状态的一致性和FlinkSQL

2023-12-15 23:24:15

状态一致性

一致性其实就是结果的正确性。精确一次是指数据有可能被处理多次,但是结果只有一个。
三个级别:

  1. 最多一次:1次或0次,有可能丢数据
  2. 至少一次:1次或n次,出错可能会重试
    • 输入端只要可以做到数据重放,即在出错后,可以重新发送一样的数据
  3. 精确一次:数据只会发送1次
    • 幂等写入:多次重复操作不影响结果,有可能出现某个值由于数据重放,导致结果回到原先的值,然后逐渐恢复。
    • 预写日志:
      1. 先把结果数据作为日志状态保存起来
      2. 进行检查点保存时,也会将这些结果数据一并做持久化存储
      3. 在收到检查点完成的通知时,将所有结果数据一次性写入外部系统
    • 预写日志缺点:这种再次确认的方式,如果写入成功返回的ack出现故障,还是会出现数据重复。
    • 两阶段提交(2PC):数据写入过程和数据提交分为两个过程,如果写入过程没有发生异常,就将事务进行提交。
      • 算子节点在收到第一个数据时,就开启一个事务,然后提交数据,在下一个检查点到达前都是预写入,如果下一个检查点正常,再进行最终提交。
      • 对外部系统有一定的要求,要能够识别事务ID,事务的重复提交应该是无效的。
      • 即barrier到来时,如果结果一致,就提交事务,否则进行事务回滚

Flink和Kafka连接时的精确一次保证

  • 开启检查点
  • 开启事务隔离级别,读已提交
  • 注意设置kafka超时时间为10分钟
public class Flink02_KafkaToFlink {
    public static void main(String[] args) {
        //1.创建运行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        //默认是最大并行度
        env.setParallelism(1);

        //开启检查点
        env.enableCheckpointing(1000L);

        //kafka source
        KafkaSource<String> kafkaSource = KafkaSource.<String>builder()
                .setBootstrapServers("hadoop102:9092,hadoop103:9092")
                .setGroupId("flinkb")
                .setTopics("topicA")
                //优先使用消费者组 记录的Offset进行消费,如果offset不存在,根据策略进行重置
                .setStartingOffsets(OffsetsInitializer.committedOffsets(OffsetResetStrategy.LATEST))
                .setValueOnlyDeserializer(new SimpleStringSchema())
                //如果还有别的配置需要指定,统一使用通用方法
                .setProperty("isolation.level", "read_committed")
                .build();

        DataStreamSource<String> ds = env.fromSource(kafkaSource, WatermarkStrategy.noWatermarks(), "kafkasource");

        //处理过程


        //kafka Sink
        KafkaSink<String> kafkaSink = KafkaSink.<String>builder()
                .setBootstrapServers("hadoop102:9092,hadoop103:9092")
                .setRecordSerializer(
                        KafkaRecordSerializationSchema.<String>builder()
                                .setTopic("first")
                                .setValueSerializationSchema(new SimpleStringSchema())
                                .build()
                )

                //语义
                //AT_LEAST_ONCE:至少一次,表示数据可能重复,需要考虑去重操作
                //EXACTLY_ONCE:精确一次
                //kafka transaction timeout is larger than broker
                //kafka超时时间:1H
                //broker超时时间:15分钟

//                .setDeliveryGuarantee(DeliveryGuarantee.AT_LEAST_ONCE)//数据传输的保障
                .setDeliveryGuarantee(DeliveryGuarantee.EXACTLY_ONCE)//数据传输的保障
                .setTransactionalIdPrefix("flink"+ RandomUtils.nextInt(0,100000))
//                .setProperty(ProducerConfig.RETRIES_CONFIG,"10")
                .setProperty(ProducerConfig.TRANSACTION_TIMEOUT_CONFIG,"60*1000*10")//10分钟
                .build();

        ds.map(
                JSON::toJSONString
        ).sinkTo(kafkaSink);//写入到kafka 生产者

        ds.sinkTo(kafkaSink);

        try {
            env.execute();
        } catch (Exception e) {
            throw new RuntimeException(e);
        }
    }
}

FlinkSQL1.17

FlinkSQL不同版本的接口仍在变化,有变动查看官网。
在官网这个位置可以查看Flink对于以来的一些官方介绍。
在这里插入图片描述
Table依赖剖析
三个依赖:
1. flink-table-api-java-uber-1.17.2.jar (所有的Java API)
2. flink-table-runtime-1.17.2.jar (包含Table运行时)
3. flink-table-planner-loader-1.17.2.jar (查询计划器,即SQL解析器)

静态导包:在import后添加static,并在类后面加上*导入全部。主要是为了方便使用下面的 $ 方法,否则 $ 方法前面都要添加Expressions的类名前缀

table.where($("vc").isGreaterOrEqual(100))
                .select($("id"),$("vc"),$("ts"))
                .execute()
                .print();

程序架构

  1. 准备环境
    • 流表环境:基于流创建表环境
    • 表环境:从操作层面与流独立,底层处理还是流
  2. 创建表
    • 基于流:将流转换为表
    • 连接器表
  3. 转换处理
    • 基于Table对象,使用API进行处理
    • 基于SQL的方式,直接写SQL处理
  4. 输出
    • 基于Table对象或连接器表,输出结果
    • 表转换为流,基于流的方式输出

流处理中的表

  • 处理的数据对象
    • 关系:字段元组的有界集合
    • 流处理:字段元组的无限序列
  • 对数据的访问
    • 关系:可以得到完整的
    • 流处理:数据是动态的

因此处理过程中的表是动态表,必须要持续查询。

流表转换

持续查询

  • 追加查询:窗口查询的结果通过追加的方式添加到表的末尾,使用toDataStream
  • 更新查询:窗口查询的结果会对原有的结果进行修改, 使用toChangeLogStream
  • 如果不清楚是什么类型,直接使用toChangeLogSteam()将表转换为流
public class Flink04_TableToStreamQQ {
    public static void main(String[] args) {
        //1.创建运行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        //默认是最大并行度
        env.setParallelism(1);

        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

        SingleOutputStreamOperator<Event> ds = env.socketTextStream("hadoop102", 8888)
                .map(
                        line -> {
                            String[] fields = line.split(",");
                            return new Event(fields[0].trim(), fields[1].trim(), Long.valueOf(fields[2].trim()));
                        }
                );

        Table table = tableEnv.fromDataStream(ds);

        tableEnv.createTemporaryView("t1", table);

        //SQL
        String appendSQL = "select user, url, ts from t1 where user <> 'zhangsan'";
        //需要在查询过程中更新上一次的值
        String updateSQL = "select user, count(*) cnt from t1 group by user";

        Table resultTable = tableEnv.sqlQuery(updateSQL);

        //表转换为流
        //doesn't support consuming update changes which is produced by node GroupAggregate(groupBy=[user], select=[user, COUNT(*) AS cnt])
//        DataStream<Row> rowDs = tableEnv.toDataStream(resultTable);

        //有更新操作时,使用toChangelogStream(),它即支持追加,也支持更新查询
        DataStream<Row> rowDs = tableEnv.toChangelogStream(resultTable);

        rowDs.print();

        try {
            env.execute();
        } catch (Exception e) {
            throw new RuntimeException(e);
        }
    }
}

将动态表转换为流

  • 仅追加流:如果表的结果都是追加查询
  • Retract撤回流:
    • 包含两类消息,添加消息和撤回消息
    • 下游需要根据这两类消息进行处理
  • 更新插入流:
    • 两种消息:更新插入消息(带key)和删除消息

连接器

  • DataGen和Print连接器
public class Flink01_DataGenPrint {
    public static void main(String[] args) {
        //TableEnvironment tableEnv = TableEnvironment.create(EnvironmentSettings.newInstance().build());
        //1. 准备表环境, 基于流环境,创建表环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);



        //DataGen
        String createTable =
                " create table t1 ( " +
                        "  id STRING , " +
                        "  vc INT ," +
                        "  ts BIGINT " +
                        " ) WITH (" +
                        "  'connector' = 'datagen' ,"  +
                        "  'rows-per-second' = '1' ," +
                        "  'fields.id.kind' = 'random' , " +
                        "  'fields.id.length' = '6' ," +
                        "  'fields.vc.kind' = 'random' , " +
                        "  'fields.vc.min' = '100' , " +
                        "  'fields.vc.max' = '1000' ," +
                        "  'fields.ts.kind' = 'sequence' , " +
                        "  'fields.ts.start' = '1000000' , " +
                        "  'fields.ts.end' = '100000000' " +
                        " )" ;


        tableEnv.executeSql(createTable);

        //Table resultTable = tableEnv.sqlQuery("select * from t1 where vc >= 200");
        //.execute().print();

        //print
        String sinkTable =
                "create table t2(" +
                        "id string," +
                        "vc int," +
                        "ts bigint" +
                        ") with (" +
                        "   'connector' = 'print', " +
                        "   'print-identifier' = 'print>' " +
                       ")";
        tableEnv.executeSql(sinkTable);
        tableEnv.executeSql("insert into t2 select id, vc, ts from t1 where vc >= 200");
    }
}
  • 文件连接器
public class Flink02_FileConnector {
    public static void main(String[] args) {
        TableEnvironment tableEnvironment = TableEnvironment.create(EnvironmentSettings.newInstance().build());

        //FileSource
        String sourceTable =
                " create table t1 ( " +
                        "  id STRING , " +
                        "  vc INT ," +
                        "  ts BIGINT," +
                        //"  `file.name` string not null METADATA," + 文件名字由于系统原因无法识别盘符后面的冒号
                        "  `file.size` bigint not null METADATA" +
                        " ) WITH (" +
                        "  'connector' = 'filesystem' ,"  +
                        "  'path' = 'input/ws.txt' ,"  +
                        "  'format' = 'csv' "  +
                        " )" ;

        tableEnvironment.executeSql(sourceTable);

        //tableEnvironment.sqlQuery(" select * from t1 ").execute().print();

        //转换处理...

        //File sink
        String sinkTable =
                " create table t2 ( " +
                        "  id STRING , " +
                        "  vc INT ," +
                        "  ts BIGINT," +
                        //"  `file.name` string not null METADATA," + 文件名字由于系统原因无法识别盘符后面的冒号
                        "  file_size bigint" +
                        " ) WITH (" +
                        "  'connector' = 'filesystem' ,"  +
                        "  'path' = 'output' ,"  +
                        "  'format' = 'json' "  +
                        " )" ;

        tableEnvironment.executeSql(sinkTable);

        tableEnvironment.executeSql("insert into t2 " +
                "select id, vc, ts, `file.size` from t1");
    }
}
  • kafka连接器
public class Flink03_KafkaConnector {
    public static void main(String[] args) {
        TableEnvironment tableEnvironment = TableEnvironment.create(EnvironmentSettings.newInstance().build());

        //kafka source
        String sourceTable =
                " create table t1 ( " +
                        "  id STRING , " +
                        "  vc INT ," +
                        "  ts BIGINT," +
                        "  `topic` string not null METADATA," +
                        "  `partition` int not null METADATA," +
                        "  `offset` bigint not null METADATA" +
                        " ) WITH (" +
                        "  'connector' = 'kafka' ,"  +
                        "  'properties.bootstrap.servers' = 'hadoop102:9092,hadoop103:9092' ,"  +
                        "  'topic' = 'topicA', "  +
                        "  'properties.group.id' = 'flinksql', "  +
                        "  'value.format' = 'csv', "  +
                        "  'scan.startup.mode' = 'group-offsets',"  +
                        "  'properties.auto.offset.reset' = 'latest' "  +
                        " )" ;

        //创建表
        tableEnvironment.executeSql(sourceTable);

        //打印查询结果
        //tableEnvironment.sqlQuery(" select * from t1 ").execute().print();

        //转换处理...

        //kafka Sink
        String sinkTable =
                " create table t2 ( " +
                        "  id STRING , " +
                        "  vc INT ," +
                        "  ts BIGINT," +
                        "  `topic` string " +
                        " ) WITH (" +
                        "  'connector' = 'kafka' ,"  +
                        "  'properties.bootstrap.servers' = 'hadoop102:9092,hadoop103:9092' ,"  +
                        "  'topic' = 'topicB', "  +
                        "  'sink.delivery-guarantee' = 'at-least-once', "  +
                        //"  'properties.transaction.timeout.ms' = '', "  +
                        //"  'sink.transactional-id-prefix' = 'xf', "  +
                        //"  'properties.group.id' = 'flinksql', "  +
                        "  'value.format' = 'json' "  +
                        //"  'scan.startup.mode' = 'group-offsets',"  +
                        //"  'properties.auto.offset.reset' = 'latest' "  +
                        " )" ;

        tableEnvironment.executeSql(sinkTable);

        tableEnvironment.executeSql("insert into t2 " +
                "select id, vc, ts, `topic` from t1");


    }
}
  • Jdbc连接器

文章来源:https://blog.csdn.net/qq_44273739/article/details/135008552
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