实时任务接入

实时作业一般由Flink Sql或Sparkstreaming来完成，流式实时任务通常配置同步生成compaction计划，异步执行计划。

• Flink SQL作业中sink端Hudi表相关配置如下：

  create table denza_hudi_sink (
  $HUDI_SINK_SQL_REPLACEABLE$
  ) PARTITIONED BY (
  years,
  months,
  days
  ) with (
  'connector' = 'hudi',                                                      //指定写入的是Hudi表
  'path' = 'obs://XXXXXXXXXXXXXXXXXX/',                                      //指定Hudi表的存储路径
  'table.type' = 'MERGE_ON_READ',                                            //Hudi表类型
  'hoodie.datasource.write.recordkey.field' = 'id',                          //主键
  'write.precombine.field' = 'vin',                                          //合并字段
  'write.tasks' = '10',                                                      //flink写入并行度
  'hoodie.datasource.write.keygenerator.type' = 'COMPLEX',                   //指定KeyGenerator，与Spark创建的Hudi表类型一致
  'hoodie.datasource.write.hive_style_partitioning' = 'true',                //使用hive支持的分区格式
  'read.streaming.enabled' = 'true',                                         //开启流读
  'read.streaming.check-interval' = '60',                                    //checkpoint间隔，单位为秒
  'index.type'='BUCKET'，                                            //指定Hudi表索引类型为BUCKET
  'hoodie.bucket.index.num.buckets'='10',                            //指定bucket桶数
  'compaction.delta_commits' = '3',                                          //compaction生成的commit间隔
  'compaction.async.enabled' = 'false',                                      //compaction异步执行关闭
  'compaction.schedule.enabled' = 'true',                                    //compaction同步生成计划
  'clean.async.enabled' = 'false',                                           //异步clean关闭
  'hoodie.archive.automatic' = 'false',                                     //自动archive关闭
  'hoodie.clean.automatic' = 'false',                                        //自动clean关闭
  'hive_sync.enable' = 'true',                                               //自动同步hive表
  'hive_sync.mode' = 'jdbc',                                                 //同步hive表方式为jdbc
  'hive_sync.jdbc_url' = '',                                                 //同步hive表的jdbc url
  'hive_sync.db' = 'hudi_cars_byd',                                          //同步hive表的database
  'hive_sync.table' = 'byd_hudi_denza_1s_mor',                               //同步hive表的tablename
  'hive_sync.metastore.uris' = 'thrift://XXXXX:9083 ',                       //同步hive表的metastore uri
  'hive_sync.support_timestamp' = 'true',                                    //同步hive表支持timestamp格式
  'hive_sync.partition_extractor_class' = 'org.apache.hudi.hive.MultiPartKeysValueExtractor'           //同步hive表的extractor类
  );

• Spark streaming写入Hudi表常用的参数如下（参数意义与上面flink类似，不再做注释）：

  hoodie.table.name=
  hoodie.index.type=BUCKET
  hoodie.bucket.index.num.buckets=3
  hoodie.datasource.write.precombine.field=
  hoodie.datasource.write.recordkey.field=
  hoodie.datasource.write.partitionpath.field=
  hoodie.datasource.write.table.type= MERGE_ON_READ
  hoodie.datasource.write.hive_style_partitioning=true
  hoodie.compact.inline=true
  hoodie.schedule.compact.only.inline=true
  hoodie.run.compact.only.inline=false
  hoodie.clean.automatic=false
  hoodie.clean.async=false
  hoodie.archive.async=false
  hoodie.archive.automatic=false
  hoodie.compact.inline.max.delta.commits=50
  hoodie.datasource.hive_sync.enable=true
  hoodie.datasource.hive_sync.partition_fields=
  hoodie.datasource.hive_sync.database=
  hoodie.datasource.hive_sync.table=
  hoodie.datasource.hive_sync.partition_extractor_class=org.apache.hudi.hive.MultiPartKeysValueExtractor