP_HOME="/home/trendwise/apache/hadoop-1.0.4");
Sys.setenv(HADOOP_CMD="/home/trendwise/apache/hadoop-1.0.4/bin/hadoop");

#optional
Sys.setenv(HADOOP_BIN="/home/trendwise/apache/hadoop-1.0.4/bin");
Sys.setenv(HADOOP_CONF_DIR="/home/trendwise/apache/hadoop-1.0.4/conf");
Sys.setenv(HADOOP_STREAMING='/home/trendwise/apache/hadoop-1.0.4/contrib/streaming/hadoop-streaming-1.0.4.jar')
Sys.setenv(LD_LIBRARY_PATH="/lib:/lib/x86_64-linux-gnu:/lib64:/usr/lib:/usr/lib64:/usr/local/lib:/usr/local/lib64:/usr/lib/jvm/jdk1.7.0_10/lib:/usr/lib/jvm/jdk1.7.0_10/jre/lib:/usr/lib/jvm/jdk1.7.0_10/jre/lib/amd64:/usr/lib/jvm/jdk1.7.0_10/jre/lib/amd64/server");

加载库
```
library(rmr2)
library(rhdfs)
```
初始化
```
hdfs.init()
```
将输入输入HDFS
```
hpInput = to.dfs(mtcars$hp)
```

运行MapReduce

mapReduceResult <- mapreduce(input=hpInput, 
          map=function(k, v) { keyval( rep(1,length(which(inputData > 150))) ,v[which(v>150)] )} ,
          reduce=function(k2, v2){  keyval(k2, sum(v2))}

查看MR输出
```
from.dfs(mapReduceResult)
```
输出
```
$key
[1] 1

$val
[1] 2804    
```

Answer 2

您可以在最新的RStudio中使用内置调试功能。只需用本地MR方式重写代码

调试R中的mapreduce（）函数

2 个答案: