昆山开发区网站制作/seo营销名词解释

前言

有个现实的需求，数据量可能在100亿条左右。现有的数据库是SQL Server，随着采集的数据不断的填充，查询的效率越来越慢（现有的SQL Server查询已经需要数十秒钟的时间），看看有没有优化的方案。

考虑过SQL Server加索引、分区表、分库分表等方案，但数据量增长太快，还是很快就会遇到瓶颈，因此需要更优化的技术。在众多的NOSQL和大数据技术之下，针对此场景，主要考虑了两种方案：

1. MongoDB：json文档型数据库，可以通过集群拓展。但更适合列比较复杂的场景快速查询。

2. Hadoop：大数据领域的瑞士军刀，周边有很多相配套的工具可以使用，后期拓展性较强。

因为此需求只是简单的根据编码找到对应的卷号，因此最终选择Hadoop实现。

部署Hadoop

直接去官方下载，https://hadoop.apache.org/。

要注意版本的问题，版本不匹配会带来很多麻烦。我这里选择的是hadoop 3.3.4的版本。

步骤：

1. 找到hadoop对应版本的winutils.exe、hadoop.dll文件

复制hadoop 3.3.4版本对应的winutils.exe和hadoop.dll文件到hadoop的bin文件夹下面。同步复制这两个文件，到C:\Windows\System32下面。

这两个文件可以去github上面搜索，一定要注意跟你的hadoop版本一致，否则不通过。

2. 文件配置（下面的配置文件都在 hadoop 3.3.4/etc/hadoop 文件夹内）

a). hadoop-env.cmd文件配置

set JAVA_HOME=C:\Users\Administrator\.jdks\corretto-11.0.21

注意：这里的JAVA_HOME是指向的openjdk（开源）的版本，oracle的jdk用不起来。必须要安装openjdk。
b). core-site.xml

<configuration><property> <name>fs.defaultFS</name> <value>hdfs://localhost:9000</value> </property>
</configuration>

c). hdfs-site.xml

<configuration><property> <name>dfs.replication</name> <value>1</value> </property> <property> <name>dfs.namenode.name.dir</name> <value>/hadoop-3.3.4/data/namenode</value> </property> <property> <name>dfs.datanode.data.dir</name> <value>/hadoop-3.3.4/data/datanode</value> </property> 
</configuration>

d). yarn-site.xml

<configuration><property> <name>yarn.nodemanager.aux-services</name> <value>mapreduce_shuffle</value> </property> <property> <name>yarn.nodemanager.auservices.mapreduce.shuffle.class</name> <value>org.apache.hadoop.mapred.ShuffleHandler</value> </property> 
</configuration>

3. 配置环境变量
   
   再添加到Path，%HADOOP_HOME%\bin
   可以在控制台输入：hadoop version，验证是否安装配置正确
   
   最后在控制台输入：start-all.cmd ，启动Hadoop。没有错误信息，表示Hadoop启动成功。
   

部署Hbase

安装Hbase可以到官网下载：https://hbase.apache.org/。

同样要非常关注版本的问题，因为我上面选择的Hadoop是3.3.4，与之配套的Hbase的版本是2.5.5。

步骤：

1. 将之前下载的winutils.exe和hadoop.dll文件拷贝到 hbase的bin目录下，比如我的：E:\hbase-2.5.5\bin。

2. 文件配置

在hbase的conf目录下，打开hbase-site.xml文件，添加如下内容：

<configuration><property><name>hbase.rootdir</name><value>file:///E:/hbase-2.5.5/root</value></property><property><name>hbase.cluster.distributed</name><value>false</value></property><property><name>hbase.zookeeper.quorum</name><value>127.0.0.1</value></property><property><name>hbase.tmp.dir</name><value>./tmp</value></property><property><name>hbase.unsafe.stream.capability.enforce</name><value>false</value></property>
</configuration>

按照上述的配置说明，在hbase目录下，添加root和tmp文件夹。

3.配置环境变量（此处省略，参考上面的hadoop的截图）

找到hbase的bin目录下的start-hbase.cmd文件，双击启动。

hbase启动完成后的界面：

基于若依进行二次开发

直接引用ruoyi的项目，在里面添加功能，当然首先需要导入相应的jar包（这些jar包在hadoop和hbase里面都有，直接引用即可）。

当然下面还有引用的jar包，这里就不截图了，供参考。

该项目基于SpringBoot框架，实现了基于HDFS、hbase的基础功能。

控制器代码如下：

package com.ruoyi.web.controller.roll;import com.ruoyi.common.core.controller.BaseController;
import com.ruoyi.common.core.domain.R;
import com.ruoyi.common.core.domain.entity.SysRole;
import com.ruoyi.common.core.page.TableDataInfo;
import com.ruoyi.common.roll.RollEntity;
import org.apache.hadoop.hbase.client.*;
import org.apache.hadoop.hbase.client.coprocessor.AggregationClient;
import org.apache.hadoop.hbase.client.coprocessor.LongColumnInterpreter;
import org.apache.hadoop.hbase.filter.*;
import org.apache.shiro.authz.annotation.RequiresPermissions;
import org.springframework.stereotype.Controller;
import org.springframework.util.StopWatch;
import org.springframework.web.bind.annotation.*;import java.io.BufferedInputStream;
import java.io.BufferedOutputStream;
import java.io.ByteArrayOutputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;
import java.net.MalformedURLException;
import java.net.URL;
import java.util.ArrayList;
import java.util.List;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.FsUrlStreamHandlerFactory;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.Cell;
import org.apache.hadoop.hbase.CellUtil;
import org.apache.hadoop.hbase.CompareOperator;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.HColumnDescriptor;
import org.apache.hadoop.hbase.HTableDescriptor;
import org.apache.hadoop.hbase.MasterNotRunningException;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.ZooKeeperConnectionException;
import org.apache.hadoop.hbase.exceptions.DeserializationException;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.IOUtils;
import org.apache.hadoop.mapreduce.Job;@Controller
@RequestMapping("/roll")
public class RollController extends BaseController {private String prefix = "/roll";/*** 新增角色*/@GetMapping("/add")public String add() {
//        long count = rowCountByCoprocessor("mytb");
//        System.out.println("总记录数->>>"+count + "");return prefix + "/add";}@PostMapping("/list")@ResponseBodypublic TableDataInfo list(String inputEPC) {
//        startPage();
//        List<SysRole> list = roleService.selectRoleList(role);//String epc = "E280117020000333BF040B34";//String epc = "E280119120006618A51D032D"; //查询的EPCString epc = inputEPC;String tableName = "mytb";String columnFamily = "mycf";//        create(tableName, columnFamily);
//        insert(tableName,columnFamily);long startTime = System.currentTimeMillis();//E280119120006BEEA4E5032String reVal = query(tableName, columnFamily, epc);long endTime = System.currentTimeMillis();System.out.println("卷号查询时间为：" + (endTime - startTime) + "ms");RollEntity model = new RollEntity();model.epc = epc;model.rollName = reVal;model.searchTime = (endTime - startTime) + "ms";List<RollEntity> list = new ArrayList<>();list.add(model);return getDataTable(list);}// 创建表public static void create(String tableName, String columnFamily) {Configuration conf = HBaseConfiguration.create();conf.set("hbase.rootdir", "hdfs://localhost:9000/hbase");conf.set("hbase.zookeeper.quorum", "localhost");try {Connection conn = ConnectionFactory.createConnection(conf);if (conn.getAdmin().tableExists(TableName.valueOf(tableName))) {System.err.println("Table exists!");} else {HTableDescriptor tableDesc = new HTableDescriptor(TableName.valueOf(tableName));try {tableDesc.addFamily(new HColumnDescriptor(columnFamily));conn.getAdmin().createTable(tableDesc);System.err.println("Create Table SUCCESS!");} catch (IOException e) {// TODO Auto-generated catch blocke.printStackTrace();}}} catch (IOException e) {// TODO Auto-generated catch blocke.printStackTrace();}}// 插入数据public static void insert(String tableName, String columnFamily) {Configuration conf = HBaseConfiguration.create();conf.set("hbase.rootdir", "hdfs://localhost:9000/hbase");conf.set("hbase.zookeeper.quorum", "localhost");try {Connection conn = ConnectionFactory.createConnection(conf);TableName tn = TableName.valueOf(tableName);Table table = conn.getTable(tn);try {//                for (int i = 17742000; i <= 100000000; i++) {
//                    Put put = new Put(Bytes.toBytes("row" + i));
//                    put.addColumn(Bytes.toBytes(columnFamily), Bytes.toBytes("code"),
//                            Bytes.toBytes("E280119120006BEEA4E5032" + i));
//                    table.put(put);
//                }//                Put put = new Put(Bytes.toBytes("E280119120006618A51D032D"));
//                put.addColumn(Bytes.toBytes(columnFamily), Bytes.toBytes("code"),
//                            Bytes.toBytes("CQ-230308009"));
//                table.put(put);Put put = new Put(Bytes.toBytes("E280117020000333BF040B34"));put.addColumn(Bytes.toBytes(columnFamily), Bytes.toBytes("code"),Bytes.toBytes("CQ-230309002"));table.put(put);table.close();// 释放资源System.err.println("record insert SUCCESS!");} catch (Exception e) {// TODO Auto-generated catch blocke.printStackTrace();}} catch (IOException e) {// TODO Auto-generated catch blocke.printStackTrace();}}// 查询public static String query(String tableName, String columnFamily, String rowName) {String reVal = "";Configuration conf = HBaseConfiguration.create();conf.set("hbase.rootdir", "hdfs://localhost:9000/hbase");conf.set("hbase.zookeeper.quorum", "localhost");try {Connection conn = ConnectionFactory.createConnection(conf);TableName tn = TableName.valueOf(tableName);Table table = conn.getTable(tn);try {Get get = new Get(rowName.getBytes());Result r = table.get(get);for (Cell cell : r.rawCells()) {String family = new String(CellUtil.cloneFamily(cell));String qualifier = new String(CellUtil.cloneQualifier(cell));String value = new String(CellUtil.cloneValue(cell));System.out.println("列:" + family + ":" + qualifier + " 值:" + value);reVal = value;break;}} catch (Exception e) {// TODO Auto-generated catch blocke.printStackTrace();} finally {conn.close();}} catch (IOException e) {// TODO Auto-generated catch blocke.printStackTrace();}return reVal;}//过滤查询public static void queryFilter(String tableName, String columnFamily, String rowName, String value) {Configuration conf = HBaseConfiguration.create();conf.set("hbase.rootdir", "hdfs://localhost:9000/hbase");conf.set("hbase.zookeeper.quorum", "localhost");try {Connection conn = ConnectionFactory.createConnection(conf);TableName tn = TableName.valueOf(tableName);Table table = conn.getTable(tn);try {Scan scan = new Scan();Filter filter = new ValueFilter(CompareOperator.EQUAL, new BinaryComparator(Bytes.toBytes(value)));scan.setFilter(filter);ResultScanner rs = table.getScanner(scan);for (Result res : rs) {System.out.println(res);}} catch (Exception e) {// TODO Auto-generated catch blocke.printStackTrace();}} catch (IOException e) {// TODO Auto-generated catch blocke.printStackTrace();}}//读取HDFS文件private static void readHDFSFileContents() {InputStream is = null;OutputStream os = null;BufferedInputStream bufferInput = null;BufferedOutputStream bufferOutput = null;try {is = new URL("hdfs://127.0.0.1:9000/myHadoop/1.txt").openStream();bufferInput = new BufferedInputStream(is);// IOUtils.copyBytes(is, os, 4096,false);byte[] contents = new byte[1024];int bytesRead = 0;String strFileContents = "";while ((bytesRead = is.read(contents)) != -1) {strFileContents += new String(contents, 0, bytesRead);}System.out.println(strFileContents);} catch (MalformedURLException e) {// TODO Auto-generated catch blocke.printStackTrace();} catch (IOException e) {// TODO Auto-generated catch blocke.printStackTrace();} finally {// IOUtils.closeStream(is);}}//创建HDFS目录private static void createHDFSDirectory() {// TODO Auto-generated method stubtry {Configuration conf = new Configuration();conf.set("fs.defaultFS", "hdfs://127.0.0.1:9000");FileSystem fs = FileSystem.get(conf);boolean result = fs.mkdirs(new Path("/myHadoop"));System.out.println(result);} catch (Exception e) {e.printStackTrace();}}//查询Hbase有多少条记录public long rowCountByCoprocessor(String tablename){long count = 0;try {Configuration conf = HBaseConfiguration.create();conf.set("hbase.rootdir", "hdfs://localhost:9000/hbase");conf.set("hbase.zookeeper.quorum", "localhost");Connection connection = ConnectionFactory.createConnection(conf);//提前创建connection和confAdmin admin = connection.getAdmin();//admin.enableTable(TableName.valueOf("mytb"));TableName name=TableName.valueOf(tablename);//先disable表，添加协处理器后再enable表//admin.disableTable(name);HTableDescriptor descriptor = new HTableDescriptor(name); //admin.getTableDescriptor(name);//descriptor.setReadOnly(false);String coprocessorClass = "org.apache.hadoop.hbase.coprocessor.AggregateImplementation";if (! descriptor.hasCoprocessor(coprocessorClass)) {descriptor.addCoprocessor(coprocessorClass);}//admin.modifyTable(name, descriptor);//admin.enableTable(name);//计时StopWatch stopWatch = new StopWatch();stopWatch.start();Scan scan = new Scan();AggregationClient aggregationClient = new AggregationClient(conf);//System.out.println("RowCount: " + aggregationClient.rowCount(name, new LongColumnInterpreter(), scan));count = aggregationClient.rowCount(name, new LongColumnInterpreter(), scan);stopWatch.stop();System.out.println("统计耗时：" +stopWatch.getTotalTimeMillis());connection.close();} catch (Throwable e) {e.printStackTrace();}return count;}
}

最终效果