spark保存数据到hdfsJ及hive

package spark88

import org.apache.spark.sql.{DataFrame, Row, SQLContext, SaveMode}
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.sql.types.{StringType, StructField, StructType}
import org.apache.spark.{SparkConf, SparkContext}

/**
* Created by hadoop on 下午8:58.
*/
object Data2HDFSWithPartition {

// 创建Datarame方式2需要
case class AccessLog(sourceip: String, port: String, url: String, time: String, dayid: String, hourid: String)

def main(args: Array[String]): Unit = {
val sparkConf = new SparkConf().setMaster("local[2]").setAppName("test")
val sc = new SparkContext(sparkConf)
val sqlContext = new HiveContext(sc)

// 数据源
val list = List(
"136.42.33.6,80,http://www.baidu.com,2018-03-22 19:50:32",
"132.92.73.7,880,http://www.google.com,2018-03-22 19:30:46",
"138.52.53.22,68,http://www.taobao.com,2018-03-22 18:50:25",
"192.62.93.56,808,http://www.qq.com,2018-03-22 18:50:24",
"101.82.33.78,99,http://www.baidu.com,2018-03-22 20:50:14",
"134.72.23.98,123,http://www.jd.com,2018-03-22 20:20:31"
)

// 根据list生成RDD
val rdd = sc.parallelize(list) //sc.makeRDD(list)
rdd.take(10).foreach(println)

// 按日/小时分区

// 方式1： 转换成dataFrame
/* import sqlContext.implicits._
val rowRDD = rdd.map(line=>getRow(line)).
map(x=>(x._1,x._2, x._3, x._4, x._5, x._6))
val df = rowRDD.toDF("sourceip", "port", "url", "time", "dayid", "hourid")
df.show()*/

// 方式2： 转换成dataFrame
/*
val rowRDD = rdd.map(line=>getRow(line))
import sqlContext.implicits._
val df = rowRDD.map(x=>AccessLog(x._1,x._2, x._3, x._4, x._5, x._6)).toDF()
df.show()*/

// 方式3： 转换成dataFrame
val rowRDD = rdd.map(x => getRow(x)).map(x => Row(x._1, x._2, x._3, x._4, x._5, x._6))
val struct = StructType(Array(
StructField("sourceip", StringType),
StructField("port", StringType),
StructField("url", StringType),
StructField("time", StringType),
StructField("dayid", StringType),
StructField("hourid", StringType)
))
val df = sqlContext.createDataFrame(rowRDD, struct)

// write2HdfsViaHive(sqlContext, df)
write2HdfsViaDF(df)
}

def write2HdfsViaHive(sqlContext: SQLContext, df:DataFrame) = {

/*
1. 建表语句
create external table testlog(sourceip string, port string, url string,
time string) partitioned by ( dayid string, hourid string)
stored as orc location '/tmp/sparkhive2';
2. 开启动态分区
sqlContext.sql("set hive.exec.dynamic.partition.mode=nonstrict")
*/

val tmpLogTable = "tmpLog"
df.registerTempTable(tmpLogTable)

sqlContext.sql("use test2")
sqlContext.sql("set hive.exec.dynamic.partition.mode=nonstrict")

val insertSQL =
s"""
|insert into testlog partition(dayid, hourid)
|select sourceip, port, url, time, dayid, hourid
|from $tmpLogTable
""".stripMargin
sqlContext.sql(insertSQL)

}

def write2HdfsViaDF(df:DataFrame) = {
// df.show(false)
// df.printSchema()
val outputPath = "/tmp/sparkdf"
df.write.format("orc").partitionBy("dayid", "hourid").mode(SaveMode.Overwrite).
save(outputPath)
}

def getRow(line: String) = {

try {
val arr = line.split(",")
val sourceip = arr(0)
val port = arr(1)
val url = arr(2)
val time = arr(3)

val dayid = time.substring(0, 10).replaceAll("-", "")
val hourid = time.substring(11, 13)
(sourceip, port, url, time, dayid, hourid)
} catch {
case e: Exception => {
(line, "-1", "-1", "-1", "-1", "-1")
}
}
}
}

标签：hdfsJ,val,df,hive,hourid,._,dayid,spark,sqlContext
来源： https://www.cnblogs.com/heguoxiu/p/10691024.html