hbase热点问题解决（预分区）
一、出现热点问题原因
1、hbase的中的数据是按照字典序排序的，当大量连续的rowkey集中写在个别的region，各个region之间数据分布不均衡；
2、创建表时没有提前预分区，创建的表默认只有一个region，大量的数据写入当前region；
3、创建表已经提前预分区，但是设计的rowkey没有规律可循，设计的rowkey应该由regionNo+messageId组成。
二、如何解决热点问题
解决这个问题，关键是要设计出可以让数据分布均匀的rowkey，与关系型数据库一样,rowkey是用来检索记录的主键。访问hbase table中的行，rowkey 可以是任意字符串(最大长度 是 64KB，实际应用中长度一般为 10-100bytes)，在hbase内部，rowkey保存为字节数组，存储时，数据按照rowkey的字典序排序存储。
创建表命令：
create 'testTable',{NAME => 'cf', DATA_BLOCK_ENCODING => 'NONE', BLOOMFILTER => 'ROW', REPLICATION_SCOPE=> '0', VERSIONS => '1', COMPRESSION => 'snappy', MIN_VERSIONS =>'0', TTL => '15552000', KEEP_DELETED_CELLS => 'false', BLOCKSIZE =>'65536', IN_MEMORY => 'false', BLOCKCACHE => 'true', METADATA =>{'ENCODE_ON_DISK' => 'true'}},{SPLITS_FILE=>'/app/soft/test/region.txt'}region.txt内容：
我这里预分10个region，执行命令之后，在hbase的console中可以看到以下信息，说明预分区ok了！！！
1、第一种设计rowkey方式：随机数+messageId，如果想让最近的数据快速get到，可以将时间戳加上，我这里的region是0001|到0009|开头的，因为hbase的数据是字典序排序的，所以如果我生成的 rowkey=0002rer4343343422,则当前这条数据就会保存到0001|~0002|这个region里，因为我的messageId都是字母+数字，“|”的ASCII值大于字母、数字。
生成regionNo的工具类：RegionUtils
package com.cn.dl;
import java.util.Random;
/**
* Created by Tiger on 2018/4/18.
*/
public class RegionUtils {
//十个预分区
private static final int REGION_NUM = 10;
//存放regionNo：0001,0002,...0009,0010
private static final String[] REGION_ARRAY = new String[REGION_NUM];
static {
initRegionArray();
}
/**
* 生成regionNo
* */
private static void initRegionArray(){
for(int i=1; i<=REGION_NUM; i++){
String regionNo = String.valueOf(i);
while (regionNo.length() < 4){
regionNo = "0" + regionNo;
}
REGION_ARRAY[i-1] = regionNo;
}
}
/**
* 随机获取regionNo
* @return regionNo
* */
public static String getRegionNo(){
Random random = new Random();
return REGION_ARRAY[random.nextInt(10)];
}
public static void main(String[] args) {
int i= 0;
while (i < 100){
System.out.println(getRegionNo());
i++;
}
}
}
生成rowKey：
// TODO: 2018/12/18 只是一个生成rowKey的案例
public void execute(Tuple tuple) {
try {
JSONObject json = JSONObject.parseObject(tuple.getStringByField("messageSpout"));
String messageId = json.getString("messageId");
// TODO: 2018/12/18 生成rowKey:regionNo+时间戳+messageId ，加上时间戳在hbase中可以提高查询效率
String rowKey = RegionUtils.getRegionNo() + System.currentTimeMillis() + messageId;
json.put("rowKey",rowKey);
System.out.println(json.toJSONString());
}catch (Exception e){
e.printStackTrace();
}finally {
collector.ack(tuple);
}
}打印结果，rowkey=regionNo+时间戳+messageId，前缀是随机的
{"name":"name2","messageId":"b998a8dfc05a4a819284213d4e727a85","age":12,"rowKey":"00041545105001972b998a8dfc05a4a819284213d4e727a85"}
{"name":"name3","messageId":"799affbf346641e8a00bfeee78ffcdb4","age":13,"rowKey":"00031545105002973799affbf346641e8a00bfeee78ffcdb4"}
{"name":"name4","messageId":"bbdf16b9a12b4fa09b060402f9522fed","age":14,"rowKey":"00051545105003973bbdf16b9a12b4fa09b060402f9522fed"}
{"name":"name5","messageId":"03c119868cd742459464df53c3827147","age":15,"rowKey":"0009154510500497403c119868cd742459464df53c3827147"}
{"name":"name6","messageId":"84c682681cdc4ac09ad3d270741074d3","age":16,"rowKey":"0002154510500597484c682681cdc4ac09ad3d270741074d3"}
{"name":"name7","messageId":"aecbd65f3f434452ab4a924d8e42b947","age":17,"rowKey":"00091545105006976aecbd65f3f434452ab4a924d8e42b947"}
{"name":"name8","messageId":"3bcb23e414e5450898b6b0eefff4d80a","age":18,"rowKey":"000315451050079783bcb23e414e5450898b6b0eefff4d80a"}
{"name":"name9","messageId":"40be62bfcea24ea799ae6f191241c5e8","age":19,"rowKey":"0002154510500897840be62bfcea24ea799ae6f191241c5e8"}
{"name":"name10","messageId":"94c220cd10d141c89cf08e61d0a48e7f","age":20,"rowKey":"0007154510500997894c220cd10d141c89cf08e61d0a48e7f"}
{"name":"name11","messageId":"0796f735b1ba43beb7b15d63c4fd4ec8","age":21,"rowKey":"000515451050109780796f735b1ba43beb7b15d63c4fd4ec8"}
{"name":"name12","messageId":"05ac8417e52443f48bf2c56879b3e2c6","age":22,"rowKey":"0009154510501197805ac8417e52443f48bf2c56879b3e2c6"}
{"name":"name13","messageId":"b87484b633b747ba8320cdf69334459b","age":23,"rowKey":"00101545105012978b87484b633b747ba8320cdf69334459b"}
{"name":"name14","messageId":"84c6daf1cdfd4c0f977a8742ee528977","age":24,"rowKey":"0005154510501397984c6daf1cdfd4c0f977a8742ee528977"}
{"name":"name15","messageId":"8e01e5c53d024de18507ed2a98e38519","age":25,"rowKey":"000415451050149808e01e5c53d024de18507ed2a98e38519"}
{"name":"name16","messageId":"48939394581946e881b91e797659d6ca","age":26,"rowKey":"0005154510501598248939394581946e881b91e797659d6ca"}
{"name":"name17","messageId":"b5c2024c721642a5a2b4cc8682c7cd40","age":27,"rowKey":"00021545105016981b5c2024c721642a5a2b4cc8682c7cd40"}
{"name":"name18","messageId":"4efb10fc351947f6a78761e7b3bf1783","age":28,"rowKey":"000215451050179824efb10fc351947f6a78761e7b3bf1783"}
{"name":"name19","messageId":"a4a27bafd5f749d0b08d9eb832120737","age":29,"rowKey":"00041545105018983a4a27bafd5f749d0b08d9eb832120737"}
{"name":"name20","messageId":"1d90758cbcc2495197b2ef0a05e58610","age":30,"rowKey":"000815451050199831d90758cbcc2495197b2ef0a05e58610"}
{"name":"name21","messageId":"47ae3fb0e3914496b75445df92d0a133","age":31,"rowKey":"0002154510502098347ae3fb0e3914496b75445df92d0a133"}
{"name":"name22","messageId":"419dccbeb2b74484997bd5373f8347af","age":32,"rowKey":"00071545105021982419dccbeb2b74484997bd5373f8347af"}
{"name":"name23","messageId":"51e38da4c9c74542bcd38be704ff3fec","age":33,"rowKey":"0005154510502298351e38da4c9c74542bcd38be704ff3fec"}
{"name":"name24","messageId":"e99e783220d14f63be1f967f82bd69fb","age":34,"rowKey":"00081545105023983e99e783220d14f63be1f967f82bd69fb"}
{"name":"name25","messageId":"0e15f181464146598dfe44976676c706","age":35,"rowKey":"000915451050249840e15f181464146598dfe44976676c706"}
{"name":"name26","messageId":"d0d8c0e939b44a688915714b85d61b1f","age":36,"rowKey":"00101545105025985d0d8c0e939b44a688915714b85d61b1f"}
{"name":"name27","messageId":"4a7d3404871a4137b9db4e24209f1346","age":37,"rowKey":"000515451050269844a7d3404871a4137b9db4e24209f1346"}
{"name":"name28","messageId":"51a5f39f032241fcaa6e5956a9ddb474","age":38,"rowKey":"0002154510502798651a5f39f032241fcaa6e5956a9ddb474"}
{"name":"name29","messageId":"c54ec6f46fa047fdae6187afad55e8f0","age":39,"rowKey":"00011545105028986c54ec6f46fa047fdae6187afad55e8f0"}
{"name":"name30","messageId":"076322780dae4748997006d53aa246c1","age":40,"rowKey":"00021545105029987076322780dae4748997006d53aa246c1"}
{"name":"name31","messageId":"563c258840a84f12ad1a5341381b163c","age":41,"rowKey":"00101545105030986563c258840a84f12ad1a5341381b163c"}
{"name":"name32","messageId":"377f3fbe63634fc4aa93b1737dd462da","age":42,"rowKey":"00051545105031988377f3fbe63634fc4aa93b1737dd462da"}
{"name":"name33","messageId":"5c1af0f26cc94d0b85d0fbca617e57ad","age":43,"rowKey":"000715451050329895c1af0f26cc94d0b85d0fbca617e57ad"}
{"name":"name34","messageId":"2d3050a0b3dd42df97ef87d45dbc0d26","age":44,"rowKey":"000715451050339902d3050a0b3dd42df97ef87d45dbc0d26"}
{"name":"name35","messageId":"526401e1f5ab45aeb95421e0ac200e4b","age":45,"rowKey":"00041545105034990526401e1f5ab45aeb95421e0ac200e4b"}总结：
这种设计的rowkey可以解决热点问题，但是要建立关联表，比如将rowkey保存到数据库或者nosql数据库中，因为前面的regionNo是随机的，不知道 对应数据在hbase的rowkey是多少；同一批数据，因为这个regionNo是随机的，所以要到多个region中get数据，不能使用startkey和endkey去get数据。
2、第二种设计rowkey的方式：通过messageId映射regionNo，这样既可以让数据均匀分布到各个region中，同时可以根据startkey和endkey可以get到同一批数据，messageId映射regionNo，使用一致性hash算法解决，一致性哈希算法在1997年由麻省理工学院的Karger等人在解决分布式Cache中提出的，设计目标是为了解决因特网中的热点(Hot spot)问题，
参考：
（https://baike.baidu.com/item/%E4%B8%80%E8%87%B4%E6%80%A7%E5%93%88%E5%B8%8C/2460889?fr=aladdin）（https://www.cnblogs.com/lpfuture/p/5796398.html）
public class ConsistentHash implements Serializable{
private static final long serialVersionUID = 1L;
private final HashFunction hashFunction;
//每个regions的虚拟节点个数
private final int numberOfReplicas;
//存储虚拟节点的hash值到真实节点的映射
private final SortedMap circle = new TreeMap();
public ConsistentHash(HashFunction hashFunction, int numberOfReplicas, Collection nodes) {
this.hashFunction = hashFunction;
this.numberOfReplicas = numberOfReplicas;
for (String node : nodes){
add(node);
}
}
/**
* 添加节点
* @param node
* @see java.util.TreeMap
* */
public void add(String node) {
for (int i = 0; i < numberOfReplicas; i++)
/*
* 不同的虚拟节点(i不同)有不同的hash值,但都对应同一个实际机器node
* 虚拟node一般是均衡分布在环上的,数据存储在顺时针方向的虚拟node上
*/
circle.put(hashFunction.getHashValue(node.toString() + i), node);
}
/**
* 移除节点
* @param node
* @see java.util.TreeMap
* */
public void remove(String node) {
for (int i = 0; i < numberOfReplicas; i++)
circle.remove(hashFunction.getHashValue(node.toString() + i));
}
/**
* 获取对应key的hashcode值，然后根据hashcode获取当前数据储存的真实节点
* */
public String get(Object key) {
if (circle.isEmpty())
return null;
//获取对应key的hashcode值
long hash = hashFunction.getHashValue((String) key);
//数据映射在两台虚拟机器所在环之间,就需要按顺时针方向寻找机器
if (!circle.containsKey(hash)) {
SortedMap tailMap = circle.tailMap(hash);
hash = tailMap.isEmpty() ? circle.firstKey() : tailMap.firstKey();
}
return circle.get(hash);
}
/**
* 获取hash环节点大小
* @return
* */
public long getSize() {
return circle.size();
}
/**
* 获取double类型数据的小数位后四位小数
* @param num
* @return
* */
public String getDecimalPoint(double num){
DecimalFormat df = new DecimalFormat("0.0000");
return df.format(num);
}
} public class HashFunction implements Serializable{
private static final long serialVersionUID = 1L;
/**
* 获取对应字符串的hashCode值
* @param key
* @return
* */
public long getHashValue(String key) {
final int p = 1677761999;
int hash = (int) 216613626111L;
for (int i = 0; i < key.length(); i++)
hash = (hash ^ key.charAt(i)) * p;
hash += hash << 13;
hash ^= hash >> 8;
hash += hash << 3;
hash ^= hash >> 18;
hash += hash << 5;
// 如果算出来的值为负数则取其绝对值
if (hash < 0)
hash = Math.abs(hash);
return hash;
}
}我目前满意第二种方式，然后在es中建立关联表，get数据时，先在es中get到rowkey，然后在hbase中获取数据，这个根据自己的业务设计。
写的内容有问题，欢迎来吐槽，我会及时修改，谢谢！