我们使用命令/home/ubuntu/spark/bin/spark-submit --master yarn --deploy-mode cluster --class "SimpleApp" /home/ubuntu/spark/examples/src/main/scala/sbt/target/scala-2.11/teste_2.11-1.0.jar
来运行下面的脚本
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql._
import org.apache.spark.sql.types._
import org.apache.spark.sql.SparkSession
import org.apache.spark._
import org.apache.spark
import org.apache.spark.sql
import org.apache.spark.SparkContext._
object SimpleApp {
def main(args: Array[String]) {
val spark = SparkSession.builder().appName("query1").master("yarn").getOrCreate
val header = StructType(Array(
StructField("medallion", StringType, true),
StructField("hack_license", StringType, true),
StructField("vendor_id", StringType, true),
StructField("rate_code", IntegerType, true),
StructField("store_and_fwd_flag", StringType, true),
StructField("pickup_datetime", TimestampType, true),
StructField("dropoff_datetime", TimestampType, true),
StructField("passenger_count", IntegerType, true),
StructField("trip_time_in_secs", IntegerType, true),
StructField("trip_distance", FloatType, true),
StructField("pickup_longitude", FloatType, true),
StructField("pickup_latitude", FloatType, true),
StructField("dropoff_longitude", FloatType, true),
StructField("dropoff_latitude", FloatType, true),
StructField("payment_type", StringType, true),
StructField("fare_amount", FloatType, true),
StructField("surcharge", FloatType, true),
StructField("mta_tax", FloatType, true),
StructField("trip_amount", FloatType, true),
StructField("tolls_amount", FloatType, true),
StructField("total_amount", FloatType, true),
StructField("zone", StringType, true)))
val nyct = spark.read.format("csv").option("delimiter", ",").option("header", "true").schema(header).load("/home/ubuntu/trip_data/trip_data_fare_1.csv")
nyct.createOrReplaceTempView("nyct_temp_table")
spark.time(spark.sql("""SELECT zone, COUNT(*) AS accesses FROM nyct_temp_table WHERE (HOUR(dropoff_datetime) >= 8 AND HOUR(dropoff_datetime) <= 19) GROUP BY zone ORDER BY accesses DESC""").show())
}
}
这个想法是将脚本中的查询运行到使用spark和Hadoop的集群中。但在执行结束时,这会生成一个从路径/home/ubuntu/trip_data/trip_data_fare_1.csv
读取csv文件的错误。This is the picture of the error
我认为问题在于节点从节点无法在主目录中找到该文件。有人知道如何修复此问题并在集群中运行此脚本?
发布于 2018-06-07 06:36:41
因为您是在集群中运行的,所以在hdfs中应该有此文件。您可以使用以下命令将文件从本地文件系统拷贝到HDFS:
hadoop fs -put source_path dest_path
然后在您的代码中使用dest_path。
对于您,请在包含本地文件的主机上执行此操作:
hadoop fs -put /home/ubuntu/trip_data/trip_data_fare_1.csv <some_hdfs_location>
通过执行以下操作,验证副本是否正常工作:
hdfs dfs -ls <some_hdfs_location>
发布于 2018-06-07 09:02:12
如果我没记错的话,那么Spark正在考虑将您的本地文件系统作为其默认文件系统,这就是为什么您面临这种error.The配置应该传递到Spark上下文中,并且您应该在所有nodes.Make中提到spark-env.sh
文件中的HADOOP_CONF_DIR
确保在所有节点中指定了HADOOP_CONF_DIR
的原因
val spCont = <Spark Context>
val config = spCont.hadoopConfiguration
config.addResource(new Path(s"${HADOOP_HOME}<path to core-site.xml>"))
https://stackoverflow.com/questions/50730513
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