【HBase分布式数据库】第七章 数据的导入导出 (2-5)
7.2 bulkload导入数据使命目的
掌握引入外部依靠包的方法
掌握eclipse打包的方法
掌握bulkload导入数据的逻辑代码
使命清单
[*]使命1：引入外部依靠包
[*]使命2：bulkload导入数据
使命步骤
使命1：引入外部依靠包
Bulkload是通过一个MapReduce Job来实现的，通过Job直接生成一个HBase的内部HFile格式文件来形成一个特别的HBase数据表，然后直接将数据文件加载到运行的集群中。使用bulk load功能最简单的方式就是使用importtsv 工具。importtsv 是从TSV文件直接加载内容至HBase的一个内置工具。它通过运行一个MapReduce Job，将数据从TSV文件中直接写入HBase的表或者写入一个HBase的自有格式数据文件。在编写代码逻辑之前，我们首先要引入程序依靠的jar包，步骤如下：
1、右键项目，选择【build path】>【configure build path】
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8.2-1
2、在弹出的对话框内，单击【libraries】> 【add external jars】
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3、弹出的对话框中，找到Hadoop存放jar包的路径，路径如图所示。
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4、当前页面下的文件夹包罗MapReduce、hdfs、yarn和common下的全部jar包，选中之后，单击底部的open按钮。需要注意的是，这4个包每个包需要单独打开，单独选中。全部添加完毕之后，单击【apply and close】
使命2：bulkload导入数据
构建BulkLoadJob类
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FsShell;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.KeyValue;
import org.apache.hadoop.hbase.client.HTable;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.HFileOutputFormat2;
import org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.IOException;
public class BulkLoadJob {
//指定的类BulkLoadJob初始化日志对象，方便在日志输出的时候，可以打印出日志信息所属的类。
static Logger logger = LoggerFactory.getLogger(BulkLoadJob.class);
//构建map端输入
public static class BulkLoadMap extends Mapper<LongWritable, Text, ImmutableBytesWritable, KeyValue> {
//map方法
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
//对输入数据进行切分
String[] valueStrSplit = value.toString().split("\t");
//拿到行键
String hkey = valueStrSplit;
//拿到列族
String family = valueStrSplit.toString().split(":");
//拿到列
String column = valueStrSplit.toString().split(":");
//拿到数值
String hvalue = valueStrSplit;
//行键转换成不可变型的字节
final byte[] rowKey = Bytes.toBytes(hkey);
final ImmutableBytesWritable HKey = new ImmutableBytesWritable(rowKey);
//把行键、列族、列和值封装成KV对儿
KeyValue kv = new KeyValue(rowKey, Bytes.toBytes(family), Bytes.toBytes(column), Bytes.toBytes(hvalue));
//写到磁盘
context.write(HKey, kv);
}
}
public static void main(String[] args) throws Exception {
//配置信息的创建
Configuration conf = HBaseConfiguration.create();
conf.set("hbase.zookeeper.property.clientPort", "2181");
conf.set("hbase.zookeeper.quorum", "localhost");
//指定数据的输入和输出
String[] dfsArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
String inputPath = dfsArgs;
System.out.println("source: " + dfsArgs);
String outputPath = dfsArgs;
System.out.println("dest: " + dfsArgs);
HTable hTable = null;
Job job = Job.getInstance(conf, "Test Import HFile & Bulkload");
job.setJarByClass(BulkLoadJob.class);
job.setMapperClass(BulkLoadJob.BulkLoadMap.class);
job.setMapOutputKeyClass(ImmutableBytesWritable.class);
job.setMapOutputValueClass(KeyValue.class);
// 避免测试task
job.setSpeculativeExecution(false);
job.setReduceSpeculativeExecution(false);
// 输入输出端的格式
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(HFileOutputFormat2.class);
FileInputFormat.setInputPaths(job, inputPath);
FileOutputFormat.setOutputPath(job, new Path(outputPath));
//指定表名
hTable = new HTable(conf, "ns:t_table");
HFileOutputFormat2.configureIncrementalLoad(job, hTable);
if (job.waitForCompletion(true)) {
FsShell shell = new FsShell(conf);
try {
shell.run(new String[] { "-chmod", "-R", "777", dfsArgs });
} catch (Exception e) {
logger.error("Couldnt change the file permissions ", e);
throw new IOException(e);
}
//数据导入hbase表
LoadIncrementalHFiles loader = new LoadIncrementalHFiles(conf);
loader.doBulkLoad(new Path(outputPath), hTable);
} else {
logger.error("loading failed.");
System.exit(1);
}
if (hTable != null) {
hTable.close();
}
}
}
创建测试表
进入hbase的shell环境，创建测试命名空间和测试表。
bin/hbase shell
create_namespace 'ns'
create 'ns:t_table','cf1','cf2'
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8.2-4
数据上传
HDFS上创建目录，把数据bulkdata.csv上传到HDFS。
cat bulkdata.csv
hadoop fs -mkdir -p /data/input
hadoop fs -put bulkdata.csv /data/input
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8.2-5
完成了数据和程序之后，就要对程序打包了。
1、选中程序所在包，右键选择【export】
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2、弹出的对话框中选择【Java】下的【jar file】，单击next。
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3、在弹出的对话框，勾选依靠，指定jar包的输出路径。单击next。
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4、本对话框不需要操纵
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5、在接下来的对话框中，需要指定运行主类。末了单击finish
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运行jar包
使用Hadoop运行jar包的命令，执行导入数据操纵。
hadoop jar /headless/Desktop/test.jar /data/input/bulkdata.csv /data/output/bulk_out
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8.2-11
检察效果
进入shell环境，检察表中是否有数据。
bin/hbase shell
scan 'ns:t_table'
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7.3 HBase的WordCount
使命目的
实践hbase的Wordcount
使命清单
使命1：预备工作
使命2：WordCount
使命步骤
使命1：预备工作
测试命名空间和测试表
进入shell环境。创建测试命名空间ns以及测试表src_table和dest_table，两张表都只有一个列族cf。
bin/hbase shell
create_namespace 'ns'
create 'ns:src_table','cf'
create 'ns:dest_table','cf'
测试数据
为测试表插入测试数据。
put 'ns:src_table','1','cf:word','hello'
put 'ns:src_table','2','cf:word','Java'
put 'ns:src_table','3','cf:word','hello'
put 'ns:src_table','4','cf:word','Scala'
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8.3-1
使命2：WordCount
程序逻辑
新建wordcount包，在包下新建HbaseWordCount类。
package wordcount;
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.client.Mutation;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.hbase.mapreduce.TableMapper;
import org.apache.hadoop.hbase.mapreduce.TableReducer;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
public class HbaseWordCount {
//日志输出的时候，可以打印出日志信息所在类
static Logger logger = LoggerFactory.getLogger(HbaseWordCount.class);
//设置服务器端口以及服务器地址
static Configuration conf = null;
static {
conf = HBaseConfiguration.create();
conf.set("hbase.zookeeper.property.clientPort", "2181");
conf.set("hbase.zookeeper.quorum", "localhost");
}
public static class HBMapper extends TableMapper<Text,IntWritable>{
private static IntWritable one = new IntWritable(1);
private static Text word = new Text();
@Override
protected void map(ImmutableBytesWritable key, Result value,
Mapper<ImmutableBytesWritable, Result, Text, IntWritable>.Context context)
throws IOException, InterruptedException {
for(Cell cell : value.rawCells()) {
word.set(CellUtil.cloneValue(cell));
context.write(word, one);
}
}
}
public static class HBReducer extends TableReducer<Text,IntWritable,ImmutableBytesWritable>{
@SuppressWarnings("deprecation")
@Override
protected void reduce(Text key, Iterable<IntWritable> values,
Reducer<Text, IntWritable, ImmutableBytesWritable, Mutation>.Context context)
throws IOException, InterruptedException {
int sum = 0;
for(IntWritable value : values) {
sum += value.get();
}
//把单词作为行键进行存储
Put put = new Put(Bytes.toBytes(key.toString()));
//数据存储到hbase表，列族为cf，列为col，值为sum
put.add(Bytes.toBytes("cf"),
Bytes.toBytes("col"),
Bytes.toBytes(String.valueOf(sum)));
//写到hbase中的需要指定行键和put
context.write(new ImmutableBytesWritable(Bytes.toBytes(key.toString())), put);
}
}
public static void main(String [] args) throws IOException, ClassNotFoundException, InterruptedException {
@SuppressWarnings("deprecation")
Job job = new Job(conf,"hbase wordcount");
Scan scan = new Scan();
//使用TableMapReduceUtil工具类初始化map，扫描源表中数据执行map操作
TableMapReduceUtil.initTableMapperJob(
"ns:src_table",
scan,
HBMapper.class,
Text.class,
IntWritable.class,
job);
//使用TableMapReduceUtil工具类初始化reduce，把reduce之后的结果存储到目标表
TableMapReduceUtil.initTableReducerJob(
"ns:dest_table",
HBReducer.class,
job);
job.waitForCompletion(true);
System.out.println("finished");
}
}
执行效果
程序完成之后，运行程序。当我们看到“finished”后，进入shell环境检察目标表中数据。
scan 'ns:dest_table'
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8.4 HDFS数据导入HBase
使命目的
掌握Hadoop与HBase的集成使用
使命清单
使命1：HDFS数据导入HBase
使命步骤
使命1：HDFS数据导入HBase
上传数据
在HDFS上新建/data/input目录，把hbase目录下的testdata中的csvdata.txt上传到该目录。
hadoop fs -mkdir -p /data/input
hadoop fs -put ./csvdata.txt /data/input
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8.4-1
程序
新建一个hdfsandhbase包，包下新建一个Hdfs2Hbase类。
package hdfsandhbase;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HColumnDescriptor;
import org.apache.hadoop.hbase.HTableDescriptor;
import org.apache.hadoop.hbase.NamespaceDescriptor;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.Admin;
import org.apache.hadoop.hbase.client.Connection;
import org.apache.hadoop.hbase.client.ConnectionFactory;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
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.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
public class Hdfs2Hbase {
public static class MyMapper extends Mapper<LongWritable,Text,Text,Text>{
@Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, Text>.Context context)
throws IOException, InterruptedException {
String line = value.toString();
String[] lines = line.split(",");
for (String s : lines) {
context.write(new Text(s), new Text(1+""));
}
}
}
public static class MyReduce extends TableReducer<Text,Text,ImmutableBytesWritable>{
@Override
protected void reduce(Text key, Iterable<Text> value,Context context)
throws IOException, InterruptedException {
int counter = 0;
for(Text t:value) {
counter += Integer.parseInt(t.toString());
}
//写出到hbase中去
Put put = new Put(Bytes.toBytes(key.toString()));
put.addColumn("data".getBytes(), "count".getBytes(), (counter+"").getBytes());
context.write(new ImmutableBytesWritable(key.getBytes()), put);
}
}
public static void main(String [] args) throws IOException, ClassNotFoundException, InterruptedException {
Configuration conf = new Configuration();
conf.set("fs.defaultFS", "hdfs://localhost:9000");
conf.set("hbase.zookeeper.quorum", "localhost");
TableName tn = TableName.valueOf("ns:test");
//对hbase进行操作
Connection conn = ConnectionFactory.createConnection(conf);
Admin admin = conn.getAdmin();
//创建命名空间
NamespaceDescriptor nsd = NamespaceDescriptor.create("ns").build();
admin.createNamespace(nsd);
//创建表
HTableDescriptor htd = new HTableDescriptor(TableName.valueOf("ns:test"));
HColumnDescriptor hcd = new HColumnDescriptor("data");
htd.addFamily(hcd);
//判断表是否存在
if(admin.tableExists(tn)) {
if(admin.isTableEnabled(tn)) {
admin.disableTable(tn);
}
admin.deleteTable(tn);
}
admin.createTable(htd);
//定义job
Job job = Job.getInstance(conf,"hdfs2hbase");
job.setMapperClass(MyMapper.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
//数据输入路径
FileInputFormat.addInputPath(job, new Path("/data/input/csvdata.txt"));
//使用TableMapreduceUtil初始化reduce
TableMapReduceUtil.initTableReducerJob(
"ns:test",
MyReduce.class,
job);
job.waitForCompletion(true);
System.out.println("finished");
}
}
检察效果
运行程序没有报错的情况下，进入shell环境，检察test表中数据。
bin/hbase shell
scan 'ns:test'
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8.4-2
8.5 HBase数据导入HDFS
使命目的
掌握hbase数据导入到HDFS的程序逻辑
使命清单
使命1：HBase数据导入HDFS
使命步骤
使命1：HBase数据导入HDFS
原始表和数据
进入shell环境，创建测试表“ns:test”，包罗一个列族cf和一个列col，并插入两条数据。
create_namespace 'ns'
create 'ns:test','cf'
put 'ns:test','1','cf:col','value1'
put 'ns:test','2','cf:col','value2'
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8.5-1
程序
在hdfsandhbase包下新建Hbase2Hdfs类。
package hdfsandhbase;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.hbase.mapreduce.TableMapper;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class Hbase2Hdfs {
public static class MyMapper extends TableMapper<Text,Text>{
@Override
protected void map(ImmutableBytesWritable key, Result value,
Mapper<ImmutableBytesWritable, Result, Text, Text>.Context context)
throws IOException, InterruptedException {
//获取对应的列族和列，设置为utf-8
String cfandc = new String(value.getValue("cf".getBytes(),
"col".getBytes()),"utf-8");
context.write(new Text(""), new Text(cfandc));
}
}
public static class MyReducer extends Reducer<Text,Text,Text,Text>{
//实例化Text用来存储获取到的数据
private Text result = new Text();
@Override
protected void reduce(Text key, Iterable<Text> values, Context context)
throws IOException, InterruptedException {
for(Text t : values) {
result.set(t);
context.write(key, result);
}
}
}
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
//配置相关信息
Configuration conf = new Configuration();
conf.set("fs.defaultFS", "hdfs://localhost:9000");
conf.set("hbase.zookeeper.quorum", "localhost");
//实例化任务
Job job = Job.getInstance(conf,"hbase2hdfs");
//设置运行主类
job.setJarByClass(Hbase2Hdfs.class);
Scan scan = new Scan();
TableMapReduceUtil.initTableMapperJob(
"ns:test",
scan,
MyMapper.class,
Text.class,
Text.class,
job);
job.setReducerClass(MyReducer.class);
//设置输出路径
FileOutputFormat.setOutputPath(job, new Path("/data/output/out"));
job.waitForCompletion(true);
System.out.println("finished");
}
}
执行效果
退出shell环境，检察输出路径下的文件效果。
quit
hadoop fs -cat /data/output/out/part-r-00000
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8.5-2
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