我火花流过程中面临的一个问题。 我得到空记录它就会流并通过了“分析”方法之后。
我的代码:
import spark.implicits._
import org.apache.spark.sql.types._
import org.apache.spark.sql.Encoders
import org.apache.spark.streaming._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.SparkSession
import spark.implicits._
import org.apache.spark.sql.types.{StructType, StructField, StringType,
IntegerType}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.SparkSession
import spark.implicits._
import org.apache.spark.sql.types.{StructType, StructField, StringType,
IntegerType}
import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.storage.StorageLevel
import java.util.regex.Pattern
import java.util.regex.Matcher
import org.apache.spark.sql.hive.HiveContext;
import org.apache.spark.sql.streaming.Trigger
import org.apache.spark.sql._
val conf = new SparkConf().setAppName("streamHive").setMaster("local[*]").set("spark.driver.allowMultipleContexts", "true")
val ssc = new StreamingContext(conf, Seconds(5))
val sc=ssc.sparkContext
val lines = ssc.textFileStream("file:///home/sadr/testHive")
case class Prices(name: String, age: String,sex: String, location: String)
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
def parse (rdd : org.apache.spark.rdd.RDD[String] ) =
{
var l = rdd.map(_.split(","))
val prices = l.map(p => Prices(p(0),p(1),p(2),p(3)))
val pricesDf = sqlContext.createDataFrame(prices)
pricesDf.registerTempTable("prices")
pricesDf.show()
var x = sqlContext.sql("select count(*) from prices")
x.show()}
lines.foreachRDD { rdd => parse(rdd)}
lines.print()
ssc.start()
我的输入文件:
cat test1.csv
Riaz,32,M,uk
tony,23,M,india
manu,33,M,china
imart,34,F,AUS
我得到这样的输出:
lines.foreachRDD { rdd => parse(rdd)}
lines.print()
ssc.start()
scala> +----+---+---+--------+
|name|age|sex|location|
+----+---+---+--------+
+----+---+---+--------+
我使用的Spark版本2.3 ....我收到以下错误之后加入X.SHOW()