：Pig 作業配置

```shell
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 * to you under the Apache License, Version 2.0 (the
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 * with the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
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 * See the License for the specific language governing permissions and
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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();
 ```

pig [user provided parameters]

-x mapreduce ossref://emr/checklist/jars/chengtao/pig/script1-hadoop-oss.pig

您也可以單擊選擇 OSS 路徑，從 OSS 中進行瀏覽和選擇，系統會自動補齊 OSS 上 Pig 指令碼的絕對路徑。請務必將 Pig 指令碼的首碼修改為 ossref（單擊切換資源類型），以保證 E-MapReduce 可以正確下載該檔案。