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阿里云E-MapReduce Pig 作业配置
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。操作步骤如下。
1.用户需要提前准备好 Pig 的脚本，例如：
```shell
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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.进入阿里云 E-MapReduce 控制台作业列表。
4.单击该页右上角的创建作业，进入创建作业页面。
5.填写作业名称。
6.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]
7.在应用参数选项框中填入 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。
8.选择执行失败后策略。
9.单击确定，Pig 作业即定义完成。
最后更新：2017-09-01 01:02:49