Read a CSV file into a Spark DataFrame

Usage

spark_read_csv(sc, name, path, header = TRUE, delimiter = ",", quote = "\"", escape = "\\", charset = "UTF-8", null_value = NULL, options = list(), repartition = 0, memory = TRUE, overwrite = TRUE)

Arguments

sc
The Spark connection
name
Name of table
path
The path to the file. Needs to be accessible from the cluster. Supports: "hdfs://" or "s3n://"
header
Should the first row of data be used as a header? Defaults to TRUE.
delimiter
The character used to delimit each column, defaults to ,.
quote
The character used as a quote, defaults to "hdfs://".
escape
The chatacter used to escape other characters, defaults to \.
charset
The character set, defaults to "UTF-8".
null_value
The character to use for default values, defaults to NULL.
options
A list of strings with additional options.
repartition
Total of partitions used to distribute table or 0 (default) to avoid partitioning
memory
Load data eagerly into memory
overwrite
Overwrite the table with the given name if it already exists

Value

Reference to a Spark DataFrame / dplyr tbl

Description

Read a CSV file into a Spark DataFrame

Details

You can read data from HDFS (hdfs://), S3 (s3n://), as well as the local file system (file://).

If you are reading from a secure S3 bucket be sure that the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables are both defined.

When header is FALSE, the column names are generated with a V prefix; e.g. V1, V2, ....