sparkR读取csv文件

sparkR读取csv文件

The general method for creating SparkDataFrames from data sources is read.df. This method takes in the path for the file to load and the type of data source, and the currently active SparkSession will be used automatically. SparkR supports reading JSON, CSV and Parquet files natively, and through packages available from sources like Third Party Projects, you can find data source connectors for popular file formats like Avro. These packages can either be added by specifying --packages with spark-submit or sparkR commands, or if initializing SparkSession with sparkPackages parameter when in an interactive R shell or from RStudio.

http://spark.apache.org/docs/latest/sparkr.html

那spark-csv_2.11-1.4.0.jar包并不是一个R包，不需要安装，在我们的机器没有网的情况下，你下载的jar包根本不知道要放置在哪里?然后我通过在有网的环境下下载并使用该jar包，得知应该放在如下路径：

(1)   你的R用户的工作目录下的一个子目录下，如：

/home/summer/.ivy2/cache/com.databricks/spark-csv_2.11/jars/spark-csv_2.11-1.4.0.jar

(2)   /root/.ivy2/cache/com.databricks/spark-csv_2.11/jars/spark-csv_2.11-1.4.0.jar

注意安装的scala版本与上面的jar包的对应，此处scala应为2.11版本。

.// bin/spark-shell

bin/spark-shell --packages com.databricks:spark-csv_2.11:1.4.0

=====================================

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

Sys.setenv('SPARKR_SUBMIT_ARGS'='"--packages"
"com.databricks:spark-csv_2.11:1.4.0" "sparkr-shell"')

sc <-
sparkR.init(master="local[*]",sparkPackages=”com.databricks:spark-csv_2.11:1.4.0”,
sparkEnvir = list(spark.driver.memory="2g"))
sqlContext <- sparkRSQL.init(sc)

setwd(“~/hgData”)

hgdata<-read.csv(sqlContext ,"db1014.csv",header = TRUE,colClasses=list('character','character','character','character','character','character','numeric','Date'))