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由于五一假期, 成文较为简略, 一些细节部分并没有详细介绍, 如有需求, 可以参考之前几篇相当MapRuduce主题的博文.
HBase实践
- 修改MapReduce阶段倒排索引的信息通过文件输出, 而每个词极其对应的
平均出现次数信息写入到Hbase的表Wuxia中(具体的要求可以查看之前的博文MapReduce实战之倒排索引) - 编写Java程序, 遍历上一步保存在HBase中的表, 并把表格的内容保存到本地文件中.
- Hive使用Hive Shell命令行创建表(
表名:Wuxia, (word string, count double)), 导入平均出现次数的数据- 查询出现次数大于300的词语
- 查询前100个出现次数最多的数
import java.io.IOException;
import java.nio.ByteBuffer;
import java.util.StringTokenizer;
import java.util.Iterator;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.Reducer.Context;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.HColumnDescriptor;
import org.apache.hadoop.hbase.HTableDescriptor;
import org.apache.hadoop.hbase.client.HBaseAdmin;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.mapreduce.TableReducer;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.hbase.mapreduce.TableOutputFormat;
import org.apache.hadoop.hbase.mapreduce.TableReducer;
import org.apache.hadoop.hbase.io.*;
import org.apache.hadoop.hbase.util.Bytes;
public class InvertedIndexHbase {
//创建表并进行简单配置
public static void createHBaseTable(Configuration conf, String tablename) throws IOException {
// HBaseConfiguration configuration = new HBaseConfiguration();
HBaseAdmin admin = new HBaseAdmin(conf);
if (admin.tableExists(tablename)) { //如果表已经存在
System.out.println("table exits, Trying recreate table!");
admin.disableTable(tablename);
admin.deleteTable(tablename);
}
HTableDescriptor htd = new HTableDescriptor(tablename); //row
HColumnDescriptor col = new HColumnDescriptor("content"); //列族
htd.addFamily(col); //创建列族
System.out.println("Create new table: " + tablename);
admin.createTable(htd); //创建表
}
//map函数不变
public static class Map
extends Mapper<Object, Text, Text, Text> {
private Text keyWord = new Text();
private Text valueDocCount = new Text();
public void map(Object key, Text value, Context context)
throws IOException, InterruptedException {
//获取文档
FileSplit fileSplit = (FileSplit)context.getInputSplit();
String fileName = fileSplit.getPath().getName();
StringTokenizer itr = new StringTokenizer(value.toString());
while(itr.hasMoreTokens()) {
keyWord.set(itr.nextToken() + ":" + fileName); // key为key#doc
valueDocCount.set("1"); // value为词频
context.write(keyWord, valueDocCount);
}
}
}
//combine函数不变
public static class InvertedIndexCombiner
extends Reducer<Text, Text, Text, Text> {
private Text wordCount = new Text();
private Text wordDoc = new Text();
//将key-value转换为word-doc:词频
public void reduce(Text key, Iterable<Text> values,
Context context) throws IOException, InterruptedException {
int sum = 0;
for (Text value : values) {
sum += Integer.parseInt(value.toString());
}
int splitIndex = key.toString().indexOf(":"); // 找到:的位置
wordDoc.set(key.toString().substring(0, splitIndex)); //key变为单词
wordCount.set(sum + ""); //value变为doc:词频
context.write(wordDoc, wordCount);
}
}
//reduce将数据存入HBase
public static class Reduce
extends TableReducer<Text, Text, NullWritable> {
private Text temp = new Text();
public void reduce(Text key, Iterable<Text> values,
Context context) throws IOException, InterruptedException {
int sum = 0;
int count = 0;
Iterator<Text> it = values.iterator();
//形成最终value
for(;it.hasNext();) {
count++;
temp.set(it.next());
sum += Integer.parseInt(temp.toString());
}
float averageCount = (float)sum / (float)count;
FloatWritable average = new FloatWritable(averageCount);
//加入row为key.toString()
Put put = new Put(Bytes.toBytes(key.toString())); //Put实例, 每一词存一行
//列族为content, 列修饰符为average表示平均出现次数, 列值为平均出现次数
put.add(Bytes.toBytes("content"), Bytes.toBytes("average"), Bytes.toBytes(average.toString()));
context.write(NullWritable.get(), put);
}
}
public static void main(String[] args) throws Exception {
String tablename = "Wuxia";
Configuration conf = HBaseConfiguration.create();
conf.set(TableOutputFormat.OUTPUT_TABLE, tablename);
createHBaseTable(conf, tablename);
Job job = Job.getInstance(conf, "Wuxia"); //配置作业名
//配置作业的各个类
job.setJarByClass(InvertedIndexHbase.class);
job.setMapperClass(Map.class);
job.setCombinerClass(InvertedIndexCombiner.class);
job.setReducerClass(Reduce.class);
// TableMapReduceUtil.initTableReducerJob(tablename, Reduce.class, job);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setOutputFormatClass(TableOutputFormat.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
然后在Hadoop执行操作.
$ hdfs dfs -mkdir /user
$ hdfs dfs -mkdir /user/input
$ hdfs dfs -put /Users/andrew_liu/Java/Hadoop/wuxia_novels/* /user/input
$ hadoop jar WorkSpace/InvertedIndexHbase.jar InvertedIndexHbase /user/input output1
执行成功结束后, 打开HBase Shell的操作
$ hbase shell
> scan 'Wuxia'
HBase中数据写入本地文件
import java.io.FileWriter;
import java.io.IOException;
import java.io.FileWriter;
import java.io.FileNotFoundException;
import java.io.FileOutputStream;
import java.io.PrintWriter;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.KeyValue;
import org.apache.hadoop.hbase.client.HTable;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.client.HBaseAdmin;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.client.ResultScanner;
import org.apache.hadoop.hbase.util.Bytes;
public class Hbase2Local {
static Configuration conf = HBaseConfiguration.create();
public static void getResultScan(String tableName, String filePath) throws IOException {
Scan scan = new Scan();
ResultScanner rs = null;
HTable table = new HTable(conf, Bytes.toBytes(tableName));
try {
rs = table.getScanner(scan);
FileWriter fos = new FileWriter(filePath, true);
for (Result r : rs) {
// System.out.println("获得rowkey: " + new String(r.getRow()));
for (KeyValue kv : r.raw()) {
// System.out.println("列: " + new String(kv.getFamily()) + " 值: " + new String(kv.getValue()));
String s = new String(r.getRow() + "\t" + kv.getValue() + "\n");
fos.write(s);
}
}
fos.close();
} catch (IOException e) {
// TODO: handle exception
e.printStackTrace();
}
rs.close();
}
public static void main(String[] args) throws Exception {
String tableName = "Wuxia";
String filePath = "/Users/andrew_liu/Java/WorkSpace/Hbaes2Local/bin/Wuxia";
getResultScan(tableName, filePath);
}
}
Hive实践
将本地数据导入Hive
hive> create table Wuxia(word string, count double) row format delimited fields terminated by '\t' stored as textfile;
Time taken: 0.049 seconds
hive> load data local inpath '/Users/andrew_liu/Downloads/Wuxia.txt' into table Wuxia;
Loading data to table default.wuxia
Table default.wuxia stats: [numFiles=1, totalSize=2065188]
OK
Time taken: 0.217 seconds
输出出现次数大于300的词语
select * from Wuxia order by count desc limit 100;
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