Scala操作hudi

Scala操作hudi

1、启动客户端2、配置信息3、 创建数据表4、插入数据5、查询数据6、更新数据7、增量查询8、时间点查询9、删除数据10、覆盖写入

//spark3.1
spark-shell 
  --packages org.apache.hudi:hudi-spark3.1.2-bundle_2.12:0.10.1,org.apache.spark:spark-avro_2.12:3.1.2 
  --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'
//spark 3.0
spark-shell 
  --packages org.apache.hudi:hudi-spark3.0.3-bundle_2.12:0.10.1,org.apache.spark:spark-avro_2.12:3.0.3 
  --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'

// spark的shell配置
import org.apache.hudi.QuickstartUtils._
import scala.collection.JavaConversions._
import org.apache.spark.sql.SaveMode._
import org.apache.hudi.DataSourceReadOptions._
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.config.HoodieWriteConfig._

val tableName = "hudi_trips_cow"
val basePath = "file:///tmp/hudi_trips_cow"
val dataGen = new DataGenerator

hudi不需要创建数据表，当使用数据表时没有数据表就会进行自动创建，因此hudi没有键表语句

// 插入数据的语法
val inserts = convertToStringList(dataGen.generateInserts(10))
val df = spark.read.json(spark.sparkContext.parallelize(inserts, 2))
df.write.format("hudi").
  options(getQuickstartWriteConfigs).
  option(PRECOMBINE_FIELD_OPT_KEY, "ts").
  option(RECORDKEY_FIELD_OPT_KEY, "uuid").
  option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").
  option(TABLE_NAME, tableName).
  mode(Overwrite).
  save(basePath)

指令参数详解

mode： 设置插入数据的模式，overwriter为覆盖模式，当数据表存在时重新创建RECORDKEY_FIELD_OPT_KEY： 设置主键PRECOMBINE_FIELD_OPT_KEY： 逻辑聚合主键PARTITIONPATH_FIELD_OPT_KEY： 设置分区

//查询语句
val tripsSnapshotDF = spark.
  read.
  format("hudi").
  load(basePath)
tripsSnapshotDF.createOrReplaceTempView("hudi_trips_snapshot")
//执行查询语句
spark.sql("select fare, begin_lon, begin_lat, ts from  hudi_trips_snapshot where fare > 20.0").show()
spark.sql("select _hoodie_commit_time, _hoodie_record_key, _hoodie_partition_path, rider, driver, fare from  hudi_trips_snapshot").show()

//数据更新语句
val updates = convertToStringList(dataGen.generateUpdates(10))
val df = spark.read.json(spark.sparkContext.parallelize(updates, 2))
df.write.format("hudi").
  options(getQuickstartWriteConfigs).
  option(PRECOMBINE_FIELD_OPT_KEY, "ts").
  option(RECORDKEY_FIELD_OPT_KEY, "uuid").
  option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").
  option(TABLE_NAME, tableName).
  mode(Append).
  save(basePath)

//增量查询
//重新加载数据
spark.
  read.
  format("hudi").
  load(basePath).
  createOrReplaceTempView("hudi_trips_snapshot")

val commits = spark.sql("select distinct(_hoodie_commit_time) as commitTime from  hudi_trips_snapshot order by commitTime").map(k => k.getString(0)).take(50)
//设置提交时间
val beginTime = commits(commits.length - 2) 

//增量查询数据
val tripsIncrementalDF = spark.read.format("hudi").
  option(QUERY_TYPE_OPT_KEY, QUERY_TYPE_INCREMENTAL_OPT_VAL).
  option(BEGIN_INSTANTTIME_OPT_KEY, beginTime).
  load(basePath)
tripsIncrementalDF.createOrReplaceTempView("hudi_trips_incremental")

spark.sql("select `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts from  hudi_trips_incremental where fare > 20.0").show()

//时间点查询
val beginTime = "000" // Represents all commits > this time.
val endTime = commits(commits.length - 2) // commit time we are interested in

//增量查询数据
val tripsPointInTimeDF = spark.read.format("hudi").
  option(QUERY_TYPE_OPT_KEY, QUERY_TYPE_INCREMENTAL_OPT_VAL).
  option(BEGIN_INSTANTTIME_OPT_KEY, beginTime).
  option(END_INSTANTTIME_OPT_KEY, endTime).
  load(basePath)
tripsPointInTimeDF.createOrReplaceTempView("hudi_trips_point_in_time")
//执行时间点查询时间查询
spark.sql("select `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts from hudi_trips_point_in_time where fare > 20.0").show()

// 删除数据
//获取所有数据记录
spark.sql("select uuid, partitionpath from hudi_trips_snapshot").count()
//获取两条记录需要删除
val ds = spark.sql("select uuid, partitionpath from hudi_trips_snapshot").limit(2)

//删除主题
val deletes = dataGen.generateDeletes(ds.collectAsList())
val df = spark.read.json(spark.sparkContext.parallelize(deletes, 2))

//重新加载数据
df.write.format("hudi").
  options(getQuickstartWriteConfigs).
  option(OPERATION_OPT_KEY,"delete").
  option(PRECOMBINE_FIELD_OPT_KEY, "ts").
  option(RECORDKEY_FIELD_OPT_KEY, "uuid").
  option(PARTITIONPATH_FIELD_OPT_KEY, "partitionpath").
  option(TABLE_NAME, tableName).
  mode(Append).
  save(basePath)

//执行相同的查询语句
val roAfterDeleteViewDF = spark.
  read.
  format("hudi").
  load(basePath)

roAfterDeleteViewDF.registerTempTable("hudi_trips_snapshot")
// 获取数据记录，不出意外应该少两条
spark.sql("select uuid, partitionpath from hudi_trips_snapshot").count()

// 覆盖写入
spark.
  read.format("hudi").
  load(basePath).
  select("uuid","partitionpath").
  sort("partitionpath","uuid").
  show(100, false)

val inserts = convertToStringList(dataGen.generateInserts(10))
val df = spark.
  read.json(spark.sparkContext.parallelize(inserts, 2)).
  filter("partitionpath = 'americas/united_states/san_francisco'")
df.write.format("hudi").
  options(getQuickstartWriteConfigs).
  option(OPERATION.key(),"insert_overwrite").
  option(PRECOMBINE_FIELD.key(), "ts").
  option(RECORDKEY_FIELD.key(), "uuid").
  option(PARTITIONPATH_FIELD.key(), "partitionpath").
  option(TBL_NAME.key(), tableName).
  mode(Append).
  save(basePath)
//查询
spark.
  read.format("hudi").
  load(basePath).
  select("uuid","partitionpath").
  sort("partitionpath","uuid").
  show(100, false)