基于HBase的冠字号查询系统1--理论部分
1. 软件版本和部署
maven：3.3.9，jdk：1.7 ，Struts2：2.3.24.1，hibernate：4.3.6，spring：4.2.5，MySQL：5.1.34，Junit：4，Myeclipse：2014;
Hadoop2.6.4，HBase1.1.2
下载：https://github.com/fansy1990/ssh_v3/releases
数据下载：http://download.csdn.net/detail/fansy 或
2. 背景&思路
目前针对钞票识别，一般都是使用看、摸、听、测四种方式，这里使用一种比较客观的方式类进行识别。 建设冠字号管理查询，以冠字号查询为手段，有效解决银行对外误付假币的问题。从源头解决伪钞问题。
本系统就是使用客观的方法来验证伪钞。本系统采用的方案是基于冠字号的，每张人民币的冠字号是唯一的，如果有一个大表可以把所有的人民币以及人民币对应的操作（在什么时间、什么地点存入或获取）记录下来，这样在进行存取时就可以根据冠字号先查询一下，看当前冠字号对应的纸币在大表中的保存的情况，这样就可以确定当前冠字号对应的纸币是否是伪钞了（这里假设在大表中的所有冠字号对应的钞票都是真钞）。
下面对应存储场景：
最近状态（表中有无）
有（此时没有无状态）
目前，基于传统存储数据一般在千万级别（受限于查询等性能），但是如果要存储所有钞票的信息以及其被存储或获取的记录信息，那么传统数据库肯定是不能胜任的。所以本系统是基于HBase的。
3. 功能指标
? 存储万级用户信息；
? 存储百万级别钞票信息；
? 支持前端业务每秒500+实时查询请求；
? 数据存储和计算能够可扩展；
? 提供统一接口，支持前端相关查询业务；
说明： 其中前两条，万级用户信息和百万级钞票信息是根据数据确定的，这里可以根据数据以及集群的大小进行调整（如果集群够大，存储信息也可以很大）；
冠字号查询系统包括下面5层：
? 数据层：包括基础数据MySQL、文档、Web数据等；
? 数据处理层：主要是数据的加载，包括MR加载方式、 API加载模式、Sqoop加载模式等；
? 数据存储层：主要是HBase存储，包括钞票的所有信息以及用户信息等；
? 数据服务层：主要是对外提供查询、存储等接口服务；
? 数据应用层：存取钞系统，在存钞时设计到伪钞识别；其他应用系统；
5.1原始数据：
冠字号存储记录（冠字号，表中是否有该冠字号（0表示没有，1表示有），存储或取时间，存储或取所在银行编号，用户id）：
用户信息表（用户Id，名字，出生日期，性别，地址，手机号，绑定银行编号）：
5.2冠字号记录
对数据进过初步探索，发现冠字号规律如下：
AAA[A~Z][]
AAB[A~Z][]
如果集群有四个节点，设置region初始为4，那么三个split点为：AAAM9999，AAAZ9999，AABM9999；
假设每个用户每天进行10次操作，如果要保存100天数据，那么设置版本数为1000，则建表语句如下：
create 'records',{NAME=&'info',VERSIONS=&1000},SPLITS =&['AAAM9999','AAAZ9999','AABM9999']
表结构描述如下：
字段值举例
表主键（钞票冠字号）
long型（可以存储用户操作的时间）
who、when、where做了哪些操作
如果用户是存储行为，那么在行为结束后，该值为1
存取钞银行
5.3用户信息
对数据进过初步探索，发现用户信息规律如下：
~92]XXXX[]
如果集群有四个节点，设置region初始为4，那么三个split点为：XXXX1991XXXX1992XXXX0000；
则建表语句如下：
create 'user',{NAME=&'info'},SPLITS =&['XXXX0000','XXXX0000','XXXX0000']
表结构描述如下:
字段值举例
用户主键（身份证号）
用户注册银行
用户出生年月
6. 数据加载
系统在投入使用的时候，已经存在历史数据，需要把历史数据批量导入到系统中；在人民币首次发行时，也需要批量导入系统中。这里的导入直接使用MR导入。
MR设计成一个通用的数据从HDFS导入HBase的MR：
6.1 主类：
package ssh.
import org.apache.hadoop.conf.C
import org.apache.hadoop.conf.C
import org.apache.hadoop.fs.P
import org.apache.hadoop.hbase.TableN
import org.apache.hadoop.hbase.mapreduce.TableMapReduceU
import org.apache.hadoop.mapreduce.J
import org.apache.hadoop.mapreduce.lib.input.FileInputF
import org.apache.hadoop.mapreduce.lib.input.TextInputF
import org.apache.hadoop.util.T
import ssh.util.HadoopU
* Job Driver驱动类
* @author fansy
public class ImportToHBase extends Configured implements Tool {
public static final String SPLITTER = &SPLITTER&;
public static final String COLSFAMILY = &COLSFAMILY&;
public static final String DATEFORMAT = &DATEFORMAT&;
public int run(String[] args) throws Exception {
if (args.length != 5) {
System.err
.println(&Usage:\n demo.job.ImportToHBase
return -1;
if (args[3] == null || args[3].length() & 1) {
System.err.println(&column family can't be null!&);
return -1;
Configuration conf = getConf();
conf.set(SPLITTER, args[2]);
conf.set(COLSFAMILY, args[3]);
conf.set(DATEFORMAT, args[4]);
TableName tableName = TableName.valueOf(args[1]);
Path inputDir = new Path(args[0]);
String jobName = &Import to & + tableName.getNameAsString();
Job job = Job.getInstance(conf, jobName);
job.setJarByClass(ImportMapper.class);
FileInputFormat.setInputPaths(job, inputDir);
job.setInputFormatClass(TextInputFormat.class);
job.setMapperClass(ImportMapper.class);
TableMapReduceUtil.initTableReducerJob(tableName.getNameAsString(),
null, job);
job.setNumReduceTasks(0);
HadoopUtils.setCurrJob(job);// 设置外部静态Job
return job.waitForCompletion(true) ? 0 : 1;
主类的run方法中使用的是传统的MR导入HBase的代码，只是设置了额外的参数，这里主类参数意思解释如下：
input: HDFS输入数据路径;
splitter : 输入数据字段分隔符;
tableName : 表名;
: 列描述， rk代表rowkey以及rowkey所在列、ts代表timestamp及其所在列；示例数据说明原始数据，第一列为rowkey，第二列为timestamp所在列，第三列属于列簇col1，同时列名为q1，第4列属于列簇col2,同时列名为q1；
date_format : timestamp日期格式，如果列描述中没有ts那么就代表原始数据中没有timestamp，则此参数没有意义；
6.2 Mapper：
package ssh.
import java.io.IOE
import java.text.ParseE
import java.text.SimpleDateF
import java.util.ArrayL
import org.apache.hadoop.hbase.client.P
import org.apache.hadoop.hbase.io.ImmutableBytesW
import org.apache.hadoop.hbase.util.B
import org.apache.hadoop.io.LongW
import org.apache.hadoop.io.T
import org.apache.hadoop.mapreduce.M
* Mapper类，接收HDFS数据，写入到HBase表中
* @author fansy
public class ImportMapper extends Mapper{
private static final String COMMA = &,&;
private static final String COLON=&:&;
private String splitter =
// private String colsStr =
private int rkIndex =0; // rowkey 下标
private int tsIndex =1; // timestamp下标
private boolean hasTs = // 原始数据是否有timestamp
private SimpleDateFormat sf =
private ArrayList colsFamily=
private Put put =
ImmutableBytesWritable rowkey = new ImmutableBytesWritable();
protected void setup(Mapper.Context context)
throws IOException, InterruptedException {
splitter = context.getConfiguration().get(ImportToHBase.SPLITTER,&,&);
String colsStr = context.getConfiguration().get(ImportToHBase.COLSFAMILY,null);
sf = context.getConfiguration().get(ImportToHBase.DATEFORMAT,null)==null
? new SimpleDateFormat(&yyyy-MM-dd HH:mm&)
:new SimpleDateFormat(context.getConfiguration().get(ImportToHBase.DATEFORMAT));
String[] cols = colsStr.split(COMMA, -1);
colsFamily =new ArrayList&&();
for(int i=0;i& cols.i++){
if(&rk&.equals(cols[i])){
colsFamily.add(null);
if(&ts&.equals(cols[i])){
colsFamily.add(null);
hasTs = // 原始数据包括ts
colsFamily.add(getCol(cols[i]));
* 获取 family：qualifier byte数组
* @param col
private byte[][] getCol(String col) {
byte[][] fam_qua = new byte[2][];
String[] fam_quaStr = col.split(COLON, -1);
fam_qua[0]=
Bytes.toBytes(fam_quaStr[0]);
fam_qua[1]=
Bytes.toBytes(fam_quaStr[1]);
return fam_
protected void map(LongWritable key, Text value,
Mapper.Context context)
throws IOException, InterruptedException {
String[] words = value.toString().split(splitter, -1);
if(words.length!=colsFamily.size()){
System.out.println(&line:&+value.toString()+& does not compatible!&);
rowkey.set(getRowKey(words[rkIndex]));
put = getValue(words,colsFamily,rowkey.copyBytes());
context.write(rowkey, put);
* 获取Put值
* @param words
* @param colsFamily
* @param bs
private Put getValue(String[] words, ArrayList colsFamily, byte[] bs) {
Put put = new Put(bs);
for(int i=0;iMapper是整个流程的核心，主要负责进行数据解析、并从HDFS导入到HBase表中的工作，其各个部分功能如下：? setup()：获取输入数据字段分隔符，获取列簇、列名，获取rowkey列标，获取ts格式及列标（如果没有的话，就按照插入数据的时间设置）；? map()：解析、过滤并提取数据（需要的字段数据），生成Put对象，写入HBase；&6.3 针对records,user MR导入：&只需要进行拼凑参数，然后直接调用即可。
7. 实时数据加载
使用Java API来操作HBase数据库，完成实时HBase数据库更新，包括冠字号查询、存取款等功能。Logstash is an open source, server-side data processing pipeline that ingests data from a multitude of sources simultaneously, transforms it, and then sends it to your favorite “stash.” (Ours is Elasticsearch, naturally.)
From 0 to 60 in 60: The Logstash Primer.
Ingest Data of All Shapes, Sizes, and Sources
Data is often scattered or siloed across many systems in many formats. Logstash supports
that pull in events from a multitude of common sources, all at the same time. Easily ingest from your logs, metrics, web applications, data stores, and various AWS services, all in continuous, streaming fashion.
Ingest Data of All Shapes, Sizes, and Sources
Data is often scattered or siloed across many systems in many formats. Logstash supports
that pull in events from a multitude of common sources, all at the same time. Easily ingest from your logs, metrics, web applications, data stores, and various AWS services, all in continuous, streaming fashion.
Parse & Transform Your Data On the Fly
5.10.83.30
user-identifier frank
[10/Oct/:36 -0700]
"GET/apache_pb.gif HTTP/1.0" 200 2326
5.10.83.30 user-identifier frank
[10/Oct/:36 -0700]
"GET /apache_pb.gif HTTP/1.0" 200 2326
5.10.83.30 user-identifier frank
[10/Oct/:36 -0700]
"GET /apache_pb.gif HTTP/1.0" 200 2326
5.10.83.30 user-identifier frank
T13:55:36.000Z.
"GET /apache_pb.gif HTTP/1.0" 200 2326
As data travels from source to store, Logstash filters&parse each event, identify named fields to build structure, and transform them to converge on a common format for easier, accelerated analysis and business value.Logstash dynamically transforms and prepare your data regardless of format or complexity:
Derive structure from unstructured data with grok
Decipher geo coordinates from IP addresses
Anonymize PII data, exclude sensitive fields completely
Ease overall processing&independent&of the data source, format, or schema.
The possibilities are endless with our rich .
Parse & Transform Your Data On the Fly
As data travels from source to store, Logstash filters&parse each event, identify named fields to build structure, and transform them to converge on a common format for easier, accelerated analysis and business value.Logstash dynamically transforms and prepare your data regardless of format or complexity:
Derive structure from unstructured data with grok
Decipher geo coordinates from IP addresses
Anonymize PII data, exclude sensitive fields completely
Ease overall processing&independent&of the data source, format, or schema.
The possibilities are endless with our rich .
Choose Your Stash, Transport Your Data
While Elasticsearch is our go-to output that opens&up a world of search and analytics possibilities, it’s not the only one available. Logstash has
that let you route data where you want, giving you the flexibility to unlock a slew of downstream use cases.
Choose Your Stash, Transport Your Data
While Elasticsearch is our go-to output that opens&up a world of search and analytics possibilities, it’s not the only one available. Logstash has
that let you route data where you want, giving you the flexibility to unlock a slew of downstream use cases.
PLUG & PLAY
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Logstash modules orchestrate a turnkey ingest-to-visualize experience with popular data sources like
and . With the power to instantly deploy ingestion pipelines and sophisticated dashboards, your data exploration starts in minutes.
EXTENSIBILITY
Create and Configure Your Pipeline, Your Way
Logstash has a pluggable framework featuring over 200 plugins. Mix, match, and orchestrate different inputs, filters, and outputs to work in pipeline harmony.
Ingesting from a custom application? Don’t see a plugin you need? Logstash plugins are easy to build. We’ve got a fantastic API for plugin development and a plugin generator to help you .
DURABILITY & SECURITY
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If Logstash nodes happen to fail, Logstash guarantees at-least-once delivery for your in-flight events with its persistent queue. Events that are not successfully processed can be shunted to a dead letter queue for introspection and replay. With the ability to absorb throughput, Logstash scales through ingestion spikes without having to use an external queueing layer.
Whether you’re running 10s or 1000s of Logstash instances, we’ve made it possible for you to fully secure your ingest pipelines. Incoming data from
along with other inputs can be encrypted over the wire, and there's full integration with .
MONITORING
Have Full Visibility into Your Deployments
Logstash pipelines are often multipurpose and can become sophisticated, making a strong understanding of pipeline performance, availability, and bottlenecks is invaluable. With , you can easily observe and study an active Logstash node or full deployment.
MANAGEMENT & ORCHESTRATION
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Take the helm of your Logstash deployments with the Pipeline Management UI, which makes orchestrating and managing your pipelines a breeze. The management controls also integrate seamlessly with the built-in X-Pack security features to prevent any unintended rewiring.
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【BUG反馈】04-28丨语音诈骗广告的ID 求系统管理封禁收藏
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