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I followed the example in this post to write out a DataFrame as a csv to an AWS S3 bucket. The result was not a single file but rather a folder with many .csv files. I'm now having trouble reading in this folder as a DataFrame in SparkR. Below is what I've tried but they do not result in the same DataFrame that I wrote out. 

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
Gaurav Bansal
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1 Answers1

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#  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)

Hope this example helps.

Arun Gunalan
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