scala代码高速拉去数据-适合同步历史数据场景
package com.longi.util
import com.longi.common.OracleTemplate
import com.longi.common.base.{HDFSPathWrapper, PropertiesHelper}
import com.longi.hadoop.{LoadKuduToDataFrame, SparkSessionPort}
import org.apache.kudu.client.SessionConfiguration
import org.apache.kudu.spark.kudu.{KuduContext, KuduWriteOptions}
import org.apache.spark.sql.functions.{coalesce, lit}
import org.apache.spark.sql.types.{DataType, DecimalType, StructField, StructType}
import org.apache.spark.sql.{Column, DataFrame, SparkSession}
/**
* @date: 2021-12-3 9:55
* @desc: Please fill in the remarks
*/
object Oracle2KuduWMS {
def main(args: Array[String]): Unit = {
//Step 0: 参数初始化
if (args.length > 7) {
throw new IllegalArgumentException("You need to pass the following parameters 1:The data of time(2020-06-01) 2:The name of Kudu table 3:Data extraction date column 4:The business Domain")
}
//(Oracle Table , Oracle Column, Begin Dt, End Dt, interval, Kudu Table)
//Oracle Table Name
val oraTableName = args(0)
//Oracle Incremental Sliding Column
val oraColumnName = args(1)
//The Begin time and End Time
val datBeginDateTime = args(2)
val datEndDateTime = args(3)
//Kudu table name
val kuduTableName = args(4)
val mesge = s"Oracle Table $oraTableName -> Kudu Table : $kuduTableName ($datBeginDateTime - $datEndDateTime) "
val fieldValueChange = args(5)
println(mesge)
val spark = SparkSessionPort(mesge)
var exeSQLCondition = ""
if (datBeginDateTime == "0000-00-00 00:00:00") {
exeSQLCondition = "1=1"
} else {
exeSQLCondition = oraColumnName.concat(">=to_date('").concat(datBeginDateTime).concat("','yyyy-mm-dd hh24:mi:ss') AND ").concat(oraColumnName).concat("<to_date('").concat(datEndDateTime).concat("','yyyy-mm-dd hh24:mi:ss')")
}
println(s"exeSQLCondition = ${exeSQLCondition}")
val exeSQL =
s"""
| (
| SELECT rownum as rs
| ,rowid as offsets -- 用这个替换cast(rowid as varchar(20)) as offsets
| ,'I' as op_type
| ,t.*
| FROM ${oraTableName} t
| WHERE ${exeSQLCondition}
| )
""".stripMargin
println(s"exeSQL = ${exeSQL}")
val exeAggrSQL =
s"""
| (
| SELECT count(1)
| FROM ${oraTableName} t
| WHERE ${exeSQLCondition}
| ) t
|""".stripMargin
println(exeAggrSQL)
val aggrDF = spark
.read
.format("jdbc")
.option("url", PropertiesHelper.getPropertiesValueFromKey("origi.wms.ora.uri"))
.option("driver", "oracle.jdbc.driver.OracleDriver")
.option("dbtable", exeAggrSQL)
.option("user", PropertiesHelper.getPropertiesValueFromKey("origi.wms.ora.username"))
.option("password", PropertiesHelper.getPropertiesValueFromKey("origi.wms.ora.password")).load()
val upperBound = aggrDF.first().getDecimal(0).toBigInteger.intValue()
val numPartitions = upperBound match {
case upperBound if (upperBound <= 100000) => 1
case upperBound if (upperBound < 100000 && upperBound <= 500000) => 3
case upperBound if (upperBound < 500000 && upperBound <= 1000000) => 10
case upperBound if (upperBound < 1000000 && upperBound <= 5000000) => 15
case _ => 20
}
val oracleDF = spark.read
.format("jdbc")
.option("url", "jdbc:oracle:thin:@xxx-st.longi.com:1521/prod")
.option("driver", "oracle.jdbc.driver.OracleDriver")
.option("dbtable", exeSQL)
.option("user", "USER")
.option("partitionColumn", "rs")
.option("lowerBound", 1)
.option("upperBound", upperBound)
.option("numPartitions", numPartitions)
.option("fetchsize", 3000)
.option("password", "XXXXXXX").load()
println(oracleDF.printSchema())
val kuduSchema = LoadKuduToDataFrame(spark, kuduTableName).schema
import org.apache.spark.sql.functions._
//按照Kudu表数据类型对Oracle数据类型进行改造
val colNames = kuduSchema.names.map(c => {
col(c).cast(kuduSchema.fields(kuduSchema.fieldIndex(c)).dataType.typeName)
})
var oracleFinalDF = oracleDF.select(colNames: _*)
//oracleFinalDF.show()
//需要转变的字段不为空
/*val fields = fieldValueChange.split(",")
fields.foreach(f => {
/*oracleFinalDF.schema.fields(kuduSchema.fieldIndex(f.split("=")(0))).dataType.typeName match {
case "Int" => coalesce(oracleFinalDF(f.split("=")(0)), lit(f.split("=")(1)))
case "String" => (f.split("=")(0))
}*/
println(s"f={$fields}, f0=${f.split("=")(0)} f1=${f.split("=")(1)}")
//coalesce(oracleFinalDF(f.split("=")(0)), lit(f.split("=")(1)))
//nvl(oracleFinalDF.col(f.split("=")(0)), f.split("=")(1))
//coalesce(col(f.split("=")(0)),lit(oracleFinalDF(f.split("=")(1))))
val tf = oracleFinalDF.withColumn(f.split("=")(0).concat("_new"), nvl(oracleFinalDF.col(f.split("=")(0)), f.split("=")(1)))
//tf.withColumn("a",oracleFinalDF.col("gl_sl_link_id"))
tf.where("").show()
val df = tf.drop(col(f.split("=")(0)))
df.show()
val rf = df.withColumnRenamed(f.split("=")(0).concat("_new"), f.split("=")(0))
rf.where("gl_sl_link_id is null or gl_sl_link_id =-1").show()
oracleFinalDF = rf
})
//将空值转为给定值
def nvl(ColIn: Column, ReplaceVal: Any): Column = {
println(ReplaceVal)
(when(ColIn.isNull, lit(ReplaceVal)).otherwise(ColIn))
}
oracleFinalDF.select("gl_sl_link_id").where("gl_sl_link_id is null or gl_sl_link_id =-1").distinct().show(200)
*/
//创建新DF，用于插入Kudu表中
val newData = spark.createDataFrame(oracleFinalDF.rdd, kuduSchema)
println(newData.printSchema())
println(s"newData.rdd.partitions.size :${newData.rdd.partitions.size}")
upsertKuduData(spark, newData, kuduTableName)
spark.stop()
}
/**
* @Description:写入Kudu数据库
* @Param: [spark, dataFrame, tabName]
* @return: void
*/
private def upsertKuduData(spark: SparkSession, dataFrame: DataFrame, tabName: String): Unit = {
val kuduContext = new KuduContext(
HDFSPathWrapper.getKuduMasterPath(),
spark.sparkContext
)
val ks = kuduContext.syncClient.newSession()
ks.setFlushMode(SessionConfiguration.FlushMode.AUTO_FLUSH_SYNC)
ks.setMutationBufferSpace(10000)
kuduContext.upsertRows(dataFrame, tabName, new KuduWriteOptions(false, true))
}
}
调用方式
spark-submit --class com.longi.util.Oracle2KuduWMS --master yarn --deploy-mode cluster --queue bdp --driver-memory 2g --num-executors 20 --executor-memory 1g --executor-cores 1 --conf spark.sql.warehouse.dir=hdfs://longi/home/data/hive/warehouse --conf spark.sql.session.timeZone=Asia/Shanghai --conf spark.executor.memoryOverhead=10G --jars /dfs/projects/etl-schedule-entry/lib/common/etl-functions-1.0.jar,/dfs/projects/etl-schedule-entry/lib/common/ojdbc6-11.2.0.3.jar,/dfs/projects/etl-schedule-entry/lib/common/kudu-spark2_2.11-1.10.0-cdh6.3.2.jar,/dfs/projects/etl-schedule-entry/lib/common/grizzled-slf4j_2.11-1.3.0.jar,/dfs/projects/etl-schedule-entry/lib/common/mysql-connector-java-5.1.44.jar /dfs/projects-test/etl-schedule-entry/lib/common/etl-tools-1.0.jar WMS_PROD.ACT_ALLOCATION_DETAILS addtime '2010-01-01 00:00:00' '2023-12-23 10:00:00' ods_po_wms.streaming_cemd_act_allocation_details 'a=1'
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