Flink-输出算子(Sink)使用

5.5 输出算子

5.5.1 概述

1. print也是一种输出类PrintSinkFunction

创建了一个PrintSinkFunction操作，然后所谓addSink方法的参数

PrintSinkFunction这个类继承自RichSinkFunction富函数类

2. RichSourceFunction类

• 继承了AbstractRichFunction富函数类

因此就可以调用富函数类的声明周期方法，例如open，close，以及获取运行时上下文，运行环境，定义状态等等

• RichSourceFunction类又实现了SinkFunction这个接口，所以本质上也是SinkFunction

• SinkFunction接口的抽象方法有invoke，传入是value，以及当前的上下文

3. 如果需要自定义输出算子

可以调用DataStream的addSink方法

然后传入自己实现的SinkFunction

4. flink提供的第三方系统连接器

5.5.2 输出到文件

1. StreamFileSink流失文件输出类

• 来源

继承RichSinkFunction类，并实现CheckpointedFunction，CheckpointListener(检查点)

• 底层

底层将数据写入bucket(桶)，桶里面分大小存储分区文件，实现了分布式存储

• 创建实例

使用Builder构建器构建

)

RowFormatBuilder是行编码

BulkFormatBuilder是列存储编码格式

2. 代码

public class SinkToFileTest {
    public static void main(String[] args) throws Exception{
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(4);

        DataStreamSource<Event> stream = env.fromElements(new Event("Mary", "./home", 1000L),
                new Event("Bob", "./cart", 2000L),
                new Event("Alice", "./prod?id=100", 3000L),
                new Event("Bob", "./prod?id=1", 3300L),
                new Event("Alice", "./prod?id=200", 3000L),
                new Event("Bob", "./home", 3500L),
                new Event("Bob", "./prod?id=2", 3800L),
                new Event("Bob", "./prod?id=3", 4200L));


        //2.为了得到并传入SinkFunction，需要构建StreamingFileSink的一个对象
        //调用forRowFormat方法或者forBulkformat方法得到一个DefaultRowFormatBuilder
            //  其中forBulkformat方法前面还有类型参数，以及传参要求一个目录名称，一个编码器
                //写入文件需要序列化，需要定义序列化方法并进行编码转换，当成Stream写入文件
        //然后再使用builder创建实例
        StreamingFileSink<String> streamingFileSink = StreamingFileSink.<String>forRowFormat(new Path("./output"),
                        new SimpleStringEncoder<>("UTF-8"))
                .withRollingPolicy(//指定滚动策略，根据事件或者文件大小新产生文件归档保存
                        DefaultRollingPolicy.builder()//使用builder构建实例
                                .withMaxPartSize(1024 * 1024 * 1024)
                                .withRolloverInterval(TimeUnit.MINUTES.toMinutes(15))//事件间隔毫秒数
                                .withInactivityInterval(TimeUnit.MINUTES.toMinutes(15))//当前不活跃的间隔事件，隔多长事件没有数据到来
                                .build()
                )
                .build();
        //1.写入文件调用addSink()方法，并传入SinkFunction
        stream
                .map(data -> data.toString())//把Event类型转换成String
                .addSink(streamingFileSink);

        env.execute();

    }
}

• 结果

5.5.3 输出到kafka

1. 代码

public class SinkToKafka {
    public static void main(String[] args) throws Exception{
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //1.从kafka中读取数据
        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers","hadoop2:9092");
        properties.setProperty("group.id", "consumer-group");

        DataStreamSource<String> kafkaStream = env.addSource(
                new FlinkKafkaConsumer<String>("clicks", new SimpleStringSchema(), properties));

        //2.用flink进行简单的etl处理转换
        SingleOutputStreamOperator<String> result = kafkaStream.map(new MapFunction<String, String>() {
            @Override
            public String map(String value) throws Exception {

                String[] fields = value.split(",");
                return new Event(fields[0].trim(), fields[1].trim(), Long.valueOf(fields[2].trim())).toString();

            }
        });

        //3.结果数据写入kafka
            //FlinkKafkaProducer传参borckList，topicid,序列化
        result.addSink(new FlinkKafkaProducer<String>(
                "hadoop2:9092","events",new SimpleStringSchema()));

        env.execute();
    }
}

2. kafka输出结果

5.5.4 输出到redis

1. 引入依赖

<dependency>
 <groupId>org.apache.bahir</groupId>
 <artifactId>flink-connector-redis_2.11</artifactId>
 <version>1.0</version>
</dependency>

2. 代码 继承自RichSinkFunction

去调构造方法，换入redis集群的配置FlinkJedisConfigBase以及RedisMapper写入命令

FlinkJedisPoolConfig用这个没毛病，直接继承的FlinkJedisConfigBase

3. 代码

public class SinkToRedis {
    public static void main(String[] args) throws Exception{
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //1.输入ClickSource是自定义输入
        DataStreamSource<Event> stream = env.addSource(new ClickSource());


        //2.创建一个jedis连接配置
        //FlinkJedisPoolConfig直接继承的FlinkJedisConfigBase
        FlinkJedisPoolConfig config = new FlinkJedisPoolConfig.Builder()
                .setHost("hadoop2")
                .build();



        //3.写入redis
        stream.addSink(new RedisSink<>(config,new MyRedisMapper()));

        env.execute();

    }

    //3.自定义类实现 redisMapper接口
    public static class MyRedisMapper implements RedisMapper<Event>{

        @Override
        public RedisCommandDescription getCommandDescription() {
            return new RedisCommandDescription(RedisCommand.HSET,"clicks");//写入哈希表
        }

        @Override
        public String getKeyFromData(Event data) {
            return data.user;
        }

        @Override
        public String getValueFromData(Event data) {
            return data.url;
        }
    }
}

4. 结果

5.5.5 输出到ElasticSearch

1. 引入依赖

<dependency>
 <groupId>org.apache.flink</groupId> 
<artifactId>flink-connector-elasticsearch6_${scala.binary.version}</artifact
Id>
<version>${flink.version}</version>
</dependency>

2. 代码

---

传入参数是List<HttpHost>和ElasticsearchSinkFunction<T>

---

public class SinToES {
    public static void main(String[] args) throws Exception{
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //1.输入
        DataStreamSource<Event> stream = env.fromElements(new Event("Mary", "./home", 1000L),
                new Event("Bob", "./cart", 2000L),
                new Event("Alice", "./prod?id=100", 3000L),
                new Event("Bob", "./prod?id=1", 3300L),
                new Event("Alice", "./prod?id=200", 3000L),
                new Event("Bob", "./home", 3500L),
                new Event("Bob", "./prod?id=2", 3800L),
                new Event("Bob", "./prod?id=3", 4200L));



        
        //2.定义hosts的列表
        ArrayList<HttpHost> httpHosts = new ArrayList<>();
        httpHosts.add(new HttpHost("hadoop",9200));

        //3.定义ElasticsearchSinkFunction<T>,是个接口，重写process方法
        //向es发送请求，并插入数据
        ElasticsearchSinkFunction<Event> elasticsearchSinkFunction = new ElasticsearchSinkFunction<Event>() {
            @Override
            //输入，运行上下文，发送任务请求
            public void process(Event element, RuntimeContext ctx, RequestIndexer indexer) {
                HashMap<String, String> map = new HashMap<>();
                map.put(element.user, element.url);

                //构建一个indexrequest
                IndexRequest request = Requests.indexRequest()
                        .index("clicks")
                        .type("types")
                        .source(map);

                indexer.add(request);
            }
        };

        //4.写入es
        //传入参数是List<HttpHost>和ElasticsearchSinkFunction<T>
        stream.addSink(new ElasticsearchSink.Builder<>(httpHosts,elasticsearchSinkFunction).build());

        env.execute();

    }
}

3. 结果

5.5.6 输入到Mysql

1. 引入依赖

<dependency>
 <groupId>org.apache.flink</groupId>
 <artifactId>flink-connector-jdbc_${scala.binary.version}</artifactId>
 <version>${flink.version}</version>
</dependency>
<dependency>
 <groupId>mysql</groupId>
 <artifactId>mysql-connector-java</artifactId>
 <version>5.1.47</version>
</dependency>

2. 代码

三个参数，sql，JdbcStatementBuilder构造，JdbcConnectionOptions等sql的连接配置

单一抽象方法，lambda使用

public class SinkToMysql {
    public static void main(String[] args) throws Exception{
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //1.输入
        DataStreamSource<Event> stream = env.fromElements(new Event("Mary", "./home", 1000L),
                new Event("Bob", "./cart", 2000L),
                new Event("Alice", "./prod?id=100", 3000L),
                new Event("Bob", "./prod?id=1", 3300L),
                new Event("Alice", "./prod?id=200", 3000L),
                new Event("Bob", "./home", 3500L),
                new Event("Bob", "./prod?id=2", 3800L),
                new Event("Bob", "./prod?id=3", 4200L));

        //三个参数，sql，JdbcStatementBuilder构造，JdbcConnectionOptions等sql的连接配置
        stream.addSink(JdbcSink.sink(
                "INSERT INTO clicks (user,url) VALUES(?,?)",
                ((statement,event)->{
                    statement.setString(1,event.user);
                    statement.setString(2,event.url);
                }),
                new JdbcConnectionOptions.JdbcConnectionOptionsBuilder()
                        .withUrl("jdbc:mysql://localhost:3306/test2")
                        .withDriverName("com.mysql.jdbc.Driver")
                        .withUsername("root")
                        .withPassword("123456")
                        .build()
        ));

        env.execute();

    }
}

3. mysql前期准备

• 创建mysql的test2
• 创建clicks表

mysql> create table clicks(
    -> user varchar(20) not null,
    -> url varchar(100) not null);
Query OK, 0 rows affected (0.02 sec)

4. 结果

5.5.7 自定义Sink输出

1. 分析

调用DataStream的addSink()方法，并传入自定义好的SinkFunction（采用富函数类），重写关键方法invoke()，并且重写富函数类的生命周期相关方法open和close

2. 导入依赖

<dependency>
 <groupId>org.apache.hbase</groupId>
 <artifactId>hbase-client</artifactId>
 <version>${hbase.version}</version>
</dependency>

3. 代码

略