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flink datastream api实现数据实时写入hudi

xiaozhch5 • • Hudi, Hudi进阶提升, 后端, 大数据 • 阅读 387

Apache Hudi（发音为“hoodie”）是下一代流数据湖平台。 Apache Hudi 将核心仓库和数据库功能直接引入数据湖。 Hudi 提供表、事务、高效的更新插入/删除、高级索引、流式摄取服务、数据集群/压缩优化和并发性，同时将您的数据保持为开源文件格式。

Hudi目前支持Flink、Spark与Java引擎实现数据写入。今天我们挑选其中一种，来看一下Flink引擎中的DataStream API写入方式。

根据官网以及hudi相关代码，目前基于Flink DataStream API写入hudi的方式也可分为hudi官网所述的如下方式（https://hudi.apache.org/docs/flink-quick-start-guide#insert-data）：

image.png

以及hudi源码中HoodieFlinkStreamer类提供的示例。

imagea744f2a9f121831a.png

image283f77a9ca3fe255.png

大概地，我们将上述两种方式分别成为HoodiePipeline方式和HoodieFlinkStreamer方式，两种方式本质上还是大同小异。下面我们简要分析一下这两种方式。

HoodiePipeline方式

Map<String, String> options = new HashMap<>();
options.put(FlinkOptions.PATH.key(), basePath);
options.put(FlinkOptions.TABLE_TYPE.key(), HoodieTableType.MERGE_ON_READ.name());
options.put(FlinkOptions.PRECOMBINE_FIELD.key(), "ts");

DataStream<RowData> dataStream = env.addSource(...);
HoodiePipeline.Builder builder = HoodiePipeline.builder(targetTable)
    .column("uuid VARCHAR(20)")
    .column("name VARCHAR(10)")
    .column("age INT")
    .column("ts TIMESTAMP(3)")
    .column("`partition` VARCHAR(20)")
    .pk("uuid")
    .partition("partition")
    .options(options);

builder.sink(dataStream, false); // The second parameter indicating whether the input data stream is bounded
env.execute("Api_Sink");

以数据写入为例，在读取数据源数据之后，基于表字段和表路径构建HoodiePipeline.Builder，进而在sink函数中传入数据源。

sink函数为具体执行者。其内容如下：

    public DataStreamSink<?> sink(DataStream<RowData> input, boolean bounded) {
      TableDescriptor tableDescriptor = getTableDescriptor();
      return HoodiePipeline.sink(input, tableDescriptor.getTableId(), tableDescriptor.getResolvedCatalogTable(), bounded);
    }

HoodiePipeline.sink内容为：

  /**
   * Returns the data stream sink with given catalog table.
   *
   * @param input        The input datastream
   * @param tablePath    The table path to the hoodie table in the catalog
   * @param catalogTable The hoodie catalog table
   * @param isBounded    A flag indicating whether the input data stream is bounded
   */
  private static DataStreamSink<?> sink(DataStream<RowData> input, ObjectIdentifier tablePath, ResolvedCatalogTable catalogTable, boolean isBounded) {
    FactoryUtil.DefaultDynamicTableContext context = Utils.getTableContext(tablePath, catalogTable, Configuration.fromMap(catalogTable.getOptions()));
    HoodieTableFactory hoodieTableFactory = new HoodieTableFactory();
    return ((DataStreamSinkProvider) hoodieTableFactory.createDynamicTableSink(context)
        .getSinkRuntimeProvider(new SinkRuntimeProviderContext(isBounded)))
        .consumeDataStream(input);
  }

分析该return语句，其调用的方法为HoodieTableFactory#createDynamicTableSink，HoodieTableSink#getSinkRuntimeProvider，上述代码即hudi扩展flink的动态表的相关方法。

其中，HoodieTableSink#getSinkRuntimeProvider内容为：

  @Override
  public SinkRuntimeProvider getSinkRuntimeProvider(Context context) {
    return (DataStreamSinkProviderAdapter) dataStream -> {

      // setup configuration
      long ckpTimeout = dataStream.getExecutionEnvironment()
          .getCheckpointConfig().getCheckpointTimeout();
      conf.setLong(FlinkOptions.WRITE_COMMIT_ACK_TIMEOUT, ckpTimeout);
      // set up default parallelism
      OptionsInference.setupSinkTasks(conf, dataStream.getExecutionConfig().getParallelism());
      // set up client id
      OptionsInference.setupClientId(conf);

      RowType rowType = (RowType) schema.toSinkRowDataType().notNull().getLogicalType();

      // bulk_insert mode
      final String writeOperation = this.conf.get(FlinkOptions.OPERATION);
      if (WriteOperationType.fromValue(writeOperation) == WriteOperationType.BULK_INSERT) {
        return Pipelines.bulkInsert(conf, rowType, dataStream);
      }

      // Append mode
      if (OptionsResolver.isAppendMode(conf)) {
        DataStream<Object> pipeline = Pipelines.append(conf, rowType, dataStream, context.isBounded());
        if (OptionsResolver.needsAsyncClustering(conf)) {
          return Pipelines.cluster(conf, rowType, pipeline);
        } else {
          return Pipelines.dummySink(pipeline);
        }
      }

      DataStream<Object> pipeline;
      // bootstrap
      final DataStream<HoodieRecord> hoodieRecordDataStream =
          Pipelines.bootstrap(conf, rowType, dataStream, context.isBounded(), overwrite);
      // write pipeline
      pipeline = Pipelines.hoodieStreamWrite(conf, hoodieRecordDataStream);
      // compaction
      if (OptionsResolver.needsAsyncCompaction(conf)) {
        // use synchronous compaction for bounded source.
        if (context.isBounded()) {
          conf.setBoolean(FlinkOptions.COMPACTION_ASYNC_ENABLED, false);
        }
        return Pipelines.compact(conf, pipeline);
      } else {
        return Pipelines.clean(conf, pipeline);
      }
    };
  }

HoodieFlinkStreamer方式

相信分析完HoodiePipeline方式，HoodieFlinkStreamer方式也就死一目了然了，其直接使用的是HoodieTableSink#getSinkRuntimeProvider方法中的代码构造DataStream。

Flink DataStream API实现Hudi数据写入

官方给了HoodiePipeline方式写入hudi的示例，但是HoodieFlinkStreamer方式给的并不全。下面我们以HoodieFlinkStreamer方式为例，读取kafka数据进而写入Hudi。

kafka发送数据

数据结构

package com.zh.ch.bigdata.examples.kafka;

import java.io.Serializable;

public class HudiSource implements Serializable {

    private int uuid;

    private String name;

    private int age;

    private int ts;

    public HudiSource() {
    }

    public HudiSource(int uuid, String name, int age, int ts) {
        this.uuid = uuid;
        this.name = name;
        this.age = age;
        this.ts = ts;
    }

    public int getUuid() {
        return uuid;
    }

    public void setUuid(int uuid) {
        this.uuid = uuid;
    }

    public String getName() {
        return name;
    }

    public void setName(String name) {
        this.name = name;
    }

    public int getAge() {
        return age;
    }

    public void setAge(int age) {
        this.age = age;
    }

    public int getTs() {
        return ts;
    }

    public void setTs(int ts) {
        this.ts = ts;
    }
}

producer类

package com.zh.ch.bigdata.examples.kafka;

import com.alibaba.fastjson2.JSON;
import com.zh.ch.bigdata.examples.utils.PropertiesUtil;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.Properties;
import java.util.concurrent.ArrayBlockingQueue;
import java.util.concurrent.ThreadPoolExecutor;
import java.util.concurrent.TimeUnit;

public class KafkaProducerExample implements Runnable {

    private static final Logger log = LoggerFactory.getLogger(KafkaProducerExample.class);

    @Override
    public void run() {
        Properties kafkaConfig = PropertiesUtil.load("kafka/src/main/resources/kafkaConfig.properties");
        try (KafkaProducer<String, Object> producer = new KafkaProducer<>(kafkaConfig)) {
            for (int i = 500; i < 600; i++) {
                producer.send(new ProducerRecord<>("hudi_topic_20230619_2", Integer.toString(i), JSON.toJSONString(new HudiSource(i, "name" + i, i, i))));
            }
        }
    }

    public static void main(String[] args) {
        ThreadPoolExecutor executor = new ThreadPoolExecutor(
                4,
                8,
                10,
                TimeUnit.SECONDS,
                new ArrayBlockingQueue<>(10));
        executor.execute(new KafkaProducerExample());
        executor.shutdown();
    }
}

相关参数：

bootstrap.servers        =10.8.0.1:30092
linger.ms                =1
acks                     =1
key.serializer           =org.apache.kafka.common.serialization.StringSerializer
value.serializer         =org.apache.kafka.common.serialization.StringSerializer

key.deserializer         =org.apache.kafka.common.serialization.StringDeserializer
value.deserializer       =org.apache.kafka.common.serialization.StringDeserializer
group.id                 =consumer-group-1

Flink消费数据写入Hudi

package com.zh.ch.bigdata.examples.hudi;

import org.apache.flink.configuration.Configuration;
import org.apache.flink.formats.common.TimestampFormat;
import org.apache.flink.formats.json.JsonRowDataDeserializationSchema;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.table.data.RowData;
import org.apache.flink.table.runtime.typeutils.InternalTypeInfo;
import org.apache.flink.table.types.logical.IntType;
import org.apache.flink.table.types.logical.RowType;
import org.apache.flink.table.types.logical.VarCharType;
import org.apache.hudi.com.beust.jcommander.JCommander;
import org.apache.hudi.common.config.DFSPropertiesConfiguration;
import org.apache.hudi.common.config.TypedProperties;
import org.apache.hudi.common.model.HoodieRecord;
import org.apache.hudi.common.util.Option;
import org.apache.hudi.configuration.OptionsInference;
import org.apache.hudi.configuration.OptionsResolver;
import org.apache.hudi.sink.transform.Transformer;
import org.apache.hudi.sink.utils.Pipelines;
import org.apache.hudi.streamer.FlinkStreamerConfig;
import org.apache.hudi.util.StreamerUtil;

import java.util.ArrayList;
import java.util.List;

/**
 * 运行参数
 * <p>
 * --table-type MERGE_ON_READ
 * --kafka-bootstrap-servers kafka:30092
 * --kafka-topic hudi_topic_20230619_2
 * --target-table hudi_tbl
 * --target-base-path file:///data/hudi/hudidb/hudi_tbl
 * --kafka-group-id consumer-group
 * --source-avro-schema
 * "{\"type\": \"record\",\"name\": \"triprec\",\"fields\": [ {\"name\": \"name\",\"type\": \"string\"},{\"name\":
 * \"age\", \"type\": \"int\"},{\"name\":\"uuid\",\"type\": \"int\"},{\"name\":\"ts\",\"type\": \"long\"}]}"
 */

public class HudiFlinkStreamer {

    public static void main(String[] args) throws Exception {
        // 创建flink DataStream执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        final FlinkStreamerConfig cfg = new FlinkStreamerConfig();
        JCommander cmd = new JCommander(cfg, null, args);
        if (cfg.help || args.length == 0) {
            cmd.usage();
            System.exit(1);
        }

        // 设置并行度
        env.setParallelism(4);

        // 设置checkpoint
        env.enableCheckpointing(30000);

        cfg.setString("rest.port", "8081");

        env.getConfig().setGlobalJobParameters(cfg);
        // We use checkpoint to trigger write operation, including instant generating and committing,
        // There can only be one checkpoint at one time.
        env.getCheckpointConfig().setMaxConcurrentCheckpoints(1);

        env.setStateBackend(cfg.stateBackend);
        if (cfg.flinkCheckPointPath != null) {
            env.getCheckpointConfig().setCheckpointStorage(cfg.flinkCheckPointPath);
        }

        Configuration conf = FlinkStreamerConfig.toFlinkConfig(cfg);

        TypedProperties kafkaProps = DFSPropertiesConfiguration.getGlobalProps();
        kafkaProps.putAll(StreamerUtil.appendKafkaProps(cfg));

        // Read from kafka source

        RowType.RowField rowField = new RowType.RowField("name", new VarCharType(VarCharType.MAX_LENGTH));
        RowType.RowField rowField1 = new RowType.RowField("age", new IntType());
        RowType.RowField rowField2 = new RowType.RowField("uuid", new IntType());
        RowType.RowField rowField3 = new RowType.RowField("ts", new IntType());

        List<RowType.RowField> rowFields = new ArrayList<>();
        rowFields.add(rowField);
        rowFields.add(rowField1);
        rowFields.add(rowField2);
        rowFields.add(rowField3);

        RowType rowType = new RowType(rowFields);

        DataStream<RowData> dataStream = env
            .addSource(new FlinkKafkaConsumer<>(cfg.kafkaTopic, new JsonRowDataDeserializationSchema(rowType,
                InternalTypeInfo.of(rowType), false, true, TimestampFormat.ISO_8601), kafkaProps))
            .name("kafka_source").uid("uid_kafka_source");

        if (cfg.transformerClassNames != null && !cfg.transformerClassNames.isEmpty()) {
            Option<Transformer> transformer = StreamerUtil.createTransformer(cfg.transformerClassNames);
            if (transformer.isPresent()) {
                dataStream = transformer.get().apply(dataStream);
            }
        }

        OptionsInference.setupSinkTasks(conf, env.getParallelism());
        DataStream<Object> pipeline;
        // Append mode
        if (OptionsResolver.isAppendMode(conf)) {
            pipeline = Pipelines.append(conf, rowType, dataStream, false);
            if (OptionsResolver.needsAsyncClustering(conf)) {
                Pipelines.cluster(conf, rowType, pipeline);
            }
            else {
                Pipelines.dummySink(pipeline);
            }
        }
        else {
            DataStream<HoodieRecord> hoodieRecordDataStream = Pipelines.bootstrap(conf, rowType, dataStream);
            pipeline = Pipelines.hoodieStreamWrite(conf, hoodieRecordDataStream);
            if (OptionsResolver.needsAsyncCompaction(conf)) {
                Pipelines.compact(conf, pipeline);
            }
            else {
                Pipelines.clean(conf, pipeline);
            }
        }
        pipeline.print();

        env.execute(cfg.targetTableName);

    }

}

总结

针对上述两种方式，我们可以发现其实都是大同小异的，最后都是调用的一段相同代码，都是相当灵活的。在使用过程中，可结合自己的业务场景分别选择。

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本文为从大数据到人工智能博主「xiaozhch5」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。

原文链接：https://lrting.top/backend/14195/

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