来自Rstudio的SparkR - 在invokeJava中给出错误(isStatic = TRUE,className,methodName,...):

Question

我正在使用RStudio.

创建会话后,如果我尝试使用R数据创建数据帧,则会出错.

Sys.setenv(SPARK_HOME = "E:/spark-2.0.0-bin-hadoop2.7/spark-2.0.0-bin-hadoop2.7")
Sys.setenv(HADOOP_HOME = "E:/winutils")
.libPaths(c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib"), .libPaths()))
Sys.setenv('SPARKR_SUBMIT_ARGS'='"sparkr-shell"')

library(SparkR)

sparkR.session(sparkConfig = list(spark.sql.warehouse.dir="C:/Temp"))

localDF <- data.frame(name=c("John", "Smith", "Sarah"), age=c(19, 23, 18))
df <- createDataFrame(localDF)

错误:

Error in invokeJava(isStatic = TRUE, className, methodName, ...) : 
  java.lang.reflect.InvocationTargetException
    at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
    at sun.reflect.NativeConstructorAccessorImpl.newInstance(Unknown Source)
    at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(Unknown Source)
    at java.lang.reflect.Constructor.newInstance(Unknown Source)
    at org.apache.spark.sql.hive.client.IsolatedClientLoader.createClient(IsolatedClientLoader.scala:258)
    at org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:359)
    at org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:263)
    at org.apache.spark.sql.hive.HiveSharedState.metadataHive$lzycompute(HiveSharedState.scala:39)
    at org.apache.spark.sql.hive.HiveSharedState.metadataHive(HiveSharedState.scala:38)
    at org.apache.spark.sql.hive.HiveSharedState.externalCatalog$lzycompute(HiveSharedState.scala:46)
    at org.apache.spark.sql.hive.HiveSharedState.externalCatalog(HiveSharedState.scala:45)
    at org.a
>

TIA.

Answer 1

非常感谢你的帮助.

1. 我必须做的是在PATH变量(winutils/bin)中设置hadoop_home路径.这应该有你的winutils.exe文件.因此,当它为hive默认德比创建Metastore时,它可以调用hive类.
2. 此外,我将hive支持设置为False,因为我没有使用它.

Sys.setenv(SPARK_HOME='E:/spark-2.0.0-bin-hadoop2.7/spark-2.0.0-bin-hadoop2.7',HADOOP_HOME='E:/winutils')

.libPaths(c(file.path(Sys.getenv('SPARK_HOME'), 'R', 'lib'),.libPaths()))

Sys.setenv('SPARKR_SUBMIT_ARGS'='"sparkr-shell"')

library(SparkR)
library(rJava)

sparkR.session(enableHiveSupport = FALSE,master = "local[*]", sparkConfig = list(spark.driver.memory = "1g",spark.sql.warehouse.dir="E:/winutils/bin/"))

df <- as.DataFrame(iris)