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重试机制
当任务出现异常的时候，会直接停止任务——解决方式，重试机制
1、设置checkpoint后，会给任务一个重启策略——无限重启
2、可以手动设置任务的重启策略
代码设置
//开启checkpoint后，默认是无限重启，可以设置该值 表示不重启
env.setRestartStrategy(RestartStrategies.noRestart());//作业失败flink中最多重启3次，每次重启的最小间隔是10s
env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3, Time.of(10, TimeUnit.SECONDS)));//2分钟内最多重启3次，每次重启的最小间隔是5秒
env.setRestartStrategy(RestartStrategies.failureRateRestart(3,Time.of(2,TimeUnit.MINUTES),Time.of(5,TimeUnit.SECONDS))
);//无限重启
env.setRestartStrategy(RestartStrategies.fixedDelayRestart(Integer.MAX_VALUE, // 无限重启次数Time.of(10, TimeUnit.SECONDS) // 每次重启的延迟时间
));维表join
所谓的维表Join: 进入Flink的数据，需要关联另外一些存储设备的数据，才能计算出来结果
那么存储在外部设备上的表称之为维表，可能存储在mysql也可能存储在hbase 等。
维表一般的特点是变化比较慢。——名词表，维度表。
解决方式
解决维表join的方式方式一：可以用一个静态代码块，或者在open方法中对一个集合初始化，用于存放想要相关联的数据。缺点：数据不能动态改变了方式二：在open中初始化连接，在map中每拿到流中的一条数据，就去mysql中查找一次缺点：数据可以动态改变，但是去mysql查找的次数太多了方式三：创建一个缓存区，用于存放数据，若过期则再去mysql中查询数据。没有缺点，可以动态获取数据了，也减少了mysql的查询次数（缓冲）唯一的是，若是多线程，可能会去mysql查询多次方式一
package com.bigdata.day06;import com.mchange.v2.c3p0.ComboPooledDataSource;
import org.apache.commons.dbutils.QueryRunner;
import org.apache.commons.dbutils.handlers.MapHandler;
import org.apache.commons.dbutils.handlers.MapListHandler;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;import java.util.List;
import java.util.Map;
import java.util.Properties;/*** 直接从mysql中拿出* 弊端 只能拿到一次 不能实现动态*/
public class _03_维表join_01 {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();Properties properties = new Properties();properties.setProperty("bootstrap.servers", "bigdata01:9092");properties.setProperty("group.id", "g1");FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>("edu",new SimpleStringSchema(),properties);DataStreamSource<String> source = env.addSource(consumer);source.map(new RichMapFunction<String, String>() {ComboPooledDataSource pool = null;QueryRunner queryRunner = null;List<Map<String, Object>> list = null;@Overridepublic void open(Configuration parameters) throws Exception {// 在open中执行sqlpool = new ComboPooledDataSource();queryRunner = new QueryRunner(pool);String sql = "select * from city ";list = queryRunner.query(sql, new MapListHandler());}@Overridepublic void close() throws Exception {pool.close();}@Overridepublic String map(String line) throws Exception {String[] split = line.split(",");Object cityName = "未知";for (Map<String, Object> map : list) {String cityId = (String)map.get("city_id");if (cityId.equals(split[1])){cityName = map.get("city_name");}}return line+","+cityName;}}).print();env.execute();}
}方式二
package com.bigdata.day06;import com.mchange.v2.c3p0.ComboPooledDataSource;
import org.apache.commons.dbutils.QueryRunner;
import org.apache.commons.dbutils.handlers.MapHandler;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;import java.util.Map;
import java.util.Properties;/*** 每次从kafka中拿到一条数据就从mysql中查一遍* 弊端 对mysql的压力加大*/
public class _03_维表join_02 {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();Properties properties = new Properties();properties.setProperty("bootstrap.servers", "bigdata01:9092");properties.setProperty("group.id", "g1");FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>("edu",new SimpleStringSchema(),properties);DataStreamSource<String> source = env.addSource(consumer);source.map(new RichMapFunction<String, String>() {ComboPooledDataSource pool = null;QueryRunner queryRunner = null;@Overridepublic void open(Configuration parameters) throws Exception {pool = new ComboPooledDataSource();queryRunner = new QueryRunner(pool);}@Overridepublic void close() throws Exception {pool.close();}@Overridepublic String map(String line) throws Exception {// 在处理逻辑中执行sqlString[] split = line.split(",");String sql = "select city_name from city where city_id = ?";Map<String, Object> rs = queryRunner.query(sql, new MapHandler(), split[1]);String cityName="未知";if (rs !=null){cityName = (String) rs.get("city_name");}return line+","+cityName;}}).print();env.execute();}
}方式三
package com.bigdata.day06;import com.mchange.v2.c3p0.ComboPooledDataSource;
import org.apache.commons.dbutils.QueryRunner;
import org.apache.commons.dbutils.handlers.MapHandler;
import org.apache.commons.dbutils.handlers.MapListHandler;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.shaded.guava18.com.google.common.cache.*;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import java.util.concurrent.TimeUnit;/*** 最终 非常好的方式* 现在内存中查 查不到在去mysql中找* 唯一的问题是，假如是多线程情况下，可能会触发多次去mysql中查找的方法*/
public class _03_维表join_03_cache {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();Properties properties = new Properties();properties.setProperty("bootstrap.servers", "bigdata01:9092");properties.setProperty("group.id", "g1");FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>("edu",new SimpleStringSchema(),properties);DataStreamSource<String> source = env.addSource(consumer);// 记得设置并行度env.setParallelism(1);source.map(new RichMapFunction<String, String>() {ComboPooledDataSource pool = null;QueryRunner queryRunner = null;// 定义一个Cache// 第一个是传入的参数类型 第二个是存放的值的类型// 也就是，传入一个参数，根据这个值获取结果，拿的时候通过传入的值 拿存放的值LoadingCache<String, String> cache;@Overridepublic void open(Configuration parameters) throws Exception {pool = new ComboPooledDataSource();queryRunner = new QueryRunner(pool);cache = CacheBuilder.newBuilder()//最多缓存个数，超过了就根据最近最少使用算法来移除缓存 LRU.maximumSize(1000)//在更新后的指定时间后就回收// 不会自动调用，而是当过期后，又用到了过期的key值数据才会触发的。.expireAfterWrite(50, TimeUnit.SECONDS)//指定移除通知.removalListener(new RemovalListener<String, String>() {@Overridepublic void onRemoval(RemovalNotification<String, String> removalNotification) {System.out.println(removalNotification.getKey() + "被移除了，值为：" + removalNotification.getValue());}}).build(//指定加载缓存的逻辑new CacheLoader<String, String>() {// 假如缓存中没有数据，会触发该方法的执行，并将结果自动保存到缓存中@Overridepublic String load(String cityId) throws Exception {String sql = "select city_name from city where city_id = ? ";Map<String, Object> rs = queryRunner.query(sql, new MapHandler(), cityId);String cityName = null;if (rs!=null){cityName = (String) rs.get("city_name");}System.out.println("进入数据库查询成功，查询的值为"+cityId+"--"+cityName);return cityName;}});}@Overridepublic void close() throws Exception {pool.close();}@Overridepublic String map(String line) throws Exception {String[] arr = line.split(",");// 使用这种方式取值String cityName = cache.get(arr[1]);return line+","+cityName;}}).print();env.execute();}
}