E-MapReduce 中,用户申请集群的时候,默认为用户提供了 Pig 环境,用户可以直接使用 Pig 来创建和操作自己的表和数据。


  1. 用户需要提前准备好 Pig 的脚本,例如:
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     -- Query Phrase Popularity (Hadoop cluster)
     -- This script processes a search query log file from the Excite search engine and finds search phrases that occur with particular high frequency during certain times of the day. 
     -- Register the tutorial JAR file so that the included UDFs can be called in the script.
     REGISTER oss://emr/checklist/jars/chengtao/pig/tutorial.jar;
     -- Use the  PigStorage function to load the excite log file into the “raw” bag as an array of records.
     -- Input: (user,time,query) 
     raw = LOAD 'oss://emr/checklist/data/chengtao/pig/excite.log.bz2' USING PigStorage('\t') AS (user, time, query);
     -- Call the NonURLDetector UDF to remove records if the query field is empty or a URL. 
     clean1 = FILTER raw BY org.apache.pig.tutorial.NonURLDetector(query);
     -- Call the ToLower UDF to change the query field to lowercase. 
     clean2 = FOREACH clean1 GENERATE user, time, org.apache.pig.tutorial.ToLower(query) as query;
     -- Because the log file only contains queries for a single day, we are only interested in the hour.
     -- The excite query log timestamp format is YYMMDDHHMMSS.
     -- Call the ExtractHour UDF to extract the hour (HH) from the time field.
     houred = FOREACH clean2 GENERATE user, org.apache.pig.tutorial.ExtractHour(time) as hour, query;
     -- Call the NGramGenerator UDF to compose the n-grams of the query.
     ngramed1 = FOREACH houred GENERATE user, hour, flatten(org.apache.pig.tutorial.NGramGenerator(query)) as ngram;
     -- Use the  DISTINCT command to get the unique n-grams for all records.
     ngramed2 = DISTINCT ngramed1;
     -- Use the  GROUP command to group records by n-gram and hour. 
     hour_frequency1 = GROUP ngramed2 BY (ngram, hour);
     -- Use the  COUNT function to get the count (occurrences) of each n-gram. 
     hour_frequency2 = FOREACH hour_frequency1 GENERATE flatten($0), COUNT($1) as count;
     -- Use the  GROUP command to group records by n-gram only. 
     -- Each group now corresponds to a distinct n-gram and has the count for each hour.
     uniq_frequency1 = GROUP hour_frequency2 BY group::ngram;
     -- For each group, identify the hour in which this n-gram is used with a particularly high frequency.
     -- Call the ScoreGenerator UDF to calculate a "popularity" score for the n-gram.
     uniq_frequency2 = FOREACH uniq_frequency1 GENERATE flatten($0), flatten(org.apache.pig.tutorial.ScoreGenerator($1));
     -- Use the  FOREACH-GENERATE command to assign names to the fields. 
     uniq_frequency3 = FOREACH uniq_frequency2 GENERATE $1 as hour, $0 as ngram, $2 as score, $3 as count, $4 as mean;
     -- Use the  FILTER command to move all records with a score less than or equal to 2.0.
     filtered_uniq_frequency = FILTER uniq_frequency3 BY score > 2.0;
     -- Use the  ORDER command to sort the remaining records by hour and score. 
     ordered_uniq_frequency = ORDER filtered_uniq_frequency BY hour, score;
     -- Use the  PigStorage function to store the results. 
     -- Output: (hour, n-gram, score, count, average_counts_among_all_hours)
     STORE ordered_uniq_frequency INTO 'oss://emr/checklist/data/chengtao/pig/script1-hadoop-results' USING PigStorage();
  2. 将该脚本保存到一个脚本文件中,例如叫 script1-hadoop-oss.pig,然后将该脚本上传到 OSS 的某个目录中(例如:oss://path/to/script1-hadoop-oss.pig)。
  3. 通过主账号登录阿里云 E-MapReduce 控制台
  4. 单击上方的数据开发页签,进入项目列表页面。
  5. 单击对应项目右侧的工作流设计,在左侧导航栏中单击作业编辑进入作业编辑页面。
  6. 在页面左侧,在需要操作的文件夹上单击右键,选择新建作业
  7. 填写作业名称,作业描述。
  8. 选择 Pig 作业类型,表示创建的作业是一个 Pig 作业。这种类型的作业,其后台实际上是通过以下的方式提交。
    pig [user provided parameters]
  9. 单击确定
    说明 您还可以通过在文件夹上单击右键,进行创建子文件夹、重命名文件夹和删除文件夹操作。
  10. 作业内容输入框中填入 Pig 命令后续的参数。例如,如果需要使用刚刚上传到 OSS 的 Pig 脚本,则填写如下:
    -x mapreduce ossref://emr/checklist/jars/chengtao/pig/script1-hadoop-oss.pig

    您也可以单击选择 OSS 路径,从 OSS 中进行浏览和选择,系统会自动补齐 OSS 上 Pig 脚本的绝对路径。请务必将 Pig 脚本的前缀修改为 ossref(单击切换资源类型),以保证 E-MapReduce 可以正确下载该文件。

  11. 单击保存,Shell 作业即定义完成。