数据准备与数据迁移

我会带着你远行 2022-10-27 06:21 168阅读 0赞

### 目录 ###

*   *  一、项目数据准备
     *  二、基于LogStash数据迁移至ES
     *  三、基于MR数据迁移至HBase

## 一、项目数据准备 ##

将备份的数据导入到mysql中

create database db_novel;
    
    mysql -u root -p199911 </opt/shell/novel.sql db_novel
    
    # 为了确保数据使用10000条
    create table novel_test as select * from novel limit 10000;

数据迁移架构  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTM2NjQ5OQ_size_16_color_FFFFFF_t_70_pic_center]

## 二、基于LogStash数据迁移至ES ##

LogStash数据迁移：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTM2NjQ5OQ_size_16_color_FFFFFF_t_70_pic_center 1]

启动LogStash数据迁移，需要编写数据迁移脚本，模版如下：

cd logstash-6.7.2
    bin/logstash -e 'input { stdin { } } output { stdout {} }'

LogStash脚本参考地址：  
[https://www.elastic.co/guide/en/logstash/6.7/index.html][https_www.elastic.co_guide_en_logstash_6.7_index.html]

根据业务需求与数据格式，编写LogStash脚本如下：

vim novel.conf

input {  
    stdin { } 
    jdbc { 
        jdbc_driver_library => "/opt/shell/mysql-connector-java-5.1.48-bin.jar"
        jdbc_driver_class => "com.mysql.jdbc.Driver"
        jdbc_connection_string => "jdbc:mysql://bigdata-pro-m04:3306/db_novel"
        jdbc_user => "root"
        jdbc_password => "199911"
        statement => "SELECT * from novel_test"
        jdbc_validate_connection => "true"
        jdbc_validation_timeout => "3600"
        connection_retry_attempts => "5"
        jdbc_paging_enabled => "true"
        jdbc_page_size => "1000"
        sql_log_level => "warn"
        lowercase_column_names => "false"
       	}
    } 
    
    filter { 
    	json { 
    		source => "message"
    		remove_field => ["message"]
    	}
    }
    
    output {  
    elasticsearch { 
    	hosts => "bigdata-pro-m04:9200"
    	index => "novel"
    	document_id => "%{id}"
        }
    stdout { 
    	codec => "json_lines"
    	} 
    }

1.将数据迁移脚本文件拷贝到/etc/logstash/conf.d这个目录下  
2.在root用户下进入到/usr/share/logstash这个目录，并运行如下命令

bin/logstash -f /etc/logstash/conf.d/novel.conf

查看是否导入数据成功：

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTM2NjQ5OQ_size_16_color_FFFFFF_t_70_pic_center 2]

通过查看Kibana可以发现数据导入成功。

## 三、基于MR数据迁移至HBase ##

创建hbase表：

create 'novel_detail','cf'

通过MR程序将mysql的数据导入到hbase表中

package com.data.migrate;
    
    import org.apache.hadoop.io.WritableComparable;
    import org.apache.hadoop.mapred.lib.db.DBWritable;
    
    import java.io.DataInput;
    import java.io.DataOutput;
    import java.io.IOException;
    import java.sql.PreparedStatement;
    import java.sql.ResultSet;
    import java.sql.SQLException;
    
    /** * @author ：caizhengjie * @description：TODO * @date ：2021/2/3 10:46 下午 */
    public class NovelDetail implements WritableComparable<NovelDetail>, DBWritable { 
    
        private int id;
        private String chapterName;
        private String authorName;
        private String novelName;
        private String chapterUrl;
    
    
        public NovelDetail() { 
    
        }
    
        public NovelDetail(int id, String authorName, String novelName, String chapterName, String chapterUrl) { 
            this.id = id;
            this.authorName = authorName;
            this.novelName = novelName;
            this.chapterName = chapterName;
            this.chapterUrl = chapterUrl;
    
        }
    
        public int getId() { 
            return id;
        }
    
        public void setId(int id) { 
            this.id = id;
        }
    
        public String getAuthorName() { 
            return authorName;
        }
    
        public void setAuthorName(String authorName) { 
            this.authorName = authorName;
        }
    
        public String getNovelName() { 
            return novelName;
        }
    
        public void setNovelName(String novelName) { 
            this.novelName = novelName;
        }
    
        public String getChapterName() { 
            return chapterName;
        }
    
        public void setChapterName(String chapterName) { 
            this.chapterName = chapterName;
        }
    
        public String getChapterUrl() { 
            return chapterUrl;
        }
    
        public void setChapterUrl(String chapterUrl) { 
            this.chapterUrl = chapterUrl;
        }
    
    
        @Override
        public int compareTo(NovelDetail o) { 
            return this.id - o.id;
        }
    
        @Override
        public void write(DataOutput dataOutput) throws IOException { 
            dataOutput.writeInt(id);
            dataOutput.writeUTF(authorName);
            dataOutput.writeUTF(novelName);
            dataOutput.writeUTF(chapterName);
            dataOutput.writeUTF(chapterUrl);
    
        }
    
        @Override
        public void readFields(DataInput dataInput) throws IOException { 
            this.id = dataInput.readInt();
            this.authorName = dataInput.readUTF();
            this.novelName = dataInput.readUTF();
            this.chapterName = dataInput.readUTF();
            this.chapterUrl = dataInput.readUTF();
    
        }
    
        @Override
        public void write(PreparedStatement preparedStatement) throws SQLException { 
            // 类似于jdbc，使用的PreparedStatement进行赋值
            int index = 1;
            preparedStatement.setInt(index++,id);
            preparedStatement.setString(index++,authorName);
            preparedStatement.setString(index++,novelName);
            preparedStatement.setString(index++,chapterName);
            preparedStatement.setString(index++,chapterUrl);
    
        }
    
        @Override
        public void readFields(ResultSet resultSet) throws SQLException { 
            // 类似于jbdc的查询
            int index = 1;
            id = resultSet.getInt(index++);
            authorName = resultSet.getString(index++);
            novelName = resultSet.getString(index++);
            chapterName = resultSet.getString(index++);
            chapterUrl = resultSet.getString(index++);
    
        }
    }

package com.data.migrate;
    
    import org.apache.commons.codec.digest.DigestUtils;
    
    /** * @author ：caizhengjie * @description：TODO * @date ：2021/2/4 9:55 上午 */
    public class JavaUtils { 
    
        /** * MD5 * @return */
        public static String md5(String key){ 
            // 加密后的字符串
            return DigestUtils.md5Hex(key);
        }
    }

package com.data.migrate;
    
    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.conf.Configured;
    import org.apache.hadoop.hbase.HBaseConfiguration;
    import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
    import org.apache.hadoop.hbase.client.Put;
    import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
    import org.apache.hadoop.hbase.mapreduce.TableReducer;
    import org.apache.hadoop.hbase.util.Bytes;
    import org.apache.hadoop.io.LongWritable;
    import org.apache.hadoop.io.NullWritable;
    import org.apache.hadoop.mapred.lib.db.DBConfiguration;
    import org.apache.hadoop.mapred.lib.db.DBInputFormat;
    import org.apache.hadoop.mapreduce.Job;
    
    import org.apache.hadoop.mapreduce.Mapper;
    import org.apache.hadoop.util.Tool;
    import org.apache.hadoop.util.ToolRunner;
    
    import java.io.IOException;
    
    /** * @author ：caizhengjie * @description：mysql数据迁移到hbase * @date ：2021/2/4 8:42 上午 */
    public class MysqlHBaseMapReduce { 
    
        /** * map */
        public static class MysqlHBaseMapper extends Mapper<LongWritable ,NovelDetail,NovelDetail, NullWritable> { 
    
            @Override
            protected void map(LongWritable key, NovelDetail value, Context context) throws IOException, InterruptedException { 
                context.write(value, NullWritable.get());
            }
        }
    
        /** * reduce */
        public static class MysqlHBaseReducer extends TableReducer<NovelDetail,NullWritable, ImmutableBytesWritable>{ 
    
            @Override
            protected void reduce(NovelDetail key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException { 
                int detailId = key.getId();
                String authorName = key.getAuthorName();
                String novelName = key.getNovelName();
                String chapterName = key.getChapterName();
                String chapterUrl = key.getChapterUrl();
    
                // 设计rowkey:小说名字加上章节名字组成的rowkey
                String rowkey = System.currentTimeMillis()+Math.random()+ JavaUtils.md5(chapterUrl);
    
                // java操作hbase先将rowkey写入
                final Put put = new Put(Bytes.toBytes(rowkey));
    
                // 接下来就是迭代赋值
                for (NullWritable value : values) { 
                    put.addColumn(Bytes.toBytes("cf"),Bytes.toBytes("id"),Bytes.toBytes(detailId));
                    put.addColumn(Bytes.toBytes("cf"),Bytes.toBytes("authorName"),Bytes.toBytes(authorName));
                    put.addColumn(Bytes.toBytes("cf"),Bytes.toBytes("novelName"),Bytes.toBytes(novelName));
                    put.addColumn(Bytes.toBytes("cf"),Bytes.toBytes("chapterName"),Bytes.toBytes(chapterName));
                    put.addColumn(Bytes.toBytes("cf"),Bytes.toBytes("chapterUrl"),Bytes.toBytes(chapterUrl));
                }
    
                // key value
                context.write(new ImmutableBytesWritable(Bytes.toBytes(rowkey)),put);
    
            }
        }
    
        public static class MysqlHBaseDriver extends Configured implements Tool { 
    
            public static void main(String[] args) throws Exception { 
                // 获取Configuration对象，读取配置信息
                Configuration configuration = HBaseConfiguration.create();
                ToolRunner.run(configuration,new MysqlHBaseDriver(),args);
            }
    
            @Override
            public int run(String[] strings) throws Exception { 
                // get conf
                Configuration conf = this.getConf();
    
                // 配置MySQL的的url,用户名和密码
                DBConfiguration.configureDB(conf,
                        "com.mysql.jdbc.Driver",
                        "jdbc:mysql://bigdata-pro-m04:3306/db_novel?useSSL=false",
                        "root",
                        "199911");
    
                // create job
                Job job = Job.getInstance(conf,this.getClass().getSimpleName());
                job.setJarByClass(MysqlHBaseDriver.class);
    
                // map，指定job的mapper和输出的类型
                job.setMapperClass(MysqlHBaseMapper.class);
                job.setMapOutputKeyClass(NovelDetail.class);
                job.setMapOutputValueClass(NullWritable.class);
    
                // set reducer and output，把数据存储到hbase的novel_detail中
                TableMapReduceUtil.initTableReducerJob("novel_detail",MysqlHBaseReducer.class,job);
    
                // 设置输入格式是从database中读取
                job.setInputFormatClass(DBInputFormat.class);
    
                // job,继承DBWritable的类，表名，查询条件，按那个字段进行排序，要读取的字段
                DBInputFormat.setInput(job, NovelDetail.class,
                        " novel_detail", null, "id", "id",
                        "author_name", "novel_name", "chapter_name", "chapter_url");
    
                job.waitForCompletion(true);
    
                return 0;
            }
        }
    }

21/02/04 14:32:37 INFO mapreduce.Job: Counters: 35
    	File System Counters
    		FILE: Number of bytes read=7070394052
    		FILE: Number of bytes written=8471690289
    		FILE: Number of read operations=0
    		FILE: Number of large read operations=0
    		FILE: Number of write operations=0
    		HDFS: Number of bytes read=0
    		HDFS: Number of bytes written=0
    		HDFS: Number of read operations=0
    		HDFS: Number of large read operations=0
    		HDFS: Number of write operations=0
    	Map-Reduce Framework
    		Map input records=10409794
    		Map output records=10409794
    		Map output bytes=1373170312
    		Map output materialized bytes=1400187855
    		Input split bytes=78
    		Combine input records=0
    		Combine output records=0
    		Reduce input groups=10409794
    		Reduce shuffle bytes=1400187855
    		Reduce input records=10409794
    		Reduce output records=10409794
    		Spilled Records=36609312
    		Shuffled Maps =1
    		Failed Shuffles=0
    		Merged Map outputs=1
    		GC time elapsed (ms)=3513
    		Total committed heap usage (bytes)=632291328
    	Shuffle Errors
    		BAD_ID=0
    		CONNECTION=0
    		IO_ERROR=0
    		WRONG_LENGTH=0
    		WRONG_MAP=0
    		WRONG_REDUCE=0
    	File Input Format Counters 
    		Bytes Read=0
    	File Output Format Counters 
    		Bytes Written=0
    
    Process finished with exit code 0

查看hbase中的数据，可以发现导入成功：

count 'novel_detail'
    10409794 row(s) in 370.3170 seconds
    
    => 10409794

--------------------

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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTM2NjQ5OQ_size_16_color_FFFFFF_t_70_pic_center 1]: /images/20221024/a8d01807434c48d9834dab9210512f7c.png
[https_www.elastic.co_guide_en_logstash_6.7_index.html]: https://www.elastic.co/guide/en/logstash/6.7/index.html
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTM2NjQ5OQ_size_16_color_FFFFFF_t_70_pic_center 2]: /images/20221024/74e1119542f04edd934803a5307b7234.png