E-MapReduce：Pig作業配置

前提條件

• 已建立好專案，詳情請參見專案管理。
• 已準備好Pig的指令碼，樣本如下。

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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();

• 已儲存該指令檔script1-hadoop-oss.pig，並上傳到OSS的某個目錄中，例如oss://path/to/script1-hadoop-oss.pig。