用以下语句生成数据

n = 1000000;
ids = rand(0..9,n);
words = rand(`apple`pig`flash`cat`dog`tomato`elevator`building,n);
data = table(ids as id, words as word);
db.createPartitionedTable(data, "words", "id").append!(data)

如何用mr函数完成每个单词个数统计？

文档举的例子太高深，太难看懂了，还是从简单需求开始吧

如下代码是否符合mr的设计思想？

def mapWords(partitionTable){    //参数是表的一个分区
    wordCount = select word,count(*) from partitionTable group by word;   //每个分区节点计算一次group
    return wordCount;
}

def reduceWords(wordsCount1,wordsCount2){     //参数是两次map的结果
    combineTable = wordsCount1.append!(wordsCount2)    //两个分区的统计结果拼接
    wordCountCombine  = select word,sum(count) as count from combineTable group by word;    //合并两次map的结果
    return wordCountCombine;
}

def finalWords(result){      //参数是最后一次reduce的结果
    return result;    //没有额外处理
}

def wordCount(ds){     //将map、reduce、final合并成一个函数，只需要传入表的所有分区数组即可
  return mr(ds, mapWords, reduceWords, finalWords)
}

wordsTable = loadTable("dfs://testdb", "wordCount")
wordCount(sqlDS(<select * from wordsTable>));

另：reduce函数是否只能是二元操作的？这与hadoop中的mr似乎有所不同