华为云FusionInsight MRS实战 - Hudi实时入湖之DeltaStreamer工具最佳实践

背景

传统大数据平台的组织架构是针对离线数据处理需求设计的，常用的数据导入方式为采用sqoop定时作业批量导入。随着数据分析对实时性要求不断提高，按小时、甚至分钟级的数据同步越来越普遍。由此展开了基于spark/flink流处理机制的（准）实时同步系统的开发。

然而实时同步从一开始就面临如下几个挑战：

小文件问题。不论是spark的microbatch模式，还是flink的逐条处理模式，每次写入HDFS时都是几MB甚至几十KB的文件。长时间下来产生的大量小文件，会对HDFS namenode产生巨大的压力。
对update操作的支持。HDFS系统本身不支持数据的修改，无法实现同步过程中对记录进行修改。
事务性。不论是追加数据还是修改数据，如何保证事务性。即数据只在流处理程序commit操作时一次性写入HDFS，当程序rollback时，已写入或部分写入的数据能随之删除。

Hudi就是针对以上问题的解决方案之一。使用Hudi自带的DeltaStreamer工具写数据到Hudi，开启–enable-hive-sync 即可同步数据到hive表。

Hudi DeltaStreamer写入工具介绍

HoodieDeltaStreamer实用工具 (hudi-utilities-bundle中的一部分) 提供了从DFS或Kafka等不同来源进行摄取的方式，并具有以下功能。

从Kafka单次摄取新事件，从Sqoop、HiveIncrementalPuller输出或DFS文件夹中的多个文件
支持json、avro或自定义记录类型的传入数据
管理检查点，回滚和恢复
利用DFS或Confluent schema注册表的Avro模式。
支持自定义转换操作

场景说明

生产库数据通过CDC工具（debezium）实时录入到MRS集群中Kafka的指定topic里。
通过Hudi提供的DeltaStreamer工具，读取Kafka指定topic里的数据并解析处理。
同时使用DeltaStreamer工具将处理后的数据写入到MRS集群的hive里。

样例数据简介

生产库MySQL原始数据：

CDC工具debezium简介

对接步骤具体参考：https://fusioninsight.github.io/ecosystem/zh-hans/Data_Integration/DEBEZIUM/

完成对接后，针对MySQL生产库分别做增、改、删除操作对应的kafka消息

增加操作: insert into hudi.hudisource3 values (11,“蒋语堂”,“38”,“女”,“图”,“播放器”,“28732”);

对应kafka消息体：

更改操作： UPDATE hudi.hudisource3 SET uname=‘Anne Marie333’ WHERE uid=11;

对应kafka消息体：

删除操作： delete from hudi.hudisource3 where uid=11;

对应kafka消息体：

调试步骤

华为云MRS Hudi样例工程获取

根据实际MRS版本登录github获取样例代码： https://github.com/huaweicloud/huaweicloud-mrs-example/tree/mrs-3.1.0

打开工程SparkOnHudiJavaExample

样例代码修改及介绍

1.debeziumJsonParser

说明：对debezium的消息体进行解析，获取到op字段。

源码如下：

package com.huawei.bigdata.hudi.examples;
import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import com.alibaba.fastjson.TypeReference;

public class debeziumJsonParser {

    public static String getOP(String message){

        JSONObject json_obj = JSON.parseObject(message);
        String op = json_obj.getJSONObject("payload").get("op").toString();
        return  op;
    }
}

2.MyJsonKafkaSource

说明：DeltaStreamer默认使用org.apache.hudi.utilities.sources.JsonKafkaSource消费kafka指定topic的数据，如果消费阶段涉及数据的解析操作，则需要重写MyJsonKafkaSource进行处理。

以下是源码，增加注释

package com.huawei.bigdata.hudi.examples;

import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import com.alibaba.fastjson.parser.Feature;
import org.apache.hudi.common.config.TypedProperties;
import org.apache.hudi.common.util.Option;
import org.apache.hudi.config.HoodieWriteConfig;
import org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamerMetrics;
import org.apache.hudi.utilities.schema.SchemaProvider;
import org.apache.hudi.utilities.sources.InputBatch;
import org.apache.hudi.utilities.sources.JsonSource;
import org.apache.hudi.utilities.sources.helpers.KafkaOffsetGen;
import org.apache.hudi.utilities.sources.helpers.KafkaOffsetGen.CheckpointUtils;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.apache.log4j.LogManager;
import org.apache.log4j.Logger;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.streaming.kafka010.KafkaUtils;
import org.apache.spark.streaming.kafka010.LocationStrategies;
import org.apache.spark.streaming.kafka010.OffsetRange;
import java.util.Map;

/**
 * Read json kafka data.
 */
public class MyJsonKafkaSource extends JsonSource {

    private static final Logger LOG = LogManager.getLogger(MyJsonKafkaSource.class);

    private final KafkaOffsetGen offsetGen;

    private final HoodieDeltaStreamerMetrics metrics;

    public MyJsonKafkaSource(TypedProperties properties, JavaSparkContext sparkContext, SparkSession sparkSession,
                             SchemaProvider schemaProvider) {
        super(properties, sparkContext, sparkSession, schemaProvider);
        HoodieWriteConfig.Builder builder = HoodieWriteConfig.newBuilder();
        this.metrics = new HoodieDeltaStreamerMetrics(builder.withProperties(properties).build());
        properties.put("key.deserializer", StringDeserializer.class);
        properties.put("value.deserializer", StringDeserializer.class);
        offsetGen = new KafkaOffsetGen(properties);
    }

    @Override
    protected InputBatch<JavaRDD<String>> fetchNewData(Option<String> lastCheckpointStr, long sourceLimit) {
        OffsetRange[] offsetRanges = offsetGen.getNextOffsetRanges(lastCheckpointStr, sourceLimit, metrics);
        long totalNewMsgs = CheckpointUtils.totalNewMessages(offsetRanges);
        LOG.info("About to read " + totalNewMsgs + " from Kafka for topic :" + offsetGen.getTopicName());
        if (totalNewMsgs <= 0) {
            return new InputBatch<>(Option.empty(), CheckpointUtils.offsetsToStr(offsetRanges));
        }
        JavaRDD<String> newDataRDD = toRDD(offsetRanges);
        return new InputBatch<>(Option.of(newDataRDD), CheckpointUtils.offsetsToStr(offsetRanges));
    }

    private JavaRDD<String> toRDD(OffsetRange[] offsetRanges) {
        return KafkaUtils.createRDD(this.sparkContext, this.offsetGen.getKafkaParams(), offsetRanges, LocationStrategies.PreferConsistent()).filter((x)->{
            //过滤空行和脏数据
            String msg = (String)x.value();
            if (msg == null) {
                return false;
            }
            try{
                String op = debeziumJsonParser.getOP(msg);
            }catch (Exception e){
                return false;
            }
            return true;
        }).map((x) -> {
            //将debezium接进来的数据解析写进map,在返回map的tostring, 这样结构改动最小
            String msg = (String)x.value();
            String op = debeziumJsonParser.getOP(msg);
            JSONObject json_obj = JSON.parseObject(msg, Feature.OrderedField);
            Boolean is_delete = false;
            String out_str = "";
            Object out_obj = new Object();
            if(op.equals("c")){
                out_obj =  json_obj.getJSONObject("payload").get("after");
            }
            else if(op.equals("u")){
                out_obj =   json_obj.getJSONObject("payload").get("after");
            }
            else {
                is_delete = true;
                out_obj =   json_obj.getJSONObject("payload").get("before");
            }
            Map out_map = (Map)out_obj;
            out_map.put("_hoodie_is_deleted",is_delete);
            out_map.put("op",op);

            return out_map.toString();
        });
    }
}

3.TransformerExample

说明：入湖hudi表或者hive表时候需要指定的字段

以下是源码，增加注释

package com.huawei.bigdata.hudi.examples;

import org.apache.hudi.common.config.TypedProperties;
import org.apache.hudi.utilities.transform.Transformer;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;

/**
 * 功能描述
 * 对获取的数据进行format
 */
public class TransformerExample implements Transformer, Serializable {

    /**
     * format data
     *
     * @param JavaSparkContext jsc
     * @param SparkSession sparkSession
     * @param Dataset<Row> rowDataset
     * @param TypedProperties properties
     * @return Dataset<Row>
     */
    @Override
    public Dataset<Row> apply(JavaSparkContext jsc, SparkSession sparkSession, Dataset<Row> rowDataset,
        TypedProperties properties) {
        JavaRDD<Row> rowJavaRdd = rowDataset.toJavaRDD();
        List<Row> rowList = new ArrayList<>();
        for (Row row : rowJavaRdd.collect()) {

            Row one_row = buildRow(row);
            rowList.add(one_row);
        }
        JavaRDD<Row> stringJavaRdd = jsc.parallelize(rowList);
        List<StructField> fields = new ArrayList<>();
        builFields(fields);
        StructType schema = DataTypes.createStructType(fields);
        Dataset<Row> dataFrame = sparkSession.createDataFrame(stringJavaRdd, schema);
        return dataFrame;
    }

    private void builFields(List<StructField> fields) {
        fields.add(DataTypes.createStructField("uid", DataTypes.IntegerType, true));
        fields.add(DataTypes.createStructField("uname", DataTypes.StringType, true));
        fields.add(DataTypes.createStructField("age", DataTypes.StringType, true));
        fields.add(DataTypes.createStructField("sex", DataTypes.StringType, true));
        fields.add(DataTypes.createStructField("mostlike", DataTypes.StringType, true));
        fields.add(DataTypes.createStructField("lastview", DataTypes.StringType, true));
        fields.add(DataTypes.createStructField("totalcost", DataTypes.StringType, true));
        fields.add(DataTypes.createStructField("_hoodie_is_deleted", DataTypes.BooleanType, true));
        fields.add(DataTypes.createStructField("op", DataTypes.StringType, true));
    }

    private Row buildRow(Row row) {
        Integer uid = row.getInt(0);
        String uname = row.getString(1);
        String age = row.getString(2);
        String sex = row.getString(3);
        String mostlike = row.getString(4);
        String lastview = row.getString(5);
        String totalcost = row.getString(6);
        Boolean _hoodie_is_deleted = row.getBoolean(7);
        String op = row.getString(8);
        Row returnRow = RowFactory.create(uid, uname, age, sex, mostlike, lastview, totalcost, _hoodie_is_deleted, op);
        return returnRow;
    }
}

4.DataSchemaProviderExample

说明：分别指定MyJsonKafkaSource返回的数据格式为source schema，TransformerExample写入的数据格式为target schema

以下是源码

package com.huawei.bigdata.hudi.examples;

import org.apache.avro.Schema;
import org.apache.hudi.common.config.TypedProperties;
import org.apache.hudi.utilities.schema.SchemaProvider;
import org.apache.spark.api.java.JavaSparkContext;

/**
 * 功能描述
 * 提供sorce和target的schema
 */
public class DataSchemaProviderExample extends SchemaProvider {

    public DataSchemaProviderExample(TypedProperties props, JavaSparkContext jssc) {
        super(props, jssc);
    }
    /**
     * source schema
     *
     * @return Schema
     */
    @Override
    public Schema getSourceSchema() {
        Schema avroSchema = new Schema.Parser().parse(
                "{\"type\":\"record\",\"name\":\"hoodie_source\",\"fields\":[{\"name\":\"uid\",\"type\":\"int\"},{\"name\":\"uname\",\"type\":\"string\"},{\"name\":\"age\",\"type\":\"string\"},{\"name\":\"sex\",\"type\":\"string\"},{\"name\":\"mostlike\",\"type\":\"string\"},{\"name\":\"lastview\",\"type\":\"string\"},{\"name\":\"totalcost\",\"type\":\"string\"},{\"name\":\"_hoodie_is_deleted\",\"type\":\"boolean\"},{\"name\":\"op\",\"type\":\"string\"}]}");
        return avroSchema;
    }
    /**
     * target schema
     *
     * @return Schema
     */
    @Override
    public Schema getTargetSchema() {
        Schema avroSchema = new Schema.Parser().parse(
            "{\"type\":\"record\",\"name\":\"mytest_record\",\"namespace\":\"hoodie.mytest\",\"fields\":[{\"name\":\"uid\",\"type\":\"int\"},{\"name\":\"uname\",\"type\":\"string\"},{\"name\":\"age\",\"type\":\"string\"},{\"name\":\"sex\",\"type\":\"string\"},{\"name\":\"mostlike\",\"type\":\"string\"},{\"name\":\"lastview\",\"type\":\"string\"},{\"name\":\"totalcost\",\"type\":\"string\"},{\"name\":\"_hoodie_is_deleted\",\"type\":\"boolean\"},{\"name\":\"op\",\"type\":\"string\"}]}");
        return avroSchema;
    }
}

将工程打包（hudi-security-examples-0.7.0.jar）以及json解析包（fastjson-1.2.4.jar）上传至MRS客户端

DeltaStreamer启动命令

登录客户端执行一下命令获取环境变量以及认证

source /opt/hadoopclient/bigdata_env
kinit developuser
source /opt/hadoopclient/Hudi/component_env

DeltaStreamer启动命令如下：

spark-submit --master yarn-client \
--jars /opt/hudi-demo2/fastjson-1.2.4.jar,/opt/hudi-demo2/hudi-security-examples-0.7.0.jar \
--driver-class-path /opt/hadoopclient/Hudi/hudi/conf:/opt/hadoopclient/Hudi/hudi/lib/*:/opt/hadoopclient/Spark2x/spark/jars/*:/opt/hudi-demo2/hudi-security-examples-0.7.0.jar \
--class org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamer \
spark-internal --props file:///opt/hudi-demo2/kafka-source.properties \
--target-base-path /tmp/huditest/delta_demo2 \
--table-type COPY_ON_WRITE  \
--target-table delta_demo2  \
--source-ordering-field uid \
--source-class com.huawei.bigdata.hudi.examples.MyJsonKafkaSource \
--schemaprovider-class com.huawei.bigdata.hudi.examples.DataSchemaProviderExample \
--transformer-class com.huawei.bigdata.hudi.examples.TransformerExample \
--enable-hive-sync --continuous

kafka.properties配置

// hudi配置
hoodie.datasource.write.recordkey.field=uid
hoodie.datasource.write.partitionpath.field=
hoodie.datasource.write.keygenerator.class=org.apache.hudi.keygen.NonpartitionedKeyGenerator
hoodie.datasource.write.hive_style_partitioning=true
hoodie.delete.shuffle.parallelism=10
hoodie.upsert.shuffle.parallelism=10
hoodie.bulkinsert.shuffle.parallelism=10
hoodie.insert.shuffle.parallelism=10
hoodie.finalize.write.parallelism=10
hoodie.cleaner.parallelism=10
hoodie.datasource.write.precombine.field=uid
hoodie.base.path = /tmp/huditest/delta_demo2
hoodie.timeline.layout.version = 1

// hive config
hoodie.datasource.hive_sync.table=delta_demo2
hoodie.datasource.hive_sync.partition_fields=
hoodie.datasource.hive_sync.assume_date_partitioning=false
hoodie.datasource.hive_sync.partition_extractor_class=org.apache.hudi.hive.NonPartitionedExtractor
hoodie.datasource.hive_sync.use_jdbc=false

// Kafka Source topic
hoodie.deltastreamer.source.kafka.topic=hudisource
// checkpoint
hoodie.deltastreamer.checkpoint.provider.path=hdfs://hacluster/tmp/delta_demo2/checkpoint/

// Kafka props
bootstrap.servers=172.16.9.117:21005
auto.offset.reset=earliest
group.id=a5
offset.rang.limit=10000

注意：kafka服务端配置 allow.everyone.if.no.acl.found 为true

使用Spark查询

spark-shell --master yarn

val roViewDF = spark.read.format("org.apache.hudi").load("/tmp/huditest/delta_demo2/*")
roViewDF.createOrReplaceTempView("hudi_ro_table")
spark.sql("select * from  hudi_ro_table").show()

Mysql增加操作对应spark中hudi表查询结果：