从 DataFrame 写出 csv 数据后将其读入 SparkR

Question

我按照this post 中的示例将DataFrame 作为csv 写入AWS S3 存储桶。结果不是一个文件，而是一个包含许多 .csv 文件的文件夹。我现在无法在 SparkR 中以 DataFrame 形式读取此文件夹。以下是我尝试过的，但它们不会导致我写出的 DataFrame 相同。

write.df(df, 's3a://bucket/df', source="csv") #Creates a folder named df in S3 bucket

df_in1 <- read.df("s3a://bucket/df", source="csv")
df_in2 <- read.df("s3a://bucket/df/*.csv", source="csv")
#Neither df_in1 or df_in2 result in DataFrames that are the same as df

Answer 1

#  Spark 1.4 is used in this example
# 
# Download the nyc flights dataset as a CSV from https://s3-us-west-2.amazonaws.com/sparkr-data/nycflights13.csv

# Launch SparkR using 
# ./bin/sparkR --packages com.databricks:spark-csv_2.10:1.0.3

# The SparkSQL context should already be created for you as sqlContext
sqlContext
# Java ref type org.apache.spark.sql.SQLContext id 1

# Load the flights CSV file using `read.df`. Note that we use the CSV reader Spark package here.
flights <- read.df(sqlContext, "./nycflights13.csv", "com.databricks.spark.csv", header="true")

# Print the first few rows
head(flights)

希望这个例子有所帮助。