Flink读写Redis(三)-读取redis数据
自定义flink的RedisSource,实现从redis中读取数据,这里借鉴了flink-connector-redis_2.11的实现逻辑,实现对redis读取的逻辑封装,flink-connector-redis_2.11的使用和介绍可参考之前的博客,项目中需要引入flink-connector-redis_2.11依赖
Flink读写Redis(一)-写入Redis
Flink读写Redis(二)-flink-redis-connector代码学习
抽象redis数据
定义MyRedisRecord类,封装redis数据类型和数据对象
package com.jike.flink.examples.redis;
import org.apache.flink.streaming.connectors.redis.common.mapper.RedisDataType;
import java.io.Serializable;
public class MyRedisRecord implements Serializable {
private Object data;
private RedisDataType redisDataType;
public MyRedisRecord(Object data, RedisDataType redisDataType) {
this.data = data;
this.redisDataType = redisDataType;
}
public Object getData() {
return data;
}
public void setData(Object data) {
this.data = data;
}
public RedisDataType getRedisDataType() {
return redisDataType;
}
public void setRedisDataType(RedisDataType redisDataType) {
this.redisDataType = redisDataType;
}
}
定义Redis数据读取类
首先定义接口类,定义redis的读取操作,目前这里只写了哈希表的get操作,可以增加更多的操作
package com.jike.flink.examples.redis;
import java.io.Serializable;
import java.util.Map;
public interface MyRedisCommandsContainer extends Serializable {
Map<String,String> hget(String key);
void close();
}
定义一个实现类,实现对redis的读取操作
package com.jike.flink.examples.redis;
import org.apache.flink.util.Preconditions;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import redis.clients.jedis.Jedis;
import redis.clients.jedis.JedisPool;
import redis.clients.jedis.JedisSentinelPool;
import java.util.HashMap;
import java.util.Map;
import java.util.Set;
public class MyRedisContainer implements MyRedisCommandsContainer,Cloneable{
private static final long serialVersionUID = 1L;
private static final Logger LOG = LoggerFactory.getLogger(MyRedisContainer.class);
private final JedisPool jedisPool;
private final JedisSentinelPool jedisSentinelPool;
public MyRedisContainer(JedisPool jedisPool) {
Preconditions.checkNotNull(jedisPool, "Jedis Pool can not be null");
this.jedisPool = jedisPool;
this.jedisSentinelPool = null;
}
public MyRedisContainer(JedisSentinelPool sentinelPool) {
Preconditions.checkNotNull(sentinelPool, "Jedis Sentinel Pool can not be null");
this.jedisPool = null;
this.jedisSentinelPool = sentinelPool;
}
@Override
public Map<String,String> hget(String key) {
Jedis jedis = null;
try {
jedis = this.getInstance();
Map<String,String> map = new HashMap<String,String>();
Set<String> fieldSet = jedis.hkeys(key);
for(String s : fieldSet){
map.put(s,jedis.hget(key,s));
}
return map;
} catch (Exception e) {
if (LOG.isErrorEnabled()) {
LOG.error("Cannot get Redis message with command HGET to key {} error message {}", new Object[]{key, e.getMessage()});
}
throw e;
} finally {
this.releaseInstance(jedis);
}
}
private Jedis getInstance() {
return this.jedisSentinelPool != null ? this.jedisSentinelPool.getResource() : this.jedisPool.getResource();
}
private void releaseInstance(Jedis jedis) {
if (jedis != null) {
try {
jedis.close();
} catch (Exception var3) {
LOG.error("Failed to close (return) instance to pool", var3);
}
}
}
public void close() {
if (this.jedisPool != null) {
this.jedisPool.close();
}
if (this.jedisSentinelPool != null) {
this.jedisSentinelPool.close();
}
}
}
定义redis读取操作对象的创建者类
该类用来根据不同的配置生成不同的对象,这里考虑了直连redis和哨兵模式两张情况,后续还可以考虑redis集群的情形
package com.jike.flink.examples.redis;
import org.apache.commons.pool2.impl.GenericObjectPoolConfig;
import org.apache.flink.streaming.connectors.redis.common.config.FlinkJedisConfigBase;
import org.apache.flink.streaming.connectors.redis.common.config.FlinkJedisPoolConfig;
import org.apache.flink.streaming.connectors.redis.common.config.FlinkJedisSentinelConfig;
import org.apache.flink.util.Preconditions;
import redis.clients.jedis.JedisPool;
import redis.clients.jedis.JedisSentinelPool;
public class MyRedisCommandsContainerBuilder {
public MyRedisCommandsContainerBuilder(){
}
public static MyRedisCommandsContainer build(FlinkJedisConfigBase flinkJedisConfigBase) {
if (flinkJedisConfigBase instanceof FlinkJedisPoolConfig) {
FlinkJedisPoolConfig flinkJedisPoolConfig = (FlinkJedisPoolConfig)flinkJedisConfigBase;
return build(flinkJedisPoolConfig);
} else if (flinkJedisConfigBase instanceof FlinkJedisSentinelConfig) {
FlinkJedisSentinelConfig flinkJedisSentinelConfig = (FlinkJedisSentinelConfig)flinkJedisConfigBase;
return build(flinkJedisSentinelConfig);
} else {
throw new IllegalArgumentException("Jedis configuration not found");
}
}
public static MyRedisCommandsContainer build(FlinkJedisPoolConfig jedisPoolConfig) {
Preconditions.checkNotNull(jedisPoolConfig, "Redis pool config should not be Null");
GenericObjectPoolConfig genericObjectPoolConfig = new GenericObjectPoolConfig();
genericObjectPoolConfig.setMaxIdle(jedisPoolConfig.getMaxIdle());
genericObjectPoolConfig.setMaxTotal(jedisPoolConfig.getMaxTotal());
genericObjectPoolConfig.setMinIdle(jedisPoolConfig.getMinIdle());
JedisPool jedisPool = new JedisPool(genericObjectPoolConfig, jedisPoolConfig.getHost(), jedisPoolConfig.getPort(), jedisPoolConfig.getConnectionTimeout(), jedisPoolConfig.getPassword(), jedisPoolConfig.getDatabase());
return new MyRedisContainer(jedisPool);
}
public static MyRedisCommandsContainer build(FlinkJedisSentinelConfig jedisSentinelConfig) {
Preconditions.checkNotNull(jedisSentinelConfig, "Redis sentinel config should not be Null");
GenericObjectPoolConfig genericObjectPoolConfig = new GenericObjectPoolConfig();
genericObjectPoolConfig.setMaxIdle(jedisSentinelConfig.getMaxIdle());
genericObjectPoolConfig.setMaxTotal(jedisSentinelConfig.getMaxTotal());
genericObjectPoolConfig.setMinIdle(jedisSentinelConfig.getMinIdle());
JedisSentinelPool jedisSentinelPool = new JedisSentinelPool(jedisSentinelConfig.getMasterName(), jedisSentinelConfig.getSentinels(), genericObjectPoolConfig, jedisSentinelConfig.getConnectionTimeout(), jedisSentinelConfig.getSoTimeout(), jedisSentinelConfig.getPassword(), jedisSentinelConfig.getDatabase());
return new MyRedisContainer(jedisSentinelPool);
}
}
redis操作描述类
package com.jike.flink.examples.redis;
import org.apache.flink.streaming.connectors.redis.common.mapper.RedisDataType;
public enum MyRedisCommand {
HGET(RedisDataType.HASH);
private RedisDataType redisDataType;
private MyRedisCommand(RedisDataType redisDataType) {
this.redisDataType = redisDataType;
}
public RedisDataType getRedisDataType() {
return this.redisDataType;
}
}
package com.jike.flink.examples.redis;
import org.apache.flink.streaming.connectors.redis.common.mapper.RedisDataType;
import org.apache.flink.util.Preconditions;
import java.io.Serializable;
public class MyRedisCommandDescription implements Serializable {
private static final long serialVersionUID = 1L;
private MyRedisCommand redisCommand;
private String additionalKey;
public MyRedisCommandDescription(MyRedisCommand redisCommand, String additionalKey) {
Preconditions.checkNotNull(redisCommand, "Redis command type can not be null");
this.redisCommand = redisCommand;
this.additionalKey = additionalKey;
if ((redisCommand.getRedisDataType() == RedisDataType.HASH || redisCommand.getRedisDataType() == RedisDataType.SORTED_SET) && additionalKey == null) {
throw new IllegalArgumentException("Hash and Sorted Set should have additional key");
}
}
public MyRedisCommandDescription(MyRedisCommand redisCommand) {
this(redisCommand, (String)null);
}
public MyRedisCommand getCommand() {
return this.redisCommand;
}
public String getAdditionalKey() {
return this.additionalKey;
}
}
RedisSource
定义flink redis source的实现,该类构造方法接收两个参数,包括redis配置信息以及要读取的redis数据类型信息;open方法会在source打开执行,用了完成redis操作类对象的创建;run方法会一直读取redis数据,并根据数据类型调用对应的redis操作,封装成MyRedisRecord对象,够后续处理
package com.jike.flink.examples.redis;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.source.RichSourceFunction;
import org.apache.flink.streaming.connectors.redis.common.config.FlinkJedisConfigBase;
import org.apache.flink.util.Preconditions;
public class RedisSource extends RichSourceFunction<MyRedisRecord>{
private static final long serialVersionUID = 1L;
private String additionalKey;
private MyRedisCommand redisCommand;
private FlinkJedisConfigBase flinkJedisConfigBase;
private MyRedisCommandsContainer redisCommandsContainer;
private volatile boolean isRunning = true;
public RedisSource(FlinkJedisConfigBase flinkJedisConfigBase, MyRedisCommandDescription redisCommandDescription) {
Preconditions.checkNotNull(flinkJedisConfigBase, "Redis connection pool config should not be null");
Preconditions.checkNotNull(redisCommandDescription, "MyRedisCommandDescription can not be null");
this.flinkJedisConfigBase = flinkJedisConfigBase;
this.redisCommand = redisCommandDescription.getCommand();
this.additionalKey = redisCommandDescription.getAdditionalKey();
}
@Override
public void open(Configuration parameters) throws Exception {
this.redisCommandsContainer = MyRedisCommandsContainerBuilder.build(this.flinkJedisConfigBase);
}
@Override
public void run(SourceContext sourceContext) throws Exception {
while (isRunning){
switch(this.redisCommand) {
case HGET:
sourceContext.collect(new MyRedisRecord(this.redisCommandsContainer.hget(this.additionalKey), this.redisCommand.getRedisDataType()));
break;
default:
throw new IllegalArgumentException("Cannot process such data type: " + this.redisCommand);
}
}
}
@Override
public void cancel() {
isRunning = false;
if (this.redisCommandsContainer != null) {
this.redisCommandsContainer.close();
}
}
}
使用
redis中的哈希表保存个各个单词的词频,统计词频最大的单词
package com.jike.flink.examples.redis;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.connectors.redis.common.mapper.RedisDataType;
import org.apache.flink.util.Collector;
import java.util.Map;
public class MyMapRedisRecordSplitter implements FlatMapFunction<MyRedisRecord, Tuple2<String,Integer>> {
@Override
public void flatMap(MyRedisRecord myRedisRecord, Collector<Tuple2<String, Integer>> collector) throws Exception {
assert myRedisRecord.getRedisDataType() == RedisDataType.HASH;
Map<String,String> map = (Map<String,String>)myRedisRecord.getData();
for(Map.Entry<String,String> e : map.entrySet()){
collector.collect(new Tuple2<>(e.getKey(),Integer.valueOf(e.getValue())));
}
}
}
package com.jike.flink.examples.redis;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.connectors.redis.common.config.FlinkJedisPoolConfig;
public class MaxCount{
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment executionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment();
FlinkJedisPoolConfig conf = new FlinkJedisPoolConfig.Builder().setHost("ip").setPort(30420).setPassword("passwd").build();
DataStreamSource<MyRedisRecord> source = executionEnvironment.addSource(new RedisSource(conf,new MyRedisCommandDescription(MyRedisCommand.HGET,"flink")));
DataStream<Tuple2<String, Integer>> max = source.flatMap(new MyMapRedisRecordSplitter()).timeWindowAll(Time.milliseconds(5000)).maxBy(1);
max.print().setParallelism(1);
executionEnvironment.execute();
}
}
结果


Flink读写Redis(三)-读取redis数据的更多相关文章
- ELK之logstash收集日志写入redis及读取redis
logstash->redis->logstash->elasticsearch 1.安装部署redis cd /usr/local/src wget http://download ...
- jxl读写excel, poi读写excel,word, 读取Excel数据到MySQL
这篇blog是介绍: 1. java中的poi技术读取Excel数据,然后保存到MySQL数据中. 2. jxl读写excel 你也可以在 : java的poi技术读取和导入Excel了解到写入Exc ...
- 《闲扯Redis三》Redis五种数据类型之List型
一.前言 Redis 提供了5种数据类型:String(字符串).Hash(哈希).List(列表).Set(集合).Zset(有序集合),理解每种数据类型的特点对于redis的开发和运维非常重要. ...
- 三、Redis基本操作——List
小喵的唠叨话:前面我们介绍了Redis的string的数据结构的原理和操作.当时我们提到Redis的键值对不仅仅是字符串.而这次我们就要介绍Redis的第二个数据结构了,List(链表).由于List ...
- redis+twemproxy实现redis集群
Redis+TwemProxy(nutcracker)集群方案部署记录 转自: http://www.cnblogs.com/kevingrace/p/5685401.html Twemproxy 又 ...
- 数据库应用之--Redis+mysql实现大量数据的读写,以及高并发
一.开发背景 在项目开发过程中中遇到了以下三个需求: 1. 多个用户同时上传数据: 2. 数据库需要支持同时读写: 3. 1分钟内存储上万条数据: 根据对Mysql的测试情况,遇到以下问题: 1. 最 ...
- 在 Istio 中实现 Redis 集群的数据分片、读写分离和流量镜像
Redis 是一个高性能的 key-value 存储系统,被广泛用于微服务架构中.如果我们想要使用 Redis 集群模式提供的高级特性,则需要对客户端代码进行改动,这带来了应用升级和维护的一些困难.利 ...
- logstash读取redis数据
类型设置: logstash中的redis插件,指定了三种方式来读取redis队列中的信息. list=>BLPOP (相当 ...
- 5.1.1 读取Redis 数据
Redis 服务器是Logstash 推荐的Broker选择,Broker 角色就意味会同时存在输入和输出两个插件. 5.1.1 读取Redis 数据 LogStash::Input::Redis 支 ...
随机推荐
- yum 方式安装mysql (完整记录)
2016-04-07 学习笔记,源代码安装比较麻烦,还是要尝试一下yum安装和rpm方式安装 一.检查系统是否安装老版本,有的话干掉 #yum list installed | grep mysqlm ...
- 「CEOI2013」Board
description 洛谷P5513 solution 用一个二进制数维护这个节点所处的位置,那么"1"操作就是这个数\(*2\),"2"操作就是这个数\(* ...
- css3系列之@font-face
@font-face 这个属性呢,必须设置在 css的根下,也就是说,不能设置在任何元素里面. @font-face: 参数: font-family: 给这个文字库 起个名字. src: url( ...
- php数字运算与格式化
浮点数高精度运算 PHP 官方手册 浮点数的精度有限.尽管取决于系统,PHP 通常使用 IEEE 754 双精度格式,则由于取整而导致的最大相对误差为 1.11e-16.非基本数学运算可能会给出更大误 ...
- NameServer路由删除
NameServer会每隔10s扫描brokerLiveTable状态表,如果BrokerLive的lastUpdateTimestamp的时间戳距当前时间超过120s,则认为Broker失效,移除改 ...
- LaTeX中的特殊字符
空白符号代码及注释: 显示效果: 控制符.排版符号.标志符号.引号.连字符.非英文字符和重音符号的代码及注释: 显示效果:
- 如何有效恢复误删的HDFS文件
HDFS是大数据领域比较知名的分布式存储系统,作为大数据相关从业人员,每天处理HDFS上的文件数据是常规操作.这就容易带来一个问题,实际操作中对重要数据文件的误删,那么如何恢复这些文件,就显得尤为重要 ...
- PP-OCR论文翻译
译者注: 我有逛豆瓣社区的习惯,因此不经意间会看到一些外文翻译成中文书的评价."书是好书,翻译太臭"."中文版别看"."有能力尽量看原版". ...
- 第3章 Python的数据类型 第3.1节 功能强大的 Python序列概述
一.概述 序列是Python中最基本的数据结构,C语言中没有这样的数据类型,只有数组有点类似,但序列跟数组差异比较大. 序列的典型特征如下: 序列使用索引来获取元素,这种索引方式适用于所有序列: 序列 ...
- PyQt学习随笔:QTableWidget的visualRow、visualColumn、logicalRow、logicalColumn(可见行、逻辑行、可见列、逻辑列)相关概念及方法探究
老猿Python博文目录 专栏:使用PyQt开发图形界面Python应用 老猿Python博客地址 一.概念 关于逻辑行logicalRow.列logicalColumn和可见行visualRow.列 ...