本文介绍在 E-MapReduce 集群中开发 Pig 作业流程。

在 Pig 中使用 OSS

在使用 OSS 路径的时候,请使用类似如下的形式:
oss://${AccessKeyId}:${AccessKeySecret}@${bucket}.${endpoint}/${path}
参数说明:
  • ${accessKeyId}:您账号的 AccessKeyId。

  • ${accessKeySecret}:该 AccessKeyId 对应的密钥。

  • ${bucket}: 该 AccessKeyId 对应的 bucket。

  • ${endpoint}:访问 OSS 使用的网络,由您集群所在的 region 决定,对应的 OSS 也需要是在集群对应的 region。

    具体的值请参考 OSS Endpoint

  • ${path}:bucket 中的路径。

实施步骤

以 Pig 中带的 script1-hadoop.pig 为例进行说明,将 Pig 中的 tutorial.jarexcite.log.bz2 上传到 OSS 中,假设上传路径分别为oss://emr/jars/tutorial.jaross://emr/data/excite.log.bz2

请参见如下操作步骤:
  1. 编写脚本
    根据准备工作中所上传的 OSS 路径,修改脚本中的 jar 包文件路径和输入输出路径。,如下所示。注意 OSS 路径设置形式为 oss://${accesskeyId}:${accessKeySecret}@${bucket}.${endpoint}/object/path
    /*
     * 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://${AccessKeyId}:${AccessKeySecret}@${bucket}.${endpoint}/data/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://${AccessKeyId}:${AccessKeySecret}@${bucket}.${endpoint}/data/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, $4 as mean;
    -- Use the  FILTER command to move all records with a score less than or equal to 2.0.
    filtered_uniq_frequency = FILTER uniq_frequency3 BY score > 2.0;
    -- Use the  ORDER command to sort the remaining records by hour and score.
    ordered_uniq_frequency = ORDER filtered_uniq_frequency BY hour, score;
    -- Use the  PigStorage function to store the results.
    -- Output: (hour, n-gram, score, count, average_counts_among_all_hours)
    STORE ordered_uniq_frequency INTO 'oss://${AccessKeyId}:${AccessKeySecret}@${bucket}.${endpoint}/data/script1-hadoop-results' USING PigStorage();
  2. 创建作业
    将步骤 1 中编写的脚本存放到 OSS 上,假设存储路径为 oss://emr/jars/script1-hadoop.pig,在 E-MapReduce 作业中创建如下作业:
    创建作业
  3. 创建执行计划并运行

    在 E-MapReduce 执行计划中创建执行计划,将上一步创建好的 Pig 作业添加到执行计划中,策略请选择立即执行,script1-hadoop 作业就会在选定集群中运行起来。