When you are applying for clusters in E-MapReduce, a Pig environment is provided by default. You can create and operate tables and data by using Pig.

The procedure is as follows.

  1. Prepare the Pig script in advance, for example:
    ```shell
     /*
     * Licensed to the Apache Software Foundation (ASF) under one
     * or more contributor license agreements.  See the NOTICE file
     * distributed with this work for additional information
     * regarding copyright ownership.  The ASF licenses this file
     * to you under the Apache License, Version 2.0 (the
     * "License"); you may not use this file except in compliance
     * with the License.  You may obtain a copy of the License at
     *
     *     http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS,
     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     * See the License for the specific language governing permissions and
     * limitations under the License.
     */
     -- 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. Save this script into a script file, such as script1-hadoop-oss.pig, and then upload it to an OSS directory(for example, oss://path/to/script1-hadoop-oss.pig).
  3. Log on to the Alibaba Cloud E-MapReduce Console.
  4. At the top of the navigation bar, click Data Platform.
  5. In the Actions column, click Design Workflow of the specified project.
  6. On the left side of the Job Editing page, right-click on the folder you want to operate and select New Job.
  7. In the New Job dialog box, enter the job name and description.
  8. Select the Pig job type to create a Pig job. This type of job is submitted in the background by using the following method:
    pig [user provided parameters]
  9. Click OK.
    Note You can also create subfolder, rename folder, and delete folder by right-clicking on the folder.
  10. Enter the parameters in the Content field with parameters subsequent to Pig commands. For example, if it is necessary to use a Pig script uploaded to OSS, the following must be entered:
    -x mapreduce ossref://emr/checklist/jars/chengtao/pig/script1-hadoop-oss.pig

    You can click Select OSS path to view and select from OSS. The system will automatically complete the path of Pig script on OSS. Switch the Pig script prefix to ossref (click Switch resource type) to guarantee this file is properly downloaded by E-MapReduce.

  11. Click Save to complete the Pig job configuration.