1. 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。
2. Hudi DeltaStreamer写入工具介绍
DeltaStreamer工具使用参考 https://hudi.apache.org/cn/docs/writing_data.html
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
3. 场景说明
1.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。2.通过Hudi提供的DeltaStreamer工具，读取Kafka指定topic里的数据并解析处理。3.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：
4. 调试步骤
4.1 华为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
4.2 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
5. 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_envkinit developusersource /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=uidhoodie.datasource.write.partitionpath.field=hoodie.datasource.write.keygenerator.class=org.apache.hudi.keygen.NonpartitionedKeyGeneratorhoodie.datasource.write.hive_style_partitioning=truehoodie.delete.shuffle.parallelism=10hoodie.upsert.shuffle.parallelism=10hoodie.bulkinsert.shuffle.parallelism=10hoodie.insert.shuffle.parallelism=10hoodie.finalize.write.parallelism=10hoodie.cleaner.parallelism=10hoodie.datasource.write.precombine.field=uidhoodie.base.path = /tmp/huditest/delta_demo2hoodie.timeline.layout.version = 1`// hive confighoodie.datasource.hive_sync.table=delta_demo2hoodie.datasource.hive_sync.partition_fields=hoodie.datasource.hive_sync.assume_date_partitioning=falsehoodie.datasource.hive_sync.partition_extractor_class=org.apache.hudi.hive.NonPartitionedExtractorhoodie.datasource.hive_sync.use_jdbc=false// Kafka Source topichoodie.deltastreamer.source.kafka.topic=hudisource// checkpointhoodie.deltastreamer.checkpoint.provider.path=hdfs://hacluster/tmp/delta_demo2/checkpoint/// Kafka propsbootstrap.servers=172.16.9.117:21005auto.offset.reset=earliestgroup.id=a5offset.rang.limit=10000
注意：kafka服务端配置 allow.everyone.if.no.acl.found 为true
5. 使用Spark查询
spark-shell --master yarnval 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：
Mysql更新操作对应spark中hudi表查询结果：
删除操作：
6. 使用Hive查询
beelineselect * from delta_demo2;
Mysql增加操作对应hive表中查询结果：
Mysql更新操作对应hive表中查询结果：
Mysql删除操作对应hive表中查询结果：
