网站建设招标信息网络推广项目代理

1. 配置MapReduce项目的日志

  在Maven项目下/src/main/resources文件夹下新建log4j.properties文件，在其中输入一下内容(log4j.appender.file.File的值是输出日志的文件名)：

log4j.rootLogger=INFO, filelog4j.appender.file=org.apache.log4j.FileAppender
log4j.appender.file.Append=false
log4j.appender.file.File=hbase-mapreduce.log
log4j.appender.file.layout=org.apache.log4j.PatternLayout
log4j.appender.file.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1}:%L - %m%n

  最后在Main方法所在类或者自定义Mapper所在类的代码中添加一句(MyMapper.class根据代码情景进行修改)：

private static final Logger logger = Logger.getLogger(MyMapper.class);

2. 通过MapReduce实现HBase中数据的插入

2.1 pom.xml中依赖配置

  <dependencies><dependency><groupId>junit</groupId><artifactId>junit</artifactId><version>4.11</version><scope>test</scope></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-common</artifactId><version>3.3.6</version><exclusions><exclusion><groupId>org.slf4j</groupId><artifactId>slf4j-log4j12</artifactId></exclusion></exclusions></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-mapreduce-client-core</artifactId><version>3.3.6</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-mapreduce-client-jobclient</artifactId><version>3.3.6</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-auth</artifactId><version>3.3.6</version></dependency><dependency><groupId>org.apache.hbase</groupId><artifactId>hbase-common</artifactId><version>2.5.10</version></dependency><dependency><groupId>org.apache.hbase</groupId><artifactId>hbase-client</artifactId><version>2.5.10</version></dependency><dependency><groupId>org.apache.hbase</groupId><artifactId>hbase-mapreduce</artifactId><version>2.5.10</version></dependency><dependency><groupId>log4j</groupId><artifactId>log4j</artifactId><version>1.2.17</version></dependency></dependencies>

2.2 工具类Util

import java.io.IOException;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.Cell;
import org.apache.hadoop.hbase.CellUtil;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.Admin;
import org.apache.hadoop.hbase.client.ColumnFamilyDescriptor;
import org.apache.hadoop.hbase.client.ColumnFamilyDescriptorBuilder;
import org.apache.hadoop.hbase.client.Connection;
import org.apache.hadoop.hbase.client.ConnectionFactory;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.client.ResultScanner;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.client.Table;
import org.apache.hadoop.hbase.client.TableDescriptorBuilder;
import org.apache.hadoop.hbase.util.Bytes;public class Util {public static Connection getConnection() throws IOException {Configuration conf = HBaseConfiguration.create();return ConnectionFactory.createConnection(conf);}public static void create(Connection conn, String tableName, String[] families) throws IOException {if (families.length == 0) {System.out.println("please provide at least one column family.");return;}if (families.length > 3) {System.out.println("please reduce the number of column families.");return;}Admin admin = conn.getAdmin();TableName tableName2 = TableName.valueOf(tableName);if (admin.tableExists(tableName2)) {System.out.println("table exists!");return;}TableDescriptorBuilder tableDescBuilder = TableDescriptorBuilder.newBuilder(tableName2);for (String family : families) {ColumnFamilyDescriptor columnFamily = ColumnFamilyDescriptorBuilder.of(family);tableDescBuilder.setColumnFamily(columnFamily);}admin.createTable(tableDescBuilder.build());System.out.println("create table success!");admin.close();}public static void delete(Connection conn, String tableName) throws IOException {Admin admin = getConnection().getAdmin();TableName tableName2 = TableName.valueOf(tableName);if (admin.tableExists(tableName2)) {admin.disableTable(tableName2);admin.deleteTable(tableName2);}admin.close();}public static void scan(Connection conn, String tableName) throws IOException {Table table = conn.getTable(TableName.valueOf(tableName));Scan scan = new Scan();ResultScanner scanner = table.getScanner(scan);System.out.println("scan: ");for (Result res = scanner.next(); res != null; res = scanner.next()) {for (Cell cell : res.listCells()) {String row = Bytes.toString(CellUtil.cloneRow(cell));String columnFamily = Bytes.toString(CellUtil.cloneFamily(cell));String column = Bytes.toString(CellUtil.cloneQualifier(cell));String data = Bytes.toString(CellUtil.cloneValue(cell));System.out.println(String.format("row: %s, family: %s, column; %s, data: %s", row, columnFamily,column, data));}}scanner.close();}
}

2.3 MyMapper和MyReducer

import java.io.IOException;import org.apache.hadoop.hbase.client.Put;
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.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.log4j.Logger;public class ImportData {private static final Logger logger = Logger.getLogger(MyMapper.class);public static class MyMapper extends Mapper<LongWritable, Text, Text, NullWritable> {;@Overrideprotected void map(LongWritable key, Text value, Context context)throws IOException, InterruptedException {context.write(value, NullWritable.get());}}public static class MyReducer extends TableReducer<Text, NullWritable, Text> {@Overrideprotected void reduce(Text key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {String[] columns = {"name", "singer", "gender", "ryghme", "terminal"};String[] splitStr = key.toString().split("\\s+");Put put = new Put(Bytes.toBytes(splitStr[0]));for (int i = 1; i < splitStr.length; i++) {put.addColumn(Bytes.toBytes("info"), Bytes.toBytes(columns[i - 1]), Bytes.toBytes(splitStr[i]));}context.write(key, put);}}
}

2.4 配置Job

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.client.Connection;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;public class App {public static void main(String[] args) throws Exception {String file = "file:///home/developer/CodeArtsProjects/mapreduce-hbase/play_records.txt";Connection conn = Util.getConnection();Util.delete(conn, "music");Util.create(conn, "music", new String[] { "info" });Configuration conf = HBaseConfiguration.create();Job job = Job.getInstance(conf, "import-data");job.setJarByClass(App.class);job.setMapperClass(ImportData.MyMapper.class);job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(NullWritable.class);job.setNumReduceTasks(2);TableMapReduceUtil.initTableReducerJob("music", ImportData.MyReducer.class, job);FileInputFormat.addInputPath(job, new Path(file));int res = job.waitForCompletion(true) ? 0 : 1;if (res == 0) {System.out.println("数据全部输入后,对music表进行扫描:");Util.scan(conn, "music");}conn.close();System.exit(res);}
}

2.5 结果

3. 利用importtsv和completebulkload实现HBase表数据的插入

3.1 删除music

  进入HBase shell，输入如下命令：

disable 'music'
drop 'music'

3.2 使用importtsv创建表

  命令：

hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.bulk.output=hdfs://172.17.0.2:9000/user/root/tmp -Dimporttsv.separator=" " -Dimporttsv.columns=HBASE_ROW_KEY,info:name,info:singer,info:gender,info:ryghme,info:terminal music file:///root/CodeProject/mapreduce-hbase/play_records.txt

3.3 使用completebulkload导入数据

  命令：

hbase org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles hdfs://172.17.0.2:9000/user/root/tmp music

参考

吴章勇 杨强著 大数据Hadoop3.X分布式处理实战